ytzi/the-stack-dedup-python-filtered · Datasets at Hugging Face

5d83ad6cff3db1b6e4ae4b1be6ce413b57641a09

9,537

py

Python

ics/mergeGatingSets.py

victorfica/utils

b61935a860838a0e70afde7c9ecf2c68f51a2c4b

[ "MIT" ]

5

2015-12-16T01:23:07.000Z

2020-04-27T11:41:43.000Z

ics/mergeGatingSets.py

victorfica/utils

b61935a860838a0e70afde7c9ecf2c68f51a2c4b

[ "MIT" ]

1

2021-05-06T23:47:20.000Z

2021-05-06T23:48:33.000Z

ics/mergeGatingSets.py

victorfica/utils

b61935a860838a0e70afde7c9ecf2c68f51a2c4b

[ "MIT" ]

6

2016-04-29T14:04:22.000Z

2021-05-06T23:49:34.000Z

#!/usr/bin/env python """ Usage examples: python /home/agartlan/gitrepo/utils/ics/mergeGatingSets.py --function functions --ncpus 4 --out functions_extract.csv sbatch -n 1 -t 3-0 -c 4 -o functions_slurm.txt --wrap="python /home/agartlan/gitrepo/utils/ics/mergeGatingSets.py --function functions --ncpus 4 --out functions_extract.csv" sbatch -n 1 -t 3-0 -c 4 -o functions_markers_slurm.txt --wrap="python /home/agartlan/gitrepo/utils/ics/mergeGatingSets.py --function functions_markers --ncpus 4 --out functions_markers_extract.csv" sbatch -n 1 -t 3-0 -c 4 -o functions_markers_sparse_slurm_gby.txt --wrap="python /home/agartlan/gitrepo/utils/ics/mergeGatingSets.py --function sparse_functions --ncpus 4 --subsets /home/agartlan/gitrepo/utils/ics/allcombs_subsets.csv --out functions_markers_sparse_24Jul2018_gby.csv" sbatch -n 1 -t 3-0 -c 4 -o cell_functions_slurm.txt --wrap="python /home/agartlan/gitrepo/utils/ics/mergeGatingSets.py --function cell_functions --ncpus 4 --out cell_functions_22Aug2018.feather --feather --subsets /home/agartlan/gitrepo/utils/ics/subsets_CD4_gd_Tcells.csv" python /home/agartlan/gitrepo/utils/ics/mergeGatingSets.py --function cell_functions --ncpus 3 --out cell_functions_extract.csv --testbatch --testsamples --feather --subsets /home/agartlan/gitrepo/utils/ics/subsets_CD4_gd_Tcells.csv python /home/agartlan/gitrepo/utils/ics/mergeGatingSets.py --function sparse_functions --ncpus 3 --out sparse_functions_extract_23Aug2018.csv --testbatch --testsamples --feather --subsets /home/agartlan/gitrepo/utils/ics/subsets_CD4_gd_Tcells.csv python /home/agartlan/gitrepo/utils/ics/mergeGatingSets.py --function bool_functions --ncpus 6 --out bool_functions_extract_05May2020.csv --testbatch --testsamples --feather --subsets /home/agartlan/gitrepo/utils/ics/subsets_CD4_gd_Tcells.csv To delete all tmp files use: find . -name \merged_tmp*.feather -type f -delete """ if __name__ == '__main__': import argparse parser = argparse.ArgumentParser(description='Extract features and merge batches into one CSV.') parser.add_argument('--folder', type=str, help='Data folder containing all batch folders.', default='/fh/fast/gilbert_p/grp/hvtn602_compass/tmpdata') parser.add_argument('--function', type=str, help='Name of extraction to apply ("functions")', default='functions') parser.add_argument('--subsets', type=str, help='Filename listing subsets for analysis.', default='/home/agartlan/gitrepo/utils/ics/sample_subsets2.csv') parser.add_argument('--out', type=str, help='Output filename for CSV.', default='merged_out.csv') parser.add_argument('--ncpus', type=int, help='Number of CPUs/cores to use for parallelization.', default=1) parser.add_argument('--testsamples', action='store_true', help='Only process two samples from each batch.') parser.add_argument('--testbatch', action='store_true', help='Only process twp samples from one batch.') parser.add_argument('--matchingonly', action='store_true', help='Only perform sample matching, to validate metadata.') parser.add_argument('--feather', action='store_true', help='Store as feather as oposed to CSV') parser.add_argument('--utils', default='/home/agartlan/gitrepo/utils', help='Location of agartland/utils repo from public github.com') args = parser.parse_args() try: import parmap from multiprocessing import Pool _PARMAP = True except: _PARMAP = False print('Could not find package "parmap", parallelization not enabled.') import itertools import pandas as pd import numpy as np from os.path import join as opj import os from functools import partial import time import sys import feather """Make sure the utils are on path before importing""" sys.path.append(args.utils) # from ics import extractFunctionsGBY, extractFunctionsMarkersGBY, parseSubsets, mergeSamples, matchSamples from ics import * if args.matchingonly: metaDf = testMatching(args.folder) metaDf.to_csv(opj(args.folder, 'metamatch_' + args.out)) print('Wrote matching metadata to %s.' % opj(args.folder, 'metamatch_' + args.out)) else: subsets, markers, functions, exclude = parseSubsets(args.subsets) features = {'sparse_functions':(extractFunctionsGBY, dict(subsets=subsets, functions=functions, mincells=5)), 'bool_functions':(extractFunctionsGBY, dict(subsets=subsets, functions=functions, mincells=0)), 'functions_markers':(extractFunctionsMarkersGBY, dict(subsets=subsets, functions=functions, markers=markers, compressions=[('ALL', 2), (['IFNg','IL2', 'TNFa'], 2)])), 'functions':(extractFunctionsGBY, dict(subsets=subsets, functions=functions, compressions=[('ALL', 1), ('ALL', 2), (['IFNg','IL2', 'TNFa'], 1), (['IFNg','IL2', 'TNFa'], 2), (['IFNg','IL2'], 1)])), 'cell_functions':(extractRawFunctions, dict(subsets=subsets, functions=functions, downsample=1))} extractionFunc, extractionKwargs = features[args.function] if args.testbatch: print('Test: processing samples from one batch') if args.testsamples: print('Test: processing two samples per batch') outFile = opj(args.folder, args.out) if args.feather: outFile = outFile.replace('.csv', '.feather') wrote = mergeBatches(args.folder, extractionFunc=extractionFunc, extractionKwargs=extractionKwargs, testsamples=args.testsamples, testbatch=args.testbatch, outFile=outFile, metaCols=['PTID', 'VISITNO', 'Global.Spec.Id', 'TESTDT', 'STIM'], filters={'STIM':['negctrl', 'TB WCL', 'BCG-Pasteur', 'Ag85B', 'TB 10.4'], 'VISITNO':[2, 6, 7, 10, 11, 12]}, useFeather=int(args.feather), ncpus=args.ncpus) if wrote == outFile: print('Wrote extracted data to %s.' % outFile) else: print('Error writing file to disk: %s' % wrote)

52.401099

284

0.554996

5d84d543ecd3e19e8c3f8af82dd1e6cc5ea16443

88

py

Python

meeting.py

zoni/ulauncher-meet

1b76627c69dfc539645acd27e30c9b8fd8fe08ae

[ "MIT" ]

4

2021-04-30T21:39:03.000Z

2021-08-30T11:38:21.000Z

meeting.py

zoni/ulauncher-meet

1b76627c69dfc539645acd27e30c9b8fd8fe08ae

[ "MIT" ]

1

2021-05-31T11:05:37.000Z

meeting.py

zoni/ulauncher-meet

1b76627c69dfc539645acd27e30c9b8fd8fe08ae

[ "MIT" ]

2

2021-05-07T09:47:20.000Z

2021-08-25T16:27:24.000Z

from dataclasses import dataclass

11

33

0.715909

5d85c596f37801463f956fbf7ef5af170636decb

1,000

py

Python

setup.py

uuosio/uuosio.gscdk

a2e364d4499c1372567aa5933e2d8e02340a8385

[ "BSD-3-Clause" ]

6

2021-09-03T09:02:39.000Z

2022-01-12T06:31:09.000Z

setup.py

uuosio/uuosio.gscdk

a2e364d4499c1372567aa5933e2d8e02340a8385

[ "BSD-3-Clause" ]

1

2021-11-01T16:46:09.000Z

2021-11-04T12:51:45.000Z

setup.py

uuosio/uuosio.gscdk

a2e364d4499c1372567aa5933e2d8e02340a8385

[ "BSD-3-Clause" ]

2

2021-11-10T01:56:15.000Z

2022-01-13T14:27:31.000Z

import os import shutil import setuptools # from skbuild import setup from distutils.core import setup from distutils.sysconfig import get_python_lib import glob # if os.path.exists('pysrc/tinygo'): # shutil.rmtree('pysrc/tinygo') # shutil.copytree('tinygo/build/release/tinygo', 'pysrc/tinygo') release_files = [] for root, dirs, files in os.walk("pysrc/tinygo"): for f in files: release_files.append(os.path.join(root.replace('pysrc/', ''), f)) # print(release_files) setup( name="gscdk", version="0.3.5", description="Go Smart Contract Development Kit", author='The UUOSIO Team', license="BSD-3-Clause", url="https://github.com/uuosio/uuosio.gscdk", packages=['gscdk'], package_dir={'gscdk': 'pysrc'}, package_data={ # "": ["*"], 'gscdk': release_files, }, setup_requires=['wheel'] # scripts=['compiler/build/release/tinygo/bin/eosio-go'], # install_requires=[ # ], # include_package_data=True )

24.390244

77

0.658

5d861ae24ab41a343997586ea4f68f7cd661d4d3

301

py

Python

tests/data_creator_action.py

michalurbanski/bkgames

69b1d16ae27d3118dd78449ce7deecbd6e1b95e7

[ "MIT" ]

null

tests/data_creator_action.py

michalurbanski/bkgames

69b1d16ae27d3118dd78449ce7deecbd6e1b95e7

[ "MIT" ]

null

tests/data_creator_action.py

michalurbanski/bkgames

69b1d16ae27d3118dd78449ce7deecbd6e1b95e7

[ "MIT" ]

null

from typing import Callable

33.444444

104

0.750831

5d8772d2443bc37d077b4e1088b8652b560de433

387

py

Python

Python/Numpy/Min and Max/min_and_max.py

brianchiang-tw/HackerRank

02a30a0033b881206fa15b8d6b4ef99b2dc420c8

[ "MIT" ]

2

2020-05-28T07:15:00.000Z

2020-07-21T08:34:06.000Z

Python/Numpy/Min and Max/min_and_max.py

brianchiang-tw/HackerRank

02a30a0033b881206fa15b8d6b4ef99b2dc420c8

[ "MIT" ]

null

Python/Numpy/Min and Max/min_and_max.py

brianchiang-tw/HackerRank

02a30a0033b881206fa15b8d6b4ef99b2dc420c8

[ "MIT" ]

null

import numpy as np if __name__ == '__main__': h, w = map( int, input().split() ) row_list = [] for i in range(h): single_row = list( map(int, input().split() ) ) np_row = np.array( single_row ) row_list.append( np_row ) min_of_each_row = np.min( row_list, axis = 1) max_of_min = np.max( min_of_each_row ) print( max_of_min )

15.48

56

0.573643

5d87b775f0d8dfc2c8f2bb9538693bb8aa0d1ec6

22,757

py

Python

allure/pytest_plugin.py

allure-framework/allure-pytest

d55180aaeb21233e7ca577ffc6f67a07837c63f2

[ "Apache-2.0" ]

112

2017-01-24T21:37:49.000Z

2022-03-25T22:32:12.000Z

venv/Lib/site-packages/allure/pytest_plugin.py

Arthii01052/conduit

3427d76d0fa364cb5d19bdd6da4aeb0a22fe9660

[ "MIT" ]

56

2017-01-21T20:01:41.000Z

2019-01-14T13:35:53.000Z

venv/Lib/site-packages/allure/pytest_plugin.py

Arthii01052/conduit

3427d76d0fa364cb5d19bdd6da4aeb0a22fe9660

[ "MIT" ]

52

2017-01-23T13:40:40.000Z

2022-03-30T00:02:31.000Z

import uuid import pickle import pytest import argparse from collections import namedtuple from six import text_type from allure.common import AllureImpl, StepContext from allure.constants import Status, AttachmentType, Severity, \ FAILED_STATUSES, Label, SKIPPED_STATUSES from allure.utils import parent_module, parent_down_from_module, labels_of, \ all_of, get_exception_message, now, mangle_testnames from allure.structure import TestCase, TestStep, Attach, TestSuite, Failure, TestLabel MASTER_HELPER = AllureHelper() CollectFail = namedtuple('CollectFail', 'name status message trace')

39.168675

183

0.572483

5d88a4a57aa7fe412e25b74cc37254832f74121b

1,113

py

Python

treenode/debug.py

domlysi/django-treenode

86e7c76e2b2d60c071cfce6ad1493b2b51f2d304

[ "MIT" ]

null

treenode/debug.py

domlysi/django-treenode

86e7c76e2b2d60c071cfce6ad1493b2b51f2d304

[ "MIT" ]

null

treenode/debug.py

domlysi/django-treenode

86e7c76e2b2d60c071cfce6ad1493b2b51f2d304

[ "MIT" ]

null

# -*- coding: utf-8 -*- from django.conf import settings from django.db import connection import logging import timeit logger = logging.getLogger(__name__)

26.5

74

0.637017

5d8a580c6383e0fa95c751c549eb6cc5184e491f

3,705

py

Python

String_tool.py

vibhorvk/BlendString

3bf62083716b3b1f4976abeb3528771eeb79e2cf

[ "MIT" ]

null

String_tool.py

vibhorvk/BlendString

3bf62083716b3b1f4976abeb3528771eeb79e2cf

[ "MIT" ]

null

String_tool.py

vibhorvk/BlendString

3bf62083716b3b1f4976abeb3528771eeb79e2cf

[ "MIT" ]

null

bl_info = { "name": "STRING", "blender": (2, 80, 0), "category": "Object", 'Author' : 'Vibhor Gupta' } import bpy import bmesh def register(): bpy.utils.register_class(STRING) def unregister(): bpy.utils.unregister_class(STRING) # This allows you to run the script directly from Blender's Text editor # to test the add-on without having to install it. if __name__ == "__main__": register()

29.879032

107

0.515789

5d8b272fb8d2699d2cf3ea1fd7de71c67f398d16

1,130

py

Python

Code/DataHandlers/__init__.py

aricsanders/pyMez3

13e2b9900af2287db0cc42a0190d31da165ce174

[ "Unlicense" ]

2

2017-01-29T00:46:01.000Z

2017-07-25T17:23:04.000Z

Code/DataHandlers/__init__.py

aricsanders/pyMez3

13e2b9900af2287db0cc42a0190d31da165ce174

[ "Unlicense" ]

1

2019-08-02T03:59:41.000Z

Code/DataHandlers/__init__.py

aricsanders/pyMez3

13e2b9900af2287db0cc42a0190d31da165ce174

[ "Unlicense" ]

null

""" The DataHandlers subpackage is designed to manipulate data, by allowing different data types to be opened, created, saved and updated. The subpackage is further divided into modules grouped by a common theme. Classes for data that are already on disk normally follows the following pattern: `instance=ClassName(file_path,**options)` For Example to open a XML file that you don't know the model, use `xml=pyMez.Code.DataHandlers.XMLModels.XMLBase('MyXML.xml')' or `xml=XMLBase('MyXML.xml')` All data models normally have save(), str() and if appropriate show() methods. Examples -------- <a href="../../../Examples/How_To_Open_S2p.html"> How to open a s2p file </a> Import Structure ---------------- DataHandlers typically import from Utils but __NOT__ from Analysis, InstrumentControl or FrontEnds Help ----- <a href="../index.html">`pyMez.Code`</a> <div> <a href="../../../pyMez_Documentation.html">Documentation Home</a> | <a href="../../index.html">API Documentation Home</a> | <a href="../../../Examples/html/Examples_Home.html">Examples</a> | <a href="../../../Reference_Index.html">Index </a> </div> """

25.681818

118

0.70531

5d8ca67bf97053442519e2fbb8cf359ecbb33654

228

py

Python

djangomail/backends/dummy.py

somenzz/djangomail

7d4f833cd71289a51eb935757d8b628e9c9f8aa1

[ "MIT" ]

1

2021-06-01T07:51:18.000Z

djangomail/backends/dummy.py

somenzz/djangomail

7d4f833cd71289a51eb935757d8b628e9c9f8aa1

[ "MIT" ]

null

djangomail/backends/dummy.py

somenzz/djangomail

7d4f833cd71289a51eb935757d8b628e9c9f8aa1

[ "MIT" ]

1

2022-01-24T13:38:14.000Z

""" Dummy email backend that does nothing. """ from djangomail.backends.base import BaseEmailBackend

20.727273

53

0.763158

5d8ccdbb74a4a8ff8a56e579b885b0bbd0743a4f

7,666

py

Python

awx/plugins/library/scan_services.py

Avinesh/awx

6310a2edd890d6062a9f6bcdeb2b46c4b876c2bf

[ "Apache-2.0" ]

17

2021-04-03T01:40:17.000Z

2022-03-03T11:45:20.000Z

awx/plugins/library/scan_services.py

Avinesh/awx

6310a2edd890d6062a9f6bcdeb2b46c4b876c2bf

[ "Apache-2.0" ]

24

2021-05-18T21:13:35.000Z

2022-03-29T10:23:52.000Z

awx/plugins/library/scan_services.py

Avinesh/awx

6310a2edd890d6062a9f6bcdeb2b46c4b876c2bf

[ "Apache-2.0" ]

14

2021-04-06T20:05:41.000Z

2022-03-24T14:16:03.000Z

#!/usr/bin/env python import re from ansible.module_utils.basic import * # noqa DOCUMENTATION = ''' --- module: scan_services short_description: Return service state information as fact data description: - Return service state information as fact data for various service management utilities version_added: "1.9" options: requirements: [ ] author: Matthew Jones ''' EXAMPLES = ''' - monit: scan_services # Example fact output: # host | success >> { # "ansible_facts": { # "services": { # "network": { # "source": "sysv", # "state": "running", # "name": "network" # }, # "arp-ethers.service": { # "source": "systemd", # "state": "stopped", # "name": "arp-ethers.service" # } # } # } ''' def main(): module = AnsibleModule(argument_spec = dict()) # noqa service_modules = (ServiceScanService, SystemctlScanService) all_services = {} incomplete_warning = False for svc_module in service_modules: svcmod = svc_module(module) svc = svcmod.gather_services() if svc is not None: all_services.update(svc) if svcmod.incomplete_warning: incomplete_warning = True if len(all_services) == 0: results = dict(skipped=True, msg="Failed to find any services. Sometimes this is due to insufficient privileges.") else: results = dict(ansible_facts=dict(services=all_services)) if incomplete_warning: results['msg'] = "WARNING: Could not find status for all services. Sometimes this is due to insufficient privileges." module.exit_json(**results) main()

40.136126

155

0.530655

5d8cdce66649554dda1ee6deb1afd812b2f3ebbf

2,146

py

Python

app.py

duckm8795/runscope-circleci

2fd42e64bddb4b8f34c437c2d834b92369c9a2bf

[ "Apache-2.0" ]

null

app.py

duckm8795/runscope-circleci

2fd42e64bddb4b8f34c437c2d834b92369c9a2bf

[ "Apache-2.0" ]

null

app.py

duckm8795/runscope-circleci

2fd42e64bddb4b8f34c437c2d834b92369c9a2bf

[ "Apache-2.0" ]

null

import requests import sys import time import os if __name__ == '__main__': main()

31.101449

154

0.605312

5d8dbbff6df38e6773044260538db7a759525964

16,585

py

Python

spyse/client.py

fabaff/spyse-python

f286514ac052ebe6fa98f877d251d8f3cd4db1c4

[ "MIT" ]

9

2021-07-28T11:59:07.000Z

2022-02-17T02:25:06.000Z

spyse/client.py

fabaff/spyse-python

f286514ac052ebe6fa98f877d251d8f3cd4db1c4

[ "MIT" ]

2

2021-11-27T02:03:03.000Z

2022-02-02T11:33:34.000Z

spyse/client.py

fabaff/spyse-python

f286514ac052ebe6fa98f877d251d8f3cd4db1c4

[ "MIT" ]

7

2021-08-05T04:02:09.000Z

2022-03-04T14:11:04.000Z

import requests from typing import List, Optional from .models import AS, Domain, IP, CVE, Account, Certificate, Email, DNSHistoricalRecord, WHOISHistoricalRecord from .response import Response from .search_query import SearchQuery from limiter import get_limiter, limit

42.308673

117

0.662466

5d8e72c2a2b92c4afc6d55b1c762592baf4c02a2

147

py

Python

talleres_inov_docente/figures/plot_helpers.py

jfcaballero/Tutorial-sobre-scikit-learn-abreviado

1e2aa1f9132c277162135a5463068801edab8d15

[ "CC0-1.0" ]

576

2016-03-20T10:05:58.000Z

2022-03-20T05:58:32.000Z

talleres_inov_docente/figures/plot_helpers.py

jfcaballero/Tutorial-sobre-scikit-learn-abreviado

1e2aa1f9132c277162135a5463068801edab8d15

[ "CC0-1.0" ]

64

2016-03-20T08:56:49.000Z

2019-03-13T15:37:55.000Z

talleres_inov_docente/figures/plot_helpers.py

jfcaballero/Tutorial-sobre-scikit-learn-abreviado

1e2aa1f9132c277162135a5463068801edab8d15

[ "CC0-1.0" ]

570

2016-03-20T19:23:07.000Z

2021-12-12T12:22:14.000Z

from matplotlib.colors import ListedColormap cm3 = ListedColormap(['#0000aa', '#ff2020', '#50ff50']) cm2 = ListedColormap(['#0000aa', '#ff2020'])

29.4

55

0.707483

5d8e73966f8c10521ed6c383e9307e6cc3d33a3d

612

py

Python

Applications/FlaskApp/errorpages.py

cemac-ccs/FlaskMWE

e8ce3cbca0d402bd9fdb1feb10290f2e7b11907b

[ "MIT" ]

null

Applications/FlaskApp/errorpages.py

cemac-ccs/FlaskMWE

e8ce3cbca0d402bd9fdb1feb10290f2e7b11907b

[ "MIT" ]

null

Applications/FlaskApp/errorpages.py

cemac-ccs/FlaskMWE

e8ce3cbca0d402bd9fdb1feb10290f2e7b11907b

[ "MIT" ]

null

from flask import render_template # Error Pages ----------------------------------------------------------------

27.818182

78

0.637255

5d8e9f525045331a16efdc9df5a7b8042480b89c

1,118

py

Python

cppgym/ToyText/BlackJack.py

anhydrous99/cppgym

0b1009a74faebfe5a31bcfd6a86c74cf13464d56

[ "MIT" ]

null

cppgym/ToyText/BlackJack.py

anhydrous99/cppgym

0b1009a74faebfe5a31bcfd6a86c74cf13464d56

[ "MIT" ]

1

2021-01-03T10:21:36.000Z

2021-01-26T03:59:07.000Z

cppgym/ToyText/BlackJack.py

anhydrous99/cppgym

0b1009a74faebfe5a31bcfd6a86c74cf13464d56

[ "MIT" ]

null

from .._BlackJack import BlackJackCPP import gym import ctypes import numpy as np from gym import spaces

26.619048

53

0.593918

5d8f21c23f88e0ce2aa150c385f666597b203749

5,827

py

Python

sdks/python/apache_beam/runners/direct/consumer_tracking_pipeline_visitor_test.py

ajothomas/beam

4774c1caf3dac3b6a7dd161f82559a26fa380920

[ "Apache-2.0", "BSD-3-Clause" ]

5

2019-07-27T11:54:33.000Z

2021-06-06T11:53:36.000Z

sdks/python/apache_beam/runners/direct/consumer_tracking_pipeline_visitor_test.py

ajothomas/beam

4774c1caf3dac3b6a7dd161f82559a26fa380920

[ "Apache-2.0", "BSD-3-Clause" ]

12

2019-04-15T15:27:23.000Z

2019-07-01T18:13:10.000Z

sdks/python/apache_beam/runners/direct/consumer_tracking_pipeline_visitor_test.py

ajothomas/beam

4774c1caf3dac3b6a7dd161f82559a26fa380920

[ "Apache-2.0", "BSD-3-Clause" ]

1

2021-06-03T19:54:48.000Z

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # """Tests for consumer_tracking_pipeline_visitor.""" # pytype: skip-file import logging import unittest import apache_beam as beam from apache_beam import pvalue from apache_beam.pipeline import Pipeline from apache_beam.pvalue import AsList from apache_beam.runners.direct import DirectRunner from apache_beam.runners.direct.consumer_tracking_pipeline_visitor import ConsumerTrackingPipelineVisitor from apache_beam.transforms import CoGroupByKey from apache_beam.transforms import Create from apache_beam.transforms import DoFn from apache_beam.transforms import FlatMap from apache_beam.transforms import Flatten from apache_beam.transforms import ParDo # Disable frequent lint warning due to pipe operator for chaining transforms. # pylint: disable=expression-not-assigned # pylint: disable=pointless-statement if __name__ == '__main__': logging.getLogger().setLevel(logging.DEBUG) unittest.main()

35.530488

105

0.727476

5d8fd5fa2bcd3f5669762aabbd18717b761f3d16

30,184

py

Python

gluon/main.py

scudette/rekall-agent-server

e553f1ae5279f75a8f5b0c0c4847766b60ed86eb

[ "BSD-3-Clause" ]

