fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f72ac5724f4c0949289c5827a02bc25b216cc4ef

687

py

Python

setup.py

DocNow/twarc-hashtags

2a8ab84c9585b6efe9696194b6030ce5486a9e7e

[ "MIT" ]

3

2021-09-09T06:22:39.000Z

2022-02-25T13:51:29.000Z

setup.py

DocNow/twarc-hashtags

2a8ab84c9585b6efe9696194b6030ce5486a9e7e

[ "MIT" ]

1

2022-01-25T11:07:05.000Z

2022-01-27T01:33:00.000Z

setup.py

DocNow/twarc-hashtags

2a8ab84c9585b6efe9696194b6030ce5486a9e7e

[ "MIT" ]

null

import setuptools with open("README.md") as f: long_description = f.read() setuptools.setup( name='twarc-hashtags', version='0.0.5', url='https://github.com/docnow/twarc-hashtags', author='Ed Summers', author_email='ehs@pobox.com', py_modules=['twarc_hashtags'], description='A twarc plugin to extract hashtags from Twitter data', long_description=long_description, long_description_content_type="text/markdown", python_requires='>=3.3', install_requires=['twarc>=2.1.1'], setup_requires=['pytest-runner'], tests_require=['pytest'], entry_points=''' [twarc.plugins] hashtags=twarc_hashtags:hashtags ''' )

27.48

71

0.6754

import setuptools with open("README.md") as f: long_description = f.read() setuptools.setup( name='twarc-hashtags', version='0.0.5', url='https://github.com/docnow/twarc-hashtags', author='Ed Summers', author_email='ehs@pobox.com', py_modules=['twarc_hashtags'], description='A twarc plugin to extract hashtags from Twitter data', long_description=long_description, long_description_content_type="text/markdown", python_requires='>=3.3', install_requires=['twarc>=2.1.1'], setup_requires=['pytest-runner'], tests_require=['pytest'], entry_points=''' [twarc.plugins] hashtags=twarc_hashtags:hashtags ''' )

true

f72ac585b2ba49e680b69313a2fa0d0a5d6a749c

137

py

Python

Python/Regex and Parsing/Validating Roman Numerals/Solution.py

PawarAditi/HackerRank

fcd9d1450ee293372ce5f1d4a3b7284ecf472657

[ "MIT" ]

219

2018-06-17T19:47:22.000Z

2022-03-27T15:28:56.000Z

Python/Regex and Parsing/Validating Roman Numerals/Solution.py

PawarAditi/HackerRank

fcd9d1450ee293372ce5f1d4a3b7284ecf472657

[ "MIT" ]

2

2020-08-12T16:47:41.000Z

2020-12-15T17:05:57.000Z

Python/Regex and Parsing/Validating Roman Numerals/Solution.py

PawarAditi/HackerRank

fcd9d1450ee293372ce5f1d4a3b7284ecf472657

[ "MIT" ]

182

2018-12-12T21:36:50.000Z

2022-03-26T17:49:51.000Z

import re regex_pattern = r'M{0,3}(C[MD]|D?C{0,3})(X[CL]|L?X{0,3})(I[VX]|V?I{0,3})$' print(str(bool(re.match(regex_pattern, input()))))

27.4

74

0.605839

import re regex_pattern = r'M{0,3}(C[MD]|D?C{0,3})(X[CL]|L?X{0,3})(I[VX]|V?I{0,3})$' print(str(bool(re.match(regex_pattern, input()))))

true

f72ac6556032482e4ba83a528d58e88c2de8f5b6

3,955

py

Python

SimpleServer.py

wanzhiguo/mininero

7dd71b02a4613478b59b2670ccf7c74a22cc2ffd

[ "BSD-3-Clause" ]

64

2015-06-12T19:29:51.000Z

2022-01-03T17:14:56.000Z

SimpleServer.py

wanzhiguo/mininero

7dd71b02a4613478b59b2670ccf7c74a22cc2ffd

[ "BSD-3-Clause" ]

4

2015-11-27T18:49:40.000Z

2017-12-14T21:32:48.000Z

SimpleServer.py

wanzhiguo/mininero

7dd71b02a4613478b59b2670ccf7c74a22cc2ffd

[ "BSD-3-Clause" ]

39

2016-02-07T08:47:02.000Z

2022-03-07T06:07:10.000Z

import MiniNero import ed25519 import binascii import PaperWallet import cherrypy import os import time import bitmonerod import SimpleXMR2 lasttime = 0 def HexSigningPubKey(s): return binascii.hexlify(ed25519.publickey(ed25519.encodeint(MiniNero.hexToInt(s)))) def Signature(m, sk): #note this seems to return nicely sized version of the signature #contrast with, i.e. tweetnacl.. sk2 = ed25519.encodeint(MiniNero.hexToInt(sk)) pk = ed25519.publickey(sk2) return binascii.hexlify(ed25519.signature(m, sk2, pk)) def Verify(sig, m, pk): return ed25519.checkvalid(binascii.unhexlify(sig), m, binascii.unhexlify(pk)) class MiniNeroServer: exposed = True def GET(self, id=None): times = str(int(time.time())) return (times) def POST(self, signature, Type, timestamp, amount=None, destination=None, pid=None, mixin=None): times= int(time.time()) pubkey = MiniNeroPk global lasttime if (abs(times - int(timestamp)) > 30): ver = False return ('fail based on timestamp too old') else: if Type == 'address': message = Type+timestamp ver = Verify(signature.encode("utf8"), message.encode("utf8"), pubkey) if (ver): print("getting address") address = bitmonerod.myAddress() return (str(address)) if Type == 'balance': message = Type+timestamp ver = Verify(signature.encode("utf8"), message.encode("utf8"), pubkey) if (ver): print("getting balance") balance = bitmonerod.balance() return (str(float(balance)/1000000000000)) if Type == 'send': message = Type+amount.replace('.', 'd')+timestamp+destination ver = Verify(signature.encode("utf8"), message.encode("utf8"), pubkey) if (ver) and (abs(times - lasttime >30 )): #create xmr2 order async, return uuid uuid, xmr_amount, xmr_addr, xmr_pid = SimpleXMR2.btc2xmr(destination, amount) bitmonerod.send(xmr_addr, float(xmr_amount), xmr_pid, 3) lasttime = times return ('order uuid: '+uuid) if Type == 'sendXMR': message = Type+amount.replace('.', 'd')+timestamp+destination ver = Verify(signature.encode("utf8"), message.encode("utf8"), pubkey) if (ver) and (abs(times - lasttime >30 )): #create xmr2 order async, return uuid #uuid, xmr_amount, xmr_addr, xmr_pid = SimpleXMR2.btc2xmr(destination, amount) lasttime = times xmr_amount = amount xmr_addr = destination xmr_pid = pid bitmonerod.send(xmr_addr, float(xmr_amount), xmr_pid, 3) return ('sent') if __name__ == '__main__': #check if api pubkey is created, if not create it: if(os.path.isfile('MiniNeroPubKey.py')): from MiniNeroPubKey import * try: MiniNeroPk except NameError: MiniNeroSk= PaperWallet.skGen() MiniNeroPk= HexSigningPubKey(MiniNeroSk) print("Your new api secret key is:") print(MiniNeroSk) print("You should save this in a password manager") print("Your pubkey will be stored in MiniNeroPubKey.py") f = open('MiniNeroPubKey.py', 'w') f.write("MiniNeroPk = \'"+MiniNeroPk+"\'") print("Your MiniNeroServer PubKey is:") print(MiniNeroPk) lasttime = 0 #Launch Cherry Server cherrypy.tree.mount( MiniNeroServer(), '/api/mininero', {'/': {'request.dispatch': cherrypy.dispatch.MethodDispatcher()} } ) cherrypy.server.socket_host = '0.0.0.0' #run on metal cherrypy.engine.start() cherrypy.engine.block()

35.3125

100

0.588369

import MiniNero import ed25519 import binascii import PaperWallet import cherrypy import os import time import bitmonerod import SimpleXMR2 lasttime = 0 def HexSigningPubKey(s): return binascii.hexlify(ed25519.publickey(ed25519.encodeint(MiniNero.hexToInt(s)))) def Signature(m, sk): sk2 = ed25519.encodeint(MiniNero.hexToInt(sk)) pk = ed25519.publickey(sk2) return binascii.hexlify(ed25519.signature(m, sk2, pk)) def Verify(sig, m, pk): return ed25519.checkvalid(binascii.unhexlify(sig), m, binascii.unhexlify(pk)) class MiniNeroServer: exposed = True def GET(self, id=None): times = str(int(time.time())) return (times) def POST(self, signature, Type, timestamp, amount=None, destination=None, pid=None, mixin=None): times= int(time.time()) pubkey = MiniNeroPk global lasttime if (abs(times - int(timestamp)) > 30): ver = False return ('fail based on timestamp too old') else: if Type == 'address': message = Type+timestamp ver = Verify(signature.encode("utf8"), message.encode("utf8"), pubkey) if (ver): print("getting address") address = bitmonerod.myAddress() return (str(address)) if Type == 'balance': message = Type+timestamp ver = Verify(signature.encode("utf8"), message.encode("utf8"), pubkey) if (ver): print("getting balance") balance = bitmonerod.balance() return (str(float(balance)/1000000000000)) if Type == 'send': message = Type+amount.replace('.', 'd')+timestamp+destination ver = Verify(signature.encode("utf8"), message.encode("utf8"), pubkey) if (ver) and (abs(times - lasttime >30 )): uuid, xmr_amount, xmr_addr, xmr_pid = SimpleXMR2.btc2xmr(destination, amount) bitmonerod.send(xmr_addr, float(xmr_amount), xmr_pid, 3) lasttime = times return ('order uuid: '+uuid) if Type == 'sendXMR': message = Type+amount.replace('.', 'd')+timestamp+destination ver = Verify(signature.encode("utf8"), message.encode("utf8"), pubkey) if (ver) and (abs(times - lasttime >30 )): lasttime = times xmr_amount = amount xmr_addr = destination xmr_pid = pid bitmonerod.send(xmr_addr, float(xmr_amount), xmr_pid, 3) return ('sent') if __name__ == '__main__': if(os.path.isfile('MiniNeroPubKey.py')): from MiniNeroPubKey import * try: MiniNeroPk except NameError: MiniNeroSk= PaperWallet.skGen() MiniNeroPk= HexSigningPubKey(MiniNeroSk) print("Your new api secret key is:") print(MiniNeroSk) print("You should save this in a password manager") print("Your pubkey will be stored in MiniNeroPubKey.py") f = open('MiniNeroPubKey.py', 'w') f.write("MiniNeroPk = \'"+MiniNeroPk+"\'") print("Your MiniNeroServer PubKey is:") print(MiniNeroPk) lasttime = 0 cherrypy.tree.mount( MiniNeroServer(), '/api/mininero', {'/': {'request.dispatch': cherrypy.dispatch.MethodDispatcher()} } ) cherrypy.server.socket_host = '0.0.0.0' cherrypy.engine.start() cherrypy.engine.block()

true

f72ac71ab4bf2592bbd31344ee98206db5efb0b0

1,390

py

Python

dvc/command/run.py

IlyaKisil/dvc

1f549d665944a314331282a132b1ba3cc3a835f5

[ "Apache-2.0" ]

null

dvc/command/run.py

IlyaKisil/dvc

1f549d665944a314331282a132b1ba3cc3a835f5

[ "Apache-2.0" ]

null

dvc/command/run.py

IlyaKisil/dvc

1f549d665944a314331282a132b1ba3cc3a835f5

[ "Apache-2.0" ]

null

import dvc.logger as logger from dvc.command.base import CmdBase from dvc.exceptions import DvcException class CmdRun(CmdBase): def _joined_cmd(self): if len(self.args.command) == 0: return '' if len(self.args.command) == 1: return self.args.command[0] cmd = '' for chunk in self.args.command: if len(chunk.split()) != 1: fmt = ' "{}"' else: fmt = ' {}' cmd += fmt.format(chunk) return cmd def run(self): overwrite = (self.args.yes or self.args.overwrite_dvcfile) try: self.project.run(cmd=self._joined_cmd(), outs=self.args.outs, outs_no_cache=self.args.outs_no_cache, metrics_no_cache=self.args.metrics_no_cache, deps=self.args.deps, fname=self.args.file, cwd=self.args.cwd, no_exec=self.args.no_exec, overwrite=overwrite, ignore_build_cache=self.args.ignore_build_cache, remove_outs=self.args.remove_outs) except DvcException: logger.error('failed to run command') return 1 return 0

33.095238

77

0.488489

import dvc.logger as logger from dvc.command.base import CmdBase from dvc.exceptions import DvcException class CmdRun(CmdBase): def _joined_cmd(self): if len(self.args.command) == 0: return '' if len(self.args.command) == 1: return self.args.command[0] cmd = '' for chunk in self.args.command: if len(chunk.split()) != 1: fmt = ' "{}"' else: fmt = ' {}' cmd += fmt.format(chunk) return cmd def run(self): overwrite = (self.args.yes or self.args.overwrite_dvcfile) try: self.project.run(cmd=self._joined_cmd(), outs=self.args.outs, outs_no_cache=self.args.outs_no_cache, metrics_no_cache=self.args.metrics_no_cache, deps=self.args.deps, fname=self.args.file, cwd=self.args.cwd, no_exec=self.args.no_exec, overwrite=overwrite, ignore_build_cache=self.args.ignore_build_cache, remove_outs=self.args.remove_outs) except DvcException: logger.error('failed to run command') return 1 return 0

true

f72ac72145f9cff31e471c1a682180a9ab441579

1,584

py

Python

python/misc.py

dnbh/kpg

c9e79b8092434919e9ac90dc199f49845403c2ba

[ "MIT" ]

69

2018-01-08T19:56:55.000Z

2022-03-05T17:14:05.000Z

python/misc.py

dnbaker/emp

c9e79b8092434919e9ac90dc199f49845403c2ba

[ "MIT" ]

6

2018-04-14T21:09:51.000Z

2021-07-17T21:08:54.000Z

python/misc.py

dnbaker/emp

c9e79b8092434919e9ac90dc199f49845403c2ba

[ "MIT" ]

11

2018-03-21T19:28:35.000Z

2021-06-29T17:33:34.000Z

#!/usr/bin/env python import sys import string from collections import defaultdict def freq(iterable): """ Returns a dictionary of counts for each item in an iterable. >>>freq("ACGTTTAAA") {'A': 4, 'C': 1, 'G': 1, 'T': 3} """ ret = defaultdict(int) for el in iterable: ret[el] += 1 return ret try: from cytoolz import frequencies as freq except ImportError: pass # Don't sweat it REV_CMP_TABLE = (str if sys.version_info[0] == 3 else string).maketrans("ACGTN", "TGCAN") def revcmp(seq): """ Returns the reverse complement of a sequence. >>>revcmp("ACGTNTTTAAATTT") 'AAATTTAAANACGT' """ return seq[::-1].translate(REV_CMP_TABLE) def xopen(path): """ Stolen from Dooplicity. (https://github.com/nellore/rail/), then stripped to only open files with open or gzip to open based on magic number presence. """ import gzip fh = (gzip.open(path, "rb") if open(path, 'rb').read(2) == '\x1f\x8b' else open(path, "r")) try: yield fh finally: fh.close() __all__ = [revcmp, REV_CMP_TABLE, freq, xopen] if __name__ == "__main__": """ Unit tests """ import unittest class Test(unittest.TestCase): def test_revcmp(self): self.assertEqual(revcmp("ACGTACCTTATATATATA"), "TATATATATAAGGTACGT") def test_freq(self): self.assertEqual(freq("ACGTTTAAA"), {'A': 4, 'C': 1, 'G': 1, 'T': 3}) unittest.main()

22.628571

73

0.571338

import sys import string from collections import defaultdict def freq(iterable): ret = defaultdict(int) for el in iterable: ret[el] += 1 return ret try: from cytoolz import frequencies as freq except ImportError: pass REV_CMP_TABLE = (str if sys.version_info[0] == 3 else string).maketrans("ACGTN", "TGCAN") def revcmp(seq): return seq[::-1].translate(REV_CMP_TABLE) def xopen(path): import gzip fh = (gzip.open(path, "rb") if open(path, 'rb').read(2) == '\x1f\x8b' else open(path, "r")) try: yield fh finally: fh.close() __all__ = [revcmp, REV_CMP_TABLE, freq, xopen] if __name__ == "__main__": import unittest class Test(unittest.TestCase): def test_revcmp(self): self.assertEqual(revcmp("ACGTACCTTATATATATA"), "TATATATATAAGGTACGT") def test_freq(self): self.assertEqual(freq("ACGTTTAAA"), {'A': 4, 'C': 1, 'G': 1, 'T': 3}) unittest.main()

true

f72ac86bdcf9c11af4e34184f7bc61e8e47c1475

1,781

py

Python

dex/dextIR/CommandListIR.py

jmorse/dexter

79cefa890d041dfc927aea2a84737aa704ddd35c

[ "MIT" ]

null

dex/dextIR/CommandListIR.py

jmorse/dexter

79cefa890d041dfc927aea2a84737aa704ddd35c

[ "MIT" ]

null

dex/dextIR/CommandListIR.py

jmorse/dexter

79cefa890d041dfc927aea2a84737aa704ddd35c

[ "MIT" ]

null

# DExTer : Debugging Experience Tester # ~~~~~~ ~ ~~ ~ ~~ # # Copyright (c) 2018 by SN Systems Ltd., Sony Interactive Entertainment Inc. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. """Serialization of the DExTer commands embedded within the files under test. """ from dex.dextIR.CommandIR import CommandIR from dex.utils.serialize import SrField, SrObject class CommandListIR(SrObject): sr_fields = [ SrField( 'command_list', CommandIR, list_of=True, required_in_init=False, default_value=list), ] def __getitem__(self, idx): return getattr(self, 'command_list')[idx] def append(self, item): return getattr(self, 'command_list').append(item)

39.577778

79

0.718136

from dex.dextIR.CommandIR import CommandIR from dex.utils.serialize import SrField, SrObject class CommandListIR(SrObject): sr_fields = [ SrField( 'command_list', CommandIR, list_of=True, required_in_init=False, default_value=list), ] def __getitem__(self, idx): return getattr(self, 'command_list')[idx] def append(self, item): return getattr(self, 'command_list').append(item)

true

f72ac92ca104149447f8f64cf75ef595d16ca300

9,128

py

Python

tests/operators/test_gcs_to_s3.py

InigoSJ/airflow

8b97a387dc30d8c88390d500ec99333798c20f1c

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

1

2019-09-06T09:55:18.000Z

tests/operators/test_gcs_to_s3.py

InigoSJ/airflow

8b97a387dc30d8c88390d500ec99333798c20f1c

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

null

tests/operators/test_gcs_to_s3.py

InigoSJ/airflow

8b97a387dc30d8c88390d500ec99333798c20f1c

[ "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0", "BSD-3-Clause" ]

1

2019-12-09T08:41:32.000Z

# -*- coding: utf-8 -*- # # 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. import unittest from airflow.operators.gcs_to_s3 import GoogleCloudStorageToS3Operator from airflow.hooks.S3_hook import S3Hook from tests.compat import mock try: from moto import mock_s3 except ImportError: mock_s3 = None TASK_ID = 'test-gcs-list-operator' GCS_BUCKET = 'test-bucket' DELIMITER = '.csv' PREFIX = 'TEST' S3_BUCKET = 's3://bucket/' MOCK_FILES = ["TEST1.csv", "TEST2.csv", "TEST3.csv"] class TestGoogleCloudStorageToS3Operator(unittest.TestCase): # Test1: incremental behaviour (just some files missing) @mock_s3 @mock.patch('airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageHook') @mock.patch('airflow.operators.gcs_to_s3.GoogleCloudStorageHook') def test_execute_incremental(self, mock_hook, mock_hook2): mock_hook.return_value.list.return_value = MOCK_FILES mock_hook.return_value.download.return_value = b"testing" mock_hook2.return_value.list.return_value = MOCK_FILES operator = GoogleCloudStorageToS3Operator(task_id=TASK_ID, bucket=GCS_BUCKET, prefix=PREFIX, delimiter=DELIMITER, dest_aws_conn_id=None, dest_s3_key=S3_BUCKET, replace=False) # create dest bucket hook = S3Hook(aws_conn_id=None) b = hook.get_bucket('bucket') b.create() b.put_object(Key=MOCK_FILES[0], Body=b'testing') # we expect all except first file in MOCK_FILES to be uploaded # and all the MOCK_FILES to be present at the S3 bucket uploaded_files = operator.execute(None) self.assertEqual(sorted(MOCK_FILES[1:]), sorted(uploaded_files)) self.assertEqual(sorted(MOCK_FILES), sorted(hook.list_keys('bucket', delimiter='/'))) # Test2: All the files are already in origin and destination without replace @mock_s3 @mock.patch('airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageHook') @mock.patch('airflow.operators.gcs_to_s3.GoogleCloudStorageHook') def test_execute_without_replace(self, mock_hook, mock_hook2): mock_hook.return_value.list.return_value = MOCK_FILES mock_hook.return_value.download.return_value = b"testing" mock_hook2.return_value.list.return_value = MOCK_FILES operator = GoogleCloudStorageToS3Operator(task_id=TASK_ID, bucket=GCS_BUCKET, prefix=PREFIX, delimiter=DELIMITER, dest_aws_conn_id=None, dest_s3_key=S3_BUCKET, replace=False) # create dest bucket with all the files hook = S3Hook(aws_conn_id=None) b = hook.get_bucket('bucket') b.create() [b.put_object(Key=MOCK_FILE, Body=b'testing') for MOCK_FILE in MOCK_FILES] # we expect nothing to be uploaded # and all the MOCK_FILES to be present at the S3 bucket uploaded_files = operator.execute(None) self.assertEqual([], uploaded_files) self.assertEqual(sorted(MOCK_FILES), sorted(hook.list_keys('bucket', delimiter='/'))) # Test3: There are no files in destination bucket @mock_s3 @mock.patch('airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageHook') @mock.patch('airflow.operators.gcs_to_s3.GoogleCloudStorageHook') def test_execute(self, mock_hook, mock_hook2): mock_hook.return_value.list.return_value = MOCK_FILES mock_hook.return_value.download.return_value = b"testing" mock_hook2.return_value.list.return_value = MOCK_FILES operator = GoogleCloudStorageToS3Operator(task_id=TASK_ID, bucket=GCS_BUCKET, prefix=PREFIX, delimiter=DELIMITER, dest_aws_conn_id=None, dest_s3_key=S3_BUCKET, replace=False) # create dest bucket without files hook = S3Hook(aws_conn_id=None) b = hook.get_bucket('bucket') b.create() # we expect all MOCK_FILES to be uploaded # and all MOCK_FILES to be present at the S3 bucket uploaded_files = operator.execute(None) self.assertEqual(sorted(MOCK_FILES), sorted(uploaded_files)) self.assertEqual(sorted(MOCK_FILES), sorted(hook.list_keys('bucket', delimiter='/'))) # Test4: Destination and Origin are in sync but replace all files in destination @mock_s3 @mock.patch('airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageHook') @mock.patch('airflow.operators.gcs_to_s3.GoogleCloudStorageHook') def test_execute_with_replace(self, mock_hook, mock_hook2): mock_hook.return_value.list.return_value = MOCK_FILES mock_hook.return_value.download.return_value = b"testing" mock_hook2.return_value.list.return_value = MOCK_FILES operator = GoogleCloudStorageToS3Operator(task_id=TASK_ID, bucket=GCS_BUCKET, prefix=PREFIX, delimiter=DELIMITER, dest_aws_conn_id=None, dest_s3_key=S3_BUCKET, replace=True) # create dest bucket with all the files hook = S3Hook(aws_conn_id=None) b = hook.get_bucket('bucket') b.create() [b.put_object(Key=MOCK_FILE, Body=b'testing') for MOCK_FILE in MOCK_FILES] # we expect all MOCK_FILES to be uploaded and replace the existing ones # and all MOCK_FILES to be present at the S3 bucket uploaded_files = operator.execute(None) self.assertEqual(sorted(MOCK_FILES), sorted(uploaded_files)) self.assertEqual(sorted(MOCK_FILES), sorted(hook.list_keys('bucket', delimiter='/'))) # Test5: Incremental sync with replace @mock_s3 @mock.patch('airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageHook') @mock.patch('airflow.operators.gcs_to_s3.GoogleCloudStorageHook') def test_execute_incremental_with_replace(self, mock_hook, mock_hook2): mock_hook.return_value.list.return_value = MOCK_FILES mock_hook.return_value.download.return_value = b"testing" mock_hook2.return_value.list.return_value = MOCK_FILES operator = GoogleCloudStorageToS3Operator(task_id=TASK_ID, bucket=GCS_BUCKET, prefix=PREFIX, delimiter=DELIMITER, dest_aws_conn_id=None, dest_s3_key=S3_BUCKET, replace=True) # create dest bucket with just two files (the first two files in MOCK_FILES) hook = S3Hook(aws_conn_id=None) b = hook.get_bucket('bucket') b.create() [b.put_object(Key=MOCK_FILE, Body=b'testing') for MOCK_FILE in MOCK_FILES[:2]] # we expect all the MOCK_FILES to be uploaded and replace the existing ones # and all MOCK_FILES to be present at the S3 bucket uploaded_files = operator.execute(None) self.assertEqual(sorted(MOCK_FILES), sorted(uploaded_files)) self.assertEqual(sorted(MOCK_FILES), sorted(hook.list_keys('bucket', delimiter='/')))

48.296296

86

0.594106

import unittest from airflow.operators.gcs_to_s3 import GoogleCloudStorageToS3Operator from airflow.hooks.S3_hook import S3Hook from tests.compat import mock try: from moto import mock_s3 except ImportError: mock_s3 = None TASK_ID = 'test-gcs-list-operator' GCS_BUCKET = 'test-bucket' DELIMITER = '.csv' PREFIX = 'TEST' S3_BUCKET = 's3://bucket/' MOCK_FILES = ["TEST1.csv", "TEST2.csv", "TEST3.csv"] class TestGoogleCloudStorageToS3Operator(unittest.TestCase): @mock_s3 @mock.patch('airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageHook') @mock.patch('airflow.operators.gcs_to_s3.GoogleCloudStorageHook') def test_execute_incremental(self, mock_hook, mock_hook2): mock_hook.return_value.list.return_value = MOCK_FILES mock_hook.return_value.download.return_value = b"testing" mock_hook2.return_value.list.return_value = MOCK_FILES operator = GoogleCloudStorageToS3Operator(task_id=TASK_ID, bucket=GCS_BUCKET, prefix=PREFIX, delimiter=DELIMITER, dest_aws_conn_id=None, dest_s3_key=S3_BUCKET, replace=False) hook = S3Hook(aws_conn_id=None) b = hook.get_bucket('bucket') b.create() b.put_object(Key=MOCK_FILES[0], Body=b'testing') uploaded_files = operator.execute(None) self.assertEqual(sorted(MOCK_FILES[1:]), sorted(uploaded_files)) self.assertEqual(sorted(MOCK_FILES), sorted(hook.list_keys('bucket', delimiter='/'))) @mock_s3 @mock.patch('airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageHook') @mock.patch('airflow.operators.gcs_to_s3.GoogleCloudStorageHook') def test_execute_without_replace(self, mock_hook, mock_hook2): mock_hook.return_value.list.return_value = MOCK_FILES mock_hook.return_value.download.return_value = b"testing" mock_hook2.return_value.list.return_value = MOCK_FILES operator = GoogleCloudStorageToS3Operator(task_id=TASK_ID, bucket=GCS_BUCKET, prefix=PREFIX, delimiter=DELIMITER, dest_aws_conn_id=None, dest_s3_key=S3_BUCKET, replace=False) hook = S3Hook(aws_conn_id=None) b = hook.get_bucket('bucket') b.create() [b.put_object(Key=MOCK_FILE, Body=b'testing') for MOCK_FILE in MOCK_FILES] uploaded_files = operator.execute(None) self.assertEqual([], uploaded_files) self.assertEqual(sorted(MOCK_FILES), sorted(hook.list_keys('bucket', delimiter='/'))) @mock_s3 @mock.patch('airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageHook') @mock.patch('airflow.operators.gcs_to_s3.GoogleCloudStorageHook') def test_execute(self, mock_hook, mock_hook2): mock_hook.return_value.list.return_value = MOCK_FILES mock_hook.return_value.download.return_value = b"testing" mock_hook2.return_value.list.return_value = MOCK_FILES operator = GoogleCloudStorageToS3Operator(task_id=TASK_ID, bucket=GCS_BUCKET, prefix=PREFIX, delimiter=DELIMITER, dest_aws_conn_id=None, dest_s3_key=S3_BUCKET, replace=False) hook = S3Hook(aws_conn_id=None) b = hook.get_bucket('bucket') b.create() uploaded_files = operator.execute(None) self.assertEqual(sorted(MOCK_FILES), sorted(uploaded_files)) self.assertEqual(sorted(MOCK_FILES), sorted(hook.list_keys('bucket', delimiter='/'))) @mock_s3 @mock.patch('airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageHook') @mock.patch('airflow.operators.gcs_to_s3.GoogleCloudStorageHook') def test_execute_with_replace(self, mock_hook, mock_hook2): mock_hook.return_value.list.return_value = MOCK_FILES mock_hook.return_value.download.return_value = b"testing" mock_hook2.return_value.list.return_value = MOCK_FILES operator = GoogleCloudStorageToS3Operator(task_id=TASK_ID, bucket=GCS_BUCKET, prefix=PREFIX, delimiter=DELIMITER, dest_aws_conn_id=None, dest_s3_key=S3_BUCKET, replace=True) hook = S3Hook(aws_conn_id=None) b = hook.get_bucket('bucket') b.create() [b.put_object(Key=MOCK_FILE, Body=b'testing') for MOCK_FILE in MOCK_FILES] uploaded_files = operator.execute(None) self.assertEqual(sorted(MOCK_FILES), sorted(uploaded_files)) self.assertEqual(sorted(MOCK_FILES), sorted(hook.list_keys('bucket', delimiter='/'))) @mock_s3 @mock.patch('airflow.contrib.operators.gcs_list_operator.GoogleCloudStorageHook') @mock.patch('airflow.operators.gcs_to_s3.GoogleCloudStorageHook') def test_execute_incremental_with_replace(self, mock_hook, mock_hook2): mock_hook.return_value.list.return_value = MOCK_FILES mock_hook.return_value.download.return_value = b"testing" mock_hook2.return_value.list.return_value = MOCK_FILES operator = GoogleCloudStorageToS3Operator(task_id=TASK_ID, bucket=GCS_BUCKET, prefix=PREFIX, delimiter=DELIMITER, dest_aws_conn_id=None, dest_s3_key=S3_BUCKET, replace=True) hook = S3Hook(aws_conn_id=None) b = hook.get_bucket('bucket') b.create() [b.put_object(Key=MOCK_FILE, Body=b'testing') for MOCK_FILE in MOCK_FILES[:2]] uploaded_files = operator.execute(None) self.assertEqual(sorted(MOCK_FILES), sorted(uploaded_files)) self.assertEqual(sorted(MOCK_FILES), sorted(hook.list_keys('bucket', delimiter='/')))

true

f72acb68ed93a51226e787125180c68eb7131f4d

5,030

py

Python

gdxpds/read_gdx.py

cdgaete/gdx-pandas

2b9b00a177268227bce189939cdab081e09cb0dc

[ "BSD-3-Clause" ]

null

gdxpds/read_gdx.py

cdgaete/gdx-pandas

2b9b00a177268227bce189939cdab081e09cb0dc

[ "BSD-3-Clause" ]

null

gdxpds/read_gdx.py

cdgaete/gdx-pandas

2b9b00a177268227bce189939cdab081e09cb0dc

[ "BSD-3-Clause" ]

null

# [LICENSE] # Copyright (c) 2018, Alliance for Sustainable Energy. # All rights reserved. # # Redistribution and use in source and binary forms, # with or without modification, are permitted provided # that the following conditions are met: # # 1. Redistributions of source code must retain the above # copyright notice, this list of conditions and the # following disclaimer. # # 2. 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. # # 3. Neither the name of the copyright holder nor the # names of its contributors may be used to endorse or # promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND # CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, # INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) # HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE # OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # [/LICENSE] from collections import OrderedDict import logging # gdxpds needs to be imported before pandas to try to avoid library conflict on # Linux that causes a segmentation fault. from gdxpds.tools import Error from gdxpds.gdx import GdxFile logger = logging.getLogger(__name__) class Translator(object): def __init__(self,gdx_file,gams_dir=None,lazy_load=False): self.__gdx = GdxFile(gams_dir=gams_dir,lazy_load=lazy_load) self.__gdx.read(gdx_file) self.__dataframes = None def __exit__(self, *args): self.__gdx.__exit__(self, *args) def __del__(self): self.__gdx.__del__() @property def gams_dir(self): return self.gdx.gams_dir @gams_dir.setter def gams_dir(self, value): self.gdx.gams_dir = value @property def gdx_file(self): return self.gdx.filename @gdx_file.setter def gdx_file(self,value): self.__gdx.__del__() self.__gdx = GdxFile(gams_dir=self.gdx.gams_dir,lazy_load=self.gdx.lazy_load) self.__gdx.read(value) self.__dataframes = None @property def gdx(self): return self.__gdx @property def dataframes(self): if self.__dataframes is None: self.__dataframes = OrderedDict() for symbol in self.__gdx: if not symbol.loaded: symbol.load() self.__dataframes[symbol.name] = symbol.dataframe.copy() return self.__dataframes @property def symbols(self): return [symbol_name for symbol_name in self.gdx] def dataframe(self, symbol_name): if not symbol_name in self.gdx: raise Error("No symbol named '{}' in '{}'.".format(symbol_name, self.gdx_file)) if not self.gdx[symbol_name].loaded: self.gdx[symbol_name].load() # This was returning { symbol_name: dataframe }, which seems intuitively off. return self.gdx[symbol_name].dataframe.copy() def to_dataframes(gdx_file,gams_dir=None): """ Primary interface for converting a GAMS GDX file to pandas DataFrames. Parameters: - gdx_file (string): path to a GDX file - gams_dir (string): optional path to GAMS directory Returns a dict of Pandas DataFrames, one item for each symbol in the GDX file, keyed with the symbol name. """ dfs = Translator(gdx_file,gams_dir=gams_dir).dataframes return dfs def list_symbols(gdx_file,gams_dir=None): """ Returns the list of symbols available in gdx_file. Parameters: - gdx_file (string): path to a GDX file - gams_dir (string): optional path to GAMS directory """ symbols = Translator(gdx_file,gams_dir=gams_dir,lazy_load=True).symbols return symbols def to_dataframe(gdx_file,symbol_name,gams_dir=None,old_interface=True): """ Interface for getting the { symbol_name: pandas.DataFrame } dict for a single symbol. Parameters: - gdx_file (string): path to a GDX file - symbol_name (string): symbol whose pandas.DataFrame is being requested - gams_dir (string): optional path to GAMS directory Returns a dict with a single entry, where the key is symbol_name and the value is the corresponding pandas.DataFrame. """ df = Translator(gdx_file,gams_dir=gams_dir,lazy_load=True).dataframe(symbol_name) return {symbol_name: df} if old_interface else df

34.689655

91

0.706362

from collections import OrderedDict import logging from gdxpds.tools import Error from gdxpds.gdx import GdxFile logger = logging.getLogger(__name__) class Translator(object): def __init__(self,gdx_file,gams_dir=None,lazy_load=False): self.__gdx = GdxFile(gams_dir=gams_dir,lazy_load=lazy_load) self.__gdx.read(gdx_file) self.__dataframes = None def __exit__(self, *args): self.__gdx.__exit__(self, *args) def __del__(self): self.__gdx.__del__() @property def gams_dir(self): return self.gdx.gams_dir @gams_dir.setter def gams_dir(self, value): self.gdx.gams_dir = value @property def gdx_file(self): return self.gdx.filename @gdx_file.setter def gdx_file(self,value): self.__gdx.__del__() self.__gdx = GdxFile(gams_dir=self.gdx.gams_dir,lazy_load=self.gdx.lazy_load) self.__gdx.read(value) self.__dataframes = None @property def gdx(self): return self.__gdx @property def dataframes(self): if self.__dataframes is None: self.__dataframes = OrderedDict() for symbol in self.__gdx: if not symbol.loaded: symbol.load() self.__dataframes[symbol.name] = symbol.dataframe.copy() return self.__dataframes @property def symbols(self): return [symbol_name for symbol_name in self.gdx] def dataframe(self, symbol_name): if not symbol_name in self.gdx: raise Error("No symbol named '{}' in '{}'.".format(symbol_name, self.gdx_file)) if not self.gdx[symbol_name].loaded: self.gdx[symbol_name].load() return self.gdx[symbol_name].dataframe.copy() def to_dataframes(gdx_file,gams_dir=None): dfs = Translator(gdx_file,gams_dir=gams_dir).dataframes return dfs def list_symbols(gdx_file,gams_dir=None): symbols = Translator(gdx_file,gams_dir=gams_dir,lazy_load=True).symbols return symbols def to_dataframe(gdx_file,symbol_name,gams_dir=None,old_interface=True): df = Translator(gdx_file,gams_dir=gams_dir,lazy_load=True).dataframe(symbol_name) return {symbol_name: df} if old_interface else df

true

f72acbaa7eb80d299ab01ae2d3c86752036d4dac

24,244

py

Python

test/api/table/test_table.py

rizwanniazigroupdocs/aspose-words-cloud-python

b943384a1e3c0710cc84df74119e6edf7356037e

[ "MIT" ]

null

test/api/table/test_table.py

rizwanniazigroupdocs/aspose-words-cloud-python

b943384a1e3c0710cc84df74119e6edf7356037e

[ "MIT" ]

null

test/api/table/test_table.py

rizwanniazigroupdocs/aspose-words-cloud-python

b943384a1e3c0710cc84df74119e6edf7356037e

[ "MIT" ]

