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
pinolo/tasks.py	piger/pinolo	2	41511	# -- coding: utf-8 -- """ pinolo.tasks ~~~~~~~~~~~~ A Task object is a `threading.Thread` instance that will be executed without blocking the main thread. This is useful to perform potentially blocking actions like fecthing resources via HTTP. :copyright: (c) 2013 <NAME> :license: BSD, see LICENSE for more details. """ import threading class Task(threading.Thread): """A task is an execution unit that will be run in a separate thread that should not block tha main thread (handling irc connections). """ def __init__(self, event, args, kwargs): self.event = event super(Task, self).__init__(args, *kwargs) @property def queue(self): return self.event.client.bot.coda @property def reply(self): return self.event.reply def run(self): raise RuntimeError("Must be implemented!") def put_results(self, data): """Task output will be sent to the main thread via the configured queue; data should be a string containing the full output, that will later be splitted on newlines.""" self.queue.put(tuple(data))	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 13, 1678, 12534, 3543, 29889, 20673, 13, 1678, 3695, 26594, 7377, 30022, 13, 13, 1678, 319, 9330, 1203, 338, 263, 421, 7097, 292, 29889, 4899, 29952, 2777, 393, 674, 367, 8283, 13, 1678, 1728, 23473, 278, 1667, 3244, 29889, 13, 1678, 910, 338, 5407, 304, 2189, 19998, 23473, 8820, 763, 285, 522, 2790, 13, 1678, 7788, 3025, 7331, 29889, 13, 13, 1678, 584, 8552, 1266, 29901, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29941, 529, 5813, 29958, 13, 1678, 584, 506, 1947, 29901, 350, 7230, 29892, 1074, 365, 2965, 1430, 1660, 363, 901, 4902, 29889, 13, 15945, 29908, 13, 5215, 3244, 292, 13, 13, 13, 1990, 9330, 29898, 7097, 292, 29889, 4899, 1125, 13, 1678, 9995, 29909, 3414, 338, 385, 8225, 5190, 393, 674, 367, 1065, 297, 263, 5004, 3244, 13, 1678, 393, 881, 451, 2908, 266, 29874, 1667, 3244, 313, 3179, 1847, 29871, 2076, 12368, 467, 13, 1678, 9995, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1741, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 1583, 29889, 3696, 353, 1741, 13, 4706, 2428, 29898, 5398, 29892, 1583, 467, 1649, 2344, 1649, 10456, 5085, 29892, 3579, 19290, 29897, 13, 13, 1678, 732, 6799, 13, 1678, 822, 9521, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 3696, 29889, 4645, 29889, 7451, 29889, 29883, 8887, 13, 13, 1678, 732, 6799, 13, 1678, 822, 8908, 29898, 1311, 1125, 13, 4706, 736, 1583, 29889, 3696, 29889, 3445, 368, 13, 13, 1678, 822, 1065, 29898, 1311, 1125, 13, 4706, 12020, 24875, 2392, 703, 29924, 504, 367, 8762, 29991, 1159, 13, 308, 13, 1678, 822, 1925, 29918, 9902, 29898, 1311, 29892, 334, 1272, 1125, 13, 4706, 9995, 5398, 1962, 674, 367, 2665, 304, 278, 1667, 3244, 3025, 278, 13252, 13, 4706, 9521, 29936, 848, 881, 367, 263, 1347, 6943, 278, 2989, 1962, 29892, 393, 674, 13, 4706, 2678, 367, 8536, 4430, 373, 716, 9012, 1213, 15945, 13, 4706, 1583, 29889, 9990, 29889, 649, 29898, 23583, 29898, 1272, 876, 13, 2 ]
setup.py	danihodovic/django-toolshed	3	17579	<gh_stars>1-10 #!/usr/bin/env python import os import re from setuptools import find_packages, setup def get_version(file_paths): filename = os.path.join(os.path.dirname(__file__), file_paths) version_file = open(filename).read() version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M) if version_match: return version_match.group(1) raise RuntimeError("Unable to find version string.") version = get_version("django_toolshed", "__init__.py") readme = open("README.md").read() setup( name="django-toolshed", version=version, description="""Your project description goes here""", long_description=readme, author="<NAME>", author_email="<EMAIL>", url="https://github.com/danihodovic/django-toolshed", packages=find_packages(), include_package_data=True, install_requires=[], license="MIT", keywords="django,app", classifiers=[ "Development Status :: 3 - Alpha", "Framework :: Django :: 2.0", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Programming Language :: Python :: 3", ], )	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 5215, 2897, 13, 5215, 337, 13, 13, 3166, 731, 21245, 8789, 1053, 1284, 29918, 8318, 29892, 6230, 13, 13, 13, 1753, 679, 29918, 3259, 10456, 1445, 29918, 24772, 1125, 13, 1678, 10422, 353, 2897, 29889, 2084, 29889, 7122, 29898, 359, 29889, 2084, 29889, 25721, 22168, 1445, 1649, 511, 334, 1445, 29918, 24772, 29897, 13, 1678, 1873, 29918, 1445, 353, 1722, 29898, 9507, 467, 949, 580, 13, 1678, 1873, 29918, 4352, 353, 337, 29889, 4478, 29898, 29878, 29908, 29985, 1649, 3259, 1649, 353, 6024, 5931, 850, 22896, 12764, 3108, 7528, 1839, 29905, 3108, 613, 1873, 29918, 1445, 29892, 337, 29889, 29924, 29897, 13, 1678, 565, 1873, 29918, 4352, 29901, 13, 4706, 736, 1873, 29918, 4352, 29889, 2972, 29898, 29896, 29897, 13, 1678, 12020, 24875, 2392, 703, 2525, 519, 304, 1284, 1873, 1347, 23157, 13, 13, 13, 3259, 353, 679, 29918, 3259, 703, 14095, 29918, 10154, 845, 287, 613, 376, 1649, 2344, 26914, 2272, 1159, 13, 13, 949, 1004, 353, 1722, 703, 16310, 2303, 29889, 3487, 2564, 949, 580, 13, 13, 14669, 29898, 13, 1678, 1024, 543, 14095, 29899, 10154, 845, 287, 613, 13, 1678, 1873, 29922, 3259, 29892, 13, 1678, 6139, 13776, 29908, 10858, 2060, 6139, 5771, 1244, 15945, 613, 13, 1678, 1472, 29918, 8216, 29922, 949, 1004, 29892, 13, 1678, 4148, 543, 29966, 5813, 28341, 13, 1678, 4148, 29918, 5269, 543, 29966, 26862, 6227, 28341, 13, 1678, 3142, 543, 991, 597, 3292, 29889, 510, 29914, 29881, 3270, 24008, 586, 293, 29914, 14095, 29899, 10154, 845, 287, 613, 13, 1678, 9741, 29922, 2886, 29918, 8318, 3285, 13, 1678, 3160, 29918, 5113, 29918, 1272, 29922, 5574, 29892, 13, 1678, 2601, 29918, 276, 339, 2658, 11759, 1402, 13, 1678, 19405, 543, 26349, 613, 13, 1678, 29361, 543, 14095, 29892, 932, 613, 13, 1678, 770, 14903, 11759, 13, 4706, 376, 21956, 358, 16034, 4761, 29871, 29941, 448, 838, 2026, 613, 13, 4706, 376, 16660, 4761, 15337, 4761, 29871, 29906, 29889, 29900, 613, 13, 4706, 376, 2928, 2760, 319, 4749, 663, 4761, 10682, 414, 613, 13, 4706, 376, 29931, 293, 1947, 4761, 438, 5425, 28268, 1490, 4761, 341, 1806, 19245, 613, 13, 4706, 376, 29940, 18771, 17088, 4761, 4223, 613, 13, 4706, 376, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 613, 13, 1678, 21251, 13, 29897, 13, 2 ]
src/code/djangotest/migrations/0001_initial.py	jielyu/notebook	2	9220	# Generated by Django 2.2.5 on 2019-10-05 23:22 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Password', fields=[ ('id', models.IntegerField(primary_key=True, serialize=False, unique=True)), ('website', models.CharField(max_length=128)), ('username', models.CharField(max_length=128)), ('pwd', models.CharField(max_length=128)), ('time_add', models.DateTimeField(auto_now_add=True, null=True)), ('time_modify', models.DateTimeField(auto_now=True)), ], options={ 'db_table': 'password_tab', }, ), ]	[ 1, 396, 3251, 630, 491, 15337, 29871, 29906, 29889, 29906, 29889, 29945, 373, 29871, 29906, 29900, 29896, 29929, 29899, 29896, 29900, 29899, 29900, 29945, 29871, 29906, 29941, 29901, 29906, 29906, 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, 2847, 353, 5852, 13, 13, 1678, 9962, 353, 518, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 4391, 3195, 29898, 13, 9651, 1024, 2433, 10048, 742, 13, 9651, 4235, 11759, 13, 18884, 6702, 333, 742, 4733, 29889, 7798, 3073, 29898, 16072, 29918, 1989, 29922, 5574, 29892, 28755, 29922, 8824, 29892, 5412, 29922, 5574, 8243, 13, 18884, 6702, 22942, 742, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29896, 29906, 29947, 8243, 13, 18884, 6702, 6786, 742, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29896, 29906, 29947, 8243, 13, 18884, 6702, 29886, 9970, 742, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29896, 29906, 29947, 8243, 13, 18884, 6702, 2230, 29918, 1202, 742, 4733, 29889, 11384, 3073, 29898, 6921, 29918, 3707, 29918, 1202, 29922, 5574, 29892, 1870, 29922, 5574, 8243, 13, 18884, 6702, 2230, 29918, 1545, 1598, 742, 4733, 29889, 11384, 3073, 29898, 6921, 29918, 3707, 29922, 5574, 8243, 13, 9651, 21251, 13, 9651, 3987, 3790, 13, 18884, 525, 2585, 29918, 2371, 2396, 525, 5630, 29918, 3891, 742, 13, 9651, 2981, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
ROS/fprime_ws/src/genfprime/src/genfprime/generate_modmk.py	genemerewether/fprime	5	2362	<gh_stars>1-10 # # Copyright 2004-2016, by the California Institute of Technology. # ALL RIGHTS RESERVED. United States Government Sponsorship # acknowledged. Any commercial use must be negotiated with the Office # of Technology Transfer at the California Institute of Technology. # # This software may be subject to U.S. export control laws and # regulations. By accepting this document, the user agrees to comply # with all U.S. export laws and regulations. User has the # responsibility to obtain export licenses, or other export authority # as may be required before exporting such information to foreign # countries or providing access to foreign persons. # from __future__ import print_function import os from genmsg import MsgGenerationException #from . name import * ## :param type_name outdir: Full path to output directory ## :returns int: status. 0 if successful def write_modmk(outdir): #, msg_types, srv_types): if not os.path.isdir(outdir): #TODO: warn? return 0 xml_in_dir = set([f for f in os.listdir(outdir) if f.endswith('.xml')]) _write_modmk(outdir, sorted(xml_in_dir)) # TODO(mereweth) if we want to independently specify the generated XML files # generated_xml = [_msg_serializable_xml_name(f) for f in sorted(msg_types)] # generated_xml.extend([_port_xml_name(f) for f in sorted(msg_types)] # write_msg_modmk(outdir, generated_xml) # generated_xml = [_srv_serializable_xml_name(f) for f in sorted(srv_types)] # generated_xml.extend([_port_xml_name(f) for f in sorted(srv_types)] # write_msg_modmk(outdir, generated_xml) return 0 def _write_modmk(outdir, generated_xml): if not os.path.exists(outdir): os.makedirs(outdir) elif not os.path.isdir(outdir): raise MsgGenerationException("file preventing the creating of Fprime directory: %s"%dir) p = os.path.join(outdir, 'mod.mk') with open(p, 'w') as f: f.write('SRC = \\\n') if len(generated_xml) != 0: for xml in generated_xml[:-1]: f.write('%s \\\n'%xml) f.write('%s\n'%generated_xml[-1]) return 0	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 13, 29937, 259, 14187, 1266, 29871, 29906, 29900, 29900, 29946, 29899, 29906, 29900, 29896, 29953, 29892, 491, 278, 8046, 8907, 310, 17968, 29889, 13, 29937, 259, 15149, 390, 22530, 29903, 390, 2890, 1001, 29963, 3352, 29889, 3303, 3900, 10354, 1706, 787, 272, 3527, 13, 29937, 259, 24084, 3192, 29889, 3139, 12128, 671, 1818, 367, 27214, 630, 411, 278, 11367, 13, 29937, 259, 310, 17968, 17934, 472, 278, 8046, 8907, 310, 17968, 29889, 13, 29937, 13, 29937, 259, 910, 7047, 1122, 367, 4967, 304, 501, 29889, 29903, 29889, 5609, 2761, 14243, 322, 13, 29937, 259, 1072, 8250, 29889, 29871, 2648, 25967, 445, 1842, 29892, 278, 1404, 8571, 267, 304, 752, 368, 13, 29937, 259, 411, 599, 501, 29889, 29903, 29889, 5609, 14243, 322, 1072, 8250, 29889, 29871, 4911, 756, 278, 13, 29937, 259, 23134, 304, 4017, 5609, 7794, 11259, 29892, 470, 916, 5609, 14329, 13, 29937, 259, 408, 1122, 367, 3734, 1434, 5609, 292, 1316, 2472, 304, 9117, 13, 29937, 259, 10916, 470, 13138, 2130, 304, 9117, 12407, 29889, 13, 29937, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 1596, 29918, 2220, 13, 13, 5215, 2897, 13, 13, 3166, 2531, 7645, 1053, 28264, 5631, 362, 2451, 13, 13, 29937, 3166, 869, 1024, 1053, 334, 13, 13, 2277, 584, 3207, 1134, 29918, 978, 714, 3972, 29901, 14846, 2224, 304, 1962, 3884, 13, 2277, 584, 18280, 938, 29901, 4660, 29889, 29871, 29900, 565, 9150, 13, 1753, 2436, 29918, 1545, 11256, 29898, 449, 3972, 1125, 396, 29892, 10191, 29918, 8768, 29892, 269, 15291, 29918, 8768, 1125, 13, 1678, 565, 451, 2897, 29889, 2084, 29889, 275, 3972, 29898, 449, 3972, 1125, 13, 4706, 396, 4986, 3970, 29901, 29383, 29973, 13, 4706, 736, 29871, 29900, 13, 13, 1678, 4903, 29918, 262, 29918, 3972, 353, 731, 4197, 29888, 363, 285, 297, 2897, 29889, 1761, 3972, 29898, 449, 3972, 29897, 13, 462, 418, 565, 285, 29889, 1975, 2541, 12839, 3134, 1495, 2314, 13, 1678, 903, 3539, 29918, 1545, 11256, 29898, 449, 3972, 29892, 12705, 29898, 3134, 29918, 262, 29918, 3972, 876, 13, 13, 29937, 14402, 29898, 29885, 406, 29893, 621, 29897, 565, 591, 864, 304, 25499, 6084, 278, 5759, 6560, 2066, 13, 29937, 268, 5759, 29918, 3134, 353, 23160, 7645, 29918, 15550, 13902, 29918, 3134, 29918, 978, 29898, 29888, 29897, 363, 285, 297, 12705, 29898, 7645, 29918, 8768, 4638, 13, 29937, 268, 5759, 29918, 3134, 29889, 21843, 4197, 29918, 637, 29918, 3134, 29918, 978, 29898, 29888, 29897, 363, 285, 297, 12705, 29898, 7645, 29918, 8768, 4638, 13, 29937, 268, 2436, 29918, 7645, 29918, 1545, 11256, 29898, 449, 3972, 29892, 5759, 29918, 3134, 29897, 13, 13, 29937, 268, 5759, 29918, 3134, 353, 23160, 29879, 15291, 29918, 15550, 13902, 29918, 3134, 29918, 978, 29898, 29888, 29897, 363, 285, 297, 12705, 29898, 29879, 15291, 29918, 8768, 4638, 13, 29937, 268, 5759, 29918, 3134, 29889, 21843, 4197, 29918, 637, 29918, 3134, 29918, 978, 29898, 29888, 29897, 363, 285, 297, 12705, 29898, 29879, 15291, 29918, 8768, 4638, 13, 29937, 268, 2436, 29918, 7645, 29918, 1545, 11256, 29898, 449, 3972, 29892, 5759, 29918, 3134, 29897, 13, 13, 1678, 736, 29871, 29900, 13, 13, 1753, 903, 3539, 29918, 1545, 11256, 29898, 449, 3972, 29892, 5759, 29918, 3134, 1125, 13, 1678, 565, 451, 2897, 29889, 2084, 29889, 9933, 29898, 449, 3972, 1125, 13, 4706, 2897, 29889, 29885, 12535, 12935, 29898, 449, 3972, 29897, 13, 1678, 25342, 451, 2897, 29889, 2084, 29889, 275, 3972, 29898, 449, 3972, 1125, 13, 4706, 12020, 28264, 5631, 362, 2451, 703, 1445, 5557, 292, 278, 4969, 310, 383, 10080, 3884, 29901, 1273, 29879, 29908, 29995, 3972, 29897, 13, 1678, 282, 353, 2897, 29889, 2084, 29889, 7122, 29898, 449, 3972, 29892, 525, 1545, 29889, 11256, 1495, 13, 1678, 411, 1722, 29898, 29886, 29892, 525, 29893, 1495, 408, 285, 29901, 13, 4706, 285, 29889, 3539, 877, 29903, 10363, 353, 320, 1966, 29876, 1495, 13, 4706, 565, 7431, 29898, 13525, 29918, 3134, 29897, 2804, 29871, 29900, 29901, 13, 9651, 363, 4903, 297, 5759, 29918, 3134, 7503, 29899, 29896, 5387, 13, 18884, 285, 29889, 3539, 877, 29995, 29879, 320, 1966, 29876, 29915, 29995, 3134, 29897, 13, 9651, 285, 29889, 3539, 877, 29995, 29879, 29905, 29876, 29915, 29995, 13525, 29918, 3134, 14352, 29896, 2314, 13, 1678, 736, 29871, 29900, 13, 2 ]
CodeWars/8 Kyu/Grasshopper - Debug sayHello.py	anubhab-code/Competitive-Programming	0	64924	<filename>CodeWars/8 Kyu/Grasshopper - Debug sayHello.py def say_hello(name): return "Hello, {0}".format(name)	[ 1, 529, 9507, 29958, 3399, 29956, 1503, 29914, 29947, 16931, 29884, 29914, 3338, 465, 1251, 2496, 448, 16171, 1827, 10994, 29889, 2272, 13, 1753, 1827, 29918, 12199, 29898, 978, 1125, 13, 1678, 736, 376, 10994, 29892, 426, 29900, 29913, 1642, 4830, 29898, 978, 29897, 2 ]
Contest/ABC086/d/main.py	mpses/AtCoder	0	35923	#!/usr/bin/env python3 # 2次元累積和 S の [x1, x2) × [y1, y2) 総和 def ac2(s, x1, x2, y1, y2): return s[x2][y2] - s[x1][y2] - s[x2][y1] + s[x1][y1] import numpy as np _, d = open(0) n, k = map(int, _.split()) B = np.zeros((2k, 2k)) for e in d: z, c = e.split() x, y = map(int, z) B[x % (2k)][(y + k (z == "W")) % (2*k)] += 1 B.cumsum(axis = 0) B.cumsum(axis = 1) B = np.tile(B, (2,2)) print(B) # 書きかけ	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 29871, 29906, 30936, 30824, 234, 183, 178, 234, 172, 144, 30503, 317, 29871, 30199, 518, 29916, 29896, 29892, 921, 29906, 29897, 13105, 518, 29891, 29896, 29892, 343, 29906, 29897, 29871, 234, 186, 146, 30503, 13, 1753, 1274, 29906, 29898, 29879, 29892, 921, 29896, 29892, 921, 29906, 29892, 343, 29896, 29892, 343, 29906, 1125, 13, 1678, 736, 269, 29961, 29916, 29906, 3816, 29891, 29906, 29962, 448, 269, 29961, 29916, 29896, 3816, 29891, 29906, 29962, 448, 269, 29961, 29916, 29906, 3816, 29891, 29896, 29962, 718, 269, 29961, 29916, 29896, 3816, 29891, 29896, 29962, 13, 13, 5215, 12655, 408, 7442, 13, 3383, 334, 29881, 353, 1722, 29898, 29900, 29897, 13, 29876, 29892, 413, 353, 2910, 29898, 524, 29892, 903, 29889, 5451, 3101, 13, 29933, 353, 7442, 29889, 3298, 359, 3552, 29906, 29930, 29895, 29892, 29871, 29906, 29930, 29895, 876, 13, 1454, 321, 297, 270, 29901, 13, 1678, 334, 29920, 29892, 274, 353, 321, 29889, 5451, 580, 13, 1678, 921, 29892, 343, 353, 2910, 29898, 524, 29892, 503, 29897, 13, 1678, 350, 29961, 29916, 1273, 313, 29906, 29930, 29895, 29897, 3816, 29898, 29891, 718, 413, 334, 313, 29920, 1275, 376, 29956, 5783, 1273, 313, 29906, 29930, 29895, 4638, 4619, 29871, 29896, 13, 29933, 29889, 29883, 398, 2083, 29898, 8990, 353, 29871, 29900, 29897, 13, 29933, 29889, 29883, 398, 2083, 29898, 8990, 353, 29871, 29896, 29897, 13, 29933, 353, 7442, 29889, 29873, 488, 29898, 29933, 29892, 313, 29906, 29892, 29906, 876, 13, 2158, 29898, 29933, 29897, 13, 29937, 29871, 30854, 30538, 30412, 30807, 2 ]
workspaces/voyage/server/src/util/smartcomponent.py	raychorn/svn_hp-projects	0	81883	##################################################### # # smartcomponent.py # # Copyright 2007 Hewlett-Packard Development Company, L.P. # # Hewlett-Packard and the Hewlett-Packard logo are trademarks of # Hewlett-Packard Development Company, L.P. in the U.S. and/or other countries. # # Confidential computer software. Valid license from Hewlett-Packard required # for possession, use or copying. Consistent with FAR 12.211 and 12.212, # Commercial Computer Software, Computer Software Documentation, and Technical # Data for Commercial Items are licensed to the U.S. Government under # vendor's standard commercial license. # # Author: # <NAME> # # Description: # Manages Smart Component objects for Breckenridge. # ##################################################### import os import tarfile import tempfile from xml.dom.minidom import parse from threading import Thread import logging log = logging.getLogger(__name__) SC_SHARE_PATH='static/sc_share' class SmartComponent: def __init__(self): self.version = "Unknown" self.name = "Unknown Component" self.filename = "" def __repr__(self): return str(self.__dict__) def json(self): return json.dumps(self.__dict__) class SmartComponentManager: def __init__(self): self.components = [] self.path = os.path.abspath(SC_SHARE_PATH) log.debug("Initializing SmartComponentManager") def get_sc_file_list(self): for root, directories, files in os.walk(self.path): return files def delete_component(self, sc_filename): sc_path = self.path + '\\' + sc_filename try: os.remove(sc_path) for component in self.components: if component.filename == sc_filename: self.components.remove(component) except: pass def discover_components(self): t = Thread(target=self.do_discover_components) t.daemon = True t.start() def do_discover_components(self): files = self.get_sc_file_list() if files: for file in files: if file.lower().endswith('.scexe') : log.debug("Discovered component %s", file) self.add_component(file) def add_component(self, filename): log.debug("Adding component %s", filename) if filename in [c.filename for c in self.components ] : log.error("Component %s already added", filename) return False component = SmartComponent() component.filename = filename digest_filename = self.extract_component_digest(filename) if digest_filename != "": self.parse_digest(digest_filename, component) # Remove the digest file. try: os.remove(self.path + '\\' + digest_filename) except: log.exception("Error removing digest file %s", digest_filename) pass self.components.append(component) # Each smart component should have an XML file inside. We'll use # tar to extract that file and obtain information about the component. # This function will return the filename of the xml file after it # has been extracted from the archive. def extract_component_digest(self, sc_filename): # The first step is to find the line where the tarfile starts. sc_path = self.path + '\\' + sc_filename sc_file = open(sc_path, "rb") line_offset = -1 while True: buf = sc_file.read(1024) if buf == "": break skip_idx = buf.find("_SKIP=") if (skip_idx != -1): buf = buf[(skip_idx+6):] newline = buf.find('\n') line_offset = buf[0:newline] break # We didn't find the line offset. We can't go any further. if line_offset == -1: sc_file.close() log.error("Unable to find tar offset in %s", sc_filename) return "" line_offset = int(line_offset)-1 sc_file.seek(0) log.debug("tar file in %s at offset %d", sc_filename, line_offset) # Now that we have our line offset, move to the correct position in the file. for line in range(0, line_offset): sc_file.readline() tar_file = tempfile.mkstemp(dir=self.path) while True: buf = sc_file.read(1024) if buf == "": break os.write(tar_file[0], buf) sc_file.close() os.close(tar_file[0]) # Now we should have a good tarfile to extract the digest. digest_name = "" try: tf = tarfile.open(tar_file[1], 'r') for filename in tf.getnames() : if filename.lower() == sc_filename.lower().replace('.scexe', '.xml') : digest_name = filename log.debug("Extracting %s from tar", digest_name) tf.extract(digest_name, self.path) tf.close() os.remove(tar_file[1]) break except: log.exception("Error getting XML file name from %s", sc_filename) os.remove(tar_file[1]) tf.close() if not digest_name : log.error("Unable to find component xml file in %s", sc_filename) return digest_name # Parses the digest file and adds the required attributes to the SmartComponent object. def parse_digest(self, filename, component): log.debug("Parsing %s", filename) digest_dom = None try: digest_dom = parse(self.path + '\\' + filename) except: log.exception("Error parsing file %s",filename) return None package_elem = digest_dom.getElementsByTagName('cpq_package') if not package_elem: log.error("No cpq_package found") return None package_elem = package_elem[0] version_elem = None name_elem = None for node in package_elem.childNodes: if node.nodeName == 'version': version_elem = node elif node.nodeName == 'name': name_elem = node # Add the version from the version element's value attribute. if version_elem: component.version = version_elem.getAttribute('value') log.debug("%s component version: %s", component.filename, component.version) else : 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test/generic_profiler_test.py	newstzpz/ClassyVision-1	0	130914	#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import unittest from test.generic.config_utils import get_test_model_configs import torch import torch.nn as nn from classy_vision.generic.profiler import ( compute_activations, compute_flops, count_params, get_shape, ) from classy_vision.models import build_model class TestModule(nn.Module): def __init__(self): super().__init__() # add parameters to the module to affect the parameter count self.linear = nn.Linear(2, 3, bias=False) def forward(self, x): return x + 1 def flops(self, x): # TODO: this should raise an exception if this function is not defined # since the FLOPs are indeterminable # need to define flops since this is an unknown class return x.numel() class TestConvModule(nn.Conv2d): def __init__(self): super().__init__(2, 3, (4, 4), bias=False) # add another (unused) layer for added complexity and to test parameters self.linear = nn.Linear(4, 5, bias=False) def forward(self, x): return x def activations(self, x, out): # TODO: this should ideally work without this function being defined return out.numel() def flops(self, x): # need to define flops since this is an unknown class return 0 class TestModel(nn.Module): def __init__(self): super().__init__() self.linear = nn.Linear(300, 300, bias=False) self.mod = TestModule() self.conv = TestConvModule() # we should be able to pick up user defined parameters as well self.extra_params = nn.Parameter(torch.randn(10, 10)) # we shouldn't count flops for an unused layer self.unused_linear = nn.Linear(2, 2, bias=False) def forward(self, x): out = self.conv(x) out = out.view(out.shape[0], -1) out = self.mod(out) return self.linear(out) class TestModel2(nn.Module): def __init__(self): super().__init__() # create a model which re-uses a module (conv_module in this case) conv_module = nn.Conv2d(3, 3, (2, 2), bias=False) self.seq_1 = nn.Sequential(conv_module) self.seq_2 = nn.Sequential(conv_module) def forward(self, x): return self.seq_1(x) + self.seq_2(x) class TestModuleWithoutFlops(nn.Module): # this module doesn't have FLOPs defined def forward(self, x): return x class TestModuleWithFlops(nn.Module): # this module does have FLOPs defined _flops = 1234 def __init__(self): super().__init__() self.mod = TestModuleWithoutFlops() # add a conv module; this shouldn't impact the FLOPs since we define # self.flops() self.conv = nn.Conv2d(3, 3, (2, 2)) def forward(self, x): return self.conv(x) def flops(self, x): return self._flops class TestProfilerFunctions(unittest.TestCase): def test_complexity_calculation_resnext(self) -> None: model_configs = get_test_model_configs() # make sure there are three configs returned self.assertEqual(len(model_configs), 3) # expected values which allow minor deviations from model changes # we only test at the 10^6 scale expected_m_flops = [4122, 7850, 8034] expected_m_params = [25, 44, 44] expected_m_activations = [11, 16, 21] for model_config, m_flops, m_params, m_activations in zip( model_configs, expected_m_flops, expected_m_params, expected_m_activations ): model = build_model(model_config) self.assertEqual(compute_activations(model) // 10 6, m_activations) self.assertEqual(compute_flops(model) // 10 6, m_flops) self.assertEqual(count_params(model) // 10 ** 6, m_params) def test_complexity_calculation(self) -> None: model = TestModel() input_shape = (3, 10, 10) num_elems = 3 * 10 * 10 self.assertEqual(compute_activations(model, input_shape=input_shape), num_elems) self.assertEqual( compute_flops(model, input_shape=input_shape), num_elems + 0 + (300 * 300), # TestModule + TestConvModule + TestModel.linear; # TestModel.unused_linear is unused and shouldn't be counted ) self.assertEqual( count_params(model), (2 * 3) + (2 * 3 * 4 * 4) + (4 * 5) + (300 * 300) + (10 * 10) + (2 * 2), ) # TestModule.linear + TestConvModule + TestConvModule.linear + # TestModel.linear + TestModel.extra_params + TestModel.unused_linear # test that we calculate complexity correctly for a model which re-uses a module model = TestModel2() in_channels = 3 out_channels = 3 out_h, out_w = 9, 9 kernel_h, kernel_w = 2, 2 conv_flops = in_channels * out_channels * out_h * out_w * kernel_h * kernel_w conv_activations = out_channels * out_h * out_w self.assertEqual( compute_activations(model, input_shape=input_shape), conv_activations * 2 ) # the conv is applied twice self.assertEqual( compute_flops(model, input_shape=input_shape), conv_flops * 2 ) # the conv is applied twice self.assertEqual( count_params(model), in_channels * out_channels * kernel_h * kernel_w ) def test_flops_calculation(self): # test that a model containing a custom module which doesn't have FLOPs defined # raises an exception model = nn.Sequential(TestModuleWithoutFlops()) input_shape = (3, 10, 10) with self.assertRaises(Exception): compute_flops(model, input_shape=input_shape) # test that a model containing a custom module does have FLOPs defined works, # even if the module has children which don't define FLOPs model = nn.Sequential(TestModuleWithFlops()) input_shape = (3, 10, 10) self.assertEqual( compute_flops(model, input_shape=input_shape), TestModuleWithFlops._flops ) # the conv is applied twice class TestHelperFunctions(unittest.TestCase): def test_get_shape(self) -> None: list_x = [torch.zeros(2, 4), torch.zeros(3, 3)] shapes = get_shape(list_x) expected_shapes = [torch.zeros(2, 4).size(), torch.zeros(3, 3).size()] for shape, expected in zip(shapes, expected_shapes): self.assertEqual(shape, expected) dict_x = {"x1": torch.zeros(2, 4), "x2": torch.zeros(3, 3)} shapes = get_shape(dict_x) expected_shapes = { "x1": torch.zeros(2, 4).size(), "x2": torch.zeros(3, 3).size(), } for k, shape in shapes.items(): self.assertEqual(shape, expected_shapes[k]) list_dict_x = [ {"x1": torch.zeros(2, 4), "x2": torch.zeros(3, 3)}, {"x1": torch.zeros(3, 4), "x2": torch.zeros(4, 5)}, ] shapes = get_shape(list_dict_x) expected_shapes = [ {"x1": torch.zeros(2, 4).size(), "x2": torch.zeros(3, 3).size()}, {"x1": torch.zeros(3, 4).size(), "x2": torch.zeros(4, 5).size()}, ] for shape, expected in zip(shapes, expected_shapes): for k, s in shape.items(): self.assertEqual(s, expected[k])	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 29937, 14187, 1266, 313, 29883, 29897, 13327, 29892, 9266, 29889, 322, 967, 23736, 1078, 29889, 13, 29937, 13, 29937, 910, 2752, 775, 338, 7794, 21144, 1090, 278, 341, 1806, 19405, 1476, 297, 278, 13, 29937, 365, 2965, 1430, 1660, 934, 297, 278, 3876, 3884, 310, 445, 2752, 5447, 29889, 13, 13, 5215, 443, 27958, 13, 3166, 1243, 29889, 19206, 29889, 2917, 29918, 13239, 1053, 679, 29918, 1688, 29918, 4299, 29918, 2917, 29879, 13, 13, 5215, 4842, 305, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 29876, 13, 3166, 770, 29891, 29918, 4924, 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app/replenishjob.py	RobbieSoutham/DanielBCF	0	192496	from app import database from app.database import Database import datetime from configparser import ConfigParser config = ConfigParser() def rep(): #Get delivery time from config config.read("app/config.ini") del_time = datetime.timedelta(days=int(config.get("Settings", "del_time"))) today = datetime.datetime.now().date() ordered_stock = Database.get("Stock") stock_out = [] for stock in ordered_stock: if stock[3] == None and stock[3] is not None: #Retrieve stock items that are out of stock stock_out.append(stock) for stock in stock_out: #If the delivery time has passed set the stock level as healthy print(stock[4]) if stock[4] - today >= del_time: Database.update("Stock", "stock_healthy", 1, "stock_healthy", "Null") rep()	[ 1, 515, 623, 1053, 2566, 13, 3166, 623, 29889, 9803, 1053, 5470, 13, 5215, 12865, 13, 3166, 2295, 16680, 1053, 12782, 11726, 13, 13, 2917, 353, 12782, 11726, 580, 13, 13, 1753, 1634, 7295, 13, 1678, 396, 2577, 28289, 931, 515, 2295, 13, 1678, 2295, 29889, 949, 703, 932, 29914, 2917, 29889, 2172, 1159, 13, 1678, 628, 29918, 2230, 353, 12865, 29889, 9346, 287, 2554, 29898, 16700, 29922, 524, 29898, 2917, 29889, 657, 703, 9585, 613, 376, 6144, 29918, 2230, 29908, 4961, 13, 1678, 9826, 353, 12865, 29889, 12673, 29889, 3707, 2141, 1256, 580, 13, 1678, 10372, 29918, 17712, 353, 5470, 29889, 657, 703, 20754, 384, 1159, 13, 1678, 10961, 29918, 449, 353, 5159, 13, 13, 1678, 363, 10961, 297, 10372, 29918, 17712, 29901, 13, 4706, 565, 10961, 29961, 29941, 29962, 1275, 6213, 322, 10961, 29961, 29941, 29962, 338, 451, 6213, 29901, 13, 9651, 396, 8015, 29878, 2418, 10961, 4452, 393, 526, 714, 310, 10961, 13, 9651, 10961, 29918, 449, 29889, 4397, 29898, 17712, 29897, 13, 13, 1678, 363, 10961, 297, 10961, 29918, 449, 29901, 13, 4706, 396, 3644, 278, 28289, 931, 756, 4502, 731, 278, 10961, 3233, 408, 9045, 29891, 13, 4706, 1596, 29898, 17712, 29961, 29946, 2314, 13, 4706, 565, 10961, 29961, 29946, 29962, 448, 9826, 6736, 628, 29918, 2230, 29901, 13, 9651, 5470, 29889, 5504, 703, 20754, 384, 613, 376, 17712, 29918, 354, 4298, 29891, 613, 29871, 29896, 29892, 376, 17712, 29918, 354, 4298, 29891, 613, 376, 7327, 1159, 13, 13, 3445, 580, 13, 2 ]
ModelServices/eventTriggerOutputDeviceMappingServices.py	tuanldchainos/HcPullData	0	119584	<reponame>tuanldchainos/HcPullData from Repository.eventTriggerOutputDeviceMappingRepo import eventTriggerOutputDeviceMappingRepo from sqlalchemy import Table from sqlalchemy.engine.base import Connection from sqlalchemy.sql.expression import BinaryExpression class eventTriggerOutputDeviceMappingServices(): __eventTriggerOutputDeviceMappingRepo: eventTriggerOutputDeviceMappingRepo def __init__(self, eventTriggerOutputDeviceMappingTable: Table, context: Connection): self.__eventTriggerOutputDeviceMappingRepo = eventTriggerOutputDeviceMappingRepo(eventTriggerOutputDeviceMappingTable, context=context) def AddManyEventTriggerOutputDeviceMappingWithCustomData(self, l: list): self.__eventTriggerOutputDeviceMappingRepo.InsertManyWithCustomData(l)	[ 1, 529, 276, 1112, 420, 29958, 9161, 273, 430, 14153, 359, 29914, 29950, 29883, 29925, 913, 1469, 13, 3166, 830, 7036, 29889, 3696, 20211, 6466, 11501, 15845, 5612, 29877, 1053, 1741, 20211, 6466, 11501, 15845, 5612, 29877, 13, 3166, 4576, 284, 305, 6764, 1053, 6137, 13, 3166, 4576, 284, 305, 6764, 29889, 10599, 29889, 3188, 1053, 15160, 13, 3166, 4576, 284, 305, 6764, 29889, 2850, 29889, 17471, 1053, 29479, 10960, 13, 13, 1990, 1741, 20211, 6466, 11501, 15845, 13779, 7295, 13, 1678, 4770, 3696, 20211, 6466, 11501, 15845, 5612, 29877, 29901, 1741, 20211, 6466, 11501, 15845, 5612, 29877, 13, 268, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1741, 20211, 6466, 11501, 15845, 3562, 29901, 6137, 29892, 3030, 29901, 15160, 1125, 13, 4706, 1583, 17255, 3696, 20211, 6466, 11501, 15845, 5612, 29877, 353, 1741, 20211, 6466, 11501, 15845, 5612, 29877, 29898, 3696, 20211, 6466, 11501, 15845, 3562, 29892, 3030, 29922, 4703, 29897, 13, 418, 13, 1678, 822, 3462, 14804, 2624, 20211, 6466, 11501, 15845, 3047, 7281, 1469, 29898, 1311, 29892, 301, 29901, 1051, 1125, 13, 4706, 1583, 17255, 3696, 20211, 6466, 11501, 15845, 5612, 29877, 29889, 17491, 14804, 3047, 7281, 1469, 29898, 29880, 29897, 1678, 2 ]
train/models.py	soymintc/brain_tumor_segmentation_web_service	1	1616968	<reponame>soymintc/brain_tumor_segmentation_web_service<gh_stars>1-10 import os import glob import psutil from django.db import models from django.conf import settings from django.contrib.auth import get_user_model from django.urls import reverse from django.core.validators import MaxValueValidator, MinValueValidator from django.core.exceptions import ValidationError def validate_optimizer(value): if value not in ['adam', 'sgd']: raise ValidationError("optimizer '{}' should be either adam or sgd".format(value)) class Train(models.Model): # Creation attrs author = models.ForeignKey( get_user_model(), on_delete=models.CASCADE, ) date = models.DateTimeField(auto_now_add=True) title = models.CharField(max_length=255) batch_size = models.PositiveIntegerField() image_size = models.PositiveIntegerField() n_validation = models.PositiveIntegerField() n_test = models.PositiveIntegerField() learning_rate = models.FloatField( validators=[MinValueValidator(1e-20), MaxValueValidator(1)],) optimizer = models.CharField(max_length=10, validators=[validate_optimizer]) group_size = models.PositiveIntegerField() filters_root = models.PositiveIntegerField() augment = models.BooleanField() # Non-input attrs pid = models.PositiveIntegerField(default=0, null=True, blank=True) tb_pid = models.PositiveIntegerField(default=0, null=True, blank=True) port = models.PositiveIntegerField(default=0, null=True, blank=True) # assign unused port cmd_str = models.CharField(max_length=1000, default='.', null=True, blank=True) def __str__(self): return self.title def pid_exists(self): return psutil.pid_exists(self.pid) def tb_pid_exists(self): return psutil.pid_exists(self.tb_pid) def run_phase(self): return self.pid == 0 def get_log_path(self): log_dir = os.path.join(settings.BASE_DIR, 'train', 'train_logs') return os.path.join(log_dir, self.title + '.log') def get_tb_log_dir(self): log_dir = os.path.join(settings.BASE_DIR, 'train', 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text/opencv_dnn_detect.py	kingemma/invoice	1,017	39174	<filename>text/opencv_dnn_detect.py from config import yoloCfg,yoloWeights,opencvFlag from config import AngleModelPb,AngleModelPbtxt from config import IMGSIZE from PIL import Image import numpy as np import cv2 if opencvFlag=='keras': ##转换为tf模型，以便GPU调用 import tensorflow as tf from tensorflow.python.platform import gfile config = tf.ConfigProto(allow_soft_placement=True) sess = tf.Session(config=config) with gfile.FastGFile(AngleModelPb, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) sess.graph.as_default() tf.import_graph_def(graph_def, name='') inputImg = sess.graph.get_tensor_by_name('input_1:0') predictions = sess.graph.get_tensor_by_name('predictions/Softmax:0') keep_prob = tf.placeholder(tf.float32) else: angleNet = cv2.dnn.readNetFromTensorflow(AngleModelPb,AngleModelPbtxt)##dnn 文字方向检测 textNet = cv2.dnn.readNetFromDarknet(yoloCfg,yoloWeights)##文字定位 def text_detect(img): thresh=0 h,w = img.shape[:2] inputBlob = cv2.dnn.blobFromImage(img, scalefactor=0.00390625, size=IMGSIZE,swapRB=True ,crop=False); textNet.setInput(inputBlob) pred = textNet.forward() cx = pred[:,0]w cy = pred[:,1]h xmin = cx - pred[:,2]w/2 xmax = cx + pred[:,2]w/2 ymin = cy - pred[:,3]h/2 ymax = cy + pred[:,3]h/2 scores = pred[:,4] indx = np.where(scores>thresh)[0] scores = scores[indx] boxes = np.array(list(zip(xmin[indx],ymin[indx],xmax[indx],ymax[indx]))) return boxes,scores def angle_detect_dnn(img,adjust=True): """ 文字方向检测 """ h,w = img.shape[:2] ROTATE = [0,90,180,270] if adjust: thesh = 0.05 xmin,ymin,xmax,ymax = int(theshw),int(theshh),w-int(theshw),h-int(theshh) img = img[ymin:ymax,xmin:xmax]##剪切图片边缘 inputBlob = cv2.dnn.blobFromImage(img, scalefactor=1.0, size=(224, 224), swapRB=True , mean=[103.939,116.779,123.68],crop=False); angleNet.setInput(inputBlob) pred = angleNet.forward() index = np.argmax(pred,axis=1)[0] return ROTATE[index] def angle_detect_tf(img,adjust=True): """ 文字方向检测 """ h,w = img.shape[:2] ROTATE = [0,90,180,270] if adjust: thesh = 0.05 xmin,ymin,xmax,ymax = int(theshw),int(theshh),w-int(theshw),h-int(theshh) img = img[ymin:ymax,xmin:xmax]##剪切图片边缘 img = cv2.resize(img,(224,224)) img = img[..., ::-1].astype(np.float32) img[..., 0] -= 103.939 img[..., 1] -= 116.779 img[..., 2] -= 123.68 img = np.array([img]) out = sess.run(predictions, feed_dict={inputImg: img, keep_prob: 0 }) index = np.argmax(out,axis=1)[0] return ROTATE[index] def angle_detect(img,adjust=True): """ 文字方向检测 """ if opencvFlag=='keras': return angle_detect_tf(img,adjust=adjust) else: return angle_detect_dnn(img,adjust=adjust)	[ 1, 529, 9507, 29958, 726, 29914, 3150, 11023, 29918, 5200, 29876, 29918, 4801, 522, 29889, 2272, 13, 3166, 2295, 1053, 343, 3543, 29907, 16434, 29892, 29891, 3543, 4806, 5861, 29892, 3150, 11023, 21979, 13, 3166, 2295, 1053, 3218, 280, 3195, 29925, 29890, 29892, 19582, 3195, 29925, 3116, 486, 13, 3166, 2295, 1053, 22313, 29954, 14226, 13, 3166, 349, 6227, 1053, 7084, 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ooobuild/csslo/view/__init__.py	Amourspirit/ooo_uno_tmpl	0	163570	<reponame>Amourspirit/ooo_uno_tmpl # coding: utf-8 # # Copyright 2022 :Barry-Thomas-Paul: Moss # # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http: // www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from ...lo.view.document_zoom_type import DocumentZoomType as DocumentZoomType from ...lo.view.duplex_mode import DuplexMode as DuplexMode from ...lo.view.office_document_view import OfficeDocumentView as OfficeDocumentView from ...lo.view.paper_format import PaperFormat as PaperFormat from ...lo.view.paper_orientation import PaperOrientation as PaperOrientation from ...lo.view.print_job_event import PrintJobEvent as PrintJobEvent from ...lo.view.print_options import PrintOptions as PrintOptions from ...lo.view.print_settings import PrintSettings as PrintSettings from ...lo.view.printable_state import PrintableState as PrintableState from ...lo.view.printable_state_event import PrintableStateEvent as PrintableStateEvent from ...lo.view.printer_descriptor import PrinterDescriptor as PrinterDescriptor from ...lo.view.render_descriptor import RenderDescriptor as RenderDescriptor from ...lo.view.render_options import RenderOptions as RenderOptions from ...lo.view.selection_type import SelectionType as SelectionType from ...lo.view.view_settings import ViewSettings as ViewSettings from ...lo.view.x_control_access import XControlAccess as XControlAccess from ...lo.view.x_form_layer_access import XFormLayerAccess as XFormLayerAccess from ...lo.view.x_line_cursor import XLineCursor as XLineCursor from ...lo.view.x_multi_selection_supplier import XMultiSelectionSupplier as XMultiSelectionSupplier from ...lo.view.x_print_job import XPrintJob as XPrintJob from ...lo.view.x_print_job_broadcaster import XPrintJobBroadcaster as XPrintJobBroadcaster from ...lo.view.x_print_job_listener import XPrintJobListener as XPrintJobListener from ...lo.view.x_print_settings_supplier import XPrintSettingsSupplier as XPrintSettingsSupplier from ...lo.view.x_printable import XPrintable as XPrintable from ...lo.view.x_printable_broadcaster import XPrintableBroadcaster as XPrintableBroadcaster from ...lo.view.x_printable_listener import XPrintableListener as XPrintableListener from ...lo.view.x_renderable import XRenderable as XRenderable from ...lo.view.x_screen_cursor import XScreenCursor as XScreenCursor from ...lo.view.x_selection_change_listener import XSelectionChangeListener as XSelectionChangeListener from ...lo.view.x_selection_supplier import XSelectionSupplier as XSelectionSupplier from ...lo.view.x_view_cursor import XViewCursor as XViewCursor from ...lo.view.x_view_settings_supplier import XViewSettingsSupplier as XViewSettingsSupplier	[ 1, 529, 276, 1112, 420, 29958, 6833, 473, 1028, 14987, 29914, 3634, 29877, 29918, 9447, 29918, 18276, 572, 13, 29937, 14137, 29901, 23616, 29899, 29947, 13, 29937, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29906, 29906, 584, 4297, 719, 29899, 1349, 18902, 29899, 18275, 29901, 341, 2209, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1159, 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, 29901, 849, 7821, 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, 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laa_court_data_api_app/models/ping.py	ministryofjustice/laa-court-data-api	1	74687	from typing import Optional from pydantic import BaseModel, Field class Ping(BaseModel): app_branch: Optional[str] = Field(None, example='test_branch') build_date: Optional[str] = Field(None, example='02022022') build_tag: Optional[str] = Field(None, example='test') commit_id: Optional[str] = Field(None, example='123456')	[ 1, 515, 19229, 1053, 28379, 13, 3166, 282, 2941, 7716, 1053, 7399, 3195, 29892, 8989, 13, 13, 13, 1990, 349, 292, 29898, 5160, 3195, 1125, 13, 1678, 623, 29918, 17519, 29901, 28379, 29961, 710, 29962, 353, 8989, 29898, 8516, 29892, 1342, 2433, 1688, 29918, 17519, 1495, 13, 1678, 2048, 29918, 1256, 29901, 28379, 29961, 710, 29962, 353, 8989, 29898, 8516, 29892, 1342, 2433, 29900, 29906, 29900, 29906, 29906, 29900, 29906, 29906, 1495, 13, 1678, 2048, 29918, 4039, 29901, 28379, 29961, 710, 29962, 353, 8989, 29898, 8516, 29892, 1342, 2433, 1688, 1495, 13, 1678, 9063, 29918, 333, 29901, 28379, 29961, 710, 29962, 353, 8989, 29898, 8516, 29892, 1342, 2433, 29896, 29906, 29941, 29946, 29945, 29953, 1495, 13, 2 ]
tests/test_linalg/test_solvers/test_probabilistic_linear_solver/test_symmetric.py	fxbriol/probnum	0	76261	<reponame>fxbriol/probnum """Tests for probabilistic linear solvers applied to symmetric linear systems.""" import pathlib import numpy as np from pytest_cases import parametrize_with_cases from probnum import linops, problems, randvars from probnum.linalg.solvers import ProbabilisticLinearSolver, beliefs case_modules = pathlib.Path("cases").stem cases_solvers = case_modules + ".solvers" cases_beliefs = case_modules + ".beliefs" cases_problems = case_modules + ".problems" @parametrize_with_cases("solver", cases=cases_solvers, has_tag="sym") @parametrize_with_cases("prior", cases=cases_beliefs, has_tag="sym") @parametrize_with_cases("problem", cases=cases_problems, has_tag="sym") def test_small_residual( solver: ProbabilisticLinearSolver, prior: beliefs.LinearSystemBelief, problem: problems.LinearSystem, ): """Test whether the output solution has small residual.""" belief, _ = solver.solve( prior=prior, problem=problem, rng=np.random.default_rng(42) ) residual_norm = np.linalg.norm(problem.b - problem.A @ belief.x.mean, ord=2) assert residual_norm < 1e-5 or residual_norm < 1e-5 * np.linalg.norm( problem.b, ord=2 ) @parametrize_with_cases("solver", cases=cases_solvers, has_tag="sym") @parametrize_with_cases("problem", cases=cases_problems, has_tag="sym") def test_perfect_information( solver: ProbabilisticLinearSolver, problem: problems.LinearSystem, ncols: int ): """Test whether a solver given perfect information converges instantly.""" # Construct prior belief with perfect information belief = beliefs.LinearSystemBelief( x=randvars.Normal( mean=problem.solution, cov=linops.Scaling(factors=0.0, shape=(ncols, ncols)) ), A=randvars.Constant(problem.A), Ainv=randvars.Constant(np.linalg.inv(problem.A @ np.eye(ncols))), ) # Run solver belief, solver_state = solver.solve( prior=belief, problem=problem, rng=np.random.default_rng(1) ) # Check for instant convergence assert solver_state.step == 0 np.testing.assert_allclose(belief.x.mean, problem.solution)	[ 1, 529, 276, 1112, 420, 29958, 11093, 29890, 374, 324, 29914, 22795, 1949, 13, 15945, 29908, 24376, 363, 23950, 4695, 5608, 899, 874, 7436, 304, 18348, 5608, 6757, 1213, 15945, 13, 5215, 2224, 1982, 13, 13, 5215, 12655, 408, 7442, 13, 3166, 11451, 1688, 29918, 11436, 1053, 25011, 374, 911, 29918, 2541, 29918, 11436, 13, 13, 3166, 2070, 1949, 1053, 6276, 3554, 29892, 4828, 29892, 20088, 16908, 13, 3166, 2070, 1949, 29889, 29880, 979, 29887, 29889, 2929, 874, 1053, 1019, 29890, 4427, 4695, 12697, 13296, 369, 29892, 17750, 29879, 13, 13, 4878, 29918, 7576, 353, 2224, 1982, 29889, 2605, 703, 11436, 2564, 303, 331, 13, 11436, 29918, 2929, 874, 353, 1206, 29918, 7576, 718, 11393, 2929, 874, 29908, 13, 11436, 29918, 6596, 2575, 29879, 353, 1206, 29918, 7576, 718, 11393, 6596, 2575, 29879, 29908, 13, 11436, 29918, 17199, 29879, 353, 1206, 29918, 7576, 718, 11393, 17199, 29879, 29908, 13, 13, 13, 29992, 3207, 300, 374, 911, 29918, 2541, 29918, 11436, 703, 2929, 369, 613, 4251, 29922, 11436, 29918, 2929, 874, 29892, 756, 29918, 4039, 543, 11967, 1159, 13, 29992, 3207, 300, 374, 911, 29918, 2541, 29918, 11436, 703, 29886, 13479, 613, 4251, 29922, 11436, 29918, 6596, 2575, 29879, 29892, 756, 29918, 4039, 543, 11967, 1159, 13, 29992, 3207, 300, 374, 911, 29918, 2541, 29918, 11436, 703, 17199, 613, 4251, 29922, 11436, 29918, 17199, 29879, 29892, 756, 29918, 4039, 543, 11967, 1159, 13, 1753, 1243, 29918, 9278, 29918, 690, 333, 950, 29898, 13, 1678, 899, 369, 29901, 1019, 29890, 4427, 4695, 12697, 13296, 369, 29892, 13, 1678, 7536, 29901, 17750, 29879, 29889, 12697, 3924, 21140, 2575, 29892, 13, 1678, 1108, 29901, 4828, 29889, 12697, 3924, 29892, 13, 1125, 13, 1678, 9995, 3057, 3692, 278, 1962, 1650, 756, 2319, 10995, 950, 1213, 15945, 13, 1678, 17750, 29892, 903, 353, 899, 369, 29889, 2929, 345, 29898, 13, 4706, 7536, 29922, 29886, 13479, 29892, 1108, 29922, 17199, 29892, 364, 865, 29922, 9302, 29889, 8172, 29889, 4381, 29918, 29878, 865, 29898, 29946, 29906, 29897, 13, 1678, 1723, 13, 13, 1678, 10995, 950, 29918, 12324, 353, 7442, 29889, 29880, 979, 29887, 29889, 12324, 29898, 17199, 29889, 29890, 448, 1108, 29889, 29909, 732, 17750, 29889, 29916, 29889, 12676, 29892, 4356, 29922, 29906, 29897, 13, 13, 1678, 4974, 10995, 950, 29918, 12324, 529, 29871, 29896, 29872, 29899, 29945, 470, 10995, 950, 29918, 12324, 529, 29871, 29896, 29872, 29899, 29945, 334, 7442, 29889, 29880, 979, 29887, 29889, 12324, 29898, 13, 4706, 1108, 29889, 29890, 29892, 4356, 29922, 29906, 13, 1678, 1723, 13, 13, 13, 29992, 3207, 300, 374, 911, 29918, 2541, 29918, 11436, 703, 2929, 369, 613, 4251, 29922, 11436, 29918, 2929, 874, 29892, 756, 29918, 4039, 543, 11967, 1159, 13, 29992, 3207, 300, 374, 911, 29918, 2541, 29918, 11436, 703, 17199, 613, 4251, 29922, 11436, 29918, 17199, 29879, 29892, 756, 29918, 4039, 543, 11967, 1159, 13, 1753, 1243, 29918, 546, 3647, 29918, 19678, 29898, 13, 1678, 899, 369, 29901, 1019, 29890, 4427, 4695, 12697, 13296, 369, 29892, 1108, 29901, 4828, 29889, 12697, 3924, 29892, 302, 22724, 29901, 938, 13, 1125, 13, 1678, 9995, 3057, 3692, 263, 899, 369, 2183, 4922, 2472, 24144, 26232, 1213, 15945, 13, 13, 1678, 396, 1281, 4984, 7536, 17750, 411, 4922, 2472, 13, 1678, 17750, 353, 17750, 29879, 29889, 12697, 3924, 21140, 2575, 29898, 13, 4706, 921, 29922, 9502, 16908, 29889, 19077, 29898, 13, 9651, 2099, 29922, 17199, 29889, 2929, 918, 29892, 18838, 29922, 1915, 3554, 29889, 29636, 292, 29898, 17028, 943, 29922, 29900, 29889, 29900, 29892, 8267, 7607, 29876, 22724, 29892, 302, 22724, 876, 13, 4706, 10353, 13, 4706, 319, 29922, 9502, 16908, 29889, 12075, 424, 29898, 17199, 29889, 29909, 511, 13, 4706, 319, 11569, 29922, 9502, 16908, 29889, 12075, 424, 29898, 9302, 29889, 29880, 979, 29887, 29889, 11569, 29898, 17199, 29889, 29909, 732, 7442, 29889, 1032, 29872, 29898, 29876, 22724, 876, 511, 13, 1678, 1723, 13, 13, 1678, 396, 7525, 899, 369, 13, 1678, 17750, 29892, 899, 369, 29918, 3859, 353, 899, 369, 29889, 2929, 345, 29898, 13, 4706, 7536, 29922, 6596, 2575, 29892, 1108, 29922, 17199, 29892, 364, 865, 29922, 9302, 29889, 8172, 29889, 4381, 29918, 29878, 865, 29898, 29896, 29897, 13, 1678, 1723, 13, 13, 1678, 396, 5399, 363, 14426, 17221, 13, 1678, 4974, 899, 369, 29918, 3859, 29889, 10568, 1275, 29871, 29900, 13, 1678, 7442, 29889, 13424, 29889, 9294, 29918, 497, 5358, 29898, 6596, 2575, 29889, 29916, 29889, 12676, 29892, 1108, 29889, 2929, 918, 29897, 13, 2 ]
sqlova/model/nl2sql/wikisql_models.py	guotong1988/Rule-SQL	15	120	# Copyright 2019-present NAVER Corp. # Apache License v2.0 # <NAME> import os, json from copy import deepcopy from matplotlib.pylab import * import torch import torch.nn as nn import torch.nn.functional as F device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from sqlova.utils.utils import topk_multi_dim from sqlova.utils.utils_wikisql import * class Seq2SQL_v1(nn.Module): def __init__(self, input_size, hidden_size, num_layer, dropout, number_cond_ops, number_agg_ops, old=False): super(Seq2SQL_v1, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.max_where_number = 4 self.number_cond_ops = number_cond_ops self.number_agg_ops = number_agg_ops self.select_column_predict = SelectColumnPredict(input_size, hidden_size, num_layer, dropout) self.select_agg_predict = SelectAggPredict(input_size, hidden_size, num_layer, dropout, number_agg_ops, old=old) self.where_number_predict = WhereNumberPredict(input_size, hidden_size, num_layer, dropout) self.wcp = WhereColumnPredict(input_size, hidden_size, num_layer, dropout) self.wop = WhereOpPredict(input_size, hidden_size, num_layer, dropout, number_cond_ops) self.wvp = WhereValuePredict_startend(input_size, hidden_size, num_layer, dropout, number_cond_ops, old=old) # start-end-search-discriminative model # emb_question, [16,26,1536] # len_question, [16] # emb_header, [102,12,1536] # len_header_token, [102] # number_header, [16] def forward(self, emb_question, len_question, emb_header, len_header_token, number_header, g_sc=None, g_sa=None, g_wn=None, g_wc=None, g_wo=None, g_wvi=None, show_p_sc=False, show_p_sa=False, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): # sc s_sc,s_sc_softmax = self.select_column_predict(emb_question, len_question, emb_header, len_header_token, number_header, show_p_sc=show_p_sc) if g_sc: pr_sc = g_sc else: pr_sc = pred_sc(s_sc) # sa s_sa,s_sa_softmax = self.select_agg_predict(emb_question, len_question, emb_header, len_header_token, number_header, pr_sc, show_p_sa=show_p_sa) if g_sa: # it's not necessary though. pr_sa = g_sa else: pr_sa = pred_sa(s_sa) # wn s_wn,s_wn_softmax = self.where_number_predict(emb_question, len_question, emb_header, len_header_token, number_header, show_p_wn=show_p_wn) if g_wn: pr_wn = g_wn else: pr_wn = pred_wn(s_wn) # wc s_wc,s_wc_softmax = self.wcp(emb_question, len_question, emb_header, len_header_token, number_header, show_p_wc=show_p_wc, penalty=True) if g_wc: pr_wc = g_wc else: pr_wc = pred_wherecolumn(pr_wn, s_wc) # wo s_wo,s_wo_softmax = self.wop(emb_question, len_question, emb_header, len_header_token, number_header, wn=pr_wn, wc=pr_wc, show_p_wo=show_p_wo) if g_wo: pr_wo = g_wo else: pr_wo = pred_wo(pr_wn, s_wo) # wv s_wv,s_wv_softmax = self.wvp(emb_question, len_question, emb_header, len_header_token, number_header, wn=pr_wn, wc=pr_wc, wo=pr_wo, show_p_wv=show_p_wv) return s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, s_sc_softmax, s_sa_softmax, s_wn_softmax, s_wc_softmax, s_wo_softmax, s_wv_softmax def beam_forward(self, emb_question, len_question, emb_header, len_header_token, l_header, engine, tb, nlu_t, nlu_wp_t, wp_to_wh_index, nlu, beam_size=4, show_p_sc=False, show_p_sa=False, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): """ Execution-guided beam decoding. """ # sc s_sc,_ = self.select_column_predict(emb_question, len_question, emb_header, len_header_token, l_header, show_p_sc=show_p_sc) prob_sc = F.softmax(s_sc, dim=-1) bS, mcL = s_sc.shape # minimum_header_length = min(l_header) # beam_size = minimum_header_length if beam_size > minimum_header_length else beam_size # sa # Construct all possible sc_sa_score prob_sc_sa = torch.zeros([bS, beam_size, self.number_agg_ops]).to(device) prob_sca = torch.zeros_like(prob_sc_sa).to(device) # get the top-k indices. pr_sc_beam = [B, beam_size] pr_sc_beam = pred_sc_beam(s_sc, beam_size) # calculate and predict s_sa. for i_beam in range(beam_size): pr_sc = list( array(pr_sc_beam)[:,i_beam] ) s_sa,_ = self.select_agg_predict(emb_question, len_question, emb_header, len_header_token, l_header, pr_sc, show_p_sa=show_p_sa) prob_sa = F.softmax(s_sa, dim=-1) prob_sc_sa[:, i_beam, :] = prob_sa prob_sc_selected = prob_sc[range(bS), pr_sc] # [B] prob_sca[:,i_beam,:] = (prob_sa.t() * prob_sc_selected).t() # [mcL, B] * [B] -> [mcL, B] (element-wise multiplication) # [mcL, B] -> [B, mcL] # Calculate the dimension of tensor # tot_dim = len(prob_sca.shape) # First flatten to 1-d idxs = topk_multi_dim(torch.tensor(prob_sca), n_topk=beam_size, batch_exist=True) # Now as sc_idx is already sorted, re-map them properly. idxs = remap_sc_idx(idxs, pr_sc_beam) # [sc_beam_idx, sa_idx] -> [sc_idx, sa_idx] idxs_arr = array(idxs) # [B, beam_size, remainig dim] # idxs[b][0] gives first probable [sc_idx, sa_idx] pairs. # idxs[b][1] gives of second. # Calculate prob_sca, a joint probability beam_idx_sca = [0] * bS beam_meet_the_final = [False] * bS while True: pr_sc = idxs_arr[range(bS),beam_idx_sca,0] pr_sa = idxs_arr[range(bS),beam_idx_sca,1] # map index properly check = check_sc_sa_pairs(tb, pr_sc, pr_sa) if sum(check) == bS: break else: for b, check1 in enumerate(check): if not check1: # wrong pair beam_idx_sca[b] += 1 if beam_idx_sca[b] >= beam_size: beam_meet_the_final[b] = True beam_idx_sca[b] -= 1 else: beam_meet_the_final[b] = True if sum(beam_meet_the_final) == bS: break # Now pr_sc, pr_sa are properly predicted. pr_sc_best = list(pr_sc) pr_sa_best = list(pr_sa) # Now, Where-clause beam search. s_wn,_ = self.where_number_predict(emb_question, len_question, emb_header, len_header_token, l_header, show_p_wn=show_p_wn) prob_wn = F.softmax(s_wn, dim=-1).detach().to('cpu').numpy() # Found "executable" most likely 4(=max_num_of_conditions) where-clauses. # wc s_wc,_ = self.wcp(emb_question, len_question, emb_header, len_header_token, l_header, show_p_wc=show_p_wc, penalty=True) prob_wc = F.sigmoid(s_wc).detach().to('cpu').numpy() # pr_wc_sorted_by_prob = pred_wc_sorted_by_prob(s_wc) # get max_wn # of most probable columns & their prob. pr_wn_max = [self.max_where_number] * bS pr_wc_max = pred_wherecolumn(pr_wn_max, s_wc) # if some column do not have executable where-claouse, omit that column prob_wc_max = zeros([bS, self.max_where_number]) for b, pr_wc_max1 in enumerate(pr_wc_max): prob_wc_max[b,:] = prob_wc[b,pr_wc_max1] # get most probable max_wn where-clouses # wo s_wo_max,_ = self.wop(emb_question, len_question, emb_header, len_header_token, l_header, wn=pr_wn_max, wc=pr_wc_max, show_p_wo=show_p_wo) prob_wo_max = F.softmax(s_wo_max, dim=-1).detach().to('cpu').numpy() # [B, max_wn, n_cond_op] pr_wvi_beam_op_list = [] prob_wvi_beam_op_list = [] for i_op in range(self.number_cond_ops - 1): pr_wo_temp = [[i_op] * self.max_where_number] * bS # wv s_wv,_ = self.wvp(emb_question, len_question, emb_header, len_header_token, l_header, wn=pr_wn_max, wc=pr_wc_max, wo=pr_wo_temp, show_p_wv=show_p_wv) prob_wv = F.softmax(s_wv, dim=-2).detach().to('cpu').numpy() # prob_wv pr_wvi_beam, prob_wvi_beam = pred_wvi_se_beam(self.max_where_number, s_wv, beam_size) pr_wvi_beam_op_list.append(pr_wvi_beam) prob_wvi_beam_op_list.append(prob_wvi_beam) # pr_wvi_beam = [B, max_wn, k_logit*2 [st, ed] paris] # pred_wv_beam # Calculate joint probability of where-clause # prob_w = [batch, wc, wo, wv] = [B, max_wn, n_cond_op, n_pairs] n_wv_beam_pairs = prob_wvi_beam.shape[2] prob_w = zeros([bS, self.max_where_number, self.number_cond_ops - 1, n_wv_beam_pairs]) for b in range(bS): for i_wn in range(self.max_where_number): for i_op in range(self.number_cond_ops - 1): # do not use final one for i_wv_beam in range(n_wv_beam_pairs): # i_wc = pr_wc_max[b][i_wn] # already done p_wc = prob_wc_max[b, i_wn] p_wo = prob_wo_max[b, i_wn, i_op] p_wv = prob_wvi_beam_op_list[i_op][b, i_wn, i_wv_beam] prob_w[b, i_wn, i_op, i_wv_beam] = p_wc p_wo * p_wv # Perform execution guided decoding conds_max = [] prob_conds_max = [] # while len(conds_max) < self.max_wn: idxs = topk_multi_dim(torch.tensor(prob_w), n_topk=beam_size, batch_exist=True) # idxs = [B, i_wc_beam, i_op, i_wv_pairs] # Construct conds1 for b, idxs1 in enumerate(idxs): conds_max1 = [] prob_conds_max1 = [] for i_wn, idxs11 in enumerate(idxs1): i_wc = pr_wc_max[b][idxs11[0]] i_op = idxs11[1] wvi = pr_wvi_beam_op_list[i_op][b][idxs11[0]][idxs11[2]] # get wv_str temp_pr_wv_str, _ = convert_pred_wvi_to_string([[wvi]], [nlu_t[b]], [nlu_wp_t[b]], [wp_to_wh_index[b]], [nlu[b]]) merged_wv11 = merge_wv_t1_eng(temp_pr_wv_str[0][0], nlu[b]) conds11 = [i_wc, i_op, merged_wv11] prob_conds11 = prob_w[b, idxs11[0], idxs11[1], idxs11[2] ] # test execution # print(nlu[b]) # print(tb[b]['id'], tb[b]['types'], pr_sc[b], pr_sa[b], [conds11]) pr_ans = engine.execute(tb[b]['id'], pr_sc[b], pr_sa[b], [conds11]) if bool(pr_ans): # pr_ans is not empty! conds_max1.append(conds11) prob_conds_max1.append(prob_conds11) conds_max.append(conds_max1) prob_conds_max.append(prob_conds_max1) # May need to do more exhuastive search? # i.e. up to.. getting all executable cases. # Calculate total probability to decide the number of where-clauses pr_sql_i = [] prob_wn_w = [] pr_wn_based_on_prob = [] for b, prob_wn1 in enumerate(prob_wn): max_executable_wn1 = len( conds_max[b] ) prob_wn_w1 = [] prob_wn_w1.append(prob_wn1[0]) # wn=0 case. for i_wn in range(max_executable_wn1): prob_wn_w11 = prob_wn1[i_wn+1] * prob_conds_max[b][i_wn] prob_wn_w1.append(prob_wn_w11) pr_wn_based_on_prob.append(argmax(prob_wn_w1)) prob_wn_w.append(prob_wn_w1) pr_sql_i1 = {'agg': pr_sa_best[b], 'sel': pr_sc_best[b], 'conds': conds_max[b][:pr_wn_based_on_prob[b]]} pr_sql_i.append(pr_sql_i1) # s_wv = [B, max_wn, max_nlu_tokens, 2] return prob_sca, prob_w, prob_wn_w, pr_sc_best, pr_sa_best, pr_wn_based_on_prob, pr_sql_i class SelectColumnPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3): super(SelectColumnPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.sc_out = nn.Sequential(nn.Tanh(), nn.Linear(2 * hidden_size, 1)) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) # emb_question, [16,26,1536] # len_question, [16] # emb_header, [102,12,1536] # len_header_token, [102] # number_header, [16] def forward(self, emb_question, len_question, emb_header, len_header_token, number_header, show_p_sc=False): # Encode encoded_question = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, number_header) # [b, header, dim] bS = len(number_header) mL_n = max(len_question) # [bS, max_len_header, 100] * [bS, 100, mL_n] -> [bS, max_len_header, mL_n] att_h = torch.bmm(encoded_header, self.W_att(encoded_question).transpose(1, 2)) # Penalty on blank parts for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att_h[b, :, l_n1:] = -10000000000 p_n = self.softmax_dim2(att_h) if show_p_sc: # p = [b, header, n] if p_n.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001, figsize=(12,3.5)) # subplot(6,2,7) subplot2grid((7,2), (3, 0), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_h in range(number_header[0]): color_idx = i_h % len(_color) plot(p_n[0][i_h][:].data.numpy() - i_h, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('sc: p_n for each h') grid(True) fig.tight_layout() fig.canvas.draw() show() # p_n [ bS, max_len_header, mL_n] -> [ bS, max_len_header, mL_n, 1] # wenc_n [ bS, mL_n, 100] -> [ bS, 1, mL_n, 100] # -> [bS, max_len_header, mL_n, 100] -> [bS, max_len_header, 100] c_n = torch.mul(p_n.unsqueeze(3), encoded_question.unsqueeze(1)).sum(dim=2) vec = torch.cat([self.W_c(c_n), self.W_header(encoded_header)], dim=2) score_select_column = self.sc_out(vec).squeeze(2) # [bS, max_len_header, 1] -> [bS, max_len_header] score_select_column_softmax = self.softmax_dim_1(score_select_column) # Penalty max_len_header = max(number_header) for b, l_header1 in enumerate(number_header): if l_header1 < max_len_header: score_select_column[b, l_header1:] = -10000000000 for b, l_header1 in enumerate(number_header): if l_header1 < max_len_header: score_select_column_softmax[b, l_header1:] = 0 return score_select_column,score_select_column_softmax class SelectAggPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, n_agg_ops=-1, old=False): super(SelectAggPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.sa_out = nn.Sequential(nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, n_agg_ops)) # Fixed number of aggregation operator. self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) if old: # for backwoard compatibility self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, pr_sc, show_p_sa=False): # Encode encoded_question = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] bS = len(l_header) mL_n = max(len_question) wenc_header_ob = encoded_header[list(range(bS)), pr_sc] # list, so one sample for each batch. # [bS, question_len, 100] * [bS, 100, 1] -> [bS, question_len] att = torch.bmm(self.W_att(encoded_question), wenc_header_ob.unsqueeze(2)).squeeze(2) # Penalty on blank parts for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b, l_n1:] = -10000000000 # [bS, question_len] p = self.softmax_dim1(att) if show_p_sa: if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); subplot(7,2,3) cla() plot(p[0].data.numpy(), '--rs', ms=7) title('sa: nlu_weight') grid(True) fig.tight_layout() fig.canvas.draw() show() # [bS, question_len, 100] * ( [bS, question_len, 1] -> [bS, question_len, 100]) # -> [bS, question_len, 100] -> [bS, 100] c_n = torch.mul(encoded_question, p.unsqueeze(2).expand_as(encoded_question)).sum(dim=1) s_sa = self.sa_out(c_n) s_sa_softmax = self.softmax_dim_1(s_sa) return s_sa,s_sa_softmax class WhereNumberPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, ): super(WhereNumberPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.mL_w = 4 # max where condition number self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att_h = nn.Linear(hidden_size, 1) self.W_hidden = nn.Linear(hidden_size, num_layer * hidden_size) self.W_cell = nn.Linear(hidden_size, num_layer * hidden_size) self.W_att_n = nn.Linear(hidden_size, 1) self.wn_out = nn.Sequential(nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, self.mL_w + 1)) # max number (4 + 1) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, show_p_wn=False): # Encode encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, max_len_header, dim] bS = len(l_header) max_len_question = max(len_question) max_len_header = max(l_header) # mL_h = max(len_header_token) # (self-attention?) column Embedding? # [B, max_len_header, 100] -> [B, max_len_header, 1] -> [B, max_len_header] att_h = self.W_att_h(encoded_header).squeeze(2) # Penalty for b, l_header1 in enumerate(l_header): if l_header1 < max_len_header: att_h[b, l_header1:] = -10000000000 p_h = self.softmax_dim1(att_h) if show_p_wn: if p_h.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); subplot(7,2,5) cla() plot(p_h[0].data.numpy(), '--rs', ms=7) title('wn: header_weight') grid(True) fig.canvas.draw() show() # input('Type Eenter to continue.') # [B, max_len_header, 100] * [ B, max_len_header, 1] -> [B, max_len_header, 100] -> [B, 100] c_header = torch.mul(encoded_header, p_h.unsqueeze(2)).sum(1) # [B, 100] --> [B, 2100] Enlarge because there are two layers. hidden = self.W_hidden(c_header) # [B, 4, 200/2] hidden = hidden.view(bS, self.num_layer 2, int( self.hidden_size / 2)) # [4, B, 100/2] # number_of_layer_layer * (bi-direction) # lstm input convention. hidden = hidden.transpose(0, 1).contiguous() cell = self.W_cell(c_header) # [B, 4, 100/2] cell = cell.view(bS, self.num_layer * 2, int(self.hidden_size / 2)) # [4, B, 100/2] cell = cell.transpose(0, 1).contiguous() wenc_n = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=(hidden, cell), last_only=False) # [b, n, dim] att_n = self.W_att_n(wenc_n).squeeze(2) # [B, max_len, 100] -> [B, max_len, 1] -> [B, max_len] # Penalty for b, l_n1 in enumerate(len_question): if l_n1 < max_len_question: att_n[b, l_n1:] = -10000000000 p_n = self.softmax_dim1(att_n) if show_p_wn: if p_n.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); subplot(7,2,6) cla() plot(p_n[0].data.numpy(), '--rs', ms=7) title('wn: nlu_weight') grid(True) fig.canvas.draw() show() # input('Type Enter to continue.') # [B, mL_n, 100] ([B, mL_n] -> [B, mL_n, 1] -> [B, mL_n, 100] ) -> [B, 100] c_n = torch.mul(wenc_n, p_n.unsqueeze(2).expand_as(wenc_n)).sum(dim=1) s_wn = self.wn_out(c_n) s_wn_softmax = self.softmax_dim_1(s_wn) return s_wn,s_wn_softmax # where column predict class WhereColumnPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3): super(WhereColumnPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.W_out = nn.Sequential( nn.Tanh(), nn.Linear(2 hidden_size, 1) ) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, show_p_wc, penalty=True): # Encode encoded_question = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] # attention # wenc = [bS, mL, hidden_size] # att = [bS, max_len_header, mL_n] # att[b, i_h, j_n] = p(j_n\| i_h) att = torch.bmm(encoded_header, self.W_att(encoded_question).transpose(1, 2)) # penalty to blank part. mL_n = max(len_question) for b_n, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b_n, :, l_n1:] = -10000000000 # make p(j_n \| i_h) p = self.softmax_dim2(att) if show_p_wc: # p = [b, header, n] if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); # subplot(6,2,7) subplot2grid((7,2), (3, 1), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_h in range(l_header[0]): color_idx = i_h % len(_color) plot(p[0][i_h][:].data.numpy() - i_h, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('wc: p_n for each h') grid(True) fig.tight_layout() fig.canvas.draw() show() # max nlu context vectors # [bS, max_len_header, mL_n][bS, max_len_header, mL_n] encoded_question = encoded_question.unsqueeze(1) # [ b, n, dim] -> [b, 1, n, dim] p = p.unsqueeze(3) # [b, header, n] -> [b, header, n, 1] c_n = torch.mul(encoded_question, p).sum(2) # -> [b, header, dim], c_n for each header. y = torch.cat([self.W_c(c_n), self.W_header(encoded_header)], dim=2) # [b, header, 2dim] score = self.W_out(y).squeeze(2) # [b, header] score[torch.isnan(score)] = 0 score_softmax = self.softmax_dim_1(score) if penalty: for b, l_header1 in enumerate(l_header): score[b, l_header1:] = -1e+10 for b, l_header1 in enumerate(l_header): score_softmax[b, l_header1:] = 0 return score,score_softmax # where op predict class WhereOpPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, n_cond_ops=3): super(WhereOpPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.mL_w = 4 # max where condition number self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.wo_out = nn.Sequential( nn.Linear(2hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, n_cond_ops) ) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, wn, wc, wenc_n=None, show_p_wo=False): # Encode if not wenc_n: wenc_n = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] bS = len(l_header) # wn wenc_header_ob = [] # observed header for b in range(bS): # [[...], [...]] # Pad list to maximum number of selections real = [encoded_header[b, col] for col in wc[b]] pad = (self.mL_w - wn[b]) [encoded_header[b, 0]] # this padding could be wrong. Test with zero padding later. wenc_header_ob1 = torch.stack(real + pad) # It is not used in the loss function. wenc_header_ob.append(wenc_header_ob1) # list to [B, 4, dim] tensor. wenc_header_ob = torch.stack(wenc_header_ob) # list to tensor. wenc_header_ob = wenc_header_ob.to(device) # [B, 1, mL_n, dim] * [B, 4, dim, 1] # -> [B, 4, mL_n, 1] -> [B, 4, mL_n] # multiplication bewteen NLq-tokens and selected column att = torch.matmul(self.W_att(wenc_n).unsqueeze(1), wenc_header_ob.unsqueeze(3) ).squeeze(3) # Penalty for blank part. mL_n = max(len_question) for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b, :, l_n1:] = -10000000000 p = self.softmax_dim2(att) # p( n\| selected_col ) if show_p_wo: # p = [b, header, n] if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001) # subplot(6,2,7) subplot2grid((7,2), (5, 0), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_wn in range(self.mL_w): color_idx = i_wn % len(_color) plot(p[0][i_wn][:].data.numpy() - i_wn, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('wo: p_n for selected h') grid(True) fig.tight_layout() fig.canvas.draw() show() # [B, 1, mL_n, dim] * [B, 4, mL_n, 1] # --> [B, 4, mL_n, dim] # --> [B, 4, dim] c_n = torch.mul(wenc_n.unsqueeze(1), p.unsqueeze(3)).sum(dim=2) # [bS, 5-1, dim] -> [bS, 5-1, 3] vec = torch.cat([self.W_c(c_n), self.W_header(wenc_header_ob)], dim=2) s_wo = self.wo_out(vec) s_wo_softmax = self.softmax_dim_1(s_wo) return s_wo,s_wo_softmax class WhereValuePredict_startend(nn.Module): """ Discriminative model Get start and end. Here, classifier for [ [투수], [팀1], [팀2], [연도], ...] Input: Encoded nlu & selected column. Algorithm: Encoded nlu & selected column. -> classifier -> mask scores -> ... """ def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, n_cond_ops=4, old=False): super(WhereValuePredict_startend, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.n_cond_ops = n_cond_ops self.mL_w = 4 # max where condition number self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.W_op = nn.Linear(n_cond_ops, hidden_size) # self.W_n = nn.Linear(hidden_size, hidden_size) if old: self.wv_out = nn.Sequential( nn.Linear(4 * hidden_size, 2) ) else: self.wv_out = nn.Sequential( nn.Linear(4 * hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, 2) ) # self.wv_out = nn.Sequential( # nn.Linear(3 * hidden_size, hidden_size), # nn.Tanh(), # nn.Linear(hidden_size, self.gdkL) # ) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, wn, wc, wo, wenc_n=None, show_p_wv=False): # Encode if not wenc_n: wenc_n, hout, cout = encode(self.enc_n, emb_question, len_question, return_hidden=True, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] bS = len(l_header) wenc_header_ob = [] # observed header for b in range(bS): # [[...], [...]] # Pad list to maximum number of selections real = [encoded_header[b, col] for col in wc[b]] pad = (self.mL_w - wn[b]) * [encoded_header[b, 0]] # this padding could be wrong. Test with zero padding later. wenc_header_ob1 = torch.stack(real + pad) # It is not used in the loss function. wenc_header_ob.append(wenc_header_ob1) # list to [B, 4, dim] tensor. wenc_header_ob = torch.stack(wenc_header_ob) # list to tensor. wenc_header_ob = wenc_header_ob.to(device) # Column attention # [B, 1, mL_n, dim] * [B, 4, dim, 1] # -> [B, 4, mL_n, 1] -> [B, 4, mL_n] # multiplication bewteen NLq-tokens and selected column att = torch.matmul(self.W_att(wenc_n).unsqueeze(1), wenc_header_ob.unsqueeze(3) ).squeeze(3) # Penalty for blank part. mL_n = max(len_question) for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b, :, l_n1:] = -10000000000 p = self.softmax_dim2(att) # p( n\| selected_col ) if show_p_wv: # p = [b, header, n] if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001) # subplot(6,2,7) subplot2grid((7,2), (5, 1), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_wn in range(self.mL_w): color_idx = i_wn % len(_color) plot(p[0][i_wn][:].data.numpy() - i_wn, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('wv: p_n for selected h') grid(True) fig.tight_layout() fig.canvas.draw() show() # [B, 1, mL_n, dim] * [B, 4, mL_n, 1] # --> [B, 4, mL_n, dim] # --> [B, 4, dim] c_n = torch.mul(wenc_n.unsqueeze(1), p.unsqueeze(3)).sum(dim=2) # Select observed headers only. # Also generate one_hot vector encoding info of the operator # [B, 4, dim] wenc_op = [] for b in range(bS): # [[...], [...]] # Pad list to maximum number of selections wenc_op1 = torch.zeros(self.mL_w, self.n_cond_ops) wo1 = wo[b] idx_scatter = [] l_wo1 = len(wo1) for i_wo11 in range(self.mL_w): if i_wo11 < l_wo1: wo11 = wo1[i_wo11] idx_scatter.append([int(wo11)]) else: idx_scatter.append([0]) # not used anyway wenc_op1 = wenc_op1.scatter(1, torch.tensor(idx_scatter), 1) wenc_op.append(wenc_op1) # list to [B, 4, dim] tensor. wenc_op = torch.stack(wenc_op) # list to tensor. wenc_op = wenc_op.to(device) # Now after concat, calculate logits for each token # [bS, 5-1, 3hidden_size] = [bS, 4, 300] vec = torch.cat([self.W_c(c_n), self.W_header(wenc_header_ob), self.W_op(wenc_op)], dim=2) # Make extended vector based on encoded nl token containing column and operator information. # wenc_n = [bS, mL, 100] # vec2 = [bS, 4, mL, 400] vec1e = vec.unsqueeze(2).expand(-1,-1, mL_n, -1) # [bS, 4, 1, 300] -> [bS, 4, mL, 300] wenc_ne = wenc_n.unsqueeze(1).expand(-1, 4, -1, -1) # [bS, 1, mL, 100] -> [bS, 4, mL, 100] vec2 = torch.cat( [vec1e, wenc_ne], dim=3) # now make logits s_wv = self.wv_out(vec2) # [bS, 4, mL, 400] -> [bS, 4, mL, 2] s_wv_softmax = self.softmax_dim_1(s_wv) # penalty for spurious tokens for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: s_wv[b, :, l_n1:, :] = -10000000000 for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: s_wv_softmax[b, :, l_n1:, :] = 0 return s_wv,s_wv_softmax def Loss_selectwhere_startend_v2(score_select_column, s_sa, s_wn, s_wc, s_wo, s_wv, ground_truth_select_column, g_sa, g_wn, g_wc, g_wo, g_wvi): """ :param s_wv: score [ B, n_conds, T, score] :param g_wn: [ B ] :param g_wvi: [B, conds, pnt], e.g. [[[0, 6, 7, 8, 15], [0, 1, 2, 3, 4, 15]], [[0, 1, 2, 3, 16], [0, 7, 8, 9, 16]]] :return: """ loss = 0 # loss += Loss_sc(score_select_column, ground_truth_select_column) # loss += Loss_sa(s_sa, g_sa) # loss += Loss_wn(s_wn, g_wn) # loss += Loss_wc(s_wc, g_wc) # loss += Loss_wo(s_wo, g_wn, g_wo) # loss += Loss_wv_se(s_wv, g_wn, g_wvi) return loss def Loss_sw_se(score_select_column, s_sa, s_wn, s_wc, s_wo, s_wv, ground_truth_select_column, g_sa, g_wn, g_wc, g_wo, g_wvi): """ :param s_wv: score [ B, n_conds, T, score] :param g_wn: [ B ] :param g_wvi: [B, conds, pnt], e.g. [[[0, 6, 7, 8, 15], [0, 1, 2, 3, 4, 15]], [[0, 1, 2, 3, 16], [0, 7, 8, 9, 16]]] :return: """ loss = 0 loss += Loss_sc(score_select_column, ground_truth_select_column) loss += Loss_sa(s_sa, g_sa) loss += Loss_wn(s_wn, g_wn) loss += Loss_wc(s_wc, g_wc) loss += Loss_wo(s_wo, g_wn, g_wo) loss += Loss_wv_se(s_wv, g_wn, g_wvi) return loss def Loss_sc(s_sc, g_sc): loss = F.cross_entropy(s_sc, torch.tensor(g_sc).to(device)) return loss def Loss_sa(s_sa, g_sa): loss = F.cross_entropy(s_sa, torch.tensor(g_sa).to(device)) return loss def Loss_wn(s_wn, g_wn): loss = F.cross_entropy(s_wn, torch.tensor(g_wn).to(device)) return loss def Loss_wc(s_wc, g_wc): # Construct index matrix bS, max_h_len = s_wc.shape im = torch.zeros([bS, max_h_len]).to(device) for b, g_wc1 in enumerate(g_wc): for g_wc11 in g_wc1: im[b, g_wc11] = 1.0 # Construct prob. p = F.sigmoid(s_wc) loss = F.binary_cross_entropy(p, im) return loss def Loss_wo(s_wo, g_wn, g_wo): # Construct index matrix loss = 0 for b, g_wn1 in enumerate(g_wn): if g_wn1 == 0: continue g_wo1 = g_wo[b] s_wo1 = s_wo[b] loss += F.cross_entropy(s_wo1[:g_wn1], torch.tensor(g_wo1).to(device)) return loss def Loss_wv_se(s_wv, g_wn, g_wvi): """ s_wv: [bS, 4, mL, 2], 4 stands for maximum # of condition, 2 tands for start & end logits. g_wvi: [ [1, 3, 2], [4,3] ] (when B=2, wn(b=1) = 3, wn(b=2) = 2). """ loss = 0 # g_wvi = torch.tensor(g_wvi).to(device) for b, g_wvi1 in enumerate(g_wvi): # for i_wn, g_wvi11 in enumerate(g_wvi1): g_wn1 = len(g_wvi1) # 有改动 # g_wn1 = g_wn[b] # 有改动 if g_wn1 == 0: continue g_wvi1 = torch.tensor(g_wvi1)[:g_wn1].to(device) # 有改动 g_st1 = g_wvi1[:,0] g_ed1 = g_wvi1[:,1] # loss from the start position loss += F.cross_entropy(s_wv[b,:g_wn1,:,0], g_st1) # print("st_login: ", s_wv[b,:g_wn1,:,0], g_st1, loss) # loss from the end position loss += F.cross_entropy(s_wv[b,:g_wn1,:,1], g_ed1) # print("ed_login: ", s_wv[b,:g_wn1,:,1], g_ed1, loss) return loss # ========= Decoder-Layer =========== class FT_s2s_1(nn.Module): """ Decoder-Layer """ def __init__(self, input_size, hidden_size, num_layer, dropout, max_seq_length, n_cond_ops, n_agg_ops, old=False): super(FT_s2s_1, self).__init__() self.input_size = input_size # input_size self.hidden_size = hidden_size # hidden_size self.ls = num_layer self.dropout = dropout self.n_cond_ops = n_cond_ops self.n_agg_ops = n_agg_ops self.n_where_num = 4 self.decoder_s2s = Decoder_s2s(input_size, hidden_size, num_layer, dropout, max_seq_length) def forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, g_pnt_idxs=None): score = self.decoder_s2s(wenc_s2s, l_input, cls_vec, pnt_start_tok, g_pnt_idxs) return score def EG_forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, pnt_end_tok, i_sql_vocab, i_nlu, i_hds, # for EG tokens, nlu, nlu_t, hds, tt_to_t_idx, # for EG tb, engine, beam_size=4, beam_only=True): """ EG-guided beam-search """ score = self.decoder_s2s.EG_forward(wenc_s2s, l_input, cls_vec, pnt_start_tok, pnt_end_tok, i_sql_vocab, i_nlu, i_hds, # for EG tokens, nlu, nlu_t, hds, tt_to_t_idx, # for EG tb, engine, beam_size, beam_only) return score class Decoder_s2s(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, max_seq_length=222, n_cond_ops=3): super(Decoder_s2s, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.mL = max_seq_length self.Tmax = 200 self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.decode_pn = nn.LSTM(input_size=max_seq_length, hidden_size=hidden_size, num_layers=num_layer, batch_first=True, dropout=dropout) self.W_s2s = nn.Linear(input_size, hidden_size) self.W_pnt = nn.Linear(hidden_size, hidden_size) self.wv_out = nn.Sequential(nn.Tanh(), nn.Linear(hidden_size, 1)) def forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, g_pnt_idxs=None,): # Encode bS, mL_input, input_size = wenc_s2s.shape # Now, pointer network. ipnt = wenc_s2s.new_zeros(bS, 1, mL_input).to(device) # [B, 1, 200] ipnt[:, 0, pnt_start_tok] = 1 # 27 is of start token under current tokenization scheme # initial (current) pointer cpnt = ipnt # reshape wenc_s2s to incorporate T later wenc_s2s = wenc_s2s.unsqueeze(1) # h_0 and c_0 from cls_vec # They are not bidirectional. h_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) c_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) for i_layer in range(self.num_layer): h_st = (2i_layer)self.hidden_size h_ed = h_st + self.hidden_size c_st = (2i_layer+1)self.hidden_size c_ed = c_st + self.hidden_size h_0[i_layer] = cls_vec[:, h_st:h_ed] # [ # of layers, batch, dim] c_0[i_layer] = cls_vec[:, c_st:c_ed] # [ # of layers, batch, dim] if g_pnt_idxs: pnt_n = torch.zeros(bS, self.Tmax, mL_input).to(device) # one hot # assign index for b, g_pnt_idxs1 in enumerate(g_pnt_idxs): for t, g_pnt_idx in enumerate(g_pnt_idxs1): pnt_n[b, t, g_pnt_idx] = 1 # Encode dec_pn, _ = self.decode_pn(pnt_n, (h_0, c_0)) dec_pn = dec_pn.contiguous() # [bS, T, input_size] dec_pn = dec_pn.unsqueeze(2) # Calculate score s_wv = self.wv_out( self.W_s2s(wenc_s2s) + self.W_pnt(dec_pn) ).squeeze(3) # [B, T, mL_input, dim] -> [B, T, mL_input, 1] -> [B, T, mL_input] # s_wv = [B, 4, T, mL_n] = [batch, conds, token idx, score] # penalty for b, l_input1 in enumerate(l_input): if l_input1 < mL_input: s_wv[b, :, l_input1:] = -10000000000 else: t = 0 s_wv_list = [] cpnt_h = (h_0, c_0) while t < self.Tmax: dec_pn, cpnt_h = self.decode_pn(cpnt, cpnt_h) # lstm # [B, 1, 100] -> [B, 1, 1, 100] dec_pn = dec_pn.unsqueeze(2) # [bS, T, input_size] # get score s_wv1 = self.wv_out( self.W_s2s(wenc_s2s) # [B, 1, mL_input, dim] + self.W_pnt(dec_pn) # [B, T=1, 1, dim] Now, T=1 ).squeeze(3) # s_wv = [B, 4, 1, mL_n, 1] = [batch, conds, token idx, score] # -> [B, 4, mL_n] # Masking -- for b, l_input1 in enumerate(l_input): if l_input1 < mL_input: s_wv1[b, :, l_input1:] = -10000000000 # Collect score-- s_wv_list.append(s_wv1) # [B, 1, mL_input] -> [B, mL_n] -> [bS(5-1)] # (max_val, max_indices) _val, pnt_n = s_wv1.view(bS, -1).max(dim=1) # formatting pnt_n as a one-hot input. cpnt = torch.zeros(bS, mL_input).to(device) # cpnt = cpnt.scatter_(dim=1, index=pnt_n.unsqueeze(1), src=1).to(device) cpnt = cpnt.scatter_(1, pnt_n.unsqueeze(1), 1) cpnt = cpnt.unsqueeze(1) # --> [B * 4, 1, 200] t += 1 s_wv = torch.stack(s_wv_list, 1) # [B, s_wv = s_wv.squeeze(2) # # # Following lines seems to be unnecessary. # # Penalty to blank parts # for b, l_input1 in enumerate(l_input): # if l_input1 < mL_input: # s_wv[b, :, l_input1:] = -10000000000 return s_wv def EG_forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, pnt_end_tok, i_sql_vocab, i_nlu, i_hds, # for EG tokens, nlu, nlu_t, hds, tt_to_t_idx, # for EG tb, engine, beam_size, beam_only=True): # Encode bS, mL_input, input_size = wenc_s2s.shape # reshape wenc_s2s to incorperate T later wenc_s2s = wenc_s2s.unsqueeze(1) # h_0 and c_0 from cls_vec # They are not bidirectional. h_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) c_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) for i_layer in range(self.num_layer): h_st = (2i_layer)self.hidden_size h_ed = h_st + self.hidden_size c_st = (2i_layer+1)self.hidden_size c_ed = c_st + self.hidden_size h_0[i_layer] = cls_vec[:, h_st:h_ed] # [ # of layers, batch, dim] c_0[i_layer] = cls_vec[:, c_st:c_ed] # [ # of layers, batch, dim] # initial (current) pointer pnt_list_beam = [] cpnt_beam = [] cpnt_h_beam = [] for i_beam in range(beam_size): pnt_list_beam1 = [] for b in range(bS): pnt_list_beam1.append( [ [pnt_start_tok], 0] ) pnt_list_beam.append(pnt_list_beam1) # initisl cpnt # Now, initialize pointer network. ipnt = wenc_s2s.new_zeros(bS, 1, mL_input).to(device) # [B, 1, 200] # Distort ipnt by i_bam on purpose to avoid initial duplication of beam-search ipnt[:, 0, pnt_start_tok] = 1 # 27 is of start token under current tokenization scheme cpnt_beam.append(ipnt) cpnt_h_beam.append( (h_0, c_0) ) t = 0 while t < self.Tmax: # s_wv1_beam = [] candidates = [ [] for b in range(bS) ] # [bS] # Generate beam for i_beam, cpnt in enumerate(cpnt_beam): cpnt_h = cpnt_h_beam[i_beam] pnt_list_beam1 = pnt_list_beam[i_beam] dec_pn, cpnt_h = self.decode_pn(cpnt, cpnt_h) # lstm cpnt_h_beam[i_beam] = cpnt_h # [B, 1, 100] -> [B, 1, 1, 100] dec_pn = dec_pn.unsqueeze(2) # [bS, T, input_size] # get score s_wv1 = self.wv_out( self.W_s2s(wenc_s2s) # [B, 1, mL_input, dim] + self.W_pnt(dec_pn) # [B, T=1, 1, dim] Now, T=1 ).squeeze(3) # s_wv = [B, 4, 1, mL_n, 1] = [batch, conds, token idx, score] # -> [B, 4, mL_n] # Masking -- for b, l_input1 in enumerate(l_input): if l_input1 < mL_input: s_wv1[b, :, l_input1:] = -10000000000 # Get the candidates only among the input space. prob, idxs = F.softmax(s_wv1.view(bS, -1), dim=1).topk(dim=1, k=max(l_input)) log_prob = torch.log(prob) # [bS, beam_size] for b, log_prob1 in enumerate(log_prob): pnt_list11, score = pnt_list_beam1[b] for i_can, log_prob11 in enumerate(log_prob1): # no update if last token was the end-token previous_pnt = pnt_list11[-1] if previous_pnt== pnt_end_tok: new_seq = pnt_list11 new_score = score else: new_seq = pnt_list11 + [idxs[b][i_can].item()] new_score = score + log_prob11.item() _candidate = [new_seq, new_score] candidates[b].append(_candidate) # Execution-guided beam filtering for b, candidates1 in enumerate(candidates): new_pnt_list_batch1 = sorted(candidates1, key=lambda list1: list1[-1], reverse=True) count = 0 selected_candidates1 = [] for new_pnt_list_batch11 in new_pnt_list_batch1: if new_pnt_list_batch11 not in selected_candidates1: if beam_only: selected_candidates1.append(new_pnt_list_batch11) pnt_list_beam[count][b] = new_pnt_list_batch11 count +=1 else: # Need to be modified here. executable = False testable = False pr_i_vg_list, pr_i_vg_sub_list = gen_i_vg_from_pnt_idxs([new_pnt_list_batch11[0]], [i_sql_vocab[b]], [i_nlu[b]], [i_hds[b]]) pr_sql_q_s2s, pr_sql_i = gen_sql_q_from_i_vg([tokens[b]], [nlu[b]], [nlu_t[b]], [hds[b]], [tt_to_t_idx[b]], pnt_start_tok, pnt_end_tok, [new_pnt_list_batch11[0]], pr_i_vg_list, pr_i_vg_sub_list) # check testability from select-clause try: # check whether basic elements presents in pr_sql_i # If so, it is testable. idx_agg = pr_sql_i[0]["agg"] idx_sel = pr_sql_i[0]["sel"] testable = True except: testable = False pass # check the presence of conds if testable: try: conds = pr_sql_i[0]["conds"] except: conds = [] try: pr_ans1 = engine.execute(tb[b]['id'], idx_sel, idx_agg, conds) executable = bool(pr_ans1) except: executable = False # if testable: if executable: add_candidate = True else: add_candidate = False else: add_candidate = True if add_candidate: selected_candidates1.append(new_pnt_list_batch11) pnt_list_beam[count][b] = new_pnt_list_batch11 count += 1 if count == beam_size: break if count < beam_size: # not executable at all.. # add junk sequence. for i_junk in range(count, beam_size): pnt_list_beam[i_junk][b] = [[pnt_end_tok],-9999999] # generate cpnt # formatting pnt_n as a one-hot input. for i_beam in range(beam_size): cpnt = torch.zeros(bS, mL_input).to(device) # cpnt = cpnt.scatter_(dim=1, index=pnt_n.unsqueeze(1), src=1).to(device) idx_batch = [seq_score[0][-1] for seq_score in pnt_list_beam[i_beam]] pnt_n = torch.tensor(idx_batch).to(device) cpnt = cpnt.scatter_(1, pnt_n.unsqueeze(1), 1) cpnt = cpnt.unsqueeze(1) # --> [B, t=1, mL_input] cpnt_beam[i_beam] = cpnt t += 1 # Generate best pr_pnt_list, p_tot pr_pnt_idxs = [] p_list = [] for b in range(bS): pnt_list_beam_best = pnt_list_beam[0] pr_pnt_idxs.append(pnt_list_beam_best[b][0]) p_list.append( pnt_list_beam_best[b][1]) return pr_pnt_idxs, p_list, pnt_list_beam # ============= Shallow-Layer =============== class FT_Scalar_1(nn.Module): """ Shallow-Layer """ def __init__(self, input_size, hidden_size, num_layer, dropout, n_cond_ops, n_agg_ops, old=False): super(FT_Scalar_1, self).__init__() self.input_size = input_size # input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.n_cond_ops = n_cond_ops self.n_agg_ops = n_agg_ops self.n_where_num = 4 def scp(self, wemb_h, l_header): bS, max_header_len, _ = wemb_h.shape # s_sc s_sc = torch.zeros(bS, max_header_len).to(device) s_sc[:, :] = wemb_h[:, :, 0] # s_sc = [B, max_header length, 1] # s_sc[:,:] = F.tanh(wemb_h[:,:,0]) # s_sc = [B, max_header length, 1] # s_sc = s_sc.squeeze(2) # masking # print(f"s_sc {s_sc}") for b, l_header1 in enumerate(l_header): s_sc[b, l_header1:] = -9999999999.0 return s_sc def sap(self, wemb_h, pr_sc, idx_st, idx_ed): bS, max_header_len, _ = wemb_h.shape # select of aggregation operator s_sa = torch.zeros([bS, self.n_agg_ops]).to(device) for b, pr_sc1 in enumerate(pr_sc): s_sa[b,:] = wemb_h[b,pr_sc1,idx_st:idx_ed] return s_sa def wnp(self, cls_vec): bS = cls_vec.shape[0] # [B,hidden_size] -> [B, n_where_num+1] s_wn = torch.zeros(bS, (self.n_where_num + 1)).to(device) s_wn[:, :] = cls_vec[:, 0:(self.n_where_num + 1)] return s_wn def wcp(self, wemb_h, l_header, idx_st, idx_ed): bS, max_header_len, _ = wemb_h.shape s_wc = torch.zeros(bS, max_header_len, 1).to(device) s_wc[:, :, :] = wemb_h[:, :, idx_st:idx_ed] s_wc = s_wc.squeeze(2) # [B, max_header_length] # masking for b, l_header1 in enumerate(l_header): s_wc[b, l_header1:] = -99999999999.0 return s_wc def wop(self, wemb_h, pr_wc, idx_st, idx_ed): bS, max_header_len, _ = wemb_h.shape s_wo = torch.zeros([bS, self.n_where_num, self.n_cond_ops]).to(device) for b, pr_wc1 in enumerate(pr_wc): if len(pr_wc1) > 0: s_wo[b, 0:len(pr_wc1), :] = wemb_h[b, pr_wc1, idx_st:idx_ed] else: pass return s_wo def wvp(self, emb_question, len_question, pr_wc): bS, _, _ = emb_question.shape s_wv = torch.zeros([bS, self.n_where_num, max(len_question), 2]).to(device) for b, pr_wc1 in enumerate(pr_wc): if len(pr_wc1) > 0: # start logit s_wv[b, 0:len(pr_wc1), :, 0] = emb_question[b, :, pr_wc1].transpose(0, 1) # end logit s_wv[b, 0:len(pr_wc1), :, 1] = emb_question[b, :, [pr_wc11 + 100 for pr_wc11 in pr_wc1]].transpose(0, 1) else: pass # masking # penalty for spurious tokens for b, l_n1 in enumerate(len_question): if l_n1 < max(len_question): s_wv[b, :, l_n1:, :] = -1e+11 return s_wv def forward(self, emb_question, len_question, wemb_h, l_header, cls_vec, g_sc=None, g_sa=None, g_wn=None, g_wc=None, g_wo=None, g_wvi=None, show_p_sc=False, show_p_sa=False, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): # emb_question = [B, max_nlu_token_length, hidden_size] # here, # of target_layer is fixed to 1. # wemb_h = [B, max_header #, hidden_size] s_sc = self.scp(wemb_h, l_header) if g_sc: pr_sc = g_sc else: pr_sc = pred_sc(s_sc) # s_sa idx_st = 1 idx_ed = 1 + self.n_agg_ops s_sa = self.sap(wemb_h, pr_sc, idx_st, idx_ed) if g_sa: pr_sa = g_sa else: pr_sa = pred_sa(s_sa) # where_number s_wn = self.wnp(cls_vec) if g_wn: pr_wn = g_wn else: pr_wn = pred_wn(s_wn) # wc idx_st = idx_ed+1 idx_ed = idx_st+1 s_wc = self.wcp(wemb_h, l_header, idx_st, idx_ed) if g_wc: pr_wc = g_wc else: pr_wc = pred_wherecolumn(pr_wn, s_wc) # wo idx_st = idx_ed+1 idx_ed = idx_st + self.n_cond_ops s_wo = self.wop(wemb_h, pr_wc, idx_st, idx_ed) if g_wo: pr_wo = g_wo else: pr_wo = pred_wo(pr_wn, s_wo) # wv # s_wv = [bS, 4, mL, 2] s_wv = self.wvp(emb_question, len_question, pr_wc) # print(s_wv) # s_wv = F.tanh(s_wv) return s_sc, s_sa, s_wn, s_wc, s_wo, s_wv def forward_EG(self, emb_question, len_question, wemb_h, l_header, cls_vec, engine, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu, beam_size=4): """ Execution-guided beam decoding. Essentially identical with that of NL2SQL Layer. """ # Select-clause prob_sca, pr_sc_best, pr_sa_best, \ p_sc_best, p_sa_best, p_select \ = self.EG_decoding_select(wemb_h, l_header, tb, beam_size=beam_size) # Where-clause prob_w, prob_wn_w, pr_wn_based_on_prob, pr_sql_i, pr_wvi_best, \ p_where, p_wn_best, p_wc_best, p_wo_best, p_wvi_best \ = self.EG_decoding_where(emb_question, len_question, wemb_h, l_header, cls_vec, engine, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu, pr_sc_best, pr_sa_best, beam_size=4) p_tot = cal_prob_tot(p_select, p_where) return pr_sc_best, pr_sa_best, pr_wn_based_on_prob, pr_wvi_best, \ pr_sql_i, p_tot, p_select, p_where, p_sc_best, p_sa_best, \ p_wn_best, p_wc_best, p_wo_best, p_wvi_best def EG_decoding_select(self, wemb_h, l_header, tb, beam_size=4, show_p_sc=False, show_p_sa=False): # sc s_sc = self.scp(wemb_h, l_header) prob_sc = F.softmax(s_sc, dim=-1) bS, mcL = s_sc.shape # minimum_header_length = min(l_header) # beam_size = minimum_header_length if beam_size > minimum_header_length else beam_size # sa # Construct all possible sc_sa_score prob_sc_sa = torch.zeros([bS, beam_size, self.n_agg_ops]).to(device) score_sc_sa = torch.zeros([bS, beam_size, self.n_agg_ops]).to(device) prob_sca = torch.zeros_like(prob_sc_sa).to(device) # get the top-k indices. pr_sc_beam = [B, beam_size] pr_sc_beam = pred_sc_beam(s_sc, beam_size) # calculate and predict s_sa. idx_st = 1 idx_ed = 1 + self.n_agg_ops for i_beam in range(beam_size): pr_sc = list(array(pr_sc_beam)[:, i_beam]) s_sa = self.sap(wemb_h, pr_sc, idx_st, idx_ed) prob_sa = F.softmax(s_sa, dim=-1) prob_sc_sa[:, i_beam, :] = prob_sa score_sc_sa[:, i_beam, :] = s_sa prob_sc_selected = prob_sc[range(bS), pr_sc] # [B] prob_sca[:, i_beam, :] = (prob_sa.t() * prob_sc_selected).t() # [mcL, B] * [B] -> [mcL, B] (element-wise multiplication) # [mcL, B] -> [B, mcL] # Calculate the dimension of tensor # tot_dim = len(prob_sca.shape) idxs = topk_multi_dim(torch.tensor(prob_sca), n_topk=beam_size, batch_exist=True) # Now as sc_idx is already sorted, re-map them properly. idxs = remap_sc_idx(idxs, pr_sc_beam) # [sc_beam_idx, sa_idx] -> [sc_idx, sa_idx] idxs_arr = array(idxs) # [B, beam_size, remainig dim] # idxs[b][0] gives first probable [sc_idx, sa_idx] pairs. # idxs[b][1] gives of second. # Calculate prob_sca, a joint probability beam_idx_sca = [0] * bS beam_meet_the_final = [False] * bS while True: pr_sc = idxs_arr[range(bS), beam_idx_sca, 0] pr_sa = idxs_arr[range(bS), beam_idx_sca, 1] # map index properly check = check_sc_sa_pairs(tb, pr_sc, pr_sa) if sum(check) == bS: break else: for b, check1 in enumerate(check): if not check1: # wrong pair beam_idx_sca[b] += 1 if beam_idx_sca[b] >= beam_size: beam_meet_the_final[b] = True beam_idx_sca[b] -= 1 else: beam_meet_the_final[b] = True if sum(beam_meet_the_final) == bS: break # Now pr_sc, pr_sa are properly predicted. pr_sc_best = list(pr_sc) pr_sa_best = list(pr_sa) # output for later analysis. p_sc_best = cal_prob_sc(s_sc, pr_sc_best) p_sa_best = cal_prob_sa(score_sc_sa[range(bS), beam_idx_sca, :].squeeze(1), pr_sa_best) p_select = cal_prob_select(p_sc_best, p_sa_best) # p_select = prob_sca[range(bS),beam_idx_sca,pr_sa_best].detach().to('cpu').numpy() return prob_sca, pr_sc_best, pr_sa_best, p_sc_best, p_sa_best, p_select def EG_decoding_where(self, emb_question, len_question, wemb_h, l_header, cls_vec, engine, tb, nlu_t, nlu_wp_t, tt_to_t_idx, nlu, pr_sc_best, pr_sa_best, beam_size=4, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): bS, max_header_len, _ = wemb_h.shape # Now, Where-clause beam search. idx_st = 1 idx_ed = 1 + self.n_agg_ops s_wn = self.wnp(cls_vec) prob_wn = F.softmax(s_wn, dim=-1).detach().to('cpu').numpy() # Found "executable" most likely 4(=max_num_of_conditions) where-clauses. # wc idx_st = idx_ed + 1 idx_ed = idx_st + 1 s_wc = self.wcp(wemb_h, l_header, idx_st, idx_ed) prob_wc = torch.sigmoid(s_wc).detach().to('cpu').numpy() # pr_wc_sorted_by_prob = pred_wc_sorted_by_prob(s_wc) # get max_wn # of most probable columns & their prob. pr_wn_max = [self.n_where_num] * bS pr_wc_max = pred_wherecolumn(pr_wn_max, s_wc) # if some column do not have executable where-claouse, omit that column prob_wc_max = zeros([bS, self.n_where_num]) for b, pr_wc_max1 in enumerate(pr_wc_max): prob_wc_max[b, :] = prob_wc[b, pr_wc_max1] # get most probable n_where_num where-clouses # wo idx_st = idx_ed + 1 idx_ed = idx_st + self.n_cond_ops s_wo_max = self.wop(wemb_h, pr_wc_max, idx_st, idx_ed) prob_wo_max = F.softmax(s_wo_max, dim=-1).detach().to('cpu').numpy() # [B, n_where_num, n_cond_op] pr_wvi_beam_op_list = [] prob_wvi_beam_op_list = [] prob_wvi_beam_st_op_list = [] prob_wvi_beam_ed_op_list = [] # To re-use code, repeat the calculation unnecessarily. for i_op in range(self.n_cond_ops - 1): pr_wo_temp = [[i_op] * self.n_where_num] * bS # wv s_wv = self.wvp(emb_question, len_question, pr_wc_max) prob_wv = F.softmax(s_wv, dim=-2).detach().to('cpu').numpy() # prob_wv pr_wvi_beam, prob_wvi_beam, prob_wvi_beam_st, prob_wvi_beam_ed = pred_wvi_se_beam(self.n_where_num, s_wv, beam_size) pr_wvi_beam_op_list.append(pr_wvi_beam) prob_wvi_beam_op_list.append(prob_wvi_beam) prob_wvi_beam_st_op_list.append(prob_wvi_beam_st) prob_wvi_beam_ed_op_list.append(prob_wvi_beam_ed) # pr_wvi_beam = [B, n_where_num, k_logit*2 [st, ed] paris] # pred_wv_beam # Calculate joint probability of where-clause # prob_w = [batch, wc, wo, wv] = [B, n_where_num, n_cond_op, n_pairs] n_wv_beam_pairs = prob_wvi_beam.shape[2] prob_w = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wc_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wo_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wvi_st_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wvi_ed_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) for b in range(bS): for i_wn in range(self.n_where_num): for i_op in range(self.n_cond_ops - 1): # do not use final one p_wc = prob_wc_max[b, i_wn] for i_wv_beam in range(n_wv_beam_pairs): # i_wc = pr_wc_max[b][i_wn] # already done p_wo = prob_wo_max[b, i_wn, i_op] p_wv = prob_wvi_beam_op_list[i_op][b, i_wn, i_wv_beam] prob_w[b, i_wn, i_op, i_wv_beam] = p_wc p_wo * p_wv prob_wc_dupl[b, i_wn, i_op, i_wv_beam] = p_wc prob_wo_dupl[b, i_wn, i_op, i_wv_beam] = p_wo p_wv_st = prob_wvi_beam_st_op_list[i_op][b, i_wn, i_wv_beam] p_wv_ed = prob_wvi_beam_ed_op_list[i_op][b, i_wn, i_wv_beam] prob_wvi_st_dupl[b, i_wn, i_op, i_wv_beam] = p_wv_st prob_wvi_ed_dupl[b, i_wn, i_op, i_wv_beam] = p_wv_ed # Perform execution guided decoding conds_max = [] prob_conds_max = [] # while len(conds_max) < self.n_where_num: idxs = topk_multi_dim(torch.tensor(prob_w), n_topk=beam_size, batch_exist=True) # idxs = [B, i_wc_beam, i_op, i_wv_pairs] # Construct conds1. Collect only executable one. It is descending order of the probability. pr_wvi_max = [] p_wc_max = [] p_wo_max = [] p_wvi_max = [] for b, idxs1 in enumerate(idxs): conds_max1 = [] prob_conds_max1 = [] pr_wvi1_max = [] p_wc1_max = [] p_wo1_max = [] p_wvi1_max = [] for i_wn, idxs11 in enumerate(idxs1): i_wc = pr_wc_max[b][idxs11[0]] i_op = idxs11[1] wvi = pr_wvi_beam_op_list[i_op][b][idxs11[0]][idxs11[2]] # idx11[0] # get wv_str temp_pr_wv_str, _ = convert_pred_wvi_to_string([[wvi]], [nlu_t[b]], [nlu_wp_t[b]], [tt_to_t_idx[b]], [nlu[b]]) merged_wv11 = merge_wv_t1_eng(temp_pr_wv_str[0][0], nlu[b]) conds11 = [i_wc, i_op, merged_wv11] prob_conds11 = prob_w[b, idxs11[0], idxs11[1], idxs11[2]] p_wc11_max = prob_wc_dupl[b, idxs11[0], idxs11[1], idxs11[2]] p_wo11_max = prob_wo_dupl[b, idxs11[0], idxs11[1], idxs11[2]] p_wvi11_max = [ prob_wvi_st_dupl[b, idxs11[0], idxs11[1], idxs11[2]], prob_wvi_ed_dupl[b, idxs11[0], idxs11[1], idxs11[2]] ] # test execution # print(nlu[b]) # print(tb[b]['id'], tb[b]['types'], pr_sc[b], pr_sa[b], [conds11]) pr_ans = engine.execute(tb[b]['id'], pr_sc_best[b], pr_sa_best[b], [conds11]) if bool(pr_ans): # pr_ans is not empty! conds_max1.append(conds11) prob_conds_max1.append(prob_conds11) pr_wvi1_max.append(wvi) p_wc1_max.append(p_wc11_max) p_wo1_max.append(p_wo11_max) p_wvi1_max.append(p_wvi11_max) conds_max.append(conds_max1) prob_conds_max.append(prob_conds_max1) pr_wvi_max.append(pr_wvi1_max) p_wc_max.append(p_wc1_max) p_wo_max.append(p_wo1_max) p_wvi_max.append(p_wvi1_max) # May need to do more exhuastive search? # i.e. up to.. getting all executable cases. # Calculate total probability to decide the number of where-clauses pr_sql_i = [] prob_wn_w = [] # total where-clause probability pr_wn_based_on_prob = [] pr_wvi_best = [] p_wc = [] p_wo = [] p_wvi = [] for b, prob_wn1 in enumerate(prob_wn): max_executable_wn1 = len(conds_max[b]) prob_wn_w1 = [] prob_wn_w1.append(prob_wn1[0]) # wn=0 case. for i_wn in range(max_executable_wn1): prob_wn_w11 = prob_wn1[i_wn + 1] * prob_conds_max[b][i_wn] prob_wn_w1.append(prob_wn_w11) pr_wn_based_on_prob.append(argmax(prob_wn_w1)) prob_wn_w.append(prob_wn_w1) pr_sql_i1 = {'agg': pr_sa_best[b], 'sel': pr_sc_best[b], 'conds': conds_max[b][:pr_wn_based_on_prob[b]]} pr_wvi_best1 = pr_wvi_max[b][:pr_wn_based_on_prob[b]] pr_sql_i.append(pr_sql_i1) pr_wvi_best.append(pr_wvi_best1) p_wc.append( p_wc_max[b][:pr_wn_based_on_prob[b]] ) p_wo.append( p_wo_max[b][:pr_wn_based_on_prob[b]] ) p_wvi.append( p_wvi_max[b][:pr_wn_based_on_prob[b]] ) # s_wv = [B, n_where_num, max_nlu_tokens, 2] p_wn = cal_prob_wn(s_wn, pr_wn_based_on_prob) p_where = cal_prob_where(p_wn, p_wc, p_wo, p_wvi) return prob_w, prob_wn_w, pr_wn_based_on_prob, pr_sql_i, pr_wvi_best, \ p_where, p_wn, p_wc, p_wo, p_wvi def Loss_s2s(score, g_pnt_idxs): """ score = [B, T, max_seq_length] """ # WHERE string part loss = 0 for b, g_pnt_idxs1 in enumerate(g_pnt_idxs): ed = len(g_pnt_idxs1) - 1 score_part = score[b, :ed] loss += F.cross_entropy(score_part, torch.tensor(g_pnt_idxs1[1:]).to(device)) # +1 shift. return loss	[ 1, 396, 14187, 1266, 29871, 29906, 29900, 29896, 29929, 29899, 6338, 8598, 5348, 2994, 29886, 29889, 13, 29937, 13380, 19245, 325, 29906, 29889, 29900, 13, 13, 29937, 529, 5813, 29958, 13, 13, 5215, 2897, 29892, 4390, 13, 3166, 3509, 1053, 6483, 8552, 13, 3166, 22889, 29889, 2272, 8205, 1053, 334, 13, 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, 13, 13, 10141, 353, 4842, 305, 29889, 10141, 703, 29883, 6191, 29908, 565, 4842, 305, 29889, 29883, 6191, 29889, 275, 29918, 16515, 580, 1683, 376, 21970, 1159, 13, 13, 3166, 18074, 417, 1564, 29889, 13239, 29889, 13239, 1053, 2246, 29895, 29918, 9910, 29918, 6229, 13, 3166, 18074, 417, 1564, 29889, 13239, 29889, 13239, 29918, 2851, 275, 1519, 1053, 334, 13, 13, 1990, 25981, 29906, 4176, 29918, 29894, 29896, 29898, 15755, 29889, 7355, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 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tests/test_db.py	schmocker/pv-FHNW	1	159902	import pytest from pvtool.db import PvModule, Measurement, MeasurementValues, FlasherData, ManufacturerData, db @pytest.mark.incremental class TestDBModels(object): def test_pv_module_insert(client, init_db): """ GIVEN a database WHEN database is initialized THEN check if pv_module can be inserted and removed """ test_pv_module = PvModule(model="TEST", manufacturer="TEST", cell_type="TEST", additional_information="TEST", price_CHF="-999", length="-999", width="-999", shunt_resistance="-999", ) db.session.add(test_pv_module) db.session.commit() query_result = db.session.query(PvModule).filter(PvModule.model == test_pv_module.model).first() assert query_result db.session.query(PvModule).filter(PvModule.model == test_pv_module.model).delete() db.session.commit() def test_measurement_insert(client, init_db): """ GIVEN a database WHEN database is initialized THEN check if pv_module can be inserted and removed """ test_measurement = Measurement(date='TEST', measurement_series='TEST', producer='TEST', pv_module_id=1, ) db.session.add(test_measurement) db.session.commit() query_result = db.session.query(Measurement).filter(Measurement.measurement_series == test_measurement.measurement_series).first() assert query_result db.session.query(Measurement).filter(Measurement.measurement_series == test_measurement.measurement_series).delete() db.session.commit() def test_measurement_values_insert(client, init_db): """ GIVEN a database WHEN database is initialized THEN check if measurement_values can be inserted and removed """ test_measurement_values = MeasurementValues(weather='TEST', _U_module=0, _U_shunt=0, _U_T_amb=0, _U_T_pan=0, _U_G_hor=0, _U_G_pan=0, _U_G_ref=0, measurement_id=1, ) db.session.add(test_measurement_values) db.session.commit() query_result = db.session.query(MeasurementValues).\ filter(MeasurementValues.weather == test_measurement_values.weather).first() assert query_result db.session.query(MeasurementValues).filter(MeasurementValues.weather == test_measurement_values.weather).delete() db.session.commit() def insert_multiple_pv_modules(client, init_db): pass	[ 1, 1053, 11451, 1688, 13, 3166, 282, 29894, 10154, 29889, 2585, 1053, 349, 29894, 7355, 29892, 2191, 3745, 358, 29892, 2191, 3745, 358, 9065, 29892, 21967, 261, 1469, 29892, 2315, 9765, 9945, 1469, 29892, 4833, 13, 13, 13, 29992, 2272, 1688, 29889, 3502, 29889, 25629, 284, 13, 1990, 4321, 4051, 23785, 29898, 3318, 1125, 13, 1678, 822, 1243, 29918, 29886, 29894, 29918, 5453, 29918, 7851, 29898, 4645, 29892, 2069, 29918, 2585, 1125, 13, 4706, 9995, 13, 9651, 402, 5667, 1430, 263, 2566, 13, 9651, 17980, 2566, 338, 16601, 13, 9651, 8183, 1423, 565, 282, 29894, 29918, 5453, 508, 367, 15478, 322, 6206, 13, 4706, 9995, 13, 4706, 1243, 29918, 29886, 29894, 29918, 5453, 353, 349, 29894, 7355, 29898, 4299, 543, 18267, 613, 13, 462, 462, 12012, 9945, 543, 18267, 613, 13, 462, 462, 3038, 29918, 1853, 543, 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7924, 29889, 15060, 580, 13, 13, 13, 1753, 4635, 29918, 20787, 29918, 29886, 29894, 29918, 7576, 29898, 4645, 29892, 2069, 29918, 2585, 1125, 13, 1678, 1209, 13, 2 ]
autobahn/wamp/gen/wamp/proto/PublisherFeatures.py	rapyuta-robotics/autobahn-python	1,670	94563	# automatically generated by the FlatBuffers compiler, do not modify # namespace: proto import flatbuffers class PublisherFeatures(object): __slots__ = ['_tab'] @classmethod def GetRootAsPublisherFeatures(cls, buf, offset): n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) x = PublisherFeatures() x.Init(buf, n + offset) return x # PublisherFeatures def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) # PublisherFeatures def PublisherIdentification(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # PublisherFeatures def PublisherExclusion(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # PublisherFeatures def SubscriberBlackwhiteListing(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # PublisherFeatures def AcknowledgeEventReceived(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # PublisherFeatures def PayloadTransparency(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # PublisherFeatures def PayloadEncryptionCryptobox(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False def PublisherFeaturesStart(builder): builder.StartObject(6) def PublisherFeaturesAddPublisherIdentification(builder, publisherIdentification): builder.PrependBoolSlot(0, publisherIdentification, 0) def PublisherFeaturesAddPublisherExclusion(builder, publisherExclusion): builder.PrependBoolSlot(1, publisherExclusion, 0) def PublisherFeaturesAddSubscriberBlackwhiteListing(builder, subscriberBlackwhiteListing): builder.PrependBoolSlot(2, subscriberBlackwhiteListing, 0) def PublisherFeaturesAddAcknowledgeEventReceived(builder, acknowledgeEventReceived): builder.PrependBoolSlot(3, acknowledgeEventReceived, 0) def PublisherFeaturesAddPayloadTransparency(builder, payloadTransparency): builder.PrependBoolSlot(4, payloadTransparency, 0) def PublisherFeaturesAddPayloadEncryptionCryptobox(builder, payloadEncryptionCryptobox): builder.PrependBoolSlot(5, payloadEncryptionCryptobox, 0) def PublisherFeaturesEnd(builder): return builder.EndObject()	[ 1, 396, 6336, 5759, 491, 278, 2379, 271, 29933, 3096, 414, 6516, 29892, 437, 451, 6623, 13, 13, 29937, 7397, 29901, 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setup.py	initOS/dob-lib	0	78135	import os from setuptools import find_packages, setup def read(fname): with open(os.path.join(os.path.dirname(__file__), fname)) as f: return f.read() deps = [ "black", "coverage", "debugpy", "flake8", "git-aggregator", "ipython", "isort>=4.3.10", "pylint_odoo", "pytest-cov", "PyYAML", ] if not os.getenv("DISABLE_PYTEST_ODOO"): deps.append("pytest-odoo") setup( name="doblib", version="0.8.3", author="<EMAIL>", author_email="<EMAIL>", description="Management tool for Odoo installations", long_description=read("README.md"), long_description_content_type="text/markdown", license="Apache License 2.0", keywords="odoo environment management", url="https://github.com/initos/dob-lib", packages=find_packages("src"), package_dir={"": "src"}, include_package_data=True, install_requires=deps, entry_points={"console_scripts": ["dob = doblib.main:main"]}, classifiers=[ "Environment :: Console", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Programming Language :: Python :: 3", ], python_requires=">=3.6", )	[ 1, 1053, 2897, 13, 13, 3166, 731, 21245, 8789, 1053, 1284, 29918, 8318, 29892, 6230, 13, 13, 13, 1753, 1303, 29898, 29888, 978, 1125, 13, 1678, 411, 1722, 29898, 359, 29889, 2084, 29889, 7122, 29898, 359, 29889, 2084, 29889, 25721, 22168, 1445, 1649, 511, 285, 978, 876, 408, 285, 29901, 13, 4706, 736, 285, 29889, 949, 580, 13, 13, 13, 311, 567, 353, 518, 13, 1678, 376, 8517, 613, 13, 1678, 376, 11911, 482, 613, 13, 1678, 376, 8382, 2272, 613, 13, 1678, 376, 29888, 433, 446, 29947, 613, 13, 1678, 376, 5559, 29899, 26193, 1061, 613, 13, 1678, 376, 666, 1656, 613, 13, 1678, 376, 275, 441, 18572, 29946, 29889, 29941, 29889, 29896, 29900, 613, 13, 1678, 376, 2272, 27854, 29918, 397, 3634, 613, 13, 1678, 376, 2272, 1688, 29899, 24542, 613, 13, 1678, 376, 19737, 29979, 23956, 613, 13, 29962, 13, 13, 361, 451, 2897, 29889, 657, 6272, 703, 23711, 6181, 29918, 20055, 18267, 29918, 29949, 3970, 29949, 29908, 1125, 13, 1678, 316, 567, 29889, 4397, 703, 2272, 1688, 29899, 397, 3634, 1159, 13, 13, 14669, 29898, 13, 1678, 1024, 543, 11152, 1982, 613, 13, 1678, 1873, 543, 29900, 29889, 29947, 29889, 29941, 613, 13, 1678, 4148, 543, 29966, 26862, 6227, 28341, 13, 1678, 4148, 29918, 5269, 543, 29966, 26862, 6227, 28341, 13, 1678, 6139, 543, 27107, 5780, 363, 438, 1867, 29877, 2601, 800, 613, 13, 1678, 1472, 29918, 8216, 29922, 949, 703, 16310, 2303, 29889, 3487, 4968, 13, 1678, 1472, 29918, 8216, 29918, 3051, 29918, 1853, 543, 726, 29914, 3502, 3204, 613, 13, 1678, 19405, 543, 17396, 1829, 19245, 29871, 29906, 29889, 29900, 613, 13, 1678, 29361, 543, 397, 3634, 5177, 10643, 613, 13, 1678, 3142, 543, 991, 597, 3292, 29889, 510, 29914, 2344, 359, 29914, 11152, 29899, 1982, 613, 13, 1678, 9741, 29922, 2886, 29918, 8318, 703, 4351, 4968, 13, 1678, 3577, 29918, 3972, 3790, 29908, 1115, 376, 4351, 10758, 13, 1678, 3160, 29918, 5113, 29918, 1272, 29922, 5574, 29892, 13, 1678, 2601, 29918, 276, 339, 2658, 29922, 311, 567, 29892, 13, 1678, 6251, 29918, 9748, 3790, 29908, 11058, 29918, 16713, 1115, 6796, 11152, 353, 20129, 1982, 29889, 3396, 29901, 3396, 3108, 1118, 13, 1678, 770, 14903, 11759, 13, 4706, 376, 18649, 4761, 9405, 613, 13, 4706, 376, 29931, 293, 1947, 4761, 438, 5425, 28268, 1490, 4761, 13380, 18540, 19245, 613, 13, 4706, 376, 7094, 1218, 2184, 4761, 6570, 25266, 613, 13, 4706, 376, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 613, 13, 1678, 21251, 13, 1678, 3017, 29918, 276, 339, 2658, 543, 18572, 29941, 29889, 29953, 613, 13, 29897, 13, 2 ]