21

2018-02-16T17:43:59.000Z

2021-12-29T12:08:28.000Z

gluon/main.py

scudette/rekall-agent-server

e553f1ae5279f75a8f5b0c0c4847766b60ed86eb

[ "BSD-3-Clause" ]

12

2017-11-01T14:54:29.000Z

2018-02-01T22:02:12.000Z

gluon/main.py

scudette/rekall-agent-server

e553f1ae5279f75a8f5b0c0c4847766b60ed86eb

[ "BSD-3-Clause" ]

8

2018-10-08T03:48:00.000Z

2022-03-31T12:13:01.000Z

#!/bin/env python # -*- coding: utf-8 -*- """ | This file is part of the web2py Web Framework | Copyrighted by Massimo Di Pierro <[email protected]> | License: LGPLv3 (http://www.gnu.org/licenses/lgpl.html) The gluon wsgi application --------------------------- """ from __future__ import print_function if False: import import_all # DO NOT REMOVE PART OF FREEZE PROCESS import gc import os import re import copy import sys import time import datetime import signal import socket import random import string from gluon._compat import Cookie, urllib2 #from thread import allocate_lock from gluon.fileutils import abspath, write_file from gluon.settings import global_settings from gluon.utils import web2py_uuid from gluon.admin import add_path_first, create_missing_folders, create_missing_app_folders from gluon.globals import current # Remarks: # calling script has inserted path to script directory into sys.path # applications_parent (path to applications/, site-packages/ etc) # defaults to that directory set sys.path to # ("", gluon_parent/site-packages, gluon_parent, ...) # # this is wrong: # web2py_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # because we do not want the path to this file which may be Library.zip # gluon_parent is the directory containing gluon, web2py.py, logging.conf # and the handlers. # applications_parent (web2py_path) is the directory containing applications/ # and routes.py # The two are identical unless web2py_path is changed via the web2py.py -f folder option # main.web2py_path is the same as applications_parent (for backward compatibility) web2py_path = global_settings.applications_parent # backward compatibility create_missing_folders() # set up logging for subsequent imports import logging import logging.config # This needed to prevent exception on Python 2.5: # NameError: name 'gluon' is not defined # See http://bugs.python.org/issue1436 # attention!, the import Tkinter in messageboxhandler, changes locale ... import gluon.messageboxhandler logging.gluon = gluon # so we must restore it! Thanks ozancag import locale locale.setlocale(locale.LC_CTYPE, "C") # IMPORTANT, web2py requires locale "C" exists = os.path.exists pjoin = os.path.join try: logging.config.fileConfig(abspath("logging.conf")) except: # fails on GAE or when logfile is missing logging.basicConfig() logger = logging.getLogger("web2py") from gluon.restricted import RestrictedError from gluon.http import HTTP, redirect from gluon.globals import Request, Response, Session from gluon.compileapp import build_environment, run_models_in, \ run_controller_in, run_view_in from gluon.contenttype import contenttype from pydal.base import BaseAdapter from gluon.validators import CRYPT from gluon.html import URL, xmlescape from gluon.utils import is_valid_ip_address, getipaddrinfo from gluon.rewrite import load as load_routes, url_in, THREAD_LOCAL as rwthread, \ try_rewrite_on_error, fixup_missing_path_info from gluon import newcron __all__ = ['wsgibase', 'save_password', 'appfactory', 'HttpServer'] requests = 0 # gc timer # Security Checks: validate URL and session_id here, # accept_language is validated in languages # pattern used to validate client address regex_client = re.compile('[\w\-:]+(\.[\w\-]+)*\.?') # ## to account for IPV6 try: version_info = open(pjoin(global_settings.gluon_parent, 'VERSION'), 'r') raw_version_string = version_info.read().split()[-1].strip() version_info.close() global_settings.web2py_version = raw_version_string web2py_version = global_settings.web2py_version except: raise RuntimeError("Cannot determine web2py version") try: from gluon import rocket except: if not global_settings.web2py_runtime_gae: logger.warn('unable to import Rocket') load_routes() HTTPS_SCHEMES = set(('https', 'HTTPS')) def get_client(env): """ Guesses the client address from the environment variables First tries 'http_x_forwarded_for', secondly 'remote_addr' if all fails, assume '127.0.0.1' or '::1' (running locally) """ eget = env.get g = regex_client.search(eget('http_x_forwarded_for', '')) client = (g.group() or '').split(',')[0] if g else None if client in (None, '', 'unknown'): g = regex_client.search(eget('remote_addr', '')) if g: client = g.group() elif env.http_host.startswith('['): # IPv6 client = '::1' else: client = '127.0.0.1' # IPv4 if not is_valid_ip_address(client): raise HTTP(400, "Bad Request (request.client=%s)" % client) return client def serve_controller(request, response, session): """ This function is used to generate a dynamic page. It first runs all models, then runs the function in the controller, and then tries to render the output using a view/template. this function must run from the [application] folder. A typical example would be the call to the url /[application]/[controller]/[function] that would result in a call to [function]() in applications/[application]/[controller].py rendered by applications/[application]/views/[controller]/[function].html """ # ################################################## # build environment for controller and view # ################################################## environment = build_environment(request, response, session) # set default view, controller can override it response.view = '%s/%s.%s' % (request.controller, request.function, request.extension) # also, make sure the flash is passed through # ################################################## # process models, controller and view (if required) # ################################################## run_models_in(environment) response._view_environment = copy.copy(environment) page = run_controller_in(request.controller, request.function, environment) if isinstance(page, dict): response._vars = page response._view_environment.update(page) page = run_view_in(response._view_environment) # logic to garbage collect after exec, not always, once every 100 requests global requests requests = ('requests' in globals()) and (requests + 1) % 100 or 0 if not requests: gc.collect() # end garbage collection logic # ################################################## # set default headers it not set # ################################################## default_headers = [ ('Content-Type', contenttype('.' + request.extension)), ('Cache-Control', 'no-store, no-cache, must-revalidate, post-check=0, pre-check=0'), ('Expires', time.strftime('%a, %d %b %Y %H:%M:%S GMT', time.gmtime())), ('Pragma', 'no-cache')] for key, value in default_headers: response.headers.setdefault(key, value) raise HTTP(response.status, page, **response.headers) def wsgibase(environ, responder): """ The gluon wsgi application. The first function called when a page is requested (static or dynamic). It can be called by paste.httpserver or by apache mod_wsgi (or any WSGI-compatible server). - fills request with info - the environment variables, replacing '.' with '_' - adds web2py path and version info - compensates for fcgi missing path_info and query_string - validates the path in url The url path must be either: 1. for static pages: - /<application>/static/<file> 2. for dynamic pages: - /<application>[/<controller>[/<function>[/<sub>]]][.<extension>] The naming conventions are: - application, controller, function and extension may only contain `[a-zA-Z0-9_]` - file and sub may also contain '-', '=', '.' and '/' """ eget = environ.get current.__dict__.clear() request = Request(environ) response = Response() session = Session() env = request.env #env.web2py_path = global_settings.applications_parent env.web2py_version = web2py_version #env.update(global_settings) static_file = False http_response = None try: try: try: # ################################################## # handle fcgi missing path_info and query_string # select rewrite parameters # rewrite incoming URL # parse rewritten header variables # parse rewritten URL # serve file if static # ################################################## fixup_missing_path_info(environ) (static_file, version, environ) = url_in(request, environ) response.status = env.web2py_status_code or response.status if static_file: if eget('QUERY_STRING', '').startswith('attachment'): response.headers['Content-Disposition'] \ = 'attachment' if version: response.headers['Cache-Control'] = 'max-age=315360000' response.headers[ 'Expires'] = 'Thu, 31 Dec 2037 23:59:59 GMT' response.stream(static_file, request=request) # ################################################## # fill in request items # ################################################## app = request.application # must go after url_in! if not global_settings.local_hosts: local_hosts = set(['127.0.0.1', '::ffff:127.0.0.1', '::1']) if not global_settings.web2py_runtime_gae: try: fqdn = socket.getfqdn() local_hosts.add(socket.gethostname()) local_hosts.add(fqdn) local_hosts.update([ addrinfo[4][0] for addrinfo in getipaddrinfo(fqdn)]) if env.server_name: local_hosts.add(env.server_name) local_hosts.update([ addrinfo[4][0] for addrinfo in getipaddrinfo(env.server_name)]) except (socket.gaierror, TypeError): pass global_settings.local_hosts = list(local_hosts) else: local_hosts = global_settings.local_hosts client = get_client(env) x_req_with = str(env.http_x_requested_with).lower() cmd_opts = global_settings.cmd_options request.update( client = client, folder = abspath('applications', app) + os.sep, ajax = x_req_with == 'xmlhttprequest', cid = env.http_web2py_component_element, is_local = (env.remote_addr in local_hosts and client == env.remote_addr), is_shell = False, is_scheduler = False, is_https = env.wsgi_url_scheme in HTTPS_SCHEMES or \ request.env.http_x_forwarded_proto in HTTPS_SCHEMES \ or env.https == 'on' ) request.url = environ['PATH_INFO'] # ################################################## # access the requested application # ################################################## disabled = pjoin(request.folder, 'DISABLED') if not exists(request.folder): if app == rwthread.routes.default_application \ and app != 'welcome': redirect(URL('welcome', 'default', 'index')) elif rwthread.routes.error_handler: _handler = rwthread.routes.error_handler redirect(URL(_handler['application'], _handler['controller'], _handler['function'], args=app)) else: raise HTTP(404, rwthread.routes.error_message % 'invalid request', web2py_error='invalid application') elif not request.is_local and exists(disabled): five0three = os.path.join(request.folder,'static','503.html') if os.path.exists(five0three): raise HTTP(503, file(five0three, 'r').read()) else: raise HTTP(503, "<html><body><h1>Temporarily down for maintenance</h1></body></html>") # ################################################## # build missing folders # ################################################## create_missing_app_folders(request) # ################################################## # get the GET and POST data # ################################################## #parse_get_post_vars(request, environ) # ################################################## # expose wsgi hooks for convenience # ################################################## request.wsgi = LazyWSGI(environ, request, response) # ################################################## # load cookies # ################################################## if env.http_cookie: for single_cookie in env.http_cookie.split(';'): single_cookie = single_cookie.strip() if single_cookie: try: request.cookies.load(single_cookie) except Cookie.CookieError: pass # single invalid cookie ignore # ################################################## # try load session or create new session file # ################################################## if not env.web2py_disable_session: session.connect(request, response) # ################################################## # run controller # ################################################## if global_settings.debugging and app != "admin": import gluon.debug # activate the debugger gluon.debug.dbg.do_debug(mainpyfile=request.folder) serve_controller(request, response, session) except HTTP as hr: http_response = hr if static_file: return http_response.to(responder, env=env) if request.body: request.body.close() if hasattr(current, 'request'): # ################################################## # on success, try store session in database # ################################################## if not env.web2py_disable_session: session._try_store_in_db(request, response) # ################################################## # on success, commit database # ################################################## if response.do_not_commit is True: BaseAdapter.close_all_instances(None) elif response.custom_commit: BaseAdapter.close_all_instances(response.custom_commit) else: BaseAdapter.close_all_instances('commit') # ################################################## # if session not in db try store session on filesystem # this must be done after trying to commit database! # ################################################## if not env.web2py_disable_session: session._try_store_in_cookie_or_file(request, response) # Set header so client can distinguish component requests. if request.cid: http_response.headers.setdefault( 'web2py-component-content', 'replace') if request.ajax: if response.flash: http_response.headers['web2py-component-flash'] = \ urllib2.quote(xmlescape(response.flash).replace(b'\n', b'')) if response.js: http_response.headers['web2py-component-command'] = \ urllib2.quote(response.js.replace('\n', '')) # ################################################## # store cookies in headers # ################################################## session._fixup_before_save() http_response.cookies2headers(response.cookies) ticket = None except RestrictedError as e: if request.body: request.body.close() # ################################################## # on application error, rollback database # ################################################## # log tickets before rollback if not in DB if not request.tickets_db: ticket = e.log(request) or 'unknown' # rollback if response._custom_rollback: response._custom_rollback() else: BaseAdapter.close_all_instances('rollback') # if tickets in db, reconnect and store it in db if request.tickets_db: ticket = e.log(request) or 'unknown' http_response = \ HTTP(500, rwthread.routes.error_message_ticket % dict(ticket=ticket), web2py_error='ticket %s' % ticket) except: if request.body: request.body.close() # ################################################## # on application error, rollback database # ################################################## try: if response._custom_rollback: response._custom_rollback() else: BaseAdapter.close_all_instances('rollback') except: pass e = RestrictedError('Framework', '', '', locals()) ticket = e.log(request) or 'unrecoverable' http_response = \ HTTP(500, rwthread.routes.error_message_ticket % dict(ticket=ticket), web2py_error='ticket %s' % ticket) finally: if response and hasattr(response, 'session_file') \ and response.session_file: response.session_file.close() session._unlock(response) http_response, new_environ = try_rewrite_on_error( http_response, request, environ, ticket) if not http_response: return wsgibase(new_environ, responder) if global_settings.web2py_crontype == 'soft': newcron.softcron(global_settings.applications_parent).start() return http_response.to(responder, env=env) def save_password(password, port): """ Used by main() to save the password in the parameters_port.py file. """ password_file = abspath('parameters_%i.py' % port) if password == '<random>': # make up a new password chars = string.letters + string.digits password = ''.join([random.choice(chars) for _ in range(8)]) cpassword = CRYPT()(password)[0] print('******************* IMPORTANT!!! ************************') print('your admin password is "%s"' % password) print('*********************************************************') elif password == '<recycle>': # reuse the current password if any if exists(password_file): return else: password = '' elif password.startswith('<pam_user:'): # use the pam password for specified user cpassword = password[1:-1] else: # use provided password cpassword = CRYPT()(password)[0] fp = open(password_file, 'w') if password: fp.write('password="%s"\n' % cpassword) else: fp.write('password=None\n') fp.close() def appfactory(wsgiapp=wsgibase, logfilename='httpserver.log', profiler_dir=None, profilerfilename=None): """ generates a wsgi application that does logging and profiling and calls wsgibase Args: wsgiapp: the base application logfilename: where to store apache-compatible requests log profiler_dir: where to store profile files """ if profilerfilename is not None: raise BaseException("Deprecated API") if profiler_dir: profiler_dir = abspath(profiler_dir) logger.warn('profiler is on. will use dir %s', profiler_dir) if not os.path.isdir(profiler_dir): try: os.makedirs(profiler_dir) except: raise BaseException("Can't create dir %s" % profiler_dir) filepath = pjoin(profiler_dir, 'wtest') try: filehandle = open( filepath, 'w' ) filehandle.close() os.unlink(filepath) except IOError: raise BaseException("Unable to write to dir %s" % profiler_dir) def app_with_logging(environ, responder): """ a wsgi app that does logging and profiling and calls wsgibase """ status_headers = [] def responder2(s, h): """ wsgi responder app """ status_headers.append(s) status_headers.append(h) return responder(s, h) time_in = time.time() ret = [0] if not profiler_dir: ret[0] = wsgiapp(environ, responder2) else: import cProfile prof = cProfile.Profile() prof.enable() ret[0] = wsgiapp(environ, responder2) prof.disable() destfile = pjoin(profiler_dir, "req_%s.prof" % web2py_uuid()) prof.dump_stats(destfile) try: line = '%s, %s, %s, %s, %s, %s, %f\n' % ( environ['REMOTE_ADDR'], datetime.datetime.today().strftime('%Y-%m-%d %H:%M:%S'), environ['REQUEST_METHOD'], environ['PATH_INFO'].replace(',', '%2C'), environ['SERVER_PROTOCOL'], (status_headers[0])[:3], time.time() - time_in, ) if not logfilename: sys.stdout.write(line) elif isinstance(logfilename, str): write_file(logfilename, line, 'a') else: logfilename.write(line) except: pass return ret[0] return app_with_logging

37.919598

117

0.52286

5d90667a56cb7c978d1072b2a27a14dbab5c4dfc

3,798

py

Python

agents/EWPublisherAgent.py

marc4gov/tokenspice2

1993383674f35b20e11e54606b3dac8e4c05c0f9

[ "Apache-2.0" ]

1

2021-01-12T08:06:21.000Z

agents/EWPublisherAgent.py

marc4gov/tokenspice2

1993383674f35b20e11e54606b3dac8e4c05c0f9

[ "Apache-2.0" ]

null

agents/EWPublisherAgent.py

marc4gov/tokenspice2

1993383674f35b20e11e54606b3dac8e4c05c0f9

[ "Apache-2.0" ]

null

import logging log = logging.getLogger('marketagents') from enforce_typing import enforce_types # type: ignore[import] import random from agents.PublisherAgent import PublisherAgent from agents.PoolAgent import PoolAgent from util import constants from util.constants import POOL_WEIGHT_DT, POOL_WEIGHT_OCEAN from web3engine import bfactory, bpool, datatoken, dtfactory, globaltokens from web3tools.web3util import toBase18

37.98

80

0.662191

5d922c20991f9207f7464898983a068c11cac2a8

1,625

py

Python

lcls_orbit/__init__.py

slaclab/lcls-orbit

e2b8738c4af2dfed40fce4b898bf9b2a820d5f56

[ "BSD-3-Clause-LBNL" ]

null

lcls_orbit/__init__.py

slaclab/lcls-orbit

e2b8738c4af2dfed40fce4b898bf9b2a820d5f56

[ "BSD-3-Clause-LBNL" ]

null

lcls_orbit/__init__.py

slaclab/lcls-orbit

e2b8738c4af2dfed40fce4b898bf9b2a820d5f56

[ "BSD-3-Clause-LBNL" ]

1

2021-11-16T01:03:53.000Z

import numpy as np from . import _version __version__ = _version.get_versions()['version'] HXR_COLORS = ("#000000", "#02004a", "#030069", "#04008f", "#0500b3", "#0700ff") SXR_COLORS = ("#000000", "#330000", "#520000", "#850000", "#ad0000", "#ff0000") HXR_AREAS = { "GUN" : [2017.911, 2018.712], "L0" : [2018.712, 2024.791], "DL1_1": [2024.791, 2031.992], "DL1_2": [2031.992, 2035.035], "L1": [2035.035, 2044.167], "BC1": [2044.167, 2059.733], "L2": [2059.733, 2410.698], "BC2": [2410.698, 2438.400], "L3": [2438.400, 3042.005], "CLTH_0": [3042.005, 3050.512], "CLTH_1": [3050.512, 3058.457], "CLTH_2": [3058.457, 3110.961], "BSYH_1": [3110.961, 3117.409], "BSYH_2": [3117.409, 3224.022], "LTUH": [3224.022, 3562.739], "UNDH": [3562.739, 3718.483], "DMPH_1": [3718.483, 3734.407], "DMPH_2": [3734.407, 3765.481] } HXR_AREAS = {np.mean(value): key for key, value in HXR_AREAS.items()} SXR_AREAS = { "GUN" : [2017.911, 2017.911], "L0" : [2018.712, 2024.791], "DL1_1": [2024.791, 2031.992], "DL1_2": [2031.992, 2035.035], "L1": [2035.035, 2044.167], "BC1": [2044.167, 2059.733], "L2": [2059.733, 2410.698], "BC2": [2410.698, 2438.400], "L3": [2438.400, 3042.005], "CLTH_0": [3042.005, 3050.512], "CLTH_1": [3050.512, 3058.457], "CLTS": [3177.650, 3224.022], "BSYS": [3224.022, 3565.656], "LTUS": [3565.656, 3718.483], "UNDS": [3718.483, 3734.407], "DMPS_1": [3734.407, 3734.407], "DMPS_2": [3734.407, 3765.481] } SXR_AREAS = {np.mean(value): key for key, value in SXR_AREAS.items()}

30.660377

79

0.563077

5d930df4a535163668fcaae7a75a25d2de903db1

13,123

py

Python

tests/test_optimizers_v2/test_optimizers_v2.py

OverLordGoldDragon/dummy

5192b91c57721f37b906f670ad954a46f98bf5b5

[ "MIT" ]

null

tests/test_optimizers_v2/test_optimizers_v2.py

OverLordGoldDragon/dummy

5192b91c57721f37b906f670ad954a46f98bf5b5

[ "MIT" ]

null

tests/test_optimizers_v2/test_optimizers_v2.py

OverLordGoldDragon/dummy

5192b91c57721f37b906f670ad954a46f98bf5b5

[ "MIT" ]

null

import os import tempfile import numpy as np import tensorflow as tf from time import time from termcolor import cprint from unittest import TestCase from .. import K from .. import Input, Dense, GRU, Bidirectional, Embedding from .. import Model, load_model from .. import l2 from .. import maxnorm from .. import Adam, Nadam, SGD from .. import AdamW, NadamW, SGDW from .. import get_weight_decays, fill_dict_in_order, reset_seeds, K_eval print("TF version: %s" % tf.__version__) tf_eager = bool(os.environ["TF_EAGER"] == "True") if tf_eager: print("TF running eagerly") else: tf.compat.v1.disable_eager_execution() print("TF running in graph mode")

44.334459

83

0.586375

5d93683480faf496a5e564f3b162607a289a4f92

21,601

py

Python

koku/reporting/migrations/0099_ocp_performance.py

Vasyka/koku

b5aa9ec41c3b0821e74afe9ff3a5ffaedb910614

[ "Apache-2.0" ]

2

2022-01-12T03:42:39.000Z

2022-01-12T03:42:40.000Z

koku/reporting/migrations/0099_ocp_performance.py

Vasyka/koku

b5aa9ec41c3b0821e74afe9ff3a5ffaedb910614

[ "Apache-2.0" ]

null

koku/reporting/migrations/0099_ocp_performance.py

Vasyka/koku

b5aa9ec41c3b0821e74afe9ff3a5ffaedb910614

[ "Apache-2.0" ]

1

2021-07-21T09:33:59.000Z

# Generated by Django 2.2.10 on 2020-02-18 12:51 import django.contrib.postgres.indexes from django.db import migrations from django.db import models

51.800959

173

0.734781

5d942ae26a14855b18361770889fe0b68867154b

1,433

py

Python

sympy/tensor/tests/test_functions.py

iamabhishek0/sympy

c461bd1ff9d178d1012b04fd0bf37ee3abb02cdd

[ "BSD-3-Clause" ]

8,323

2015-01-02T15:51:43.000Z

2022-03-31T13:13:19.000Z

sympy/tensor/tests/test_functions.py

iamabhishek0/sympy

c461bd1ff9d178d1012b04fd0bf37ee3abb02cdd

[ "BSD-3-Clause" ]

15,102

2015-01-01T01:33:17.000Z

2022-03-31T22:53:13.000Z

sympy/tensor/tests/test_functions.py

iamabhishek0/sympy

c461bd1ff9d178d1012b04fd0bf37ee3abb02cdd

[ "BSD-3-Clause" ]

4,490

2015-01-01T17:48:07.000Z

2022-03-31T17:24:05.000Z

from sympy.tensor.functions import TensorProduct from sympy import MatrixSymbol, Matrix, Array from sympy.abc import x, y, z from sympy.abc import i, j, k, l A = MatrixSymbol("A", 3, 3) B = MatrixSymbol("B", 3, 3) C = MatrixSymbol("C", 3, 3)

25.589286

74

0.567341

5d94582a86f1cb5da7910dcf3af0df5fef4be108

898

py

Python

app/views.py

Kgermando/sem

c76e97e1d526d4e92a925adb6bceee426f999655

[ "Apache-2.0" ]

null

app/views.py

Kgermando/sem

c76e97e1d526d4e92a925adb6bceee426f999655

[ "Apache-2.0" ]

null

app/views.py

Kgermando/sem

c76e97e1d526d4e92a925adb6bceee426f999655

[ "Apache-2.0" ]

null

from django.shortcuts import render # Create your views here.