null

# ----------------------------------------------------------------------------------- # <copyright company="Aspose" file="test_table.py"> # Copyright (c) 2020 Aspose.Words for Cloud # </copyright> # <summary> # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # </summary> # ----------------------------------------------------------------------------------- import os import dateutil.parser import asposewordscloud.models.requests from test.base_test_context import BaseTestContext # # Example of how to work wtih table. # class TestTable(BaseTestContext): # # Test for getting tables. # def test_get_tables(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTables.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTablesRequest(name=remoteFileName, node_path='', folder=remoteDataFolder) result = self.words_api.get_tables(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.tables, 'Validate GetTables response') self.assertIsNotNone(result.tables.table_link_list, 'Validate GetTables response') self.assertEqual(5, len(result.tables.table_link_list)) self.assertEqual('0.0.1', result.tables.table_link_list[0].node_id) # # Test for getting tables without node path. # def test_get_tables_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTablesWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTablesRequest(name=remoteFileName, folder=remoteDataFolder) result = self.words_api.get_tables(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.tables, 'Validate GetTablesWithoutNodePath response') self.assertIsNotNone(result.tables.table_link_list, 'Validate GetTablesWithoutNodePath response') self.assertEqual(5, len(result.tables.table_link_list)) self.assertEqual('0.0.1', result.tables.table_link_list[0].node_id) # # Test for getting table. # def test_get_table(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTable.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableRequest(name=remoteFileName, index=1, node_path='', folder=remoteDataFolder) result = self.words_api.get_table(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.table, 'Validate GetTable response') self.assertIsNotNone(result.table.table_row_list, 'Validate GetTable response') self.assertEqual(1, len(result.table.table_row_list)) self.assertIsNotNone(result.table.table_row_list[0].table_cell_list, 'Validate GetTable response') self.assertEqual(2, len(result.table.table_row_list[0].table_cell_list)) # # Test for getting table without node path. # def test_get_table_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableRequest(name=remoteFileName, index=1, folder=remoteDataFolder) result = self.words_api.get_table(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.table, 'Validate GetTableWithoutNodePath response') self.assertIsNotNone(result.table.table_row_list, 'Validate GetTableWithoutNodePath response') self.assertEqual(1, len(result.table.table_row_list)) self.assertIsNotNone(result.table.table_row_list[0].table_cell_list, 'Validate GetTableWithoutNodePath response') self.assertEqual(2, len(result.table.table_row_list[0].table_cell_list)) # # Test for deleting table. # def test_delete_table(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestDeleteTable.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.DeleteTableRequest(name=remoteFileName, index=1, node_path='', folder=remoteDataFolder) self.words_api.delete_table(request) # # Test for deleting table without node path. # def test_delete_table_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestDeleteTableWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.DeleteTableRequest(name=remoteFileName, index=1, folder=remoteDataFolder) self.words_api.delete_table(request) # # Test for adding table. # def test_insert_table(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestInsertTable.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestTable = asposewordscloud.TableInsert(columns_count=5, rows_count=4) request = asposewordscloud.models.requests.InsertTableRequest(name=remoteFileName, table=requestTable, node_path='', folder=remoteDataFolder) result = self.words_api.insert_table(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.table, 'Validate InsertTable response') self.assertIsNotNone(result.table.table_row_list, 'Validate InsertTable response') self.assertEqual(4, len(result.table.table_row_list)) self.assertIsNotNone(result.table.table_row_list[0].table_cell_list, 'Validate InsertTable response') self.assertEqual(5, len(result.table.table_row_list[0].table_cell_list)) # # Test for adding table without node path. # def test_insert_table_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestInsertTableWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestTable = asposewordscloud.TableInsert(columns_count=5, rows_count=4) request = asposewordscloud.models.requests.InsertTableRequest(name=remoteFileName, table=requestTable, folder=remoteDataFolder) result = self.words_api.insert_table(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.table, 'Validate InsertTableWithoutNodePath response') self.assertIsNotNone(result.table.table_row_list, 'Validate InsertTableWithoutNodePath response') self.assertEqual(4, len(result.table.table_row_list)) self.assertIsNotNone(result.table.table_row_list[0].table_cell_list, 'Validate InsertTableWithoutNodePath response') self.assertEqual(5, len(result.table.table_row_list[0].table_cell_list)) # # Test for getting document properties. # def test_get_table_properties(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableProperties.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTablePropertiesRequest(name=remoteFileName, index=1, node_path='', folder=remoteDataFolder) result = self.words_api.get_table_properties(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.properties, 'Validate GetTableProperties response') self.assertEqual('Table Grid', result.properties.style_name) # # Test for getting document properties without node path. # def test_get_table_properties_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTablePropertiesWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTablePropertiesRequest(name=remoteFileName, index=1, folder=remoteDataFolder) result = self.words_api.get_table_properties(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.properties, 'Validate GetTablePropertiesWithoutNodePath response') self.assertEqual('Table Grid', result.properties.style_name) # # Test for updating table properties. # def test_update_table_properties(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestUpdateTableProperties.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestProperties = asposewordscloud.TableProperties(alignment='Right', allow_auto_fit=False, bidi=True, bottom_padding=1, cell_spacing=2.0, style_options='ColumnBands') request = asposewordscloud.models.requests.UpdateTablePropertiesRequest(name=remoteFileName, properties=requestProperties, index=1, node_path='', folder=remoteDataFolder) result = self.words_api.update_table_properties(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.properties, 'Validate UpdateTableProperties response') self.assertFalse(result.properties.allow_auto_fit, 'Validate UpdateTableProperties response') self.assertTrue(result.properties.bidi, 'Validate UpdateTableProperties response') self.assertEqual(1.0, result.properties.bottom_padding) self.assertEqual(2.0, result.properties.cell_spacing) # # Test for updating table properties without node path. # def test_update_table_properties_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestUpdateTablePropertiesWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestProperties = asposewordscloud.TableProperties(alignment='Right', allow_auto_fit=False, bidi=True, bottom_padding=1.0, cell_spacing=2.0, style_options='ColumnBands') request = asposewordscloud.models.requests.UpdateTablePropertiesRequest(name=remoteFileName, properties=requestProperties, index=1, folder=remoteDataFolder) result = self.words_api.update_table_properties(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.properties, 'Validate UpdateTablePropertiesWithoutNodePath response') self.assertFalse(result.properties.allow_auto_fit, 'Validate UpdateTablePropertiesWithoutNodePath response') self.assertTrue(result.properties.bidi, 'Validate UpdateTablePropertiesWithoutNodePath response') self.assertEqual(1.0, result.properties.bottom_padding) self.assertEqual(2.0, result.properties.cell_spacing) # # Test for getting table row. # def test_get_table_row(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableRow.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableRowRequest(name=remoteFileName, table_path='tables/1', index=0, folder=remoteDataFolder) result = self.words_api.get_table_row(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.row, 'Validate GetTableRow response') self.assertIsNotNone(result.row.table_cell_list, 'Validate GetTableRow response') self.assertEqual(2, len(result.row.table_cell_list)) # # Test for deleting table row. # def test_delete_table_row(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestDeleteTableRow.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.DeleteTableRowRequest(name=remoteFileName, table_path='tables/1', index=0, folder=remoteDataFolder) self.words_api.delete_table_row(request) # # Test for adding row. # def test_insert_table_row(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestInsertTableRow.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestRow = asposewordscloud.TableRowInsert(columns_count=5) request = asposewordscloud.models.requests.InsertTableRowRequest(name=remoteFileName, row=requestRow, table_path='sections/0/tables/2', folder=remoteDataFolder) result = self.words_api.insert_table_row(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.row, 'Validate InsertTableRow response') self.assertIsNotNone(result.row.table_cell_list, 'Validate InsertTableRow response') self.assertEqual(5, len(result.row.table_cell_list)) # # Test for getting row format. # def test_get_table_row_format(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableRowFormat.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableRowFormatRequest(name=remoteFileName, table_path='sections/0/tables/2', index=0, folder=remoteDataFolder) result = self.words_api.get_table_row_format(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.row_format, 'Validate GetTableRowFormat response') self.assertTrue(result.row_format.allow_break_across_pages, 'Validate GetTableRowFormat response') # # Test updating row format. # def test_update_table_row_format(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestUpdateTableRowFormat.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestFormat = asposewordscloud.TableRowFormat(allow_break_across_pages=True, heading_format=True, height=10.0, height_rule='Exactly') request = asposewordscloud.models.requests.UpdateTableRowFormatRequest(name=remoteFileName, format=requestFormat, table_path='sections/0/tables/2', index=0, folder=remoteDataFolder) result = self.words_api.update_table_row_format(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.row_format, 'Validate UpdateTableRowFormat response') self.assertTrue(result.row_format.allow_break_across_pages, 'Validate UpdateTableRowFormat response') self.assertTrue(result.row_format.heading_format, 'Validate UpdateTableRowFormat response') self.assertEqual(10.0, result.row_format.height) # # Test for getting table cell. # def test_get_table_cell(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableCell.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableCellRequest(name=remoteFileName, table_row_path='sections/0/tables/2/rows/0', index=0, folder=remoteDataFolder) result = self.words_api.get_table_cell(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.cell, 'Validate GetTableCell response') self.assertEqual('0.0.5.0.0', result.cell.node_id) # # Test for deleting cell. # def test_delete_table_cell(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestDeleteTableCell.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.DeleteTableCellRequest(name=remoteFileName, table_row_path='sections/0/tables/2/rows/0', index=0, folder=remoteDataFolder) self.words_api.delete_table_cell(request) # # Test for adding cell. # def test_insert_table_cell(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestInsertTableCell.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestCell = asposewordscloud.TableCellInsert() request = asposewordscloud.models.requests.InsertTableCellRequest(name=remoteFileName, cell=requestCell, table_row_path='sections/0/tables/2/rows/0', folder=remoteDataFolder) result = self.words_api.insert_table_cell(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.cell, 'Validate InsertTableCell response') self.assertEqual('0.0.5.0.3', result.cell.node_id) # # Test for getting cell format. # def test_get_table_cell_format(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableCellFormat.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableCellFormatRequest(name=remoteFileName, table_row_path='sections/0/tables/2/rows/0', index=0, folder=remoteDataFolder) result = self.words_api.get_table_cell_format(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.cell_format, 'Validate GetTableCellFormat response') self.assertTrue(result.cell_format.wrap_text, 'Validate GetTableCellFormat response') # # Test for updating cell format. # def test_update_table_cell_format(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestUpdateTableCellFormat.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestFormat = asposewordscloud.TableCellFormat(bottom_padding=5.0, fit_text=True, horizontal_merge='First', wrap_text=True) request = asposewordscloud.models.requests.UpdateTableCellFormatRequest(name=remoteFileName, format=requestFormat, table_row_path='sections/0/tables/2/rows/0', index=0, folder=remoteDataFolder) result = self.words_api.update_table_cell_format(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.cell_format, 'Validate UpdateTableCellFormat response') self.assertEqual(5.0, result.cell_format.bottom_padding) self.assertTrue(result.cell_format.fit_text, 'Validate UpdateTableCellFormat response') self.assertTrue(result.cell_format.wrap_text, 'Validate UpdateTableCellFormat response') # # Test for table rendering. # def test_render_table(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestRenderTable.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.RenderTableRequest(name=remoteFileName, format='png', index=0, node_path='', folder=remoteDataFolder) result = self.words_api.render_table(request) self.assertIsNotNone(result, 'Error has occurred.') # # Test for table rendering without node path. # def test_render_table_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestRenderTableWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.RenderTableRequest(name=remoteFileName, format='png', index=0, folder=remoteDataFolder) result = self.words_api.render_table(request) self.assertIsNotNone(result, 'Error has occurred.')

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import os import dateutil.parser import asposewordscloud.models.requests from test.base_test_context import BaseTestContext class TestTable(BaseTestContext): def test_get_tables(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTables.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTablesRequest(name=remoteFileName, node_path='', folder=remoteDataFolder) result = self.words_api.get_tables(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.tables, 'Validate GetTables response') self.assertIsNotNone(result.tables.table_link_list, 'Validate GetTables response') self.assertEqual(5, len(result.tables.table_link_list)) self.assertEqual('0.0.1', result.tables.table_link_list[0].node_id) def test_get_tables_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTablesWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTablesRequest(name=remoteFileName, folder=remoteDataFolder) result = self.words_api.get_tables(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.tables, 'Validate GetTablesWithoutNodePath response') self.assertIsNotNone(result.tables.table_link_list, 'Validate GetTablesWithoutNodePath response') self.assertEqual(5, len(result.tables.table_link_list)) self.assertEqual('0.0.1', result.tables.table_link_list[0].node_id) def test_get_table(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTable.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableRequest(name=remoteFileName, index=1, node_path='', folder=remoteDataFolder) result = self.words_api.get_table(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.table, 'Validate GetTable response') self.assertIsNotNone(result.table.table_row_list, 'Validate GetTable response') self.assertEqual(1, len(result.table.table_row_list)) self.assertIsNotNone(result.table.table_row_list[0].table_cell_list, 'Validate GetTable response') self.assertEqual(2, len(result.table.table_row_list[0].table_cell_list)) def test_get_table_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableRequest(name=remoteFileName, index=1, folder=remoteDataFolder) result = self.words_api.get_table(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.table, 'Validate GetTableWithoutNodePath response') self.assertIsNotNone(result.table.table_row_list, 'Validate GetTableWithoutNodePath response') self.assertEqual(1, len(result.table.table_row_list)) self.assertIsNotNone(result.table.table_row_list[0].table_cell_list, 'Validate GetTableWithoutNodePath response') self.assertEqual(2, len(result.table.table_row_list[0].table_cell_list)) def test_delete_table(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestDeleteTable.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.DeleteTableRequest(name=remoteFileName, index=1, node_path='', folder=remoteDataFolder) self.words_api.delete_table(request) def test_delete_table_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestDeleteTableWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.DeleteTableRequest(name=remoteFileName, index=1, folder=remoteDataFolder) self.words_api.delete_table(request) def test_insert_table(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestInsertTable.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestTable = asposewordscloud.TableInsert(columns_count=5, rows_count=4) request = asposewordscloud.models.requests.InsertTableRequest(name=remoteFileName, table=requestTable, node_path='', folder=remoteDataFolder) result = self.words_api.insert_table(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.table, 'Validate InsertTable response') self.assertIsNotNone(result.table.table_row_list, 'Validate InsertTable response') self.assertEqual(4, len(result.table.table_row_list)) self.assertIsNotNone(result.table.table_row_list[0].table_cell_list, 'Validate InsertTable response') self.assertEqual(5, len(result.table.table_row_list[0].table_cell_list)) def test_insert_table_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestInsertTableWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestTable = asposewordscloud.TableInsert(columns_count=5, rows_count=4) request = asposewordscloud.models.requests.InsertTableRequest(name=remoteFileName, table=requestTable, folder=remoteDataFolder) result = self.words_api.insert_table(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.table, 'Validate InsertTableWithoutNodePath response') self.assertIsNotNone(result.table.table_row_list, 'Validate InsertTableWithoutNodePath response') self.assertEqual(4, len(result.table.table_row_list)) self.assertIsNotNone(result.table.table_row_list[0].table_cell_list, 'Validate InsertTableWithoutNodePath response') self.assertEqual(5, len(result.table.table_row_list[0].table_cell_list)) def test_get_table_properties(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableProperties.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTablePropertiesRequest(name=remoteFileName, index=1, node_path='', folder=remoteDataFolder) result = self.words_api.get_table_properties(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.properties, 'Validate GetTableProperties response') self.assertEqual('Table Grid', result.properties.style_name) def test_get_table_properties_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTablePropertiesWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTablePropertiesRequest(name=remoteFileName, index=1, folder=remoteDataFolder) result = self.words_api.get_table_properties(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.properties, 'Validate GetTablePropertiesWithoutNodePath response') self.assertEqual('Table Grid', result.properties.style_name) def test_update_table_properties(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestUpdateTableProperties.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestProperties = asposewordscloud.TableProperties(alignment='Right', allow_auto_fit=False, bidi=True, bottom_padding=1, cell_spacing=2.0, style_options='ColumnBands') request = asposewordscloud.models.requests.UpdateTablePropertiesRequest(name=remoteFileName, properties=requestProperties, index=1, node_path='', folder=remoteDataFolder) result = self.words_api.update_table_properties(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.properties, 'Validate UpdateTableProperties response') self.assertFalse(result.properties.allow_auto_fit, 'Validate UpdateTableProperties response') self.assertTrue(result.properties.bidi, 'Validate UpdateTableProperties response') self.assertEqual(1.0, result.properties.bottom_padding) self.assertEqual(2.0, result.properties.cell_spacing) def test_update_table_properties_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestUpdateTablePropertiesWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestProperties = asposewordscloud.TableProperties(alignment='Right', allow_auto_fit=False, bidi=True, bottom_padding=1.0, cell_spacing=2.0, style_options='ColumnBands') request = asposewordscloud.models.requests.UpdateTablePropertiesRequest(name=remoteFileName, properties=requestProperties, index=1, folder=remoteDataFolder) result = self.words_api.update_table_properties(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.properties, 'Validate UpdateTablePropertiesWithoutNodePath response') self.assertFalse(result.properties.allow_auto_fit, 'Validate UpdateTablePropertiesWithoutNodePath response') self.assertTrue(result.properties.bidi, 'Validate UpdateTablePropertiesWithoutNodePath response') self.assertEqual(1.0, result.properties.bottom_padding) self.assertEqual(2.0, result.properties.cell_spacing) def test_get_table_row(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableRow.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableRowRequest(name=remoteFileName, table_path='tables/1', index=0, folder=remoteDataFolder) result = self.words_api.get_table_row(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.row, 'Validate GetTableRow response') self.assertIsNotNone(result.row.table_cell_list, 'Validate GetTableRow response') self.assertEqual(2, len(result.row.table_cell_list)) def test_delete_table_row(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestDeleteTableRow.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.DeleteTableRowRequest(name=remoteFileName, table_path='tables/1', index=0, folder=remoteDataFolder) self.words_api.delete_table_row(request) def test_insert_table_row(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestInsertTableRow.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestRow = asposewordscloud.TableRowInsert(columns_count=5) request = asposewordscloud.models.requests.InsertTableRowRequest(name=remoteFileName, row=requestRow, table_path='sections/0/tables/2', folder=remoteDataFolder) result = self.words_api.insert_table_row(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.row, 'Validate InsertTableRow response') self.assertIsNotNone(result.row.table_cell_list, 'Validate InsertTableRow response') self.assertEqual(5, len(result.row.table_cell_list)) def test_get_table_row_format(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableRowFormat.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableRowFormatRequest(name=remoteFileName, table_path='sections/0/tables/2', index=0, folder=remoteDataFolder) result = self.words_api.get_table_row_format(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.row_format, 'Validate GetTableRowFormat response') self.assertTrue(result.row_format.allow_break_across_pages, 'Validate GetTableRowFormat response') def test_update_table_row_format(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestUpdateTableRowFormat.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestFormat = asposewordscloud.TableRowFormat(allow_break_across_pages=True, heading_format=True, height=10.0, height_rule='Exactly') request = asposewordscloud.models.requests.UpdateTableRowFormatRequest(name=remoteFileName, format=requestFormat, table_path='sections/0/tables/2', index=0, folder=remoteDataFolder) result = self.words_api.update_table_row_format(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.row_format, 'Validate UpdateTableRowFormat response') self.assertTrue(result.row_format.allow_break_across_pages, 'Validate UpdateTableRowFormat response') self.assertTrue(result.row_format.heading_format, 'Validate UpdateTableRowFormat response') self.assertEqual(10.0, result.row_format.height) def test_get_table_cell(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableCell.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableCellRequest(name=remoteFileName, table_row_path='sections/0/tables/2/rows/0', index=0, folder=remoteDataFolder) result = self.words_api.get_table_cell(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.cell, 'Validate GetTableCell response') self.assertEqual('0.0.5.0.0', result.cell.node_id) def test_delete_table_cell(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestDeleteTableCell.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.DeleteTableCellRequest(name=remoteFileName, table_row_path='sections/0/tables/2/rows/0', index=0, folder=remoteDataFolder) self.words_api.delete_table_cell(request) def test_insert_table_cell(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestInsertTableCell.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestCell = asposewordscloud.TableCellInsert() request = asposewordscloud.models.requests.InsertTableCellRequest(name=remoteFileName, cell=requestCell, table_row_path='sections/0/tables/2/rows/0', folder=remoteDataFolder) result = self.words_api.insert_table_cell(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.cell, 'Validate InsertTableCell response') self.assertEqual('0.0.5.0.3', result.cell.node_id) def test_get_table_cell_format(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestGetTableCellFormat.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.GetTableCellFormatRequest(name=remoteFileName, table_row_path='sections/0/tables/2/rows/0', index=0, folder=remoteDataFolder) result = self.words_api.get_table_cell_format(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.cell_format, 'Validate GetTableCellFormat response') self.assertTrue(result.cell_format.wrap_text, 'Validate GetTableCellFormat response') def test_update_table_cell_format(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestUpdateTableCellFormat.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) requestFormat = asposewordscloud.TableCellFormat(bottom_padding=5.0, fit_text=True, horizontal_merge='First', wrap_text=True) request = asposewordscloud.models.requests.UpdateTableCellFormatRequest(name=remoteFileName, format=requestFormat, table_row_path='sections/0/tables/2/rows/0', index=0, folder=remoteDataFolder) result = self.words_api.update_table_cell_format(request) self.assertIsNotNone(result, 'Error has occurred.') self.assertIsNotNone(result.cell_format, 'Validate UpdateTableCellFormat response') self.assertEqual(5.0, result.cell_format.bottom_padding) self.assertTrue(result.cell_format.fit_text, 'Validate UpdateTableCellFormat response') self.assertTrue(result.cell_format.wrap_text, 'Validate UpdateTableCellFormat response') def test_render_table(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestRenderTable.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.RenderTableRequest(name=remoteFileName, format='png', index=0, node_path='', folder=remoteDataFolder) result = self.words_api.render_table(request) self.assertIsNotNone(result, 'Error has occurred.') def test_render_table_without_node_path(self): remoteDataFolder = self.remote_test_folder + '/DocumentElements/Tables' localFile = 'DocumentElements/Tables/TablesGet.docx' remoteFileName = 'TestRenderTableWithoutNodePath.docx' self.upload_file(remoteDataFolder + '/' + remoteFileName, open(os.path.join(self.local_test_folder, localFile), 'rb')) request = asposewordscloud.models.requests.RenderTableRequest(name=remoteFileName, format='png', index=0, folder=remoteDataFolder) result = self.words_api.render_table(request) self.assertIsNotNone(result, 'Error has occurred.')

true

f72accd8900bf752d4868f03ba6ce4c1c4210e08

7,851

py

Python

kaggle/ghouls-goblins-and-ghosts-boo/script_3.py

josepablocam/janus-public

4713092b27d02386bdb408213d8edc0dc5859eec

[ "MIT" ]

null

kaggle/ghouls-goblins-and-ghosts-boo/script_3.py

josepablocam/janus-public

4713092b27d02386bdb408213d8edc0dc5859eec

[ "MIT" ]

null

kaggle/ghouls-goblins-and-ghosts-boo/script_3.py

josepablocam/janus-public

4713092b27d02386bdb408213d8edc0dc5859eec

[ "MIT" ]

null

#Libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns sns.set_style('whitegrid') from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.calibration import CalibratedClassifierCV import xgboost as xgb from sklearn.model_selection import GridSearchCV from sklearn.model_selection import StratifiedKFold from sklearn.feature_selection import SelectFromModel from sklearn.linear_model import LogisticRegression from sklearn import svm from sklearn.ensemble import VotingClassifier from sklearn.naive_bayes import GaussianNB train = pd.read_csv('../input/train.csv') test = pd.read_csv('../input/test.csv') train.info() train.describe(include='all') train.head() plt.subplot(1,4,1) train.groupby('type').mean()['rotting_flesh'].plot(kind='bar',figsize=(7,4), color='r') plt.subplot(1,4,2) train.groupby('type').mean()['bone_length'].plot(kind='bar',figsize=(7,4), color='g') plt.subplot(1,4,3) train.groupby('type').mean()['hair_length'].plot(kind='bar',figsize=(7,4), color='y') plt.subplot(1,4,4) train.groupby('type').mean()['has_soul'].plot(kind='bar',figsize=(7,4), color='teal') sns.factorplot("type", col="color", col_wrap=4, data=train, kind="count", size=2.4, aspect=.8) #The graphs look much better with higher figsize. fig, ax = plt.subplots(2, 2, figsize = (16, 12)) sns.pointplot(x="color", y="rotting_flesh", hue="type", data=train, ax = ax[0, 0]) sns.pointplot(x="color", y="bone_length", hue="type", data=train, ax = ax[0, 1]) sns.pointplot(x="color", y="hair_length", hue="type", data=train, ax = ax[1, 0]) sns.pointplot(x="color", y="has_soul", hue="type", data=train, ax = ax[1, 1]) sns.pairplot(train, hue='type') train['hair_soul'] = train['hair_length'] * train['has_soul'] train['hair_bone'] = train['hair_length'] * train['bone_length'] test['hair_soul'] = test['hair_length'] * test['has_soul'] test['hair_bone'] = test['hair_length'] * test['bone_length'] train['hair_soul_bone'] = train['hair_length'] * train['has_soul'] * train['bone_length'] test['hair_soul_bone'] = test['hair_length'] * test['has_soul'] * test['bone_length'] #test_id will be used later, so save it test_id = test['id'] train.drop(['id'], axis=1, inplace=True) test.drop(['id'], axis=1, inplace=True) #Deal with 'color' column col = 'color' dummies = pd.get_dummies(train[col], drop_first=False) dummies = dummies.add_prefix("{}#".format(col)) train.drop(col, axis=1, inplace=True) train = train.join(dummies) dummies = pd.get_dummies(test[col], drop_first=False) dummies = dummies.add_prefix("{}#".format(col)) test.drop(col, axis=1, inplace=True) test = test.join(dummies) X_train = train.drop('type', axis=1) le = LabelEncoder() Y_train = le.fit_transform(train.type.values) X_test = test clf = RandomForestClassifier(n_estimators=200) clf = clf.fit(X_train, Y_train) indices = np.argsort(clf.feature_importances_)[::-1] # Print the feature ranking print('Feature ranking:') for f in range(X_train.shape[1]): print('%d. feature %d %s (%f)' % (f + 1, indices[f], X_train.columns[indices[f]], clf.feature_importances_[indices[f]])) best_features=X_train.columns[indices[0:7]] X = X_train[best_features] Xt = X_test[best_features] #Splitting data for validation Xtrain, Xtest, ytrain, ytest = train_test_split(X, Y_train, test_size=0.20, random_state=36) forest = RandomForestClassifier(max_depth = 100, min_samples_split =7, min_weight_fraction_leaf = 0.0, max_leaf_nodes = 60) parameter_grid = {'n_estimators' : [10, 20, 100, 150], 'criterion' : ['gini', 'entropy'], 'max_features' : ['auto', 'sqrt', 'log2', None] } grid_search = GridSearchCV(forest, param_grid=parameter_grid, scoring='accuracy', cv=StratifiedKFold(5)) grid_search.fit(Xtrain, ytrain) print('Best score: {}'.format(grid_search.best_score_)) print('Best parameters: {}'.format(grid_search.best_params_)) forest = RandomForestClassifier(n_estimators = 150, criterion = 'entropy', max_features = 'auto') parameter_grid = { 'max_depth' : [None, 5, 20, 100], 'min_samples_split' : [2, 5, 7], 'min_weight_fraction_leaf' : [0.0, 0.1], 'max_leaf_nodes' : [40, 60, 80], } grid_search = GridSearchCV(forest, param_grid=parameter_grid, scoring='accuracy', cv=StratifiedKFold(5)) grid_search.fit(Xtrain, ytrain) print('Best score: {}'.format(grid_search.best_score_)) print('Best parameters: {}'.format(grid_search.best_params_)) #Optimal parameters clf = RandomForestClassifier(n_estimators=150, n_jobs=-1, criterion = 'entropy', max_features = 'auto', min_samples_split=7, min_weight_fraction_leaf=0.0, max_leaf_nodes=40, max_depth=20) #Calibration improves probability predictions calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated_clf.fit(Xtrain, ytrain) y_val = calibrated_clf.predict_proba(Xtest) print("Validation accuracy: ", sum(pd.DataFrame(y_val, columns=le.classes_).idxmax(axis=1).values == le.inverse_transform(ytest))/len(ytest)) svc = svm.SVC(kernel='linear') svc.fit(Xtrain, ytrain) y_val_s = svc.predict(Xtest) print("Validation accuracy: ", sum(le.inverse_transform(y_val_s) == le.inverse_transform(ytest))/len(ytest)) #The last model is logistic regression logreg = LogisticRegression() parameter_grid = {'solver' : ['newton-cg', 'lbfgs'], 'multi_class' : ['ovr', 'multinomial'], 'C' : [0.005, 0.01, 1, 10, 100, 1000], 'tol': [0.0001, 0.001, 0.005] } grid_search = GridSearchCV(logreg, param_grid=parameter_grid, cv=StratifiedKFold(5)) grid_search.fit(Xtrain, ytrain) print('Best score: {}'.format(grid_search.best_score_)) print('Best parameters: {}'.format(grid_search.best_params_)) log_reg = LogisticRegression(C = 1, tol = 0.0001, solver='newton-cg', multi_class='multinomial') log_reg.fit(Xtrain, ytrain) y_val_l = log_reg.predict_proba(Xtest) print("Validation accuracy: ", sum(pd.DataFrame(y_val_l, columns=le.classes_).idxmax(axis=1).values == le.inverse_transform(ytest))/len(ytest)) clf = RandomForestClassifier(n_estimators=20, n_jobs=-1, criterion = 'gini', max_features = 'sqrt', min_samples_split=2, min_weight_fraction_leaf=0.0, max_leaf_nodes=40, max_depth=100) calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid', cv=5) log_reg = LogisticRegression(C = 1, tol = 0.0001, solver='newton-cg', multi_class='multinomial') gnb = GaussianNB() calibrated_clf1 = CalibratedClassifierCV(RandomForestClassifier()) log_reg1 = LogisticRegression() gnb1 = GaussianNB() Vclf1 = VotingClassifier(estimators=[('LR', log_reg1), ('CRF', calibrated_clf1), ('GNB', gnb1)], voting='hard') Vclf = VotingClassifier(estimators=[('LR', log_reg), ('CRF', calibrated_clf), ('GNB', gnb)], voting='soft', weights=[1,1,1]) hard_predict = le.inverse_transform(Vclf1.fit(X, Y_train).predict(Xt)) soft_predict = le.inverse_transform(Vclf.fit(X, Y_train).predict(Xt)) #Let's see the differences: for i in range(len(hard_predict)): if hard_predict[i] != soft_predict[i]: print(i, hard_predict[i], soft_predict[i]) submission = pd.DataFrame({'id':test_id, 'type':hard_predict}) submission.to_csv('GGG_submission.csv', index=False)

47.011976

104

0.672271

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns sns.set_style('whitegrid') from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.calibration import CalibratedClassifierCV import xgboost as xgb from sklearn.model_selection import GridSearchCV from sklearn.model_selection import StratifiedKFold from sklearn.feature_selection import SelectFromModel from sklearn.linear_model import LogisticRegression from sklearn import svm from sklearn.ensemble import VotingClassifier from sklearn.naive_bayes import GaussianNB train = pd.read_csv('../input/train.csv') test = pd.read_csv('../input/test.csv') train.info() train.describe(include='all') train.head() plt.subplot(1,4,1) train.groupby('type').mean()['rotting_flesh'].plot(kind='bar',figsize=(7,4), color='r') plt.subplot(1,4,2) train.groupby('type').mean()['bone_length'].plot(kind='bar',figsize=(7,4), color='g') plt.subplot(1,4,3) train.groupby('type').mean()['hair_length'].plot(kind='bar',figsize=(7,4), color='y') plt.subplot(1,4,4) train.groupby('type').mean()['has_soul'].plot(kind='bar',figsize=(7,4), color='teal') sns.factorplot("type", col="color", col_wrap=4, data=train, kind="count", size=2.4, aspect=.8) fig, ax = plt.subplots(2, 2, figsize = (16, 12)) sns.pointplot(x="color", y="rotting_flesh", hue="type", data=train, ax = ax[0, 0]) sns.pointplot(x="color", y="bone_length", hue="type", data=train, ax = ax[0, 1]) sns.pointplot(x="color", y="hair_length", hue="type", data=train, ax = ax[1, 0]) sns.pointplot(x="color", y="has_soul", hue="type", data=train, ax = ax[1, 1]) sns.pairplot(train, hue='type') train['hair_soul'] = train['hair_length'] * train['has_soul'] train['hair_bone'] = train['hair_length'] * train['bone_length'] test['hair_soul'] = test['hair_length'] * test['has_soul'] test['hair_bone'] = test['hair_length'] * test['bone_length'] train['hair_soul_bone'] = train['hair_length'] * train['has_soul'] * train['bone_length'] test['hair_soul_bone'] = test['hair_length'] * test['has_soul'] * test['bone_length'] test_id = test['id'] train.drop(['id'], axis=1, inplace=True) test.drop(['id'], axis=1, inplace=True) col = 'color' dummies = pd.get_dummies(train[col], drop_first=False) dummies = dummies.add_prefix("{}#".format(col)) train.drop(col, axis=1, inplace=True) train = train.join(dummies) dummies = pd.get_dummies(test[col], drop_first=False) dummies = dummies.add_prefix("{}#".format(col)) test.drop(col, axis=1, inplace=True) test = test.join(dummies) X_train = train.drop('type', axis=1) le = LabelEncoder() Y_train = le.fit_transform(train.type.values) X_test = test clf = RandomForestClassifier(n_estimators=200) clf = clf.fit(X_train, Y_train) indices = np.argsort(clf.feature_importances_)[::-1] print('Feature ranking:') for f in range(X_train.shape[1]): print('%d. feature %d %s (%f)' % (f + 1, indices[f], X_train.columns[indices[f]], clf.feature_importances_[indices[f]])) best_features=X_train.columns[indices[0:7]] X = X_train[best_features] Xt = X_test[best_features] Xtrain, Xtest, ytrain, ytest = train_test_split(X, Y_train, test_size=0.20, random_state=36) forest = RandomForestClassifier(max_depth = 100, min_samples_split =7, min_weight_fraction_leaf = 0.0, max_leaf_nodes = 60) parameter_grid = {'n_estimators' : [10, 20, 100, 150], 'criterion' : ['gini', 'entropy'], 'max_features' : ['auto', 'sqrt', 'log2', None] } grid_search = GridSearchCV(forest, param_grid=parameter_grid, scoring='accuracy', cv=StratifiedKFold(5)) grid_search.fit(Xtrain, ytrain) print('Best score: {}'.format(grid_search.best_score_)) print('Best parameters: {}'.format(grid_search.best_params_)) forest = RandomForestClassifier(n_estimators = 150, criterion = 'entropy', max_features = 'auto') parameter_grid = { 'max_depth' : [None, 5, 20, 100], 'min_samples_split' : [2, 5, 7], 'min_weight_fraction_leaf' : [0.0, 0.1], 'max_leaf_nodes' : [40, 60, 80], } grid_search = GridSearchCV(forest, param_grid=parameter_grid, scoring='accuracy', cv=StratifiedKFold(5)) grid_search.fit(Xtrain, ytrain) print('Best score: {}'.format(grid_search.best_score_)) print('Best parameters: {}'.format(grid_search.best_params_)) clf = RandomForestClassifier(n_estimators=150, n_jobs=-1, criterion = 'entropy', max_features = 'auto', min_samples_split=7, min_weight_fraction_leaf=0.0, max_leaf_nodes=40, max_depth=20) calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated_clf.fit(Xtrain, ytrain) y_val = calibrated_clf.predict_proba(Xtest) print("Validation accuracy: ", sum(pd.DataFrame(y_val, columns=le.classes_).idxmax(axis=1).values == le.inverse_transform(ytest))/len(ytest)) svc = svm.SVC(kernel='linear') svc.fit(Xtrain, ytrain) y_val_s = svc.predict(Xtest) print("Validation accuracy: ", sum(le.inverse_transform(y_val_s) == le.inverse_transform(ytest))/len(ytest)) logreg = LogisticRegression() parameter_grid = {'solver' : ['newton-cg', 'lbfgs'], 'multi_class' : ['ovr', 'multinomial'], 'C' : [0.005, 0.01, 1, 10, 100, 1000], 'tol': [0.0001, 0.001, 0.005] } grid_search = GridSearchCV(logreg, param_grid=parameter_grid, cv=StratifiedKFold(5)) grid_search.fit(Xtrain, ytrain) print('Best score: {}'.format(grid_search.best_score_)) print('Best parameters: {}'.format(grid_search.best_params_)) log_reg = LogisticRegression(C = 1, tol = 0.0001, solver='newton-cg', multi_class='multinomial') log_reg.fit(Xtrain, ytrain) y_val_l = log_reg.predict_proba(Xtest) print("Validation accuracy: ", sum(pd.DataFrame(y_val_l, columns=le.classes_).idxmax(axis=1).values == le.inverse_transform(ytest))/len(ytest)) clf = RandomForestClassifier(n_estimators=20, n_jobs=-1, criterion = 'gini', max_features = 'sqrt', min_samples_split=2, min_weight_fraction_leaf=0.0, max_leaf_nodes=40, max_depth=100) calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid', cv=5) log_reg = LogisticRegression(C = 1, tol = 0.0001, solver='newton-cg', multi_class='multinomial') gnb = GaussianNB() calibrated_clf1 = CalibratedClassifierCV(RandomForestClassifier()) log_reg1 = LogisticRegression() gnb1 = GaussianNB() Vclf1 = VotingClassifier(estimators=[('LR', log_reg1), ('CRF', calibrated_clf1), ('GNB', gnb1)], voting='hard') Vclf = VotingClassifier(estimators=[('LR', log_reg), ('CRF', calibrated_clf), ('GNB', gnb)], voting='soft', weights=[1,1,1]) hard_predict = le.inverse_transform(Vclf1.fit(X, Y_train).predict(Xt)) soft_predict = le.inverse_transform(Vclf.fit(X, Y_train).predict(Xt)) for i in range(len(hard_predict)): if hard_predict[i] != soft_predict[i]: print(i, hard_predict[i], soft_predict[i]) submission = pd.DataFrame({'id':test_id, 'type':hard_predict}) submission.to_csv('GGG_submission.csv', index=False)

true

f72acedfe31ef0d6425a9d5e280c234bf012eb1c

2,456

py

Python

example.py

macky168/gaopt

bf2785325d3cb4489513f47ed06f745a059262f8

[ "MIT" ]

null

example.py

macky168/gaopt

bf2785325d3cb4489513f47ed06f745a059262f8

[ "MIT" ]

null

example.py

macky168/gaopt

bf2785325d3cb4489513f47ed06f745a059262f8

[ "MIT" ]

null

import gaopt from gaopt import search_space import pandas as pd import numpy as np import lightgbm as lgb from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score from sklearn.datasets import load_diabetes params_range={ 'lambda_l1': search_space.discrete_int(-8, 2), 'lambda_l2': search_space.discrete_int(-8, 2), 'num_leaves': search_space.discrete(2, 100, 4), 'feature_fraction': search_space.discrete(0.1, 1.0, 0.02), 'bagging_fraction': search_space.discrete(0.1, 1.0, 0.02), 'bagging_freq': search_space.discrete_int(0,1), 'min_child_samples': search_space.discrete_int(1,30), } cal_time_lst = [] date_start = None def objective1(params): diabetes = load_diabetes() X = diabetes.data y = diabetes.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0) X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size = 0.3, random_state = 0) lgb_train = lgb.Dataset(data=X_train, label=y_train) lgb_valid = lgb.Dataset(data=X_valid, label=y_valid) params ={ 'lambda_l1': 10**params.lambda_l1, 'lambda_l2': 10**params.lambda_l2, 'num_leaves': params.num_leaves, 'feature_fraction': params.feature_fraction, 'bagging_fraction': params.bagging_fraction, 'bagging_freq': params.bagging_freq, 'min_child_samples': params.min_child_samples, 'objective': 'regression', 'metric': 'rmse', "verbosity": -1, "seed": 0 } model = lgb.train(params, train_set=lgb_train, valid_sets=lgb_valid, verbose_eval=False ) y_pred_lgb = model.predict(X_test) fitness = r2_score(y_test, y_pred_lgb) return fitness def main(): p_m = 0.10 p_c = 0.7 population = 30 generation = 50 instance = gaopt.GAOpt(params_range, objective=objective1, generation=generation, population=population, p_m=p_m, p_c=p_c, elitism=True, history=2, verbose=2, maximizing=True) best_params, best_fitness, best_fitness_lst, worst_fitness_lst, mean_fitness_lst, median_fitness_lst, sd_fitness_lst, search_history_lst = instance.fit() print("best params: ", best_params) print("best fitness: ", best_fitness) if __name__ == '__main__': main()

31.487179

157

0.664088

import gaopt from gaopt import search_space import pandas as pd import numpy as np import lightgbm as lgb from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score from sklearn.datasets import load_diabetes params_range={ 'lambda_l1': search_space.discrete_int(-8, 2), 'lambda_l2': search_space.discrete_int(-8, 2), 'num_leaves': search_space.discrete(2, 100, 4), 'feature_fraction': search_space.discrete(0.1, 1.0, 0.02), 'bagging_fraction': search_space.discrete(0.1, 1.0, 0.02), 'bagging_freq': search_space.discrete_int(0,1), 'min_child_samples': search_space.discrete_int(1,30), } cal_time_lst = [] date_start = None def objective1(params): diabetes = load_diabetes() X = diabetes.data y = diabetes.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0) X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size = 0.3, random_state = 0) lgb_train = lgb.Dataset(data=X_train, label=y_train) lgb_valid = lgb.Dataset(data=X_valid, label=y_valid) params ={ 'lambda_l1': 10**params.lambda_l1, 'lambda_l2': 10**params.lambda_l2, 'num_leaves': params.num_leaves, 'feature_fraction': params.feature_fraction, 'bagging_fraction': params.bagging_fraction, 'bagging_freq': params.bagging_freq, 'min_child_samples': params.min_child_samples, 'objective': 'regression', 'metric': 'rmse', "verbosity": -1, "seed": 0 } model = lgb.train(params, train_set=lgb_train, valid_sets=lgb_valid, verbose_eval=False ) y_pred_lgb = model.predict(X_test) fitness = r2_score(y_test, y_pred_lgb) return fitness def main(): p_m = 0.10 p_c = 0.7 population = 30 generation = 50 instance = gaopt.GAOpt(params_range, objective=objective1, generation=generation, population=population, p_m=p_m, p_c=p_c, elitism=True, history=2, verbose=2, maximizing=True) best_params, best_fitness, best_fitness_lst, worst_fitness_lst, mean_fitness_lst, median_fitness_lst, sd_fitness_lst, search_history_lst = instance.fit() print("best params: ", best_params) print("best fitness: ", best_fitness) if __name__ == '__main__': main()

true

f72acf6685fa304f560b7aba21b3cc59df08af86

1,407

py

Python

plotly/validators/contour/colorbar/_tickfont.py

faezs/plotly.py

6009b5b9c746e5d2a2849ad255a4eb234b551ed7

[ "MIT" ]

1

2018-07-16T01:51:47.000Z

plotly/validators/contour/colorbar/_tickfont.py

faezs/plotly.py

6009b5b9c746e5d2a2849ad255a4eb234b551ed7

[ "MIT" ]

null

plotly/validators/contour/colorbar/_tickfont.py

faezs/plotly.py

6009b5b9c746e5d2a2849ad255a4eb234b551ed7

[ "MIT" ]

1

2019-02-18T04:12:56.000Z

import _plotly_utils.basevalidators class TickfontValidator(_plotly_utils.basevalidators.CompoundValidator): def __init__( self, plotly_name='tickfont', parent_name='contour.colorbar', **kwargs ): super(TickfontValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, data_class_str='Tickfont', data_docs=""" color family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include *Arial*, *Balto*, *Courier New*, *Droid Sans*,, *Droid Serif*, *Droid Sans Mono*, *Gravitas One*, *Old Standard TT*, *Open Sans*, *Overpass*, *PT Sans Narrow*, *Raleway*, *Times New Roman*. size """, **kwargs )

39.083333

78

0.570007

import _plotly_utils.basevalidators class TickfontValidator(_plotly_utils.basevalidators.CompoundValidator): def __init__( self, plotly_name='tickfont', parent_name='contour.colorbar', **kwargs ): super(TickfontValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, data_class_str='Tickfont', data_docs=""" color family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include *Arial*, *Balto*, *Courier New*, *Droid Sans*,, *Droid Serif*, *Droid Sans Mono*, *Gravitas One*, *Old Standard TT*, *Open Sans*, *Overpass*, *PT Sans Narrow*, *Raleway*, *Times New Roman*. size """, **kwargs )

true

f72acf916cc7270f998cfd07db89c1ac93ca5b18

1,812

py

Python

src/scripts/extract_syscall.py

Manouchehri/Triton-docker

ce49ce9ba49965a5e7f814f2b46e50cc74b704de

[ "BSD-3-Clause" ]

1

2020-11-15T15:21:12.000Z

src/scripts/extract_syscall.py

Manouchehri/Triton-docker

ce49ce9ba49965a5e7f814f2b46e50cc74b704de

[ "BSD-3-Clause" ]

null

src/scripts/extract_syscall.py

Manouchehri/Triton-docker

ce49ce9ba49965a5e7f814f2b46e50cc74b704de

[ "BSD-3-Clause" ]

null

#! /usr/bin/env python # # This script is used to generate the files src/utils/syscalls{32,64}.cpp. # As the list of syscalls depends of your Kernel version. We must # generate the list at the compile time. # from __future__ import print_function import argparse import sys import re import platform HEADER = """ /*! \\file */ #if defined(__unix__) || defined(__APPLE__) #include <syscalls.hpp> namespace triton { namespace os { namespace unix { """ FOOTER = """ }; /* unix namespace */ }; /* os namespace */ }; /* triton namespace */ #endif """ if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("file", help="this file must contains the syscalls definitions", type=str) parser.add_argument("arch", help="syscall architecture - 32 or 64", type=str) args = parser.parse_args() if platform.system() == 'Linux': regex = re.compile(r"#define\s+(__NR_)(\w+)\s+(\d+)") elif platform.system() == 'Darwin': regex = re.compile(r"#define\s+(SYS_)(\w+)\s+(\d+)") else: sys.exit(0) with open(args.file) as hfile: print(HEADER) print(" const char* syscallmap%s[] = {" % args.arch) counter = 0 for match in regex.finditer(hfile.read()): prefix = str(match.groups()[0]) name = str(match.groups()[1]) sysid = int(match.groups()[2]) if counter != sysid: for i in range(sysid - counter): print(' "UNDEF", // undefined') counter += 1 print(' "%s", // %s%s' % (name.upper(), prefix, name)) counter += 1 print(" };") print() print(" const unsigned int NB_SYSCALL%s = %d;" % (args.arch, counter)) print(FOOTER)