yummyPie.py	BoomBanger/EatPie	0	1610018	import time time.sleep(5) import datetime import os import git import pyglet from pyglet.window import Platform dir = os.path.dirname(os.path.realpath(__file__)) repo = git.cmd.Git(dir) t = -1 lastpull = 0 with open(os.path.join(dir, "dinnerTime.txt"), "r") as f: timestr = f.read() #####################################################04/02/22 04:03 t = time.mktime(datetime.datetime.strptime(timestr, "%m/%d/%y %H:%M:%S").timetuple()) #t += 53600 #only for replit, thinks we are in london def pull(): global time, dir, repo try: repo.pull() with open(os.path.join(dir, "dinnerTime.txt"), "r") as f: timestr = f.read() #####################################################04/02/22 04:03 t = time.mktime(datetime.datetime.strptime(timestr, "%d/%m/%y %H:%M:%S").timetuple()) #t += 53600 #only for replit, thinks we are in london except: pass while time.time() < t: print("waiting") if time.time() - lastpull > 46060: pull() lastpull = time.time() if time.time() < t + 106060: monitor = Platform().get_default_display().get_default_screen() sprite = pyglet.sprite.Sprite(pyglet.resource.animation(os.path.join(dir, "text.gif"))) H_ratio = max(sprite.height, monitor.height) / min(sprite.height, monitor.height) W_ratio = max(sprite.width, monitor.width) / min(sprite.width, monitor.width) sprite.scale = min(H_ratio, W_ratio) window = pyglet.window.Window(width=monitor.width, height=monitor.height, fullscreen=True) pyglet.gl.glClearColor(1, 1, 1, 1) @window.event def on_draw(): window.clear() sprite.draw() pyglet.app.run()	[ 1, 1053, 931, 13, 2230, 29889, 17059, 29898, 29945, 29897, 13, 13, 5215, 12865, 13, 5215, 2897, 13, 5215, 6315, 13, 5215, 19484, 1026, 13, 3166, 19484, 1026, 29889, 7165, 1053, 28096, 13, 13, 3972, 353, 2897, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 6370, 2084, 22168, 1445, 1649, 876, 13, 13, 20095, 353, 6315, 29889, 9006, 29889, 28712, 29898, 3972, 29897, 13, 13, 29873, 353, 448, 29896, 13, 4230, 26746, 353, 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tests/ut/python/dataset/test_datasets_obs_mindrecord.py	httpsgithu/mindspore	1	183851	# Copyright 2022 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """ Test OBSMindDataset operator """ import pytest from mindspore.dataset.engine.datasets_standard_format import OBSMindDataset from mindspore import log as logger DATA_DIR = ["s3://dataset/imagenet0", "s3://dataset/imagenet1"] def test_obs_mindrecord_exception(): """ Feature: Test OBSMindDataset. Description: invalid input. Expectation: raise exception. """ logger.info("Test error cases for MnistDataset") error_msg_0 = "Argument dataset_files" with pytest.raises(TypeError, match=error_msg_0): OBSMindDataset("err_dataset", "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir") error_msg_0_1 = "Item of dataset files" with pytest.raises(TypeError, match=error_msg_0_1): OBSMindDataset([1, 2], "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir") error_msg_1 = "Argument server" with pytest.raises(TypeError, match=error_msg_1): OBSMindDataset(DATA_DIR, 12, "dummy_ak", "dummy_sk", "s3://dummy_sync_dir") error_msg_1_1 = "server should" with pytest.raises(ValueError, match=error_msg_1_1): OBSMindDataset(DATA_DIR, "ftp://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir") error_msg_2 = "Argument ak" with pytest.raises(TypeError, match=error_msg_2): OBSMindDataset(DATA_DIR, "https://dummy_site", 12, "dummy_sk", "s3://dummy_sync_dir") error_msg_3 = "Argument sk" with pytest.raises(TypeError, match=error_msg_3): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", 12, "s3://dummy_sync_dir") error_msg_4 = "Argument sync_obs_path" with pytest.raises(TypeError, match=error_msg_4): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", "dummy_sk", 12) error_msg_5 = "Input shard_id is not within the required interval" with pytest.raises(ValueError, match=error_msg_5): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir", num_shards=2, shard_id=-1) with pytest.raises(ValueError, match=error_msg_5): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir", num_shards=4, shard_id=4) with pytest.raises(ValueError, match=error_msg_5): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir", num_shards=2, shard_id=4) error_msg_7 = "Argument shard_equal_rows" with pytest.raises(TypeError, match=error_msg_7): OBSMindDataset(DATA_DIR, "https://dummy_site", 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importanize/groups.py	xiachufang/importanize	0	13649	# -- coding: utf-8 -- from __future__ import absolute_import, print_function, unicode_literals import itertools import operator from collections import OrderedDict, defaultdict from functools import reduce import six from .formatters import DEFAULT_FORMATTER, DEFAULT_LENGTH from .utils import is_site_package, is_std_lib @six.python_2_unicode_compatible class BaseImportGroup(object): def __init__(self, config=None, *kwargs): self.config = config or {} self.statements = kwargs.get("statements", []) self.file_artifacts = kwargs.get("file_artifacts", {}) @property def unique_statements(self): return sorted(list(set(self.merged_statements))) @property def merged_statements(self): """ Merge statements with the same import stems """ leafless_counter = defaultdict(list) counter = defaultdict(list) for statement in self.statements: if statement.leafs: counter[statement.stem].append(statement) else: leafless_counter[statement.stem].append(statement) merged_statements = list(itertools.chain(leafless_counter.values())) def merge(statements): _special = [] _statements = [] for i in statements: if i.leafs and i.leafs[0].name == "": _special.append(i) else: _statements.append(i) _reduced = [] if _statements: _reduced = [reduce(lambda a, b: a + b, _statements)] return _special + _reduced for statements in counter.values(): merged_statements.extend(merge(statements)) return merged_statements def all_line_numbers(self): return sorted( list( set( list( itertools.chain( map( operator.attrgetter("line_numbers"), self.statements, ) ) ) ) ) ) def should_add_statement(self, statement): raise NotImplementedError def add_statement(self, statement): if self.should_add_statement(statement): self.statements.append(statement) return True return False def as_string(self): sep = self.file_artifacts.get("sep", "\n") return sep.join( map(operator.methodcaller("as_string"), self.unique_statements) ) def formatted(self, formatter=DEFAULT_FORMATTER, length=DEFAULT_LENGTH): sep = self.file_artifacts.get("sep", "\n") return sep.join( map( operator.methodcaller( "formatted", formatter=formatter, length=length ), self.unique_statements, ) ) def __str__(self): return self.as_string() class StdLibGroup(BaseImportGroup): def should_add_statement(self, statement): return is_std_lib(statement.root_module) class SitePackagesGroup(BaseImportGroup): def should_add_statement(self, statement): return is_site_package(statement.root_module) class PackagesGroup(BaseImportGroup): def __init__(self, args, kwargs): super(PackagesGroup, self).__init__(args, *kwargs) if "packages" not in self.config: msg = ( '"package" config must be supplied ' "for packages import group" ) raise ValueError(msg) def should_add_statement(self, statement): return statement.root_module in self.config.get("packages", []) class LocalGroup(BaseImportGroup): def should_add_statement(self, statement): return statement.stem.startswith(".") class RemainderGroup(BaseImportGroup): def should_add_statement(self, statement): return True # -- RemainderGroup goes last and catches everything left over GROUP_MAPPING = OrderedDict( ( ("stdlib", StdLibGroup), ("sitepackages", SitePackagesGroup), ("packages", PackagesGroup), ("local", LocalGroup), ("remainder", RemainderGroup), ) ) def sort_groups(groups): return sorted( groups, key=lambda i: list(GROUP_MAPPING.values()).index(type(i)) ) @six.python_2_unicode_compatible class ImportGroups(list): def __init__(self, args, *kwargs): super(ImportGroups, self).__init__(args) self.file_artifacts = kwargs.get("file_artifacts", {}) def all_line_numbers(self): return sorted( list( set( list( itertools.chain( map( operator.methodcaller("all_line_numbers"), self ) ) ) ) ) ) def add_group(self, config): if "type" not in config: msg = '"type" must be specified in ' "import group config" raise ValueError(msg) if config["type"] not in GROUP_MAPPING: msg = '"{}" is not supported import group'.format(config["type"]) raise ValueError(msg) self.append(GROUP_MAPPING[config["type"]](config)) def add_statement_to_group(self, statement): groups_by_priority = sort_groups(self) added = False for group in groups_by_priority: if group.add_statement(statement): added = True break if not added: msg = ( "Import statement was not added into " "any of the import groups. 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src/download_dataset.py	thomasreolon/DeepfakeDetection	2	181910	<gh_stars>1-10 import os, pathlib from gdrive import GoogleDriveDownloader as gdd from videoanalizer import VideoAnalizer os.chdir(pathlib.Path(__file__).parent.absolute()) #GDRIVE_CODE = '1La_4SVYRNT8ePgLzZGwlxuY1V9x989DC' # old dataset GDRIVE_CODE = '1djFwp9vLkmOtd65ylXSYYn7C1ju08lJ7' ######## Create Directory & Download Videos if ('test_data' not in os.listdir('../')): # if folder is empty, get videos from gdrive print("Default dataset size: 6GB") gdd.download_file_from_google_drive(file_id=GDRIVE_CODE, # my GDrive dest_path='../dataset.zip', unzip=True, showsize=True) os.system('rm ../dataset.zip') ######## This will fail and ask you to install OpenFace # press y to install OpenFace VideoAnalizer()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 2897, 29892, 2224, 1982, 13, 3166, 330, 21594, 1053, 5087, 29928, 4401, 6767, 12657, 408, 330, 1289, 13, 3166, 4863, 7054, 3950, 1053, 13987, 21067, 3950, 13, 13, 359, 29889, 305, 3972, 29898, 2084, 1982, 29889, 2605, 22168, 1445, 1649, 467, 3560, 29889, 23552, 3101, 13, 13, 29937, 29954, 29928, 3960, 12064, 29918, 16524, 353, 525, 29896, 5661, 29918, 29946, 7597, 29979, 29934, 20321, 29947, 29872, 29925, 29887, 29931, 29920, 29999, 29954, 29893, 29880, 29916, 29884, 29979, 29896, 29963, 29929, 29916, 29929, 29947, 29929, 12696, 29915, 29871, 396, 2030, 8783, 13, 29954, 29928, 3960, 12064, 29918, 16524, 353, 525, 29896, 19776, 29943, 11912, 29929, 29894, 29931, 8848, 29949, 1594, 29953, 29945, 2904, 29990, 14816, 29979, 29876, 29955, 29907, 29896, 4900, 29900, 29947, 29880, 29967, 29955, 29915, 13, 13, 7346, 6204, 18862, 669, 25553, 11812, 13, 13, 361, 6702, 1688, 29918, 1272, 29915, 451, 297, 2897, 29889, 1761, 3972, 877, 6995, 8785, 29901, 13, 1678, 396, 565, 4138, 338, 4069, 29892, 679, 19707, 515, 330, 21594, 13, 1678, 1596, 703, 4592, 8783, 2159, 29901, 29871, 29953, 7210, 1159, 13, 1678, 330, 1289, 29889, 10382, 29918, 1445, 29918, 3166, 29918, 3608, 29918, 21594, 29898, 1445, 29918, 333, 29922, 29954, 29928, 3960, 12064, 29918, 16524, 29892, 1678, 396, 590, 402, 29928, 4401, 13, 462, 462, 4706, 2731, 29918, 2084, 2433, 6995, 24713, 29889, 7554, 742, 13, 462, 462, 4706, 443, 7554, 29922, 5574, 29892, 13, 462, 462, 4706, 3697, 675, 29922, 5574, 29897, 13, 1678, 2897, 29889, 5205, 877, 1758, 29772, 24713, 29889, 7554, 1495, 13, 13, 13, 13, 7346, 910, 674, 4418, 322, 2244, 366, 304, 2601, 4673, 23360, 13, 13, 29937, 3965, 343, 304, 2601, 4673, 23360, 13, 15167, 21067, 3950, 580, 13, 2 ]
luxai2021/game/resource.py	nofate090/LuxPythonEnvGym	0	143923	'''Implements /src/Resource/index.ts''' class Resource: ''' Enum implemenation ''' class Types: WOOD = 'wood' COAL = 'coal' URANIUM = 'uranium' def __init__(self, type, amount) -> None: self.type = type self.amount = amount	[ 1, 29871, 13, 12008, 1888, 9711, 847, 4351, 29914, 6848, 29914, 2248, 29889, 1372, 12008, 13, 13, 1990, 18981, 29901, 13, 1678, 14550, 1174, 398, 527, 552, 1527, 362, 14550, 13, 1678, 770, 28025, 29901, 13, 4706, 399, 29949, 13668, 353, 525, 6115, 29915, 13, 4706, 4810, 1964, 353, 525, 1111, 284, 29915, 13, 4706, 501, 29934, 2190, 29902, 5005, 353, 525, 332, 273, 1974, 29915, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1134, 29892, 5253, 29897, 1599, 6213, 29901, 13, 4706, 1583, 29889, 1853, 353, 1134, 13, 4706, 1583, 29889, 14506, 353, 5253, 13, 2 ]
gocrawl/request.py	stephanebruckert/GoCrawl	0	152916	#!/usr/bin/python import requests from pyquery import PyQuery def stringified_page(url): ''' Request a webpage ''' r = requests.get(url) if r.status_code == 200: return str(PyQuery(r.text)) else: raise Exception(r.status_code)	[ 1, 18787, 4855, 29914, 2109, 29914, 4691, 13, 13, 5215, 7274, 13, 3166, 11451, 1972, 1053, 10772, 3010, 13, 13, 13, 1753, 1347, 2164, 29918, 3488, 29898, 2271, 1125, 13, 1678, 14550, 13, 1678, 10729, 263, 24499, 13, 1678, 14550, 13, 1678, 364, 353, 7274, 29889, 657, 29898, 2271, 29897, 13, 13, 1678, 565, 364, 29889, 4882, 29918, 401, 1275, 29871, 29906, 29900, 29900, 29901, 13, 4706, 736, 851, 29898, 19737, 3010, 29898, 29878, 29889, 726, 876, 13, 1678, 1683, 29901, 13, 4706, 12020, 8960, 29898, 29878, 29889, 4882, 29918, 401, 29897, 13, 2 ]
cfanalytics/tests/test_affiliatelist.py	raybellwaves/cfanalytics	23	125641	<filename>cfanalytics/tests/test_affiliatelist.py import pytest import requests # HTTP library from . import TestCase class TestAffiliatelist(TestCase): def setUp(self): self.basepath = 'https://map.crossfit.com' self.headers = {'Host': 'map.crossfit.com', 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_2'+\ ') AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.13'+\ '2 Safari/537.36'} def test_get_lat_lons(self): expected = 40.3604 response = requests.get(self.basepath+'/getAllAffiliates.php').json() actual = response[0][0] assert expected == actual	[ 1, 529, 9507, 29958, 6854, 7054, 22026, 29914, 21150, 29914, 1688, 29918, 3470, 2638, 14830, 391, 29889, 2272, 13, 5215, 11451, 1688, 13, 5215, 7274, 396, 7331, 3489, 13, 13, 3166, 869, 1053, 4321, 8259, 13, 13, 13, 1990, 4321, 27867, 2638, 14830, 391, 29898, 3057, 8259, 1125, 268, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 1583, 29889, 3188, 2084, 353, 525, 991, 597, 1958, 29889, 19128, 9202, 29889, 510, 29915, 13, 4706, 1583, 29889, 13662, 353, 11117, 8514, 2396, 525, 1958, 29889, 19128, 9202, 29889, 510, 742, 13, 1669, 525, 2659, 29899, 19661, 2396, 525, 29924, 2112, 2911, 29914, 29945, 29889, 29900, 313, 15735, 524, 10578, 29936, 18555, 4326, 6570, 1060, 29871, 29896, 29900, 29918, 29896, 29941, 29918, 29906, 29915, 3124, 13, 1669, 25710, 12113, 3609, 13117, 29914, 29945, 29941, 29955, 29889, 29941, 29953, 313, 29968, 7020, 29892, 763, 1879, 27604, 29897, 10228, 29914, 29953, 29941, 29889, 29900, 29889, 29941, 29906, 29941, 29929, 29889, 29896, 29941, 29915, 3124, 13, 1669, 525, 29906, 24544, 29914, 29945, 29941, 29955, 29889, 29941, 29953, 10827, 13, 13, 13, 1678, 822, 1243, 29918, 657, 29918, 5066, 29918, 29880, 787, 29898, 1311, 1125, 13, 4706, 3806, 353, 29871, 29946, 29900, 29889, 29941, 29953, 29900, 29946, 13, 4706, 2933, 353, 7274, 29889, 657, 29898, 1311, 29889, 3188, 2084, 23097, 29914, 657, 3596, 27867, 2638, 1078, 29889, 1961, 2824, 3126, 580, 13, 4706, 3935, 353, 2933, 29961, 29900, 3816, 29900, 29962, 13, 4706, 4974, 3806, 1275, 3935, 2 ]
server/ball_speed_placement.py	Rohansjamadagni/topspin-tracker	2	103678	import sys import cv2 import math import os import json import pandas as pd import numpy as np import json stroke_data = { "data" : [] } stroke_data_ = open('result.json', 'r') stroke_data = json.loads(stroke_data_.read()) print(stroke_data['data']) stroke_data_.close() speed_for_each_stroke = {} def read_coordinates(): f = open("coordinates.txt", 'r') data_ = f.read() data = data_.strip() str_table_coordinates = data.split(' ') table_coordinates = [] for i in str_table_coordinates: table_coordinates.append(list(map(int, (i.split(','))))) return table_coordinates def find_net(table_coordinates): """ Finding net coordinates, taking avg of x1 and x4 (mid points of table) """ top_x = table_coordinates[1][0] bottom_x = table_coordinates[4][0] avg = int((top_x+bottom_x)/2) return avg def get_coordinate_of_net_cross(x_coords, midpoint_x=270): print(list(x_coords), midpoint_x) for pos, x_coordinate in enumerate(list(x_coords)): if x_coordinate >= midpoint_x: return pos # 1 2 3 4 5 6 7 8 9 9 10 def euclidian_distance(x1, y1, x2, y2): distance = math.sqrt(((x1 - x2)2) + ((y1 - y2)2)) return distance def get_speed(buffer=3, fps = 20): speed_for_each_stroke = {} table_coordinates = read_coordinates() midpoint_x = find_net(table_coordinates) coordinates_df = pd.read_csv('final_result.csv') stroke_number = coordinates_df['stroke_number'] x_coords = coordinates_df['x'] y_coords = coordinates_df['y'] x_coords = list(x_coords) y_coords = list(y_coords) strokes_x_y = [] prev_stroke_number = list(stroke_number)[0] stroke = [] # separate strokes into array for i in coordinates_df.iterrows(): # print(i[1]['stroke_number']) if prev_stroke_number != i[1]['stroke_number']: prev_stroke_number = i[1]['stroke_number'] strokes_x_y.append(np.asarray(stroke)) stroke = [] stroke.append([i[1]['x'], i[1]['y']]) strokes_x_y.append(np.asarray(stroke)) # import sys # sys.exit(1) left_midpoint_coordinate = [ ((table_coordinates[0][0] + table_coordinates[5][0]) / 2), ((table_coordinates[0][1] + table_coordinates[5][1]) / 2)] right_midpoint_coordinate = [ ((table_coordinates[2][0] + table_coordinates[3][0]) / 2), ((table_coordinates[2][1] + table_coordinates[3][1]) / 2)] strokes_x_y = np.asarray(strokes_x_y) table_distance = euclidian_distance( left_midpoint_coordinate[0], left_midpoint_coordinate[1], right_midpoint_coordinate[0], right_midpoint_coordinate[1]) # print(stroke) import sys print(len(strokes_x_y)) for pos, stroke in enumerate(strokes_x_y): print('ffs', pos) x_coords = stroke[:, 0] y_coords = stroke[:, 1] speed_list = [] position_of_net_cross = get_coordinate_of_net_cross(x_coords, midpoint_x) distances = [] print(position_of_net_cross, 'pos', midpoint_x) # sys.exit(1) print(len(x_coords), len(y_coords)) try: for point in range(position_of_net_cross-buffer, position_of_net_cross+buffer+1): speed_list.append([x_coords[point], y_coords[point]]) except: pass for i in range(len(speed_list)-1): distances.append(euclidian_distance( speed_list[i][0], speed_list[i][1], speed_list[i+1][0], speed_list[i+1][1] )) speed = sum(distances) / (1 / fps * ((len(distances)+1))) speed = (speed * 2.74) / table_distance # find speed for each stroke speed = 18 / 5 speed_for_each_stroke[pos+1] = speed return speed_for_each_stroke def get_zone(ball_coordinates, no_columns=6, no_rows=3): tt_table = np.zeros((3, 6), np.int32) table_map = { "03" : 1, "04" : 2, "05" : 3, "13" : 4, "14" : 5, "15" : 6, "23" : 7, "24" : 8, "25" : 9 } col_seg = 1920 / no_columns row_seg = 1080 / no_rows bounce_col = 0 bounce_row = 0 for i in range(1, no_columns+1): if ball_coordinates[0] < col_seg i: bounce_col = i break for i in range(1, no_rows+1): if ball_coordinates[1] < row_seg * i: bounce_row = i break # tt_table[bounce_row-1][bounce_col-1] += 1 valid_check = str(bounce_row) + str(bounce_col) if valid_check in table_map: return str(table_map[valid_check]) else: print('wrong coords') return str(3) def warp_coordinates(x, y, matrix): print('warping') print(x, y, 'pints') p = (x, y) px = (matrix[0][0] * p[0] + matrix[0][1] * p[1] + matrix[0][2]) / \ ((matrix[2][0] * p[0] + matrix[2][1] * p[1] + matrix[2][2])) py = (matrix[1][0] * p[0] + matrix[1][1] * p[1] + matrix[1][2]) / \ ((matrix[2][0] * p[0] + matrix[2][1] * p[1] + matrix[2][2])) print(px, py, ' warped pints') return [int(px), int(py)] def get_transposed_coordinates(x, y): table_coordinates = read_coordinates() table_coordinates_corners = np.array([ table_coordinates[0], table_coordinates[2], table_coordinates[3], table_coordinates[5] ], np.float32) print(table_coordinates, 'table coords') warped_dimensions = np.array([[0, 0], [1920, 0], [0, 1080], [1920, 1080]], np.float32) matrix = cv2.getPerspectiveTransform(table_coordinates_corners, warped_dimensions) x, y = warp_coordinates(x, y, matrix) position = get_zone([x,y]) return position def caluclate_speed_and_placements(): global stroke_data # do speed tomorrow result = pd.read_csv('final_result.csv') left_bounces = result.groupby("stroke_number").first().reset_index() right_bounces = result.groupby("stroke_number").last().reset_index() speed_for_each_stroke = get_speed() print(speed_for_each_stroke, 'speed', len(left_bounces)) bc = 0 for bounces in zip(left_bounces.iterrows(), right_bounces.iterrows()): print(bc, 'bc') left, right = bounces[0][1], bounces[1][1] print(left['x'], right['x'], 'lr') data_ = { "stroke_name" : stroke_data['data'][bc]['stroke_name'], "stroke_number" : left['stroke_number'], "position" : get_transposed_coordinates(right['x'], right['y']), "speed" : speed_for_each_stroke[left['stroke_number']] } stroke_data['data'][bc] = data_ # if bc == 3: # break bc += 1 file_ = open('stroke_speed_result.json', 'w') json.dump(stroke_data, file_) if __name__ == "__main__": caluclate_speed_and_placements()	[ 1, 1053, 10876, 13, 5215, 13850, 29906, 13, 5215, 5844, 13, 5215, 2897, 13, 5215, 4390, 13, 5215, 11701, 408, 10518, 13, 5215, 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memsql_loader/api/caller.py	yonglehou/memsql-loader	1	181929	<filename>memsql_loader/api/caller.py import re from memsql_loader.api.exceptions import ApiException from memsql_loader.api.jobs import Jobs from memsql_loader.api.job import Job from memsql_loader.api.tasks import Tasks from memsql_loader.api.task import Task APIS = [ Jobs, Job, Tasks, Task ] __underscorer1 = re.compile(r'(.)([A-Z][a-z]+)') __underscorer2 = re.compile('([a-z0-9])([A-Z])') def __camelcase_convertor(s): subbed = __underscorer1.sub(r'\1_\2', s) return __underscorer2.sub(r'\1_\2', subbed).lower() API_NAME_MAP = { __camelcase_convertor(ApiClass.__name__): ApiClass for ApiClass in APIS } class ApiCaller(object): """ This class allows you to query any of the API's by name """ def __init__(self): for name, ApiClass in API_NAME_MAP.items(): setattr(self, name, ApiClass()) def call(self, api, params): return self.get(api).query(params) def get(self, api): if not hasattr(self, api): raise ApiException('Api not found: %s' % api) return getattr(self, api)	[ 1, 529, 9507, 29958, 29885, 1567, 1519, 29918, 12657, 29914, 2754, 29914, 4804, 261, 29889, 2272, 13, 5215, 337, 13, 13, 3166, 286, 1567, 1519, 29918, 12657, 29889, 2754, 29889, 11739, 29879, 1053, 29749, 2451, 13, 13, 3166, 286, 1567, 1519, 29918, 12657, 29889, 2754, 29889, 9057, 29879, 1053, 17163, 29879, 13, 3166, 286, 1567, 1519, 29918, 12657, 29889, 2754, 29889, 9057, 1053, 17163, 13, 3166, 286, 1567, 1519, 29918, 12657, 29889, 2754, 29889, 20673, 1053, 9330, 29879, 13, 3166, 286, 1567, 1519, 29918, 12657, 29889, 2754, 29889, 7662, 1053, 9330, 13, 8787, 29903, 353, 518, 17163, 29879, 29892, 17163, 29892, 9330, 29879, 29892, 9330, 4514, 13, 13, 1649, 870, 414, 2616, 261, 29896, 353, 337, 29889, 12198, 29898, 29878, 12215, 1846, 4197, 29909, 29899, 29999, 3816, 29874, 29899, 29920, 10062, 29897, 1495, 13, 1649, 870, 414, 2616, 261, 29906, 353, 337, 29889, 12198, 877, 4197, 29874, 29899, 29920, 29900, 29899, 29929, 2314, 4197, 29909, 29899, 29999, 2314, 1495, 13, 13, 1753, 4770, 11108, 295, 4878, 29918, 13441, 272, 29898, 29879, 1125, 13, 1678, 1014, 2580, 353, 4770, 870, 414, 2616, 261, 29896, 29889, 1491, 29898, 29878, 12764, 29896, 3187, 29906, 742, 269, 29897, 13, 1678, 736, 4770, 870, 414, 2616, 261, 29906, 29889, 1491, 29898, 29878, 12764, 29896, 3187, 29906, 742, 1014, 2580, 467, 13609, 580, 13, 13, 8787, 29918, 5813, 29918, 23827, 353, 426, 4770, 11108, 295, 4878, 29918, 13441, 272, 29898, 11713, 2385, 17255, 978, 1649, 1125, 29749, 2385, 363, 29749, 2385, 297, 3450, 29903, 500, 13, 13, 1990, 29749, 5594, 261, 29898, 3318, 1125, 13, 1678, 9995, 910, 770, 6511, 366, 304, 2346, 738, 310, 278, 3450, 29915, 29879, 491, 1024, 13, 1678, 9995, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 13, 4706, 363, 1024, 29892, 29749, 2385, 297, 3450, 29918, 5813, 29918, 23827, 29889, 7076, 7295, 13, 9651, 731, 5552, 29898, 1311, 29892, 1024, 29892, 29749, 2385, 3101, 13, 13, 1678, 822, 1246, 29898, 1311, 29892, 7882, 29892, 8636, 1125, 13, 4706, 736, 1583, 29889, 657, 29898, 2754, 467, 1972, 29898, 7529, 29897, 13, 13, 1678, 822, 679, 29898, 1311, 29892, 7882, 1125, 13, 4706, 565, 451, 756, 5552, 29898, 1311, 29892, 7882, 1125, 13, 9651, 12020, 29749, 2451, 877, 11713, 451, 1476, 29901, 1273, 29879, 29915, 1273, 7882, 29897, 13, 4706, 736, 679, 5552, 29898, 1311, 29892, 7882, 29897, 13, 2 ]
src/jomiel_kore/version.py	guendto/jomiel-kore	0	29931	<filename>src/jomiel_kore/version.py # # jomiel-kore # # Copyright # 2019-2020 <NAME> # # # SPDX-License-Identifier: Apache-2.0 # """TODO.""" try: # py38+ from importlib.metadata import version as metadata_version from importlib.metadata import PackageNotFoundError except ModuleNotFoundError: from importlib_metadata import version as metadata_version from importlib_metadata import PackageNotFoundError def package_version(package_name, destination): """Returns the package version string Args: package_name (str): the package name to look up destination (list): the list to store the result (tuple) to """ try: version = metadata_version(package_name) except PackageNotFoundError: version = "<unavailable>" if package_name == "pyzmq": from zmq import zmq_version version = "{} (libzmq version {})".format( version, zmq_version(), ) destination.append((package_name, version)) # vim: set ts=4 sw=4 tw=72 expandtab:	[ 1, 529, 9507, 29958, 4351, 29914, 29926, 290, 709, 29918, 29895, 487, 29914, 3259, 29889, 2272, 13, 29937, 13, 29937, 432, 290, 709, 29899, 29895, 487, 13, 29937, 13, 29937, 14187, 1266, 13, 29937, 259, 29906, 29900, 29896, 29929, 29899, 29906, 29900, 29906, 29900, 529, 5813, 29958, 13, 29937, 13, 29937, 13, 29937, 10937, 29928, 29990, 29899, 29931, 293, 1947, 29899, 12889, 29901, 13380, 29899, 29906, 29889, 29900, 13, 29937, 13, 15945, 29908, 4986, 3970, 1213, 15945, 13, 13, 2202, 29901, 29871, 396, 11451, 29941, 29947, 29974, 13, 1678, 515, 1053, 1982, 29889, 19635, 1053, 1873, 408, 15562, 29918, 3259, 13, 1678, 515, 1053, 1982, 29889, 19635, 1053, 22029, 17413, 2392, 13, 19499, 15591, 17413, 2392, 29901, 13, 1678, 515, 1053, 1982, 29918, 19635, 1053, 1873, 408, 15562, 29918, 3259, 13, 1678, 515, 1053, 1982, 29918, 19635, 1053, 22029, 17413, 2392, 13, 13, 13, 1753, 3577, 29918, 3259, 29898, 5113, 29918, 978, 29892, 12551, 1125, 13, 1678, 9995, 11609, 29879, 278, 3577, 1873, 1347, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 3577, 29918, 978, 313, 710, 1125, 278, 3577, 1024, 304, 1106, 701, 13, 4706, 12551, 313, 1761, 1125, 278, 1051, 304, 3787, 278, 1121, 313, 23583, 29897, 304, 13, 13, 1678, 9995, 13, 1678, 1018, 29901, 13, 4706, 1873, 353, 15562, 29918, 3259, 29898, 5113, 29918, 978, 29897, 13, 1678, 5174, 22029, 17413, 2392, 29901, 13, 4706, 1873, 353, 9872, 348, 16515, 11903, 13, 13, 1678, 565, 3577, 29918, 978, 1275, 376, 2272, 14018, 29939, 1115, 13, 4706, 515, 12162, 29939, 1053, 12162, 29939, 29918, 3259, 13, 13, 4706, 1873, 353, 376, 8875, 313, 1982, 14018, 29939, 1873, 426, 1800, 1642, 4830, 29898, 13, 9651, 1873, 29892, 13, 9651, 12162, 29939, 29918, 3259, 3285, 13, 4706, 1723, 13, 13, 1678, 12551, 29889, 4397, 3552, 5113, 29918, 978, 29892, 1873, 876, 13, 13, 13, 29937, 325, 326, 29901, 731, 18696, 29922, 29946, 2381, 29922, 29946, 3252, 29922, 29955, 29906, 7985, 3891, 29901, 13, 2 ]
oslo_db/sqlalchemy/enginefacade.py	calsoft-internal/oslo.db-internal	0	68130	# 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 contextlib import functools import operator import threading import warnings import debtcollector.moves import debtcollector.removals import debtcollector.renames from oslo_config import cfg from oslo_utils import excutils from oslo_db import exception from oslo_db import options from oslo_db.sqlalchemy import engines from oslo_db.sqlalchemy import orm from oslo_db.sqlalchemy import utils from oslo_db import warning class _symbol(object): """represent a fixed symbol.""" __slots__ = 'name', def __init__(self, name): self.name = name def __repr__(self): return "symbol(%r)" % self.name _ASYNC_READER = _symbol('ASYNC_READER') """Represent the transaction state of "async reader". This state indicates that the transaction is a read-only and is safe to use on an asynchronously updated slave database. """ _READER = _symbol('READER') """Represent the transaction state of "reader". This state indicates that the transaction is a read-only and is only safe to use on a synchronously updated slave database; otherwise the master database should be used. """ _WRITER = _symbol('WRITER') """Represent the transaction state of "writer". This state indicates that the transaction writes data and should be directed at the master database. """ class _Default(object): """Mark a value as a default value. A value in the local configuration dictionary wrapped with _Default() will not take precedence over a value that is specified in cfg.CONF. Values that are set after the fact using configure() will supersede those in cfg.CONF. """ __slots__ = 'value', _notset = _symbol("NOTSET") def __init__(self, value=_notset): self.value = value @classmethod def resolve(cls, value): if isinstance(value, _Default): v = value.value if v is cls._notset: return None else: return v else: return value @classmethod def resolve_w_conf(cls, value, conf_namespace, key): if isinstance(value, _Default): v = getattr(conf_namespace, key, value.value) if v is cls._notset: return None else: return v else: return value @classmethod def is_set(cls, value): return not isinstance(value, _Default) or \ value.value is not cls._notset @classmethod def is_set_w_conf(cls, value, conf_namespace, key): return not isinstance(value, _Default) or \ value.value is not cls._notset or \ hasattr(conf_namespace, key) class AlreadyStartedError(TypeError): """Raises when a factory is being asked to initialize a second time. Subclasses :class:`.TypeError` for legacy support. """ class _TransactionFactory(object): """A factory for :class:`._TransactionContext` objects. By default, there is just one of these, set up based on CONF, however instance-level :class:`._TransactionFactory` objects can be made, as is the case with the :class:`._TestTransactionFactory` subclass used by the oslo.db test suite. """ def __init__(self): self._url_cfg = { 'connection': _Default(), 'slave_connection': _Default(), } self._engine_cfg = { 'sqlite_fk': _Default(False), 'mysql_sql_mode': _Default('TRADITIONAL'), 'mysql_enable_ndb': _Default(False), 'connection_recycle_time': _Default(3600), 'connection_debug': _Default(0), 'max_pool_size': _Default(), 'max_overflow': _Default(), 'pool_timeout': _Default(), 'sqlite_synchronous': _Default(True), 'connection_trace': _Default(False), 'max_retries': _Default(10), 'retry_interval': _Default(10), 'thread_checkin': _Default(True), 'json_serializer': _Default(None), 'json_deserializer': _Default(None), 'logging_name': _Default(None), 'connection_parameters': _Default(None) } self._maker_cfg = { 'expire_on_commit': _Default(False), '__autocommit': True } self._transaction_ctx_cfg = { 'rollback_reader_sessions': False, 'flush_on_subtransaction': False, } self._facade_cfg = { 'synchronous_reader': True, 'on_engine_create': [], } # other options that are defined in oslo_db.options.database_opts # or oslo_db.concurrency.tpool_opts but do not apply to the standard # enginefacade arguments (most seem to apply to api.DBAPI). self._ignored_cfg = dict( (k, _Default(None)) for k in [ 'db_max_retries', 'db_inc_retry_interval', 'use_db_reconnect', 'db_retry_interval', 'db_max_retry_interval', 'backend', 'use_tpool']) self._started = False self._legacy_facade = None self._start_lock = threading.Lock() @debtcollector.renames.renamed_kwarg( "idle_timeout", "connection_recycle_time", replace=True) def configure_defaults(self, kw): """Apply default configurational options. This method can only be called before any specific transaction-beginning methods have been called. Configurational options are within a fixed set of keys, and fall under three categories: URL configuration, engine configuration, and session configuration. Each key given will be tested against these three configuration sets to see which one is applicable; if it is not applicable to any set, an exception is raised. The configurational options given here act as defaults when the :class:`._TransactionFactory` is configured using a :class:`oslo_config.cfg.ConfigOpts` object; the options present within the :class:`oslo_config.cfg.ConfigOpts` take precedence versus the arguments passed here. By default, the :class:`._TransactionFactory` loads in the configuration from :data:`oslo_config.cfg.CONF`, after applying the :data:`oslo_db.options.database_opts` configurational defaults to it. :param connection: database URL :param slave_connection: database URL :param sqlite_fk: whether to enable SQLite foreign key pragma; default False :param mysql_sql_mode: MySQL SQL mode, defaults to TRADITIONAL :param mysql_enable_ndb: enable MySQL Cluster (NDB) support :param connection_recycle_time: connection pool recycle time, defaults to 3600. Note the connection does not actually have to be "idle" to be recycled. :param connection_debug: engine logging level, defaults to 0. set to 50 for INFO, 100 for DEBUG. :param connection_parameters: additional parameters to append onto the database URL query string, pass as "param1=value1&param2=value2&..." :param max_pool_size: max size of connection pool, uses CONF for default :param max_overflow: max overflow for connection pool, uses CONF for default :param sqlite_synchronous: disable SQLite SYNCHRONOUS pragma if False; defaults to True :param connection_trace: enable tracing comments in logging :param max_retries: max retries to connect, defaults to !0 :param retry_interval: time in seconds between retries, defaults to 10 :param thread_checkin: add sleep(0) on connection checkin to allow greenlet yields, defaults to True :param json_serializer: JSON serializer for PostgreSQL connections :param json_deserializer: JSON deserializer for PostgreSQL connections :param logging_name: logging name for engine :param expire_on_commit: sets expire_on_commit for SQLAlchemy sessionmaker; defaults to False :param rollback_reader_sessions: if True, a :class:`.Session` object will have its :meth:`.Session.rollback` method invoked at the end of a ``@reader`` block, actively rolling back the transaction and expiring the objects within, before the :class:`.Session` moves on to be closed, which has the effect of releasing connection resources back to the connection pool and detaching all objects. If False, the :class:`.Session` is not affected at the end of a ``@reader`` block; the underlying connection referred to by this :class:`.Session` will still be released in the enclosing context via the :meth:`.Session.close` method, which still ensures that the DBAPI connection is rolled back, however the objects associated with the :class:`.Session` retain their database-persisted contents after they are detached. .. seealso:: http://docs.sqlalchemy.org/en/rel_0_9/glossary.html#term-released\ SQLAlchemy documentation on what "releasing resources" means. :param synchronous_reader: whether or not to assume a "reader" context needs to guarantee it can read data committed by a "writer" assuming replication lag is present; defaults to True. When False, a @reader context works the same as @async_reader and will select the "slave" database if present. :param flush_on_subtransaction: if True, a :class:`.Session` object will have its :meth:`.Session.flush` method invoked whenever a context manager or decorator that is not itself the originator of the top- level or savepoint :class:`.Session` transaction exits - in this way it behaves like a "subtransaction" from a :class:`.Session` perspective. .. seealso:: :meth:`._TransactionFactory.configure` """ self._configure(True, kw) @debtcollector.renames.renamed_kwarg( "idle_timeout", "connection_recycle_time", replace=True) def configure(self, kw): """Apply configurational options. This method can only be called before any specific transaction-beginning methods have been called. Behavior here is the same as that of :meth:`._TransactionFactory.configure_defaults`, with the exception that values specified here will supersede those setup in the :class:`oslo_config.cfg.ConfigOpts` options. See that method for a listing of all keyword arguments. .. seealso:: :meth:`._TransactionFactory.configure_defaults` """ self._configure(False, kw) def _configure(self, as_defaults, kw): if self._started: raise AlreadyStartedError( "this TransactionFactory is already started") not_supported = [] for k, v in kw.items(): for dict_ in ( self._url_cfg, self._engine_cfg, self._maker_cfg, self._ignored_cfg, self._facade_cfg, self._transaction_ctx_cfg): if k in dict_: dict_[k] = _Default(v) if as_defaults else v break else: not_supported.append(k) if not_supported: # would like to raise ValueError here, but there are just # too many unrecognized (obsolete?) configuration options # coming in from projects warnings.warn( "Configuration option(s) %r not supported" % sorted(not_supported), warning.NotSupportedWarning ) def get_legacy_facade(self): """Return a :class:`.LegacyEngineFacade` for this factory. This facade will make use of the same engine and sessionmaker as this factory, however will not share the same transaction context; the legacy facade continues to work the old way of returning a new Session each time get_session() is called. """ if not self._legacy_facade: self._legacy_facade = LegacyEngineFacade(None, _factory=self) if not self._started: self._start() return self._legacy_facade def get_writer_engine(self): """Return the writer engine for this factory. Implies start. """ if not self._started: self._start() return self._writer_engine def get_reader_engine(self): """Return the reader engine for this factory. Implies start. """ if not self._started: self._start() return self._reader_engine def get_writer_maker(self): """Return the writer sessionmaker for this factory. Implies start. """ if not self._started: self._start() return self._writer_maker def get_reader_maker(self): """Return the reader sessionmaker for this factory. Implies start. """ if not self._started: self._start() return self._reader_maker def _create_connection(self, mode): if not self._started: self._start() if mode is _WRITER: return self._writer_engine.connect() elif mode is _ASYNC_READER or \ (mode is _READER and not self.synchronous_reader): return self._reader_engine.connect() else: return self._writer_engine.connect() def _create_session(self, mode, bind=None): if not self._started: self._start() kw = {} # don't pass 'bind' if bind is None; the sessionmaker # already has a bind to the engine. if bind: kw['bind'] = bind if mode is _WRITER: return self._writer_maker(kw) elif mode is _ASYNC_READER or \ (mode is _READER and not self.synchronous_reader): return self._reader_maker(kw) else: return self._writer_maker(kw) def _create_factory_copy(self): factory = _TransactionFactory() factory._url_cfg.update(self._url_cfg) factory._engine_cfg.update(self._engine_cfg) factory._maker_cfg.update(self._maker_cfg) factory._transaction_ctx_cfg.update(self._transaction_ctx_cfg) factory._facade_cfg.update(self._facade_cfg) return factory def _args_for_conf(self, default_cfg, conf): if conf is None: return dict( (key, _Default.resolve(value)) for key, value in default_cfg.items() if _Default.is_set(value) ) else: return dict( (key, _Default.resolve_w_conf(value, conf.database, key)) for key, value in default_cfg.items() if _Default.is_set_w_conf(value, conf.database, key) ) def _url_args_for_conf(self, conf): return self._args_for_conf(self._url_cfg, conf) def _engine_args_for_conf(self, conf): return self._args_for_conf(self._engine_cfg, conf) def _maker_args_for_conf(self, conf): maker_args = self._args_for_conf(self._maker_cfg, conf) maker_args['autocommit'] = maker_args.pop('__autocommit') return maker_args def dispose_pool(self): """Call engine.pool.dispose() on underlying Engine objects.""" with self._start_lock: if not self._started: return self._writer_engine.pool.dispose() if self._reader_engine is not self._writer_engine: self._reader_engine.pool.dispose() @property def is_started(self): """True if this :class:`._TransactionFactory` is already started.""" return self._started def _start(self, conf=False, connection=None, slave_connection=None): with self._start_lock: # self._started has been checked on the outside # when _start() was called. Within the lock, # check the flag once more to detect the case where # the start process proceeded while this thread was waiting # for the lock. if self._started: return if conf is False: conf = cfg.CONF # perform register_opts() local to actually using # the cfg.CONF to maintain exact compatibility with # the EngineFacade design. This can be changed if needed. if conf is not None: conf.register_opts(options.database_opts, 'database') url_args = self._url_args_for_conf(conf) if connection: url_args['connection'] = connection if slave_connection: url_args['slave_connection'] = slave_connection engine_args = self._engine_args_for_conf(conf) maker_args = self._maker_args_for_conf(conf) self._writer_engine, self._writer_maker = \ self._setup_for_connection( url_args['connection'], engine_args, maker_args) if url_args.get('slave_connection'): self._reader_engine, self._reader_maker = \ self._setup_for_connection( url_args['slave_connection'], engine_args, maker_args) else: self._reader_engine, self._reader_maker = \ self._writer_engine, self._writer_maker self.synchronous_reader = self._facade_cfg['synchronous_reader'] # set up _started last, so that in case of exceptions # we try the whole thing again and report errors # correctly self._started = True def _setup_for_connection( self, sql_connection, engine_kwargs, maker_kwargs): if sql_connection is None: raise exception.CantStartEngineError( "No sql_connection parameter is established") engine = engines.create_engine( sql_connection=sql_connection, engine_kwargs) for hook in self._facade_cfg['on_engine_create']: hook(engine) sessionmaker = orm.get_maker(engine=engine, maker_kwargs) return engine, sessionmaker class _TestTransactionFactory(_TransactionFactory): """A :class:`._TransactionFactory` used by test suites. This is a :class:`._TransactionFactory` that can be directly injected with an existing engine and sessionmaker. Note that while this is used by oslo.db's own tests of the enginefacade system, it is also exported for use by the test suites of other projects, first as an element of the oslo_db.sqlalchemy.test_base module, and secondly may be used by external test suites directly. Includes a feature to inject itself temporarily as the factory within the global :class:`._TransactionContextManager`. """ @debtcollector.removals.removed_kwarg( 'synchronous_reader', 'argument value is propagated from the parent _TransactionFactory') def __init__(self, engine, maker, apply_global, from_factory=None, kw): # NOTE(zzzeek): kw needed for backwards compability self._reader_engine = self._writer_engine = engine self._reader_maker = self._writer_maker = maker self._started = True self._legacy_facade = None if from_factory is None: from_factory = _context_manager._factory self._facade_cfg = from_factory._facade_cfg self._transaction_ctx_cfg = from_factory._transaction_ctx_cfg self.synchronous_reader = self._facade_cfg['synchronous_reader'] if apply_global: self.existing_factory = _context_manager._factory _context_manager._root_factory = self def dispose_global(self): _context_manager._root_factory = self.existing_factory class _TransactionContext(object): """Represent a single database transaction in progress.""" def __init__(self, factory, global_factory=None): """Construct a new :class:`.TransactionContext`. :param factory: the :class:`.TransactionFactory` which will serve as a source of connectivity. :param global_factory: the "global" factory which will be used by the global ``_context_manager`` for new ``_TransactionContext`` objects created under this one. When left as None the actual "global" factory is used. """ self.factory = factory self.global_factory = global_factory self.mode = None self.session = None self.connection = None self.transaction = None kw = self.factory._transaction_ctx_cfg self.rollback_reader_sessions = kw['rollback_reader_sessions'] self.flush_on_subtransaction = kw['flush_on_subtransaction'] @contextlib.contextmanager def _connection(self, savepoint=False, context=None): if self.connection is None: try: if self.session is not None: # use existing session, which is outer to us self.connection = self.session.connection() if savepoint: with self.connection.begin_nested(), \ self._add_context(self.connection, context): yield self.connection else: with self._add_context(self.connection, context): yield self.connection else: # is outermost self.connection = self.factory._create_connection( mode=self.mode) self.transaction = self.connection.begin() try: with self._add_context(self.connection, context): yield self.connection self._end_connection_transaction(self.transaction) except Exception: self.transaction.rollback() # TODO(zzzeek) do we need save_and_reraise() here, # or do newer eventlets not have issues? we are using # raw "raise" in many other places in oslo.db already raise finally: self.transaction = None self.connection.close() finally: self.connection = None else: # use existing connection, which is outer to us if savepoint: with self.connection.begin_nested(), \ self._add_context(self.connection, context): yield self.connection else: with self._add_context(self.connection, context): yield self.connection @contextlib.contextmanager def _session(self, savepoint=False, context=None): if self.session is None: self.session = self.factory._create_session( bind=self.connection, mode=self.mode) try: self.session.begin() with self._add_context(self.session, context): yield self.session self._end_session_transaction(self.session) except Exception: with excutils.save_and_reraise_exception(): self.session.rollback() finally: self.session.close() self.session = None else: # use existing session, which is outer to us if savepoint: with self.session.begin_nested(): with self._add_context(self.session, context): yield self.session else: with self._add_context(self.session, context): yield self.session if self.flush_on_subtransaction: self.session.flush() @contextlib.contextmanager def _add_context(self, connection, context): restore_context = connection.info.get('using_context') connection.info['using_context'] = context yield connection connection.info['using_context'] = restore_context def _end_session_transaction(self, session): if self.mode is _WRITER: session.commit() elif self.rollback_reader_sessions: session.rollback() # In the absence of calling session.rollback(), # the next call is session.close(). This releases all # objects from the session into the detached state, and # releases the connection as well; the connection when returned # to the pool is either rolled back in any case, or closed fully. def _end_connection_transaction(self, transaction): if self.mode is _WRITER: transaction.commit() else: transaction.rollback() def _produce_block(self, mode, connection, savepoint, allow_async=False, context=None): if mode is _WRITER: self._writer() elif mode is _ASYNC_READER: self._async_reader() else: self._reader(allow_async) if connection: return self._connection(savepoint, context=context) else: return self._session(savepoint, context=context) def _writer(self): if self.mode is None: self.mode = _WRITER elif self.mode is _READER: raise TypeError( "Can't upgrade a READER transaction " "to a WRITER mid-transaction") elif self.mode is _ASYNC_READER: raise TypeError( "Can't upgrade an ASYNC_READER transaction " "to a WRITER mid-transaction") def _reader(self, allow_async=False): if self.mode is None: self.mode = _READER elif self.mode is _ASYNC_READER and not allow_async: raise TypeError( "Can't upgrade an ASYNC_READER transaction " "to a READER mid-transaction") def _async_reader(self): if self.mode is None: self.mode = _ASYNC_READER class _TransactionContextTLocal(threading.local): def __deepcopy__(self, memo): return self def __reduce__(self): return _TransactionContextTLocal, () class _TransactionContextManager(object): """Provide context-management and decorator patterns for transactions. This object integrates user-defined "context" objects with the :class:`._TransactionContext` class, on behalf of a contained :class:`._TransactionFactory`. """ def __init__( self, root=None, mode=None, independent=False, savepoint=False, connection=False, replace_global_factory=None, _is_global_manager=False, allow_async=False): if root is None: self._root = self self._root_factory = _TransactionFactory() else: self._root = root self._replace_global_factory = replace_global_factory self._is_global_manager = _is_global_manager self._mode = mode self._independent = independent self._savepoint = savepoint if self._savepoint and self._independent: raise TypeError( "setting savepoint and independent makes no sense.") self._connection = connection self._allow_async = allow_async @property def _factory(self): """The :class:`._TransactionFactory` associated with this context.""" return self._root._root_factory @property def is_started(self): """True if this manager is already started.""" return self._factory.is_started def configure(self, kw): """Apply configurational options to the factory. This method can only be called before any specific transaction-beginning methods have been called. """ self._factory.configure(kw) def append_on_engine_create(self, fn): """Append a listener function to _facade_cfg["on_engine_create"]""" self._factory._facade_cfg['on_engine_create'].append(fn) def get_legacy_facade(self): """Return a :class:`.LegacyEngineFacade` for factory from this context. This facade will make use of the same engine and sessionmaker as this factory, however will not share the same transaction context; the legacy facade continues to work the old way of returning a new Session each time get_session() is called. """ return self._factory.get_legacy_facade() def get_engine(self): """Return the Engine in use. This will be based on the state being WRITER or READER. This implies a start operation. """ if self._mode is _WRITER: return self._factory.get_writer_engine() elif self._mode is _READER: return self._factory.get_reader_engine() else: raise ValueError("mode should be WRITER or READER") def get_sessionmaker(self): """Return the sessionmaker in use. This will be based on the state being WRITER or READER. This implies a start operation. """ if self._mode is _WRITER: return self._factory.get_writer_maker() elif self._mode is _READER: return self._factory.get_reader_maker() else: raise ValueError("mode should be WRITER or READER") def dispose_pool(self): """Call engine.pool.dispose() on underlying Engine objects.""" self._factory.dispose_pool() def make_new_manager(self): """Create a new, independent _TransactionContextManager from this one. Copies the underlying _TransactionFactory to a new one, so that it can be further configured with new options. Used for test environments where the application-wide _TransactionContextManager may be used as a factory for test-local managers. """ new = self._clone() new._root = new new._root_factory = self._root_factory._create_factory_copy() if new._factory._started: raise AssertionError('TransactionFactory is already started') return new def patch_factory(self, factory_or_manager): """Patch a _TransactionFactory into this manager. Replaces this manager's factory with the given one, and returns a callable that will reset the factory back to what we started with. Only works for root factories. Is intended for test suites that need to patch in alternate database configurations. The given argument may be a _TransactionContextManager or a _TransactionFactory. """ if isinstance(factory_or_manager, _TransactionContextManager): factory = factory_or_manager._factory elif isinstance(factory_or_manager, _TransactionFactory): factory = factory_or_manager else: raise ValueError( "_TransactionContextManager or " "_TransactionFactory expected.") if self._root is not self: raise AssertionError('patch_factory only works for root factory.') existing_factory = self._root_factory self._root_factory = factory def reset(): self._root_factory = existing_factory return reset def patch_engine(self, engine): """Patch an Engine into this manager. Replaces this manager's factory with a _TestTransactionFactory that will use the given Engine, and returns a callable that will reset the factory back to what we started with. Only works for root factories. Is intended for test suites that need to patch in alternate database configurations. """ existing_factory = self._factory if not existing_factory._started: existing_factory._start() maker = existing_factory._writer_maker maker_kwargs = existing_factory._maker_args_for_conf(cfg.CONF) maker = orm.get_maker(engine=engine, maker_kwargs) factory = _TestTransactionFactory( engine, maker, apply_global=False, from_factory=existing_factory ) return self.patch_factory(factory) @property def replace(self): """Modifier to replace the global transaction factory with this one.""" return self._clone(replace_global_factory=self._factory) @property def writer(self): """Modifier to set the transaction to WRITER.""" return self._clone(mode=_WRITER) @property def reader(self): """Modifier to set the transaction to READER.""" return self._clone(mode=_READER) @property def allow_async(self): """Modifier to allow async operations Allows async operations if asynchronous session is already started in this context. Marking DB API methods with READER would make it impossible to use them in ASYNC_READER transactions, and marking them with ASYNC_READER would require a modification of all the places these DB API methods are called to force READER mode, where the latest DB state is required. In Nova DB API methods should have a 'safe' default (i.e. READER), so that they can start sessions on their own, but it would also be useful for them to be able to participate in an existing ASYNC_READER session, if one was started up the stack. """ if self._mode is _WRITER: raise TypeError("Setting async on a WRITER makes no sense") return self._clone(allow_async=True) @property def independent(self): """Modifier to start a transaction independent from any enclosing.""" return self._clone(independent=True) @property def savepoint(self): """Modifier to start a SAVEPOINT if a transaction already exists.""" return self._clone(savepoint=True) @property def connection(self): """Modifier to return a core Connection object instead of Session.""" return self._clone(connection=True) @property def async_(self): """Modifier to set a READER operation to ASYNC_READER.""" if self._mode is _WRITER: raise TypeError("Setting async on a WRITER makes no sense") return self._clone(mode=_ASYNC_READER) def using(self, context): """Provide a context manager block that will use the given context.""" return self._transaction_scope(context) def __call__(self, fn): """Decorate a function.""" argspec = utils.getargspec(fn) if argspec.args[0] == 'self' or argspec.args[0] == 'cls': context_index = 1 else: context_index = 0 context_kw = argspec.args[context_index] @functools.wraps(fn) def wrapper(args, kwargs): context = kwargs.get(context_kw, None) if not context: context = args[context_index] with self._transaction_scope(context): return fn(args, kwargs) return wrapper def _clone(self, kw): default_kw = { "independent": self._independent, "mode": self._mode, "connection": self._connection } default_kw.update(kw) return _TransactionContextManager(root=self._root, default_kw) @contextlib.contextmanager def _transaction_scope(self, context): new_transaction = self._independent transaction_contexts_by_thread = \ _transaction_contexts_by_thread(context) current = restore = getattr( transaction_contexts_by_thread, "current", None) use_factory = self._factory global_factory = None if self._replace_global_factory: use_factory = global_factory = self._replace_global_factory elif current is not None and current.global_factory: global_factory = current.global_factory if self._root._is_global_manager: use_factory = global_factory if current is not None and ( new_transaction or current.factory is not use_factory ): current = None if current is None: current = transaction_contexts_by_thread.current = \ _TransactionContext(use_factory, global_factory=global_factory) try: if self._mode is not None: with current._produce_block( mode=self._mode, connection=self._connection, savepoint=self._savepoint, allow_async=self._allow_async, context=context) as resource: yield resource else: yield finally: if restore is None: del transaction_contexts_by_thread.current elif current is not restore: transaction_contexts_by_thread.current = restore @property @debtcollector.moves.moved_property("async_") def async_compat(self): return self.async_ setattr( _TransactionContextManager, "async", async_compat ) def _context_descriptor(attr=None): getter = operator.attrgetter(attr) def _property_for_context(context): try: transaction_context = context.transaction_ctx except exception.NoEngineContextEstablished: raise exception.NoEngineContextEstablished( "No TransactionContext is established for " "this %s object within the current thread; " "the %r attribute is unavailable." % (context, attr) ) else: result = getter(transaction_context) if result is None: raise exception.ContextNotRequestedError( "The '%s' context attribute was requested but " "it has not been established for this context." % attr ) return result return property(_property_for_context) def _transaction_ctx_for_context(context): by_thread = _transaction_contexts_by_thread(context) try: return by_thread.current except AttributeError: raise exception.NoEngineContextEstablished( "No TransactionContext is established for " "this %s object within the current thread. " % context ) def _transaction_contexts_by_thread(context): transaction_contexts_by_thread = getattr( context, '_enginefacade_context', None) if transaction_contexts_by_thread is None: transaction_contexts_by_thread = \ context._enginefacade_context = _TransactionContextTLocal() return transaction_contexts_by_thread def transaction_context_provider(klass): """Decorate a class with ``session`` and ``connection`` attributes.""" setattr( klass, 'transaction_ctx', property(_transaction_ctx_for_context)) # Graft transaction context attributes as context properties for attr in ('session', 'connection', 'transaction'): setattr(klass, attr, _context_descriptor(attr)) return klass _context_manager = _TransactionContextManager(_is_global_manager=True) """default context manager.""" def transaction_context(): """Construct a local transaction context. """ return _TransactionContextManager() def configure(kw): """Apply configurational options to the global factory. This method can only be called before any specific transaction-beginning methods have been called. .. seealso:: :meth:`._TransactionFactory.configure` """ _context_manager._factory.configure(kw) def get_legacy_facade(): """Return a :class:`.LegacyEngineFacade` for the global factory. This facade will make use of the same engine and sessionmaker as this factory, however will not share the same transaction context; the legacy facade continues to work the old way of returning a new Session each time get_session() is called. """ return _context_manager.get_legacy_facade() reader = _context_manager.reader """The global 'reader' starting point.""" writer = _context_manager.writer """The global 'writer' starting point.""" class LegacyEngineFacade(object): """A helper class for removing of global engine instances from oslo.db. .. deprecated:: 1.12.0 Please use :mod:`oslo_db.sqlalchemy.enginefacade` for new development. As a library, oslo.db can't decide where to store/when to create engine and sessionmaker instances, so this must be left for a target application. On the other hand, in order to simplify the adoption of oslo.db changes, we'll provide a helper class, which creates engine and sessionmaker on its instantiation and provides get_engine()/get_session() methods that are compatible with corresponding utility functions that currently exist in target projects, e.g. in Nova. engine/sessionmaker instances will still be global (and they are meant to be global), but they will be stored in the app context, rather that in the oslo.db context. Two important things to remember: 1. An Engine instance is effectively a pool of DB connections, so it's meant to be shared (and it's thread-safe). 2. A Session instance is not meant to be shared and represents a DB transactional context (i.e. it's not thread-safe). sessionmaker is a factory of sessions. :param sql_connection: the connection string for the database to use :type sql_connection: string :param slave_connection: the connection string for the 'slave' database to use. If not provided, the master database will be used for all operations. Note: this is meant to be used for offloading of read operations to asynchronously replicated slaves to reduce the load on the master database. :type slave_connection: string :param sqlite_fk: enable foreign keys in SQLite :type sqlite_fk: bool :param autocommit: use autocommit mode for created Session instances :type autocommit: bool :param expire_on_commit: expire session objects on commit :type expire_on_commit: bool Keyword arguments: :keyword mysql_sql_mode: the SQL mode to be used for MySQL sessions. (defaults to TRADITIONAL) :keyword mysql_enable_ndb: If True, transparently enables support for handling MySQL Cluster (NDB). (defaults to False) :keyword connection_recycle_time: Time period for connections to be recycled upon checkout (defaults to 3600) :keyword connection_debug: verbosity of SQL debugging information. -1=Off, 0=None, 100=Everything (defaults to 0) :keyword max_pool_size: maximum number of SQL connections to keep open in a pool (defaults to SQLAlchemy settings) :keyword max_overflow: if set, use this value for max_overflow with sqlalchemy (defaults to SQLAlchemy settings) :keyword pool_timeout: if set, use this value for pool_timeout with sqlalchemy (defaults to SQLAlchemy settings) :keyword sqlite_synchronous: if True, SQLite uses synchronous mode (defaults to True) :keyword connection_trace: add python stack traces to SQL as comment strings (defaults to False) :keyword max_retries: maximum db connection retries during startup. (setting -1 implies an infinite retry count) (defaults to 10) :keyword retry_interval: interval between retries of opening a sql connection (defaults to 10) :keyword thread_checkin: boolean that indicates that between each engine checkin event a sleep(0) will occur to allow other greenthreads to run (defaults to True) """ def __init__(self, sql_connection, slave_connection=None, sqlite_fk=False, autocommit=True, expire_on_commit=False, _conf=None, _factory=None, kwargs): warnings.warn( "EngineFacade is deprecated; please use " "oslo_db.sqlalchemy.enginefacade", warning.OsloDBDeprecationWarning, stacklevel=2) if _factory: self._factory = _factory else: self._factory = _TransactionFactory() self._factory.configure( sqlite_fk=sqlite_fk, __autocommit=autocommit, expire_on_commit=expire_on_commit, kwargs ) # make sure passed-in urls are favored over that # of config self._factory._start( _conf, connection=sql_connection, slave_connection=slave_connection) def _check_factory_started(self): if not self._factory._started: self._factory._start() def get_engine(self, use_slave=False): """Get the engine instance (note, that it's shared). :param use_slave: if possible, use 'slave' database for this engine. If the connection string for the slave database wasn't provided, 'master' engine will be returned. (defaults to False) :type use_slave: bool """ self._check_factory_started() if use_slave: return self._factory._reader_engine else: return self._factory._writer_engine def get_session(self, use_slave=False, kwargs): """Get a Session instance. :param use_slave: if possible, use 'slave' database connection for this session. If the connection string for the slave database wasn't provided, a session bound to the 'master' engine will be returned. (defaults to False) :type use_slave: bool Keyword arguments will be passed to a sessionmaker instance as is (if passed, they will override the ones used when the sessionmaker instance was created). See SQLAlchemy Session docs for details. """ self._check_factory_started() if use_slave: return self._factory._reader_maker(kwargs) else: return self._factory._writer_maker(kwargs) def get_sessionmaker(self, use_slave=False): """Get the sessionmaker instance used to create a Session. This can be called for those cases where the sessionmaker() is to be temporarily injected with some state such as a specific connection. 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transform_docker/server.py	briglx/AzureConvertJson	0	143152	<filename>transform_docker/server.py<gh_stars>0 """HTTP Server to transform json to json using liquid formatter.""" import json import logging import os import uuid from aiohttp import web from liquid import Liquid, filter_manager def generate_guid(): """Generate a UUID string.""" return str(uuid.uuid4()) def convert_quality_filter(quality): """Jinja2 Filter to convert quality score.""" quality_map = { "A01": "Poor", "A02": "OK", "A03": "Moderate", "A04": "Good", } if quality not in quality_map: raise ValueError("The quality is not a valid value. It must be A01 - A04") return quality_map[quality] def render(data, template_path, template_name, filters, debug=False): """Render data as a string for a given template.""" file_name = os.path.join(template_path, template_name) # Register Filter for filter_name, filter_code in filters.items(): filter_manager.register(filter_name)(filter_code) with open(file_name, "r") as template_file: template_string = template_file.read() template = Liquid(template_string, liquid_config={"debug": debug}) # Transform result_string = template.render(**data) # Unpack argument lists return result_string def render_dict(data, template_path, template_name, filters): """Render data as Json Dictionary for a given template.""" result_string = render(data, template_path, template_name, filters) return json.loads(result_string) async def post_api_transform(request): """Handle transform api call.""" LOGGER.info("post_api_transform called") # Get payload data = await request.json() # Add tracking guid data["system_guid"] = generate_guid() # set filters filters = {"convert": convert_quality_filter} # Transform message = render_dict(data, TEMPLATE_PATH, TEMPLATE_NAME, filters) return web.json_response(message) if __name__ == "__main__": LOGGER = logging.getLogger(__name__) LOGGER.info("Starting Python HTTP web server.") TEMPLATE_PATH = os.environ.get("TEMPLATE_PATH") TEMPLATE_NAME = os.environ.get("TEMPLATE_NAME") if not TEMPLATE_PATH: raise ValueError( "Template path is required." "Have you set the TEMPLATE_PATH env variable?" ) if not TEMPLATE_NAME: raise ValueError( "Template transform is required." 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raiden_client/interfaces/cli_commands/channels.py	s0b0lev/raiden-client-python	3	181743	<filename>raiden_client/interfaces/cli_commands/channels.py from argparse import ArgumentParser, Namespace, _SubParsersAction from raiden_client import Client, utils def configure_parser(arg_parser: ArgumentParser, subparser: _SubParsersAction) -> None: channels = subparser.add_parser("channels", help="Request a list of all unsettled channels") channels.add_argument("-t", "--token-address", required=False, help="For the given token address") channels.set_defaults(func=parser_function) def parser_function(args: Namespace) -> str: client = Client(endpoint=args.endpoint, version=args.version) channels = client.channels(token_address=args.token_address) return utils.print_stdout(channels)	[ 1, 529, 9507, 29958, 336, 3615, 29918, 4645, 29914, 1639, 8726, 29914, 11303, 29918, 26381, 29914, 305, 12629, 29889, 2272, 13, 3166, 1852, 5510, 1053, 23125, 11726, 29892, 14706, 3535, 29892, 903, 4035, 29925, 1503, 414, 4276, 13, 13, 3166, 1153, 3615, 29918, 4645, 1053, 12477, 29892, 3667, 29879, 13, 13, 13, 1753, 10822, 29918, 16680, 29898, 1191, 29918, 16680, 29901, 23125, 11726, 29892, 1014, 16680, 29901, 903, 4035, 29925, 1503, 414, 4276, 29897, 1599, 6213, 29901, 13, 1678, 18196, 353, 1014, 16680, 29889, 1202, 29918, 16680, 703, 305, 12629, 613, 1371, 543, 3089, 263, 1051, 310, 599, 443, 9915, 839, 18196, 1159, 13, 1678, 18196, 29889, 1202, 29918, 23516, 703, 29899, 29873, 613, 376, 489, 6979, 29899, 7328, 613, 3734, 29922, 8824, 29892, 1371, 543, 2831, 278, 2183, 5993, 3211, 1159, 13, 1678, 18196, 29889, 842, 29918, 4381, 29879, 29898, 9891, 29922, 16680, 29918, 2220, 29897, 13, 13, 13, 1753, 13812, 29918, 2220, 29898, 5085, 29901, 14706, 3535, 29897, 1599, 851, 29901, 13, 1678, 3132, 353, 12477, 29898, 29734, 29922, 5085, 29889, 29734, 29892, 1873, 29922, 5085, 29889, 3259, 29897, 13, 1678, 18196, 353, 3132, 29889, 305, 12629, 29898, 6979, 29918, 7328, 29922, 5085, 29889, 6979, 29918, 7328, 29897, 13, 1678, 736, 3667, 29879, 29889, 2158, 29918, 25393, 29898, 305, 12629, 29897, 13, 2 ]
manimpy/right_angle.py	Jin-Yuhan/manimpy	1	48193	# from @cigar666 # cgnb!!!! from manimlib.imports import * class RightAngle(VGroup): CONFIG = { 'size': 0.25, 'stroke_color': WHITE, 'stroke_width': 3.2, 'fill_color': BLUE, 'fill_opacity': 0.5, 'on_the_right': True, } def __init__(self, corner=ORIGIN, angle=0, kwargs): VGroup.__init__(self, kwargs) self.corner = ORIGIN self.angle = 0 r = UR if self.on_the_right else UL self.add( Polygon(ORIGIN, RIGHT * self.size * r, UR * self.size * r, UP * self.size * r, stroke_width=0, fill_color=self.fill_color, fill_opacity=self.fill_opacity), Line(RIGHT * self.size * r, UR * self.size * r + UP * self.stroke_width / 100 / 2 * 0.8, stroke_width=self.stroke_width, stroke_color=self.stroke_color), Line(UR * self.size * r + RIGHT * self.stroke_width / 100 / 2 * r * 0.8, UP * self.size * r, stroke_width=self.stroke_width, stroke_color=self.stroke_color), ) self.move_corner_to(corner) self.change_angle_to(angle) def move_corner_to(self, new_corner): self.shift(new_corner - self.corner) self.corner = new_corner return self def change_angle_to(self, new_angle): self.rotate(new_angle - self.angle, about_point=self.corner) self.angle = new_angle return self	[ 1, 396, 515, 732, 29883, 335, 279, 29953, 29953, 29953, 13, 29937, 274, 5138, 29890, 6824, 6824, 13, 13, 3166, 767, 326, 1982, 29889, 326, 4011, 1053, 334, 13, 13, 13, 1990, 10428, 19582, 29898, 29963, 4782, 1125, 13, 1678, 8707, 18667, 353, 426, 13, 4706, 525, 2311, 2396, 29871, 29900, 29889, 29906, 29945, 29892, 13, 4706, 525, 25893, 29918, 2780, 2396, 12317, 9094, 29892, 13, 4706, 525, 25893, 29918, 2103, 2396, 29871, 29941, 29889, 29906, 29892, 13, 4706, 525, 5589, 29918, 2780, 2396, 350, 29931, 4462, 29892, 13, 4706, 525, 5589, 29918, 28193, 2396, 29871, 29900, 29889, 29945, 29892, 13, 4706, 525, 265, 29918, 1552, 29918, 1266, 2396, 5852, 29892, 13, 1678, 500, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 11155, 29922, 1955, 6259, 1177, 29892, 10696, 29922, 29900, 29892, 3579, 19290, 1125, 13, 4706, 478, 4782, 17255, 2344, 12035, 1311, 29892, 3579, 19290, 29897, 13, 4706, 1583, 29889, 2616, 1089, 353, 438, 22789, 1177, 13, 4706, 1583, 29889, 2521, 353, 29871, 29900, 13, 4706, 364, 353, 501, 29934, 565, 1583, 29889, 265, 29918, 1552, 29918, 1266, 1683, 501, 29931, 13, 4706, 1583, 29889, 1202, 29898, 13, 18884, 2043, 17125, 29898, 1955, 6259, 1177, 29892, 390, 22530, 334, 1583, 29889, 2311, 334, 364, 29892, 501, 29934, 334, 1583, 29889, 2311, 334, 364, 29892, 11901, 334, 1583, 29889, 2311, 334, 364, 29892, 19782, 29918, 2103, 29922, 29900, 29892, 13, 462, 4706, 5445, 29918, 2780, 29922, 1311, 29889, 5589, 29918, 2780, 29892, 5445, 29918, 28193, 29922, 1311, 29889, 5589, 29918, 28193, 511, 13, 13, 18884, 7407, 29898, 22789, 3912, 334, 1583, 29889, 2311, 334, 364, 29892, 501, 29934, 334, 1583, 29889, 2311, 334, 364, 718, 11901, 334, 1583, 29889, 25893, 29918, 2103, 847, 29871, 29896, 29900, 29900, 847, 29871, 29906, 334, 29871, 29900, 29889, 29947, 29892, 13, 462, 268, 19782, 29918, 2103, 29922, 1311, 29889, 25893, 29918, 2103, 29892, 19782, 29918, 2780, 29922, 1311, 29889, 25893, 29918, 2780, 511, 13, 13, 18884, 7407, 29898, 4574, 334, 1583, 29889, 2311, 334, 364, 718, 390, 22530, 334, 1583, 29889, 25893, 29918, 2103, 847, 29871, 29896, 29900, 29900, 847, 29871, 29906, 334, 364, 334, 29871, 29900, 29889, 29947, 29892, 11901, 334, 1583, 29889, 2311, 334, 364, 29892, 13, 462, 268, 19782, 29918, 2103, 29922, 1311, 29889, 25893, 29918, 2103, 29892, 19782, 29918, 2780, 29922, 1311, 29889, 25893, 29918, 2780, 511, 13, 462, 1723, 13, 4706, 1583, 29889, 11631, 29918, 2616, 1089, 29918, 517, 29898, 2616, 1089, 29897, 13, 4706, 1583, 29889, 3167, 29918, 2521, 29918, 517, 29898, 2521, 29897, 13, 13, 1678, 822, 4337, 29918, 2616, 1089, 29918, 517, 29898, 1311, 29892, 716, 29918, 2616, 1089, 1125, 13, 4706, 1583, 29889, 10889, 29898, 1482, 29918, 2616, 1089, 448, 1583, 29889, 2616, 1089, 29897, 13, 4706, 1583, 29889, 2616, 1089, 353, 716, 29918, 2616, 1089, 13, 4706, 736, 1583, 13, 13, 1678, 822, 1735, 29918, 2521, 29918, 517, 29898, 1311, 29892, 716, 29918, 2521, 1125, 13, 4706, 1583, 29889, 23361, 29898, 1482, 29918, 2521, 448, 1583, 29889, 2521, 29892, 1048, 29918, 3149, 29922, 1311, 29889, 2616, 1089, 29897, 13, 4706, 1583, 29889, 2521, 353, 716, 29918, 2521, 13, 4706, 736, 1583, 13, 13, 2 ]
web_scraper/extract/common.py	rarc41/web_scraper_pro	0	3010	<reponame>rarc41/web_scraper_pro<filename>web_scraper/extract/common.py import yaml __config=None def config(): global __config if not __config: with open('config.yaml', mode='r') as f: __config=yaml.safe_load(f) return __config	[ 1, 529, 276, 1112, 420, 29958, 29878, 5666, 29946, 29896, 29914, 2676, 29918, 1557, 336, 546, 29918, 771, 29966, 9507, 29958, 2676, 29918, 1557, 336, 546, 29914, 21111, 29914, 9435, 29889, 2272, 13, 5215, 343, 8807, 13, 13, 13, 1649, 2917, 29922, 8516, 13, 13, 13, 1753, 2295, 7295, 13, 1678, 5534, 4770, 2917, 13, 1678, 565, 451, 4770, 2917, 29901, 13, 4706, 411, 1722, 877, 2917, 29889, 25162, 742, 4464, 2433, 29878, 1495, 408, 285, 29901, 13, 9651, 4770, 2917, 29922, 25162, 29889, 11177, 29918, 1359, 29898, 29888, 29897, 13, 632, 13, 1678, 736, 4770, 2917, 2 ]
000-025/p010/inle.py	CDL-Project-Euler/Solutions	3	75509	def checkprime(value: int): factor = 2 sqrt = value ** 0.5 while factor <= sqrt: if value % factor == 0: return False factor += 1 return True def sum_primes_under(n: int): num = 2 sum = 0 while num < n: if checkprime(num): sum += num num += 1 return(sum) if __name__ == "__main__": print(sum_primes_under(10))	[ 1, 822, 1423, 10080, 29898, 1767, 29901, 938, 1125, 13, 1678, 7329, 353, 29871, 29906, 13, 1678, 18074, 2273, 353, 995, 3579, 29871, 29900, 29889, 29945, 13, 1678, 1550, 7329, 5277, 18074, 2273, 29901, 13, 4706, 565, 995, 1273, 7329, 1275, 29871, 29900, 29901, 13, 9651, 736, 7700, 13, 4706, 7329, 4619, 29871, 29896, 13, 1678, 736, 5852, 13, 13, 13, 1753, 2533, 29918, 558, 1355, 29918, 5062, 29898, 29876, 29901, 938, 1125, 13, 1678, 954, 353, 29871, 29906, 13, 1678, 2533, 353, 29871, 29900, 13, 1678, 1550, 954, 529, 302, 29901, 13, 4706, 565, 1423, 10080, 29898, 1949, 1125, 13, 9651, 2533, 4619, 954, 13, 4706, 954, 4619, 29871, 29896, 13, 1678, 736, 29898, 2083, 29897, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1596, 29898, 2083, 29918, 558, 1355, 29918, 5062, 29898, 29896, 29900, 876, 2 ]
sc_utils/dhcp_listener.py	jkulawik/ServiceChecker	0	171543	#!/usr/bin/env python3 """scapy-dhcp-listener.py Listen for DHCP packets using scapy to learn when LAN hosts request IP addresses from DHCP Servers. Copyright (C) 2018 <NAME> https://jcutrer.com/python/scapy-dhcp-listener License Dual MIT, 0BSD Extended by jkulawik, 2020 """ from __future__ import print_function from scapy.all import * from scapy.layers.dhcp import DHCP from scapy.layers.dhcp import Ether from scapy.layers.dhcp import BOOTP from scapy.layers.dhcp import IP # Logging from datetime import date from inspect import getsourcefile from sc_utils import mac_vendor from scapy.layers.l2 import getmacbyip whitelist_file = 'MAC_whitelist.txt' __version__ = "0.0.4" def add_zero_to_time(time): if time < 10: return '0'+ time else: return time # print_and_log current time def pal_time(): todays_date = datetime.now() hour = add_zero_to_time(todays_date.hour) minute = add_zero_to_time(todays_date.minute) curr_time = '\n{}:{}'.format(hour, minute) print_and_log(curr_time) def check_whitelist(mac): with open(whitelist_file, 'r') as file: whitelist = file.read() return mac in whitelist def print_and_log(message): print(message) log(message) # Print a message into today's log in /logs def log(message): dir = 'logs' if not os.path.exists(dir): # The folder gets created in the run directory automatically... os.makedirs(dir) # ...but for file writing a full path is needed current_dir = os.path.dirname(getsourcefile(lambda: 0)) path = os.path.join(current_dir, 'logs') file_name = str(date.today()) + '-log.txt' file_path = os.path.join(path, file_name) file = open(file_path, "a") file.write(message + '\n') file.close() # Fixup function to extract dhcp_options by key def get_option(dhcp_options, key): must_decode = ['hostname', 'domain', 'vendor_class_id'] try: for i in dhcp_options: if i[0] == key: # If DHCP Server Returned multiple name servers # return all as comma separated string. if key == 'name_server' and len(i) > 2: return ",".join(i[1:]) # domain and hostname are binary strings, # decode to unicode string before returning elif key in must_decode: return i[1].decode() else: return i[1] except: pass def handle_dhcp_packet(packet): # Match DHCP discover if DHCP in packet and packet[DHCP].options[0][1] == 1: print('---') print('New DHCP Discover') #print(packet.summary()) #print(ls(packet)) hostname = get_option(packet[DHCP].options, 'hostname') mac = packet[Ether].src if check_whitelist(mac): print(f'Whitelisted host {hostname} asked for an IP.') print(f'Host vendor: {mac_vendor.get_str(mac)}') print(f'Host MAC: {mac}') return pal_time() print_and_log(f"Unknown host {hostname} asked for an IP.") print_and_log(f'Host vendor: {mac_vendor.get_str(mac)}') print_and_log(f'Host MAC: {mac}') # Match DHCP ack elif DHCP in packet and packet[DHCP].options[0][1] == 5\ and packet[BOOTP].yiaddr != '0.0.0.0': print('---') print('New DHCP Ack') #print(packet.summary()) #print(ls(packet)) subnet_mask = get_option(packet[DHCP].options, 'subnet_mask') lease_time = get_option(packet[DHCP].options, 'lease_time') router = get_option(packet[DHCP].options, 'router') name_server = get_option(packet[DHCP].options, 'name_server') server_mac = packet[Ether].src server_ip = packet[IP].src sus_ip = packet[BOOTP].yiaddr sus_mac = str(getmacbyip(sus_ip)) sus_vendor = mac_vendor.get_str(sus_mac) if check_whitelist(sus_mac): print(f"DHCP Server {server_ip} ({server_mac}) acknowledged a whitelisted device on IP {sus_ip}") print(f'Host vendor: {mac_vendor.get_str(sus_vendor)}') print(f'Host MAC: {sus_mac}\n') return pal_time() print_and_log(f"DHCP Server {server_ip} ({server_mac}) acknowledged unknown device on IP {sus_ip}") print_and_log(f'Unknown host vendor: {mac_vendor.get_str(sus_vendor)}') print_and_log(f'Unknown host MAC: {sus_mac}\n') print(f"DHCP Options: subnet_mask: {subnet_mask}, lease_time: " f"{lease_time}, router: {router}, name_server: {name_server}") # Match DHCP inform elif DHCP in packet and packet[DHCP].options[0][1] == 8: print('---') print('New DHCP Inform') #print(packet.summary()) #print(ls(packet)) hostname = get_option(packet[DHCP].options, 'hostname') vendor_class_id = get_option(packet[DHCP].options, 'vendor_class_id') print(f"DHCP Inform from {packet[IP].src} ({packet[Ether].src}) " f"hostname: {hostname}, vendor_class_id: {vendor_class_id}") else: print('---') print('Some Other DHCP Packet') print(packet.summary()) #print(ls(packet)) return # This is just to use this script as a dependency def start_sniffing(): # Create MAC whitelist if not os.path.exists(whitelist_file): open(whitelist_file, "w+") print('Sniffing DHCP broadcasts...') print('Press Ctrl+C to stop.') sniff(filter="udp and (port 67 or 68)", prn=handle_dhcp_packet) if __name__ == "__main__": sniff(filter="udp and (port 67 or 68)", prn=handle_dhcp_packet)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 15945, 29908, 1557, 27580, 29899, 12744, 6814, 29899, 25894, 29889, 2272, 13, 1293, 264, 363, 360, 29950, 6271, 23912, 773, 885, 27580, 304, 5110, 746, 365, 2190, 29871, 13, 23525, 2009, 5641, 14157, 515, 360, 29950, 6271, 1816, 874, 29889, 13, 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authz/test/test_obp_helper.py	shivdeep-singh/conversational-ai-chatbot	11	15295	<reponame>shivdeep-singh/conversational-ai-chatbot<gh_stars>10-100 import unittest from zmq_integration_lib import RPCClient, RPCServer import unittest.mock as mock class TestOBPHelper(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def logout(self, mock_zmq): self.assertTrue(True) def get_login_token(self, mock_zmq): self.assertTrue(True)	[ 1, 529, 276, 1112, 420, 29958, 845, 440, 24535, 29899, 2976, 29882, 29914, 535, 874, 1288, 29899, 1794, 29899, 13496, 7451, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 5215, 443, 27958, 13, 3166, 12162, 29939, 29918, 27925, 29918, 1982, 1053, 390, 9026, 4032, 29892, 390, 9026, 6004, 13, 5215, 443, 27958, 29889, 17640, 408, 11187, 13, 13, 13, 1990, 4321, 14824, 29925, 10739, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 1209, 13, 13, 1678, 822, 734, 279, 6767, 29898, 1311, 1125, 13, 4706, 1209, 13, 13, 1678, 822, 1480, 449, 29898, 1311, 29892, 11187, 29918, 14018, 29939, 1125, 13, 4706, 1583, 29889, 9294, 5574, 29898, 5574, 29897, 13, 268, 13, 1678, 822, 679, 29918, 7507, 29918, 6979, 29898, 1311, 29892, 11187, 29918, 14018, 29939, 1125, 13, 4706, 1583, 29889, 9294, 5574, 29898, 5574, 29897, 13, 268, 13, 2 ]
pkpdapp/pkpdapp/dash_apps/auce_app.py	pkpdapp-team/pkpdapp	4	118826	# # This file is part of PKPDApp (https://github.com/pkpdapp-team/pkpdapp) which # is released under the BSD 3-clause license. See accompanying LICENSE.md for # copyright notice and full license details. # import dash_core_components as dcc import dash_html_components as html import dash_bootstrap_components as dbc from dash.dependencies import Input, Output import plotly.graph_objects as go from .auce_figure import AuceFigure import pandas as pd import django_plotly_dash as dpd import json # Create data view app = dpd.DjangoDash( name='auce_view', add_bootstrap_links=True ) # Create dash app app.layout = dbc.Container([ dbc.Row(children=[ dbc.Col(width=5, children=[ html.Label("Select datasets to use:"), dcc.Dropdown( id='dataset-dropdown', ), ]) ]), dbc.Row([ dbc.Col( children=[ html.P(children=( 'Note: AUCE app requries Dosing Groups corresponding to ' 'numerical concentrations ' )), dbc.Col(width=3, children=[ html.Label("Choose variable:"), dcc.Dropdown( id='auce-biomarker-dropdown', ), ]), dcc.Graph( id='initial-doses-dashboard', figure=go.Figure(), style={'width': '100%'} ), dcc.Graph( id='auce-dashboard', figure=go.Figure(), style={'width': '100%'} ) ], ), ]), ]) def rehydrate_state(session_state): if session_state is None: raise NotImplementedError("Cannot handle a missing session state") state = session_state.get('auce_view', None) if state is None: raise NotImplementedError('AuceState missing in session state') return AuceState.from_json(state) @app.callback( [ Output('dataset-dropdown', 'options'), Output('dataset-dropdown', 'value'), ], [Input('auce-dashboard', 'style')], ) def update_datasets(_, session_state=None): state = rehydrate_state(session_state) return state._dataset_options, state._selected_dataset @app.callback( [ Output('auce-biomarker-dropdown', 'options'), Output('auce-biomarker-dropdown', 'value'), ], [ Input('dataset-dropdown', 'value'), ] ) def update_nca_auce_options(dataset_dropdown, session_state=None): state = rehydrate_state(session_state) state._selected_dataset = dataset_dropdown biomarkers = state._get_biomarkers() return biomarkers, biomarkers[0]['value'] @app.callback( [ Output('initial-doses-dashboard', 'figure'), Output('auce-dashboard', 'figure'), ], [ Input('auce-biomarker-dropdown', 'value'), Input('dataset-dropdown', 'value'), ], ) def update_auce(auce_biomarker_dropdown, dataset_dropdown, session_state=None): state = rehydrate_state(session_state) state._selected_dataset = dataset_dropdown return state.generate_auce_figures(auce_biomarker_dropdown) class AuceState: """ """ _id_key = 'ID' _biomarker_key = 'Biomarker' _compound_key = 'Compound' def __init__(self): self._selected_dataset = [] self._datasets = [] self._dataset_options = [] self._auce_biomarker = None def add_datasets(self, datasets, dataset_names): # handle empty datasets if not datasets: return # by default show last datset self._selected_dataset = 0 self._datasets = datasets for dataset, dataset_name in zip(self._datasets, dataset_names): dataset['Dataset'] = dataset_name self._dataset_options = [ {'label': name, 'value': i} for i, name in enumerate(dataset_names) ] # by default use first biomarker self._auce_biomarker = self._datasets[ self._selected_dataset ][self._biomarker_key][0] def to_json(self): state_dict = { '_selected_dataset': self._selected_dataset, '_datasets': [d.to_dict() for d in self._datasets], '_dataset_options': self._dataset_options, '_auce_biomarker': self._auce_biomarker, } return json.dumps(state_dict) @classmethod def from_json(cls, j): data_dict = json.loads(j) o = cls() o._selected_dataset = data_dict['_selected_dataset'] o._datasets = [ pd.DataFrame.from_dict(d) for d in data_dict['_datasets'] ] o._dataset_options = data_dict['_dataset_options'] o._auce_biomarker = data_dict['_auce_biomarker'] return o def generate_auce_figures(self, new_biomarker=None): if not self._datasets: return go.Figure(), go.Figure() if new_biomarker: self._auce_biomarker = new_biomarker self._auce_figure = AuceFigure( self._datasets[self._selected_dataset], self._auce_biomarker ) return self._auce_figure.figures() 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descriptor/customer6b.py	ambleacme/decorators-descriptors	78	132319	""" A client with name and e-mail: >>> jack = Customer('<NAME>', '<EMAIL>') >>> jack.full_email() '<NAME> <<EMAIL>>' A client cannot be created with a blank e-mail: >>> joe = Customer('<NAME>', '') Traceback (most recent call last): ... ValueError: 'email' must not be empty >>> joe = Customer('<NAME>', '<EMAIL>') >>> joe.full_email() '<NAME> <<EMAIL>>' Assigning a blank e-mail later is not allowed either: >>> joe.email = '' Traceback (most recent call last): ... ValueError: 'email' must not be empty `NonBlank` fields must also be strings: >>> dummy = Customer('Buster', 99) Traceback (most recent call last): ... TypeError: 'email' must be of type 'str' The `NonBlank` field values are stored in properly named `Customer` instance attributes: >>> for key, value in sorted(jack.__dict__.items()): ... print('{:>10} : {!r}'.format(key, value)) email : '<EMAIL>' fidelity : 0 name : '<NAME>' """ from model6b import Model, NonBlank class Customer(Model): name = NonBlank() email = NonBlank() def __init__(self, name, email, fidelity=0): self.name = name self.email = email self.fidelity = fidelity def full_email(self): return '{0} <{1}>'.format(self.name, self.email)	[ 1, 9995, 13, 29909, 3132, 411, 1024, 322, 321, 29899, 2549, 29901, 13, 13, 1678, 8653, 28015, 353, 21886, 877, 29966, 5813, 29958, 742, 12801, 26862, 6227, 29958, 1495, 13, 1678, 8653, 28015, 29889, 8159, 29918, 5269, 580, 13, 1678, 12801, 5813, 29958, 3532, 26862, 6227, 6778, 29915, 13, 13, 29909, 3132, 2609, 367, 2825, 411, 263, 9654, 321, 29899, 2549, 29901, 13, 13, 1678, 8653, 2958, 29872, 353, 21886, 877, 29966, 5813, 29958, 742, 27255, 13, 1678, 29243, 313, 3242, 7786, 1246, 1833, 1125, 13, 418, 2023, 13, 1678, 7865, 2392, 29901, 525, 5269, 29915, 1818, 451, 367, 4069, 13, 1678, 8653, 2958, 29872, 353, 21886, 877, 29966, 5813, 29958, 742, 12801, 26862, 6227, 29958, 1495, 13, 1678, 8653, 2958, 29872, 29889, 8159, 29918, 5269, 580, 13, 1678, 12801, 5813, 29958, 3532, 26862, 6227, 6778, 29915, 13, 13, 7900, 647, 292, 263, 9654, 321, 29899, 2549, 2678, 338, 451, 6068, 2845, 29901, 13, 13, 1678, 8653, 2958, 29872, 29889, 5269, 353, 6629, 13, 1678, 29243, 313, 3242, 7786, 1246, 1833, 1125, 13, 418, 2023, 13, 1678, 7865, 2392, 29901, 525, 5269, 29915, 1818, 451, 367, 4069, 13, 13, 29952, 12283, 10358, 804, 29952, 4235, 1818, 884, 367, 6031, 29901, 13, 13, 1678, 8653, 20254, 353, 21886, 877, 29933, 5402, 742, 29871, 29929, 29929, 29897, 13, 1678, 29243, 313, 3242, 7786, 1246, 1833, 1125, 13, 418, 2023, 13, 1678, 20948, 29901, 525, 5269, 29915, 1818, 367, 310, 1134, 525, 710, 29915, 13, 13, 1576, 421, 12283, 10358, 804, 29952, 1746, 1819, 526, 6087, 297, 6284, 4257, 13, 29952, 15122, 29952, 2777, 8393, 29901, 13, 13, 1678, 8653, 363, 1820, 29892, 995, 297, 12705, 29898, 21452, 17255, 8977, 26914, 7076, 580, 1125, 13, 1678, 2023, 268, 1596, 877, 25641, 29958, 29896, 29900, 29913, 584, 426, 29991, 29878, 29913, 4286, 4830, 29898, 1989, 29892, 995, 876, 13, 308, 4876, 584, 12801, 26862, 6227, 16299, 13, 418, 285, 10652, 537, 584, 29871, 29900, 13, 3986, 1024, 584, 12801, 5813, 16299, 13, 13, 15945, 29908, 13, 13, 3166, 1904, 29953, 29890, 1053, 8125, 29892, 10050, 10358, 804, 13, 13, 13, 1990, 21886, 29898, 3195, 1125, 13, 13, 1678, 1024, 353, 10050, 10358, 804, 580, 13, 1678, 4876, 353, 10050, 10358, 804, 580, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1024, 29892, 4876, 29892, 285, 10652, 537, 29922, 29900, 1125, 13, 4706, 1583, 29889, 978, 353, 1024, 13, 4706, 1583, 29889, 5269, 353, 4876, 13, 4706, 1583, 29889, 29888, 10652, 537, 353, 285, 10652, 537, 13, 13, 1678, 822, 2989, 29918, 5269, 29898, 1311, 1125, 13, 4706, 736, 22372, 29900, 29913, 529, 29912, 29896, 17428, 4286, 4830, 29898, 1311, 29889, 978, 29892, 1583, 29889, 5269, 29897, 13, 2 ]
src/bot.py	shirin1996/PyBot	1	194874	import telebot import os bot = telebot.TeleBot(os.environ['BOT_TOKEN'], parse_mode='HTML')	[ 1, 1053, 4382, 7451, 13, 5215, 2897, 13, 13, 7451, 353, 4382, 7451, 29889, 29911, 6146, 29933, 327, 29898, 359, 29889, 21813, 1839, 29933, 2891, 29918, 4986, 29968, 1430, 7464, 6088, 29918, 8513, 2433, 7020, 1495, 2 ]
SBOL2Excel/utils/sbol2excel.py	abamaj/SBOL-to-Excel	0	7384	import sbol2 import pandas as pd import os import logging from openpyxl import load_workbook from openpyxl.worksheet.table import Table, TableStyleInfo from openpyxl.utils.dataframe import dataframe_to_rows from openpyxl.styles import Font, PatternFill, Border, Side from requests_html import HTMLSession #wasderivedfrom: source #remove identity, persistenID, displayID, version #remove attachment (if empty) #add library sheets #add postprocessing function to remove unecessaries class seqFile: def __init__(self, file_path_in, output_path): # global varibales for homespace, document, and sheet self.homeSpace = 'https://sys-bio.org' self.document = file_path_in self.file_location_path = os.path.dirname(__file__) self.sheet = os.path.join(self.file_location_path, 'ontologies.xlsx') self.output_template = os.path.join(self.file_location_path, 'Template_to_Output_Into_v001.xlsx') self.output_path = output_path def roleVariables(self): # set Excel file into a dataframe df = pd.read_excel(self.sheet, index_col=0, sheet_name=1, usecols=[1, 2]) # convert the dataframe into a dictionary roleConvertDict = df.to_dict() # set dictionary indices and values (use column 'URI' in excel sheet) roleName = roleConvertDict['URI'] # switch indices' and values' postions roleDictionary = {uri: role for role, uri in roleName.items()} return roleDictionary def orgVariables(self): # set Excel file into a dataframe df = pd.read_excel(self.sheet, index_col=0, sheet_name=2, usecols=[0, 1]) # convert the dataframe into a dictionary organismConvertDict = df.to_dict() # set dictionary indices and values (use column 'txid' in excel sheet) organismName = organismConvertDict['txid'] # switch indices' and values' postions organismDictionary = {str(txid): organism for organism, txid in organismName.items()} return organismDictionary # def inspectDocInfo(self): # # declare homespace # sbol2.setHomespace(self.homeSpace) # doc = sbol2.Document() # doc.read('../tests/test_files/' + self.document) # # doc.read(self.document) # # print document information # print(doc) # def printDocContents(self): # # declare homespace # sbol2.setHomespace(self.homeSpace) # doc = sbol2.Document() # doc.read('../tests/test_files/' + self.document) # # doc.read(self.document) # # print document contents # for obj in doc: # print(obj) def readDocChart(self): # declare homespace sbol2.setHomespace(self.homeSpace) doc = sbol2.Document() doc.read(self.document) # create a dictionary to hold all the component defintions' information componentDefinitions = {} # iterate through the component definitions roleDict = self.roleVariables() orgDict = self.orgVariables() for cd in doc.componentDefinitions: cdType = cd.type # create a dictionary that has a key for the # component definition's identity, # and a value for all of its features componentFeatures = {} persistentIdentity = cd.properties['http://sbols.org/v2#persistentIdentity'][0] # iterate through the properties of the component defintions # and set them equal to propValue variable for prop in cd.properties: try: propValue = cd.properties[prop][0] except (IndexError): propValue = cd.properties[prop] # extract attribute property type if propValue == []: propValue = '' prop = self.prop_convert(prop) propValue = columnMethods(prop, propValue, doc, cdType, roleDict, orgDict).colV componentFeatures[prop] = str(propValue) # append each componentFeatures dictionary as a # value into the componentDefinitions # dictionary with the 'persistentIdentity' serving as the key componentDefinitions[persistentIdentity] = componentFeatures # return the dictionary of information (temporary, maybe # return true if read in correctly) doc_chart = pd.DataFrame.from_dict(componentDefinitions, orient="index") return doc_chart def prop_convert(self, prop): if type(prop) is str: idx = prop.find('#') # if parsing conditions meet, append them into the # componentFeatures dictionary as necessary if idx >= 1: prop = prop[idx + 1:] if prop == 'type': prop = 'types' if prop == 'http://purl.org/dc/terms/title': prop = 'title' if prop == 'http://purl.org/dc/terms/description': prop = 'description' if prop == 'http://purl.obolibrary.org/obo/OBI_0001617': prop = 'OBI_0001617' return (prop) else: raise ValueError() def displayDocChart(self): #display the dataframe return pd.DataFrame.from_dict(self.readDocChart(), orient = "index") def TEMP_readDocChart1(self): #demo of table column names columnNames = ['Part Name', 'Role', 'Design Notes', 'Altered Sequence', 'Part Description', 'Data Source Prefix', 'Data Source', 'Source Organism', 'Target Organism', 'Circular', 'length (bp)', 'Sequence', 'Data Source', 'Composite'] #import dataframe dictionary #convert dictionary to dataframe df = self.displayDocChart() #type caste dataframe to a set dfSet = set(df) #type caste column names to a set columnNameOrder = set(columnNames) #check difference between the dataframe set and the column name order dfSetDifference = dfSet.difference(columnNameOrder) #check intersection between the datframe set and the column name order dfSetIntersection = dfSet.intersection(columnNameOrder) #combine the type casted difference and intersection finalSetList = list(dfSetIntersection) + list(dfSetDifference) #set list to dictionary return finalSetList # def displayDocChart(self): # # display the dataframe # return pd.DataFrame.from_dict(self.readDocChart(), orient="index") def columnString(self, n): # loop through column length in order to get string appropriate # values for excel sheet rows and columns string = "" while n > 0: n, remainder = divmod(n - 1, 26) string = chr(65 + remainder) + string return string def returnExcelChart(self): start_row = 18 start_cell = f'A{start_row}' # load a workbook wb = load_workbook(self.output_template) ws = wb.active # load raw dataframe to df df = self.readDocChart() # set font features ft1 = Font(name='Arial', size=12, color='548235') ft2 = Font(name='Calibri', size=11, bold=True) hold = dataframe_to_rows(df, index=False, header=True) # counter = 0 # loop through worksheet ws[start_cell].value = '' for r in hold: # if a specific cell is empty, continue to loop past it if r == [None]: continue ws.append(r) # counter += 1 # set table features tab = Table(displayName="Parts_Lib", ref=f"A{start_row +1}:{self.columnString(len(df.columns))}{(len(df) * 2) - 2}") style = TableStyleInfo(name="TableStyleLight7", showFirstColumn=False, showLastColumn=False, showRowStripes=True, showColumnStripes=False) cellColor = PatternFill(patternType='solid', fgColor='DDEBF7') cellBorder = Side(border_style='medium', color="000000") # cellIndex = len(x) # gives cells within specified range their table attributes for col in range(1, len(df.columns) + 1): alpha = self.columnString(col) ws[f'{alpha}{start_row+1}'].fill = cellColor ws[f'{alpha}{start_row+1}'].border = Border(top=cellBorder) tab.tableStyleInfo = style ws.add_table(tab) # counter = 0 # gives cells within specified range their font attributes for row in range(len(df) - 1, (len(df) * 2 - 1)): # counter = counter + 1 for cell in ws[row]: cell.font = ft1 # gives cells within specified range their font attributes # (these are special features for the title) num_rows = len(df) if num_rows % 2 > 0: num_rows = num_rows - 1 for j in range(19, num_rows): for x in ws[j]: x.font = ft2 # output the file wb.save(self.output_path) wb.close() logging.warning(f'Your converted file has been output at {self.output_path}') class columnMethods: def __init__(self, colN, colV, doc, cdType, roleDict, orgDict): # global varibales for dataframe switch statements self.colN = colN self.colV = colV self.doc = doc self.cdType = cdType self.roleDict = roleDict self.orgDict = orgDict # if the column name matches the function name, call the function try: return getattr(self, self.colN)() # if the column name does not match the function name, call 'no_change' except AttributeError: return getattr(self, 'no_change')() def no_change(self): pass # if the specified column role value is within the role column def role(self): roleVal = str(self.colV) if roleVal in self.roleDict: self.colV = self.roleDict[roleVal] def types(self): self.colV = self.colV.split('#')[-1] def sequence(self): self.colV = self.doc.getSequence(self.colV).elements def sourceOrganism(self): orgVal = str(self.colV) orgVal = orgVal.split('=')[-1] txid = self.colV.split('=')[-1] if orgVal in self.orgDict: self.colV = self.orgDict[orgVal] else: session = HTMLSession() r = session.get(self.colV) v = r.html.find('strong', first=True) self.colV = v.text self.orgDict[txid] = self.colV def targetOrganism(self): orgVal = str(self.colV) orgVal = orgVal.split('=')[-1] txid = self.colV.split('=')[-1] if orgVal in self.orgDict: self.colV = self.orgDict[orgVal] else: session = HTMLSession() r = session.get(self.colV) v = r.html.find('strong', first=True) self.colV = v.text self.orgDict[txid] = self.colV	[ 1, 1053, 269, 2095, 29906, 13, 5215, 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quantization/tensorRT_test.py	Jihunn-Kim/khu_capstone_1	1	108250	<reponame>Jihunn-Kim/khu_capstone_1 import tensorrt as trt import pycuda.driver as cuda import numpy as np import torch import pycuda.autoinit import dataset import model import time # print(dir(trt)) tensorrt_file_name = 'bert.plan' TRT_LOGGER = trt.Logger(trt.Logger.WARNING) trt_runtime = trt.Runtime(TRT_LOGGER) with open(tensorrt_file_name, 'rb') as f: engine_data = f.read() engine = trt_runtime.deserialize_cuda_engine(engine_data) context = engine.create_execution_context() # class HostDeviceMem(object): # def __init__(self, host_mem, device_mem): # self.host = host_mem # self.device = device_mem # def __str__(self): # return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device) # def __repr__(self): # return self.__str__() # inputs, outputs, bindings, stream = [], [], [], [] # for binding in engine: # size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size # dtype = trt.nptype(engine.get_binding_dtype(binding)) # host_mem = cuda.pagelocked_empty(size, dtype) # device_mem = cuda.mem_alloc(host_mem.nbytes) # bindings.append(int(device_mem)) # if engine.binding_is_input(binding): # inputs.append( HostDeviceMem(host_mem, device_mem) ) # else: # outputs.append(HostDeviceMem(host_mem, device_mem)) # input_ids = np.ones([1, 1, 29, 29]) # numpy_array_input = [input_ids] # hosts = [input.host for input in inputs] # trt_types = [trt.int32] # for numpy_array, host, trt_types in zip(numpy_array_input, hosts, trt_types): # numpy_array = np.asarray(numpy_array).ravel() # np.copyto(host, numpy_array) # def do_inference(context, bindings, inputs, outputs, stream): # [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] # context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) # [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] # stream.synchronize() # return [out.host for out in outputs] # trt_outputs = do_inference( # context=context, # bindings=bindings, # inputs=inputs, # outputs=outputs, # stream=stream) def infer(context, input_img, output_size, batch_size): # Load engine # engine = context.get_engine() # assert(engine.get_nb_bindings() == 2) # Convert input data to float32 input_img = input_img.astype(np.float32) # Create host buffer to receive data output = np.empty(output_size, dtype = np.float32) # Allocate device memory d_input = cuda.mem_alloc(batch_size * input_img.size * input_img.dtype.itemsize) d_output = cuda.mem_alloc(batch_size * output.size * output.dtype.itemsize) bindings = [int(d_input), int(d_output)] stream = cuda.Stream() # Transfer input data to device cuda.memcpy_htod_async(d_input, input_img, stream) # Execute model context.execute_async(batch_size, bindings, stream.handle, None) # Transfer predictions back cuda.memcpy_dtoh_async(output, d_output, stream) # Synchronize threads stream.synchronize() # Return predictions return output # kwargs = {"./dataset/DoS_dataset.csv" : './DoS_dataset.txt'} # train_data_set, data_idx_map, net_class_count, net_data_count, test_data_set = dataset.GetCanDatasetUsingTxtKwarg(100, 0, **kwargs) # testloader = torch.utils.data.DataLoader(test_data_set, batch_size=256, # shuffle=False, num_workers=2) check_time = time.time() cnt = 0 temp = np.ones([256, 1, 29, 29]) for idx in range(100): # for i, (inputs, labels) in enumerate(testloader): trt_outputs = infer(context, temp, (256, 2), 256) print(trt_outputs.shape) # print(trt_outputs) # print(np.argmax(trt_outputs, axis=0)) # cnt += 1 # if cnt == 100: # break print(time.time() - check_time) tensorrt_file_name = 'bert_int.plan' TRT_LOGGER = trt.Logger(trt.Logger.WARNING) trt_runtime = trt.Runtime(TRT_LOGGER) with open(tensorrt_file_name, 'rb') as f: engine_data = f.read() engine = trt_runtime.deserialize_cuda_engine(engine_data) context = engine.create_execution_context() check_time = time.time() cnt = 0 temp = np.ones([256, 1, 29, 29]) for idx in range(100): # for i, (inputs, labels) in enumerate(testloader): trt_outputs = infer(context, temp, (256, 2), 256) print(trt_outputs.shape) # print(trt_outputs) # print(np.argmax(trt_outputs, axis=0)) # cnt += 1 # if cnt == 100: # break print(time.time() - check_time) test_model = model.Net().cuda() check_time = time.time() cnt = 0 temp = torch.randn(256, 1, 29, 29).cuda() for idx in range(100): # for i, (inputs, labels) in enumerate(testloader): # inputs = inputs.float().cuda() normal_outputs = test_model(temp) # print(normal_outputs) print(normal_outputs.shape) cnt += 1 if cnt == 100: break print(time.time() - check_time) import tensorrt as trt import numpy as np import pycuda.autoinit import pycuda.driver as cuda import time model_path = "bert.onnx" input_size = 32 TRT_LOGGER = trt.Logger(trt.Logger.WARNING) # def build_engine(model_path): # with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.OnnxParser(network, TRT_LOGGER) as parser: # builder.max_workspace_size = 1<<20 # builder.max_batch_size = 1 # with open(model_path, "rb") as f: # parser.parse(f.read()) # engine = builder.build_cuda_engine(network) # return engine def alloc_buf(engine): # host cpu mem h_in_size = trt.volume(engine.get_binding_shape(0)) h_out_size = trt.volume(engine.get_binding_shape(1)) h_in_dtype = trt.nptype(engine.get_binding_dtype(0)) h_out_dtype = trt.nptype(engine.get_binding_dtype(1)) in_cpu = cuda.pagelocked_empty(h_in_size, h_in_dtype) out_cpu = cuda.pagelocked_empty(h_out_size, h_out_dtype) # allocate gpu mem in_gpu = cuda.mem_alloc(in_cpu.nbytes) out_gpu = cuda.mem_alloc(out_cpu.nbytes) stream = cuda.Stream() return in_cpu, out_cpu, in_gpu, out_gpu, stream def inference(engine, context, inputs, out_cpu, in_gpu, out_gpu, stream): # async version # with engine.create_execution_context() as context: # cost time to initialize # cuda.memcpy_htod_async(in_gpu, inputs, stream) # context.execute_async(1, [int(in_gpu), int(out_gpu)], stream.handle, None) # cuda.memcpy_dtoh_async(out_cpu, out_gpu, stream) # stream.synchronize() # sync version cuda.memcpy_htod(in_gpu, inputs) context.execute(1, [int(in_gpu), int(out_gpu)]) cuda.memcpy_dtoh(out_cpu, out_gpu) return out_cpu if __name__ == "__main__": inputs = np.random.random((1, 1, 29, 29)).astype(np.float32) tensorrt_file_name = '/content/drive/My Drive/capstone1/CAN/bert.plan' TRT_LOGGER = trt.Logger(trt.Logger.WARNING) trt_runtime = trt.Runtime(TRT_LOGGER) with open(tensorrt_file_name, 'rb') as f: engine_data = f.read() engine = trt_runtime.deserialize_cuda_engine(engine_data) # engine = build_engine(model_path) context = engine.create_execution_context() for _ in range(10): t1 = time.time() in_cpu, out_cpu, in_gpu, out_gpu, stream = alloc_buf(engine) res = inference(engine, context, inputs.reshape(-1), out_cpu, in_gpu, out_gpu, stream) print(res) print("cost time: ", time.time()-t1)	[ 1, 529, 276, 1112, 420, 29958, 29967, 4861, 5963, 29899, 29968, 326, 29914, 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dagger/dag_creator/airflow/utils/utils.py	jorgetagle/dagger	5	143709	from pathlib import Path def get_sql_queries(path): sql_queries = {} for query_file in Path(path).glob("*.sql"): with open(query_file, "r") as f: sql_queries[query_file.stem] = f.read() return sql_queries	[ 1, 515, 2224, 1982, 1053, 10802, 13, 13, 13, 1753, 679, 29918, 2850, 29918, 339, 6358, 29898, 2084, 1125, 13, 1678, 4576, 29918, 339, 6358, 353, 6571, 13, 1678, 363, 2346, 29918, 1445, 297, 10802, 29898, 2084, 467, 23705, 703, 10521, 2850, 29908, 1125, 13, 4706, 411, 1722, 29898, 1972, 29918, 1445, 29892, 376, 29878, 1159, 408, 285, 29901, 13, 9651, 4576, 29918, 339, 6358, 29961, 1972, 29918, 1445, 29889, 303, 331, 29962, 353, 285, 29889, 949, 580, 13, 1678, 736, 4576, 29918, 339, 6358, 13, 2 ]