26.411765

59

0.604677

5d9653a594b04879fa4c04919b21be2e555546ba

1,033

py

Python

webcrawler/crawler/spiders/baselensspider.py

HansZimmer5000/LensComparison

e4d9b68211604c4569c4ca9b1e1b4fce2a8c1ea8

[ "Apache-2.0" ]

null

webcrawler/crawler/spiders/baselensspider.py

HansZimmer5000/LensComparison

e4d9b68211604c4569c4ca9b1e1b4fce2a8c1ea8

[ "Apache-2.0" ]

null

webcrawler/crawler/spiders/baselensspider.py

HansZimmer5000/LensComparison

e4d9b68211604c4569c4ca9b1e1b4fce2a8c1ea8

[ "Apache-2.0" ]

null

# This module is about my webcrawler with the use of scrapy. # Its a generell web crawler, but the import and use of GhAdapter makes it usefull for geizhals.de sites. from abc import ABC, abstractmethod import scrapy

27.184211

105

0.804453

5d968294f2b19ad9cf4d5cc885fbe7be0f0e3330

15,289

py

Python

byol_train.py

fjbriones/deep-text-recognition-benchmark

c85d12aa56495fe221656bac4c8cb159a28456b1

[ "Apache-2.0" ]

null

byol_train.py

fjbriones/deep-text-recognition-benchmark

c85d12aa56495fe221656bac4c8cb159a28456b1

[ "Apache-2.0" ]

null

byol_train.py

fjbriones/deep-text-recognition-benchmark

c85d12aa56495fe221656bac4c8cb159a28456b1

[ "Apache-2.0" ]

null

import os import sys import time import random import string import argparse import torch import torch.backends.cudnn as cudnn import torch.nn.init as init import torch.optim as optim import torch.utils.data import numpy as np from utils import CTCLabelConverter, CTCLabelConverterForBaiduWarpctc, AttnLabelConverter, Averager from simclr_dataset import hierarchical_dataset, AlignCollate, Batch_Balanced_Dataset from simclr_model import FeaturesModel as Model from test import validation from byol_pytorch import BYOL from imgaug import augmenters as iaa import imgaug as ia from tqdm import tqdm import matplotlib.pyplot as plt device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') def train(opt): """ dataset preparation """ if not opt.data_filtering_off: print('Filtering the images containing characters which are not in opt.character') print('Filtering the images whose label is longer than opt.batch_max_length') # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130 opt.select_data = opt.select_data.split('-') opt.batch_ratio = opt.batch_ratio.split('-') train_dataset = Batch_Balanced_Dataset(opt) log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a') ia.seed(1) image_transforms = iaa.Sequential([iaa.SomeOf((1, 5), [iaa.LinearContrast((0.5, 1.0)), iaa.GaussianBlur((0.5, 1.5)), iaa.Crop(percent=((0, 0.4),(0, 0),(0, 0.4),(0, 0.0)), keep_size=True), iaa.Crop(percent=((0, 0.0),(0, 0.02),(0, 0),(0, 0.02)), keep_size=True), iaa.Sharpen(alpha=(0.0, 0.5), lightness=(0.0, 0.5)), iaa.PiecewiseAffine(scale=(0.02, 0.03), mode='edge'), iaa.PerspectiveTransform(scale=(0.01, 0.02))], random_order=True)]) AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, image_transforms=image_transforms) valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=opt.batch_size, shuffle=True, # 'True' to check training progress with validation function. num_workers=int(opt.workers), collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() if opt.rgb: opt.input_channel = 3 model = Model(opt) print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel, opt.hidden_size, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction, opt.SequenceModeling) # weight initialization for name, param in model.named_parameters(): if 'localization_fc2' in name: print(f'Skip {name} as it is already initialized') continue try: if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) except Exception as e: # for batchnorm. if 'weight' in name: param.data.fill_(1) continue # data parallel for multi-GPU model = torch.nn.DataParallel(model).to(device) model.train() if opt.saved_model != '': print(f'loading pretrained model from {opt.saved_model}') if opt.FT: model.load_state_dict(torch.load(opt.saved_model), strict=False) else: model.load_state_dict(torch.load(opt.saved_model)) print("Model:") print(model) image_transforms = iaa.Sequential([iaa.SomeOf((1, 5), [iaa.LinearContrast((0.5, 1.0)), iaa.GaussianBlur((0.5, 1.5)), iaa.Crop(percent=((0, 0.4),(0, 0),(0, 0.4),(0, 0.0)), keep_size=True), iaa.Crop(percent=((0, 0.0),(0, 0.02),(0, 0),(0, 0.02)), keep_size=True), iaa.Sharpen(alpha=(0.0, 0.5), lightness=(0.0, 0.5)), iaa.PiecewiseAffine(scale=(0.02, 0.03), mode='edge'), iaa.PerspectiveTransform(scale=(0.01, 0.02))], random_order=True)]) byol_learner = BYOL( model, image_size=(32,100), hidden_layer=-1, channels=1, augment_fn=image_transforms, augmented=True) print(byol_learner) # filter that only require gradient decent filtered_parameters = [] params_num = [] for p in filter(lambda p: p.requires_grad, byol_learner.parameters()): filtered_parameters.append(p) params_num.append(np.prod(p.size())) print('Trainable params num : ', sum(params_num)) # setup optimizer if opt.optimizer == 'adam': optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) elif opt.optimizer == 'adadelta': optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps, weight_decay=opt.weight_decay) elif opt.optimizer == 'sgd': optimizer = optim.SGD(filtered_parameters, lr=opt.lr, momentum=opt.momentum, weight_decay=opt.weight_decay, nesterov=opt.nesterov) else: raise Exception('Unknown optimizer') print("Optimizer:") print(optimizer) """ final options """ # print(opt) with open(f'./saved_models/{opt.exp_name}/opt.txt', 'a') as opt_file: opt_log = '------------ Options -------------\n' args = vars(opt) for k, v in args.items(): opt_log += f'{str(k)}: {str(v)}\n' opt_log += '---------------------------------------\n' print(opt_log) opt_file.write(opt_log) """ start training """ start_iter = 0 if opt.saved_model != '': try: start_iter = int(opt.saved_model.split('_')[-1].split('.')[0]) print(f'continue to train, start_iter: {start_iter}') except: pass #LR Scheduler: scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[int(0.6*opt.num_iter), int(0.8*opt.num_iter)], last_epoch=start_iter-1, gamma=0.1) best_loss = None iteration = start_iter print(device) loss_avg = Averager() valid_loss_avg = Averager() # kl_loss_avg = Averager() # kl_loss = torch.nn.KLDivLoss() epoch = 0 while(True): # train part for i in tqdm(range(opt.valInterval)): image_tensors, _ = train_dataset.get_batch() image = image_tensors.to(device) optimizer.zero_grad() loss = byol_learner(image) loss.backward() if opt.grad_clip: torch.nn.utils.clip_grad_norm_(byol_learner.parameters(), opt.grad_clip) optimizer.step() scheduler.step() byol_learner.update_moving_average() loss_avg.add(loss) if iteration==0: print("Epoch {:06d} Loss: {:.04f}".format(iteration, loss_avg.val())) iteration += 1 byol_learner.eval() model.eval() with torch.no_grad(): for image_tensors, _ in valid_loader: image = image_tensors.to(device) val_loss = byol_learner(image) valid_loss_avg.add(val_loss) # features = model(image) # features = features.view(-1, 26, features.shape[1]) # kl_div = kl_loss(features[:int(features.shape[0]/2)], features[int(features.shape[0]/2):]) # kl_loss_avg.add(kl_div) model.train() byol_learner.train() with open(f'./saved_models/{opt.exp_name}/log_train.txt', 'a') as log: log.write("Iteration {:06d} Loss: {:.06f} Val loss: {:06f}".format(iteration, loss_avg.val(), valid_loss_avg.val()) + '\n') print("Iteration {:06d} Loss: {:.06f} Val loss: {:06f}".format(iteration, loss_avg.val(), valid_loss_avg.val())) if best_loss is None: best_loss = valid_loss_avg.val() torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/iter_{iteration+1}.pth') elif best_loss > valid_loss_avg.val(): best_loss = valid_loss_avg.val() torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/iter_{iteration+1}.pth') scheduler.step() loss_avg.reset() valid_loss_avg.reset() if epoch % 5 == 0: torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/iter_{iteration+1}.pth') if (iteration + 1) >= opt.num_iter: print('end the training') torch.save(model.state_dict(), f'./saved_models/{opt.exp_name}/iter_{iteration+1}.pth') sys.exit() epoch +=1 if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--exp_name', help='Where to store logs and models') parser.add_argument('--train_data', required=True, help='path to training dataset') parser.add_argument('--valid_data', required=True, help='path to validation dataset') parser.add_argument('--manualSeed', type=int, default=1111, help='for random seed setting') parser.add_argument('--workers', type=int, help='number of data loading workers', default=4) parser.add_argument('--batch_size', type=int, default=192, help='input batch size') parser.add_argument('--num_iter', type=int, default=300000, help='number of iterations to train for') parser.add_argument('--valInterval', type=int, default=2000, help='Interval between each validation') parser.add_argument('--saved_model', default='', help="path to model to continue training") parser.add_argument('--FT', action='store_true', help='whether to do fine-tuning') parser.add_argument('--optimizer', type=str, choices=['adam', 'adadelta', 'sgd'], help="Optimizer") parser.add_argument('--lr', type=float, default=1, help='learning rate, default=1.0 for Adadelta') parser.add_argument('--beta1', type=float, default=0.9, help='beta1 for adam. default=0.9') parser.add_argument('--rho', type=float, default=0.95, help='decay rate rho for Adadelta. default=0.95') parser.add_argument('--eps', type=float, default=1e-8, help='eps for Adadelta. default=1e-8') parser.add_argument('--nesterov', action='store_true', help='Use Nesterov momentum for SGD') parser.add_argument('--momentum', type=float, default=0.9, help='Momentum for SGD') parser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping value. default=5') parser.add_argument('--baiduCTC', action='store_true', help='for data_filtering_off mode') """ Data processing """ parser.add_argument('--select_data', type=str, default='MJ-ST', help='select training data (default is MJ-ST, which means MJ and ST used as training data)') parser.add_argument('--batch_ratio', type=str, default='0.5-0.5', help='assign ratio for each selected data in the batch') parser.add_argument('--total_data_usage_ratio', type=str, default='1.0', help='total data usage ratio, this ratio is multiplied to total number of data.') parser.add_argument('--batch_max_length', type=int, default=25, help='maximum-label-length') parser.add_argument('--imgH', type=int, default=32, help='the height of the input image') parser.add_argument('--imgW', type=int, default=100, help='the width of the input image') parser.add_argument('--rgb', action='store_true', help='use rgb input') parser.add_argument('--character', type=str, default='0123456789abcdefghijklmnopqrstuvwxyz', help='character label') parser.add_argument('--sensitive', action='store_true', help='for sensitive character mode') parser.add_argument('--PAD', action='store_true', help='whether to keep ratio then pad for image resize') parser.add_argument('--data_filtering_off', action='store_true', help='for data_filtering_off mode') """ Model Architecture """ parser.add_argument('--Transformation', type=str, required=True, help='Transformation stage. None|TPS') parser.add_argument('--FeatureExtraction', type=str, required=True, help='FeatureExtraction stage. VGG|RCNN|ResNet') parser.add_argument('--SequenceModeling', type=str, required=True, help='SequenceModeling stage. None|BiLSTM') parser.add_argument('--num_fiducial', type=int, default=20, help='number of fiducial points of TPS-STN') parser.add_argument('--input_channel', type=int, default=1, help='the number of input channel of Feature extractor') parser.add_argument('--output_channel', type=int, default=512, help='the number of output channel of Feature extractor') parser.add_argument('--hidden_size', type=int, default=256, help='the size of the LSTM hidden state') parser.add_argument('--weight_decay', type=float, default=10e-4, help='Weight decay') parser.add_argument('--FinalLayer', action='store_true', help='Use a nonlinear projection head during training') parser.add_argument('--final_feature', type=int, default=256, help='the size of the output of the final layer') opt = parser.parse_args() if not opt.exp_name: opt.exp_name = f'{opt.Transformation}-{opt.FeatureExtraction}-{opt.SequenceModeling}-BYOL' opt.exp_name += f'-Seed{opt.manualSeed}' # print(opt.exp_name) os.makedirs(f'./saved_models/{opt.exp_name}', exist_ok=True) """ vocab / character number configuration """ if opt.sensitive: # opt.character += 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' opt.character = string.printable[:-6] # same with ASTER setting (use 94 char). """ Seed and GPU setting """ # print("Random Seed: ", opt.manualSeed) random.seed(opt.manualSeed) np.random.seed(opt.manualSeed) torch.manual_seed(opt.manualSeed) torch.cuda.manual_seed(opt.manualSeed) cudnn.benchmark = True cudnn.deterministic = True opt.num_gpu = torch.cuda.device_count() # print('device count', opt.num_gpu) if opt.num_gpu > 1: print('------ Use multi-GPU setting ------') print('if you stuck too long time with multi-GPU setting, try to set --workers 0') # check multi-GPU issue https://github.com/clovaai/deep-text-recognition-benchmark/issues/1 opt.workers = opt.workers * opt.num_gpu opt.batch_size = opt.batch_size * opt.num_gpu """ previous version print('To equlize batch stats to 1-GPU setting, the batch_size is multiplied with num_gpu and multiplied batch_size is ', opt.batch_size) opt.batch_size = opt.batch_size * opt.num_gpu print('To equalize the number of epochs to 1-GPU setting, num_iter is divided with num_gpu by default.') If you dont care about it, just commnet out these line.) opt.num_iter = int(opt.num_iter / opt.num_gpu) """ train(opt)

45.502976

152

0.636013

5d96fee6a1d130e8653363f3a24275073276610b

1,496

py

Python

app/__init__.py

dulin/tornado-test

8ceeb9f2b50b4cd0f18baa9149140721feec1925

[ "MIT" ]

null

app/__init__.py

dulin/tornado-test

8ceeb9f2b50b4cd0f18baa9149140721feec1925

[ "MIT" ]

null

app/__init__.py

dulin/tornado-test

8ceeb9f2b50b4cd0f18baa9149140721feec1925

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # -*- mode: python -*- import aiopg import psycopg2 import tornado.locks from tornado.options import define, options from tornado.web import Application from app.application import Application define('port', default=8080, help="listening port") define('bind_address', default="", help="bind address") define("db_host", default="127.0.0.1", help="database host") define("db_port", default=5432, help="database port") define("db_database", default="tornado", help="database name") define("db_user", default="tornado", help="database user") define("db_password", default="tornado", help="database password")

32.521739

81

0.675802

5d9879f28b9c13e35ac740f4cb5632764b5c35dd

2,426

py

Python

pipescaler/core/stage.py

KarlTDebiec/PipeScaler

b990ece8f3dd2c3506c226ed871871997fc57beb

[ "BSD-3-Clause" ]

1

2022-02-07T03:47:53.000Z

pipescaler/core/stage.py

KarlTDebiec/PipeScaler

b990ece8f3dd2c3506c226ed871871997fc57beb

[ "BSD-3-Clause" ]

49

2022-01-17T15:16:22.000Z

2022-03-28T03:00:39.000Z

pipescaler/core/stage.py

KarlTDebiec/PipeScaler

b990ece8f3dd2c3506c226ed871871997fc57beb

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # pipescaler/core/stage.py # # Copyright (C) 2020-2021 Karl T Debiec # All rights reserved. # # This software may be modified and distributed under the terms of the # BSD license. from __future__ import annotations from abc import ABC, abstractmethod from importlib.util import module_from_spec, spec_from_file_location from typing import Any, List, Optional from pipescaler.common import validate_input_path

27.568182

83

0.633965

5d99e63583440bf3da1b852644d47a0c0ec5d4a3

349

py

Python

src/project/api/rankings/urls.py

jSkrod/djangae-react-browser-games-app

28c5064f0a126021afb08b195839305aba6b35a2

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

null

src/project/api/rankings/urls.py

jSkrod/djangae-react-browser-games-app

28c5064f0a126021afb08b195839305aba6b35a2

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

null

src/project/api/rankings/urls.py

jSkrod/djangae-react-browser-games-app

28c5064f0a126021afb08b195839305aba6b35a2

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

null

from django.conf.urls import url, include from project.api.rankings.api import AddRanking, AddScore, GetScoresUser, GetScoresGame urlpatterns = [ url(r'add_ranking$', AddRanking.as_view()), url(r'add_score$', AddScore.as_view()), url(r'get_scores_game$', GetScoresGame.as_view()), url(r'get_scores_user$', GetScoresUser.as_view()) ]

38.777778

87

0.739255

5d9a08394431a2356f36800cd3badfa0bed3c07f

7,730

py

Python

qiskit_metal/qlibrary/lumped/cap_n_interdigital.py

wdczdj/qiskit-metal

c77805f66da60021ef8d10d668715c1dc2ebcd1d

[ "Apache-2.0" ]

null

qiskit_metal/qlibrary/lumped/cap_n_interdigital.py

wdczdj/qiskit-metal

c77805f66da60021ef8d10d668715c1dc2ebcd1d

[ "Apache-2.0" ]

null

qiskit_metal/qlibrary/lumped/cap_n_interdigital.py

wdczdj/qiskit-metal

c77805f66da60021ef8d10d668715c1dc2ebcd1d

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM 2017, 2021. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. from qiskit_metal import draw, Dict from qiskit_metal.qlibrary.core import QComponent import numpy as np

41.336898

100

0.51022

5d9a69d3f7389d018e9e0d4577b31c493762c8e2

4,895

py

Python

ThirdParty/protobuf-registry/python/protobufs/services/feature/actions/get_flags_pb2.py

getcircle/luno-ios

d18260abb537496d86cf607c170dd5e91c406f0f

[ "MIT" ]

null

ThirdParty/protobuf-registry/python/protobufs/services/feature/actions/get_flags_pb2.py

getcircle/luno-ios

d18260abb537496d86cf607c170dd5e91c406f0f

[ "MIT" ]

null

ThirdParty/protobuf-registry/python/protobufs/services/feature/actions/get_flags_pb2.py

getcircle/luno-ios

d18260abb537496d86cf607c170dd5e91c406f0f

[ "MIT" ]

null

# Generated by the protocol buffer compiler. DO NOT EDIT! # source: protobufs/services/feature/actions/get_flags.proto from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database from google.protobuf import descriptor_pb2 # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='protobufs/services/feature/actions/get_flags.proto', package='services.feature.actions.get_flags', syntax='proto3', serialized_pb=b'\n2protobufs/services/feature/actions/get_flags.proto\x12\"services.feature.actions.get_flags\"\x0b\n\tRequestV1\"\x84\x01\n\nResponseV1\x12H\n\x05\x66lags\x18\x01 \x03(\x0b\x32\x39.services.feature.actions.get_flags.ResponseV1.FlagsEntry\x1a,\n\nFlagsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x08:\x02\x38\x01\x62\x06proto3' ) _sym_db.RegisterFileDescriptor(DESCRIPTOR) _REQUESTV1 = _descriptor.Descriptor( name='RequestV1', full_name='services.feature.actions.get_flags.RequestV1', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=90, serialized_end=101, ) _RESPONSEV1_FLAGSENTRY = _descriptor.Descriptor( name='FlagsEntry', full_name='services.feature.actions.get_flags.ResponseV1.FlagsEntry', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='key', full_name='services.feature.actions.get_flags.ResponseV1.FlagsEntry.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), _descriptor.FieldDescriptor( name='value', full_name='services.feature.actions.get_flags.ResponseV1.FlagsEntry.value', index=1, number=2, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], enum_types=[ ], options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), b'8\001'), is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=192, serialized_end=236, ) _RESPONSEV1 = _descriptor.Descriptor( name='ResponseV1', full_name='services.feature.actions.get_flags.ResponseV1', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='flags', full_name='services.feature.actions.get_flags.ResponseV1.flags', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[_RESPONSEV1_FLAGSENTRY, ], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=104, serialized_end=236, ) _RESPONSEV1_FLAGSENTRY.containing_type = _RESPONSEV1 _RESPONSEV1.fields_by_name['flags'].message_type = _RESPONSEV1_FLAGSENTRY DESCRIPTOR.message_types_by_name['RequestV1'] = _REQUESTV1 DESCRIPTOR.message_types_by_name['ResponseV1'] = _RESPONSEV1 RequestV1 = _reflection.GeneratedProtocolMessageType('RequestV1', (_message.Message,), dict( DESCRIPTOR = _REQUESTV1, __module__ = 'protobufs.services.feature.actions.get_flags_pb2' # @@protoc_insertion_point(class_scope:services.feature.actions.get_flags.RequestV1) )) _sym_db.RegisterMessage(RequestV1) ResponseV1 = _reflection.GeneratedProtocolMessageType('ResponseV1', (_message.Message,), dict( FlagsEntry = _reflection.GeneratedProtocolMessageType('FlagsEntry', (_message.Message,), dict( DESCRIPTOR = _RESPONSEV1_FLAGSENTRY, __module__ = 'protobufs.services.feature.actions.get_flags_pb2' # @@protoc_insertion_point(class_scope:services.feature.actions.get_flags.ResponseV1.FlagsEntry) )) , DESCRIPTOR = _RESPONSEV1, __module__ = 'protobufs.services.feature.actions.get_flags_pb2' # @@protoc_insertion_point(class_scope:services.feature.actions.get_flags.ResponseV1) )) _sym_db.RegisterMessage(ResponseV1) _sym_db.RegisterMessage(ResponseV1.FlagsEntry) _RESPONSEV1_FLAGSENTRY.has_options = True _RESPONSEV1_FLAGSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), b'8\001') # @@protoc_insertion_point(module_scope)

32.852349

371

0.765475

5d9cfb201fcd48e23406da7a37202a4d1d0051f3

1,758

py

Python

Medium/102_2.py

Hellofafar/Leetcode

7a459e9742958e63be8886874904e5ab2489411a

[ "CNRI-Python" ]

6

2017-09-25T18:05:50.000Z

2019-03-27T00:23:15.000Z

Medium/102_2.py

Hellofafar/Leetcode

7a459e9742958e63be8886874904e5ab2489411a

[ "CNRI-Python" ]

1

2017-10-29T12:04:41.000Z

2018-08-16T18:00:37.000Z

Medium/102_2.py

Hellofafar/Leetcode

7a459e9742958e63be8886874904e5ab2489411a

[ "CNRI-Python" ]

null

# ------------------------------ # Binary Tree Level Order Traversal # # Description: # Given a binary tree, return the level order traversal of its nodes' values. (ie, from # left to right, level by level). # # For example: # Given binary tree [3,9,20,null,null,15,7], # 3 # / \ # 9 20 # / \ # 15 7 # return its level order traversal as: # [ # [3], # [9,20], # [15,7] # ] # # Version: 2.0 # 11/11/19 by Jianfa # ------------------------------ # Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None # Used for testing if __name__ == "__main__": test = Solution() # ------------------------------ # Summary: # Similar BFS solution but use a little more spaces. # On 102.py, using list.pop(0) actually takes O(n) time because it needs to remap the index # of values. Use collections.deque instead. # # O(N) time O(N) space

26.636364

108

0.527873

5d9e54e4e20b43e465756409507c8caedb39d5b5

7,969

py

Python

mturk/comparison_among_different_models/sample_from_models_for_comparison.py

qiaone/GIF

2c551e844748c72395fc91fb080c7a2f9c8d5285

[ "MIT" ]

322

2020-08-28T22:23:09.000Z

2022-03-25T09:42:12.000Z

mturk/comparison_among_different_models/sample_from_models_for_comparison.py

qiaone/GIF

2c551e844748c72395fc91fb080c7a2f9c8d5285

[ "MIT" ]

25

2020-11-03T02:03:51.000Z

2022-03-18T13:06:42.000Z

mturk/comparison_among_different_models/sample_from_models_for_comparison.py

qiaone/GIF

2c551e844748c72395fc91fb080c7a2f9c8d5285

[ "MIT" ]