25.885714

98

0.570088

from __future__ import print_function import argparse import sys import re import platform HEADER = """ /*! \\file */ #if defined(__unix__) || defined(__APPLE__) #include <syscalls.hpp> namespace triton { namespace os { namespace unix { """ FOOTER = """ }; /* unix namespace */ }; /* os namespace */ }; /* triton namespace */ #endif """ if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("file", help="this file must contains the syscalls definitions", type=str) parser.add_argument("arch", help="syscall architecture - 32 or 64", type=str) args = parser.parse_args() if platform.system() == 'Linux': regex = re.compile(r"#define\s+(__NR_)(\w+)\s+(\d+)") elif platform.system() == 'Darwin': regex = re.compile(r"#define\s+(SYS_)(\w+)\s+(\d+)") else: sys.exit(0) with open(args.file) as hfile: print(HEADER) print(" const char* syscallmap%s[] = {" % args.arch) counter = 0 for match in regex.finditer(hfile.read()): prefix = str(match.groups()[0]) name = str(match.groups()[1]) sysid = int(match.groups()[2]) if counter != sysid: for i in range(sysid - counter): print(' "UNDEF", // undefined') counter += 1 print(' "%s", // %s%s' % (name.upper(), prefix, name)) counter += 1 print(" };") print() print(" const unsigned int NB_SYSCALL%s = %d;" % (args.arch, counter)) print(FOOTER)

true

f72ad055c9ca2d52827b7e4aa011c2370f6292dc

15,695

py

Python

electrum_ltc/tests/test_lnpeer.py

SynchrotronCoinDev/electrum-ltc

178589f30ce57ca84e4d8bc7587f39522e9d17b3

[ "MIT" ]

null

electrum_ltc/tests/test_lnpeer.py

SynchrotronCoinDev/electrum-ltc

178589f30ce57ca84e4d8bc7587f39522e9d17b3

[ "MIT" ]

null

electrum_ltc/tests/test_lnpeer.py

SynchrotronCoinDev/electrum-ltc

178589f30ce57ca84e4d8bc7587f39522e9d17b3

[ "MIT" ]

null

import asyncio import tempfile from decimal import Decimal import os from contextlib import contextmanager from collections import defaultdict import logging import concurrent from concurrent import futures import unittest from aiorpcx import TaskGroup from electrum_ltc import constants from electrum_ltc.network import Network from electrum_ltc.ecc import ECPrivkey from electrum_ltc import simple_config, lnutil from electrum_ltc.lnaddr import lnencode, LnAddr, lndecode from electrum_ltc.bitcoin import COIN, sha256 from electrum_ltc.util import bh2u, create_and_start_event_loop from electrum_ltc.lnpeer import Peer from electrum_ltc.lnutil import LNPeerAddr, Keypair, privkey_to_pubkey from electrum_ltc.lnutil import LightningPeerConnectionClosed, RemoteMisbehaving from electrum_ltc.lnutil import PaymentFailure, LnLocalFeatures, HTLCOwner from electrum_ltc.lnchannel import channel_states, peer_states, Channel from electrum_ltc.lnrouter import LNPathFinder from electrum_ltc.channel_db import ChannelDB from electrum_ltc.lnworker import LNWallet, NoPathFound from electrum_ltc.lnmsg import encode_msg, decode_msg from electrum_ltc.logging import console_stderr_handler, Logger from electrum_ltc.lnworker import PaymentInfo, RECEIVED, PR_UNPAID from .test_lnchannel import create_test_channels from .test_bitcoin import needs_test_with_all_chacha20_implementations from . import ElectrumTestCase def keypair(): priv = ECPrivkey.generate_random_key().get_secret_bytes() k1 = Keypair( pubkey=privkey_to_pubkey(priv), privkey=priv) return k1 @contextmanager def noop_lock(): yield class MockNetwork: def __init__(self, tx_queue): self.callbacks = defaultdict(list) self.lnwatcher = None self.interface = None user_config = {} user_dir = tempfile.mkdtemp(prefix="electrum-lnpeer-test-") self.config = simple_config.SimpleConfig(user_config, read_user_dir_function=lambda: user_dir) self.asyncio_loop = asyncio.get_event_loop() self.channel_db = ChannelDB(self) self.channel_db.data_loaded.set() self.path_finder = LNPathFinder(self.channel_db) self.tx_queue = tx_queue @property def callback_lock(self): return noop_lock() register_callback = Network.register_callback unregister_callback = Network.unregister_callback trigger_callback = Network.trigger_callback def get_local_height(self): return 0 async def broadcast_transaction(self, tx): if self.tx_queue: await self.tx_queue.put(tx) async def try_broadcasting(self, tx, name): self.broadcast_transaction(tx) class MockWallet: def set_label(self, x, y): pass def save_db(self): pass def is_lightning_backup(self): return False class MockLNWallet(Logger): def __init__(self, remote_keypair, local_keypair, chan: 'Channel', tx_queue): Logger.__init__(self) self.remote_keypair = remote_keypair self.node_keypair = local_keypair self.network = MockNetwork(tx_queue) self.channels = {chan.channel_id: chan} self.payments = {} self.logs = defaultdict(list) self.wallet = MockWallet() self.localfeatures = LnLocalFeatures(0) self.localfeatures |= LnLocalFeatures.OPTION_DATA_LOSS_PROTECT_OPT self.pending_payments = defaultdict(asyncio.Future) chan.lnworker = self chan.node_id = remote_keypair.pubkey # used in tests self.enable_htlc_settle = asyncio.Event() self.enable_htlc_settle.set() def get_invoice_status(self, key): pass @property def lock(self): return noop_lock() @property def peers(self): return {self.remote_keypair.pubkey: self.peer} def channels_for_peer(self, pubkey): return self.channels def get_channel_by_short_id(self, short_channel_id): with self.lock: for chan in self.channels.values(): if chan.short_channel_id == short_channel_id: return chan def save_channel(self, chan): print("Ignoring channel save") is_routing = set() preimages = {} get_payment_info = LNWallet.get_payment_info save_payment_info = LNWallet.save_payment_info set_invoice_status = LNWallet.set_invoice_status set_payment_status = LNWallet.set_payment_status get_payment_status = LNWallet.get_payment_status await_payment = LNWallet.await_payment payment_received = LNWallet.payment_received payment_sent = LNWallet.payment_sent payment_failed = LNWallet.payment_failed save_preimage = LNWallet.save_preimage get_preimage = LNWallet.get_preimage _create_route_from_invoice = LNWallet._create_route_from_invoice _check_invoice = staticmethod(LNWallet._check_invoice) _pay_to_route = LNWallet._pay_to_route _pay = LNWallet._pay force_close_channel = LNWallet.force_close_channel try_force_closing = LNWallet.try_force_closing get_first_timestamp = lambda self: 0 class MockTransport: def __init__(self, name): self.queue = asyncio.Queue() self._name = name def name(self): return self._name async def read_messages(self): while True: yield await self.queue.get() class NoFeaturesTransport(MockTransport): """ This answers the init message with a init that doesn't signal any features. Used for testing that we require DATA_LOSS_PROTECT. """ def send_bytes(self, data): decoded = decode_msg(data) print(decoded) if decoded[0] == 'init': self.queue.put_nowait(encode_msg('init', lflen=1, gflen=1, localfeatures=b"\x00", globalfeatures=b"\x00")) class PutIntoOthersQueueTransport(MockTransport): def __init__(self, name): super().__init__(name) self.other_mock_transport = None def send_bytes(self, data): self.other_mock_transport.queue.put_nowait(data) def transport_pair(name1, name2): t1 = PutIntoOthersQueueTransport(name1) t2 = PutIntoOthersQueueTransport(name2) t1.other_mock_transport = t2 t2.other_mock_transport = t1 return t1, t2 class TestPeer(ElectrumTestCase): @classmethod def setUpClass(cls): super().setUpClass() console_stderr_handler.setLevel(logging.DEBUG) def setUp(self): super().setUp() self.asyncio_loop, self._stop_loop, self._loop_thread = create_and_start_event_loop() def tearDown(self): super().tearDown() self.asyncio_loop.call_soon_threadsafe(self._stop_loop.set_result, 1) self._loop_thread.join(timeout=1) def prepare_peers(self, alice_channel, bob_channel): k1, k2 = keypair(), keypair() t1, t2 = transport_pair(alice_channel.name, bob_channel.name) q1, q2 = asyncio.Queue(), asyncio.Queue() w1 = MockLNWallet(k1, k2, alice_channel, tx_queue=q1) w2 = MockLNWallet(k2, k1, bob_channel, tx_queue=q2) p1 = Peer(w1, k1.pubkey, t1) p2 = Peer(w2, k2.pubkey, t2) w1.peer = p1 w2.peer = p2 # mark_open won't work if state is already OPEN. # so set it to FUNDED alice_channel._state = channel_states.FUNDED bob_channel._state = channel_states.FUNDED # this populates the channel graph: p1.mark_open(alice_channel) p2.mark_open(bob_channel) return p1, p2, w1, w2, q1, q2 @staticmethod def prepare_invoice( w2, # receiver *, amount_sat=100_000, ): amount_btc = amount_sat/Decimal(COIN) payment_preimage = os.urandom(32) RHASH = sha256(payment_preimage) info = PaymentInfo(RHASH, amount_sat, RECEIVED, PR_UNPAID) w2.save_preimage(RHASH, payment_preimage) w2.save_payment_info(info) lnaddr = LnAddr( RHASH, amount_btc, tags=[('c', lnutil.MIN_FINAL_CLTV_EXPIRY_FOR_INVOICE), ('d', 'coffee') ]) return lnencode(lnaddr, w2.node_keypair.privkey) def test_reestablish(self): alice_channel, bob_channel = create_test_channels() p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel, bob_channel) for chan in (alice_channel, bob_channel): chan.peer_state = peer_states.DISCONNECTED async def reestablish(): await asyncio.gather( p1.reestablish_channel(alice_channel), p2.reestablish_channel(bob_channel)) self.assertEqual(alice_channel.peer_state, peer_states.GOOD) self.assertEqual(bob_channel.peer_state, peer_states.GOOD) gath.cancel() gath = asyncio.gather(reestablish(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p1.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) @needs_test_with_all_chacha20_implementations def test_reestablish_with_old_state(self): alice_channel, bob_channel = create_test_channels() alice_channel_0, bob_channel_0 = create_test_channels() # these are identical p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel, bob_channel) pay_req = self.prepare_invoice(w2) async def pay(): result = await w1._pay(pay_req) self.assertEqual(result, True) gath.cancel() gath = asyncio.gather(pay(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel_0, bob_channel) for chan in (alice_channel_0, bob_channel): chan.peer_state = peer_states.DISCONNECTED async def reestablish(): await asyncio.gather( p1.reestablish_channel(alice_channel_0), p2.reestablish_channel(bob_channel)) self.assertEqual(alice_channel_0.peer_state, peer_states.BAD) self.assertEqual(bob_channel._state, channel_states.FORCE_CLOSING) # wait so that pending messages are processed #await asyncio.sleep(1) gath.cancel() gath = asyncio.gather(reestablish(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) @needs_test_with_all_chacha20_implementations def test_payment(self): alice_channel, bob_channel = create_test_channels() p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel, bob_channel) pay_req = self.prepare_invoice(w2) async def pay(): result = await w1._pay(pay_req) self.assertTrue(result) gath.cancel() gath = asyncio.gather(pay(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) #@unittest.skip("too expensive") #@needs_test_with_all_chacha20_implementations def test_payments_stresstest(self): alice_channel, bob_channel = create_test_channels() p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel, bob_channel) alice_init_balance_msat = alice_channel.balance(HTLCOwner.LOCAL) bob_init_balance_msat = bob_channel.balance(HTLCOwner.LOCAL) num_payments = 50 #pay_reqs1 = [self.prepare_invoice(w1, amount_sat=1) for i in range(num_payments)] pay_reqs2 = [self.prepare_invoice(w2, amount_sat=1) for i in range(num_payments)] max_htlcs_in_flight = asyncio.Semaphore(5) async def single_payment(pay_req): async with max_htlcs_in_flight: await w1._pay(pay_req) async def many_payments(): async with TaskGroup() as group: for pay_req in pay_reqs2: await group.spawn(single_payment(pay_req)) gath.cancel() gath = asyncio.gather(many_payments(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) self.assertEqual(alice_init_balance_msat - num_payments * 1000, alice_channel.balance(HTLCOwner.LOCAL)) self.assertEqual(alice_init_balance_msat - num_payments * 1000, bob_channel.balance(HTLCOwner.REMOTE)) self.assertEqual(bob_init_balance_msat + num_payments * 1000, bob_channel.balance(HTLCOwner.LOCAL)) self.assertEqual(bob_init_balance_msat + num_payments * 1000, alice_channel.balance(HTLCOwner.REMOTE)) @needs_test_with_all_chacha20_implementations def test_close(self): alice_channel, bob_channel = create_test_channels() p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel, bob_channel) w1.network.config.set_key('dynamic_fees', False) w2.network.config.set_key('dynamic_fees', False) w1.network.config.set_key('fee_per_kb', 5000) w2.network.config.set_key('fee_per_kb', 1000) w2.enable_htlc_settle.clear() pay_req = self.prepare_invoice(w2) lnaddr = lndecode(pay_req, expected_hrp=constants.net.SEGWIT_HRP) async def pay(): await asyncio.wait_for(p1.initialized, 1) await asyncio.wait_for(p2.initialized, 1) # alice sends htlc route = w1._create_route_from_invoice(decoded_invoice=lnaddr) htlc = p1.pay(route, alice_channel, int(lnaddr.amount * COIN * 1000), lnaddr.paymenthash, lnaddr.get_min_final_cltv_expiry()) # alice closes await p1.close_channel(alice_channel.channel_id) gath.cancel() async def set_settle(): await asyncio.sleep(0.1) w2.enable_htlc_settle.set() gath = asyncio.gather(pay(), set_settle(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) def test_channel_usage_after_closing(self): alice_channel, bob_channel = create_test_channels() p1, p2, w1, w2, q1, q2 = self.prepare_peers(alice_channel, bob_channel) pay_req = self.prepare_invoice(w2) addr = w1._check_invoice(pay_req) route = w1._create_route_from_invoice(decoded_invoice=addr) run(w1.force_close_channel(alice_channel.channel_id)) # check if a tx (commitment transaction) was broadcasted: assert q1.qsize() == 1 with self.assertRaises(NoPathFound) as e: w1._create_route_from_invoice(decoded_invoice=addr) peer = w1.peers[route[0].node_id] # AssertionError is ok since we shouldn't use old routes, and the # route finding should fail when channel is closed async def f(): await asyncio.gather(w1._pay_to_route(route, addr), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) with self.assertRaises(PaymentFailure): run(f()) def run(coro): return asyncio.run_coroutine_threadsafe(coro, loop=asyncio.get_event_loop()).result()

39.633838

139

0.680663

import asyncio import tempfile from decimal import Decimal import os from contextlib import contextmanager from collections import defaultdict import logging import concurrent from concurrent import futures import unittest from aiorpcx import TaskGroup from electrum_ltc import constants from electrum_ltc.network import Network from electrum_ltc.ecc import ECPrivkey from electrum_ltc import simple_config, lnutil from electrum_ltc.lnaddr import lnencode, LnAddr, lndecode from electrum_ltc.bitcoin import COIN, sha256 from electrum_ltc.util import bh2u, create_and_start_event_loop from electrum_ltc.lnpeer import Peer from electrum_ltc.lnutil import LNPeerAddr, Keypair, privkey_to_pubkey from electrum_ltc.lnutil import LightningPeerConnectionClosed, RemoteMisbehaving from electrum_ltc.lnutil import PaymentFailure, LnLocalFeatures, HTLCOwner from electrum_ltc.lnchannel import channel_states, peer_states, Channel from electrum_ltc.lnrouter import LNPathFinder from electrum_ltc.channel_db import ChannelDB from electrum_ltc.lnworker import LNWallet, NoPathFound from electrum_ltc.lnmsg import encode_msg, decode_msg from electrum_ltc.logging import console_stderr_handler, Logger from electrum_ltc.lnworker import PaymentInfo, RECEIVED, PR_UNPAID from .test_lnchannel import create_test_channels from .test_bitcoin import needs_test_with_all_chacha20_implementations from . import ElectrumTestCase def keypair(): priv = ECPrivkey.generate_random_key().get_secret_bytes() k1 = Keypair( pubkey=privkey_to_pubkey(priv), privkey=priv) return k1 @contextmanager def noop_lock(): yield class MockNetwork: def __init__(self, tx_queue): self.callbacks = defaultdict(list) self.lnwatcher = None self.interface = None user_config = {} user_dir = tempfile.mkdtemp(prefix="electrum-lnpeer-test-") self.config = simple_config.SimpleConfig(user_config, read_user_dir_function=lambda: user_dir) self.asyncio_loop = asyncio.get_event_loop() self.channel_db = ChannelDB(self) self.channel_db.data_loaded.set() self.path_finder = LNPathFinder(self.channel_db) self.tx_queue = tx_queue @property def callback_lock(self): return noop_lock() register_callback = Network.register_callback unregister_callback = Network.unregister_callback trigger_callback = Network.trigger_callback def get_local_height(self): return 0 async def broadcast_transaction(self, tx): if self.tx_queue: await self.tx_queue.put(tx) async def try_broadcasting(self, tx, name): self.broadcast_transaction(tx) class MockWallet: def set_label(self, x, y): pass def save_db(self): pass def is_lightning_backup(self): return False class MockLNWallet(Logger): def __init__(self, remote_keypair, local_keypair, chan: 'Channel', tx_queue): Logger.__init__(self) self.remote_keypair = remote_keypair self.node_keypair = local_keypair self.network = MockNetwork(tx_queue) self.channels = {chan.channel_id: chan} self.payments = {} self.logs = defaultdict(list) self.wallet = MockWallet() self.localfeatures = LnLocalFeatures(0) self.localfeatures |= LnLocalFeatures.OPTION_DATA_LOSS_PROTECT_OPT self.pending_payments = defaultdict(asyncio.Future) chan.lnworker = self chan.node_id = remote_keypair.pubkey self.enable_htlc_settle = asyncio.Event() self.enable_htlc_settle.set() def get_invoice_status(self, key): pass @property def lock(self): return noop_lock() @property def peers(self): return {self.remote_keypair.pubkey: self.peer} def channels_for_peer(self, pubkey): return self.channels def get_channel_by_short_id(self, short_channel_id): with self.lock: for chan in self.channels.values(): if chan.short_channel_id == short_channel_id: return chan def save_channel(self, chan): print("Ignoring channel save") is_routing = set() preimages = {} get_payment_info = LNWallet.get_payment_info save_payment_info = LNWallet.save_payment_info set_invoice_status = LNWallet.set_invoice_status set_payment_status = LNWallet.set_payment_status get_payment_status = LNWallet.get_payment_status await_payment = LNWallet.await_payment payment_received = LNWallet.payment_received payment_sent = LNWallet.payment_sent payment_failed = LNWallet.payment_failed save_preimage = LNWallet.save_preimage get_preimage = LNWallet.get_preimage _create_route_from_invoice = LNWallet._create_route_from_invoice _check_invoice = staticmethod(LNWallet._check_invoice) _pay_to_route = LNWallet._pay_to_route _pay = LNWallet._pay force_close_channel = LNWallet.force_close_channel try_force_closing = LNWallet.try_force_closing get_first_timestamp = lambda self: 0 class MockTransport: def __init__(self, name): self.queue = asyncio.Queue() self._name = name def name(self): return self._name async def read_messages(self): while True: yield await self.queue.get() class NoFeaturesTransport(MockTransport): def send_bytes(self, data): decoded = decode_msg(data) print(decoded) if decoded[0] == 'init': self.queue.put_nowait(encode_msg('init', lflen=1, gflen=1, localfeatures=b"\x00", globalfeatures=b"\x00")) class PutIntoOthersQueueTransport(MockTransport): def __init__(self, name): super().__init__(name) self.other_mock_transport = None def send_bytes(self, data): self.other_mock_transport.queue.put_nowait(data) def transport_pair(name1, name2): t1 = PutIntoOthersQueueTransport(name1) t2 = PutIntoOthersQueueTransport(name2) t1.other_mock_transport = t2 t2.other_mock_transport = t1 return t1, t2 class TestPeer(ElectrumTestCase): @classmethod def setUpClass(cls): super().setUpClass() console_stderr_handler.setLevel(logging.DEBUG) def setUp(self): super().setUp() self.asyncio_loop, self._stop_loop, self._loop_thread = create_and_start_event_loop() def tearDown(self): super().tearDown() self.asyncio_loop.call_soon_threadsafe(self._stop_loop.set_result, 1) self._loop_thread.join(timeout=1) def prepare_peers(self, alice_channel, bob_channel): k1, k2 = keypair(), keypair() t1, t2 = transport_pair(alice_channel.name, bob_channel.name) q1, q2 = asyncio.Queue(), asyncio.Queue() w1 = MockLNWallet(k1, k2, alice_channel, tx_queue=q1) w2 = MockLNWallet(k2, k1, bob_channel, tx_queue=q2) p1 = Peer(w1, k1.pubkey, t1) p2 = Peer(w2, k2.pubkey, t2) w1.peer = p1 w2.peer = p2 # so set it to FUNDED alice_channel._state = channel_states.FUNDED bob_channel._state = channel_states.FUNDED # this populates the channel graph: p1.mark_open(alice_channel) p2.mark_open(bob_channel) return p1, p2, w1, w2, q1, q2 @staticmethod def prepare_invoice( w2, # receiver *, amount_sat=100_000, ): amount_btc = amount_sat/Decimal(COIN) payment_preimage = os.urandom(32) RHASH = sha256(payment_preimage) info = PaymentInfo(RHASH, amount_sat, RECEIVED, PR_UNPAID) w2.save_preimage(RHASH, payment_preimage) w2.save_payment_info(info) lnaddr = LnAddr( RHASH, amount_btc, tags=[('c', lnutil.MIN_FINAL_CLTV_EXPIRY_FOR_INVOICE), ('d', 'coffee') ]) return lnencode(lnaddr, w2.node_keypair.privkey) def test_reestablish(self): alice_channel, bob_channel = create_test_channels() p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel, bob_channel) for chan in (alice_channel, bob_channel): chan.peer_state = peer_states.DISCONNECTED async def reestablish(): await asyncio.gather( p1.reestablish_channel(alice_channel), p2.reestablish_channel(bob_channel)) self.assertEqual(alice_channel.peer_state, peer_states.GOOD) self.assertEqual(bob_channel.peer_state, peer_states.GOOD) gath.cancel() gath = asyncio.gather(reestablish(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p1.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) @needs_test_with_all_chacha20_implementations def test_reestablish_with_old_state(self): alice_channel, bob_channel = create_test_channels() alice_channel_0, bob_channel_0 = create_test_channels() # these are identical p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel, bob_channel) pay_req = self.prepare_invoice(w2) async def pay(): result = await w1._pay(pay_req) self.assertEqual(result, True) gath.cancel() gath = asyncio.gather(pay(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel_0, bob_channel) for chan in (alice_channel_0, bob_channel): chan.peer_state = peer_states.DISCONNECTED async def reestablish(): await asyncio.gather( p1.reestablish_channel(alice_channel_0), p2.reestablish_channel(bob_channel)) self.assertEqual(alice_channel_0.peer_state, peer_states.BAD) self.assertEqual(bob_channel._state, channel_states.FORCE_CLOSING) # wait so that pending messages are processed #await asyncio.sleep(1) gath.cancel() gath = asyncio.gather(reestablish(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) @needs_test_with_all_chacha20_implementations def test_payment(self): alice_channel, bob_channel = create_test_channels() p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel, bob_channel) pay_req = self.prepare_invoice(w2) async def pay(): result = await w1._pay(pay_req) self.assertTrue(result) gath.cancel() gath = asyncio.gather(pay(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) #@unittest.skip("too expensive") #@needs_test_with_all_chacha20_implementations def test_payments_stresstest(self): alice_channel, bob_channel = create_test_channels() p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel, bob_channel) alice_init_balance_msat = alice_channel.balance(HTLCOwner.LOCAL) bob_init_balance_msat = bob_channel.balance(HTLCOwner.LOCAL) num_payments = 50 #pay_reqs1 = [self.prepare_invoice(w1, amount_sat=1) for i in range(num_payments)] pay_reqs2 = [self.prepare_invoice(w2, amount_sat=1) for i in range(num_payments)] max_htlcs_in_flight = asyncio.Semaphore(5) async def single_payment(pay_req): async with max_htlcs_in_flight: await w1._pay(pay_req) async def many_payments(): async with TaskGroup() as group: for pay_req in pay_reqs2: await group.spawn(single_payment(pay_req)) gath.cancel() gath = asyncio.gather(many_payments(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) self.assertEqual(alice_init_balance_msat - num_payments * 1000, alice_channel.balance(HTLCOwner.LOCAL)) self.assertEqual(alice_init_balance_msat - num_payments * 1000, bob_channel.balance(HTLCOwner.REMOTE)) self.assertEqual(bob_init_balance_msat + num_payments * 1000, bob_channel.balance(HTLCOwner.LOCAL)) self.assertEqual(bob_init_balance_msat + num_payments * 1000, alice_channel.balance(HTLCOwner.REMOTE)) @needs_test_with_all_chacha20_implementations def test_close(self): alice_channel, bob_channel = create_test_channels() p1, p2, w1, w2, _q1, _q2 = self.prepare_peers(alice_channel, bob_channel) w1.network.config.set_key('dynamic_fees', False) w2.network.config.set_key('dynamic_fees', False) w1.network.config.set_key('fee_per_kb', 5000) w2.network.config.set_key('fee_per_kb', 1000) w2.enable_htlc_settle.clear() pay_req = self.prepare_invoice(w2) lnaddr = lndecode(pay_req, expected_hrp=constants.net.SEGWIT_HRP) async def pay(): await asyncio.wait_for(p1.initialized, 1) await asyncio.wait_for(p2.initialized, 1) # alice sends htlc route = w1._create_route_from_invoice(decoded_invoice=lnaddr) htlc = p1.pay(route, alice_channel, int(lnaddr.amount * COIN * 1000), lnaddr.paymenthash, lnaddr.get_min_final_cltv_expiry()) # alice closes await p1.close_channel(alice_channel.channel_id) gath.cancel() async def set_settle(): await asyncio.sleep(0.1) w2.enable_htlc_settle.set() gath = asyncio.gather(pay(), set_settle(), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) async def f(): await gath with self.assertRaises(concurrent.futures.CancelledError): run(f()) def test_channel_usage_after_closing(self): alice_channel, bob_channel = create_test_channels() p1, p2, w1, w2, q1, q2 = self.prepare_peers(alice_channel, bob_channel) pay_req = self.prepare_invoice(w2) addr = w1._check_invoice(pay_req) route = w1._create_route_from_invoice(decoded_invoice=addr) run(w1.force_close_channel(alice_channel.channel_id)) # check if a tx (commitment transaction) was broadcasted: assert q1.qsize() == 1 with self.assertRaises(NoPathFound) as e: w1._create_route_from_invoice(decoded_invoice=addr) peer = w1.peers[route[0].node_id] # AssertionError is ok since we shouldn't use old routes, and the async def f(): await asyncio.gather(w1._pay_to_route(route, addr), p1._message_loop(), p2._message_loop(), p1.htlc_switch(), p2.htlc_switch()) with self.assertRaises(PaymentFailure): run(f()) def run(coro): return asyncio.run_coroutine_threadsafe(coro, loop=asyncio.get_event_loop()).result()

true

f72ad123a16de5b88d83b7f0efe6887a58556b76

1,491

py

Python

examples/map_view_simple_example.py

TomSchimansky/TkinterMapView

eb84f600e9b6bb8c60d88149e277b3abee704a70

[ "CC0-1.0" ]

43

2022-01-02T04:23:28.000Z

2022-03-30T03:04:03.000Z

examples/map_view_simple_example.py

TomSchimansky/TkinterMapView

eb84f600e9b6bb8c60d88149e277b3abee704a70

[ "CC0-1.0" ]

6

2022-02-24T09:19:35.000Z

2022-03-24T18:32:22.000Z

examples/map_view_simple_example.py

TomSchimansky/TkinterMapView

eb84f600e9b6bb8c60d88149e277b3abee704a70

[ "CC0-1.0" ]

4

2022-01-03T16:49:04.000Z

2022-03-21T09:25:44.000Z

import tkinter import tkintermapview # create tkinter window root_tk = tkinter.Tk() root_tk.geometry(f"{1000}x{700}") root_tk.title("map_view_simple_example.py") # create map widget map_widget = tkintermapview.TkinterMapView(root_tk, width=1000, height=700, corner_radius=0) map_widget.pack(fill="both", expand=True) # set other tile server (standard is OpenStreetMap) # map_widget.set_tile_server("https://mt0.google.com/vt/lyrs=m&hl=en&x={x}&y={y}&z={z}&s=Ga", max_zoom=22) # google normal # map_widget.set_tile_server("https://mt0.google.com/vt/lyrs=s&hl=en&x={x}&y={y}&z={z}&s=Ga", max_zoom=22) # google satellite # set current position and zoom # map_widget.set_position(52.516268, 13.377695, marker=False) # Berlin, Germany # map_widget.set_zoom(17) # set current position with address # map_widget.set_address("Berlin Germany", marker=False) def marker_click(marker): print(f"marker clicked - text: {marker.text} position: {marker.position}") # set a position marker (also with a custom color and command on click) marker_2 = map_widget.set_marker(52.516268, 13.377695, text="Brandenburger Tor", command=marker_click) marker_3 = map_widget.set_marker(52.55, 13.4, text="52.55, 13.4") # marker_3.set_position(...) # marker_3.set_text(...) # marker_3.delete() # set a path path_1 = map_widget.set_path([marker_2.position, marker_3.position, (52.568, 13.4), (52.569, 13.35)]) # path_1.add_position(...) # path_1.remove_position(...) # path_1.delete() root_tk.mainloop()

36.365854

126

0.739772

import tkinter import tkintermapview root_tk = tkinter.Tk() root_tk.geometry(f"{1000}x{700}") root_tk.title("map_view_simple_example.py") map_widget = tkintermapview.TkinterMapView(root_tk, width=1000, height=700, corner_radius=0) map_widget.pack(fill="both", expand=True) rker clicked - text: {marker.text} position: {marker.position}") marker_2 = map_widget.set_marker(52.516268, 13.377695, text="Brandenburger Tor", command=marker_click) marker_3 = map_widget.set_marker(52.55, 13.4, text="52.55, 13.4") path_1 = map_widget.set_path([marker_2.position, marker_3.position, (52.568, 13.4), (52.569, 13.35)]) root_tk.mainloop()

true

f72ad17de09166bbcef6aaac4ff6b283c77049fa

2,206

py

Python

retrieve_response.py

kit-data-manager/gemma

0ae4e64f966b389c7e7c5619c8fd09bef78c8c87

[ "Apache-2.0" ]

null

retrieve_response.py

kit-data-manager/gemma

0ae4e64f966b389c7e7c5619c8fd09bef78c8c87

[ "Apache-2.0" ]

null

retrieve_response.py

kit-data-manager/gemma

0ae4e64f966b389c7e7c5619c8fd09bef78c8c87

[ "Apache-2.0" ]

null

import http.client import os import json import wget import mapping_functions import pprint import sys HOST = 'episteme2.scc.kit.edu' PORT = '8080' URL = os.path.join('http://' + HOST + ':' + PORT, 'api/v1/dataresources') output_folder = sys.argv[1] payload = "{\n \t\"resourceType\": {\n \t\t\"typeGeneral\":\"TEXT\"\n \t}\n}" headers = {'Content-Type': "application/json", 'cache-control': "no-cache"} size = 20 page = 0 def http_call(TYPE, host=HOST, port=PORT, endpoint='', search='', query='', payload='', headers={}): check_http_method(TYPE) conn = http.client.HTTPConnection(host, port) if search != '' or query != '': endpoint = os.path.join(endpoint, search + query) url = os.path.join(URL, endpoint) print('URL: ', url) conn.request(TYPE, url, payload, headers) res = conn.getresponse() data = json.loads(res.read().decode('utf-8')) return data def check_http_method(method): assert(isinstance(method, str)), 'method must be a string' list = ['POST', 'GET', 'PUT', 'PATCH', 'DELETE'] if method not in list: print("{} not allowed. Use: 'POST', 'GET', 'PUT', 'PATCH', 'DELETE'".format(method)) return def download_file(file_id, extention='xml'): endpoint = 'data/manuscript_metadata.' + extention url = os.path.join(URL, file_id, endpoint) output_file = file_id + "." + extention wget.download(url, os.path.join(output_folder, output_file)) while True: retrieve = 'search?size=' + str(size) + '&page=' + str(page) data = http_call('POST', search=retrieve, payload=payload, headers=headers) print('{} results at page {}'.format(len(data), page)) if len(data) == 0: break for resourse in data: manuscript_id = resourse['id'] print("manuscript id: {}".format(manuscript_id)) if resourse['state'] == "REVOKED": print("Status of resource {} is {}".format(resourse, resourse['state'])) continue assert(resourse['resourceType']['value'] == 'manuscriptMetadata'), "resourceType is not manuscriptMetadata" download_file(manuscript_id, 'json') if len(data) == size: page += 1 else: break

30.638889

115

0.629193

import http.client import os import json import wget import mapping_functions import pprint import sys HOST = 'episteme2.scc.kit.edu' PORT = '8080' URL = os.path.join('http://' + HOST + ':' + PORT, 'api/v1/dataresources') output_folder = sys.argv[1] payload = "{\n \t\"resourceType\": {\n \t\t\"typeGeneral\":\"TEXT\"\n \t}\n}" headers = {'Content-Type': "application/json", 'cache-control': "no-cache"} size = 20 page = 0 def http_call(TYPE, host=HOST, port=PORT, endpoint='', search='', query='', payload='', headers={}): check_http_method(TYPE) conn = http.client.HTTPConnection(host, port) if search != '' or query != '': endpoint = os.path.join(endpoint, search + query) url = os.path.join(URL, endpoint) print('URL: ', url) conn.request(TYPE, url, payload, headers) res = conn.getresponse() data = json.loads(res.read().decode('utf-8')) return data def check_http_method(method): assert(isinstance(method, str)), 'method must be a string' list = ['POST', 'GET', 'PUT', 'PATCH', 'DELETE'] if method not in list: print("{} not allowed. Use: 'POST', 'GET', 'PUT', 'PATCH', 'DELETE'".format(method)) return def download_file(file_id, extention='xml'): endpoint = 'data/manuscript_metadata.' + extention url = os.path.join(URL, file_id, endpoint) output_file = file_id + "." + extention wget.download(url, os.path.join(output_folder, output_file)) while True: retrieve = 'search?size=' + str(size) + '&page=' + str(page) data = http_call('POST', search=retrieve, payload=payload, headers=headers) print('{} results at page {}'.format(len(data), page)) if len(data) == 0: break for resourse in data: manuscript_id = resourse['id'] print("manuscript id: {}".format(manuscript_id)) if resourse['state'] == "REVOKED": print("Status of resource {} is {}".format(resourse, resourse['state'])) continue assert(resourse['resourceType']['value'] == 'manuscriptMetadata'), "resourceType is not manuscriptMetadata" download_file(manuscript_id, 'json') if len(data) == size: page += 1 else: break

true

f72ad2f82bf260bd112b090bded6d3c5ba2e8a43

1,180

py

Python

profiles_api/serializers.py

Atique-7/drf-genesis

a333564d285885c7661e3324d5503488d9ced6ae

[ "MIT" ]

null

profiles_api/serializers.py

Atique-7/drf-genesis

a333564d285885c7661e3324d5503488d9ced6ae

[ "MIT" ]

null

profiles_api/serializers.py

Atique-7/drf-genesis

a333564d285885c7661e3324d5503488d9ced6ae

[ "MIT" ]

null

from rest_framework import serializers from profiles_api import models class UserProfileSerializer(serializers.ModelSerializer): """serializes a user profile object""" class Meta: model = models.UserProfile fields = ('id', 'name', 'email', 'password') extra_kwargs = { 'password' : { 'write_only' : True, 'style' : { 'input_type' : 'password' } } } # We now take over the default create function. def create(self, validated_data): """create and return a new user""" user = models.UserProfile.objects.create_user( email = validated_data['email'], name = validated_data['name'], password = validated_data['password'] ) return user class ProfileFeedItemSerializer(serializers.ModelSerializer): """serializes profile feed items""" class Meta: model = models.ProfileFeedItem fields = ('id', 'user_profile', 'status_text', 'created_on') extra_kwargs = { 'user_profile' : { 'read_only' : True } }

29.5

68

0.561864

from rest_framework import serializers from profiles_api import models class UserProfileSerializer(serializers.ModelSerializer): class Meta: model = models.UserProfile fields = ('id', 'name', 'email', 'password') extra_kwargs = { 'password' : { 'write_only' : True, 'style' : { 'input_type' : 'password' } } } def create(self, validated_data): user = models.UserProfile.objects.create_user( email = validated_data['email'], name = validated_data['name'], password = validated_data['password'] ) return user class ProfileFeedItemSerializer(serializers.ModelSerializer): class Meta: model = models.ProfileFeedItem fields = ('id', 'user_profile', 'status_text', 'created_on') extra_kwargs = { 'user_profile' : { 'read_only' : True } }

true

f72ad439a6e7cf5dac1b087074d4ee471a260a4b

52

py

Python

tests/python/overload1.py

jacereda/py2nim

56fc2699d31241c60bed726f59efea4bf46be238

[ "MIT" ]

10

2020-03-10T12:01:01.000Z

2021-05-23T19:47:06.000Z

tests/python/overload1.py

jacereda/py2nim

56fc2699d31241c60bed726f59efea4bf46be238

[ "MIT" ]

null

tests/python/overload1.py

jacereda/py2nim

56fc2699d31241c60bed726f59efea4bf46be238

[ "MIT" ]

1

2020-07-17T11:20:56.000Z

def a(z, b): print(z + b) a(0, 0.0) a('e', '')

8.666667

16

0.365385

def a(z, b): print(z + b) a(0, 0.0) a('e', '')

true

f72ad5b39fcaee399cd011abf25e5fda0c0342a6

24,914

py

Python

jina/flow/mixin/async_crud.py

liushuigs/jina

b3550e901b2a340924330b5ba2801603e493c933

[ "Apache-2.0" ]

null

jina/flow/mixin/async_crud.py

liushuigs/jina

b3550e901b2a340924330b5ba2801603e493c933

[ "Apache-2.0" ]