action-server/covidflow/actions/action_daily_ci_early_opt_out.py	dialoguemd/covidflow	7	162107	from typing import Any, Dict, List, Text from rasa_sdk import Action, Tracker from rasa_sdk.events import SlotSet from rasa_sdk.executor import CollectingDispatcher from covidflow.constants import CONTINUE_CI_SLOT from covidflow.utils.persistence import cancel_reminder from .lib.log_util import bind_logger ACTION_NAME = "action_daily_ci_early_opt_out" class ActionDailyCiEarlyOptOut(Action): def name(self) -> Text: return ACTION_NAME def run( self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict[Text, Any], ) -> List[Dict[Text, Any]]: bind_logger(tracker) dispatcher.utter_message( template="utter_daily_ci__early_opt_out__acknowledge_cancel_ci" ) dispatcher.utter_message( template="utter_daily_ci__early_opt_out__cancel_ci_recommendation" ) cancel_reminder(tracker.current_slot_values()) return [SlotSet(CONTINUE_CI_SLOT, False)]	[ 1, 515, 19229, 1053, 3139, 29892, 360, 919, 29892, 2391, 29892, 3992, 13, 13, 3166, 364, 11290, 29918, 15348, 1053, 9123, 29892, 3201, 4937, 13, 3166, 364, 11290, 29918, 15348, 29889, 13604, 1053, 317, 8276, 2697, 13, 3166, 364, 11290, 29918, 15348, 29889, 4258, 3406, 1053, 24930, 292, 23669, 13, 13, 3166, 18838, 333, 1731, 29889, 3075, 1934, 1053, 8707, 29911, 1177, 4462, 29918, 8426, 29918, 12750, 2891, 13, 3166, 18838, 333, 1731, 29889, 13239, 29889, 28249, 1053, 12611, 29918, 1745, 4995, 13, 13, 3166, 869, 1982, 29889, 1188, 29918, 4422, 1053, 7868, 29918, 21707, 13, 13, 24705, 29918, 5813, 353, 376, 2467, 29918, 29881, 8683, 29918, 455, 29918, 799, 368, 29918, 3670, 29918, 449, 29908, 13, 13, 13, 1990, 9123, 29928, 8683, 29907, 29875, 29923, 279, 368, 20624, 3744, 29898, 4276, 1125, 13, 1678, 822, 1024, 29898, 1311, 29897, 1599, 3992, 29901, 13, 4706, 736, 319, 9838, 29918, 5813, 13, 13, 1678, 822, 1065, 29898, 13, 4706, 1583, 29892, 13, 4706, 13916, 261, 29901, 24930, 292, 23669, 29892, 13, 4706, 1020, 4937, 29901, 3201, 4937, 29892, 13, 4706, 5354, 29901, 360, 919, 29961, 1626, 29892, 3139, 1402, 13, 1678, 1723, 1599, 2391, 29961, 21533, 29961, 1626, 29892, 3139, 5262, 29901, 13, 4706, 7868, 29918, 21707, 29898, 3018, 4937, 29897, 13, 4706, 13916, 261, 29889, 6463, 29918, 4906, 29898, 13, 9651, 4472, 543, 6463, 29918, 29881, 8683, 29918, 455, 1649, 799, 368, 29918, 3670, 29918, 449, 1649, 547, 3707, 5485, 29918, 20713, 29918, 455, 29908, 13, 4706, 1723, 13, 13, 4706, 13916, 261, 29889, 6463, 29918, 4906, 29898, 13, 9651, 4472, 543, 6463, 29918, 29881, 8683, 29918, 455, 1649, 799, 368, 29918, 3670, 29918, 449, 1649, 20713, 29918, 455, 29918, 276, 2055, 355, 362, 29908, 13, 4706, 1723, 13, 13, 4706, 12611, 29918, 1745, 4995, 29898, 3018, 4937, 29889, 3784, 29918, 2536, 327, 29918, 5975, 3101, 13, 13, 4706, 736, 518, 29903, 8276, 2697, 29898, 22412, 1177, 4462, 29918, 8426, 29918, 12750, 2891, 29892, 7700, 4638, 13, 2 ]
LambdaFunction/index.py	aws-samples/integrate-amazon-connect-with-smart-on-fhir-backend-services	3	92474	import json, random, string, os, math, time, datetime, dateutil.parser import boto3 from datetime import datetime, timedelta, timezone import logging from FHIRClient import FHIRClient logger = logging.getLogger() logger.setLevel(logging.INFO) ## Establish the connection to SMART on FHIR backend services fhirclient = FHIRClient( os.environ['client_id'], os.environ['endpoint_token'], os.environ['endpoint_stu3'], os.environ['kms_key_id'] ) """ --- Helpers to build responses which match the structure of the necessary dialog actions --- """ def elicit_slot(session_attributes, intent_name, slots, slot_to_elicit, message, response_card): return { 'sessionAttributes': session_attributes, 'dialogAction': { 'type': 'ElicitSlot', 'intentName': intent_name, 'slots': slots, 'slotToElicit': slot_to_elicit, 'message': message, 'responseCard': response_card } } def confirm_intent(session_attributes, intent_name, slots, message, response_card): return { 'sessionAttributes': session_attributes, 'dialogAction': { 'type': 'ConfirmIntent', 'intentName': intent_name, 'slots': slots, 'message': message, 'responseCard': response_card } } def close(session_attributes, fulfillment_state, message): response = { 'sessionAttributes': session_attributes, 'dialogAction': { 'type': 'Close', 'fulfillmentState': fulfillment_state, 'message': message } } return response def delegate(session_attributes, slots): return { 'sessionAttributes': session_attributes, 'dialogAction': { 'type': 'Delegate', 'slots': slots } } def build_response_card(title, subtitle, options): """ Build a responseCard with a title, subtitle, and an optional set of options which should be displayed as buttons. """ buttons = None if len(options) > 1: buttons = [] for i in range(min(5, len(options))): buttons.append(options[i]) return { 'contentType': 'application/vnd.amazonaws.card.generic', 'version': 1, 'genericAttachments': [{ 'title': title, 'subTitle': subtitle, 'buttons': buttons }] } else: return { 'contentType': 'application/vnd.amazonaws.card.generic', 'version': 1, 'genericAttachments': [{ 'title': title, 'subTitle': subtitle }] } """ --- Functions that control the bot's behavior --- """ def getPatientAuth(intent_request): """ Authenticate caller and return FHIR STU3 patient ID for the following query """ logger.debug('intent request: {}'.format(intent_request)) output_session_attributes = intent_request['currentIntent']['slots'] if intent_request['currentIntent']['slots'] is not None else {} telecom = intent_request['sessionAttributes']['telecom'] if intent_request['sessionAttributes']['telecom'] is not None else {} ## format phone number telecom = '{0}-{1}-{2}'.format(telecom[-10:-7], telecom[-7:-4], telecom[-4:]) logger.debug('phone number after tranformation: {}'.format(telecom)) patientinfo = { 'birthdate': output_session_attributes['patientBirthday'], 'gender': output_session_attributes['patientGender'], 'telecom': telecom } r = fhirclient.get_patient(patientinfo) output_session_attributes['patientid']=r['response'] if r['status']==200: statusMessage = "Thank you for authenticating" else: statusMessage = "I'm sorry, I didn't find a patient with that information" return close( output_session_attributes, 'Fulfilled', { 'contentType': 'PlainText', 'content': statusMessage } ) def getMedHelp(intent_request): """ Retrieve Medication Information for a given patient """ logger.info('intent request: {}'.format(intent_request)) output_session_attributes = intent_request['sessionAttributes'] if intent_request['sessionAttributes'] is not None else {} patientid = output_session_attributes['patientid'] if output_session_attributes['patientid'] is not None else {} res = fhirclient.get_meds(patientid) logger.debug(res) if res['status']==200: if type(res['response'])==str: outputtext = res['response'] else: outputtext = 'I have found the following medications and instructions for you. ' for med in res["response"]: outputtext += med['medicationReference'] + '. ' if 'dosage' in med and 'patientInstruction' in med['dosage'][0]: outputtext += 'The dosage for this is the following... ' + med['dosage'][0]['patientInstruction'] else: outputtext += 'I did not find a dosage for this medication. ' else: outputtext = 'I do not have any medication for you.' return close( output_session_attributes, 'Fulfilled', { 'contentType': 'PlainText', 'content': outputtext } ) """ --- Intents --- """ def dispatch(intent_request): """ Called when the user specifies an intent for this bot. 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app/recipe/tests/test_tags_api.py	leo-hoet/recipe-app-api	0	52291	<gh_stars>0 from django.contrib.auth import get_user_model from django.urls import reverse from django.test import TestCase from rest_framework import status from rest_framework.test import APIClient from recipe.tests.test_recipe_api import sample_recipe, sample_tag from core.models import Tag from recipe.serializers import TagSerializer TAGS_URL = reverse('recipe:tag-list') class PubliTagsApiTests(TestCase): """Test the publicly available endpoints""" def setUp(self) -> None: self.client = APIClient() def test_login_requiered(self): """Test that login is requeired for retrieveing tags""" res = self.client.get(TAGS_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateTagsApiTests(TestCase): """User tags API tests with authentication""" def setUp(self) -> None: self.user = get_user_model().objects.create_user( email='<EMAIL>', password='<PASSWORD>', ) self.client = APIClient() self.client.force_authenticate(self.user) def test_retrieve_tags(self): """Test retrieving tags""" Tag.objects.create(user=self.user, name='Vegan') Tag.objects.create(user=self.user, name='Dessert') res = self.client.get(TAGS_URL) tags = Tag.objects.all().order_by('-name') serializer = TagSerializer(tags, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_tag_limited_to_user(self): """Test that tags returned are for the authenticated user""" user2 = get_user_model().objects.create_user( email='<EMAIL>', password='<PASSWORD>', ) Tag.objects.create(user=user2, name='Fruity') tag = Tag.objects.create(user=self.user, name='Comfort Food') res = self.client.get(TAGS_URL) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data[0]['name'], tag.name) def test_create_tag_successful(self): """Test creating a new tag""" payload = { 'name': 'Test tag' } res = self.client.post(TAGS_URL, payload) exists = Tag.objects.filter( user=self.user, name=payload['name'] ).exists() self.assertTrue(exists) self.assertEqual(res.status_code, status.HTTP_201_CREATED) def test_create_tag_invalid(self): """Test creating an invalid tag""" payload = {'name': ''} res = self.client.post(TAGS_URL, payload) self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_get_tags_in_use_succesful(self): """Test getting al ussed tags by recipes""" tag1 = sample_tag(self.user, 'ta') tag2 = sample_tag(self.user, 'tb') tag3 = sample_tag(self.user, 'tc') recip1 = sample_recipe(self.user, title='recip1') recip1.tags.add(tag1) recip1.tags.add(tag2) recip2 = sample_recipe(self.user, title='recip2') recip2.tags.add(tag2) payload = { 'in_use': 1 } res = self.client.get(TAGS_URL, payload) serializer1 = TagSerializer(tag1) serializer2 = TagSerializer(tag2) serializer3 = TagSerializer(tag3) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 2) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) def test_get_tags_in_use_invalid_inuse(self): payload = { 'in_use': 'invalid' } res = self.client.get(TAGS_URL, payload) self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_get_tags_in_use_invalid_key(self): payload = { 'asdasd': 0 } res = self.client.get(TAGS_URL, payload) self.assertEqual(res.status_code, status.HTTP_200_OK)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 9557, 29889, 21570, 29889, 5150, 1053, 679, 29918, 1792, 29918, 4299, 13, 3166, 9557, 29889, 26045, 1053, 11837, 13, 3166, 9557, 29889, 1688, 1053, 4321, 8259, 13, 13, 3166, 1791, 29918, 4468, 1053, 4660, 13, 3166, 1791, 29918, 4468, 29889, 1688, 1053, 3450, 4032, 13, 3166, 9522, 412, 29889, 21150, 29889, 1688, 29918, 4361, 412, 29918, 2754, 1053, 4559, 29918, 4361, 412, 29892, 4559, 29918, 4039, 13, 13, 3166, 7136, 29889, 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pydis_site/apps/api/migrations/0026_use_proper_default_for_infraction_insertion_date.py	Transfusion/site	700	175151	<filename>pydis_site/apps/api/migrations/0026_use_proper_default_for_infraction_insertion_date.py<gh_stars>100-1000 # Generated by Django 2.1.5 on 2019-01-09 19:50 from django.db import migrations, models import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('api', '0025_allow_custom_inserted_at_infraction_field'), ] operations = [ migrations.AlterField( model_name='infraction', name='inserted_at', field=models.DateTimeField(default=django.utils.timezone.now, help_text='The date and time of the creation of this infraction.'), ), ]	[ 1, 529, 9507, 29958, 2272, 2218, 29918, 2746, 29914, 13371, 29914, 2754, 29914, 26983, 800, 29914, 29900, 29900, 29906, 29953, 29918, 1509, 29918, 771, 546, 29918, 4381, 29918, 1454, 29918, 7192, 13857, 29918, 7851, 291, 29918, 1256, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29899, 29896, 29900, 29900, 29900, 13, 29937, 3251, 630, 491, 15337, 29871, 29906, 29889, 29896, 29889, 29945, 373, 29871, 29906, 29900, 29896, 29929, 29899, 29900, 29896, 29899, 29900, 29929, 29871, 29896, 29929, 29901, 29945, 29900, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 29892, 4733, 13, 5215, 9557, 29889, 13239, 29889, 2230, 8028, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 2754, 742, 525, 29900, 29900, 29906, 29945, 29918, 9536, 29918, 6341, 29918, 7851, 287, 29918, 271, 29918, 7192, 13857, 29918, 2671, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 2499, 357, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 7192, 13857, 742, 13, 9651, 1024, 2433, 7851, 287, 29918, 271, 742, 13, 9651, 1746, 29922, 9794, 29889, 11384, 3073, 29898, 4381, 29922, 14095, 29889, 13239, 29889, 2230, 8028, 29889, 3707, 29892, 1371, 29918, 726, 2433, 1576, 2635, 322, 931, 310, 278, 11265, 310, 445, 3041, 13857, 29889, 5477, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
configs/deepim/ycbvPbrSO/FlowNet512_1.5AugCosyAAEGray_NoiseRandom_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_ycbvPbr_SO/FlowNet512_1.5AugCosyAAEGray_NoiseRandom_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_Pbr_16_36WoodBlock.py	THU-DA-6D-Pose-Group/self6dpp	33	2538	<gh_stars>10-100 _base_ = "./FlowNet512_1.5AugCosyAAEGray_NoiseRandom_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_Pbr_01_02MasterChefCan.py" OUTPUT_DIR = "output/deepim/ycbvPbrSO/FlowNet512_1.5AugCosyAAEGray_NoiseRandom_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_ycbvPbr_SO/16_36WoodBlock" DATASETS = dict(TRAIN=("ycbv_036_wood_block_train_pbr",))	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29918, 3188, 29918, 353, 376, 6904, 17907, 6779, 29945, 29896, 29906, 29918, 29896, 29889, 29945, 29909, 688, 29907, 359, 29891, 6344, 11787, 764, 29918, 3782, 895, 17875, 29918, 14769, 3663, 573, 29934, 29918, 29907, 3466, 25584, 29918, 11093, 29888, 29891, 29896, 29918, 29928, 7516, 29900, 29896, 29918, 3403, 29928, 29920, 29918, 13427, 29896, 29900, 29918, 29943, 5066, 29918, 29925, 1182, 29918, 29900, 29896, 29918, 29900, 29906, 19203, 26856, 29888, 6028, 29889, 2272, 29908, 13, 12015, 12336, 29918, 9464, 353, 376, 4905, 29914, 24535, 326, 29914, 29891, 10702, 29894, 29925, 1182, 6156, 29914, 17907, 6779, 29945, 29896, 29906, 29918, 29896, 29889, 29945, 29909, 688, 29907, 359, 29891, 6344, 11787, 764, 29918, 3782, 895, 17875, 29918, 14769, 3663, 573, 29934, 29918, 29907, 3466, 25584, 29918, 11093, 29888, 29891, 29896, 29918, 29928, 7516, 29900, 29896, 29918, 3403, 29928, 29920, 29918, 13427, 29896, 29900, 29918, 29943, 5066, 29918, 29891, 10702, 29894, 29925, 1182, 29918, 6156, 29914, 29896, 29953, 29918, 29941, 29953, 29956, 2092, 7445, 29908, 13, 25832, 8127, 9375, 353, 9657, 29898, 29911, 4717, 1177, 29922, 703, 29891, 10702, 29894, 29918, 29900, 29941, 29953, 29918, 6115, 29918, 1271, 29918, 14968, 29918, 29886, 1182, 613, 876, 13, 2 ]
sources/args.py	NougatCA/SPT-Code	10	129158	<reponame>NougatCA/SPT-Code import dataclasses from dataclasses import dataclass, field import os import enums @dataclass class RuntimeArguments: """Arguments for runtime.""" do_pre_train: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to pre-train'} ) pre_train_tasks: str = field( default=','.join(enums.PRE_TRAIN_TASKS), metadata={'help': 'Pre-training tasks in order, split by commas, ' 'for example (default) {}'.format(','.join(enums.PRE_TRAIN_TASKS))} ) do_fine_tune: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to fine_tune, task can be specific by `--task`'} ) only_test: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to test only'} ) task: str = field( default='summarization', metadata={'help': 'Downstream task', 'choices': enums.ALL_DOWNSTREAM_TASKS} ) trained_vocab: str = field( default='../pre_trained/vocabs/', metadata={'help': 'Directory of trained vocabs'} ) trained_model: str = field( default='../pre_trained/models/all/', metadata={'help': 'Directory of trained model'} ) train_from_scratch: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to train from scratch, will ignore `--trained_model`'} ) random_seed: int = field( default=42, metadata={'help': 'Specific random seed manually for all operations, 0 to disable'} ) n_epoch: int = field( default=50, metadata={'help': 'Number of data iterations for training'} ) batch_size: int = field( default=64, metadata={'help': 'Batch size for training on each device'} ) eval_batch_size: int = field( default=64, metadata={'help': 'Batch size for evaluation on each device'} ) beam_width: int = field( default=5, metadata={'help': 'Beam width when using beam decoding, 1 to greedy decode'} ) logging_steps: int = field( default=100, metadata={'help': 'Log training state every n steps'} ) cuda_visible_devices: str = field( default=None, metadata={'help': 'Visible cuda devices, string formatted, device number divided by \',\', e.g., \'0, 2\', ' '\'None\' will use all'} ) fp16: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to use mixed precision'} ) @dataclass class DatasetArguments: """Arguments for dataset loading.""" dataset_root: str = field( default='../../dataset/', metadata={'help': 'Root of the dataset'} ) train_subset_ratio: float = field( default=None, metadata={'help': 'Ratio of train subset'} ) pre_train_subset_ratio: float = field( default=None, metadata={'help': 'Ratio of pre-train subset'} ) @dataclass class SavingArguments: """Arguments for saving and loading.""" model_name: str = field( default='default_model', metadata={'help': 'Name of the model'} ) dataset_save_dir: str = field( default=os.path.join(DatasetArguments.dataset_root, 'dataset_saved'), metadata={'help': 'Directory to save and load dataset pickle instance'} ) vocab_save_dir: str = field( default=os.path.join(DatasetArguments.dataset_root, 'vocab_saved'), metadata={'help': 'Directory to save and load vocab pickle instance'} ) @dataclass class PreprocessingArguments: """Arguments for data preprocessing.""" code_vocab_size: int = field( default=50000, metadata={'help': 'Maximum size of code vocab'} ) nl_vocab_size: int = field( default=30000, metadata={'help': 'Maximum size of nl vocab'} ) code_vocab_name: str = field( default='code', metadata={'help': 'Name of the code vocab'} ) ast_vocab_name: str = field( default='ast', metadata={'help': 'Name of the ast vocab'} ) nl_vocab_name: str = field( default='nl', metadata={'help': 'Name of the nl vocab'} ) max_code_len: int = field( default=256, metadata={'help': 'Maximum length of code sequence'} ) max_ast_len: int = field( default=32, metadata={'help': 'Maximum length of ast sequence'} ) max_nl_len: int = field( default=64, metadata={'help': 'Maximum length of the nl sequence'} ) code_tokenize_method: str = field( default='bpe', metadata={'help': 'Tokenize method of code', 'choices': ['word', 'bpe']} ) nl_tokenize_method: str = field( default='bpe', metadata={'help': 'Tokenize method of nl', 'choices': ['word', 'bpe']} ) no_ast: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to eliminate AST from input'} ) no_nl: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to eliminate natural language from input'} ) @dataclass class ModelArguments: """Arguments for model related hyper-parameters""" d_model: int = field( default=768, metadata={'help': 'Dimension of the model'} ) d_ff: int = field( default=3072, metadata={'help': 'Dimension of the feed forward layer'} ) n_head: int = field( default=12, metadata={'help': 'Number of head of self attention'} ) n_layer: int = field( default=12, metadata={'help': 'Number of layer'} ) dropout: float = field( default=0.1, metadata={'help': 'Dropout probability'} ) @dataclass class OptimizerArguments: """Arguments for optimizer, early stopping, warmup, grad clipping, label smoothing.""" learning_rate: float = field( default=5e-5, metadata={'help': 'Learning rate'} ) lr_decay_rate: float = field( default=0, metadata={'help': 'Decay ratio for learning rate, 0 to disable'} ) early_stop_patience: int = field( default=20, metadata={'help': 'Stop training if performance does not improve in n epoch, 0 to disable'} ) warmup_steps: int = field( default=1000, metadata={'help': 'Warmup steps for optimizer, 0 to disable'} ) grad_clipping_norm: float = field( default=1.0, metadata={'help': 'Gradient clipping norm, 0 to disable'} ) gradient_accumulation_steps: int = field( default=1, metadata={'help': 'Gradient accumulation steps, default to 1'} ) label_smoothing: float = field( default=0.1, metadata={'help': 'Label smoothing ratio, 0 to disable'} ) @dataclass class TaskArguments: """Arguments for specific tasks""" mass_mask_ratio: float = field( default=0.5, metadata={'help': 'Ratio between number of masked tokens and number of total tokens, in MASS'} ) summarization_language: str = field( default='java', metadata={'help': 'Language of the source code in code summarization, also the directory of the dataset dir'} ) completion_max_len: int = field( default=32, metadata={'help': 'Max length of code to completion'} ) translation_source_language: str = field( default='java', metadata={'help': 'Source language of the code translation', 'choices': ['java', 'c_sharp']} ) translation_target_language: str = field( default='c_sharp', metadata={'help': 'Target language of the code translation', 'choices': ['java', 'c_sharp']} ) search_language: str = field( default='java', metadata={'help': 'Language of the source code in code search, also the directory of the dataset dir'} ) bug_fix_scale: str = field( default='small', metadata={'help': 'Scale of the bug fix dataset.', 'choices': ['small', 'medium']} ) def transfer_arg_name(name): return '--' + name.replace('_', '-') def 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{{cookiecutter.project_hyphen}}/{{cookiecutter.project_slug}}/__init__.py	zhangxianbing/cookiecutter-pypackage	1	9816	"""{{ cookiecutter.project_name }} - {{ cookiecutter.project_short_description }}""" __version__ = "{{ cookiecutter.project_version }}" __author__ = """{{ cookiecutter.author_name }}""" __email__ = "{{ cookiecutter.author_email }}" prog_name = "{{ cookiecutter.project_hyphen }}"	[ 1, 9995, 6224, 15327, 29883, 6463, 29889, 4836, 29918, 978, 9156, 448, 8620, 15327, 29883, 6463, 29889, 4836, 29918, 12759, 29918, 8216, 500, 5038, 15945, 13, 13, 1649, 3259, 1649, 353, 376, 6224, 15327, 29883, 6463, 29889, 4836, 29918, 3259, 500, 5038, 13, 1649, 8921, 1649, 353, 9995, 6224, 15327, 29883, 6463, 29889, 8921, 29918, 978, 500, 5038, 15945, 13, 1649, 5269, 1649, 353, 376, 6224, 15327, 29883, 6463, 29889, 8921, 29918, 5269, 500, 5038, 13, 13, 29097, 29918, 978, 353, 376, 6224, 15327, 29883, 6463, 29889, 4836, 29918, 5819, 9789, 500, 5038, 13, 2 ]
hummingbot/connector/exchange/k2/k2_in_flight_order.py	d3alek/hummingbot	0	26659	<reponame>d3alek/hummingbot<filename>hummingbot/connector/exchange/k2/k2_in_flight_order.py<gh_stars>0 import asyncio from decimal import Decimal from typing import ( Any, Dict, Optional, ) from hummingbot.connector.exchange.k2.k2_utils import convert_from_exchange_trading_pair from hummingbot.connector.in_flight_order_base import InFlightOrderBase from hummingbot.core.event.events import ( OrderType, TradeType ) class K2InFlightOrder(InFlightOrderBase): def __init__(self, client_order_id: str, exchange_order_id: Optional[str], trading_pair: str, order_type: OrderType, trade_type: TradeType, price: Decimal, amount: Decimal, creation_timestamp: int, initial_state: str = "New", ): super().__init__( client_order_id, exchange_order_id, trading_pair, order_type, trade_type, price, amount, creation_timestamp, initial_state, ) self.last_executed_amount_base = Decimal("nan") self.trade_id_set = set() self.cancelled_event = asyncio.Event() @property def is_done(self) -> bool: return self.last_state in {"Filled", "Cancelled"} @property def is_failure(self) -> bool: return self.last_state in {"No Balance"} @property def is_cancelled(self) -> bool: return self.last_state in {"Cancelled", "Expired"} def update_with_trade_update(self, trade_update: Dict[str, Any]) -> bool: """ Update the InFlightOrder with the trade update from Private/GetHistory API endpoint return: True if the order gets updated successfully otherwise False """ trade_id: str = str(trade_update["id"]) trade_order_id: str = str(trade_update["orderid"]) if trade_order_id != self.exchange_order_id or trade_id in self.trade_id_set: return False self.trade_id_set.add(trade_id) trade_price: Decimal = Decimal(str(trade_update["price"])) trade_amount: Decimal = Decimal(str(trade_update["amount"])) if trade_update["type"] == "Buy": self.executed_amount_base += trade_amount self.executed_amount_quote += trade_price * trade_amount else: self.executed_amount_quote += trade_amount self.executed_amount_base += trade_amount / trade_price self.fee_paid += Decimal(str(trade_update["fee"])) if not self.fee_asset: base, quote = convert_from_exchange_trading_pair(trade_update["symbol"]).split("-") self.fee_asset = base if trade_update["type"] == "Buy" else quote return True	[ 1, 529, 276, 1112, 420, 29958, 29881, 29941, 744, 29895, 29914, 16063, 4056, 7451, 29966, 9507, 29958, 16063, 4056, 7451, 29914, 11958, 2801, 29914, 6543, 29914, 29895, 29906, 29914, 29895, 29906, 29918, 262, 29918, 1579, 523, 29918, 2098, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 408, 948, 3934, 13, 13, 3166, 13677, 1053, 3826, 3039, 13, 3166, 19229, 1053, 313, 13, 1678, 3139, 29892, 13, 1678, 360, 919, 29892, 13, 1678, 28379, 29892, 13, 29897, 13, 13, 3166, 3165, 4056, 7451, 29889, 11958, 2801, 29889, 6543, 29889, 29895, 29906, 29889, 29895, 29906, 29918, 13239, 1053, 3588, 29918, 3166, 29918, 6543, 29918, 509, 9382, 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api.py	mr-mixas/Nested-Diff-RESTful	0	39193	<filename>api.py # -- coding: utf-8 -- # # Copyright 2021 <NAME> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from flask import Flask, Response, jsonify, render_template, request from nested_diff import Differ, Patcher from nested_diff.fmt import HtmlFormatter, TextFormatter, TermFormatter app = Flask(__name__) def format_diff_response(fmt, diff, opts): mimetype = 'text/plain' if fmt == 'text': formatter = TextFormatter elif fmt == 'term': formatter = TermFormatter elif fmt == 'html': formatter = HtmlFormatter else: return Response('Unsupported format ' + fmt, status=400) return Response( formatter(opts).format(diff), status=200, mimetype=mimetype, ) @app.route('/') def index(): # this page is almost never reloaded, so it is OK to embed css in it return render_template('index.html', html_fmt_css=HtmlFormatter.get_css()) @app.route('/ping') def health_check(): return Response('pong', status=200) @app.route('/api/v1/diff', methods=['POST']) def diff(): try: diff_opts = request.json.get('diff_opts', {'U': False}) except AttributeError: return Response('Object expected', status=400) try: diff = Differ(diff_opts).diff( request.json.get('a', None), request.json.get('b', None), ) except Exception: return Response('Incorrect options', status=400) ofmt = request.json.get('ofmt', 'json') if ofmt == 'json': return jsonify(diff) ofmt_opts = request.json.get('ofmt_opts', {}) return format_diff_response(ofmt, diff, ofmt_opts) @app.route('/api/v1/format', methods=['POST']) def format(): try: diff = request.json.get('diff', {}) ofmt = request.json.get('ofmt', None) if ofmt is None: return jsonify(diff) ofmt_opts = request.json.get('ofmt_opts', {}) return format_diff_response(ofmt, diff, ofmt_opts) except Exception: return Response('Incorrect arguments', status=400) @app.route('/api/v1/patch', methods=['POST']) def patch(): try: return jsonify(Patcher().patch( request.json['target'], request.json['patch'], )) except Exception: return Response('Incorrect arguments', status=400) if __name__ == '__main__': app.run(host='0.0.0.0', port=8080, debug=True)	[ 1, 529, 9507, 29958, 2754, 29889, 2272, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29906, 29896, 529, 5813, 29958, 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, 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experiments/messaging/pyuv/pyuv_server.py	chartbeat-labs/wade	16	74009	<reponame>chartbeat-labs/wade<gh_stars>10-100 import time import signal import pyuv import msgpack count = 0 st_time = 0 packer = msgpack.Packer() unpacker = msgpack.Unpacker() def on_read(client, data, error): if data is None: client.close() clients.remove(client) return unpacker.feed(data) try: msg = unpacker.unpack() except msgpack.OutOfData: return client.write(packer.pack(123)) global count global st_time if count % 10000 == 0: right_now = time.time() print count / (right_now - st_time) st_time = right_now count = 0 count += 1 def on_connection(server, error): client = pyuv.TCP(server.loop) server.accept(client) clients.append(client) client.start_read(on_read) def signal_cb(handle, signum): [c.close() for c in clients] signal_h.close() server.close() print("PyUV version %s" % pyuv.__version__) loop = pyuv.Loop.default_loop() clients = [] server = pyuv.TCP(loop) server.bind(("0.0.0.0", 12345)) server.listen(on_connection) signal_h = pyuv.Signal(loop) signal_h.start(signal_cb, signal.SIGINT) loop.run() print("Stopped!")	[ 1, 529, 276, 1112, 420, 29958, 15425, 915, 271, 29899, 29880, 6897, 29914, 29893, 1943, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 13, 5215, 931, 13, 13, 5215, 7182, 13, 5215, 11451, 4090, 13, 5215, 10191, 4058, 13, 13, 13, 2798, 353, 29871, 29900, 13, 303, 29918, 2230, 353, 29871, 29900, 13, 13, 4058, 261, 353, 10191, 4058, 29889, 16638, 261, 580, 13, 348, 4058, 261, 353, 10191, 4058, 29889, 2525, 4058, 261, 580, 13, 13, 13, 1753, 373, 29918, 949, 29898, 4645, 29892, 848, 29892, 1059, 1125, 13, 1678, 565, 848, 338, 6213, 29901, 13, 4706, 3132, 29889, 5358, 580, 13, 4706, 13154, 29889, 5992, 29898, 4645, 29897, 13, 4706, 736, 13, 13, 1678, 443, 4058, 261, 29889, 18798, 29898, 1272, 29897, 13, 1678, 1018, 29901, 13, 4706, 10191, 353, 443, 4058, 261, 29889, 348, 4058, 580, 13, 1678, 5174, 10191, 4058, 29889, 3744, 2776, 1469, 29901, 13, 4706, 736, 13, 13, 1678, 3132, 29889, 3539, 29898, 4058, 261, 29889, 4058, 29898, 29896, 29906, 29941, 876, 13, 13, 1678, 5534, 2302, 13, 1678, 5534, 380, 29918, 2230, 13, 13, 1678, 565, 2302, 1273, 29871, 29896, 29900, 29900, 29900, 29900, 1275, 29871, 29900, 29901, 13, 4706, 1492, 29918, 3707, 353, 931, 29889, 2230, 580, 13, 4706, 1596, 2302, 847, 313, 1266, 29918, 3707, 448, 380, 29918, 2230, 29897, 13, 4706, 380, 29918, 2230, 353, 1492, 29918, 3707, 13, 4706, 2302, 353, 29871, 29900, 13, 13, 1678, 2302, 4619, 29871, 29896, 13, 13, 1753, 373, 29918, 9965, 29898, 2974, 29892, 1059, 1125, 13, 1678, 3132, 353, 11451, 4090, 29889, 29911, 6271, 29898, 2974, 29889, 7888, 29897, 13, 1678, 1923, 29889, 16044, 29898, 4645, 29897, 13, 1678, 13154, 29889, 4397, 29898, 4645, 29897, 13, 1678, 3132, 29889, 2962, 29918, 949, 29898, 265, 29918, 949, 29897, 13, 13, 1753, 7182, 29918, 10702, 29898, 8411, 29892, 1804, 398, 1125, 13, 1678, 518, 29883, 29889, 5358, 580, 363, 274, 297, 13154, 29962, 13, 1678, 7182, 29918, 29882, 29889, 5358, 580, 13, 1678, 1923, 29889, 5358, 580, 13, 13, 13, 2158, 703, 19737, 29965, 29963, 1873, 1273, 29879, 29908, 1273, 11451, 4090, 17255, 3259, 1649, 29897, 13, 13, 7888, 353, 11451, 4090, 29889, 18405, 29889, 4381, 29918, 7888, 580, 13, 11303, 1237, 353, 5159, 13, 13, 2974, 353, 11451, 4090, 29889, 29911, 6271, 29898, 7888, 29897, 13, 2974, 29889, 5355, 29898, 703, 29900, 29889, 29900, 29889, 29900, 29889, 29900, 613, 29871, 29896, 29906, 29941, 29946, 29945, 876, 13, 2974, 29889, 20631, 29898, 265, 29918, 9965, 29897, 13, 13, 25436, 29918, 29882, 353, 11451, 4090, 29889, 10140, 284, 29898, 7888, 29897, 13, 25436, 29918, 29882, 29889, 2962, 29898, 25436, 29918, 10702, 29892, 7182, 29889, 5425, 29954, 10192, 29897, 13, 13, 7888, 29889, 3389, 580, 13, 2158, 703, 20754, 2986, 29991, 1159, 13, 2 ]
scripts/benchmarking/parallel_benchmark.py	BerkeleyAutomation/pyhull	69	187898	<reponame>BerkeleyAutomation/pyhull #!/usr/bin/env python """ TODO: Change the module doc. """ from __future__ import division __author__ = "<NAME>" __version__ = "0.1" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Beta" __date__ = "11/19/12" from multiprocessing import Pool import numpy as np from pyhull.convex_hull import ConvexHull num_proc = 100 def get_vertices(data): return ConvexHull(data).vertices def serial_test(data): d = [] for i in xrange(num_proc): d.append(get_vertices(data)) return d def parallel_test(data): p = Pool(4) all_data = p.map(get_vertices, [data] * num_proc) assert all([all_data[i] == all_data[0] for i in xrange(num_proc)]) if __name__ == "__main__": import timeit global data for npts in [100, 1000, 10000]: for dim in [3, 4, 5]: data = np.random.randn(npts,dim) t = timeit.timeit("serial_test(data)", setup="from __main__ import serial_test, data", number=1) print ("Serial: {} {} {:.5f}".format(npts, dim, t)) t = timeit.timeit("parallel_test(data)", setup="from __main__ import parallel_test, data", number=1) print ("Parallel: {} {} {:.5f}".format(npts, dim, t))	[ 1, 529, 276, 1112, 420, 29958, 17104, 27279, 28451, 362, 29914, 2272, 29882, 913, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 15945, 29908, 13, 4986, 3970, 29901, 10726, 278, 3883, 1574, 29889, 13, 15945, 29908, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 8542, 13, 13, 1649, 8921, 1649, 353, 9872, 5813, 11903, 13, 1649, 3259, 1649, 353, 376, 29900, 29889, 29896, 29908, 13, 1649, 29885, 2365, 4008, 1649, 353, 9872, 5813, 11903, 13, 1649, 5269, 1649, 353, 9872, 26862, 6227, 11903, 13, 1649, 4882, 1649, 353, 376, 29933, 1187, 29908, 13, 1649, 1256, 1649, 353, 376, 29896, 29896, 29914, 29896, 29929, 29914, 29896, 29906, 29908, 13, 13, 3166, 6674, 307, 985, 292, 1053, 28625, 13, 13, 5215, 12655, 408, 7442, 13, 13, 3166, 11451, 29882, 913, 29889, 535, 13809, 29918, 29882, 913, 1053, 1281, 13809, 29950, 913, 13, 13, 1949, 29918, 15439, 353, 29871, 29896, 29900, 29900, 13, 13, 1753, 679, 29918, 1765, 1575, 29898, 1272, 1125, 13, 1678, 736, 1281, 13809, 29950, 913, 29898, 1272, 467, 1765, 1575, 13, 13, 1753, 7797, 29918, 1688, 29898, 1272, 1125, 13, 1678, 270, 353, 5159, 13, 1678, 363, 474, 297, 921, 3881, 29898, 1949, 29918, 15439, 1125, 13, 4706, 270, 29889, 4397, 29898, 657, 29918, 1765, 1575, 29898, 1272, 876, 13, 1678, 736, 270, 13, 13, 1753, 8943, 29918, 1688, 29898, 1272, 1125, 13, 1678, 282, 353, 28625, 29898, 29946, 29897, 13, 1678, 599, 29918, 1272, 353, 282, 29889, 1958, 29898, 657, 29918, 1765, 1575, 29892, 518, 1272, 29962, 334, 954, 29918, 15439, 29897, 13, 1678, 4974, 599, 4197, 497, 29918, 1272, 29961, 29875, 29962, 1275, 599, 29918, 1272, 29961, 29900, 29962, 363, 474, 297, 921, 3881, 29898, 1949, 29918, 15439, 29897, 2314, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1053, 931, 277, 13, 1678, 5534, 848, 13, 1678, 363, 302, 16485, 297, 518, 29896, 29900, 29900, 29892, 29871, 29896, 29900, 29900, 29900, 29892, 29871, 29896, 29900, 29900, 29900, 29900, 5387, 13, 4706, 363, 3964, 297, 518, 29941, 29892, 29871, 29946, 29892, 29871, 29945, 5387, 13, 9651, 848, 353, 7442, 29889, 8172, 29889, 9502, 29876, 29898, 29876, 16485, 29892, 6229, 29897, 13, 9651, 260, 353, 931, 277, 29889, 2230, 277, 703, 15550, 29918, 1688, 29898, 1272, 19123, 13, 462, 795, 6230, 543, 3166, 4770, 3396, 1649, 1053, 7797, 29918, 1688, 29892, 848, 613, 13, 462, 795, 1353, 29922, 29896, 29897, 13, 9651, 1596, 4852, 9125, 29901, 259, 6571, 6571, 12365, 29889, 29945, 29888, 29913, 1642, 4830, 29898, 29876, 16485, 29892, 3964, 29892, 260, 876, 13, 9651, 260, 353, 931, 277, 29889, 2230, 277, 703, 23482, 29918, 1688, 29898, 1272, 19123, 13, 462, 795, 6230, 543, 3166, 4770, 3396, 1649, 1053, 8943, 29918, 1688, 29892, 848, 613, 13, 462, 795, 1353, 29922, 29896, 29897, 13, 9651, 1596, 4852, 2177, 6553, 29901, 6571, 6571, 12365, 29889, 29945, 29888, 29913, 1642, 4830, 29898, 29876, 16485, 29892, 3964, 29892, 260, 876, 13, 2 ]
addons14/project_timeline/tests/__init__.py	odoochain/addons_oca	1	166723	from . import test_project_timeline	[ 1, 515, 869, 1053, 1243, 29918, 4836, 29918, 9346, 5570, 13, 2 ]
computational-linguistics/LSTM-POS-tagger/main.py	sangeet2020/ws-20-21	0	89208	<reponame>sangeet2020/ws-20-21 #!/usr/bin/python3 # -- coding: utf-8 -- # @Author: <NAME> # @Date: 2021-03-08 18:19:28 # @Email: <EMAIL> # @Organization: Universität des Saarlandes # @Last Modified time: 2021-03-15 23:28:47 """ LSTM based POS tagger """ import os import io import sys import pdb import nltk import zipfile import argparse import numpy as np import pandas as pd import time import urllib.request from nltk import corpus import matplotlib.pyplot as plt from gensim.models import KeyedVectors from keras.preprocessing.text import Tokenizer import seaborn; seaborn.set() # plot formatting from keras.utils.np_utils import to_categorical from sklearn.model_selection import train_test_split from tensorflow import keras from keras.preprocessing.sequence import pad_sequences from keras.models import Sequential from keras.layers import Embedding from keras.layers import Dense, Input from keras.layers import TimeDistributed from keras.layers import LSTM, Bidirectional from keras.models import Model from sklearn.metrics import classification_report MAX_SEQ_LEN = 100 EMBEDDING_SIZE = 300 class Vectorizer(object): def __init__(self, sentences, pos_tags, word2vec): self.sentences = sentences self.pos_tags = pos_tags self.word2vec = word2vec def vectorize(self): # Vectorize X and Y self.t_words = Tokenizer() self.t_words.fit_on_texts(self.sentences) X = self.t_words.texts_to_sequences(self.sentences) self.t_tags = Tokenizer() self.t_tags.fit_on_texts(self.pos_tags) y = self.t_tags.texts_to_sequences(self.pos_tags) # Padding and truncation to make all sequences of uniform length self.X = pad_sequences(X, maxlen=MAX_SEQ_LEN, truncating='post') self.y = pad_sequences(y, maxlen=MAX_SEQ_LEN, truncating='post') self.embedding_weights = self.prep_embeddings() self.target_labs = [self.t_tags.word_index] return self.X, self.y, self.target_labs, self.embedding_weights def prep_embeddings(self): Vectorizer.prep_embeddings.VOCABULARY_SIZE = len(self.t_words.word_index) + 1 self.embedding_weights = np.zeros((Vectorizer.prep_embeddings.VOCABULARY_SIZE, EMBEDDING_SIZE)) word2id = self.t_words.word_index for word, index in word2id.items(): try: self.embedding_weights[index, :] = self.word2vec[word] except KeyError: pass self._encodings() return self.embedding_weights def _encodings(self): # one-hot encoding self.y = to_categorical(self.y) class LSTM_model(object): def __init__(self, X, y, target_labs, embedding_weights, BiLSTM=False): self.X = X self.y = y self.target_labs = target_labs self.embedding_weights = embedding_weights self.BiLSTM = BiLSTM def train_test_split(self): self.X_train, self.X_test, self.Y_train, self.Y_test = train_test_split(self.X, self.y, test_size=0.2, random_state=123) self.X_train, self.X_validation, self.Y_train, self.Y_validation = train_test_split(self.X_train, self.Y_train, test_size=0.2, random_state=123) def model_init(self, args): self.lstm_model = Sequential() self.target_labs = self.y.shape[2] VOCABULARY_SIZE = Vectorizer.prep_embeddings.VOCABULARY_SIZE self.lstm_model.add(Embedding( input_dim = VOCABULARY_SIZE, output_dim = EMBEDDING_SIZE, input_length = MAX_SEQ_LEN, weights = [self.embedding_weights], trainable = False )) if self.BiLSTM: print('-'20,"Model architecture: Bi-LSTM",'-'20) self.lstm_model.add(Bidirectional(LSTM(64, return_sequences=True))) else: print('-'20,"Model architecture: LSTM",'-'20) self.lstm_model.add(LSTM(64, return_sequences=True)) self.lstm_model.add(TimeDistributed(Dense(self.target_labs, activation='softmax'))) path = args.out_dir + "/model_arch.png" keras.utils.plot_model(self.lstm_model, path, show_shapes=True) def train(self, args): self.train_test_split() self.model_init(args) self.lstm_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc']) print(self.lstm_model.summary()) self.lstm_training = self.lstm_model.fit(self.X_train, self.Y_train, batch_size=128, epochs=10, validation_data=(self.X_validation, self.Y_validation)) self.training_report(args) loss, accuracy = self.lstm_model.evaluate(self.X_test, self.Y_test, verbose = 1) print("Loss: {:.2f} %,\nAccuracy: {:.2f} %".format(loss100, accuracy*100)) if self.BiLSTM: self.lstm_model.save(args.out_dir + '/model_Bi-LSTM.h5') else: self.lstm_model.save(args.out_dir + '/model_LSTM.h5') def training_report(self, args): # summarize history for accuracy plt.plot(self.lstm_training.history['acc']) plt.plot(self.lstm_training.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') if self.BiLSTM: plt.savefig(args.out_dir + '/accuray_plot_Bi-LSTM.eps', format='eps') else: plt.savefig(args.out_dir + '/accuray_plot_LSTM.eps', format='eps') # summarize history for loss plt.plot(self.lstm_training.history['acc']) plt.plot(self.lstm_training.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') if self.BiLSTM: plt.savefig(args.out_dir + '/loss_plot_Bi-LSTM.eps', format='eps') else: plt.savefig(args.out_dir + '/loss_plot_LSTM.eps', format='eps') def download_data(args): """ purpose of my function """ # Download dataset nltk.download('treebank') nltk.download('brown') nltk.download('conll2000') nltk.download('universal_tagset') # Download pre-trained embeddings. 1M words with subword info if args.subword_info: print("Using Subword info") url = 'https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M-subword.vec.zip' else: print("Not using Subword info") url = 'https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M.vec.zip' fname = url.split('/')[-1] if not os.path.exists(fname): print("Downloading embeddings...") command = str("! wget -nc ") + url print(command) os.system(command) with zipfile.ZipFile(fname, 'r') as zip_ref: zip_ref.extractall('.') else: print("Pre-trained embeddings already present; not retrieving.") return KeyedVectors.load_word2vec_format(fname.strip('.zip')) def data_preparation(): treebank_corpus = corpus.treebank.tagged_sents(tagset='universal') brown_corpus = corpus.brown.tagged_sents(tagset='universal') conll_corpus = corpus.conll2000.tagged_sents(tagset='universal') return treebank_corpus + brown_corpus + conll_corpus def data_loader(tagged_sentences): sentences = [] pos_tags = [] for item in tagged_sentences: sentences.append([token[0] for token in item]) pos_tags.append([token[1] for token in item]) return sentences, pos_tags def main(): """ main method """ args = parse_arguments() start = time.time() os.makedirs(args.out_dir, exist_ok=True) word2vec = download_data(args) tagged_sentences = data_preparation() sentences, pos_tags = data_loader(tagged_sentences) vectorizer = Vectorizer(sentences, pos_tags, word2vec) X, y, target_labs, embedding_weights = vectorizer.vectorize() if args.model_choice=="BiLSTM": LSTM_model(X, y, target_labs, embedding_weights,BiLSTM=True).train(args) elif args.model_choice=="LSTM": LSTM_model(X, y, target_labs, embedding_weights,BiLSTM=False).train(args) else: print("Specified model architecture not understood. Try again!") end = time.time() print("Total runtime: %.3f s" % (end-start)) def parse_arguments(): """ parse arguments """ parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("out_dir", help="path to save the plots and results") parser.add_argument("model_choice",default=None,choices=['LSTM','BiLSTM'], type=str,help='Choice of training architecture') parser.add_argument("-subword_info", default=None, type=bool, help='use pre-embeddings with subword information') args = parser.parse_args() return args if __name__ == "__main__": main()	[ 1, 529, 276, 1112, 420, 29958, 29879, 927, 300, 29906, 29900, 29906, 29900, 29914, 5652, 29899, 29906, 29900, 29899, 29906, 29896, 13, 29937, 14708, 4855, 29914, 2109, 29914, 4691, 29941, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 29937, 732, 13720, 29901, 529, 5813, 29958, 13, 29937, 732, 2539, 29901, 1678, 29906, 29900, 29906, 29896, 29899, 29900, 29941, 29899, 29900, 29947, 29871, 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ihan/__init__.py	marklit/ihan	0	173734	<reponame>marklit/ihan<gh_stars>0 from __future__ import print_function from itertools import chain, islice import json from os.path import expanduser, join from random import randint import subprocess import sys from time import sleep import requests def eprint(args, kwargs): print(args, file=sys.stderr, **kwargs) def group(num_items, iterable): iterable = iter(iterable) while True: chunk = islice(iterable, num_items) try: first_element = next(chunk) except StopIteration: return yield chain((first_element,), chunk) def send_it(endpoint, api_key, api_secret, logs): r = requests.post(endpoint, json={'lines': logs, 'api_key': api_key, 'api_secret': api_secret}) assert r.status_code == 200, r.status_code assert r.json()['lines'] == len(logs), 'Line counts failed to match' has_binary = lambda s: "\x00" in s or any(ord(x) > 0x80 for x in s) # noqa:E731 def feed_file(filename, from_beginning, batch_size, endpoint, sleep_interval, backfill): try: config_file = join(expanduser("~"), ".ihan/config") api = json.loads(open(config_file, 'r').read()) assert len(api['api_key']) > 10 and len(api['api_secret']) > 10 except Exception as exc: print(exc) print("Please run this first: ihan login") return filename = '/dev/stdin' if filename.strip() == '-' else filename # WIP this can't detect the end of a stream when # gunzip -c data is being piped in. # If filename is /dev/stdin don't use tail, just use # something in python to follow stdin. if backfill: proc = subprocess.Popen(['cat', filename], stdout=subprocess.PIPE) else: proc = subprocess.Popen(['tail', '--lines=+0' if from_beginning else '-n 1000', '--follow=name', '--retry', '--quiet', '--silent', '--sleep-interval=%d' % sleep_interval, filename], stdout=subprocess.PIPE) for n_batch, lines in enumerate(group(batch_size, iter(proc.stdout.readline, ''))): eprint('batch: ' + str(n_batch)) # Don't let a few binary bytes stop this in its tracks. try: logs = list([l.rstrip().decode('utf-8') for l in lines]) except (AttributeError, # Python 3 UnicodeDecodeError) as exc: # Python 2 eprint('Compressed logs are not supported') sleep(randint(30, 60)) continue if has_binary(''.join(logs)): eprint('Compressed logs are not supported') sleep(randint(30, 60)) continue while True: try: send_it(endpoint, api['api_key'], api['api_secret'], logs) sleep(0.25) # WIP: Make this optional break except (KeyError, ValueError, AssertionError, requests.exceptions.ConnectionError) as exc: eprint(exc) # WIP: Set configuration for this _interval = randint(10, 180) eprint('Sleeping for ' + str(_interval) + 's') sleep(_interval)	[ 1, 529, 276, 1112, 420, 29958, 3502, 19411, 29914, 4861, 273, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 4770, 29888, 9130, 1649, 1053, 1596, 29918, 2220, 13, 13, 3166, 259, 4256, 8504, 1053, 9704, 29892, 338, 5897, 13, 5215, 4390, 13, 3166, 259, 2897, 29889, 2084, 1053, 7985, 1792, 29892, 5988, 13, 3166, 259, 4036, 1053, 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prediction/rawDataReader.py	garagonc/optimization-framework	0	138484	<filename>prediction/rawDataReader.py """ Created on Okt 04 13:51 2018 @author: nishit """ import datetime import os import time from senml import senml from utils_intern.messageLogger import MessageLogger logger = MessageLogger.get_logger_parent() class RawDataReader: @staticmethod def read_from_file(file_path, topic_name): try: if os.path.exists(file_path): with open(file_path) as file: data = file.readlines() file.close() return data else: logger.info("Saved data not available "+str(topic_name)) return [] except Exception as e: logger.error(e) return [] @staticmethod def time_conversion(time): time = int(time) if len(str(time)) > 10: new_t = time / (10 ** (len(str(time)) - 10)) return new_t else: return time @staticmethod def format_data(data=[]): new_data = [] doc = None try: doc = senml.SenMLDocument.from_json(data) except Exception as e: pass if not doc: try: meas = senml.SenMLMeasurement.from_json(data) doc = senml.SenMLDocument([meas]) except Exception as e: pass if doc: for meas in doc.measurements: try: n = meas.name v = meas.value t = meas.time t = RawDataReader.time_conversion(t) cols = [t,v] new_data.append(cols) except Exception as e: logger.error("Exception in formating meas " + str(e)) return new_data else: for row in data: try: cols = row.replace('\n', '').strip().split(",") dateTime = float(cols[0]) cols = cols[1:] cols = list(map(float, cols)) cols.insert(0, dateTime) new_data.append(cols) except Exception as e: logger.error("Exception in formating line "+str(row)+ " "+ str(e)) return new_data @staticmethod def get_raw_data(file_path, topic_name, data_length=None): data = RawDataReader.read_from_file(file_path, topic_name) if data_length is not None and len(data) > data_length: data = data[-data_length:] if len(data) > 0: indexes = [] for i,v in enumerate(data): if "inf" in str(v).lower() or "nan" in str(v).lower(): indexes.append(i) logger.debug("len indexes " + str(len(indexes))) indexes.sort(reverse=True) for index in indexes: data.pop(index) return RawDataReader.format_data(data) @staticmethod def get_raw_data_by_time(file_path, topic_name, start_time, end_time): #logger.debug("start time "+str(start_time)+" end time "+str(end_time)) flag = False new_data = [] if start_time < end_time: data = RawDataReader.read_from_file(file_path, topic_name) start_time = int(start_time) end_time = int(end_time) for row in data: try: col = row.replace('\n', '').strip().split(",") t = float(col[0]) v = float(col[1]) if not flag and start_time <= t: flag = True if flag and end_time < t: break if flag: new_data.append([t,v]) except Exception as e: logger.error("Exception in formating line " + str(row) + " " + str(e)) return new_data @staticmethod def save_to_file(file_path, topic_name, minute_data, max_file_size_mins=-1, overwrite=False): try: logger.info("Saving raw data to file " + str(file_path)) if overwrite: old_data = [] else: old_data = RawDataReader.read_from_file(file_path, topic_name) for item in minute_data: line = ','.join(map(str, item[:2])) + "\n" old_data.append(line) if max_file_size_mins > 0: old_data = old_data[-max_file_size_mins:] update_data = [] for i, line in enumerate(old_data): c = line.count(",") if c == 2: s = line.split(",") m = s[1] mv = m[:-12] mt = m[-12:] l1 = [float(s[0]), float(mv)] l1 = ','.join(map(str, l1[:2])) + "\n" l2 = [float(mt), float(s[2].replace("\n", ""))] l2 = ','.join(map(str, l2[:2])) + "\n" update_data.append([i, l1, l2]) elif c != 1: update_data.append([i, None, None]) shift = 0 for d in update_data: if d[1] is not None and d[2] is not None: old_data.pop(d[0] + shift) old_data.insert(d[0] + shift, d[1]) old_data.insert(d[0] + shift + 1, d[2]) shift += 1 else: old_data.pop(d[0] + shift) shift -= 1 with open(file_path, 'w+') as file: file.writelines(old_data) file.close() return [] except Exception as e: logger.error("failed to save_to_file " + str(e)) return minute_data @staticmethod def del_file(file_path, topic_name): try: if os.path.exists(file_path): os.remove(file_path) except Exception as e: logger.error("failed to del_to_file " + str(e)+" "+str(topic_name)) @staticmethod def removed_data_before_timestamp(file_path, topic_name, lastest_timestamp): data = RawDataReader.get_raw_data_by_time(file_path, topic_name, lastest_timestamp, time.time()) RawDataReader.save_to_file(file_path, topic_name, data, overwrite=True) @staticmethod def save_to_influx(influxdb, topic_name, minute_data, id): try: logger.info("Saving raw data to influx " + str(topic_name)) json_body = influxdb.timeseries_list_to_influx_json(minute_data, topic_name, "raw", id) if not influxdb.write(json_body): return minute_data else: return [] except Exception as e: logger.error("failed to save_to_influx " + str(e)) return minute_data @staticmethod def get_raw_data_influx(influxdb, topic_name, id, data_length=None): start_time = None if data_length: start_time = datetime.datetime.now() 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rasa/utils/io.py	chws0508/rasa	0	133978	import asyncio import filecmp import logging import os import pickle import tempfile import warnings import re from asyncio import AbstractEventLoop from pathlib import Path from typing import Text, Any, Union, List, Type, Callable, TYPE_CHECKING, Pattern import rasa.shared.constants import rasa.shared.utils.io if TYPE_CHECKING: from prompt_toolkit.validation import Validator def configure_colored_logging(loglevel: Text) -> None: import coloredlogs loglevel = loglevel or os.environ.get( rasa.shared.constants.ENV_LOG_LEVEL, rasa.shared.constants.DEFAULT_LOG_LEVEL ) field_styles = coloredlogs.DEFAULT_FIELD_STYLES.copy() field_styles["asctime"] = {} level_styles = coloredlogs.DEFAULT_LEVEL_STYLES.copy() level_styles["debug"] = {} coloredlogs.install( level=loglevel, use_chroot=False, fmt="%(asctime)s %(levelname)-8s %(name)s - %(message)s", level_styles=level_styles, field_styles=field_styles, ) def enable_async_loop_debugging( event_loop: AbstractEventLoop, slow_callback_duration: float = 0.1 ) -> AbstractEventLoop: logging.info( "Enabling coroutine debugging. Loop id {}.".format(id(asyncio.get_event_loop())) ) # Enable debugging event_loop.set_debug(True) # Make the threshold for "slow" tasks very very small for # illustration. The default is 0.1 (= 100 milliseconds). event_loop.slow_callback_duration = slow_callback_duration # Report all mistakes managing asynchronous resources. warnings.simplefilter("always", ResourceWarning) return event_loop def pickle_dump(filename: Union[Text, Path], obj: Any) -> None: """Saves object to file. Args: filename: the filename to save the object to obj: the object to store """ with open(filename, "wb") as f: pickle.dump(obj, f) def pickle_load(filename: Union[Text, Path]) -> Any: """Loads an object from a file. Args: filename: the filename to load the object from Returns: the loaded object """ with open(filename, "rb") as f: return pickle.load(f) def create_temporary_file(data: Any, suffix: Text = "", mode: Text = "w+") -> Text: """Creates a tempfile.NamedTemporaryFile object for data.""" encoding = None if "b" in mode else rasa.shared.utils.io.DEFAULT_ENCODING f = tempfile.NamedTemporaryFile( mode=mode, suffix=suffix, delete=False, encoding=encoding ) f.write(data) f.close() return f.name def create_temporary_directory() -> Text: """Creates a tempfile.TemporaryDirectory.""" f = tempfile.TemporaryDirectory() return f.name def create_path(file_path: Text) -> None: """Makes sure all directories in the 'file_path' exists.""" parent_dir = os.path.dirname(os.path.abspath(file_path)) if not os.path.exists(parent_dir): os.makedirs(parent_dir) def file_type_validator( valid_file_types: List[Text], error_message: Text ) -> Type["Validator"]: """Creates a `Validator` class which can be used with `questionary` to validate file paths. """ def is_valid(path: Text) -> bool: return path is not None and any( [path.endswith(file_type) for file_type in valid_file_types] ) return create_validator(is_valid, error_message) def not_empty_validator(error_message: Text) -> Type["Validator"]: """Creates a `Validator` class which can be used with `questionary` to validate that the user entered something other than whitespace. """ def is_valid(input: Text) -> bool: return input is not None and input.strip() != "" return create_validator(is_valid, error_message) def create_validator( function: Callable[[Text], bool], error_message: Text ) -> Type["Validator"]: """Helper method to create `Validator` classes from callable functions. Should be removed when questionary supports `Validator` objects.""" from prompt_toolkit.validation import Validator, ValidationError from prompt_toolkit.document import Document class FunctionValidator(Validator): @staticmethod def validate(document: Document) -> None: is_valid = function(document.text) if not is_valid: raise ValidationError(message=error_message) return FunctionValidator def json_unpickle( file_name: Union[Text, Path], encode_non_string_keys: bool = False ) -> Any: """Unpickle an object from file using json. Args: file_name: the file to load the object from encode_non_string_keys: If set to `True` then jsonpickle will encode non-string dictionary keys instead of coercing them into strings via `repr()`. Returns: the object """ import jsonpickle.ext.numpy as jsonpickle_numpy import jsonpickle jsonpickle_numpy.register_handlers() file_content = rasa.shared.utils.io.read_file(file_name) return jsonpickle.loads(file_content, keys=encode_non_string_keys) def json_pickle( file_name: Union[Text, Path], obj: Any, encode_non_string_keys: bool = False ) -> None: """Pickle an object to a file using json. Args: file_name: the file to store the object to obj: the object to store encode_non_string_keys: If set to `True` then jsonpickle will encode non-string dictionary keys instead of coercing them into strings via `repr()`. """ import jsonpickle.ext.numpy as jsonpickle_numpy import jsonpickle jsonpickle_numpy.register_handlers() rasa.shared.utils.io.write_text_file( jsonpickle.dumps(obj, keys=encode_non_string_keys), file_name ) def get_emoji_regex() -> Pattern: """Returns regex to identify emojis.""" return re.compile( "[" "\U0001F600-\U0001F64F" # emoticons "\U0001F300-\U0001F5FF" # symbols & pictographs "\U0001F680-\U0001F6FF" # transport & map symbols "\U0001F1E0-\U0001F1FF" # flags (iOS) "\U00002702-\U000027B0" "\U000024C2-\U0001F251" "\u200d" # zero width joiner "\u200c" # zero width non-joiner "]+", flags=re.UNICODE, ) def are_directories_equal(dir1: Path, dir2: Path) -> bool: """Compares two directories recursively. Files in each directory are assumed to be equal if their names and contents are equal. Args: dir1: The first directory. dir2: The second directory. Returns: `True` if they are equal, `False` otherwise. """ dirs_cmp = filecmp.dircmp(dir1, dir2) if dirs_cmp.left_only or dirs_cmp.right_only: return False (_, mismatches, errors) = filecmp.cmpfiles( dir1, dir2, dirs_cmp.common_files, shallow=False ) if mismatches or errors: return False for common_dir in dirs_cmp.common_dirs: new_dir1 = Path(dir1, common_dir) new_dir2 = Path(dir2, common_dir) is_equal = are_directories_equal(new_dir1, new_dir2) if not is_equal: return False return True	[ 1, 1053, 408, 948, 3934, 13, 5215, 934, 21058, 13, 5215, 12183, 13, 5215, 2897, 13, 5215, 5839, 280, 13, 5215, 5694, 1445, 13, 5215, 18116, 13, 5215, 337, 13, 3166, 408, 948, 3934, 1053, 25513, 2624, 18405, 13, 3166, 2224, 1982, 1053, 10802, 13, 3166, 19229, 1053, 3992, 29892, 3139, 29892, 7761, 29892, 2391, 29892, 5167, 29892, 8251, 519, 29892, 323, 6959, 29918, 3210, 16658, 4214, 29892, 25860, 13, 13, 5215, 364, 11290, 29889, 12366, 29889, 3075, 1934, 13, 5215, 364, 11290, 29889, 12366, 29889, 13239, 29889, 601, 13, 13, 361, 323, 6959, 29918, 3210, 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mosaic_letter.py	ryhoh/mosaicDesigner_py	0	88866	<gh_stars>0 ''' mosaic_letter.py Created by <NAME> on 2018/01/04. Copyright © 2018年 <NAME>. All rights reserved. モザイク文字画像の情報を持つクラス - フィールド - colors 整数リスト：使用するカラーコード（背景，色1，色2） - gridSize 整数：モザイクのグリッドの大きさ - grid_parameter np配列：背景の部分を0，色ありの部分を1で表現 - grid_pattern np配列：gridの各部分の色を指定 - メソッド - __init__ フィールドへ代入 - input 標準入力から色とグリッドの大きさとパターンを入力 - mask parameterが0でない部分は，patternの色を保持 parameterが0の部分は，patternに背景色を入れる - output bmp形式で出力 ''' import struct import numpy as np class MosaicLetter: def __init__(self): self.colors = list() self.gridSize = None self.grid_parameter = list() self.grid_pattern = list() self.input() # 入力を受ける def input(self): print("First, input 3 colorcodes.") for i in range(3): self.colors.append(int(input(), base=16)) print("Second, input size of grid.") self.gridSize = int(input()) print("Third, input grid parameter.") for i in range(self.gridSize): self.grid_parameter.append([int(j) for j in input()]) self.grid_parameter = np.array(self.grid_parameter) print("Finally, input grid pattern.") for i in range(self.gridSize): self.grid_pattern.append([int(j) for j in input()]) self.grid_pattern = np.array(self.grid_pattern) def mask(self): # grid_parameterに基づいてgrid_patternを編集する # grid_parameter[i][j]が0ならgrid_pattern[i][j]も0 # grid_parameter[i][j]が1ならgrid_pattern[i][j]はそのまま self.grid_pattern = self.grid_parameter def output(self, name="output.bmp"): # 定数 FILEHEADER = 14 # ファイルヘッダの大きさ INFOHEADER = 12 # 情報ヘッダの大きさ SQUARESIZE = 32 # 小さな正方形の一辺の長さ FRAMETHICK = 1 # 縁取りの線の厚さ IMAGE_LEN = SQUARESIZE self.gridSize # 画像の1辺の長さ # 書き込み with open(name, "wb") as f: # 符号なし4byte整数を書き込む関数 writeInt = (lambda x: f.write(struct.pack("I", x))) writeShort = (lambda x: f.write(struct.pack("H", x))) writeByte = (lambda x: f.write(struct.pack("B", x))) # ファイルヘッダ ---------------- f.write(struct.pack("BB", ord("B"), ord("M"))) # ファイルヘッダ # ファイルサイズ（4bit=1/2byteの画像であるので画素数を1/2する） writeInt(FILEHEADER + INFOHEADER+ 316 + IMAGE_LEN2 // 2) writeInt(0) # 予約領域 # 先頭から画像までのオフセット = ファイルヘッダ + 情報ヘッダ + パレットデータ writeInt(FILEHEADER + INFOHEADER + 316) # 情報ヘッダ ---------------- writeInt(INFOHEADER) # 情報ヘッダの大きさ writeShort(IMAGE_LEN) # 縦横の長さ writeShort(IMAGE_LEN) # 正方形なので同じ数値を2つ writeShort(1) # bcプレーン数（？）は常に1 writeShort(4) # 画像の色数（4bit） # パレットデータ ---------------- # 最初の3色にcolorsのデータを入れ、残りは0に設定 for color in self.colors: # リトルエンディアンで1byteずつ書き込む writeByte(color & 0x0000FF) writeByte((color & 0x00FF00) >> 8) writeByte((color & 0xFF0000) >> 16) for i in range(16 - len(self.colors)): for j in range(3): writeByte(0) # 画像データ ---------------- # 1Byteに2pixelのデータが入ることに注意 # 画像データは左下から右上に記録される # 縁の内外を判定して書き込み ------------------------------------- def checkAndWrite(v_i, v_j, h_i, h_j): if FRAMETHICK <= v_j < SQUARESIZE-FRAMETHICK \ and FRAMETHICK <= h_j < SQUARESIZE-FRAMETHICK: # 縁の内側 data = self.grid_pattern[self.gridSize - v_i - 1][h_i] \ + (self.grid_pattern[self.gridSize - v_i - 1][h_i] << 4) else: # 縁 data = 0 # 書き込み writeByte(data) # ----------------------------------------------------------- for v_i in range(self.gridSize): # 縦のパターンの数だけループ for v_j in range(SQUARESIZE): # 小さい正方形の縦の長さだけループ for h_i in range(self.gridSize): # 横のパターンの数だけループ for h_j in range(SQUARESIZE // 2): # 小さい正方形の横の長さ/2だけループ # 1byteに2画素書き込むためループ数を1/2とする checkAndWrite(v_i, v_j, h_i, h_j) if __name__ == '__main__': ml = MosaicLetter() ml.mask() ml.output()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 12008, 13, 1678, 286, 3628, 293, 29918, 15670, 29889, 2272, 13, 13, 1678, 6760, 630, 491, 529, 5813, 29958, 373, 29871, 29906, 29900, 29896, 29947, 29914, 29900, 29896, 29914, 29900, 29946, 29889, 13, 1678, 14187, 1266, 29871, 30211, 29871, 29906, 29900, 29896, 29947, 30470, 529, 5813, 15513, 2178, 10462, 21676, 29889, 13, 13, 268, 30761, 31123, 30260, 30305, 30333, 30578, 31046, 31551, 30199, 30993, 31519, 30396, 31695, 30773, 30305, 30281, 30255, 13, 13, 1678, 448, 29871, 30423, 30532, 30185, 30258, 30335, 13, 4706, 448, 11955, 29871, 233, 152, 183, 30354, 30303, 30255, 30279, 30383, 30785, 30406, 30427, 30332, 30439, 30281, 30185, 30459, 30185, 30335, 30419, 235, 134, 143, 31495, 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qualtrics/share.py	anhiancheong/topics_evaluation	0	196153	<gh_stars>0 import argparse import json import requests API_KEY = "<KEY>" ENDPOINT = "https://iad1.qualtrics.com/API/v3" users = [ "UR_56juIFbMBWLU9jo", "UR_bIMXWqoIF8GNR7E", "UR_4O2EvhVhx5UVoQS" ] survey_ids = [ "SV_ezlYrHXuI4oxV8W", "SV_0Va3pTzbIGiS8tM", "SV_50zRZfCRm5wvvxA", "SV_80UG9Vuo5KHKbFI", "SV_3z2XeepZD1uheui", "SV_6GuDFnyQX0IhAeq", "SV_4Ob1KEJ0trA5KCy", "SV_1NyEUJ5LX8KtHFQ", "SV_3z661hKZxFMnewC", "SV_bqpnsYfAAixidLM" ] for survey_id in survey_ids: for user_id in users: resp = requests.post( f"https://ca1.qualtrics.com/API/v3/surveys/{survey_id}/permissions/collaborations", headers={"X-API-TOKEN": API_KEY}, json={ "recipientId": user_id, "permissions": { "surveyDefinitionManipulation": { "copySurveyQuestions": True, "editSurveyFlow": True, "useBlocks": True, "useSkipLogic": True, "useConjoint": True, "useTriggers": True, "useQuotas": True, "setSurveyOptions": True, "editQuestions": True, "deleteSurveyQuestions": True, "useTableOfContents": True, "useAdvancedQuotas": True }, "surveyManagement": { "editSurveys": True, "activateSurveys": True, "deactivateSurveys": True, "copySurveys": True, "distributeSurveys": True, "deleteSurveys": True, "translateSurveys": True }, "response": { "editSurveyResponses": True, "createResponseSets": True, "viewResponseId": True, "useCrossTabs": True, "useScreenouts": True }, "result": { "downloadSurveyResults": True, "viewSurveyResults": True, "filterSurveyResults": True, "viewPersonalData": True } } }) print(resp.status_code) if resp.status_code > 299: print(resp.__dict__)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 13, 13, 5215, 1852, 5510, 13, 5215, 4390, 13, 5215, 7274, 13, 13, 8787, 29918, 10818, 353, 9872, 10818, 11903, 13, 13, 1430, 11191, 6992, 29911, 353, 376, 991, 597, 29875, 328, 29896, 29889, 15380, 509, 1199, 29889, 510, 29914, 8787, 29914, 29894, 29941, 29908, 13, 13, 13, 7193, 353, 518, 13, 1678, 376, 4574, 29918, 29945, 29953, 4900, 6545, 29890, 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tests/test_client.py	nyush-se-spring2021-forum/OurTieba	0	22001	<gh_stars>0 class TestClient: """ Test before_request and after_request decorators in __init__.py. """ def test_1(self, client): # disallowed methods res = client.put("/") assert res.status_code == 405 assert b"Method Not Allowed" in res.content res = client.options("/api/post/add") assert res.status_code == 405 assert b"Method Not Allowed" in res.content res = client.delete("/notifications") assert res.status_code == 405 assert b"Method Not Allowed" in res.content def test_2(self, client): # empty/fake user agent res = client.get("/", headers={"User-Agent": ""}) assert res.status_code == 403 assert b"No Scrappers!" in res.content res = client.get("/board/2", headers={"User-Agent": "python/3.8"}) assert res.status_code == 403 assert b"No Scrappers!" in res.content	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 1990, 4321, 4032, 29901, 13, 1678, 9995, 13, 1678, 4321, 1434, 29918, 3827, 322, 1156, 29918, 3827, 10200, 4097, 297, 4770, 2344, 26914, 2272, 29889, 13, 1678, 9995, 13, 13, 1678, 822, 1243, 29918, 29896, 29898, 1311, 29892, 3132, 1125, 29871, 396, 766, 24622, 3519, 13, 4706, 620, 353, 3132, 29889, 649, 11974, 1159, 13, 4706, 4974, 620, 29889, 4882, 29918, 401, 1275, 29871, 29946, 29900, 29945, 13, 4706, 4974, 289, 29908, 4062, 2216, 2178, 20937, 29908, 297, 620, 29889, 3051, 13, 13, 4706, 620, 353, 3132, 29889, 6768, 11974, 2754, 29914, 2490, 29914, 1202, 1159, 13, 4706, 4974, 620, 29889, 4882, 29918, 401, 1275, 29871, 29946, 29900, 29945, 13, 4706, 4974, 289, 29908, 4062, 2216, 2178, 20937, 29908, 297, 620, 29889, 3051, 13, 13, 4706, 620, 353, 3132, 29889, 8143, 11974, 1333, 8232, 1159, 13, 4706, 4974, 620, 29889, 4882, 29918, 401, 1275, 29871, 29946, 29900, 29945, 13, 4706, 4974, 289, 29908, 4062, 2216, 2178, 20937, 29908, 297, 620, 29889, 3051, 13, 13, 1678, 822, 1243, 29918, 29906, 29898, 1311, 29892, 3132, 1125, 29871, 396, 4069, 29914, 29888, 1296, 1404, 10823, 13, 4706, 620, 353, 3132, 29889, 657, 11974, 613, 9066, 3790, 29908, 2659, 29899, 19661, 1115, 5124, 1800, 13, 4706, 4974, 620, 29889, 4882, 29918, 401, 1275, 29871, 29946, 29900, 29941, 13, 4706, 4974, 289, 29908, 3782, 2522, 336, 22437, 3850, 297, 620, 29889, 3051, 13, 13, 4706, 620, 353, 3132, 29889, 657, 11974, 3377, 29914, 29906, 613, 9066, 3790, 29908, 2659, 29899, 19661, 1115, 376, 4691, 29914, 29941, 29889, 29947, 29908, 1800, 13, 4706, 4974, 620, 29889, 4882, 29918, 401, 1275, 29871, 29946, 29900, 29941, 13, 4706, 4974, 289, 29908, 3782, 2522, 336, 22437, 3850, 297, 620, 29889, 3051, 13, 2 ]
qemu/scripts/codeconverter/codeconverter/test_patching.py	hyunjoy/scripts	44	9469	# Copyright (C) 2020 Red Hat Inc. # # Authors: # <NAME> <<EMAIL>> # # This work is licensed under the terms of the GNU GPL, version 2. See # the COPYING file in the top-level directory. from tempfile import NamedTemporaryFile from .patching import FileInfo, FileMatch, Patch, FileList from .regexps import * class BasicPattern(FileMatch): regexp = '[abc]{3}' @property def name(self): return self.group(0) def replacement(self) -> str: # replace match with the middle character repeated 5 times return self.group(0)[1].upper()5 def test_pattern_patching(): of = NamedTemporaryFile('wt') of.writelines(['one line\n', 'this pattern will be patched: defbbahij\n', 'third line\n', 'another pattern: jihaabfed']) of.flush() files = FileList() f = FileInfo(files, of.name) f.load() matches = f.matches_of_type(BasicPattern) assert len(matches) == 2 p2 = matches[1] # manually add patch, to see if .append() works: f.patches.append(p2.append('XXX')) # apply all patches: f.gen_patches(matches) patched = f.get_patched_content() assert patched == ('one line\n'+ 'this pattern will be patched: defBBBBBhij\n'+ 'third line\n'+ 'another pattern: jihAAAAAXXXfed') class Function(FileMatch): regexp = S(r'BEGIN\s+', NAMED('name', RE_IDENTIFIER), r'\n', r'(.\n)?END\n') class Statement(FileMatch): regexp = S(r'^\s', NAMED('name', RE_IDENTIFIER), r'\(\)\n') def test_container_match(): of = NamedTemporaryFile('wt') of.writelines(['statement1()\n', 'statement2()\n', 'BEGIN function1\n', ' statement3()\n', ' statement4()\n', 'END\n', 'BEGIN function2\n', ' statement5()\n', ' statement6()\n', 'END\n', 'statement7()\n']) of.flush() files = FileList() f = FileInfo(files, of.name) f.load() assert len(f.matches_of_type(Function)) == 2 print(' '.join(m.name for m in f.matches_of_type(Statement))) assert len(f.matches_of_type(Statement)) == 7 f1 = f.find_match(Function, 'function1') f2 = f.find_match(Function, 'function2') st1 = f.find_match(Statement, 'statement1') st2 = f.find_match(Statement, 'statement2') st3 = f.find_match(Statement, 'statement3') st4 = f.find_match(Statement, 'statement4') st5 = f.find_match(Statement, 'statement5') st6 = f.find_match(Statement, 'statement6') st7 = f.find_match(Statement, 'statement7') assert not f1.contains(st1) assert not f1.contains(st2) assert not f1.contains(st2) assert f1.contains(st3) assert f1.contains(st4) assert not f1.contains(st5) assert not f1.contains(st6) assert not f1.contains(st7) assert not f2.contains(st1) assert not f2.contains(st2) assert not f2.contains(st2) assert not f2.contains(st3) assert not f2.contains(st4) assert f2.contains(st5) assert f2.contains(st6) assert not f2.contains(st7)	[ 1, 396, 14187, 1266, 313, 29907, 29897, 29871, 29906, 29900, 29906, 29900, 4367, 25966, 9266, 29889, 13, 29937, 13, 29937, 13189, 943, 29901, 13, 29937, 29871, 529, 5813, 29958, 3532, 26862, 6227, 6778, 13, 29937, 13, 29937, 910, 664, 338, 7794, 21144, 1090, 278, 4958, 310, 278, 15143, 402, 7390, 29892, 1873, 29871, 29906, 29889, 29871, 2823, 13, 29937, 278, 315, 4590, 29979, 4214, 934, 297, 278, 2246, 29899, 5563, 3884, 29889, 13, 3166, 5694, 1445, 1053, 405, 2795, 5776, 1971, 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wbia_cnn/netrun.py	WildMeOrg/wbia-plugin-cnn	0	31590	#!/usr/bin/env python # -- coding: utf-8 -- r""" FIXME: sometimes you have to chown -R user:user ~/.theano or run with sudo the first time after roboot, otherwise you get errors CommandLineHelp: python -m wbia_cnn --tf netrun <networkmodel> --dataset, --ds = <dstag>:<subtag> dstag is the main dataset name (eg PZ_MTEST), subtag are parameters to modify (max_examples=3) --weights, -w = \\|new\\|<checkpoint_tag>\\|<dstag>:<checkpoint_tag> (default: <checkpoint_tag>) new will initialize clean weights. a checkpoint tag will try to to match a saved model state in the history. can load weights from an external dataset. <checkpoint_tag> defaults to current --arch, -a = <archtag> model architecture tag (eg siaml2_128, siam2stream, viewpoint) --device = <processor> sets theano device flag to a processor like gpu0, gpu1, or cpu0 """ import logging from wbia_cnn import models from wbia_cnn import ingest_data from wbia_cnn import experiments import utool as ut import sys print, rrr, profile = ut.inject2(__name__) logger = logging.getLogger() # This is more of a history tag CHECKPOINT_TAG_ALIAS = { 'current': None, '': None, } # second level of alias indirection # This is more of a dataset tag DS_TAG_ALIAS2 = { 'flankhack': "dict(acfg_name='ctrl:pername=None,excluderef=False,contributor_contains=FlankHack', colorspace='gray', db='PZ_Master1')", 'pzmtest-bgr': "PZ_MTEST;dict(colorspace='bgr', controlled=True, max_examples=None, num_top=None)", # NOQA 'pzmtest': "PZ_MTEST;dict(colorspace='gray', controlled=True, max_examples=None, num_top=None)", # NOQA 'gz-gray': "GZ_ALL;dict(colorspace='gray', controlled=False, max_examples=None, num_top=None)", # NOQA 'liberty': "liberty;dict(detector='dog', pairs=250000)", 'combo': 'combo_vdsujffw', 'timectrl_pzmaster1': "PZ_Master1;dict(acfg_name='timectrl', colorspace='gray', min_featweight=0.8)", # NOQA 'pzm2': "PZ_Master1;dict(acfg_name='timectrl:pername=None', colorspace='gray', min_featweight=0.8)", # NOQA 'pzm3': "PZ_Master1;dict(acfg_name=None, colorspace='gray', controlled=True, min_featweight=0.8)", #'pzm3' : "PZ_Master1;dict(acfg_name='default:is_known=True,qmin_pername=2,view=primary,species=primary,minqual=ok', colorspace='gray', min_featweight=0.8)", # NOQA 'pzm4': "PZ_Master1;dict(acfg_name='default:is_known=True,qmin_pername=2,view=primary,species=primary,minqual=ok', colorspace='gray', min_featweight=0.8)", } def netrun(): r""" CommandLine: # --- UTILITY python -m wbia_cnn --tf get_juction_dpath --show # --- DATASET BUILDING --- # Build Dataset Aliases python -m wbia_cnn --tf netrun --db PZ_MTEST --acfg ctrl --ensuredata --show python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg timectrl --ensuredata python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg timectrl:pername=None --ensuredata python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg timectrl:pername=None --ensuredata python -m wbia_cnn --tf netrun --db mnist --ensuredata --show python -m wbia_cnn --tf netrun --db mnist --ensuredata --show --datatype=category python -m wbia_cnn --tf netrun --db mnist --ensuredata --show --datatype=siam-patch python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg ctrl:pername=None,excluderef=False,contributor_contains=FlankHack --ensuredata --show --datatype=siam-part # Parts based datasets python -m wbia_cnn --tf netrun --db PZ_MTEST --acfg ctrl --datatype=siam-part --ensuredata --show # Patch based dataset (big one) python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg default:is_known=True,qmin_pername=2,view=primary,species=primary,minqual=ok --ensuredata --show --vtd python -m wbia_cnn --tf netrun --ds pzm4 --weights=new --arch=siaml2_128 --train --monitor python -m wbia_cnn --tf netrun --ds pzm4 --arch=siaml2_128 --test python -m wbia_cnn --tf netrun --ds pzm4 --arch=siaml2_128 --veryverbose --no-flask # --- TRAINING --- python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg default:is_known=True,qmin_pername=2,view=primary,species=primary,minqual=ok --weights=new --arch=siaml2_128 --train --monitor python -m wbia_cnn --tf netrun --ds timectrl_pzmaster1 --acfg ctrl:pername=None,excluderef=False,contributor_contains=FlankHack --train --weights=new --arch=siaml2_128 --monitor # NOQA python -m wbia_cnn --tf netrun --ds timectrl_pzmaster1 --acfg ctrl:pername=None,excluderef=False --train --weights=new --arch=siaml2_128 --monitor # NOQA python -m wbia_cnn --tf netrun --ds pzmtest --weights=new --arch=siaml2_128 --train --monitor --DEBUG_AUGMENTATION python -m wbia_cnn --tf netrun --ds pzmtest --weights=new --arch=siaml2_128 --train --monitor python -m wbia_cnn --tf netrun --ds flankhack --weights=new --arch=siaml2_partmatch --train --monitor --learning_rate=.00001 python -m wbia_cnn --tf netrun --ds flankhack --weights=new --arch=siam_deepfaceish --train --monitor --learning_rate=.00001 # Different ways to train mnist python -m wbia_cnn --tf netrun --db mnist --weights=new --arch=mnist_siaml2 --train --monitor --datatype=siam-patch python -m wbia_cnn --tf netrun --db mnist --weights=new --arch=mnist-category --train --monitor --datatype=category # --- INITIALIZED-TRAINING --- python -m wbia_cnn --tf netrun --ds pzmtest --arch=siaml2_128 --weights=gz-gray:current --train --monitor # --- TESTING --- python -m wbia_cnn --tf netrun --db liberty --weights=liberty:current --arch=siaml2_128 --test python -m wbia_cnn --tf netrun --db PZ_Master0 --weights=combo:current --arch=siaml2_128 --testall Example: >>> # DISABLE_DOCTEST >>> from wbia_cnn.netrun import * # NOQA >>> netrun() >>> ut.show_if_requested() """ ut.colorprint('[netrun] NET RUN', 'red') requests, hyperparams, tags = parse_args() ds_tag = tags['ds_tag'] datatype = tags['datatype'] extern_ds_tag = tags['extern_ds_tag'] arch_tag = tags['arch_tag'] checkpoint_tag = tags['checkpoint_tag'] # ---------------------------- # Choose the main dataset ut.colorprint('[netrun] Ensuring Dataset', 'yellow') dataset = ingest_data.grab_dataset(ds_tag, datatype) if extern_ds_tag is not None: extern_dpath = ingest_data.get_extern_training_dpath(extern_ds_tag) else: extern_dpath = None logger.info('dataset.training_dpath = %r' % (dataset.training_dpath,)) logger.info('Dataset Alias Key: %r' % (dataset.alias_key,)) logger.info( 'Current Dataset Tag: %r' % (ut.invert_dict(DS_TAG_ALIAS2).get(dataset.alias_key, None),) ) if requests['ensuredata']: # Print alias key that maps to this particular dataset if ut.show_was_requested(): interact_ = dataset.interact() # NOQA return logger.info('...exiting') sys.exit(1) # ---------------------------- # Choose model architecture # TODO: data will need to return info about number of labels in viewpoint models # Specify model archichitecture ut.colorprint('[netrun] Architecture Specification', 'yellow') if arch_tag == 'siam2stream': model = models.SiameseCenterSurroundModel( data_shape=dataset.data_shape, training_dpath=dataset.training_dpath, hyperparams ) elif arch_tag.startswith('siam'): model = models.SiameseL2( data_shape=dataset.data_shape, arch_tag=arch_tag, training_dpath=dataset.training_dpath, hyperparams ) elif arch_tag == 'mnist-category': model = models.MNISTModel( data_shape=dataset.data_shape, output_dims=dataset.output_dims, arch_tag=arch_tag, training_dpath=dataset.training_dpath, *hyperparams ) pass else: raise ValueError('Unknown arch_tag=%r' % (arch_tag,)) ut.colorprint('[netrun] Initialize archchitecture', 'yellow') model.init_arch() # ---------------------------- # Choose weight initialization ut.colorprint('[netrun] Setting weights', 'yellow') if checkpoint_tag == 'new': ut.colorprint('[netrun] Initializing new weights', 'lightgray') model.reinit_weights() else: checkpoint_tag = model.resolve_fuzzy_checkpoint_pattern( checkpoint_tag, extern_dpath ) ut.colorprint( '[netrun] * Resolving weights checkpoint_tag=%r' % (checkpoint_tag,), 'lightgray', ) if extern_dpath is not None: model.load_extern_weights(dpath=extern_dpath, checkpoint_tag=checkpoint_tag) elif model.has_saved_state(checkpoint_tag=checkpoint_tag): model.load_model_state(checkpoint_tag=checkpoint_tag) else: model_state_fpath = model.get_model_state_fpath(checkpoint_tag=checkpoint_tag) logger.info('model_state_fpath = %r' % (model_state_fpath,)) ut.checkpath(model_state_fpath, verbose=True) logger.info( 'Known checkpoints are: ' + ut.repr3(model.list_saved_checkpoints()) ) raise ValueError( ('Unresolved weight init: ' 'checkpoint_tag=%r, extern_ds_tag=%r') % ( checkpoint_tag, extern_ds_tag, ) ) # logger.info('Model State:') # logger.info(model.get_state_str()) # ---------------------------- if not model.is_train_state_initialized(): ut.colorprint('[netrun] Need to initialize training state', 'yellow') X_train, y_train = dataset.subset('train') model.ensure_data_params(X_train, y_train) # Run Actions if requests['train']: ut.colorprint('[netrun] Training Requested', 'yellow') # parse training arguments config = ut.argparse_dict( dict( era_size=15, max_epochs=1200, rate_decay=0.8, ) ) model.monitor_config.update(*config) X_train, y_train = dataset.subset('train') X_valid, y_valid = dataset.subset('valid') model.fit(X_train, y_train, X_valid=X_valid, y_valid=y_valid) elif requests['test']: # assert model.best_results['epoch'] is not None ut.colorprint('[netrun] Test Requested', 'yellow') if requests['testall']: ut.colorprint('[netrun] Testing on all data', 'lightgray') X_test, y_test = dataset.subset('all') flat_metadata = dataset.subset_metadata('all') else: ut.colorprint('[netrun] * Testing on test subset', 'lightgray') X_test, y_test = dataset.subset('test') flat_metadata = dataset.subset_metadata('test') data, labels = X_test, y_test dataname = dataset.alias_key experiments.test_siamese_performance(model, data, labels, flat_metadata, dataname) else: if not ut.get_argflag('--cmd'): raise ValueError('nothing here. need to train or test') if requests['publish']: ut.colorprint('[netrun] Publish Requested', 'yellow') publish_dpath = ut.truepath('~/Dropbox/IBEIS') published_model_state = ut.unixjoin( publish_dpath, model.arch_tag + '_model_state.pkl' ) ut.copy(model.get_model_state_fpath(), published_model_state) ut.view_directory(publish_dpath) logger.info( 'You need to get the dropbox link and ' 'register it into the appropriate file' ) # pip install dropbox # https://www.dropbox.com/developers/core/start/python # import dropbox # need oauth # client.share('/myfile.txt', short_url=False) # https://wildbookiarepository.azureedge.net/models/siaml2_128_model_state.pkl if ut.get_argflag('--cmd'): ut.embed() def parse_args(): ds_default = None arch_default = 'siaml2_128' weights_tag_default = None # Test values if False: ds_default = 'liberty' weights_tag_default = 'current' assert ut.inIPython() # Parse commandline args ds_tag = ut.get_argval(('--dataset', '--ds'), type_=str, default=ds_default) datatype = ut.get_argval(('--datatype', '--dt'), type_=str, default='siam-patch') arch_tag = ut.get_argval(('--arch', '-a'), default=arch_default) weights_tag = ut.get_argval( ('--weights', '+w'), type_=str, default=weights_tag_default ) # Incorporate new config stuff? # NEW = False # if NEW: # default_dstag_cfg = { # 'ds': 'PZ_MTEST', # 'mode': 'patches', # 'arch': arch_default # } # named_defaults_dict = { # '': default_dstag_cfg # } # ut.parse_argv_cfg('dstag', named_defaults_dict=named_defaults_dict) hyperparams = ut.argparse_dict( { #'batch_size': 128, 'batch_size': 256, #'learning_rate': .0005, 'learning_rate': 0.1, 'momentum': 0.9, #'weight_decay': 0.0005, 'weight_decay': 0.0001, }, alias_dict={ 'weight_decay': ['decay'], 'learning_rate': ['learn_rate'], }, ) requests = ut.argparse_dict( { 'train': False, 'test': False, 'testall': False, 'publish': False, 'ensuredata': False, } ) requests['test'] = requests['test'] or requests['testall'] # breakup weights tag into extern_ds and checkpoint if weights_tag is not None and ':' in weights_tag: extern_ds_tag, checkpoint_tag = weights_tag.split(':') else: extern_ds_tag = None checkpoint_tag = weights_tag # resolve aliases ds_tag = DS_TAG_ALIAS2.get(ds_tag, ds_tag) extern_ds_tag = DS_TAG_ALIAS2.get(extern_ds_tag, extern_ds_tag) checkpoint_tag = CHECKPOINT_TAG_ALIAS.get(checkpoint_tag, checkpoint_tag) tags = { 'ds_tag': ds_tag, 'extern_ds_tag': extern_ds_tag, 'checkpoint_tag': checkpoint_tag, 'arch_tag': arch_tag, 'datatype': datatype, } ut.colorprint('[netrun] * ds_tag=%r' % (ds_tag,), 'lightgray') ut.colorprint('[netrun] * arch_tag=%r' % (arch_tag,), 'lightgray') ut.colorprint('[netrun] * extern_ds_tag=%r' % (extern_ds_tag,), 'lightgray') ut.colorprint('[netrun] * checkpoint_tag=%r' % (checkpoint_tag,), 'lightgray') return requests, hyperparams, tags def merge_ds_tags(ds_alias_list): r""" CommandLine: python -m wbia_cnn --tf merge_ds_tags --alias-list gz-gray girm pzmtest nnp TODO: http://stackoverflow.com/questions/18492273/combining-hdf5-files Example: >>> # DISABLE_DOCTEST >>> from wbia_cnn.netrun import * # NOQA >>> ds_alias_list = ut.get_argval('--alias-list', type_=list, default=[]) >>> result = merge_ds_tags(ds_alias_list) >>> print(result) """ ds_tag_list = [DS_TAG_ALIAS2.get(ds_tag, ds_tag) for ds_tag in ds_alias_list] dataset_list = [ingest_data.grab_siam_dataset(ds_tag) for ds_tag in ds_tag_list] merged_dataset = ingest_data.merge_datasets(dataset_list) logger.info(merged_dataset.alias_key) return merged_dataset if __name__ == '__main__': """ CommandLine: python -m wbia_cnn.netrun python -m wbia_cnn.netrun --allexamples python -m wbia_cnn.netrun --allexamples --noface --nosrc """ # train_pz() import multiprocessing multiprocessing.freeze_support() # for win32 import utool as ut # NOQA # import warnings # with warnings.catch_warnings(): # # Cause all warnings to always be triggered. # warnings.filterwarnings("error", ".get_all_non_bias_params.") ut.doctest_funcs()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 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upcfcardsearch/c133.py	ProfessorSean/Kasutamaiza	0	1615344	import discord from discord.ext import commands from discord.utils import get class c133(commands.Cog, name="c133"): def __init__(self, bot: commands.Bot): self.bot = bot @commands.command(name='Cyberse-Cop', aliases=['c133']) async def example_embed(self, ctx): embed = discord.Embed(title='Cyberse-Cop', color=0xFDE68A) embed.set_thumbnail(url='https://www.duelingbook.com/images/custom-pics/2300000/2334898.jpg') embed.add_field(name='Status (Archetype)', value='Casual:3/Tournament:3', inline=True) embed.add_field(name='Type (Attribute)', value='Cyberse/Normal (FIRE)', inline=False) embed.add_field(name='Level (ATK/DEF)', value='7 (3000/2500)', inline=False) embed.add_field(name='Lore Text', value='Constantly on guard, the Cyberse-Cops stop at nothing to ensure truth and justice is upheld. They are constanly updated on the crime in the area and are swift in taking action.', inline=False) embed.set_footer(text='Set Code: ANCF') await ctx.send(embed=embed) def setup(bot: commands.Bot): bot.add_cog(c133(bot))	[ 1, 1053, 2313, 536, 13, 3166, 2313, 536, 29889, 1062, 1053, 8260, 13, 3166, 2313, 536, 29889, 13239, 1053, 679, 13, 13, 1990, 274, 29896, 29941, 29941, 29898, 26381, 29889, 29907, 468, 29892, 1024, 543, 29883, 29896, 29941, 29941, 29908, 1125, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 9225, 29901, 8260, 29889, 29933, 327, 1125, 13, 4706, 1583, 29889, 7451, 353, 9225, 13, 1678, 732, 26381, 29889, 6519, 29898, 978, 2433, 29733, 495, 344, 29899, 29907, 459, 742, 14430, 2129, 29922, 1839, 29883, 29896, 29941, 29941, 11287, 13, 1678, 7465, 822, 1342, 29918, 17987, 29898, 1311, 29892, 12893, 1125, 13, 4706, 8297, 353, 2313, 536, 29889, 6026, 2580, 29898, 3257, 2433, 29733, 495, 344, 29899, 29907, 459, 742, 13, 462, 795, 2927, 29922, 29900, 29916, 29943, 2287, 29953, 29947, 29909, 29897, 13, 4706, 8297, 29889, 842, 29918, 386, 21145, 29898, 2271, 2433, 991, 597, 1636, 29889, 700, 14067, 2909, 29889, 510, 29914, 8346, 29914, 6341, 29899, 29886, 1199, 29914, 29906, 29941, 29900, 29900, 29900, 29900, 29900, 29914, 29906, 29941, 29941, 29946, 29947, 29929, 29947, 29889, 6173, 1495, 13, 13, 4706, 8297, 29889, 1202, 29918, 2671, 29898, 978, 2433, 5709, 313, 13197, 300, 668, 29897, 742, 995, 2433, 29907, 294, 950, 29901, 29941, 29914, 29911, 2905, 1166, 29901, 29941, 742, 10583, 29922, 5574, 29897, 13, 4706, 8297, 29889, 1202, 29918, 2671, 29898, 978, 2433, 1542, 313, 6708, 29897, 742, 995, 2433, 29733, 495, 344, 29914, 19077, 313, 3738, 1525, 29897, 742, 10583, 29922, 8824, 29897, 13, 4706, 8297, 29889, 1202, 29918, 2671, 29898, 978, 2433, 10108, 313, 1299, 29968, 29914, 24405, 29897, 742, 995, 2433, 29955, 313, 29941, 29900, 29900, 29900, 29914, 29906, 29945, 29900, 29900, 29897, 742, 10583, 29922, 8824, 29897, 13, 4706, 8297, 29889, 1202, 29918, 2671, 29898, 978, 2433, 29931, 487, 3992, 742, 995, 2433, 12075, 10835, 373, 8372, 29892, 278, 8045, 495, 344, 29899, 29907, 3554, 5040, 472, 3078, 304, 9801, 8760, 322, 15426, 338, 318, 561, 2495, 29889, 2688, 526, 1040, 273, 368, 4784, 373, 278, 17268, 297, 278, 4038, 322, 526, 12086, 297, 5622, 3158, 29889, 742, 10583, 29922, 8824, 29897, 13, 4706, 8297, 29889, 842, 29918, 21720, 29898, 726, 2433, 2697, 5920, 29901, 13764, 9207, 1495, 13, 13, 4706, 7272, 12893, 29889, 6717, 29898, 17987, 29922, 17987, 29897, 13, 13, 1753, 6230, 29898, 7451, 29901, 8260, 29889, 29933, 327, 1125, 13, 1678, 9225, 29889, 1202, 29918, 29883, 468, 29898, 29883, 29896, 29941, 29941, 29898, 7451, 876, 2 ]
cs15211/StoneGame.py	JulyKikuAkita/PythonPrac	1	29901	<gh_stars>1-10 __source__ = 'https://leetcode.com/problems/stone-game/' # Time: O() # Space: O() # # Description: Leetcode # 877. Stone Game # # Alex and Lee play a game with piles of stones. # There are an even number of piles arranged in a row, # and each pile has a positive integer number of stones piles[i]. # # The objective of the game is to end with the most stones. # The total number of stones is odd, so there are no ties. # # Alex and Lee take turns, with Alex starting first. # Each turn, a player takes the entire pile of stones from either the beginning # or the end of the row. This continues until there are no more piles left, # at which point the person with the most stones wins. # # Assuming Alex and Lee play optimally, return True if and only if Alex wins the game. # # # # Example 1: # # Input: [5,3,4,5] # Output: true # Explanation: # Alex starts first, and can only take the first 5 or the last 5. # Say he takes the first 5, so that the row becomes [3, 4, 5]. # If Lee takes 3, then the board is [4, 5], and Alex takes 5 to win with 10 points. # If Lee takes the last 5, then the board is [3, 4], and Alex takes 4 to win with 9 points. # This demonstrated that taking the first 5 was a winning move for Alex, so we return true. # # # Note: # # 2 <= piles.length <= 500 # piles.length is even. # 1 <= piles[i] <= 500 # sum(piles) is odd. # import unittest class Solution(object): def stoneGame(self, piles): """ :type piles: List[int] :rtype: bool """ return True class SolutionDP(object): def stoneGame(self, piles): """ :type piles: List[int] :rtype: bool """ n = len(piles) dp = [[0] * n for _ in range(n)] for i in range(n): dp[i][i] = piles[i] for l in range(2, n + 1): for i in range(n - l + 1): j = i + l - 1 dp[i][j] = max(piles[i] - dp[i + 1][j], piles[j] - dp[i][j - 1]) return dp[0][n - 1] > 0 class TestMethods(unittest.TestCase): def test_Local(self): self.assertEqual(1, 1) if __name__ == '__main__': unittest.main() Java = ''' # Thought: https://leetcode.com/problems/stone-game/solution/ Approach 1: Dynamic Programming Complexity Analysis Time Complexity: O(N^2), where N is the number of piles. Space Complexity: O(N^2), the space used storing the intermediate results of each subgame. # 10ms 36.14% class Solution { public boolean stoneGame(int[] piles) { int N = piles.length; // dp[i+1][j+1] = the value of the game [piles[i], ..., piles[j]]. int[][] dp = new int[N+2][N+2]; for (int size = 1; size <= N; ++ size) { for (int i = 0; i + size <= N; ++i) { int j = i + size - 1; int parity = ( j + i + N) % 2; // j - i - N; but +x = -x (mod 2) if (parity == 1) { dp[i + 1][j + 1] = Math.max(piles[i] + dp[i +2][j + 1], piles[j] + dp[i + 1][j]); } else { dp[i + 1][j + 1] = Math.min(-piles[i] + dp[i +2][j + 1], -piles[j] + dp[i + 1][j]); } } } return dp[1][N] > 0; } } Approach 2: Mathematical Complexity Analysis Time and Space Complexity: O(1) # 3ms 53.69% class Solution { public boolean stoneGame(int[] piles) { return true; } } # 2ms 99.64% class Solution { public boolean stoneGame(int[] piles) { int left = 0; int right = piles.length-1; int alex = 0; int lee = 0; boolean alexTurn = true; while (left < right) { if (alexTurn) { if (piles[left] > piles[right]) { alex += piles[left]; left++; } else { alex += piles[right]; right--; } } else { if (piles[left] > piles[right]) { lee += piles[left]; left++; } else { lee += piles[right]; right--; } } } return alex > lee; } } '''	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 1649, 4993, 1649, 353, 525, 991, 597, 280, 300, 401, 29889, 510, 29914, 17199, 29879, 29914, 12734, 29899, 11802, 22208, 13, 29937, 5974, 29901, 29871, 438, 580, 13, 29937, 14121, 29901, 438, 580, 13, 29937, 13, 29937, 12953, 29901, 951, 300, 401, 396, 29871, 29947, 29955, 29955, 29889, 15681, 8448, 13, 29937, 13, 29937, 4827, 322, 9371, 1708, 263, 3748, 411, 282, 5475, 310, 25702, 29889, 13, 29937, 1670, 526, 385, 1584, 1353, 310, 282, 5475, 21050, 297, 263, 1948, 29892, 13, 29937, 322, 1269, 282, 488, 756, 263, 6374, 6043, 1353, 310, 25702, 282, 5475, 29961, 29875, 1822, 13, 29937, 13, 29937, 450, 12091, 310, 278, 3748, 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Python/_old/Euler048.py	sosheskaz/Project-Euler	0	169137	#!/usr/bin/env python3 def main(): s = 0 for j in range(1, 1001): s += j ** j print(str(s)[-10:]) if __name__ == '__main__': main()	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 1753, 1667, 7295, 13, 1678, 269, 353, 29871, 29900, 13, 1678, 363, 432, 297, 3464, 29898, 29896, 29892, 29871, 29896, 29900, 29900, 29896, 1125, 13, 4706, 269, 4619, 432, 3579, 432, 13, 1678, 1596, 29898, 710, 29898, 29879, 9601, 29899, 29896, 29900, 29901, 2314, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1667, 580, 13, 2 ]
eutl_database/DataAccessLayer.py	jabrell/eutl_scraper	0	167247	<gh_stars>0 from getpass import getpass from sqlalchemy.exc import ProgrammingError from sqlalchemy.inspection import inspect from .model import Base from sqlalchemy import create_engine, MetaData from sqlalchemy.orm import sessionmaker from io import StringIO import csv class DataAccessLayer: """Class managing database access""" def __init__(self, user, host, db, passw, echo=False, encoding="utf-8", connect=True, base=None): """Constructor for data access class. Default access is to local database :param user: <string> user name :param host: <string> host address :param db: <string> database name :param echo: <string, boolean> whther to echo sql statements, "debug" for verbose output :param encoding: <string> database encoding :param connect: <boolean> True to establish immideiate connection to database default: True "param base: <qlalchemy.ext.declarative.declarative_base> """ self.engine = None self.user = user self.host = host self.db = db if base is None: # no custum declarative base, so use the one provided by eutl orm self.Base = Base else: self.Base = base self.conn_string = "postgresql+psycopg2://%s:%s@%s/%s" % (user, passw, host, db) self.encoding = encoding self.echo = echo if connect: self.connect() def connect(self): """ Connects to database """ if self.engine is None: self.engine = create_engine(self.conn_string, echo=self.echo, encoding=self.encoding) self.Base.metadata.create_all(self.engine) self.metadata = self.metadata = MetaData(bind=self.engine) self.metadata.reflect() self.Session = sessionmaker(bind=self.engine) self.session = self.Session() def empty_database(self, askConfirmation=True): """ Deletes all tables from database connectd by engine askConfirmation: <boolean> true to ask for typed confirmation""" self.metadata = MetaData(bind=self.engine) self.metadata.reflect() if len(self.engine.table_names()) > 0: if askConfirmation: confirm = getpass("Do really want to drop all tables? Enter Yes for confirmation: ") else: confirm = "yes" if confirm.lower() == "yes": for i, tbl in enumerate(reversed(self.metadata.sorted_tables)): if tbl.name in ["spatial_ref_sys"]: continue print(i, tbl) tbl.drop() print("Tables deleted") else: print("#### Tables still in database ####") self.metadata.reflect() self.Base.metadata.create_all(self.engine) def insert_df(self, df, obj, update=False, bulk_insert=False, verbose=False): """Inserts dataframe to database using session and ORM object. Dataframe has to have columns matching fields of the OMR objects :param df: <pd.DataFrame> with data :param obj: <ORM object> :param update: <boolean> True to update existing rows :param bulk_insert: <boolean> True for buli insert of intems :parma verbose: <boolean> to print all keys not inserted """ printed = False # get primary key names pk_names = [i.name for i in inspect(obj).primary_key] # replace Null values by None df_ = self._replace_null(df) to_add = [] for item in df_.to_dict(orient="records"): # get primary key pk = {k: v for k, v in item.items() if k in pk_names} exists = False try: qry = self.session.query(obj).filter_by(pk) exists = qry.count() > 0 except ProgrammingError: pass if exists: # with same key already in database if update: qry.delete() else: if verbose: print("Did not insert", pk) else: if not printed: print("Some entries not inserted due to key duplication.") printed = True continue obj_to_insert = obj(item) if bulk_insert: to_add.append(obj_to_insert) else: self.session.add(obj_to_insert) if bulk_insert: self.session.add_all(to_add) self.session.commit() def insert_df_large(self, df, name, integerColumns=None, schema=None, if_exists="fail", index=False, index_label=None, chunksize=1000000, dtype=None): """ Wrapper for pandas to_sql function using a more efficient insertion function. Likely only works under psycopg2 and postrgres :parm df: <pd.DataFrame> to be inserted :param name: <string> name if table :param integerColumns: <list: string> name of columns to be inserted as int :param schema: <string> pecify the schema (if database flavor supports this). If None, use default schema. :param chunksize: <int> number of rows to be inserted at once. In contrast to pandas implementeation here we use explicit slicing of the dataframe. :param if_exists: {"fail", "replace", "append"}, default "fail", How to behave if the table already exists. :param index: <boolean> whetehr to also insert index :param index_label: <string> Column label for index column(s) :param dtype: <dict> or <scalar> Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns. """ def psql_insert_copy(table, con, keys, data_iter): """ Execute SQL statement inserting data :param table : pandas.io.sql.SQLTable :parm con : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection :param keys : list of str Column names :param data_iter : Iterable that iterates the values to be inserted""" # gets a DBAPI connection that can provide a cursor dbapi_con = con.connection with dbapi_con.cursor() as cur: s_buf = StringIO() writer = csv.writer(s_buf) writer.writerows(data_iter) s_buf.seek(0) columns = ', '.join('"{}"'.format(k) for k in keys) if table.schema: table_name = '{}.{}'.format(table.schema, table.name) else: table_name = table.name sql = 'COPY {} ({}) FROM STDIN WITH CSV'.format( table_name, columns) cur.copy_expert(sql=sql, file=s_buf) def df_to_list_of_chuncks(df, chunksize=100000): lst_df = [df[i: i + chunksize] for i in range(0, df.shape[0], chunksize)] return lst_df def prepare_int_cols_for_sql_insert(df, int_cols): """ For fast insertion into the database using the csv IO stream we need interegers to be properly formated. However, as long as we have Null values in the column, the column is of type float and integers are foramtted with .0 at the end. Thus we cast these integers to string with correct format. :param df: <pd.DataFrame> :param int_cols: <list:string> names of columns to be converted """ def int_to_string(x): try: return "%d" % x except (TypeError, ValueError): return x for c in int_cols: df[c] = df[c].map(int_to_string) return df # convert int columns if integerColumns is not None: df_ = prepare_int_cols_for_sql_insert(df, int_cols=integerColumns) else: df_ = df.copy() # create chunks of dataframe and slice over it lst_df = df_to_list_of_chuncks(df_, chunksize=chunksize) for i, df_out in enumerate(lst_df): if (i % 10 == 0) and (i > 0): print("#### Commit chunck %d of %d" % ((i + 1), len(lst_df))) # insert data df_out.to_sql(name=name, con=self.session.get_bind(), if_exists=if_exists, schema=schema, index=index, index_label=index_label, dtype=dtype, method=psql_insert_copy) @staticmethod def _replace_null(df): """replaces nan and nat in dataframe by None values for database insertion""" df_ = df.copy() dt_cols = df_.select_dtypes(include=['datetime64']).columns for c in dt_cols: # convert datetimes to objects to replace nat df_[c] = df_[c].astype("object") df_ = df_.where(df.notnull(), None) return df_ @staticmethod def prepare_int_cols_for_sql_insert(df, int_cols): """ For fast insertion into the database using the csv IO stream we need interegers to be properly formated. 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22724, 29901, 529, 1761, 29901, 1807, 29958, 2983, 310, 4341, 304, 367, 11543, 13, 4706, 9995, 13, 4706, 822, 938, 29918, 517, 29918, 1807, 29898, 29916, 1125, 13, 9651, 1018, 29901, 13, 18884, 736, 11860, 29881, 29908, 1273, 921, 13, 9651, 5174, 313, 1542, 2392, 29892, 7865, 2392, 1125, 13, 18884, 736, 921, 13, 4706, 363, 274, 297, 938, 29918, 22724, 29901, 13, 9651, 4489, 29961, 29883, 29962, 353, 4489, 29961, 29883, 1822, 1958, 29898, 524, 29918, 517, 29918, 1807, 29897, 13, 4706, 736, 4489, 268, 13, 2 ]
configs/m4c/m4c_textvqa.py	inspur-hsslab/iMIX	23	174565	_base_ = [ '../_base_/datasets/textvqa_dataset.py', '../_base_/models/m4c_config.py', '../_base_/schedules/schedule_textvqa.py', '../_base_/textvqa_default_runtime.py' ] # yapf:disable	[ 1, 903, 3188, 29918, 353, 518, 13, 1678, 525, 6995, 29918, 3188, 29918, 29914, 14538, 1691, 29914, 726, 29894, 25621, 29918, 24713, 29889, 2272, 742, 13, 1678, 525, 6995, 29918, 3188, 29918, 29914, 9794, 29914, 29885, 29946, 29883, 29918, 2917, 29889, 2272, 742, 13, 1678, 525, 6995, 29918, 3188, 29918, 29914, 816, 287, 2540, 29914, 816, 11272, 29918, 726, 29894, 25621, 29889, 2272, 742, 13, 1678, 525, 6995, 29918, 3188, 29918, 29914, 726, 29894, 25621, 29918, 4381, 29918, 15634, 29889, 2272, 29915, 13, 29962, 29871, 396, 343, 481, 29888, 29901, 20472, 13, 2 ]
leetcode/python/841_keys_and_rooms.py	VVKot/leetcode-solutions	4	198838	<filename>leetcode/python/841_keys_and_rooms.py class Solution: def canVisitAllRooms(self, rooms): stack = [0] visited = set(stack) while stack: curr = stack.pop() for room in rooms[curr]: if room not in visited: stack.append(room) visited.add(room) if len(visited) == len(rooms): return True return len(visited) == len(rooms)	[ 1, 529, 9507, 29958, 280, 300, 401, 29914, 4691, 29914, 29947, 29946, 29896, 29918, 8149, 29918, 392, 29918, 18901, 29889, 2272, 13, 1990, 24380, 29901, 13, 1678, 822, 508, 6116, 277, 3596, 9588, 4835, 29898, 1311, 29892, 19600, 1125, 13, 4706, 5096, 353, 518, 29900, 29962, 13, 4706, 16669, 353, 731, 29898, 1429, 29897, 13, 4706, 1550, 5096, 29901, 13, 9651, 16256, 353, 5096, 29889, 7323, 580, 13, 9651, 363, 5716, 297, 19600, 29961, 21962, 5387, 13, 18884, 565, 5716, 451, 297, 16669, 29901, 13, 462, 1678, 5096, 29889, 4397, 29898, 8345, 29897, 13, 462, 1678, 16669, 29889, 1202, 29898, 8345, 29897, 13, 462, 1678, 565, 7431, 29898, 1730, 1573, 29897, 1275, 7431, 29898, 18901, 1125, 736, 5852, 13, 4706, 736, 7431, 29898, 1730, 1573, 29897, 1275, 7431, 29898, 18901, 29897, 2 ]