59

2020-08-28T23:32:08.000Z

2022-03-30T03:29:35.000Z

import sys sys.path.append('../../') import constants as cnst import os os.environ['PYTHONHASHSEED'] = '2' import tqdm from model.stg2_generator import StyledGenerator import numpy as np from my_utils.visualize_flame_overlay import OverLayViz from my_utils.flm_dynamic_fit_overlay import camera_ringnetpp from my_utils.generate_gif import generate_from_flame_sequence from my_utils.generic_utils import save_set_of_images from my_utils import compute_fid import constants from dataset_loaders import fast_image_reshape import torch from my_utils import generic_utils from my_utils.eye_centering import position_to_given_location # General settings save_images = True code_size = 236 use_inst_norm = True core_tensor_res = 4 resolution = 256 alpha = 1 step_max = int(np.log2(resolution) - 2) root_out_dir = f'{cnst.output_root}sample/' num_smpl_to_eval_on = 1000 use_styled_conv_stylegan2 = True flength = 5000 cam_t = np.array([0., 0., 0]) camera_params = camera_ringnetpp((512, 512), trans=cam_t, focal=flength) run_ids_1 = [29, ] # with sqrt(2) # run_ids_1 = [7, 24, 8, 3] # run_ids_1 = [7, 8, 3] settings_for_runs = \ {24: {'name': 'vector_cond', 'model_idx': '216000_1', 'normal_maps_as_cond': False, 'rendered_flame_as_condition': False, 'apply_sqrt2_fac_in_eq_lin': False}, 29: {'name': 'full_model', 'model_idx': '294000_1', 'normal_maps_as_cond': True, 'rendered_flame_as_condition': True, 'apply_sqrt2_fac_in_eq_lin': True}, 7: {'name': 'flm_rndr_tex_interp', 'model_idx': '051000_1', 'normal_maps_as_cond': False, 'rendered_flame_as_condition': True, 'apply_sqrt2_fac_in_eq_lin': False}, 3: {'name': 'norm_mp_tex_interp', 'model_idx': '203000_1', 'normal_maps_as_cond': True, 'rendered_flame_as_condition': False, 'apply_sqrt2_fac_in_eq_lin': False}, 8: {'name': 'norm_map_rend_flm_no_tex_interp', 'model_idx': '009000_1', 'normal_maps_as_cond': True, 'rendered_flame_as_condition': True, 'apply_sqrt2_fac_in_eq_lin': False},} overlay_visualizer = OverLayViz() # overlay_visualizer.setup_renderer(mesh_file=None) flm_params = np.zeros((num_smpl_to_eval_on, code_size)).astype('float32') fl_param_dict = np.load(cnst.all_flame_params_file, allow_pickle=True).item() for i, key in enumerate(fl_param_dict): flame_param = fl_param_dict[key] flame_param = np.hstack((flame_param['shape'], flame_param['exp'], flame_param['pose'], flame_param['cam'], flame_param['tex'], flame_param['lit'].flatten())) # tz = camera_params['f'][0] / (camera_params['c'][0] * flame_param[:, 156:157]) # flame_param[:, 156:159] = np.concatenate((flame_param[:, 157:], tz), axis=1) # import ipdb; ipdb.set_trace() flm_params[i, :] = flame_param.astype('float32') if i == num_smpl_to_eval_on - 1: break batch_size = 64 flame_decoder = overlay_visualizer.deca.flame.eval() for run_idx in run_ids_1: # import ipdb; ipdb.set_trace() generator_1 = torch.nn.DataParallel( StyledGenerator(embedding_vocab_size=69158, rendered_flame_ascondition=settings_for_runs[run_idx]['rendered_flame_as_condition'], normal_maps_as_cond=settings_for_runs[run_idx]['normal_maps_as_cond'], core_tensor_res=core_tensor_res, w_truncation_factor=1.0, apply_sqrt2_fac_in_eq_lin=settings_for_runs[run_idx]['apply_sqrt2_fac_in_eq_lin'], n_mlp=8)).cuda() model_idx = settings_for_runs[run_idx]['model_idx'] ckpt1 = torch.load(f'{cnst.output_root}checkpoint/{run_idx}/{model_idx}.model') generator_1.load_state_dict(ckpt1['generator_running']) generator_1 = generator_1.eval() # images = np.zeros((num_smpl_to_eval_on, 3, resolution, resolution)).astype('float32') pbar = tqdm.tqdm(range(0, num_smpl_to_eval_on, batch_size)) pbar.set_description('Generating_images') flame_mesh_imgs = None mdl_id = 'mdl2_' if settings_for_runs[run_idx]['name'] == 'full_model': mdl_id = 'mdl1_' for batch_idx in pbar: flm_batch = flm_params[batch_idx:batch_idx+batch_size, :] flm_batch = torch.from_numpy(flm_batch).cuda() flm_batch = position_to_given_location(flame_decoder, flm_batch) batch_size_true = flm_batch.shape[0] if settings_for_runs[run_idx]['normal_maps_as_cond'] or \ settings_for_runs[run_idx]['rendered_flame_as_condition']: cam = flm_batch[:, constants.DECA_IDX['cam'][0]:constants.DECA_IDX['cam'][1]:] shape = flm_batch[:, constants.INDICES['SHAPE'][0]:constants.INDICES['SHAPE'][1]] exp = flm_batch[:, constants.INDICES['EXP'][0]:constants.INDICES['EXP'][1]] pose = flm_batch[:, constants.INDICES['POSE'][0]:constants.INDICES['POSE'][1]] # import ipdb; ipdb.set_trace() light_code = \ flm_batch[:, constants.DECA_IDX['lit'][0]:constants.DECA_IDX['lit'][1]:].view((batch_size_true, 9, 3)) texture_code = flm_batch[:, constants.DECA_IDX['tex'][0]:constants.DECA_IDX['tex'][1]:] norma_map_img, _, _, _, rend_flm = \ overlay_visualizer.get_rendered_mesh(flame_params=(shape, exp, pose, light_code, texture_code), camera_params=cam) rend_flm = torch.clamp(rend_flm, 0, 1) * 2 - 1 norma_map_img = torch.clamp(norma_map_img, 0, 1) * 2 - 1 rend_flm = fast_image_reshape(rend_flm, height_out=256, width_out=256, mode='bilinear') norma_map_img = fast_image_reshape(norma_map_img, height_out=256, width_out=256, mode='bilinear') else: rend_flm = None norma_map_img = None gen_1_in = ge_gen_in(flm_batch, rend_flm, norma_map_img, settings_for_runs[run_idx]['normal_maps_as_cond'], settings_for_runs[run_idx]['rendered_flame_as_condition']) # torch.manual_seed(2) identity_embeddings = torch.randint(low=0, high=69158, size=(gen_1_in.shape[0], ), dtype=torch.long, device='cuda') mdl_1_gen_images = generic_utils.get_images_from_flame_params( flame_params=gen_1_in.cpu().numpy(), pose=None, model=generator_1, step=step_max, alpha=alpha, input_indices=identity_embeddings.cpu().numpy()) # import ipdb; ipdb.set_trace() images = torch.clamp(mdl_1_gen_images, -1, 1).cpu().numpy() flame_mesh_imgs = torch.clamp(rend_flm, -1, 1).cpu().numpy() save_path_current_id = os.path.join(root_out_dir, 'inter_model_comparison', settings_for_runs[run_idx]['name']) save_set_of_images(path=save_path_current_id, prefix=f'{mdl_id}_{batch_idx}', images=(images + 1) / 2, show_prog_bar=True) #save flam rndr save_path_current_id_flm_rndr = os.path.join(root_out_dir, 'inter_model_comparison', settings_for_runs[run_idx]['name']) save_set_of_images(path=save_path_current_id_flm_rndr, prefix=f'mesh_{batch_idx}', images=(flame_mesh_imgs + 1) / 2, show_prog_bar=True) # save_set_of_images(path=save_path_this_expt, prefix='mesh_', images=((norma_map_img + 1) / 2).cpu().numpy()) # save_set_of_images(path=save_path_this_expt, prefix='mdl1_', images=((mdl_1_gen_images + 1) / 2).cpu().numpy()) # save_set_of_images(path=save_path_this_expt, prefix='mdl2_', images=((mdl_2_gen_images + 1) / 2).cpu().numpy())

47.718563

119

0.676371

5d9ff4014ab10b4aacbc6a629a0aa9ded18d3d4a

895

py

Python

setup.py

philippWassibauer/django-activity-stream

766a372aea4803ef5fe051a5de16dde5b5efcc72

[ "BSD-3-Clause" ]

4

2015-05-21T04:28:43.000Z

2019-04-27T15:12:32.000Z

setup.py

philippWassibauer/django-activity-stream

766a372aea4803ef5fe051a5de16dde5b5efcc72

[ "BSD-3-Clause" ]

null

setup.py

philippWassibauer/django-activity-stream

766a372aea4803ef5fe051a5de16dde5b5efcc72

[ "BSD-3-Clause" ]

2

2018-02-10T22:31:07.000Z

2021-02-14T07:43:35.000Z

from distutils.core import setup """ django-activity-stream instalation script """ setup( name = 'activity_stream', description = 'generic activity feed system for users', author = 'Philipp Wassibauer', author_email = '[email protected]', url='http://github.com/philippWassibauer/django-activity-stream', download_url='http://github.com/philippWassibauer/django-activity-stream/tarball/master', license='MIT', version = __import__('activity_stream').__version__, classifiers=[ 'Development Status :: 3 - Alpha', 'Environment :: Web Environment', 'Framework :: Django', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Topic :: Software Development :: Libraries :: Python Modules', ], )

33.148148

93

0.660335

5da0ff4d3e7dbb3fe7c21095720798fb7df7ef6b

742

py

Python

02/selenium.02.py

study-machine-learning/dongheon.shin

6103ef9c73b162603bc39a27e4ecca0f1ac35e57

[ "MIT" ]

2

2017-09-24T02:29:48.000Z

2017-10-05T11:15:22.000Z

02/selenium.02.py

study-machine-learning/dongheon.shin

6103ef9c73b162603bc39a27e4ecca0f1ac35e57

[ "MIT" ]

null

02/selenium.02.py

study-machine-learning/dongheon.shin

6103ef9c73b162603bc39a27e4ecca0f1ac35e57

[ "MIT" ]

null

from selenium import webdriver username = "henlix" password = "my_password" browser = webdriver.PhantomJS() browser.implicitly_wait(5) url_login = "https://nid.naver.com/nidlogin.login" browser.get(url_login) el = browser.find_element_by_id("id") el.clear() el.send_keys(username) el = browser.find_element_by_id("pw") el.clear() el.send_keys(password) form = browser.find_element_by_css_selector("input.btn_global[type=submit]") form.submit() url_shopping_list = "https://order.pay.naver.com/home?tabMenu=SHOPPING" browser.get(url_shopping_list) products = browser.find_elements_by_css_selector(".p_info span") for product in products: print("- ", product.text) # PYTHONIOENCODING=utf-8:surrogateescape python3 selenium.02.py

22.484848

76

0.777628

5da246d54547ba7b297b610234129f3853586daf

343

py

Python

visualization/matplotlib/barwitherror.py

Licas/datascienceexamples

cbb1293dbae875cb3f166dbde00b2ab629a43ece

[ "MIT" ]

null

visualization/matplotlib/barwitherror.py

Licas/datascienceexamples

cbb1293dbae875cb3f166dbde00b2ab629a43ece

[ "MIT" ]

null

visualization/matplotlib/barwitherror.py

Licas/datascienceexamples

cbb1293dbae875cb3f166dbde00b2ab629a43ece

[ "MIT" ]

null

from matplotlib import pyplot as plt drinks = ["cappuccino", "latte", "chai", "americano", "mocha", "espresso"] ounces_of_milk = [6, 9, 4, 0, 9, 0] error = [0.6, 0.9, 0.4, 0, 0.9, 0] #Yerr -> element at i position represents +/- error[i] variance on bar[i] value plt.bar( range(len(drinks)),ounces_of_milk, yerr=error, capsize=15) plt.show()

38.111111

79

0.667638

5da2dcdc36e76038fe4e53bad3d9602bb03e2dea

38,961

py

Python

Packs/mnemonicMDR/Integrations/ArgusManagedDefence/ArgusManagedDefence.py

matan-xmcyber/content

7f02301c140b35956af3cd20cb8dfc64f34afb3e

[ "MIT" ]

null

Packs/mnemonicMDR/Integrations/ArgusManagedDefence/ArgusManagedDefence.py

matan-xmcyber/content

7f02301c140b35956af3cd20cb8dfc64f34afb3e

[ "MIT" ]

3

2019-12-13T13:27:20.000Z

2020-01-01T14:27:45.000Z

Packs/mnemonicMDR/Integrations/ArgusManagedDefence/ArgusManagedDefence.py

matan-xmcyber/content

7f02301c140b35956af3cd20cb8dfc64f34afb3e

[ "MIT" ]

null

import demistomock as demisto from CommonServerPython import * """ IMPORTS """ import json import urllib3 import dateparser import traceback from typing import Any, Dict, List, Union import logging from argus_api import session as argus_session from argus_api.api.currentuser.v1.user import get_current_user from argus_api.api.cases.v2.case import ( add_case_tag, add_comment, advanced_case_search, close_case, create_case, delete_case, delete_comment, download_attachment, edit_comment, get_attachment, get_case_metadata_by_id, list_case_attachments, list_case_tags, list_case_comments, remove_case_tag_by_id, remove_case_tag_by_key_value, update_case, ) from argus_api.api.events.v1 import get_event_by_path from argus_api.api.events.v1.case.case import get_events_for_case from argus_api.api.events.v1.aggregated import ( find_aggregated_events, list_aggregated_events, ) from argus_api.api.events.v1.payload import get_payload from argus_api.api.events.v1.pcap import get_pcap from argus_api.api.events.v1.nids import find_n_i_d_s_events, list_n_i_d_s_events from argus_api.api.pdns.v3.search import search_records from argus_api.api.reputation.v1.observation import ( fetch_observations_for_domain, fetch_observations_for_i_p, ) # Disable insecure warnings urllib3.disable_warnings() """ CONSTANTS """ DATE_FORMAT = "%Y-%m-%dT%H:%M:%SZ" PRETTY_DATE_FORMAT = "%b %d, %Y, %H:%M:%S" FETCH_TAG = demisto.params().get("fetch_tag") """ HELPER FUNCTIONS """ """ COMMAND FUNCTIONS """ """ MAIN FUNCTION """ """ ENTRY POINT """ if __name__ in ("__main__", "__builtin__", "builtins"): main()

35.711274

117

0.65273

5da38d402c0b885654d90358b5f682eddb296488

672

py

Python

03.py

SnowWolf75/aoc-2020

1745a6cf46dac097869e5af99194b710e78bed28

[ "Unlicense" ]

null

03.py

SnowWolf75/aoc-2020

1745a6cf46dac097869e5af99194b710e78bed28

[ "Unlicense" ]

null

03.py

SnowWolf75/aoc-2020

1745a6cf46dac097869e5af99194b710e78bed28

[ "Unlicense" ]

null

#!/usr/bin/env python3 import sys, os import unittest from lib.common import * filename = "inputs/2020_12_03_input.txt"

16.8

40

0.638393

5da38e943cdd95f554ae0517d32417a9a5d31b05

699

py

Python

scripts/examples/tools/capturebat.py

fortinet/ips-bph-framework

145e14cced2181f388ade07d78b4f0e9452143dd

[ "Apache-2.0" ]

21

2019-10-24T04:59:52.000Z

2021-05-11T12:47:17.000Z

scripts/examples/tools/capturebat.py

fortinet/ips-bph-framework

145e14cced2181f388ade07d78b4f0e9452143dd

[ "Apache-2.0" ]

null

scripts/examples/tools/capturebat.py

fortinet/ips-bph-framework

145e14cced2181f388ade07d78b4f0e9452143dd

[ "Apache-2.0" ]

9

2019-10-26T16:56:08.000Z

2021-03-15T14:10:21.000Z

# Tool Imports from bph.tools.windows.capturebat import BphCaptureBat as CaptureBat # Core Imports from bph.core.server.template import BphTemplateServer as TemplateServer from bph.core.sample import BphSample as Sample from bph.core.sample import BphLabFile as LabFile from bph.core.session import BphSession as Session session = Session(project_name='blackhat_arsenal_2019') session.start() templateserver = TemplateServer() templateserver.start() capturebat = CaptureBat() capturebat.cleanup() capturebat.execute() capturebat.start() capturebat.execute(delay=15) capturebat.stop() capturebat.execute() capturebat.collect() capturebat.execute() capturebat.files()

24.964286

73

0.786838

5da47e4e4410b3e8309f308ed349c9a9599c9032

2,225

py

Python

dymos/utils/test/test_hermite.py

kaushikponnapalli/dymos

3fba91d0fc2c0e8460717b1bec80774676287739

[ "Apache-2.0" ]

104

2018-09-08T16:52:27.000Z

2022-03-10T23:35:30.000Z

dymos/utils/test/test_hermite.py

kaushikponnapalli/dymos

3fba91d0fc2c0e8460717b1bec80774676287739

[ "Apache-2.0" ]

628

2018-06-27T20:32:59.000Z

2022-03-31T19:24:32.000Z

dymos/utils/test/test_hermite.py

kaushikponnapalli/dymos

3fba91d0fc2c0e8460717b1bec80774676287739

[ "Apache-2.0" ]

46

2018-06-27T20:54:07.000Z

2021-12-19T07:23:32.000Z

import unittest import numpy as np from numpy.testing import assert_almost_equal from dymos.utils.hermite import hermite_matrices if __name__ == '__main__': # pragma: no cover unittest.main()

31.785714

79

0.613483

5da53bca28edc1a3193db977a15e4f5897d0d909

2,569

py

Python

xl_auth/settings.py

libris/xl_auth

33d705c287d2ecd81920d37c3751d947cd52588c

[ "Apache-2.0" ]

7

2017-09-04T10:24:02.000Z

2019-12-02T13:12:30.000Z

xl_auth/settings.py

libris/xl_auth

33d705c287d2ecd81920d37c3751d947cd52588c

[ "Apache-2.0" ]

140

2017-09-06T07:02:18.000Z

2022-02-26T01:26:25.000Z

xl_auth/settings.py

libris/xl_auth

33d705c287d2ecd81920d37c3751d947cd52588c

[ "Apache-2.0" ]

2

2017-09-13T16:42:57.000Z

2018-02-15T15:32:40.000Z

# -*- coding: utf-8 -*- """Application configuration.""" from __future__ import absolute_import, division, print_function, unicode_literals import os from . import __author__, __name__, __version__

34.716216

82

0.696769

5da5c1523876b5ad6f15a38ad4bcfea7774fd3c9

3,083

py

Python

tests/mrp/test_mrp_auth.py

evanreichard/pyatv

d41bd749bbf8f8a9365e7fd36c1164543e334565

[ "MIT" ]

null

tests/mrp/test_mrp_auth.py

evanreichard/pyatv

d41bd749bbf8f8a9365e7fd36c1164543e334565

[ "MIT" ]

1

2020-06-13T15:14:47.000Z

tests/mrp/test_mrp_auth.py

evanreichard/pyatv

d41bd749bbf8f8a9365e7fd36c1164543e334565

[ "MIT" ]

null

"""Functional authentication tests with fake MRP Apple TV.""" import inspect from aiohttp.test_utils import AioHTTPTestCase, unittest_run_loop import pyatv from pyatv import exceptions from pyatv.const import Protocol from pyatv.conf import MrpService, AppleTV from pyatv.mrp.server_auth import PIN_CODE, CLIENT_IDENTIFIER, CLIENT_CREDENTIALS from tests.fake_device import FakeAppleTV

32.114583

81

0.705157

5da69a858193b1623616f277374a6ced50dc8b34

352

py

Python

tests_app/tests/functional/key_constructor/bits/models.py

maryokhin/drf-extensions

8223db2bdddaf3cd99f951b2291210c5fd5b0e6f

[ "MIT" ]

1

2019-06-18T16:40:33.000Z

tests_app/tests/functional/key_constructor/bits/models.py

maryokhin/drf-extensions

8223db2bdddaf3cd99f951b2291210c5fd5b0e6f

[ "MIT" ]

null

tests_app/tests/functional/key_constructor/bits/models.py

maryokhin/drf-extensions

8223db2bdddaf3cd99f951b2291210c5fd5b0e6f

[ "MIT" ]

1

2018-07-17T00:13:19.000Z

# -*- coding: utf-8 -*- from django.db import models

22

60

0.713068

5da7c35c6f555424a35c54ce0dd94e20ac56d5b8

4,116

py

Python

ngraph_onnx/onnx_importer/utils/numeric_limits.py

cliveseldon/ngraph-onnx

a2d20afdc7acd5064e4717612ad372d864d03d3d

[ "Apache-2.0" ]

null

ngraph_onnx/onnx_importer/utils/numeric_limits.py

cliveseldon/ngraph-onnx

a2d20afdc7acd5064e4717612ad372d864d03d3d

[ "Apache-2.0" ]

null

ngraph_onnx/onnx_importer/utils/numeric_limits.py

cliveseldon/ngraph-onnx

a2d20afdc7acd5064e4717612ad372d864d03d3d

[ "Apache-2.0" ]

null

# ****************************************************************************** # Copyright 2018 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ****************************************************************************** from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import numpy as np import numbers from typing import Union

34.588235

94

0.640671

5da817172273224f419b42630ca0117dc06b1363

5,158

py

Python

curvpack/utils.py

AbhilashReddyM/curvpack

74351624ec9ec50ec4445c7be85a48a4eabb029a

[ "BSD-3-Clause" ]

8

2019-04-30T19:31:57.000Z

2022-02-25T14:50:56.000Z

curvpack/utils.py

AbhilashReddyM/curvpack

74351624ec9ec50ec4445c7be85a48a4eabb029a

[ "BSD-3-Clause" ]

1

2019-06-14T06:32:40.000Z

2019-06-14T18:26:01.000Z

curvpack/utils.py

AbhilashReddyM/curvpack

74351624ec9ec50ec4445c7be85a48a4eabb029a

[ "BSD-3-Clause" ]

3

2020-04-18T10:13:55.000Z

2022-02-02T03:53:04.000Z

import numpy as np # The first two functions are modified from MNE surface project. LIcense follows # This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative. # # Copyright (c) 2011-2019, authors of MNE-Python. All rights reserved. # # Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: # # Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. # Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. # Neither the names of MNE-Python authors nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. # # This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright owner or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage. def triangle_neighbors(tris, npts): """Efficiently compute vertex neighboring triangles. Returns the triangles in the 1-ring of a given vertex """ # this code replaces the following, but is faster (vectorized): # # this['neighbor_tri'] = [list() for _ in xrange(this['np'])] # for p in xrange(this['ntri']): # verts = this['tris'][p] # this['neighbor_tri'][verts[0]].append(p) # this['neighbor_tri'][verts[1]].append(p) # this['neighbor_tri'][verts[2]].append(p) # this['neighbor_tri'] = [np.array(nb, int) for nb in this['neighbor_tri']] # verts = tris.ravel() counts = np.bincount(verts, minlength=npts) reord = np.argsort(verts) tri_idx = np.unravel_index(reord, (len(tris), 3))[0] idx = np.cumsum(np.r_[0, counts]) # the sort below slows it down a bit, but is needed for equivalence neighbor_tri = np.array([np.sort(tri_idx[v1:v2]) for v1, v2 in zip(idx[:-1], idx[1:])]) return neighbor_tri def get_surf_neighbors(tris,neighbor_tri, k): """Get vertices of 1-ring """ verts = tris[neighbor_tri[k]] verts = np.setdiff1d(verts, [k], assume_unique=False) nneighbors = len(verts) return verts def GetVertexNormals(vertices,faces,FaceNormals,e0,e1,e2): """ INPUT: Vertices : vertices Faces : vertex connectivity FaceNormals : Outer Normal per face, having magnitude equal to area of face e0,e1,e2 : edge vectors OUTPUT: VertNormals : Unit normal at the vertex """ VertNormals =np.zeros(vertices.shape) #edge lengths de0=np.sqrt(e0[:,0]**2+e0[:,1]**2+e0[:,2]**2) de1=np.sqrt(e1[:,0]**2+e1[:,1]**2+e1[:,2]**2) de2=np.sqrt(e2[:,0]**2+e2[:,1]**2+e2[:,2]**2) L2=np.c_[de0**2,de1**2,de2**2] #Calculate weights according to N.Max [1999] for normals wfv1=FaceNormals/(L2[:,1]*L2[:,2])[:,np.newaxis] wfv2=FaceNormals/(L2[:,2]*L2[:,0])[:,np.newaxis] wfv3=FaceNormals/(L2[:,0]*L2[:,1])[:,np.newaxis] # #Calculate the weights according to MWA for normals # wfv1=FaceNormals*np.arcsin(2*Af/(de1*de2))[:,np.newaxis] # wfv2=FaceNormals*np.arcsin(2*Af/(de2*de0))[:,np.newaxis] # wfv3=FaceNormals*np.arcsin(2*Af/(de0*de1))[:,np.newaxis] verts=faces.T[0] for j in [0,1,2]: VertNormals[:,j]+=np.bincount(verts,minlength=vertices.shape[0],weights=wfv1[:,j]) verts=faces.T[1] for j in [0,1,2]: VertNormals[:,j]+=np.bincount(verts,minlength=vertices.shape[0],weights=wfv2[:,j]) verts=faces.T[2] for j in [0,1,2]: VertNormals[:,j]+=np.bincount(verts,minlength=vertices.shape[0],weights=wfv3[:,j]) VertNormals=normr(VertNormals) return VertNormals def fastcross(x, y): """Compute cross product between list of 3D vectors Input x : Mx3 array y : Mx3 array Output z : Mx3 array Cross product of x and y. """ if max([x.shape[0], y.shape[0]]) >= 500: return np.c_[x[:, 1] * y[:, 2] - x[:, 2] * y[:, 1], x[:, 2] * y[:, 0] - x[:, 0] * y[:, 2], x[:, 0] * y[:, 1] - x[:, 1] * y[:, 0]] else: return np.cross(x, y) def normr(vec): """ Normalizes an array of vectors. e.g. to convert a np array of vectors to unit vectors """ return vec/np.sqrt((vec**2).sum(axis=1))[:,np.newaxis]

44.852174

757

0.65917

5da84607f0ca3d7ead02486a100adef7e245823f

14,805

py

Python

tests/unit/core/streams/test_stream_zero.py

tethys-platform/tethys

c27daf5a832b05f9d771b04355001c331bc08766

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

2

2020-05-20T19:03:14.000Z

2020-06-03T20:43:34.000Z

tests/unit/core/streams/test_stream_zero.py

tethys-platform/tethys

c27daf5a832b05f9d771b04355001c331bc08766

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

null

tests/unit/core/streams/test_stream_zero.py

tethys-platform/tethys

c27daf5a832b05f9d771b04355001c331bc08766

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

null

# Copyright 2020 Konstruktor, 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 gc import platform import time from unittest import mock from unittest.mock import patch, call from pytest import fixture from tethys.core.pipes.pipe_zero import ZeroPipe from tethys.core.sessions.sess_zero import ZeroSession from tethys.core.stations.station_zero import ZeroStation from tethys.core.streams.stream_zero import ZeroStream from tethys.core.transports.transport_zero import ZeroTransport # conn context def test_new_connection_context(self, stream): with stream.connection_context(): MockTransport.connect.assert_called_once_with(stream) MockTransport.disconnect.assert_not_called() MockTransport.disconnect.assert_called_once_with(stream) def test_old_connection_context(self, stream): MockTransport._connections[stream.id] = stream with stream.connection_context(): MockTransport.connect.assert_not_called() MockTransport.disconnect.assert_not_called() # heartbeat # open # close # read # write # ack # redirect # open/close context

30.779626

88

0.640662

5da8c5b2385d6f73170c02cb6de27d3641c827fa

6,653

py

Python

amd64-linux/lib/ppc64_simple_components.py

qiyancos/Simics-3.0.31

9bd52d5abad023ee87a37306382a338abf7885f1

[ "BSD-4-Clause", "FSFAP" ]

1

2020-06-15T10:41:18.000Z

amd64-linux/lib/ppc64_simple_components.py

qiyancos/Simics-3.0.31

9bd52d5abad023ee87a37306382a338abf7885f1

[ "BSD-4-Clause", "FSFAP" ]

null

amd64-linux/lib/ppc64_simple_components.py

qiyancos/Simics-3.0.31

9bd52d5abad023ee87a37306382a338abf7885f1

[ "BSD-4-Clause", "FSFAP" ]

3

2020-08-10T10:25:02.000Z

2021-09-12T01:12:09.000Z

## Copyright 2005-2007 Virtutech AB ## ## The contents herein are Source Code which are a subset of Licensed ## Software pursuant to the terms of the Virtutech Simics Software ## License Agreement (the "Agreement"), and are being distributed under ## the Agreement. You should have received a copy of the Agreement with ## this Licensed Software; if not, please contact Virtutech for a copy ## of the Agreement prior to using this Licensed Software. ## ## By using this Source Code, you agree to be bound by all of the terms ## of the Agreement, and use of this Source Code is subject to the terms ## the Agreement. ## ## This Source Code and any derivatives thereof are provided on an "as ## is" basis. Virtutech makes no warranties with respect to the Source ## Code or any derivatives thereof and disclaims all implied warranties, ## including, without limitation, warranties of merchantability and ## fitness for a particular purpose and non-infringement. from sim_core import * from components import * import time # Generic Simple System for PPC64 Processors ppc64_simple_attributes = [ ['cpu_frequency', Sim_Attr_Required, 'f', 'Processor frequency in MHz.'], ['memory_megs', Sim_Attr_Required, 'i', 'The amount of RAM in megabytes.'], ['map_offset', Sim_Attr_Optional, 'i', 'Base address for device mappings. ' \ 'Offsets at 4 GB and above will not work'], ['time_of_day', Sim_Attr_Optional, 's', 'Date and time to initialize the OpenFirmware RTC to']]