2

2021-02-15T01:40:38.000Z

2021-02-15T02:00:21.000Z

jina/flow/mixin/async_crud.py

liushuigs/jina

b3550e901b2a340924330b5ba2801603e493c933

[ "Apache-2.0" ]

null

import warnings from typing import Union, Iterable, TextIO, Dict, Optional import numpy as np from ...clients.base import InputType, CallbackFnType from ...enums import DataInputType from ...helper import deprecated_alias class AsyncCRUDFlowMixin: """The asynchronous version of the Mixin for CRUD in Flow""" @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def train( self, inputs: InputType, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Do training on the current Flow :param inputs: An iterator of bytes. If not given, then you have to specify it in **kwargs**. :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ warnings.warn(f'{self.train} is under heavy refactoring', FutureWarning) async for r in self._get_client(**kwargs).train( inputs, on_done, on_error, on_always, **kwargs ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def index_ndarray( self, array: 'np.ndarray', axis: int = 0, size: Optional[int] = None, shuffle: bool = False, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Using numpy ndarray as the index source for the current Flow :param array: the numpy ndarray data source :param axis: iterate over that axis :param size: the maximum number of the sub arrays :param shuffle: shuffle the the numpy data source beforehand :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ from ...clients.sugary_io import _input_ndarray async for r in self._get_client(**kwargs).index( _input_ndarray(array, axis, size, shuffle), on_done, on_error, on_always, data_type=DataInputType.CONTENT, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def search_ndarray( self, array: 'np.ndarray', axis: int = 0, size: Optional[int] = None, shuffle: bool = False, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Use a numpy ndarray as the query source for searching on the current Flow :param array: the numpy ndarray data source :param axis: iterate over that axis :param size: the maximum number of the sub arrays :param shuffle: shuffle the the numpy data source beforehand :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ from ...clients.sugary_io import _input_ndarray async for r in self._get_client(**kwargs).search( _input_ndarray(array, axis, size, shuffle), on_done, on_error, on_always, data_type=DataInputType.CONTENT, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def index_lines( self, lines: Optional[Union[Iterable[str], TextIO]] = None, filepath: Optional[str] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, read_mode: str = 'r', line_format: str = 'json', field_resolver: Optional[Dict[str, str]] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Use a list of lines as the index source for indexing on the current Flow :param lines: a list of strings, each is considered as d document :param filepath: a text file that each line contains a document :param size: the maximum number of the documents :param sampling_rate: the sampling rate between [0, 1] :param read_mode: specifies the mode in which the file is opened. 'r' for reading in text mode, 'rb' for reading in binary :param line_format: the format of each line: ``json`` or ``csv`` :param field_resolver: a map from field names defined in ``document`` (JSON, dict) to the field names defined in Protobuf. This is only used when the given ``document`` is a JSON string or a Python dict. :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ from ...clients.sugary_io import _input_lines async for r in self._get_client(**kwargs).index( _input_lines( lines, filepath, size=size, sampling_rate=sampling_rate, read_mode=read_mode, line_format=line_format, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.AUTO, **kwargs, ): yield r async def index_csv( self, lines: Union[Iterable[str], TextIO], field_resolver: Dict[str, str] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Use a list of lines as the index source for indexing on the current Flow :param lines: a list of strings, each is considered as d document :param size: the maximum number of the documents :param sampling_rate: the sampling rate between [0, 1] :param field_resolver: a map from field names defined in ``document`` (JSON, dict) to the field names defined in Protobuf. This is only used when the given ``document`` is a JSON string or a Python dict. :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ from ...clients.sugary_io import _input_csv async for r in self._get_client(**kwargs).index( _input_csv( lines, size=size, sampling_rate=sampling_rate, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.AUTO, **kwargs, ): yield r async def index_ndjson( self, lines: Union[Iterable[str], TextIO], field_resolver: Optional[Dict[str, str]] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Use a list of lines as the index source for indexing on the current Flow :param lines: a list of strings, each is considered as d document :param size: the maximum number of the documents :param sampling_rate: the sampling rate between [0, 1] :param field_resolver: a map from field names defined in ``document`` (JSON, dict) to the field names defined in Protobuf. This is only used when the given ``document`` is a JSON string or a Python dict. :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ from ...clients.sugary_io import _input_ndjson async for r in self._get_client(**kwargs).index( _input_ndjson( lines, size=size, sampling_rate=sampling_rate, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.AUTO, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def index_files( self, patterns: Union[str, Iterable[str]], recursive: bool = True, size: Optional[int] = None, sampling_rate: Optional[float] = None, read_mode: Optional[str] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Use a set of files as the index source for indexing on the current Flow :param patterns: The pattern may contain simple shell-style wildcards, e.g. '\*.py', '[\*.zip, \*.gz]' :param recursive: If recursive is true, the pattern '**' will match any files and zero or more directories and subdirectories. :param size: the maximum number of the files :param sampling_rate: the sampling rate between [0, 1] :param read_mode: specifies the mode in which the file is opened. 'r' for reading in text mode, 'rb' for reading in binary mode :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ from ...clients.sugary_io import _input_files async for r in self._get_client(**kwargs).index( _input_files(patterns, recursive, size, sampling_rate, read_mode), on_done, on_error, on_always, data_type=DataInputType.CONTENT, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def search_files( self, patterns: Union[str, Iterable[str]], recursive: bool = True, size: Optional[int] = None, sampling_rate: Optional[float] = None, read_mode: Optional[str] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Use a set of files as the query source for searching on the current Flow :param patterns: The pattern may contain simple shell-style wildcards, e.g. '\*.py', '[\*.zip, \*.gz]' :param recursive: If recursive is true, the pattern '**' will match any files and zero or more directories and subdirectories. :param size: the maximum number of the files :param sampling_rate: the sampling rate between [0, 1] :param read_mode: specifies the mode in which the file is opened. 'r' for reading in text mode, 'rb' for reading in :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ from ...clients.sugary_io import _input_files async for r in self._get_client(**kwargs).search( _input_files(patterns, recursive, size, sampling_rate, read_mode), on_done, on_error, on_always, data_type=DataInputType.CONTENT, **kwargs, ): yield r async def search_ndjson( self, lines: Union[Iterable[str], TextIO], field_resolver: Optional[Dict[str, str]] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Use a list of files as the query source for searching on the current Flow :param lines: a list of strings, each is considered as d document :param size: the maximum number of the documents :param sampling_rate: the sampling rate between [0, 1] :param field_resolver: a map from field names defined in ``document`` (JSON, dict) to the field names defined in Protobuf. This is only used when the given ``document`` is a JSON string or a Python dict. :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ from ...clients.sugary_io import _input_ndjson async for r in self._get_client(**kwargs).search( _input_ndjson( lines, size=size, sampling_rate=sampling_rate, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.AUTO, **kwargs, ): yield r async def search_csv( self, lines: Union[Iterable[str], TextIO], field_resolver: Optional[Dict[str, str]] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Use a list of lines as the index source for indexing on the current Flow :param lines: a list of strings, each is considered as d document :param size: the maximum number of the documents :param sampling_rate: the sampling rate between [0, 1] :param field_resolver: a map from field names defined in ``document`` (JSON, dict) to the field names defined in Protobuf. This is only used when the given ``document`` is a JSON string or a Python dict. :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ from ...clients.sugary_io import _input_csv async for r in self._get_client(**kwargs).search( _input_csv( lines, size=size, sampling_rate=sampling_rate, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.AUTO, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def search_lines( self, lines: Optional[Union[Iterable[str], TextIO]] = None, filepath: Optional[str] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, read_mode: str = 'r', line_format: str = 'json', field_resolver: Optional[Dict[str, str]] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Use a list of files as the query source for searching on the current Flow :param filepath: a text file that each line contains a document :param lines: a list of strings, each is considered as d document :param size: the maximum number of the documents :param sampling_rate: the sampling rate between [0, 1] :param read_mode: specifies the mode in which the file is opened. 'r' for reading in text mode, 'rb' for reading in binary :param line_format: the format of each line: ``json`` or ``csv`` :param field_resolver: a map from field names defined in ``document`` (JSON, dict) to the field names defined in Protobuf. This is only used when the given ``document`` is a JSON string or a Python dict. :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ from ...clients.sugary_io import _input_lines async for r in self._get_client(**kwargs).search( _input_lines( lines, filepath, size=size, sampling_rate=sampling_rate, read_mode=read_mode, line_format=line_format, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.CONTENT, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def index( self, inputs: InputType, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Do indexing on the current Flow It will start a :py:class:`CLIClient` and call :py:func:`index`. :param inputs: An iterator of bytes. If not given, then you have to specify it in **kwargs**. :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ async for r in self._get_client(**kwargs).index( inputs, on_done, on_error, on_always, **kwargs ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def update( self, inputs: InputType, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Do updates on the current Flow It will start a :py:class:`CLIClient` and call :py:func:`index`. :param inputs: An iterator of bytes. If not given, then you have to specify it in **kwargs**. :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ async for r in self._get_client(**kwargs).update( inputs, on_done, on_error, on_always, **kwargs ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def delete( self, ids: Iterable[str], on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Do deletion on the current Flow :param ids: An iterable of ids :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ async for r in self._get_client(**kwargs).delete( ids, on_done, on_error, on_always, **kwargs ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def search( self, inputs: InputType, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): """Do searching on the current Flow It will start a :py:class:`CLIClient` and call :py:func:`search`. :param inputs: An iterator of bytes. If not given, then you have to specify it in **kwargs**. :param on_done: the function to be called when the :class:`Request` object is resolved. :param on_error: the function to be called when the :class:`Request` object is rejected. :param on_always: the function to be called when the :class:`Request` object is is either resolved or rejected. :param kwargs: accepts all keyword arguments of `jina client` CLI :yields: results """ async for r in self._get_client(**kwargs).search( inputs, on_done, on_error, on_always, **kwargs ): yield r

40.70915

120

0.603837

import warnings from typing import Union, Iterable, TextIO, Dict, Optional import numpy as np from ...clients.base import InputType, CallbackFnType from ...enums import DataInputType from ...helper import deprecated_alias class AsyncCRUDFlowMixin: @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def train( self, inputs: InputType, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): warnings.warn(f'{self.train} is under heavy refactoring', FutureWarning) async for r in self._get_client(**kwargs).train( inputs, on_done, on_error, on_always, **kwargs ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def index_ndarray( self, array: 'np.ndarray', axis: int = 0, size: Optional[int] = None, shuffle: bool = False, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): from ...clients.sugary_io import _input_ndarray async for r in self._get_client(**kwargs).index( _input_ndarray(array, axis, size, shuffle), on_done, on_error, on_always, data_type=DataInputType.CONTENT, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def search_ndarray( self, array: 'np.ndarray', axis: int = 0, size: Optional[int] = None, shuffle: bool = False, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): from ...clients.sugary_io import _input_ndarray async for r in self._get_client(**kwargs).search( _input_ndarray(array, axis, size, shuffle), on_done, on_error, on_always, data_type=DataInputType.CONTENT, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def index_lines( self, lines: Optional[Union[Iterable[str], TextIO]] = None, filepath: Optional[str] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, read_mode: str = 'r', line_format: str = 'json', field_resolver: Optional[Dict[str, str]] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): from ...clients.sugary_io import _input_lines async for r in self._get_client(**kwargs).index( _input_lines( lines, filepath, size=size, sampling_rate=sampling_rate, read_mode=read_mode, line_format=line_format, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.AUTO, **kwargs, ): yield r async def index_csv( self, lines: Union[Iterable[str], TextIO], field_resolver: Dict[str, str] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): from ...clients.sugary_io import _input_csv async for r in self._get_client(**kwargs).index( _input_csv( lines, size=size, sampling_rate=sampling_rate, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.AUTO, **kwargs, ): yield r async def index_ndjson( self, lines: Union[Iterable[str], TextIO], field_resolver: Optional[Dict[str, str]] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): from ...clients.sugary_io import _input_ndjson async for r in self._get_client(**kwargs).index( _input_ndjson( lines, size=size, sampling_rate=sampling_rate, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.AUTO, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def index_files( self, patterns: Union[str, Iterable[str]], recursive: bool = True, size: Optional[int] = None, sampling_rate: Optional[float] = None, read_mode: Optional[str] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): from ...clients.sugary_io import _input_files async for r in self._get_client(**kwargs).index( _input_files(patterns, recursive, size, sampling_rate, read_mode), on_done, on_error, on_always, data_type=DataInputType.CONTENT, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def search_files( self, patterns: Union[str, Iterable[str]], recursive: bool = True, size: Optional[int] = None, sampling_rate: Optional[float] = None, read_mode: Optional[str] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): from ...clients.sugary_io import _input_files async for r in self._get_client(**kwargs).search( _input_files(patterns, recursive, size, sampling_rate, read_mode), on_done, on_error, on_always, data_type=DataInputType.CONTENT, **kwargs, ): yield r async def search_ndjson( self, lines: Union[Iterable[str], TextIO], field_resolver: Optional[Dict[str, str]] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): from ...clients.sugary_io import _input_ndjson async for r in self._get_client(**kwargs).search( _input_ndjson( lines, size=size, sampling_rate=sampling_rate, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.AUTO, **kwargs, ): yield r async def search_csv( self, lines: Union[Iterable[str], TextIO], field_resolver: Optional[Dict[str, str]] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): from ...clients.sugary_io import _input_csv async for r in self._get_client(**kwargs).search( _input_csv( lines, size=size, sampling_rate=sampling_rate, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.AUTO, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def search_lines( self, lines: Optional[Union[Iterable[str], TextIO]] = None, filepath: Optional[str] = None, size: Optional[int] = None, sampling_rate: Optional[float] = None, read_mode: str = 'r', line_format: str = 'json', field_resolver: Optional[Dict[str, str]] = None, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): from ...clients.sugary_io import _input_lines async for r in self._get_client(**kwargs).search( _input_lines( lines, filepath, size=size, sampling_rate=sampling_rate, read_mode=read_mode, line_format=line_format, field_resolver=field_resolver, ), on_done, on_error, on_always, data_type=DataInputType.CONTENT, **kwargs, ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def index( self, inputs: InputType, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): async for r in self._get_client(**kwargs).index( inputs, on_done, on_error, on_always, **kwargs ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def update( self, inputs: InputType, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): async for r in self._get_client(**kwargs).update( inputs, on_done, on_error, on_always, **kwargs ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def delete( self, ids: Iterable[str], on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): async for r in self._get_client(**kwargs).delete( ids, on_done, on_error, on_always, **kwargs ): yield r @deprecated_alias( input_fn=('inputs', 0), buffer=('inputs', 1), callback=('on_done', 1), output_fn=('on_done', 1), ) async def search( self, inputs: InputType, on_done: CallbackFnType = None, on_error: CallbackFnType = None, on_always: CallbackFnType = None, **kwargs, ): async for r in self._get_client(**kwargs).search( inputs, on_done, on_error, on_always, **kwargs ): yield r

true

f72ad5bc7ad2d8fb6d61ac7005b04ae01a495d56

1,629

py

Python

packages/tool_util/tests/test_tool_linters.py

lawrence14701/galaxy

7eb2fcb708e7b63e17800c87613ddfa5497c0654

[ "CC-BY-3.0" ]

2

2017-03-28T12:11:41.000Z

2017-04-22T02:58:25.000Z

packages/tool_util/tests/test_tool_linters.py

lawrence14701/galaxy

7eb2fcb708e7b63e17800c87613ddfa5497c0654

[ "CC-BY-3.0" ]

12

2020-07-24T23:55:19.000Z

2021-12-19T11:40:06.000Z

packages/tool_util/tests/test_tool_linters.py

lawrence14701/galaxy

7eb2fcb708e7b63e17800c87613ddfa5497c0654

[ "CC-BY-3.0" ]

1

2019-01-16T22:21:54.000Z

import pytest from galaxy.tool_util.lint import LintContext from galaxy.tool_util.linters import inputs from galaxy.util import etree NO_SECTIONS_XML = """ <tool name="BWA Mapper" id="bwa" version="1.0.1" is_multi_byte="true" display_interface="true" require_login="true" hidden="true"> <description>The BWA Mapper</description> <version_command interpreter="python">bwa.py --version</version_command> </tool> """ NO_WHEN_IN_CONDITIONAL_XML = """ <tool name="BWA Mapper" id="bwa" version="1.0.1" is_multi_byte="true" display_interface="true" require_login="true" hidden="true"> <description>The BWA Mapper</description> <version_command interpreter="python">bwa.py --version</version_command> <inputs> <conditional name="labels"> <param name="label_select" type="select" label="Points to label"> <option value="none" selected="True">None</option> </param> </conditional> </inputs> </tool> """ TESTS = [ (NO_SECTIONS_XML, inputs.lint_inputs, lambda x: 'Found no input parameters.' in x.warn_messages), (NO_WHEN_IN_CONDITIONAL_XML, inputs.lint_inputs, lambda x: 'No <when /> block found for select option \'none\' inside conditional \'labels\'' in x.warn_messages), ] @pytest.mark.parametrize('tool_xml,lint_func,assert_func', TESTS, ids=['Lint no sections', 'lint no when']) def test_tool_xml(tool_xml, lint_func, assert_func): lint_ctx = LintContext('all') tree = etree.ElementTree(element=etree.fromstring(tool_xml)) lint_ctx.lint(name="test_lint", lint_func=lint_func, lint_target=tree) assert assert_func(lint_ctx)

39.731707

166

0.715163

import pytest from galaxy.tool_util.lint import LintContext from galaxy.tool_util.linters import inputs from galaxy.util import etree NO_SECTIONS_XML = """ <tool name="BWA Mapper" id="bwa" version="1.0.1" is_multi_byte="true" display_interface="true" require_login="true" hidden="true"> <description>The BWA Mapper</description> <version_command interpreter="python">bwa.py --version</version_command> </tool> """ NO_WHEN_IN_CONDITIONAL_XML = """ <tool name="BWA Mapper" id="bwa" version="1.0.1" is_multi_byte="true" display_interface="true" require_login="true" hidden="true"> <description>The BWA Mapper</description> <version_command interpreter="python">bwa.py --version</version_command> <inputs> <conditional name="labels"> <param name="label_select" type="select" label="Points to label"> <option value="none" selected="True">None</option> </param> </conditional> </inputs> </tool> """ TESTS = [ (NO_SECTIONS_XML, inputs.lint_inputs, lambda x: 'Found no input parameters.' in x.warn_messages), (NO_WHEN_IN_CONDITIONAL_XML, inputs.lint_inputs, lambda x: 'No <when /> block found for select option \'none\' inside conditional \'labels\'' in x.warn_messages), ] @pytest.mark.parametrize('tool_xml,lint_func,assert_func', TESTS, ids=['Lint no sections', 'lint no when']) def test_tool_xml(tool_xml, lint_func, assert_func): lint_ctx = LintContext('all') tree = etree.ElementTree(element=etree.fromstring(tool_xml)) lint_ctx.lint(name="test_lint", lint_func=lint_func, lint_target=tree) assert assert_func(lint_ctx)

true

f72ad5f44335464611bcb3461699a32b7602d505

7,802

py

Python

virtual/lib/python3.6/site-packages/PIL/PsdImagePlugin.py

Ruterana/clone_instagram

a068587ef1d1a93ec8d1c08086bf11c0fb274b83

[ "MIT" ]

99

2019-10-09T16:14:46.000Z

2022-03-17T02:23:47.000Z

virtual/lib/python3.6/site-packages/PIL/PsdImagePlugin.py

Ruterana/clone_instagram

a068587ef1d1a93ec8d1c08086bf11c0fb274b83

[ "MIT" ]

123

2019-09-10T14:48:01.000Z

2019-11-28T21:24:06.000Z

virtual/lib/python3.6/site-packages/PIL/PsdImagePlugin.py

Ruterana/clone_instagram

a068587ef1d1a93ec8d1c08086bf11c0fb274b83

[ "MIT" ]

98

2019-10-17T14:48:28.000Z

2022-01-21T03:33:38.000Z

# # The Python Imaging Library # $Id$ # # Adobe PSD 2.5/3.0 file handling # # History: # 1995-09-01 fl Created # 1997-01-03 fl Read most PSD images # 1997-01-18 fl Fixed P and CMYK support # 2001-10-21 fl Added seek/tell support (for layers) # # Copyright (c) 1997-2001 by Secret Labs AB. # Copyright (c) 1995-2001 by Fredrik Lundh # # See the README file for information on usage and redistribution. # # __version__ is deprecated and will be removed in a future version. Use # PIL.__version__ instead. __version__ = "0.4" import io from . import Image, ImageFile, ImagePalette from ._binary import i8, i16be as i16, i32be as i32 MODES = { # (photoshop mode, bits) -> (pil mode, required channels) (0, 1): ("1", 1), (0, 8): ("L", 1), (1, 8): ("L", 1), (2, 8): ("P", 1), (3, 8): ("RGB", 3), (4, 8): ("CMYK", 4), (7, 8): ("L", 1), # FIXME: multilayer (8, 8): ("L", 1), # duotone (9, 8): ("LAB", 3), } # --------------------------------------------------------------------. # read PSD images def _accept(prefix): return prefix[:4] == b"8BPS" ## # Image plugin for Photoshop images. class PsdImageFile(ImageFile.ImageFile): format = "PSD" format_description = "Adobe Photoshop" _close_exclusive_fp_after_loading = False def _open(self): read = self.fp.read # # header s = read(26) if s[:4] != b"8BPS" or i16(s[4:]) != 1: raise SyntaxError("not a PSD file") psd_bits = i16(s[22:]) psd_channels = i16(s[12:]) psd_mode = i16(s[24:]) mode, channels = MODES[(psd_mode, psd_bits)] if channels > psd_channels: raise IOError("not enough channels") self.mode = mode self._size = i32(s[18:]), i32(s[14:]) # # color mode data size = i32(read(4)) if size: data = read(size) if mode == "P" and size == 768: self.palette = ImagePalette.raw("RGB;L", data) # # image resources self.resources = [] size = i32(read(4)) if size: # load resources end = self.fp.tell() + size while self.fp.tell() < end: read(4) # signature id = i16(read(2)) name = read(i8(read(1))) if not (len(name) & 1): read(1) # padding data = read(i32(read(4))) if len(data) & 1: read(1) # padding self.resources.append((id, name, data)) if id == 1039: # ICC profile self.info["icc_profile"] = data # # layer and mask information self.layers = [] size = i32(read(4)) if size: end = self.fp.tell() + size size = i32(read(4)) if size: self.layers = _layerinfo(self.fp) self.fp.seek(end) # # image descriptor self.tile = _maketile(self.fp, mode, (0, 0) + self.size, channels) # keep the file open self.__fp = self.fp self.frame = 1 self._min_frame = 1 @property def n_frames(self): return len(self.layers) @property def is_animated(self): return len(self.layers) > 1 def seek(self, layer): if not self._seek_check(layer): return # seek to given layer (1..max) try: name, mode, bbox, tile = self.layers[layer - 1] self.mode = mode self.tile = tile self.frame = layer self.fp = self.__fp return name, bbox except IndexError: raise EOFError("no such layer") def tell(self): # return layer number (0=image, 1..max=layers) return self.frame def load_prepare(self): # create image memory if necessary if not self.im or self.im.mode != self.mode or self.im.size != self.size: self.im = Image.core.fill(self.mode, self.size, 0) # create palette (optional) if self.mode == "P": Image.Image.load(self) def _close__fp(self): try: if self.__fp != self.fp: self.__fp.close() except AttributeError: pass finally: self.__fp = None def _layerinfo(file): # read layerinfo block layers = [] read = file.read for i in range(abs(i16(read(2)))): # bounding box y0 = i32(read(4)) x0 = i32(read(4)) y1 = i32(read(4)) x1 = i32(read(4)) # image info info = [] mode = [] types = list(range(i16(read(2)))) if len(types) > 4: continue for i in types: type = i16(read(2)) if type == 65535: m = "A" else: m = "RGBA"[type] mode.append(m) size = i32(read(4)) info.append((m, size)) # figure out the image mode mode.sort() if mode == ["R"]: mode = "L" elif mode == ["B", "G", "R"]: mode = "RGB" elif mode == ["A", "B", "G", "R"]: mode = "RGBA" else: mode = None # unknown # skip over blend flags and extra information read(12) # filler name = "" size = i32(read(4)) # length of the extra data field combined = 0 if size: data_end = file.tell() + size length = i32(read(4)) if length: file.seek(length - 16, io.SEEK_CUR) combined += length + 4 length = i32(read(4)) if length: file.seek(length, io.SEEK_CUR) combined += length + 4 length = i8(read(1)) if length: # Don't know the proper encoding, # Latin-1 should be a good guess name = read(length).decode("latin-1", "replace") combined += length + 1 file.seek(data_end) layers.append((name, mode, (x0, y0, x1, y1))) # get tiles i = 0 for name, mode, bbox in layers: tile = [] for m in mode: t = _maketile(file, m, bbox, 1) if t: tile.extend(t) layers[i] = name, mode, bbox, tile i += 1 return layers def _maketile(file, mode, bbox, channels): tile = None read = file.read compression = i16(read(2)) xsize = bbox[2] - bbox[0] ysize = bbox[3] - bbox[1] offset = file.tell() if compression == 0: # # raw compression tile = [] for channel in range(channels): layer = mode[channel] if mode == "CMYK": layer += ";I" tile.append(("raw", bbox, offset, layer)) offset = offset + xsize * ysize elif compression == 1: # # packbits compression i = 0 tile = [] bytecount = read(channels * ysize * 2) offset = file.tell() for channel in range(channels): layer = mode[channel] if mode == "CMYK": layer += ";I" tile.append(("packbits", bbox, offset, layer)) for y in range(ysize): offset = offset + i16(bytecount[i : i + 2]) i += 2 file.seek(offset) if offset & 1: read(1) # padding return tile # -------------------------------------------------------------------- # registry Image.register_open(PsdImageFile.format, PsdImageFile, _accept) Image.register_extension(PsdImageFile.format, ".psd")

24.38125

81

0.481671

__version__ = "0.4" import io from . import Image, ImageFile, ImagePalette from ._binary import i8, i16be as i16, i32be as i32 MODES = { (0, 1): ("1", 1), (0, 8): ("L", 1), (1, 8): ("L", 1), (2, 8): ("P", 1), (3, 8): ("RGB", 3), (4, 8): ("CMYK", 4), (7, 8): ("L", 1), (8, 8): ("L", 1), (9, 8): ("LAB", 3), } def _accept(prefix): return prefix[:4] == b"8BPS" class PsdImageFile(ImageFile.ImageFile): format = "PSD" format_description = "Adobe Photoshop" _close_exclusive_fp_after_loading = False def _open(self): read = self.fp.read s = read(26) if s[:4] != b"8BPS" or i16(s[4:]) != 1: raise SyntaxError("not a PSD file") psd_bits = i16(s[22:]) psd_channels = i16(s[12:]) psd_mode = i16(s[24:]) mode, channels = MODES[(psd_mode, psd_bits)] if channels > psd_channels: raise IOError("not enough channels") self.mode = mode self._size = i32(s[18:]), i32(s[14:]) size = i32(read(4)) if size: data = read(size) if mode == "P" and size == 768: self.palette = ImagePalette.raw("RGB;L", data) self.resources = [] size = i32(read(4)) if size: end = self.fp.tell() + size while self.fp.tell() < end: read(4) id = i16(read(2)) name = read(i8(read(1))) if not (len(name) & 1): read(1) data = read(i32(read(4))) if len(data) & 1: read(1) self.resources.append((id, name, data)) if id == 1039: self.info["icc_profile"] = data self.layers = [] size = i32(read(4)) if size: end = self.fp.tell() + size size = i32(read(4)) if size: self.layers = _layerinfo(self.fp) self.fp.seek(end) self.tile = _maketile(self.fp, mode, (0, 0) + self.size, channels) self.__fp = self.fp self.frame = 1 self._min_frame = 1 @property def n_frames(self): return len(self.layers) @property def is_animated(self): return len(self.layers) > 1 def seek(self, layer): if not self._seek_check(layer): return try: name, mode, bbox, tile = self.layers[layer - 1] self.mode = mode self.tile = tile self.frame = layer self.fp = self.__fp return name, bbox except IndexError: raise EOFError("no such layer") def tell(self): return self.frame def load_prepare(self): if not self.im or self.im.mode != self.mode or self.im.size != self.size: self.im = Image.core.fill(self.mode, self.size, 0) if self.mode == "P": Image.Image.load(self) def _close__fp(self): try: if self.__fp != self.fp: self.__fp.close() except AttributeError: pass finally: self.__fp = None def _layerinfo(file): layers = [] read = file.read for i in range(abs(i16(read(2)))): y0 = i32(read(4)) x0 = i32(read(4)) y1 = i32(read(4)) x1 = i32(read(4)) info = [] mode = [] types = list(range(i16(read(2)))) if len(types) > 4: continue for i in types: type = i16(read(2)) if type == 65535: m = "A" else: m = "RGBA"[type] mode.append(m) size = i32(read(4)) info.append((m, size)) mode.sort() if mode == ["R"]: mode = "L" elif mode == ["B", "G", "R"]: mode = "RGB" elif mode == ["A", "B", "G", "R"]: mode = "RGBA" else: mode = None read(12) name = "" size = i32(read(4)) combined = 0 if size: data_end = file.tell() + size length = i32(read(4)) if length: file.seek(length - 16, io.SEEK_CUR) combined += length + 4 length = i32(read(4)) if length: file.seek(length, io.SEEK_CUR) combined += length + 4 length = i8(read(1)) if length: # Latin-1 should be a good guess name = read(length).decode("latin-1", "replace") combined += length + 1 file.seek(data_end) layers.append((name, mode, (x0, y0, x1, y1))) # get tiles i = 0 for name, mode, bbox in layers: tile = [] for m in mode: t = _maketile(file, m, bbox, 1) if t: tile.extend(t) layers[i] = name, mode, bbox, tile i += 1 return layers def _maketile(file, mode, bbox, channels): tile = None read = file.read compression = i16(read(2)) xsize = bbox[2] - bbox[0] ysize = bbox[3] - bbox[1] offset = file.tell() if compression == 0: # # raw compression tile = [] for channel in range(channels): layer = mode[channel] if mode == "CMYK": layer += ";I" tile.append(("raw", bbox, offset, layer)) offset = offset + xsize * ysize elif compression == 1: # # packbits compression i = 0 tile = [] bytecount = read(channels * ysize * 2) offset = file.tell() for channel in range(channels): layer = mode[channel] if mode == "CMYK": layer += ";I" tile.append(("packbits", bbox, offset, layer)) for y in range(ysize): offset = offset + i16(bytecount[i : i + 2]) i += 2 file.seek(offset) if offset & 1: read(1) # padding return tile # -------------------------------------------------------------------- # registry Image.register_open(PsdImageFile.format, PsdImageFile, _accept) Image.register_extension(PsdImageFile.format, ".psd")

true

f72ad8ba1938d20c873989d306f99b76c1ee53bf

11,515

py

Python

qiskit/tools/jupyter/backend_overview.py

t-imamichi/qiskit-core

8d2eeeac44f97af1e10514cdae4157e5923ff2e5

[ "Apache-2.0" ]

92

2018-06-05T11:18:38.000Z

2018-07-01T23:50:44.000Z

qiskit/tools/jupyter/backend_overview.py

t-imamichi/qiskit-core

8d2eeeac44f97af1e10514cdae4157e5923ff2e5

[ "Apache-2.0" ]

107

2018-06-05T08:41:19.000Z

2018-07-02T12:10:53.000Z

qiskit/tools/jupyter/backend_overview.py

t-imamichi/qiskit-core

8d2eeeac44f97af1e10514cdae4157e5923ff2e5

[ "Apache-2.0" ]

39

2018-06-05T09:55:56.000Z

2018-07-02T08:47:35.000Z

# This code is part of Qiskit. # # (C) Copyright IBM 2017, 2018. # # 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. """A module for monitoring backends.""" import time import threading import types from IPython.display import display from IPython.core.magic import line_magic, Magics, magics_class from IPython.core import magic_arguments import matplotlib.pyplot as plt import ipywidgets as widgets from qiskit.tools.monitor.overview import get_unique_backends from qiskit.visualization.gate_map import plot_gate_map @magics_class class BackendOverview(Magics): """A class of status magic functions.""" @line_magic @magic_arguments.magic_arguments() @magic_arguments.argument( "-i", "--interval", type=float, default=60, help="Interval for status check." ) def qiskit_backend_overview(self, line=""): """A Jupyter magic function to monitor backends.""" args = magic_arguments.parse_argstring(self.qiskit_backend_overview, line) unique_hardware_backends = get_unique_backends() _value = "<h2 style ='color:#ffffff; background-color:#000000;" _value += "padding-top: 1%; padding-bottom: 1%;padding-left: 1%;" _value += "margin-top: 0px'>Backend Overview</h2>" backend_title = widgets.HTML(value=_value, layout=widgets.Layout(margin="0px 0px 0px 0px")) build_back_widgets = [backend_widget(b) for b in unique_hardware_backends] _backends = [] # Sort backends by operational or not oper_ord_backends = [] for n, back in enumerate(unique_hardware_backends): if back.status().operational: oper_ord_backends = [build_back_widgets[n]] + oper_ord_backends _backends = [back] + _backends else: oper_ord_backends = oper_ord_backends + [build_back_widgets[n]] _backends = _backends + [back] qubit_label = widgets.Label(value="Num. Qubits") qv_label = widgets.Label(value="Quantum Vol.") pend_label = widgets.Label( value="Pending Jobs", layout=widgets.Layout(margin="5px 0px 0px 0px") ) least_label = widgets.Label( value="Least Busy", layout=widgets.Layout(margin="10px 0px 0px 0px") ) oper_label = widgets.Label( value="Operational", layout=widgets.Layout(margin="5px 0px 0px 0px") ) t12_label = widgets.Label( value="Avg. T1 / T2", layout=widgets.Layout(margin="10px 0px 0px 0px") ) cx_label = widgets.Label( value="Avg. CX Err.", layout=widgets.Layout(margin="8px 0px 0px 0px") ) meas_label = widgets.Label( value="Avg. Meas. Err.", layout=widgets.Layout(margin="8px 0px 0px 0px") ) labels_widget = widgets.VBox( [ qubit_label, qv_label, pend_label, oper_label, least_label, t12_label, cx_label, meas_label, ], layout=widgets.Layout(margin="295px 0px 0px 0px", min_width="100px"), ) backend_grid = GridBox_with_thread( children=oper_ord_backends, layout=widgets.Layout( grid_template_columns="250px " * len(unique_hardware_backends), grid_template_rows="auto", grid_gap="0px 25px", ), ) backend_grid._backends = _backends # pylint: disable=attribute-defined-outside-init backend_grid._update = types.MethodType( # pylint: disable=attribute-defined-outside-init update_backend_info, backend_grid ) backend_grid._thread = threading.Thread( # pylint: disable=attribute-defined-outside-init target=backend_grid._update, args=(args.interval,) ) backend_grid._thread.start() back_box = widgets.HBox([labels_widget, backend_grid]) back_monitor = widgets.VBox([backend_title, back_box]) display(back_monitor) class GridBox_with_thread(widgets.GridBox): # pylint: disable=invalid-name """A GridBox that will close an attached thread""" def __del__(self): """Object disposal""" if hasattr(self, "_thread"): try: self._thread.do_run = False self._thread.join() except Exception: # pylint: disable=broad-except pass self.close() def backend_widget(backend): """Creates a backend widget.""" config = backend.configuration().to_dict() props = backend.properties().to_dict() name = widgets.HTML(value=f"<h4>{backend.name()}</h4>", layout=widgets.Layout()) num_qubits = config["n_qubits"] qv_val = "-" if "quantum_volume" in config.keys(): if config["quantum_volume"]: qv_val = config["quantum_volume"] qubit_count = widgets.HTML( value=f"<h5><b>{num_qubits}</b></h5>", layout=widgets.Layout(justify_content="center"), ) qv_value = widgets.HTML( value=f"<h5>{qv_val}</h5>", layout=widgets.Layout(justify_content="center"), ) cmap = widgets.Output( layout=widgets.Layout( min_width="250px", max_width="250px", max_height="250px", min_height="250px", justify_content="center", align_items="center", margin="0px 0px 0px 0px", ) ) with cmap: _cmap_fig = plot_gate_map(backend, plot_directed=False, label_qubits=False) if _cmap_fig is not None: display(_cmap_fig) # Prevents plot from showing up twice. plt.close(_cmap_fig) pending = generate_jobs_pending_widget() is_oper = widgets.HTML(value="<h5></h5>", layout=widgets.Layout(justify_content="center")) least_busy = widgets.HTML(value="<h5></h5>", layout=widgets.Layout(justify_content="center")) t1_units = props["qubits"][0][0]["unit"] avg_t1 = round(sum(q[0]["value"] for q in props["qubits"]) / num_qubits, 1) avg_t2 = round(sum(q[1]["value"] for q in props["qubits"]) / num_qubits, 1) t12_widget = widgets.HTML( value=f"<h5>{avg_t1} / {avg_t2} {t1_units}</h5>", layout=widgets.Layout(), ) avg_cx_err = "NA" if config["coupling_map"]: sum_cx_err = 0 num_cx = 0 for gate in props["gates"]: if gate["gate"] == "cx": for param in gate["parameters"]: if param["name"] == "gate_error": # Value == 1.0 means gate effectively off if param["value"] != 1.0: sum_cx_err += param["value"] num_cx += 1 if num_cx > 0: avg_cx_err = round(sum_cx_err / num_cx, 4) cx_widget = widgets.HTML(value=f"<h5>{avg_cx_err}</h5>", layout=widgets.Layout()) avg_meas_err = 0 for qub in props["qubits"]: for item in qub: if item["name"] == "readout_error": avg_meas_err += item["value"] avg_meas_err = round(avg_meas_err / num_qubits, 4) meas_widget = widgets.HTML(value=f"<h5>{avg_meas_err}</h5>", layout=widgets.Layout()) out = widgets.VBox( [ name, cmap, qubit_count, qv_value, pending, is_oper, least_busy, t12_widget, cx_widget, meas_widget, ], layout=widgets.Layout(display="inline-flex", flex_flow="column", align_items="center"), ) out._is_alive = True return out def update_backend_info(self, interval=60): """Updates the monitor info Called from another thread. """ my_thread = threading.current_thread() current_interval = 0 started = False all_dead = False stati = [None] * len(self._backends) while getattr(my_thread, "do_run", True) and not all_dead: if current_interval == interval or started is False: for ind, back in enumerate(self._backends): _value = self.children[ind].children[2].value _head = _value.split("<b>")[0] try: _status = back.status() stati[ind] = _status except Exception: # pylint: disable=broad-except self.children[ind].children[2].value = _value.replace( _head, "<h5 style='color:#ff5c49'>" ) self.children[ind]._is_alive = False else: self.children[ind]._is_alive = True self.children[ind].children[2].value = _value.replace(_head, "<h5>") idx = list(range(len(self._backends))) pending = [s.pending_jobs for s in stati] _, least_idx = zip(*sorted(zip(pending, idx))) # Make sure least pending is operational for ind in least_idx: if stati[ind].operational: least_pending_idx = ind break for var in idx: if var == least_pending_idx: self.children[var].children[6].value = "<h5 style='color:#34bc6e'>True</h5>" else: self.children[var].children[6].value = "<h5 style='color:#dc267f'>False</h5>" self.children[var].children[4].children[1].max = max( self.children[var].children[4].children[1].max, pending[var] + 10 ) self.children[var].children[4].children[1].value = pending[var] if stati[var].operational: self.children[var].children[5].value = "<h5 style='color:#34bc6e'>True</h5>" else: self.children[var].children[5].value = "<h5 style='color:#dc267f'>False</h5>" started = True current_interval = 0 time.sleep(1) all_dead = not any(wid._is_alive for wid in self.children) current_interval += 1 def generate_jobs_pending_widget(): """Generates a jobs_pending progress bar widget.""" pbar = widgets.IntProgress( value=0, min=0, max=50, description="", orientation="horizontal", layout=widgets.Layout(max_width="180px"), ) pbar.style.bar_color = "#71cddd" pbar_current = widgets.Label(value=str(pbar.value), layout=widgets.Layout(min_width="auto")) pbar_max = widgets.Label(value=str(pbar.max), layout=widgets.Layout(min_width="auto")) def _on_max_change(change): pbar_max.value = str(change["new"]) def _on_val_change(change): pbar_current.value = str(change["new"]) pbar.observe(_on_max_change, names="max") pbar.observe(_on_val_change, names="value") jobs_widget = widgets.HBox( [pbar_current, pbar, pbar_max], layout=widgets.Layout(max_width="250px", min_width="250px", justify_content="center"), ) return jobs_widget

35.650155

99

0.590881

import time import threading import types from IPython.display import display from IPython.core.magic import line_magic, Magics, magics_class from IPython.core import magic_arguments import matplotlib.pyplot as plt import ipywidgets as widgets from qiskit.tools.monitor.overview import get_unique_backends from qiskit.visualization.gate_map import plot_gate_map @magics_class class BackendOverview(Magics): @line_magic @magic_arguments.magic_arguments() @magic_arguments.argument( "-i", "--interval", type=float, default=60, help="Interval for status check." ) def qiskit_backend_overview(self, line=""): args = magic_arguments.parse_argstring(self.qiskit_backend_overview, line) unique_hardware_backends = get_unique_backends() _value = "<h2 style ='color:#ffffff; background-color:#000000;" _value += "padding-top: 1%; padding-bottom: 1%;padding-left: 1%;" _value += "margin-top: 0px'>Backend Overview</h2>" backend_title = widgets.HTML(value=_value, layout=widgets.Layout(margin="0px 0px 0px 0px")) build_back_widgets = [backend_widget(b) for b in unique_hardware_backends] _backends = [] oper_ord_backends = [] for n, back in enumerate(unique_hardware_backends): if back.status().operational: oper_ord_backends = [build_back_widgets[n]] + oper_ord_backends _backends = [back] + _backends else: oper_ord_backends = oper_ord_backends + [build_back_widgets[n]] _backends = _backends + [back] qubit_label = widgets.Label(value="Num. Qubits") qv_label = widgets.Label(value="Quantum Vol.") pend_label = widgets.Label( value="Pending Jobs", layout=widgets.Layout(margin="5px 0px 0px 0px") ) least_label = widgets.Label( value="Least Busy", layout=widgets.Layout(margin="10px 0px 0px 0px") ) oper_label = widgets.Label( value="Operational", layout=widgets.Layout(margin="5px 0px 0px 0px") ) t12_label = widgets.Label( value="Avg. T1 / T2", layout=widgets.Layout(margin="10px 0px 0px 0px") ) cx_label = widgets.Label( value="Avg. CX Err.", layout=widgets.Layout(margin="8px 0px 0px 0px") ) meas_label = widgets.Label( value="Avg. Meas. Err.", layout=widgets.Layout(margin="8px 0px 0px 0px") ) labels_widget = widgets.VBox( [ qubit_label, qv_label, pend_label, oper_label, least_label, t12_label, cx_label, meas_label, ], layout=widgets.Layout(margin="295px 0px 0px 0px", min_width="100px"), ) backend_grid = GridBox_with_thread( children=oper_ord_backends, layout=widgets.Layout( grid_template_columns="250px " * len(unique_hardware_backends), grid_template_rows="auto", grid_gap="0px 25px", ), ) backend_grid._backends = _backends backend_grid._update = types.MethodType( update_backend_info, backend_grid ) backend_grid._thread = threading.Thread( target=backend_grid._update, args=(args.interval,) ) backend_grid._thread.start() back_box = widgets.HBox([labels_widget, backend_grid]) back_monitor = widgets.VBox([backend_title, back_box]) display(back_monitor) class GridBox_with_thread(widgets.GridBox): def __del__(self): if hasattr(self, "_thread"): try: self._thread.do_run = False self._thread.join() except Exception: pass self.close() def backend_widget(backend): config = backend.configuration().to_dict() props = backend.properties().to_dict() name = widgets.HTML(value=f"<h4>{backend.name()}</h4>", layout=widgets.Layout()) num_qubits = config["n_qubits"] qv_val = "-" if "quantum_volume" in config.keys(): if config["quantum_volume"]: qv_val = config["quantum_volume"] qubit_count = widgets.HTML( value=f"<h5><b>{num_qubits}</b></h5>", layout=widgets.Layout(justify_content="center"), ) qv_value = widgets.HTML( value=f"<h5>{qv_val}</h5>", layout=widgets.Layout(justify_content="center"), ) cmap = widgets.Output( layout=widgets.Layout( min_width="250px", max_width="250px", max_height="250px", min_height="250px", justify_content="center", align_items="center", margin="0px 0px 0px 0px", ) ) with cmap: _cmap_fig = plot_gate_map(backend, plot_directed=False, label_qubits=False) if _cmap_fig is not None: display(_cmap_fig) plt.close(_cmap_fig) pending = generate_jobs_pending_widget() is_oper = widgets.HTML(value="<h5></h5>", layout=widgets.Layout(justify_content="center")) least_busy = widgets.HTML(value="<h5></h5>", layout=widgets.Layout(justify_content="center")) t1_units = props["qubits"][0][0]["unit"] avg_t1 = round(sum(q[0]["value"] for q in props["qubits"]) / num_qubits, 1) avg_t2 = round(sum(q[1]["value"] for q in props["qubits"]) / num_qubits, 1) t12_widget = widgets.HTML( value=f"<h5>{avg_t1} / {avg_t2} {t1_units}</h5>", layout=widgets.Layout(), ) avg_cx_err = "NA" if config["coupling_map"]: sum_cx_err = 0 num_cx = 0 for gate in props["gates"]: if gate["gate"] == "cx": for param in gate["parameters"]: if param["name"] == "gate_error": if param["value"] != 1.0: sum_cx_err += param["value"] num_cx += 1 if num_cx > 0: avg_cx_err = round(sum_cx_err / num_cx, 4) cx_widget = widgets.HTML(value=f"<h5>{avg_cx_err}</h5>", layout=widgets.Layout()) avg_meas_err = 0 for qub in props["qubits"]: for item in qub: if item["name"] == "readout_error": avg_meas_err += item["value"] avg_meas_err = round(avg_meas_err / num_qubits, 4) meas_widget = widgets.HTML(value=f"<h5>{avg_meas_err}</h5>", layout=widgets.Layout()) out = widgets.VBox( [ name, cmap, qubit_count, qv_value, pending, is_oper, least_busy, t12_widget, cx_widget, meas_widget, ], layout=widgets.Layout(display="inline-flex", flex_flow="column", align_items="center"), ) out._is_alive = True return out def update_backend_info(self, interval=60): my_thread = threading.current_thread() current_interval = 0 started = False all_dead = False stati = [None] * len(self._backends) while getattr(my_thread, "do_run", True) and not all_dead: if current_interval == interval or started is False: for ind, back in enumerate(self._backends): _value = self.children[ind].children[2].value _head = _value.split("<b>")[0] try: _status = back.status() stati[ind] = _status except Exception: self.children[ind].children[2].value = _value.replace( _head, "<h5 style='color:#ff5c49'>" ) self.children[ind]._is_alive = False else: self.children[ind]._is_alive = True self.children[ind].children[2].value = _value.replace(_head, "<h5>") idx = list(range(len(self._backends))) pending = [s.pending_jobs for s in stati] _, least_idx = zip(*sorted(zip(pending, idx))) for ind in least_idx: if stati[ind].operational: least_pending_idx = ind break for var in idx: if var == least_pending_idx: self.children[var].children[6].value = "<h5 style='color:#34bc6e'>True</h5>" else: self.children[var].children[6].value = "<h5 style='color:#dc267f'>False</h5>" self.children[var].children[4].children[1].max = max( self.children[var].children[4].children[1].max, pending[var] + 10 ) self.children[var].children[4].children[1].value = pending[var] if stati[var].operational: self.children[var].children[5].value = "<h5 style='color:#34bc6e'>True</h5>" else: self.children[var].children[5].value = "<h5 style='color:#dc267f'>False</h5>" started = True current_interval = 0 time.sleep(1) all_dead = not any(wid._is_alive for wid in self.children) current_interval += 1 def generate_jobs_pending_widget(): pbar = widgets.IntProgress( value=0, min=0, max=50, description="", orientation="horizontal", layout=widgets.Layout(max_width="180px"), ) pbar.style.bar_color = "#71cddd" pbar_current = widgets.Label(value=str(pbar.value), layout=widgets.Layout(min_width="auto")) pbar_max = widgets.Label(value=str(pbar.max), layout=widgets.Layout(min_width="auto")) def _on_max_change(change): pbar_max.value = str(change["new"]) def _on_val_change(change): pbar_current.value = str(change["new"]) pbar.observe(_on_max_change, names="max") pbar.observe(_on_val_change, names="value") jobs_widget = widgets.HBox( [pbar_current, pbar, pbar_max], layout=widgets.Layout(max_width="250px", min_width="250px", justify_content="center"), ) return jobs_widget

true

f72ada2ce523c5d4764bb97fbbec0c1d62c192e2

897

py

Python

idaes/generic_models/unit_models/column_models/__init__.py

eslickj/idaes-pse

328ed07ffb0b4d98c03e972675ea32c41dd2531a

[ "RSA-MD" ]