ChatBot/chatbot_search/chatbot_tfserving/indexAnnoy.py	yongzhuo/nlp_xiaojiang	1,379	144409	<reponame>yongzhuo/nlp_xiaojiang<filename>ChatBot/chatbot_search/chatbot_tfserving/indexAnnoy.py # !/usr/bin/python # -- coding: utf-8 -- # @time : 2021/4/18 21:04 # @author : Mo # @function: annoy search from annoy import AnnoyIndex import numpy as np import os class AnnoySearch: def __init__(self, dim=768, n_cluster=100): # metric可选“angular”（余弦距离）、“euclidean”（欧几里得距离）、 “ manhattan”（曼哈顿距离）或“hamming”（海明距离） self.annoy_index = AnnoyIndex(dim, metric="angular") self.n_cluster = n_cluster self.dim = dim def k_neighbors(self, vectors, k=18): """ 搜索 """ annoy_tops = [] for v in vectors: idx, dist = self.annoy_index.get_nns_by_vector(v, k, search_k=32k, include_distances=True) annoy_tops.append([dist, idx]) return annoy_tops def fit(self, vectors): """ annoy构建 """ for i, v in enumerate(vectors): self.annoy_index.add_item(i, v) self.annoy_index.build(self.n_cluster) def save(self, path): """ 存储 """ self.annoy_index.save(path) def load(self, path): """ 加载 """ self.annoy_index.load(path) if __name__ == '__main__': ### 索引 import random path = "model.ann" dim = 768 vectors = [[random.gauss(0, 1) for z in range(768)] for i in range(10)] an_model = AnnoySearch(dim, n_cluster=32) # Length of item vector that will be indexed an_model.fit(vectors) an_model.save(path) tops = an_model.k_neighbors([vectors[0]], 18) print(tops) del an_model ### 下载, 搜索 an_model = AnnoySearch(dim, n_cluster=32) an_model.load(path) tops = an_model.k_neighbors([vectors[0]], 6) print(tops) """ # example from annoy import AnnoyIndex import random dim = 768 vectors = [[random.gauss(0, 1) for z in range(768)] for i in range(10)] ann_model = AnnoyIndex(dim, 'angular') # Length of item vector that will be indexed for i,v in enumerate(vectors): ann_model.add_item(i, v) ann_model.build(10) # 10 trees ann_model.save("tet.ann") del ann_model u = AnnoyIndex(dim, "angular") u.load('tet.ann') # super fast, will just mmap the file v = vectors[1] idx, dist = u.get_nns_by_vector(v, 10, search_k=50 10, include_distances=True) print([idx, dist]) """ ### 备注说明: annoy索引无法增删会改查	[ 1, 529, 276, 1112, 420, 29958, 29891, 549, 29920, 6905, 29877, 29914, 12938, 29886, 29918, 29916, 29653, 2397, 574, 29966, 9507, 29958, 1451, 271, 29933, 327, 29914, 13496, 7451, 29918, 4478, 29914, 13496, 7451, 29918, 13264, 643, 1747, 29914, 2248, 2744, 1217, 29891, 29889, 2272, 13, 29937, 1738, 29914, 4855, 29914, 2109, 29914, 4691, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 732, 2230, 1678, 584, 29871, 29906, 29900, 29906, 29896, 29914, 29946, 29914, 29896, 29947, 29871, 29906, 29896, 29901, 29900, 29946, 13, 29937, 732, 8921, 29871, 584, 4546, 13, 29937, 732, 2220, 29901, 12327, 29891, 2740, 13, 13, 13, 3166, 12327, 29891, 1053, 530, 1217, 29891, 3220, 13, 5215, 12655, 408, 7442, 13, 5215, 2897, 13, 13, 13, 1990, 530, 1217, 29891, 7974, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 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utils/regression/classes/module_run.py	noahsherrill/force-riscv	111	1613424	<gh_stars>100-1000 # # Copyright (C) [2020] Futurewei Technologies, Inc. # # FORCE-RISCV is 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 # # THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES # OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO # NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the License for the specific language governing permissions and # limitations under the License. # # @package module_run.py # module_run.py # # Provides command line argument parsing and access as base class of # master_run.py and forrest_run.py # import sys import os from abc import ABC, abstractmethod from common.path_utils import PathUtils from common.msg_utils import Msg # # Parses and accesses parsed command line options. Also can be used to set # message level... and directly query for the force directory. class ModuleRun(ABC): def __init__(self, arg_msg_lev, arg_def_lev): self.m_app_setup = None self.m_app_info = None self.module_dir, self.module_name = PathUtils.split_path( PathUtils.real_path(sys.argv[0]) ) self.init_app_setup() self.load_message_levels(arg_msg_lev, arg_def_lev) @abstractmethod def init_app_setup(self): pass ## # resolves the desired message types def load_message_levels(self, arg_msg_lev, arg_def_lev): # load from the command line if specified or use the default my_lev_str = self.option_def(arg_msg_lev, arg_def_lev) # if a (+) or a (-) is found then the command line will be appended # to or demoted by if (my_lev_str[0] == "+") or (my_lev_str[0] == "-"): # use the default string to build a format string, then append # the passed in value my_fmt_str = "%s%%s" % arg_def_lev my_lev_str = my_fmt_str % (my_lev_str) my_level = Msg.translate_levelstr(my_lev_str) Msg.set_level(my_level) # return the force path def get_force_dir(self): return self.force_path # Reflects the supplied default value in arg_def_val if none matching the # arg_switch can be found in the parsed options. def option_def(self, aSwitch, aDefVal=None, aConversionFunc=None): return_value = self.m_app_info.mCmdLineOpts.option_def( aSwitch, aDefVal ) if aConversionFunc and return_value is not None: try: return_value = aConversionFunc(return_value) except (TypeError, ValueError) as ex: Msg.warn( 'Invalid value "{}" provided for "{}". ' "Using default.".format(repr(return_value), aSwitch) ) return aDefVal return return_value def get_arguments(self): return self.m_app_info.mCmdLineOpts.get_arguments() def get_unknown_arguments(self): return self.m_app_info.mCmdLineOpts.get_unknown_arguments() def print_help(self): self.m_app_info.mCmdLineOpts.print_help()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29899, 29896, 29900, 29900, 29900, 13, 29937, 13, 29937, 14187, 1266, 313, 29907, 29897, 518, 29906, 29900, 29906, 29900, 29962, 16367, 26599, 8364, 11763, 29892, 9266, 29889, 13, 29937, 13, 29937, 15842, 4741, 29899, 29934, 3235, 15633, 338, 7794, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 13, 29937, 29871, 313, 1552, 376, 29931, 293, 1947, 1496, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 13, 29937, 29871, 411, 278, 19245, 29889, 29871, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 29871, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 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endpoints/bookstore-grpc/bookstore.py	bxue16/bx_byte1	4	177638	# Copyright 2016 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. import threading class ShelfInfo(object): """The contents of a single shelf.""" def __init__(self, shelf): self._shelf = shelf self._last_book_id = 0 self._books = dict() class Bookstore(object): """An in-memory backend for storing Bookstore data.""" def __init__(self): self._last_shelf_id = 0 self._shelves = dict() self._lock = threading.Lock() def list_shelf(self): with self._lock: return [s._shelf for (_, s) in self._shelves.iteritems()] def create_shelf(self, shelf): with self._lock: self._last_shelf_id += 1 shelf_id = self._last_shelf_id shelf.id = shelf_id self._shelves[shelf_id] = ShelfInfo(shelf) return (shelf, shelf_id) def get_shelf(self, shelf_id): with self._lock: return self._shelves[shelf_id]._shelf def delete_shelf(self, shelf_id): with self._lock: del self._shelves[shelf_id] def list_books(self, shelf_id): with self._lock: return [book for ( _, book) in self._shelves[shelf_id]._books.iteritems()] def create_book(self, shelf_id, book): with self._lock: shelf_info = self._shelves[shelf_id] shelf_info._last_book_id += 1 book_id = shelf_info._last_book_id book.id = book_id shelf_info._books[book_id] = book return book def get_book(self, shelf_id, book_id): with self._lock: return self._shelves[shelf_id]._books[book_id] def delete_book(self, shelf_id, book_id): with self._lock: del self._shelves[shelf_id]._books[book_id]	[ 1, 396, 14187, 1266, 29871, 29906, 29900, 29896, 29953, 5087, 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linekey.py	alex-west-met-office/IMBS_MO	1	19926	''' A module for defining and producing the linekey object, which is used to determine and store information about data format in a CRREL ice mass balance buoy.''' class linekey: def __init__(self,date_index = 0): self.date_index = date_index self.value_index = [] self.phenomena_names = [] self.lon_flip_ew = (False,-1,-1) self.lat_flip_ns = (False,-1,-1) self.vertical_scale = 1. self.fliplon = False def add_value_index(self,phenomenon,index): self.value_index.append(index) self.phenomena_names.append(phenomenon) def ns(self,index_flippee,index_flipper): self.lat_flip_ns = (True,index_flippee,index_flipper) def ew(self,index_flippee,index_flipper): self.lon_flip_ew = (True,index_flippee,index_flipper) def get_temp_linekey(data_file): import csv fileh = open(data_file) rows = csv.reader(fileh) found_key = False found_date = False for row in rows: print(row) for (i,strtest) in enumerate(row): if ('Date' in strtest) or ('DATE' in strtest): key = linekey(date_index = i) found_date = True break if found_date: temp_codes = {} temp_type = '' for (i,strtest) in enumerate(row): result = classify_temp_header(strtest) if result[0]==1: if temp_type == 'subjective': print('Unable to determine temperature type') return None temp_type = 'objective' prefix = 'TO' if result[0]==2: if temp_type == 'objective': print('Unable to determine temperature type') return None temp_type = 'subjective' prefix = 'TS' temp_codes[i] = classify_temp_header(strtest) if result[0]!=0: key.add_value_index(prefix+str(result[1]),i) break return key def get_linekey(data_file,variable_list,buoy_name): import dictionaries import csv fileh = open(data_file) rows = csv.reader(fileh) found_key = False found_date = False td = dictionaries.title_dic() variable_keys_list = [td[variable_name] for variable_name in variable_list] vertical_scale = 1. fliplon = False for row in rows: if not found_key: for (i,strtest) in enumerate(row): if ('Date' in strtest) or ('DATE' in strtest): key = linekey(date_index = i) found_date = True break if found_date: for (varno,variable_keys) in enumerate(variable_keys_list): found_key = False for string in variable_keys: for (i,strtest) in enumerate(row): if (string == strtest.strip()): key.add_value_index(variable_list[varno],i) found_key = True i_key = i if '(cm)' in string: vertical_scale = 0.01 if '(m)' in string: vertical_scale = 1. if string=='Longitude (W)': fliplon = True if not found_key: key.add_value_index(variable_list[varno],-1) if variable_list[varno]=='latitude': for (i,strtest) in enumerate(row): if (strtest == 'N/S'): key.ns(i_key,i) if variable_list[varno]=='longitude': for (i,strtest) in enumerate(row): if (strtest == 'E/W'): key.ew(i_key,i) if True in [('units are cm') in item for item in row]: vertical_scale = 0.01 if 'E/W' in row and 'longitude' in key.phenomena_names: i_flipper = row.index('E/W') i_flippee = key.value_index[key.phenomena_names.index('longitude')] key.ew(i_flippee,i_flipper) if 'N/S' in row and 'latitude' in key.phenomena_names: i_flipper = row.index('N/S') i_flippee = key.value_index[key.phenomena_names.index('latitude')] key.ew(i_flippee,i_flipper) if not found_date: print('Could not find date') fileh.close() return None key.vertical_scale = vertical_scale key.fliplon = fliplon fileh.close() return key def classify_temp_header(string): import functions if functions.is_number(string): number = float(string) return (1,number) elif string[0:1]=='T' and string[-3:]=='(C)' and functions.is_number(string[1:-3]): number = int(string[1:-3]) return (2,number) elif string[0:1]=='T' and functions.is_number(string[1:]): number = int(string[1:]) return (2,number) elif len(string) >= 4: if string[0:4]=='TEMP' and functions.is_number(string[4:]): number = int(string[4:]) return (2,number) elif string[0:5]=='Temp ' and functions.is_number(string[5:]): number = int(string[5:]) return (2,number) else: return (0,0) else: return (0,0)	[ 1, 14550, 319, 3883, 363, 16184, 322, 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test_motor.py	ankworld/Rpi_Therapy	0	98410	<reponame>ankworld/Rpi_Therapy<filename>test_motor.py from myLib import controlcore from time import sleep motor = controlcore.Motor(11, 13, 1000) dc = 100 for i in range(10): motor.l_drive(dc) sleep(5) motor.r_drive(dc) sleep(5)	[ 1, 529, 276, 1112, 420, 29958, 804, 11526, 29914, 29934, 1631, 29918, 1349, 261, 27580, 29966, 9507, 29958, 1688, 29918, 14817, 272, 29889, 2272, 13, 3166, 590, 14868, 1053, 2761, 3221, 13, 3166, 931, 1053, 8709, 13, 13, 14817, 272, 353, 2761, 3221, 29889, 29924, 327, 272, 29898, 29896, 29896, 29892, 29871, 29896, 29941, 29892, 29871, 29896, 29900, 29900, 29900, 29897, 13, 13, 13891, 353, 29871, 29896, 29900, 29900, 13, 13, 13, 1454, 474, 297, 3464, 29898, 29896, 29900, 1125, 13, 1678, 10992, 29889, 29880, 29918, 21594, 29898, 13891, 29897, 13, 13, 1678, 8709, 29898, 29945, 29897, 13, 13, 1678, 10992, 29889, 29878, 29918, 21594, 29898, 13891, 29897, 13, 13, 1678, 8709, 29898, 29945, 29897, 13, 2 ]
train.py	Mario-Medvedovic/deepPhosAPI	0	71287	<gh_stars>0 import functools import itertools import os import random import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np from sklearn import metrics from sklearn import preprocessing from sklearn.model_selection import train_test_split, KFold, cross_val_score from keras.layers import Dense, Activation, Flatten, Dropout, Reshape from keras.layers import Conv1D,Conv2D, MaxPooling2D from keras.models import Sequential,Model from keras.utils.np_utils import to_categorical from keras import optimizers from keras.optimizers import Adam,SGD from keras.layers.normalization import BatchNormalization from keras.regularizers import l2 import copy def train_for_deepphos(train_file_name,site,predictFrame = 'general',background_weight = None): ''' :param train_file_name: input of your train file it must be a .csv file and theinput format is label,proteinName, postion,sites, shortsequence, :param site: the sites predict: site = 'S','T' OR 'Y' :param predictFrame: 'general' or 'kinase' :param background_weight: the model you want load to pretrain new model usually used in kinase training :return: ''' win1 = 51 win2 = 33 win3 = 15 from methods.dataprocess_train import getMatrixLabel X_train1 , y_train = getMatrixLabel(train_file_name, sites, win1) X_train2, _ = getMatrixLabel(train_file_name, sites, win2) X_train3, _ = getMatrixLabel(train_file_name, sites, win3) modelname = "general_{:s}".format(site) if predictFrame == 'general': modelname ="general_model_{:s}".format(site) if predictFrame == 'kinase': modelname = "kinase_model_{:s}".format(site) from methods.model_n import model_net model = model_net(X_train1, X_train2, X_train3, y_train, weights=background_weight) model.save_weights(modelname+'.h5',overwrite=True)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 2090, 312, 8789, 13, 5215, 4256, 8504, 13, 5215, 2897, 13, 5215, 4036, 13, 13, 5215, 22889, 13, 2922, 17357, 29889, 1509, 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data_scripts/data_loader.py	luis-armando-perez-rey/renderSHREC2021	0	167885	<filename>data_scripts/data_loader.py import os import h5py import numpy as np from data_scripts.factor_dataset import FactorImageDataset AVAILABLE_COLLECTIONS = ["Shape", "Culture"] AVAILABLE_DATA_TYPES = ["train", "test"] def load_factor_data(data, root_path=None, kwargs): options_dict = { "shrec2021": get_h5_saved_data, } return options_dict[data](root_path, kwargs) def get_h5_saved_data(root_path, collection_list, data_type, dataset_directory, normalize=True): """ Returns a TransformImage object created from ModelNet40 dataset of objects with periodic colors and rotated Args: root_path: path to the root of the project dataset_filename: filename of the .h5 data to be loaded object_type: type of object saved in the data file normalize: whether data should be in the range [0,1] (True) or [0, 255] (False). Returns: FactorImageDataset object :param normalize: :param dataset_directory: :param data_type: :param collection_list: """ image_list = [] views_list = [] labels_list = [] ids_list = [] for collection in collection_list: assert collection in AVAILABLE_COLLECTIONS, "collection_list = {} is not available. Possible values are {}".format( collection, AVAILABLE_COLLECTIONS) assert data_type in AVAILABLE_DATA_TYPES, "data_type = {} is not available. Possible values are {}".format( data_type, AVAILABLE_DATA_TYPES) dataset_filename = collection + "_" + data_type + ".h5" print(dataset_filename) dataset_filepath = os.path.join(root_path, dataset_directory, dataset_filename) # Read the images images = read_data_h5(dataset_filepath, "images") if normalize: images = images.astype('float32') / np.amax(images) image_list.append(images) # Read the rotations views = read_data_h5(dataset_filepath, "views") views_list.append(views) # Read category integer class_labels try: labels = read_data_h5(dataset_filepath, "class_int") except: print("No labels detected in the dataset") labels = None labels_list.append(labels) ids = read_data_h5(dataset_filepath, "ids") ids_list.append(ids) images = np.concatenate(image_list, axis=0) ids = np.concatenate(ids_list, axis=0) labels = np.concatenate(labels_list, axis=0) views = np.concatenate(views_list, axis=0) # Convert integer range to angular range unique_views = np.unique(views) unique_ids = np.unique(ids) # Create FactorImageDataset lists factor_values = [unique_ids, unique_views] max_factor_values = [np.amax(factor) for factor in factor_values] return FactorImageDataset(images=images, factor_values_list=factor_values, max_factor_values=max_factor_values, factor_names=["object_ids", "rotation_angle"], labels=labels) def read_data_h5(data_filepath, data_type): """ Read data from h5 file with 1 level of hierarchy Args: data_filepath: path to the .h5 file data_type: key value from which data is read Returns: """ with h5py.File(data_filepath, "r") as file: data = np.array(file.get(data_type)) return data	[ 1, 529, 9507, 29958, 1272, 29918, 16713, 29914, 1272, 29918, 12657, 29889, 2272, 13, 5215, 2897, 13, 5215, 298, 29945, 2272, 13, 5215, 12655, 408, 7442, 13, 3166, 848, 29918, 16713, 29889, 19790, 29918, 24713, 1053, 383, 7168, 2940, 16390, 24541, 13, 13, 26612, 6227, 6181, 29918, 15032, 3281, 27946, 353, 6796, 24111, 613, 376, 27926, 3108, 13, 26612, 6227, 6181, 29918, 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causal_discovery/graph_fitting.py	codeaudit/ENCO	0	176412	import torch import torch.nn.functional as F import numpy as np import math import random import sys sys.path.append("../") from causal_graphs.variable_distributions import _random_categ from causal_discovery.datasets import InterventionalDataset class GraphFitting(object): def __init__(self, model, graph, num_batches, num_graphs, theta_only_num_graphs, batch_size, lambda_sparse, max_graph_stacking=200): """ Creates a DistributionFitting object that summarizes all functionalities for performing the graph fitting stage of ENCO. Parameters ---------- model : MultivarMLP PyTorch module of the neural networks that model the conditional distributions. graph : CausalDAG Causal graph on which we want to perform causal structure learning. num_batches : int Number of batches to use per MC sample in the graph fitting stage. Usually 1, only higher needed if GPU is running out of memory for common batch sizes. num_graphs : int Number of graph samples to use for estimating the gradients in the graph fitting stage. Usually in the range 20-100. theta_only_num_graphs : int Number of graph samples to use in the graph fitting stage if gamma is frozen. Needs to be an even number, and usually 2 or 4. batch_size : int Size of the batches to use in the gradient estimators. lambda_sparse : float Sparsity regularizer value to use in the graph fitting stage. max_graph_stacking : int Number of graphs that can maximally evaluated in parallel on the device. If you run out of GPU memory, try to lower this number. It will then evaluate the graph sequentially, which can be slightly slower but uses less memory. """ self.model = model self.graph = graph self.num_batches = num_batches self.num_graphs = num_graphs self.batch_size = batch_size self.lambda_sparse = lambda_sparse self.max_graph_stacking = max_graph_stacking self.theta_only_num_graphs = theta_only_num_graphs self.inter_vars = [] if self.graph.num_vars >= 100 or hasattr(self.graph, "data_int"): self.dataset = InterventionalDataset(self.graph, dataset_size=4096, batch_size=self.batch_size) def perform_update_step(self, gamma, theta, var_idx=-1, only_theta=False): """ Performs a full update step of the graph fitting stage. We first sample a batch of graphs, evaluate them on a interventional data batch, and estimate the gradients for gamma and theta based on the log-likelihoods. Parameters ---------- gamma : nn.Parameter Parameter tensor representing the gamma parameters in ENCO. theta : nn.Parameter Parameter tensor representing the theta parameters in ENCO. var_idx : int Variable on which should be intervened to obtain the update. If none is given, i.e., a negative value, the variable will be randomly selected. only_theta : bool If True, gamma is frozen and the gradients are only estimated for theta. See Appendix D.2 in the paper for details on the gamma freezing stage. """ # Obtain log-likelihood estimates for randomly sampled graph structures if not only_theta: MC_samp = self.get_MC_samples(gamma, theta, num_batches=self.num_batches, num_graphs=self.num_graphs, batch_size=self.batch_size, var_idx=var_idx, mirror_graphs=False) else: MC_samp = self.get_MC_samples(gamma, theta, num_batches=self.num_batches, num_graphs=self.theta_only_num_graphs, batch_size=self.batch_size, var_idx=var_idx, mirror_graphs=True) adj_matrices, log_likelihoods, var_idx = MC_samp # Determine gradients for gamma and theta gamma_grads, theta_grads, theta_mask = self.gradient_estimator( adj_matrices, log_likelihoods, gamma, theta, var_idx) gamma.grad = gamma_grads theta.grad = theta_grads return theta_mask, var_idx @torch.no_grad() def get_MC_samples(self, gamma, theta, num_batches, num_graphs, batch_size, var_idx=-1, mirror_graphs=False): """ Samples and evaluates a batch of graph structures on a batch of interventional data. Parameters ---------- gamma : nn.Parameter Parameter tensor representing the gamma parameters in ENCO. theta : nn.Parameter Parameter tensor representing the theta parameters in ENCO. num_batches : int Number of batches to use per MC sample. num_graphs : int Number of graph structures to sample. batch_size : int Size of interventional data batches. var_idx : int Variable on which should be intervened to obtain the update. If none is given, i.e., a negative value, the variable will be randomly selected. mirror_graphs : bool This variable should be true if only theta is optimized. In this case, the first half of the graph structure samples is identical to the second half, except that the values of the outgoing edges of the intervened variable are flipped. This allows for more efficient, low-variance gradient estimators. See details in the paper. """ if mirror_graphs: assert num_graphs % 2 == 0, "Number of graphs must be divisible by two for mirroring" device = self.get_device() # Sample data batch if hasattr(self, "dataset"): # Pre-sampled data var_idx = self.sample_next_var_idx() int_sample = torch.cat([self.dataset.get_batch(var_idx) for _ in range(num_batches)], dim=0).to(device) else: # If no dataset exists, data is newly sampled from the graph intervention_dict, var_idx = self.sample_intervention(self.graph, dataset_size=num_batchesbatch_size, var_idx=var_idx) int_sample = self.graph.sample(interventions=intervention_dict, batch_size=num_batchesbatch_size, as_array=True) int_sample = torch.from_numpy(int_sample).long().to(device) # Split number of graph samples acorss multiple iterations if not all can fit into memory num_graphs_list = [min(self.max_graph_stacking, num_graphs-iself.max_graph_stacking) for i in range(math.ceil(num_graphs 1.0 / self.max_graph_stacking))] num_graphs_list = [(num_graphs_list[i], sum(num_graphs_list[:i])) for i in range(len(num_graphs_list))] # Tensors needed for sampling edge_prob = (torch.sigmoid(gamma) * torch.sigmoid(theta)).detach() edge_prob_batch = edge_prob[None].expand(num_graphs, -1, -1) # Inner function for sampling a batch of random adjacency matrices from current belief probabilities def sample_adj_matrix(): sample_matrix = torch.bernoulli(edge_prob_batch) sample_matrix = sample_matrix * (1 - torch.eye(sample_matrix.shape[-1], device=sample_matrix.device)[None]) if mirror_graphs: # First and second half of tensors are identical, except the intervened variable sample_matrix[num_graphs//2:] = sample_matrix[:num_graphs//2] sample_matrix[num_graphs//2:, var_idx] = 1 - sample_matrix[num_graphs//2:, var_idx] sample_matrix[:, var_idx, var_idx] = 0. return sample_matrix # Evaluate log-likelihoods under sampled adjacency matrix and data adj_matrices = [] log_likelihoods = [] for n_idx in range(num_batches): batch = int_sample[n_idxbatch_size:(n_idx+1)batch_size] if n_idx == 0: adj_matrix = sample_adj_matrix() adj_matrices.append(adj_matrix) for c_idx, (graph_count, start_idx) in enumerate(num_graphs_list): adj_matrix_expanded = adj_matrix[start_idx:start_idx+graph_count, None].expand(-1, batch_size, -1, -1).flatten(0, 1) batch_exp = batch[None, :].expand(graph_count, -1, -1).flatten(0, 1) nll = self.evaluate_likelihoods(batch_exp, adj_matrix_expanded, var_idx) nll = nll.reshape(graph_count, batch_size, -1) if n_idx == 0: log_likelihoods.append(nll.mean(dim=1)) else: log_likelihoods[c_idx] += nll.mean(dim=1) # Combine all data adj_matrices = torch.cat(adj_matrices, dim=0) log_likelihoods = torch.cat(log_likelihoods, dim=0) / num_batches return adj_matrices, log_likelihoods, var_idx @torch.no_grad() def gradient_estimator(self, adj_matrices, log_likelihoods, gamma, theta, var_idx): """ Returns the estimated gradients for gamma and theta. It uses the low-variance gradient estimators proposed in Section 3.3 of the paper. Parameters ---------- adj_matrices : torch.FloatTensor, shape [batch_size, num_vars, num_vars] The adjacency matrices on which the interventional data has been evaluated on. log_likelihoods : torch.FloatTensor, shape [batch_size, num_vars] The average log-likelihood under the adjacency matrices for all variables in the graph. gamma : nn.Parameter Parameter tensor representing the gamma parameters in ENCO. theta : nn.Parameter Parameter tensor representing the theta parameters in ENCO. var_idx : int Variable on which the intervention was performed. """ batch_size = adj_matrices.shape[0] log_likelihoods = log_likelihoods.unsqueeze(dim=1) orient_probs = torch.sigmoid(theta) edge_probs = torch.sigmoid(gamma) # Gradient calculation num_pos = adj_matrices.sum(dim=0) num_neg = batch_size - num_pos mask = ((num_pos > 0) * (num_neg > 0)).float() pos_grads = (log_likelihoods * adj_matrices).sum(dim=0) / num_pos.clamp_(min=1e-5) neg_grads = (log_likelihoods * (1 - adj_matrices)).sum(dim=0) / num_neg.clamp_(min=1e-5) gamma_grads = mask * edge_probs * (1 - edge_probs) * orient_probs * (pos_grads - neg_grads + self.lambda_sparse) theta_grads = mask * orient_probs * (1 - orient_probs) * edge_probs * (pos_grads - neg_grads) # Masking gamma for incoming edges to intervened variable gamma_grads[:, var_idx] = 0. gamma_grads[torch.arange(gamma_grads.shape[0]), torch.arange(gamma_grads.shape[1])] = 0. # Masking all theta's except the ones with a intervened variable theta_grads[:var_idx] = 0. theta_grads[var_idx+1:] = 0. theta_grads -= theta_grads.transpose(0, 1) # theta_ij = -theta_ji # Creating a mask which theta's are actually updated for the optimizer theta_mask = torch.zeros_like(theta_grads) theta_mask[var_idx] = 1. theta_mask[:, var_idx] = 1. theta_mask[var_idx, var_idx] = 0. return gamma_grads, theta_grads, theta_mask def sample_next_var_idx(self): """ Returns next variable to intervene on. We iterate through the variables in a shuffled order, like a standard dataset. """ if len(self.inter_vars) == 0: # If an epoch finished, reshuffle variables self.inter_vars = [i for i in range(len(self.graph.variables))] random.shuffle(self.inter_vars) var_idx = self.inter_vars.pop() return var_idx def sample_intervention(self, graph, dataset_size, var_idx=-1): """ Returns a new data batch for an intervened variable. """ # Select variable to intervene on if var_idx < 0: var_idx = self.sample_next_var_idx() var = graph.variables[var_idx] # Soft, perfect intervention => replace p(X_n) by random categorical # Scale is set to 0.0, which represents a uniform distribution. int_dist = _random_categ(size=(var.prob_dist.num_categs,), scale=0.0, axis=-1) # Sample from interventional distribution value = np.random.multinomial(n=1, pvals=int_dist, size=(dataset_size,)) value = np.argmax(value, axis=-1) # One-hot to index intervention_dict = {var.name: value} return intervention_dict, var_idx @torch.no_grad() def evaluate_likelihoods(self, int_sample, adj_matrix, var_idx): """ Evaluates the negative log-likelihood of the interventional data batch (int_sample) on the given graph structures (adj_matrix) and the intervened variable (var_idx). 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modeling/player/load.py	cpratim/Fantasy-Predictions	0	62804	<gh_stars>0 import numpy as np from helpers import * from config import * def load_xy(player): player = '_'.join([l.lower() for l in player.split(' ')]) X, y = [], [] stats_record = load_json(f'{player_dir}/{player}.json') for season, stats in stats_record.items(): X.append(int(season[-2:])) y.append(list(stats.values())) return np.array(X), np.array(y).T	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 12655, 408, 7442, 13, 3166, 1371, 414, 1053, 334, 13, 3166, 2295, 1053, 334, 13, 13, 13, 1753, 2254, 29918, 3594, 29898, 9106, 1125, 13, 13, 1678, 4847, 353, 22868, 4286, 7122, 4197, 29880, 29889, 13609, 580, 363, 301, 297, 4847, 29889, 5451, 877, 25710, 2314, 13, 13, 1678, 1060, 29892, 343, 353, 19997, 5159, 13, 1678, 13, 1678, 22663, 29918, 11651, 353, 2254, 29918, 3126, 29898, 29888, 29915, 29912, 9106, 29918, 3972, 6822, 29912, 9106, 1836, 3126, 1495, 13, 1678, 363, 4259, 29892, 22663, 297, 22663, 29918, 11651, 29889, 7076, 7295, 13, 13, 4706, 1060, 29889, 4397, 29898, 524, 29898, 25682, 14352, 29906, 29901, 12622, 13, 4706, 343, 29889, 4397, 29898, 1761, 29898, 16202, 29889, 5975, 22130, 13, 13, 1678, 736, 7442, 29889, 2378, 29898, 29990, 511, 7442, 29889, 2378, 29898, 29891, 467, 29911, 13, 2 ]
Docker image/pdx-analysis-workflows/JAX_RNA/picard_alignment/picard_alignment_metrics.py	BorisYourich/EurOPDX-Galaxy	0	86632	#! /usr/bin/env python """ Picard Alignment Metrics. Version: 0.1.1 """ from __future__ import print_function import argparse import os import shutil import sys def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('files', nargs='+', help="The file[s] to use.") return parser.parse_args() def main(): args = parse_args() sample_name = sys.argv[4] RGID = "4" RGPL = "illumina" RGPU = "unit1" RGSM = "20" picard_stat_file = sample_name + "_picard_stat" interim_results_dir = '/mnt/volume/shared/reference-data/' # Check whether interim results directory exists or not. If not create it. if not os.path.exists(interim_results_dir): os.mkdir(interim_results_dir) # Remove existing files to avoid files exist exception. if os.path.exists(interim_results_dir + picard_stat_file): os.remove(interim_results_dir + picard_stat_file) # Adding readgroup information input_bam = args.files[0] try: os.system("picard AddOrReplaceReadGroups \ INPUT=" + input_bam + " \ OUTPUT=genome_bam_read_group.bam \ SORT_ORDER=coordinate \ RGID=" + RGID + " \ RGLB=" + sample_name + " \ RGPL=" + RGPL + " \ RGPU=" + RGPU + " \ RGSM=" + RGSM + " \ CREATE_INDEX=true" ) except Exception as e: print('Error executing picard AddOrReplaceReadGroups -> %s' % e) sys.exit(1) # Create dictionary for the reference file # SEQUENCE_DICTIONARY = os.path.splitext(args.files[3])[0] + ".dict" try: SEQUENCE_DICTIONARY = args.files[2] + ".dict" if not os.path.exists(SEQUENCE_DICTIONARY): symlink_to_fa = args.files[2] + ".fa" command = "ln -fs " + args.files[2] + " " + symlink_to_fa os.system(command) print("Command: " + command) os.system("picard CreateSequenceDictionary REFERENCE=" + symlink_to_fa) except Exception as e: print('Error executing picard CreateSequenceDictionary -> %s' % e) sys.exit(1) # Picard Reorder Bam file try: os.system("picard ReorderSam \ INPUT=genome_bam_read_group.bam \ OUTPUT=genome_bam_read_group_reorder.bam \ SEQUENCE_DICTIONARY=" + SEQUENCE_DICTIONARY + " \ ALLOW_INCOMPLETE_DICT_CONCORDANCE=true \ CREATE_INDEX=true" ) except Exception as e: print('Error executing picard ReorderSam -> %s' % e) sys.exit(1) # Picard SortSam (generating sorted alignment bam file) try: os.system("picard SortSam \ SORT_ORDER=coordinate \ INPUT=genome_bam_read_group_reorder.bam \ OUTPUT=genome_bam_read_group_reorder_sorted.bam \ VALIDATION_STRINGENCY=SILENT \ CREATE_INDEX=true" ) except Exception as e: print('Error executing picard SortSam -> %s' % e) sys.exit(1) # CollectRnaSeqMetrics try: os.system("picard CollectRnaSeqMetrics \ INPUT=genome_bam_read_group_reorder_sorted.bam \ OUTPUT=" + picard_stat_file + " \ REF_FLAT=" + args.files[1] + " \ STRAND=NONE \ VALIDATION_STRINGENCY=SILENT \ CHART_OUTPUT=File" ) except Exception as e: print('Error executing picard CollectRnaSeqMetrics -> %s' % e) sys.exit(1) #shutil.move(picard_stat_file, interim_results_dir) # Summary metrics compilation # try: # os.system( # "perl ../../../../../tools/pdx-analysis-workflows/RNA_PDX/summary_QC_metrics.pl " + interim_results_dir + quality_stat_file + " " + interim_results_dir + xenome_stat_file + " " + interim_results_dir + rsem_stat_file + " " + interim_results_dir + picard_stat_file + " > " + interim_results_dir + summary_stat_file) # shutil.copy(interim_results_dir + summary_stat_file, os.getcwd()) # except Exception as e: # print('Error executing summary_QC_metrics.pl -> %s' % e) # sys.exit(1) if __name__ == '__main__': main()	[ 1, 396, 29991, 847, 4855, 29914, 2109, 29914, 6272, 3017, 13, 15945, 29908, 13, 29925, 293, 538, 838, 10194, 4737, 10817, 29889, 13, 6594, 29901, 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tests/color_threshold.py	hunterjsb/CalculateArea	0	154545	import numpy as np from calculatearea import AreaEstimator from math import sqrt area_estimator = AreaEstimator(filename='rancho_rd.jpg', # name of the image in the directory color_range=((0, 0, 118), (100, 100, 255)), default_area_color=(255, 20, 160)) # BGR purple A_px = area_estimator.get_area(return_pixels=True) ft_per_pixel = sqrt(131193.8 / A_px) # plot size - 30.56 acres, img size - 447x588 px print(ft_per_pixel) # area_estimator.show_images() # with tighter restraints area_estimator.color_lower_limit = np.array([0, 0, 130], np.uint8) area_estimator.color_upper_limit = np.array([50, 50, 255], np.uint8) area_estimator.area_color = (100, 200, 200) A_px = area_estimator.get_area() area_estimator.show_images() # guess feet per pixel ft_per_pixel = sqrt(131193.8 / A_px) print(ft_per_pixel)	[ 1, 1053, 12655, 408, 7442, 13, 13, 3166, 8147, 6203, 1053, 18320, 12787, 326, 1061, 13, 3166, 5844, 1053, 18074, 2273, 13, 13, 6203, 29918, 342, 326, 1061, 353, 18320, 12787, 326, 1061, 29898, 9507, 2433, 661, 1859, 29918, 5499, 29889, 6173, 742, 29871, 396, 1024, 310, 278, 1967, 297, 278, 3884, 13, 462, 1669, 2927, 29918, 3881, 29922, 3552, 29900, 29892, 29871, 29900, 29892, 29871, 29896, 29896, 29947, 511, 313, 29896, 29900, 29900, 29892, 29871, 29896, 29900, 29900, 29892, 29871, 29906, 29945, 29945, 8243, 13, 462, 1669, 2322, 29918, 6203, 29918, 2780, 7607, 29906, 29945, 29945, 29892, 29871, 29906, 29900, 29892, 29871, 29896, 29953, 29900, 876, 29871, 396, 350, 14345, 3708, 552, 13, 13, 29909, 29918, 1756, 353, 4038, 29918, 342, 326, 1061, 29889, 657, 29918, 6203, 29898, 2457, 29918, 29886, 861, 1379, 29922, 5574, 29897, 13, 615, 29918, 546, 29918, 29886, 15711, 353, 18074, 2273, 29898, 29896, 29941, 29896, 29896, 29929, 29941, 29889, 29947, 847, 319, 29918, 1756, 29897, 29871, 396, 6492, 2159, 448, 29871, 29941, 29900, 29889, 29945, 29953, 23931, 29892, 10153, 2159, 448, 29871, 29946, 29946, 29955, 29916, 29945, 29947, 29947, 282, 29916, 13, 2158, 29898, 615, 29918, 546, 29918, 29886, 15711, 29897, 13, 13, 29937, 4038, 29918, 342, 326, 1061, 29889, 4294, 29918, 8346, 580, 13, 13, 29937, 411, 260, 14643, 1791, 5270, 29879, 13, 6203, 29918, 342, 326, 1061, 29889, 2780, 29918, 13609, 29918, 13400, 353, 7442, 29889, 2378, 4197, 29900, 29892, 29871, 29900, 29892, 29871, 29896, 29941, 29900, 1402, 7442, 29889, 13470, 29947, 29897, 13, 6203, 29918, 342, 326, 1061, 29889, 2780, 29918, 21064, 29918, 13400, 353, 7442, 29889, 2378, 4197, 29945, 29900, 29892, 29871, 29945, 29900, 29892, 29871, 29906, 29945, 29945, 1402, 7442, 29889, 13470, 29947, 29897, 13, 6203, 29918, 342, 326, 1061, 29889, 6203, 29918, 2780, 353, 313, 29896, 29900, 29900, 29892, 29871, 29906, 29900, 29900, 29892, 29871, 29906, 29900, 29900, 29897, 13, 13, 29909, 29918, 1756, 353, 4038, 29918, 342, 326, 1061, 29889, 657, 29918, 6203, 580, 13, 6203, 29918, 342, 326, 1061, 29889, 4294, 29918, 8346, 580, 13, 13, 29937, 4140, 6900, 639, 15526, 13, 615, 29918, 546, 29918, 29886, 15711, 353, 18074, 2273, 29898, 29896, 29941, 29896, 29896, 29929, 29941, 29889, 29947, 847, 319, 29918, 1756, 29897, 13, 2158, 29898, 615, 29918, 546, 29918, 29886, 15711, 29897, 13, 2 ]
corehq/apps/users/bulkupload.py	dborowiecki/commcare-hq	0	90465	import logging import uuid from django import forms from django.conf import settings from django.core.exceptions import ValidationError from django.core.validators import validate_email from django.utils.translation import ugettext as _ from couchdbkit.exceptions import ( BulkSaveError, MultipleResultsFound, ResourceNotFound, ) from couchexport.writers import Excel2007ExportWriter from dimagi.utils.chunked import chunked from dimagi.utils.parsing import string_to_boolean from soil import DownloadBase from soil.util import expose_download, get_download_file_path from corehq import privileges from corehq.apps.accounting.utils import domain_has_privilege from corehq.apps.commtrack.util import get_supply_point_and_location from corehq.apps.custom_data_fields.models import CustomDataFieldsDefinition from corehq.apps.domain.forms import clean_password from corehq.apps.domain.models import Domain from corehq.apps.groups.models import Group from corehq.apps.locations.models import SQLLocation from corehq.apps.users.dbaccessors.all_commcare_users import ( get_commcare_users_by_filters, get_existing_usernames, ) from corehq.apps.users.models import UserRole from corehq.util.workbook_json.excel import ( alphanumeric_sort_key, flatten_json, json_to_headers, ) from .forms import get_mobile_worker_max_username_length from .models import CommCareUser, CouchUser from .util import normalize_username, raw_username class UserUploadError(Exception): pass required_headers = set(['username']) allowed_headers = set([ 'data', 'email', 'group', 'language', 'name', 'password', 'phone-number', 'uncategorized_data', 'user_id', 'is_active', 'location_code', 'role', 'User IMEIs (read only)', 'registered_on (read only)', ]) \| required_headers old_headers = { # 'old_header_name': 'new_header_name' 'location-sms-code': 'location_code' } def check_headers(user_specs): messages = [] headers = set(user_specs.fieldnames) # Backwards warnings for (old_name, new_name) in old_headers.items(): if old_name in headers: messages.append( _("'The column header '{old_name}' is deprecated, please use '{new_name}' instead.").format( old_name=old_name, new_name=new_name )) headers.discard(old_name) illegal_headers = headers - allowed_headers missing_headers = required_headers - headers for header_set, label in (missing_headers, 'required'), (illegal_headers, 'illegal'): if header_set: messages.append(_('The following are {label} column headers: {headers}.').format( label=label, headers=', '.join(header_set))) if messages: raise UserUploadError('\n'.join(messages)) def check_duplicate_usernames(user_specs): usernames = set() duplicated_usernames = set() for row in user_specs: username = row.get('username') if username and username in usernames: duplicated_usernames.add(username) usernames.add(username) if duplicated_usernames: raise UserUploadError(_("The following usernames have duplicate entries in " "your file: " + ', '.join(duplicated_usernames))) def check_existing_usernames(user_specs, domain): usernames_without_ids = set() invalid_usernames = set() for row in user_specs: username = row.get('username') user_id = row.get('user_id') if username and user_id: continue try: usernames_without_ids.add(normalize_username(username, domain)) except ValidationError: invalid_usernames.add(username) if invalid_usernames: raise UserUploadError(_('The following usernames are invalid: ' + ', '.join(invalid_usernames))) existing_usernames = set() for usernames in chunked(usernames_without_ids, 500): existing_usernames.update(get_existing_usernames(usernames)) if existing_usernames: raise UserUploadError(_("The following usernames already exist and must " "have an id specified to be updated: " + ', '.join(existing_usernames))) class GroupMemoizer(object): """ If you use this to get a group, do not set group.name directly; use group_memoizer.rename_group(group, name) instead. """ def __init__(self, domain): self.groups_by_name = {} self.groups_by_id = {} self.groups = set() self.domain = domain def load_all(self): for group in Group.by_domain(self.domain): self.add_group(group) def add_group(self, new_group): # todo # this has the possibility of missing two rows one with id one with name # that actually refer to the same group # and overwriting one with the other assert new_group.name if new_group.get_id: self.groups_by_id[new_group.get_id] = new_group self.groups_by_name[new_group.name] = new_group self.groups.add(new_group) def by_name(self, group_name): if group_name not in self.groups_by_name: group = Group.by_name(self.domain, group_name) if not group: self.groups_by_name[group_name] = None return None self.add_group(group) return self.groups_by_name[group_name] def get(self, group_id): if group_id not in self.groups_by_id: group = Group.get(group_id) if group.domain != self.domain: raise ResourceNotFound() self.add_group(group) return self.groups_by_id[group_id] def create(self, domain, name): group = Group(domain=domain, name=name) self.add_group(group) return group def rename_group(self, group, name): # This isn't always true, you can rename A => B and then B => C, # and what was A will now be called B when you try to change # what was B to be called C. That's fine, but you don't want to # delete someone else's entry if self.groups_by_name.get(group.name) is group: del self.groups_by_name[group.name] group.name = name self.add_group(group) def save_all(self): Group.bulk_save(self.groups) def _fmt_phone(phone_number): if phone_number and not isinstance(phone_number, str): phone_number = str(int(phone_number)) return phone_number.lstrip("+") class BulkCacheBase(object): def __init__(self, domain): self.domain = domain self.cache = {} def get(self, key): if not key: return None if key not in self.cache: self.cache[key] = self.lookup(key) return self.cache[key] def lookup(self, key): # base classes must implement this themselves raise NotImplementedError class SiteCodeToSupplyPointCache(BulkCacheBase): """ Cache the lookup of a supply point object from the site code used in upload. """ def lookup(self, site_code): case_location = get_supply_point_and_location( self.domain, site_code ) return case_location.case class SiteCodeToLocationCache(BulkCacheBase): def __init__(self, domain): self.non_admin_types = [ loc_type.name for loc_type in Domain.get_by_name(domain).location_types if not loc_type.administrative ] super(SiteCodeToLocationCache, self).__init__(domain) def lookup(self, site_code): """ Note that this can raise SQLLocation.DoesNotExist if the location with the given site code is not found. """ return SQLLocation.objects.using('default').get( domain=self.domain, site_code__iexact=site_code ) class LocationIdToSiteCodeCache(BulkCacheBase): def lookup(self, location_id): return SQLLocation.objects.get( domain=self.domain, # this is only for safety location_id=location_id ).site_code def create_or_update_groups(domain, group_specs, log): group_memoizer = GroupMemoizer(domain) group_memoizer.load_all() group_names = set() for row in group_specs: group_id = row.get('id') group_name = str(row.get('name') or '') case_sharing = row.get('case-sharing') reporting = row.get('reporting') data = row.get('data') # check that group_names are unique if group_name in group_names: log['errors'].append('Your spreadsheet has multiple groups called "%s" and only the first was processed' % group_name) continue else: group_names.add(group_name) # check that there's a group_id or a group_name if not group_id and not group_name: log['errors'].append('Your spreadsheet has a group with no name or id and it has been ignored') continue try: if group_id: group = group_memoizer.get(group_id) else: group = group_memoizer.by_name(group_name) if not group: group = group_memoizer.create(domain=domain, name=group_name) except ResourceNotFound: log["errors"].append('There are no groups on CommCare HQ with id "%s"' % group_id) except MultipleResultsFound: log["errors"].append("There are multiple groups on CommCare HQ named: %s" % group_name) else: if group_name: group_memoizer.rename_group(group, group_name) group.case_sharing = case_sharing group.reporting = reporting group.metadata = data return group_memoizer def get_location_from_site_code(site_code, location_cache): if isinstance(site_code, str): site_code = site_code.lower() elif isinstance(site_code, int): site_code = str(site_code) else: raise UserUploadError( _("Unexpected format received for site code '%(site_code)s'") % {'site_code': site_code} ) try: return location_cache.get(site_code) except SQLLocation.DoesNotExist: raise UserUploadError( _("Could not find organization with site code '%(site_code)s'") % {'site_code': site_code} ) def is_password(password): if not password: return False for c in password: if c != "": return True return False def users_with_duplicate_passwords(rows): password_dict = dict() for row in rows: username = row.get('username') password = str(row.get('password')) if not is_password(password): continue if password_dict.get(password): password_dict[password].add(username) else: password_dict[password] = {username} ret = set() for usernames in password_dict.values(): if len(usernames) > 1: ret = ret.union(usernames) return ret def create_or_update_users_and_groups(domain, user_specs, group_specs, task=None): from corehq.apps.users.views.mobile.custom_data_fields import UserFieldsView custom_data_validator = UserFieldsView.get_validator(domain) ret = {"errors": [], "rows": []} total = len(user_specs) + len(group_specs) def _set_progress(progress): if task is not None: DownloadBase.set_progress(task, progress, total) group_memoizer = create_or_update_groups(domain, group_specs, log=ret) current = len(group_specs) usernames = set() user_ids = set() allowed_groups = set(group_memoizer.groups) allowed_group_names = [group.name for group in allowed_groups] allowed_roles = UserRole.by_domain(domain) roles_by_name = {role.name: role for role in allowed_roles} can_assign_locations = domain_has_privilege(domain, privileges.LOCATIONS) if can_assign_locations: location_cache = SiteCodeToLocationCache(domain) domain_obj = Domain.get_by_name(domain) usernames_with_dupe_passwords = users_with_duplicate_passwords(user_specs) try: for row in user_specs: _set_progress(current) current += 1 data = row.get('data') email = row.get('email') group_names = list(map(str, row.get('group') or [])) language = row.get('language') name = row.get('name') password = <PASSWORD>.<PASSWORD>('password') phone_number = row.get('phone-number') uncategorized_data = row.get('uncategorized_data') user_id = row.get('user_id') username = row.get('username') location_codes = row.get('location_code') or [] if location_codes and not isinstance(location_codes, list): location_codes = [location_codes] # ignore empty location_codes = [code for code in location_codes if code] role = row.get('role', '') if password: password = <PASSWORD>) try: username = normalize_username(str(username), domain) except TypeError: username = None except ValidationError: ret['rows'].append({ 'username': username, 'row': row, 'flag': _('username cannot contain spaces or symbols'), }) continue status_row = { 'username': raw_username(username) if username else None, 'row': row, } is_active = row.get('is_active') if isinstance(is_active, str): try: is_active = string_to_boolean(is_active) if is_active else None except ValueError: ret['rows'].append({ 'username': username, 'row': row, 'flag': _("'is_active' column can only contain 'true' or 'false'"), }) continue if username in usernames or user_id in user_ids: status_row['flag'] = 'repeat' elif not username and not user_id: status_row['flag'] = 'missing-data' else: try: if username: usernames.add(username) if user_id: user_ids.add(user_id) if user_id: user = CommCareUser.get_by_user_id(user_id, domain) else: user = CommCareUser.get_by_username(username) if domain_obj.strong_mobile_passwords and is_password(password): if raw_username(username) in usernames_with_dupe_passwords: raise UserUploadError(_("Provide a unique password for each mobile worker")) try: clean_password(password) except forms.ValidationError: if settings.ENABLE_DRACONIAN_SECURITY_FEATURES: msg = _("Mobile Worker passwords must be 8 " "characters long with at least 1 capital " "letter, 1 special character and 1 number") else: msg = _("Please provide a stronger password") raise UserUploadError(msg) if user: if user.domain != domain: raise UserUploadError(_( 'User with username %(username)r is ' 'somehow in domain %(domain)r' ) % {'username': user.username, 'domain': user.domain}) if username and user.username != username: raise UserUploadError(_( 'Changing usernames is not supported: %(username)r to %(new_username)r' ) % {'username': user.username, 'new_username': username}) if is_password(password): user.set_password(password) status_row['flag'] = 'updated' else: max_username_length = get_mobile_worker_max_username_length(domain) if len(raw_username(username)) > max_username_length: ret['rows'].append({ 'username': username, 'row': row, 'flag': _("username cannot contain greater than %d characters" % max_username_length) }) continue if not is_password(password): raise UserUploadError(_("Cannot create a new user with a blank password")) user = CommCareUser.create(domain, username, password, commit=False) status_row['flag'] = 'created' if phone_number: user.add_phone_number(_fmt_phone(phone_number), default=True) if name: user.set_full_name(str(name)) if data: error = custom_data_validator(data) if error: raise UserUploadError(error) user.user_data.update(data) if uncategorized_data: user.user_data.update(uncategorized_data) if language: user.language = language if email: try: validate_email(email) except ValidationError: raise UserUploadError(_("User has an invalid email address")) user.email = email.lower() if is_active is not None: user.is_active = is_active if can_assign_locations: # Do this here so that we validate the location code before we # save any other information to the user, this way either all of # the user's information is updated, or none of it location_ids = [] for code in location_codes: loc = get_location_from_site_code(code, location_cache) location_ids.append(loc.location_id) if role: if role in roles_by_name: user.set_role(domain, roles_by_name[role].get_qualified_id()) else: raise UserUploadError(_( "Role '%s' does not exist" ) % role) if can_assign_locations: locations_updated = set(user.assigned_location_ids) != set(location_ids) primary_location_removed = (user.location_id and not location_ids or user.location_id not in location_ids) if primary_location_removed: user.unset_location(commit=False) if locations_updated: user.reset_locations(location_ids, commit=False) user.save() if is_password(password): # Without this line, digest auth doesn't work. # With this line, digest auth works. # Other than that, I'm not sure what's going on # Passing use_primary_db=True because of https://dimagi-dev.atlassian.net/browse/ICDS-465 user.get_django_user(use_primary_db=True).check_password(password) for group_id in Group.by_user_id(user.user_id, wrap=False): group = group_memoizer.get(group_id) if group.name not in group_names: group.remove_user(user) for group_name in group_names: if group_name not in allowed_group_names: raise UserUploadError(_( "Can't add to group '%s' " "(try adding it to your spreadsheet)" ) % group_name) group_memoizer.by_name(group_name).add_user(user, save=False) except (UserUploadError, CouchUser.Inconsistent) as e: status_row['flag'] = str(e) ret["rows"].append(status_row) finally: try: group_memoizer.save_all() except BulkSaveError as e: _error_message = ( "Oops! We were not able to save some of your group changes. " "Please make sure no one else is editing your groups " "and try again." ) logging.exception(( 'BulkSaveError saving groups. ' 'User saw error message "%s". Errors: %s' ) % (_error_message, e.errors)) ret['errors'].append(_error_message) _set_progress(total) return ret class GroupNameError(Exception): def __init__(self, blank_groups): self.blank_groups = blank_groups @property def message(self): return "The following group ids have a blank name: %s." % ( ', '.join([group.get_id for group in self.blank_groups]) ) def build_data_headers(keys, header_prefix='data'): return json_to_headers( {header_prefix: {key: None for key in keys}} ) def parse_users(group_memoizer, domain, user_data_model, location_cache, user_filters, task, total_count): def _get_group_names(user): return sorted([ group_memoizer.get(id).name for id in Group.by_user_id(user.user_id, wrap=False) ], key=alphanumeric_sort_key) def _get_devices(user): """ Returns a comma-separated list of IMEI numbers of the user's devices, sorted with most-recently-used first """ return ', '.join([device.device_id for device in sorted( user.devices, key=lambda d: d.last_used, reverse=True )]) def _make_user_dict(user, group_names, location_cache): model_data, uncategorized_data = ( user_data_model.get_model_and_uncategorized(user.user_data) ) role = user.get_role(domain) location_codes = [] try: location_codes.append(location_cache.get(user.location_id)) except SQLLocation.DoesNotExist: pass for location_id in user.assigned_location_ids: # skip if primary location_id, as it is already added to the start of list above if location_id != user.location_id: try: location_codes.append(location_cache.get(location_id)) except SQLLocation.DoesNotExist: pass return { 'data': model_data, 'uncategorized_data': uncategorized_data, 'group': group_names, 'name': user.full_name, 'password': "*******", # dummy display string for passwords 'phone-number': user.phone_number, 'email': user.email, 'username': user.raw_username, 'language': user.language, 'user_id': user._id, 'is_active': str(user.is_active), 'User IMEIs (read only)': _get_devices(user), 'location_code': location_codes, 'role': role.name if role else '', 'registered_on (read only)': user.created_on.strftime('%Y-%m-%d %H:%M:%S') if user.created_on else '' } unrecognized_user_data_keys = set() user_groups_length = 0 max_location_length = 0 user_dicts = [] for n, user in enumerate(get_commcare_users_by_filters(domain, user_filters)): group_names = _get_group_names(user) user_dict = _make_user_dict(user, group_names, location_cache) user_dicts.append(user_dict) unrecognized_user_data_keys.update(user_dict['uncategorized_data']) user_groups_length = max(user_groups_length, len(group_names)) max_location_length = max(max_location_length, len(user_dict["location_code"])) DownloadBase.set_progress(task, n, total_count) user_headers = [ 'username', 'password', 'name', 'phone-number', 'email', 'language', 'role', 'user_id', 'is_active', 'User IMEIs (read only)', 'registered_on (read only)'] user_data_fields = [f.slug for f in user_data_model.get_fields(include_system=False)] user_headers.extend(build_data_headers(user_data_fields)) user_headers.extend(build_data_headers( unrecognized_user_data_keys, header_prefix='uncategorized_data' )) user_headers.extend(json_to_headers( {'group': list(range(1, user_groups_length + 1))} )) if domain_has_privilege(domain, privileges.LOCATIONS): user_headers.extend(json_to_headers( {'location_code': list(range(1, max_location_length + 1))} )) def _user_rows(): for user_dict in user_dicts: row = dict(flatten_json(user_dict)) yield [row.get(header) or '' for header in user_headers] return user_headers, _user_rows() def parse_groups(groups): def _make_group_dict(group): return { 'id': group.get_id, 'name': group.name, 'case-sharing': group.case_sharing, 'reporting': group.reporting, 'data': group.metadata, } group_data_keys = set() group_dicts = [] sorted_groups = sorted( groups, key=lambda group: alphanumeric_sort_key(group.name) ) for group in sorted_groups: group_dicts.append(_make_group_dict(group)) group_data_keys.update(group.metadata if group.metadata else []) group_headers = ['id', 'name', 'case-sharing?', 'reporting?'] group_headers.extend(build_data_headers(group_data_keys)) def _get_group_rows(): for group_dict in group_dicts: row = dict(flatten_json(group_dict)) yield [row.get(header) or '' for header in group_headers] return group_headers, _get_group_rows() def count_users_and_groups(domain, user_filters, group_memoizer): users_count = get_commcare_users_by_filters(domain, user_filters, count_only=True) groups_count = len(group_memoizer.groups) return users_count + groups_count def dump_users_and_groups(domain, download_id, user_filters, task): from corehq.apps.users.views.mobile.custom_data_fields import UserFieldsView def _load_memoizer(domain): group_memoizer = GroupMemoizer(domain=domain) # load groups manually instead of calling group_memoizer.load_all() # so that we can detect blank groups blank_groups = set() for group in Group.by_domain(domain): if group.name: group_memoizer.add_group(group) else: blank_groups.add(group) if blank_groups: raise GroupNameError(blank_groups=blank_groups) return group_memoizer writer = Excel2007ExportWriter(format_as_text=True) group_memoizer = _load_memoizer(domain) location_cache = LocationIdToSiteCodeCache(domain) users_groups_count = count_users_and_groups(domain, user_filters, group_memoizer) DownloadBase.set_progress(task, 0, users_groups_count) user_data_model = CustomDataFieldsDefinition.get_or_create( domain, UserFieldsView.field_type ) user_headers, user_rows = parse_users( group_memoizer, domain, user_data_model, location_cache, user_filters, task, users_groups_count, ) group_headers, group_rows = parse_groups(group_memoizer.groups) headers = [ ('users', [user_headers]), ('groups', [group_headers]), ] rows = [ ('users', user_rows), ('groups', group_rows), ] use_transfer = settings.SHARED_DRIVE_CONF.transfer_enabled filename = "{}_users_{}.xlsx".format(domain, uuid.uuid4().hex) file_path = 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train_xgboost.py	mmcenta/missing-link	0	141824	<filename>train_xgboost.py import pickle import argparse import numpy as np import xgboost as xgb from xgboost.sklearn import XGBClassifier from sklearn.metrics import accuracy_score from util.dataset_io import load_dataset parser = argparse.ArgumentParser(description='Train a xgboost model.') parser.add_argument('--train_name', required=True, help='name of the training dataset') parser.add_argument('--val_name', help='name of the validation dataset') parser.add_argument('--model_name', default='base', help='name of the model for saving') parser.add_argument('--n_estimators', type=int, default=100, help='number of trees to fit') parser.add_argument('--gpu', action='store_true', help='whether to use a gpu when training') parser.add_argument('--max_depth', type=int, default=3, help='maximum tree depth for base learners') parser.add_argument('--learning_rate', type=float, default=0.01, help='boosting learning rate') parser.add_argument('--subsample', type=float, default=0.8, help='subsample ratio of the training instance') parser.add_argument('--colsample_bytree', type=float, default=0.8, help='subsample ratio of columns when constructing each tree') parser.add_argument('--gamma', type=float, default=0, help='minimum loss reduction required to make a further partition on a leaf node of the tree') parser.add_argument('--scale_pos_weight', type=float, default=1.0) parser.add_argument('--min_child_weight', type=int, default=1) parser.add_argument('--reg_alpha', type=float, default=0.0) if __name__ == "__main__": args = parser.parse_args() print("Loading training datataset...") X_train, y_train = load_dataset(args.train_name) print("Done.\nTraining...") if not args.gpu: model = XGBClassifier(silent=False, scale_pos_weight=args.scale_pos_weight, min_child_weight=args.min_child_weight, learning_rate=args.learning_rate, colsample_bytree=args.colsample_bytree, subsample=args.subsample, objective='binary:logistic', n_estimators=args.n_estimators, reg_alpha=args.reg_alpha, max_depth=args.max_depth, gamma=args.gamma) else: model = XGBClassifier(tree_method="gpu_hist", gpu_id=0, silent=False, scale_pos_weight=args.scale_pos_weight, min_child_weight=args.min_child_weight, learning_rate=args.learning_rate, colsample_bytree=args.colsample_bytree, subsample=args.subsample, objective='binary:logistic', n_estimators=args.n_estimators, reg_alpha=args.reg_alpha, max_depth=args.max_depth, gamma=args.gamma) model.fit(X_train, y_train) print("Done.\nSaving...") with open("./models/" + args.model_name + ".pickle", "wb") as f: pickle.dump(model, f) print("Done.") if args.val_name is not None: print("Validating...") preds = model.predict(X_train) pred_labels = np.rint(preds) print("\ttrain accuracy:", accuracy_score(y_train, pred_labels)) X_val, y_val = load_dataset(args.val_name) preds = model.predict(X_val) pred_labels = np.rint(preds) print("\tval accuracy:", accuracy_score(y_val, pred_labels)) print("Done.")	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merger.py	lorabit/workspace_merger	0	178069	from shutil import copy2 from os import path, remove from random import randint import argparse class WorkspaceReader: def __init__(self, fname): self.maven_jars = [] self.java_libraries = [] module_name = 'tmp'+str(randint(1000,9999)) new_name = path.join(path.dirname(__file__), module_name +'.py') copy2(fname, new_name) workspace = __import__(module_name) workspace.native = self workspace.generated_java_libraries() workspace.generated_maven_jars() remove(new_name) def maven_jar(self, name = None, artifact = None, sha1 = None): self.maven_jars.append( { "name": name, "artifact": artifact, "sha1" : sha1 } ) def java_library(self, name = None, visibility = [], exports = [], runtime_deps = []): self.java_libraries.append( { "name": name, "visibility": visibility, "exports": exports, "runtime_deps": runtime_deps } ) class WorkspaceMerger: def __init__(self): self.maven_jars = {} self.java_libraries = {} def merge(self, reader): for maven_jar in reader.maven_jars: self.maven_jars[maven_jar["name"]] = maven_jar for java_library in reader.java_libraries: self.java_libraries[java_library["name"]] = java_library def export(self, fname): def format_array(array, indent = 6): if len(array) == 0: return "[]" if len(array) == 1: return "[\"%s\"]" % (array[0]) ret = [((" "(indent+4)+"\"%s\",\n") %(i,)) for i in array] return "[\n"+"".join(ret)+" "indent+"]" with open(fname, 'w') as f: f.write("# The following dependencies were calculated by workspace_merger. \n# https://github.com/lorabit/workspace_merger\n\n\n") f.write("def generated_maven_jars():\n") for maven_jar_name in self.maven_jars: maven_jar = self.maven_jars[maven_jar_name] f.write(" native.maven_jar(\n") f.write(" name = \"%s\",\n" % (maven_jar["name"],)) if maven_jar.get("artifact")!=None: f.write(" artifact = \"%s\",\n" % (maven_jar["artifact"])) if maven_jar.get("sha1")!=None: f.write(" sha1 = \"%s\",\n" % (maven_jar["sha1"])) f.write(" )\n\n") f.write("def generated_java_libraries():\n") for java_library_name in self.java_libraries: java_library = self.java_libraries[java_library_name] f.write(" native.java_library(\n") f.write(" name = \"%s\",\n" % (java_library["name"],)) if java_library.get("visibility")!=None: f.write(" visibility = %s,\n" % (format_array(java_library["visibility"]))) if java_library.get("exports")!=None: f.write(" exports = %s,\n" % (format_array(java_library["exports"]))) if java_library.get("runtime_deps")!=None: f.write(" runtime_deps = %s,\n" % (format_array(java_library["runtime_deps"]))) f.write(" )\n\n") if __name__ == '__main__': parser = argparse.ArgumentParser( description='Merge generated bazel workspaces for maven dependencies.') _arg = parser.add_argument _arg( '-s', '--source', type=str, action='store', metavar='PATH', help='The source bzl file path.', required = True ) _arg( '-d', '--destination', type=str, action='store', metavar='PATH', help='The destination bzl file path. If the file does not exist, a new file will be created.', required = True ) args = parser.parse_args() if not path.isfile(args.source): print("%s does not exist.\n" % (args.source)) exit(0) merger = WorkspaceMerger() if path.isfile(args.destination): dest = WorkspaceReader(args.destination) merger.merge(dest) source = WorkspaceReader(args.source) merger.merge(source) merger.export(args.destination) print('Done!')	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suites/DatabaseApi/SideChainEthereum.py	dbulka/pytests	0	137996	<gh_stars>0 # -- coding: utf-8 -- import lemoncheesecake.api as lcc @lcc.suite("SideChainEthereum") class SideChainEthereum: pass	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 5215, 454, 3712, 1173, 267, 687, 1296, 29889, 2754, 408, 301, 617, 13, 13, 13, 29992, 29880, 617, 29889, 13495, 703, 23908, 14688, 29923, 12711, 398, 1159, 13, 1990, 19160, 14688, 29923, 12711, 398, 29901, 13, 1678, 1209, 13, 2 ]
authentik/policies/password/models.py	BeryJu/passbook	15	175958	"""user field matcher models""" import re from django.db import models from django.utils.translation import gettext as _ from rest_framework.serializers import BaseSerializer from structlog.stdlib import get_logger from authentik.policies.models import Policy from authentik.policies.types import PolicyRequest, PolicyResult from authentik.stages.prompt.stage import PLAN_CONTEXT_PROMPT LOGGER = get_logger() RE_LOWER = re.compile("[a-z]") RE_UPPER = re.compile("[A-Z]") RE_DIGITS = re.compile("[0-9]") class PasswordPolicy(Policy): """Policy to make sure passwords have certain properties""" password_field = models.TextField( default="password", help_text=_("Field key to check, field keys defined in Prompt stages are available."), ) amount_digits = models.PositiveIntegerField(default=0) amount_uppercase = models.PositiveIntegerField(default=0) amount_lowercase = models.PositiveIntegerField(default=0) amount_symbols = models.PositiveIntegerField(default=0) length_min = models.PositiveIntegerField(default=0) symbol_charset = models.TextField(default=r"!\"#$%&'()*+,-./:;<=>?@[\]^_`{\|}~ ") error_message = models.TextField() @property def serializer(self) -> BaseSerializer: from authentik.policies.password.api import PasswordPolicySerializer return PasswordPolicySerializer @property def component(self) -> str: return "ak-policy-password-form" # pylint: disable=too-many-return-statements def passes(self, request: PolicyRequest) -> PolicyResult: if ( self.password_field not in request.context and self.password_field not in request.context.get(PLAN_CONTEXT_PROMPT, {}) ): LOGGER.warning( "Password field not set in Policy Request", field=self.password_field, fields=request.context.keys(), prompt_fields=request.context.get(PLAN_CONTEXT_PROMPT, {}).keys(), ) return PolicyResult(False, _("Password not set in context")) if self.password_field in request.context: password = request.context[self.password_field] else: password = request.context[PLAN_CONTEXT_PROMPT][self.password_field] if len(password) < self.length_min: LOGGER.debug("password failed", reason="length") return PolicyResult(False, self.error_message) if self.amount_digits > 0 and len(RE_DIGITS.findall(password)) < self.amount_digits: LOGGER.debug("password failed", reason="amount_digits") return PolicyResult(False, self.error_message) if self.amount_lowercase > 0 and len(RE_LOWER.findall(password)) < self.amount_lowercase: LOGGER.debug("password failed", reason="amount_lowercase") return PolicyResult(False, self.error_message) if self.amount_uppercase > 0 and len(RE_UPPER.findall(password)) < self.amount_lowercase: LOGGER.debug("password failed", reason="amount_uppercase") return PolicyResult(False, self.error_message) if self.amount_symbols > 0: count = 0 for symbol in self.symbol_charset: count += password.count(symbol) if count < self.amount_symbols: LOGGER.debug("password failed", reason="amount_symbols") return 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Database/splParser/databaseconnection.py	snapmeds/snapmeds	1	134492	# -- coding: utf-8 -- import urllib2, urllib import drug def write_to_database(drug): mydata = get_array_of_tuples_from_drug(drug) mydata = mydata + [("username","YOURUSERNAMEHERE"),("password","<PASSWORD>")] mydata=urllib.urlencode(mydata) print mydata path='YOURDATABASEURL' req=urllib2.Request(path, mydata) req.add_header("Content-type", "application/x-www-form-urlencoded") page=urllib2.urlopen(req).read() return convert(page) def convert(input): if isinstance(input, dict): return {convert(key): convert(value) for key, value in input.iteritems()} elif isinstance(input, list): return [convert(element) for element in input] elif isinstance(input, unicode): return input.encode('utf-8') else: return input def get_array_of_tuples_from_drug(drug): ret = [] ret.append( ('setid',drug.setid)) ret.append( ('ndc',drug.ndc)) ret.append( ('warnings',drug.warnings)) ret.append( ('genericnames',drug.genericnames)) ret.append( ('uses',drug.uses)) ret.append( ('adversereactions',drug.adversereactions)) ret.append( ('boxwarnings',drug.boxwarnings)) ret.append( ('precautions',drug.precautions)) ret.append( ('conflictingconditions',drug.conflictingconditions)) ret.append( ('name', drug.name)) ret.append( ('medicationguide', drug.medicationguide)) return ret	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 30004, 13, 30004, 13, 5215, 3142, 1982, 29906, 29892, 3142, 1982, 30004, 13, 5215, 15721, 30004, 13, 1753, 2436, 29918, 517, 29918, 9803, 29898, 29881, 11124, 1125, 30004, 13, 1678, 590, 1272, 353, 679, 29918, 2378, 29918, 974, 29918, 9161, 2701, 29918, 3166, 29918, 29881, 11124, 29898, 29881, 11124, 8443, 13, 1678, 590, 1272, 353, 590, 1272, 718, 518, 703, 6786, 3284, 29979, 22970, 11889, 5813, 5006, 4968, 703, 5630, 3284, 29966, 25711, 17013, 29958, 13531, 30004, 13, 1678, 590, 1272, 29922, 2271, 1982, 29889, 2271, 12508, 29898, 1357, 1272, 8443, 13, 1678, 1596, 590, 1272, 30004, 13, 1678, 2224, 2433, 29979, 22970, 25832, 27982, 4219, 29915, 30004, 13, 1678, 12428, 29922, 2271, 1982, 29906, 29889, 3089, 29898, 2084, 29892, 590, 1272, 8443, 13, 1678, 12428, 29889, 1202, 29918, 6672, 703, 3916, 29899, 1853, 613, 376, 6214, 29914, 29916, 29899, 1636, 29899, 689, 29899, 2271, 26716, 1159, 30004, 13, 1678, 1813, 29922, 2271, 1982, 29906, 29889, 332, 417, 2238, 29898, 7971, 467, 949, 26471, 13, 1678, 736, 3588, 29898, 3488, 8443, 13, 30004, 13, 1753, 3588, 29898, 2080, 1125, 30004, 13, 1678, 565, 338, 8758, 29898, 2080, 29892, 9657, 1125, 30004, 13, 4706, 736, 426, 13441, 29898, 1989, 1125, 3588, 29898, 1767, 29897, 363, 1820, 29892, 995, 297, 1881, 29889, 1524, 7076, 580, 8117, 13, 1678, 25342, 338, 8758, 29898, 2080, 29892, 1051, 1125, 30004, 13, 4706, 736, 518, 13441, 29898, 5029, 29897, 363, 1543, 297, 1881, 29962, 30004, 13, 1678, 25342, 338, 8758, 29898, 2080, 29892, 29104, 1125, 30004, 13, 4706, 736, 1881, 29889, 12508, 877, 9420, 29899, 29947, 1495, 30004, 13, 1678, 1683, 29901, 30004, 13, 4706, 736, 1881, 30004, 13, 4706, 6756, 13, 1753, 679, 29918, 2378, 29918, 974, 29918, 9161, 2701, 29918, 3166, 29918, 29881, 11124, 29898, 29881, 11124, 1125, 30004, 13, 1678, 3240, 353, 5159, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 842, 333, 742, 29881, 11124, 29889, 842, 333, 876, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 299, 29883, 742, 29881, 11124, 29889, 299, 29883, 876, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 25442, 886, 742, 29881, 11124, 29889, 25442, 886, 876, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 19206, 7039, 742, 29881, 11124, 29889, 19206, 7039, 876, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 6394, 742, 29881, 11124, 29889, 6394, 876, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 328, 874, 406, 7387, 742, 29881, 11124, 29889, 328, 874, 406, 7387, 876, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 1884, 25442, 886, 742, 29881, 11124, 29889, 1884, 25442, 886, 876, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 1457, 1113, 17925, 742, 29881, 11124, 29889, 1457, 1113, 17925, 876, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 5527, 506, 1259, 1116, 2187, 742, 29881, 11124, 29889, 5527, 506, 1259, 1116, 2187, 876, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 978, 742, 15721, 29889, 978, 876, 30004, 13, 1678, 3240, 29889, 4397, 29898, 6702, 2168, 293, 362, 13075, 742, 15721, 29889, 2168, 293, 362, 13075, 876, 30004, 13, 1678, 736, 3240, 30004, 13, 30004, 13, 268, 2 ]
dpkt/vrrp.py	lkash/test	0	164732	<gh_stars>0 # $Id: vrrp.py 88 2013-03-05 19:43:17Z <EMAIL> $ # -- coding: utf-8 -- """Virtual Router Redundancy Protocol.""" import dpkt from decorators import deprecated class VRRP(dpkt.Packet): __hdr__ = ( ('_v_type', 'B', 0x21), ('vrid', 'B', 0), ('priority', 'B', 0), ('count', 'B', 0), ('atype', 'B', 0), ('advtime', 'B', 0), ('sum', 'H', 0), ) addrs = () auth = '' @property def v(self): # high 4 bits of _v_type return self._v_type >> 4 @v.setter def v(self, v): self._v_type = (self._v_type & 0x0f) \| (v << 4) @property def type(self): # low 4 bits of _v_type return self._v_type & 0x0f @type.setter def type(self, v): self._v_type = (self._v_type & 0xf0) \| (v & 0x0f) # Deprecated methods, will be removed in the future # ================================================= @deprecated('v') def _get_v(self): return self.v @deprecated('v') def _set_v(self, v): self.v = v @deprecated('type') def _get_type(self): return self.type @deprecated('type') def _set_type(self, v): self.type = v # ================================================= def unpack(self, buf): dpkt.Packet.unpack(self, buf) l = [] off = 0 for off in range(0, 4 * self.count, 4): l.append(self.data[off:off + 4]) self.addrs = l self.auth = self.data[off + 4:] self.data = '' def __len__(self): return self.__hdr_len__ + (4 * self.count) + len(self.auth) def __str__(self): data = ''.join(self.addrs) + self.auth if not self.sum: self.sum = dpkt.in_cksum(self.pack_hdr() + data) return self.pack_hdr() + data def test_vrrp(): # no addresses s = '\x00\x00\x00\x00\x00\x00\xff\xff' v = VRRP(s) assert v.sum == 0xffff assert str(v) == s # have address s = '\x21\x01\x64\x01\x00\x01\xba\x52\xc0\xa8\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00' v = VRRP(s) assert v.count == 1 assert v.addrs == ['\xc0\xa8\x00\x01'] # 192.168.0.1 assert str(v) == s # test checksum generation v.sum = 0 assert str(v) == s # test length assert len(v) == len(s) # test getters assert v.v == 2 assert v.type == 1 # test setters v.v = 3 v.type = 2 assert str(v)[0] == '\x32' if __name__ == '__main__': test_vrrp() print 'Tests Successful...'	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projeto_sensorino.py	rafaelbeloduarte/leitor_arduino	0	81251	# -- coding: utf-8 -- import tkinter import serial import serial.tools.list_ports import threading from tkinter import filedialog from tkinter import * import sys import os from tkinter import messagebox from tkinter.scrolledtext import ScrolledText import matplotlib.pyplot as plt import matplotlib.animation as animation # funções------------------------------------------------------------------------------------- def sel(): # LISTA AS PORTAS DISPONÍVEIS comlist = serial.tools.list_ports.comports() connected = [] for element in comlist: connected.append(element.device) def graficoinst(): plt.close() fig = plt.figure() grafico = open('grafico.dat', 'r').read() vetores = grafico.split('\n') vetores = filter(None, vetores) vetores = list(vetores) for i in range(len(vetores)): aux = vetores[i].split(",") for j in range(len(aux)): aux[j] = float(aux[j]) vetores[i] = aux matriz_graf = vetores plt.clf() eixo = fig.add_subplot(1, 1, 1) eixo.plot(matriz_graf) # eixo.legend() plt.show() def grafico(): plt.close() fig = plt.figure() def animar(i): grafico = open('grafico.dat', 'r').read() vetores = grafico.split('\n') vetores = filter(None, vetores) vetores = list(vetores) for i in range(len(vetores)): aux = vetores[i].split(",") for j in range(len(aux)): aux[j] = float(aux[j]) vetores[i] = aux matriz_graf = vetores plt.clf() eixo = fig.add_subplot(1, 1, 1) eixo.plot(matriz_graf) # eixo.legend() ani = animation.FuncAnimation(fig, animar, interval=1000) plt.show() def selbaud(): text.insert(END, "\nBaudrate: " + str(varbaud.get()) + "\n") text.see(END) def handle_leitura(): try: ser = serial.Serial(var.get(), baudrate=varbaud.get(), timeout=1, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS) ser.close() except Exception as e: messagebox.showinfo("Eita!", "Erro: " + str(e) + "\nProvável causa: a porta já está sendo utilizada.", icon='error') return None if not var.get() or not varbaud.get(): messagebox.showinfo("Aviso!", "Selecione uma porta/baudrate.") return None if not n_texto.get(): messagebox.showinfo("Aviso!", "Por favor, informe o número de entradas.") return None try: salvararquivo = filedialog.asksaveasfilename(defaultextension=".txt", initialfile="dados") arquivousuario = open(salvararquivo, 'w') arquivousuario.close() arquivo_sinal = open(salvararquivo.replace(".txt", "") + "_sinal.txt", "w") arquivo_sinal.close() except Exception as e: messagebox.showinfo(e) return None ser = serial.Serial(var.get(), baudrate=varbaud.get(), timeout=1, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS) instr_arquivo = Label(top, text="Para finalizar a leitura apenas feche o programa, seus dados são salvos automaticamente.") instr_arquivo.grid(row = 1, column=1, columnspan=5) instr_arquivo.configure(background='blue', foreground='white', borderwidth=3, relief='groove') def iniciar(): # Esta função lê o inpu # t da porta selecionada i_limpa_texto = 0 arquivousuario = open(salvararquivo, 'a') arquivo_sinal = open(salvararquivo.replace(".txt", "") + "_sinal.txt", 'a') rol = IntVar() rol_parar = Radiobutton(top, text="Parar rolagem", variable=rol, value=0) rol_parar.grid(row=20, column=2, columnspan = 1) rol_parar.configure(background='#F2F2F2', indicatoron=0, width=15) rol_iniciar = Radiobutton(top, text="Auto rolagem", variable=rol, value=1) rol_iniciar.grid(row=20, column=3, columnspan=1) rol_iniciar.configure(background='#F2F2F2', indicatoron=0, width=15) rol_iniciar.select() graf = open("grafico.dat", 'w') graf.close() if os.path.isfile(abre_poli.get()) == False and os.path.isfile("polinomios.txt") == False: messagebox.showinfo("Atenção!", "Arquivo não encontrado, o sinal será apresentado nu.") poli = open("polinomios.txt", 'w') n_entradas = int(n_texto.get()) for i in range(n_entradas): poli.write("1 0\n") if abre_poli.get(): if os.stat(abre_poli.get()).st_size == 0: messagebox.showinfo("Atenção!", "Arquivo vazio, será preenchido com y=x.") poli = open(abre_poli.get(), 'w') n_entradas = int(n_texto.get()) for i in range(n_entradas): poli.write("1 0\n") poli.close() poli = open(abre_poli.get()).readlines() i = 0 linhas = i + 1 if linhas < int(n_texto.get()): messagebox.showinfo("Atenção!", "Número de equações menor que o número de entradas. Os sinais das entradas restantes serão exibidos inalterados.") escrever_polinomios = open(abre_poli.get(), 'a') restantes = int(n_texto.get()) - len(poli) for i in range(restantes): escrever_polinomios.write("1 0\n") escrever_polinomios.close() else: poli = open("polinomios.txt").readlines() i = 0 with open("polinomios.txt") as arquivo: for i, arquivo in enumerate(arquivo): pass linhas = i + 1 if linhas < int(n_texto.get()): messagebox.showinfo("Atenção!", "Número de equações menor que o número de entradas. Os sinais das entradas restantes serão exibidos inalterados.") escrever_polinomios = open("polinomios.txt", 'a') restantes = int(n_texto.get()) - len(poli) for i in range(restantes): escrever_polinomios.write("1 0\n") escrever_polinomios.close() for i in range(len(poli)): poli[i] = poli[i].replace("\n", "").split(" ") for i in range(len(poli)): for j in range(len(poli[i])): poli[i][j] = float(poli[i][j]) text.insert(END, "\n" + str(poli) + "\n\n") text.see(END) try: # LOOP DE LEITURA while True: serial_bytes = ser.readline() serial_texto = serial_bytes.decode('utf-8') a = serial_texto.split(' ') graf = open("grafico.dat", 'a') dados = [] lista_dados = [] if a: dados = '[%s]' % ', '.join(map(str, a)) # map serve para aplicarmos uma função a cada elemento de uma lista, # retornando uma nova lista contendo os elementos resultantes da aplicação da função. # The %s token allows to insert (and potentially format) a string. # Notice that the %s token is replaced by whatever I pass to the string after the % symbol. dados = dados.replace('\n', '').replace('\r', '').replace('[', '').replace(']', '').replace(" ", "").replace("'", "") lista_dados = dados.split(",") lista_dados = filter(None, lista_dados) lista_dados = list(lista_dados) for i in range(len(lista_dados)): lista_dados[i] = float(lista_dados[i]) if lista_dados: for i in range(len(poli)): lista_dados[i] = poli[i][0]lista_dados[i] + poli[i][1] arquivousuario.write(str(lista_dados).replace('[', '').replace(']', '') + '\n') arquivousuario.flush() arquivo_sinal.write(dados + '\n') arquivo_sinal.flush() graf.write(str(lista_dados).replace('[', '').replace(']', '') + '\n') graf.flush() text.insert(END, str(lista_dados).replace('[', '').replace(']', '') + '\n') if rol.get() == 1: text.see(END) i_limpa_texto += 1 if i_limpa_texto > 1000: text.delete(1.0, END) graf = open('grafico.dat', 'r+') graf.truncate(0) i_limpa_texto = 0 if limpar_graf.get() == 1: graf = open('grafico.dat', 'w') graf.truncate(0) limpar_graf.set(0) # time.sleep(1) except Exception as e: messagebox.showinfo("Erro!", "Erro: " + str(e), icon='error') pass ser.close() graf.close() arquivousuario.close() arquivo_sinal.close() # poli.close() t = threading.Thread(target=iniciar) t.daemon = True t.start() def atualizarporta(): portas_sub_menu.delete(0, 'end') var.set("") ports = list(serial.tools.list_ports.comports()) comlist = serial.tools.list_ports.comports() connected = [] for element in comlist: connected.append(element.device) for i in range(len(ports)): ports[i] = str(ports[i]) for k in range(len(connected)): portas_sub_menu.add_radiobutton(label = connected[k], variable=var, value=str(connected[k]), command=sel) if "Arduino" or "Serial USB" in ports[k]: var.set(str(connected[k])) portas_sub_menu.activate(k) text.insert(END, "\nPorta selecionada: " + str(var.get()) + "\n") text.see(END) def reiniciar(): if messagebox.askokcancel("Reiniciando...", "Tem certeza?"): python = sys.executable os.execl(python, python, sys.argv) else: return None def fechando(): if messagebox.askokcancel("Fechando...", "Tem certeza?"): plt.close('all') top.destroy() # fim das funções----------------------------------------------------------------------------- # Código para os widgets vai aqui.------------------------------------------------------------ # define a janela principal---------------------------------- top = tkinter.Tk() top.wm_title("Sensorino Salva!") top.minsize(500, 500) top.geometry("950x600") top.configure(background='#000000') # ----------------------------------------------------------- comlist = serial.tools.list_ports.comports() connected = [] for element in comlist: connected.append(element.device) if not connected: messagebox.showinfo("Aviso!", "Nenhuma porta disponível, verifique se seu dispositivo está conectado.") label_n = Label(top, text = "Número de entradas:") label_n.grid(row = 2, column = 1, columnspan = 2, padx = 10, pady = 10) label_n.configure(background='#000000', foreground='white', width = 20) n_texto = StringVar() n = Entry(top, textvariable = n_texto) n.grid(row = 2, column=3, padx = 10, pady = 10) n.configure(width = 10) label_eqs = Label(top, text = "Equação da reta: y = ax+b, padrão: y=x") label_eqs.grid(row = 2, column = 6, columnspan = 3) label_eqs.configure(background='#000000', foreground='white') label_a = Label(top, text = "a:") label_a.grid(row = 3, column = 6) label_a.configure(background='#000000', foreground='white') arquivo_coef = open("polinomios.txt", 'w') arquivo_coef.close() abre_poli = StringVar() def abre_polinomio(): try: abre_poli.set(filedialog.askopenfilename(initialdir = "",title = "Selecione seu arquivo",filetypes = (("Arquivo de texto",".txt"),("Todos os arquivos",".")))) if abre_poli.get(): text.insert(END, "\nCarregado com sucesso.\n") text.see(END) except Exception as e: messagebox.showinfo(e) return None button_abre_poli = Button(top, text = "Carregar equações", command = abre_polinomio) button_abre_poli.grid(row=6, column=7) button_abre_poli.configure(activebackground='#000000', activeforeground='#FFFFFF') def pega_coef(): if not n_texto.get(): messagebox.showinfo("Aviso!", "Por favor, informe o número de entradas.") return None coef_a = a.get() coef_b = b.get() text.insert(END, "y = " + coef_a + "*x" + " + " + coef_b + '\n') text.see(END) arquivo_coef = open("polinomios.txt", 'a') arquivo_coef.write(coef_a + " ") arquivo_coef.write(coef_b + "\n") arquivo_coef.close() a.delete(0, END) b.delete(0, END) arquivo_coef.close() def salva_polinomio(): try: salvar_poli = filedialog.asksaveasfilename(defaultextension=".txt", initialfile="meus_polinomios") arquivo_poli = open(salvar_poli, 'w') texto = open("polinomios.txt").read() texto = texto.replace('\r', '').replace('[', '').replace(']', '').replace(",", "").replace("'", "") arquivo_poli.write(texto) arquivo_poli.close() except Exception as e: messagebox.showinfo(e) return None limpar_graf = IntVar() def limpar_graf_estado(): limpar_graf.set(1) button_salva_poli = Button(top, text = "Salvar equações", command = salva_polinomio) button_salva_poli.grid(row=5, column=7) button_salva_poli.configure(activebackground='#000000', activeforeground='#FFFFFF') a_texto = StringVar() a = Entry(top, textvariable = a_texto) a.grid(row=3, column=7) label_b = Label(top, text = "b:") label_b.grid(row = 4, column = 6) label_b.configure(background='#000000', foreground='white') b_texto = StringVar() b = Entry(top, textvariable = b_texto) b.grid(row=4, column=7) button_entrar = Button(top, text = "Inserir", command = pega_coef) button_entrar.grid(row=3, column=8, rowspan = 2, padx = 15) button_entrar.configure(activebackground='#000000', activeforeground='#FFFFFF') text = ScrolledText(top, width=50, height=20) text.grid(row=3, column=1, columnspan=5, rowspan=10, padx = 10, pady = 10) text.insert(END, " INSTRUÇÕES\n") text.insert(END, "\n") text.insert(END, ">O loop de seu controlador deve\nretornar os dados da seguinte forma:\n") text.insert(END, "a b c ...\n") text.insert(END, "Ex: 1 2 3 4 5\n") text.insert(END, "\n>Declare o número de entradas\nantes de iniciar a leitura.\n") text.insert(END, "\n") text.see(END) # criar menu menubar = Menu(top) menubar.add_command(label="Iniciar leitura", command=handle_leitura) graf_menu = Menu(menubar, tearoff=0) menubar.add_cascade(label="Gráficos", menu=graf_menu) graf_menu.add_command(label="Exibir gráfico em tempo real", command=grafico) graf_menu.add_command(label="Exibir gráfico", command=graficoinst) graf_menu.add_separator() graf_menu.add_command(label="Limpar gráfico", command=limpar_graf_estado) controlador_menu = Menu(menubar, tearoff=0) menubar.add_cascade(label="Controlador", menu=controlador_menu) portas_sub_menu = Menu(controlador_menu, tearoff = 0) controlador_menu.add_cascade(label="Portas", menu=portas_sub_menu) var = StringVar() atualizarporta() baud_sub_menu = Menu(controlador_menu, tearoff = 0) controlador_menu.add_cascade(label = "Baudrate", menu = baud_sub_menu) # pega o baud rate, varbaud é o baudrate=============================================== #4800, 9600, 38400, 57600, 115200, 230400 varbaud = IntVar() baud_sub_menu.add_radiobutton(label = "4800", variable = varbaud, value = 4800, command = selbaud) baud_sub_menu.add_radiobutton(label = "9600", variable = varbaud, value = 9600, command = selbaud) baud_sub_menu.add_radiobutton(label = "38400", variable = varbaud, value = 38400, command = selbaud) baud_sub_menu.add_radiobutton(label = "57600", variable = varbaud, value = 57600, command = selbaud) baud_sub_menu.add_radiobutton(label = "115200", variable = varbaud, value = 115200, command = selbaud) baud_sub_menu.add_radiobutton(label = "230400", variable = varbaud, value = 230400, command = selbaud) baud_sub_menu.invoke(1) # ===================================================================================== controlador_menu.add_separator() controlador_menu.add_command(label = "Atualizar portas", command = atualizarporta) sair_menu = Menu(menubar, tearoff=0) 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Chapter 8 - Functions & Recursion/04_factorial.py	alex-dsouza777/Python-Basics	0	86100	<reponame>alex-dsouza777/Python-Basics #n! = 1 * 2 * 3 * 4 ..... * n #n! = [1 * 2 * 3 * 4 ..... n-1]* n #n! = n * (n-1)! # n = 7 # product = 1 # for i in range(n): # product = product * (i+1) # print(product) def factorial_iter(n): product = 1 for i in range(n): product = product * (i+1) return product def factorial_recursive(n): if n == 1 or n ==0: return 1 return n * factorial_recursive(n-1) # f = factorial_iter(5) f = factorial_recursive(3) print(f)	[ 1, 529, 276, 1112, 420, 29958, 744, 29916, 29899, 6289, 283, 1362, 29955, 29955, 29955, 29914, 11980, 29899, 9496, 1199, 13, 29937, 29876, 29991, 353, 29871, 29896, 334, 29871, 29906, 334, 29871, 29941, 334, 29871, 29946, 6317, 856, 334, 302, 13, 29937, 29876, 29991, 353, 29871, 518, 29896, 334, 29871, 29906, 334, 29871, 29941, 334, 29871, 29946, 6317, 856, 302, 29899, 29896, 14178, 302, 13, 29937, 29876, 29991, 353, 302, 334, 313, 29876, 29899, 29896, 20198, 13, 13, 29937, 302, 353, 29871, 29955, 13, 29937, 3234, 353, 29871, 29896, 13, 29937, 363, 474, 297, 3464, 29898, 29876, 1125, 13, 29937, 268, 3234, 353, 3234, 334, 313, 29875, 29974, 29896, 29897, 13, 29937, 1596, 29898, 4704, 29897, 13, 13, 1753, 7329, 616, 29918, 1524, 29898, 29876, 1125, 13, 1678, 3234, 353, 29871, 29896, 13, 1678, 363, 474, 297, 3464, 29898, 29876, 1125, 13, 4706, 3234, 353, 3234, 334, 313, 29875, 29974, 29896, 29897, 13, 1678, 736, 3234, 13, 13, 1753, 7329, 616, 29918, 3757, 25397, 29898, 29876, 1125, 13, 1678, 565, 302, 1275, 29871, 29896, 470, 302, 1275, 29900, 29901, 13, 4706, 736, 29871, 29896, 13, 1678, 736, 302, 334, 7329, 616, 29918, 3757, 25397, 29898, 29876, 29899, 29896, 29897, 13, 29937, 285, 353, 7329, 616, 29918, 1524, 29898, 29945, 29897, 13, 29888, 353, 7329, 616, 29918, 3757, 25397, 29898, 29941, 29897, 13, 2158, 29898, 29888, 29897, 13, 2 ]
pythonfiles/testing.py	amrut-prabhu/loan-default-prediction	2	23179	import numpy as np import pandas as pd from sklearn.externals import joblib #from sklearn.ensemble import RandomForestRegressor #from sklearn.multioutput import MultiOutputRegressor #from sklearn.multioutput import MultiOutputRegressor from sklearn.model_selection import train_test_split df = pd.read_csv('https://drive.google.com/uc?export=download&id=1XoV8SfvHmzaxRuDRe81OWSQu10dYTbO5',sep=',') df_X = df.iloc[:, 2:13].copy() df_X = pd.get_dummies(df_X) df_y1 = df.iloc[:, 13:16].copy() df_y1 = pd.get_dummies(df_y1) df_y2 = df.iloc[:, 16:20].copy() df_y2 = pd.get_dummies(df_y2) #X_train, df_X, y_train, df_y1 = train_test_split(df_X, df_y1, test_size=0.2, random_state=0) def accuracy(model, X_test, y_test): predictions = model.predict(X_test) print(model.score(X_test, y_test)) errors = np.abs(predictions - y_test) mape = 100 * (errors / y_test) for col in mape: accuracy = 100 - np.mean(mape[col]) print('Accuracy:', round(accuracy, 2), '%.') def test(model, df_X, df_y1, num): accuracy(model, df_X, df_y1) predictions = pd.DataFrame(model.predict(df_X)) errors = np.abs(predictions - df_y1) print(type(predictions)) print(type(errors)) for i in range(num): #results = df_X.iloc[:, 0:10].values #results = np.append(results, df_y1.ix[:, i]) #results = np.append(results, predictions[:, i]) #results = np.append(results, errors.ix[:, i]) #result_df = pd.DataFrame(results) df_X.join(df_y1.ix[:, i]).join(predictions.ix[:, i]).to_csv("ModelPredictions" + str(num) + str(i) + ".csv") #model = RandomForestRegressor(n_estimators = 1900, max_depth = 70, random_state = 50, learning_rate = 0.1, min_samples_split = 0.1, max_features = 'sqrt', loss = 'lad', warm_start = True, min_samples_leaf = 0.0005) #model1 = MultipleOutputRegressor(model) #model2 = MultipleOutputRegressor(model, n_jobs = -1) #model1.fit(X_train, y_train) model = joblib.load("RegressorChainGradientBoostingRegressorEarningsNew.pkl") test(model, df_X, df_y1, 3) model = joblib.load("RegressorChainGradientBoostingRegressorRepaymentNew.pkl") test(model, df_X, df_y2, 4) #model2.fit(X_train, y_train) #joblib.dump(model2, "MultiplepleOutputRandomForestRegressorEarnings.pkl") #print("Model 2: "); #accuracy(model2, df_X, df_y1) #X_train, df_X, y_train, df_y1 = train_test_split(df_X, df_y2, test_size=0.2, random_state=0) #model1.fit(X_train, y_train) #joblib.dump(model1, "MultipleOutputRegressorRandomForestRegressorRepayment.pkl") #print("Model 3: "); #accuracy(model1, df_X, df_y1) #model2.fit(X_train, y_train) #joblib.dump(model2, "MultiplepleOutputRandomForestRegressorRepayment.pkl") #print("Model 4: "); #accuracy(model2, df_X, df_y1)	[ 1, 1053, 12655, 408, 7442, 13, 5215, 11701, 408, 10518, 13, 3166, 2071, 19668, 29889, 735, 725, 1338, 1053, 4982, 1982, 13, 29937, 3166, 2071, 19668, 29889, 24031, 1053, 16968, 2831, 342, 4597, 1253, 272, 13, 29937, 3166, 2071, 19668, 29889, 9910, 4905, 1053, 14974, 6466, 4597, 1253, 272, 29871, 13, 29937, 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client/cryptog.py	yoelbassin/chat	8	77531	<gh_stars>1-10 import const import config import rsa from cryptography.fernet import Fernet def key_ex(): config.client.send((const.public_key + str(config.public_key.n) + ' ' + str(config.public_key.e)).encode()) def send_symmetric_key(): self_id = int(''.join([str(ord(c)) for c in config.uname])) dst_id = int(''.join([str(ord(c)) for c in config.active_chat])) if dst_id > self_id: generate_key() encrypted_message = rsa.encrypt(config.chat_key, config.dst_pub) config.client.send(const.symmetric_key.encode() + encrypted_message) config.chat_key = Fernet(config.chat_key) def get_key(key): key = key[5:].split(' ') config.dst_pub = rsa.PublicKey(int(key[0]), int(key[1])) def get_symmetric_key(key): key = key.encode() self_id = int(''.join([str(ord(c)) for c in config.uname])) dst_id = int(''.join([str(ord(c)) for c in config.active_chat])) if dst_id < self_id: config.chat_key = Fernet(key) def get_message_rsa(message): code = message[:const.CODE_LEN].decode() message = rsa.decrypt(message[const.CODE_LEN:], config.private_key) message = code + message.decode() return message def get_enc_message(message): code = message[:const.CODE_LEN].decode() if code == const.msg_code: message = decrypt_message(message[const.CODE_LEN:]) else: message = rsa.decrypt(message[const.CODE_LEN:], config.private_key) return code + message.decode() def generate_key(): """ Generates a key and save it into a file """ config.chat_key = Fernet.generate_key() def encrypt_message(message): """ Encrypts a message """ encoded_message = message.encode() return config.chat_key.encrypt(encoded_message) def decrypt_message(encrypted_message): """ Decrypts an encrypted message """ return config.chat_key.decrypt(encrypted_message)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 5215, 1040, 13, 5215, 2295, 13, 5215, 364, 4977, 13, 3166, 24941, 5275, 29889, 571, 1212, 1053, 7139, 300, 13, 13, 13, 1753, 1820, 29918, 735, 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qgenda/pipeline/pre.py	jjorissen52/python-qgenda	2	1601888	import time from qgenda import helpers def gzip_headers(method_self, caller=None, params=None): if caller not in method_self.gzip_safe: use_gzip = params.pop('gzip', False) params['headers'] = method_self.headers if use_gzip else { key: value for key, value in method_self.headers.items() if value != 'gzip' } return method_self, caller, params def keep_authenticated(method_self, caller=None, params=None): """ """ request_start = time.time() auth_details = method_self.auth_details expiration_time = auth_details.get("expiration_time", 0) if auth_details else 0 if method_self.leader and not auth_details: method_self.authenticate() auth_details = method_self.auth_details else: retries = 10 while retries: if expiration_time - request_start <= 0: time.sleep(1) auth_details = method_self.auth_details if auth_details: break retries -= 1 if "access_token" in auth_details: method_self.headers.update({"Authorization": f'bearer {auth_details["access_token"]}'}) params['headers'].update({"Authorization": f'bearer {auth_details["access_token"]}'}) return method_self, caller, params def prepare_odata(logger): odata_filters = ['$filter', '$select', '$orderby'] def real_decorator(method_self, caller, params): odata_kwargs = params.get('odata_kwargs', {}) extra = [] if odata_kwargs: extra = [key for key in odata_kwargs.keys() if key not in odata_filters] if extra: for e in extra: odata_kwargs.pop(e) logger.warning(f'Extra OData filter(s) removed from kwargs. 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ngallery/core/services/solana.py	manti-by/ngallery	0	127454	from django.conf import settings from solana.rpc.api import Client solana_client = Client(settings.SOLANA_NETWORK_URL)	[ 1, 515, 9557, 29889, 5527, 1053, 6055, 13, 3166, 899, 1648, 29889, 29878, 6739, 29889, 2754, 1053, 12477, 13, 13, 2929, 1648, 29918, 4645, 353, 12477, 29898, 11027, 29889, 29903, 5607, 2190, 29909, 29918, 6006, 11686, 29968, 29918, 4219, 29897, 13, 2 ]
model.py	FutureCoderme/week5_heroku	0	125841	<filename>model.py import pandas as pan import pickle from sklearn.linear_model import LinearRegression data = pan.read_excel('BP_simple_data.xlsx') x1 = data.iloc[:,1:] x2 = data.iloc[:,0:1] reg = LinearRegression() reg.fit(x1,x2) prediction = reg.predict(x1) pickle.dump(reg, open('model.pkl','wb'))	[ 1, 529, 9507, 29958, 4299, 29889, 2272, 13, 5215, 11701, 408, 7243, 13, 5215, 5839, 280, 13, 3166, 2071, 19668, 29889, 10660, 29918, 4299, 1053, 22985, 4597, 23881, 13, 13, 1272, 353, 7243, 29889, 949, 29918, 24633, 877, 29933, 29925, 29918, 12857, 29918, 1272, 29889, 20267, 29916, 1495, 13, 13, 29916, 29896, 353, 848, 29889, 309, 542, 7503, 29892, 29896, 17531, 13, 29916, 29906, 353, 848, 29889, 309, 542, 7503, 29892, 29900, 29901, 29896, 29962, 13, 13, 1727, 353, 22985, 4597, 23881, 580, 13, 1727, 29889, 9202, 29898, 29916, 29896, 29892, 29916, 29906, 29897, 13, 13, 11965, 2463, 353, 1072, 29889, 27711, 29898, 29916, 29896, 29897, 13, 23945, 280, 29889, 15070, 29898, 1727, 29892, 1722, 877, 4299, 29889, 29886, 6321, 3788, 29893, 29890, 8785, 13, 2 ]
fetch_data.py	bitfag/bt-macd-binance	0	3815	<gh_stars>0 #!/usr/bin/env python from btmacd.binance_fetcher import BinanceFetcher def main(): fetcher = BinanceFetcher("BTCUSDT", filename="binance_ohlc.csv", start_date="01.01.2018") fetcher.fetch() if __name__ == "__main__": main()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 13, 3166, 289, 29873, 8628, 29881, 29889, 2109, 749, 29918, 9155, 261, 1053, 27662, 749, 20927, 261, 13, 13, 13, 1753, 1667, 7295, 13, 1678, 6699, 261, 353, 27662, 749, 20927, 261, 703, 29933, 9472, 3308, 12972, 613, 10422, 543, 2109, 749, 29918, 10330, 29883, 29889, 7638, 613, 1369, 29918, 1256, 543, 29900, 29896, 29889, 29900, 29896, 29889, 29906, 29900, 29896, 29947, 1159, 13, 1678, 6699, 261, 29889, 9155, 580, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1667, 580, 13, 2 ]
background_task/models_completed.py	AMRivkin/django-background-tasks	0	1617853	<gh_stars>0 # -- coding: utf-8 -- import os from compat.models import GenericForeignKey from django.contrib.contenttypes.models import ContentType from django.db import models from django.utils import timezone from django.utils.six import python_2_unicode_compatible from background_task.models import Task class CompletedTaskQuerySet(models.QuerySet): def created_by(self, creator): """ :return: A CompletedTask queryset filtered by creator """ content_type = ContentType.objects.get_for_model(creator) return self.filter( creator_content_type=content_type, creator_object_id=creator.id, ) def failed(self, within=None): """ :param within: A timedelta object :return: A queryset of CompletedTasks that failed within the given timeframe (e.g. less than 1h ago) """ qs = self.filter( failed_at__isnull=False, ) if within: time_limit = timezone.now() - within qs = qs.filter(failed_at__gt=time_limit) return qs def succeeded(self, within=None): """ :param within: A timedelta object :return: A queryset of CompletedTasks that completed successfully within the given timeframe (e.g. less than 1h ago) """ qs = self.filter( failed_at__isnull=True, ) if within: time_limit = timezone.now() - within qs = qs.filter(run_at__gt=time_limit) return qs @python_2_unicode_compatible class CompletedTask(models.Model): # the "name" of the task/function to be run task_name = models.CharField(max_length=190, db_index=True) # the json encoded parameters to pass to the task task_params = models.TextField() # a sha1 hash of the name and params, to lookup already scheduled tasks task_hash = models.CharField(max_length=40, db_index=True) verbose_name = models.CharField(max_length=255, null=True, blank=True) # what priority the task has priority = models.IntegerField(default=0, db_index=True) # when the task should be run run_at = models.DateTimeField(db_index=True) repeat = models.BigIntegerField(choices=Task.REPEAT_CHOICES, default=Task.NEVER) repeat_until = models.DateTimeField(null=True, blank=True) # the "name" of the queue this is to be run on queue = models.CharField(max_length=190, db_index=True, null=True, blank=True) # how many times the task has been tried attempts = models.IntegerField(default=0, db_index=True) # when the task last failed failed_at = models.DateTimeField(db_index=True, null=True, blank=True) # details of the error that occurred last_error = models.TextField(blank=True) # details of who's trying to run the task at the moment locked_by = models.CharField(max_length=64, db_index=True, null=True, blank=True) locked_at = models.DateTimeField(db_index=True, null=True, blank=True) creator_content_type = models.ForeignKey( ContentType, null=True, blank=True, related_name='completed_background_task', on_delete=models.CASCADE ) creator_object_id = models.PositiveIntegerField(null=True, blank=True) creator = GenericForeignKey('creator_content_type', 'creator_object_id') objects = CompletedTaskQuerySet.as_manager() def locked_by_pid_running(self): """ Check if the locked_by process is still running. 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app.py	CosteaPaul/strokeCoach_falsk	0	120146	from flask import Flask, render_template, request app = Flask(__name__) @app.route('/') def upload(): return render_template('upload.html') @app.route('/uploader', methods = ['GET', 'POST']) def upload_file(): if request.method == 'POST': f = request.files['file'] #f.save(secure_filename(f.filename)) return f.read() if __name__ == '__main__': # Threaded option to enable multiple instances for multiple user access support app.run(threaded=True, port=5000)	[ 1, 515, 29784, 1053, 2379, 1278, 29892, 4050, 29918, 6886, 29892, 2009, 13, 13, 932, 353, 2379, 1278, 22168, 978, 1649, 29897, 13, 13, 29992, 932, 29889, 13134, 11219, 1495, 13, 1753, 6441, 7295, 13, 259, 736, 4050, 29918, 6886, 877, 9009, 29889, 1420, 1495, 13, 13, 29992, 932, 29889, 13134, 11219, 9009, 261, 742, 3519, 353, 6024, 7194, 742, 525, 5438, 11287, 13, 1753, 6441, 29918, 1445, 7295, 13, 259, 565, 2009, 29889, 5696, 1275, 525, 5438, 2396, 13, 418, 285, 353, 2009, 29889, 5325, 1839, 1445, 2033, 13, 418, 396, 29888, 29889, 7620, 29898, 24216, 29918, 9507, 29898, 29888, 29889, 9507, 876, 13, 418, 736, 285, 29889, 949, 580, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 396, 10480, 287, 2984, 304, 9025, 2999, 8871, 363, 2999, 1404, 2130, 2304, 13, 1678, 623, 29889, 3389, 29898, 7097, 287, 29922, 5574, 29892, 2011, 29922, 29945, 29900, 29900, 29900, 29897, 13, 2 ]
utils/state.py	DeWa/PiBooth	1	90550	<filename>utils/state.py class State: state = {} @staticmethod def init(): State.state = { "lowres_frames": None, "highres_frames": None, "photopath": "", "frame": 0, "current_photo": None, "share_code": "", "preview_image_width": 0, "photo_countdown": 0, "reset_time": 0, "upload_url": "", "photo_url": "", "api_key": "" } return State.state @staticmethod def set(value): State.state[value[0]] = value[1] return State.state @staticmethod def set_dict(value): State.state = {State.state, value} return State.state @staticmethod def get(key): try: return State.state.get(key) except KeyError: print("No key found") return None @staticmethod def print(): print(State.state)	[ 1, 529, 9507, 29958, 13239, 29914, 3859, 29889, 2272, 13, 1990, 4306, 29901, 13, 1678, 2106, 353, 6571, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 2069, 7295, 13, 4706, 4306, 29889, 3859, 353, 426, 13, 9651, 376, 677, 690, 29918, 19935, 1115, 6213, 29892, 13, 9651, 376, 9812, 690, 29918, 19935, 1115, 6213, 29892, 13, 9651, 376, 561, 327, 459, 493, 1115, 12633, 13, 9651, 376, 2557, 1115, 29871, 29900, 29892, 13, 9651, 376, 3784, 29918, 21596, 1115, 6213, 29892, 13, 9651, 376, 13653, 29918, 401, 1115, 12633, 13, 9651, 376, 25347, 29918, 3027, 29918, 2103, 1115, 29871, 29900, 29892, 13, 9651, 376, 21596, 29918, 2798, 3204, 1115, 29871, 29900, 29892, 13, 9651, 376, 12071, 29918, 2230, 1115, 29871, 29900, 29892, 13, 9651, 376, 9009, 29918, 2271, 1115, 12633, 13, 9651, 376, 21596, 29918, 2271, 1115, 12633, 13, 9651, 376, 2754, 29918, 1989, 1115, 5124, 13, 4706, 500, 13, 4706, 736, 4306, 29889, 3859, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 731, 29898, 1767, 1125, 13, 4706, 4306, 29889, 3859, 29961, 1767, 29961, 29900, 5262, 353, 995, 29961, 29896, 29962, 13, 4706, 736, 4306, 29889, 3859, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 731, 29918, 8977, 29898, 1767, 1125, 13, 4706, 4306, 29889, 3859, 353, 426, 1068, 2792, 29889, 3859, 29892, 3579, 1767, 29913, 13, 4706, 736, 4306, 29889, 3859, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 679, 29898, 1989, 1125, 13, 4706, 1018, 29901, 13, 9651, 736, 4306, 29889, 3859, 29889, 657, 29898, 1989, 29897, 13, 4706, 5174, 7670, 2392, 29901, 13, 9651, 1596, 703, 3782, 1820, 1476, 1159, 13, 9651, 736, 6213, 13, 13, 1678, 732, 7959, 5696, 13, 1678, 822, 1596, 7295, 13, 4706, 1596, 29898, 2792, 29889, 3859, 29897, 13, 2 ]
andes/models/dc/__init__.py	cuihantao/Andes	16	86876	""" DC models. """ from andes.models.dc.ground import Ground # NOQA from andes.models.dc.node import Node # noqa from andes.models.dc.rlc import R, L, C, RCp, RLCp, RLCs, RCs, RLs # noqa	[ 1, 9995, 13, 12696, 4733, 29889, 13, 15945, 29908, 13, 13, 3166, 322, 267, 29889, 9794, 29889, 13891, 29889, 2057, 1053, 1632, 618, 29871, 396, 11698, 29984, 29909, 13, 3166, 322, 267, 29889, 9794, 29889, 13891, 29889, 3177, 1053, 9071, 29871, 396, 694, 25621, 13, 3166, 322, 267, 29889, 9794, 29889, 13891, 29889, 2096, 29883, 1053, 390, 29892, 365, 29892, 315, 29892, 29138, 29886, 29892, 390, 12182, 29886, 29892, 390, 12182, 29879, 29892, 29138, 29879, 29892, 390, 29931, 29879, 29871, 396, 694, 25621, 13, 2 ]