37.587571

78

0.599579

5da9e73a716a3d83801c56b0312fd8f4d87f351c

385

py

Python

Front-end (Django)/course/migrations/0002_subject_number_of_questions.py

shadow0403bsr/AutomatedGradingSoftware

5031d22683a05f937615b3b8997152c285a2f930

[ "MIT" ]

null

Front-end (Django)/course/migrations/0002_subject_number_of_questions.py

shadow0403bsr/AutomatedGradingSoftware

5031d22683a05f937615b3b8997152c285a2f930

[ "MIT" ]

null

Front-end (Django)/course/migrations/0002_subject_number_of_questions.py

shadow0403bsr/AutomatedGradingSoftware

5031d22683a05f937615b3b8997152c285a2f930

[ "MIT" ]

null

# Generated by Django 3.0.1 on 2020-02-15 06:02 from django.db import migrations, models

20.263158

49

0.597403

5da9e91d7d69e5378260fe7c404a58e9aa312b9e

3,101

py

Python

cornflow/tests/unit/test_dags.py

pchtsp/corn

2811ad400f3f3681a159984eabf4fee1fc99b433

[ "MIT" ]

5

2021-11-24T02:43:22.000Z

2021-12-10T09:28:32.000Z

cornflow/tests/unit/test_dags.py

pchtsp/corn

2811ad400f3f3681a159984eabf4fee1fc99b433

[ "MIT" ]

125

2021-09-01T12:06:48.000Z

2022-03-30T11:32:57.000Z

cornflow/tests/unit/test_dags.py

pchtsp/corn

2811ad400f3f3681a159984eabf4fee1fc99b433

[ "MIT" ]

1

2022-03-23T17:57:59.000Z

""" Unit test for the DAG endpoints """ # Import from libraries import json # Import from internal modules from cornflow.shared.const import EXEC_STATE_CORRECT, EXEC_STATE_MANUAL from cornflow.tests.const import ( DAG_URL, EXECUTION_URL_NORUN, CASE_PATH, INSTANCE_URL, ) from cornflow.tests.unit.test_executions import TestExecutionsDetailEndpointMock

27.936937

80

0.563689

5daa62bfbc58bf60d68725712bf46468f85577d3

10,673

py

Python

nets/resnet.py

xwshi/faster-rcnn-keras

bfd99e3d0e786ada75a212c007111364b2c86312

[ "MIT" ]

null

nets/resnet.py

xwshi/faster-rcnn-keras

bfd99e3d0e786ada75a212c007111364b2c86312

[ "MIT" ]

null

nets/resnet.py

xwshi/faster-rcnn-keras

bfd99e3d0e786ada75a212c007111364b2c86312

[ "MIT" ]

null

#-------------------------------------------------------------# # ResNet50 #-------------------------------------------------------------# import keras.backend as K from keras import backend as K from keras import initializers, layers, regularizers from keras.engine import InputSpec, Layer from keras.initializers import random_normal from keras.layers import (Activation, Add, AveragePooling2D, Conv2D, MaxPooling2D, TimeDistributed, ZeroPadding2D)

45.417021

148

0.599082

5daa836b2adc23c7d1169d134b820b47732f82c0

1,309

py

Python

app/api/deps.py

congdh/fastapi-realworld

42c8630aedf594b69bc96a327b04dfe636a785fe

[ "MIT" ]

null

app/api/deps.py

congdh/fastapi-realworld

42c8630aedf594b69bc96a327b04dfe636a785fe

[ "MIT" ]

null

app/api/deps.py

congdh/fastapi-realworld

42c8630aedf594b69bc96a327b04dfe636a785fe

[ "MIT" ]

null

from typing import Generator from fastapi import Depends, HTTPException from fastapi.security import APIKeyHeader from sqlalchemy.orm import Session from starlette import status from app import crud, models from app.core import security from app.db.session import SessionLocal JWT_TOKEN_PREFIX = "Token" # noqa: S105

26.714286

81

0.694423

5daaea4f5cbe880e71d3bbf0f6ec12e332c717ab

2,343

py

Python

src/raiden_libs/contract_info.py

netcriptus/raiden-services

3955d91852c616f6ba0a3a979757edbd852b2c6d

[ "MIT" ]

13

2019-02-07T23:23:33.000Z

2021-07-03T16:00:53.000Z

src/raiden_libs/contract_info.py

netcriptus/raiden-services

3955d91852c616f6ba0a3a979757edbd852b2c6d

[ "MIT" ]

1,095

2019-01-21T09:30:57.000Z

2022-03-25T05:13:30.000Z

src/raiden_libs/contract_info.py

netcriptus/raiden-services

3955d91852c616f6ba0a3a979757edbd852b2c6d

[ "MIT" ]

18

2019-01-21T09:17:19.000Z

2022-02-23T15:53:17.000Z

import sys from typing import Dict, List, Tuple import structlog from eth_utils import to_canonical_address from raiden.utils.typing import Address, BlockNumber, ChainID, Optional from raiden_contracts.contract_manager import ( ContractDevEnvironment, ContractManager, contracts_precompiled_path, get_contracts_deployment_info, ) log = structlog.get_logger(__name__) CONTRACT_MANAGER = ContractManager(contracts_precompiled_path()) def get_contract_addresses_and_start_block( chain_id: ChainID, contracts: List[str], address_overwrites: Dict[str, Address], development_environment: ContractDevEnvironment = ContractDevEnvironment.DEMO, contracts_version: Optional[str] = None, ) -> Tuple[Dict[str, Address], BlockNumber]: """Returns contract addresses and start query block for a given chain and contracts version. The default contracts can be overwritten by the additional parameters. Args: chain_id: The chain id to look for deployed contracts. contracts: The list of contracts which should be considered address_overwrites: Dict of addresses which should be used instead of the ones in the requested deployment. contracts_version: The version of the contracts to use. Returns: A dictionary with the contract addresses and start block for the given information """ contract_data = get_contracts_deployment_info( chain_id=chain_id, version=contracts_version, development_environment=development_environment, ) if not contract_data: log.error( "No deployed contracts were found at the default registry", contracts_version=contracts_version, ) sys.exit(1) # Get deployed addresses for those contracts which have no overwrites addresses = { c: ( address_overwrites.get(c) or to_canonical_address(contract_data["contracts"][c]["address"]) ) for c in contracts } # Set start block to zero if any contract addresses are overwritten if any(address_overwrites.values()): start_block = BlockNumber(0) else: start_block = BlockNumber( max(0, min(contract_data["contracts"][c]["block_number"] for c in contracts)) ) return addresses, start_block

33.956522

96

0.714042

5dab624f1bba960c93bdbcfc0dd2115a637b7aae

8,749

py

Python

meta_dataset/models/functional_classifiers.py

letyrodridc/meta-dataset

d868ea1c767cce46fa6723f6f77c29552754fcc9

[ "Apache-2.0" ]

null

meta_dataset/models/functional_classifiers.py

letyrodridc/meta-dataset

d868ea1c767cce46fa6723f6f77c29552754fcc9

[ "Apache-2.0" ]

null

meta_dataset/models/functional_classifiers.py

letyrodridc/meta-dataset

d868ea1c767cce46fa6723f6f77c29552754fcc9

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2022 The Meta-Dataset Authors. # # 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. # Lint as: python2,python3 """Classifier-related code.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import gin.tf from meta_dataset.models import functional_backbones import tensorflow.compat.v1 as tf def linear_classifier_forward_pass(embeddings, w_fc, b_fc, cosine_classifier, cosine_logits_multiplier, use_weight_norm): """Passes embeddings through the linear layer defined by w_fc and b_fc. Args: embeddings: A Tensor of size [batch size, embedding dim]. w_fc: A Tensor of size [embedding dim, num outputs]. b_fc: Either None, or a Tensor of size [num outputs] or []. If cosine_classifier is False, it can not be None. cosine_classifier: A bool. If true, a cosine classifier is used which does not require the bias b_fc. cosine_logits_multiplier: A float. Only used if cosine_classifier is True, and multiplies the resulting logits. use_weight_norm: A bool. Whether weight norm was used. If so, then if using cosine classifier, normalize only the embeddings but not the weights. Returns: logits: A Tensor of size [batch size, num outputs]. """ if cosine_classifier: # Each column of the weight matrix may be interpreted as a class # representation (of the same dimenionality as the embedding space). The # logit for an embedding vector belonging to that class is the cosine # similarity between that embedding and that class representation. embeddings = tf.nn.l2_normalize(embeddings, axis=1, epsilon=1e-3) if not use_weight_norm: # Only normalize the weights if weight norm was not used. w_fc = tf.nn.l2_normalize(w_fc, axis=0, epsilon=1e-3) logits = tf.matmul(embeddings, w_fc) # Scale the logits as passing numbers in [-1, 1] to softmax is not very # expressive. logits *= cosine_logits_multiplier else: assert b_fc is not None logits = tf.matmul(embeddings, w_fc) + b_fc return logits

41.861244

80

0.686821

5dacac5c524e8494c9a0a1e27e5a00cc81bbbd7d

13,499

py

Python

app.py

Shrinidhi-C/Context-Based-Question-Answering

f2e0bbc03003aae65f4cabddecd5cd9fcdbfb333

[ "Apache-2.0" ]

16

2021-03-09T17:00:27.000Z

2022-01-07T15:49:46.000Z

app.py

Shrinidhi-C/Context-Based-Question-Answering

f2e0bbc03003aae65f4cabddecd5cd9fcdbfb333

[ "Apache-2.0" ]

1

2021-06-03T13:01:41.000Z

app.py

Karthik-Bhaskar/Context-Based-Question-Answering

f2e0bbc03003aae65f4cabddecd5cd9fcdbfb333

[ "Apache-2.0" ]

7

2021-03-10T11:33:18.000Z

2022-01-07T17:48:17.000Z

import os import threading import shutil from datetime import timedelta, datetime from flask import Flask, render_template, request, session, jsonify, url_for, redirect from haystack.document_store.elasticsearch import * from haystack.preprocessor.utils import convert_files_to_dicts from haystack.preprocessor.cleaning import clean_wiki_text from haystack import Finder from haystack.retriever.sparse import ElasticsearchRetriever from haystack.reader.transformers import TransformersReader from elasticsearch import Elasticsearch es = ( Elasticsearch() ) # Replace with Elasticsearch(["http://elasticsearch:9200/"], verify_certs=True) to build docker image session_time = 60 # Session Timeout in Minutes app = Flask(__name__) app.secret_key = "cbqa_123" app.permanent_session_lifetime = timedelta(minutes=session_time) user_id = 0 # User ID to keep track w.r.t sessions and context data current_users = dict() # Used to store user id with time of login user_doc_store = dict() # Document store object of the user id user_settings = dict() # User settings for GPU and Pre-trained models choice # Handles pre-processing the context and uploads the pre-processed context to Elasticsearch # Each user is assigned with a separate Elasticsearch index starting with "user_{user_id}" # Documents & textual context are deleted from them temp folder named with user_id under users dir after uploading to Es # Handles setting up reader and retriever # Handles deletion of context data completely from the server after the session time ends and deletes user id from dict session_timer = threading.Thread(target=user_session_timer) session_timer.start() # Handles users w.r.t new session or already in session # Handles context documents uploads # Handles context added through the textbox # Provides extracted answers for the posted question # Handles GPU setting changes. # Handles pre-trained model choice setting changes. # Handles session timeout redirection # Handles removing of session identifier from session dict, This works only when app tab is open until session completes # Comment the below block in case of building a docker image or running on WSGI server like gunicorn if __name__ == "__main__": app.run(host="0.0.0.0")

38.132768

120

0.588414

5dad62563785343452980a6c164a9cfda04650c2

7,486

py

Python

timevortex/utils/filestorage.py

timevortexproject/timevortex

2bc1a50b255524af8582e6624dee280d64d3c9f3

[ "MIT" ]

null

timevortex/utils/filestorage.py

timevortexproject/timevortex

2bc1a50b255524af8582e6624dee280d64d3c9f3

[ "MIT" ]

null

timevortex/utils/filestorage.py

timevortexproject/timevortex

2bc1a50b255524af8582e6624dee280d64d3c9f3

[ "MIT" ]

null

#!/usr/bin/python3 # -*- coding: utf8 -*- # -*- Mode: Python; py-indent-offset: 4 -*- """File storage adapter for timevortex project""" import os from os import listdir, makedirs from os.path import isfile, join, exists from time import tzname from datetime import datetime import pytz import dateutil.parser from django.conf import settings from django.utils import timezone from timevortex.utils.globals import LOGGER, KEY_ERROR, KEY_SITE_ID, KEY_VARIABLE_ID, KEY_VALUE, KEY_DATE from timevortex.utils.globals import KEY_DST_TIMEZONE, KEY_NON_DST_TIMEZONE, SYSTEM_SITE_ID SETTINGS_FILE_STORAGE_FOLDER = "SETTINGS_FILE_STORAGE_FOLDER" SETTINGS_DEFAULT_FILE_STORAGE_FOLDER = "/tmp/data/" def get_lines_number(file_path): """Get lines number """ return sum(1 for line in open(file_path)) def get_series_per_file(site_folder, file_prefix): """Get series per file """ series = {} for filename in listdir(site_folder): is_file = isfile(join(site_folder, filename)) if is_file and file_prefix in filename: complete_filename = "%s/%s" % (site_folder, filename) with open(complete_filename, "r") as filed: temp_series = filed.readlines() for line in temp_series: array_line = line.split("\t") if len(array_line) >= 2: series[array_line[1]] = array_line[0] return series def get_last_file_name(site_folder, file_prefix): """Get last filename """ old_date = None last_filename = "" for new_filename in listdir(site_folder): is_file = isfile(join(site_folder, new_filename)) if is_file and file_prefix in new_filename: old_date, last_filename = update_last_file_name(file_prefix, old_date, last_filename, new_filename) return last_filename def update_last_file_name(file_prefix, old_date, last_filename, new_filename): """Update last file name """ try: new_date = new_filename.replace(file_prefix, "") new_date = datetime.strptime(new_date, "%Y-%m-%d") if old_date is None or new_date > old_date: return new_date, new_filename except ValueError: LOGGER.error("Not right file") return old_date, last_filename FILE_STORAGE_SPACE = FileStorage(getattr(settings, SETTINGS_FILE_STORAGE_FOLDER, SETTINGS_DEFAULT_FILE_STORAGE_FOLDER))

34.497696

119

0.612209

5daed49fa4c053c06f93d18e081f06b652a982e8

4,078

py

Python

main_tg.py

olegush/quiz-bot

ae370d42f32c42b290a507924a801c63901d5148

[ "MIT" ]

null

main_tg.py

olegush/quiz-bot

ae370d42f32c42b290a507924a801c63901d5148

[ "MIT" ]

null

main_tg.py

olegush/quiz-bot

ae370d42f32c42b290a507924a801c63901d5148

[ "MIT" ]

null

import os import logging import logging.config from functools import partial from dotenv import load_dotenv from telegram import Bot, ReplyKeyboardMarkup, ReplyKeyboardRemove from telegram.ext import (Updater, CommandHandler, MessageHandler, RegexHandler, ConversationHandler, Filters) from redis import Redis from tg_logging import create_logger from quiz_tools import get_question_and_answer, format_answer, format_question QUESTION, ATTEMPT = range(2) if __name__ == '__main__': main()

31.612403

102

0.632908

5daf4ad3d9f3b39d8355c443ca683a3b5708554c

3,082

py

Python

tf_fourier_features/fourier_features_mlp.py

titu1994/tf_fourier_features

3aead078ae79a278b9975e21f44560a7f51e3f31

[ "MIT" ]

37

2020-06-20T21:39:30.000Z

2021-11-08T09:31:22.000Z

tf_fourier_features/fourier_features_mlp.py

titu1994/tf_fourier_features

3aead078ae79a278b9975e21f44560a7f51e3f31

[ "MIT" ]

null

tf_fourier_features/fourier_features_mlp.py

titu1994/tf_fourier_features

3aead078ae79a278b9975e21f44560a7f51e3f31

[ "MIT" ]

5

2020-06-22T10:24:11.000Z

2021-09-10T10:40:08.000Z

import tensorflow as tf from typing import Optional from tf_fourier_features import fourier_features

48.15625

156

0.633679

5db0d4e1070be8121a1c33bca072f550967ebe82

6,969

py

Python

eek/spider.py

fusionbox/eek

8e962b7ad80c594a3498190fead016db826771e0

[ "BSD-2-Clause-FreeBSD" ]

5

2015-05-11T18:13:51.000Z

2021-07-17T04:53:27.000Z

eek/spider.py

fusionbox/eek

8e962b7ad80c594a3498190fead016db826771e0

[ "BSD-2-Clause-FreeBSD" ]

1

2015-03-06T20:32:14.000Z

eek/spider.py

fusionbox/eek

8e962b7ad80c594a3498190fead016db826771e0

[ "BSD-2-Clause-FreeBSD" ]

2

2015-07-15T12:41:32.000Z

2015-10-12T21:40:14.000Z

import urlparse import csv import sys import re import collections import time import requests from eek import robotparser # this project's version from bs4 import BeautifulSoup try: import lxml except ImportError: HTML_PARSER = None else: HTML_PARSER = 'lxml' encoding_re = re.compile("charset\s*=\s*(\S+?)(;|$)") html_re = re.compile("text/html") headers = ['url', 'title', 'description', 'keywords', 'allow', 'disallow', 'noindex', 'meta robots', 'canonical', 'referer', 'status'] def encoding_from_content_type(content_type): """ Extracts the charset from a Content-Type header. >>> encoding_from_content_type('text/html; charset=utf-8') 'utf-8' >>> encoding_from_content_type('text/html') >>> """ if not content_type: return None match = encoding_re.search(content_type) return match and match.group(1) or None def lremove(string, prefix): """ Remove a prefix from a string, if it exists. >>> lremove('www.foo.com', 'www.') 'foo.com' >>> lremove('foo.com', 'www.') 'foo.com' """ if string.startswith(prefix): return string[len(prefix):] else: return string def beautify(response): content_type = response.headers.get('content-type') if content_type: if not html_re.search(content_type): raise NotHtmlException encoding = encoding_from_content_type(content_type) else: encoding = None try: return BeautifulSoup( response.content, features=HTML_PARSER, from_encoding=encoding, ) except UnicodeEncodeError: raise NotHtmlException def get_links(response): if 300 <= response.status_code < 400 and response.headers['location']: # redirect yield urlparse.urldefrag( urlparse.urljoin(response.url, response.headers['location'], False) )[0] try: html = beautify(response) for i in html.find_all('a', href=True): yield urlparse.urldefrag(urlparse.urljoin(response.url, i['href'], False))[0] except NotHtmlException: pass

29.529661

99

0.605252

5db204f5af9206eceaf400a510a5e3d05316e861

2,647

py

Python

observations/r/zea_mays.py

hajime9652/observations

2c8b1ac31025938cb17762e540f2f592e302d5de

[ "Apache-2.0" ]

199

2017-07-24T01:34:27.000Z

2022-01-29T00:50:55.000Z

observations/r/zea_mays.py

hajime9652/observations

2c8b1ac31025938cb17762e540f2f592e302d5de

[ "Apache-2.0" ]

46

2017-09-05T19:27:20.000Z

2019-01-07T09:47:26.000Z

observations/r/zea_mays.py

hajime9652/observations

2c8b1ac31025938cb17762e540f2f592e302d5de

[ "Apache-2.0" ]

45

2017-07-26T00:10:44.000Z

2022-03-16T20:44:59.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function import csv import numpy as np import os import sys from observations.util import maybe_download_and_extract def zea_mays(path): """Darwin's Heights of Cross- and Self-fertilized Zea May Pairs Darwin (1876) studied the growth of pairs of zea may (aka corn) seedlings, one produced by cross-fertilization and the other produced by self-fertilization, but otherwise grown under identical conditions. His goal was to demonstrate the greater vigour of the cross-fertilized plants. The data recorded are the final height (inches, to the nearest 1/8th) of the plants in each pair. In the *Design of Experiments*, Fisher (1935) used these data to illustrate a paired t-test (well, a one-sample test on the mean difference, `cross - self`). Later in the book (section 21), he used this data to illustrate an early example of a non-parametric permutation test, treating each paired difference as having (randomly) either a positive or negative sign. A data frame with 15 observations on the following 4 variables. `pair` pair number, a numeric vector `pot` pot, a factor with levels `1` `2` `3` `4` `cross` height of cross fertilized plant, a numeric vector `self` height of self fertilized plant, a numeric vector `diff` `cross - self` for each pair Darwin, C. (1876). *The Effect of Cross- and Self-fertilization in the Vegetable Kingdom*, 2nd Ed. London: John Murray. Andrews, D. and Herzberg, A. (1985) *Data: a collection of problems from many fields for the student and research worker*. New York: Springer. Data retrieved from: `https://www.stat.cmu.edu/StatDat/` Args: path: str. Path to directory which either stores file or otherwise file will be downloaded and extracted there. Filename is `zea_mays.csv`. Returns: Tuple of np.ndarray `x_train` with 15 rows and 5 columns and dictionary `metadata` of column headers (feature names). """ import pandas as pd path = os.path.expanduser(path) filename = 'zea_mays.csv' if not os.path.exists(os.path.join(path, filename)): url = 'http://dustintran.com/data/r/HistData/ZeaMays.csv' maybe_download_and_extract(path, url, save_file_name='zea_mays.csv', resume=False) data = pd.read_csv(os.path.join(path, filename), index_col=0, parse_dates=True) x_train = data.values metadata = {'columns': data.columns} return x_train, metadata

32.679012

74

0.705327

5db3dae6928f712e933165c643051e536448b1fb

359

py

Python

ois_api_client/v3_0/dto/Lines.py

peterkulik/ois_api_client

51dabcc9f920f89982c4419bb058f5a88193cee0

[ "MIT" ]

7

2020-10-22T08:15:29.000Z

2022-01-27T07:59:39.000Z

ois_api_client/v3_0/dto/Lines.py

peterkulik/ois_api_client

51dabcc9f920f89982c4419bb058f5a88193cee0

[ "MIT" ]

null

ois_api_client/v3_0/dto/Lines.py

peterkulik/ois_api_client

51dabcc9f920f89982c4419bb058f5a88193cee0

[ "MIT" ]

null

from typing import List from dataclasses import dataclass from .Line import Line

22.4375

117

0.743733

5db3e4eb84b3d9fc5559048f6229e0e36618f2f4

1,545

py

Python

parsing_documents.py

leylafenix/belief-network-irs

9094e4cde738bd93ed1747dc958b5acb0e0fa684

[ "MIT" ]

null

parsing_documents.py

leylafenix/belief-network-irs

9094e4cde738bd93ed1747dc958b5acb0e0fa684

[ "MIT" ]

null

parsing_documents.py

leylafenix/belief-network-irs

9094e4cde738bd93ed1747dc958b5acb0e0fa684

[ "MIT" ]

null

__author__ = 'Jose Gabriel' import os import pprint if __name__ == '__main__': s = "adi" + os.sep + "ADI.ALL" out_folder = "test_index" try: # averiguar como preguntar si una carpeta o fichero existe en python os.mkdir(out_folder) except FileExistsError: pass parse_all(s, out_folder)

26.186441

79

0.514563

5db477aeb7079f4d5d6c834082c575659d075877

161

py

Python

groups/admin.py

caktus/rapidsms-groups

eda6f30cdc60cf57833f1d37ba08e59454da8987

[ "BSD-3-Clause" ]

1

2016-06-19T07:34:19.000Z

groups/admin.py

caktus/rapidsms-groups

eda6f30cdc60cf57833f1d37ba08e59454da8987

[ "BSD-3-Clause" ]

null

groups/admin.py

caktus/rapidsms-groups

eda6f30cdc60cf57833f1d37ba08e59454da8987

[ "BSD-3-Clause" ]

1

2016-08-31T05:02:03.000Z

#!/usr/bin/env python # vim: ai ts=4 sts=4 et sw=4 encoding=utf-8 from django.contrib import admin from groups.models import Group admin.site.register(Group)

17.888889

43

0.751553

5db48c51cdf7033a6dcea32b1d26408dd6d2dbc0

1,891

py

Python

avod/datasets/kitti/kitti_aug_test.py

Ascend-Huawei/AVOD

ea62372517bbfa9d4020bc5ab2739ee182c63c56

[ "BSD-2-Clause" ]

null

avod/datasets/kitti/kitti_aug_test.py

Ascend-Huawei/AVOD

ea62372517bbfa9d4020bc5ab2739ee182c63c56

[ "BSD-2-Clause" ]

null

avod/datasets/kitti/kitti_aug_test.py

Ascend-Huawei/AVOD

ea62372517bbfa9d4020bc5ab2739ee182c63c56

[ "BSD-2-Clause" ]

null

# Copyright 2017 The TensorFlow Authors. 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. # ============================================================================ # Copyright 2021 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. from npu_bridge.npu_init import * import unittest import numpy as np from avod.datasets.kitti import kitti_aug

33.767857

78

0.670016

5db5a1d6975c3995d47712215ed2acd01be9b8ad

208

py

Python

application/model/_base.py

keysona/blog

783e0bdbed1e4d8ec9857ee609b39c9dfb958670

[ "MIT" ]

null

application/model/_base.py

keysona/blog

783e0bdbed1e4d8ec9857ee609b39c9dfb958670

[ "MIT" ]

null

application/model/_base.py

keysona/blog

783e0bdbed1e4d8ec9857ee609b39c9dfb958670

[ "MIT" ]

null

from flask_sqlalchemy import SQLAlchemy, Model # class BaseModel(Model): # def save(self): # db.session.add(self) # db.session.commit(self) # def delete(self): # db.session. db = SQLAlchemy()