112

2019-02-11T23:16:36.000Z

2022-03-23T20:59:57.000Z

idaes/generic_models/unit_models/column_models/__init__.py

eslickj/idaes-pse

328ed07ffb0b4d98c03e972675ea32c41dd2531a

[ "RSA-MD" ]

621

2019-03-01T14:44:12.000Z

2022-03-31T19:49:25.000Z

idaes/generic_models/unit_models/column_models/__init__.py

eslickj/idaes-pse

328ed07ffb0b4d98c03e972675ea32c41dd2531a

[ "RSA-MD" ]

154

2019-02-01T23:46:33.000Z

2022-03-23T15:07:10.000Z

################################################################################# # The Institute for the Design of Advanced Energy Systems Integrated Platform # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES), and is copyright (c) 2018-2021 # by the software owners: The Regents of the University of California, through # Lawrence Berkeley National Laboratory, National Technology & Engineering # Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia University # Research Corporation, et al. All rights reserved. # # Please see the files COPYRIGHT.md and LICENSE.md for full copyright and # license information. ################################################################################# from .condenser import Condenser from .reboiler import Reboiler from .tray import Tray from .tray_column import TrayColumn

52.764706

81

0.654404

true

f72adb7883b52f3f1c6bf8306f57b1dd0008ab29

868

py

Python

enviorment/colors.py

JLMadsen/TetrisAI

c6f2ef47a57e60b1ec73666406931ca46c9d1233

[ "MIT" ]

1

2020-11-23T22:11:33.000Z

enviorment/colors.py

JLMadsen/TetrisAI

c6f2ef47a57e60b1ec73666406931ca46c9d1233

[ "MIT" ]

1

2021-07-13T15:31:00.000Z

enviorment/colors.py

JLMadsen/TetrisAI

c6f2ef47a57e60b1ec73666406931ca46c9d1233

[ "MIT" ]

1

2021-02-02T14:11:57.000Z

class Color: WHITE = (255, 255, 255) BLACK = (0, 0, 0 ) GRAY = (100, 100, 100) RED = (220, 20, 60 ) GREEN = (50, 205, 50 ) YELLOW = (255, 255, 0 ) PURPLE = (218, 112, 214) ALL = [WHITE, BLACK, GRAY, RED, GREEN] # just for printing colors in terminal class bcolors: HEADER = '\033[95m' OKBLUE = '\033[94m' OKCYAN = '\033[96m' OKGREEN = '\033[92m' WARNING = '\033[93m' FAIL = '\033[91m' ENDC = '\033[0m' BOLD = '\033[1m' UNDERLINE = '\033[4m' def green(msg): return bcolors.OKGREEN+msg+bcolors.ENDC def header(msg): return bcolors.HEADER+msg+bcolors.ENDC def fail(msg): return bcolors.FAIL+msg+bcolors.ENDC def cyan(msg): return bcolors.OKCYAN+msg+bcolors.ENDC def warning(msg): return bcolors.WARNING+msg+bcolors.ENDC

22.842105

43

0.562212

class Color: WHITE = (255, 255, 255) BLACK = (0, 0, 0 ) GRAY = (100, 100, 100) RED = (220, 20, 60 ) GREEN = (50, 205, 50 ) YELLOW = (255, 255, 0 ) PURPLE = (218, 112, 214) ALL = [WHITE, BLACK, GRAY, RED, GREEN] class bcolors: HEADER = '\033[95m' OKBLUE = '\033[94m' OKCYAN = '\033[96m' OKGREEN = '\033[92m' WARNING = '\033[93m' FAIL = '\033[91m' ENDC = '\033[0m' BOLD = '\033[1m' UNDERLINE = '\033[4m' def green(msg): return bcolors.OKGREEN+msg+bcolors.ENDC def header(msg): return bcolors.HEADER+msg+bcolors.ENDC def fail(msg): return bcolors.FAIL+msg+bcolors.ENDC def cyan(msg): return bcolors.OKCYAN+msg+bcolors.ENDC def warning(msg): return bcolors.WARNING+msg+bcolors.ENDC

true

f72adc47d855b9bd8cfb880f4445828ea9fe2109

9,267

py

Python

pysot/datasets/dataset_template.py

wattanapong/DFA

c05851beca2f8739f80531eb4de2f61639715cab

[ "Apache-2.0" ]

null

pysot/datasets/dataset_template.py

wattanapong/DFA

c05851beca2f8739f80531eb4de2f61639715cab

[ "Apache-2.0" ]

null

pysot/datasets/dataset_template.py

wattanapong/DFA

c05851beca2f8739f80531eb4de2f61639715cab

[ "Apache-2.0" ]

null

# Copyright (c) SenseTime. All Rights Reserved. from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import json import logging import sys import os import cv2 import numpy as np from torch.utils.data import Dataset from pysot.utils.bbox import center2corner, Center from pysot.datasets.anchor_target import AnchorTarget from pysot.datasets.augmentation import Augmentation from pysot.core.config import cfg logger = logging.getLogger("global") # setting opencv pyv = sys.version[0] if pyv[0] == '3': cv2.ocl.setUseOpenCL(False) class SubDataset(object): def __init__(self, name, root, anno, frame_range, num_use, start_idx): cur_path = os.path.dirname(os.path.realpath(__file__)) self.name = name self.root = os.path.join(cur_path, '../../', root) self.anno = os.path.join(cur_path, '../../', anno) self.frame_range = frame_range self.num_use = num_use self.start_idx = start_idx logger.info("loading " + name) with open(self.anno, 'r') as f: meta_data = json.load(f) meta_data = self._filter_zero(meta_data) for video in list(meta_data.keys()): for track in meta_data[video]: frames = meta_data[video][track] frames = list(map(int, filter(lambda x: x.isdigit(), frames.keys()))) frames.sort() meta_data[video][track]['frames'] = frames if len(frames) <= 0: logger.warning("{}/{} has no frames".format(video, track)) del meta_data[video][track] for video in list(meta_data.keys()): if len(meta_data[video]) <= 0: logger.warning("{} has no tracks".format(video)) del meta_data[video] self.labels = meta_data self.num = len(self.labels) self.num_use = self.num if self.num_use == -1 else self.num_use self.videos = list(meta_data.keys()) logger.info("{} loaded".format(self.name)) self.path_format = '{}.{}.{}.jpg' self.pick = self.shuffle() def _filter_zero(self, meta_data): meta_data_new = {} for video, tracks in meta_data.items(): new_tracks = {} for trk, frames in tracks.items(): new_frames = {} for frm, bbox in frames.items(): if not isinstance(bbox, dict): if len(bbox) == 4: x1, y1, x2, y2 = bbox w, h = x2 - x1, y2 - y1 else: w, h = bbox if w <= 0 or h <= 0: continue new_frames[frm] = bbox if len(new_frames) > 0: new_tracks[trk] = new_frames if len(new_tracks) > 0: meta_data_new[video] = new_tracks return meta_data_new def log(self): logger.info("{} start-index {} select [{}/{}] path_format {}".format( self.name, self.start_idx, self.num_use, self.num, self.path_format)) def shuffle(self): lists = list(range(self.start_idx, self.start_idx + self.num)) pick = [] while len(pick) < self.num_use: np.random.shuffle(lists) pick += lists return pick[:self.num_use] def get_image_anno(self, video, track, frame): frame = "{:06d}".format(frame) image_path = os.path.join(self.root, video, self.path_format.format(frame, track, 'x')) image_anno = self.labels[video][track][frame] return image_path, image_anno # track is tracking object in video # video is one of subfolder under ILSVRC2015_VID_train_000{0-3}, for example, ILSVRC2015_train_00004000 def get_positive_pair(self, index): video_name = self.videos[index] video = self.labels[video_name] track = np.random.choice(list(video.keys())) track_info = video[track] frames = track_info['frames'] template_frame = np.random.randint(0, len(frames)) template_frame = frames[template_frame] return self.get_image_anno(video_name, track, template_frame) def get_random_target(self, index=-1): if index == -1: index = np.random.randint(0, self.num) video_name = self.videos[index] video = self.labels[video_name] track = np.random.choice(list(video.keys())) track_info = video[track] frames = track_info['frames'] frame = np.random.choice(frames) return self.get_image_anno(video_name, track, frame) def __len__(self): return self.num class TrkDataset(Dataset): def __init__(self,): super(TrkDataset, self).__init__() desired_size = (cfg.TRAIN.SEARCH_SIZE - cfg.TRAIN.EXEMPLAR_SIZE) / \ cfg.ANCHOR.STRIDE + 1 + cfg.TRAIN.BASE_SIZE if desired_size != cfg.TRAIN.OUTPUT_SIZE: raise Exception('size not match!') # create anchor target self.anchor_target = AnchorTarget() # create sub dataset self.all_dataset = [] start = 0 self.num = 0 for name in cfg.DATASET.NAMES: subdata_cfg = getattr(cfg.DATASET, name) sub_dataset = SubDataset( name, subdata_cfg.ROOT, subdata_cfg.ANNO, subdata_cfg.FRAME_RANGE, subdata_cfg.NUM_USE, start ) start += sub_dataset.num self.num += sub_dataset.num_use sub_dataset.log() self.all_dataset.append(sub_dataset) # data augmentation self.template_aug = Augmentation( cfg.DATASET.TEMPLATE.SHIFT, cfg.DATASET.TEMPLATE.SCALE, cfg.DATASET.TEMPLATE.BLUR, cfg.DATASET.TEMPLATE.FLIP, cfg.DATASET.TEMPLATE.COLOR ) self.search_aug = Augmentation( cfg.DATASET.SEARCH.SHIFT, cfg.DATASET.SEARCH.SCALE, cfg.DATASET.SEARCH.BLUR, cfg.DATASET.SEARCH.FLIP, cfg.DATASET.SEARCH.COLOR ) videos_per_epoch = cfg.DATASET.VIDEOS_PER_EPOCH self.num = videos_per_epoch if videos_per_epoch > 0 else self.num self.num *= cfg.TRAIN.EPOCH self.pick = self.shuffle() def shuffle(self): pick = [] m = 0 while m < self.num: p = [] for sub_dataset in self.all_dataset: sub_p = sub_dataset.pick p += sub_p np.random.shuffle(p) pick += p m = len(pick) logger.info("shuffle done!") logger.info("dataset length {}".format(self.num)) return pick[:self.num] def _find_dataset(self, index): for dataset in self.all_dataset: if dataset.start_idx + dataset.num > index: return dataset, index - dataset.start_idx def _get_bbox(self, image, shape): imh, imw = image.shape[:2] if len(shape) == 4: w, h = shape[2]-shape[0], shape[3]-shape[1] else: w, h = shape context_amount = 0.5 exemplar_size = cfg.TRAIN.EXEMPLAR_SIZE wc_z = w + context_amount * (w+h) hc_z = h + context_amount * (w+h) s_z = np.sqrt(wc_z * hc_z) scale_z = exemplar_size / s_z w = w*scale_z h = h*scale_z cx, cy = imw//2, imh//2 bbox = center2corner(Center(cx, cy, w, h)) return bbox def __len__(self): return self.num def __getitem__(self, index): index = self.pick[index] dataset, index = self._find_dataset(index) gray = cfg.DATASET.GRAY and cfg.DATASET.GRAY > np.random.random() neg = cfg.DATASET.NEG and cfg.DATASET.NEG > np.random.random() # get one dataset if neg: print('please check this suspension due to it was removed negative function (distractor)') import pdb pdb.set_trace() template = dataset.get_random_target(index) search = np.random.choice(self.all_dataset).get_random_target() else: template = dataset.get_positive_pair(index) if not os.path.exists(template[0]): print(template[0]) # get image template_image = cv2.imread(template[0]) # get bounding box template_box = self._get_bbox(template_image, template[1]) # augmentation template, _ = self.template_aug(template_image, template_box, cfg.TRAIN.EXEMPLAR_SIZE, gray=gray) template = template.transpose((2, 0, 1)).astype(np.float32) return { 'template': template, 'gt': template_box }

34.449814

107

0.555627

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import json import logging import sys import os import cv2 import numpy as np from torch.utils.data import Dataset from pysot.utils.bbox import center2corner, Center from pysot.datasets.anchor_target import AnchorTarget from pysot.datasets.augmentation import Augmentation from pysot.core.config import cfg logger = logging.getLogger("global") pyv = sys.version[0] if pyv[0] == '3': cv2.ocl.setUseOpenCL(False) class SubDataset(object): def __init__(self, name, root, anno, frame_range, num_use, start_idx): cur_path = os.path.dirname(os.path.realpath(__file__)) self.name = name self.root = os.path.join(cur_path, '../../', root) self.anno = os.path.join(cur_path, '../../', anno) self.frame_range = frame_range self.num_use = num_use self.start_idx = start_idx logger.info("loading " + name) with open(self.anno, 'r') as f: meta_data = json.load(f) meta_data = self._filter_zero(meta_data) for video in list(meta_data.keys()): for track in meta_data[video]: frames = meta_data[video][track] frames = list(map(int, filter(lambda x: x.isdigit(), frames.keys()))) frames.sort() meta_data[video][track]['frames'] = frames if len(frames) <= 0: logger.warning("{}/{} has no frames".format(video, track)) del meta_data[video][track] for video in list(meta_data.keys()): if len(meta_data[video]) <= 0: logger.warning("{} has no tracks".format(video)) del meta_data[video] self.labels = meta_data self.num = len(self.labels) self.num_use = self.num if self.num_use == -1 else self.num_use self.videos = list(meta_data.keys()) logger.info("{} loaded".format(self.name)) self.path_format = '{}.{}.{}.jpg' self.pick = self.shuffle() def _filter_zero(self, meta_data): meta_data_new = {} for video, tracks in meta_data.items(): new_tracks = {} for trk, frames in tracks.items(): new_frames = {} for frm, bbox in frames.items(): if not isinstance(bbox, dict): if len(bbox) == 4: x1, y1, x2, y2 = bbox w, h = x2 - x1, y2 - y1 else: w, h = bbox if w <= 0 or h <= 0: continue new_frames[frm] = bbox if len(new_frames) > 0: new_tracks[trk] = new_frames if len(new_tracks) > 0: meta_data_new[video] = new_tracks return meta_data_new def log(self): logger.info("{} start-index {} select [{}/{}] path_format {}".format( self.name, self.start_idx, self.num_use, self.num, self.path_format)) def shuffle(self): lists = list(range(self.start_idx, self.start_idx + self.num)) pick = [] while len(pick) < self.num_use: np.random.shuffle(lists) pick += lists return pick[:self.num_use] def get_image_anno(self, video, track, frame): frame = "{:06d}".format(frame) image_path = os.path.join(self.root, video, self.path_format.format(frame, track, 'x')) image_anno = self.labels[video][track][frame] return image_path, image_anno def get_positive_pair(self, index): video_name = self.videos[index] video = self.labels[video_name] track = np.random.choice(list(video.keys())) track_info = video[track] frames = track_info['frames'] template_frame = np.random.randint(0, len(frames)) template_frame = frames[template_frame] return self.get_image_anno(video_name, track, template_frame) def get_random_target(self, index=-1): if index == -1: index = np.random.randint(0, self.num) video_name = self.videos[index] video = self.labels[video_name] track = np.random.choice(list(video.keys())) track_info = video[track] frames = track_info['frames'] frame = np.random.choice(frames) return self.get_image_anno(video_name, track, frame) def __len__(self): return self.num class TrkDataset(Dataset): def __init__(self,): super(TrkDataset, self).__init__() desired_size = (cfg.TRAIN.SEARCH_SIZE - cfg.TRAIN.EXEMPLAR_SIZE) / \ cfg.ANCHOR.STRIDE + 1 + cfg.TRAIN.BASE_SIZE if desired_size != cfg.TRAIN.OUTPUT_SIZE: raise Exception('size not match!') self.anchor_target = AnchorTarget() self.all_dataset = [] start = 0 self.num = 0 for name in cfg.DATASET.NAMES: subdata_cfg = getattr(cfg.DATASET, name) sub_dataset = SubDataset( name, subdata_cfg.ROOT, subdata_cfg.ANNO, subdata_cfg.FRAME_RANGE, subdata_cfg.NUM_USE, start ) start += sub_dataset.num self.num += sub_dataset.num_use sub_dataset.log() self.all_dataset.append(sub_dataset) self.template_aug = Augmentation( cfg.DATASET.TEMPLATE.SHIFT, cfg.DATASET.TEMPLATE.SCALE, cfg.DATASET.TEMPLATE.BLUR, cfg.DATASET.TEMPLATE.FLIP, cfg.DATASET.TEMPLATE.COLOR ) self.search_aug = Augmentation( cfg.DATASET.SEARCH.SHIFT, cfg.DATASET.SEARCH.SCALE, cfg.DATASET.SEARCH.BLUR, cfg.DATASET.SEARCH.FLIP, cfg.DATASET.SEARCH.COLOR ) videos_per_epoch = cfg.DATASET.VIDEOS_PER_EPOCH self.num = videos_per_epoch if videos_per_epoch > 0 else self.num self.num *= cfg.TRAIN.EPOCH self.pick = self.shuffle() def shuffle(self): pick = [] m = 0 while m < self.num: p = [] for sub_dataset in self.all_dataset: sub_p = sub_dataset.pick p += sub_p np.random.shuffle(p) pick += p m = len(pick) logger.info("shuffle done!") logger.info("dataset length {}".format(self.num)) return pick[:self.num] def _find_dataset(self, index): for dataset in self.all_dataset: if dataset.start_idx + dataset.num > index: return dataset, index - dataset.start_idx def _get_bbox(self, image, shape): imh, imw = image.shape[:2] if len(shape) == 4: w, h = shape[2]-shape[0], shape[3]-shape[1] else: w, h = shape context_amount = 0.5 exemplar_size = cfg.TRAIN.EXEMPLAR_SIZE wc_z = w + context_amount * (w+h) hc_z = h + context_amount * (w+h) s_z = np.sqrt(wc_z * hc_z) scale_z = exemplar_size / s_z w = w*scale_z h = h*scale_z cx, cy = imw//2, imh//2 bbox = center2corner(Center(cx, cy, w, h)) return bbox def __len__(self): return self.num def __getitem__(self, index): index = self.pick[index] dataset, index = self._find_dataset(index) gray = cfg.DATASET.GRAY and cfg.DATASET.GRAY > np.random.random() neg = cfg.DATASET.NEG and cfg.DATASET.NEG > np.random.random() if neg: print('please check this suspension due to it was removed negative function (distractor)') import pdb pdb.set_trace() template = dataset.get_random_target(index) search = np.random.choice(self.all_dataset).get_random_target() else: template = dataset.get_positive_pair(index) if not os.path.exists(template[0]): print(template[0]) template_image = cv2.imread(template[0]) template_box = self._get_bbox(template_image, template[1]) template, _ = self.template_aug(template_image, template_box, cfg.TRAIN.EXEMPLAR_SIZE, gray=gray) template = template.transpose((2, 0, 1)).astype(np.float32) return { 'template': template, 'gt': template_box }

true

f72addc1225c0aa169e2bb36069de6d370480522

12,614

py

Python

src/gluonnlp/data/utils.py

yifeim/gluon-nlp

ea30d3399d87404b731d513535af9a31a5672799

[ "Apache-2.0" ]

null

src/gluonnlp/data/utils.py

yifeim/gluon-nlp

ea30d3399d87404b731d513535af9a31a5672799

[ "Apache-2.0" ]

2

2019-02-13T09:10:26.000Z

2019-02-20T02:59:43.000Z

src/gluonnlp/data/utils.py

yifeim/gluon-nlp

ea30d3399d87404b731d513535af9a31a5672799

[ "Apache-2.0" ]

1

2019-02-13T03:07:06.000Z

# coding: utf-8 # 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. """Utility classes and functions. They help organize and keep statistics of datasets.""" from __future__ import absolute_import from __future__ import print_function __all__ = [ 'Counter', 'count_tokens', 'concat_sequence', 'slice_sequence', 'train_valid_split', 'line_splitter', 'whitespace_splitter', 'Splitter' ] import os import collections import zipfile import tarfile import numpy as np from mxnet.gluon.data import SimpleDataset from mxnet.gluon.utils import _get_repo_url, download, check_sha1 from .. import _constants as C class Counter(collections.Counter): # pylint: disable=abstract-method """Counter class for keeping token frequencies.""" def discard(self, min_freq, unknown_token): """Discards tokens with frequency below min_frequency and represents them as `unknown_token`. Parameters ---------- min_freq: int Tokens whose frequency is under min_freq is counted as `unknown_token` in the Counter returned. unknown_token: str The representation for any unknown token. Returns ------- The Counter instance. Examples -------- >>> a = gluonnlp.data.Counter({'a': 10, 'b': 1, 'c': 1}) >>> a.discard(3, '<unk>') Counter({'a': 10, '<unk>': 2}) """ freq = 0 ret = Counter({}) for token, count in self.items(): if count < min_freq: freq += count else: ret[token] = count ret[unknown_token] = ret.get(unknown_token, 0) + freq return ret class DefaultLookupDict(dict): """Dictionary class with fall-back look-up with default value set in the constructor.""" def __init__(self, default, d=None): if d: super(DefaultLookupDict, self).__init__(d) else: super(DefaultLookupDict, self).__init__() self._default = default def __getitem__(self, k): return self.get(k, self._default) def count_tokens(tokens, to_lower=False, counter=None): r"""Counts tokens in the specified string. For token_delim='(td)' and seq_delim='(sd)', a specified string of two sequences of tokens may look like:: (td)token1(td)token2(td)token3(td)(sd)(td)token4(td)token5(td)(sd) Parameters ---------- tokens : list of str A source list of tokens. to_lower : bool, default False Whether to convert the source source_str to the lower case. counter : Counter or None, default None The Counter instance to be updated with the counts of `tokens`. If None, return a new Counter instance counting tokens from `tokens`. Returns ------- The `counter` Counter instance after being updated with the token counts of `source_str`. If `counter` is None, return a new Counter instance counting tokens from `source_str`. Examples -------- >>> import re >>> source_str = ' Life is great ! \n life is good . \n' >>> source_str_tokens = filter(None, re.split(' |\n', source_str)) >>> gluonnlp.data.count_tokens(source_str_tokens) Counter({'is': 2, 'Life': 1, 'great': 1, '!': 1, 'life': 1, 'good': 1, '.': 1}) """ if to_lower: tokens = [t.lower() for t in tokens] if counter is None: return Counter(tokens) else: counter.update(tokens) return counter def concat_sequence(sequences): """Concatenate sequences of tokens into a single flattened list of tokens. Parameters ---------- sequences : list of list of object Sequences of tokens, each of which is an iterable of tokens. Returns ------- Flattened list of tokens. """ return [token for seq in sequences for token in seq if token] def slice_sequence(sequence, length, pad_last=False, pad_val=C.PAD_TOKEN, overlap=0): """Slice a flat sequence of tokens into sequences tokens, with each inner sequence's length equal to the specified `length`, taking into account the requested sequence overlap. Parameters ---------- sequence : list of object A flat list of tokens. length : int The length of each of the samples. pad_last : bool, default False Whether to pad the last sequence when its length doesn't align. If the last sequence's length doesn't align and ``pad_last`` is False, it will be dropped. pad_val : object, default The padding value to use when the padding of the last sequence is enabled. In general, the type of ``pad_val`` should be the same as the tokens. overlap : int, default 0 The extra number of items in current sample that should overlap with the next sample. Returns ------- List of list of tokens, with the length of each inner list equal to `length`. """ if length <= overlap: raise ValueError('length needs to be larger than overlap') if pad_last: pad_len = _slice_pad_length(len(sequence), length, overlap) sequence = sequence + [pad_val] * pad_len num_samples = (len(sequence)-length) // (length-overlap) + 1 return [sequence[i*(length-overlap):((i+1)*length-i*overlap)] for i in range(num_samples)] def _slice_pad_length(num_items, length, overlap=0): """Calculate the padding length needed for sliced samples in order not to discard data. Parameters ---------- num_items : int Number of items in dataset before collating. length : int The length of each of the samples. overlap : int, default 0 The extra number of items in current sample that should overlap with the next sample. Returns ------- Length of paddings. """ if length <= overlap: raise ValueError('length needs to be larger than overlap') step = length-overlap span = num_items-length residual = span % step if residual: return step - residual else: return 0 _vocab_sha1 = {'wikitext-2': 'be36dc5238c2e7d69720881647ab72eb506d0131', 'gbw': 'ebb1a287ca14d8fa6f167c3a779e5e7ed63ac69f', 'WMT2014_src': '230ebb817b1d86950d71e2e765f192a4e4f34415', 'WMT2014_tgt': '230ebb817b1d86950d71e2e765f192a4e4f34415', 'book_corpus_wiki_en_cased': '2d62af22535ed51f35cc8e2abb607723c89c2636', 'book_corpus_wiki_en_uncased': 'a66073971aa0b1a262453fe51342e57166a8abcf', 'wiki_multilingual_cased': '71bb9e248dc75dce9227d3c8c16fde3993588b9e', 'wiki_cn': 'a1e06f8e39ae51ab8a92b8458e6a658b8b1f72bf', 'wiki_multilingual': '2b2514cc539047b9179e9d98a4e68c36db05c97a'} _url_format = '{repo_url}gluon/dataset/vocab/{file_name}.zip' def train_valid_split(dataset, valid_ratio=0.05): """Split the dataset into training and validation sets. Parameters ---------- train : list A list of training samples. valid_ratio : float, default 0.05 Proportion of training samples to use for validation set range: [0, 1] Returns ------- train : SimpleDataset valid : SimpleDataset """ if not 0.0 <= valid_ratio <= 1.0: raise ValueError('valid_ratio should be in [0, 1]') num_train = len(dataset) num_valid = np.ceil(num_train * valid_ratio).astype('int') indices = np.arange(num_train) np.random.shuffle(indices) valid = SimpleDataset([dataset[indices[i]] for i in range(num_valid)]) train = SimpleDataset([dataset[indices[i + num_valid]] for i in range(num_train - num_valid)]) return train, valid def short_hash(name): if name not in _vocab_sha1: raise ValueError('Vocabulary for {name} is not available.'.format(name=name)) return _vocab_sha1[name][:8] def _load_pretrained_vocab(name, root=os.path.join('~', '.mxnet', 'models'), cls=None): """Load the accompanying vocabulary object for pre-trained model. Parameters ---------- name : str Name of the vocabulary, usually the name of the dataset. root : str, default '~/.mxnet/models' Location for keeping the model parameters. cls : nlp.Vocab or nlp.vocab.BERTVocab, default nlp.Vocab Returns ------- Vocab or nlp.bert.BERTVocab Loaded vocabulary object for the pre-trained model. """ file_name = '{name}-{short_hash}'.format(name=name, short_hash=short_hash(name)) root = os.path.expanduser(root) file_path = os.path.join(root, file_name+'.vocab') sha1_hash = _vocab_sha1[name] if os.path.exists(file_path): if check_sha1(file_path, sha1_hash): return _load_vocab_file(file_path, cls) else: print('Detected mismatch in the content of model vocab file. Downloading again.') else: print('Vocab file is not found. Downloading.') if not os.path.exists(root): os.makedirs(root) zip_file_path = os.path.join(root, file_name+'.zip') repo_url = _get_repo_url() if repo_url[-1] != '/': repo_url = repo_url + '/' download(_url_format.format(repo_url=repo_url, file_name=file_name), path=zip_file_path, overwrite=True) with zipfile.ZipFile(zip_file_path) as zf: zf.extractall(root) os.remove(zip_file_path) if check_sha1(file_path, sha1_hash): return _load_vocab_file(file_path, cls) else: raise ValueError('Downloaded file has different hash. Please try again.') def _load_vocab_file(file_path, cls): with open(file_path, 'r') as f: if cls is None: from ..vocab import Vocab cls = Vocab return cls.from_json(f.read()) def _get_home_dir(): """Get home directory for storing datasets/models/pre-trained word embeddings""" _home_dir = os.environ.get('MXNET_HOME', os.path.join('~', '.mxnet')) # expand ~ to actual path _home_dir = os.path.expanduser(_home_dir) return _home_dir def _extract_archive(file, target_dir): """Extract archive file Parameters ---------- file : str Absolute path of the archive file. target_dir : str Target directory of the archive to be uncompressed """ if file.endswith('.gz') or file.endswith('.tar') or file.endswith('.tgz'): archive = tarfile.open(file, 'r') elif file.endswith('.zip'): archive = zipfile.ZipFile(file, 'r') else: raise Exception('Unrecognized file type: ' + file) archive.extractall(path=target_dir) archive.close() def line_splitter(s): """Split a string at newlines. Parameters ---------- s : str The string to be split Returns -------- List[str] List of strings. Obtained by calling s.splitlines(). """ return s.splitlines() def whitespace_splitter(s): """Split a string at whitespace (space, tab, newline, return, formfeed). Parameters ---------- s : str The string to be split Returns -------- List[str] List of strings. Obtained by calling s.split(). """ return s.split() class Splitter(object): """Split a string based on a separator. Parameters ---------- separator : str The separator based on which string is split. """ def __init__(self, separator=None): self._separator = separator def __call__(self, s): """Split a string based on the separator. Parameters ---------- s : str The string to be split Returns -------- List[str] List of strings. Obtained by calling s.split(separator). """ return s.split(self._separator)

30.616505

98

0.641589

from __future__ import absolute_import from __future__ import print_function __all__ = [ 'Counter', 'count_tokens', 'concat_sequence', 'slice_sequence', 'train_valid_split', 'line_splitter', 'whitespace_splitter', 'Splitter' ] import os import collections import zipfile import tarfile import numpy as np from mxnet.gluon.data import SimpleDataset from mxnet.gluon.utils import _get_repo_url, download, check_sha1 from .. import _constants as C class Counter(collections.Counter): def discard(self, min_freq, unknown_token): freq = 0 ret = Counter({}) for token, count in self.items(): if count < min_freq: freq += count else: ret[token] = count ret[unknown_token] = ret.get(unknown_token, 0) + freq return ret class DefaultLookupDict(dict): def __init__(self, default, d=None): if d: super(DefaultLookupDict, self).__init__(d) else: super(DefaultLookupDict, self).__init__() self._default = default def __getitem__(self, k): return self.get(k, self._default) def count_tokens(tokens, to_lower=False, counter=None): if to_lower: tokens = [t.lower() for t in tokens] if counter is None: return Counter(tokens) else: counter.update(tokens) return counter def concat_sequence(sequences): return [token for seq in sequences for token in seq if token] def slice_sequence(sequence, length, pad_last=False, pad_val=C.PAD_TOKEN, overlap=0): if length <= overlap: raise ValueError('length needs to be larger than overlap') if pad_last: pad_len = _slice_pad_length(len(sequence), length, overlap) sequence = sequence + [pad_val] * pad_len num_samples = (len(sequence)-length) // (length-overlap) + 1 return [sequence[i*(length-overlap):((i+1)*length-i*overlap)] for i in range(num_samples)] def _slice_pad_length(num_items, length, overlap=0): if length <= overlap: raise ValueError('length needs to be larger than overlap') step = length-overlap span = num_items-length residual = span % step if residual: return step - residual else: return 0 _vocab_sha1 = {'wikitext-2': 'be36dc5238c2e7d69720881647ab72eb506d0131', 'gbw': 'ebb1a287ca14d8fa6f167c3a779e5e7ed63ac69f', 'WMT2014_src': '230ebb817b1d86950d71e2e765f192a4e4f34415', 'WMT2014_tgt': '230ebb817b1d86950d71e2e765f192a4e4f34415', 'book_corpus_wiki_en_cased': '2d62af22535ed51f35cc8e2abb607723c89c2636', 'book_corpus_wiki_en_uncased': 'a66073971aa0b1a262453fe51342e57166a8abcf', 'wiki_multilingual_cased': '71bb9e248dc75dce9227d3c8c16fde3993588b9e', 'wiki_cn': 'a1e06f8e39ae51ab8a92b8458e6a658b8b1f72bf', 'wiki_multilingual': '2b2514cc539047b9179e9d98a4e68c36db05c97a'} _url_format = '{repo_url}gluon/dataset/vocab/{file_name}.zip' def train_valid_split(dataset, valid_ratio=0.05): if not 0.0 <= valid_ratio <= 1.0: raise ValueError('valid_ratio should be in [0, 1]') num_train = len(dataset) num_valid = np.ceil(num_train * valid_ratio).astype('int') indices = np.arange(num_train) np.random.shuffle(indices) valid = SimpleDataset([dataset[indices[i]] for i in range(num_valid)]) train = SimpleDataset([dataset[indices[i + num_valid]] for i in range(num_train - num_valid)]) return train, valid def short_hash(name): if name not in _vocab_sha1: raise ValueError('Vocabulary for {name} is not available.'.format(name=name)) return _vocab_sha1[name][:8] def _load_pretrained_vocab(name, root=os.path.join('~', '.mxnet', 'models'), cls=None): file_name = '{name}-{short_hash}'.format(name=name, short_hash=short_hash(name)) root = os.path.expanduser(root) file_path = os.path.join(root, file_name+'.vocab') sha1_hash = _vocab_sha1[name] if os.path.exists(file_path): if check_sha1(file_path, sha1_hash): return _load_vocab_file(file_path, cls) else: print('Detected mismatch in the content of model vocab file. Downloading again.') else: print('Vocab file is not found. Downloading.') if not os.path.exists(root): os.makedirs(root) zip_file_path = os.path.join(root, file_name+'.zip') repo_url = _get_repo_url() if repo_url[-1] != '/': repo_url = repo_url + '/' download(_url_format.format(repo_url=repo_url, file_name=file_name), path=zip_file_path, overwrite=True) with zipfile.ZipFile(zip_file_path) as zf: zf.extractall(root) os.remove(zip_file_path) if check_sha1(file_path, sha1_hash): return _load_vocab_file(file_path, cls) else: raise ValueError('Downloaded file has different hash. Please try again.') def _load_vocab_file(file_path, cls): with open(file_path, 'r') as f: if cls is None: from ..vocab import Vocab cls = Vocab return cls.from_json(f.read()) def _get_home_dir(): _home_dir = os.environ.get('MXNET_HOME', os.path.join('~', '.mxnet')) _home_dir = os.path.expanduser(_home_dir) return _home_dir def _extract_archive(file, target_dir): if file.endswith('.gz') or file.endswith('.tar') or file.endswith('.tgz'): archive = tarfile.open(file, 'r') elif file.endswith('.zip'): archive = zipfile.ZipFile(file, 'r') else: raise Exception('Unrecognized file type: ' + file) archive.extractall(path=target_dir) archive.close() def line_splitter(s): return s.splitlines() def whitespace_splitter(s): return s.split() class Splitter(object): def __init__(self, separator=None): self._separator = separator def __call__(self, s): return s.split(self._separator)

true

f72addc1825c766c27b5ea9433ca8b1b439ac3e5

33,419

py

Python

cirq/ops/common_gates.py

philiptmassey/Cirq

b8b457c2fc484d76bf8a82a73f6ecc11756229a6

[ "Apache-2.0" ]

null

cirq/ops/common_gates.py

philiptmassey/Cirq

b8b457c2fc484d76bf8a82a73f6ecc11756229a6

[ "Apache-2.0" ]

null

cirq/ops/common_gates.py

philiptmassey/Cirq

b8b457c2fc484d76bf8a82a73f6ecc11756229a6

[ "Apache-2.0" ]