pinolo/tasks.py

piger/pinolo

2

41511

# -*- coding: utf-8 -*- """ pinolo.tasks ~~~~~~~~~~~~ A Task object is a `threading.Thread` instance that will be executed without blocking the main thread. This is useful to perform potentially blocking actions like fecthing resources via HTTP. :copyright: (c) 2013 <NAME> :license: BSD, see LICENSE for more details. """ import threading class Task(threading.Thread): """A task is an execution unit that will be run in a separate thread that should not block tha main thread (handling irc connections). """ def __init__(self, event, *args, **kwargs): self.event = event super(Task, self).__init__(*args, **kwargs) @property def queue(self): return self.event.client.bot.coda @property def reply(self): return self.event.reply def run(self): raise RuntimeError("Must be implemented!") def put_results(self, *data): """Task output will be sent to the main thread via the configured queue; data should be a string containing the full output, that will later be splitted on newlines.""" self.queue.put(tuple(data))

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setup.py

danihodovic/django-toolshed

3

17579

<gh_stars>1-10 #!/usr/bin/env python import os import re from setuptools import find_packages, setup def get_version(*file_paths): filename = os.path.join(os.path.dirname(__file__), *file_paths) version_file = open(filename).read() version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M) if version_match: return version_match.group(1) raise RuntimeError("Unable to find version string.") version = get_version("django_toolshed", "__init__.py") readme = open("README.md").read() setup( name="django-toolshed", version=version, description="""Your project description goes here""", long_description=readme, author="<NAME>", author_email="<EMAIL>", url="https://github.com/danihodovic/django-toolshed", packages=find_packages(), include_package_data=True, install_requires=[], license="MIT", keywords="django,app", classifiers=[ "Development Status :: 3 - Alpha", "Framework :: Django :: 2.0", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Programming Language :: Python :: 3", ], )

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src/code/djangotest/migrations/0001_initial.py

jielyu/notebook

2

9220

# Generated by Django 2.2.5 on 2019-10-05 23:22 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Password', fields=[ ('id', models.IntegerField(primary_key=True, serialize=False, unique=True)), ('website', models.CharField(max_length=128)), ('username', models.CharField(max_length=128)), ('pwd', models.CharField(max_length=128)), ('time_add', models.DateTimeField(auto_now_add=True, null=True)), ('time_modify', models.DateTimeField(auto_now=True)), ], options={ 'db_table': 'password_tab', }, ), ]

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ROS/fprime_ws/src/genfprime/src/genfprime/generate_modmk.py

genemerewether/fprime

5

2362

<gh_stars>1-10 # # Copyright 2004-2016, by the California Institute of Technology. # ALL RIGHTS RESERVED. United States Government Sponsorship # acknowledged. Any commercial use must be negotiated with the Office # of Technology Transfer at the California Institute of Technology. # # This software may be subject to U.S. export control laws and # regulations. By accepting this document, the user agrees to comply # with all U.S. export laws and regulations. User has the # responsibility to obtain export licenses, or other export authority # as may be required before exporting such information to foreign # countries or providing access to foreign persons. # from __future__ import print_function import os from genmsg import MsgGenerationException #from . name import * ## :param type_name outdir: Full path to output directory ## :returns int: status. 0 if successful def write_modmk(outdir): #, msg_types, srv_types): if not os.path.isdir(outdir): #TODO: warn? return 0 xml_in_dir = set([f for f in os.listdir(outdir) if f.endswith('.xml')]) _write_modmk(outdir, sorted(xml_in_dir)) # TODO(mereweth) if we want to independently specify the generated XML files # generated_xml = [_msg_serializable_xml_name(f) for f in sorted(msg_types)] # generated_xml.extend([_port_xml_name(f) for f in sorted(msg_types)] # write_msg_modmk(outdir, generated_xml) # generated_xml = [_srv_serializable_xml_name(f) for f in sorted(srv_types)] # generated_xml.extend([_port_xml_name(f) for f in sorted(srv_types)] # write_msg_modmk(outdir, generated_xml) return 0 def _write_modmk(outdir, generated_xml): if not os.path.exists(outdir): os.makedirs(outdir) elif not os.path.isdir(outdir): raise MsgGenerationException("file preventing the creating of Fprime directory: %s"%dir) p = os.path.join(outdir, 'mod.mk') with open(p, 'w') as f: f.write('SRC = \\\n') if len(generated_xml) != 0: for xml in generated_xml[:-1]: f.write('%s \\\n'%xml) f.write('%s\n'%generated_xml[-1]) return 0

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CodeWars/8 Kyu/Grasshopper - Debug sayHello.py

anubhab-code/Competitive-Programming

0

64924

<filename>CodeWars/8 Kyu/Grasshopper - Debug sayHello.py def say_hello(name): return "Hello, {0}".format(name)

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Contest/ABC086/d/main.py

mpses/AtCoder

0

35923

#!/usr/bin/env python3 # 2次元累積和 S の [x1, x2) × [y1, y2) 総和 def ac2(s, x1, x2, y1, y2): return s[x2][y2] - s[x1][y2] - s[x2][y1] + s[x1][y1] import numpy as np _, *d = open(0) n, k = map(int, _.split()) B = np.zeros((2*k, 2*k)) for e in d: *z, c = e.split() x, y = map(int, z) B[x % (2*k)][(y + k * (z == "W")) % (2*k)] += 1 B.cumsum(axis = 0) B.cumsum(axis = 1) B = np.tile(B, (2,2)) print(B) # 書きかけ

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workspaces/voyage/server/src/util/smartcomponent.py

raychorn/svn_hp-projects

0

81883

##################################################### # # smartcomponent.py # # Copyright 2007 Hewlett-Packard Development Company, L.P. # # Hewlett-Packard and the Hewlett-Packard logo are trademarks of # Hewlett-Packard Development Company, L.P. in the U.S. and/or other countries. # # Confidential computer software. Valid license from Hewlett-Packard required # for possession, use or copying. Consistent with FAR 12.211 and 12.212, # Commercial Computer Software, Computer Software Documentation, and Technical # Data for Commercial Items are licensed to the U.S. Government under # vendor's standard commercial license. # # Author: # <NAME> # # Description: # Manages Smart Component objects for Breckenridge. # ##################################################### import os import tarfile import tempfile from xml.dom.minidom import parse from threading import Thread import logging log = logging.getLogger(__name__) SC_SHARE_PATH='static/sc_share' class SmartComponent: def __init__(self): self.version = "Unknown" self.name = "Unknown Component" self.filename = "" def __repr__(self): return str(self.__dict__) def json(self): return json.dumps(self.__dict__) class SmartComponentManager: def __init__(self): self.components = [] self.path = os.path.abspath(SC_SHARE_PATH) log.debug("Initializing SmartComponentManager") def get_sc_file_list(self): for root, directories, files in os.walk(self.path): return files def delete_component(self, sc_filename): sc_path = self.path + '\\' + sc_filename try: os.remove(sc_path) for component in self.components: if component.filename == sc_filename: self.components.remove(component) except: pass def discover_components(self): t = Thread(target=self.do_discover_components) t.daemon = True t.start() def do_discover_components(self): files = self.get_sc_file_list() if files: for file in files: if file.lower().endswith('.scexe') : log.debug("Discovered component %s", file) self.add_component(file) def add_component(self, filename): log.debug("Adding component %s", filename) if filename in [c.filename for c in self.components ] : log.error("Component %s already added", filename) return False component = SmartComponent() component.filename = filename digest_filename = self.extract_component_digest(filename) if digest_filename != "": self.parse_digest(digest_filename, component) # Remove the digest file. try: os.remove(self.path + '\\' + digest_filename) except: log.exception("Error removing digest file %s", digest_filename) pass self.components.append(component) # Each smart component should have an XML file inside. We'll use # tar to extract that file and obtain information about the component. # This function will return the filename of the xml file after it # has been extracted from the archive. def extract_component_digest(self, sc_filename): # The first step is to find the line where the tarfile starts. sc_path = self.path + '\\' + sc_filename sc_file = open(sc_path, "rb") line_offset = -1 while True: buf = sc_file.read(1024) if buf == "": break skip_idx = buf.find("_SKIP=") if (skip_idx != -1): buf = buf[(skip_idx+6):] newline = buf.find('\n') line_offset = buf[0:newline] break # We didn't find the line offset. We can't go any further. if line_offset == -1: sc_file.close() log.error("Unable to find tar offset in %s", sc_filename) return "" line_offset = int(line_offset)-1 sc_file.seek(0) log.debug("tar file in %s at offset %d", sc_filename, line_offset) # Now that we have our line offset, move to the correct position in the file. for line in range(0, line_offset): sc_file.readline() tar_file = tempfile.mkstemp(dir=self.path) while True: buf = sc_file.read(1024) if buf == "": break os.write(tar_file[0], buf) sc_file.close() os.close(tar_file[0]) # Now we should have a good tarfile to extract the digest. digest_name = "" try: tf = tarfile.open(tar_file[1], 'r') for filename in tf.getnames() : if filename.lower() == sc_filename.lower().replace('.scexe', '.xml') : digest_name = filename log.debug("Extracting %s from tar", digest_name) tf.extract(digest_name, self.path) tf.close() os.remove(tar_file[1]) break except: log.exception("Error getting XML file name from %s", sc_filename) os.remove(tar_file[1]) tf.close() if not digest_name : log.error("Unable to find component xml file in %s", sc_filename) return digest_name # Parses the digest file and adds the required attributes to the SmartComponent object. def parse_digest(self, filename, component): log.debug("Parsing %s", filename) digest_dom = None try: digest_dom = parse(self.path + '\\' + filename) except: log.exception("Error parsing file %s",filename) return None package_elem = digest_dom.getElementsByTagName('cpq_package') if not package_elem: log.error("No cpq_package found") return None package_elem = package_elem[0] version_elem = None name_elem = None for node in package_elem.childNodes: if node.nodeName == 'version': version_elem = node elif node.nodeName == 'name': name_elem = node # Add the version from the version element's value attribute. if version_elem: component.version = version_elem.getAttribute('value') log.debug("%s component version: %s", component.filename, component.version) else : log.error("Error: no component version in %s", filename) if name_elem: for node in name_elem.childNodes: if node.nodeName == "name_xlate" and node.getAttribute("lang") == "en": for cn in node.childNodes: component.name = cn.wholeText log.debug("%s component name: %s", component.filename, component.name) else : log.error("Error: no component name in %s", filename) def json(self): json_list = [] for component in self.components: json_list.append(component.__dict__) return json_list # The main smart component manager object. __scm = SmartComponentManager() def get_scm(): return __scm

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test/generic_profiler_test.py

newstzpz/ClassyVision-1

0

130914

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import unittest from test.generic.config_utils import get_test_model_configs import torch import torch.nn as nn from classy_vision.generic.profiler import ( compute_activations, compute_flops, count_params, get_shape, ) from classy_vision.models import build_model class TestModule(nn.Module): def __init__(self): super().__init__() # add parameters to the module to affect the parameter count self.linear = nn.Linear(2, 3, bias=False) def forward(self, x): return x + 1 def flops(self, x): # TODO: this should raise an exception if this function is not defined # since the FLOPs are indeterminable # need to define flops since this is an unknown class return x.numel() class TestConvModule(nn.Conv2d): def __init__(self): super().__init__(2, 3, (4, 4), bias=False) # add another (unused) layer for added complexity and to test parameters self.linear = nn.Linear(4, 5, bias=False) def forward(self, x): return x def activations(self, x, out): # TODO: this should ideally work without this function being defined return out.numel() def flops(self, x): # need to define flops since this is an unknown class return 0 class TestModel(nn.Module): def __init__(self): super().__init__() self.linear = nn.Linear(300, 300, bias=False) self.mod = TestModule() self.conv = TestConvModule() # we should be able to pick up user defined parameters as well self.extra_params = nn.Parameter(torch.randn(10, 10)) # we shouldn't count flops for an unused layer self.unused_linear = nn.Linear(2, 2, bias=False) def forward(self, x): out = self.conv(x) out = out.view(out.shape[0], -1) out = self.mod(out) return self.linear(out) class TestModel2(nn.Module): def __init__(self): super().__init__() # create a model which re-uses a module (conv_module in this case) conv_module = nn.Conv2d(3, 3, (2, 2), bias=False) self.seq_1 = nn.Sequential(conv_module) self.seq_2 = nn.Sequential(conv_module) def forward(self, x): return self.seq_1(x) + self.seq_2(x) class TestModuleWithoutFlops(nn.Module): # this module doesn't have FLOPs defined def forward(self, x): return x class TestModuleWithFlops(nn.Module): # this module does have FLOPs defined _flops = 1234 def __init__(self): super().__init__() self.mod = TestModuleWithoutFlops() # add a conv module; this shouldn't impact the FLOPs since we define # self.flops() self.conv = nn.Conv2d(3, 3, (2, 2)) def forward(self, x): return self.conv(x) def flops(self, x): return self._flops class TestProfilerFunctions(unittest.TestCase): def test_complexity_calculation_resnext(self) -> None: model_configs = get_test_model_configs() # make sure there are three configs returned self.assertEqual(len(model_configs), 3) # expected values which allow minor deviations from model changes # we only test at the 10^6 scale expected_m_flops = [4122, 7850, 8034] expected_m_params = [25, 44, 44] expected_m_activations = [11, 16, 21] for model_config, m_flops, m_params, m_activations in zip( model_configs, expected_m_flops, expected_m_params, expected_m_activations ): model = build_model(model_config) self.assertEqual(compute_activations(model) // 10 ** 6, m_activations) self.assertEqual(compute_flops(model) // 10 ** 6, m_flops) self.assertEqual(count_params(model) // 10 ** 6, m_params) def test_complexity_calculation(self) -> None: model = TestModel() input_shape = (3, 10, 10) num_elems = 3 * 10 * 10 self.assertEqual(compute_activations(model, input_shape=input_shape), num_elems) self.assertEqual( compute_flops(model, input_shape=input_shape), num_elems + 0 + (300 * 300), # TestModule + TestConvModule + TestModel.linear; # TestModel.unused_linear is unused and shouldn't be counted ) self.assertEqual( count_params(model), (2 * 3) + (2 * 3 * 4 * 4) + (4 * 5) + (300 * 300) + (10 * 10) + (2 * 2), ) # TestModule.linear + TestConvModule + TestConvModule.linear + # TestModel.linear + TestModel.extra_params + TestModel.unused_linear # test that we calculate complexity correctly for a model which re-uses a module model = TestModel2() in_channels = 3 out_channels = 3 out_h, out_w = 9, 9 kernel_h, kernel_w = 2, 2 conv_flops = in_channels * out_channels * out_h * out_w * kernel_h * kernel_w conv_activations = out_channels * out_h * out_w self.assertEqual( compute_activations(model, input_shape=input_shape), conv_activations * 2 ) # the conv is applied twice self.assertEqual( compute_flops(model, input_shape=input_shape), conv_flops * 2 ) # the conv is applied twice self.assertEqual( count_params(model), in_channels * out_channels * kernel_h * kernel_w ) def test_flops_calculation(self): # test that a model containing a custom module which doesn't have FLOPs defined # raises an exception model = nn.Sequential(TestModuleWithoutFlops()) input_shape = (3, 10, 10) with self.assertRaises(Exception): compute_flops(model, input_shape=input_shape) # test that a model containing a custom module does have FLOPs defined works, # even if the module has children which don't define FLOPs model = nn.Sequential(TestModuleWithFlops()) input_shape = (3, 10, 10) self.assertEqual( compute_flops(model, input_shape=input_shape), TestModuleWithFlops._flops ) # the conv is applied twice class TestHelperFunctions(unittest.TestCase): def test_get_shape(self) -> None: list_x = [torch.zeros(2, 4), torch.zeros(3, 3)] shapes = get_shape(list_x) expected_shapes = [torch.zeros(2, 4).size(), torch.zeros(3, 3).size()] for shape, expected in zip(shapes, expected_shapes): self.assertEqual(shape, expected) dict_x = {"x1": torch.zeros(2, 4), "x2": torch.zeros(3, 3)} shapes = get_shape(dict_x) expected_shapes = { "x1": torch.zeros(2, 4).size(), "x2": torch.zeros(3, 3).size(), } for k, shape in shapes.items(): self.assertEqual(shape, expected_shapes[k]) list_dict_x = [ {"x1": torch.zeros(2, 4), "x2": torch.zeros(3, 3)}, {"x1": torch.zeros(3, 4), "x2": torch.zeros(4, 5)}, ] shapes = get_shape(list_dict_x) expected_shapes = [ {"x1": torch.zeros(2, 4).size(), "x2": torch.zeros(3, 3).size()}, {"x1": torch.zeros(3, 4).size(), "x2": torch.zeros(4, 5).size()}, ] for shape, expected in zip(shapes, expected_shapes): for k, s in shape.items(): self.assertEqual(s, expected[k])

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app/replenishjob.py

RobbieSoutham/DanielBCF

0

192496

from app import database from app.database import Database import datetime from configparser import ConfigParser config = ConfigParser() def rep(): #Get delivery time from config config.read("app/config.ini") del_time = datetime.timedelta(days=int(config.get("Settings", "del_time"))) today = datetime.datetime.now().date() ordered_stock = Database.get("Stock") stock_out = [] for stock in ordered_stock: if stock[3] == None and stock[3] is not None: #Retrieve stock items that are out of stock stock_out.append(stock) for stock in stock_out: #If the delivery time has passed set the stock level as healthy print(stock[4]) if stock[4] - today >= del_time: Database.update("Stock", "stock_healthy", 1, "stock_healthy", "Null") rep()

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ModelServices/eventTriggerOutputDeviceMappingServices.py

tuanldchainos/HcPullData

0

119584

<reponame>tuanldchainos/HcPullData from Repository.eventTriggerOutputDeviceMappingRepo import eventTriggerOutputDeviceMappingRepo from sqlalchemy import Table from sqlalchemy.engine.base import Connection from sqlalchemy.sql.expression import BinaryExpression class eventTriggerOutputDeviceMappingServices(): __eventTriggerOutputDeviceMappingRepo: eventTriggerOutputDeviceMappingRepo def __init__(self, eventTriggerOutputDeviceMappingTable: Table, context: Connection): self.__eventTriggerOutputDeviceMappingRepo = eventTriggerOutputDeviceMappingRepo(eventTriggerOutputDeviceMappingTable, context=context) def AddManyEventTriggerOutputDeviceMappingWithCustomData(self, l: list): self.__eventTriggerOutputDeviceMappingRepo.InsertManyWithCustomData(l)

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train/models.py

soymintc/brain_tumor_segmentation_web_service

1

1616968

<reponame>soymintc/brain_tumor_segmentation_web_service<gh_stars>1-10 import os import glob import psutil from django.db import models from django.conf import settings from django.contrib.auth import get_user_model from django.urls import reverse from django.core.validators import MaxValueValidator, MinValueValidator from django.core.exceptions import ValidationError def validate_optimizer(value): if value not in ['adam', 'sgd']: raise ValidationError("optimizer '{}' should be either adam or sgd".format(value)) class Train(models.Model): # Creation attrs author = models.ForeignKey( get_user_model(), on_delete=models.CASCADE, ) date = models.DateTimeField(auto_now_add=True) title = models.CharField(max_length=255) batch_size = models.PositiveIntegerField() image_size = models.PositiveIntegerField() n_validation = models.PositiveIntegerField() n_test = models.PositiveIntegerField() learning_rate = models.FloatField( validators=[MinValueValidator(1e-20), MaxValueValidator(1)],) optimizer = models.CharField(max_length=10, validators=[validate_optimizer]) group_size = models.PositiveIntegerField() filters_root = models.PositiveIntegerField() augment = models.BooleanField() # Non-input attrs pid = models.PositiveIntegerField(default=0, null=True, blank=True) tb_pid = models.PositiveIntegerField(default=0, null=True, blank=True) port = models.PositiveIntegerField(default=0, null=True, blank=True) # assign unused port cmd_str = models.CharField(max_length=1000, default='.', null=True, blank=True) def __str__(self): return self.title def pid_exists(self): return psutil.pid_exists(self.pid) def tb_pid_exists(self): return psutil.pid_exists(self.tb_pid) def run_phase(self): return self.pid == 0 def get_log_path(self): log_dir = os.path.join(settings.BASE_DIR, 'train', 'train_logs') return os.path.join(log_dir, self.title + '.log') def get_tb_log_dir(self): log_dir = os.path.join(settings.BASE_DIR, 'train', 'tb_logs', self.author.username) return log_dir def get_absolute_url(self): # page to return after save #return reverse('train_detail', args=[str(self.id)]) #return reverse('train_log', args=[self.pk]) return reverse('train_detail', args=[self.pk])

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text/opencv_dnn_detect.py

kingemma/invoice

1,017

39174

<filename>text/opencv_dnn_detect.py from config import yoloCfg,yoloWeights,opencvFlag from config import AngleModelPb,AngleModelPbtxt from config import IMGSIZE from PIL import Image import numpy as np import cv2 if opencvFlag=='keras': ##转换为tf模型，以便GPU调用 import tensorflow as tf from tensorflow.python.platform import gfile config = tf.ConfigProto(allow_soft_placement=True) sess = tf.Session(config=config) with gfile.FastGFile(AngleModelPb, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) sess.graph.as_default() tf.import_graph_def(graph_def, name='') inputImg = sess.graph.get_tensor_by_name('input_1:0') predictions = sess.graph.get_tensor_by_name('predictions/Softmax:0') keep_prob = tf.placeholder(tf.float32) else: angleNet = cv2.dnn.readNetFromTensorflow(AngleModelPb,AngleModelPbtxt)##dnn 文字方向检测 textNet = cv2.dnn.readNetFromDarknet(yoloCfg,yoloWeights)##文字定位 def text_detect(img): thresh=0 h,w = img.shape[:2] inputBlob = cv2.dnn.blobFromImage(img, scalefactor=0.00390625, size=IMGSIZE,swapRB=True ,crop=False); textNet.setInput(inputBlob) pred = textNet.forward() cx = pred[:,0]*w cy = pred[:,1]*h xmin = cx - pred[:,2]*w/2 xmax = cx + pred[:,2]*w/2 ymin = cy - pred[:,3]*h/2 ymax = cy + pred[:,3]*h/2 scores = pred[:,4] indx = np.where(scores>thresh)[0] scores = scores[indx] boxes = np.array(list(zip(xmin[indx],ymin[indx],xmax[indx],ymax[indx]))) return boxes,scores def angle_detect_dnn(img,adjust=True): """ 文字方向检测 """ h,w = img.shape[:2] ROTATE = [0,90,180,270] if adjust: thesh = 0.05 xmin,ymin,xmax,ymax = int(thesh*w),int(thesh*h),w-int(thesh*w),h-int(thesh*h) img = img[ymin:ymax,xmin:xmax]##剪切图片边缘 inputBlob = cv2.dnn.blobFromImage(img, scalefactor=1.0, size=(224, 224), swapRB=True , mean=[103.939,116.779,123.68],crop=False); angleNet.setInput(inputBlob) pred = angleNet.forward() index = np.argmax(pred,axis=1)[0] return ROTATE[index] def angle_detect_tf(img,adjust=True): """ 文字方向检测 """ h,w = img.shape[:2] ROTATE = [0,90,180,270] if adjust: thesh = 0.05 xmin,ymin,xmax,ymax = int(thesh*w),int(thesh*h),w-int(thesh*w),h-int(thesh*h) img = img[ymin:ymax,xmin:xmax]##剪切图片边缘 img = cv2.resize(img,(224,224)) img = img[..., ::-1].astype(np.float32) img[..., 0] -= 103.939 img[..., 1] -= 116.779 img[..., 2] -= 123.68 img = np.array([img]) out = sess.run(predictions, feed_dict={inputImg: img, keep_prob: 0 }) index = np.argmax(out,axis=1)[0] return ROTATE[index] def angle_detect(img,adjust=True): """ 文字方向检测 """ if opencvFlag=='keras': return angle_detect_tf(img,adjust=adjust) else: return angle_detect_dnn(img,adjust=adjust)

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ooobuild/csslo/view/__init__.py

Amourspirit/ooo_uno_tmpl

0

163570

<reponame>Amourspirit/ooo_uno_tmpl # coding: utf-8 # # Copyright 2022 :Barry-Thomas-Paul: Moss # # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http: // www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from ...lo.view.document_zoom_type import DocumentZoomType as DocumentZoomType from ...lo.view.duplex_mode import DuplexMode as DuplexMode from ...lo.view.office_document_view import OfficeDocumentView as OfficeDocumentView from ...lo.view.paper_format import PaperFormat as PaperFormat from ...lo.view.paper_orientation import PaperOrientation as PaperOrientation from ...lo.view.print_job_event import PrintJobEvent as PrintJobEvent from ...lo.view.print_options import PrintOptions as PrintOptions from ...lo.view.print_settings import PrintSettings as PrintSettings from ...lo.view.printable_state import PrintableState as PrintableState from ...lo.view.printable_state_event import PrintableStateEvent as PrintableStateEvent from ...lo.view.printer_descriptor import PrinterDescriptor as PrinterDescriptor from ...lo.view.render_descriptor import RenderDescriptor as RenderDescriptor from ...lo.view.render_options import RenderOptions as RenderOptions from ...lo.view.selection_type import SelectionType as SelectionType from ...lo.view.view_settings import ViewSettings as ViewSettings from ...lo.view.x_control_access import XControlAccess as XControlAccess from ...lo.view.x_form_layer_access import XFormLayerAccess as XFormLayerAccess from ...lo.view.x_line_cursor import XLineCursor as XLineCursor from ...lo.view.x_multi_selection_supplier import XMultiSelectionSupplier as XMultiSelectionSupplier from ...lo.view.x_print_job import XPrintJob as XPrintJob from ...lo.view.x_print_job_broadcaster import XPrintJobBroadcaster as XPrintJobBroadcaster from ...lo.view.x_print_job_listener import XPrintJobListener as XPrintJobListener from ...lo.view.x_print_settings_supplier import XPrintSettingsSupplier as XPrintSettingsSupplier from ...lo.view.x_printable import XPrintable as XPrintable from ...lo.view.x_printable_broadcaster import XPrintableBroadcaster as XPrintableBroadcaster from ...lo.view.x_printable_listener import XPrintableListener as XPrintableListener from ...lo.view.x_renderable import XRenderable as XRenderable from ...lo.view.x_screen_cursor import XScreenCursor as XScreenCursor from ...lo.view.x_selection_change_listener import XSelectionChangeListener as XSelectionChangeListener from ...lo.view.x_selection_supplier import XSelectionSupplier as XSelectionSupplier from ...lo.view.x_view_cursor import XViewCursor as XViewCursor from ...lo.view.x_view_settings_supplier import XViewSettingsSupplier as XViewSettingsSupplier

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laa_court_data_api_app/models/ping.py

ministryofjustice/laa-court-data-api

1

74687

from typing import Optional from pydantic import BaseModel, Field class Ping(BaseModel): app_branch: Optional[str] = Field(None, example='test_branch') build_date: Optional[str] = Field(None, example='02022022') build_tag: Optional[str] = Field(None, example='test') commit_id: Optional[str] = Field(None, example='123456')

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tests/test_linalg/test_solvers/test_probabilistic_linear_solver/test_symmetric.py

fxbriol/probnum

0

76261

<reponame>fxbriol/probnum """Tests for probabilistic linear solvers applied to symmetric linear systems.""" import pathlib import numpy as np from pytest_cases import parametrize_with_cases from probnum import linops, problems, randvars from probnum.linalg.solvers import ProbabilisticLinearSolver, beliefs case_modules = pathlib.Path("cases").stem cases_solvers = case_modules + ".solvers" cases_beliefs = case_modules + ".beliefs" cases_problems = case_modules + ".problems" @parametrize_with_cases("solver", cases=cases_solvers, has_tag="sym") @parametrize_with_cases("prior", cases=cases_beliefs, has_tag="sym") @parametrize_with_cases("problem", cases=cases_problems, has_tag="sym") def test_small_residual( solver: ProbabilisticLinearSolver, prior: beliefs.LinearSystemBelief, problem: problems.LinearSystem, ): """Test whether the output solution has small residual.""" belief, _ = solver.solve( prior=prior, problem=problem, rng=np.random.default_rng(42) ) residual_norm = np.linalg.norm(problem.b - problem.A @ belief.x.mean, ord=2) assert residual_norm < 1e-5 or residual_norm < 1e-5 * np.linalg.norm( problem.b, ord=2 ) @parametrize_with_cases("solver", cases=cases_solvers, has_tag="sym") @parametrize_with_cases("problem", cases=cases_problems, has_tag="sym") def test_perfect_information( solver: ProbabilisticLinearSolver, problem: problems.LinearSystem, ncols: int ): """Test whether a solver given perfect information converges instantly.""" # Construct prior belief with perfect information belief = beliefs.LinearSystemBelief( x=randvars.Normal( mean=problem.solution, cov=linops.Scaling(factors=0.0, shape=(ncols, ncols)) ), A=randvars.Constant(problem.A), Ainv=randvars.Constant(np.linalg.inv(problem.A @ np.eye(ncols))), ) # Run solver belief, solver_state = solver.solve( prior=belief, problem=problem, rng=np.random.default_rng(1) ) # Check for instant convergence assert solver_state.step == 0 np.testing.assert_allclose(belief.x.mean, problem.solution)

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sqlova/model/nl2sql/wikisql_models.py