16

46

0.673077

5db6309005811059a99432092acd5ed62236c399

23,282

py

Python

kvmagent/kvmagent/plugins/prometheus.py

qianfei11/zstack-utility

e791bc6b6ae3a74e202f6fce84bde498c715aee8

[ "Apache-2.0" ]

null

kvmagent/kvmagent/plugins/prometheus.py

qianfei11/zstack-utility

e791bc6b6ae3a74e202f6fce84bde498c715aee8

[ "Apache-2.0" ]

null

kvmagent/kvmagent/plugins/prometheus.py

qianfei11/zstack-utility

e791bc6b6ae3a74e202f6fce84bde498c715aee8

[ "Apache-2.0" ]

null

import os.path import threading import typing from prometheus_client import start_http_server from prometheus_client.core import GaugeMetricFamily, REGISTRY from kvmagent import kvmagent from zstacklib.utils import http from zstacklib.utils import jsonobject from zstacklib.utils import lock from zstacklib.utils import lvm from zstacklib.utils import misc from zstacklib.utils import thread from zstacklib.utils.bash import * from zstacklib.utils.ip import get_nic_supported_max_speed logger = log.get_logger(__name__) collector_dict = {} # type: Dict[str, threading.Thread] latest_collect_result = {} collectResultLock = threading.RLock() QEMU_CMD = kvmagent.get_qemu_path().split("/")[-1] def convert_raid_state_to_int(state): """ :type state: str """ state = state.lower() if state == "optimal": return 0 elif state == "degraded": return 5 else: return 100 def convert_disk_state_to_int(state): """ :type state: str """ state = state.lower() if "online" in state or "jobd" in state: return 0 elif "rebuild" in state: return 5 elif "failed" in state: return 10 elif "unconfigured" in state: return 15 else: return 100 collect_node_disk_wwid_last_time = None collect_node_disk_wwid_last_result = None kvmagent.register_prometheus_collector(collect_host_network_statistics) kvmagent.register_prometheus_collector(collect_host_capacity_statistics) kvmagent.register_prometheus_collector(collect_vm_statistics) kvmagent.register_prometheus_collector(collect_node_disk_wwid) if misc.isMiniHost(): kvmagent.register_prometheus_collector(collect_lvm_capacity_statistics) kvmagent.register_prometheus_collector(collect_raid_state) kvmagent.register_prometheus_collector(collect_equipment_state)

36.492163

157

0.583541

5db634e6fdac00dd4f3ce30f7fe7fbdaae184512

6,924

py

Python

recipes/libstudxml/all/conanfile.py

rockandsalt/conan-center-index

d739adcec3e4dd4c250eff559ceb738e420673dd

[ "MIT" ]

562

2019-09-04T12:23:43.000Z

2022-03-29T16:41:43.000Z

recipes/libstudxml/all/conanfile.py

rockandsalt/conan-center-index

d739adcec3e4dd4c250eff559ceb738e420673dd

[ "MIT" ]

9,799

2019-09-04T12:02:11.000Z

2022-03-31T23:55:45.000Z

recipes/libstudxml/all/conanfile.py

rockandsalt/conan-center-index

d739adcec3e4dd4c250eff559ceb738e420673dd

[ "MIT" ]

1,126

2019-09-04T11:57:46.000Z

2022-03-31T16:43:38.000Z

from conans import ConanFile, AutoToolsBuildEnvironment, MSBuild, tools from conans.errors import ConanInvalidConfiguration import os import shutil required_conan_version = ">=1.33.0"

41.963636

124

0.633449

5db692792275a8f7aff10d7781c4cef5d88900db

6,263

py

Python

dataset/WebCariA.py

KeleiHe/DAAN

04e153c55f8d63e824adbee828e524573afe6a1c

[ "Apache-2.0" ]

9

2020-07-24T03:32:17.000Z

2022-03-25T12:01:24.000Z

dataset/WebCariA.py

KeleiHe/DAAN

04e153c55f8d63e824adbee828e524573afe6a1c

[ "Apache-2.0" ]

1

2020-10-14T17:22:43.000Z

dataset/WebCariA.py

KeleiHe/DAAN

04e153c55f8d63e824adbee828e524573afe6a1c

[ "Apache-2.0" ]

null

# Copyright 2020 Wen Ji & Kelei He ([email protected]) # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os

38.900621

113

0.440683

5db7554e0b55f70192702d11bfb40d5a1d8f2459

4,081

py

Python

moonworm/crawler/state/json_state.py

zomglings/moonworm

930e60199629b6a04adecc7f9ff9450e51bb4640

[ "Apache-2.0" ]

10

2021-12-08T22:35:58.000Z

2022-03-30T07:38:12.000Z

moonworm/crawler/state/json_state.py

zomglings/moonworm

930e60199629b6a04adecc7f9ff9450e51bb4640

[ "Apache-2.0" ]

29

2021-11-04T12:30:31.000Z

2022-03-03T21:29:08.000Z

moonworm/crawler/state/json_state.py

zomglings/moonworm

930e60199629b6a04adecc7f9ff9450e51bb4640

[ "Apache-2.0" ]

5

2021-11-06T02:25:09.000Z

2022-02-15T03:09:26.000Z

import datetime import json import time from typing import Optional from web3.datastructures import AttributeDict from .event_scanner_state import EventScannerState

35.486957

109

0.626072

5db81c5e24b93ba19d16beaadd48634b1c9fd58a

4,934

py

Python

npbench/benchmarks/nbody/nbody_dace.py

frahlg/npbench

1bc4d9e2e22f3ca67fa2bc7f40e2e751a9c8dd26

[ "BSD-3-Clause" ]

27

2021-05-10T11:49:13.000Z

2022-03-22T18:07:19.000Z

npbench/benchmarks/nbody/nbody_dace.py

frahlg/npbench

1bc4d9e2e22f3ca67fa2bc7f40e2e751a9c8dd26

[ "BSD-3-Clause" ]

3

2021-12-01T13:03:17.000Z

2022-03-17T10:53:00.000Z

npbench/benchmarks/nbody/nbody_dace.py

frahlg/npbench

1bc4d9e2e22f3ca67fa2bc7f40e2e751a9c8dd26

[ "BSD-3-Clause" ]

7

2021-06-24T03:40:25.000Z

2022-01-26T09:04:33.000Z

# Adapted from https://github.com/pmocz/nbody-python/blob/master/nbody.py # TODO: Add GPL-3.0 License import numpy as np import dace as dc """ Create Your Own N-body Simulation (With Python) Philip Mocz (2020) Princeton Univeristy, @PMocz Simulate orbits of stars interacting due to gravity Code calculates pairwise forces according to Newton's Law of Gravity """ N, Nt = (dc.symbol(s, dtype=dc.int64) for s in ('N', 'Nt')) # @dc.program # def hstack(out: dc.float64[N, 3], a: dc.float64[N], # b: dc.float64[N], c: dc.float64[N]): # out[:, 0] = a # out[:, 1] = b # out[:, 2] = c

29.195266

85

0.561005

5db8b350508cfde3359da0d0ee1d9036c8e97549

817

py

Python

application/__init__.py

Healthy-Kokoro/Hiroshima

87c6c533f97f55ceb33553a2409076bcd21a36d2

[ "MIT" ]

null

application/__init__.py

Healthy-Kokoro/Hiroshima

87c6c533f97f55ceb33553a2409076bcd21a36d2

[ "MIT" ]

null

application/__init__.py

Healthy-Kokoro/Hiroshima

87c6c533f97f55ceb33553a2409076bcd21a36d2

[ "MIT" ]

null

# Third-party imports from flask import Flask from flask_sqlalchemy import SQLAlchemy configurations = { 'development': 'configurations.DevelopmentConfiguration', 'testing': 'configurations.TestingConfiguration', 'staging': 'configurations.StagingConfiguration', 'production': 'configurations.ProductionConfiguration' } database = SQLAlchemy()

30.259259

64

0.831089

5db8ca5f5d703991674fff33fa5c1ac47210c351

692

py

Python

lesson5/exceptions_except.py

drednout/letspython

9747442d63873b5f71e2c15ed5528bd98ad5ac31

[ "BSD-2-Clause" ]

1

2015-11-26T15:53:58.000Z

lesson5/exceptions_except.py

drednout/letspython

9747442d63873b5f71e2c15ed5528bd98ad5ac31

[ "BSD-2-Clause" ]

null

lesson5/exceptions_except.py

drednout/letspython

9747442d63873b5f71e2c15ed5528bd98ad5ac31

[ "BSD-2-Clause" ]

null

if __name__ == "__main__": fridge = { "beer": 2, "milk": 1, "meat": 3, } print("I wanna drink 1 bottle of beer...") take_beer(fridge) print("Oooh, great!") print("I wanna drink 2 bottle of beer...") try: take_beer(fridge, 2) except Exception as e: print("Error: {}. Let's continue".format(e)) print("Fallback. Try to take 1 bottle of beer...") take_beer(fridge, 1) print("Oooh, awesome!")

22.322581

54

0.559249

5db9f201356818f114d992f32b2d46869da4d326

23,877

py

Python

synapse/storage/data_stores/state/store.py

juhovan/synapse

57feeab364325374b14ff67ac97c288983cc5cde

[ "Apache-2.0" ]

1

2020-07-12T00:18:52.000Z

synapse/storage/data_stores/state/store.py

juhovan/synapse

57feeab364325374b14ff67ac97c288983cc5cde

[ "Apache-2.0" ]

null

synapse/storage/data_stores/state/store.py

juhovan/synapse

57feeab364325374b14ff67ac97c288983cc5cde

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2014-2016 OpenMarket 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. import logging from collections import namedtuple from typing import Dict, Iterable, List, Set, Tuple from twisted.internet import defer from synapse.api.constants import EventTypes from synapse.storage._base import SQLBaseStore from synapse.storage.data_stores.state.bg_updates import StateBackgroundUpdateStore from synapse.storage.database import Database from synapse.storage.state import StateFilter from synapse.types import StateMap from synapse.util.caches.descriptors import cached from synapse.util.caches.dictionary_cache import DictionaryCache logger = logging.getLogger(__name__) MAX_STATE_DELTA_HOPS = 100 def _get_state_for_groups_using_cache( self, groups: Iterable[int], cache: DictionaryCache, state_filter: StateFilter ) -> Tuple[Dict[int, StateMap[str]], Set[int]]: """Gets the state at each of a list of state groups, optionally filtering by type/state_key, querying from a specific cache. Args: groups: list of state groups for which we want to get the state. cache: the cache of group ids to state dicts which we will pass through - either the normal state cache or the specific members state cache. state_filter: The state filter used to fetch state from the database. Returns: Tuple of dict of state_group_id to state map of entries in the cache, and the state group ids either missing from the cache or incomplete. """ results = {} incomplete_groups = set() for group in set(groups): state_dict_ids, got_all = self._get_state_for_group_using_cache( cache, group, state_filter ) results[group] = state_dict_ids if not got_all: incomplete_groups.add(group) return results, incomplete_groups def _insert_into_cache( self, group_to_state_dict, state_filter, cache_seq_num_members, cache_seq_num_non_members, ): """Inserts results from querying the database into the relevant cache. Args: group_to_state_dict (dict): The new entries pulled from database. Map from state group to state dict state_filter (StateFilter): The state filter used to fetch state from the database. cache_seq_num_members (int): Sequence number of member cache since last lookup in cache cache_seq_num_non_members (int): Sequence number of member cache since last lookup in cache """ # We need to work out which types we've fetched from the DB for the # member vs non-member caches. This should be as accurate as possible, # but can be an underestimate (e.g. when we have wild cards) member_filter, non_member_filter = state_filter.get_member_split() if member_filter.is_full(): # We fetched all member events member_types = None else: # `concrete_types()` will only return a subset when there are wild # cards in the filter, but that's fine. member_types = member_filter.concrete_types() if non_member_filter.is_full(): # We fetched all non member events non_member_types = None else: non_member_types = non_member_filter.concrete_types() for group, group_state_dict in group_to_state_dict.items(): state_dict_members = {} state_dict_non_members = {} for k, v in group_state_dict.items(): if k[0] == EventTypes.Member: state_dict_members[k] = v else: state_dict_non_members[k] = v self._state_group_members_cache.update( cache_seq_num_members, key=group, value=state_dict_members, fetched_keys=member_types, ) self._state_group_cache.update( cache_seq_num_non_members, key=group, value=state_dict_non_members, fetched_keys=non_member_types, ) def store_state_group( self, event_id, room_id, prev_group, delta_ids, current_state_ids ): """Store a new set of state, returning a newly assigned state group. Args: event_id (str): The event ID for which the state was calculated room_id (str) prev_group (int|None): A previous state group for the room, optional. delta_ids (dict|None): The delta between state at `prev_group` and `current_state_ids`, if `prev_group` was given. Same format as `current_state_ids`. current_state_ids (dict): The state to store. Map of (type, state_key) to event_id. Returns: Deferred[int]: The state group ID """ return self.db.runInteraction("store_state_group", _store_state_group_txn) def purge_unreferenced_state_groups( self, room_id: str, state_groups_to_delete ) -> defer.Deferred: """Deletes no longer referenced state groups and de-deltas any state groups that reference them. Args: room_id: The room the state groups belong to (must all be in the same room). state_groups_to_delete (Collection[int]): Set of all state groups to delete. """ return self.db.runInteraction( "purge_unreferenced_state_groups", self._purge_unreferenced_state_groups, room_id, state_groups_to_delete, ) def purge_room_state(self, room_id, state_groups_to_delete): """Deletes all record of a room from state tables Args: room_id (str): state_groups_to_delete (list[int]): State groups to delete """ return self.db.runInteraction( "purge_room_state", self._purge_room_state_txn, room_id, state_groups_to_delete, )

37.307813

88

0.590568

5dba0da51e77fecfd4eb4bbfdb42e2e652206d09

1,322

py

Python

core/migrations/0011_itemvariation_variation.py

manulangat1/djcommerce

2cd92631479ef949e0f05a255f2f50feca728802

[ "MIT" ]

1

2020-02-08T16:29:41.000Z

core/migrations/0011_itemvariation_variation.py

manulangat1/djcommerce

2cd92631479ef949e0f05a255f2f50feca728802

[ "MIT" ]

15

2020-05-04T13:22:32.000Z

2022-03-12T00:27:28.000Z

core/migrations/0011_itemvariation_variation.py

manulangat1/djcommerce

2cd92631479ef949e0f05a255f2f50feca728802

[ "MIT" ]

1

2020-10-17T08:54:31.000Z

# Generated by Django 2.2.6 on 2020-02-09 12:24 from django.db import migrations, models import django.db.models.deletion

34.789474

115

0.555219

5dba8f581c63a89cafcdb31c2be81f0648adb964

1,422

py

Python

mnist/convolutional.py

Colins-Ford/mnist-webapp

20e9b6f5520d5bda957d9501347f787450555db8

[ "Apache-2.0" ]

null

mnist/convolutional.py

Colins-Ford/mnist-webapp

20e9b6f5520d5bda957d9501347f787450555db8

[ "Apache-2.0" ]

null

mnist/convolutional.py

Colins-Ford/mnist-webapp

20e9b6f5520d5bda957d9501347f787450555db8

[ "Apache-2.0" ]

null

import os from mnist import model import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data data = input_data.read_data_sets("data/dataset/", one_hot=True) # model with tf.variable_scope("convolutional"): x = tf.placeholder(tf.float32, [None, 784]) keep_prob = tf.placeholder(tf.float32) y, variables = model.convolutional(x, keep_prob) # train y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = -tf.reduce_sum(y_ * tf.log(y)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) saver = tf.train.Saver(variables) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(20000): batch = data.train.next_batch(50) if i % 100 == 0: train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0}) print("step %d, training accuracy %g" % (i, train_accuracy)) sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) print(sess.run(accuracy, feed_dict={x: data.test.images, y_: data.test.labels, keep_prob: 1.0})) path = saver.save( sess, os.path.join(os.path.dirname(__file__), 'data', 'convolutional.ckpt'), write_meta_graph=False, write_state=False) print("Saved:", path)

37.421053

100

0.69339

5dba9613244bd2e35eb89625f766b4f652fe90d8

2,901

py

Python

parse_scripts/import_osm.py

nokout/au_address

07138ecd8fedab9566435b609cb8124b67ad42ff

[ "MIT" ]

1

2018-11-16T15:41:38.000Z

training/parse_scripts/import_osm.py

crccheck/us-address-parser

826fd365cba065a0588fa013cddbb23a8dac27a9

[ "MIT" ]

6

2016-10-05T11:21:36.000Z

2016-10-18T15:11:20.000Z

parse_scripts/import_osm.py

nokout/au_address

07138ecd8fedab9566435b609cb8124b67ad42ff

[ "MIT" ]

null

import requests import codecs query1 = """<union> <query type="way"> <has-kv k="addr:housenumber"/> <has-kv k="addr:street:name"/> <has-kv k="addr:street:type"/> <has-kv k="addr:state"/> <bbox-query e="%s" n="%s" s="%s" w="%s"/> </query> <query type="way"> <has-kv k="addr:housenumber"/> <has-kv k="addr:street:name"/> <has-kv k="addr:street:type"/> <has-kv k="addr:city"/> <bbox-query e="%s" n="%s" s="%s" w="%s"/> </query> <query type="way"> <has-kv k="addr:housenumber"/> <has-kv k="addr:street:name"/> <has-kv k="addr:street:type"/> <has-kv k="addr:postcode"/> <bbox-query e="%s" n="%s" s="%s" w="%s"/> </query> <query type="node"> <has-kv k="addr:housenumber"/> <has-kv k="addr:street:name"/> <has-kv k="addr:street:type"/> <has-kv k="addr:state"/> <bbox-query e="%s" n="%s" s="%s" w="%s"/> </query> <query type="node"> <has-kv k="addr:housenumber"/> <has-kv k="addr:street:name"/> <has-kv k="addr:street:type"/> <has-kv k="addr:city"/> <bbox-query e="%s" n="%s" s="%s" w="%s"/> </query> <query type="node"> <has-kv k="addr:housenumber"/> <has-kv k="addr:street:name"/> <has-kv k="addr:street:type"/> <has-kv k="addr:postcode"/> <bbox-query e="%s" n="%s" s="%s" w="%s"/> </query> </union> <print/>""" % ((-70.000000, 50.000000, 25.000000, -125.000000) * 6) r1 = requests.post('http://overpass-api.de/api/interpreter/', data=query1) r1.encoding = 'utf-8' f = codecs.open('data/osm_data.xml', encoding='utf-8' , mode='w+') f.write(r1.text) query2 = """<union> <query type="way"> <has-kv k="addr:street"/> <has-kv k="addr:street:name"/> <has-kv k="addr:street:prefix"/> <has-kv k="addr:street:type"/> <bbox-query e="%s" n="%s" s="%s" w="%s"/> </query> <query type="node"> <has-kv k="addr:street"/> <has-kv k="addr:street:name"/> <has-kv k="addr:street:prefix"/> <has-kv k="addr:street:type"/> <bbox-query e="%s" n="%s" s="%s" w="%s"/> </query> </union> <print/>""" % ((-87.61309146881104, 41.890042371392965, 41.87234107841773, -87.64235973358154) * 2) #r2 = requests.post('http://overpass-api.de/api/interpreter/', data=query2) #f = codecs.open("data/osm_data_street.xml", "wb", "utf-8") #r2.encoding = 'utf-8' #f.write(r2.text) query3 = """<union> <query type="way"> <has-kv k="addr:full" regv="^[0-9]+.*[a-z]+.*[0-9]{5}.*"/> <bbox-query e="%s" n="%s" s="%s" w="%s"/> </query> <query type="node"> <has-kv k="addr:full" regv="^[0-9]+.*[a-z]+.*[0-9]{5}.*"/> <bbox-query e="%s" n="%s" s="%s" w="%s"/> </query> </union> <print/> """ % ((-70.000000, 50.000000, 25.000000, -125.000000) * 2) if __name__ == '__main__' : r3 = requests.post('http://overpass-api.de/api/interpreter/', data=query3) f = codecs.open("data/osm_data_full_addr.xml", "wb", "utf-8") r3.encoding = 'utf-8' f.write(r3.text)

28.441176

99

0.558083

5dbd482917f27cdd677d99ffd355bb76525f3a13

4,110

py

Python

tools/test_net_batch.py

abhirevan/pedestrian-detector

f4fa4cd59315ea515ace3c529b716ff3173e2205

[ "BSD-2-Clause" ]

null

tools/test_net_batch.py

abhirevan/pedestrian-detector

f4fa4cd59315ea515ace3c529b716ff3173e2205

[ "BSD-2-Clause" ]

null

tools/test_net_batch.py

abhirevan/pedestrian-detector

f4fa4cd59315ea515ace3c529b716ff3173e2205

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- """Test a Fast R-CNN network on an image database.""" import _init_paths from fast_rcnn.test import test_net from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list from datasets.factory import get_imdb import caffe import argparse import pprint import time, os, sys import pandas as pd def parse_args(): """ Parse input arguments """ parser = argparse.ArgumentParser(description='Test a Fast R-CNN network pipeline') parser.add_argument('--gpu', dest='gpu_id', help='GPU id to use', default=0, type=int, required=True) parser.add_argument('--dir', dest='dir', help='Directory of the model files', default="", type=str, required=True) parser.add_argument('--models', dest='model_files', help='Text file with names of models', default=None, type=str, required=True) parser.add_argument('--prototxt', dest='prototxt', help='prototxt', default=None, type=str, required=True) parser.add_argument('--imdb', dest='imdb_name', help='dataset to test', default='ped_test_small', type=str, required=True) parser.add_argument('--cfg', dest='cfg_file', help='cfg', default='experiments/cfgs/faster_rcnn_end2end.yml', type=str) parser.add_argument('--res', dest='res_file', help='result file', default='', type=str, required=True) args = parser.parse_args() return args if __name__ == '__main__': # args = parse_args() gpu_id = 0 # dir = '/home/abhijitcbim/git/pedestrian-detector/output/faster_rcnn_end2end/train/backup' # model_files = 'test.txt' args = parse_args() print('Called with args:') print(args) run_test_nets(args.gpu_id, args.dir, args.model_files, args.prototxt, args.imdb_name, args.cfg_file, args.res_file) # run_test_net(gpu_id,caffemodel, prototxt, imdb_name, cfg_file)

33.145161

119

0.615572

5dbe1aa985f0f74b54e5721ad988a0ced87ead89

469

py

Python

mygallary/urls.py

mangowilliam/my_gallary

4c87fe055e5c28d6ca6a27ea5bde7df380750006

[ "MIT" ]

null

mygallary/urls.py

mangowilliam/my_gallary

4c87fe055e5c28d6ca6a27ea5bde7df380750006

[ "MIT" ]

6

2021-03-19T02:06:21.000Z

2022-03-11T23:53:21.000Z

mygallary/urls.py

mangowilliam/my_gallary

4c87fe055e5c28d6ca6a27ea5bde7df380750006

[ "MIT" ]

null

from django.conf import settings from django.conf.urls.static import static from django.conf.urls import url from . import views urlpatterns = [ url('^$', views.gallary,name = 'gallary'), url(r'^search/', views.search_image, name='search_image'), url(r'^details/(\d+)',views.search_location,name ='images') ] if settings.DEBUG: urlpatterns+= static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT)

20.391304

81

0.656716

5dbebf189d084ec54743890289ba79eb7c5bba5c

5,831

py

Python

yolox/data/datasets/mot.py

ldelzott/ByteTrack

5f8ab49a913a551d041918607a0bd2473602ad39

[ "MIT" ]

null

yolox/data/datasets/mot.py

ldelzott/ByteTrack

5f8ab49a913a551d041918607a0bd2473602ad39

[ "MIT" ]

null

yolox/data/datasets/mot.py

ldelzott/ByteTrack

5f8ab49a913a551d041918607a0bd2473602ad39

[ "MIT" ]

null

import cv2 import numpy as np from pycocotools.coco import COCO import os from ..dataloading import get_yolox_datadir from .datasets_wrapper import Dataset

40.776224

165

0.510547

5dbf449975065338e5216b26f0b50de7db0d2cd0

4,740

py

Python

src/poetry/console/commands/remove.py

pkoch/poetry

d22c5a7187d8b5a30196a7df58111b3c90be7d22

[ "MIT" ]

null

src/poetry/console/commands/remove.py

pkoch/poetry

d22c5a7187d8b5a30196a7df58111b3c90be7d22

[ "MIT" ]

null

src/poetry/console/commands/remove.py

pkoch/poetry

d22c5a7187d8b5a30196a7df58111b3c90be7d22

[ "MIT" ]

null

from __future__ import annotations from typing import Any from cleo.helpers import argument from cleo.helpers import option from tomlkit.toml_document import TOMLDocument try: from poetry.core.packages.dependency_group import MAIN_GROUP except ImportError: MAIN_GROUP = "default" from poetry.console.commands.installer_command import InstallerCommand

34.347826

88

0.59789

5dbff166d1570c685dadc7e901e806b3102dde0f

3,316

py

Python

orrinjelo/aoc2021/day_11.py

orrinjelo/AdventOfCode2021

6fce5c48ec3dc602b393824f592a5c6db2a8b66f

[ "MIT" ]

null

orrinjelo/aoc2021/day_11.py

orrinjelo/AdventOfCode2021

6fce5c48ec3dc602b393824f592a5c6db2a8b66f

[ "MIT" ]

null

orrinjelo/aoc2021/day_11.py

orrinjelo/AdventOfCode2021

6fce5c48ec3dc602b393824f592a5c6db2a8b66f

[ "MIT" ]

null

from orrinjelo.utils.decorators import timeit import numpy as np visited = [] # = Test ================================================ inputlist = [ '5483143223', '2745854711', '5264556173', '6141336146', '6357385478', '4167524645', '2176841721', '6882881134', '4846848554', '5283751526', ] import pygame import sys