null

# Copyright 2018 The Cirq Developers # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Quantum gates that are commonly used in the literature. This module creates Gate instances for the following gates: X,Y,Z: Pauli gates. H,S: Clifford gates. T: A non-Clifford gate. CZ: Controlled phase gate. CNOT: Controlled not gate. SWAP: the swap gate. ISWAP: a swap gate with a phase on the swapped subspace. Each of these are implemented as EigenGates, which means that they can be raised to a power (i.e. cirq.H**0.5). See the definition in EigenGate. In addition MeasurementGate is defined and convenience methods for measurements are provided measure measure_each """ from typing import ( Any, Callable, cast, Iterable, List, Optional, Tuple, Union, ) import numpy as np from cirq import linalg, protocols, value from cirq.ops import gate_features, eigen_gate, raw_types, gate_operation from cirq.type_workarounds import NotImplementedType # Note: avoiding 'from/as' because it creates a circular dependency in python 2. import cirq.ops.phased_x_gate class XPowGate(eigen_gate.EigenGate, gate_features.SingleQubitGate): """A gate that rotates around the X axis of the Bloch sphere. The unitary matrix of ``XPowGate(exponent=t)`` is: [[g·c, -i·g·s], [-i·g·s, g·c]] where: c = cos(π·t/2) s = sin(π·t/2) g = exp(i·π·t/2). Note in particular that this gate has a global phase factor of e^{i·π·t/2} vs the traditionally defined rotation matrices about the Pauli X axis. See `cirq.Rx` for rotations without the global phase. The global phase factor can be adjusted by using the `global_shift` parameter when initializing. `cirq.X`, the Pauli X gate, is an instance of this gate at exponent=1. """ def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if self._exponent != 1: return None zero = args.subspace_index(0) one = args.subspace_index(1) args.available_buffer[zero] = args.target_tensor[one] args.available_buffer[one] = args.target_tensor[zero] p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.available_buffer *= p return args.available_buffer def _eigen_components(self): return [ (0, np.array([[0.5, 0.5], [0.5, 0.5]])), (1, np.array([[0.5, -0.5], [-0.5, 0.5]])), ] def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> Union[str, protocols.CircuitDiagramInfo]: if self._global_shift == -0.5: return _rads_func_symbol( 'Rx', args, self._diagram_exponent(args, ignore_global_phase=False)) return protocols.CircuitDiagramInfo( wire_symbols=('X',), exponent=self._diagram_exponent(args)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: args.validate_version('2.0') if self._exponent == 1: return args.format('x {0};\n', qubits[0]) else: return args.format('rx({0:half_turns}) {1};\n', self._exponent, qubits[0]) def _phase_by_(self, phase_turns, qubit_index): """See `cirq.SupportsPhase`.""" return cirq.ops.phased_x_gate.PhasedXPowGate( exponent=self._exponent, phase_exponent=phase_turns * 2) def __str__(self) -> str: if self._exponent == 1: return 'X' return 'X**{!r}'.format(self._exponent) def __repr__(self) -> str: if self._global_shift == -0.5 and not protocols.is_parameterized(self): return 'cirq.Rx(np.pi*{!r})'.format(self._exponent) if self._global_shift == 0: if self._exponent == 1: return 'cirq.X' return '(cirq.X**{!r})'.format(self._exponent) return ( 'cirq.XPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) class YPowGate(eigen_gate.EigenGate, gate_features.SingleQubitGate): """A gate that rotates around the Y axis of the Bloch sphere. The unitary matrix of ``YPowGate(exponent=t)`` is: [[g·c, g·s], [-g·s, g·c]] where: c = cos(π·t/2) s = sin(π·t/2) g = exp(i·π·t/2). Note in particular that this gate has a global phase factor of e^{i·π·t/2} vs the traditionally defined rotation matrices about the Pauli Y axis. See `cirq.Ry` for rotations without the global phase. The global phase factor can be adjusted by using the `global_shift` parameter when initializing. `cirq.Y`, the Pauli Y gate, is an instance of this gate at exponent=1. """ def _eigen_components(self): return [ (0, np.array([[0.5, -0.5j], [0.5j, 0.5]])), (1, np.array([[0.5, 0.5j], [-0.5j, 0.5]])), ] def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> Union[str, protocols.CircuitDiagramInfo]: if self._global_shift == -0.5: return _rads_func_symbol( 'Ry', args, self._diagram_exponent(args, ignore_global_phase=False)) return protocols.CircuitDiagramInfo( wire_symbols=('Y',), exponent=self._diagram_exponent(args)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: args.validate_version('2.0') if self._exponent == 1: return args.format('y {0};\n', qubits[0]) else: return args.format('ry({0:half_turns}) {1};\n', self._exponent, qubits[0]) def _phase_by_(self, phase_turns, qubit_index): """See `cirq.SupportsPhase`.""" return cirq.ops.phased_x_gate.PhasedXPowGate( exponent=self._exponent, phase_exponent=0.5 + phase_turns * 2) def __str__(self) -> str: if self._exponent == 1: return 'Y' return 'Y**{!r}'.format(self._exponent) def __repr__(self) -> str: if self._global_shift == -0.5 and not protocols.is_parameterized(self): return 'cirq.Ry(np.pi*{!r})'.format(self._exponent) if self._global_shift == 0: if self._exponent == 1: return 'cirq.Y' return '(cirq.Y**{!r})'.format(self._exponent) return ( 'cirq.YPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) class ZPowGate(eigen_gate.EigenGate, gate_features.SingleQubitGate): """A gate that rotates around the Z axis of the Bloch sphere. The unitary matrix of ``ZPowGate(exponent=t)`` is: [[1, 0], [0, g]] where: g = exp(i·π·t). Note in particular that this gate has a global phase factor of e^{i·π·t/2} vs the traditionally defined rotation matrices about the Pauli Z axis. See `cirq.Rz` for rotations without the global phase. The global phase factor can be adjusted by using the `global_shift` parameter when initializing. `cirq.Z`, the Pauli Z gate, is an instance of this gate at exponent=1. """ def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if protocols.is_parameterized(self): return None one = args.subspace_index(1) c = 1j**(self._exponent * 2) args.target_tensor[one] *= c p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.target_tensor *= p return args.target_tensor def _eigen_components(self): return [ (0, np.diag([1, 0])), (1, np.diag([0, 1])), ] def _phase_by_(self, phase_turns: float, qubit_index: int): return self def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> Union[str, protocols.CircuitDiagramInfo]: if self._global_shift == -0.5: return _rads_func_symbol( 'Rz', args, self._diagram_exponent(args, ignore_global_phase=False)) e = self._diagram_exponent(args) if e in [-0.25, 0.25]: return protocols.CircuitDiagramInfo( wire_symbols=('T',), exponent=cast(float, e) * 4) if e in [-0.5, 0.5]: return protocols.CircuitDiagramInfo( wire_symbols=('S',), exponent=cast(float, e) * 2) return protocols.CircuitDiagramInfo( wire_symbols=('Z',), exponent=e) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: args.validate_version('2.0') if self._exponent == 1: return args.format('z {0};\n', qubits[0]) else: return args.format('rz({0:half_turns}) {1};\n', self._exponent, qubits[0]) def __str__(self) -> str: if self._exponent == 0.25: return 'T' if self._exponent == -0.25: return 'T**-1' if self._exponent == 0.5: return 'S' if self._exponent == -0.5: return 'S**-1' if self._exponent == 1: return 'Z' return 'Z**{}'.format(self._exponent) def __repr__(self) -> str: if self._global_shift == -0.5 and not protocols.is_parameterized(self): return 'cirq.Rz(np.pi*{!r})'.format(self._exponent) if self._global_shift == 0: if self._exponent == 0.25: return 'cirq.T' if self._exponent == -0.25: return '(cirq.T**-1)' if self._exponent == 0.5: return 'cirq.S' if self._exponent == -0.5: return '(cirq.S**-1)' if self._exponent == 1: return 'cirq.Z' return '(cirq.Z**{!r})'.format(self._exponent) return ( 'cirq.ZPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) @value.value_equality class MeasurementGate(raw_types.Gate): """A gate that measures qubits in the computational basis. The measurement gate contains a key that is used to identify results of measurements. """ def __init__(self, key: str = '', invert_mask: Tuple[bool, ...] = ()) -> None: """ Args: key: The string key of the measurement. invert_mask: A list of values indicating whether the corresponding qubits should be flipped. The list's length must not be longer than the number of qubits, but it is permitted to be shorter. Qubits with indices past the end of the mask are not flipped. """ self.key = key self.invert_mask = invert_mask or () @staticmethod def is_measurement(op: Union[raw_types.Gate, raw_types.Operation]) -> bool: if isinstance(op, MeasurementGate): return True if (isinstance(op, gate_operation.GateOperation) and isinstance(op.gate, MeasurementGate)): return True return False def with_bits_flipped(self, *bit_positions: int) -> 'MeasurementGate': """Toggles whether or not the measurement inverts various outputs.""" old_mask = self.invert_mask or () n = max(len(old_mask) - 1, *bit_positions) + 1 new_mask = [k < len(old_mask) and old_mask[k] for k in range(n)] for b in bit_positions: new_mask[b] = not new_mask[b] return MeasurementGate(key=self.key, invert_mask=tuple(new_mask)) def validate_args(self, qubits): if (self.invert_mask is not None and len(self.invert_mask) > len(qubits)): raise ValueError('len(invert_mask) > len(qubits)') def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: n = (max(1, len(self.invert_mask)) if args.known_qubit_count is None else args.known_qubit_count) symbols = ['M'] * n # Show which output bits are negated. if self.invert_mask: for i, b in enumerate(self.invert_mask): if b: symbols[i] = '!M' # Mention the measurement key. if (not args.known_qubits or self.key != _default_measurement_key(args.known_qubits)): symbols[0] += "('{}')".format(self.key) return protocols.CircuitDiagramInfo(tuple(symbols)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: args.validate_version('2.0') invert_mask = self.invert_mask if len(invert_mask) < len(qubits): invert_mask = (invert_mask + (False,) * (len(qubits) - len(invert_mask))) lines = [] for i, (qubit, inv) in enumerate(zip(qubits, invert_mask)): if inv: lines.append(args.format( 'x {0}; // Invert the following measurement\n', qubit)) lines.append(args.format('measure {0} -> {1:meas}[{2}];\n', qubit, self.key, i)) return ''.join(lines) def __repr__(self): return 'cirq.MeasurementGate({}, {})'.format(repr(self.key), repr(self.invert_mask)) def _value_equality_values_(self): return self.key, self.invert_mask def _default_measurement_key(qubits: Iterable[raw_types.QubitId]) -> str: return ','.join(str(q) for q in qubits) def measure(*qubits: raw_types.QubitId, key: Optional[str] = None, invert_mask: Tuple[bool, ...] = () ) -> gate_operation.GateOperation: """Returns a single MeasurementGate applied to all the given qubits. The qubits are measured in the computational basis. Args: *qubits: The qubits that the measurement gate should measure. key: The string key of the measurement. If this is None, it defaults to a comma-separated list of the target qubits' str values. invert_mask: A list of Truthy or Falsey values indicating whether the corresponding qubits should be flipped. None indicates no inverting should be done. Returns: An operation targeting the given qubits with a measurement. Raises: ValueError if the qubits are not instances of QubitId. """ for qubit in qubits: if isinstance(qubit, np.ndarray): raise ValueError( 'measure() was called a numpy ndarray. Perhaps you meant ' 'to call measure_state_vector on numpy array?' ) elif not isinstance(qubit, raw_types.QubitId): raise ValueError( 'measure() was called with type different than QubitId.') if key is None: key = _default_measurement_key(qubits) return MeasurementGate(key, invert_mask).on(*qubits) def measure_each(*qubits: raw_types.QubitId, key_func: Callable[[raw_types.QubitId], str] = str ) -> List[gate_operation.GateOperation]: """Returns a list of operations individually measuring the given qubits. The qubits are measured in the computational basis. Args: *qubits: The qubits to measure. key_func: Determines the key of the measurements of each qubit. Takes the qubit and returns the key for that qubit. Defaults to str. Returns: A list of operations individually measuring the given qubits. """ return [MeasurementGate(key_func(q)).on(q) for q in qubits] class HPowGate(eigen_gate.EigenGate, gate_features.SingleQubitGate): """A Gate that performs a rotation around the X+Z axis of the Bloch sphere. The unitary matrix of ``HPowGate(exponent=t)`` is: [[g·(c-i·s/sqrt(2)), -i·g·s/sqrt(2)], [-i·g·s/sqrt(2)], g·(c+i·s/sqrt(2))]] where c = cos(π·t/2) s = sin(π·t/2) g = exp(i·π·t/2). Note in particular that for `t=1`, this gives the Hadamard matrix. `cirq.H`, the Hadamard gate, is an instance of this gate at `exponent=1`. """ def _eigen_components(self): s = np.sqrt(2) component0 = np.array([ [3 + 2 * s, 1 + s], [1 + s, 1] ]) / (4 + 2 * s) component1 = np.array([ [3 - 2 * s, 1 - s], [1 - s, 1] ]) / (4 - 2 * s) return [(0, component0), (1, component1)] def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if self._exponent != 1: return None zero = args.subspace_index(0) one = args.subspace_index(1) args.target_tensor[one] -= args.target_tensor[zero] args.target_tensor[one] *= -0.5 args.target_tensor[zero] -= args.target_tensor[one] p = 1j**(2 * self._exponent * self._global_shift) args.target_tensor *= np.sqrt(2) * p return args.target_tensor def _decompose_(self, qubits): q = qubits[0] if self._exponent == 1: yield cirq.Y(q)**0.5 yield cirq.XPowGate(global_shift=-0.25).on(q) return yield Y(q)**0.25 yield X(q)**self._exponent yield Y(q)**-0.25 def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: return protocols.CircuitDiagramInfo(('H',)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: args.validate_version('2.0') if self._exponent == 1: return args.format('h {0};\n', qubits[0]) else: return args.format('ry({0:half_turns}) {3};\n' 'rx({1:half_turns}) {3};\n' 'ry({2:half_turns}) {3};\n', 0.25, self._exponent, -0.25, qubits[0]) def __str__(self): if self._exponent == 1: return 'H' return 'H^{}'.format(self._exponent) def __repr__(self): if self._global_shift == 0: if self._exponent == 1: return 'cirq.H' return '(cirq.H**{!r})'.format(self._exponent) return ( 'cirq.HPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) class CZPowGate(eigen_gate.EigenGate, gate_features.TwoQubitGate, gate_features.InterchangeableQubitsGate): """A gate that applies a phase to the |11⟩ state of two qubits. The unitary matrix of `CZPowGate(exponent=t)` is: [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, g]] where: g = exp(i·π·t/2). `cirq.CZ`, the controlled Z gate, is an instance of this gate at `exponent=1`. """ def _eigen_components(self): return [ (0, np.diag([1, 1, 1, 0])), (1, np.diag([0, 0, 0, 1])), ] def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Union[np.ndarray, NotImplementedType]: if protocols.is_parameterized(self): return NotImplemented c = 1j**(2 * self._exponent) one_one = linalg.slice_for_qubits_equal_to(args.axes, 0b11) args.target_tensor[one_one] *= c p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.target_tensor *= p return args.target_tensor def _phase_by_(self, phase_turns, qubit_index): return self def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: return protocols.CircuitDiagramInfo( wire_symbols=('@', '@'), exponent=self._diagram_exponent(args)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: if self._exponent != 1: return None # Don't have an equivalent gate in QASM args.validate_version('2.0') return args.format('cz {0},{1};\n', qubits[0], qubits[1]) def __str__(self) -> str: if self._exponent == 1: return 'CZ' return 'CZ**{!r}'.format(self._exponent) def __repr__(self) -> str: if self._global_shift == 0: if self._exponent == 1: return 'cirq.CZ' return '(cirq.CZ**{!r})'.format(self._exponent) return ( 'cirq.CZPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) def _rads_func_symbol(func_name: str, args: protocols.CircuitDiagramInfoArgs, half_turns: Any) -> str: unit = 'π' if args.use_unicode_characters else 'pi' if half_turns == 1: return '{}({})'.format(func_name, unit) if half_turns == -1: return '{}(-{})'.format(func_name, unit) return '{}({}{})'.format(func_name, half_turns, unit) class CNotPowGate(eigen_gate.EigenGate, gate_features.TwoQubitGate): """A gate that applies a controlled power of an X gate. When applying CNOT (controlled-not) to qubits, you can either use positional arguments CNOT(q1, q2), where q2 is toggled when q1 is on, or named arguments CNOT(control=q1, target=q2). (Mixing the two is not permitted.) The unitary matrix of `CNotPowGate(exponent=t)` is: [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, g·c, -i·g·s], [0, 0, -i·g·s, g·c]] where: c = cos(π·t/2) s = sin(π·t/2) g = exp(i·π·t/2). `cirq.CNOT`, the controlled NOT gate, is an instance of this gate at `exponent=1`. """ def _decompose_(self, qubits): c, t = qubits yield Y(t)**-0.5 yield CZ(c, t)**self._exponent yield Y(t)**0.5 def _eigen_components(self): return [ (0, np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0.5, 0.5], [0, 0, 0.5, 0.5]])), (1, np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0.5, -0.5], [0, 0, -0.5, 0.5]])), ] def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: return protocols.CircuitDiagramInfo( wire_symbols=('@', 'X'), exponent=self._diagram_exponent(args)) def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if self._exponent != 1: return None oo = args.subspace_index(0b11) zo = args.subspace_index(0b01) args.available_buffer[oo] = args.target_tensor[oo] args.target_tensor[oo] = args.target_tensor[zo] args.target_tensor[zo] = args.available_buffer[oo] p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.target_tensor *= p return args.target_tensor def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: if self._exponent != 1: return None # Don't have an equivalent gate in QASM args.validate_version('2.0') return args.format('cx {0},{1};\n', qubits[0], qubits[1]) def __str__(self) -> str: if self._exponent == 1: return 'CNOT' return 'CNOT**{!r}'.format(self._exponent) def __repr__(self): if self._global_shift == 0: if self._exponent == 1: return 'cirq.CNOT' return '(cirq.CNOT**{!r})'.format(self._exponent) return ( 'cirq.CNotPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) def on(self, *args: raw_types.QubitId, **kwargs: raw_types.QubitId) -> gate_operation.GateOperation: if not kwargs: return super().on(*args) if not args and set(kwargs.keys()) == {'control', 'target'}: return super().on(kwargs['control'], kwargs['target']) raise ValueError( "Expected two positional argument or else 'target' AND 'control' " "keyword arguments. But got args={!r}, kwargs={!r}.".format( args, kwargs)) class SwapPowGate(eigen_gate.EigenGate, gate_features.TwoQubitGate, gate_features.InterchangeableQubitsGate): """The SWAP gate, possibly raised to a power. Exchanges qubits. SwapPowGate()**t = SwapPowGate(exponent=t) and acts on two qubits in the computational basis as the matrix: [[1, 0, 0, 0], [0, g·c, -i·g·s, 0], [0, -i·g·s, g·c, 0], [0, 0, 0, 1]] where: c = cos(π·t/2) s = sin(π·t/2) g = exp(i·π·t/2). `cirq.SWAP`, the swap gate, is an instance of this gate at exponent=1. """ def _decompose_(self, qubits): """See base class.""" a, b = qubits yield CNOT(a, b) yield CNOT(b, a) ** self._exponent yield CNOT(a, b) def _eigen_components(self): return [ (0, np.array([[1, 0, 0, 0], [0, 0.5, 0.5, 0], [0, 0.5, 0.5, 0], [0, 0, 0, 1]])), (1, np.array([[0, 0, 0, 0], [0, 0.5, -0.5, 0], [0, -0.5, 0.5, 0], [0, 0, 0, 0]])), ] def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if self._exponent != 1: return None zo = args.subspace_index(0b01) oz = args.subspace_index(0b10) args.available_buffer[zo] = args.target_tensor[zo] args.target_tensor[zo] = args.target_tensor[oz] args.target_tensor[oz] = args.available_buffer[zo] p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.target_tensor *= p return args.target_tensor def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: if not args.use_unicode_characters: return protocols.CircuitDiagramInfo( wire_symbols=('swap', 'swap'), exponent=self._diagram_exponent(args)) return protocols.CircuitDiagramInfo( wire_symbols=('×', '×'), exponent=self._diagram_exponent(args)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: if self._exponent != 1: return None # Don't have an equivalent gate in QASM args.validate_version('2.0') return args.format('swap {0},{1};\n', qubits[0], qubits[1]) def __str__(self) -> str: if self._exponent == 1: return 'SWAP' return 'SWAP**{!r}'.format(self._exponent) def __repr__(self): if self._global_shift == 0: if self._exponent == 1: return 'cirq.SWAP' return '(cirq.SWAP**{!r})'.format(self._exponent) return ( 'cirq.SwapPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) class ISwapPowGate(eigen_gate.EigenGate, gate_features.InterchangeableQubitsGate, gate_features.TwoQubitGate): """Rotates the |01⟩-vs-|10⟩ subspace of two qubits around its Bloch X-axis. When exponent=1, swaps the two qubits and phases |01⟩ and |10⟩ by i. More generally, this gate's matrix is defined as follows: ISWAP**t ≡ exp(+i π t (X⊗X + Y⊗Y) / 4) which is given by the matrix: [[1, 0, 0, 0], [0, c, i·s, 0], [0, i·s, c, 0], [0, 0, 0, 1]] where: c = cos(π·t/2) s = sin(π·t/2) `cirq.ISWAP`, the swap gate that applies -i to the |01> and |10> states, is an instance of this gate at exponent=1. """ def _eigen_components(self): return [ (0, np.diag([1, 0, 0, 1])), (+0.5, np.array([[0, 0, 0, 0], [0, 0.5, 0.5, 0], [0, 0.5, 0.5, 0], [0, 0, 0, 0]])), (-0.5, np.array([[0, 0, 0, 0], [0, 0.5, -0.5, 0], [0, -0.5, 0.5, 0], [0, 0, 0, 0]])), ] def _decompose_(self, qubits): a, b = qubits yield CNOT(a, b) yield H(a) yield CNOT(b, a) yield S(a)**self._exponent yield CNOT(b, a) yield S(a)**-self._exponent yield H(a) yield CNOT(a, b) def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if self._exponent != 1: return None zo = args.subspace_index(0b01) oz = args.subspace_index(0b10) args.available_buffer[zo] = args.target_tensor[zo] args.target_tensor[zo] = args.target_tensor[oz] args.target_tensor[oz] = args.available_buffer[zo] args.target_tensor[zo] *= 1j args.target_tensor[oz] *= 1j p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.target_tensor *= p return args.target_tensor def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: return protocols.CircuitDiagramInfo( wire_symbols=('iSwap', 'iSwap'), exponent=self._diagram_exponent(args)) def __str__(self) -> str: if self._exponent == 1: return 'ISWAP' return 'ISWAP**{!r}'.format(self._exponent) def __repr__(self): if self._global_shift == 0: if self._exponent == 1: return 'cirq.ISWAP' return '(cirq.ISWAP**{!r})'.format(self._exponent) return ( 'cirq.ISwapPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) def Rx(rads: float) -> XPowGate: """Returns a gate with the matrix e^{-i X rads / 2}.""" return XPowGate(exponent=rads / np.pi, global_shift=-0.5) def Ry(rads: float) -> YPowGate: """Returns a gate with the matrix e^{-i Y rads / 2}.""" return YPowGate(exponent=rads / np.pi, global_shift=-0.5) def Rz(rads: float) -> ZPowGate: """Returns a gate with the matrix e^{-i Z rads / 2}.""" return ZPowGate(exponent=rads / np.pi, global_shift=-0.5) X = XPowGate() """The Pauli X gate. Matrix: [[0, 1], [1, 0]] """ #: The Pauli Y gate. #: #: Matrix: #: #: [[0, -i], #: [i, 0]] Y = YPowGate() # The Pauli Z gate. # # Matrix: # # [[1, 0], # [0, -1]] Z = ZPowGate() # The Hadamard gate. # # Matrix: # # [[s, s], # [s, -s]] # where s = sqrt(0.5). H = HPowGate() # The Clifford S gate. # # Matrix: # # [[1, 0], # [0, i]] S = Z**0.5 # The T gate. # # Matrix: # # [[1, 0] # [0, exp(i pi / 4)]] T = Z**0.25 # The controlled Z gate. # # Matrix: # # [[1, 0, 0, 0], # [0, 1, 0, 0], # [0, 0, 1, 0], # [0, 0, 0, -1]] CZ = CZPowGate() # The controlled NOT gate. # # Matrix: # # [[1, 0, 0, 0], # [0, 1, 0, 0], # [0, 0, 0, 1], # [0, 0, 1, 0]] CNOT = CNotPowGate() # The swap gate. # # Matrix: # # [[1, 0, 0, 0], # [0, 0, 1, 0], # [0, 1, 0, 0], # [0, 0, 0, 1]] SWAP = SwapPowGate() # The iswap gate. # # Matrix: # # [[1, 0, 0, 0], # [0, 0, i, 0], # [0, i, 0, 0], # [0, 0, 0, 1]] ISWAP = ISwapPowGate()

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from typing import ( Any, Callable, cast, Iterable, List, Optional, Tuple, Union, ) import numpy as np from cirq import linalg, protocols, value from cirq.ops import gate_features, eigen_gate, raw_types, gate_operation from cirq.type_workarounds import NotImplementedType import cirq.ops.phased_x_gate class XPowGate(eigen_gate.EigenGate, gate_features.SingleQubitGate): def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if self._exponent != 1: return None zero = args.subspace_index(0) one = args.subspace_index(1) args.available_buffer[zero] = args.target_tensor[one] args.available_buffer[one] = args.target_tensor[zero] p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.available_buffer *= p return args.available_buffer def _eigen_components(self): return [ (0, np.array([[0.5, 0.5], [0.5, 0.5]])), (1, np.array([[0.5, -0.5], [-0.5, 0.5]])), ] def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> Union[str, protocols.CircuitDiagramInfo]: if self._global_shift == -0.5: return _rads_func_symbol( 'Rx', args, self._diagram_exponent(args, ignore_global_phase=False)) return protocols.CircuitDiagramInfo( wire_symbols=('X',), exponent=self._diagram_exponent(args)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: args.validate_version('2.0') if self._exponent == 1: return args.format('x {0};\n', qubits[0]) else: return args.format('rx({0:half_turns}) {1};\n', self._exponent, qubits[0]) def _phase_by_(self, phase_turns, qubit_index): return cirq.ops.phased_x_gate.PhasedXPowGate( exponent=self._exponent, phase_exponent=phase_turns * 2) def __str__(self) -> str: if self._exponent == 1: return 'X' return 'X**{!r}'.format(self._exponent) def __repr__(self) -> str: if self._global_shift == -0.5 and not protocols.is_parameterized(self): return 'cirq.Rx(np.pi*{!r})'.format(self._exponent) if self._global_shift == 0: if self._exponent == 1: return 'cirq.X' return '(cirq.X**{!r})'.format(self._exponent) return ( 'cirq.XPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) class YPowGate(eigen_gate.EigenGate, gate_features.SingleQubitGate): def _eigen_components(self): return [ (0, np.array([[0.5, -0.5j], [0.5j, 0.5]])), (1, np.array([[0.5, 0.5j], [-0.5j, 0.5]])), ] def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> Union[str, protocols.CircuitDiagramInfo]: if self._global_shift == -0.5: return _rads_func_symbol( 'Ry', args, self._diagram_exponent(args, ignore_global_phase=False)) return protocols.CircuitDiagramInfo( wire_symbols=('Y',), exponent=self._diagram_exponent(args)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: args.validate_version('2.0') if self._exponent == 1: return args.format('y {0};\n', qubits[0]) else: return args.format('ry({0:half_turns}) {1};\n', self._exponent, qubits[0]) def _phase_by_(self, phase_turns, qubit_index): return cirq.ops.phased_x_gate.PhasedXPowGate( exponent=self._exponent, phase_exponent=0.5 + phase_turns * 2) def __str__(self) -> str: if self._exponent == 1: return 'Y' return 'Y**{!r}'.format(self._exponent) def __repr__(self) -> str: if self._global_shift == -0.5 and not protocols.is_parameterized(self): return 'cirq.Ry(np.pi*{!r})'.format(self._exponent) if self._global_shift == 0: if self._exponent == 1: return 'cirq.Y' return '(cirq.Y**{!r})'.format(self._exponent) return ( 'cirq.YPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) class ZPowGate(eigen_gate.EigenGate, gate_features.SingleQubitGate): def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if protocols.is_parameterized(self): return None one = args.subspace_index(1) c = 1j**(self._exponent * 2) args.target_tensor[one] *= c p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.target_tensor *= p return args.target_tensor def _eigen_components(self): return [ (0, np.diag([1, 0])), (1, np.diag([0, 1])), ] def _phase_by_(self, phase_turns: float, qubit_index: int): return self def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> Union[str, protocols.CircuitDiagramInfo]: if self._global_shift == -0.5: return _rads_func_symbol( 'Rz', args, self._diagram_exponent(args, ignore_global_phase=False)) e = self._diagram_exponent(args) if e in [-0.25, 0.25]: return protocols.CircuitDiagramInfo( wire_symbols=('T',), exponent=cast(float, e) * 4) if e in [-0.5, 0.5]: return protocols.CircuitDiagramInfo( wire_symbols=('S',), exponent=cast(float, e) * 2) return protocols.CircuitDiagramInfo( wire_symbols=('Z',), exponent=e) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: args.validate_version('2.0') if self._exponent == 1: return args.format('z {0};\n', qubits[0]) else: return args.format('rz({0:half_turns}) {1};\n', self._exponent, qubits[0]) def __str__(self) -> str: if self._exponent == 0.25: return 'T' if self._exponent == -0.25: return 'T**-1' if self._exponent == 0.5: return 'S' if self._exponent == -0.5: return 'S**-1' if self._exponent == 1: return 'Z' return 'Z**{}'.format(self._exponent) def __repr__(self) -> str: if self._global_shift == -0.5 and not protocols.is_parameterized(self): return 'cirq.Rz(np.pi*{!r})'.format(self._exponent) if self._global_shift == 0: if self._exponent == 0.25: return 'cirq.T' if self._exponent == -0.25: return '(cirq.T**-1)' if self._exponent == 0.5: return 'cirq.S' if self._exponent == -0.5: return '(cirq.S**-1)' if self._exponent == 1: return 'cirq.Z' return '(cirq.Z**{!r})'.format(self._exponent) return ( 'cirq.ZPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) @value.value_equality class MeasurementGate(raw_types.Gate): def __init__(self, key: str = '', invert_mask: Tuple[bool, ...] = ()) -> None: self.key = key self.invert_mask = invert_mask or () @staticmethod def is_measurement(op: Union[raw_types.Gate, raw_types.Operation]) -> bool: if isinstance(op, MeasurementGate): return True if (isinstance(op, gate_operation.GateOperation) and isinstance(op.gate, MeasurementGate)): return True return False def with_bits_flipped(self, *bit_positions: int) -> 'MeasurementGate': old_mask = self.invert_mask or () n = max(len(old_mask) - 1, *bit_positions) + 1 new_mask = [k < len(old_mask) and old_mask[k] for k in range(n)] for b in bit_positions: new_mask[b] = not new_mask[b] return MeasurementGate(key=self.key, invert_mask=tuple(new_mask)) def validate_args(self, qubits): if (self.invert_mask is not None and len(self.invert_mask) > len(qubits)): raise ValueError('len(invert_mask) > len(qubits)') def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: n = (max(1, len(self.invert_mask)) if args.known_qubit_count is None else args.known_qubit_count) symbols = ['M'] * n if self.invert_mask: for i, b in enumerate(self.invert_mask): if b: symbols[i] = '!M' if (not args.known_qubits or self.key != _default_measurement_key(args.known_qubits)): symbols[0] += "('{}')".format(self.key) return protocols.CircuitDiagramInfo(tuple(symbols)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: args.validate_version('2.0') invert_mask = self.invert_mask if len(invert_mask) < len(qubits): invert_mask = (invert_mask + (False,) * (len(qubits) - len(invert_mask))) lines = [] for i, (qubit, inv) in enumerate(zip(qubits, invert_mask)): if inv: lines.append(args.format( 'x {0}; // Invert the following measurement\n', qubit)) lines.append(args.format('measure {0} -> {1:meas}[{2}];\n', qubit, self.key, i)) return ''.join(lines) def __repr__(self): return 'cirq.MeasurementGate({}, {})'.format(repr(self.key), repr(self.invert_mask)) def _value_equality_values_(self): return self.key, self.invert_mask def _default_measurement_key(qubits: Iterable[raw_types.QubitId]) -> str: return ','.join(str(q) for q in qubits) def measure(*qubits: raw_types.QubitId, key: Optional[str] = None, invert_mask: Tuple[bool, ...] = () ) -> gate_operation.GateOperation: for qubit in qubits: if isinstance(qubit, np.ndarray): raise ValueError( 'measure() was called a numpy ndarray. Perhaps you meant ' 'to call measure_state_vector on numpy array?' ) elif not isinstance(qubit, raw_types.QubitId): raise ValueError( 'measure() was called with type different than QubitId.') if key is None: key = _default_measurement_key(qubits) return MeasurementGate(key, invert_mask).on(*qubits) def measure_each(*qubits: raw_types.QubitId, key_func: Callable[[raw_types.QubitId], str] = str ) -> List[gate_operation.GateOperation]: return [MeasurementGate(key_func(q)).on(q) for q in qubits] class HPowGate(eigen_gate.EigenGate, gate_features.SingleQubitGate): def _eigen_components(self): s = np.sqrt(2) component0 = np.array([ [3 + 2 * s, 1 + s], [1 + s, 1] ]) / (4 + 2 * s) component1 = np.array([ [3 - 2 * s, 1 - s], [1 - s, 1] ]) / (4 - 2 * s) return [(0, component0), (1, component1)] def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if self._exponent != 1: return None zero = args.subspace_index(0) one = args.subspace_index(1) args.target_tensor[one] -= args.target_tensor[zero] args.target_tensor[one] *= -0.5 args.target_tensor[zero] -= args.target_tensor[one] p = 1j**(2 * self._exponent * self._global_shift) args.target_tensor *= np.sqrt(2) * p return args.target_tensor def _decompose_(self, qubits): q = qubits[0] if self._exponent == 1: yield cirq.Y(q)**0.5 yield cirq.XPowGate(global_shift=-0.25).on(q) return yield Y(q)**0.25 yield X(q)**self._exponent yield Y(q)**-0.25 def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: return protocols.CircuitDiagramInfo(('H',)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: args.validate_version('2.0') if self._exponent == 1: return args.format('h {0};\n', qubits[0]) else: return args.format('ry({0:half_turns}) {3};\n' 'rx({1:half_turns}) {3};\n' 'ry({2:half_turns}) {3};\n', 0.25, self._exponent, -0.25, qubits[0]) def __str__(self): if self._exponent == 1: return 'H' return 'H^{}'.format(self._exponent) def __repr__(self): if self._global_shift == 0: if self._exponent == 1: return 'cirq.H' return '(cirq.H**{!r})'.format(self._exponent) return ( 'cirq.HPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) class CZPowGate(eigen_gate.EigenGate, gate_features.TwoQubitGate, gate_features.InterchangeableQubitsGate): def _eigen_components(self): return [ (0, np.diag([1, 1, 1, 0])), (1, np.diag([0, 0, 0, 1])), ] def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Union[np.ndarray, NotImplementedType]: if protocols.is_parameterized(self): return NotImplemented c = 1j**(2 * self._exponent) one_one = linalg.slice_for_qubits_equal_to(args.axes, 0b11) args.target_tensor[one_one] *= c p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.target_tensor *= p return args.target_tensor def _phase_by_(self, phase_turns, qubit_index): return self def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: return protocols.CircuitDiagramInfo( wire_symbols=('@', '@'), exponent=self._diagram_exponent(args)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: if self._exponent != 1: return None args.validate_version('2.0') return args.format('cz {0},{1};\n', qubits[0], qubits[1]) def __str__(self) -> str: if self._exponent == 1: return 'CZ' return 'CZ**{!r}'.format(self._exponent) def __repr__(self) -> str: if self._global_shift == 0: if self._exponent == 1: return 'cirq.CZ' return '(cirq.CZ**{!r})'.format(self._exponent) return ( 'cirq.CZPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) def _rads_func_symbol(func_name: str, args: protocols.CircuitDiagramInfoArgs, half_turns: Any) -> str: unit = 'π' if args.use_unicode_characters else 'pi' if half_turns == 1: return '{}({})'.format(func_name, unit) if half_turns == -1: return '{}(-{})'.format(func_name, unit) return '{}({}{})'.format(func_name, half_turns, unit) class CNotPowGate(eigen_gate.EigenGate, gate_features.TwoQubitGate): def _decompose_(self, qubits): c, t = qubits yield Y(t)**-0.5 yield CZ(c, t)**self._exponent yield Y(t)**0.5 def _eigen_components(self): return [ (0, np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0.5, 0.5], [0, 0, 0.5, 0.5]])), (1, np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0.5, -0.5], [0, 0, -0.5, 0.5]])), ] def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: return protocols.CircuitDiagramInfo( wire_symbols=('@', 'X'), exponent=self._diagram_exponent(args)) def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if self._exponent != 1: return None oo = args.subspace_index(0b11) zo = args.subspace_index(0b01) args.available_buffer[oo] = args.target_tensor[oo] args.target_tensor[oo] = args.target_tensor[zo] args.target_tensor[zo] = args.available_buffer[oo] p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.target_tensor *= p return args.target_tensor def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: if self._exponent != 1: return None # Don't have an equivalent gate in QASM args.validate_version('2.0') return args.format('cx {0},{1};\n', qubits[0], qubits[1]) def __str__(self) -> str: if self._exponent == 1: return 'CNOT' return 'CNOT**{!r}'.format(self._exponent) def __repr__(self): if self._global_shift == 0: if self._exponent == 1: return 'cirq.CNOT' return '(cirq.CNOT**{!r})'.format(self._exponent) return ( 'cirq.CNotPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) def on(self, *args: raw_types.QubitId, **kwargs: raw_types.QubitId) -> gate_operation.GateOperation: if not kwargs: return super().on(*args) if not args and set(kwargs.keys()) == {'control', 'target'}: return super().on(kwargs['control'], kwargs['target']) raise ValueError( "Expected two positional argument or else 'target' AND 'control' " "keyword arguments. But got args={!r}, kwargs={!r}.".format( args, kwargs)) class SwapPowGate(eigen_gate.EigenGate, gate_features.TwoQubitGate, gate_features.InterchangeableQubitsGate): def _decompose_(self, qubits): a, b = qubits yield CNOT(a, b) yield CNOT(b, a) ** self._exponent yield CNOT(a, b) def _eigen_components(self): return [ (0, np.array([[1, 0, 0, 0], [0, 0.5, 0.5, 0], [0, 0.5, 0.5, 0], [0, 0, 0, 1]])), (1, np.array([[0, 0, 0, 0], [0, 0.5, -0.5, 0], [0, -0.5, 0.5, 0], [0, 0, 0, 0]])), ] def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if self._exponent != 1: return None zo = args.subspace_index(0b01) oz = args.subspace_index(0b10) args.available_buffer[zo] = args.target_tensor[zo] args.target_tensor[zo] = args.target_tensor[oz] args.target_tensor[oz] = args.available_buffer[zo] p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.target_tensor *= p return args.target_tensor def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: if not args.use_unicode_characters: return protocols.CircuitDiagramInfo( wire_symbols=('swap', 'swap'), exponent=self._diagram_exponent(args)) return protocols.CircuitDiagramInfo( wire_symbols=('×', '×'), exponent=self._diagram_exponent(args)) def _qasm_(self, args: protocols.QasmArgs, qubits: Tuple[raw_types.QubitId, ...]) -> Optional[str]: if self._exponent != 1: return None args.validate_version('2.0') return args.format('swap {0},{1};\n', qubits[0], qubits[1]) def __str__(self) -> str: if self._exponent == 1: return 'SWAP' return 'SWAP**{!r}'.format(self._exponent) def __repr__(self): if self._global_shift == 0: if self._exponent == 1: return 'cirq.SWAP' return '(cirq.SWAP**{!r})'.format(self._exponent) return ( 'cirq.SwapPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) class ISwapPowGate(eigen_gate.EigenGate, gate_features.InterchangeableQubitsGate, gate_features.TwoQubitGate): def _eigen_components(self): return [ (0, np.diag([1, 0, 0, 1])), (+0.5, np.array([[0, 0, 0, 0], [0, 0.5, 0.5, 0], [0, 0.5, 0.5, 0], [0, 0, 0, 0]])), (-0.5, np.array([[0, 0, 0, 0], [0, 0.5, -0.5, 0], [0, -0.5, 0.5, 0], [0, 0, 0, 0]])), ] def _decompose_(self, qubits): a, b = qubits yield CNOT(a, b) yield H(a) yield CNOT(b, a) yield S(a)**self._exponent yield CNOT(b, a) yield S(a)**-self._exponent yield H(a) yield CNOT(a, b) def _apply_unitary_(self, args: protocols.ApplyUnitaryArgs ) -> Optional[np.ndarray]: if self._exponent != 1: return None zo = args.subspace_index(0b01) oz = args.subspace_index(0b10) args.available_buffer[zo] = args.target_tensor[zo] args.target_tensor[zo] = args.target_tensor[oz] args.target_tensor[oz] = args.available_buffer[zo] args.target_tensor[zo] *= 1j args.target_tensor[oz] *= 1j p = 1j**(2 * self._exponent * self._global_shift) if p != 1: args.target_tensor *= p return args.target_tensor def _circuit_diagram_info_(self, args: protocols.CircuitDiagramInfoArgs ) -> protocols.CircuitDiagramInfo: return protocols.CircuitDiagramInfo( wire_symbols=('iSwap', 'iSwap'), exponent=self._diagram_exponent(args)) def __str__(self) -> str: if self._exponent == 1: return 'ISWAP' return 'ISWAP**{!r}'.format(self._exponent) def __repr__(self): if self._global_shift == 0: if self._exponent == 1: return 'cirq.ISWAP' return '(cirq.ISWAP**{!r})'.format(self._exponent) return ( 'cirq.ISwapPowGate(exponent={!r}, ' 'global_shift={!r})' ).format(self._exponent, self._global_shift) def Rx(rads: float) -> XPowGate: return XPowGate(exponent=rads / np.pi, global_shift=-0.5) def Ry(rads: float) -> YPowGate: return YPowGate(exponent=rads / np.pi, global_shift=-0.5) def Rz(rads: float) -> ZPowGate: return ZPowGate(exponent=rads / np.pi, global_shift=-0.5) X = XPowGate() #: The Pauli Y gate. #: #: Matrix: #: #: [[0, -i], #: [i, 0]] Y = YPowGate() # The Pauli Z gate. # # Matrix: # # [[1, 0], # [0, -1]] Z = ZPowGate() # The Hadamard gate. # # Matrix: # # [[s, s], # [s, -s]] # where s = sqrt(0.5). H = HPowGate() # The Clifford S gate. # # Matrix: # # [[1, 0], # [0, i]] S = Z**0.5 # The T gate. # # Matrix: # # [[1, 0] # [0, exp(i pi / 4)]] T = Z**0.25 # The controlled Z gate. # # Matrix: # # [[1, 0, 0, 0], # [0, 1, 0, 0], # [0, 0, 1, 0], # [0, 0, 0, -1]] CZ = CZPowGate() # The controlled NOT gate. # # Matrix: # # [[1, 0, 0, 0], # [0, 1, 0, 0], # [0, 0, 0, 1], # [0, 0, 1, 0]] CNOT = CNotPowGate() # The swap gate. # # Matrix: # # [[1, 0, 0, 0], # [0, 0, 1, 0], # [0, 1, 0, 0], # [0, 0, 0, 1]] SWAP = SwapPowGate() # The iswap gate. # # Matrix: # # [[1, 0, 0, 0], # [0, 0, i, 0], # [0, i, 0, 0], # [0, 0, 0, 1]] ISWAP = ISwapPowGate()

true

f72adde5fd070ac204654007f643a021dddeff3a

4,967

py

Python

sensirion_shdlc_sensorbridge/commands/firmware_update.py

Sensirion/python-shdlc-sensorbridge

c441c17d89697ecf0f7b61955f54c3da195e30e6

[ "BSD-3-Clause" ]

null

sensirion_shdlc_sensorbridge/commands/firmware_update.py

Sensirion/python-shdlc-sensorbridge

c441c17d89697ecf0f7b61955f54c3da195e30e6

[ "BSD-3-Clause" ]

1

2021-03-28T22:15:29.000Z

2021-11-03T09:06:14.000Z

sensirion_shdlc_sensorbridge/commands/firmware_update.py

Sensirion/python-shdlc-sensorbridge

c441c17d89697ecf0f7b61955f54c3da195e30e6

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # (c) Copyright 2020 Sensirion AG, Switzerland ############################################################################## ############################################################################## # _____ _ _ _______ _____ ____ _ _ # / ____| /\ | | | |__ __|_ _/ __ \| \ | | # | | / \ | | | | | | | || | | | \| | # | | / /\ \| | | | | | | || | | | . ` | # | |____ / ____ \ |__| | | | _| || |__| | |\ | # \_____/_/ \_\____/ |_| |_____\____/|_| \_| # # THIS FILE IS AUTOMATICALLY GENERATED AND MUST NOT BE EDITED MANUALLY! # # Generator: sensirion-shdlc-interface-generator 0.5.1 # Product: Sensor Bridge # Version: 0.1.0 # ############################################################################## ############################################################################## # flake8: noqa from __future__ import absolute_import, division, print_function from sensirion_shdlc_driver.command import ShdlcCommand from struct import pack, unpack import logging log = logging.getLogger(__name__) class SensorBridgeCmdFirmwareUpdateBase(ShdlcCommand): """ SHDLC command 0xF3: "Firmware Update". """ def __init__(self, *args, **kwargs): super(SensorBridgeCmdFirmwareUpdateBase, self).__init__( 0xF3, *args, **kwargs) class SensorBridgeCmdEnterBootloader(SensorBridgeCmdFirmwareUpdateBase): def __init__(self): """ Enter Bootloader Command Command to enter into the bootloader mode. The device will reboot into bootloader mode and wait until the new Firmware is received (start update command expected). Even after a power reset, the device returns into bootloader mode. The response frame is sent before the reset. .. note:: After the response frame is received, the device will not accept new commands until fully booted (wait at least 1 s). """ super(SensorBridgeCmdEnterBootloader, self).__init__( data=[], max_response_time=0.5, post_processing_time=1.0, min_response_length=0, max_response_length=0 ) class SensorBridgeCmdStartUpdate(SensorBridgeCmdFirmwareUpdateBase): def __init__(self): """ Start Update Command Command to start the firmware update. The devices flash will be erased (except bootloader) and the internal pointers resetted. The device is then ready to receive the new firmware with the update data command. .. note:: Only supported when in bootloader mode. """ super(SensorBridgeCmdStartUpdate, self).__init__( data=b"".join([bytes(bytearray([0x01]))]), max_response_time=0.5, post_processing_time=0.0, min_response_length=0, max_response_length=0 ) class SensorBridgeCmdUpdateData(SensorBridgeCmdFirmwareUpdateBase): def __init__(self, data): """ Update Data Command Command to send the new firmware data as hex code in binary format. .. note:: Only supported when in bootloader mode after receiving the start update command. Send even number of bytes except for the last frame. :param bytes data: Firmware hex data in binary format. """ super(SensorBridgeCmdUpdateData, self).__init__( data=b"".join([bytes(bytearray([0x02])), bytes(bytearray(data))]), max_response_time=0.5, post_processing_time=0.0, min_response_length=0, max_response_length=0 ) class SensorBridgeCmdStopUpdate(SensorBridgeCmdFirmwareUpdateBase): def __init__(self, checksum): """ Stop Update Command After all update data frames are sent, the stop update marks the end of the update sequence. The checksum is sent to the device and verification is done. The device state represents the success of the update sequence. If successfully, the device writes the signature and reboots into the application. .. note:: The checksum is calculated the same way as the SHDLC checksum. First sum all firmware update data bytes and then take the LSB of the result and invert it. This will be the checksum. :param int checksum: Checksum of the firmware data. """ super(SensorBridgeCmdStopUpdate, self).__init__( data=b"".join([bytes(bytearray([0x03])), pack(">B", checksum)]), max_response_time=1.0, post_processing_time=0.0, min_response_length=0, max_response_length=0 )