guotong1988/Rule-SQL

15

120

# Copyright 2019-present NAVER Corp. # Apache License v2.0 # <NAME> import os, json from copy import deepcopy from matplotlib.pylab import * import torch import torch.nn as nn import torch.nn.functional as F device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from sqlova.utils.utils import topk_multi_dim from sqlova.utils.utils_wikisql import * class Seq2SQL_v1(nn.Module): def __init__(self, input_size, hidden_size, num_layer, dropout, number_cond_ops, number_agg_ops, old=False): super(Seq2SQL_v1, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.max_where_number = 4 self.number_cond_ops = number_cond_ops self.number_agg_ops = number_agg_ops self.select_column_predict = SelectColumnPredict(input_size, hidden_size, num_layer, dropout) self.select_agg_predict = SelectAggPredict(input_size, hidden_size, num_layer, dropout, number_agg_ops, old=old) self.where_number_predict = WhereNumberPredict(input_size, hidden_size, num_layer, dropout) self.wcp = WhereColumnPredict(input_size, hidden_size, num_layer, dropout) self.wop = WhereOpPredict(input_size, hidden_size, num_layer, dropout, number_cond_ops) self.wvp = WhereValuePredict_startend(input_size, hidden_size, num_layer, dropout, number_cond_ops, old=old) # start-end-search-discriminative model # emb_question, [16,26,1536] # len_question, [16] # emb_header, [102,12,1536] # len_header_token, [102] # number_header, [16] def forward(self, emb_question, len_question, emb_header, len_header_token, number_header, g_sc=None, g_sa=None, g_wn=None, g_wc=None, g_wo=None, g_wvi=None, show_p_sc=False, show_p_sa=False, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): # sc s_sc,s_sc_softmax = self.select_column_predict(emb_question, len_question, emb_header, len_header_token, number_header, show_p_sc=show_p_sc) if g_sc: pr_sc = g_sc else: pr_sc = pred_sc(s_sc) # sa s_sa,s_sa_softmax = self.select_agg_predict(emb_question, len_question, emb_header, len_header_token, number_header, pr_sc, show_p_sa=show_p_sa) if g_sa: # it's not necessary though. pr_sa = g_sa else: pr_sa = pred_sa(s_sa) # wn s_wn,s_wn_softmax = self.where_number_predict(emb_question, len_question, emb_header, len_header_token, number_header, show_p_wn=show_p_wn) if g_wn: pr_wn = g_wn else: pr_wn = pred_wn(s_wn) # wc s_wc,s_wc_softmax = self.wcp(emb_question, len_question, emb_header, len_header_token, number_header, show_p_wc=show_p_wc, penalty=True) if g_wc: pr_wc = g_wc else: pr_wc = pred_wherecolumn(pr_wn, s_wc) # wo s_wo,s_wo_softmax = self.wop(emb_question, len_question, emb_header, len_header_token, number_header, wn=pr_wn, wc=pr_wc, show_p_wo=show_p_wo) if g_wo: pr_wo = g_wo else: pr_wo = pred_wo(pr_wn, s_wo) # wv s_wv,s_wv_softmax = self.wvp(emb_question, len_question, emb_header, len_header_token, number_header, wn=pr_wn, wc=pr_wc, wo=pr_wo, show_p_wv=show_p_wv) return s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, s_sc_softmax, s_sa_softmax, s_wn_softmax, s_wc_softmax, s_wo_softmax, s_wv_softmax def beam_forward(self, emb_question, len_question, emb_header, len_header_token, l_header, engine, tb, nlu_t, nlu_wp_t, wp_to_wh_index, nlu, beam_size=4, show_p_sc=False, show_p_sa=False, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): """ Execution-guided beam decoding. """ # sc s_sc,_ = self.select_column_predict(emb_question, len_question, emb_header, len_header_token, l_header, show_p_sc=show_p_sc) prob_sc = F.softmax(s_sc, dim=-1) bS, mcL = s_sc.shape # minimum_header_length = min(l_header) # beam_size = minimum_header_length if beam_size > minimum_header_length else beam_size # sa # Construct all possible sc_sa_score prob_sc_sa = torch.zeros([bS, beam_size, self.number_agg_ops]).to(device) prob_sca = torch.zeros_like(prob_sc_sa).to(device) # get the top-k indices. pr_sc_beam = [B, beam_size] pr_sc_beam = pred_sc_beam(s_sc, beam_size) # calculate and predict s_sa. for i_beam in range(beam_size): pr_sc = list( array(pr_sc_beam)[:,i_beam] ) s_sa,_ = self.select_agg_predict(emb_question, len_question, emb_header, len_header_token, l_header, pr_sc, show_p_sa=show_p_sa) prob_sa = F.softmax(s_sa, dim=-1) prob_sc_sa[:, i_beam, :] = prob_sa prob_sc_selected = prob_sc[range(bS), pr_sc] # [B] prob_sca[:,i_beam,:] = (prob_sa.t() * prob_sc_selected).t() # [mcL, B] * [B] -> [mcL, B] (element-wise multiplication) # [mcL, B] -> [B, mcL] # Calculate the dimension of tensor # tot_dim = len(prob_sca.shape) # First flatten to 1-d idxs = topk_multi_dim(torch.tensor(prob_sca), n_topk=beam_size, batch_exist=True) # Now as sc_idx is already sorted, re-map them properly. idxs = remap_sc_idx(idxs, pr_sc_beam) # [sc_beam_idx, sa_idx] -> [sc_idx, sa_idx] idxs_arr = array(idxs) # [B, beam_size, remainig dim] # idxs[b][0] gives first probable [sc_idx, sa_idx] pairs. # idxs[b][1] gives of second. # Calculate prob_sca, a joint probability beam_idx_sca = [0] * bS beam_meet_the_final = [False] * bS while True: pr_sc = idxs_arr[range(bS),beam_idx_sca,0] pr_sa = idxs_arr[range(bS),beam_idx_sca,1] # map index properly check = check_sc_sa_pairs(tb, pr_sc, pr_sa) if sum(check) == bS: break else: for b, check1 in enumerate(check): if not check1: # wrong pair beam_idx_sca[b] += 1 if beam_idx_sca[b] >= beam_size: beam_meet_the_final[b] = True beam_idx_sca[b] -= 1 else: beam_meet_the_final[b] = True if sum(beam_meet_the_final) == bS: break # Now pr_sc, pr_sa are properly predicted. pr_sc_best = list(pr_sc) pr_sa_best = list(pr_sa) # Now, Where-clause beam search. s_wn,_ = self.where_number_predict(emb_question, len_question, emb_header, len_header_token, l_header, show_p_wn=show_p_wn) prob_wn = F.softmax(s_wn, dim=-1).detach().to('cpu').numpy() # Found "executable" most likely 4(=max_num_of_conditions) where-clauses. # wc s_wc,_ = self.wcp(emb_question, len_question, emb_header, len_header_token, l_header, show_p_wc=show_p_wc, penalty=True) prob_wc = F.sigmoid(s_wc).detach().to('cpu').numpy() # pr_wc_sorted_by_prob = pred_wc_sorted_by_prob(s_wc) # get max_wn # of most probable columns & their prob. pr_wn_max = [self.max_where_number] * bS pr_wc_max = pred_wherecolumn(pr_wn_max, s_wc) # if some column do not have executable where-claouse, omit that column prob_wc_max = zeros([bS, self.max_where_number]) for b, pr_wc_max1 in enumerate(pr_wc_max): prob_wc_max[b,:] = prob_wc[b,pr_wc_max1] # get most probable max_wn where-clouses # wo s_wo_max,_ = self.wop(emb_question, len_question, emb_header, len_header_token, l_header, wn=pr_wn_max, wc=pr_wc_max, show_p_wo=show_p_wo) prob_wo_max = F.softmax(s_wo_max, dim=-1).detach().to('cpu').numpy() # [B, max_wn, n_cond_op] pr_wvi_beam_op_list = [] prob_wvi_beam_op_list = [] for i_op in range(self.number_cond_ops - 1): pr_wo_temp = [[i_op] * self.max_where_number] * bS # wv s_wv,_ = self.wvp(emb_question, len_question, emb_header, len_header_token, l_header, wn=pr_wn_max, wc=pr_wc_max, wo=pr_wo_temp, show_p_wv=show_p_wv) prob_wv = F.softmax(s_wv, dim=-2).detach().to('cpu').numpy() # prob_wv pr_wvi_beam, prob_wvi_beam = pred_wvi_se_beam(self.max_where_number, s_wv, beam_size) pr_wvi_beam_op_list.append(pr_wvi_beam) prob_wvi_beam_op_list.append(prob_wvi_beam) # pr_wvi_beam = [B, max_wn, k_logit**2 [st, ed] paris] # pred_wv_beam # Calculate joint probability of where-clause # prob_w = [batch, wc, wo, wv] = [B, max_wn, n_cond_op, n_pairs] n_wv_beam_pairs = prob_wvi_beam.shape[2] prob_w = zeros([bS, self.max_where_number, self.number_cond_ops - 1, n_wv_beam_pairs]) for b in range(bS): for i_wn in range(self.max_where_number): for i_op in range(self.number_cond_ops - 1): # do not use final one for i_wv_beam in range(n_wv_beam_pairs): # i_wc = pr_wc_max[b][i_wn] # already done p_wc = prob_wc_max[b, i_wn] p_wo = prob_wo_max[b, i_wn, i_op] p_wv = prob_wvi_beam_op_list[i_op][b, i_wn, i_wv_beam] prob_w[b, i_wn, i_op, i_wv_beam] = p_wc * p_wo * p_wv # Perform execution guided decoding conds_max = [] prob_conds_max = [] # while len(conds_max) < self.max_wn: idxs = topk_multi_dim(torch.tensor(prob_w), n_topk=beam_size, batch_exist=True) # idxs = [B, i_wc_beam, i_op, i_wv_pairs] # Construct conds1 for b, idxs1 in enumerate(idxs): conds_max1 = [] prob_conds_max1 = [] for i_wn, idxs11 in enumerate(idxs1): i_wc = pr_wc_max[b][idxs11[0]] i_op = idxs11[1] wvi = pr_wvi_beam_op_list[i_op][b][idxs11[0]][idxs11[2]] # get wv_str temp_pr_wv_str, _ = convert_pred_wvi_to_string([[wvi]], [nlu_t[b]], [nlu_wp_t[b]], [wp_to_wh_index[b]], [nlu[b]]) merged_wv11 = merge_wv_t1_eng(temp_pr_wv_str[0][0], nlu[b]) conds11 = [i_wc, i_op, merged_wv11] prob_conds11 = prob_w[b, idxs11[0], idxs11[1], idxs11[2] ] # test execution # print(nlu[b]) # print(tb[b]['id'], tb[b]['types'], pr_sc[b], pr_sa[b], [conds11]) pr_ans = engine.execute(tb[b]['id'], pr_sc[b], pr_sa[b], [conds11]) if bool(pr_ans): # pr_ans is not empty! conds_max1.append(conds11) prob_conds_max1.append(prob_conds11) conds_max.append(conds_max1) prob_conds_max.append(prob_conds_max1) # May need to do more exhuastive search? # i.e. up to.. getting all executable cases. # Calculate total probability to decide the number of where-clauses pr_sql_i = [] prob_wn_w = [] pr_wn_based_on_prob = [] for b, prob_wn1 in enumerate(prob_wn): max_executable_wn1 = len( conds_max[b] ) prob_wn_w1 = [] prob_wn_w1.append(prob_wn1[0]) # wn=0 case. for i_wn in range(max_executable_wn1): prob_wn_w11 = prob_wn1[i_wn+1] * prob_conds_max[b][i_wn] prob_wn_w1.append(prob_wn_w11) pr_wn_based_on_prob.append(argmax(prob_wn_w1)) prob_wn_w.append(prob_wn_w1) pr_sql_i1 = {'agg': pr_sa_best[b], 'sel': pr_sc_best[b], 'conds': conds_max[b][:pr_wn_based_on_prob[b]]} pr_sql_i.append(pr_sql_i1) # s_wv = [B, max_wn, max_nlu_tokens, 2] return prob_sca, prob_w, prob_wn_w, pr_sc_best, pr_sa_best, pr_wn_based_on_prob, pr_sql_i class SelectColumnPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3): super(SelectColumnPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.sc_out = nn.Sequential(nn.Tanh(), nn.Linear(2 * hidden_size, 1)) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) # emb_question, [16,26,1536] # len_question, [16] # emb_header, [102,12,1536] # len_header_token, [102] # number_header, [16] def forward(self, emb_question, len_question, emb_header, len_header_token, number_header, show_p_sc=False): # Encode encoded_question = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, number_header) # [b, header, dim] bS = len(number_header) mL_n = max(len_question) # [bS, max_len_header, 100] * [bS, 100, mL_n] -> [bS, max_len_header, mL_n] att_h = torch.bmm(encoded_header, self.W_att(encoded_question).transpose(1, 2)) # Penalty on blank parts for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att_h[b, :, l_n1:] = -10000000000 p_n = self.softmax_dim2(att_h) if show_p_sc: # p = [b, header, n] if p_n.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001, figsize=(12,3.5)) # subplot(6,2,7) subplot2grid((7,2), (3, 0), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_h in range(number_header[0]): color_idx = i_h % len(_color) plot(p_n[0][i_h][:].data.numpy() - i_h, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('sc: p_n for each h') grid(True) fig.tight_layout() fig.canvas.draw() show() # p_n [ bS, max_len_header, mL_n] -> [ bS, max_len_header, mL_n, 1] # wenc_n [ bS, mL_n, 100] -> [ bS, 1, mL_n, 100] # -> [bS, max_len_header, mL_n, 100] -> [bS, max_len_header, 100] c_n = torch.mul(p_n.unsqueeze(3), encoded_question.unsqueeze(1)).sum(dim=2) vec = torch.cat([self.W_c(c_n), self.W_header(encoded_header)], dim=2) score_select_column = self.sc_out(vec).squeeze(2) # [bS, max_len_header, 1] -> [bS, max_len_header] score_select_column_softmax = self.softmax_dim_1(score_select_column) # Penalty max_len_header = max(number_header) for b, l_header1 in enumerate(number_header): if l_header1 < max_len_header: score_select_column[b, l_header1:] = -10000000000 for b, l_header1 in enumerate(number_header): if l_header1 < max_len_header: score_select_column_softmax[b, l_header1:] = 0 return score_select_column,score_select_column_softmax class SelectAggPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, n_agg_ops=-1, old=False): super(SelectAggPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.sa_out = nn.Sequential(nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, n_agg_ops)) # Fixed number of aggregation operator. self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) if old: # for backwoard compatibility self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, pr_sc, show_p_sa=False): # Encode encoded_question = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] bS = len(l_header) mL_n = max(len_question) wenc_header_ob = encoded_header[list(range(bS)), pr_sc] # list, so one sample for each batch. # [bS, question_len, 100] * [bS, 100, 1] -> [bS, question_len] att = torch.bmm(self.W_att(encoded_question), wenc_header_ob.unsqueeze(2)).squeeze(2) # Penalty on blank parts for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b, l_n1:] = -10000000000 # [bS, question_len] p = self.softmax_dim1(att) if show_p_sa: if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); subplot(7,2,3) cla() plot(p[0].data.numpy(), '--rs', ms=7) title('sa: nlu_weight') grid(True) fig.tight_layout() fig.canvas.draw() show() # [bS, question_len, 100] * ( [bS, question_len, 1] -> [bS, question_len, 100]) # -> [bS, question_len, 100] -> [bS, 100] c_n = torch.mul(encoded_question, p.unsqueeze(2).expand_as(encoded_question)).sum(dim=1) s_sa = self.sa_out(c_n) s_sa_softmax = self.softmax_dim_1(s_sa) return s_sa,s_sa_softmax class WhereNumberPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, ): super(WhereNumberPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.mL_w = 4 # max where condition number self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att_h = nn.Linear(hidden_size, 1) self.W_hidden = nn.Linear(hidden_size, num_layer * hidden_size) self.W_cell = nn.Linear(hidden_size, num_layer * hidden_size) self.W_att_n = nn.Linear(hidden_size, 1) self.wn_out = nn.Sequential(nn.Linear(hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, self.mL_w + 1)) # max number (4 + 1) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, show_p_wn=False): # Encode encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, max_len_header, dim] bS = len(l_header) max_len_question = max(len_question) max_len_header = max(l_header) # mL_h = max(len_header_token) # (self-attention?) column Embedding? # [B, max_len_header, 100] -> [B, max_len_header, 1] -> [B, max_len_header] att_h = self.W_att_h(encoded_header).squeeze(2) # Penalty for b, l_header1 in enumerate(l_header): if l_header1 < max_len_header: att_h[b, l_header1:] = -10000000000 p_h = self.softmax_dim1(att_h) if show_p_wn: if p_h.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); subplot(7,2,5) cla() plot(p_h[0].data.numpy(), '--rs', ms=7) title('wn: header_weight') grid(True) fig.canvas.draw() show() # input('Type Eenter to continue.') # [B, max_len_header, 100] * [ B, max_len_header, 1] -> [B, max_len_header, 100] -> [B, 100] c_header = torch.mul(encoded_header, p_h.unsqueeze(2)).sum(1) # [B, 100] --> [B, 2*100] Enlarge because there are two layers. hidden = self.W_hidden(c_header) # [B, 4, 200/2] hidden = hidden.view(bS, self.num_layer * 2, int( self.hidden_size / 2)) # [4, B, 100/2] # number_of_layer_layer * (bi-direction) # lstm input convention. hidden = hidden.transpose(0, 1).contiguous() cell = self.W_cell(c_header) # [B, 4, 100/2] cell = cell.view(bS, self.num_layer * 2, int(self.hidden_size / 2)) # [4, B, 100/2] cell = cell.transpose(0, 1).contiguous() wenc_n = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=(hidden, cell), last_only=False) # [b, n, dim] att_n = self.W_att_n(wenc_n).squeeze(2) # [B, max_len, 100] -> [B, max_len, 1] -> [B, max_len] # Penalty for b, l_n1 in enumerate(len_question): if l_n1 < max_len_question: att_n[b, l_n1:] = -10000000000 p_n = self.softmax_dim1(att_n) if show_p_wn: if p_n.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); subplot(7,2,6) cla() plot(p_n[0].data.numpy(), '--rs', ms=7) title('wn: nlu_weight') grid(True) fig.canvas.draw() show() # input('Type Enter to continue.') # [B, mL_n, 100] *([B, mL_n] -> [B, mL_n, 1] -> [B, mL_n, 100] ) -> [B, 100] c_n = torch.mul(wenc_n, p_n.unsqueeze(2).expand_as(wenc_n)).sum(dim=1) s_wn = self.wn_out(c_n) s_wn_softmax = self.softmax_dim_1(s_wn) return s_wn,s_wn_softmax # where column predict class WhereColumnPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3): super(WhereColumnPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.W_out = nn.Sequential( nn.Tanh(), nn.Linear(2 * hidden_size, 1) ) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, show_p_wc, penalty=True): # Encode encoded_question = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] # attention # wenc = [bS, mL, hidden_size] # att = [bS, max_len_header, mL_n] # att[b, i_h, j_n] = p(j_n| i_h) att = torch.bmm(encoded_header, self.W_att(encoded_question).transpose(1, 2)) # penalty to blank part. mL_n = max(len_question) for b_n, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b_n, :, l_n1:] = -10000000000 # make p(j_n | i_h) p = self.softmax_dim2(att) if show_p_wc: # p = [b, header, n] if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001); # subplot(6,2,7) subplot2grid((7,2), (3, 1), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_h in range(l_header[0]): color_idx = i_h % len(_color) plot(p[0][i_h][:].data.numpy() - i_h, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('wc: p_n for each h') grid(True) fig.tight_layout() fig.canvas.draw() show() # max nlu context vectors # [bS, max_len_header, mL_n]*[bS, max_len_header, mL_n] encoded_question = encoded_question.unsqueeze(1) # [ b, n, dim] -> [b, 1, n, dim] p = p.unsqueeze(3) # [b, header, n] -> [b, header, n, 1] c_n = torch.mul(encoded_question, p).sum(2) # -> [b, header, dim], c_n for each header. y = torch.cat([self.W_c(c_n), self.W_header(encoded_header)], dim=2) # [b, header, 2*dim] score = self.W_out(y).squeeze(2) # [b, header] score[torch.isnan(score)] = 0 score_softmax = self.softmax_dim_1(score) if penalty: for b, l_header1 in enumerate(l_header): score[b, l_header1:] = -1e+10 for b, l_header1 in enumerate(l_header): score_softmax[b, l_header1:] = 0 return score,score_softmax # where op predict class WhereOpPredict(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, n_cond_ops=3): super(WhereOpPredict, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.mL_w = 4 # max where condition number self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.wo_out = nn.Sequential( nn.Linear(2*hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, n_cond_ops) ) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, wn, wc, wenc_n=None, show_p_wo=False): # Encode if not wenc_n: wenc_n = encode(self.enc_n, emb_question, len_question, return_hidden=False, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] bS = len(l_header) # wn wenc_header_ob = [] # observed header for b in range(bS): # [[...], [...]] # Pad list to maximum number of selections real = [encoded_header[b, col] for col in wc[b]] pad = (self.mL_w - wn[b]) * [encoded_header[b, 0]] # this padding could be wrong. Test with zero padding later. wenc_header_ob1 = torch.stack(real + pad) # It is not used in the loss function. wenc_header_ob.append(wenc_header_ob1) # list to [B, 4, dim] tensor. wenc_header_ob = torch.stack(wenc_header_ob) # list to tensor. wenc_header_ob = wenc_header_ob.to(device) # [B, 1, mL_n, dim] * [B, 4, dim, 1] # -> [B, 4, mL_n, 1] -> [B, 4, mL_n] # multiplication bewteen NLq-tokens and selected column att = torch.matmul(self.W_att(wenc_n).unsqueeze(1), wenc_header_ob.unsqueeze(3) ).squeeze(3) # Penalty for blank part. mL_n = max(len_question) for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b, :, l_n1:] = -10000000000 p = self.softmax_dim2(att) # p( n| selected_col ) if show_p_wo: # p = [b, header, n] if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001) # subplot(6,2,7) subplot2grid((7,2), (5, 0), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_wn in range(self.mL_w): color_idx = i_wn % len(_color) plot(p[0][i_wn][:].data.numpy() - i_wn, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('wo: p_n for selected h') grid(True) fig.tight_layout() fig.canvas.draw() show() # [B, 1, mL_n, dim] * [B, 4, mL_n, 1] # --> [B, 4, mL_n, dim] # --> [B, 4, dim] c_n = torch.mul(wenc_n.unsqueeze(1), p.unsqueeze(3)).sum(dim=2) # [bS, 5-1, dim] -> [bS, 5-1, 3] vec = torch.cat([self.W_c(c_n), self.W_header(wenc_header_ob)], dim=2) s_wo = self.wo_out(vec) s_wo_softmax = self.softmax_dim_1(s_wo) return s_wo,s_wo_softmax class WhereValuePredict_startend(nn.Module): """ Discriminative model Get start and end. Here, classifier for [ [투수], [팀1], [팀2], [연도], ...] Input: Encoded nlu & selected column. Algorithm: Encoded nlu & selected column. -> classifier -> mask scores -> ... """ def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, n_cond_ops=4, old=False): super(WhereValuePredict_startend, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.n_cond_ops = n_cond_ops self.mL_w = 4 # max where condition number self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.W_att = nn.Linear(hidden_size, hidden_size) self.W_c = nn.Linear(hidden_size, hidden_size) self.W_header = nn.Linear(hidden_size, hidden_size) self.W_op = nn.Linear(n_cond_ops, hidden_size) # self.W_n = nn.Linear(hidden_size, hidden_size) if old: self.wv_out = nn.Sequential( nn.Linear(4 * hidden_size, 2) ) else: self.wv_out = nn.Sequential( nn.Linear(4 * hidden_size, hidden_size), nn.Tanh(), nn.Linear(hidden_size, 2) ) # self.wv_out = nn.Sequential( # nn.Linear(3 * hidden_size, hidden_size), # nn.Tanh(), # nn.Linear(hidden_size, self.gdkL) # ) self.softmax_dim1 = nn.Softmax(dim=1) self.softmax_dim2 = nn.Softmax(dim=2) self.softmax_dim_1 = nn.Softmax(dim=-1) def forward(self, emb_question, len_question, emb_header, len_header_token, l_header, wn, wc, wo, wenc_n=None, show_p_wv=False): # Encode if not wenc_n: wenc_n, hout, cout = encode(self.enc_n, emb_question, len_question, return_hidden=True, hc0=None, last_only=False) # [b, n, dim] encoded_header = encode_header(self.enc_h, emb_header, len_header_token, l_header) # [b, header, dim] bS = len(l_header) wenc_header_ob = [] # observed header for b in range(bS): # [[...], [...]] # Pad list to maximum number of selections real = [encoded_header[b, col] for col in wc[b]] pad = (self.mL_w - wn[b]) * [encoded_header[b, 0]] # this padding could be wrong. Test with zero padding later. wenc_header_ob1 = torch.stack(real + pad) # It is not used in the loss function. wenc_header_ob.append(wenc_header_ob1) # list to [B, 4, dim] tensor. wenc_header_ob = torch.stack(wenc_header_ob) # list to tensor. wenc_header_ob = wenc_header_ob.to(device) # Column attention # [B, 1, mL_n, dim] * [B, 4, dim, 1] # -> [B, 4, mL_n, 1] -> [B, 4, mL_n] # multiplication bewteen NLq-tokens and selected column att = torch.matmul(self.W_att(wenc_n).unsqueeze(1), wenc_header_ob.unsqueeze(3) ).squeeze(3) # Penalty for blank part. mL_n = max(len_question) for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: att[b, :, l_n1:] = -10000000000 p = self.softmax_dim2(att) # p( n| selected_col ) if show_p_wv: # p = [b, header, n] if p.shape[0] != 1: raise Exception("Batch size should be 1.") fig=figure(2001) # subplot(6,2,7) subplot2grid((7,2), (5, 1), rowspan=2) cla() _color='rgbkcm' _symbol='.......' for i_wn in range(self.mL_w): color_idx = i_wn % len(_color) plot(p[0][i_wn][:].data.numpy() - i_wn, '--'+_symbol[color_idx]+_color[color_idx], ms=7) title('wv: p_n for selected h') grid(True) fig.tight_layout() fig.canvas.draw() show() # [B, 1, mL_n, dim] * [B, 4, mL_n, 1] # --> [B, 4, mL_n, dim] # --> [B, 4, dim] c_n = torch.mul(wenc_n.unsqueeze(1), p.unsqueeze(3)).sum(dim=2) # Select observed headers only. # Also generate one_hot vector encoding info of the operator # [B, 4, dim] wenc_op = [] for b in range(bS): # [[...], [...]] # Pad list to maximum number of selections wenc_op1 = torch.zeros(self.mL_w, self.n_cond_ops) wo1 = wo[b] idx_scatter = [] l_wo1 = len(wo1) for i_wo11 in range(self.mL_w): if i_wo11 < l_wo1: wo11 = wo1[i_wo11] idx_scatter.append([int(wo11)]) else: idx_scatter.append([0]) # not used anyway wenc_op1 = wenc_op1.scatter(1, torch.tensor(idx_scatter), 1) wenc_op.append(wenc_op1) # list to [B, 4, dim] tensor. wenc_op = torch.stack(wenc_op) # list to tensor. wenc_op = wenc_op.to(device) # Now after concat, calculate logits for each token # [bS, 5-1, 3*hidden_size] = [bS, 4, 300] vec = torch.cat([self.W_c(c_n), self.W_header(wenc_header_ob), self.W_op(wenc_op)], dim=2) # Make extended vector based on encoded nl token containing column and operator information. # wenc_n = [bS, mL, 100] # vec2 = [bS, 4, mL, 400] vec1e = vec.unsqueeze(2).expand(-1,-1, mL_n, -1) # [bS, 4, 1, 300] -> [bS, 4, mL, 300] wenc_ne = wenc_n.unsqueeze(1).expand(-1, 4, -1, -1) # [bS, 1, mL, 100] -> [bS, 4, mL, 100] vec2 = torch.cat( [vec1e, wenc_ne], dim=3) # now make logits s_wv = self.wv_out(vec2) # [bS, 4, mL, 400] -> [bS, 4, mL, 2] s_wv_softmax = self.softmax_dim_1(s_wv) # penalty for spurious tokens for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: s_wv[b, :, l_n1:, :] = -10000000000 for b, l_n1 in enumerate(len_question): if l_n1 < mL_n: s_wv_softmax[b, :, l_n1:, :] = 0 return s_wv,s_wv_softmax def Loss_selectwhere_startend_v2(score_select_column, s_sa, s_wn, s_wc, s_wo, s_wv, ground_truth_select_column, g_sa, g_wn, g_wc, g_wo, g_wvi): """ :param s_wv: score [ B, n_conds, T, score] :param g_wn: [ B ] :param g_wvi: [B, conds, pnt], e.g. [[[0, 6, 7, 8, 15], [0, 1, 2, 3, 4, 15]], [[0, 1, 2, 3, 16], [0, 7, 8, 9, 16]]] :return: """ loss = 0 # loss += Loss_sc(score_select_column, ground_truth_select_column) # loss += Loss_sa(s_sa, g_sa) # loss += Loss_wn(s_wn, g_wn) # loss += Loss_wc(s_wc, g_wc) # loss += Loss_wo(s_wo, g_wn, g_wo) # loss += Loss_wv_se(s_wv, g_wn, g_wvi) return loss def Loss_sw_se(score_select_column, s_sa, s_wn, s_wc, s_wo, s_wv, ground_truth_select_column, g_sa, g_wn, g_wc, g_wo, g_wvi): """ :param s_wv: score [ B, n_conds, T, score] :param g_wn: [ B ] :param g_wvi: [B, conds, pnt], e.g. [[[0, 6, 7, 8, 15], [0, 1, 2, 3, 4, 15]], [[0, 1, 2, 3, 16], [0, 7, 8, 9, 16]]] :return: """ loss = 0 loss += Loss_sc(score_select_column, ground_truth_select_column) loss += Loss_sa(s_sa, g_sa) loss += Loss_wn(s_wn, g_wn) loss += Loss_wc(s_wc, g_wc) loss += Loss_wo(s_wo, g_wn, g_wo) loss += Loss_wv_se(s_wv, g_wn, g_wvi) return loss def Loss_sc(s_sc, g_sc): loss = F.cross_entropy(s_sc, torch.tensor(g_sc).to(device)) return loss def Loss_sa(s_sa, g_sa): loss = F.cross_entropy(s_sa, torch.tensor(g_sa).to(device)) return loss def Loss_wn(s_wn, g_wn): loss = F.cross_entropy(s_wn, torch.tensor(g_wn).to(device)) return loss def Loss_wc(s_wc, g_wc): # Construct index matrix bS, max_h_len = s_wc.shape im = torch.zeros([bS, max_h_len]).to(device) for b, g_wc1 in enumerate(g_wc): for g_wc11 in g_wc1: im[b, g_wc11] = 1.0 # Construct prob. p = F.sigmoid(s_wc) loss = F.binary_cross_entropy(p, im) return loss def Loss_wo(s_wo, g_wn, g_wo): # Construct index matrix loss = 0 for b, g_wn1 in enumerate(g_wn): if g_wn1 == 0: continue g_wo1 = g_wo[b] s_wo1 = s_wo[b] loss += F.cross_entropy(s_wo1[:g_wn1], torch.tensor(g_wo1).to(device)) return loss def Loss_wv_se(s_wv, g_wn, g_wvi): """ s_wv: [bS, 4, mL, 2], 4 stands for maximum # of condition, 2 tands for start & end logits. g_wvi: [ [1, 3, 2], [4,3] ] (when B=2, wn(b=1) = 3, wn(b=2) = 2). """ loss = 0 # g_wvi = torch.tensor(g_wvi).to(device) for b, g_wvi1 in enumerate(g_wvi): # for i_wn, g_wvi11 in enumerate(g_wvi1): g_wn1 = len(g_wvi1) # 有改动 # g_wn1 = g_wn[b] # 有改动 if g_wn1 == 0: continue g_wvi1 = torch.tensor(g_wvi1)[:g_wn1].to(device) # 有改动 g_st1 = g_wvi1[:,0] g_ed1 = g_wvi1[:,1] # loss from the start position loss += F.cross_entropy(s_wv[b,:g_wn1,:,0], g_st1) # print("st_login: ", s_wv[b,:g_wn1,:,0], g_st1, loss) # loss from the end position loss += F.cross_entropy(s_wv[b,:g_wn1,:,1], g_ed1) # print("ed_login: ", s_wv[b,:g_wn1,:,1], g_ed1, loss) return loss # ========= Decoder-Layer =========== class FT_s2s_1(nn.Module): """ Decoder-Layer """ def __init__(self, input_size, hidden_size, num_layer, dropout, max_seq_length, n_cond_ops, n_agg_ops, old=False): super(FT_s2s_1, self).__init__() self.input_size = input_size # input_size self.hidden_size = hidden_size # hidden_size self.ls = num_layer self.dropout = dropout self.n_cond_ops = n_cond_ops self.n_agg_ops = n_agg_ops self.n_where_num = 4 self.decoder_s2s = Decoder_s2s(input_size, hidden_size, num_layer, dropout, max_seq_length) def forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, g_pnt_idxs=None): score = self.decoder_s2s(wenc_s2s, l_input, cls_vec, pnt_start_tok, g_pnt_idxs) return score def EG_forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, pnt_end_tok, i_sql_vocab, i_nlu, i_hds, # for EG tokens, nlu, nlu_t, hds, tt_to_t_idx, # for EG tb, engine, beam_size=4, beam_only=True): """ EG-guided beam-search """ score = self.decoder_s2s.EG_forward(wenc_s2s, l_input, cls_vec, pnt_start_tok, pnt_end_tok, i_sql_vocab, i_nlu, i_hds, # for EG tokens, nlu, nlu_t, hds, tt_to_t_idx, # for EG tb, engine, beam_size, beam_only) return score class Decoder_s2s(nn.Module): def __init__(self, input_size=300, hidden_size=100, num_layer=2, dropout=0.3, max_seq_length=222, n_cond_ops=3): super(Decoder_s2s, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.mL = max_seq_length self.Tmax = 200 self.enc_h = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.enc_n = nn.LSTM(input_size=input_size, hidden_size=int(hidden_size / 2), num_layers=num_layer, batch_first=True, dropout=dropout, bidirectional=True) self.decode_pn = nn.LSTM(input_size=max_seq_length, hidden_size=hidden_size, num_layers=num_layer, batch_first=True, dropout=dropout) self.W_s2s = nn.Linear(input_size, hidden_size) self.W_pnt = nn.Linear(hidden_size, hidden_size) self.wv_out = nn.Sequential(nn.Tanh(), nn.Linear(hidden_size, 1)) def forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, g_pnt_idxs=None,): # Encode bS, mL_input, input_size = wenc_s2s.shape # Now, pointer network. ipnt = wenc_s2s.new_zeros(bS, 1, mL_input).to(device) # [B, 1, 200] ipnt[:, 0, pnt_start_tok] = 1 # 27 is of start token under current tokenization scheme # initial (current) pointer cpnt = ipnt # reshape wenc_s2s to incorporate T later wenc_s2s = wenc_s2s.unsqueeze(1) # h_0 and c_0 from cls_vec # They are not bidirectional. h_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) c_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) for i_layer in range(self.num_layer): h_st = (2*i_layer)*self.hidden_size h_ed = h_st + self.hidden_size c_st = (2*i_layer+1)*self.hidden_size c_ed = c_st + self.hidden_size h_0[i_layer] = cls_vec[:, h_st:h_ed] # [ # of layers, batch, dim] c_0[i_layer] = cls_vec[:, c_st:c_ed] # [ # of layers, batch, dim] if g_pnt_idxs: pnt_n = torch.zeros(bS, self.Tmax, mL_input).to(device) # one hot # assign index for b, g_pnt_idxs1 in enumerate(g_pnt_idxs): for t, g_pnt_idx in enumerate(g_pnt_idxs1): pnt_n[b, t, g_pnt_idx] = 1 # Encode dec_pn, _ = self.decode_pn(pnt_n, (h_0, c_0)) dec_pn = dec_pn.contiguous() # [bS, T, input_size] dec_pn = dec_pn.unsqueeze(2) # Calculate score s_wv = self.wv_out( self.W_s2s(wenc_s2s) + self.W_pnt(dec_pn) ).squeeze(3) # [B, T, mL_input, dim] -> [B, T, mL_input, 1] -> [B, T, mL_input] # s_wv = [B, 4, T, mL_n] = [batch, conds, token idx, score] # penalty for b, l_input1 in enumerate(l_input): if l_input1 < mL_input: s_wv[b, :, l_input1:] = -10000000000 else: t = 0 s_wv_list = [] cpnt_h = (h_0, c_0) while t < self.Tmax: dec_pn, cpnt_h = self.decode_pn(cpnt, cpnt_h) # lstm # [B, 1, 100] -> [B, 1, 1, 100] dec_pn = dec_pn.unsqueeze(2) # [bS, T, input_size] # get score s_wv1 = self.wv_out( self.W_s2s(wenc_s2s) # [B, 1, mL_input, dim] + self.W_pnt(dec_pn) # [B, T=1, 1, dim] Now, T=1 ).squeeze(3) # s_wv = [B, 4, 1, mL_n, 1] = [batch, conds, token idx, score] # -> [B, 4, mL_n] # Masking -- for b, l_input1 in enumerate(l_input): if l_input1 < mL_input: s_wv1[b, :, l_input1:] = -10000000000 # Collect score-- s_wv_list.append(s_wv1) # [B, 1, mL_input] -> [B, mL_n] -> [bS*(5-1)] # (max_val, max_indices) _val, pnt_n = s_wv1.view(bS, -1).max(dim=1) # formatting pnt_n as a one-hot input. cpnt = torch.zeros(bS, mL_input).to(device) # cpnt = cpnt.scatter_(dim=1, index=pnt_n.unsqueeze(1), src=1).to(device) cpnt = cpnt.scatter_(1, pnt_n.unsqueeze(1), 1) cpnt = cpnt.unsqueeze(1) # --> [B * 4, 1, 200] t += 1 s_wv = torch.stack(s_wv_list, 1) # [B, s_wv = s_wv.squeeze(2) # # # Following lines seems to be unnecessary. # # Penalty to blank parts # for b, l_input1 in enumerate(l_input): # if l_input1 < mL_input: # s_wv[b, :, l_input1:] = -10000000000 return s_wv def EG_forward(self, wenc_s2s, l_input, cls_vec, pnt_start_tok, pnt_end_tok, i_sql_vocab, i_nlu, i_hds, # for EG tokens, nlu, nlu_t, hds, tt_to_t_idx, # for EG tb, engine, beam_size, beam_only=True): # Encode bS, mL_input, input_size = wenc_s2s.shape # reshape wenc_s2s to incorperate T later wenc_s2s = wenc_s2s.unsqueeze(1) # h_0 and c_0 from cls_vec # They are not bidirectional. h_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) c_0 = torch.zeros([self.num_layer, bS, self.hidden_size]).to(device) for i_layer in range(self.num_layer): h_st = (2*i_layer)*self.hidden_size h_ed = h_st + self.hidden_size c_st = (2*i_layer+1)*self.hidden_size c_ed = c_st + self.hidden_size h_0[i_layer] = cls_vec[:, h_st:h_ed] # [ # of layers, batch, dim] c_0[i_layer] = cls_vec[:, c_st:c_ed] # [ # of layers, batch, dim] # initial (current) pointer pnt_list_beam = [] cpnt_beam = [] cpnt_h_beam = [] for i_beam in range(beam_size): pnt_list_beam1 = [] for b in range(bS): pnt_list_beam1.append( [ [pnt_start_tok], 0] ) pnt_list_beam.append(pnt_list_beam1) # initisl cpnt # Now, initialize pointer network. ipnt = wenc_s2s.new_zeros(bS, 1, mL_input).to(device) # [B, 1, 200] # Distort ipnt by i_bam on purpose to avoid initial duplication of beam-search ipnt[:, 0, pnt_start_tok] = 1 # 27 is of start token under current tokenization scheme cpnt_beam.append(ipnt) cpnt_h_beam.append( (h_0, c_0) ) t = 0 while t < self.Tmax: # s_wv1_beam = [] candidates = [ [] for b in range(bS) ] # [bS] # Generate beam for i_beam, cpnt in enumerate(cpnt_beam): cpnt_h = cpnt_h_beam[i_beam] pnt_list_beam1 = pnt_list_beam[i_beam] dec_pn, cpnt_h = self.decode_pn(cpnt, cpnt_h) # lstm cpnt_h_beam[i_beam] = cpnt_h # [B, 1, 100] -> [B, 1, 1, 100] dec_pn = dec_pn.unsqueeze(2) # [bS, T, input_size] # get score s_wv1 = self.wv_out( self.W_s2s(wenc_s2s) # [B, 1, mL_input, dim] + self.W_pnt(dec_pn) # [B, T=1, 1, dim] Now, T=1 ).squeeze(3) # s_wv = [B, 4, 1, mL_n, 1] = [batch, conds, token idx, score] # -> [B, 4, mL_n] # Masking -- for b, l_input1 in enumerate(l_input): if l_input1 < mL_input: s_wv1[b, :, l_input1:] = -10000000000 # Get the candidates only among the input space. prob, idxs = F.softmax(s_wv1.view(bS, -1), dim=1).topk(dim=1, k=max(l_input)) log_prob = torch.log(prob) # [bS, beam_size] for b, log_prob1 in enumerate(log_prob): pnt_list11, score = pnt_list_beam1[b] for i_can, log_prob11 in enumerate(log_prob1): # no update if last token was the end-token previous_pnt = pnt_list11[-1] if previous_pnt== pnt_end_tok: new_seq = pnt_list11 new_score = score else: new_seq = pnt_list11 + [idxs[b][i_can].item()] new_score = score + log_prob11.item() _candidate = [new_seq, new_score] candidates[b].append(_candidate) # Execution-guided beam filtering for b, candidates1 in enumerate(candidates): new_pnt_list_batch1 = sorted(candidates1, key=lambda list1: list1[-1], reverse=True) count = 0 selected_candidates1 = [] for new_pnt_list_batch11 in new_pnt_list_batch1: if new_pnt_list_batch11 not in selected_candidates1: if beam_only: selected_candidates1.append(new_pnt_list_batch11) pnt_list_beam[count][b] = new_pnt_list_batch11 count +=1 else: # Need to be modified here. executable = False testable = False pr_i_vg_list, pr_i_vg_sub_list = gen_i_vg_from_pnt_idxs([new_pnt_list_batch11[0]], [i_sql_vocab[b]], [i_nlu[b]], [i_hds[b]]) pr_sql_q_s2s, pr_sql_i = gen_sql_q_from_i_vg([tokens[b]], [nlu[b]], [nlu_t[b]], [hds[b]], [tt_to_t_idx[b]], pnt_start_tok, pnt_end_tok, [new_pnt_list_batch11[0]], pr_i_vg_list, pr_i_vg_sub_list) # check testability from select-clause try: # check whether basic elements presents in pr_sql_i # If so, it is testable. idx_agg = pr_sql_i[0]["agg"] idx_sel = pr_sql_i[0]["sel"] testable = True except: testable = False pass # check the presence of conds if testable: try: conds = pr_sql_i[0]["conds"] except: conds = [] try: pr_ans1 = engine.execute(tb[b]['id'], idx_sel, idx_agg, conds) executable = bool(pr_ans1) except: executable = False # if testable: if executable: add_candidate = True else: add_candidate = False else: add_candidate = True if add_candidate: selected_candidates1.append(new_pnt_list_batch11) pnt_list_beam[count][b] = new_pnt_list_batch11 count += 1 if count == beam_size: break if count < beam_size: # not executable at all.. # add junk sequence. for i_junk in range(count, beam_size): pnt_list_beam[i_junk][b] = [[pnt_end_tok],-9999999] # generate cpnt # formatting pnt_n as a one-hot input. for i_beam in range(beam_size): cpnt = torch.zeros(bS, mL_input).to(device) # cpnt = cpnt.scatter_(dim=1, index=pnt_n.unsqueeze(1), src=1).to(device) idx_batch = [seq_score[0][-1] for seq_score in pnt_list_beam[i_beam]] pnt_n = torch.tensor(idx_batch).to(device) cpnt = cpnt.scatter_(1, pnt_n.unsqueeze(1), 1) cpnt = cpnt.unsqueeze(1) # --> [B, t=1, mL_input] cpnt_beam[i_beam] = cpnt t += 1 # Generate best pr_pnt_list, p_tot pr_pnt_idxs = [] p_list = [] for b in range(bS): pnt_list_beam_best = pnt_list_beam[0] pr_pnt_idxs.append(pnt_list_beam_best[b][0]) p_list.append( pnt_list_beam_best[b][1]) return pr_pnt_idxs, p_list, pnt_list_beam # ============= Shallow-Layer =============== class FT_Scalar_1(nn.Module): """ Shallow-Layer """ def __init__(self, input_size, hidden_size, num_layer, dropout, n_cond_ops, n_agg_ops, old=False): super(FT_Scalar_1, self).__init__() self.input_size = input_size # input_size self.hidden_size = hidden_size self.num_layer = num_layer self.dropout = dropout self.n_cond_ops = n_cond_ops self.n_agg_ops = n_agg_ops self.n_where_num = 4 def scp(self, wemb_h, l_header): bS, max_header_len, _ = wemb_h.shape # s_sc s_sc = torch.zeros(bS, max_header_len).to(device) s_sc[:, :] = wemb_h[:, :, 0] # s_sc = [B, max_header length, 1] # s_sc[:,:] = F.tanh(wemb_h[:,:,0]) # s_sc = [B, max_header length, 1] # s_sc = s_sc.squeeze(2) # masking # print(f"s_sc {s_sc}") for b, l_header1 in enumerate(l_header): s_sc[b, l_header1:] = -9999999999.0 return s_sc def sap(self, wemb_h, pr_sc, idx_st, idx_ed): bS, max_header_len, _ = wemb_h.shape # select of aggregation operator s_sa = torch.zeros([bS, self.n_agg_ops]).to(device) for b, pr_sc1 in enumerate(pr_sc): s_sa[b,:] = wemb_h[b,pr_sc1,idx_st:idx_ed] return s_sa def wnp(self, cls_vec): bS = cls_vec.shape[0] # [B,hidden_size] -> [B, n_where_num+1] s_wn = torch.zeros(bS, (self.n_where_num + 1)).to(device) s_wn[:, :] = cls_vec[:, 0:(self.n_where_num + 1)] return s_wn def wcp(self, wemb_h, l_header, idx_st, idx_ed): bS, max_header_len, _ = wemb_h.shape s_wc = torch.zeros(bS, max_header_len, 1).to(device) s_wc[:, :, :] = wemb_h[:, :, idx_st:idx_ed] s_wc = s_wc.squeeze(2) # [B, max_header_length] # masking for b, l_header1 in enumerate(l_header): s_wc[b, l_header1:] = -99999999999.0 return s_wc def wop(self, wemb_h, pr_wc, idx_st, idx_ed): bS, max_header_len, _ = wemb_h.shape s_wo = torch.zeros([bS, self.n_where_num, self.n_cond_ops]).to(device) for b, pr_wc1 in enumerate(pr_wc): if len(pr_wc1) > 0: s_wo[b, 0:len(pr_wc1), :] = wemb_h[b, pr_wc1, idx_st:idx_ed] else: pass return s_wo def wvp(self, emb_question, len_question, pr_wc): bS, _, _ = emb_question.shape s_wv = torch.zeros([bS, self.n_where_num, max(len_question), 2]).to(device) for b, pr_wc1 in enumerate(pr_wc): if len(pr_wc1) > 0: # start logit s_wv[b, 0:len(pr_wc1), :, 0] = emb_question[b, :, pr_wc1].transpose(0, 1) # end logit s_wv[b, 0:len(pr_wc1), :, 1] = emb_question[b, :, [pr_wc11 + 100 for pr_wc11 in pr_wc1]].transpose(0, 1) else: pass # masking # penalty for spurious tokens for b, l_n1 in enumerate(len_question): if l_n1 < max(len_question): s_wv[b, :, l_n1:, :] = -1e+11 return s_wv def forward(self, emb_question, len_question, wemb_h, l_header, cls_vec, g_sc=None, g_sa=None, g_wn=None, g_wc=None, g_wo=None, g_wvi=None, show_p_sc=False, show_p_sa=False, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): # emb_question = [B, max_nlu_token_length, hidden_size] # here, # of target_layer is fixed to 1. # wemb_h = [B, max_header #, hidden_size] s_sc = self.scp(wemb_h, l_header) if g_sc: pr_sc = g_sc else: pr_sc = pred_sc(s_sc) # s_sa idx_st = 1 idx_ed = 1 + self.n_agg_ops s_sa = self.sap(wemb_h, pr_sc, idx_st, idx_ed) if g_sa: pr_sa = g_sa else: pr_sa = pred_sa(s_sa) # where_number s_wn = self.wnp(cls_vec) if g_wn: pr_wn = g_wn else: pr_wn = pred_wn(s_wn) # wc idx_st = idx_ed+1 idx_ed = idx_st+1 s_wc = self.wcp(wemb_h, l_header, idx_st, idx_ed) if g_wc: pr_wc = g_wc else: pr_wc = pred_wherecolumn(pr_wn, s_wc) # wo idx_st = idx_ed+1 idx_ed = idx_st + self.n_cond_ops s_wo = self.wop(wemb_h, pr_wc, idx_st, idx_ed) if g_wo: pr_wo = g_wo else: pr_wo = pred_wo(pr_wn, s_wo) # wv # s_wv = [bS, 4, mL, 2] s_wv = self.wvp(emb_question, len_question, pr_wc) # print(s_wv) # s_wv = F.tanh(s_wv) return s_sc, s_sa, s_wn, s_wc, s_wo, s_wv def forward_EG(self, emb_question, len_question, wemb_h, l_header, cls_vec, engine, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu, beam_size=4): """ Execution-guided beam decoding. Essentially identical with that of NL2SQL Layer. """ # Select-clause prob_sca, pr_sc_best, pr_sa_best, \ p_sc_best, p_sa_best, p_select \ = self.EG_decoding_select(wemb_h, l_header, tb, beam_size=beam_size) # Where-clause prob_w, prob_wn_w, pr_wn_based_on_prob, pr_sql_i, pr_wvi_best, \ p_where, p_wn_best, p_wc_best, p_wo_best, p_wvi_best \ = self.EG_decoding_where(emb_question, len_question, wemb_h, l_header, cls_vec, engine, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu, pr_sc_best, pr_sa_best, beam_size=4) p_tot = cal_prob_tot(p_select, p_where) return pr_sc_best, pr_sa_best, pr_wn_based_on_prob, pr_wvi_best, \ pr_sql_i, p_tot, p_select, p_where, p_sc_best, p_sa_best, \ p_wn_best, p_wc_best, p_wo_best, p_wvi_best def EG_decoding_select(self, wemb_h, l_header, tb, beam_size=4, show_p_sc=False, show_p_sa=False): # sc s_sc = self.scp(wemb_h, l_header) prob_sc = F.softmax(s_sc, dim=-1) bS, mcL = s_sc.shape # minimum_header_length = min(l_header) # beam_size = minimum_header_length if beam_size > minimum_header_length else beam_size # sa # Construct all possible sc_sa_score prob_sc_sa = torch.zeros([bS, beam_size, self.n_agg_ops]).to(device) score_sc_sa = torch.zeros([bS, beam_size, self.n_agg_ops]).to(device) prob_sca = torch.zeros_like(prob_sc_sa).to(device) # get the top-k indices. pr_sc_beam = [B, beam_size] pr_sc_beam = pred_sc_beam(s_sc, beam_size) # calculate and predict s_sa. idx_st = 1 idx_ed = 1 + self.n_agg_ops for i_beam in range(beam_size): pr_sc = list(array(pr_sc_beam)[:, i_beam]) s_sa = self.sap(wemb_h, pr_sc, idx_st, idx_ed) prob_sa = F.softmax(s_sa, dim=-1) prob_sc_sa[:, i_beam, :] = prob_sa score_sc_sa[:, i_beam, :] = s_sa prob_sc_selected = prob_sc[range(bS), pr_sc] # [B] prob_sca[:, i_beam, :] = (prob_sa.t() * prob_sc_selected).t() # [mcL, B] * [B] -> [mcL, B] (element-wise multiplication) # [mcL, B] -> [B, mcL] # Calculate the dimension of tensor # tot_dim = len(prob_sca.shape) idxs = topk_multi_dim(torch.tensor(prob_sca), n_topk=beam_size, batch_exist=True) # Now as sc_idx is already sorted, re-map them properly. idxs = remap_sc_idx(idxs, pr_sc_beam) # [sc_beam_idx, sa_idx] -> [sc_idx, sa_idx] idxs_arr = array(idxs) # [B, beam_size, remainig dim] # idxs[b][0] gives first probable [sc_idx, sa_idx] pairs. # idxs[b][1] gives of second. # Calculate prob_sca, a joint probability beam_idx_sca = [0] * bS beam_meet_the_final = [False] * bS while True: pr_sc = idxs_arr[range(bS), beam_idx_sca, 0] pr_sa = idxs_arr[range(bS), beam_idx_sca, 1] # map index properly check = check_sc_sa_pairs(tb, pr_sc, pr_sa) if sum(check) == bS: break else: for b, check1 in enumerate(check): if not check1: # wrong pair beam_idx_sca[b] += 1 if beam_idx_sca[b] >= beam_size: beam_meet_the_final[b] = True beam_idx_sca[b] -= 1 else: beam_meet_the_final[b] = True if sum(beam_meet_the_final) == bS: break # Now pr_sc, pr_sa are properly predicted. pr_sc_best = list(pr_sc) pr_sa_best = list(pr_sa) # output for later analysis. p_sc_best = cal_prob_sc(s_sc, pr_sc_best) p_sa_best = cal_prob_sa(score_sc_sa[range(bS), beam_idx_sca, :].squeeze(1), pr_sa_best) p_select = cal_prob_select(p_sc_best, p_sa_best) # p_select = prob_sca[range(bS),beam_idx_sca,pr_sa_best].detach().to('cpu').numpy() return prob_sca, pr_sc_best, pr_sa_best, p_sc_best, p_sa_best, p_select def EG_decoding_where(self, emb_question, len_question, wemb_h, l_header, cls_vec, engine, tb, nlu_t, nlu_wp_t, tt_to_t_idx, nlu, pr_sc_best, pr_sa_best, beam_size=4, show_p_wn=False, show_p_wc=False, show_p_wo=False, show_p_wv=False): bS, max_header_len, _ = wemb_h.shape # Now, Where-clause beam search. idx_st = 1 idx_ed = 1 + self.n_agg_ops s_wn = self.wnp(cls_vec) prob_wn = F.softmax(s_wn, dim=-1).detach().to('cpu').numpy() # Found "executable" most likely 4(=max_num_of_conditions) where-clauses. # wc idx_st = idx_ed + 1 idx_ed = idx_st + 1 s_wc = self.wcp(wemb_h, l_header, idx_st, idx_ed) prob_wc = torch.sigmoid(s_wc).detach().to('cpu').numpy() # pr_wc_sorted_by_prob = pred_wc_sorted_by_prob(s_wc) # get max_wn # of most probable columns & their prob. pr_wn_max = [self.n_where_num] * bS pr_wc_max = pred_wherecolumn(pr_wn_max, s_wc) # if some column do not have executable where-claouse, omit that column prob_wc_max = zeros([bS, self.n_where_num]) for b, pr_wc_max1 in enumerate(pr_wc_max): prob_wc_max[b, :] = prob_wc[b, pr_wc_max1] # get most probable n_where_num where-clouses # wo idx_st = idx_ed + 1 idx_ed = idx_st + self.n_cond_ops s_wo_max = self.wop(wemb_h, pr_wc_max, idx_st, idx_ed) prob_wo_max = F.softmax(s_wo_max, dim=-1).detach().to('cpu').numpy() # [B, n_where_num, n_cond_op] pr_wvi_beam_op_list = [] prob_wvi_beam_op_list = [] prob_wvi_beam_st_op_list = [] prob_wvi_beam_ed_op_list = [] # To re-use code, repeat the calculation unnecessarily. for i_op in range(self.n_cond_ops - 1): pr_wo_temp = [[i_op] * self.n_where_num] * bS # wv s_wv = self.wvp(emb_question, len_question, pr_wc_max) prob_wv = F.softmax(s_wv, dim=-2).detach().to('cpu').numpy() # prob_wv pr_wvi_beam, prob_wvi_beam, prob_wvi_beam_st, prob_wvi_beam_ed = pred_wvi_se_beam(self.n_where_num, s_wv, beam_size) pr_wvi_beam_op_list.append(pr_wvi_beam) prob_wvi_beam_op_list.append(prob_wvi_beam) prob_wvi_beam_st_op_list.append(prob_wvi_beam_st) prob_wvi_beam_ed_op_list.append(prob_wvi_beam_ed) # pr_wvi_beam = [B, n_where_num, k_logit**2 [st, ed] paris] # pred_wv_beam # Calculate joint probability of where-clause # prob_w = [batch, wc, wo, wv] = [B, n_where_num, n_cond_op, n_pairs] n_wv_beam_pairs = prob_wvi_beam.shape[2] prob_w = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wc_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wo_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wvi_st_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) prob_wvi_ed_dupl = zeros([bS, self.n_where_num, self.n_cond_ops - 1, n_wv_beam_pairs]) for b in range(bS): for i_wn in range(self.n_where_num): for i_op in range(self.n_cond_ops - 1): # do not use final one p_wc = prob_wc_max[b, i_wn] for i_wv_beam in range(n_wv_beam_pairs): # i_wc = pr_wc_max[b][i_wn] # already done p_wo = prob_wo_max[b, i_wn, i_op] p_wv = prob_wvi_beam_op_list[i_op][b, i_wn, i_wv_beam] prob_w[b, i_wn, i_op, i_wv_beam] = p_wc * p_wo * p_wv prob_wc_dupl[b, i_wn, i_op, i_wv_beam] = p_wc prob_wo_dupl[b, i_wn, i_op, i_wv_beam] = p_wo p_wv_st = prob_wvi_beam_st_op_list[i_op][b, i_wn, i_wv_beam] p_wv_ed = prob_wvi_beam_ed_op_list[i_op][b, i_wn, i_wv_beam] prob_wvi_st_dupl[b, i_wn, i_op, i_wv_beam] = p_wv_st prob_wvi_ed_dupl[b, i_wn, i_op, i_wv_beam] = p_wv_ed # Perform execution guided decoding conds_max = [] prob_conds_max = [] # while len(conds_max) < self.n_where_num: idxs = topk_multi_dim(torch.tensor(prob_w), n_topk=beam_size, batch_exist=True) # idxs = [B, i_wc_beam, i_op, i_wv_pairs] # Construct conds1. Collect only executable one. It is descending order of the probability. pr_wvi_max = [] p_wc_max = [] p_wo_max = [] p_wvi_max = [] for b, idxs1 in enumerate(idxs): conds_max1 = [] prob_conds_max1 = [] pr_wvi1_max = [] p_wc1_max = [] p_wo1_max = [] p_wvi1_max = [] for i_wn, idxs11 in enumerate(idxs1): i_wc = pr_wc_max[b][idxs11[0]] i_op = idxs11[1] wvi = pr_wvi_beam_op_list[i_op][b][idxs11[0]][idxs11[2]] # idx11[0] # get wv_str temp_pr_wv_str, _ = convert_pred_wvi_to_string([[wvi]], [nlu_t[b]], [nlu_wp_t[b]], [tt_to_t_idx[b]], [nlu[b]]) merged_wv11 = merge_wv_t1_eng(temp_pr_wv_str[0][0], nlu[b]) conds11 = [i_wc, i_op, merged_wv11] prob_conds11 = prob_w[b, idxs11[0], idxs11[1], idxs11[2]] p_wc11_max = prob_wc_dupl[b, idxs11[0], idxs11[1], idxs11[2]] p_wo11_max = prob_wo_dupl[b, idxs11[0], idxs11[1], idxs11[2]] p_wvi11_max = [ prob_wvi_st_dupl[b, idxs11[0], idxs11[1], idxs11[2]], prob_wvi_ed_dupl[b, idxs11[0], idxs11[1], idxs11[2]] ] # test execution # print(nlu[b]) # print(tb[b]['id'], tb[b]['types'], pr_sc[b], pr_sa[b], [conds11]) pr_ans = engine.execute(tb[b]['id'], pr_sc_best[b], pr_sa_best[b], [conds11]) if bool(pr_ans): # pr_ans is not empty! conds_max1.append(conds11) prob_conds_max1.append(prob_conds11) pr_wvi1_max.append(wvi) p_wc1_max.append(p_wc11_max) p_wo1_max.append(p_wo11_max) p_wvi1_max.append(p_wvi11_max) conds_max.append(conds_max1) prob_conds_max.append(prob_conds_max1) pr_wvi_max.append(pr_wvi1_max) p_wc_max.append(p_wc1_max) p_wo_max.append(p_wo1_max) p_wvi_max.append(p_wvi1_max) # May need to do more exhuastive search? # i.e. up to.. getting all executable cases. # Calculate total probability to decide the number of where-clauses pr_sql_i = [] prob_wn_w = [] # total where-clause probability pr_wn_based_on_prob = [] pr_wvi_best = [] p_wc = [] p_wo = [] p_wvi = [] for b, prob_wn1 in enumerate(prob_wn): max_executable_wn1 = len(conds_max[b]) prob_wn_w1 = [] prob_wn_w1.append(prob_wn1[0]) # wn=0 case. for i_wn in range(max_executable_wn1): prob_wn_w11 = prob_wn1[i_wn + 1] * prob_conds_max[b][i_wn] prob_wn_w1.append(prob_wn_w11) pr_wn_based_on_prob.append(argmax(prob_wn_w1)) prob_wn_w.append(prob_wn_w1) pr_sql_i1 = {'agg': pr_sa_best[b], 'sel': pr_sc_best[b], 'conds': conds_max[b][:pr_wn_based_on_prob[b]]} pr_wvi_best1 = pr_wvi_max[b][:pr_wn_based_on_prob[b]] pr_sql_i.append(pr_sql_i1) pr_wvi_best.append(pr_wvi_best1) p_wc.append( p_wc_max[b][:pr_wn_based_on_prob[b]] ) p_wo.append( p_wo_max[b][:pr_wn_based_on_prob[b]] ) p_wvi.append( p_wvi_max[b][:pr_wn_based_on_prob[b]] ) # s_wv = [B, n_where_num, max_nlu_tokens, 2] p_wn = cal_prob_wn(s_wn, pr_wn_based_on_prob) p_where = cal_prob_where(p_wn, p_wc, p_wo, p_wvi) return prob_w, prob_wn_w, pr_wn_based_on_prob, pr_sql_i, pr_wvi_best, \ p_where, p_wn, p_wc, p_wo, p_wvi def Loss_s2s(score, g_pnt_idxs): """ score = [B, T, max_seq_length] """ # WHERE string part loss = 0 for b, g_pnt_idxs1 in enumerate(g_pnt_idxs): ed = len(g_pnt_idxs1) - 1 score_part = score[b, :ed] loss += F.cross_entropy(score_part, torch.tensor(g_pnt_idxs1[1:]).to(device)) # +1 shift. return loss

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tests/test_db.py

schmocker/pv-FHNW

1

159902

import pytest from pvtool.db import PvModule, Measurement, MeasurementValues, FlasherData, ManufacturerData, db @pytest.mark.incremental class TestDBModels(object): def test_pv_module_insert(client, init_db): """ GIVEN a database WHEN database is initialized THEN check if pv_module can be inserted and removed """ test_pv_module = PvModule(model="TEST", manufacturer="TEST", cell_type="TEST", additional_information="TEST", price_CHF="-999", length="-999", width="-999", shunt_resistance="-999", ) db.session.add(test_pv_module) db.session.commit() query_result = db.session.query(PvModule).filter(PvModule.model == test_pv_module.model).first() assert query_result db.session.query(PvModule).filter(PvModule.model == test_pv_module.model).delete() db.session.commit() def test_measurement_insert(client, init_db): """ GIVEN a database WHEN database is initialized THEN check if pv_module can be inserted and removed """ test_measurement = Measurement(date='TEST', measurement_series='TEST', producer='TEST', pv_module_id=1, ) db.session.add(test_measurement) db.session.commit() query_result = db.session.query(Measurement).filter(Measurement.measurement_series == test_measurement.measurement_series).first() assert query_result db.session.query(Measurement).filter(Measurement.measurement_series == test_measurement.measurement_series).delete() db.session.commit() def test_measurement_values_insert(client, init_db): """ GIVEN a database WHEN database is initialized THEN check if measurement_values can be inserted and removed """ test_measurement_values = MeasurementValues(weather='TEST', _U_module=0, _U_shunt=0, _U_T_amb=0, _U_T_pan=0, _U_G_hor=0, _U_G_pan=0, _U_G_ref=0, measurement_id=1, ) db.session.add(test_measurement_values) db.session.commit() query_result = db.session.query(MeasurementValues).\ filter(MeasurementValues.weather == test_measurement_values.weather).first() assert query_result db.session.query(MeasurementValues).filter(MeasurementValues.weather == test_measurement_values.weather).delete() db.session.commit() def insert_multiple_pv_modules(client, init_db): pass

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autobahn/wamp/gen/wamp/proto/PublisherFeatures.py

rapyuta-robotics/autobahn-python

1,670

94563

# automatically generated by the FlatBuffers compiler, do not modify # namespace: proto import flatbuffers class PublisherFeatures(object): __slots__ = ['_tab'] @classmethod def GetRootAsPublisherFeatures(cls, buf, offset): n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) x = PublisherFeatures() x.Init(buf, n + offset) return x # PublisherFeatures def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) # PublisherFeatures def PublisherIdentification(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # PublisherFeatures def PublisherExclusion(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # PublisherFeatures def SubscriberBlackwhiteListing(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # PublisherFeatures def AcknowledgeEventReceived(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # PublisherFeatures def PayloadTransparency(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # PublisherFeatures def PayloadEncryptionCryptobox(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) if o != 0: return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False def PublisherFeaturesStart(builder): builder.StartObject(6) def PublisherFeaturesAddPublisherIdentification(builder, publisherIdentification): builder.PrependBoolSlot(0, publisherIdentification, 0) def PublisherFeaturesAddPublisherExclusion(builder, publisherExclusion): builder.PrependBoolSlot(1, publisherExclusion, 0) def PublisherFeaturesAddSubscriberBlackwhiteListing(builder, subscriberBlackwhiteListing): builder.PrependBoolSlot(2, subscriberBlackwhiteListing, 0) def PublisherFeaturesAddAcknowledgeEventReceived(builder, acknowledgeEventReceived): builder.PrependBoolSlot(3, acknowledgeEventReceived, 0) def PublisherFeaturesAddPayloadTransparency(builder, payloadTransparency): builder.PrependBoolSlot(4, payloadTransparency, 0) def PublisherFeaturesAddPayloadEncryptionCryptobox(builder, payloadEncryptionCryptobox): builder.PrependBoolSlot(5, payloadEncryptionCryptobox, 0) def PublisherFeaturesEnd(builder): return builder.EndObject()

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setup.py

initOS/dob-lib

0

78135

import os from setuptools import find_packages, setup def read(fname): with open(os.path.join(os.path.dirname(__file__), fname)) as f: return f.read() deps = [ "black", "coverage", "debugpy", "flake8", "git-aggregator", "ipython", "isort>=4.3.10", "pylint_odoo", "pytest-cov", "PyYAML", ] if not os.getenv("DISABLE_PYTEST_ODOO"): deps.append("pytest-odoo") setup( name="doblib", version="0.8.3", author="<EMAIL>", author_email="<EMAIL>", description="Management tool for Odoo installations", long_description=read("README.md"), long_description_content_type="text/markdown", license="Apache License 2.0", keywords="odoo environment management", url="https://github.com/initos/dob-lib", packages=find_packages("src"), package_dir={"": "src"}, include_package_data=True, install_requires=deps, entry_points={"console_scripts": ["dob = doblib.main:main"]}, classifiers=[ "Environment :: Console", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Programming Language :: Python :: 3", ], python_requires=">=3.6", )

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yummyPie.py

BoomBanger/EatPie

0

1610018

import time time.sleep(5) import datetime import os import git import pyglet from pyglet.window import Platform dir = os.path.dirname(os.path.realpath(__file__)) repo = git.cmd.Git(dir) t = -1 lastpull = 0 with open(os.path.join(dir, "dinnerTime.txt"), "r") as f: timestr = f.read() #####################################################04/02/22 04:03 t = time.mktime(datetime.datetime.strptime(timestr, "%m/%d/%y %H:%M:%S").timetuple()) #t += 5*3600 #only for replit, thinks we are in london def pull(): global time, dir, repo try: repo.pull() with open(os.path.join(dir, "dinnerTime.txt"), "r") as f: timestr = f.read() #####################################################04/02/22 04:03 t = time.mktime(datetime.datetime.strptime(timestr, "%d/%m/%y %H:%M:%S").timetuple()) #t += 5*3600 #only for replit, thinks we are in london except: pass while time.time() < t: print("waiting") if time.time() - lastpull > 4*60*60: pull() lastpull = time.time() if time.time() < t + 10*60*60: monitor = Platform().get_default_display().get_default_screen() sprite = pyglet.sprite.Sprite(pyglet.resource.animation(os.path.join(dir, "text.gif"))) H_ratio = max(sprite.height, monitor.height) / min(sprite.height, monitor.height) W_ratio = max(sprite.width, monitor.width) / min(sprite.width, monitor.width) sprite.scale = min(H_ratio, W_ratio) window = pyglet.window.Window(width=monitor.width, height=monitor.height, fullscreen=True) pyglet.gl.glClearColor(1, 1, 1, 1) @window.event def on_draw(): window.clear() sprite.draw() pyglet.app.run()

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tests/ut/python/dataset/test_datasets_obs_mindrecord.py

httpsgithu/mindspore

1

183851

# Copyright 2022 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """ Test OBSMindDataset operator """ import pytest from mindspore.dataset.engine.datasets_standard_format import OBSMindDataset from mindspore import log as logger DATA_DIR = ["s3://dataset/imagenet0", "s3://dataset/imagenet1"] def test_obs_mindrecord_exception(): """ Feature: Test OBSMindDataset. Description: invalid input. Expectation: raise exception. """ logger.info("Test error cases for MnistDataset") error_msg_0 = "Argument dataset_files" with pytest.raises(TypeError, match=error_msg_0): OBSMindDataset("err_dataset", "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir") error_msg_0_1 = "Item of dataset files" with pytest.raises(TypeError, match=error_msg_0_1): OBSMindDataset([1, 2], "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir") error_msg_1 = "Argument server" with pytest.raises(TypeError, match=error_msg_1): OBSMindDataset(DATA_DIR, 12, "dummy_ak", "dummy_sk", "s3://dummy_sync_dir") error_msg_1_1 = "server should" with pytest.raises(ValueError, match=error_msg_1_1): OBSMindDataset(DATA_DIR, "ftp://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir") error_msg_2 = "Argument ak" with pytest.raises(TypeError, match=error_msg_2): OBSMindDataset(DATA_DIR, "https://dummy_site", 12, "dummy_sk", "s3://dummy_sync_dir") error_msg_3 = "Argument sk" with pytest.raises(TypeError, match=error_msg_3): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", 12, "s3://dummy_sync_dir") error_msg_4 = "Argument sync_obs_path" with pytest.raises(TypeError, match=error_msg_4): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", "dummy_sk", 12) error_msg_5 = "Input shard_id is not within the required interval" with pytest.raises(ValueError, match=error_msg_5): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir", num_shards=2, shard_id=-1) with pytest.raises(ValueError, match=error_msg_5): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir", num_shards=4, shard_id=4) with pytest.raises(ValueError, match=error_msg_5): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir", num_shards=2, shard_id=4) error_msg_7 = "Argument shard_equal_rows" with pytest.raises(TypeError, match=error_msg_7): OBSMindDataset(DATA_DIR, "https://dummy_site", "dummy_ak", "dummy_sk", "s3://dummy_sync_dir", shard_equal_rows=1) if __name__ == '__main__': test_obs_mindrecord_exception()

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importanize/groups.py

xiachufang/importanize

0

13649

# -*- coding: utf-8 -*- from __future__ import absolute_import, print_function, unicode_literals import itertools import operator from collections import OrderedDict, defaultdict from functools import reduce import six from .formatters import DEFAULT_FORMATTER, DEFAULT_LENGTH from .utils import is_site_package, is_std_lib @six.python_2_unicode_compatible class BaseImportGroup(object): def __init__(self, config=None, **kwargs): self.config = config or {} self.statements = kwargs.get("statements", []) self.file_artifacts = kwargs.get("file_artifacts", {}) @property def unique_statements(self): return sorted(list(set(self.merged_statements))) @property def merged_statements(self): """ Merge statements with the same import stems """ leafless_counter = defaultdict(list) counter = defaultdict(list) for statement in self.statements: if statement.leafs: counter[statement.stem].append(statement) else: leafless_counter[statement.stem].append(statement) merged_statements = list(itertools.chain(*leafless_counter.values())) def merge(statements): _special = [] _statements = [] for i in statements: if i.leafs and i.leafs[0].name == "*": _special.append(i) else: _statements.append(i) _reduced = [] if _statements: _reduced = [reduce(lambda a, b: a + b, _statements)] return _special + _reduced for statements in counter.values(): merged_statements.extend(merge(statements)) return merged_statements def all_line_numbers(self): return sorted( list( set( list( itertools.chain( *map( operator.attrgetter("line_numbers"), self.statements, ) ) ) ) ) ) def should_add_statement(self, statement): raise NotImplementedError def add_statement(self, statement): if self.should_add_statement(statement): self.statements.append(statement) return True return False def as_string(self): sep = self.file_artifacts.get("sep", "\n") return sep.join( map(operator.methodcaller("as_string"), self.unique_statements) ) def formatted(self, formatter=DEFAULT_FORMATTER, length=DEFAULT_LENGTH): sep = self.file_artifacts.get("sep", "\n") return sep.join( map( operator.methodcaller( "formatted", formatter=formatter, length=length ), self.unique_statements, ) ) def __str__(self): return self.as_string() class StdLibGroup(BaseImportGroup): def should_add_statement(self, statement): return is_std_lib(statement.root_module) class SitePackagesGroup(BaseImportGroup): def should_add_statement(self, statement): return is_site_package(statement.root_module) class PackagesGroup(BaseImportGroup): def __init__(self, *args, **kwargs): super(PackagesGroup, self).__init__(*args, **kwargs) if "packages" not in self.config: msg = ( '"package" config must be supplied ' "for packages import group" ) raise ValueError(msg) def should_add_statement(self, statement): return statement.root_module in self.config.get("packages", []) class LocalGroup(BaseImportGroup): def should_add_statement(self, statement): return statement.stem.startswith(".") class RemainderGroup(BaseImportGroup): def should_add_statement(self, statement): return True # -- RemainderGroup goes last and catches everything left over GROUP_MAPPING = OrderedDict( ( ("stdlib", StdLibGroup), ("sitepackages", SitePackagesGroup), ("packages", PackagesGroup), ("local", LocalGroup), ("remainder", RemainderGroup), ) ) def sort_groups(groups): return sorted( groups, key=lambda i: list(GROUP_MAPPING.values()).index(type(i)) ) @six.python_2_unicode_compatible class ImportGroups(list): def __init__(self, *args, **kwargs): super(ImportGroups, self).__init__(*args) self.file_artifacts = kwargs.get("file_artifacts", {}) def all_line_numbers(self): return sorted( list( set( list( itertools.chain( *map( operator.methodcaller("all_line_numbers"), self ) ) ) ) ) ) def add_group(self, config): if "type" not in config: msg = '"type" must be specified in ' "import group config" raise ValueError(msg) if config["type"] not in GROUP_MAPPING: msg = '"{}" is not supported import group'.format(config["type"]) raise ValueError(msg) self.append(GROUP_MAPPING[config["type"]](config)) def add_statement_to_group(self, statement): groups_by_priority = sort_groups(self) added = False for group in groups_by_priority: if group.add_statement(statement): added = True break if not added: msg = ( "Import statement was not added into " "any of the import groups. " "Perhaps you can consider adding " '"remaining" import group which will ' "catch all remaining import statements." ) raise ValueError(msg) def as_string(self): sep = self.file_artifacts.get("sep", "\n") * 2 return sep.join( filter(None, map(operator.methodcaller("as_string"), self)) ) def formatted(self, formatter=DEFAULT_FORMATTER, length=DEFAULT_LENGTH): sep = self.file_artifacts.get("sep", "\n") * 2 return sep.join( filter( None, map( operator.methodcaller( "formatted", formatter=formatter, length=length ), self, ), ) ) def __str__(self): return self.as_string()

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src/download_dataset.py

thomasreolon/DeepfakeDetection

2

181910

<gh_stars>1-10 import os, pathlib from gdrive import GoogleDriveDownloader as gdd from videoanalizer import VideoAnalizer os.chdir(pathlib.Path(__file__).parent.absolute()) #GDRIVE_CODE = '1La_4SVYRNT8ePgLzZGwlxuY1V9x989DC' # old dataset GDRIVE_CODE = '1djFwp9vLkmOtd65ylXSYYn7C1ju08lJ7' ######## Create Directory & Download Videos if ('test_data' not in os.listdir('../')): # if folder is empty, get videos from gdrive print("Default dataset size: 6GB") gdd.download_file_from_google_drive(file_id=GDRIVE_CODE, # my GDrive dest_path='../dataset.zip', unzip=True, showsize=True) os.system('rm ../dataset.zip') ######## This will fail and ask you to install OpenFace # press y to install OpenFace VideoAnalizer()

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luxai2021/game/resource.py

nofate090/LuxPythonEnvGym

0

143923

'''Implements /src/Resource/index.ts''' class Resource: ''' Enum implemenation ''' class Types: WOOD = 'wood' COAL = 'coal' URANIUM = 'uranium' def __init__(self, type, amount) -> None: self.type = type self.amount = amount

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gocrawl/request.py

stephanebruckert/GoCrawl

0

152916

#!/usr/bin/python import requests from pyquery import PyQuery def stringified_page(url): ''' Request a webpage ''' r = requests.get(url) if r.status_code == 200: return str(PyQuery(r.text)) else: raise Exception(r.status_code)

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cfanalytics/tests/test_affiliatelist.py

raybellwaves/cfanalytics

23

125641

<filename>cfanalytics/tests/test_affiliatelist.py import pytest import requests # HTTP library from . import TestCase class TestAffiliatelist(TestCase): def setUp(self): self.basepath = 'https://map.crossfit.com' self.headers = {'Host': 'map.crossfit.com', 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_2'+\ ') AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.13'+\ '2 Safari/537.36'} def test_get_lat_lons(self): expected = 40.3604 response = requests.get(self.basepath+'/getAllAffiliates.php').json() actual = response[0][0] assert expected == actual

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server/ball_speed_placement.py

Rohansjamadagni/topspin-tracker

2

103678

import sys import cv2 import math import os import json import pandas as pd import numpy as np import json stroke_data = { "data" : [] } stroke_data_ = open('result.json', 'r') stroke_data = json.loads(stroke_data_.read()) print(stroke_data['data']) stroke_data_.close() speed_for_each_stroke = {} def read_coordinates(): f = open("coordinates.txt", 'r') data_ = f.read() data = data_.strip() str_table_coordinates = data.split(' ') table_coordinates = [] for i in str_table_coordinates: table_coordinates.append(list(map(int, (i.split(','))))) return table_coordinates def find_net(table_coordinates): """ Finding net coordinates, taking avg of x1 and x4 (mid points of table) """ top_x = table_coordinates[1][0] bottom_x = table_coordinates[4][0] avg = int((top_x+bottom_x)/2) return avg def get_coordinate_of_net_cross(x_coords, midpoint_x=270): print(list(x_coords), midpoint_x) for pos, x_coordinate in enumerate(list(x_coords)): if x_coordinate >= midpoint_x: return pos # 1 2 3 4 5 6 7 8 9 9 10 def euclidian_distance(x1, y1, x2, y2): distance = math.sqrt(((x1 - x2)**2) + ((y1 - y2)**2)) return distance def get_speed(buffer=3, fps = 20): speed_for_each_stroke = {} table_coordinates = read_coordinates() midpoint_x = find_net(table_coordinates) coordinates_df = pd.read_csv('final_result.csv') stroke_number = coordinates_df['stroke_number'] x_coords = coordinates_df['x'] y_coords = coordinates_df['y'] x_coords = list(x_coords) y_coords = list(y_coords) strokes_x_y = [] prev_stroke_number = list(stroke_number)[0] stroke = [] # separate strokes into array for i in coordinates_df.iterrows(): # print(i[1]['stroke_number']) if prev_stroke_number != i[1]['stroke_number']: prev_stroke_number = i[1]['stroke_number'] strokes_x_y.append(np.asarray(stroke)) stroke = [] stroke.append([i[1]['x'], i[1]['y']]) strokes_x_y.append(np.asarray(stroke)) # import sys # sys.exit(1) left_midpoint_coordinate = [ ((table_coordinates[0][0] + table_coordinates[5][0]) / 2), ((table_coordinates[0][1] + table_coordinates[5][1]) / 2)] right_midpoint_coordinate = [ ((table_coordinates[2][0] + table_coordinates[3][0]) / 2), ((table_coordinates[2][1] + table_coordinates[3][1]) / 2)] strokes_x_y = np.asarray(strokes_x_y) table_distance = euclidian_distance( left_midpoint_coordinate[0], left_midpoint_coordinate[1], right_midpoint_coordinate[0], right_midpoint_coordinate[1]) # print(stroke) import sys print(len(strokes_x_y)) for pos, stroke in enumerate(strokes_x_y): print('ffs', pos) x_coords = stroke[:, 0] y_coords = stroke[:, 1] speed_list = [] position_of_net_cross = get_coordinate_of_net_cross(x_coords, midpoint_x) distances = [] print(position_of_net_cross, 'pos', midpoint_x) # sys.exit(1) print(len(x_coords), len(y_coords)) try: for point in range(position_of_net_cross-buffer, position_of_net_cross+buffer+1): speed_list.append([x_coords[point], y_coords[point]]) except: pass for i in range(len(speed_list)-1): distances.append(euclidian_distance( speed_list[i][0], speed_list[i][1], speed_list[i+1][0], speed_list[i+1][1] )) speed = sum(distances) / (1 / fps * ((len(distances)+1))) speed = (speed * 2.74) / table_distance # find speed for each stroke speed *= 18 / 5 speed_for_each_stroke[pos+1] = speed return speed_for_each_stroke def get_zone(ball_coordinates, no_columns=6, no_rows=3): tt_table = np.zeros((3, 6), np.int32) table_map = { "03" : 1, "04" : 2, "05" : 3, "13" : 4, "14" : 5, "15" : 6, "23" : 7, "24" : 8, "25" : 9 } col_seg = 1920 / no_columns row_seg = 1080 / no_rows bounce_col = 0 bounce_row = 0 for i in range(1, no_columns+1): if ball_coordinates[0] < col_seg * i: bounce_col = i break for i in range(1, no_rows+1): if ball_coordinates[1] < row_seg * i: bounce_row = i break # tt_table[bounce_row-1][bounce_col-1] += 1 valid_check = str(bounce_row) + str(bounce_col) if valid_check in table_map: return str(table_map[valid_check]) else: print('wrong coords') return str(3) def warp_coordinates(x, y, matrix): print('warping') print(x, y, 'pints') p = (x, y) px = (matrix[0][0] * p[0] + matrix[0][1] * p[1] + matrix[0][2]) / \ ((matrix[2][0] * p[0] + matrix[2][1] * p[1] + matrix[2][2])) py = (matrix[1][0] * p[0] + matrix[1][1] * p[1] + matrix[1][2]) / \ ((matrix[2][0] * p[0] + matrix[2][1] * p[1] + matrix[2][2])) print(px, py, ' warped pints') return [int(px), int(py)] def get_transposed_coordinates(x, y): table_coordinates = read_coordinates() table_coordinates_corners = np.array([ table_coordinates[0], table_coordinates[2], table_coordinates[3], table_coordinates[5] ], np.float32) print(table_coordinates, 'table coords') warped_dimensions = np.array([[0, 0], [1920, 0], [0, 1080], [1920, 1080]], np.float32) matrix = cv2.getPerspectiveTransform(table_coordinates_corners, warped_dimensions) x, y = warp_coordinates(x, y, matrix) position = get_zone([x,y]) return position def caluclate_speed_and_placements(): global stroke_data # do speed tomorrow result = pd.read_csv('final_result.csv') left_bounces = result.groupby("stroke_number").first().reset_index() right_bounces = result.groupby("stroke_number").last().reset_index() speed_for_each_stroke = get_speed() print(speed_for_each_stroke, 'speed', len(left_bounces)) bc = 0 for bounces in zip(left_bounces.iterrows(), right_bounces.iterrows()): print(bc, 'bc') left, right = bounces[0][1], bounces[1][1] print(left['x'], right['x'], 'lr') data_ = { "stroke_name" : stroke_data['data'][bc]['stroke_name'], "stroke_number" : left['stroke_number'], "position" : get_transposed_coordinates(right['x'], right['y']), "speed" : speed_for_each_stroke[left['stroke_number']] } stroke_data['data'][bc] = data_ # if bc == 3: # break bc += 1 file_ = open('stroke_speed_result.json', 'w') json.dump(stroke_data, file_) if __name__ == "__main__": caluclate_speed_and_placements()

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memsql_loader/api/caller.py

yonglehou/memsql-loader

1

181929

<filename>memsql_loader/api/caller.py import re from memsql_loader.api.exceptions import ApiException from memsql_loader.api.jobs import Jobs from memsql_loader.api.job import Job from memsql_loader.api.tasks import Tasks from memsql_loader.api.task import Task APIS = [ Jobs, Job, Tasks, Task ] __underscorer1 = re.compile(r'(.)([A-Z][a-z]+)') __underscorer2 = re.compile('([a-z0-9])([A-Z])') def __camelcase_convertor(s): subbed = __underscorer1.sub(r'\1_\2', s) return __underscorer2.sub(r'\1_\2', subbed).lower() API_NAME_MAP = { __camelcase_convertor(ApiClass.__name__): ApiClass for ApiClass in APIS } class ApiCaller(object): """ This class allows you to query any of the API's by name """ def __init__(self): for name, ApiClass in API_NAME_MAP.items(): setattr(self, name, ApiClass()) def call(self, api, params): return self.get(api).query(params) def get(self, api): if not hasattr(self, api): raise ApiException('Api not found: %s' % api) return getattr(self, api)

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src/jomiel_kore/version.py

guendto/jomiel-kore

0

29931

<filename>src/jomiel_kore/version.py # # jomiel-kore # # Copyright # 2019-2020 <NAME> # # # SPDX-License-Identifier: Apache-2.0 # """TODO.""" try: # py38+ from importlib.metadata import version as metadata_version from importlib.metadata import PackageNotFoundError except ModuleNotFoundError: from importlib_metadata import version as metadata_version from importlib_metadata import PackageNotFoundError def package_version(package_name, destination): """Returns the package version string Args: package_name (str): the package name to look up destination (list): the list to store the result (tuple) to """ try: version = metadata_version(package_name) except PackageNotFoundError: version = "<unavailable>" if package_name == "pyzmq": from zmq import zmq_version version = "{} (libzmq version {})".format( version, zmq_version(), ) destination.append((package_name, version)) # vim: set ts=4 sw=4 tw=72 expandtab:

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oslo_db/sqlalchemy/enginefacade.py

calsoft-internal/oslo.db-internal

0

68130

# 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 contextlib import functools import operator import threading import warnings import debtcollector.moves import debtcollector.removals import debtcollector.renames from oslo_config import cfg from oslo_utils import excutils from oslo_db import exception from oslo_db import options from oslo_db.sqlalchemy import engines from oslo_db.sqlalchemy import orm from oslo_db.sqlalchemy import utils from oslo_db import warning class _symbol(object): """represent a fixed symbol.""" __slots__ = 'name', def __init__(self, name): self.name = name def __repr__(self): return "symbol(%r)" % self.name _ASYNC_READER = _symbol('ASYNC_READER') """Represent the transaction state of "async reader". This state indicates that the transaction is a read-only and is safe to use on an asynchronously updated slave database. """ _READER = _symbol('READER') """Represent the transaction state of "reader". This state indicates that the transaction is a read-only and is only safe to use on a synchronously updated slave database; otherwise the master database should be used. """ _WRITER = _symbol('WRITER') """Represent the transaction state of "writer". This state indicates that the transaction writes data and should be directed at the master database. """ class _Default(object): """Mark a value as a default value. A value in the local configuration dictionary wrapped with _Default() will not take precedence over a value that is specified in cfg.CONF. Values that are set after the fact using configure() will supersede those in cfg.CONF. """ __slots__ = 'value', _notset = _symbol("NOTSET") def __init__(self, value=_notset): self.value = value @classmethod def resolve(cls, value): if isinstance(value, _Default): v = value.value if v is cls._notset: return None else: return v else: return value @classmethod def resolve_w_conf(cls, value, conf_namespace, key): if isinstance(value, _Default): v = getattr(conf_namespace, key, value.value) if v is cls._notset: return None else: return v else: return value @classmethod def is_set(cls, value): return not isinstance(value, _Default) or \ value.value is not cls._notset @classmethod def is_set_w_conf(cls, value, conf_namespace, key): return not isinstance(value, _Default) or \ value.value is not cls._notset or \ hasattr(conf_namespace, key) class AlreadyStartedError(TypeError): """Raises when a factory is being asked to initialize a second time. Subclasses :class:`.TypeError` for legacy support. """ class _TransactionFactory(object): """A factory for :class:`._TransactionContext` objects. By default, there is just one of these, set up based on CONF, however instance-level :class:`._TransactionFactory` objects can be made, as is the case with the :class:`._TestTransactionFactory` subclass used by the oslo.db test suite. """ def __init__(self): self._url_cfg = { 'connection': _Default(), 'slave_connection': _Default(), } self._engine_cfg = { 'sqlite_fk': _Default(False), 'mysql_sql_mode': _Default('TRADITIONAL'), 'mysql_enable_ndb': _Default(False), 'connection_recycle_time': _Default(3600), 'connection_debug': _Default(0), 'max_pool_size': _Default(), 'max_overflow': _Default(), 'pool_timeout': _Default(), 'sqlite_synchronous': _Default(True), 'connection_trace': _Default(False), 'max_retries': _Default(10), 'retry_interval': _Default(10), 'thread_checkin': _Default(True), 'json_serializer': _Default(None), 'json_deserializer': _Default(None), 'logging_name': _Default(None), 'connection_parameters': _Default(None) } self._maker_cfg = { 'expire_on_commit': _Default(False), '__autocommit': True } self._transaction_ctx_cfg = { 'rollback_reader_sessions': False, 'flush_on_subtransaction': False, } self._facade_cfg = { 'synchronous_reader': True, 'on_engine_create': [], } # other options that are defined in oslo_db.options.database_opts # or oslo_db.concurrency.tpool_opts but do not apply to the standard # enginefacade arguments (most seem to apply to api.DBAPI). self._ignored_cfg = dict( (k, _Default(None)) for k in [ 'db_max_retries', 'db_inc_retry_interval', 'use_db_reconnect', 'db_retry_interval', 'db_max_retry_interval', 'backend', 'use_tpool']) self._started = False self._legacy_facade = None self._start_lock = threading.Lock() @debtcollector.renames.renamed_kwarg( "idle_timeout", "connection_recycle_time", replace=True) def configure_defaults(self, **kw): """Apply default configurational options. This method can only be called before any specific transaction-beginning methods have been called. Configurational options are within a fixed set of keys, and fall under three categories: URL configuration, engine configuration, and session configuration. Each key given will be tested against these three configuration sets to see which one is applicable; if it is not applicable to any set, an exception is raised. The configurational options given here act as **defaults** when the :class:`._TransactionFactory` is configured using a :class:`oslo_config.cfg.ConfigOpts` object; the options present within the :class:`oslo_config.cfg.ConfigOpts` **take precedence** versus the arguments passed here. By default, the :class:`._TransactionFactory` loads in the configuration from :data:`oslo_config.cfg.CONF`, after applying the :data:`oslo_db.options.database_opts` configurational defaults to it. :param connection: database URL :param slave_connection: database URL :param sqlite_fk: whether to enable SQLite foreign key pragma; default False :param mysql_sql_mode: MySQL SQL mode, defaults to TRADITIONAL :param mysql_enable_ndb: enable MySQL Cluster (NDB) support :param connection_recycle_time: connection pool recycle time, defaults to 3600. Note the connection does not actually have to be "idle" to be recycled. :param connection_debug: engine logging level, defaults to 0. set to 50 for INFO, 100 for DEBUG. :param connection_parameters: additional parameters to append onto the database URL query string, pass as "param1=value1&param2=value2&..." :param max_pool_size: max size of connection pool, uses CONF for default :param max_overflow: max overflow for connection pool, uses CONF for default :param sqlite_synchronous: disable SQLite SYNCHRONOUS pragma if False; defaults to True :param connection_trace: enable tracing comments in logging :param max_retries: max retries to connect, defaults to !0 :param retry_interval: time in seconds between retries, defaults to 10 :param thread_checkin: add sleep(0) on connection checkin to allow greenlet yields, defaults to True :param json_serializer: JSON serializer for PostgreSQL connections :param json_deserializer: JSON deserializer for PostgreSQL connections :param logging_name: logging name for engine :param expire_on_commit: sets expire_on_commit for SQLAlchemy sessionmaker; defaults to False :param rollback_reader_sessions: if True, a :class:`.Session` object will have its :meth:`.Session.rollback` method invoked at the end of a ``@reader`` block, actively rolling back the transaction and expiring the objects within, before the :class:`.Session` moves on to be closed, which has the effect of releasing connection resources back to the connection pool and detaching all objects. If False, the :class:`.Session` is not affected at the end of a ``@reader`` block; the underlying connection referred to by this :class:`.Session` will still be released in the enclosing context via the :meth:`.Session.close` method, which still ensures that the DBAPI connection is rolled back, however the objects associated with the :class:`.Session` retain their database-persisted contents after they are detached. .. seealso:: http://docs.sqlalchemy.org/en/rel_0_9/glossary.html#term-released\ SQLAlchemy documentation on what "releasing resources" means. :param synchronous_reader: whether or not to assume a "reader" context needs to guarantee it can read data committed by a "writer" assuming replication lag is present; defaults to True. When False, a @reader context works the same as @async_reader and will select the "slave" database if present. :param flush_on_subtransaction: if True, a :class:`.Session` object will have its :meth:`.Session.flush` method invoked whenever a context manager or decorator that is not itself the originator of the top- level or savepoint :class:`.Session` transaction exits - in this way it behaves like a "subtransaction" from a :class:`.Session` perspective. .. seealso:: :meth:`._TransactionFactory.configure` """ self._configure(True, kw) @debtcollector.renames.renamed_kwarg( "idle_timeout", "connection_recycle_time", replace=True) def configure(self, **kw): """Apply configurational options. This method can only be called before any specific transaction-beginning methods have been called. Behavior here is the same as that of :meth:`._TransactionFactory.configure_defaults`, with the exception that values specified here will **supersede** those setup in the :class:`oslo_config.cfg.ConfigOpts` options. See that method for a listing of all keyword arguments. .. seealso:: :meth:`._TransactionFactory.configure_defaults` """ self._configure(False, kw) def _configure(self, as_defaults, kw): if self._started: raise AlreadyStartedError( "this TransactionFactory is already started") not_supported = [] for k, v in kw.items(): for dict_ in ( self._url_cfg, self._engine_cfg, self._maker_cfg, self._ignored_cfg, self._facade_cfg, self._transaction_ctx_cfg): if k in dict_: dict_[k] = _Default(v) if as_defaults else v break else: not_supported.append(k) if not_supported: # would like to raise ValueError here, but there are just # too many unrecognized (obsolete?) configuration options # coming in from projects warnings.warn( "Configuration option(s) %r not supported" % sorted(not_supported), warning.NotSupportedWarning ) def get_legacy_facade(self): """Return a :class:`.LegacyEngineFacade` for this factory. This facade will make use of the same engine and sessionmaker as this factory, however will not share the same transaction context; the legacy facade continues to work the old way of returning a new Session each time get_session() is called. """ if not self._legacy_facade: self._legacy_facade = LegacyEngineFacade(None, _factory=self) if not self._started: self._start() return self._legacy_facade def get_writer_engine(self): """Return the writer engine for this factory. Implies start. """ if not self._started: self._start() return self._writer_engine def get_reader_engine(self): """Return the reader engine for this factory. Implies start. """ if not self._started: self._start() return self._reader_engine def get_writer_maker(self): """Return the writer sessionmaker for this factory. Implies start. """ if not self._started: self._start() return self._writer_maker def get_reader_maker(self): """Return the reader sessionmaker for this factory. Implies start. """ if not self._started: self._start() return self._reader_maker def _create_connection(self, mode): if not self._started: self._start() if mode is _WRITER: return self._writer_engine.connect() elif mode is _ASYNC_READER or \ (mode is _READER and not self.synchronous_reader): return self._reader_engine.connect() else: return self._writer_engine.connect() def _create_session(self, mode, bind=None): if not self._started: self._start() kw = {} # don't pass 'bind' if bind is None; the sessionmaker # already has a bind to the engine. if bind: kw['bind'] = bind if mode is _WRITER: return self._writer_maker(**kw) elif mode is _ASYNC_READER or \ (mode is _READER and not self.synchronous_reader): return self._reader_maker(**kw) else: return self._writer_maker(**kw) def _create_factory_copy(self): factory = _TransactionFactory() factory._url_cfg.update(self._url_cfg) factory._engine_cfg.update(self._engine_cfg) factory._maker_cfg.update(self._maker_cfg) factory._transaction_ctx_cfg.update(self._transaction_ctx_cfg) factory._facade_cfg.update(self._facade_cfg) return factory def _args_for_conf(self, default_cfg, conf): if conf is None: return dict( (key, _Default.resolve(value)) for key, value in default_cfg.items() if _Default.is_set(value) ) else: return dict( (key, _Default.resolve_w_conf(value, conf.database, key)) for key, value in default_cfg.items() if _Default.is_set_w_conf(value, conf.database, key) ) def _url_args_for_conf(self, conf): return self._args_for_conf(self._url_cfg, conf) def _engine_args_for_conf(self, conf): return self._args_for_conf(self._engine_cfg, conf) def _maker_args_for_conf(self, conf): maker_args = self._args_for_conf(self._maker_cfg, conf) maker_args['autocommit'] = maker_args.pop('__autocommit') return maker_args def dispose_pool(self): """Call engine.pool.dispose() on underlying Engine objects.""" with self._start_lock: if not self._started: return self._writer_engine.pool.dispose() if self._reader_engine is not self._writer_engine: self._reader_engine.pool.dispose() @property def is_started(self): """True if this :class:`._TransactionFactory` is already started.""" return self._started def _start(self, conf=False, connection=None, slave_connection=None): with self._start_lock: # self._started has been checked on the outside # when _start() was called. Within the lock, # check the flag once more to detect the case where # the start process proceeded while this thread was waiting # for the lock. if self._started: return if conf is False: conf = cfg.CONF # perform register_opts() local to actually using # the cfg.CONF to maintain exact compatibility with # the EngineFacade design. This can be changed if needed. if conf is not None: conf.register_opts(options.database_opts, 'database') url_args = self._url_args_for_conf(conf) if connection: url_args['connection'] = connection if slave_connection: url_args['slave_connection'] = slave_connection engine_args = self._engine_args_for_conf(conf) maker_args = self._maker_args_for_conf(conf) self._writer_engine, self._writer_maker = \ self._setup_for_connection( url_args['connection'], engine_args, maker_args) if url_args.get('slave_connection'): self._reader_engine, self._reader_maker = \ self._setup_for_connection( url_args['slave_connection'], engine_args, maker_args) else: self._reader_engine, self._reader_maker = \ self._writer_engine, self._writer_maker self.synchronous_reader = self._facade_cfg['synchronous_reader'] # set up _started last, so that in case of exceptions # we try the whole thing again and report errors # correctly self._started = True def _setup_for_connection( self, sql_connection, engine_kwargs, maker_kwargs): if sql_connection is None: raise exception.CantStartEngineError( "No sql_connection parameter is established") engine = engines.create_engine( sql_connection=sql_connection, **engine_kwargs) for hook in self._facade_cfg['on_engine_create']: hook(engine) sessionmaker = orm.get_maker(engine=engine, **maker_kwargs) return engine, sessionmaker class _TestTransactionFactory(_TransactionFactory): """A :class:`._TransactionFactory` used by test suites. This is a :class:`._TransactionFactory` that can be directly injected with an existing engine and sessionmaker. Note that while this is used by oslo.db's own tests of the enginefacade system, it is also exported for use by the test suites of other projects, first as an element of the oslo_db.sqlalchemy.test_base module, and secondly may be used by external test suites directly. Includes a feature to inject itself temporarily as the factory within the global :class:`._TransactionContextManager`. """ @debtcollector.removals.removed_kwarg( 'synchronous_reader', 'argument value is propagated from the parent _TransactionFactory') def __init__(self, engine, maker, apply_global, from_factory=None, **kw): # NOTE(zzzeek): **kw needed for backwards compability self._reader_engine = self._writer_engine = engine self._reader_maker = self._writer_maker = maker self._started = True self._legacy_facade = None if from_factory is None: from_factory = _context_manager._factory self._facade_cfg = from_factory._facade_cfg self._transaction_ctx_cfg = from_factory._transaction_ctx_cfg self.synchronous_reader = self._facade_cfg['synchronous_reader'] if apply_global: self.existing_factory = _context_manager._factory _context_manager._root_factory = self def dispose_global(self): _context_manager._root_factory = self.existing_factory class _TransactionContext(object): """Represent a single database transaction in progress.""" def __init__(self, factory, global_factory=None): """Construct a new :class:`.TransactionContext`. :param factory: the :class:`.TransactionFactory` which will serve as a source of connectivity. :param global_factory: the "global" factory which will be used by the global ``_context_manager`` for new ``_TransactionContext`` objects created under this one. When left as None the actual "global" factory is used. """ self.factory = factory self.global_factory = global_factory self.mode = None self.session = None self.connection = None self.transaction = None kw = self.factory._transaction_ctx_cfg self.rollback_reader_sessions = kw['rollback_reader_sessions'] self.flush_on_subtransaction = kw['flush_on_subtransaction'] @contextlib.contextmanager def _connection(self, savepoint=False, context=None): if self.connection is None: try: if self.session is not None: # use existing session, which is outer to us self.connection = self.session.connection() if savepoint: with self.connection.begin_nested(), \ self._add_context(self.connection, context): yield self.connection else: with self._add_context(self.connection, context): yield self.connection else: # is outermost self.connection = self.factory._create_connection( mode=self.mode) self.transaction = self.connection.begin() try: with self._add_context(self.connection, context): yield self.connection self._end_connection_transaction(self.transaction) except Exception: self.transaction.rollback() # TODO(zzzeek) do we need save_and_reraise() here, # or do newer eventlets not have issues? we are using # raw "raise" in many other places in oslo.db already raise finally: self.transaction = None self.connection.close() finally: self.connection = None else: # use existing connection, which is outer to us if savepoint: with self.connection.begin_nested(), \ self._add_context(self.connection, context): yield self.connection else: with self._add_context(self.connection, context): yield self.connection @contextlib.contextmanager def _session(self, savepoint=False, context=None): if self.session is None: self.session = self.factory._create_session( bind=self.connection, mode=self.mode) try: self.session.begin() with self._add_context(self.session, context): yield self.session self._end_session_transaction(self.session) except Exception: with excutils.save_and_reraise_exception(): self.session.rollback() finally: self.session.close() self.session = None else: # use existing session, which is outer to us if savepoint: with self.session.begin_nested(): with self._add_context(self.session, context): yield self.session else: with self._add_context(self.session, context): yield self.session if self.flush_on_subtransaction: self.session.flush() @contextlib.contextmanager def _add_context(self, connection, context): restore_context = connection.info.get('using_context') connection.info['using_context'] = context yield connection connection.info['using_context'] = restore_context def _end_session_transaction(self, session): if self.mode is _WRITER: session.commit() elif self.rollback_reader_sessions: session.rollback() # In the absence of calling session.rollback(), # the next call is session.close(). This releases all # objects from the session into the detached state, and # releases the connection as well; the connection when returned # to the pool is either rolled back in any case, or closed fully. def _end_connection_transaction(self, transaction): if self.mode is _WRITER: transaction.commit() else: transaction.rollback() def _produce_block(self, mode, connection, savepoint, allow_async=False, context=None): if mode is _WRITER: self._writer() elif mode is _ASYNC_READER: self._async_reader() else: self._reader(allow_async) if connection: return self._connection(savepoint, context=context) else: return self._session(savepoint, context=context) def _writer(self): if self.mode is None: self.mode = _WRITER elif self.mode is _READER: raise TypeError( "Can't upgrade a READER transaction " "to a WRITER mid-transaction") elif self.mode is _ASYNC_READER: raise TypeError( "Can't upgrade an ASYNC_READER transaction " "to a WRITER mid-transaction") def _reader(self, allow_async=False): if self.mode is None: self.mode = _READER elif self.mode is _ASYNC_READER and not allow_async: raise TypeError( "Can't upgrade an ASYNC_READER transaction " "to a READER mid-transaction") def _async_reader(self): if self.mode is None: self.mode = _ASYNC_READER class _TransactionContextTLocal(threading.local): def __deepcopy__(self, memo): return self def __reduce__(self): return _TransactionContextTLocal, () class _TransactionContextManager(object): """Provide context-management and decorator patterns for transactions. This object integrates user-defined "context" objects with the :class:`._TransactionContext` class, on behalf of a contained :class:`._TransactionFactory`. """ def __init__( self, root=None, mode=None, independent=False, savepoint=False, connection=False, replace_global_factory=None, _is_global_manager=False, allow_async=False): if root is None: self._root = self self._root_factory = _TransactionFactory() else: self._root = root self._replace_global_factory = replace_global_factory self._is_global_manager = _is_global_manager self._mode = mode self._independent = independent self._savepoint = savepoint if self._savepoint and self._independent: raise TypeError( "setting savepoint and independent makes no sense.") self._connection = connection self._allow_async = allow_async @property def _factory(self): """The :class:`._TransactionFactory` associated with this context.""" return self._root._root_factory @property def is_started(self): """True if this manager is already started.""" return self._factory.is_started def configure(self, **kw): """Apply configurational options to the factory. This method can only be called before any specific transaction-beginning methods have been called. """ self._factory.configure(**kw) def append_on_engine_create(self, fn): """Append a listener function to _facade_cfg["on_engine_create"]""" self._factory._facade_cfg['on_engine_create'].append(fn) def get_legacy_facade(self): """Return a :class:`.LegacyEngineFacade` for factory from this context. This facade will make use of the same engine and sessionmaker as this factory, however will not share the same transaction context; the legacy facade continues to work the old way of returning a new Session each time get_session() is called. """ return self._factory.get_legacy_facade() def get_engine(self): """Return the Engine in use. This will be based on the state being WRITER or READER. This implies a start operation. """ if self._mode is _WRITER: return self._factory.get_writer_engine() elif self._mode is _READER: return self._factory.get_reader_engine() else: raise ValueError("mode should be WRITER or READER") def get_sessionmaker(self): """Return the sessionmaker in use. This will be based on the state being WRITER or READER. This implies a start operation. """ if self._mode is _WRITER: return self._factory.get_writer_maker() elif self._mode is _READER: return self._factory.get_reader_maker() else: raise ValueError("mode should be WRITER or READER") def dispose_pool(self): """Call engine.pool.dispose() on underlying Engine objects.""" self._factory.dispose_pool() def make_new_manager(self): """Create a new, independent _TransactionContextManager from this one. Copies the underlying _TransactionFactory to a new one, so that it can be further configured with new options. Used for test environments where the application-wide _TransactionContextManager may be used as a factory for test-local managers. """ new = self._clone() new._root = new new._root_factory = self._root_factory._create_factory_copy() if new._factory._started: raise AssertionError('TransactionFactory is already started') return new def patch_factory(self, factory_or_manager): """Patch a _TransactionFactory into this manager. Replaces this manager's factory with the given one, and returns a callable that will reset the factory back to what we started with. Only works for root factories. Is intended for test suites that need to patch in alternate database configurations. The given argument may be a _TransactionContextManager or a _TransactionFactory. """ if isinstance(factory_or_manager, _TransactionContextManager): factory = factory_or_manager._factory elif isinstance(factory_or_manager, _TransactionFactory): factory = factory_or_manager else: raise ValueError( "_TransactionContextManager or " "_TransactionFactory expected.") if self._root is not self: raise AssertionError('patch_factory only works for root factory.') existing_factory = self._root_factory self._root_factory = factory def reset(): self._root_factory = existing_factory return reset def patch_engine(self, engine): """Patch an Engine into this manager. Replaces this manager's factory with a _TestTransactionFactory that will use the given Engine, and returns a callable that will reset the factory back to what we started with. Only works for root factories. Is intended for test suites that need to patch in alternate database configurations. """ existing_factory = self._factory if not existing_factory._started: existing_factory._start() maker = existing_factory._writer_maker maker_kwargs = existing_factory._maker_args_for_conf(cfg.CONF) maker = orm.get_maker(engine=engine, **maker_kwargs) factory = _TestTransactionFactory( engine, maker, apply_global=False, from_factory=existing_factory ) return self.patch_factory(factory) @property def replace(self): """Modifier to replace the global transaction factory with this one.""" return self._clone(replace_global_factory=self._factory) @property def writer(self): """Modifier to set the transaction to WRITER.""" return self._clone(mode=_WRITER) @property def reader(self): """Modifier to set the transaction to READER.""" return self._clone(mode=_READER) @property def allow_async(self): """Modifier to allow async operations Allows async operations if asynchronous session is already started in this context. Marking DB API methods with READER would make it impossible to use them in ASYNC_READER transactions, and marking them with ASYNC_READER would require a modification of all the places these DB API methods are called to force READER mode, where the latest DB state is required. In Nova DB API methods should have a 'safe' default (i.e. READER), so that they can start sessions on their own, but it would also be useful for them to be able to participate in an existing ASYNC_READER session, if one was started up the stack. """ if self._mode is _WRITER: raise TypeError("Setting async on a WRITER makes no sense") return self._clone(allow_async=True) @property def independent(self): """Modifier to start a transaction independent from any enclosing.""" return self._clone(independent=True) @property def savepoint(self): """Modifier to start a SAVEPOINT if a transaction already exists.""" return self._clone(savepoint=True) @property def connection(self): """Modifier to return a core Connection object instead of Session.""" return self._clone(connection=True) @property def async_(self): """Modifier to set a READER operation to ASYNC_READER.""" if self._mode is _WRITER: raise TypeError("Setting async on a WRITER makes no sense") return self._clone(mode=_ASYNC_READER) def using(self, context): """Provide a context manager block that will use the given context.""" return self._transaction_scope(context) def __call__(self, fn): """Decorate a function.""" argspec = utils.getargspec(fn) if argspec.args[0] == 'self' or argspec.args[0] == 'cls': context_index = 1 else: context_index = 0 context_kw = argspec.args[context_index] @functools.wraps(fn) def wrapper(*args, **kwargs): context = kwargs.get(context_kw, None) if not context: context = args[context_index] with self._transaction_scope(context): return fn(*args, **kwargs) return wrapper def _clone(self, **kw): default_kw = { "independent": self._independent, "mode": self._mode, "connection": self._connection } default_kw.update(kw) return _TransactionContextManager(root=self._root, **default_kw) @contextlib.contextmanager def _transaction_scope(self, context): new_transaction = self._independent transaction_contexts_by_thread = \ _transaction_contexts_by_thread(context) current = restore = getattr( transaction_contexts_by_thread, "current", None) use_factory = self._factory global_factory = None if self._replace_global_factory: use_factory = global_factory = self._replace_global_factory elif current is not None and current.global_factory: global_factory = current.global_factory if self._root._is_global_manager: use_factory = global_factory if current is not None and ( new_transaction or current.factory is not use_factory ): current = None if current is None: current = transaction_contexts_by_thread.current = \ _TransactionContext(use_factory, global_factory=global_factory) try: if self._mode is not None: with current._produce_block( mode=self._mode, connection=self._connection, savepoint=self._savepoint, allow_async=self._allow_async, context=context) as resource: yield resource else: yield finally: if restore is None: del transaction_contexts_by_thread.current elif current is not restore: transaction_contexts_by_thread.current = restore @property @debtcollector.moves.moved_property("async_") def async_compat(self): return self.async_ setattr( _TransactionContextManager, "async", async_compat ) def _context_descriptor(attr=None): getter = operator.attrgetter(attr) def _property_for_context(context): try: transaction_context = context.transaction_ctx except exception.NoEngineContextEstablished: raise exception.NoEngineContextEstablished( "No TransactionContext is established for " "this %s object within the current thread; " "the %r attribute is unavailable." % (context, attr) ) else: result = getter(transaction_context) if result is None: raise exception.ContextNotRequestedError( "The '%s' context attribute was requested but " "it has not been established for this context." % attr ) return result return property(_property_for_context) def _transaction_ctx_for_context(context): by_thread = _transaction_contexts_by_thread(context) try: return by_thread.current except AttributeError: raise exception.NoEngineContextEstablished( "No TransactionContext is established for " "this %s object within the current thread. " % context ) def _transaction_contexts_by_thread(context): transaction_contexts_by_thread = getattr( context, '_enginefacade_context', None) if transaction_contexts_by_thread is None: transaction_contexts_by_thread = \ context._enginefacade_context = _TransactionContextTLocal() return transaction_contexts_by_thread def transaction_context_provider(klass): """Decorate a class with ``session`` and ``connection`` attributes.""" setattr( klass, 'transaction_ctx', property(_transaction_ctx_for_context)) # Graft transaction context attributes as context properties for attr in ('session', 'connection', 'transaction'): setattr(klass, attr, _context_descriptor(attr)) return klass _context_manager = _TransactionContextManager(_is_global_manager=True) """default context manager.""" def transaction_context(): """Construct a local transaction context. """ return _TransactionContextManager() def configure(**kw): """Apply configurational options to the global factory. This method can only be called before any specific transaction-beginning methods have been called. .. seealso:: :meth:`._TransactionFactory.configure` """ _context_manager._factory.configure(**kw) def get_legacy_facade(): """Return a :class:`.LegacyEngineFacade` for the global factory. This facade will make use of the same engine and sessionmaker as this factory, however will not share the same transaction context; the legacy facade continues to work the old way of returning a new Session each time get_session() is called. """ return _context_manager.get_legacy_facade() reader = _context_manager.reader """The global 'reader' starting point.""" writer = _context_manager.writer """The global 'writer' starting point.""" class LegacyEngineFacade(object): """A helper class for removing of global engine instances from oslo.db. .. deprecated:: 1.12.0 Please use :mod:`oslo_db.sqlalchemy.enginefacade` for new development. As a library, oslo.db can't decide where to store/when to create engine and sessionmaker instances, so this must be left for a target application. On the other hand, in order to simplify the adoption of oslo.db changes, we'll provide a helper class, which creates engine and sessionmaker on its instantiation and provides get_engine()/get_session() methods that are compatible with corresponding utility functions that currently exist in target projects, e.g. in Nova. engine/sessionmaker instances will still be global (and they are meant to be global), but they will be stored in the app context, rather that in the oslo.db context. Two important things to remember: 1. An Engine instance is effectively a pool of DB connections, so it's meant to be shared (and it's thread-safe). 2. A Session instance is not meant to be shared and represents a DB transactional context (i.e. it's not thread-safe). sessionmaker is a factory of sessions. :param sql_connection: the connection string for the database to use :type sql_connection: string :param slave_connection: the connection string for the 'slave' database to use. If not provided, the master database will be used for all operations. Note: this is meant to be used for offloading of read operations to asynchronously replicated slaves to reduce the load on the master database. :type slave_connection: string :param sqlite_fk: enable foreign keys in SQLite :type sqlite_fk: bool :param autocommit: use autocommit mode for created Session instances :type autocommit: bool :param expire_on_commit: expire session objects on commit :type expire_on_commit: bool Keyword arguments: :keyword mysql_sql_mode: the SQL mode to be used for MySQL sessions. (defaults to TRADITIONAL) :keyword mysql_enable_ndb: If True, transparently enables support for handling MySQL Cluster (NDB). (defaults to False) :keyword connection_recycle_time: Time period for connections to be recycled upon checkout (defaults to 3600) :keyword connection_debug: verbosity of SQL debugging information. -1=Off, 0=None, 100=Everything (defaults to 0) :keyword max_pool_size: maximum number of SQL connections to keep open in a pool (defaults to SQLAlchemy settings) :keyword max_overflow: if set, use this value for max_overflow with sqlalchemy (defaults to SQLAlchemy settings) :keyword pool_timeout: if set, use this value for pool_timeout with sqlalchemy (defaults to SQLAlchemy settings) :keyword sqlite_synchronous: if True, SQLite uses synchronous mode (defaults to True) :keyword connection_trace: add python stack traces to SQL as comment strings (defaults to False) :keyword max_retries: maximum db connection retries during startup. (setting -1 implies an infinite retry count) (defaults to 10) :keyword retry_interval: interval between retries of opening a sql connection (defaults to 10) :keyword thread_checkin: boolean that indicates that between each engine checkin event a sleep(0) will occur to allow other greenthreads to run (defaults to True) """ def __init__(self, sql_connection, slave_connection=None, sqlite_fk=False, autocommit=True, expire_on_commit=False, _conf=None, _factory=None, **kwargs): warnings.warn( "EngineFacade is deprecated; please use " "oslo_db.sqlalchemy.enginefacade", warning.OsloDBDeprecationWarning, stacklevel=2) if _factory: self._factory = _factory else: self._factory = _TransactionFactory() self._factory.configure( sqlite_fk=sqlite_fk, __autocommit=autocommit, expire_on_commit=expire_on_commit, **kwargs ) # make sure passed-in urls are favored over that # of config self._factory._start( _conf, connection=sql_connection, slave_connection=slave_connection) def _check_factory_started(self): if not self._factory._started: self._factory._start() def get_engine(self, use_slave=False): """Get the engine instance (note, that it's shared). :param use_slave: if possible, use 'slave' database for this engine. If the connection string for the slave database wasn't provided, 'master' engine will be returned. (defaults to False) :type use_slave: bool """ self._check_factory_started() if use_slave: return self._factory._reader_engine else: return self._factory._writer_engine def get_session(self, use_slave=False, **kwargs): """Get a Session instance. :param use_slave: if possible, use 'slave' database connection for this session. If the connection string for the slave database wasn't provided, a session bound to the 'master' engine will be returned. (defaults to False) :type use_slave: bool Keyword arguments will be passed to a sessionmaker instance as is (if passed, they will override the ones used when the sessionmaker instance was created). See SQLAlchemy Session docs for details. """ self._check_factory_started() if use_slave: return self._factory._reader_maker(**kwargs) else: return self._factory._writer_maker(**kwargs) def get_sessionmaker(self, use_slave=False): """Get the sessionmaker instance used to create a Session. This can be called for those cases where the sessionmaker() is to be temporarily injected with some state such as a specific connection. """ self._check_factory_started() if use_slave: return self._factory._reader_maker else: return self._factory._writer_maker @classmethod def from_config(cls, conf, sqlite_fk=False, autocommit=True, expire_on_commit=False): """Initialize EngineFacade using oslo.config config instance options. :param conf: oslo.config config instance :type conf: oslo_config.cfg.ConfigOpts :param sqlite_fk: enable foreign keys in SQLite :type sqlite_fk: bool :param autocommit: use autocommit mode for created Session instances :type autocommit: bool :param expire_on_commit: expire session objects on commit :type expire_on_commit: bool """ return cls( None, sqlite_fk=sqlite_fk, autocommit=autocommit, expire_on_commit=expire_on_commit, _conf=conf)

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transform_docker/server.py

briglx/AzureConvertJson

0

143152

<filename>transform_docker/server.py<gh_stars>0 """HTTP Server to transform json to json using liquid formatter.""" import json import logging import os import uuid from aiohttp import web from liquid import Liquid, filter_manager def generate_guid(): """Generate a UUID string.""" return str(uuid.uuid4()) def convert_quality_filter(quality): """Jinja2 Filter to convert quality score.""" quality_map = { "A01": "Poor", "A02": "OK", "A03": "Moderate", "A04": "Good", } if quality not in quality_map: raise ValueError("The quality is not a valid value. It must be A01 - A04") return quality_map[quality] def render(data, template_path, template_name, filters, debug=False): """Render data as a string for a given template.""" file_name = os.path.join(template_path, template_name) # Register Filter for filter_name, filter_code in filters.items(): filter_manager.register(filter_name)(filter_code) with open(file_name, "r") as template_file: template_string = template_file.read() template = Liquid(template_string, liquid_config={"debug": debug}) # Transform result_string = template.render(**data) # Unpack argument lists return result_string def render_dict(data, template_path, template_name, filters): """Render data as Json Dictionary for a given template.""" result_string = render(data, template_path, template_name, filters) return json.loads(result_string) async def post_api_transform(request): """Handle transform api call.""" LOGGER.info("post_api_transform called") # Get payload data = await request.json() # Add tracking guid data["system_guid"] = generate_guid() # set filters filters = {"convert": convert_quality_filter} # Transform message = render_dict(data, TEMPLATE_PATH, TEMPLATE_NAME, filters) return web.json_response(message) if __name__ == "__main__": LOGGER = logging.getLogger(__name__) LOGGER.info("Starting Python HTTP web server.") TEMPLATE_PATH = os.environ.get("TEMPLATE_PATH") TEMPLATE_NAME = os.environ.get("TEMPLATE_NAME") if not TEMPLATE_PATH: raise ValueError( "Template path is required." "Have you set the TEMPLATE_PATH env variable?" ) if not TEMPLATE_NAME: raise ValueError( "Template transform is required." "Have you set the TEMPLATE_NAME env variable?" ) app = web.Application() app.add_routes([web.post("/api/liquid_json_transform", post_api_transform)]) web.run_app(app)

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raiden_client/interfaces/cli_commands/channels.py

s0b0lev/raiden-client-python

3

181743

<filename>raiden_client/interfaces/cli_commands/channels.py from argparse import ArgumentParser, Namespace, _SubParsersAction from raiden_client import Client, utils def configure_parser(arg_parser: ArgumentParser, subparser: _SubParsersAction) -> None: channels = subparser.add_parser("channels", help="Request a list of all unsettled channels") channels.add_argument("-t", "--token-address", required=False, help="For the given token address") channels.set_defaults(func=parser_function) def parser_function(args: Namespace) -> str: client = Client(endpoint=args.endpoint, version=args.version) channels = client.channels(token_address=args.token_address) return utils.print_stdout(channels)