22.557823

85

0.518697

5dc01810c4c1797d877a743bdf67e61535eee657

1,914

py

Python

exercise_2/exercise_2.1.py

lukaszbinden/ethz-iacv-2020

271de804315de98b816cda3e2498958ffa87ad59

[ "MIT" ]

null

exercise_2/exercise_2.1.py

lukaszbinden/ethz-iacv-2020

271de804315de98b816cda3e2498958ffa87ad59

[ "MIT" ]

null

exercise_2/exercise_2.1.py

lukaszbinden/ethz-iacv-2020

271de804315de98b816cda3e2498958ffa87ad59

[ "MIT" ]

null

camera_width = 640 camera_height = 480 film_back_width = 1.417 film_back_height = 0.945 x_center = 320 y_center = 240 P_1 = (-0.023, -0.261, 2.376) p_11 = P_1[0] p_12 = P_1[1] p_13 = P_1[2] P_2 = (0.659, -0.071, 2.082) p_21 = P_2[0] p_22 = P_2[1] p_23 = P_2[2] p_1_prime = (52, 163) x_1 = p_1_prime[0] y_1 = p_1_prime[1] p_2_prime = (218, 216) x_2 = p_2_prime[0] y_2 = p_2_prime[1] f = 1.378 k_x = camera_width / film_back_width k_y = camera_height / film_back_height # f_k_x = f * k_x f_k_x = f # f_k_y = f * k_y f_k_y = f u_1_prime = (x_1 - x_center) / k_x v_1_prime = (y_1 - y_center) / k_y u_2_prime = (x_2 - x_center) / k_x v_2_prime = (y_2 - y_center) / k_y c_1_prime = (f_k_x * p_21 + (p_13 - p_23) * u_2_prime - u_2_prime/u_1_prime * f_k_x * p_11) / (f_k_x * (1 - u_2_prime/u_1_prime)) c_2_prime = (f_k_y * p_22 - (p_23 - (p_13*u_1_prime - f_k_x*(p_11 - c_1_prime))/u_1_prime) * v_2_prime) / f_k_y c_2_prime_alt = (f_k_y * p_12 - (p_13 - (p_13*u_1_prime - f_k_x*(p_11 - c_1_prime))/u_1_prime) * v_1_prime) / f_k_y c_3_prime = p_13 - (f_k_x / u_1_prime) * (p_11 - c_1_prime) rho_1_prime = p_13 - c_3_prime rho_2_prime = p_23 - c_3_prime print(f"C' = ({c_1_prime}, {c_2_prime}, {c_3_prime})") print(f"c_2_prime_alt = {c_2_prime_alt}") print(f"rho_1_prime = {rho_1_prime}") print(f"rho_2_prime = {rho_2_prime}") print("------------------") r_11 = f_k_x * (p_11 - c_1_prime) r_12 = f_k_y * (p_12 - c_2_prime) r_13 = 1 * (p_13 - c_3_prime) l_11 = rho_1_prime * u_1_prime l_12 = rho_1_prime * v_1_prime l_13 = rho_1_prime * 1 print(f"L: ({l_11}, {l_12}, {l_13})") print(f"R: ({r_11}, {r_12}, {r_13})") print("------------------") r_21 = f_k_x * (p_21 - c_1_prime) r_22 = f_k_y * (p_22 - c_2_prime) r_23 = 1 * (p_23 - c_3_prime) l_21 = rho_2_prime * u_2_prime l_22 = rho_2_prime * v_2_prime l_23 = rho_2_prime * 1 print(f"L: ({l_11}, {l_12}, {l_13})") print(f"R: ({r_11}, {r_12}, {r_13})")

23.060241

129

0.642633

5dc0c299dbdb6b798fc1619ba108af859bcce78e

3,329

py

Python

services/train/single.py

paper2code/torch2vec-restful-service

6c4412d84d067268bf988b1f31cef716a2ed23a5

[ "MIT" ]

2

2020-09-13T18:08:52.000Z

2020-09-19T05:26:50.000Z

services/train/single.py

paper2code/torch2vec-restful-service

6c4412d84d067268bf988b1f31cef716a2ed23a5

[ "MIT" ]

null

services/train/single.py

paper2code/torch2vec-restful-service

6c4412d84d067268bf988b1f31cef716a2ed23a5

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Aug 26 19:15:34 2020 @author: deviantpadam """ import pandas as pd import numpy as np import concurrent.futures import os import tqdm from collections import Counter from torch2vec.data import DataPreparation from torch2vec.torch2vec import DM # train = pd.read_csv('/home/deviantpadam/Downloads/example.csv',delimiter='\t') # train = pd.read_csv('/home/deviantpadam/Downloads/example (1).csv') train = pd.read_csv('../data/suggest_dump.txt',delimiter='\t') train = cleaner(train) corpus = train['authors']+' '+train['title']+' '+train['summary']+' '+train['subject1']+' '+train['subject2']+' '+train['task'] corpus.name = 'text' corpus = pd.concat([train['subject1'],train['subject2'],train['task'],corpus],axis=1) data = DataPreparation(corpus.reset_index(),f_size=3) data.tokenize() bigrams = _get_bigrams(data.corpus.values,min_count=700) data.corpus = phraser(data.corpus.values) bigrams = _get_bigrams(data.corpus.values,min_count=500) data.corpus = phraser(data.corpus.values) data.vocab_builder() doc, context, target_noise_ids = data.get_data(window_size=5,num_noise_words=10) model = DM(vec_dim=100,num_docs=len(data),num_words=data.vocab_size).cuda() num_workers = os.cpu_count() model.fit(doc_ids=doc,context=context,target_noise_ids=target_noise_ids,epochs=20,batch_size=8000,num_workers=num_workers) model.save_model(data.document_ids,data.args,file_name='weights')

36.988889

151

0.681586

5dc0fa811b71f512df88503ac7e13855083e0792

8,399

py

Python

tests/sources/test_document_oereblex.py

geo-bl-ch/pyramid_oereb

767375a4adda4589e12c4257377fc30258cdfcb3

[ "BSD-2-Clause" ]

null

tests/sources/test_document_oereblex.py

geo-bl-ch/pyramid_oereb

767375a4adda4589e12c4257377fc30258cdfcb3

[ "BSD-2-Clause" ]

null

tests/sources/test_document_oereblex.py

geo-bl-ch/pyramid_oereb

767375a4adda4589e12c4257377fc30258cdfcb3

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- import datetime import pytest import requests_mock from geolink_formatter.entity import Document, File from requests.auth import HTTPBasicAuth from pyramid_oereb.contrib.sources.document import OEREBlexSource from pyramid_oereb.lib.records.documents import DocumentRecord, LegalProvisionRecord from pyramid_oereb.lib.records.office import OfficeRecord from tests.mockrequest import MockParameter def test_read(): with requests_mock.mock() as m: with open('./tests/resources/geolink_v1.1.1.xml', 'rb') as f: m.get('http://oereblex.example.com/api/geolinks/100.xml', content=f.read()) source = OEREBlexSource(host='http://oereblex.example.com', language='de', canton='BL') source.read(MockParameter(), 100) assert len(source.records) == 2 document = source.records[0] assert isinstance(document, DocumentRecord) assert isinstance(document.responsible_office, OfficeRecord) assert document.responsible_office.name == {'de': 'Landeskanzlei'} assert document.canton == 'BL' assert document.text_at_web == { 'de': 'http://oereblex.example.com/api/attachments/313' } assert len(document.references) == 5 def test_read_related_decree_as_main(): with requests_mock.mock() as m: with open('./tests/resources/geolink_v1.1.1.xml', 'rb') as f: m.get('http://oereblex.example.com/api/geolinks/100.xml', content=f.read()) source = OEREBlexSource(host='http://oereblex.example.com', language='de', canton='BL', related_decree_as_main=True) source.read(MockParameter(), 100) assert len(source.records) == 3 document = source.records[0] assert isinstance(document, DocumentRecord) assert isinstance(document.responsible_office, OfficeRecord) assert document.responsible_office.name == {'de': 'Landeskanzlei'} assert document.canton == 'BL' assert document.text_at_web == { 'de': 'http://oereblex.example.com/api/attachments/313' } assert len(document.references) == 4 def test_read_with_version_in_url(): with requests_mock.mock() as m: with open('./tests/resources/geolink_v1.1.1.xml', 'rb') as f: m.get('http://oereblex.example.com/api/1.1.1/geolinks/100.xml', content=f.read()) source = OEREBlexSource(host='http://oereblex.example.com', language='de', canton='BL', pass_version=True) source.read(MockParameter(), 100) assert len(source.records) == 2 def test_read_with_specified_version(): with requests_mock.mock() as m: with open('./tests/resources/geolink_v1.0.0.xml', 'rb') as f: m.get('http://oereblex.example.com/api/1.0.0/geolinks/100.xml', content=f.read()) source = OEREBlexSource(host='http://oereblex.example.com', language='de', canton='BL', pass_version=True, version='1.0.0') source.read(MockParameter(), 100) assert len(source.records) == 2 def test_read_with_specified_language(): with requests_mock.mock() as m: with open('./tests/resources/geolink_v1.1.1.xml', 'rb') as f: m.get('http://oereblex.example.com/api/geolinks/100.xml?locale=fr', content=f.read()) source = OEREBlexSource(host='http://oereblex.example.com', language='de', canton='BL') params = MockParameter() params.set_language('fr') source.read(params, 100) assert len(source.records) == 2 document = source.records[0] assert document.responsible_office.name == {'fr': 'Landeskanzlei'} assert document.text_at_web == { 'fr': 'http://oereblex.example.com/api/attachments/313' } def test_authentication(): auth = { 'username': 'test', 'password': 'test' } source = OEREBlexSource(host='http://oereblex.example.com', language='de', canton='BL', auth=auth) assert isinstance(source._auth, HTTPBasicAuth) def test_get_document_title(): document = Document([], id='1', title='Test') result = {'de': 'Test'} assert OEREBlexSource._get_document_title(document, File(), 'de') == result

37.328889

102

0.604239

5dc1607dc008e8af7451051e5d28ffb9f945411a

998

py

Python

apps/zsh/singletons.py

codecat555/codecat555-fidgetingbits_knausj_talon

62f9be0459e6631c99d58eee97054ddd970cc5f3

[ "MIT" ]

4

2021-02-04T07:36:05.000Z

2021-07-03T06:53:30.000Z

apps/zsh/singletons.py

codecat555/codecat555-fidgetingbits_knausj_talon

62f9be0459e6631c99d58eee97054ddd970cc5f3

[ "MIT" ]

null

apps/zsh/singletons.py

codecat555/codecat555-fidgetingbits_knausj_talon

62f9be0459e6631c99d58eee97054ddd970cc5f3

[ "MIT" ]

null

# A rarely-updated module to assist in writing reload-safe talon modules using # things like threads, which are not normally safe for reloading with talon. # If this file is ever updated, you'll need to restart talon. import logging _singletons = {}

28.514286

78

0.645291

5dc1f18fa3f023890a5249859cd11435ad90ffca

1,088

py

Python

trainNN/run_bichrom.py

yztxwd/Bichrom

3939b8e52816a02b34122feef27c8e0a06e31d8e

[ "MIT" ]

3

2021-02-09T14:07:48.000Z

2021-06-21T18:31:54.000Z

trainNN/run_bichrom.py

yztxwd/Bichrom

3939b8e52816a02b34122feef27c8e0a06e31d8e

[ "MIT" ]

5

2021-02-05T03:46:37.000Z

2022-03-16T16:34:41.000Z

trainNN/run_bichrom.py

yztxwd/Bichrom

3939b8e52816a02b34122feef27c8e0a06e31d8e

[ "MIT" ]

4

2021-01-09T19:59:51.000Z

2021-11-12T21:08:40.000Z

import argparse import yaml from subprocess import call from train import train_bichrom if __name__ == '__main__': # parsing parser = argparse.ArgumentParser(description='Train and Evaluate Bichrom') parser.add_argument('-training_schema_yaml', required=True, help='YAML file with paths to train, test and val data') parser.add_argument('-len', help='Size of genomic windows', required=True, type=int) parser.add_argument('-outdir', required=True, help='Output directory') parser.add_argument('-nbins', type=int, required=True, help='Number of bins') args = parser.parse_args() # load the yaml file with input data paths: with open(args.training_schema_yaml, 'r') as f: try: data_paths = yaml.safe_load(f) except yaml.YAMLError as exc: print(exc) # create the output directory: outdir = args.outdir call(['mkdir', outdir]) train_bichrom(data_paths=data_paths, outdir=outdir, seq_len=args.len, bin_size=int(args.len/args.nbins))

38.857143

81

0.662684

5dc26a359dc3c1de1c2351ad0bab013c4dbc10a0

3,687

py

Python

setup.py

Fronius-SED/rapidyaml

20d44ff0c43085d08cb17f37fd6b0b305938a3ea

[ "MIT" ]

null

setup.py

Fronius-SED/rapidyaml

20d44ff0c43085d08cb17f37fd6b0b305938a3ea

[ "MIT" ]

null

setup.py

Fronius-SED/rapidyaml

20d44ff0c43085d08cb17f37fd6b0b305938a3ea

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # SPDX-License-Identifier: MIT import os import shutil import sys from pathlib import Path from distutils import log from setuptools import setup from setuptools.command.sdist import sdist as SdistCommand from cmake_build_extension import BuildExtension, CMakeExtension TOP_DIR = (Path(__file__).parent).resolve() # Where the Python library is actually found. PYTHON_DIR = "api/python" setup_kw = {} # Read in the package version when not in a git repository. VERSION_FILE = os.path.join(PYTHON_DIR, 'ryml', 'version.py') if not (TOP_DIR / '.git').exists() and os.path.exists(VERSION_FILE): exec(open(VERSION_FILE).read()) setup_kw['version'] = version else: setup_kw['use_scm_version']= { "version_scheme": "post-release", "local_scheme": "no-local-version", "write_to": VERSION_FILE, } # Read in the module description from the README.md file. README_FILE = TOP_DIR / "README.md" if README_FILE.exists(): with open(TOP_DIR / "README.md", "r") as fh: setup_kw['long_description'] = fh.read() setup_kw['long_description_content_type'] = "text/markdown" # define a CMake package cmake_args = dict( name='ryml.ryml', install_prefix='', source_dir='', cmake_component='python', cmake_configure_options=[ "-DRYML_BUILD_API:BOOL=ON", # Force cmake to use the Python interpreter we are currently using to # run setup.py "-DPython3_EXECUTABLE:FILEPATH="+sys.executable, ], ) try: ext = CMakeExtension(**cmake_args) except TypeError: del cmake_args['cmake_component'] ext = CMakeExtension(**cmake_args) # If the CMakeExtension doesn't support `cmake_component` then we have to # do some manual cleanup. _BuildExtension=BuildExtension setup( # Package human readable information name='rapidyaml', #author='Joao Paulo Magalhaes', description='Rapid YAML - a library to parse and emit YAML, and do it fast.', url='https://github.com/biojppm/rapidyaml', license='MIT', license_files=['LICENSE.txt'], # Package contents control cmdclass={ "build_ext": BuildExtension, }, package_dir={"": PYTHON_DIR}, packages=['ryml'], ext_modules=[ext], include_package_data=True, # Requirements python_requires=">=3.7", setup_requires=['setuptools_scm'], # Extra arguments **setup_kw, )

32.342105

81

0.641714

5dc297d8b74fa875a21b1642232b22d90653124f

5,193

py

Python

litex_boards/targets/digilent_arty_z7.py

machdyne/litex-boards

2311db18f8c92f80f03226fa984e6110caf25b88

[ "BSD-2-Clause" ]

null

litex_boards/targets/digilent_arty_z7.py

machdyne/litex-boards

2311db18f8c92f80f03226fa984e6110caf25b88

[ "BSD-2-Clause" ]

null

litex_boards/targets/digilent_arty_z7.py

machdyne/litex-boards

2311db18f8c92f80f03226fa984e6110caf25b88

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 # # This file is part of LiteX-Boards. # # Copyright (c) 2021 Gwenhael Goavec-Merou <[email protected]> # SPDX-License-Identifier: BSD-2-Clause import argparse import subprocess from migen import * from litex_boards.platforms import digilent_arty_z7 from litex.build import tools from litex.build.xilinx import common as xil_common from litex.build.xilinx.vivado import vivado_build_args, vivado_build_argdict from litex.soc.interconnect import axi from litex.soc.interconnect import wishbone from litex.soc.cores.clock import * from litex.soc.integration.soc_core import * from litex.soc.integration.soc import SoCRegion from litex.soc.integration.builder import * from litex.soc.cores.led import LedChaser # CRG ---------------------------------------------------------------------------------------------- # BaseSoC ------------------------------------------------------------------------------------------ # Build -------------------------------------------------------------------------------------------- def main(): parser = argparse.ArgumentParser(description="LiteX SoC on Arty Z7") parser.add_argument("--toolchain", default="vivado", help="FPGA toolchain (vivado, symbiflow or yosys+nextpnr).") parser.add_argument("--build", action="store_true", help="Build bitstream.") parser.add_argument("--load", action="store_true", help="Load bitstream.") parser.add_argument("--variant", default="z7-20", help="Board variant (z7-20 or z7-10).") parser.add_argument("--sys-clk-freq", default=125e6, help="System clock frequency.") builder_args(parser) soc_core_args(parser) vivado_build_args(parser) parser.set_defaults(cpu_type="zynq7000") args = parser.parse_args() soc = BaseSoC( variant = args.variant, toolchain = args.toolchain, sys_clk_freq=int(float(args.sys_clk_freq)), **soc_core_argdict(args) ) builder = Builder(soc, **builder_argdict(args)) builder_kwargs = vivado_build_argdict(args) if args.toolchain == "vivado" else {} builder.build(**builder_kwargs, run=args.build) if args.load: prog = soc.platform.create_programmer() prog.load_bitstream(os.path.join(builder.gateware_dir, soc.build_name + ".bit")) if __name__ == "__main__": main()

38.753731

123

0.561333

5dc2ee5e1dddc721c798287488da3cf41eba8ae1

6,899

py

Python

goose/parsers.py

allmalaysianews/article-extractor

8d0ff3ed01258d0fad56fc22d2c1852e603096b4

[ "Apache-2.0" ]

null

goose/parsers.py

allmalaysianews/article-extractor

8d0ff3ed01258d0fad56fc22d2c1852e603096b4

[ "Apache-2.0" ]

null

goose/parsers.py

allmalaysianews/article-extractor

8d0ff3ed01258d0fad56fc22d2c1852e603096b4

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """\ This is a python port of "Goose" orignialy licensed to Gravity.com under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. Python port was written by Xavier Grangier for Recrutae Gravity.com licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import lxml.html as lxmlhtml from lxml.html import soupparser from lxml import etree from copy import deepcopy from goose.text import innerTrim from goose.text import encodeValue

28.159184

84

0.584433

5dc3595f9215ec36727d03f139d3d859982ac98f

2,352

py

Python

src/infrastructure/database/postgres/sqlhandler.py

SoyBeansLab/daizu-online-judge-backend

873f81fdad2f216e28b83341a6d88b0e21078d6e

[ "MIT" ]

7

2019-05-14T08:40:35.000Z

2019-08-20T08:15:21.000Z

src/infrastructure/database/postgres/sqlhandler.py

SoyBeansLab/daizu-online-judge-backend

873f81fdad2f216e28b83341a6d88b0e21078d6e

[ "MIT" ]

76

2019-05-14T08:56:40.000Z

2020-10-18T16:25:33.000Z

src/infrastructure/database/postgres/sqlhandler.py

SoyBeansLab/daizu-online-judge-backend

873f81fdad2f216e28b83341a6d88b0e21078d6e

[ "MIT" ]

3

2019-12-12T01:44:31.000Z

2020-11-22T03:24:40.000Z

from logging import getLogger import os from typing import List, Union import psycopg2 from interface.database.sqlhandler import Cursor as AbsCursor from interface.database.sqlhandler import Result as AbsResult from interface.database.sqlhandler import SqlHandler as AbsSqlHandler from exceptions.waf import SqlTransactionException logger = getLogger("daizu").getChild("infrastracture.SqlHandler")

29.4

75

0.627551

5dc369e87fe800f32ded0cd2dc49e361f6723160

1,001

py

Python

virtualisation/wrapper/parser/xmlparser.py

CityPulse/CP_Resourcemanagement

aa670fa89d5e086a98ade3ccc152518be55abf2e

[ "MIT" ]

2

2016-11-03T14:57:45.000Z

2019-05-13T13:21:08.000Z

virtualisation/wrapper/parser/xmlparser.py

CityPulse/CP_Resourcemanagement

aa670fa89d5e086a98ade3ccc152518be55abf2e

[ "MIT" ]

null

virtualisation/wrapper/parser/xmlparser.py

CityPulse/CP_Resourcemanagement

aa670fa89d5e086a98ade3ccc152518be55abf2e

[ "MIT" ]

1

2020-07-23T11:27:15.000Z

from virtualisation.clock.abstractclock import AbstractClock __author__ = 'Marten Fischer ([email protected])' from virtualisation.wrapper.parser.abstractparser import AbstractParser from virtualisation.misc.jsonobject import JSONObject as JOb import datetime as dt

31.28125

91

0.682318

5dc48b0f27c0b76d7893695e9d44f12dbfa7a376

19,404

py

Python

plaso/formatters/interface.py

jonathan-greig/plaso

b88a6e54c06a162295d09b016bddbfbfe7ca9070

[ "Apache-2.0" ]

1,253

2015-01-02T13:58:02.000Z

2022-03-31T08:43:39.000Z

plaso/formatters/interface.py

jonathan-greig/plaso

b88a6e54c06a162295d09b016bddbfbfe7ca9070

[ "Apache-2.0" ]

3,388

2015-01-02T11:17:58.000Z

2022-03-30T10:21:45.000Z

plaso/formatters/interface.py

jonathan-greig/plaso

b88a6e54c06a162295d09b016bddbfbfe7ca9070

[ "Apache-2.0" ]

376

2015-01-20T07:04:54.000Z

2022-03-04T23:53:00.000Z

# -*- coding: utf-8 -*- """This file contains the event formatters interface classes. The l2t_csv and other formats are dependent on a message field, referred to as description_long and description_short in l2t_csv. Plaso no longer stores these field explicitly. A formatter, with a format string definition, is used to convert the event object values into a formatted string that is similar to the description_long and description_short field. """ import abc import re from plaso.formatters import logger def AddHelper(self, helper): """Adds an event formatter helper. Args: helper (EventFormatterHelper): event formatter helper to add. """ self.helpers.append(helper)

33.112628

79

0.704958

5dc4b2786f8172c270a1fc651693530424b90630

190

py

Python

python_program/condition.py

LiuKaiqiang94/PyStudyExample

b30212718b218c71e06b68677f55c33e3a1dbf46

[ "MIT" ]

5

2018-09-10T02:52:35.000Z

2018-09-20T07:50:42.000Z

python_program/condition.py

LiuKaiqiang94/PyStudyExample

b30212718b218c71e06b68677f55c33e3a1dbf46

[ "MIT" ]

null

python_program/condition.py

LiuKaiqiang94/PyStudyExample

b30212718b218c71e06b68677f55c33e3a1dbf46

[ "MIT" ]

null

main()

13.571429

33

0.442105

5dc534af6da39531ee8b4ae7b4baf8841a23115e

1,608

py

Python

Annotated_video/test/Annotatedvideo_worm.py

Rukaume/LRCN

0d1928cc72544f59a4335fea7febc561d3dfc118

[ "MIT" ]

1

2020-11-07T05:57:32.000Z

Annotated_video/test/Annotatedvideo_worm.py

Rukaume/LRCN

0d1928cc72544f59a4335fea7febc561d3dfc118

[ "MIT" ]

1

2020-11-07T00:30:22.000Z

2021-01-26T02:22:16.000Z

Annotated_video/test/Annotatedvideo_worm.py

Rukaume/LRCN

0d1928cc72544f59a4335fea7febc561d3dfc118

[ "MIT" ]

1

2020-11-07T05:57:52.000Z

# -*- coding: utf-8 -*- """ Created on Fri Sep 4 22:27:11 2020 @author: Miyazaki """ imdir = "C:/Users/Miyazaki/Desktop/hayashi_lab/20200527_lethargus_analysis/renamed_pillar_chamber-N2/chamber3" resultdir= "C:/Users/Miyazaki/Desktop/hayashi_lab/20200527_lethargus_analysis/renamed_pillar_chamber-N2/result0918.csv" import os, cv2, shutil from tqdm import tqdm import pandas as pd os.chdir(imdir) os.makedirs("../annotatedimages", exist_ok = True) imlist = os.listdir("./") imlist = [i for i in imlist if os.path.splitext(i)[1] == '.jpg' \ or os.path.splitext(i)[1] == '.png'] imlist.sort() result = pd.read_csv(resultdir) font = cv2.FONT_HERSHEY_SIMPLEX for i in tqdm(range(len(imlist))): if int(result.loc[i]) == 0: tempim = cv2.imread(imlist[i]) tempim = cv2.putText(tempim,'quiescent',(10,500), font, 1,(255,0,0),2,cv2.LINE_AA) cv2.imwrite('../annotatedimages/{}'.format(imlist[i]), tempim) elif int(result.loc[i]) == 1: tempim = cv2.imread(imlist[i]) tempim = cv2.putText(tempim,'dwell',(10,500), font, 1,(0,255,0),2,cv2.LINE_AA) cv2.imwrite('../annotatedimages/{}'.format(imlist[i]), tempim) elif int(result.loc[i]) == 2: tempim = cv2.imread(imlist[i]) tempim = cv2.putText(tempim,'forward',(10,500), font, 1,(0,0,255),2,cv2.LINE_AA) cv2.imwrite('../annotatedimages/{}'.format(imlist[i]), tempim) elif int(result.loc[i]) == 3: tempim = cv2.imread(imlist[i]) tempim = cv2.putText(tempim,'backward',(10,500), font, 1,(100,100,0),2,cv2.LINE_AA) cv2.imwrite('../annotatedimages/{}'.format(imlist[i]), tempim) else: pass