35.733813

79

0.562915

true

f72adf1f6af0532364f442d4ae606bac033e4b53

584

py

Python

tutorials/migrations/0031_auto_20210211_1605.py

ericrobskyhuntley/vialab.mit.edu

1318d03b8eeb106c1662052e1caa53290e206ae7

[ "MIT" ]

null

tutorials/migrations/0031_auto_20210211_1605.py

ericrobskyhuntley/vialab.mit.edu

1318d03b8eeb106c1662052e1caa53290e206ae7

[ "MIT" ]

null

tutorials/migrations/0031_auto_20210211_1605.py

ericrobskyhuntley/vialab.mit.edu

1318d03b8eeb106c1662052e1caa53290e206ae7

[ "MIT" ]

null

# Generated by Django 3.0.4 on 2021-02-11 21:05 from django.db import migrations import martor.models class Migration(migrations.Migration): dependencies = [ ('tutorials', '0030_auto_20200408_1257'), ] operations = [ migrations.AlterField( model_name='historicalsoftware', name='desc', field=martor.models.MartorField(max_length=400), ), migrations.AlterField( model_name='software', name='desc', field=martor.models.MartorField(max_length=400), ), ]

23.36

60

0.601027

from django.db import migrations import martor.models class Migration(migrations.Migration): dependencies = [ ('tutorials', '0030_auto_20200408_1257'), ] operations = [ migrations.AlterField( model_name='historicalsoftware', name='desc', field=martor.models.MartorField(max_length=400), ), migrations.AlterField( model_name='software', name='desc', field=martor.models.MartorField(max_length=400), ), ]

true

f72adfbd0b4913e9c0e119e52b6aa8237cc00b2a

2,757

py

Python

tools/count_opsize.py

VDIGPKU/OPANAS

873ff09a65d3253ce8351e54880a642517f7e8b5

[ "Apache-2.0" ]

39

2021-03-31T21:15:48.000Z

2022-03-30T03:34:14.000Z

tools/count_opsize.py

VDIGPKU/OPANAS

873ff09a65d3253ce8351e54880a642517f7e8b5

[ "Apache-2.0" ]

8

2021-04-06T07:58:03.000Z

2022-01-11T17:10:51.000Z

tools/count_opsize.py

VDIGPKU/OPANAS

873ff09a65d3253ce8351e54880a642517f7e8b5

[ "Apache-2.0" ]

4

2021-04-06T03:28:56.000Z

2022-03-06T19:57:50.000Z

import argparse import os import warnings import mmcv import torch from mmcv import Config, DictAction from mmcv.cnn import fuse_conv_bn from mmcv.parallel import MMDataParallel, MMDistributedDataParallel from mmcv.runner import get_dist_info, init_dist, load_checkpoint from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner, OptimizerHook, build_optimizer) from mmdet.apis import multi_gpu_test_search, single_gpu_test_search from mmdet.core import wrap_fp16_model from mmdet.datasets import (build_dataloader, build_dataset, replace_ImageToTensor) from mmdet.models import build_detector import numpy as np from torch.autograd import Variable import collections import sys import time import copy from mmdet.core import encode_mask_results, tensor2imgs import logging sys.setrecursionlimit(10000) import argparse import torch.distributed as dist import functools import random import os from mmdet.models.necks.spos_opsc import OPS PRIMITIVES = ['TDM_dcn', 'BUM_dcn', 'PCONV_dcn', 'FSM_dcn'] def countop(paths, channel): opsize = 0 fp = 0 for path in paths: op = OPS[path](channel, channel, True, True) opsize += op.size fp += op.fp #print(opsize) return opsize, fp def parse_args(): parser = argparse.ArgumentParser(description='Train a detector') parser.add_argument('log', help='train log file path', default='./work_dirs/faster_rcnn_r50_sposfpn3_uniform_dcn_p4st12_c64_256_1x_coco/epoch_12_ea_prun_0_20210104_075032.log') args = parser.parse_args() return args def main(): args = parse_args() print(args) name = args.log print(os.getcwd()) print(name) #name = '/data/liangtingting/projects/panas_super/work_dirs/faster_rcnn_r50_sposfpn3_uniform_dcn_p4st12_c64_256_1x_coco/epoch_12_ea_prun_0_20210104_075032.log' op_name = os.path.splitext(name)[0] + '.txt' print(op_name) f = open(name, 'r') wf = open(op_name,'w') for line in f: if '[' in line and 'AP' in line: st = line.index('(') ed = line.index(')') paths = str(line[st+1:ed]) paths = paths.split(', ') op_paths = [int(i) for i in paths] channel = op_paths[-1] cand = [PRIMITIVES[i] for i in op_paths[:-1]] opsize, fp = countop(cand, channel) ap = line.index('AP') map = line[ap+3:ap+15] wf.write(str(cand) + ' ' + str(channel) + ' ' + map + ' ' + str(opsize) + ' ' + str(fp) + '\n') print(cand, channel, map, opsize, fp) if 'top 50 result' in line: break if __name__ == '__main__': main()

31.689655

163

0.660863

import argparse import os import warnings import mmcv import torch from mmcv import Config, DictAction from mmcv.cnn import fuse_conv_bn from mmcv.parallel import MMDataParallel, MMDistributedDataParallel from mmcv.runner import get_dist_info, init_dist, load_checkpoint from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner, OptimizerHook, build_optimizer) from mmdet.apis import multi_gpu_test_search, single_gpu_test_search from mmdet.core import wrap_fp16_model from mmdet.datasets import (build_dataloader, build_dataset, replace_ImageToTensor) from mmdet.models import build_detector import numpy as np from torch.autograd import Variable import collections import sys import time import copy from mmdet.core import encode_mask_results, tensor2imgs import logging sys.setrecursionlimit(10000) import argparse import torch.distributed as dist import functools import random import os from mmdet.models.necks.spos_opsc import OPS PRIMITIVES = ['TDM_dcn', 'BUM_dcn', 'PCONV_dcn', 'FSM_dcn'] def countop(paths, channel): opsize = 0 fp = 0 for path in paths: op = OPS[path](channel, channel, True, True) opsize += op.size fp += op.fp return opsize, fp def parse_args(): parser = argparse.ArgumentParser(description='Train a detector') parser.add_argument('log', help='train log file path', default='./work_dirs/faster_rcnn_r50_sposfpn3_uniform_dcn_p4st12_c64_256_1x_coco/epoch_12_ea_prun_0_20210104_075032.log') args = parser.parse_args() return args def main(): args = parse_args() print(args) name = args.log print(os.getcwd()) print(name) op_name = os.path.splitext(name)[0] + '.txt' print(op_name) f = open(name, 'r') wf = open(op_name,'w') for line in f: if '[' in line and 'AP' in line: st = line.index('(') ed = line.index(')') paths = str(line[st+1:ed]) paths = paths.split(', ') op_paths = [int(i) for i in paths] channel = op_paths[-1] cand = [PRIMITIVES[i] for i in op_paths[:-1]] opsize, fp = countop(cand, channel) ap = line.index('AP') map = line[ap+3:ap+15] wf.write(str(cand) + ' ' + str(channel) + ' ' + map + ' ' + str(opsize) + ' ' + str(fp) + '\n') print(cand, channel, map, opsize, fp) if 'top 50 result' in line: break if __name__ == '__main__': main()

true

f72ae0a27f7cd75894571c6fa943dd5463f7ef49

15,394

py

Python

tests/test_rtc_parse_aec.py

fyntex/lib-cl-sii-python

b6ffb72be1f173a1d2e44b17ae5c08caf96ebf34

[ "MIT" ]

8

2020-03-07T19:58:40.000Z

2021-12-15T13:47:40.000Z

tests/test_rtc_parse_aec.py

fyntex/lib-cl-sii-python

b6ffb72be1f173a1d2e44b17ae5c08caf96ebf34

[ "MIT" ]

141

2020-01-17T22:47:35.000Z

2022-03-31T18:29:47.000Z

tests/test_rtc_parse_aec.py

fyntex/lib-cl-sii-python

b6ffb72be1f173a1d2e44b17ae5c08caf96ebf34

[ "MIT" ]

3

2020-03-07T20:30:02.000Z

2021-03-22T03:14:26.000Z

from __future__ import annotations import unittest from datetime import date, datetime from cl_sii.dte.data_models import DteDataL1, DteXmlData from cl_sii.dte.constants import TipoDteEnum from cl_sii.dte.parse import DTE_XMLNS from cl_sii.libs import encoding_utils from cl_sii.libs import tz_utils from cl_sii.libs import xml_utils from cl_sii.rut import Rut from cl_sii.rtc.data_models_aec import CesionAecXml, AecXml from cl_sii.rtc.parse_aec import AEC_XML_SCHEMA_OBJ, parse_aec_xml, validate_aec_xml from .utils import read_test_file_bytes class AecXmlSchemaTest(unittest.TestCase): """ Tests for AEC XML schema. """ @unittest.skip("TODO: Implement for 'AEC_XML_SCHEMA_OBJ'.") def test_AEC_XML_SCHEMA_OBJ(self): self.assertIsNotNone(AEC_XML_SCHEMA_OBJ) class AecXmlValidatorTest(unittest.TestCase): """ Tests for :func:`validate_aec_xml`. """ def _set_obj_1(self) -> None: aec_xml_bytes: bytes = read_test_file_bytes( 'test_data/sii-rtc/AEC--76354771-K--33--170--SEQ-2.xml', ) self.aec_1_xml_bytes = aec_xml_bytes def _set_obj_2(self) -> None: aec_xml_bytes: bytes = read_test_file_bytes( 'test_data/sii-rtc/AEC--76399752-9--33--25568--SEQ-1.xml', ) self.aec_2_xml_bytes = aec_xml_bytes def test_validate_aec_xml_ok_1(self) -> None: self._set_obj_1() aec_xml_bytes = self.aec_1_xml_bytes xml_doc = xml_utils.parse_untrusted_xml(aec_xml_bytes) try: validate_aec_xml(xml_doc) except xml_utils.XmlSchemaDocValidationError as exc: self.fail(f'{exc.__class__.__name__} raised') expected_xml_root_tag = '{%s}AEC' % DTE_XMLNS self.assertEqual(xml_doc.getroottree().getroot().tag, expected_xml_root_tag) def test_validate_aec_xml_ok_2(self) -> None: self._set_obj_2() aec_xml_bytes = self.aec_2_xml_bytes xml_doc = xml_utils.parse_untrusted_xml(aec_xml_bytes) try: validate_aec_xml(xml_doc) except xml_utils.XmlSchemaDocValidationError as exc: self.fail(f'{exc.__class__.__name__} raised') expected_xml_root_tag = '{%s}AEC' % DTE_XMLNS self.assertEqual(xml_doc.getroottree().getroot().tag, expected_xml_root_tag) @unittest.skip("TODO: Implement for 'validate_aec_xml'.") def test_validate_aec_xml_fail(self) -> None: self.assertIsNotNone(validate_aec_xml) class AecXmlParserTest(unittest.TestCase): """ Tests for :func:`parse_aec_xml`. """ def _set_obj_1(self) -> None: aec_xml_bytes: bytes = read_test_file_bytes( 'test_data/sii-rtc/AEC--76354771-K--33--170--SEQ-2.xml', ) aec_signature_value: bytes = encoding_utils.decode_base64_strict( read_test_file_bytes( 'test_data/sii-crypto/AEC--76354771-K--33--170--SEQ-2-signature-value-base64.txt', ), ) aec_cert_der_bytes: bytes = read_test_file_bytes( 'test_data/sii-crypto/AEC--76354771-K--33--170--SEQ-2-cert.der', ) aec_dte_cert_der_bytes: bytes = read_test_file_bytes( 'test_data/sii-crypto/DTE--76354771-K--33--170-cert.der', ) aec_dte_signature_value: bytes = encoding_utils.decode_base64_strict( read_test_file_bytes( 'test_data/sii-crypto/DTE--76354771-K--33--170-signature-value-base64.txt', ), ) self.aec_1_xml_bytes = aec_xml_bytes self.aec_1_signature_value = aec_signature_value self.aec_1_cert_der_bytes = aec_cert_der_bytes self.aec_1_dte_cert_der_bytes = aec_dte_cert_der_bytes self.aec_1_dte_signature_value = aec_dte_signature_value def _set_obj_2(self) -> None: aec_xml_bytes: bytes = read_test_file_bytes( 'test_data/sii-rtc/AEC--76399752-9--33--25568--SEQ-1.xml', ) aec_signature_value: bytes = encoding_utils.decode_base64_strict( read_test_file_bytes( 'test_data/sii-crypto/AEC--76399752-9--33--25568--SEQ-1-signature-value-base64.txt', ), ) aec_cert_der_bytes: bytes = read_test_file_bytes( 'test_data/sii-crypto/AEC--76399752-9--33--25568--SEQ-1-cert.der', ) aec_dte_cert_der_bytes: bytes = read_test_file_bytes( 'test_data/sii-crypto/DTE--76399752-9--33--25568-cert.der', ) aec_dte_signature_value: bytes = encoding_utils.decode_base64_strict( read_test_file_bytes( 'test_data/sii-crypto/DTE--76399752-9--33--25568-signature-value-base64.txt', ), ) self.aec_2_xml_bytes = aec_xml_bytes self.aec_2_signature_value = aec_signature_value self.aec_2_cert_der_bytes = aec_cert_der_bytes self.aec_2_dte_cert_der_bytes = aec_dte_cert_der_bytes self.aec_2_dte_signature_value = aec_dte_signature_value def test_parse_aec_xml_ok_1(self) -> None: self._set_obj_1() aec_xml_bytes = self.aec_1_xml_bytes aec_signature_value = self.aec_1_signature_value aec_cert_der_bytes = self.aec_1_cert_der_bytes aec_dte_signature_value = self.aec_1_dte_signature_value aec_dte_cert_der_bytes = self.aec_1_dte_cert_der_bytes expected_output = AecXml( dte=DteXmlData( emisor_rut=Rut('76354771-K'), tipo_dte=TipoDteEnum.FACTURA_ELECTRONICA, folio=170, fecha_emision_date=date(2019, 4, 1), receptor_rut=Rut('96790240-3'), monto_total=2996301, emisor_razon_social='INGENIERIA ENACON SPA', receptor_razon_social='MINERA LOS PELAMBRES', fecha_vencimiento_date=None, firma_documento_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 1, 1, 36, 40), tz=DteXmlData.DATETIME_FIELDS_TZ, ), signature_value=aec_dte_signature_value, signature_x509_cert_der=aec_dte_cert_der_bytes, emisor_giro='Ingenieria y Construccion', emisor_email='ENACONLTDA@GMAIL.COM', receptor_email=None, ), cedente_rut=Rut('76389992-6'), cesionario_rut=Rut('76598556-0'), fecha_firma_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 5, 12, 57, 32), tz=AecXml.DATETIME_FIELDS_TZ, ), signature_value=aec_signature_value, signature_x509_cert_der=aec_cert_der_bytes, cesiones=[ CesionAecXml( dte=DteDataL1( emisor_rut=Rut('76354771-K'), tipo_dte=TipoDteEnum.FACTURA_ELECTRONICA, folio=170, fecha_emision_date=date(2019, 4, 1), receptor_rut=Rut('96790240-3'), monto_total=2996301, ), seq=1, cedente_rut=Rut('76354771-K'), cesionario_rut=Rut('76389992-6'), monto_cesion=2996301, fecha_cesion_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 1, 10, 22, 2), tz=CesionAecXml.DATETIME_FIELDS_TZ, ), fecha_ultimo_vencimiento=date(2019, 5, 1), cedente_razon_social='SERVICIOS BONILLA Y LOPEZ Y COMPAÑIA LIMITADA', cedente_direccion='MERCED 753 16 ARBOLEDA DE QUIILOTA', cedente_email='enaconltda@gmail.com', cedente_persona_autorizada_rut=Rut('76354771-K'), cedente_persona_autorizada_nombre='SERVICIOS BONILLA Y LOPEZ Y COMPAÑIA LIM', cesionario_razon_social='ST CAPITAL S.A.', cesionario_direccion='Isidora Goyenechea 2939 Oficina 602', cesionario_email='fynpal-app-notif-st-capital@fynpal.com', dte_deudor_email=None, cedente_declaracion_jurada=( 'Se declara bajo juramento que SERVICIOS BONILLA Y LOPEZ Y COMPAÑIA ' 'LIMITADA, RUT 76354771-K ha puesto a disposición del cesionario ST ' 'CAPITAL S.A., RUT 76389992-6, el o los documentos donde constan los ' 'recibos de las mercaderías entregadas o servicios prestados, entregados ' 'por parte del deudor de la factura MINERA LOS PELAMBRES, RUT 96790240-3, ' 'deacuerdo a lo establecido en la Ley N°19.983.' ), ), CesionAecXml( dte=DteDataL1( emisor_rut=Rut('76354771-K'), tipo_dte=TipoDteEnum.FACTURA_ELECTRONICA, folio=170, fecha_emision_date=date(2019, 4, 1), receptor_rut=Rut('96790240-3'), monto_total=2996301, ), seq=2, cedente_rut=Rut('76389992-6'), cesionario_rut=Rut('76598556-0'), monto_cesion=2996301, fecha_cesion_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 5, 12, 57, 32), tz=CesionAecXml.DATETIME_FIELDS_TZ, ), fecha_ultimo_vencimiento=date(2019, 5, 1), cedente_razon_social='ST CAPITAL S.A.', cedente_direccion='Isidora Goyenechea 2939 Oficina 602', cedente_email='APrat@Financiaenlinea.com', cesionario_razon_social='Fondo de Inversión Privado Deuda y Facturas', cesionario_direccion='Arrayan 2750 Oficina 703 Providencia', cesionario_email='solicitudes@stcapital.cl', cedente_persona_autorizada_rut=Rut('16360379-9'), cedente_persona_autorizada_nombre='ANDRES PRATS VIAL', dte_deudor_email=None, cedente_declaracion_jurada=( 'Se declara bajo juramento que ST CAPITAL S.A., RUT 76389992-6 ha puesto ' 'a disposicion del cesionario Fondo de Inversión Privado Deuda y Facturas, ' 'RUT 76598556-0, el documento validamente emitido al deudor MINERA LOS ' 'PELAMBRES, RUT 96790240-3.' ), ), ], contacto_nombre='ST Capital Servicios Financieros', contacto_telefono=None, contacto_email='APrat@Financiaenlinea.com', ) xml_doc = xml_utils.parse_untrusted_xml(aec_xml_bytes) aec_xml = parse_aec_xml(xml_doc) self.assertEqual(aec_xml, expected_output) def test_parse_aec_xml_ok_2(self) -> None: self._set_obj_2() aec_xml_bytes = self.aec_2_xml_bytes aec_signature_value = self.aec_2_signature_value aec_cert_der_bytes = self.aec_2_cert_der_bytes aec_dte_signature_value = self.aec_2_dte_signature_value aec_dte_cert_der_bytes = self.aec_2_dte_cert_der_bytes expected_output = AecXml( dte=DteXmlData( emisor_rut=Rut('76399752-9'), tipo_dte=TipoDteEnum.FACTURA_ELECTRONICA, folio=25568, fecha_emision_date=date(2019, 3, 29), receptor_rut=Rut('96874030-K'), monto_total=230992, emisor_razon_social='COMERCIALIZADORA INNOVA MOBEL SPA', receptor_razon_social='EMPRESAS LA POLAR S.A.', fecha_vencimiento_date=None, firma_documento_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 3, 28, 13, 59, 52), tz=DteXmlData.DATETIME_FIELDS_TZ, ), signature_value=aec_dte_signature_value, signature_x509_cert_der=aec_dte_cert_der_bytes, emisor_giro='COMERCIALIZACION DE PRODUCTOS PARA EL HOGAR', emisor_email='ANGEL.PEZO@APCASESORIAS.CL', receptor_email=None, ), cedente_rut=Rut('76399752-9'), cesionario_rut=Rut('76389992-6'), fecha_firma_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 4, 9, 9, 52), tz=AecXml.DATETIME_FIELDS_TZ, ), signature_value=aec_signature_value, signature_x509_cert_der=aec_cert_der_bytes, cesiones=[ CesionAecXml( dte=DteDataL1( emisor_rut=Rut('76399752-9'), tipo_dte=TipoDteEnum.FACTURA_ELECTRONICA, folio=25568, fecha_emision_date=date(2019, 3, 29), receptor_rut=Rut('96874030-K'), monto_total=230992, ), seq=1, cedente_rut=Rut('76399752-9'), cesionario_rut=Rut('76389992-6'), monto_cesion=230992, fecha_cesion_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 4, 9, 9, 52), tz=CesionAecXml.DATETIME_FIELDS_TZ, ), fecha_ultimo_vencimiento=date(2019, 4, 28), cedente_razon_social='COMERCIALIZADORA INNOVA MOBEL SPA', cedente_direccion='LOS CIPRESES 2834', cedente_email='camilo.perez@innovamobel.cl', cedente_persona_autorizada_rut=Rut('76399752-9'), cedente_persona_autorizada_nombre='COMERCIALIZADORA INNOVA MOBEL SPA', cesionario_razon_social='ST CAPITAL S.A.', cesionario_direccion='Isidora Goyenechea 2939 Oficina 602', cesionario_email='fynpal-app-notif-st-capital@fynpal.com', dte_deudor_email=None, cedente_declaracion_jurada=( 'Se declara bajo juramento que COMERCIALIZADORA INNOVA MOBEL SPA, RUT ' '76399752-9 ha puesto a disposición del cesionario ST CAPITAL S.A., RUT ' '76389992-6, el o los documentos donde constan los recibos de las ' 'mercaderías entregadas o servicios prestados, entregados por parte del ' 'deudor de la factura EMPRESAS LA POLAR S.A., RUT 96874030-K, deacuerdo a ' 'lo establecido en la Ley N°19.983.' ), ), ], contacto_nombre=None, contacto_telefono=None, contacto_email='fynpal-app-notif-st-capital@fynpal.com', ) xml_doc = xml_utils.parse_untrusted_xml(aec_xml_bytes) aec_xml = parse_aec_xml(xml_doc) self.assertEqual(aec_xml, expected_output)

44.235632

100

0.59023

from __future__ import annotations import unittest from datetime import date, datetime from cl_sii.dte.data_models import DteDataL1, DteXmlData from cl_sii.dte.constants import TipoDteEnum from cl_sii.dte.parse import DTE_XMLNS from cl_sii.libs import encoding_utils from cl_sii.libs import tz_utils from cl_sii.libs import xml_utils from cl_sii.rut import Rut from cl_sii.rtc.data_models_aec import CesionAecXml, AecXml from cl_sii.rtc.parse_aec import AEC_XML_SCHEMA_OBJ, parse_aec_xml, validate_aec_xml from .utils import read_test_file_bytes class AecXmlSchemaTest(unittest.TestCase): @unittest.skip("TODO: Implement for 'AEC_XML_SCHEMA_OBJ'.") def test_AEC_XML_SCHEMA_OBJ(self): self.assertIsNotNone(AEC_XML_SCHEMA_OBJ) class AecXmlValidatorTest(unittest.TestCase): def _set_obj_1(self) -> None: aec_xml_bytes: bytes = read_test_file_bytes( 'test_data/sii-rtc/AEC--76354771-K--33--170--SEQ-2.xml', ) self.aec_1_xml_bytes = aec_xml_bytes def _set_obj_2(self) -> None: aec_xml_bytes: bytes = read_test_file_bytes( 'test_data/sii-rtc/AEC--76399752-9--33--25568--SEQ-1.xml', ) self.aec_2_xml_bytes = aec_xml_bytes def test_validate_aec_xml_ok_1(self) -> None: self._set_obj_1() aec_xml_bytes = self.aec_1_xml_bytes xml_doc = xml_utils.parse_untrusted_xml(aec_xml_bytes) try: validate_aec_xml(xml_doc) except xml_utils.XmlSchemaDocValidationError as exc: self.fail(f'{exc.__class__.__name__} raised') expected_xml_root_tag = '{%s}AEC' % DTE_XMLNS self.assertEqual(xml_doc.getroottree().getroot().tag, expected_xml_root_tag) def test_validate_aec_xml_ok_2(self) -> None: self._set_obj_2() aec_xml_bytes = self.aec_2_xml_bytes xml_doc = xml_utils.parse_untrusted_xml(aec_xml_bytes) try: validate_aec_xml(xml_doc) except xml_utils.XmlSchemaDocValidationError as exc: self.fail(f'{exc.__class__.__name__} raised') expected_xml_root_tag = '{%s}AEC' % DTE_XMLNS self.assertEqual(xml_doc.getroottree().getroot().tag, expected_xml_root_tag) @unittest.skip("TODO: Implement for 'validate_aec_xml'.") def test_validate_aec_xml_fail(self) -> None: self.assertIsNotNone(validate_aec_xml) class AecXmlParserTest(unittest.TestCase): def _set_obj_1(self) -> None: aec_xml_bytes: bytes = read_test_file_bytes( 'test_data/sii-rtc/AEC--76354771-K--33--170--SEQ-2.xml', ) aec_signature_value: bytes = encoding_utils.decode_base64_strict( read_test_file_bytes( 'test_data/sii-crypto/AEC--76354771-K--33--170--SEQ-2-signature-value-base64.txt', ), ) aec_cert_der_bytes: bytes = read_test_file_bytes( 'test_data/sii-crypto/AEC--76354771-K--33--170--SEQ-2-cert.der', ) aec_dte_cert_der_bytes: bytes = read_test_file_bytes( 'test_data/sii-crypto/DTE--76354771-K--33--170-cert.der', ) aec_dte_signature_value: bytes = encoding_utils.decode_base64_strict( read_test_file_bytes( 'test_data/sii-crypto/DTE--76354771-K--33--170-signature-value-base64.txt', ), ) self.aec_1_xml_bytes = aec_xml_bytes self.aec_1_signature_value = aec_signature_value self.aec_1_cert_der_bytes = aec_cert_der_bytes self.aec_1_dte_cert_der_bytes = aec_dte_cert_der_bytes self.aec_1_dte_signature_value = aec_dte_signature_value def _set_obj_2(self) -> None: aec_xml_bytes: bytes = read_test_file_bytes( 'test_data/sii-rtc/AEC--76399752-9--33--25568--SEQ-1.xml', ) aec_signature_value: bytes = encoding_utils.decode_base64_strict( read_test_file_bytes( 'test_data/sii-crypto/AEC--76399752-9--33--25568--SEQ-1-signature-value-base64.txt', ), ) aec_cert_der_bytes: bytes = read_test_file_bytes( 'test_data/sii-crypto/AEC--76399752-9--33--25568--SEQ-1-cert.der', ) aec_dte_cert_der_bytes: bytes = read_test_file_bytes( 'test_data/sii-crypto/DTE--76399752-9--33--25568-cert.der', ) aec_dte_signature_value: bytes = encoding_utils.decode_base64_strict( read_test_file_bytes( 'test_data/sii-crypto/DTE--76399752-9--33--25568-signature-value-base64.txt', ), ) self.aec_2_xml_bytes = aec_xml_bytes self.aec_2_signature_value = aec_signature_value self.aec_2_cert_der_bytes = aec_cert_der_bytes self.aec_2_dte_cert_der_bytes = aec_dte_cert_der_bytes self.aec_2_dte_signature_value = aec_dte_signature_value def test_parse_aec_xml_ok_1(self) -> None: self._set_obj_1() aec_xml_bytes = self.aec_1_xml_bytes aec_signature_value = self.aec_1_signature_value aec_cert_der_bytes = self.aec_1_cert_der_bytes aec_dte_signature_value = self.aec_1_dte_signature_value aec_dte_cert_der_bytes = self.aec_1_dte_cert_der_bytes expected_output = AecXml( dte=DteXmlData( emisor_rut=Rut('76354771-K'), tipo_dte=TipoDteEnum.FACTURA_ELECTRONICA, folio=170, fecha_emision_date=date(2019, 4, 1), receptor_rut=Rut('96790240-3'), monto_total=2996301, emisor_razon_social='INGENIERIA ENACON SPA', receptor_razon_social='MINERA LOS PELAMBRES', fecha_vencimiento_date=None, firma_documento_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 1, 1, 36, 40), tz=DteXmlData.DATETIME_FIELDS_TZ, ), signature_value=aec_dte_signature_value, signature_x509_cert_der=aec_dte_cert_der_bytes, emisor_giro='Ingenieria y Construccion', emisor_email='ENACONLTDA@GMAIL.COM', receptor_email=None, ), cedente_rut=Rut('76389992-6'), cesionario_rut=Rut('76598556-0'), fecha_firma_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 5, 12, 57, 32), tz=AecXml.DATETIME_FIELDS_TZ, ), signature_value=aec_signature_value, signature_x509_cert_der=aec_cert_der_bytes, cesiones=[ CesionAecXml( dte=DteDataL1( emisor_rut=Rut('76354771-K'), tipo_dte=TipoDteEnum.FACTURA_ELECTRONICA, folio=170, fecha_emision_date=date(2019, 4, 1), receptor_rut=Rut('96790240-3'), monto_total=2996301, ), seq=1, cedente_rut=Rut('76354771-K'), cesionario_rut=Rut('76389992-6'), monto_cesion=2996301, fecha_cesion_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 1, 10, 22, 2), tz=CesionAecXml.DATETIME_FIELDS_TZ, ), fecha_ultimo_vencimiento=date(2019, 5, 1), cedente_razon_social='SERVICIOS BONILLA Y LOPEZ Y COMPAÑIA LIMITADA', cedente_direccion='MERCED 753 16 ARBOLEDA DE QUIILOTA', cedente_email='enaconltda@gmail.com', cedente_persona_autorizada_rut=Rut('76354771-K'), cedente_persona_autorizada_nombre='SERVICIOS BONILLA Y LOPEZ Y COMPAÑIA LIM', cesionario_razon_social='ST CAPITAL S.A.', cesionario_direccion='Isidora Goyenechea 2939 Oficina 602', cesionario_email='fynpal-app-notif-st-capital@fynpal.com', dte_deudor_email=None, cedente_declaracion_jurada=( 'Se declara bajo juramento que SERVICIOS BONILLA Y LOPEZ Y COMPAÑIA ' 'LIMITADA, RUT 76354771-K ha puesto a disposición del cesionario ST ' 'CAPITAL S.A., RUT 76389992-6, el o los documentos donde constan los ' 'recibos de las mercaderías entregadas o servicios prestados, entregados ' 'por parte del deudor de la factura MINERA LOS PELAMBRES, RUT 96790240-3, ' 'deacuerdo a lo establecido en la Ley N°19.983.' ), ), CesionAecXml( dte=DteDataL1( emisor_rut=Rut('76354771-K'), tipo_dte=TipoDteEnum.FACTURA_ELECTRONICA, folio=170, fecha_emision_date=date(2019, 4, 1), receptor_rut=Rut('96790240-3'), monto_total=2996301, ), seq=2, cedente_rut=Rut('76389992-6'), cesionario_rut=Rut('76598556-0'), monto_cesion=2996301, fecha_cesion_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 5, 12, 57, 32), tz=CesionAecXml.DATETIME_FIELDS_TZ, ), fecha_ultimo_vencimiento=date(2019, 5, 1), cedente_razon_social='ST CAPITAL S.A.', cedente_direccion='Isidora Goyenechea 2939 Oficina 602', cedente_email='APrat@Financiaenlinea.com', cesionario_razon_social='Fondo de Inversión Privado Deuda y Facturas', cesionario_direccion='Arrayan 2750 Oficina 703 Providencia', cesionario_email='solicitudes@stcapital.cl', cedente_persona_autorizada_rut=Rut('16360379-9'), cedente_persona_autorizada_nombre='ANDRES PRATS VIAL', dte_deudor_email=None, cedente_declaracion_jurada=( 'Se declara bajo juramento que ST CAPITAL S.A., RUT 76389992-6 ha puesto ' 'a disposicion del cesionario Fondo de Inversión Privado Deuda y Facturas, ' 'RUT 76598556-0, el documento validamente emitido al deudor MINERA LOS ' 'PELAMBRES, RUT 96790240-3.' ), ), ], contacto_nombre='ST Capital Servicios Financieros', contacto_telefono=None, contacto_email='APrat@Financiaenlinea.com', ) xml_doc = xml_utils.parse_untrusted_xml(aec_xml_bytes) aec_xml = parse_aec_xml(xml_doc) self.assertEqual(aec_xml, expected_output) def test_parse_aec_xml_ok_2(self) -> None: self._set_obj_2() aec_xml_bytes = self.aec_2_xml_bytes aec_signature_value = self.aec_2_signature_value aec_cert_der_bytes = self.aec_2_cert_der_bytes aec_dte_signature_value = self.aec_2_dte_signature_value aec_dte_cert_der_bytes = self.aec_2_dte_cert_der_bytes expected_output = AecXml( dte=DteXmlData( emisor_rut=Rut('76399752-9'), tipo_dte=TipoDteEnum.FACTURA_ELECTRONICA, folio=25568, fecha_emision_date=date(2019, 3, 29), receptor_rut=Rut('96874030-K'), monto_total=230992, emisor_razon_social='COMERCIALIZADORA INNOVA MOBEL SPA', receptor_razon_social='EMPRESAS LA POLAR S.A.', fecha_vencimiento_date=None, firma_documento_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 3, 28, 13, 59, 52), tz=DteXmlData.DATETIME_FIELDS_TZ, ), signature_value=aec_dte_signature_value, signature_x509_cert_der=aec_dte_cert_der_bytes, emisor_giro='COMERCIALIZACION DE PRODUCTOS PARA EL HOGAR', emisor_email='ANGEL.PEZO@APCASESORIAS.CL', receptor_email=None, ), cedente_rut=Rut('76399752-9'), cesionario_rut=Rut('76389992-6'), fecha_firma_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 4, 9, 9, 52), tz=AecXml.DATETIME_FIELDS_TZ, ), signature_value=aec_signature_value, signature_x509_cert_der=aec_cert_der_bytes, cesiones=[ CesionAecXml( dte=DteDataL1( emisor_rut=Rut('76399752-9'), tipo_dte=TipoDteEnum.FACTURA_ELECTRONICA, folio=25568, fecha_emision_date=date(2019, 3, 29), receptor_rut=Rut('96874030-K'), monto_total=230992, ), seq=1, cedente_rut=Rut('76399752-9'), cesionario_rut=Rut('76389992-6'), monto_cesion=230992, fecha_cesion_dt=tz_utils.convert_naive_dt_to_tz_aware( dt=datetime(2019, 4, 4, 9, 9, 52), tz=CesionAecXml.DATETIME_FIELDS_TZ, ), fecha_ultimo_vencimiento=date(2019, 4, 28), cedente_razon_social='COMERCIALIZADORA INNOVA MOBEL SPA', cedente_direccion='LOS CIPRESES 2834', cedente_email='camilo.perez@innovamobel.cl', cedente_persona_autorizada_rut=Rut('76399752-9'), cedente_persona_autorizada_nombre='COMERCIALIZADORA INNOVA MOBEL SPA', cesionario_razon_social='ST CAPITAL S.A.', cesionario_direccion='Isidora Goyenechea 2939 Oficina 602', cesionario_email='fynpal-app-notif-st-capital@fynpal.com', dte_deudor_email=None, cedente_declaracion_jurada=( 'Se declara bajo juramento que COMERCIALIZADORA INNOVA MOBEL SPA, RUT ' '76399752-9 ha puesto a disposición del cesionario ST CAPITAL S.A., RUT ' '76389992-6, el o los documentos donde constan los recibos de las ' 'mercaderías entregadas o servicios prestados, entregados por parte del ' 'deudor de la factura EMPRESAS LA POLAR S.A., RUT 96874030-K, deacuerdo a ' 'lo establecido en la Ley N°19.983.' ), ), ], contacto_nombre=None, contacto_telefono=None, contacto_email='fynpal-app-notif-st-capital@fynpal.com', ) xml_doc = xml_utils.parse_untrusted_xml(aec_xml_bytes) aec_xml = parse_aec_xml(xml_doc) self.assertEqual(aec_xml, expected_output)

true

f72ae161a0eb4e5d0974932d1ca4ef7364cf371f

152

py

Python

aiocloudflare/api/zones/dns_records/import_/import_.py

Stewart86/aioCloudflare

341c0941f8f888a8b7e696e64550bce5da4949e6

[ "MIT" ]

2

2021-09-14T13:20:55.000Z

2022-02-24T14:18:24.000Z

aiocloudflare/api/zones/dns_records/import_/import_.py

Stewart86/aioCloudflare

341c0941f8f888a8b7e696e64550bce5da4949e6

[ "MIT" ]

46

2021-09-08T08:39:45.000Z

2022-03-29T12:31:05.000Z

aiocloudflare/api/zones/dns_records/import_/import_.py

Stewart86/aioCloudflare

341c0941f8f888a8b7e696e64550bce5da4949e6

[ "MIT" ]

1

2021-12-30T23:02:23.000Z

from aiocloudflare.commons.auth import Auth class Import_(Auth): _endpoint1 = "zones" _endpoint2 = "dns_records/import" _endpoint3 = None

19

43

0.723684

from aiocloudflare.commons.auth import Auth class Import_(Auth): _endpoint1 = "zones" _endpoint2 = "dns_records/import" _endpoint3 = None

true

f72ae291978b1bc7fcf2a7bbfa465ce316156938

596

py

Python

ROSpractice/src/topics_quiz/src/topics_quiz_node.py

kasiv008/Robotics

302b3336005acd81202ebbbb0c52a4b2692fa9c7

[ "MIT" ]

1

2021-07-19T10:15:08.000Z

ROSpractice/src/topics_quiz/src/topics_quiz_node.py

kasiv008/Robotics

302b3336005acd81202ebbbb0c52a4b2692fa9c7

[ "MIT" ]

null

ROSpractice/src/topics_quiz/src/topics_quiz_node.py

kasiv008/Robotics

302b3336005acd81202ebbbb0c52a4b2692fa9c7

[ "MIT" ]

null

#!/usr/bin/env python import rospy from geometry_msgs.msg import Twist from sensor_msgs.msg import LaserScan def callback(msg): L,M,R = msg.ranges[719],msg.ranges[360],msg.ranges[0] move.linear.x = .2 if M < 1.2: move.linear.x = .05 move.angular.z = .1 elif L > 30 and R > 30 and M > 30: move.linear.x = .2 move.angular.z = 0 pub.publish(move) rospy.init_node('topics_quiz_node') pub = rospy.Publisher('/cmd_vel',Twist) sub = rospy.Subscriber('/kobuki/laser/scan', LaserScan,callback) rate = rospy.Rate(2) move = Twist() rospy.spin()

24.833333

57

0.642617

import rospy from geometry_msgs.msg import Twist from sensor_msgs.msg import LaserScan def callback(msg): L,M,R = msg.ranges[719],msg.ranges[360],msg.ranges[0] move.linear.x = .2 if M < 1.2: move.linear.x = .05 move.angular.z = .1 elif L > 30 and R > 30 and M > 30: move.linear.x = .2 move.angular.z = 0 pub.publish(move) rospy.init_node('topics_quiz_node') pub = rospy.Publisher('/cmd_vel',Twist) sub = rospy.Subscriber('/kobuki/laser/scan', LaserScan,callback) rate = rospy.Rate(2) move = Twist() rospy.spin()

true

f72ae3fa136caa90b5e27aab7455fdec4407560e

2,016

py

Python

alipay/aop/api/domain/KoubeiSalesKbassetStuffProduceqrcodeBatchqueryModel.py

articuly/alipay-sdk-python-all

0259cd28eca0f219b97dac7f41c2458441d5e7a6

[ "Apache-2.0" ]

null

alipay/aop/api/domain/KoubeiSalesKbassetStuffProduceqrcodeBatchqueryModel.py

articuly/alipay-sdk-python-all

0259cd28eca0f219b97dac7f41c2458441d5e7a6

[ "Apache-2.0" ]

null

alipay/aop/api/domain/KoubeiSalesKbassetStuffProduceqrcodeBatchqueryModel.py

articuly/alipay-sdk-python-all

0259cd28eca0f219b97dac7f41c2458441d5e7a6

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import simplejson as json from alipay.aop.api.constant.ParamConstants import * class KoubeiSalesKbassetStuffProduceqrcodeBatchqueryModel(object): def __init__(self): self._batch_id = None self._page_size = None self._produce_order_id = None @property def batch_id(self): return self._batch_id @batch_id.setter def batch_id(self, value): self._batch_id = value @property def page_size(self): return self._page_size @page_size.setter def page_size(self, value): self._page_size = value @property def produce_order_id(self): return self._produce_order_id @produce_order_id.setter def produce_order_id(self, value): self._produce_order_id = value def to_alipay_dict(self): params = dict() if self.batch_id: if hasattr(self.batch_id, 'to_alipay_dict'): params['batch_id'] = self.batch_id.to_alipay_dict() else: params['batch_id'] = self.batch_id if self.page_size: if hasattr(self.page_size, 'to_alipay_dict'): params['page_size'] = self.page_size.to_alipay_dict() else: params['page_size'] = self.page_size if self.produce_order_id: if hasattr(self.produce_order_id, 'to_alipay_dict'): params['produce_order_id'] = self.produce_order_id.to_alipay_dict() else: params['produce_order_id'] = self.produce_order_id return params @staticmethod def from_alipay_dict(d): if not d: return None o = KoubeiSalesKbassetStuffProduceqrcodeBatchqueryModel() if 'batch_id' in d: o.batch_id = d['batch_id'] if 'page_size' in d: o.page_size = d['page_size'] if 'produce_order_id' in d: o.produce_order_id = d['produce_order_id'] return o