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manimpy/right_angle.py

Jin-Yuhan/manimpy

1

48193

# from @cigar666 # cgnb!!!! from manimlib.imports import * class RightAngle(VGroup): CONFIG = { 'size': 0.25, 'stroke_color': WHITE, 'stroke_width': 3.2, 'fill_color': BLUE, 'fill_opacity': 0.5, 'on_the_right': True, } def __init__(self, corner=ORIGIN, angle=0, **kwargs): VGroup.__init__(self, **kwargs) self.corner = ORIGIN self.angle = 0 r = UR if self.on_the_right else UL self.add( Polygon(ORIGIN, RIGHT * self.size * r, UR * self.size * r, UP * self.size * r, stroke_width=0, fill_color=self.fill_color, fill_opacity=self.fill_opacity), Line(RIGHT * self.size * r, UR * self.size * r + UP * self.stroke_width / 100 / 2 * 0.8, stroke_width=self.stroke_width, stroke_color=self.stroke_color), Line(UR * self.size * r + RIGHT * self.stroke_width / 100 / 2 * r * 0.8, UP * self.size * r, stroke_width=self.stroke_width, stroke_color=self.stroke_color), ) self.move_corner_to(corner) self.change_angle_to(angle) def move_corner_to(self, new_corner): self.shift(new_corner - self.corner) self.corner = new_corner return self def change_angle_to(self, new_angle): self.rotate(new_angle - self.angle, about_point=self.corner) self.angle = new_angle return self

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web_scraper/extract/common.py

rarc41/web_scraper_pro

0

3010

<reponame>rarc41/web_scraper_pro<filename>web_scraper/extract/common.py import yaml __config=None def config(): global __config if not __config: with open('config.yaml', mode='r') as f: __config=yaml.safe_load(f) return __config

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000-025/p010/inle.py

CDL-Project-Euler/Solutions

3

75509

def checkprime(value: int): factor = 2 sqrt = value ** 0.5 while factor <= sqrt: if value % factor == 0: return False factor += 1 return True def sum_primes_under(n: int): num = 2 sum = 0 while num < n: if checkprime(num): sum += num num += 1 return(sum) if __name__ == "__main__": print(sum_primes_under(10))

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sc_utils/dhcp_listener.py

jkulawik/ServiceChecker

0

171543

#!/usr/bin/env python3 """scapy-dhcp-listener.py Listen for DHCP packets using scapy to learn when LAN hosts request IP addresses from DHCP Servers. Copyright (C) 2018 <NAME> https://jcutrer.com/python/scapy-dhcp-listener License Dual MIT, 0BSD Extended by jkulawik, 2020 """ from __future__ import print_function from scapy.all import * from scapy.layers.dhcp import DHCP from scapy.layers.dhcp import Ether from scapy.layers.dhcp import BOOTP from scapy.layers.dhcp import IP # Logging from datetime import date from inspect import getsourcefile from sc_utils import mac_vendor from scapy.layers.l2 import getmacbyip whitelist_file = 'MAC_whitelist.txt' __version__ = "0.0.4" def add_zero_to_time(time): if time < 10: return '0'+ time else: return time # print_and_log current time def pal_time(): todays_date = datetime.now() hour = add_zero_to_time(todays_date.hour) minute = add_zero_to_time(todays_date.minute) curr_time = '\n{}:{}'.format(hour, minute) print_and_log(curr_time) def check_whitelist(mac): with open(whitelist_file, 'r') as file: whitelist = file.read() return mac in whitelist def print_and_log(message): print(message) log(message) # Print a message into today's log in /logs def log(message): dir = 'logs' if not os.path.exists(dir): # The folder gets created in the run directory automatically... os.makedirs(dir) # ...but for file writing a full path is needed current_dir = os.path.dirname(getsourcefile(lambda: 0)) path = os.path.join(current_dir, 'logs') file_name = str(date.today()) + '-log.txt' file_path = os.path.join(path, file_name) file = open(file_path, "a") file.write(message + '\n') file.close() # Fixup function to extract dhcp_options by key def get_option(dhcp_options, key): must_decode = ['hostname', 'domain', 'vendor_class_id'] try: for i in dhcp_options: if i[0] == key: # If DHCP Server Returned multiple name servers # return all as comma separated string. if key == 'name_server' and len(i) > 2: return ",".join(i[1:]) # domain and hostname are binary strings, # decode to unicode string before returning elif key in must_decode: return i[1].decode() else: return i[1] except: pass def handle_dhcp_packet(packet): # Match DHCP discover if DHCP in packet and packet[DHCP].options[0][1] == 1: print('---') print('New DHCP Discover') #print(packet.summary()) #print(ls(packet)) hostname = get_option(packet[DHCP].options, 'hostname') mac = packet[Ether].src if check_whitelist(mac): print(f'Whitelisted host {hostname} asked for an IP.') print(f'Host vendor: {mac_vendor.get_str(mac)}') print(f'Host MAC: {mac}') return pal_time() print_and_log(f"Unknown host {hostname} asked for an IP.") print_and_log(f'Host vendor: {mac_vendor.get_str(mac)}') print_and_log(f'Host MAC: {mac}') # Match DHCP ack elif DHCP in packet and packet[DHCP].options[0][1] == 5\ and packet[BOOTP].yiaddr != '0.0.0.0': print('---') print('New DHCP Ack') #print(packet.summary()) #print(ls(packet)) subnet_mask = get_option(packet[DHCP].options, 'subnet_mask') lease_time = get_option(packet[DHCP].options, 'lease_time') router = get_option(packet[DHCP].options, 'router') name_server = get_option(packet[DHCP].options, 'name_server') server_mac = packet[Ether].src server_ip = packet[IP].src sus_ip = packet[BOOTP].yiaddr sus_mac = str(getmacbyip(sus_ip)) sus_vendor = mac_vendor.get_str(sus_mac) if check_whitelist(sus_mac): print(f"DHCP Server {server_ip} ({server_mac}) acknowledged a whitelisted device on IP {sus_ip}") print(f'Host vendor: {mac_vendor.get_str(sus_vendor)}') print(f'Host MAC: {sus_mac}\n') return pal_time() print_and_log(f"DHCP Server {server_ip} ({server_mac}) acknowledged unknown device on IP {sus_ip}") print_and_log(f'Unknown host vendor: {mac_vendor.get_str(sus_vendor)}') print_and_log(f'Unknown host MAC: {sus_mac}\n') print(f"DHCP Options: subnet_mask: {subnet_mask}, lease_time: " f"{lease_time}, router: {router}, name_server: {name_server}") # Match DHCP inform elif DHCP in packet and packet[DHCP].options[0][1] == 8: print('---') print('New DHCP Inform') #print(packet.summary()) #print(ls(packet)) hostname = get_option(packet[DHCP].options, 'hostname') vendor_class_id = get_option(packet[DHCP].options, 'vendor_class_id') print(f"DHCP Inform from {packet[IP].src} ({packet[Ether].src}) " f"hostname: {hostname}, vendor_class_id: {vendor_class_id}") else: print('---') print('Some Other DHCP Packet') print(packet.summary()) #print(ls(packet)) return # This is just to use this script as a dependency def start_sniffing(): # Create MAC whitelist if not os.path.exists(whitelist_file): open(whitelist_file, "w+") print('Sniffing DHCP broadcasts...') print('Press Ctrl+C to stop.') sniff(filter="udp and (port 67 or 68)", prn=handle_dhcp_packet) if __name__ == "__main__": sniff(filter="udp and (port 67 or 68)", prn=handle_dhcp_packet)

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authz/test/test_obp_helper.py

shivdeep-singh/conversational-ai-chatbot

11

15295

<reponame>shivdeep-singh/conversational-ai-chatbot<gh_stars>10-100 import unittest from zmq_integration_lib import RPCClient, RPCServer import unittest.mock as mock class TestOBPHelper(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def logout(self, mock_zmq): self.assertTrue(True) def get_login_token(self, mock_zmq): self.assertTrue(True)

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pkpdapp/pkpdapp/dash_apps/auce_app.py

pkpdapp-team/pkpdapp

4

118826

# # This file is part of PKPDApp (https://github.com/pkpdapp-team/pkpdapp) which # is released under the BSD 3-clause license. See accompanying LICENSE.md for # copyright notice and full license details. # import dash_core_components as dcc import dash_html_components as html import dash_bootstrap_components as dbc from dash.dependencies import Input, Output import plotly.graph_objects as go from .auce_figure import AuceFigure import pandas as pd import django_plotly_dash as dpd import json # Create data view app = dpd.DjangoDash( name='auce_view', add_bootstrap_links=True ) # Create dash app app.layout = dbc.Container([ dbc.Row(children=[ dbc.Col(width=5, children=[ html.Label("Select datasets to use:"), dcc.Dropdown( id='dataset-dropdown', ), ]) ]), dbc.Row([ dbc.Col( children=[ html.P(children=( 'Note: AUCE app requries Dosing Groups corresponding to ' 'numerical concentrations ' )), dbc.Col(width=3, children=[ html.Label("Choose variable:"), dcc.Dropdown( id='auce-biomarker-dropdown', ), ]), dcc.Graph( id='initial-doses-dashboard', figure=go.Figure(), style={'width': '100%'} ), dcc.Graph( id='auce-dashboard', figure=go.Figure(), style={'width': '100%'} ) ], ), ]), ]) def rehydrate_state(session_state): if session_state is None: raise NotImplementedError("Cannot handle a missing session state") state = session_state.get('auce_view', None) if state is None: raise NotImplementedError('AuceState missing in session state') return AuceState.from_json(state) @app.callback( [ Output('dataset-dropdown', 'options'), Output('dataset-dropdown', 'value'), ], [Input('auce-dashboard', 'style')], ) def update_datasets(_, session_state=None): state = rehydrate_state(session_state) return state._dataset_options, state._selected_dataset @app.callback( [ Output('auce-biomarker-dropdown', 'options'), Output('auce-biomarker-dropdown', 'value'), ], [ Input('dataset-dropdown', 'value'), ] ) def update_nca_auce_options(dataset_dropdown, session_state=None): state = rehydrate_state(session_state) state._selected_dataset = dataset_dropdown biomarkers = state._get_biomarkers() return biomarkers, biomarkers[0]['value'] @app.callback( [ Output('initial-doses-dashboard', 'figure'), Output('auce-dashboard', 'figure'), ], [ Input('auce-biomarker-dropdown', 'value'), Input('dataset-dropdown', 'value'), ], ) def update_auce(auce_biomarker_dropdown, dataset_dropdown, session_state=None): state = rehydrate_state(session_state) state._selected_dataset = dataset_dropdown return state.generate_auce_figures(auce_biomarker_dropdown) class AuceState: """ """ _id_key = 'ID' _biomarker_key = 'Biomarker' _compound_key = 'Compound' def __init__(self): self._selected_dataset = [] self._datasets = [] self._dataset_options = [] self._auce_biomarker = None def add_datasets(self, datasets, dataset_names): # handle empty datasets if not datasets: return # by default show last datset self._selected_dataset = 0 self._datasets = datasets for dataset, dataset_name in zip(self._datasets, dataset_names): dataset['Dataset'] = dataset_name self._dataset_options = [ {'label': name, 'value': i} for i, name in enumerate(dataset_names) ] # by default use first biomarker self._auce_biomarker = self._datasets[ self._selected_dataset ][self._biomarker_key][0] def to_json(self): state_dict = { '_selected_dataset': self._selected_dataset, '_datasets': [d.to_dict() for d in self._datasets], '_dataset_options': self._dataset_options, '_auce_biomarker': self._auce_biomarker, } return json.dumps(state_dict) @classmethod def from_json(cls, j): data_dict = json.loads(j) o = cls() o._selected_dataset = data_dict['_selected_dataset'] o._datasets = [ pd.DataFrame.from_dict(d) for d in data_dict['_datasets'] ] o._dataset_options = data_dict['_dataset_options'] o._auce_biomarker = data_dict['_auce_biomarker'] return o def generate_auce_figures(self, new_biomarker=None): if not self._datasets: return go.Figure(), go.Figure() if new_biomarker: self._auce_biomarker = new_biomarker self._auce_figure = AuceFigure( self._datasets[self._selected_dataset], self._auce_biomarker ) return self._auce_figure.figures() def _get_biomarkers(self): biomarkers = set() biomarkers = self._datasets[self._selected_dataset][ self._biomarker_key ].unique().tolist() return [{'label': str(i), 'value': i} for i in biomarkers]

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descriptor/customer6b.py

ambleacme/decorators-descriptors

78

132319

""" A client with name and e-mail: >>> jack = Customer('<NAME>', '<EMAIL>') >>> jack.full_email() '<NAME> <<EMAIL>>' A client cannot be created with a blank e-mail: >>> joe = Customer('<NAME>', '') Traceback (most recent call last): ... ValueError: 'email' must not be empty >>> joe = Customer('<NAME>', '<EMAIL>') >>> joe.full_email() '<NAME> <<EMAIL>>' Assigning a blank e-mail later is not allowed either: >>> joe.email = '' Traceback (most recent call last): ... ValueError: 'email' must not be empty `NonBlank` fields must also be strings: >>> dummy = Customer('Buster', 99) Traceback (most recent call last): ... TypeError: 'email' must be of type 'str' The `NonBlank` field values are stored in properly named `Customer` instance attributes: >>> for key, value in sorted(jack.__dict__.items()): ... print('{:>10} : {!r}'.format(key, value)) email : '<EMAIL>' fidelity : 0 name : '<NAME>' """ from model6b import Model, NonBlank class Customer(Model): name = NonBlank() email = NonBlank() def __init__(self, name, email, fidelity=0): self.name = name self.email = email self.fidelity = fidelity def full_email(self): return '{0} <{1}>'.format(self.name, self.email)

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src/bot.py

shirin1996/PyBot

1

194874

import telebot import os bot = telebot.TeleBot(os.environ['BOT_TOKEN'], parse_mode='HTML')

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SBOL2Excel/utils/sbol2excel.py

abamaj/SBOL-to-Excel

0

7384

import sbol2 import pandas as pd import os import logging from openpyxl import load_workbook from openpyxl.worksheet.table import Table, TableStyleInfo from openpyxl.utils.dataframe import dataframe_to_rows from openpyxl.styles import Font, PatternFill, Border, Side from requests_html import HTMLSession #wasderivedfrom: source #remove identity, persistenID, displayID, version #remove attachment (if empty) #add library sheets #add postprocessing function to remove unecessaries class seqFile: def __init__(self, file_path_in, output_path): # global varibales for homespace, document, and sheet self.homeSpace = 'https://sys-bio.org' self.document = file_path_in self.file_location_path = os.path.dirname(__file__) self.sheet = os.path.join(self.file_location_path, 'ontologies.xlsx') self.output_template = os.path.join(self.file_location_path, 'Template_to_Output_Into_v001.xlsx') self.output_path = output_path def roleVariables(self): # set Excel file into a dataframe df = pd.read_excel(self.sheet, index_col=0, sheet_name=1, usecols=[1, 2]) # convert the dataframe into a dictionary roleConvertDict = df.to_dict() # set dictionary indices and values (use column 'URI' in excel sheet) roleName = roleConvertDict['URI'] # switch indices' and values' postions roleDictionary = {uri: role for role, uri in roleName.items()} return roleDictionary def orgVariables(self): # set Excel file into a dataframe df = pd.read_excel(self.sheet, index_col=0, sheet_name=2, usecols=[0, 1]) # convert the dataframe into a dictionary organismConvertDict = df.to_dict() # set dictionary indices and values (use column 'txid' in excel sheet) organismName = organismConvertDict['txid'] # switch indices' and values' postions organismDictionary = {str(txid): organism for organism, txid in organismName.items()} return organismDictionary # def inspectDocInfo(self): # # declare homespace # sbol2.setHomespace(self.homeSpace) # doc = sbol2.Document() # doc.read('../tests/test_files/' + self.document) # # doc.read(self.document) # # print document information # print(doc) # def printDocContents(self): # # declare homespace # sbol2.setHomespace(self.homeSpace) # doc = sbol2.Document() # doc.read('../tests/test_files/' + self.document) # # doc.read(self.document) # # print document contents # for obj in doc: # print(obj) def readDocChart(self): # declare homespace sbol2.setHomespace(self.homeSpace) doc = sbol2.Document() doc.read(self.document) # create a dictionary to hold all the component defintions' information componentDefinitions = {} # iterate through the component definitions roleDict = self.roleVariables() orgDict = self.orgVariables() for cd in doc.componentDefinitions: cdType = cd.type # create a dictionary that has a key for the # component definition's identity, # and a value for all of its features componentFeatures = {} persistentIdentity = cd.properties['http://sbols.org/v2#persistentIdentity'][0] # iterate through the properties of the component defintions # and set them equal to propValue variable for prop in cd.properties: try: propValue = cd.properties[prop][0] except (IndexError): propValue = cd.properties[prop] # extract attribute property type if propValue == []: propValue = '' prop = self.prop_convert(prop) propValue = columnMethods(prop, propValue, doc, cdType, roleDict, orgDict).colV componentFeatures[prop] = str(propValue) # append each componentFeatures dictionary as a # value into the componentDefinitions # dictionary with the 'persistentIdentity' serving as the key componentDefinitions[persistentIdentity] = componentFeatures # return the dictionary of information (temporary, maybe # return true if read in correctly) doc_chart = pd.DataFrame.from_dict(componentDefinitions, orient="index") return doc_chart def prop_convert(self, prop): if type(prop) is str: idx = prop.find('#') # if parsing conditions meet, append them into the # componentFeatures dictionary as necessary if idx >= 1: prop = prop[idx + 1:] if prop == 'type': prop = 'types' if prop == 'http://purl.org/dc/terms/title': prop = 'title' if prop == 'http://purl.org/dc/terms/description': prop = 'description' if prop == 'http://purl.obolibrary.org/obo/OBI_0001617': prop = 'OBI_0001617' return (prop) else: raise ValueError() def displayDocChart(self): #display the dataframe return pd.DataFrame.from_dict(self.readDocChart(), orient = "index") def TEMP_readDocChart1(self): #demo of table column names columnNames = ['Part Name', 'Role', 'Design Notes', 'Altered Sequence', 'Part Description', 'Data Source Prefix', 'Data Source', 'Source Organism', 'Target Organism', 'Circular', 'length (bp)', 'Sequence', 'Data Source', 'Composite'] #import dataframe dictionary #convert dictionary to dataframe df = self.displayDocChart() #type caste dataframe to a set dfSet = set(df) #type caste column names to a set columnNameOrder = set(columnNames) #check difference between the dataframe set and the column name order dfSetDifference = dfSet.difference(columnNameOrder) #check intersection between the datframe set and the column name order dfSetIntersection = dfSet.intersection(columnNameOrder) #combine the type casted difference and intersection finalSetList = list(dfSetIntersection) + list(dfSetDifference) #set list to dictionary return finalSetList # def displayDocChart(self): # # display the dataframe # return pd.DataFrame.from_dict(self.readDocChart(), orient="index") def columnString(self, n): # loop through column length in order to get string appropriate # values for excel sheet rows and columns string = "" while n > 0: n, remainder = divmod(n - 1, 26) string = chr(65 + remainder) + string return string def returnExcelChart(self): start_row = 18 start_cell = f'A{start_row}' # load a workbook wb = load_workbook(self.output_template) ws = wb.active # load raw dataframe to df df = self.readDocChart() # set font features ft1 = Font(name='Arial', size=12, color='548235') ft2 = Font(name='Calibri', size=11, bold=True) hold = dataframe_to_rows(df, index=False, header=True) # counter = 0 # loop through worksheet ws[start_cell].value = '' for r in hold: # if a specific cell is empty, continue to loop past it if r == [None]: continue ws.append(r) # counter += 1 # set table features tab = Table(displayName="Parts_Lib", ref=f"A{start_row +1}:{self.columnString(len(df.columns))}{(len(df) * 2) - 2}") style = TableStyleInfo(name="TableStyleLight7", showFirstColumn=False, showLastColumn=False, showRowStripes=True, showColumnStripes=False) cellColor = PatternFill(patternType='solid', fgColor='DDEBF7') cellBorder = Side(border_style='medium', color="000000") # cellIndex = len(x) # gives cells within specified range their table attributes for col in range(1, len(df.columns) + 1): alpha = self.columnString(col) ws[f'{alpha}{start_row+1}'].fill = cellColor ws[f'{alpha}{start_row+1}'].border = Border(top=cellBorder) tab.tableStyleInfo = style ws.add_table(tab) # counter = 0 # gives cells within specified range their font attributes for row in range(len(df) - 1, (len(df) * 2 - 1)): # counter = counter + 1 for cell in ws[row]: cell.font = ft1 # gives cells within specified range their font attributes # (these are special features for the title) num_rows = len(df) if num_rows % 2 > 0: num_rows = num_rows - 1 for j in range(19, num_rows): for x in ws[j]: x.font = ft2 # output the file wb.save(self.output_path) wb.close() logging.warning(f'Your converted file has been output at {self.output_path}') class columnMethods: def __init__(self, colN, colV, doc, cdType, roleDict, orgDict): # global varibales for dataframe switch statements self.colN = colN self.colV = colV self.doc = doc self.cdType = cdType self.roleDict = roleDict self.orgDict = orgDict # if the column name matches the function name, call the function try: return getattr(self, self.colN)() # if the column name does not match the function name, call 'no_change' except AttributeError: return getattr(self, 'no_change')() def no_change(self): pass # if the specified column role value is within the role column def role(self): roleVal = str(self.colV) if roleVal in self.roleDict: self.colV = self.roleDict[roleVal] def types(self): self.colV = self.colV.split('#')[-1] def sequence(self): self.colV = self.doc.getSequence(self.colV).elements def sourceOrganism(self): orgVal = str(self.colV) orgVal = orgVal.split('=')[-1] txid = self.colV.split('=')[-1] if orgVal in self.orgDict: self.colV = self.orgDict[orgVal] else: session = HTMLSession() r = session.get(self.colV) v = r.html.find('strong', first=True) self.colV = v.text self.orgDict[txid] = self.colV def targetOrganism(self): orgVal = str(self.colV) orgVal = orgVal.split('=')[-1] txid = self.colV.split('=')[-1] if orgVal in self.orgDict: self.colV = self.orgDict[orgVal] else: session = HTMLSession() r = session.get(self.colV) v = r.html.find('strong', first=True) self.colV = v.text self.orgDict[txid] = self.colV

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quantization/tensorRT_test.py

Jihunn-Kim/khu_capstone_1

1

108250

<reponame>Jihunn-Kim/khu_capstone_1 import tensorrt as trt import pycuda.driver as cuda import numpy as np import torch import pycuda.autoinit import dataset import model import time # print(dir(trt)) tensorrt_file_name = 'bert.plan' TRT_LOGGER = trt.Logger(trt.Logger.WARNING) trt_runtime = trt.Runtime(TRT_LOGGER) with open(tensorrt_file_name, 'rb') as f: engine_data = f.read() engine = trt_runtime.deserialize_cuda_engine(engine_data) context = engine.create_execution_context() # class HostDeviceMem(object): # def __init__(self, host_mem, device_mem): # self.host = host_mem # self.device = device_mem # def __str__(self): # return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device) # def __repr__(self): # return self.__str__() # inputs, outputs, bindings, stream = [], [], [], [] # for binding in engine: # size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size # dtype = trt.nptype(engine.get_binding_dtype(binding)) # host_mem = cuda.pagelocked_empty(size, dtype) # device_mem = cuda.mem_alloc(host_mem.nbytes) # bindings.append(int(device_mem)) # if engine.binding_is_input(binding): # inputs.append( HostDeviceMem(host_mem, device_mem) ) # else: # outputs.append(HostDeviceMem(host_mem, device_mem)) # input_ids = np.ones([1, 1, 29, 29]) # numpy_array_input = [input_ids] # hosts = [input.host for input in inputs] # trt_types = [trt.int32] # for numpy_array, host, trt_types in zip(numpy_array_input, hosts, trt_types): # numpy_array = np.asarray(numpy_array).ravel() # np.copyto(host, numpy_array) # def do_inference(context, bindings, inputs, outputs, stream): # [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] # context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) # [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] # stream.synchronize() # return [out.host for out in outputs] # trt_outputs = do_inference( # context=context, # bindings=bindings, # inputs=inputs, # outputs=outputs, # stream=stream) def infer(context, input_img, output_size, batch_size): # Load engine # engine = context.get_engine() # assert(engine.get_nb_bindings() == 2) # Convert input data to float32 input_img = input_img.astype(np.float32) # Create host buffer to receive data output = np.empty(output_size, dtype = np.float32) # Allocate device memory d_input = cuda.mem_alloc(batch_size * input_img.size * input_img.dtype.itemsize) d_output = cuda.mem_alloc(batch_size * output.size * output.dtype.itemsize) bindings = [int(d_input), int(d_output)] stream = cuda.Stream() # Transfer input data to device cuda.memcpy_htod_async(d_input, input_img, stream) # Execute model context.execute_async(batch_size, bindings, stream.handle, None) # Transfer predictions back cuda.memcpy_dtoh_async(output, d_output, stream) # Synchronize threads stream.synchronize() # Return predictions return output # kwargs = {"./dataset/DoS_dataset.csv" : './DoS_dataset.txt'} # train_data_set, data_idx_map, net_class_count, net_data_count, test_data_set = dataset.GetCanDatasetUsingTxtKwarg(100, 0, **kwargs) # testloader = torch.utils.data.DataLoader(test_data_set, batch_size=256, # shuffle=False, num_workers=2) check_time = time.time() cnt = 0 temp = np.ones([256, 1, 29, 29]) for idx in range(100): # for i, (inputs, labels) in enumerate(testloader): trt_outputs = infer(context, temp, (256, 2), 256) print(trt_outputs.shape) # print(trt_outputs) # print(np.argmax(trt_outputs, axis=0)) # cnt += 1 # if cnt == 100: # break print(time.time() - check_time) tensorrt_file_name = 'bert_int.plan' TRT_LOGGER = trt.Logger(trt.Logger.WARNING) trt_runtime = trt.Runtime(TRT_LOGGER) with open(tensorrt_file_name, 'rb') as f: engine_data = f.read() engine = trt_runtime.deserialize_cuda_engine(engine_data) context = engine.create_execution_context() check_time = time.time() cnt = 0 temp = np.ones([256, 1, 29, 29]) for idx in range(100): # for i, (inputs, labels) in enumerate(testloader): trt_outputs = infer(context, temp, (256, 2), 256) print(trt_outputs.shape) # print(trt_outputs) # print(np.argmax(trt_outputs, axis=0)) # cnt += 1 # if cnt == 100: # break print(time.time() - check_time) test_model = model.Net().cuda() check_time = time.time() cnt = 0 temp = torch.randn(256, 1, 29, 29).cuda() for idx in range(100): # for i, (inputs, labels) in enumerate(testloader): # inputs = inputs.float().cuda() normal_outputs = test_model(temp) # print(normal_outputs) print(normal_outputs.shape) cnt += 1 if cnt == 100: break print(time.time() - check_time) import tensorrt as trt import numpy as np import pycuda.autoinit import pycuda.driver as cuda import time model_path = "bert.onnx" input_size = 32 TRT_LOGGER = trt.Logger(trt.Logger.WARNING) # def build_engine(model_path): # with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.OnnxParser(network, TRT_LOGGER) as parser: # builder.max_workspace_size = 1<<20 # builder.max_batch_size = 1 # with open(model_path, "rb") as f: # parser.parse(f.read()) # engine = builder.build_cuda_engine(network) # return engine def alloc_buf(engine): # host cpu mem h_in_size = trt.volume(engine.get_binding_shape(0)) h_out_size = trt.volume(engine.get_binding_shape(1)) h_in_dtype = trt.nptype(engine.get_binding_dtype(0)) h_out_dtype = trt.nptype(engine.get_binding_dtype(1)) in_cpu = cuda.pagelocked_empty(h_in_size, h_in_dtype) out_cpu = cuda.pagelocked_empty(h_out_size, h_out_dtype) # allocate gpu mem in_gpu = cuda.mem_alloc(in_cpu.nbytes) out_gpu = cuda.mem_alloc(out_cpu.nbytes) stream = cuda.Stream() return in_cpu, out_cpu, in_gpu, out_gpu, stream def inference(engine, context, inputs, out_cpu, in_gpu, out_gpu, stream): # async version # with engine.create_execution_context() as context: # cost time to initialize # cuda.memcpy_htod_async(in_gpu, inputs, stream) # context.execute_async(1, [int(in_gpu), int(out_gpu)], stream.handle, None) # cuda.memcpy_dtoh_async(out_cpu, out_gpu, stream) # stream.synchronize() # sync version cuda.memcpy_htod(in_gpu, inputs) context.execute(1, [int(in_gpu), int(out_gpu)]) cuda.memcpy_dtoh(out_cpu, out_gpu) return out_cpu if __name__ == "__main__": inputs = np.random.random((1, 1, 29, 29)).astype(np.float32) tensorrt_file_name = '/content/drive/My Drive/capstone1/CAN/bert.plan' TRT_LOGGER = trt.Logger(trt.Logger.WARNING) trt_runtime = trt.Runtime(TRT_LOGGER) with open(tensorrt_file_name, 'rb') as f: engine_data = f.read() engine = trt_runtime.deserialize_cuda_engine(engine_data) # engine = build_engine(model_path) context = engine.create_execution_context() for _ in range(10): t1 = time.time() in_cpu, out_cpu, in_gpu, out_gpu, stream = alloc_buf(engine) res = inference(engine, context, inputs.reshape(-1), out_cpu, in_gpu, out_gpu, stream) print(res) print("cost time: ", time.time()-t1)

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dagger/dag_creator/airflow/utils/utils.py

jorgetagle/dagger

5

143709

from pathlib import Path def get_sql_queries(path): sql_queries = {} for query_file in Path(path).glob("*.sql"): with open(query_file, "r") as f: sql_queries[query_file.stem] = f.read() return sql_queries

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action-server/covidflow/actions/action_daily_ci_early_opt_out.py

dialoguemd/covidflow

7

162107

from typing import Any, Dict, List, Text from rasa_sdk import Action, Tracker from rasa_sdk.events import SlotSet from rasa_sdk.executor import CollectingDispatcher from covidflow.constants import CONTINUE_CI_SLOT from covidflow.utils.persistence import cancel_reminder from .lib.log_util import bind_logger ACTION_NAME = "action_daily_ci_early_opt_out" class ActionDailyCiEarlyOptOut(Action): def name(self) -> Text: return ACTION_NAME def run( self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict[Text, Any], ) -> List[Dict[Text, Any]]: bind_logger(tracker) dispatcher.utter_message( template="utter_daily_ci__early_opt_out__acknowledge_cancel_ci" ) dispatcher.utter_message( template="utter_daily_ci__early_opt_out__cancel_ci_recommendation" ) cancel_reminder(tracker.current_slot_values()) return [SlotSet(CONTINUE_CI_SLOT, False)]

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LambdaFunction/index.py

aws-samples/integrate-amazon-connect-with-smart-on-fhir-backend-services

3

92474

import json, random, string, os, math, time, datetime, dateutil.parser import boto3 from datetime import datetime, timedelta, timezone import logging from FHIRClient import FHIRClient logger = logging.getLogger() logger.setLevel(logging.INFO) ## Establish the connection to SMART on FHIR backend services fhirclient = FHIRClient( os.environ['client_id'], os.environ['endpoint_token'], os.environ['endpoint_stu3'], os.environ['kms_key_id'] ) """ --- Helpers to build responses which match the structure of the necessary dialog actions --- """ def elicit_slot(session_attributes, intent_name, slots, slot_to_elicit, message, response_card): return { 'sessionAttributes': session_attributes, 'dialogAction': { 'type': 'ElicitSlot', 'intentName': intent_name, 'slots': slots, 'slotToElicit': slot_to_elicit, 'message': message, 'responseCard': response_card } } def confirm_intent(session_attributes, intent_name, slots, message, response_card): return { 'sessionAttributes': session_attributes, 'dialogAction': { 'type': 'ConfirmIntent', 'intentName': intent_name, 'slots': slots, 'message': message, 'responseCard': response_card } } def close(session_attributes, fulfillment_state, message): response = { 'sessionAttributes': session_attributes, 'dialogAction': { 'type': 'Close', 'fulfillmentState': fulfillment_state, 'message': message } } return response def delegate(session_attributes, slots): return { 'sessionAttributes': session_attributes, 'dialogAction': { 'type': 'Delegate', 'slots': slots } } def build_response_card(title, subtitle, options): """ Build a responseCard with a title, subtitle, and an optional set of options which should be displayed as buttons. """ buttons = None if len(options) > 1: buttons = [] for i in range(min(5, len(options))): buttons.append(options[i]) return { 'contentType': 'application/vnd.amazonaws.card.generic', 'version': 1, 'genericAttachments': [{ 'title': title, 'subTitle': subtitle, 'buttons': buttons }] } else: return { 'contentType': 'application/vnd.amazonaws.card.generic', 'version': 1, 'genericAttachments': [{ 'title': title, 'subTitle': subtitle }] } """ --- Functions that control the bot's behavior --- """ def getPatientAuth(intent_request): """ Authenticate caller and return FHIR STU3 patient ID for the following query """ logger.debug('intent request: {}'.format(intent_request)) output_session_attributes = intent_request['currentIntent']['slots'] if intent_request['currentIntent']['slots'] is not None else {} telecom = intent_request['sessionAttributes']['telecom'] if intent_request['sessionAttributes']['telecom'] is not None else {} ## format phone number telecom = '{0}-{1}-{2}'.format(telecom[-10:-7], telecom[-7:-4], telecom[-4:]) logger.debug('phone number after tranformation: {}'.format(telecom)) patientinfo = { 'birthdate': output_session_attributes['patientBirthday'], 'gender': output_session_attributes['patientGender'], 'telecom': telecom } r = fhirclient.get_patient(patientinfo) output_session_attributes['patientid']=r['response'] if r['status']==200: statusMessage = "Thank you for authenticating" else: statusMessage = "I'm sorry, I didn't find a patient with that information" return close( output_session_attributes, 'Fulfilled', { 'contentType': 'PlainText', 'content': statusMessage } ) def getMedHelp(intent_request): """ Retrieve Medication Information for a given patient """ logger.info('intent request: {}'.format(intent_request)) output_session_attributes = intent_request['sessionAttributes'] if intent_request['sessionAttributes'] is not None else {} patientid = output_session_attributes['patientid'] if output_session_attributes['patientid'] is not None else {} res = fhirclient.get_meds(patientid) logger.debug(res) if res['status']==200: if type(res['response'])==str: outputtext = res['response'] else: outputtext = 'I have found the following medications and instructions for you. ' for med in res["response"]: outputtext += med['medicationReference'] + '. ' if 'dosage' in med and 'patientInstruction' in med['dosage'][0]: outputtext += 'The dosage for this is the following... ' + med['dosage'][0]['patientInstruction'] else: outputtext += 'I did not find a dosage for this medication. ' else: outputtext = 'I do not have any medication for you.' return close( output_session_attributes, 'Fulfilled', { 'contentType': 'PlainText', 'content': outputtext } ) """ --- Intents --- """ def dispatch(intent_request): """ Called when the user specifies an intent for this bot. """ logger.debug (intent_request) intent_name = intent_request['currentIntent']['name'] # Dispatch to your bot's intent handlers if intent_name == 'getAppointments': return findFutureAppt(intent_request) if intent_name == 'getMedication': return getMedHelp(intent_request) if intent_name == 'authenticateUser': return getPatientAuth(intent_request) raise Exception('Intent with name ' + intent_name + ' not supported') """ --- Main handler --- """ def lambda_handler(event, context): """ Route the incoming request based on intent. The JSON body of the request is provided in the event slot. """ # By default, treat the user request as coming from the America/New_York time zone. os.environ['TZ'] = 'America/New_York' time.tzset() logger.debug('event.bot.name={}'.format(event['bot']['name'])) return dispatch(event)

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app/recipe/tests/test_tags_api.py

leo-hoet/recipe-app-api

0

52291

<gh_stars>0 from django.contrib.auth import get_user_model from django.urls import reverse from django.test import TestCase from rest_framework import status from rest_framework.test import APIClient from recipe.tests.test_recipe_api import sample_recipe, sample_tag from core.models import Tag from recipe.serializers import TagSerializer TAGS_URL = reverse('recipe:tag-list') class PubliTagsApiTests(TestCase): """Test the publicly available endpoints""" def setUp(self) -> None: self.client = APIClient() def test_login_requiered(self): """Test that login is requeired for retrieveing tags""" res = self.client.get(TAGS_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateTagsApiTests(TestCase): """User tags API tests with authentication""" def setUp(self) -> None: self.user = get_user_model().objects.create_user( email='<EMAIL>', password='<PASSWORD>', ) self.client = APIClient() self.client.force_authenticate(self.user) def test_retrieve_tags(self): """Test retrieving tags""" Tag.objects.create(user=self.user, name='Vegan') Tag.objects.create(user=self.user, name='Dessert') res = self.client.get(TAGS_URL) tags = Tag.objects.all().order_by('-name') serializer = TagSerializer(tags, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_tag_limited_to_user(self): """Test that tags returned are for the authenticated user""" user2 = get_user_model().objects.create_user( email='<EMAIL>', password='<PASSWORD>', ) Tag.objects.create(user=user2, name='Fruity') tag = Tag.objects.create(user=self.user, name='Comfort Food') res = self.client.get(TAGS_URL) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data[0]['name'], tag.name) def test_create_tag_successful(self): """Test creating a new tag""" payload = { 'name': 'Test tag' } res = self.client.post(TAGS_URL, payload) exists = Tag.objects.filter( user=self.user, name=payload['name'] ).exists() self.assertTrue(exists) self.assertEqual(res.status_code, status.HTTP_201_CREATED) def test_create_tag_invalid(self): """Test creating an invalid tag""" payload = {'name': ''} res = self.client.post(TAGS_URL, payload) self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_get_tags_in_use_succesful(self): """Test getting al ussed tags by recipes""" tag1 = sample_tag(self.user, 'ta') tag2 = sample_tag(self.user, 'tb') tag3 = sample_tag(self.user, 'tc') recip1 = sample_recipe(self.user, title='recip1') recip1.tags.add(tag1) recip1.tags.add(tag2) recip2 = sample_recipe(self.user, title='recip2') recip2.tags.add(tag2) payload = { 'in_use': 1 } res = self.client.get(TAGS_URL, payload) serializer1 = TagSerializer(tag1) serializer2 = TagSerializer(tag2) serializer3 = TagSerializer(tag3) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 2) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) def test_get_tags_in_use_invalid_inuse(self): payload = { 'in_use': 'invalid' } res = self.client.get(TAGS_URL, payload) self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_get_tags_in_use_invalid_key(self): payload = { 'asdasd': 0 } res = self.client.get(TAGS_URL, payload) self.assertEqual(res.status_code, status.HTTP_200_OK)

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pydis_site/apps/api/migrations/0026_use_proper_default_for_infraction_insertion_date.py

Transfusion/site

700

175151

<filename>pydis_site/apps/api/migrations/0026_use_proper_default_for_infraction_insertion_date.py<gh_stars>100-1000 # Generated by Django 2.1.5 on 2019-01-09 19:50 from django.db import migrations, models import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('api', '0025_allow_custom_inserted_at_infraction_field'), ] operations = [ migrations.AlterField( model_name='infraction', name='inserted_at', field=models.DateTimeField(default=django.utils.timezone.now, help_text='The date and time of the creation of this infraction.'), ), ]

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configs/deepim/ycbvPbrSO/FlowNet512_1.5AugCosyAAEGray_NoiseRandom_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_ycbvPbr_SO/FlowNet512_1.5AugCosyAAEGray_NoiseRandom_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_Pbr_16_36WoodBlock.py

THU-DA-6D-Pose-Group/self6dpp

33

2538

<gh_stars>10-100 _base_ = "./FlowNet512_1.5AugCosyAAEGray_NoiseRandom_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_Pbr_01_02MasterChefCan.py" OUTPUT_DIR = "output/deepim/ycbvPbrSO/FlowNet512_1.5AugCosyAAEGray_NoiseRandom_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_ycbvPbr_SO/16_36WoodBlock" DATASETS = dict(TRAIN=("ycbv_036_wood_block_train_pbr",))

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sources/args.py

NougatCA/SPT-Code

10

129158

<reponame>NougatCA/SPT-Code import dataclasses from dataclasses import dataclass, field import os import enums @dataclass class RuntimeArguments: """Arguments for runtime.""" do_pre_train: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to pre-train'} ) pre_train_tasks: str = field( default=','.join(enums.PRE_TRAIN_TASKS), metadata={'help': 'Pre-training tasks in order, split by commas, ' 'for example (default) {}'.format(','.join(enums.PRE_TRAIN_TASKS))} ) do_fine_tune: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to fine_tune, task can be specific by `--task`'} ) only_test: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to test only'} ) task: str = field( default='summarization', metadata={'help': 'Downstream task', 'choices': enums.ALL_DOWNSTREAM_TASKS} ) trained_vocab: str = field( default='../pre_trained/vocabs/', metadata={'help': 'Directory of trained vocabs'} ) trained_model: str = field( default='../pre_trained/models/all/', metadata={'help': 'Directory of trained model'} ) train_from_scratch: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to train from scratch, will ignore `--trained_model`'} ) random_seed: int = field( default=42, metadata={'help': 'Specific random seed manually for all operations, 0 to disable'} ) n_epoch: int = field( default=50, metadata={'help': 'Number of data iterations for training'} ) batch_size: int = field( default=64, metadata={'help': 'Batch size for training on each device'} ) eval_batch_size: int = field( default=64, metadata={'help': 'Batch size for evaluation on each device'} ) beam_width: int = field( default=5, metadata={'help': 'Beam width when using beam decoding, 1 to greedy decode'} ) logging_steps: int = field( default=100, metadata={'help': 'Log training state every n steps'} ) cuda_visible_devices: str = field( default=None, metadata={'help': 'Visible cuda devices, string formatted, device number divided by \',\', e.g., \'0, 2\', ' '\'None\' will use all'} ) fp16: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to use mixed precision'} ) @dataclass class DatasetArguments: """Arguments for dataset loading.""" dataset_root: str = field( default='../../dataset/', metadata={'help': 'Root of the dataset'} ) train_subset_ratio: float = field( default=None, metadata={'help': 'Ratio of train subset'} ) pre_train_subset_ratio: float = field( default=None, metadata={'help': 'Ratio of pre-train subset'} ) @dataclass class SavingArguments: """Arguments for saving and loading.""" model_name: str = field( default='default_model', metadata={'help': 'Name of the model'} ) dataset_save_dir: str = field( default=os.path.join(DatasetArguments.dataset_root, 'dataset_saved'), metadata={'help': 'Directory to save and load dataset pickle instance'} ) vocab_save_dir: str = field( default=os.path.join(DatasetArguments.dataset_root, 'vocab_saved'), metadata={'help': 'Directory to save and load vocab pickle instance'} ) @dataclass class PreprocessingArguments: """Arguments for data preprocessing.""" code_vocab_size: int = field( default=50000, metadata={'help': 'Maximum size of code vocab'} ) nl_vocab_size: int = field( default=30000, metadata={'help': 'Maximum size of nl vocab'} ) code_vocab_name: str = field( default='code', metadata={'help': 'Name of the code vocab'} ) ast_vocab_name: str = field( default='ast', metadata={'help': 'Name of the ast vocab'} ) nl_vocab_name: str = field( default='nl', metadata={'help': 'Name of the nl vocab'} ) max_code_len: int = field( default=256, metadata={'help': 'Maximum length of code sequence'} ) max_ast_len: int = field( default=32, metadata={'help': 'Maximum length of ast sequence'} ) max_nl_len: int = field( default=64, metadata={'help': 'Maximum length of the nl sequence'} ) code_tokenize_method: str = field( default='bpe', metadata={'help': 'Tokenize method of code', 'choices': ['word', 'bpe']} ) nl_tokenize_method: str = field( default='bpe', metadata={'help': 'Tokenize method of nl', 'choices': ['word', 'bpe']} ) no_ast: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to eliminate AST from input'} ) no_nl: bool = field( default=False, metadata={'action': 'store_true', 'help': 'Whether to eliminate natural language from input'} ) @dataclass class ModelArguments: """Arguments for model related hyper-parameters""" d_model: int = field( default=768, metadata={'help': 'Dimension of the model'} ) d_ff: int = field( default=3072, metadata={'help': 'Dimension of the feed forward layer'} ) n_head: int = field( default=12, metadata={'help': 'Number of head of self attention'} ) n_layer: int = field( default=12, metadata={'help': 'Number of layer'} ) dropout: float = field( default=0.1, metadata={'help': 'Dropout probability'} ) @dataclass class OptimizerArguments: """Arguments for optimizer, early stopping, warmup, grad clipping, label smoothing.""" learning_rate: float = field( default=5e-5, metadata={'help': 'Learning rate'} ) lr_decay_rate: float = field( default=0, metadata={'help': 'Decay ratio for learning rate, 0 to disable'} ) early_stop_patience: int = field( default=20, metadata={'help': 'Stop training if performance does not improve in n epoch, 0 to disable'} ) warmup_steps: int = field( default=1000, metadata={'help': 'Warmup steps for optimizer, 0 to disable'} ) grad_clipping_norm: float = field( default=1.0, metadata={'help': 'Gradient clipping norm, 0 to disable'} ) gradient_accumulation_steps: int = field( default=1, metadata={'help': 'Gradient accumulation steps, default to 1'} ) label_smoothing: float = field( default=0.1, metadata={'help': 'Label smoothing ratio, 0 to disable'} ) @dataclass class TaskArguments: """Arguments for specific tasks""" mass_mask_ratio: float = field( default=0.5, metadata={'help': 'Ratio between number of masked tokens and number of total tokens, in MASS'} ) summarization_language: str = field( default='java', metadata={'help': 'Language of the source code in code summarization, also the directory of the dataset dir'} ) completion_max_len: int = field( default=32, metadata={'help': 'Max length of code to completion'} ) translation_source_language: str = field( default='java', metadata={'help': 'Source language of the code translation', 'choices': ['java', 'c_sharp']} ) translation_target_language: str = field( default='c_sharp', metadata={'help': 'Target language of the code translation', 'choices': ['java', 'c_sharp']} ) search_language: str = field( default='java', metadata={'help': 'Language of the source code in code search, also the directory of the dataset dir'} ) bug_fix_scale: str = field( default='small', metadata={'help': 'Scale of the bug fix dataset.', 'choices': ['small', 'medium']} ) def transfer_arg_name(name): return '--' + name.replace('_', '-') def add_args(parser): """Add all arguments to the given parser.""" for data_container in [RuntimeArguments, DatasetArguments, SavingArguments, PreprocessingArguments, ModelArguments, OptimizerArguments, TaskArguments]: group = parser.add_argument_group(data_container.__name__) for data_field in dataclasses.fields(data_container): if 'action' in data_field.metadata: group.add_argument(transfer_arg_name(data_field.name), default=data_field.default, **data_field.metadata) else: group.add_argument(transfer_arg_name(data_field.name), type=data_field.type, default=data_field.default, **data_field.metadata)

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{{cookiecutter.project_hyphen}}/{{cookiecutter.project_slug}}/__init__.py

zhangxianbing/cookiecutter-pypackage

1

9816

"""{{ cookiecutter.project_name }} - {{ cookiecutter.project_short_description }}""" __version__ = "{{ cookiecutter.project_version }}" __author__ = """{{ cookiecutter.author_name }}""" __email__ = "{{ cookiecutter.author_email }}" prog_name = "{{ cookiecutter.project_hyphen }}"

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hummingbot/connector/exchange/k2/k2_in_flight_order.py

d3alek/hummingbot

0

26659

<reponame>d3alek/hummingbot<filename>hummingbot/connector/exchange/k2/k2_in_flight_order.py<gh_stars>0 import asyncio from decimal import Decimal from typing import ( Any, Dict, Optional, ) from hummingbot.connector.exchange.k2.k2_utils import convert_from_exchange_trading_pair from hummingbot.connector.in_flight_order_base import InFlightOrderBase from hummingbot.core.event.events import ( OrderType, TradeType ) class K2InFlightOrder(InFlightOrderBase): def __init__(self, client_order_id: str, exchange_order_id: Optional[str], trading_pair: str, order_type: OrderType, trade_type: TradeType, price: Decimal, amount: Decimal, creation_timestamp: int, initial_state: str = "New", ): super().__init__( client_order_id, exchange_order_id, trading_pair, order_type, trade_type, price, amount, creation_timestamp, initial_state, ) self.last_executed_amount_base = Decimal("nan") self.trade_id_set = set() self.cancelled_event = asyncio.Event() @property def is_done(self) -> bool: return self.last_state in {"Filled", "Cancelled"} @property def is_failure(self) -> bool: return self.last_state in {"No Balance"} @property def is_cancelled(self) -> bool: return self.last_state in {"Cancelled", "Expired"} def update_with_trade_update(self, trade_update: Dict[str, Any]) -> bool: """ Update the InFlightOrder with the trade update from Private/GetHistory API endpoint return: True if the order gets updated successfully otherwise False """ trade_id: str = str(trade_update["id"]) trade_order_id: str = str(trade_update["orderid"]) if trade_order_id != self.exchange_order_id or trade_id in self.trade_id_set: return False self.trade_id_set.add(trade_id) trade_price: Decimal = Decimal(str(trade_update["price"])) trade_amount: Decimal = Decimal(str(trade_update["amount"])) if trade_update["type"] == "Buy": self.executed_amount_base += trade_amount self.executed_amount_quote += trade_price * trade_amount else: self.executed_amount_quote += trade_amount self.executed_amount_base += trade_amount / trade_price self.fee_paid += Decimal(str(trade_update["fee"])) if not self.fee_asset: base, quote = convert_from_exchange_trading_pair(trade_update["symbol"]).split("-") self.fee_asset = base if trade_update["type"] == "Buy" else quote return True

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api.py

mr-mixas/Nested-Diff-RESTful

0

39193

<filename>api.py # -*- coding: utf-8 -*- # # Copyright 2021 <NAME> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from flask import Flask, Response, jsonify, render_template, request from nested_diff import Differ, Patcher from nested_diff.fmt import HtmlFormatter, TextFormatter, TermFormatter app = Flask(__name__) def format_diff_response(fmt, diff, opts): mimetype = 'text/plain' if fmt == 'text': formatter = TextFormatter elif fmt == 'term': formatter = TermFormatter elif fmt == 'html': formatter = HtmlFormatter else: return Response('Unsupported format ' + fmt, status=400) return Response( formatter(**opts).format(diff), status=200, mimetype=mimetype, ) @app.route('/') def index(): # this page is almost never reloaded, so it is OK to embed css in it return render_template('index.html', html_fmt_css=HtmlFormatter.get_css()) @app.route('/ping') def health_check(): return Response('pong', status=200) @app.route('/api/v1/diff', methods=['POST']) def diff(): try: diff_opts = request.json.get('diff_opts', {'U': False}) except AttributeError: return Response('Object expected', status=400) try: diff = Differ(**diff_opts).diff( request.json.get('a', None), request.json.get('b', None), ) except Exception: return Response('Incorrect options', status=400) ofmt = request.json.get('ofmt', 'json') if ofmt == 'json': return jsonify(diff) ofmt_opts = request.json.get('ofmt_opts', {}) return format_diff_response(ofmt, diff, ofmt_opts) @app.route('/api/v1/format', methods=['POST']) def format(): try: diff = request.json.get('diff', {}) ofmt = request.json.get('ofmt', None) if ofmt is None: return jsonify(diff) ofmt_opts = request.json.get('ofmt_opts', {}) return format_diff_response(ofmt, diff, ofmt_opts) except Exception: return Response('Incorrect arguments', status=400) @app.route('/api/v1/patch', methods=['POST']) def patch(): try: return jsonify(Patcher().patch( request.json['target'], request.json['patch'], )) except Exception: return Response('Incorrect arguments', status=400) if __name__ == '__main__': app.run(host='0.0.0.0', port=8080, debug=True)

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experiments/messaging/pyuv/pyuv_server.py

chartbeat-labs/wade

16

74009

<reponame>chartbeat-labs/wade<gh_stars>10-100 import time import signal import pyuv import msgpack count = 0 st_time = 0 packer = msgpack.Packer() unpacker = msgpack.Unpacker() def on_read(client, data, error): if data is None: client.close() clients.remove(client) return unpacker.feed(data) try: msg = unpacker.unpack() except msgpack.OutOfData: return client.write(packer.pack(123)) global count global st_time if count % 10000 == 0: right_now = time.time() print count / (right_now - st_time) st_time = right_now count = 0 count += 1 def on_connection(server, error): client = pyuv.TCP(server.loop) server.accept(client) clients.append(client) client.start_read(on_read) def signal_cb(handle, signum): [c.close() for c in clients] signal_h.close() server.close() print("PyUV version %s" % pyuv.__version__) loop = pyuv.Loop.default_loop() clients = [] server = pyuv.TCP(loop) server.bind(("0.0.0.0", 12345)) server.listen(on_connection) signal_h = pyuv.Signal(loop) signal_h.start(signal_cb, signal.SIGINT) loop.run() print("Stopped!")

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scripts/benchmarking/parallel_benchmark.py

BerkeleyAutomation/pyhull

69

187898

<reponame>BerkeleyAutomation/pyhull #!/usr/bin/env python """ TODO: Change the module doc. """ from __future__ import division __author__ = "<NAME>" __version__ = "0.1" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Beta" __date__ = "11/19/12" from multiprocessing import Pool import numpy as np from pyhull.convex_hull import ConvexHull num_proc = 100 def get_vertices(data): return ConvexHull(data).vertices def serial_test(data): d = [] for i in xrange(num_proc): d.append(get_vertices(data)) return d def parallel_test(data): p = Pool(4) all_data = p.map(get_vertices, [data] * num_proc) assert all([all_data[i] == all_data[0] for i in xrange(num_proc)]) if __name__ == "__main__": import timeit global data for npts in [100, 1000, 10000]: for dim in [3, 4, 5]: data = np.random.randn(npts,dim) t = timeit.timeit("serial_test(data)", setup="from __main__ import serial_test, data", number=1) print ("Serial: {} {} {:.5f}".format(npts, dim, t)) t = timeit.timeit("parallel_test(data)", setup="from __main__ import parallel_test, data", number=1) print ("Parallel: {} {} {:.5f}".format(npts, dim, t))

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addons14/project_timeline/tests/__init__.py

odoochain/addons_oca

1

166723

from . import test_project_timeline

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computational-linguistics/LSTM-POS-tagger/main.py

sangeet2020/ws-20-21

0

89208

<reponame>sangeet2020/ws-20-21 #!/usr/bin/python3 # -*- coding: utf-8 -*- # @Author: <NAME> # @Date: 2021-03-08 18:19:28 # @Email: <EMAIL> # @Organization: Universität des Saarlandes # @Last Modified time: 2021-03-15 23:28:47 """ LSTM based POS tagger """ import os import io import sys import pdb import nltk import zipfile import argparse import numpy as np import pandas as pd import time import urllib.request from nltk import corpus import matplotlib.pyplot as plt from gensim.models import KeyedVectors from keras.preprocessing.text import Tokenizer import seaborn; seaborn.set() # plot formatting from keras.utils.np_utils import to_categorical from sklearn.model_selection import train_test_split from tensorflow import keras from keras.preprocessing.sequence import pad_sequences from keras.models import Sequential from keras.layers import Embedding from keras.layers import Dense, Input from keras.layers import TimeDistributed from keras.layers import LSTM, Bidirectional from keras.models import Model from sklearn.metrics import classification_report MAX_SEQ_LEN = 100 EMBEDDING_SIZE = 300 class Vectorizer(object): def __init__(self, sentences, pos_tags, word2vec): self.sentences = sentences self.pos_tags = pos_tags self.word2vec = word2vec def vectorize(self): # Vectorize X and Y self.t_words = Tokenizer() self.t_words.fit_on_texts(self.sentences) X = self.t_words.texts_to_sequences(self.sentences) self.t_tags = Tokenizer() self.t_tags.fit_on_texts(self.pos_tags) y = self.t_tags.texts_to_sequences(self.pos_tags) # Padding and truncation to make all sequences of uniform length self.X = pad_sequences(X, maxlen=MAX_SEQ_LEN, truncating='post') self.y = pad_sequences(y, maxlen=MAX_SEQ_LEN, truncating='post') self.embedding_weights = self.prep_embeddings() self.target_labs = [*self.t_tags.word_index] return self.X, self.y, self.target_labs, self.embedding_weights def prep_embeddings(self): Vectorizer.prep_embeddings.VOCABULARY_SIZE = len(self.t_words.word_index) + 1 self.embedding_weights = np.zeros((Vectorizer.prep_embeddings.VOCABULARY_SIZE, EMBEDDING_SIZE)) word2id = self.t_words.word_index for word, index in word2id.items(): try: self.embedding_weights[index, :] = self.word2vec[word] except KeyError: pass self._encodings() return self.embedding_weights def _encodings(self): # one-hot encoding self.y = to_categorical(self.y) class LSTM_model(object): def __init__(self, X, y, target_labs, embedding_weights, BiLSTM=False): self.X = X self.y = y self.target_labs = target_labs self.embedding_weights = embedding_weights self.BiLSTM = BiLSTM def train_test_split(self): self.X_train, self.X_test, self.Y_train, self.Y_test = train_test_split(self.X, self.y, test_size=0.2, random_state=123) self.X_train, self.X_validation, self.Y_train, self.Y_validation = train_test_split(self.X_train, self.Y_train, test_size=0.2, random_state=123) def model_init(self, args): self.lstm_model = Sequential() self.target_labs = self.y.shape[2] VOCABULARY_SIZE = Vectorizer.prep_embeddings.VOCABULARY_SIZE self.lstm_model.add(Embedding( input_dim = VOCABULARY_SIZE, output_dim = EMBEDDING_SIZE, input_length = MAX_SEQ_LEN, weights = [self.embedding_weights], trainable = False )) if self.BiLSTM: print('-'*20,"Model architecture: Bi-LSTM",'-'*20) self.lstm_model.add(Bidirectional(LSTM(64, return_sequences=True))) else: print('-'*20,"Model architecture: LSTM",'-'*20) self.lstm_model.add(LSTM(64, return_sequences=True)) self.lstm_model.add(TimeDistributed(Dense(self.target_labs, activation='softmax'))) path = args.out_dir + "/model_arch.png" keras.utils.plot_model(self.lstm_model, path, show_shapes=True) def train(self, args): self.train_test_split() self.model_init(args) self.lstm_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc']) print(self.lstm_model.summary()) self.lstm_training = self.lstm_model.fit(self.X_train, self.Y_train, batch_size=128, epochs=10, validation_data=(self.X_validation, self.Y_validation)) self.training_report(args) loss, accuracy = self.lstm_model.evaluate(self.X_test, self.Y_test, verbose = 1) print("Loss: {:.2f} %,\nAccuracy: {:.2f} %".format(loss*100, accuracy*100)) if self.BiLSTM: self.lstm_model.save(args.out_dir + '/model_Bi-LSTM.h5') else: self.lstm_model.save(args.out_dir + '/model_LSTM.h5') def training_report(self, args): # summarize history for accuracy plt.plot(self.lstm_training.history['acc']) plt.plot(self.lstm_training.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') if self.BiLSTM: plt.savefig(args.out_dir + '/accuray_plot_Bi-LSTM.eps', format='eps') else: plt.savefig(args.out_dir + '/accuray_plot_LSTM.eps', format='eps') # summarize history for loss plt.plot(self.lstm_training.history['acc']) plt.plot(self.lstm_training.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') if self.BiLSTM: plt.savefig(args.out_dir + '/loss_plot_Bi-LSTM.eps', format='eps') else: plt.savefig(args.out_dir + '/loss_plot_LSTM.eps', format='eps') def download_data(args): """ purpose of my function """ # Download dataset nltk.download('treebank') nltk.download('brown') nltk.download('conll2000') nltk.download('universal_tagset') # Download pre-trained embeddings. 1M words with subword info if args.subword_info: print("Using Subword info") url = 'https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M-subword.vec.zip' else: print("Not using Subword info") url = 'https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M.vec.zip' fname = url.split('/')[-1] if not os.path.exists(fname): print("Downloading embeddings...") command = str("! wget -nc ") + url print(command) os.system(command) with zipfile.ZipFile(fname, 'r') as zip_ref: zip_ref.extractall('.') else: print("Pre-trained embeddings already present; not retrieving.") return KeyedVectors.load_word2vec_format(fname.strip('.zip')) def data_preparation(): treebank_corpus = corpus.treebank.tagged_sents(tagset='universal') brown_corpus = corpus.brown.tagged_sents(tagset='universal') conll_corpus = corpus.conll2000.tagged_sents(tagset='universal') return treebank_corpus + brown_corpus + conll_corpus def data_loader(tagged_sentences): sentences = [] pos_tags = [] for item in tagged_sentences: sentences.append([token[0] for token in item]) pos_tags.append([token[1] for token in item]) return sentences, pos_tags def main(): """ main method """ args = parse_arguments() start = time.time() os.makedirs(args.out_dir, exist_ok=True) word2vec = download_data(args) tagged_sentences = data_preparation() sentences, pos_tags = data_loader(tagged_sentences) vectorizer = Vectorizer(sentences, pos_tags, word2vec) X, y, target_labs, embedding_weights = vectorizer.vectorize() if args.model_choice=="BiLSTM": LSTM_model(X, y, target_labs, embedding_weights,BiLSTM=True).train(args) elif args.model_choice=="LSTM": LSTM_model(X, y, target_labs, embedding_weights,BiLSTM=False).train(args) else: print("Specified model architecture not understood. Try again!") end = time.time() print("Total runtime: %.3f s" % (end-start)) def parse_arguments(): """ parse arguments """ parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("out_dir", help="path to save the plots and results") parser.add_argument("model_choice",default=None,choices=['LSTM','BiLSTM'], type=str,help='Choice of training architecture') parser.add_argument("-subword_info", default=None, type=bool, help='use pre-embeddings with subword information') args = parser.parse_args() return args if __name__ == "__main__": main()

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ihan/__init__.py

marklit/ihan

0

173734

<reponame>marklit/ihan<gh_stars>0 from __future__ import print_function from itertools import chain, islice import json from os.path import expanduser, join from random import randint import subprocess import sys from time import sleep import requests def eprint(*args, **kwargs): print(*args, file=sys.stderr, **kwargs) def group(num_items, iterable): iterable = iter(iterable) while True: chunk = islice(iterable, num_items) try: first_element = next(chunk) except StopIteration: return yield chain((first_element,), chunk) def send_it(endpoint, api_key, api_secret, logs): r = requests.post(endpoint, json={'lines': logs, 'api_key': api_key, 'api_secret': api_secret}) assert r.status_code == 200, r.status_code assert r.json()['lines'] == len(logs), 'Line counts failed to match' has_binary = lambda s: "\x00" in s or any(ord(x) > 0x80 for x in s) # noqa:E731 def feed_file(filename, from_beginning, batch_size, endpoint, sleep_interval, backfill): try: config_file = join(expanduser("~"), ".ihan/config") api = json.loads(open(config_file, 'r').read()) assert len(api['api_key']) > 10 and len(api['api_secret']) > 10 except Exception as exc: print(exc) print("Please run this first: ihan login") return filename = '/dev/stdin' if filename.strip() == '-' else filename # WIP this can't detect the end of a stream when # gunzip -c data is being piped in. # If filename is /dev/stdin don't use tail, just use # something in python to follow stdin. if backfill: proc = subprocess.Popen(['cat', filename], stdout=subprocess.PIPE) else: proc = subprocess.Popen(['tail', '--lines=+0' if from_beginning else '-n 1000', '--follow=name', '--retry', '--quiet', '--silent', '--sleep-interval=%d' % sleep_interval, filename], stdout=subprocess.PIPE) for n_batch, lines in enumerate(group(batch_size, iter(proc.stdout.readline, ''))): eprint('batch: ' + str(n_batch)) # Don't let a few binary bytes stop this in its tracks. try: logs = list([l.rstrip().decode('utf-8') for l in lines]) except (AttributeError, # Python 3 UnicodeDecodeError) as exc: # Python 2 eprint('Compressed logs are not supported') sleep(randint(30, 60)) continue if has_binary(''.join(logs)): eprint('Compressed logs are not supported') sleep(randint(30, 60)) continue while True: try: send_it(endpoint, api['api_key'], api['api_secret'], logs) sleep(0.25) # WIP: Make this optional break except (KeyError, ValueError, AssertionError, requests.exceptions.ConnectionError) as exc: eprint(exc) # WIP: Set configuration for this _interval = randint(10, 180) eprint('Sleeping for ' + str(_interval) + 's') sleep(_interval)

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prediction/rawDataReader.py

garagonc/optimization-framework

0

138484

<filename>prediction/rawDataReader.py """ Created on Okt 04 13:51 2018 @author: nishit """ import datetime import os import time from senml import senml from utils_intern.messageLogger import MessageLogger logger = MessageLogger.get_logger_parent() class RawDataReader: @staticmethod def read_from_file(file_path, topic_name): try: if os.path.exists(file_path): with open(file_path) as file: data = file.readlines() file.close() return data else: logger.info("Saved data not available "+str(topic_name)) return [] except Exception as e: logger.error(e) return [] @staticmethod def time_conversion(time): time = int(time) if len(str(time)) > 10: new_t = time / (10 ** (len(str(time)) - 10)) return new_t else: return time @staticmethod def format_data(data=[]): new_data = [] doc = None try: doc = senml.SenMLDocument.from_json(data) except Exception as e: pass if not doc: try: meas = senml.SenMLMeasurement.from_json(data) doc = senml.SenMLDocument([meas]) except Exception as e: pass if doc: for meas in doc.measurements: try: n = meas.name v = meas.value t = meas.time t = RawDataReader.time_conversion(t) cols = [t,v] new_data.append(cols) except Exception as e: logger.error("Exception in formating meas " + str(e)) return new_data else: for row in data: try: cols = row.replace('\n', '').strip().split(",") dateTime = float(cols[0]) cols = cols[1:] cols = list(map(float, cols)) cols.insert(0, dateTime) new_data.append(cols) except Exception as e: logger.error("Exception in formating line "+str(row)+ " "+ str(e)) return new_data @staticmethod def get_raw_data(file_path, topic_name, data_length=None): data = RawDataReader.read_from_file(file_path, topic_name) if data_length is not None and len(data) > data_length: data = data[-data_length:] if len(data) > 0: indexes = [] for i,v in enumerate(data): if "inf" in str(v).lower() or "nan" in str(v).lower(): indexes.append(i) logger.debug("len indexes " + str(len(indexes))) indexes.sort(reverse=True) for index in indexes: data.pop(index) return RawDataReader.format_data(data) @staticmethod def get_raw_data_by_time(file_path, topic_name, start_time, end_time): #logger.debug("start time "+str(start_time)+" end time "+str(end_time)) flag = False new_data = [] if start_time < end_time: data = RawDataReader.read_from_file(file_path, topic_name) start_time = int(start_time) end_time = int(end_time) for row in data: try: col = row.replace('\n', '').strip().split(",") t = float(col[0]) v = float(col[1]) if not flag and start_time <= t: flag = True if flag and end_time < t: break if flag: new_data.append([t,v]) except Exception as e: logger.error("Exception in formating line " + str(row) + " " + str(e)) return new_data @staticmethod def save_to_file(file_path, topic_name, minute_data, max_file_size_mins=-1, overwrite=False): try: logger.info("Saving raw data to file " + str(file_path)) if overwrite: old_data = [] else: old_data = RawDataReader.read_from_file(file_path, topic_name) for item in minute_data: line = ','.join(map(str, item[:2])) + "\n" old_data.append(line) if max_file_size_mins > 0: old_data = old_data[-max_file_size_mins:] update_data = [] for i, line in enumerate(old_data): c = line.count(",") if c == 2: s = line.split(",") m = s[1] mv = m[:-12] mt = m[-12:] l1 = [float(s[0]), float(mv)] l1 = ','.join(map(str, l1[:2])) + "\n" l2 = [float(mt), float(s[2].replace("\n", ""))] l2 = ','.join(map(str, l2[:2])) + "\n" update_data.append([i, l1, l2]) elif c != 1: update_data.append([i, None, None]) shift = 0 for d in update_data: if d[1] is not None and d[2] is not None: old_data.pop(d[0] + shift) old_data.insert(d[0] + shift, d[1]) old_data.insert(d[0] + shift + 1, d[2]) shift += 1 else: old_data.pop(d[0] + shift) shift -= 1 with open(file_path, 'w+') as file: file.writelines(old_data) file.close() return [] except Exception as e: logger.error("failed to save_to_file " + str(e)) return minute_data @staticmethod def del_file(file_path, topic_name): try: if os.path.exists(file_path): os.remove(file_path) except Exception as e: logger.error("failed to del_to_file " + str(e)+" "+str(topic_name)) @staticmethod def removed_data_before_timestamp(file_path, topic_name, lastest_timestamp): data = RawDataReader.get_raw_data_by_time(file_path, topic_name, lastest_timestamp, time.time()) RawDataReader.save_to_file(file_path, topic_name, data, overwrite=True) @staticmethod def save_to_influx(influxdb, topic_name, minute_data, id): try: logger.info("Saving raw data to influx " + str(topic_name)) json_body = influxdb.timeseries_list_to_influx_json(minute_data, topic_name, "raw", id) if not influxdb.write(json_body): return minute_data else: return [] except Exception as e: logger.error("failed to save_to_influx " + str(e)) return minute_data @staticmethod def get_raw_data_influx(influxdb, topic_name, id, data_length=None): start_time = None if data_length: start_time = datetime.datetime.now() - datetime.timedelta(minutes=data_length+720) # half day extra data = influxdb.read(topic_name, "raw", instance_id=id, start_time=start_time) if data_length is not None and len(data) > data_length: data = data[-data_length:] if len(data) > 0: indexes = [] for i, v in enumerate(data): if "inf" in str(v).lower() or "nan" in str(v).lower(): indexes.append(i) logger.debug("len indexes " + str(len(indexes))) indexes.sort(reverse=True) for index in indexes: data.pop(index) return data @staticmethod def get_raw_data_by_time_influx(influxdb, topic_name, start_time, end_time, id): new_data = [] if start_time < end_time: new_data = influxdb.read(topic_name, "raw", instance_id=id, start_time=start_time, end_time=end_time) return new_data

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rasa/utils/io.py

chws0508/rasa

0

133978

import asyncio import filecmp import logging import os import pickle import tempfile import warnings import re from asyncio import AbstractEventLoop from pathlib import Path from typing import Text, Any, Union, List, Type, Callable, TYPE_CHECKING, Pattern import rasa.shared.constants import rasa.shared.utils.io if TYPE_CHECKING: from prompt_toolkit.validation import Validator def configure_colored_logging(loglevel: Text) -> None: import coloredlogs loglevel = loglevel or os.environ.get( rasa.shared.constants.ENV_LOG_LEVEL, rasa.shared.constants.DEFAULT_LOG_LEVEL ) field_styles = coloredlogs.DEFAULT_FIELD_STYLES.copy() field_styles["asctime"] = {} level_styles = coloredlogs.DEFAULT_LEVEL_STYLES.copy() level_styles["debug"] = {} coloredlogs.install( level=loglevel, use_chroot=False, fmt="%(asctime)s %(levelname)-8s %(name)s - %(message)s", level_styles=level_styles, field_styles=field_styles, ) def enable_async_loop_debugging( event_loop: AbstractEventLoop, slow_callback_duration: float = 0.1 ) -> AbstractEventLoop: logging.info( "Enabling coroutine debugging. Loop id {}.".format(id(asyncio.get_event_loop())) ) # Enable debugging event_loop.set_debug(True) # Make the threshold for "slow" tasks very very small for # illustration. The default is 0.1 (= 100 milliseconds). event_loop.slow_callback_duration = slow_callback_duration # Report all mistakes managing asynchronous resources. warnings.simplefilter("always", ResourceWarning) return event_loop def pickle_dump(filename: Union[Text, Path], obj: Any) -> None: """Saves object to file. Args: filename: the filename to save the object to obj: the object to store """ with open(filename, "wb") as f: pickle.dump(obj, f) def pickle_load(filename: Union[Text, Path]) -> Any: """Loads an object from a file. Args: filename: the filename to load the object from Returns: the loaded object """ with open(filename, "rb") as f: return pickle.load(f) def create_temporary_file(data: Any, suffix: Text = "", mode: Text = "w+") -> Text: """Creates a tempfile.NamedTemporaryFile object for data.""" encoding = None if "b" in mode else rasa.shared.utils.io.DEFAULT_ENCODING f = tempfile.NamedTemporaryFile( mode=mode, suffix=suffix, delete=False, encoding=encoding ) f.write(data) f.close() return f.name def create_temporary_directory() -> Text: """Creates a tempfile.TemporaryDirectory.""" f = tempfile.TemporaryDirectory() return f.name def create_path(file_path: Text) -> None: """Makes sure all directories in the 'file_path' exists.""" parent_dir = os.path.dirname(os.path.abspath(file_path)) if not os.path.exists(parent_dir): os.makedirs(parent_dir) def file_type_validator( valid_file_types: List[Text], error_message: Text ) -> Type["Validator"]: """Creates a `Validator` class which can be used with `questionary` to validate file paths. """ def is_valid(path: Text) -> bool: return path is not None and any( [path.endswith(file_type) for file_type in valid_file_types] ) return create_validator(is_valid, error_message) def not_empty_validator(error_message: Text) -> Type["Validator"]: """Creates a `Validator` class which can be used with `questionary` to validate that the user entered something other than whitespace. """ def is_valid(input: Text) -> bool: return input is not None and input.strip() != "" return create_validator(is_valid, error_message) def create_validator( function: Callable[[Text], bool], error_message: Text ) -> Type["Validator"]: """Helper method to create `Validator` classes from callable functions. Should be removed when questionary supports `Validator` objects.""" from prompt_toolkit.validation import Validator, ValidationError from prompt_toolkit.document import Document class FunctionValidator(Validator): @staticmethod def validate(document: Document) -> None: is_valid = function(document.text) if not is_valid: raise ValidationError(message=error_message) return FunctionValidator def json_unpickle( file_name: Union[Text, Path], encode_non_string_keys: bool = False ) -> Any: """Unpickle an object from file using json. Args: file_name: the file to load the object from encode_non_string_keys: If set to `True` then jsonpickle will encode non-string dictionary keys instead of coercing them into strings via `repr()`. Returns: the object """ import jsonpickle.ext.numpy as jsonpickle_numpy import jsonpickle jsonpickle_numpy.register_handlers() file_content = rasa.shared.utils.io.read_file(file_name) return jsonpickle.loads(file_content, keys=encode_non_string_keys) def json_pickle( file_name: Union[Text, Path], obj: Any, encode_non_string_keys: bool = False ) -> None: """Pickle an object to a file using json. Args: file_name: the file to store the object to obj: the object to store encode_non_string_keys: If set to `True` then jsonpickle will encode non-string dictionary keys instead of coercing them into strings via `repr()`. """ import jsonpickle.ext.numpy as jsonpickle_numpy import jsonpickle jsonpickle_numpy.register_handlers() rasa.shared.utils.io.write_text_file( jsonpickle.dumps(obj, keys=encode_non_string_keys), file_name ) def get_emoji_regex() -> Pattern: """Returns regex to identify emojis.""" return re.compile( "[" "\U0001F600-\U0001F64F" # emoticons "\U0001F300-\U0001F5FF" # symbols & pictographs "\U0001F680-\U0001F6FF" # transport & map symbols "\U0001F1E0-\U0001F1FF" # flags (iOS) "\U00002702-\U000027B0" "\U000024C2-\U0001F251" "\u200d" # zero width joiner "\u200c" # zero width non-joiner "]+", flags=re.UNICODE, ) def are_directories_equal(dir1: Path, dir2: Path) -> bool: """Compares two directories recursively. Files in each directory are assumed to be equal if their names and contents are equal. Args: dir1: The first directory. dir2: The second directory. Returns: `True` if they are equal, `False` otherwise. """ dirs_cmp = filecmp.dircmp(dir1, dir2) if dirs_cmp.left_only or dirs_cmp.right_only: return False (_, mismatches, errors) = filecmp.cmpfiles( dir1, dir2, dirs_cmp.common_files, shallow=False ) if mismatches or errors: return False for common_dir in dirs_cmp.common_dirs: new_dir1 = Path(dir1, common_dir) new_dir2 = Path(dir2, common_dir) is_equal = are_directories_equal(new_dir1, new_dir2) if not is_equal: return False return True

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mosaic_letter.py

ryhoh/mosaicDesigner_py

0

88866

<gh_stars>0 ''' mosaic_letter.py Created by <NAME> on 2018/01/04. Copyright © 2018年 <NAME>. All rights reserved. モザイク文字画像の情報を持つクラス - フィールド - colors 整数リスト：使用するカラーコード（背景，色1，色2） - gridSize 整数：モザイクのグリッドの大きさ - grid_parameter np配列：背景の部分を0，色ありの部分を1で表現 - grid_pattern np配列：gridの各部分の色を指定 - メソッド - __init__ フィールドへ代入 - input 標準入力から色とグリッドの大きさとパターンを入力 - mask parameterが0でない部分は，patternの色を保持 parameterが0の部分は，patternに背景色を入れる - output bmp形式で出力 ''' import struct import numpy as np class MosaicLetter: def __init__(self): self.colors = list() self.gridSize = None self.grid_parameter = list() self.grid_pattern = list() self.input() # 入力を受ける def input(self): print("First, input 3 colorcodes.") for i in range(3): self.colors.append(int(input(), base=16)) print("Second, input size of grid.") self.gridSize = int(input()) print("Third, input grid parameter.") for i in range(self.gridSize): self.grid_parameter.append([int(j) for j in input()]) self.grid_parameter = np.array(self.grid_parameter) print("Finally, input grid pattern.") for i in range(self.gridSize): self.grid_pattern.append([int(j) for j in input()]) self.grid_pattern = np.array(self.grid_pattern) def mask(self): # grid_parameterに基づいてgrid_patternを編集する # grid_parameter[i][j]が0ならgrid_pattern[i][j]も0 # grid_parameter[i][j]が1ならgrid_pattern[i][j]はそのまま self.grid_pattern *= self.grid_parameter def output(self, name="output.bmp"): # 定数 FILEHEADER = 14 # ファイルヘッダの大きさ INFOHEADER = 12 # 情報ヘッダの大きさ SQUARESIZE = 32 # 小さな正方形の一辺の長さ FRAMETHICK = 1 # 縁取りの線の厚さ IMAGE_LEN = SQUARESIZE * self.gridSize # 画像の1辺の長さ # 書き込み with open(name, "wb") as f: # 符号なし4byte整数を書き込む関数 writeInt = (lambda x: f.write(struct.pack("I", x))) writeShort = (lambda x: f.write(struct.pack("H", x))) writeByte = (lambda x: f.write(struct.pack("B", x))) # ファイルヘッダ ---------------- f.write(struct.pack("BB", ord("B"), ord("M"))) # ファイルヘッダ # ファイルサイズ（4bit=1/2byteの画像であるので画素数を1/2する） writeInt(FILEHEADER + INFOHEADER+ 3*16 + IMAGE_LEN**2 // 2) writeInt(0) # 予約領域 # 先頭から画像までのオフセット = ファイルヘッダ + 情報ヘッダ + パレットデータ writeInt(FILEHEADER + INFOHEADER + 3*16) # 情報ヘッダ ---------------- writeInt(INFOHEADER) # 情報ヘッダの大きさ writeShort(IMAGE_LEN) # 縦横の長さ writeShort(IMAGE_LEN) # 正方形なので同じ数値を2つ writeShort(1) # bcプレーン数（？）は常に1 writeShort(4) # 画像の色数（4bit） # パレットデータ ---------------- # 最初の3色にcolorsのデータを入れ、残りは0に設定 for color in self.colors: # リトルエンディアンで1byteずつ書き込む writeByte(color & 0x0000FF) writeByte((color & 0x00FF00) >> 8) writeByte((color & 0xFF0000) >> 16) for i in range(16 - len(self.colors)): for j in range(3): writeByte(0) # 画像データ ---------------- # 1Byteに2pixelのデータが入ることに注意 # 画像データは左下から右上に記録される # 縁の内外を判定して書き込み ------------------------------------- def checkAndWrite(v_i, v_j, h_i, h_j): if FRAMETHICK <= v_j < SQUARESIZE-FRAMETHICK \ and FRAMETHICK <= h_j < SQUARESIZE-FRAMETHICK: # 縁の内側 data = self.grid_pattern[self.gridSize - v_i - 1][h_i] \ + (self.grid_pattern[self.gridSize - v_i - 1][h_i] << 4) else: # 縁 data = 0 # 書き込み writeByte(data) # ----------------------------------------------------------- for v_i in range(self.gridSize): # 縦のパターンの数だけループ for v_j in range(SQUARESIZE): # 小さい正方形の縦の長さだけループ for h_i in range(self.gridSize): # 横のパターンの数だけループ for h_j in range(SQUARESIZE // 2): # 小さい正方形の横の長さ/2だけループ # 1byteに2画素書き込むためループ数を1/2とする checkAndWrite(v_i, v_j, h_i, h_j) if __name__ == '__main__': ml = MosaicLetter() ml.mask() ml.output()

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qualtrics/share.py

anhiancheong/topics_evaluation

0

196153

<gh_stars>0 import argparse import json import requests API_KEY = "<KEY>" ENDPOINT = "https://iad1.qualtrics.com/API/v3" users = [ "UR_56juIFbMBWLU9jo", "UR_bIMXWqoIF8GNR7E", "UR_4O2EvhVhx5UVoQS" ] survey_ids = [ "SV_ezlYrHXuI4oxV8W", "SV_0Va3pTzbIGiS8tM", "SV_50zRZfCRm5wvvxA", "SV_80UG9Vuo5KHKbFI", "SV_3z2XeepZD1uheui", "SV_6GuDFnyQX0IhAeq", "SV_4Ob1KEJ0trA5KCy", "SV_1NyEUJ5LX8KtHFQ", "SV_3z661hKZxFMnewC", "SV_bqpnsYfAAixidLM" ] for survey_id in survey_ids: for user_id in users: resp = requests.post( f"https://ca1.qualtrics.com/API/v3/surveys/{survey_id}/permissions/collaborations", headers={"X-API-TOKEN": API_KEY}, json={ "recipientId": user_id, "permissions": { "surveyDefinitionManipulation": { "copySurveyQuestions": True, "editSurveyFlow": True, "useBlocks": True, "useSkipLogic": True, "useConjoint": True, "useTriggers": True, "useQuotas": True, "setSurveyOptions": True, "editQuestions": True, "deleteSurveyQuestions": True, "useTableOfContents": True, "useAdvancedQuotas": True }, "surveyManagement": { "editSurveys": True, "activateSurveys": True, "deactivateSurveys": True, "copySurveys": True, "distributeSurveys": True, "deleteSurveys": True, "translateSurveys": True }, "response": { "editSurveyResponses": True, "createResponseSets": True, "viewResponseId": True, "useCrossTabs": True, "useScreenouts": True }, "result": { "downloadSurveyResults": True, "viewSurveyResults": True, "filterSurveyResults": True, "viewPersonalData": True } } }) print(resp.status_code) if resp.status_code > 299: print(resp.__dict__)

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tests/test_client.py

nyush-se-spring2021-forum/OurTieba

0

22001

<gh_stars>0 class TestClient: """ Test before_request and after_request decorators in __init__.py. """ def test_1(self, client): # disallowed methods res = client.put("/") assert res.status_code == 405 assert b"Method Not Allowed" in res.content res = client.options("/api/post/add") assert res.status_code == 405 assert b"Method Not Allowed" in res.content res = client.delete("/notifications") assert res.status_code == 405 assert b"Method Not Allowed" in res.content def test_2(self, client): # empty/fake user agent res = client.get("/", headers={"User-Agent": ""}) assert res.status_code == 403 assert b"No Scrappers!" in res.content res = client.get("/board/2", headers={"User-Agent": "python/3.8"}) assert res.status_code == 403 assert b"No Scrappers!" in res.content

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qemu/scripts/codeconverter/codeconverter/test_patching.py

hyunjoy/scripts

44

9469

# Copyright (C) 2020 Red Hat Inc. # # Authors: # <NAME> <<EMAIL>> # # This work is licensed under the terms of the GNU GPL, version 2. See # the COPYING file in the top-level directory. from tempfile import NamedTemporaryFile from .patching import FileInfo, FileMatch, Patch, FileList from .regexps import * class BasicPattern(FileMatch): regexp = '[abc]{3}' @property def name(self): return self.group(0) def replacement(self) -> str: # replace match with the middle character repeated 5 times return self.group(0)[1].upper()*5 def test_pattern_patching(): of = NamedTemporaryFile('wt') of.writelines(['one line\n', 'this pattern will be patched: defbbahij\n', 'third line\n', 'another pattern: jihaabfed']) of.flush() files = FileList() f = FileInfo(files, of.name) f.load() matches = f.matches_of_type(BasicPattern) assert len(matches) == 2 p2 = matches[1] # manually add patch, to see if .append() works: f.patches.append(p2.append('XXX')) # apply all patches: f.gen_patches(matches) patched = f.get_patched_content() assert patched == ('one line\n'+ 'this pattern will be patched: defBBBBBhij\n'+ 'third line\n'+ 'another pattern: jihAAAAAXXXfed') class Function(FileMatch): regexp = S(r'BEGIN\s+', NAMED('name', RE_IDENTIFIER), r'\n', r'(.*\n)*?END\n') class Statement(FileMatch): regexp = S(r'^\s*', NAMED('name', RE_IDENTIFIER), r'\n') def test_container_match(): of = NamedTemporaryFile('wt') of.writelines(['statement1()\n', 'statement2()\n', 'BEGIN function1\n', ' statement3()\n', ' statement4()\n', 'END\n', 'BEGIN function2\n', ' statement5()\n', ' statement6()\n', 'END\n', 'statement7()\n']) of.flush() files = FileList() f = FileInfo(files, of.name) f.load() assert len(f.matches_of_type(Function)) == 2 print(' '.join(m.name for m in f.matches_of_type(Statement))) assert len(f.matches_of_type(Statement)) == 7 f1 = f.find_match(Function, 'function1') f2 = f.find_match(Function, 'function2') st1 = f.find_match(Statement, 'statement1') st2 = f.find_match(Statement, 'statement2') st3 = f.find_match(Statement, 'statement3') st4 = f.find_match(Statement, 'statement4') st5 = f.find_match(Statement, 'statement5') st6 = f.find_match(Statement, 'statement6') st7 = f.find_match(Statement, 'statement7') assert not f1.contains(st1) assert not f1.contains(st2) assert not f1.contains(st2) assert f1.contains(st3) assert f1.contains(st4) assert not f1.contains(st5) assert not f1.contains(st6) assert not f1.contains(st7) assert not f2.contains(st1) assert not f2.contains(st2) assert not f2.contains(st2) assert not f2.contains(st3) assert not f2.contains(st4) assert f2.contains(st5) assert f2.contains(st6) assert not f2.contains(st7)

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wbia_cnn/netrun.py

WildMeOrg/wbia-plugin-cnn

0

31590

#!/usr/bin/env python # -*- coding: utf-8 -*- r""" FIXME: sometimes you have to chown -R user:user ~/.theano or run with sudo the first time after roboot, otherwise you get errors CommandLineHelp: python -m wbia_cnn --tf netrun <networkmodel> --dataset, --ds = <dstag>:<subtag> dstag is the main dataset name (eg PZ_MTEST), subtag are parameters to modify (max_examples=3) --weights, -w = \|new\|<checkpoint_tag>\|<dstag>:<checkpoint_tag> (default: <checkpoint_tag>) new will initialize clean weights. a checkpoint tag will try to to match a saved model state in the history. can load weights from an external dataset. <checkpoint_tag> defaults to current --arch, -a = <archtag> model architecture tag (eg siaml2_128, siam2stream, viewpoint) --device = <processor> sets theano device flag to a processor like gpu0, gpu1, or cpu0 """ import logging from wbia_cnn import models from wbia_cnn import ingest_data from wbia_cnn import experiments import utool as ut import sys print, rrr, profile = ut.inject2(__name__) logger = logging.getLogger() # This is more of a history tag CHECKPOINT_TAG_ALIAS = { 'current': None, '': None, } # second level of alias indirection # This is more of a dataset tag DS_TAG_ALIAS2 = { 'flankhack': "dict(acfg_name='ctrl:pername=None,excluderef=False,contributor_contains=FlankHack', colorspace='gray', db='PZ_Master1')", 'pzmtest-bgr': "PZ_MTEST;dict(colorspace='bgr', controlled=True, max_examples=None, num_top=None)", # NOQA 'pzmtest': "PZ_MTEST;dict(colorspace='gray', controlled=True, max_examples=None, num_top=None)", # NOQA 'gz-gray': "GZ_ALL;dict(colorspace='gray', controlled=False, max_examples=None, num_top=None)", # NOQA 'liberty': "liberty;dict(detector='dog', pairs=250000)", 'combo': 'combo_vdsujffw', 'timectrl_pzmaster1': "PZ_Master1;dict(acfg_name='timectrl', colorspace='gray', min_featweight=0.8)", # NOQA 'pzm2': "PZ_Master1;dict(acfg_name='timectrl:pername=None', colorspace='gray', min_featweight=0.8)", # NOQA 'pzm3': "PZ_Master1;dict(acfg_name=None, colorspace='gray', controlled=True, min_featweight=0.8)", #'pzm3' : "PZ_Master1;dict(acfg_name='default:is_known=True,qmin_pername=2,view=primary,species=primary,minqual=ok', colorspace='gray', min_featweight=0.8)", # NOQA 'pzm4': "PZ_Master1;dict(acfg_name='default:is_known=True,qmin_pername=2,view=primary,species=primary,minqual=ok', colorspace='gray', min_featweight=0.8)", } def netrun(): r""" CommandLine: # --- UTILITY python -m wbia_cnn --tf get_juction_dpath --show # --- DATASET BUILDING --- # Build Dataset Aliases python -m wbia_cnn --tf netrun --db PZ_MTEST --acfg ctrl --ensuredata --show python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg timectrl --ensuredata python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg timectrl:pername=None --ensuredata python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg timectrl:pername=None --ensuredata python -m wbia_cnn --tf netrun --db mnist --ensuredata --show python -m wbia_cnn --tf netrun --db mnist --ensuredata --show --datatype=category python -m wbia_cnn --tf netrun --db mnist --ensuredata --show --datatype=siam-patch python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg ctrl:pername=None,excluderef=False,contributor_contains=FlankHack --ensuredata --show --datatype=siam-part # Parts based datasets python -m wbia_cnn --tf netrun --db PZ_MTEST --acfg ctrl --datatype=siam-part --ensuredata --show # Patch based dataset (big one) python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg default:is_known=True,qmin_pername=2,view=primary,species=primary,minqual=ok --ensuredata --show --vtd python -m wbia_cnn --tf netrun --ds pzm4 --weights=new --arch=siaml2_128 --train --monitor python -m wbia_cnn --tf netrun --ds pzm4 --arch=siaml2_128 --test python -m wbia_cnn --tf netrun --ds pzm4 --arch=siaml2_128 --veryverbose --no-flask # --- TRAINING --- python -m wbia_cnn --tf netrun --db PZ_Master1 --acfg default:is_known=True,qmin_pername=2,view=primary,species=primary,minqual=ok --weights=new --arch=siaml2_128 --train --monitor python -m wbia_cnn --tf netrun --ds timectrl_pzmaster1 --acfg ctrl:pername=None,excluderef=False,contributor_contains=FlankHack --train --weights=new --arch=siaml2_128 --monitor # NOQA python -m wbia_cnn --tf netrun --ds timectrl_pzmaster1 --acfg ctrl:pername=None,excluderef=False --train --weights=new --arch=siaml2_128 --monitor # NOQA python -m wbia_cnn --tf netrun --ds pzmtest --weights=new --arch=siaml2_128 --train --monitor --DEBUG_AUGMENTATION python -m wbia_cnn --tf netrun --ds pzmtest --weights=new --arch=siaml2_128 --train --monitor python -m wbia_cnn --tf netrun --ds flankhack --weights=new --arch=siaml2_partmatch --train --monitor --learning_rate=.00001 python -m wbia_cnn --tf netrun --ds flankhack --weights=new --arch=siam_deepfaceish --train --monitor --learning_rate=.00001 # Different ways to train mnist python -m wbia_cnn --tf netrun --db mnist --weights=new --arch=mnist_siaml2 --train --monitor --datatype=siam-patch python -m wbia_cnn --tf netrun --db mnist --weights=new --arch=mnist-category --train --monitor --datatype=category # --- INITIALIZED-TRAINING --- python -m wbia_cnn --tf netrun --ds pzmtest --arch=siaml2_128 --weights=gz-gray:current --train --monitor # --- TESTING --- python -m wbia_cnn --tf netrun --db liberty --weights=liberty:current --arch=siaml2_128 --test python -m wbia_cnn --tf netrun --db PZ_Master0 --weights=combo:current --arch=siaml2_128 --testall Example: >>> # DISABLE_DOCTEST >>> from wbia_cnn.netrun import * # NOQA >>> netrun() >>> ut.show_if_requested() """ ut.colorprint('[netrun] NET RUN', 'red') requests, hyperparams, tags = parse_args() ds_tag = tags['ds_tag'] datatype = tags['datatype'] extern_ds_tag = tags['extern_ds_tag'] arch_tag = tags['arch_tag'] checkpoint_tag = tags['checkpoint_tag'] # ---------------------------- # Choose the main dataset ut.colorprint('[netrun] Ensuring Dataset', 'yellow') dataset = ingest_data.grab_dataset(ds_tag, datatype) if extern_ds_tag is not None: extern_dpath = ingest_data.get_extern_training_dpath(extern_ds_tag) else: extern_dpath = None logger.info('dataset.training_dpath = %r' % (dataset.training_dpath,)) logger.info('Dataset Alias Key: %r' % (dataset.alias_key,)) logger.info( 'Current Dataset Tag: %r' % (ut.invert_dict(DS_TAG_ALIAS2).get(dataset.alias_key, None),) ) if requests['ensuredata']: # Print alias key that maps to this particular dataset if ut.show_was_requested(): interact_ = dataset.interact() # NOQA return logger.info('...exiting') sys.exit(1) # ---------------------------- # Choose model architecture # TODO: data will need to return info about number of labels in viewpoint models # Specify model archichitecture ut.colorprint('[netrun] Architecture Specification', 'yellow') if arch_tag == 'siam2stream': model = models.SiameseCenterSurroundModel( data_shape=dataset.data_shape, training_dpath=dataset.training_dpath, **hyperparams ) elif arch_tag.startswith('siam'): model = models.SiameseL2( data_shape=dataset.data_shape, arch_tag=arch_tag, training_dpath=dataset.training_dpath, **hyperparams ) elif arch_tag == 'mnist-category': model = models.MNISTModel( data_shape=dataset.data_shape, output_dims=dataset.output_dims, arch_tag=arch_tag, training_dpath=dataset.training_dpath, **hyperparams ) pass else: raise ValueError('Unknown arch_tag=%r' % (arch_tag,)) ut.colorprint('[netrun] Initialize archchitecture', 'yellow') model.init_arch() # ---------------------------- # Choose weight initialization ut.colorprint('[netrun] Setting weights', 'yellow') if checkpoint_tag == 'new': ut.colorprint('[netrun] * Initializing new weights', 'lightgray') model.reinit_weights() else: checkpoint_tag = model.resolve_fuzzy_checkpoint_pattern( checkpoint_tag, extern_dpath ) ut.colorprint( '[netrun] * Resolving weights checkpoint_tag=%r' % (checkpoint_tag,), 'lightgray', ) if extern_dpath is not None: model.load_extern_weights(dpath=extern_dpath, checkpoint_tag=checkpoint_tag) elif model.has_saved_state(checkpoint_tag=checkpoint_tag): model.load_model_state(checkpoint_tag=checkpoint_tag) else: model_state_fpath = model.get_model_state_fpath(checkpoint_tag=checkpoint_tag) logger.info('model_state_fpath = %r' % (model_state_fpath,)) ut.checkpath(model_state_fpath, verbose=True) logger.info( 'Known checkpoints are: ' + ut.repr3(model.list_saved_checkpoints()) ) raise ValueError( ('Unresolved weight init: ' 'checkpoint_tag=%r, extern_ds_tag=%r') % ( checkpoint_tag, extern_ds_tag, ) ) # logger.info('Model State:') # logger.info(model.get_state_str()) # ---------------------------- if not model.is_train_state_initialized(): ut.colorprint('[netrun] Need to initialize training state', 'yellow') X_train, y_train = dataset.subset('train') model.ensure_data_params(X_train, y_train) # Run Actions if requests['train']: ut.colorprint('[netrun] Training Requested', 'yellow') # parse training arguments config = ut.argparse_dict( dict( era_size=15, max_epochs=1200, rate_decay=0.8, ) ) model.monitor_config.update(**config) X_train, y_train = dataset.subset('train') X_valid, y_valid = dataset.subset('valid') model.fit(X_train, y_train, X_valid=X_valid, y_valid=y_valid) elif requests['test']: # assert model.best_results['epoch'] is not None ut.colorprint('[netrun] Test Requested', 'yellow') if requests['testall']: ut.colorprint('[netrun] * Testing on all data', 'lightgray') X_test, y_test = dataset.subset('all') flat_metadata = dataset.subset_metadata('all') else: ut.colorprint('[netrun] * Testing on test subset', 'lightgray') X_test, y_test = dataset.subset('test') flat_metadata = dataset.subset_metadata('test') data, labels = X_test, y_test dataname = dataset.alias_key experiments.test_siamese_performance(model, data, labels, flat_metadata, dataname) else: if not ut.get_argflag('--cmd'): raise ValueError('nothing here. need to train or test') if requests['publish']: ut.colorprint('[netrun] Publish Requested', 'yellow') publish_dpath = ut.truepath('~/Dropbox/IBEIS') published_model_state = ut.unixjoin( publish_dpath, model.arch_tag + '_model_state.pkl' ) ut.copy(model.get_model_state_fpath(), published_model_state) ut.view_directory(publish_dpath) logger.info( 'You need to get the dropbox link and ' 'register it into the appropriate file' ) # pip install dropbox # https://www.dropbox.com/developers/core/start/python # import dropbox # need oauth # client.share('/myfile.txt', short_url=False) # https://wildbookiarepository.azureedge.net/models/siaml2_128_model_state.pkl if ut.get_argflag('--cmd'): ut.embed() def parse_args(): ds_default = None arch_default = 'siaml2_128' weights_tag_default = None # Test values if False: ds_default = 'liberty' weights_tag_default = 'current' assert ut.inIPython() # Parse commandline args ds_tag = ut.get_argval(('--dataset', '--ds'), type_=str, default=ds_default) datatype = ut.get_argval(('--datatype', '--dt'), type_=str, default='siam-patch') arch_tag = ut.get_argval(('--arch', '-a'), default=arch_default) weights_tag = ut.get_argval( ('--weights', '+w'), type_=str, default=weights_tag_default ) # Incorporate new config stuff? # NEW = False # if NEW: # default_dstag_cfg = { # 'ds': 'PZ_MTEST', # 'mode': 'patches', # 'arch': arch_default # } # named_defaults_dict = { # '': default_dstag_cfg # } # ut.parse_argv_cfg('dstag', named_defaults_dict=named_defaults_dict) hyperparams = ut.argparse_dict( { #'batch_size': 128, 'batch_size': 256, #'learning_rate': .0005, 'learning_rate': 0.1, 'momentum': 0.9, #'weight_decay': 0.0005, 'weight_decay': 0.0001, }, alias_dict={ 'weight_decay': ['decay'], 'learning_rate': ['learn_rate'], }, ) requests = ut.argparse_dict( { 'train': False, 'test': False, 'testall': False, 'publish': False, 'ensuredata': False, } ) requests['test'] = requests['test'] or requests['testall'] # breakup weights tag into extern_ds and checkpoint if weights_tag is not None and ':' in weights_tag: extern_ds_tag, checkpoint_tag = weights_tag.split(':') else: extern_ds_tag = None checkpoint_tag = weights_tag # resolve aliases ds_tag = DS_TAG_ALIAS2.get(ds_tag, ds_tag) extern_ds_tag = DS_TAG_ALIAS2.get(extern_ds_tag, extern_ds_tag) checkpoint_tag = CHECKPOINT_TAG_ALIAS.get(checkpoint_tag, checkpoint_tag) tags = { 'ds_tag': ds_tag, 'extern_ds_tag': extern_ds_tag, 'checkpoint_tag': checkpoint_tag, 'arch_tag': arch_tag, 'datatype': datatype, } ut.colorprint('[netrun] * ds_tag=%r' % (ds_tag,), 'lightgray') ut.colorprint('[netrun] * arch_tag=%r' % (arch_tag,), 'lightgray') ut.colorprint('[netrun] * extern_ds_tag=%r' % (extern_ds_tag,), 'lightgray') ut.colorprint('[netrun] * checkpoint_tag=%r' % (checkpoint_tag,), 'lightgray') return requests, hyperparams, tags def merge_ds_tags(ds_alias_list): r""" CommandLine: python -m wbia_cnn --tf merge_ds_tags --alias-list gz-gray girm pzmtest nnp TODO: http://stackoverflow.com/questions/18492273/combining-hdf5-files Example: >>> # DISABLE_DOCTEST >>> from wbia_cnn.netrun import * # NOQA >>> ds_alias_list = ut.get_argval('--alias-list', type_=list, default=[]) >>> result = merge_ds_tags(ds_alias_list) >>> print(result) """ ds_tag_list = [DS_TAG_ALIAS2.get(ds_tag, ds_tag) for ds_tag in ds_alias_list] dataset_list = [ingest_data.grab_siam_dataset(ds_tag) for ds_tag in ds_tag_list] merged_dataset = ingest_data.merge_datasets(dataset_list) logger.info(merged_dataset.alias_key) return merged_dataset if __name__ == '__main__': """ CommandLine: python -m wbia_cnn.netrun python -m wbia_cnn.netrun --allexamples python -m wbia_cnn.netrun --allexamples --noface --nosrc """ # train_pz() import multiprocessing multiprocessing.freeze_support() # for win32 import utool as ut # NOQA # import warnings # with warnings.catch_warnings(): # # Cause all warnings to always be triggered. # warnings.filterwarnings("error", ".*get_all_non_bias_params.*") ut.doctest_funcs()

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upcfcardsearch/c133.py

ProfessorSean/Kasutamaiza

0

1615344

import discord from discord.ext import commands from discord.utils import get class c133(commands.Cog, name="c133"): def __init__(self, bot: commands.Bot): self.bot = bot @commands.command(name='Cyberse-Cop', aliases=['c133']) async def example_embed(self, ctx): embed = discord.Embed(title='Cyberse-Cop', color=0xFDE68A) embed.set_thumbnail(url='https://www.duelingbook.com/images/custom-pics/2300000/2334898.jpg') embed.add_field(name='Status (Archetype)', value='Casual:3/Tournament:3', inline=True) embed.add_field(name='Type (Attribute)', value='Cyberse/Normal (FIRE)', inline=False) embed.add_field(name='Level (ATK/DEF)', value='7 (3000/2500)', inline=False) embed.add_field(name='Lore Text', value='Constantly on guard, the Cyberse-Cops stop at nothing to ensure truth and justice is upheld. They are constanly updated on the crime in the area and are swift in taking action.', inline=False) embed.set_footer(text='Set Code: ANCF') await ctx.send(embed=embed) def setup(bot: commands.Bot): bot.add_cog(c133(bot))