36.545455

119

0.670398

5dc789560fb397b3832cccec69534dcbf26e36d2

5,902

py

Python

emilia/modules/math.py

masterisira/ELIZA_OF-master

02a7dbf48e4a3d4ee0981e6a074529ab1497aafe

[ "Unlicense" ]

null

emilia/modules/math.py

masterisira/ELIZA_OF-master

02a7dbf48e4a3d4ee0981e6a074529ab1497aafe

[ "Unlicense" ]

null

emilia/modules/math.py

masterisira/ELIZA_OF-master

02a7dbf48e4a3d4ee0981e6a074529ab1497aafe

[ "Unlicense" ]

null

from typing import List import requests from telegram import Message, Update, Bot, MessageEntity from telegram.ext import CommandHandler, run_async from emilia import dispatcher from emilia.modules.disable import DisableAbleCommandHandler from emilia.modules.helper_funcs.alternate import send_message import pynewtonmath as newton import math __help__ = """ Under Developmeent.. More features soon - /cos: Cosine `/cos pi` - /sin: Sine `/sin 0` - /tan: Tangent `/tan 0` - /arccos: Inverse Cosine `/arccos 1` - /arcsin: Inverse Sine `/arcsin 0` - /arctan: Inverse Tangent `/arctan 0` - /abs: Absolute Value `/abs -1` - /log: Logarithm `/log 2l8` __Keep in mind__: To find the tangent line of a function at a certain x value, send the request as c|f(x) where c is the given x value and f(x) is the function expression, the separator is a vertical bar '|'. See the table above for an example request. To find the area under a function, send the request as c:d|f(x) where c is the starting x value, d is the ending x value, and f(x) is the function under which you want the curve between the two x values. To compute fractions, enter expressions as numerator(over)denominator. For example, to process 2/4 you must send in your expression as 2(over)4. The result expression will be in standard math notation (1/2, 3/4). """ SIMPLIFY_HANDLER = DisableAbleCommandHandler("math", simplify, pass_args=True) FACTOR_HANDLER = DisableAbleCommandHandler("factor", factor, pass_args=True) DERIVE_HANDLER = DisableAbleCommandHandler("derive", derive, pass_args=True) INTEGRATE_HANDLER = DisableAbleCommandHandler("integrate", integrate, pass_args=True) ZEROES_HANDLER = DisableAbleCommandHandler("zeroes", zeroes, pass_args=True) TANGENT_HANDLER = DisableAbleCommandHandler("tangent", tangent, pass_args=True) AREA_HANDLER = DisableAbleCommandHandler("area", area, pass_args=True) COS_HANDLER = DisableAbleCommandHandler("cos", cos, pass_args=True) SIN_HANDLER = DisableAbleCommandHandler("sin", sin, pass_args=True) TAN_HANDLER = DisableAbleCommandHandler("tan", tan, pass_args=True) ARCCOS_HANDLER = DisableAbleCommandHandler("arccos", arccos, pass_args=True) ARCSIN_HANDLER = DisableAbleCommandHandler("arcsin", arcsin, pass_args=True) ARCTAN_HANDLER = DisableAbleCommandHandler("arctan", arctan, pass_args=True) ABS_HANDLER = DisableAbleCommandHandler("abs", abs, pass_args=True) LOG_HANDLER = DisableAbleCommandHandler("log", log, pass_args=True) dispatcher.add_handler(SIMPLIFY_HANDLER) dispatcher.add_handler(FACTOR_HANDLER) dispatcher.add_handler(DERIVE_HANDLER) dispatcher.add_handler(INTEGRATE_HANDLER) dispatcher.add_handler(ZEROES_HANDLER) dispatcher.add_handler(TANGENT_HANDLER) dispatcher.add_handler(AREA_HANDLER) dispatcher.add_handler(COS_HANDLER) dispatcher.add_handler(SIN_HANDLER) dispatcher.add_handler(TAN_HANDLER) dispatcher.add_handler(ARCCOS_HANDLER) dispatcher.add_handler(ARCSIN_HANDLER) dispatcher.add_handler(ARCTAN_HANDLER) dispatcher.add_handler(ABS_HANDLER) dispatcher.add_handler(LOG_HANDLER) __mod_name__ = "Math" __command_list__ = ["math","factor","derive","integrate","zeroes","tangent","area","cos","sin","tan","arccos","arcsin","arctan","abs","log"] __handlers__ = [ SIMPLIFY_HANDLER,FACTOR_HANDLER,DERIVE_HANDLER,INTEGRATE_HANDLER,TANGENT_HANDLER,ZEROES_HANDLER,AREA_HANDLER,COS_HANDLER,SIN_HANDLER,TAN_HANDLER,ARCCOS_HANDLER,ARCSIN_HANDLER,ARCTAN_HANDLER,ABS_HANDLER,LOG_HANDLER ]

35.769697

252

0.763978

5dc7dadf50bfb05ab92b9d5e96fde0df19295e15

4,996

py

Python

services/IAm.py

matteobjornsson/serverless-rock-paper-scissors

32b6f11644c59dc3bb159ee9e1118fed26a3983d

[ "MIT" ]

null

services/IAm.py

matteobjornsson/serverless-rock-paper-scissors

32b6f11644c59dc3bb159ee9e1118fed26a3983d

[ "MIT" ]

null

services/IAm.py

matteobjornsson/serverless-rock-paper-scissors

32b6f11644c59dc3bb159ee9e1118fed26a3983d

[ "MIT" ]

1

2021-04-20T23:55:37.000Z

# # Created on Thu Apr 22 2021 # Matteo Bjornsson # import boto3 from botocore.exceptions import ClientError import logging logging.basicConfig(filename="rps.log", level=logging.INFO) iam_resource = boto3.resource("iam") sts_client = boto3.client("sts") def create_role( iam_role_name: str, assume_role_policy_json: str, policy_arns: list ) -> iam_resource.Role: """ Create an IAM role with a given policy. :param assume_role_policy_json: A json string that represents the assume role policy defining what resources are allowed to assume the role. :param policy_arns: a list of strings representing existing policy arns to also attach to the role :return: IAM role object This method was adapted from the create_iam_role_for_lambda() method found here: https://docs.aws.amazon.com/code-samples/latest/catalog/python-lambda-boto_client_examples-lambda_basics.py.html """ try: role = iam_resource.create_role( RoleName=iam_role_name, AssumeRolePolicyDocument=assume_role_policy_json, ) # wait for the creation to complete iam_resource.meta.client.get_waiter("role_exists").wait(RoleName=iam_role_name) # attach the additional supplied policies for arn in policy_arns: role.attach_policy(PolicyArn=arn) except ClientError as error: if error.response["Error"]["Code"] == "EntityAlreadyExists": role = iam_resource.Role(iam_role_name) logging.warning("The role %s already exists. Using it.", iam_role_name) return role else: logging.error(error.response["Error"]["Message"]) logging.exception( "Couldn't create role %s or attach policy %s.", iam_role_name, str(policy_arns), ) raise else: logging.info("Created IAM role %s.", role.name) logging.info("Attached policies %s to role %s.", policy_arns, role.name) return role def create_policy(policy_name: str, policy_json: str) -> iam_resource.Policy: """ Create an IAM policy of given name and json description. Policies define permissions in AWS and can be associated with IAM roles. :param policy_json: just be a valid policy json string :return: IAM Policy object """ try: policy = iam_resource.create_policy( PolicyName=policy_name, PolicyDocument=policy_json ) except ClientError as error: if error.response["Error"]["Code"] == "EntityAlreadyExists": policy = get_policy_by_name(policy_name) logging.warning("The policy %s already exists. Using it.", policy.arn) return policy else: logging.error(error.response["Error"]["Message"]) logging.exception("Couldn't create policy %s", policy_name) raise else: logging.info("Created Policy '%s'", policy_name) return policy def get_policy_by_name(policy_name: str) -> iam_resource.Policy: """ Get an existing policy by name. :return: IAM Policy object """ # sts provides the account number of the current credentials account_id = sts_client.get_caller_identity()["Account"] # policy arns consist of an account id and policy name policy_arn = f"arn:aws:iam::{account_id}:policy/{policy_name}" # policies are created in the Python SDK via their arn policy = iam_resource.Policy(policy_arn) return policy def delete_role(iam_role) -> dict: """ Delete a role. :param iam_role: this parameter is an IAM role object, such as returned by create_role() """ try: # remove all policies before deleting role for policy in iam_role.attached_policies.all(): policy.detach_role(RoleName=iam_role.name) response = iam_role.delete() except ClientError as error: logging.error(error.response["Error"]["Message"]) logging.error("Couldn't delete role %s", iam_role.name) else: logging.info("Deleted role '%s'", iam_role.name) return response def delete_policy(iam_policy) -> dict: """ Delete a role. :param iam_policy: this parameter is an IAM policy object, such as returned by create_policy() """ try: response = iam_policy.delete() except ClientError as error: logging.error(error.response["Error"]["Message"]) logging.error("Couldn't delete policy %s", iam_policy.arn) else: logging.info("Deleted policy '%s'", iam_policy.arn) return response if __name__ == "__main__": # brief functionality test with delete() cleanup at end policy_json_file = "./policy/lambda_policy.json" with open(policy_json_file) as file: policy_json = file.read() policy_name = "test_policy" policy = create_policy(policy_name, policy_json) print("new policy arn: ", policy.arn) policy.delete()

34.937063

116

0.664532

5dcb3695e4bb82f323f1875e14dd30d0eb26c6e3

199

py

Python

stograde/common/run_status.py

babatana/stograde

c1c447e99c44c23cef9dd857e669861f3708ae77

[ "MIT" ]

null

stograde/common/run_status.py

babatana/stograde

c1c447e99c44c23cef9dd857e669861f3708ae77

[ "MIT" ]

null

stograde/common/run_status.py

babatana/stograde

c1c447e99c44c23cef9dd857e669861f3708ae77

[ "MIT" ]

null

from enum import auto, Enum

19.9

33

0.683417

5dcb43dbc68228752388169be1e8b17fd1bf9290

112

py

Python

recsys/__init__.py

shenghuiliuu/recsys

d706d1ae2558816c1e11ca790baeb7748200b404

[ "MIT" ]

50

2016-10-27T07:28:35.000Z

2022-03-30T01:32:32.000Z

recsys/__init__.py

shenghuiliuu/recsys

d706d1ae2558816c1e11ca790baeb7748200b404

[ "MIT" ]

5

2016-11-10T16:22:37.000Z

2020-09-16T10:26:59.000Z

recsys/__init__.py

shenghuiliuu/recsys

d706d1ae2558816c1e11ca790baeb7748200b404

[ "MIT" ]

22

2016-11-19T08:56:22.000Z

2021-06-23T16:13:10.000Z

__all__ = ['cross_validation', 'metrics', 'datasets', 'recommender']

14

31

0.4375

5dcbed3a8321b9c6b63677f0f51fde0daacfda04

21,917

py

Python

audiomate/annotations/label_list.py

CostanzoPablo/audiomate

080402eadaa81f77f64c8680510a2de64bc18e74

[ "MIT" ]

133

2018-05-18T13:54:10.000Z

2022-02-15T02:14:20.000Z

audiomate/annotations/label_list.py

CostanzoPablo/audiomate

080402eadaa81f77f64c8680510a2de64bc18e74

[ "MIT" ]

68

2018-06-03T16:42:09.000Z

2021-01-29T10:58:30.000Z

audiomate/annotations/label_list.py

CostanzoPablo/audiomate

080402eadaa81f77f64c8680510a2de64bc18e74

[ "MIT" ]

37

2018-11-02T02:40:29.000Z

2021-11-30T07:44:50.000Z

import collections import copy import intervaltree from .label import Label def addl(self, value, start=0.0, end=float('inf')): """ Shortcut for ``add(Label(value, start, end))``. """ self.add(Label(value, start=start, end=end)) def update(self, labels): """ Add a list of labels to the end of the list. Args: labels (list): Labels to add. """ ivs = [] for label in labels: label.label_list = self ivs.append(intervaltree.Interval(label.start, label.end, label)) self.label_tree.update(ivs) def apply(self, fn): """ Apply the given function `fn` to every label in this label list. `fn` is a function of one argument that receives the current label which can then be edited in place. Args: fn (func): Function to apply to every label Example: >>> ll = LabelList(labels=[ ... Label('a_label', 1.0, 2.0), ... Label('another_label', 2.0, 3.0) ... ]) >>> def shift_labels(label): ... label.start += 1.0 ... label.end += 1.0 ... >>> ll.apply(shift_labels) >>> ll.labels [Label(a_label, 2.0, 3.0), Label(another_label, 3.0, 4.0)] """ for label in self.labels: fn(label) def merge_overlaps(self, threshold=0.0): """ Merge overlapping labels with the same value. Two labels are considered overlapping, if ``l2.start - l1.end < threshold``. Args: threshold (float): Maximal distance between two labels to be considered as overlapping. (default: 0.0) Example: >>> ll = LabelList(labels=[ ... Label('a_label', 1.0, 2.0), ... Label('a_label', 1.5, 2.7), ... Label('b_label', 1.0, 2.0), ... ]) >>> ll.merge_overlapping_labels() >>> ll.labels [ Label('a_label', 1.0, 2.7), Label('b_label', 1.0, 2.0), ] """ updated_labels = [] all_intervals = self.label_tree.copy() # recursivly find a group of overlapping labels with the same value # For every remaining interval # - Find overlapping intervals recursively # - Remove them # - Create a concatenated new label while not all_intervals.is_empty(): next_interval = list(all_intervals)[0] overlapping = recursive_overlaps(next_interval) ov_start = float('inf') ov_end = 0.0 ov_value = next_interval.data.value for overlap in overlapping: ov_start = min(ov_start, overlap.begin) ov_end = max(ov_end, overlap.end) all_intervals.discard(overlap) updated_labels.append(Label( ov_value, ov_start, ov_end )) # Replace the old labels with the updated ones self.label_tree.clear() self.update(updated_labels) # # Statistics # def label_total_duration(self): """ Return for each distinct label value the total duration of all occurrences. Returns: dict: A dictionary containing for every label-value (key) the total duration in seconds (value). Example: >>> ll = LabelList(labels=[ >>> Label('a', 3, 5), >>> Label('b', 5, 8), >>> Label('a', 8, 10), >>> Label('b', 10, 14), >>> Label('a', 15, 18.5) >>> ]) >>> ll.label_total_duration() {'a': 7.5 'b': 7.0} """ durations = collections.defaultdict(float) for label in self: durations[label.value] += label.duration return durations def label_values(self): """ Return a list of all occuring label values. Returns: list: Lexicographically sorted list (str) of label values. Example: >>> ll = LabelList(labels=[ >>> Label('a', 3.2, 4.5), >>> Label('b', 5.1, 8.9), >>> Label('c', 7.2, 10.5), >>> Label('d', 10.5, 14), >>> Label('d', 15, 18) >>> ]) >>> ll.label_values() ['a', 'b', 'c', 'd'] """ all_labels = {l.value for l in self} return sorted(all_labels) def label_count(self): """ Return for each label the number of occurrences within the list. Returns: dict: A dictionary containing for every label-value (key) the number of occurrences (value). Example: >>> ll = LabelList(labels=[ >>> Label('a', 3.2, 4.5), >>> Label('b', 5.1, 8.9), >>> Label('a', 7.2, 10.5), >>> Label('b', 10.5, 14), >>> Label('a', 15, 18) >>> ]) >>> ll.label_count() {'a': 3 'b': 2} """ occurrences = collections.defaultdict(int) for label in self: occurrences[label.value] += 1 return occurrences def all_tokens(self, delimiter=' '): """ Return a list of all tokens occurring in the label-list. Args: delimiter (str): The delimiter used to split labels into tokens. See :meth:`audiomate.annotations.Label.tokenized` Returns: :class:`set`: A set of distinct tokens. """ tokens = set() for label in self: tokens = tokens.union(set(label.tokenized(delimiter=delimiter))) return tokens # # Query Label Values # def join(self, delimiter=' ', overlap_threshold=0.1): """ Return a string with all labels concatenated together. The order of the labels is defined by the start of the label. If the overlapping between two labels is greater than ``overlap_threshold``, an Exception is thrown. Args: delimiter (str): A string to join two consecutive labels. overlap_threshold (float): Maximum overlap between two consecutive labels. Returns: str: A string with all labels concatenated together. Example: >>> ll = LabelList(idx='some', labels=[ >>> Label('a', start=0, end=4), >>> Label('b', start=3.95, end=6.0), >>> Label('c', start=7.0, end=10.2), >>> Label('d', start=10.3, end=14.0) >>> ]) >>> ll.join(' - ') 'a - b - c - d' """ sorted_by_start = sorted(self.labels) concat_values = [] last_label_end = None for label in sorted_by_start: if last_label_end is None or (last_label_end - label.start < overlap_threshold and last_label_end > 0): concat_values.append(label.value) last_label_end = label.end else: raise ValueError('Labels overlap, not able to define the correct order') return delimiter.join(concat_values) def tokenized(self, delimiter=' ', overlap_threshold=0.1): """ Return a ordered list of tokens based on all labels. Joins all token from all labels (``label.tokenized()```). If the overlapping between two labels is greater than ``overlap_threshold``, an Exception is thrown. Args: delimiter (str): The delimiter used to split labels into tokens. (default: space) overlap_threshold (float): Maximum overlap between two consecutive labels. Returns: str: A list containing tokens of all labels ordered according to the label order. Example: >>> ll = LabelList(idx='some', labels=[ >>> Label('a d q', start=0, end=4), >>> Label('b', start=3.95, end=6.0), >>> Label('c a', start=7.0, end=10.2), >>> Label('f g', start=10.3, end=14.0) >>> ]) >>> ll.tokenized(delimiter=' ', overlap_threshold=0.1) ['a', 'd', 'q', 'b', 'c', 'a', 'f', 'g'] """ sorted_by_start = sorted(self.labels) tokens = [] last_label_end = None for label in sorted_by_start: if last_label_end is None or (last_label_end - label.start < overlap_threshold and last_label_end > 0): tokens.extend(label.tokenized(delimiter=delimiter)) last_label_end = label.end else: raise ValueError('Labels overlap, not able to define the correct order') return tokens # # Restructuring # def separated(self): """ Create a separate Label-List for every distinct label-value. Returns: dict: A dictionary with distinct label-values as keys. Every value is a LabelList containing only labels with the same value. Example: >>> ll = LabelList(idx='some', labels=[ >>> Label('a', start=0, end=4), >>> Label('b', start=3.95, end=6.0), >>> Label('a', start=7.0, end=10.2), >>> Label('b', start=10.3, end=14.0) >>> ]) >>> s = ll.separate() >>> s['a'].labels [Label('a', start=0, end=4), Label('a', start=7.0, end=10.2)] >>> s['b'].labels [Label('b', start=3.95, end=6.0), Label('b', start=10.3, end=14.0)] """ separated_lls = collections.defaultdict(LabelList) for label in self.labels: separated_lls[label.value].add(label) for ll in separated_lls.values(): ll.idx = self.idx return separated_lls def labels_in_range(self, start, end, fully_included=False): """ Return a list of labels, that are within the given range. Also labels that only overlap are included. Args: start(float): Start-time in seconds. end(float): End-time in seconds. fully_included(bool): If ``True``, only labels fully included in the range are returned. Otherwise also overlapping ones are returned. (default ``False``) Returns: list: List of labels in the range. Example: >>> ll = LabelList(labels=[ >>> Label('a', 3.2, 4.5), >>> Label('b', 5.1, 8.9), >>> Label('c', 7.2, 10.5), >>> Label('d', 10.5, 14) >>>]) >>> ll.labels_in_range(6.2, 10.1) [Label('b', 5.1, 8.9), Label('c', 7.2, 10.5)] """ if fully_included: intervals = self.label_tree.envelop(start, end) else: intervals = self.label_tree.overlap(start, end) return [iv.data for iv in intervals] def split(self, cutting_points, shift_times=False, overlap=0.0): """ Split the label-list into x parts and return them as new label-lists. x is defined by the number of cutting-points (``x == len(cutting_points) + 1``). The result is a list of label-lists corresponding to each part. Label-list 0 contains labels between ``0`` and ``cutting_points[0]``. Label-list 1 contains labels between ``cutting_points[0]`` and ``cutting_points[1]``. And so on. Args: cutting_points(list): List of floats defining the points in seconds, where the label-list is splitted. shift_times(bool): If True, start and end-time are shifted in splitted label-lists. So the start is relative to the cutting point and not to the beginning of the original label-list. overlap(float): Amount of overlap in seconds. This amount is subtracted from a start-cutting-point, and added to a end-cutting-point. Returns: list: A list of of: class: `audiomate.annotations.LabelList`. Example: >>> ll = LabelList(labels=[ >>> Label('a', 0, 5), >>> Label('b', 5, 10), >>> Label('c', 11, 15), >>>]) >>> >>> res = ll.split([4.1, 8.9, 12.0]) >>> len(res) 4 >>> res[0].labels [Label('a', 0.0, 4.1)] >>> res[1].labels [ Label('a', 4.1, 5.0), Label('b', 5.0, 8.9) ] >>> res[2].labels [ Label('b', 8.9, 10.0), Label('c', 11.0, 12.0) ] >>> res[3].labels [Label('c', 12.0, 15.0)] If ``shift_times = True``, the times are adjusted to be relative to the cutting-points for every label-list but the first. >>> ll = LabelList(labels=[ >>> Label('a', 0, 5), >>> Label('b', 5, 10), >>>]) >>> >>> res = ll.split([4.6]) >>> len(res) 4 >>> res[0].labels [Label('a', 0.0, 4.6)] >>> res[1].labels [ Label('a', 0.0, 0.4), Label('b', 0.4, 5.4) ] """ if len(cutting_points) == 0: raise ValueError('At least one cutting-point is needed!') # we have to loop in sorted order cutting_points = sorted(cutting_points) splits = [] iv_start = 0.0 for i in range(len(cutting_points) + 1): if i < len(cutting_points): iv_end = cutting_points[i] else: iv_end = float('inf') # get all intervals intersecting range intervals = self.label_tree.overlap( iv_start - overlap, iv_end + overlap ) cp_splits = LabelList(idx=self.idx) # Extract labels from intervals with updated times for iv in intervals: label = copy.deepcopy(iv.data) label.start = max(0, iv_start - overlap, label.start) label.end = min(iv_end + overlap, label.end) if shift_times: orig_start = max(0, iv_start - overlap) label.start -= orig_start label.end -= orig_start cp_splits.add(label) splits.append(cp_splits) iv_start = iv_end return splits # # Convenience Constructors #

31.580692

115

0.505087

5dcd4858a80507237d1cda30d4f8de4336f40710

3,044

py

Python

src/views/age_results_widget.py

RubyMarsden/Crayfish

33bbb1248beec2fc40eee59e462711dd8cbc33da

[ "MIT" ]

null

src/views/age_results_widget.py

RubyMarsden/Crayfish

33bbb1248beec2fc40eee59e462711dd8cbc33da

[ "MIT" ]

8

2021-03-19T06:35:48.000Z

2021-03-31T14:23:24.000Z

src/views/age_results_widget.py

RubyMarsden/Crayfish

33bbb1248beec2fc40eee59e462711dd8cbc33da

[ "MIT" ]

null

import matplotlib from PyQt5.QtCore import Qt from PyQt5.QtWidgets import QHBoxLayout, QDialog, QPushButton, QWidget, QVBoxLayout, QLabel matplotlib.use('QT5Agg') import matplotlib.pyplot as plt from models.data_key import DataKey from utils import ui_utils

30.747475

100

0.598555

5dcd80b6fc81b5df240724a020510872ace9a270

791

py

Python

examples/single_run/ocaes_single_run.py

EnergyModels/OCAES

d848d9fa621767e036824110de87450d524b7687

[ "MIT" ]

null

examples/single_run/ocaes_single_run.py

EnergyModels/OCAES

d848d9fa621767e036824110de87450d524b7687

[ "MIT" ]

null

examples/single_run/ocaes_single_run.py

EnergyModels/OCAES

d848d9fa621767e036824110de87450d524b7687

[ "MIT" ]

null

import pandas as pd from OCAES import ocaes # ---------------------- # create and run model # ---------------------- data = pd.read_csv('timeseries_inputs_2019.csv') inputs = ocaes.get_default_inputs() # inputs['C_well'] = 5000.0 # inputs['X_well'] = 50.0 # inputs['L_well'] = 50.0 # inputs['X_cmp'] = 0 # inputs['X_exp'] = 0 model = ocaes(data, inputs) df, s = model.get_full_results() revenue, LCOE, COVE, avoided_emissions = model.post_process(s) s['revenue'] = revenue s['LCOE'] = LCOE s['COVE'] = COVE s['avoided_emissions'] = avoided_emissions df.to_csv('results_timeseries.csv') s.to_csv('results_values.csv') print(model.calculate_LCOE(s)) # ---------------------- # create plots using built-in functions # ---------------------- model.plot_overview() model.plot_power_energy()

23.969697

62

0.637168