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cs15211/StoneGame.py

JulyKikuAkita/PythonPrac

1

29901

<gh_stars>1-10 __source__ = 'https://leetcode.com/problems/stone-game/' # Time: O() # Space: O() # # Description: Leetcode # 877. Stone Game # # Alex and Lee play a game with piles of stones. # There are an even number of piles arranged in a row, # and each pile has a positive integer number of stones piles[i]. # # The objective of the game is to end with the most stones. # The total number of stones is odd, so there are no ties. # # Alex and Lee take turns, with Alex starting first. # Each turn, a player takes the entire pile of stones from either the beginning # or the end of the row. This continues until there are no more piles left, # at which point the person with the most stones wins. # # Assuming Alex and Lee play optimally, return True if and only if Alex wins the game. # # # # Example 1: # # Input: [5,3,4,5] # Output: true # Explanation: # Alex starts first, and can only take the first 5 or the last 5. # Say he takes the first 5, so that the row becomes [3, 4, 5]. # If Lee takes 3, then the board is [4, 5], and Alex takes 5 to win with 10 points. # If Lee takes the last 5, then the board is [3, 4], and Alex takes 4 to win with 9 points. # This demonstrated that taking the first 5 was a winning move for Alex, so we return true. # # # Note: # # 2 <= piles.length <= 500 # piles.length is even. # 1 <= piles[i] <= 500 # sum(piles) is odd. # import unittest class Solution(object): def stoneGame(self, piles): """ :type piles: List[int] :rtype: bool """ return True class SolutionDP(object): def stoneGame(self, piles): """ :type piles: List[int] :rtype: bool """ n = len(piles) dp = [[0] * n for _ in range(n)] for i in range(n): dp[i][i] = piles[i] for l in range(2, n + 1): for i in range(n - l + 1): j = i + l - 1 dp[i][j] = max(piles[i] - dp[i + 1][j], piles[j] - dp[i][j - 1]) return dp[0][n - 1] > 0 class TestMethods(unittest.TestCase): def test_Local(self): self.assertEqual(1, 1) if __name__ == '__main__': unittest.main() Java = ''' # Thought: https://leetcode.com/problems/stone-game/solution/ Approach 1: Dynamic Programming Complexity Analysis Time Complexity: O(N^2), where N is the number of piles. Space Complexity: O(N^2), the space used storing the intermediate results of each subgame. # 10ms 36.14% class Solution { public boolean stoneGame(int[] piles) { int N = piles.length; // dp[i+1][j+1] = the value of the game [piles[i], ..., piles[j]]. int[][] dp = new int[N+2][N+2]; for (int size = 1; size <= N; ++ size) { for (int i = 0; i + size <= N; ++i) { int j = i + size - 1; int parity = ( j + i + N) % 2; // j - i - N; but +x = -x (mod 2) if (parity == 1) { dp[i + 1][j + 1] = Math.max(piles[i] + dp[i +2][j + 1], piles[j] + dp[i + 1][j]); } else { dp[i + 1][j + 1] = Math.min(-piles[i] + dp[i +2][j + 1], -piles[j] + dp[i + 1][j]); } } } return dp[1][N] > 0; } } Approach 2: Mathematical Complexity Analysis Time and Space Complexity: O(1) # 3ms 53.69% class Solution { public boolean stoneGame(int[] piles) { return true; } } # 2ms 99.64% class Solution { public boolean stoneGame(int[] piles) { int left = 0; int right = piles.length-1; int alex = 0; int lee = 0; boolean alexTurn = true; while (left < right) { if (alexTurn) { if (piles[left] > piles[right]) { alex += piles[left]; left++; } else { alex += piles[right]; right--; } } else { if (piles[left] > piles[right]) { lee += piles[left]; left++; } else { lee += piles[right]; right--; } } } return alex > lee; } } '''

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Python/_old/Euler048.py

sosheskaz/Project-Euler

0

169137

#!/usr/bin/env python3 def main(): s = 0 for j in range(1, 1001): s += j ** j print(str(s)[-10:]) if __name__ == '__main__': main()

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eutl_database/DataAccessLayer.py

jabrell/eutl_scraper

0

167247

<gh_stars>0 from getpass import getpass from sqlalchemy.exc import ProgrammingError from sqlalchemy.inspection import inspect from .model import Base from sqlalchemy import create_engine, MetaData from sqlalchemy.orm import sessionmaker from io import StringIO import csv class DataAccessLayer: """Class managing database access""" def __init__(self, user, host, db, passw, echo=False, encoding="utf-8", connect=True, base=None): """Constructor for data access class. Default access is to local database :param user: <string> user name :param host: <string> host address :param db: <string> database name :param echo: <string, boolean> whther to echo sql statements, "debug" for verbose output :param encoding: <string> database encoding :param connect: <boolean> True to establish immideiate connection to database default: True "param base: <qlalchemy.ext.declarative.declarative_base> """ self.engine = None self.user = user self.host = host self.db = db if base is None: # no custum declarative base, so use the one provided by eutl orm self.Base = Base else: self.Base = base self.conn_string = "postgresql+psycopg2://%s:%s@%s/%s" % (user, passw, host, db) self.encoding = encoding self.echo = echo if connect: self.connect() def connect(self): """ Connects to database """ if self.engine is None: self.engine = create_engine(self.conn_string, echo=self.echo, encoding=self.encoding) self.Base.metadata.create_all(self.engine) self.metadata = self.metadata = MetaData(bind=self.engine) self.metadata.reflect() self.Session = sessionmaker(bind=self.engine) self.session = self.Session() def empty_database(self, askConfirmation=True): """ Deletes all tables from database connectd by engine askConfirmation: <boolean> true to ask for typed confirmation""" self.metadata = MetaData(bind=self.engine) self.metadata.reflect() if len(self.engine.table_names()) > 0: if askConfirmation: confirm = getpass("Do really want to drop all tables? Enter Yes for confirmation: ") else: confirm = "yes" if confirm.lower() == "yes": for i, tbl in enumerate(reversed(self.metadata.sorted_tables)): if tbl.name in ["spatial_ref_sys"]: continue print(i, tbl) tbl.drop() print("Tables deleted") else: print("#### Tables still in database ####") self.metadata.reflect() self.Base.metadata.create_all(self.engine) def insert_df(self, df, obj, update=False, bulk_insert=False, verbose=False): """Inserts dataframe to database using session and ORM object. Dataframe has to have columns matching fields of the OMR objects :param df: <pd.DataFrame> with data :param obj: <ORM object> :param update: <boolean> True to update existing rows :param bulk_insert: <boolean> True for buli insert of intems :parma verbose: <boolean> to print all keys not inserted """ printed = False # get primary key names pk_names = [i.name for i in inspect(obj).primary_key] # replace Null values by None df_ = self._replace_null(df) to_add = [] for item in df_.to_dict(orient="records"): # get primary key pk = {k: v for k, v in item.items() if k in pk_names} exists = False try: qry = self.session.query(obj).filter_by(**pk) exists = qry.count() > 0 except ProgrammingError: pass if exists: # with same key already in database if update: qry.delete() else: if verbose: print("Did not insert", pk) else: if not printed: print("Some entries not inserted due to key duplication.") printed = True continue obj_to_insert = obj(**item) if bulk_insert: to_add.append(obj_to_insert) else: self.session.add(obj_to_insert) if bulk_insert: self.session.add_all(to_add) self.session.commit() def insert_df_large(self, df, name, integerColumns=None, schema=None, if_exists="fail", index=False, index_label=None, chunksize=1000000, dtype=None): """ Wrapper for pandas to_sql function using a more efficient insertion function. Likely only works under psycopg2 and postrgres :parm df: <pd.DataFrame> to be inserted :param name: <string> name if table :param integerColumns: <list: string> name of columns to be inserted as int :param schema: <string> pecify the schema (if database flavor supports this). If None, use default schema. :param chunksize: <int> number of rows to be inserted at once. In contrast to pandas implementeation here we use explicit slicing of the dataframe. :param if_exists: {"fail", "replace", "append"}, default "fail", How to behave if the table already exists. :param index: <boolean> whetehr to also insert index :param index_label: <string> Column label for index column(s) :param dtype: <dict> or <scalar> Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns. """ def psql_insert_copy(table, con, keys, data_iter): """ Execute SQL statement inserting data :param table : pandas.io.sql.SQLTable :parm con : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection :param keys : list of str Column names :param data_iter : Iterable that iterates the values to be inserted""" # gets a DBAPI connection that can provide a cursor dbapi_con = con.connection with dbapi_con.cursor() as cur: s_buf = StringIO() writer = csv.writer(s_buf) writer.writerows(data_iter) s_buf.seek(0) columns = ', '.join('"{}"'.format(k) for k in keys) if table.schema: table_name = '{}.{}'.format(table.schema, table.name) else: table_name = table.name sql = 'COPY {} ({}) FROM STDIN WITH CSV'.format( table_name, columns) cur.copy_expert(sql=sql, file=s_buf) def df_to_list_of_chuncks(df, chunksize=100000): lst_df = [df[i: i + chunksize] for i in range(0, df.shape[0], chunksize)] return lst_df def prepare_int_cols_for_sql_insert(df, int_cols): """ For fast insertion into the database using the csv IO stream we need interegers to be properly formated. However, as long as we have Null values in the column, the column is of type float and integers are foramtted with .0 at the end. Thus we cast these integers to string with correct format. :param df: <pd.DataFrame> :param int_cols: <list:string> names of columns to be converted """ def int_to_string(x): try: return "%d" % x except (TypeError, ValueError): return x for c in int_cols: df[c] = df[c].map(int_to_string) return df # convert int columns if integerColumns is not None: df_ = prepare_int_cols_for_sql_insert(df, int_cols=integerColumns) else: df_ = df.copy() # create chunks of dataframe and slice over it lst_df = df_to_list_of_chuncks(df_, chunksize=chunksize) for i, df_out in enumerate(lst_df): if (i % 10 == 0) and (i > 0): print("#### Commit chunck %d of %d" % ((i + 1), len(lst_df))) # insert data df_out.to_sql(name=name, con=self.session.get_bind(), if_exists=if_exists, schema=schema, index=index, index_label=index_label, dtype=dtype, method=psql_insert_copy) @staticmethod def _replace_null(df): """replaces nan and nat in dataframe by None values for database insertion""" df_ = df.copy() dt_cols = df_.select_dtypes(include=['datetime64']).columns for c in dt_cols: # convert datetimes to objects to replace nat df_[c] = df_[c].astype("object") df_ = df_.where(df.notnull(), None) return df_ @staticmethod def prepare_int_cols_for_sql_insert(df, int_cols): """ For fast insertion into the database using the csv IO stream we need interegers to be properly formated. However, as long as we have Null values in the column, the column is of type float and integers are foramtted with .0 at the end. Thus we cast these integers to string with correct format. :param df: <pd.DataFrame> :param int_cols: <list:string> names of columns to be converted """ def int_to_string(x): try: return "%d" % x except (TypeError, ValueError): return x for c in int_cols: df[c] = df[c].map(int_to_string) return df

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configs/m4c/m4c_textvqa.py

inspur-hsslab/iMIX

23

174565

_base_ = [ '../_base_/datasets/textvqa_dataset.py', '../_base_/models/m4c_config.py', '../_base_/schedules/schedule_textvqa.py', '../_base_/textvqa_default_runtime.py' ] # yapf:disable

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leetcode/python/841_keys_and_rooms.py

VVKot/leetcode-solutions

4

198838

<filename>leetcode/python/841_keys_and_rooms.py class Solution: def canVisitAllRooms(self, rooms): stack = [0] visited = set(stack) while stack: curr = stack.pop() for room in rooms[curr]: if room not in visited: stack.append(room) visited.add(room) if len(visited) == len(rooms): return True return len(visited) == len(rooms)

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ChatBot/chatbot_search/chatbot_tfserving/indexAnnoy.py

yongzhuo/nlp_xiaojiang

1,379

144409

<reponame>yongzhuo/nlp_xiaojiang<filename>ChatBot/chatbot_search/chatbot_tfserving/indexAnnoy.py # !/usr/bin/python # -*- coding: utf-8 -*- # @time : 2021/4/18 21:04 # @author : Mo # @function: annoy search from annoy import AnnoyIndex import numpy as np import os class AnnoySearch: def __init__(self, dim=768, n_cluster=100): # metric可选“angular”（余弦距离）、“euclidean”（欧几里得距离）、 “ manhattan”（曼哈顿距离）或“hamming”（海明距离） self.annoy_index = AnnoyIndex(dim, metric="angular") self.n_cluster = n_cluster self.dim = dim def k_neighbors(self, vectors, k=18): """ 搜索 """ annoy_tops = [] for v in vectors: idx, dist = self.annoy_index.get_nns_by_vector(v, k, search_k=32*k, include_distances=True) annoy_tops.append([dist, idx]) return annoy_tops def fit(self, vectors): """ annoy构建 """ for i, v in enumerate(vectors): self.annoy_index.add_item(i, v) self.annoy_index.build(self.n_cluster) def save(self, path): """ 存储 """ self.annoy_index.save(path) def load(self, path): """ 加载 """ self.annoy_index.load(path) if __name__ == '__main__': ### 索引 import random path = "model.ann" dim = 768 vectors = [[random.gauss(0, 1) for z in range(768)] for i in range(10)] an_model = AnnoySearch(dim, n_cluster=32) # Length of item vector that will be indexed an_model.fit(vectors) an_model.save(path) tops = an_model.k_neighbors([vectors[0]], 18) print(tops) del an_model ### 下载, 搜索 an_model = AnnoySearch(dim, n_cluster=32) an_model.load(path) tops = an_model.k_neighbors([vectors[0]], 6) print(tops) """ # example from annoy import AnnoyIndex import random dim = 768 vectors = [[random.gauss(0, 1) for z in range(768)] for i in range(10)] ann_model = AnnoyIndex(dim, 'angular') # Length of item vector that will be indexed for i,v in enumerate(vectors): ann_model.add_item(i, v) ann_model.build(10) # 10 trees ann_model.save("tet.ann") del ann_model u = AnnoyIndex(dim, "angular") u.load('tet.ann') # super fast, will just mmap the file v = vectors[1] idx, dist = u.get_nns_by_vector(v, 10, search_k=50 * 10, include_distances=True) print([idx, dist]) """ ### 备注说明: annoy索引无法增删会改查

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utils/regression/classes/module_run.py

noahsherrill/force-riscv

111

1613424

<gh_stars>100-1000 # # Copyright (C) [2020] Futurewei Technologies, Inc. # # FORCE-RISCV is 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 # # THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES # OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO # NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. # See the License for the specific language governing permissions and # limitations under the License. # # @package module_run.py # module_run.py # # Provides command line argument parsing and access as base class of # master_run.py and forrest_run.py # import sys import os from abc import ABC, abstractmethod from common.path_utils import PathUtils from common.msg_utils import Msg # # Parses and accesses parsed command line options. Also can be used to set # message level... and directly query for the force directory. class ModuleRun(ABC): def __init__(self, arg_msg_lev, arg_def_lev): self.m_app_setup = None self.m_app_info = None self.module_dir, self.module_name = PathUtils.split_path( PathUtils.real_path(sys.argv[0]) ) self.init_app_setup() self.load_message_levels(arg_msg_lev, arg_def_lev) @abstractmethod def init_app_setup(self): pass ## # resolves the desired message types def load_message_levels(self, arg_msg_lev, arg_def_lev): # load from the command line if specified or use the default my_lev_str = self.option_def(arg_msg_lev, arg_def_lev) # if a (+) or a (-) is found then the command line will be appended # to or demoted by if (my_lev_str[0] == "+") or (my_lev_str[0] == "-"): # use the default string to build a format string, then append # the passed in value my_fmt_str = "%s%%s" % arg_def_lev my_lev_str = my_fmt_str % (my_lev_str) my_level = Msg.translate_levelstr(my_lev_str) Msg.set_level(my_level) # return the force path def get_force_dir(self): return self.force_path # Reflects the supplied default value in arg_def_val if none matching the # arg_switch can be found in the parsed options. def option_def(self, aSwitch, aDefVal=None, aConversionFunc=None): return_value = self.m_app_info.mCmdLineOpts.option_def( aSwitch, aDefVal ) if aConversionFunc and return_value is not None: try: return_value = aConversionFunc(return_value) except (TypeError, ValueError) as ex: Msg.warn( 'Invalid value "{}" provided for "{}". ' "Using default.".format(repr(return_value), aSwitch) ) return aDefVal return return_value def get_arguments(self): return self.m_app_info.mCmdLineOpts.get_arguments() def get_unknown_arguments(self): return self.m_app_info.mCmdLineOpts.get_unknown_arguments() def print_help(self): self.m_app_info.mCmdLineOpts.print_help()

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endpoints/bookstore-grpc/bookstore.py

bxue16/bx_byte1

4

177638

# Copyright 2016 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. import threading class ShelfInfo(object): """The contents of a single shelf.""" def __init__(self, shelf): self._shelf = shelf self._last_book_id = 0 self._books = dict() class Bookstore(object): """An in-memory backend for storing Bookstore data.""" def __init__(self): self._last_shelf_id = 0 self._shelves = dict() self._lock = threading.Lock() def list_shelf(self): with self._lock: return [s._shelf for (_, s) in self._shelves.iteritems()] def create_shelf(self, shelf): with self._lock: self._last_shelf_id += 1 shelf_id = self._last_shelf_id shelf.id = shelf_id self._shelves[shelf_id] = ShelfInfo(shelf) return (shelf, shelf_id) def get_shelf(self, shelf_id): with self._lock: return self._shelves[shelf_id]._shelf def delete_shelf(self, shelf_id): with self._lock: del self._shelves[shelf_id] def list_books(self, shelf_id): with self._lock: return [book for ( _, book) in self._shelves[shelf_id]._books.iteritems()] def create_book(self, shelf_id, book): with self._lock: shelf_info = self._shelves[shelf_id] shelf_info._last_book_id += 1 book_id = shelf_info._last_book_id book.id = book_id shelf_info._books[book_id] = book return book def get_book(self, shelf_id, book_id): with self._lock: return self._shelves[shelf_id]._books[book_id] def delete_book(self, shelf_id, book_id): with self._lock: del self._shelves[shelf_id]._books[book_id]

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linekey.py

alex-west-met-office/IMBS_MO

1

19926

''' A module for defining and producing the linekey object, which is used to determine and store information about data format in a CRREL ice mass balance buoy.''' class linekey: def __init__(self,date_index = 0): self.date_index = date_index self.value_index = [] self.phenomena_names = [] self.lon_flip_ew = (False,-1,-1) self.lat_flip_ns = (False,-1,-1) self.vertical_scale = 1. self.fliplon = False def add_value_index(self,phenomenon,index): self.value_index.append(index) self.phenomena_names.append(phenomenon) def ns(self,index_flippee,index_flipper): self.lat_flip_ns = (True,index_flippee,index_flipper) def ew(self,index_flippee,index_flipper): self.lon_flip_ew = (True,index_flippee,index_flipper) def get_temp_linekey(data_file): import csv fileh = open(data_file) rows = csv.reader(fileh) found_key = False found_date = False for row in rows: print(row) for (i,strtest) in enumerate(row): if ('Date' in strtest) or ('DATE' in strtest): key = linekey(date_index = i) found_date = True break if found_date: temp_codes = {} temp_type = '' for (i,strtest) in enumerate(row): result = classify_temp_header(strtest) if result[0]==1: if temp_type == 'subjective': print('Unable to determine temperature type') return None temp_type = 'objective' prefix = 'TO' if result[0]==2: if temp_type == 'objective': print('Unable to determine temperature type') return None temp_type = 'subjective' prefix = 'TS' temp_codes[i] = classify_temp_header(strtest) if result[0]!=0: key.add_value_index(prefix+str(result[1]),i) break return key def get_linekey(data_file,variable_list,buoy_name): import dictionaries import csv fileh = open(data_file) rows = csv.reader(fileh) found_key = False found_date = False td = dictionaries.title_dic() variable_keys_list = [td[variable_name] for variable_name in variable_list] vertical_scale = 1. fliplon = False for row in rows: if not found_key: for (i,strtest) in enumerate(row): if ('Date' in strtest) or ('DATE' in strtest): key = linekey(date_index = i) found_date = True break if found_date: for (varno,variable_keys) in enumerate(variable_keys_list): found_key = False for string in variable_keys: for (i,strtest) in enumerate(row): if (string == strtest.strip()): key.add_value_index(variable_list[varno],i) found_key = True i_key = i if '(cm)' in string: vertical_scale = 0.01 if '(m)' in string: vertical_scale = 1. if string=='Longitude (W)': fliplon = True if not found_key: key.add_value_index(variable_list[varno],-1) if variable_list[varno]=='latitude': for (i,strtest) in enumerate(row): if (strtest == 'N/S'): key.ns(i_key,i) if variable_list[varno]=='longitude': for (i,strtest) in enumerate(row): if (strtest == 'E/W'): key.ew(i_key,i) if True in [('units are cm') in item for item in row]: vertical_scale = 0.01 if 'E/W' in row and 'longitude' in key.phenomena_names: i_flipper = row.index('E/W') i_flippee = key.value_index[key.phenomena_names.index('longitude')] key.ew(i_flippee,i_flipper) if 'N/S' in row and 'latitude' in key.phenomena_names: i_flipper = row.index('N/S') i_flippee = key.value_index[key.phenomena_names.index('latitude')] key.ew(i_flippee,i_flipper) if not found_date: print('Could not find date') fileh.close() return None key.vertical_scale = vertical_scale key.fliplon = fliplon fileh.close() return key def classify_temp_header(string): import functions if functions.is_number(string): number = float(string) return (1,number) elif string[0:1]=='T' and string[-3:]=='(C)' and functions.is_number(string[1:-3]): number = int(string[1:-3]) return (2,number) elif string[0:1]=='T' and functions.is_number(string[1:]): number = int(string[1:]) return (2,number) elif len(string) >= 4: if string[0:4]=='TEMP' and functions.is_number(string[4:]): number = int(string[4:]) return (2,number) elif string[0:5]=='Temp ' and functions.is_number(string[5:]): number = int(string[5:]) return (2,number) else: return (0,0) else: return (0,0)

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test_motor.py

ankworld/Rpi_Therapy

0

98410

<reponame>ankworld/Rpi_Therapy<filename>test_motor.py from myLib import controlcore from time import sleep motor = controlcore.Motor(11, 13, 1000) dc = 100 for i in range(10): motor.l_drive(dc) sleep(5) motor.r_drive(dc) sleep(5)

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train.py

Mario-Medvedovic/deepPhosAPI

0

71287

<gh_stars>0 import functools import itertools import os import random import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np from sklearn import metrics from sklearn import preprocessing from sklearn.model_selection import train_test_split, KFold, cross_val_score from keras.layers import Dense, Activation, Flatten, Dropout, Reshape from keras.layers import Conv1D,Conv2D, MaxPooling2D from keras.models import Sequential,Model from keras.utils.np_utils import to_categorical from keras import optimizers from keras.optimizers import Adam,SGD from keras.layers.normalization import BatchNormalization from keras.regularizers import l2 import copy def train_for_deepphos(train_file_name,site,predictFrame = 'general',background_weight = None): ''' :param train_file_name: input of your train file it must be a .csv file and theinput format is label,proteinName, postion,sites, shortsequence, :param site: the sites predict: site = 'S','T' OR 'Y' :param predictFrame: 'general' or 'kinase' :param background_weight: the model you want load to pretrain new model usually used in kinase training :return: ''' win1 = 51 win2 = 33 win3 = 15 from methods.dataprocess_train import getMatrixLabel X_train1 , y_train = getMatrixLabel(train_file_name, sites, win1) X_train2, _ = getMatrixLabel(train_file_name, sites, win2) X_train3, _ = getMatrixLabel(train_file_name, sites, win3) modelname = "general_{:s}".format(site) if predictFrame == 'general': modelname ="general_model_{:s}".format(site) if predictFrame == 'kinase': modelname = "kinase_model_{:s}".format(site) from methods.model_n import model_net model = model_net(X_train1, X_train2, X_train3, y_train, weights=background_weight) model.save_weights(modelname+'.h5',overwrite=True)

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data_scripts/data_loader.py

luis-armando-perez-rey/renderSHREC2021

0

167885

<filename>data_scripts/data_loader.py import os import h5py import numpy as np from data_scripts.factor_dataset import FactorImageDataset AVAILABLE_COLLECTIONS = ["Shape", "Culture"] AVAILABLE_DATA_TYPES = ["train", "test"] def load_factor_data(data, root_path=None, **kwargs): options_dict = { "shrec2021": get_h5_saved_data, } return options_dict[data](root_path, **kwargs) def get_h5_saved_data(root_path, collection_list, data_type, dataset_directory, normalize=True): """ Returns a TransformImage object created from ModelNet40 dataset of objects with periodic colors and rotated Args: root_path: path to the root of the project dataset_filename: filename of the .h5 data to be loaded object_type: type of object saved in the data file normalize: whether data should be in the range [0,1] (True) or [0, 255] (False). Returns: FactorImageDataset object :param normalize: :param dataset_directory: :param data_type: :param collection_list: """ image_list = [] views_list = [] labels_list = [] ids_list = [] for collection in collection_list: assert collection in AVAILABLE_COLLECTIONS, "collection_list = {} is not available. Possible values are {}".format( collection, AVAILABLE_COLLECTIONS) assert data_type in AVAILABLE_DATA_TYPES, "data_type = {} is not available. Possible values are {}".format( data_type, AVAILABLE_DATA_TYPES) dataset_filename = collection + "_" + data_type + ".h5" print(dataset_filename) dataset_filepath = os.path.join(root_path, dataset_directory, dataset_filename) # Read the images images = read_data_h5(dataset_filepath, "images") if normalize: images = images.astype('float32') / np.amax(images) image_list.append(images) # Read the rotations views = read_data_h5(dataset_filepath, "views") views_list.append(views) # Read category integer class_labels try: labels = read_data_h5(dataset_filepath, "class_int") except: print("No labels detected in the dataset") labels = None labels_list.append(labels) ids = read_data_h5(dataset_filepath, "ids") ids_list.append(ids) images = np.concatenate(image_list, axis=0) ids = np.concatenate(ids_list, axis=0) labels = np.concatenate(labels_list, axis=0) views = np.concatenate(views_list, axis=0) # Convert integer range to angular range unique_views = np.unique(views) unique_ids = np.unique(ids) # Create FactorImageDataset lists factor_values = [unique_ids, unique_views] max_factor_values = [np.amax(factor) for factor in factor_values] return FactorImageDataset(images=images, factor_values_list=factor_values, max_factor_values=max_factor_values, factor_names=["object_ids", "rotation_angle"], labels=labels) def read_data_h5(data_filepath, data_type): """ Read data from h5 file with 1 level of hierarchy Args: data_filepath: path to the .h5 file data_type: key value from which data is read Returns: """ with h5py.File(data_filepath, "r") as file: data = np.array(file.get(data_type)) return data

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causal_discovery/graph_fitting.py

codeaudit/ENCO

0

176412

import torch import torch.nn.functional as F import numpy as np import math import random import sys sys.path.append("../") from causal_graphs.variable_distributions import _random_categ from causal_discovery.datasets import InterventionalDataset class GraphFitting(object): def __init__(self, model, graph, num_batches, num_graphs, theta_only_num_graphs, batch_size, lambda_sparse, max_graph_stacking=200): """ Creates a DistributionFitting object that summarizes all functionalities for performing the graph fitting stage of ENCO. Parameters ---------- model : MultivarMLP PyTorch module of the neural networks that model the conditional distributions. graph : CausalDAG Causal graph on which we want to perform causal structure learning. num_batches : int Number of batches to use per MC sample in the graph fitting stage. Usually 1, only higher needed if GPU is running out of memory for common batch sizes. num_graphs : int Number of graph samples to use for estimating the gradients in the graph fitting stage. Usually in the range 20-100. theta_only_num_graphs : int Number of graph samples to use in the graph fitting stage if gamma is frozen. Needs to be an even number, and usually 2 or 4. batch_size : int Size of the batches to use in the gradient estimators. lambda_sparse : float Sparsity regularizer value to use in the graph fitting stage. max_graph_stacking : int Number of graphs that can maximally evaluated in parallel on the device. If you run out of GPU memory, try to lower this number. It will then evaluate the graph sequentially, which can be slightly slower but uses less memory. """ self.model = model self.graph = graph self.num_batches = num_batches self.num_graphs = num_graphs self.batch_size = batch_size self.lambda_sparse = lambda_sparse self.max_graph_stacking = max_graph_stacking self.theta_only_num_graphs = theta_only_num_graphs self.inter_vars = [] if self.graph.num_vars >= 100 or hasattr(self.graph, "data_int"): self.dataset = InterventionalDataset(self.graph, dataset_size=4096, batch_size=self.batch_size) def perform_update_step(self, gamma, theta, var_idx=-1, only_theta=False): """ Performs a full update step of the graph fitting stage. We first sample a batch of graphs, evaluate them on a interventional data batch, and estimate the gradients for gamma and theta based on the log-likelihoods. Parameters ---------- gamma : nn.Parameter Parameter tensor representing the gamma parameters in ENCO. theta : nn.Parameter Parameter tensor representing the theta parameters in ENCO. var_idx : int Variable on which should be intervened to obtain the update. If none is given, i.e., a negative value, the variable will be randomly selected. only_theta : bool If True, gamma is frozen and the gradients are only estimated for theta. See Appendix D.2 in the paper for details on the gamma freezing stage. """ # Obtain log-likelihood estimates for randomly sampled graph structures if not only_theta: MC_samp = self.get_MC_samples(gamma, theta, num_batches=self.num_batches, num_graphs=self.num_graphs, batch_size=self.batch_size, var_idx=var_idx, mirror_graphs=False) else: MC_samp = self.get_MC_samples(gamma, theta, num_batches=self.num_batches, num_graphs=self.theta_only_num_graphs, batch_size=self.batch_size, var_idx=var_idx, mirror_graphs=True) adj_matrices, log_likelihoods, var_idx = MC_samp # Determine gradients for gamma and theta gamma_grads, theta_grads, theta_mask = self.gradient_estimator( adj_matrices, log_likelihoods, gamma, theta, var_idx) gamma.grad = gamma_grads theta.grad = theta_grads return theta_mask, var_idx @torch.no_grad() def get_MC_samples(self, gamma, theta, num_batches, num_graphs, batch_size, var_idx=-1, mirror_graphs=False): """ Samples and evaluates a batch of graph structures on a batch of interventional data. Parameters ---------- gamma : nn.Parameter Parameter tensor representing the gamma parameters in ENCO. theta : nn.Parameter Parameter tensor representing the theta parameters in ENCO. num_batches : int Number of batches to use per MC sample. num_graphs : int Number of graph structures to sample. batch_size : int Size of interventional data batches. var_idx : int Variable on which should be intervened to obtain the update. If none is given, i.e., a negative value, the variable will be randomly selected. mirror_graphs : bool This variable should be true if only theta is optimized. In this case, the first half of the graph structure samples is identical to the second half, except that the values of the outgoing edges of the intervened variable are flipped. This allows for more efficient, low-variance gradient estimators. See details in the paper. """ if mirror_graphs: assert num_graphs % 2 == 0, "Number of graphs must be divisible by two for mirroring" device = self.get_device() # Sample data batch if hasattr(self, "dataset"): # Pre-sampled data var_idx = self.sample_next_var_idx() int_sample = torch.cat([self.dataset.get_batch(var_idx) for _ in range(num_batches)], dim=0).to(device) else: # If no dataset exists, data is newly sampled from the graph intervention_dict, var_idx = self.sample_intervention(self.graph, dataset_size=num_batches*batch_size, var_idx=var_idx) int_sample = self.graph.sample(interventions=intervention_dict, batch_size=num_batches*batch_size, as_array=True) int_sample = torch.from_numpy(int_sample).long().to(device) # Split number of graph samples acorss multiple iterations if not all can fit into memory num_graphs_list = [min(self.max_graph_stacking, num_graphs-i*self.max_graph_stacking) for i in range(math.ceil(num_graphs * 1.0 / self.max_graph_stacking))] num_graphs_list = [(num_graphs_list[i], sum(num_graphs_list[:i])) for i in range(len(num_graphs_list))] # Tensors needed for sampling edge_prob = (torch.sigmoid(gamma) * torch.sigmoid(theta)).detach() edge_prob_batch = edge_prob[None].expand(num_graphs, -1, -1) # Inner function for sampling a batch of random adjacency matrices from current belief probabilities def sample_adj_matrix(): sample_matrix = torch.bernoulli(edge_prob_batch) sample_matrix = sample_matrix * (1 - torch.eye(sample_matrix.shape[-1], device=sample_matrix.device)[None]) if mirror_graphs: # First and second half of tensors are identical, except the intervened variable sample_matrix[num_graphs//2:] = sample_matrix[:num_graphs//2] sample_matrix[num_graphs//2:, var_idx] = 1 - sample_matrix[num_graphs//2:, var_idx] sample_matrix[:, var_idx, var_idx] = 0. return sample_matrix # Evaluate log-likelihoods under sampled adjacency matrix and data adj_matrices = [] log_likelihoods = [] for n_idx in range(num_batches): batch = int_sample[n_idx*batch_size:(n_idx+1)*batch_size] if n_idx == 0: adj_matrix = sample_adj_matrix() adj_matrices.append(adj_matrix) for c_idx, (graph_count, start_idx) in enumerate(num_graphs_list): adj_matrix_expanded = adj_matrix[start_idx:start_idx+graph_count, None].expand(-1, batch_size, -1, -1).flatten(0, 1) batch_exp = batch[None, :].expand(graph_count, -1, -1).flatten(0, 1) nll = self.evaluate_likelihoods(batch_exp, adj_matrix_expanded, var_idx) nll = nll.reshape(graph_count, batch_size, -1) if n_idx == 0: log_likelihoods.append(nll.mean(dim=1)) else: log_likelihoods[c_idx] += nll.mean(dim=1) # Combine all data adj_matrices = torch.cat(adj_matrices, dim=0) log_likelihoods = torch.cat(log_likelihoods, dim=0) / num_batches return adj_matrices, log_likelihoods, var_idx @torch.no_grad() def gradient_estimator(self, adj_matrices, log_likelihoods, gamma, theta, var_idx): """ Returns the estimated gradients for gamma and theta. It uses the low-variance gradient estimators proposed in Section 3.3 of the paper. Parameters ---------- adj_matrices : torch.FloatTensor, shape [batch_size, num_vars, num_vars] The adjacency matrices on which the interventional data has been evaluated on. log_likelihoods : torch.FloatTensor, shape [batch_size, num_vars] The average log-likelihood under the adjacency matrices for all variables in the graph. gamma : nn.Parameter Parameter tensor representing the gamma parameters in ENCO. theta : nn.Parameter Parameter tensor representing the theta parameters in ENCO. var_idx : int Variable on which the intervention was performed. """ batch_size = adj_matrices.shape[0] log_likelihoods = log_likelihoods.unsqueeze(dim=1) orient_probs = torch.sigmoid(theta) edge_probs = torch.sigmoid(gamma) # Gradient calculation num_pos = adj_matrices.sum(dim=0) num_neg = batch_size - num_pos mask = ((num_pos > 0) * (num_neg > 0)).float() pos_grads = (log_likelihoods * adj_matrices).sum(dim=0) / num_pos.clamp_(min=1e-5) neg_grads = (log_likelihoods * (1 - adj_matrices)).sum(dim=0) / num_neg.clamp_(min=1e-5) gamma_grads = mask * edge_probs * (1 - edge_probs) * orient_probs * (pos_grads - neg_grads + self.lambda_sparse) theta_grads = mask * orient_probs * (1 - orient_probs) * edge_probs * (pos_grads - neg_grads) # Masking gamma for incoming edges to intervened variable gamma_grads[:, var_idx] = 0. gamma_grads[torch.arange(gamma_grads.shape[0]), torch.arange(gamma_grads.shape[1])] = 0. # Masking all theta's except the ones with a intervened variable theta_grads[:var_idx] = 0. theta_grads[var_idx+1:] = 0. theta_grads -= theta_grads.transpose(0, 1) # theta_ij = -theta_ji # Creating a mask which theta's are actually updated for the optimizer theta_mask = torch.zeros_like(theta_grads) theta_mask[var_idx] = 1. theta_mask[:, var_idx] = 1. theta_mask[var_idx, var_idx] = 0. return gamma_grads, theta_grads, theta_mask def sample_next_var_idx(self): """ Returns next variable to intervene on. We iterate through the variables in a shuffled order, like a standard dataset. """ if len(self.inter_vars) == 0: # If an epoch finished, reshuffle variables self.inter_vars = [i for i in range(len(self.graph.variables))] random.shuffle(self.inter_vars) var_idx = self.inter_vars.pop() return var_idx def sample_intervention(self, graph, dataset_size, var_idx=-1): """ Returns a new data batch for an intervened variable. """ # Select variable to intervene on if var_idx < 0: var_idx = self.sample_next_var_idx() var = graph.variables[var_idx] # Soft, perfect intervention => replace p(X_n) by random categorical # Scale is set to 0.0, which represents a uniform distribution. int_dist = _random_categ(size=(var.prob_dist.num_categs,), scale=0.0, axis=-1) # Sample from interventional distribution value = np.random.multinomial(n=1, pvals=int_dist, size=(dataset_size,)) value = np.argmax(value, axis=-1) # One-hot to index intervention_dict = {var.name: value} return intervention_dict, var_idx @torch.no_grad() def evaluate_likelihoods(self, int_sample, adj_matrix, var_idx): """ Evaluates the negative log-likelihood of the interventional data batch (int_sample) on the given graph structures (adj_matrix) and the intervened variable (var_idx). """ self.model.eval() device = self.get_device() int_sample = int_sample.to(device) adj_matrix = adj_matrix.to(device) # Transpose for mask because adj[i,j] means that i->j mask_adj_matrix = adj_matrix.transpose(1, 2) preds = self.model(int_sample, mask=mask_adj_matrix) # Evaluate negative log-likelihood of predictions preds = preds.flatten(0, 1) labels = int_sample.clone() labels[:, var_idx] = -1 # Perfect interventions => no predictions of the intervened variable labels = labels.reshape(-1) nll = F.cross_entropy(preds, labels, reduction='none', ignore_index=-1) nll = nll.reshape(*int_sample.shape) self.model.train() return nll def get_device(self): return self.model.device

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modeling/player/load.py

cpratim/Fantasy-Predictions

0

62804

<gh_stars>0 import numpy as np from helpers import * from config import * def load_xy(player): player = '_'.join([l.lower() for l in player.split(' ')]) X, y = [], [] stats_record = load_json(f'{player_dir}/{player}.json') for season, stats in stats_record.items(): X.append(int(season[-2:])) y.append(list(stats.values())) return np.array(X), np.array(y).T

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Docker image/pdx-analysis-workflows/JAX_RNA/picard_alignment/picard_alignment_metrics.py

BorisYourich/EurOPDX-Galaxy

0

86632

#! /usr/bin/env python """ Picard Alignment Metrics. Version: 0.1.1 """ from __future__ import print_function import argparse import os import shutil import sys def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('files', nargs='+', help="The file[s] to use.") return parser.parse_args() def main(): args = parse_args() sample_name = sys.argv[4] RGID = "4" RGPL = "illumina" RGPU = "unit1" RGSM = "20" picard_stat_file = sample_name + "_picard_stat" interim_results_dir = '/mnt/volume/shared/reference-data/' # Check whether interim results directory exists or not. If not create it. if not os.path.exists(interim_results_dir): os.mkdir(interim_results_dir) # Remove existing files to avoid files exist exception. if os.path.exists(interim_results_dir + picard_stat_file): os.remove(interim_results_dir + picard_stat_file) # Adding readgroup information input_bam = args.files[0] try: os.system("picard AddOrReplaceReadGroups \ INPUT=" + input_bam + " \ OUTPUT=genome_bam_read_group.bam \ SORT_ORDER=coordinate \ RGID=" + RGID + " \ RGLB=" + sample_name + " \ RGPL=" + RGPL + " \ RGPU=" + RGPU + " \ RGSM=" + RGSM + " \ CREATE_INDEX=true" ) except Exception as e: print('Error executing picard AddOrReplaceReadGroups -> %s' % e) sys.exit(1) # Create dictionary for the reference file # SEQUENCE_DICTIONARY = os.path.splitext(args.files[3])[0] + ".dict" try: SEQUENCE_DICTIONARY = args.files[2] + ".dict" if not os.path.exists(SEQUENCE_DICTIONARY): symlink_to_fa = args.files[2] + ".fa" command = "ln -fs " + args.files[2] + " " + symlink_to_fa os.system(command) print("Command: " + command) os.system("picard CreateSequenceDictionary REFERENCE=" + symlink_to_fa) except Exception as e: print('Error executing picard CreateSequenceDictionary -> %s' % e) sys.exit(1) # Picard Reorder Bam file try: os.system("picard ReorderSam \ INPUT=genome_bam_read_group.bam \ OUTPUT=genome_bam_read_group_reorder.bam \ SEQUENCE_DICTIONARY=" + SEQUENCE_DICTIONARY + " \ ALLOW_INCOMPLETE_DICT_CONCORDANCE=true \ CREATE_INDEX=true" ) except Exception as e: print('Error executing picard ReorderSam -> %s' % e) sys.exit(1) # Picard SortSam (generating sorted alignment bam file) try: os.system("picard SortSam \ SORT_ORDER=coordinate \ INPUT=genome_bam_read_group_reorder.bam \ OUTPUT=genome_bam_read_group_reorder_sorted.bam \ VALIDATION_STRINGENCY=SILENT \ CREATE_INDEX=true" ) except Exception as e: print('Error executing picard SortSam -> %s' % e) sys.exit(1) # CollectRnaSeqMetrics try: os.system("picard CollectRnaSeqMetrics \ INPUT=genome_bam_read_group_reorder_sorted.bam \ OUTPUT=" + picard_stat_file + " \ REF_FLAT=" + args.files[1] + " \ STRAND=NONE \ VALIDATION_STRINGENCY=SILENT \ CHART_OUTPUT=File" ) except Exception as e: print('Error executing picard CollectRnaSeqMetrics -> %s' % e) sys.exit(1) #shutil.move(picard_stat_file, interim_results_dir) # Summary metrics compilation # try: # os.system( # "perl ../../../../../tools/pdx-analysis-workflows/RNA_PDX/summary_QC_metrics.pl " + interim_results_dir + quality_stat_file + " " + interim_results_dir + xenome_stat_file + " " + interim_results_dir + rsem_stat_file + " " + interim_results_dir + picard_stat_file + " > " + interim_results_dir + summary_stat_file) # shutil.copy(interim_results_dir + summary_stat_file, os.getcwd()) # except Exception as e: # print('Error executing summary_QC_metrics.pl -> %s' % e) # sys.exit(1) if __name__ == '__main__': main()

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tests/color_threshold.py

hunterjsb/CalculateArea

0

154545

import numpy as np from calculatearea import AreaEstimator from math import sqrt area_estimator = AreaEstimator(filename='rancho_rd.jpg', # name of the image in the directory color_range=((0, 0, 118), (100, 100, 255)), default_area_color=(255, 20, 160)) # BGR purple A_px = area_estimator.get_area(return_pixels=True) ft_per_pixel = sqrt(131193.8 / A_px) # plot size - 30.56 acres, img size - 447x588 px print(ft_per_pixel) # area_estimator.show_images() # with tighter restraints area_estimator.color_lower_limit = np.array([0, 0, 130], np.uint8) area_estimator.color_upper_limit = np.array([50, 50, 255], np.uint8) area_estimator.area_color = (100, 200, 200) A_px = area_estimator.get_area() area_estimator.show_images() # guess feet per pixel ft_per_pixel = sqrt(131193.8 / A_px) print(ft_per_pixel)

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corehq/apps/users/bulkupload.py

dborowiecki/commcare-hq

0

90465

import logging import uuid from django import forms from django.conf import settings from django.core.exceptions import ValidationError from django.core.validators import validate_email from django.utils.translation import ugettext as _ from couchdbkit.exceptions import ( BulkSaveError, MultipleResultsFound, ResourceNotFound, ) from couchexport.writers import Excel2007ExportWriter from dimagi.utils.chunked import chunked from dimagi.utils.parsing import string_to_boolean from soil import DownloadBase from soil.util import expose_download, get_download_file_path from corehq import privileges from corehq.apps.accounting.utils import domain_has_privilege from corehq.apps.commtrack.util import get_supply_point_and_location from corehq.apps.custom_data_fields.models import CustomDataFieldsDefinition from corehq.apps.domain.forms import clean_password from corehq.apps.domain.models import Domain from corehq.apps.groups.models import Group from corehq.apps.locations.models import SQLLocation from corehq.apps.users.dbaccessors.all_commcare_users import ( get_commcare_users_by_filters, get_existing_usernames, ) from corehq.apps.users.models import UserRole from corehq.util.workbook_json.excel import ( alphanumeric_sort_key, flatten_json, json_to_headers, ) from .forms import get_mobile_worker_max_username_length from .models import CommCareUser, CouchUser from .util import normalize_username, raw_username class UserUploadError(Exception): pass required_headers = set(['username']) allowed_headers = set([ 'data', 'email', 'group', 'language', 'name', 'password', 'phone-number', 'uncategorized_data', 'user_id', 'is_active', 'location_code', 'role', 'User IMEIs (read only)', 'registered_on (read only)', ]) | required_headers old_headers = { # 'old_header_name': 'new_header_name' 'location-sms-code': 'location_code' } def check_headers(user_specs): messages = [] headers = set(user_specs.fieldnames) # Backwards warnings for (old_name, new_name) in old_headers.items(): if old_name in headers: messages.append( _("'The column header '{old_name}' is deprecated, please use '{new_name}' instead.").format( old_name=old_name, new_name=new_name )) headers.discard(old_name) illegal_headers = headers - allowed_headers missing_headers = required_headers - headers for header_set, label in (missing_headers, 'required'), (illegal_headers, 'illegal'): if header_set: messages.append(_('The following are {label} column headers: {headers}.').format( label=label, headers=', '.join(header_set))) if messages: raise UserUploadError('\n'.join(messages)) def check_duplicate_usernames(user_specs): usernames = set() duplicated_usernames = set() for row in user_specs: username = row.get('username') if username and username in usernames: duplicated_usernames.add(username) usernames.add(username) if duplicated_usernames: raise UserUploadError(_("The following usernames have duplicate entries in " "your file: " + ', '.join(duplicated_usernames))) def check_existing_usernames(user_specs, domain): usernames_without_ids = set() invalid_usernames = set() for row in user_specs: username = row.get('username') user_id = row.get('user_id') if username and user_id: continue try: usernames_without_ids.add(normalize_username(username, domain)) except ValidationError: invalid_usernames.add(username) if invalid_usernames: raise UserUploadError(_('The following usernames are invalid: ' + ', '.join(invalid_usernames))) existing_usernames = set() for usernames in chunked(usernames_without_ids, 500): existing_usernames.update(get_existing_usernames(usernames)) if existing_usernames: raise UserUploadError(_("The following usernames already exist and must " "have an id specified to be updated: " + ', '.join(existing_usernames))) class GroupMemoizer(object): """ If you use this to get a group, do not set group.name directly; use group_memoizer.rename_group(group, name) instead. """ def __init__(self, domain): self.groups_by_name = {} self.groups_by_id = {} self.groups = set() self.domain = domain def load_all(self): for group in Group.by_domain(self.domain): self.add_group(group) def add_group(self, new_group): # todo # this has the possibility of missing two rows one with id one with name # that actually refer to the same group # and overwriting one with the other assert new_group.name if new_group.get_id: self.groups_by_id[new_group.get_id] = new_group self.groups_by_name[new_group.name] = new_group self.groups.add(new_group) def by_name(self, group_name): if group_name not in self.groups_by_name: group = Group.by_name(self.domain, group_name) if not group: self.groups_by_name[group_name] = None return None self.add_group(group) return self.groups_by_name[group_name] def get(self, group_id): if group_id not in self.groups_by_id: group = Group.get(group_id) if group.domain != self.domain: raise ResourceNotFound() self.add_group(group) return self.groups_by_id[group_id] def create(self, domain, name): group = Group(domain=domain, name=name) self.add_group(group) return group def rename_group(self, group, name): # This isn't always true, you can rename A => B and then B => C, # and what was A will now be called B when you try to change # what was B to be called C. That's fine, but you don't want to # delete someone else's entry if self.groups_by_name.get(group.name) is group: del self.groups_by_name[group.name] group.name = name self.add_group(group) def save_all(self): Group.bulk_save(self.groups) def _fmt_phone(phone_number): if phone_number and not isinstance(phone_number, str): phone_number = str(int(phone_number)) return phone_number.lstrip("+") class BulkCacheBase(object): def __init__(self, domain): self.domain = domain self.cache = {} def get(self, key): if not key: return None if key not in self.cache: self.cache[key] = self.lookup(key) return self.cache[key] def lookup(self, key): # base classes must implement this themselves raise NotImplementedError class SiteCodeToSupplyPointCache(BulkCacheBase): """ Cache the lookup of a supply point object from the site code used in upload. """ def lookup(self, site_code): case_location = get_supply_point_and_location( self.domain, site_code ) return case_location.case class SiteCodeToLocationCache(BulkCacheBase): def __init__(self, domain): self.non_admin_types = [ loc_type.name for loc_type in Domain.get_by_name(domain).location_types if not loc_type.administrative ] super(SiteCodeToLocationCache, self).__init__(domain) def lookup(self, site_code): """ Note that this can raise SQLLocation.DoesNotExist if the location with the given site code is not found. """ return SQLLocation.objects.using('default').get( domain=self.domain, site_code__iexact=site_code ) class LocationIdToSiteCodeCache(BulkCacheBase): def lookup(self, location_id): return SQLLocation.objects.get( domain=self.domain, # this is only for safety location_id=location_id ).site_code def create_or_update_groups(domain, group_specs, log): group_memoizer = GroupMemoizer(domain) group_memoizer.load_all() group_names = set() for row in group_specs: group_id = row.get('id') group_name = str(row.get('name') or '') case_sharing = row.get('case-sharing') reporting = row.get('reporting') data = row.get('data') # check that group_names are unique if group_name in group_names: log['errors'].append('Your spreadsheet has multiple groups called "%s" and only the first was processed' % group_name) continue else: group_names.add(group_name) # check that there's a group_id or a group_name if not group_id and not group_name: log['errors'].append('Your spreadsheet has a group with no name or id and it has been ignored') continue try: if group_id: group = group_memoizer.get(group_id) else: group = group_memoizer.by_name(group_name) if not group: group = group_memoizer.create(domain=domain, name=group_name) except ResourceNotFound: log["errors"].append('There are no groups on CommCare HQ with id "%s"' % group_id) except MultipleResultsFound: log["errors"].append("There are multiple groups on CommCare HQ named: %s" % group_name) else: if group_name: group_memoizer.rename_group(group, group_name) group.case_sharing = case_sharing group.reporting = reporting group.metadata = data return group_memoizer def get_location_from_site_code(site_code, location_cache): if isinstance(site_code, str): site_code = site_code.lower() elif isinstance(site_code, int): site_code = str(site_code) else: raise UserUploadError( _("Unexpected format received for site code '%(site_code)s'") % {'site_code': site_code} ) try: return location_cache.get(site_code) except SQLLocation.DoesNotExist: raise UserUploadError( _("Could not find organization with site code '%(site_code)s'") % {'site_code': site_code} ) def is_password(password): if not password: return False for c in password: if c != "*": return True return False def users_with_duplicate_passwords(rows): password_dict = dict() for row in rows: username = row.get('username') password = str(row.get('password')) if not is_password(password): continue if password_dict.get(password): password_dict[password].add(username) else: password_dict[password] = {username} ret = set() for usernames in password_dict.values(): if len(usernames) > 1: ret = ret.union(usernames) return ret def create_or_update_users_and_groups(domain, user_specs, group_specs, task=None): from corehq.apps.users.views.mobile.custom_data_fields import UserFieldsView custom_data_validator = UserFieldsView.get_validator(domain) ret = {"errors": [], "rows": []} total = len(user_specs) + len(group_specs) def _set_progress(progress): if task is not None: DownloadBase.set_progress(task, progress, total) group_memoizer = create_or_update_groups(domain, group_specs, log=ret) current = len(group_specs) usernames = set() user_ids = set() allowed_groups = set(group_memoizer.groups) allowed_group_names = [group.name for group in allowed_groups] allowed_roles = UserRole.by_domain(domain) roles_by_name = {role.name: role for role in allowed_roles} can_assign_locations = domain_has_privilege(domain, privileges.LOCATIONS) if can_assign_locations: location_cache = SiteCodeToLocationCache(domain) domain_obj = Domain.get_by_name(domain) usernames_with_dupe_passwords = users_with_duplicate_passwords(user_specs) try: for row in user_specs: _set_progress(current) current += 1 data = row.get('data') email = row.get('email') group_names = list(map(str, row.get('group') or [])) language = row.get('language') name = row.get('name') password = <PASSWORD>.<PASSWORD>('password') phone_number = row.get('phone-number') uncategorized_data = row.get('uncategorized_data') user_id = row.get('user_id') username = row.get('username') location_codes = row.get('location_code') or [] if location_codes and not isinstance(location_codes, list): location_codes = [location_codes] # ignore empty location_codes = [code for code in location_codes if code] role = row.get('role', '') if password: password = <PASSWORD>) try: username = normalize_username(str(username), domain) except TypeError: username = None except ValidationError: ret['rows'].append({ 'username': username, 'row': row, 'flag': _('username cannot contain spaces or symbols'), }) continue status_row = { 'username': raw_username(username) if username else None, 'row': row, } is_active = row.get('is_active') if isinstance(is_active, str): try: is_active = string_to_boolean(is_active) if is_active else None except ValueError: ret['rows'].append({ 'username': username, 'row': row, 'flag': _("'is_active' column can only contain 'true' or 'false'"), }) continue if username in usernames or user_id in user_ids: status_row['flag'] = 'repeat' elif not username and not user_id: status_row['flag'] = 'missing-data' else: try: if username: usernames.add(username) if user_id: user_ids.add(user_id) if user_id: user = CommCareUser.get_by_user_id(user_id, domain) else: user = CommCareUser.get_by_username(username) if domain_obj.strong_mobile_passwords and is_password(password): if raw_username(username) in usernames_with_dupe_passwords: raise UserUploadError(_("Provide a unique password for each mobile worker")) try: clean_password(password) except forms.ValidationError: if settings.ENABLE_DRACONIAN_SECURITY_FEATURES: msg = _("Mobile Worker passwords must be 8 " "characters long with at least 1 capital " "letter, 1 special character and 1 number") else: msg = _("Please provide a stronger password") raise UserUploadError(msg) if user: if user.domain != domain: raise UserUploadError(_( 'User with username %(username)r is ' 'somehow in domain %(domain)r' ) % {'username': user.username, 'domain': user.domain}) if username and user.username != username: raise UserUploadError(_( 'Changing usernames is not supported: %(username)r to %(new_username)r' ) % {'username': user.username, 'new_username': username}) if is_password(password): user.set_password(password) status_row['flag'] = 'updated' else: max_username_length = get_mobile_worker_max_username_length(domain) if len(raw_username(username)) > max_username_length: ret['rows'].append({ 'username': username, 'row': row, 'flag': _("username cannot contain greater than %d characters" % max_username_length) }) continue if not is_password(password): raise UserUploadError(_("Cannot create a new user with a blank password")) user = CommCareUser.create(domain, username, password, commit=False) status_row['flag'] = 'created' if phone_number: user.add_phone_number(_fmt_phone(phone_number), default=True) if name: user.set_full_name(str(name)) if data: error = custom_data_validator(data) if error: raise UserUploadError(error) user.user_data.update(data) if uncategorized_data: user.user_data.update(uncategorized_data) if language: user.language = language if email: try: validate_email(email) except ValidationError: raise UserUploadError(_("User has an invalid email address")) user.email = email.lower() if is_active is not None: user.is_active = is_active if can_assign_locations: # Do this here so that we validate the location code before we # save any other information to the user, this way either all of # the user's information is updated, or none of it location_ids = [] for code in location_codes: loc = get_location_from_site_code(code, location_cache) location_ids.append(loc.location_id) if role: if role in roles_by_name: user.set_role(domain, roles_by_name[role].get_qualified_id()) else: raise UserUploadError(_( "Role '%s' does not exist" ) % role) if can_assign_locations: locations_updated = set(user.assigned_location_ids) != set(location_ids) primary_location_removed = (user.location_id and not location_ids or user.location_id not in location_ids) if primary_location_removed: user.unset_location(commit=False) if locations_updated: user.reset_locations(location_ids, commit=False) user.save() if is_password(password): # Without this line, digest auth doesn't work. # With this line, digest auth works. # Other than that, I'm not sure what's going on # Passing use_primary_db=True because of https://dimagi-dev.atlassian.net/browse/ICDS-465 user.get_django_user(use_primary_db=True).check_password(password) for group_id in Group.by_user_id(user.user_id, wrap=False): group = group_memoizer.get(group_id) if group.name not in group_names: group.remove_user(user) for group_name in group_names: if group_name not in allowed_group_names: raise UserUploadError(_( "Can't add to group '%s' " "(try adding it to your spreadsheet)" ) % group_name) group_memoizer.by_name(group_name).add_user(user, save=False) except (UserUploadError, CouchUser.Inconsistent) as e: status_row['flag'] = str(e) ret["rows"].append(status_row) finally: try: group_memoizer.save_all() except BulkSaveError as e: _error_message = ( "Oops! We were not able to save some of your group changes. " "Please make sure no one else is editing your groups " "and try again." ) logging.exception(( 'BulkSaveError saving groups. ' 'User saw error message "%s". Errors: %s' ) % (_error_message, e.errors)) ret['errors'].append(_error_message) _set_progress(total) return ret class GroupNameError(Exception): def __init__(self, blank_groups): self.blank_groups = blank_groups @property def message(self): return "The following group ids have a blank name: %s." % ( ', '.join([group.get_id for group in self.blank_groups]) ) def build_data_headers(keys, header_prefix='data'): return json_to_headers( {header_prefix: {key: None for key in keys}} ) def parse_users(group_memoizer, domain, user_data_model, location_cache, user_filters, task, total_count): def _get_group_names(user): return sorted([ group_memoizer.get(id).name for id in Group.by_user_id(user.user_id, wrap=False) ], key=alphanumeric_sort_key) def _get_devices(user): """ Returns a comma-separated list of IMEI numbers of the user's devices, sorted with most-recently-used first """ return ', '.join([device.device_id for device in sorted( user.devices, key=lambda d: d.last_used, reverse=True )]) def _make_user_dict(user, group_names, location_cache): model_data, uncategorized_data = ( user_data_model.get_model_and_uncategorized(user.user_data) ) role = user.get_role(domain) location_codes = [] try: location_codes.append(location_cache.get(user.location_id)) except SQLLocation.DoesNotExist: pass for location_id in user.assigned_location_ids: # skip if primary location_id, as it is already added to the start of list above if location_id != user.location_id: try: location_codes.append(location_cache.get(location_id)) except SQLLocation.DoesNotExist: pass return { 'data': model_data, 'uncategorized_data': uncategorized_data, 'group': group_names, 'name': user.full_name, 'password': "********", # dummy display string for passwords 'phone-number': user.phone_number, 'email': user.email, 'username': user.raw_username, 'language': user.language, 'user_id': user._id, 'is_active': str(user.is_active), 'User IMEIs (read only)': _get_devices(user), 'location_code': location_codes, 'role': role.name if role else '', 'registered_on (read only)': user.created_on.strftime('%Y-%m-%d %H:%M:%S') if user.created_on else '' } unrecognized_user_data_keys = set() user_groups_length = 0 max_location_length = 0 user_dicts = [] for n, user in enumerate(get_commcare_users_by_filters(domain, user_filters)): group_names = _get_group_names(user) user_dict = _make_user_dict(user, group_names, location_cache) user_dicts.append(user_dict) unrecognized_user_data_keys.update(user_dict['uncategorized_data']) user_groups_length = max(user_groups_length, len(group_names)) max_location_length = max(max_location_length, len(user_dict["location_code"])) DownloadBase.set_progress(task, n, total_count) user_headers = [ 'username', 'password', 'name', 'phone-number', 'email', 'language', 'role', 'user_id', 'is_active', 'User IMEIs (read only)', 'registered_on (read only)'] user_data_fields = [f.slug for f in user_data_model.get_fields(include_system=False)] user_headers.extend(build_data_headers(user_data_fields)) user_headers.extend(build_data_headers( unrecognized_user_data_keys, header_prefix='uncategorized_data' )) user_headers.extend(json_to_headers( {'group': list(range(1, user_groups_length + 1))} )) if domain_has_privilege(domain, privileges.LOCATIONS): user_headers.extend(json_to_headers( {'location_code': list(range(1, max_location_length + 1))} )) def _user_rows(): for user_dict in user_dicts: row = dict(flatten_json(user_dict)) yield [row.get(header) or '' for header in user_headers] return user_headers, _user_rows() def parse_groups(groups): def _make_group_dict(group): return { 'id': group.get_id, 'name': group.name, 'case-sharing': group.case_sharing, 'reporting': group.reporting, 'data': group.metadata, } group_data_keys = set() group_dicts = [] sorted_groups = sorted( groups, key=lambda group: alphanumeric_sort_key(group.name) ) for group in sorted_groups: group_dicts.append(_make_group_dict(group)) group_data_keys.update(group.metadata if group.metadata else []) group_headers = ['id', 'name', 'case-sharing?', 'reporting?'] group_headers.extend(build_data_headers(group_data_keys)) def _get_group_rows(): for group_dict in group_dicts: row = dict(flatten_json(group_dict)) yield [row.get(header) or '' for header in group_headers] return group_headers, _get_group_rows() def count_users_and_groups(domain, user_filters, group_memoizer): users_count = get_commcare_users_by_filters(domain, user_filters, count_only=True) groups_count = len(group_memoizer.groups) return users_count + groups_count def dump_users_and_groups(domain, download_id, user_filters, task): from corehq.apps.users.views.mobile.custom_data_fields import UserFieldsView def _load_memoizer(domain): group_memoizer = GroupMemoizer(domain=domain) # load groups manually instead of calling group_memoizer.load_all() # so that we can detect blank groups blank_groups = set() for group in Group.by_domain(domain): if group.name: group_memoizer.add_group(group) else: blank_groups.add(group) if blank_groups: raise GroupNameError(blank_groups=blank_groups) return group_memoizer writer = Excel2007ExportWriter(format_as_text=True) group_memoizer = _load_memoizer(domain) location_cache = LocationIdToSiteCodeCache(domain) users_groups_count = count_users_and_groups(domain, user_filters, group_memoizer) DownloadBase.set_progress(task, 0, users_groups_count) user_data_model = CustomDataFieldsDefinition.get_or_create( domain, UserFieldsView.field_type ) user_headers, user_rows = parse_users( group_memoizer, domain, user_data_model, location_cache, user_filters, task, users_groups_count, ) group_headers, group_rows = parse_groups(group_memoizer.groups) headers = [ ('users', [user_headers]), ('groups', [group_headers]), ] rows = [ ('users', user_rows), ('groups', group_rows), ] use_transfer = settings.SHARED_DRIVE_CONF.transfer_enabled filename = "{}_users_{}.xlsx".format(domain, uuid.uuid4().hex) file_path = get_download_file_path(use_transfer, filename) writer.open( header_table=headers, file=file_path, ) writer.write(rows) writer.close() expose_download(use_transfer, file_path, filename, download_id, 'xlsx') DownloadBase.set_progress(task, users_groups_count, users_groups_count)

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train_xgboost.py

mmcenta/missing-link

0

141824

<filename>train_xgboost.py import pickle import argparse import numpy as np import xgboost as xgb from xgboost.sklearn import XGBClassifier from sklearn.metrics import accuracy_score from util.dataset_io import load_dataset parser = argparse.ArgumentParser(description='Train a xgboost model.') parser.add_argument('--train_name', required=True, help='name of the training dataset') parser.add_argument('--val_name', help='name of the validation dataset') parser.add_argument('--model_name', default='base', help='name of the model for saving') parser.add_argument('--n_estimators', type=int, default=100, help='number of trees to fit') parser.add_argument('--gpu', action='store_true', help='whether to use a gpu when training') parser.add_argument('--max_depth', type=int, default=3, help='maximum tree depth for base learners') parser.add_argument('--learning_rate', type=float, default=0.01, help='boosting learning rate') parser.add_argument('--subsample', type=float, default=0.8, help='subsample ratio of the training instance') parser.add_argument('--colsample_bytree', type=float, default=0.8, help='subsample ratio of columns when constructing each tree') parser.add_argument('--gamma', type=float, default=0, help='minimum loss reduction required to make a further partition on a leaf node of the tree') parser.add_argument('--scale_pos_weight', type=float, default=1.0) parser.add_argument('--min_child_weight', type=int, default=1) parser.add_argument('--reg_alpha', type=float, default=0.0) if __name__ == "__main__": args = parser.parse_args() print("Loading training datataset...") X_train, y_train = load_dataset(args.train_name) print("Done.\nTraining...") if not args.gpu: model = XGBClassifier(silent=False, scale_pos_weight=args.scale_pos_weight, min_child_weight=args.min_child_weight, learning_rate=args.learning_rate, colsample_bytree=args.colsample_bytree, subsample=args.subsample, objective='binary:logistic', n_estimators=args.n_estimators, reg_alpha=args.reg_alpha, max_depth=args.max_depth, gamma=args.gamma) else: model = XGBClassifier(tree_method="gpu_hist", gpu_id=0, silent=False, scale_pos_weight=args.scale_pos_weight, min_child_weight=args.min_child_weight, learning_rate=args.learning_rate, colsample_bytree=args.colsample_bytree, subsample=args.subsample, objective='binary:logistic', n_estimators=args.n_estimators, reg_alpha=args.reg_alpha, max_depth=args.max_depth, gamma=args.gamma) model.fit(X_train, y_train) print("Done.\nSaving...") with open("./models/" + args.model_name + ".pickle", "wb") as f: pickle.dump(model, f) print("Done.") if args.val_name is not None: print("Validating...") preds = model.predict(X_train) pred_labels = np.rint(preds) print("\ttrain accuracy:", accuracy_score(y_train, pred_labels)) X_val, y_val = load_dataset(args.val_name) preds = model.predict(X_val) pred_labels = np.rint(preds) print("\tval accuracy:", accuracy_score(y_val, pred_labels)) print("Done.")

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merger.py

lorabit/workspace_merger

0

178069

from shutil import copy2 from os import path, remove from random import randint import argparse class WorkspaceReader: def __init__(self, fname): self.maven_jars = [] self.java_libraries = [] module_name = 'tmp'+str(randint(1000,9999)) new_name = path.join(path.dirname(__file__), module_name +'.py') copy2(fname, new_name) workspace = __import__(module_name) workspace.native = self workspace.generated_java_libraries() workspace.generated_maven_jars() remove(new_name) def maven_jar(self, name = None, artifact = None, sha1 = None): self.maven_jars.append( { "name": name, "artifact": artifact, "sha1" : sha1 } ) def java_library(self, name = None, visibility = [], exports = [], runtime_deps = []): self.java_libraries.append( { "name": name, "visibility": visibility, "exports": exports, "runtime_deps": runtime_deps } ) class WorkspaceMerger: def __init__(self): self.maven_jars = {} self.java_libraries = {} def merge(self, reader): for maven_jar in reader.maven_jars: self.maven_jars[maven_jar["name"]] = maven_jar for java_library in reader.java_libraries: self.java_libraries[java_library["name"]] = java_library def export(self, fname): def format_array(array, indent = 6): if len(array) == 0: return "[]" if len(array) == 1: return "[\"%s\"]" % (array[0]) ret = [((" "*(indent+4)+"\"%s\",\n") %(i,)) for i in array] return "[\n"+"".join(ret)+" "*indent+"]" with open(fname, 'w') as f: f.write("# The following dependencies were calculated by workspace_merger. \n# https://github.com/lorabit/workspace_merger\n\n\n") f.write("def generated_maven_jars():\n") for maven_jar_name in self.maven_jars: maven_jar = self.maven_jars[maven_jar_name] f.write(" native.maven_jar(\n") f.write(" name = \"%s\",\n" % (maven_jar["name"],)) if maven_jar.get("artifact")!=None: f.write(" artifact = \"%s\",\n" % (maven_jar["artifact"])) if maven_jar.get("sha1")!=None: f.write(" sha1 = \"%s\",\n" % (maven_jar["sha1"])) f.write(" )\n\n") f.write("def generated_java_libraries():\n") for java_library_name in self.java_libraries: java_library = self.java_libraries[java_library_name] f.write(" native.java_library(\n") f.write(" name = \"%s\",\n" % (java_library["name"],)) if java_library.get("visibility")!=None: f.write(" visibility = %s,\n" % (format_array(java_library["visibility"]))) if java_library.get("exports")!=None: f.write(" exports = %s,\n" % (format_array(java_library["exports"]))) if java_library.get("runtime_deps")!=None: f.write(" runtime_deps = %s,\n" % (format_array(java_library["runtime_deps"]))) f.write(" )\n\n") if __name__ == '__main__': parser = argparse.ArgumentParser( description='Merge generated bazel workspaces for maven dependencies.') _arg = parser.add_argument _arg( '-s', '--source', type=str, action='store', metavar='PATH', help='The source bzl file path.', required = True ) _arg( '-d', '--destination', type=str, action='store', metavar='PATH', help='The destination bzl file path. If the file does not exist, a new file will be created.', required = True ) args = parser.parse_args() if not path.isfile(args.source): print("%s does not exist.\n" % (args.source)) exit(0) merger = WorkspaceMerger() if path.isfile(args.destination): dest = WorkspaceReader(args.destination) merger.merge(dest) source = WorkspaceReader(args.source) merger.merge(source) merger.export(args.destination) print('Done!')

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suites/DatabaseApi/SideChainEthereum.py

dbulka/pytests

0

137996

<gh_stars>0 # -*- coding: utf-8 -*- import lemoncheesecake.api as lcc @lcc.suite("SideChainEthereum") class SideChainEthereum: pass

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authentik/policies/password/models.py

BeryJu/passbook

15

175958

"""user field matcher models""" import re from django.db import models from django.utils.translation import gettext as _ from rest_framework.serializers import BaseSerializer from structlog.stdlib import get_logger from authentik.policies.models import Policy from authentik.policies.types import PolicyRequest, PolicyResult from authentik.stages.prompt.stage import PLAN_CONTEXT_PROMPT LOGGER = get_logger() RE_LOWER = re.compile("[a-z]") RE_UPPER = re.compile("[A-Z]") RE_DIGITS = re.compile("[0-9]") class PasswordPolicy(Policy): """Policy to make sure passwords have certain properties""" password_field = models.TextField( default="password", help_text=_("Field key to check, field keys defined in Prompt stages are available."), ) amount_digits = models.PositiveIntegerField(default=0) amount_uppercase = models.PositiveIntegerField(default=0) amount_lowercase = models.PositiveIntegerField(default=0) amount_symbols = models.PositiveIntegerField(default=0) length_min = models.PositiveIntegerField(default=0) symbol_charset = models.TextField(default=r"!\"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ ") error_message = models.TextField() @property def serializer(self) -> BaseSerializer: from authentik.policies.password.api import PasswordPolicySerializer return PasswordPolicySerializer @property def component(self) -> str: return "ak-policy-password-form" # pylint: disable=too-many-return-statements def passes(self, request: PolicyRequest) -> PolicyResult: if ( self.password_field not in request.context and self.password_field not in request.context.get(PLAN_CONTEXT_PROMPT, {}) ): LOGGER.warning( "Password field not set in Policy Request", field=self.password_field, fields=request.context.keys(), prompt_fields=request.context.get(PLAN_CONTEXT_PROMPT, {}).keys(), ) return PolicyResult(False, _("Password not set in context")) if self.password_field in request.context: password = request.context[self.password_field] else: password = request.context[PLAN_CONTEXT_PROMPT][self.password_field] if len(password) < self.length_min: LOGGER.debug("password failed", reason="length") return PolicyResult(False, self.error_message) if self.amount_digits > 0 and len(RE_DIGITS.findall(password)) < self.amount_digits: LOGGER.debug("password failed", reason="amount_digits") return PolicyResult(False, self.error_message) if self.amount_lowercase > 0 and len(RE_LOWER.findall(password)) < self.amount_lowercase: LOGGER.debug("password failed", reason="amount_lowercase") return PolicyResult(False, self.error_message) if self.amount_uppercase > 0 and len(RE_UPPER.findall(password)) < self.amount_lowercase: LOGGER.debug("password failed", reason="amount_uppercase") return PolicyResult(False, self.error_message) if self.amount_symbols > 0: count = 0 for symbol in self.symbol_charset: count += password.count(symbol) if count < self.amount_symbols: LOGGER.debug("password failed", reason="amount_symbols") return PolicyResult(False, self.error_message) return PolicyResult(True) class Meta: verbose_name = _("Password Policy") verbose_name_plural = _("Password Policies")

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Database/splParser/databaseconnection.py

snapmeds/snapmeds

1

134492

# -*- coding: utf-8 -*- import urllib2, urllib import drug def write_to_database(drug): mydata = get_array_of_tuples_from_drug(drug) mydata = mydata + [("username","YOURUSERNAMEHERE"),("password","<PASSWORD>")] mydata=urllib.urlencode(mydata) print mydata path='YOURDATABASEURL' req=urllib2.Request(path, mydata) req.add_header("Content-type", "application/x-www-form-urlencoded") page=urllib2.urlopen(req).read() return convert(page) def convert(input): if isinstance(input, dict): return {convert(key): convert(value) for key, value in input.iteritems()} elif isinstance(input, list): return [convert(element) for element in input] elif isinstance(input, unicode): return input.encode('utf-8') else: return input def get_array_of_tuples_from_drug(drug): ret = [] ret.append( ('setid',drug.setid)) ret.append( ('ndc',drug.ndc)) ret.append( ('warnings',drug.warnings)) ret.append( ('genericnames',drug.genericnames)) ret.append( ('uses',drug.uses)) ret.append( ('adversereactions',drug.adversereactions)) ret.append( ('boxwarnings',drug.boxwarnings)) ret.append( ('precautions',drug.precautions)) ret.append( ('conflictingconditions',drug.conflictingconditions)) ret.append( ('name', drug.name)) ret.append( ('medicationguide', drug.medicationguide)) return ret

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dpkt/vrrp.py

lkash/test

0

164732

<gh_stars>0 # $Id: vrrp.py 88 2013-03-05 19:43:17Z <EMAIL> $ # -*- coding: utf-8 -*- """Virtual Router Redundancy Protocol.""" import dpkt from decorators import deprecated class VRRP(dpkt.Packet): __hdr__ = ( ('_v_type', 'B', 0x21), ('vrid', 'B', 0), ('priority', 'B', 0), ('count', 'B', 0), ('atype', 'B', 0), ('advtime', 'B', 0), ('sum', 'H', 0), ) addrs = () auth = '' @property def v(self): # high 4 bits of _v_type return self._v_type >> 4 @v.setter def v(self, v): self._v_type = (self._v_type & 0x0f) | (v << 4) @property def type(self): # low 4 bits of _v_type return self._v_type & 0x0f @type.setter def type(self, v): self._v_type = (self._v_type & 0xf0) | (v & 0x0f) # Deprecated methods, will be removed in the future # ================================================= @deprecated('v') def _get_v(self): return self.v @deprecated('v') def _set_v(self, v): self.v = v @deprecated('type') def _get_type(self): return self.type @deprecated('type') def _set_type(self, v): self.type = v # ================================================= def unpack(self, buf): dpkt.Packet.unpack(self, buf) l = [] off = 0 for off in range(0, 4 * self.count, 4): l.append(self.data[off:off + 4]) self.addrs = l self.auth = self.data[off + 4:] self.data = '' def __len__(self): return self.__hdr_len__ + (4 * self.count) + len(self.auth) def __str__(self): data = ''.join(self.addrs) + self.auth if not self.sum: self.sum = dpkt.in_cksum(self.pack_hdr() + data) return self.pack_hdr() + data def test_vrrp(): # no addresses s = '\x00\x00\x00\x00\x00\x00\xff\xff' v = VRRP(s) assert v.sum == 0xffff assert str(v) == s # have address s = '\x21\x01\x64\x01\x00\x01\xba\x52\xc0\xa8\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00' v = VRRP(s) assert v.count == 1 assert v.addrs == ['\xc0\xa8\x00\x01'] # 192.168.0.1 assert str(v) == s # test checksum generation v.sum = 0 assert str(v) == s # test length assert len(v) == len(s) # test getters assert v.v == 2 assert v.type == 1 # test setters v.v = 3 v.type = 2 assert str(v)[0] == '\x32' if __name__ == '__main__': test_vrrp() print 'Tests Successful...'

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projeto_sensorino.py

rafaelbeloduarte/leitor_arduino

0

81251

# -*- coding: utf-8 -*- import tkinter import serial import serial.tools.list_ports import threading from tkinter import filedialog from tkinter import * import sys import os from tkinter import messagebox from tkinter.scrolledtext import ScrolledText import matplotlib.pyplot as plt import matplotlib.animation as animation # funções------------------------------------------------------------------------------------- def sel(): # LISTA AS PORTAS DISPONÍVEIS comlist = serial.tools.list_ports.comports() connected = [] for element in comlist: connected.append(element.device) def graficoinst(): plt.close() fig = plt.figure() grafico = open('grafico.dat', 'r').read() vetores = grafico.split('\n') vetores = filter(None, vetores) vetores = list(vetores) for i in range(len(vetores)): aux = vetores[i].split(",") for j in range(len(aux)): aux[j] = float(aux[j]) vetores[i] = aux matriz_graf = vetores plt.clf() eixo = fig.add_subplot(1, 1, 1) eixo.plot(matriz_graf) # eixo.legend() plt.show() def grafico(): plt.close() fig = plt.figure() def animar(i): grafico = open('grafico.dat', 'r').read() vetores = grafico.split('\n') vetores = filter(None, vetores) vetores = list(vetores) for i in range(len(vetores)): aux = vetores[i].split(",") for j in range(len(aux)): aux[j] = float(aux[j]) vetores[i] = aux matriz_graf = vetores plt.clf() eixo = fig.add_subplot(1, 1, 1) eixo.plot(matriz_graf) # eixo.legend() ani = animation.FuncAnimation(fig, animar, interval=1000) plt.show() def selbaud(): text.insert(END, "\nBaudrate: " + str(varbaud.get()) + "\n") text.see(END) def handle_leitura(): try: ser = serial.Serial(var.get(), baudrate=varbaud.get(), timeout=1, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS) ser.close() except Exception as e: messagebox.showinfo("Eita!", "Erro: " + str(e) + "\nProvável causa: a porta já está sendo utilizada.", icon='error') return None if not var.get() or not varbaud.get(): messagebox.showinfo("Aviso!", "Selecione uma porta/baudrate.") return None if not n_texto.get(): messagebox.showinfo("Aviso!", "Por favor, informe o número de entradas.") return None try: salvararquivo = filedialog.asksaveasfilename(defaultextension=".txt", initialfile="dados") arquivousuario = open(salvararquivo, 'w') arquivousuario.close() arquivo_sinal = open(salvararquivo.replace(".txt", "") + "_sinal.txt", "w") arquivo_sinal.close() except Exception as e: messagebox.showinfo(e) return None ser = serial.Serial(var.get(), baudrate=varbaud.get(), timeout=1, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS) instr_arquivo = Label(top, text="Para finalizar a leitura apenas feche o programa, seus dados são salvos automaticamente.") instr_arquivo.grid(row = 1, column=1, columnspan=5) instr_arquivo.configure(background='blue', foreground='white', borderwidth=3, relief='groove') def iniciar(): # Esta função lê o inpu # t da porta selecionada i_limpa_texto = 0 arquivousuario = open(salvararquivo, 'a') arquivo_sinal = open(salvararquivo.replace(".txt", "") + "_sinal.txt", 'a') rol = IntVar() rol_parar = Radiobutton(top, text="Parar rolagem", variable=rol, value=0) rol_parar.grid(row=20, column=2, columnspan = 1) rol_parar.configure(background='#F2F2F2', indicatoron=0, width=15) rol_iniciar = Radiobutton(top, text="Auto rolagem", variable=rol, value=1) rol_iniciar.grid(row=20, column=3, columnspan=1) rol_iniciar.configure(background='#F2F2F2', indicatoron=0, width=15) rol_iniciar.select() graf = open("grafico.dat", 'w') graf.close() if os.path.isfile(abre_poli.get()) == False and os.path.isfile("polinomios.txt") == False: messagebox.showinfo("Atenção!", "Arquivo não encontrado, o sinal será apresentado nu.") poli = open("polinomios.txt", 'w') n_entradas = int(n_texto.get()) for i in range(n_entradas): poli.write("1 0\n") if abre_poli.get(): if os.stat(abre_poli.get()).st_size == 0: messagebox.showinfo("Atenção!", "Arquivo vazio, será preenchido com y=x.") poli = open(abre_poli.get(), 'w') n_entradas = int(n_texto.get()) for i in range(n_entradas): poli.write("1 0\n") poli.close() poli = open(abre_poli.get()).readlines() i = 0 linhas = i + 1 if linhas < int(n_texto.get()): messagebox.showinfo("Atenção!", "Número de equações menor que o número de entradas. Os sinais das entradas restantes serão exibidos inalterados.") escrever_polinomios = open(abre_poli.get(), 'a') restantes = int(n_texto.get()) - len(poli) for i in range(restantes): escrever_polinomios.write("1 0\n") escrever_polinomios.close() else: poli = open("polinomios.txt").readlines() i = 0 with open("polinomios.txt") as arquivo: for i, arquivo in enumerate(arquivo): pass linhas = i + 1 if linhas < int(n_texto.get()): messagebox.showinfo("Atenção!", "Número de equações menor que o número de entradas. Os sinais das entradas restantes serão exibidos inalterados.") escrever_polinomios = open("polinomios.txt", 'a') restantes = int(n_texto.get()) - len(poli) for i in range(restantes): escrever_polinomios.write("1 0\n") escrever_polinomios.close() for i in range(len(poli)): poli[i] = poli[i].replace("\n", "").split(" ") for i in range(len(poli)): for j in range(len(poli[i])): poli[i][j] = float(poli[i][j]) text.insert(END, "\n" + str(poli) + "\n\n") text.see(END) try: # LOOP DE LEITURA while True: serial_bytes = ser.readline() serial_texto = serial_bytes.decode('utf-8') a = serial_texto.split(' ') graf = open("grafico.dat", 'a') dados = [] lista_dados = [] if a: dados = '[%s]' % ', '.join(map(str, a)) # map serve para aplicarmos uma função a cada elemento de uma lista, # retornando uma nova lista contendo os elementos resultantes da aplicação da função. # The %s token allows to insert (and potentially format) a string. # Notice that the %s token is replaced by whatever I pass to the string after the % symbol. dados = dados.replace('\n', '').replace('\r', '').replace('[', '').replace(']', '').replace(" ", "").replace("'", "") lista_dados = dados.split(",") lista_dados = filter(None, lista_dados) lista_dados = list(lista_dados) for i in range(len(lista_dados)): lista_dados[i] = float(lista_dados[i]) if lista_dados: for i in range(len(poli)): lista_dados[i] = poli[i][0]*lista_dados[i] + poli[i][1] arquivousuario.write(str(lista_dados).replace('[', '').replace(']', '') + '\n') arquivousuario.flush() arquivo_sinal.write(dados + '\n') arquivo_sinal.flush() graf.write(str(lista_dados).replace('[', '').replace(']', '') + '\n') graf.flush() text.insert(END, str(lista_dados).replace('[', '').replace(']', '') + '\n') if rol.get() == 1: text.see(END) i_limpa_texto += 1 if i_limpa_texto > 1000: text.delete(1.0, END) graf = open('grafico.dat', 'r+') graf.truncate(0) i_limpa_texto = 0 if limpar_graf.get() == 1: graf = open('grafico.dat', 'w') graf.truncate(0) limpar_graf.set(0) # time.sleep(1) except Exception as e: messagebox.showinfo("Erro!", "Erro: " + str(e), icon='error') pass ser.close() graf.close() arquivousuario.close() arquivo_sinal.close() # poli.close() t = threading.Thread(target=iniciar) t.daemon = True t.start() def atualizarporta(): portas_sub_menu.delete(0, 'end') var.set("") ports = list(serial.tools.list_ports.comports()) comlist = serial.tools.list_ports.comports() connected = [] for element in comlist: connected.append(element.device) for i in range(len(ports)): ports[i] = str(ports[i]) for k in range(len(connected)): portas_sub_menu.add_radiobutton(label = connected[k], variable=var, value=str(connected[k]), command=sel) if "Arduino" or "Serial USB" in ports[k]: var.set(str(connected[k])) portas_sub_menu.activate(k) text.insert(END, "\nPorta selecionada: " + str(var.get()) + "\n") text.see(END) def reiniciar(): if messagebox.askokcancel("Reiniciando...", "Tem certeza?"): python = sys.executable os.execl(python, python, *sys.argv) else: return None def fechando(): if messagebox.askokcancel("Fechando...", "Tem certeza?"): plt.close('all') top.destroy() # fim das funções----------------------------------------------------------------------------- # Código para os widgets vai aqui.------------------------------------------------------------ # define a janela principal---------------------------------- top = tkinter.Tk() top.wm_title("Sensorino Salva!") top.minsize(500, 500) top.geometry("950x600") top.configure(background='#000000') # ----------------------------------------------------------- comlist = serial.tools.list_ports.comports() connected = [] for element in comlist: connected.append(element.device) if not connected: messagebox.showinfo("Aviso!", "Nenhuma porta disponível, verifique se seu dispositivo está conectado.") label_n = Label(top, text = "Número de entradas:") label_n.grid(row = 2, column = 1, columnspan = 2, padx = 10, pady = 10) label_n.configure(background='#000000', foreground='white', width = 20) n_texto = StringVar() n = Entry(top, textvariable = n_texto) n.grid(row = 2, column=3, padx = 10, pady = 10) n.configure(width = 10) label_eqs = Label(top, text = "Equação da reta: y = a*x+b, padrão: y=x") label_eqs.grid(row = 2, column = 6, columnspan = 3) label_eqs.configure(background='#000000', foreground='white') label_a = Label(top, text = "a:") label_a.grid(row = 3, column = 6) label_a.configure(background='#000000', foreground='white') arquivo_coef = open("polinomios.txt", 'w') arquivo_coef.close() abre_poli = StringVar() def abre_polinomio(): try: abre_poli.set(filedialog.askopenfilename(initialdir = "",title = "Selecione seu arquivo",filetypes = (("Arquivo de texto","*.txt"),("Todos os arquivos","*.*")))) if abre_poli.get(): text.insert(END, "\nCarregado com sucesso.\n") text.see(END) except Exception as e: messagebox.showinfo(e) return None button_abre_poli = Button(top, text = "Carregar equações", command = abre_polinomio) button_abre_poli.grid(row=6, column=7) button_abre_poli.configure(activebackground='#000000', activeforeground='#FFFFFF') def pega_coef(): if not n_texto.get(): messagebox.showinfo("Aviso!", "Por favor, informe o número de entradas.") return None coef_a = a.get() coef_b = b.get() text.insert(END, "y = " + coef_a + "*x" + " + " + coef_b + '\n') text.see(END) arquivo_coef = open("polinomios.txt", 'a') arquivo_coef.write(coef_a + " ") arquivo_coef.write(coef_b + "\n") arquivo_coef.close() a.delete(0, END) b.delete(0, END) arquivo_coef.close() def salva_polinomio(): try: salvar_poli = filedialog.asksaveasfilename(defaultextension=".txt", initialfile="meus_polinomios") arquivo_poli = open(salvar_poli, 'w') texto = open("polinomios.txt").read() texto = texto.replace('\r', '').replace('[', '').replace(']', '').replace(",", "").replace("'", "") arquivo_poli.write(texto) arquivo_poli.close() except Exception as e: messagebox.showinfo(e) return None limpar_graf = IntVar() def limpar_graf_estado(): limpar_graf.set(1) button_salva_poli = Button(top, text = "Salvar equações", command = salva_polinomio) button_salva_poli.grid(row=5, column=7) button_salva_poli.configure(activebackground='#000000', activeforeground='#FFFFFF') a_texto = StringVar() a = Entry(top, textvariable = a_texto) a.grid(row=3, column=7) label_b = Label(top, text = "b:") label_b.grid(row = 4, column = 6) label_b.configure(background='#000000', foreground='white') b_texto = StringVar() b = Entry(top, textvariable = b_texto) b.grid(row=4, column=7) button_entrar = Button(top, text = "Inserir", command = pega_coef) button_entrar.grid(row=3, column=8, rowspan = 2, padx = 15) button_entrar.configure(activebackground='#000000', activeforeground='#FFFFFF') text = ScrolledText(top, width=50, height=20) text.grid(row=3, column=1, columnspan=5, rowspan=10, padx = 10, pady = 10) text.insert(END, " INSTRUÇÕES\n") text.insert(END, "\n") text.insert(END, ">O loop de seu controlador deve\nretornar os dados da seguinte forma:\n") text.insert(END, "a b c ...\n") text.insert(END, "Ex: 1 2 3 4 5\n") text.insert(END, "\n>Declare o número de entradas\nantes de iniciar a leitura.\n") text.insert(END, "\n") text.see(END) # criar menu menubar = Menu(top) menubar.add_command(label="Iniciar leitura", command=handle_leitura) graf_menu = Menu(menubar, tearoff=0) menubar.add_cascade(label="Gráficos", menu=graf_menu) graf_menu.add_command(label="Exibir gráfico em tempo real", command=grafico) graf_menu.add_command(label="Exibir gráfico", command=graficoinst) graf_menu.add_separator() graf_menu.add_command(label="Limpar gráfico", command=limpar_graf_estado) controlador_menu = Menu(menubar, tearoff=0) menubar.add_cascade(label="Controlador", menu=controlador_menu) portas_sub_menu = Menu(controlador_menu, tearoff = 0) controlador_menu.add_cascade(label="Portas", menu=portas_sub_menu) var = StringVar() atualizarporta() baud_sub_menu = Menu(controlador_menu, tearoff = 0) controlador_menu.add_cascade(label = "Baudrate", menu = baud_sub_menu) # pega o baud rate, varbaud é o baudrate=============================================== #4800, 9600, 38400, 57600, 115200, 230400 varbaud = IntVar() baud_sub_menu.add_radiobutton(label = "4800", variable = varbaud, value = 4800, command = selbaud) baud_sub_menu.add_radiobutton(label = "9600", variable = varbaud, value = 9600, command = selbaud) baud_sub_menu.add_radiobutton(label = "38400", variable = varbaud, value = 38400, command = selbaud) baud_sub_menu.add_radiobutton(label = "57600", variable = varbaud, value = 57600, command = selbaud) baud_sub_menu.add_radiobutton(label = "115200", variable = varbaud, value = 115200, command = selbaud) baud_sub_menu.add_radiobutton(label = "230400", variable = varbaud, value = 230400, command = selbaud) baud_sub_menu.invoke(1) # ===================================================================================== controlador_menu.add_separator() controlador_menu.add_command(label = "Atualizar portas", command = atualizarporta) sair_menu = Menu(menubar, tearoff=0) menubar.add_cascade(label="Sair/Reiniciar", menu=sair_menu) sair_menu.add_command(label = "Reiniciar", command = reiniciar) sair_menu.add_command(label="Sair", command=fechando) # mostrar o menu top.config(menu=menubar) # chama o mainloop -> abre a janela com os itens anteriores top.protocol("WM_DELETE_WINDOW", fechando) top.mainloop()

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Chapter 8 - Functions & Recursion/04_factorial.py

alex-dsouza777/Python-Basics

0

86100

<reponame>alex-dsouza777/Python-Basics #n! = 1 * 2 * 3 * 4 ..... * n #n! = [1 * 2 * 3 * 4 ..... n-1]* n #n! = n * (n-1)! # n = 7 # product = 1 # for i in range(n): # product = product * (i+1) # print(product) def factorial_iter(n): product = 1 for i in range(n): product = product * (i+1) return product def factorial_recursive(n): if n == 1 or n ==0: return 1 return n * factorial_recursive(n-1) # f = factorial_iter(5) f = factorial_recursive(3) print(f)

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pythonfiles/testing.py

amrut-prabhu/loan-default-prediction

2

23179

import numpy as np import pandas as pd from sklearn.externals import joblib #from sklearn.ensemble import RandomForestRegressor #from sklearn.multioutput import MultiOutputRegressor #from sklearn.multioutput import MultiOutputRegressor from sklearn.model_selection import train_test_split df = pd.read_csv('https://drive.google.com/uc?export=download&id=1XoV8SfvHmzaxRuDRe81OWSQu10dYTbO5',sep=',') df_X = df.iloc[:, 2:13].copy() df_X = pd.get_dummies(df_X) df_y1 = df.iloc[:, 13:16].copy() df_y1 = pd.get_dummies(df_y1) df_y2 = df.iloc[:, 16:20].copy() df_y2 = pd.get_dummies(df_y2) #X_train, df_X, y_train, df_y1 = train_test_split(df_X, df_y1, test_size=0.2, random_state=0) def accuracy(model, X_test, y_test): predictions = model.predict(X_test) print(model.score(X_test, y_test)) errors = np.abs(predictions - y_test) mape = 100 * (errors / y_test) for col in mape: accuracy = 100 - np.mean(mape[col]) print('Accuracy:', round(accuracy, 2), '%.') def test(model, df_X, df_y1, num): accuracy(model, df_X, df_y1) predictions = pd.DataFrame(model.predict(df_X)) errors = np.abs(predictions - df_y1) print(type(predictions)) print(type(errors)) for i in range(num): #results = df_X.iloc[:, 0:10].values #results = np.append(results, df_y1.ix[:, i]) #results = np.append(results, predictions[:, i]) #results = np.append(results, errors.ix[:, i]) #result_df = pd.DataFrame(results) df_X.join(df_y1.ix[:, i]).join(predictions.ix[:, i]).to_csv("ModelPredictions" + str(num) + str(i) + ".csv") #model = RandomForestRegressor(n_estimators = 1900, max_depth = 70, random_state = 50, learning_rate = 0.1, min_samples_split = 0.1, max_features = 'sqrt', loss = 'lad', warm_start = True, min_samples_leaf = 0.0005) #model1 = MultipleOutputRegressor(model) #model2 = MultipleOutputRegressor(model, n_jobs = -1) #model1.fit(X_train, y_train) model = joblib.load("RegressorChainGradientBoostingRegressorEarningsNew.pkl") test(model, df_X, df_y1, 3) model = joblib.load("RegressorChainGradientBoostingRegressorRepaymentNew.pkl") test(model, df_X, df_y2, 4) #model2.fit(X_train, y_train) #joblib.dump(model2, "MultiplepleOutputRandomForestRegressorEarnings.pkl") #print("Model 2: "); #accuracy(model2, df_X, df_y1) #X_train, df_X, y_train, df_y1 = train_test_split(df_X, df_y2, test_size=0.2, random_state=0) #model1.fit(X_train, y_train) #joblib.dump(model1, "MultipleOutputRegressorRandomForestRegressorRepayment.pkl") #print("Model 3: "); #accuracy(model1, df_X, df_y1) #model2.fit(X_train, y_train) #joblib.dump(model2, "MultiplepleOutputRandomForestRegressorRepayment.pkl") #print("Model 4: "); #accuracy(model2, df_X, df_y1)

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client/cryptog.py

yoelbassin/chat

8

77531

<gh_stars>1-10 import const import config import rsa from cryptography.fernet import Fernet def key_ex(): config.client.send((const.public_key + str(config.public_key.n) + ' ' + str(config.public_key.e)).encode()) def send_symmetric_key(): self_id = int(''.join([str(ord(c)) for c in config.uname])) dst_id = int(''.join([str(ord(c)) for c in config.active_chat])) if dst_id > self_id: generate_key() encrypted_message = rsa.encrypt(config.chat_key, config.dst_pub) config.client.send(const.symmetric_key.encode() + encrypted_message) config.chat_key = Fernet(config.chat_key) def get_key(key): key = key[5:].split(' ') config.dst_pub = rsa.PublicKey(int(key[0]), int(key[1])) def get_symmetric_key(key): key = key.encode() self_id = int(''.join([str(ord(c)) for c in config.uname])) dst_id = int(''.join([str(ord(c)) for c in config.active_chat])) if dst_id < self_id: config.chat_key = Fernet(key) def get_message_rsa(message): code = message[:const.CODE_LEN].decode() message = rsa.decrypt(message[const.CODE_LEN:], config.private_key) message = code + message.decode() return message def get_enc_message(message): code = message[:const.CODE_LEN].decode() if code == const.msg_code: message = decrypt_message(message[const.CODE_LEN:]) else: message = rsa.decrypt(message[const.CODE_LEN:], config.private_key) return code + message.decode() def generate_key(): """ Generates a key and save it into a file """ config.chat_key = Fernet.generate_key() def encrypt_message(message): """ Encrypts a message """ encoded_message = message.encode() return config.chat_key.encrypt(encoded_message) def decrypt_message(encrypted_message): """ Decrypts an encrypted message """ return config.chat_key.decrypt(encrypted_message)

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qgenda/pipeline/pre.py

jjorissen52/python-qgenda

2

1601888

import time from qgenda import helpers def gzip_headers(method_self, caller=None, params=None): if caller not in method_self.gzip_safe: use_gzip = params.pop('gzip', False) params['headers'] = method_self.headers if use_gzip else { key: value for key, value in method_self.headers.items() if value != 'gzip' } return method_self, caller, params def keep_authenticated(method_self, caller=None, params=None): """ """ request_start = time.time() auth_details = method_self.auth_details expiration_time = auth_details.get("expiration_time", 0) if auth_details else 0 if method_self.leader and not auth_details: method_self.authenticate() auth_details = method_self.auth_details else: retries = 10 while retries: if expiration_time - request_start <= 0: time.sleep(1) auth_details = method_self.auth_details if auth_details: break retries -= 1 if "access_token" in auth_details: method_self.headers.update({"Authorization": f'bearer {auth_details["access_token"]}'}) params['headers'].update({"Authorization": f'bearer {auth_details["access_token"]}'}) return method_self, caller, params def prepare_odata(logger): odata_filters = ['$filter', '$select', '$orderby'] def real_decorator(method_self, caller, params): odata_kwargs = params.get('odata_kwargs', {}) extra = [] if odata_kwargs: extra = [key for key in odata_kwargs.keys() if key not in odata_filters] if extra: for e in extra: odata_kwargs.pop(e) logger.warning(f'Extra OData filter(s) removed from kwargs. Invalid filter {extra}') params['odata_kwargs'] = odata_kwargs return method_self, caller, params return real_decorator

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ngallery/core/services/solana.py

manti-by/ngallery

0

127454

from django.conf import settings from solana.rpc.api import Client solana_client = Client(settings.SOLANA_NETWORK_URL)

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model.py

FutureCoderme/week5_heroku

0

125841

<filename>model.py import pandas as pan import pickle from sklearn.linear_model import LinearRegression data = pan.read_excel('BP_simple_data.xlsx') x1 = data.iloc[:,1:] x2 = data.iloc[:,0:1] reg = LinearRegression() reg.fit(x1,x2) prediction = reg.predict(x1) pickle.dump(reg, open('model.pkl','wb'))

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fetch_data.py

bitfag/bt-macd-binance

0

3815

<gh_stars>0 #!/usr/bin/env python from btmacd.binance_fetcher import BinanceFetcher def main(): fetcher = BinanceFetcher("BTCUSDT", filename="binance_ohlc.csv", start_date="01.01.2018") fetcher.fetch() if __name__ == "__main__": main()

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background_task/models_completed.py

AMRivkin/django-background-tasks

0

1617853

<gh_stars>0 # -*- coding: utf-8 -*- import os from compat.models import GenericForeignKey from django.contrib.contenttypes.models import ContentType from django.db import models from django.utils import timezone from django.utils.six import python_2_unicode_compatible from background_task.models import Task class CompletedTaskQuerySet(models.QuerySet): def created_by(self, creator): """ :return: A CompletedTask queryset filtered by creator """ content_type = ContentType.objects.get_for_model(creator) return self.filter( creator_content_type=content_type, creator_object_id=creator.id, ) def failed(self, within=None): """ :param within: A timedelta object :return: A queryset of CompletedTasks that failed within the given timeframe (e.g. less than 1h ago) """ qs = self.filter( failed_at__isnull=False, ) if within: time_limit = timezone.now() - within qs = qs.filter(failed_at__gt=time_limit) return qs def succeeded(self, within=None): """ :param within: A timedelta object :return: A queryset of CompletedTasks that completed successfully within the given timeframe (e.g. less than 1h ago) """ qs = self.filter( failed_at__isnull=True, ) if within: time_limit = timezone.now() - within qs = qs.filter(run_at__gt=time_limit) return qs @python_2_unicode_compatible class CompletedTask(models.Model): # the "name" of the task/function to be run task_name = models.CharField(max_length=190, db_index=True) # the json encoded parameters to pass to the task task_params = models.TextField() # a sha1 hash of the name and params, to lookup already scheduled tasks task_hash = models.CharField(max_length=40, db_index=True) verbose_name = models.CharField(max_length=255, null=True, blank=True) # what priority the task has priority = models.IntegerField(default=0, db_index=True) # when the task should be run run_at = models.DateTimeField(db_index=True) repeat = models.BigIntegerField(choices=Task.REPEAT_CHOICES, default=Task.NEVER) repeat_until = models.DateTimeField(null=True, blank=True) # the "name" of the queue this is to be run on queue = models.CharField(max_length=190, db_index=True, null=True, blank=True) # how many times the task has been tried attempts = models.IntegerField(default=0, db_index=True) # when the task last failed failed_at = models.DateTimeField(db_index=True, null=True, blank=True) # details of the error that occurred last_error = models.TextField(blank=True) # details of who's trying to run the task at the moment locked_by = models.CharField(max_length=64, db_index=True, null=True, blank=True) locked_at = models.DateTimeField(db_index=True, null=True, blank=True) creator_content_type = models.ForeignKey( ContentType, null=True, blank=True, related_name='completed_background_task', on_delete=models.CASCADE ) creator_object_id = models.PositiveIntegerField(null=True, blank=True) creator = GenericForeignKey('creator_content_type', 'creator_object_id') objects = CompletedTaskQuerySet.as_manager() def locked_by_pid_running(self): """ Check if the locked_by process is still running. """ if self.locked_by: try: # won't kill the process. kill is a bad named system call os.kill(int(self.locked_by), 0) return True except: return False else: return None locked_by_pid_running.boolean = True def has_error(self): """ Check if the last_error field is empty. """ return bool(self.last_error) has_error.boolean = True def __str__(self): return u'{} - {}'.format( self.verbose_name or self.task_name, self.run_at, )

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app.py

CosteaPaul/strokeCoach_falsk

0

120146

from flask import Flask, render_template, request app = Flask(__name__) @app.route('/') def upload(): return render_template('upload.html') @app.route('/uploader', methods = ['GET', 'POST']) def upload_file(): if request.method == 'POST': f = request.files['file'] #f.save(secure_filename(f.filename)) return f.read() if __name__ == '__main__': # Threaded option to enable multiple instances for multiple user access support app.run(threaded=True, port=5000)

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utils/state.py

DeWa/PiBooth

1

90550

<filename>utils/state.py class State: state = {} @staticmethod def init(): State.state = { "lowres_frames": None, "highres_frames": None, "photopath": "", "frame": 0, "current_photo": None, "share_code": "", "preview_image_width": 0, "photo_countdown": 0, "reset_time": 0, "upload_url": "", "photo_url": "", "api_key": "" } return State.state @staticmethod def set(value): State.state[value[0]] = value[1] return State.state @staticmethod def set_dict(value): State.state = {**State.state, **value} return State.state @staticmethod def get(key): try: return State.state.get(key) except KeyError: print("No key found") return None @staticmethod def print(): print(State.state)

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andes/models/dc/__init__.py

cuihantao/Andes

16

86876

""" DC models. """ from andes.models.dc.ground import Ground # NOQA from andes.models.dc.node import Node # noqa from andes.models.dc.rlc import R, L, C, RCp, RLCp, RLCs, RCs, RLs # noqa

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