28.394366

83

0.613095

import simplejson as json from alipay.aop.api.constant.ParamConstants import * class KoubeiSalesKbassetStuffProduceqrcodeBatchqueryModel(object): def __init__(self): self._batch_id = None self._page_size = None self._produce_order_id = None @property def batch_id(self): return self._batch_id @batch_id.setter def batch_id(self, value): self._batch_id = value @property def page_size(self): return self._page_size @page_size.setter def page_size(self, value): self._page_size = value @property def produce_order_id(self): return self._produce_order_id @produce_order_id.setter def produce_order_id(self, value): self._produce_order_id = value def to_alipay_dict(self): params = dict() if self.batch_id: if hasattr(self.batch_id, 'to_alipay_dict'): params['batch_id'] = self.batch_id.to_alipay_dict() else: params['batch_id'] = self.batch_id if self.page_size: if hasattr(self.page_size, 'to_alipay_dict'): params['page_size'] = self.page_size.to_alipay_dict() else: params['page_size'] = self.page_size if self.produce_order_id: if hasattr(self.produce_order_id, 'to_alipay_dict'): params['produce_order_id'] = self.produce_order_id.to_alipay_dict() else: params['produce_order_id'] = self.produce_order_id return params @staticmethod def from_alipay_dict(d): if not d: return None o = KoubeiSalesKbassetStuffProduceqrcodeBatchqueryModel() if 'batch_id' in d: o.batch_id = d['batch_id'] if 'page_size' in d: o.page_size = d['page_size'] if 'produce_order_id' in d: o.produce_order_id = d['produce_order_id'] return o

true

f72ae4fe9cb98106976c818db916bbe6b063c51a

2,243

py

Python

sdk/python/tests/unit/test_feature_views.py

kevjumba/feast

44d53fda71b5a82d9fb6e044b01d97080c2d018c

[ "Apache-2.0" ]

810

2018-12-25T15:16:11.000Z

2020-05-14T09:49:40.000Z

sdk/python/tests/unit/test_feature_views.py

kevjumba/feast

44d53fda71b5a82d9fb6e044b01d97080c2d018c

[ "Apache-2.0" ]

701

2018-12-21T05:18:43.000Z

2020-05-16T01:30:21.000Z

sdk/python/tests/unit/test_feature_views.py

kevjumba/feast

44d53fda71b5a82d9fb6e044b01d97080c2d018c

[ "Apache-2.0" ]

155

2018-12-22T11:05:04.000Z

2020-05-14T07:33:41.000Z

from datetime import timedelta import pytest from feast import PushSource from feast.batch_feature_view import BatchFeatureView from feast.data_format import AvroFormat from feast.data_source import KafkaSource from feast.infra.offline_stores.file_source import FileSource from feast.stream_feature_view import StreamFeatureView def test_create_batch_feature_view(): batch_source = FileSource(path="some path") BatchFeatureView( name="test batch feature view", entities=[], ttl=timedelta(days=30), source=batch_source, ) with pytest.raises(ValueError): BatchFeatureView( name="test batch feature view", entities=[], ttl=timedelta(days=30) ) stream_source = KafkaSource( name="kafka", timestamp_field="", bootstrap_servers="", message_format=AvroFormat(""), topic="topic", batch_source=FileSource(path="some path"), ) with pytest.raises(ValueError): BatchFeatureView( name="test batch feature view", entities=[], ttl=timedelta(days=30), source=stream_source, ) def test_create_stream_feature_view(): stream_source = KafkaSource( name="kafka", timestamp_field="", bootstrap_servers="", message_format=AvroFormat(""), topic="topic", batch_source=FileSource(path="some path"), ) StreamFeatureView( name="test kafka stream feature view", entities=[], ttl=timedelta(days=30), source=stream_source, ) push_source = PushSource( name="push source", batch_source=FileSource(path="some path") ) StreamFeatureView( name="test push source feature view", entities=[], ttl=timedelta(days=30), source=push_source, ) with pytest.raises(ValueError): StreamFeatureView( name="test batch feature view", entities=[], ttl=timedelta(days=30) ) with pytest.raises(ValueError): StreamFeatureView( name="test batch feature view", entities=[], ttl=timedelta(days=30), source=FileSource(path="some path"), )

27.353659

79

0.628622

from datetime import timedelta import pytest from feast import PushSource from feast.batch_feature_view import BatchFeatureView from feast.data_format import AvroFormat from feast.data_source import KafkaSource from feast.infra.offline_stores.file_source import FileSource from feast.stream_feature_view import StreamFeatureView def test_create_batch_feature_view(): batch_source = FileSource(path="some path") BatchFeatureView( name="test batch feature view", entities=[], ttl=timedelta(days=30), source=batch_source, ) with pytest.raises(ValueError): BatchFeatureView( name="test batch feature view", entities=[], ttl=timedelta(days=30) ) stream_source = KafkaSource( name="kafka", timestamp_field="", bootstrap_servers="", message_format=AvroFormat(""), topic="topic", batch_source=FileSource(path="some path"), ) with pytest.raises(ValueError): BatchFeatureView( name="test batch feature view", entities=[], ttl=timedelta(days=30), source=stream_source, ) def test_create_stream_feature_view(): stream_source = KafkaSource( name="kafka", timestamp_field="", bootstrap_servers="", message_format=AvroFormat(""), topic="topic", batch_source=FileSource(path="some path"), ) StreamFeatureView( name="test kafka stream feature view", entities=[], ttl=timedelta(days=30), source=stream_source, ) push_source = PushSource( name="push source", batch_source=FileSource(path="some path") ) StreamFeatureView( name="test push source feature view", entities=[], ttl=timedelta(days=30), source=push_source, ) with pytest.raises(ValueError): StreamFeatureView( name="test batch feature view", entities=[], ttl=timedelta(days=30) ) with pytest.raises(ValueError): StreamFeatureView( name="test batch feature view", entities=[], ttl=timedelta(days=30), source=FileSource(path="some path"), )

true

f72ae59ecb83441e8b44b0616951c153ac6dd839

8,726

py

Python

lambda/py/mutagen/_file.py

frivas/alexa-mixed-polly

bf0fde9005a66f3d6f0193799eacef934d166de7

[ "W3C" ]

2

2019-07-29T15:45:31.000Z

2019-11-17T23:33:58.000Z

lambda/py/mutagen/_file.py

frivas/alexa-mixed-polly

bf0fde9005a66f3d6f0193799eacef934d166de7

[ "W3C" ]

null

lambda/py/mutagen/_file.py

frivas/alexa-mixed-polly

bf0fde9005a66f3d6f0193799eacef934d166de7

[ "W3C" ]

1

2019-01-06T15:18:58.000Z

# -*- coding: utf-8 -*- # Copyright (C) 2005 Michael Urman # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. import warnings from mutagen._util import DictMixin, loadfile from mutagen._compat import izip class FileType(DictMixin): """FileType(filething, **kwargs) Args: filething (filething): A filename or a file-like object Subclasses might take further options via keyword arguments. An abstract object wrapping tags and audio stream information. Each file format has different potential tags and stream information. FileTypes implement an interface very similar to Metadata; the dict interface, save, load, and delete calls on a FileType call the appropriate methods on its tag data. Attributes: info (`StreamInfo`): contains length, bitrate, sample rate tags (`Tags`): metadata tags, if any, otherwise `None` """ __module__ = "mutagen" info = None tags = None filename = None _mimes = ["application/octet-stream"] def __init__(self, *args, **kwargs): if not args and not kwargs: warnings.warn("FileType constructor requires a filename", DeprecationWarning) else: self.load(*args, **kwargs) @loadfile() def load(self, filething, *args, **kwargs): raise NotImplementedError def __getitem__(self, key): """Look up a metadata tag key. If the file has no tags at all, a KeyError is raised. """ if self.tags is None: raise KeyError(key) else: return self.tags[key] def __setitem__(self, key, value): """Set a metadata tag. If the file has no tags, an appropriate format is added (but not written until save is called). """ if self.tags is None: self.add_tags() self.tags[key] = value def __delitem__(self, key): """Delete a metadata tag key. If the file has no tags at all, a KeyError is raised. """ if self.tags is None: raise KeyError(key) else: del(self.tags[key]) def keys(self): """Return a list of keys in the metadata tag. If the file has no tags at all, an empty list is returned. """ if self.tags is None: return [] else: return self.tags.keys() @loadfile(writable=True) def delete(self, filething=None): """delete(filething=None) Remove tags from a file. In cases where the tagging format is independent of the file type (for example `mutagen.id3.ID3`) all traces of the tagging format will be removed. In cases where the tag is part of the file type, all tags and padding will be removed. The tags attribute will be cleared as well if there is one. Does nothing if the file has no tags. Raises: mutagen.MutagenError: if deleting wasn't possible """ if self.tags is not None: return self.tags.delete(filething) @loadfile(writable=True) def save(self, filething=None, **kwargs): """save(filething=None, **kwargs) Save metadata tags. Raises: MutagenError: if saving wasn't possible """ if self.tags is not None: return self.tags.save(filething, **kwargs) def pprint(self): """ Returns: text: stream information and comment key=value pairs. """ stream = "%s (%s)" % (self.info.pprint(), self.mime[0]) try: tags = self.tags.pprint() except AttributeError: return stream else: return stream + ((tags and "\n" + tags) or "") def add_tags(self): """Adds new tags to the file. Raises: mutagen.MutagenError: if tags already exist or adding is not possible. """ raise NotImplementedError @property def mime(self): """A list of mime types (:class:`mutagen.text`)""" mimes = [] for Kind in type(self).__mro__: for mime in getattr(Kind, '_mimes', []): if mime not in mimes: mimes.append(mime) return mimes @staticmethod def score(filename, fileobj, header): """Returns a score for how likely the file can be parsed by this type. Args: filename (fspath): a file path fileobj (fileobj): a file object open in rb mode. Position is undefined header (bytes): data of undefined length, starts with the start of the file. Returns: int: negative if definitely not a matching type, otherwise a score, the bigger the more certain that the file can be loaded. """ raise NotImplementedError class StreamInfo(object): """Abstract stream information object. Provides attributes for length, bitrate, sample rate etc. See the implementations for details. """ __module__ = "mutagen" def pprint(self): """ Returns: text: Print stream information """ raise NotImplementedError @loadfile(method=False) def File(filething, options=None, easy=False): """File(filething, options=None, easy=False) Guess the type of the file and try to open it. The file type is decided by several things, such as the first 128 bytes (which usually contains a file type identifier), the filename extension, and the presence of existing tags. If no appropriate type could be found, None is returned. Args: filething (filething) options: Sequence of :class:`FileType` implementations, defaults to all included ones. easy (bool): If the easy wrappers should be returnd if available. For example :class:`EasyMP3 <mp3.EasyMP3>` instead of :class:`MP3 <mp3.MP3>`. Returns: FileType: A FileType instance for the detected type or `None` in case the type couln't be determined. Raises: MutagenError: in case the detected type fails to load the file. """ if options is None: from mutagen.asf import ASF from mutagen.apev2 import APEv2File from mutagen.flac import FLAC if easy: from mutagen.easyid3 import EasyID3FileType as ID3FileType else: from mutagen.id3 import ID3FileType if easy: from mutagen.mp3 import EasyMP3 as MP3 else: from mutagen.mp3 import MP3 from mutagen.oggflac import OggFLAC from mutagen.oggspeex import OggSpeex from mutagen.oggtheora import OggTheora from mutagen.oggvorbis import OggVorbis from mutagen.oggopus import OggOpus if easy: from mutagen.trueaudio import EasyTrueAudio as TrueAudio else: from mutagen.trueaudio import TrueAudio from mutagen.wavpack import WavPack if easy: from mutagen.easymp4 import EasyMP4 as MP4 else: from mutagen.mp4 import MP4 from mutagen.musepack import Musepack from mutagen.monkeysaudio import MonkeysAudio from mutagen.optimfrog import OptimFROG from mutagen.aiff import AIFF from mutagen.aac import AAC from mutagen.smf import SMF from mutagen.dsf import DSF options = [MP3, TrueAudio, OggTheora, OggSpeex, OggVorbis, OggFLAC, FLAC, AIFF, APEv2File, MP4, ID3FileType, WavPack, Musepack, MonkeysAudio, OptimFROG, ASF, OggOpus, AAC, SMF, DSF] if not options: return None fileobj = filething.fileobj try: header = fileobj.read(128) except IOError: header = b"" # Sort by name after score. Otherwise import order affects # Kind sort order, which affects treatment of things with # equals scores. results = [(Kind.score(filething.name, fileobj, header), Kind.__name__) for Kind in options] results = list(izip(results, options)) results.sort() (score, name), Kind = results[-1] if score > 0: try: fileobj.seek(0, 0) except IOError: pass return Kind(fileobj, filename=filething.filename) else: return None

28.990033

79

0.607609

import warnings from mutagen._util import DictMixin, loadfile from mutagen._compat import izip class FileType(DictMixin): __module__ = "mutagen" info = None tags = None filename = None _mimes = ["application/octet-stream"] def __init__(self, *args, **kwargs): if not args and not kwargs: warnings.warn("FileType constructor requires a filename", DeprecationWarning) else: self.load(*args, **kwargs) @loadfile() def load(self, filething, *args, **kwargs): raise NotImplementedError def __getitem__(self, key): if self.tags is None: raise KeyError(key) else: return self.tags[key] def __setitem__(self, key, value): if self.tags is None: self.add_tags() self.tags[key] = value def __delitem__(self, key): if self.tags is None: raise KeyError(key) else: del(self.tags[key]) def keys(self): if self.tags is None: return [] else: return self.tags.keys() @loadfile(writable=True) def delete(self, filething=None): if self.tags is not None: return self.tags.delete(filething) @loadfile(writable=True) def save(self, filething=None, **kwargs): if self.tags is not None: return self.tags.save(filething, **kwargs) def pprint(self): stream = "%s (%s)" % (self.info.pprint(), self.mime[0]) try: tags = self.tags.pprint() except AttributeError: return stream else: return stream + ((tags and "\n" + tags) or "") def add_tags(self): raise NotImplementedError @property def mime(self): mimes = [] for Kind in type(self).__mro__: for mime in getattr(Kind, '_mimes', []): if mime not in mimes: mimes.append(mime) return mimes @staticmethod def score(filename, fileobj, header): raise NotImplementedError class StreamInfo(object): __module__ = "mutagen" def pprint(self): raise NotImplementedError @loadfile(method=False) def File(filething, options=None, easy=False): if options is None: from mutagen.asf import ASF from mutagen.apev2 import APEv2File from mutagen.flac import FLAC if easy: from mutagen.easyid3 import EasyID3FileType as ID3FileType else: from mutagen.id3 import ID3FileType if easy: from mutagen.mp3 import EasyMP3 as MP3 else: from mutagen.mp3 import MP3 from mutagen.oggflac import OggFLAC from mutagen.oggspeex import OggSpeex from mutagen.oggtheora import OggTheora from mutagen.oggvorbis import OggVorbis from mutagen.oggopus import OggOpus if easy: from mutagen.trueaudio import EasyTrueAudio as TrueAudio else: from mutagen.trueaudio import TrueAudio from mutagen.wavpack import WavPack if easy: from mutagen.easymp4 import EasyMP4 as MP4 else: from mutagen.mp4 import MP4 from mutagen.musepack import Musepack from mutagen.monkeysaudio import MonkeysAudio from mutagen.optimfrog import OptimFROG from mutagen.aiff import AIFF from mutagen.aac import AAC from mutagen.smf import SMF from mutagen.dsf import DSF options = [MP3, TrueAudio, OggTheora, OggSpeex, OggVorbis, OggFLAC, FLAC, AIFF, APEv2File, MP4, ID3FileType, WavPack, Musepack, MonkeysAudio, OptimFROG, ASF, OggOpus, AAC, SMF, DSF] if not options: return None fileobj = filething.fileobj try: header = fileobj.read(128) except IOError: header = b"" results = [(Kind.score(filething.name, fileobj, header), Kind.__name__) for Kind in options] results = list(izip(results, options)) results.sort() (score, name), Kind = results[-1] if score > 0: try: fileobj.seek(0, 0) except IOError: pass return Kind(fileobj, filename=filething.filename) else: return None

true

f72ae5ad21d0d2e7c0cc825a649cff1858a27800

5,781

py

Python

src/coolbeans/extort/ib.py

runarp/coolbeans

128a7f2e45690d2d22b05608e555c44334f46859

[ "MIT" ]

5

2020-05-17T04:48:25.000Z

2022-01-27T09:36:45.000Z

src/coolbeans/extort/ib.py

runarp/coolbeans

128a7f2e45690d2d22b05608e555c44334f46859

[ "MIT" ]

1

2020-05-17T06:21:52.000Z

2020-05-22T13:49:33.000Z

src/coolbeans/extort/ib.py

runarp/coolbeans

128a7f2e45690d2d22b05608e555c44334f46859

[ "MIT" ]

1

2021-01-28T03:00:27.000Z

"""Example Extorter, useful as a starting point""" import typing import logging import dataclasses import datetime # 3rdparty import slugify # We use ibflex from ibflex import parser, FlexStatement, CashAction from coolbeans.extort.base import ExtortionProtocol from coolbeans.tools.seeds import Trade, Transfer, Expense, Income, EventDetail logger = logging.getLogger(__name__) def trade_key(trade): if trade.openCloseIndicator: o = trade.openCloseIndicator.name + ':' else: o = '' return f"{o}{trade.tradeDate.strftime('%Y-%m-%d')}:{trade.ibOrderID}" def clean_symbol(symbol: str) -> str: symbol = slugify.slugify(symbol, separator='_') if symbol[0].isdigit(): symbol = "X" + symbol symbol = symbol.upper() return symbol class Extorter(ExtortionProtocol): FILE_OPEN_MODE = None # This requires a file-name, not a ib_account_id = "" def extort(self, stream: typing.Union[typing.IO[typing.AnyStr], str]): """Extract as much information as possible from the workbook""" for statement in parser.parse(stream).FlexStatements: for record in self.extract_cash(statement): yield dataclasses.asdict(record) for trade in self.extract_trades(statement): yield dataclasses.asdict(trade) @staticmethod def extract_cash(statement: FlexStatement): """ Args: statement: The Statement to extract entries from Returns: iterator of DataClass instances for these records """ for record in statement.CashTransactions: date = record.dateTime if record.type in ( CashAction.DEPOSITWITHDRAW, ): yield Transfer( id=record.transactionID, date=date, amount=record.amount, currency=record.currency, subaccount=record.accountId, narration=record.description, event_detail=EventDetail.TRANSFER_DEPOSIT.name if record.amount > 0 else EventDetail.TRANSFER_WITHDRAWAL.name, meta={ 'type': record.type.value, 'rate': record.fxRateToBase } ) elif record.amount < 0: event_detail = EventDetail.EXPENSE_FEES if record.type in (CashAction.BONDINTPAID, CashAction.BROKERINTPAID): event_detail = EventDetail.EXPENSE_INTEREST if record.type == CashAction.WHTAX: event_detail = EventDetail.EXPENSE_TAX yield Expense( id=record.transactionID, date=date, amount=record.amount, event_detail=event_detail, currency=record.currency, subaccount=record.accountId, narration=record.description, meta={ 'type': record.type.value, 'rate': record.fxRateToBase } ) else: yield Income( id=record.transactionID, date=date, amount=record.amount, currency=record.currency, subaccount=record.accountId, narration=record.description, meta={ 'type': record.type.value, 'rate': record.fxRateToBase } ) @staticmethod def extract_trades(statement: FlexStatement): """Pull Trades from a FlexStatement """ by_order: typing.Dict[str, Trade] = {} for trade in statement.Trades: key = trade_key(trade) assert key.strip(), f"Invalid Key {len(key)}" if not trade.openCloseIndicator: # This isn't a trade at all. continue if key in by_order: combined = by_order[key] combined.add_trade( quantity=trade.quantity * trade.multiplier, price=trade.tradePrice, fees=trade.ibCommission ) else: seed = Trade( id=key, date=trade.tradeDate, price=trade.tradePrice, currency=trade.currency, quantity=trade.quantity * trade.multiplier, commodity=clean_symbol(trade.symbol), fees=trade.ibCommission, fees_currency=trade.ibCommissionCurrency, subaccount=trade.accountId, event_detail=EventDetail.TRADE_OPEN if trade.openCloseIndicator.name == 'OPEN' else EventDetail.TRADE_CLOSE, meta={ 'exchange': trade.exchange, 'symbol': trade.symbol, } ) by_order[key] = seed for trade in by_order.values(): yield trade # if trade.securityID is None and "." in trade.symbol: # # FOREX Trade, not really a valid Symbol at all # # TODO: Better check than blank securityID # # Usually [currency].[commodity]. For example GBP.JPY # # In that case trade.currency is JPY, so we just need to parse out the GBP part # safe_symbol, _ = trade.symbol.split('.') # else: # safe_symbol = self.clean_symbol(trade.symbol)

33.034286

130

0.530877

import typing import logging import dataclasses import datetime import slugify from ibflex import parser, FlexStatement, CashAction from coolbeans.extort.base import ExtortionProtocol from coolbeans.tools.seeds import Trade, Transfer, Expense, Income, EventDetail logger = logging.getLogger(__name__) def trade_key(trade): if trade.openCloseIndicator: o = trade.openCloseIndicator.name + ':' else: o = '' return f"{o}{trade.tradeDate.strftime('%Y-%m-%d')}:{trade.ibOrderID}" def clean_symbol(symbol: str) -> str: symbol = slugify.slugify(symbol, separator='_') if symbol[0].isdigit(): symbol = "X" + symbol symbol = symbol.upper() return symbol class Extorter(ExtortionProtocol): FILE_OPEN_MODE = None ib_account_id = "" def extort(self, stream: typing.Union[typing.IO[typing.AnyStr], str]): for statement in parser.parse(stream).FlexStatements: for record in self.extract_cash(statement): yield dataclasses.asdict(record) for trade in self.extract_trades(statement): yield dataclasses.asdict(trade) @staticmethod def extract_cash(statement: FlexStatement): for record in statement.CashTransactions: date = record.dateTime if record.type in ( CashAction.DEPOSITWITHDRAW, ): yield Transfer( id=record.transactionID, date=date, amount=record.amount, currency=record.currency, subaccount=record.accountId, narration=record.description, event_detail=EventDetail.TRANSFER_DEPOSIT.name if record.amount > 0 else EventDetail.TRANSFER_WITHDRAWAL.name, meta={ 'type': record.type.value, 'rate': record.fxRateToBase } ) elif record.amount < 0: event_detail = EventDetail.EXPENSE_FEES if record.type in (CashAction.BONDINTPAID, CashAction.BROKERINTPAID): event_detail = EventDetail.EXPENSE_INTEREST if record.type == CashAction.WHTAX: event_detail = EventDetail.EXPENSE_TAX yield Expense( id=record.transactionID, date=date, amount=record.amount, event_detail=event_detail, currency=record.currency, subaccount=record.accountId, narration=record.description, meta={ 'type': record.type.value, 'rate': record.fxRateToBase } ) else: yield Income( id=record.transactionID, date=date, amount=record.amount, currency=record.currency, subaccount=record.accountId, narration=record.description, meta={ 'type': record.type.value, 'rate': record.fxRateToBase } ) @staticmethod def extract_trades(statement: FlexStatement): by_order: typing.Dict[str, Trade] = {} for trade in statement.Trades: key = trade_key(trade) assert key.strip(), f"Invalid Key {len(key)}" if not trade.openCloseIndicator: continue if key in by_order: combined = by_order[key] combined.add_trade( quantity=trade.quantity * trade.multiplier, price=trade.tradePrice, fees=trade.ibCommission ) else: seed = Trade( id=key, date=trade.tradeDate, price=trade.tradePrice, currency=trade.currency, quantity=trade.quantity * trade.multiplier, commodity=clean_symbol(trade.symbol), fees=trade.ibCommission, fees_currency=trade.ibCommissionCurrency, subaccount=trade.accountId, event_detail=EventDetail.TRADE_OPEN if trade.openCloseIndicator.name == 'OPEN' else EventDetail.TRADE_CLOSE, meta={ 'exchange': trade.exchange, 'symbol': trade.symbol, } ) by_order[key] = seed for trade in by_order.values(): yield trade # if trade.securityID is None and "." in trade.symbol: # # FOREX Trade, not really a valid Symbol at all # # TODO: Better check than blank securityID # # Usually [currency].[commodity]. For example GBP.JPY # # In that case trade.currency is JPY, so we just need to parse out the GBP part # safe_symbol, _ = trade.symbol.split('.') # else: # safe_symbol = self.clean_symbol(trade.symbol)

true

f72ae5e176716f5b8b5bebf5ecd595df75c371dc

1,555

py

Python

example/run_SolveOneAgent_online.py

zehuilu/DrMaMP-Distributed-Real-time-Multi-agent-Mission-Planning-Algorithm

894875ebddf7d1f6bbf7a47ce82f05d7be2bafdc

[ "Apache-2.0" ]

4

2022-02-22T05:12:18.000Z

2022-03-29T01:56:37.000Z

example/run_SolveOneAgent_online.py

zehuilu/DrMaMP-Distributed-Real-time-Multi-agent-Mission-Planning-Algorithm

894875ebddf7d1f6bbf7a47ce82f05d7be2bafdc

[ "Apache-2.0" ]

null

example/run_SolveOneAgent_online.py

zehuilu/DrMaMP-Distributed-Real-time-Multi-agent-Mission-Planning-Algorithm

894875ebddf7d1f6bbf7a47ce82f05d7be2bafdc

[ "Apache-2.0" ]

3

2022-02-23T03:14:56.000Z

2022-03-14T12:22:05.000Z

#!/usr/bin/env python3 import asyncio import random import matplotlib.pyplot as plt import pathmagic with pathmagic.context(): from Simulator import Simulator from MissionPlanner import MissionPlanner if __name__ == "__main__": # define the world map_width_meter = 25.0 map_height_meter = 25.0 map_resolution = 2 value_non_obs = 0 # the cell is empty value_obs = 255 # the cell is blocked # create a simulator MySimulator = Simulator(map_width_meter, map_height_meter, map_resolution, value_non_obs, value_obs) # number of obstacles num_obs = 250 # [width, length] size of each obstacle [meter] size_obs = [1, 1] # generate random obstacles MySimulator.generate_random_obs(num_obs, size_obs) # randomly generate agents and targets num_agents = 1 num_targets = 8 agents_position, targets_position = MySimulator.generate_agents_and_targets(num_agents, num_targets) # average agent velocity in cells agent_velocity_ave = [random.randint(4,8) for i in range(num_agents)] # planning and visualization frequency in Hz planning_frequency = 5 # initialize a planner MyPlanner = MissionPlanner(MySimulator) # run the planner online asyncio.run(MyPlanner.run_planner({"agents_position": agents_position, "targets_position": targets_position, "agent_velocity_ave": agent_velocity_ave, "planning_frequency": planning_frequency}))

34.555556

104

0.686817

import asyncio import random import matplotlib.pyplot as plt import pathmagic with pathmagic.context(): from Simulator import Simulator from MissionPlanner import MissionPlanner if __name__ == "__main__": map_width_meter = 25.0 map_height_meter = 25.0 map_resolution = 2 value_non_obs = 0 value_obs = 255 MySimulator = Simulator(map_width_meter, map_height_meter, map_resolution, value_non_obs, value_obs) num_obs = 250 size_obs = [1, 1] MySimulator.generate_random_obs(num_obs, size_obs) num_agents = 1 num_targets = 8 agents_position, targets_position = MySimulator.generate_agents_and_targets(num_agents, num_targets) agent_velocity_ave = [random.randint(4,8) for i in range(num_agents)] planning_frequency = 5 MyPlanner = MissionPlanner(MySimulator) asyncio.run(MyPlanner.run_planner({"agents_position": agents_position, "targets_position": targets_position, "agent_velocity_ave": agent_velocity_ave, "planning_frequency": planning_frequency}))

true

f72ae622b3e7a87cfbd8de23dda483349b388bb1

26,682

py

Python

test/functional/tests/io_class/test_io_classification.py

josehu07/open-cas-linux-mf

5c6870be8bbb6816645955b6e479c9b5c7c0074d

[ "BSD-3-Clause-Clear" ]

2

2021-08-13T14:44:45.000Z

2022-01-10T07:41:40.000Z

test/functional/tests/io_class/test_io_classification.py

josehu07/open-cas-linux-mf

5c6870be8bbb6816645955b6e479c9b5c7c0074d

[ "BSD-3-Clause-Clear" ]

null

test/functional/tests/io_class/test_io_classification.py

josehu07/open-cas-linux-mf

5c6870be8bbb6816645955b6e479c9b5c7c0074d

[ "BSD-3-Clause-Clear" ]

null

# # Copyright(c) 2019-2020 Intel Corporation # SPDX-License-Identifier: BSD-3-Clause-Clear # import random from itertools import permutations import pytest from api.cas.ioclass_config import IoClass from storage_devices.disk import DiskType, DiskTypeSet, DiskTypeLowerThan from test_tools import fs_utils from test_tools.dd import Dd from test_tools.disk_utils import Filesystem from test_tools.fio.fio import Fio from test_tools.fio.fio_param import ReadWrite, IoEngine from test_utils.filesystem.file import File from test_utils.os_utils import sync, Udev from .io_class_common import * @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) def test_ioclass_lba(): """Write data to random lba and check if it is cached according to range defined in ioclass rule""" cache, core = prepare() ioclass_id = 1 min_cached_lba = 56 max_cached_lba = 200 iterations = 100 dd_size = Size(1, Unit.Blocks512) dd_count = 1 # Prepare ioclass config ioclass_config.add_ioclass( ioclass_id=ioclass_id, eviction_priority=1, allocation=True, rule=f"lba:ge:{min_cached_lba}&lba:le:{max_cached_lba}&done", ioclass_config_path=ioclass_config_path, ) # Prepare cache for test casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) cache.flush_cache() # Check if lbas from defined range are cached dirty_count = 0 # '8' step is set to prevent writing cache line more than once TestRun.LOGGER.info(f"Writing to one sector in each cache line from range.") for lba in range(min_cached_lba, max_cached_lba, 8): dd = ( Dd() .input("/dev/zero") .output(f"{core.system_path}") .count(dd_count) .block_size(dd_size) .seek(lba) ) dd.run() sync() dirty_count += 1 dirty = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.dirty if dirty.get_value(Unit.Blocks4096) != dirty_count: TestRun.LOGGER.error(f"LBA {lba} not cached") cache.flush_cache() # Check if lba outside of defined range are not cached TestRun.LOGGER.info(f"Writing to random sectors outside of cached range.") for i in range(iterations): rand_lba = random.randrange(2000) if min_cached_lba <= rand_lba <= max_cached_lba: continue dd = ( Dd() .input("/dev/zero") .output(f"{core.system_path}") .count(dd_count) .block_size(dd_size) .seek(rand_lba) ) dd.run() sync() dirty = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.dirty if dirty.get_value(Unit.Blocks4096) != 0: TestRun.LOGGER.error(f"Inappropriately cached lba: {rand_lba}") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) def test_ioclass_request_size(): cache, core = prepare() ioclass_id = 1 iterations = 100 ioclass_config.add_ioclass( ioclass_id=ioclass_id, eviction_priority=1, allocation=True, rule=f"request_size:ge:8192&request_size:le:16384&done", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) Udev.disable() # Check if requests with appropriate size are cached TestRun.LOGGER.info( f"Check if requests with size within defined range are cached" ) cached_req_sizes = [Size(2, Unit.Blocks4096), Size(4, Unit.Blocks4096)] for i in range(iterations): cache.flush_cache() req_size = random.choice(cached_req_sizes) dd = ( Dd() .input("/dev/zero") .output(core.system_path) .count(1) .block_size(req_size) .oflag("direct") ) dd.run() dirty = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.dirty if dirty.get_value(Unit.Blocks4096) != req_size.value / Unit.Blocks4096.value: TestRun.fail("Incorrect number of dirty blocks!") cache.flush_cache() # Check if requests with inappropriate size are not cached TestRun.LOGGER.info( f"Check if requests with size outside defined range are not cached" ) not_cached_req_sizes = [ Size(1, Unit.Blocks4096), Size(8, Unit.Blocks4096), Size(16, Unit.Blocks4096), ] for i in range(iterations): req_size = random.choice(not_cached_req_sizes) dd = ( Dd() .input("/dev/zero") .output(core.system_path) .count(1) .block_size(req_size) .oflag("direct") ) dd.run() dirty = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.dirty if dirty.get_value(Unit.Blocks4096) != 0: TestRun.fail("Dirty data present!") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", list(Filesystem) + [False]) def test_ioclass_direct(filesystem): """ Perform buffered/direct IO to/from files or raw block device. Data from buffered IO should be cached. Data from buffered IO should not be cached and if performed to/from already cached data should cause reclassification to unclassified IO class. """ cache, core = prepare() Udev.disable() ioclass_id = 1 io_size = Size(random.randint(1000, 2000), Unit.Blocks4096) # direct IO class ioclass_config.add_ioclass( ioclass_id=ioclass_id, eviction_priority=1, allocation=True, rule="direct", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) fio = ( Fio().create_command() .io_engine(IoEngine.libaio) .size(io_size) .offset(io_size) .read_write(ReadWrite.write) .target(f"{mountpoint}/tmp_file" if filesystem else core.system_path) ) if filesystem: TestRun.LOGGER.info( f"Preparing {filesystem.name} filesystem and mounting {core.system_path} at" f" {mountpoint}" ) core.create_filesystem(filesystem) core.mount(mountpoint) sync() else: TestRun.LOGGER.info("Testing on raw exported object") base_occupancy = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.occupancy TestRun.LOGGER.info(f"Buffered writes to {'file' if filesystem else 'device'}") fio.run() sync() new_occupancy = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.occupancy if new_occupancy != base_occupancy: TestRun.fail("Buffered writes were cached!\n" f"Expected: {base_occupancy}, actual: {new_occupancy}") TestRun.LOGGER.info(f"Direct writes to {'file' if filesystem else 'device'}") fio.direct() fio.run() sync() new_occupancy = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.occupancy if new_occupancy != base_occupancy + io_size: TestRun.fail("Wrong number of direct writes was cached!\n" f"Expected: {base_occupancy + io_size}, actual: {new_occupancy}") TestRun.LOGGER.info(f"Buffered reads from {'file' if filesystem else 'device'}") fio.remove_param("readwrite").remove_param("direct") fio.read_write(ReadWrite.read) fio.run() sync() new_occupancy = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.occupancy if new_occupancy != base_occupancy: TestRun.fail("Buffered reads did not cause reclassification!" f"Expected occupancy: {base_occupancy}, actual: {new_occupancy}") TestRun.LOGGER.info(f"Direct reads from {'file' if filesystem else 'device'}") fio.direct() fio.run() sync() new_occupancy = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.occupancy if new_occupancy != base_occupancy + io_size: TestRun.fail("Wrong number of direct reads was cached!\n" f"Expected: {base_occupancy + io_size}, actual: {new_occupancy}") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", Filesystem) def test_ioclass_metadata(filesystem): """ Perform operations on files that cause metadata update. Determine if every such operation results in increased writes to cached metadata. Exact values may not be tested as each file system has different metadata structure. """ cache, core = prepare() Udev.disable() ioclass_id = random.randint(1, ioclass_config.MAX_IO_CLASS_ID) # metadata IO class ioclass_config.add_ioclass( ioclass_id=ioclass_id, eviction_priority=1, allocation=True, rule="metadata&done", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) TestRun.LOGGER.info(f"Preparing {filesystem.name} filesystem " f"and mounting {core.system_path} at {mountpoint}") core.create_filesystem(filesystem) core.mount(mountpoint) sync() requests_to_metadata_before = cache.get_io_class_statistics( io_class_id=ioclass_id).request_stats.write TestRun.LOGGER.info("Creating 20 test files") files = [] for i in range(1, 21): file_path = f"{mountpoint}/test_file_{i}" dd = ( Dd() .input("/dev/urandom") .output(file_path) .count(random.randint(5, 50)) .block_size(Size(1, Unit.MebiByte)) .oflag("sync") ) dd.run() files.append(File(file_path)) TestRun.LOGGER.info("Checking requests to metadata") requests_to_metadata_after = cache.get_io_class_statistics( io_class_id=ioclass_id).request_stats.write if requests_to_metadata_after == requests_to_metadata_before: TestRun.fail("No requests to metadata while creating files!") requests_to_metadata_before = requests_to_metadata_after TestRun.LOGGER.info("Renaming all test files") for file in files: file.move(f"{file.full_path}_renamed") sync() TestRun.LOGGER.info("Checking requests to metadata") requests_to_metadata_after = cache.get_io_class_statistics( io_class_id=ioclass_id).request_stats.write if requests_to_metadata_after == requests_to_metadata_before: TestRun.fail("No requests to metadata while renaming files!") requests_to_metadata_before = requests_to_metadata_after test_dir_path = f"{mountpoint}/test_dir" TestRun.LOGGER.info(f"Creating directory {test_dir_path}") fs_utils.create_directory(path=test_dir_path) TestRun.LOGGER.info(f"Moving test files into {test_dir_path}") for file in files: file.move(test_dir_path) sync() TestRun.LOGGER.info("Checking requests to metadata") requests_to_metadata_after = cache.get_io_class_statistics( io_class_id=ioclass_id).request_stats.write if requests_to_metadata_after == requests_to_metadata_before: TestRun.fail("No requests to metadata while moving files!") TestRun.LOGGER.info(f"Removing {test_dir_path}") fs_utils.remove(path=test_dir_path, force=True, recursive=True) TestRun.LOGGER.info("Checking requests to metadata") requests_to_metadata_after = cache.get_io_class_statistics( io_class_id=ioclass_id).request_stats.write if requests_to_metadata_after == requests_to_metadata_before: TestRun.fail("No requests to metadata while deleting directory with files!") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", Filesystem) def test_ioclass_id_as_condition(filesystem): """ Load config in which IO class ids are used as conditions in other IO class definitions. Check if performed IO is properly classified. """ cache, core = prepare() Udev.disable() base_dir_path = f"{mountpoint}/base_dir" ioclass_file_size = Size(random.randint(25, 50), Unit.MebiByte) ioclass_file_size_bytes = int(ioclass_file_size.get_value(Unit.Byte)) # directory condition ioclass_config.add_ioclass( ioclass_id=1, eviction_priority=1, allocation=True, rule=f"directory:{base_dir_path}", ioclass_config_path=ioclass_config_path, ) # file size condition ioclass_config.add_ioclass( ioclass_id=2, eviction_priority=1, allocation=True, rule=f"file_size:eq:{ioclass_file_size_bytes}", ioclass_config_path=ioclass_config_path, ) # direct condition ioclass_config.add_ioclass( ioclass_id=3, eviction_priority=1, allocation=True, rule="direct", ioclass_config_path=ioclass_config_path, ) # IO class 1 OR 2 condition ioclass_config.add_ioclass( ioclass_id=4, eviction_priority=1, allocation=True, rule="io_class:1|io_class:2", ioclass_config_path=ioclass_config_path, ) # IO class 4 AND file size condition (same as IO class 2) ioclass_config.add_ioclass( ioclass_id=5, eviction_priority=1, allocation=True, rule=f"io_class:4&file_size:eq:{ioclass_file_size_bytes}", ioclass_config_path=ioclass_config_path, ) # IO class 3 condition ioclass_config.add_ioclass( ioclass_id=6, eviction_priority=1, allocation=True, rule="io_class:3", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) TestRun.LOGGER.info(f"Preparing {filesystem.name} filesystem " f"and mounting {core.system_path} at {mountpoint}") core.create_filesystem(filesystem) core.mount(mountpoint) fs_utils.create_directory(base_dir_path) sync() # IO fulfilling IO class 1 condition (and not IO class 2) # Should be classified as IO class 4 base_occupancy = cache.get_io_class_statistics(io_class_id=4).usage_stats.occupancy non_ioclass_file_size = Size(random.randrange(1, 25), Unit.MebiByte) (Fio().create_command() .io_engine(IoEngine.libaio) .size(non_ioclass_file_size) .read_write(ReadWrite.write) .target(f"{base_dir_path}/test_file_1") .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=4).usage_stats.occupancy if new_occupancy != base_occupancy + non_ioclass_file_size: TestRun.fail("Writes were not properly cached!\n" f"Expected: {base_occupancy + non_ioclass_file_size}, actual: {new_occupancy}") # IO fulfilling IO class 2 condition (and not IO class 1) # Should be classified as IO class 5 base_occupancy = cache.get_io_class_statistics(io_class_id=5).usage_stats.occupancy (Fio().create_command() .io_engine(IoEngine.libaio) .size(ioclass_file_size) .read_write(ReadWrite.write) .target(f"{mountpoint}/test_file_2") .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=5).usage_stats.occupancy if new_occupancy != base_occupancy + ioclass_file_size: TestRun.fail("Writes were not properly cached!\n" f"Expected: {base_occupancy + ioclass_file_size}, actual: {new_occupancy}") # IO fulfilling IO class 1 and 2 conditions # Should be classified as IO class 5 base_occupancy = new_occupancy (Fio().create_command() .io_engine(IoEngine.libaio) .size(ioclass_file_size) .read_write(ReadWrite.write) .target(f"{base_dir_path}/test_file_3") .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=5).usage_stats.occupancy if new_occupancy != base_occupancy + ioclass_file_size: TestRun.fail("Writes were not properly cached!\n" f"Expected: {base_occupancy + ioclass_file_size}, actual: {new_occupancy}") # Same IO but direct # Should be classified as IO class 6 base_occupancy = cache.get_io_class_statistics(io_class_id=6).usage_stats.occupancy (Fio().create_command() .io_engine(IoEngine.libaio) .size(ioclass_file_size) .read_write(ReadWrite.write) .target(f"{base_dir_path}/test_file_3") .direct() .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=6).usage_stats.occupancy if new_occupancy != base_occupancy + ioclass_file_size: TestRun.fail("Writes were not properly cached!\n" f"Expected: {base_occupancy + ioclass_file_size}, actual: {new_occupancy}") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", Filesystem) def test_ioclass_conditions_or(filesystem): """ Load config with IO class combining 5 contradicting conditions connected by OR operator. Check if every IO fulfilling one condition is classified properly. """ cache, core = prepare() Udev.disable() # directories OR condition ioclass_config.add_ioclass( ioclass_id=1, eviction_priority=1, allocation=True, rule=f"directory:{mountpoint}/dir1|directory:{mountpoint}/dir2|directory:" f"{mountpoint}/dir3|directory:{mountpoint}/dir4|directory:{mountpoint}/dir5", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) TestRun.LOGGER.info(f"Preparing {filesystem.name} filesystem " f"and mounting {core.system_path} at {mountpoint}") core.create_filesystem(filesystem) core.mount(mountpoint) for i in range(1, 6): fs_utils.create_directory(f"{mountpoint}/dir{i}") sync() # Perform IO fulfilling each condition and check if occupancy raises for i in range(1, 6): file_size = Size(random.randint(25, 50), Unit.MebiByte) base_occupancy = cache.get_io_class_statistics(io_class_id=1).usage_stats.occupancy (Fio().create_command() .io_engine(IoEngine.libaio) .size(file_size) .read_write(ReadWrite.write) .target(f"{mountpoint}/dir{i}/test_file") .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=1).usage_stats.occupancy if new_occupancy != base_occupancy + file_size: TestRun.fail("Occupancy has not increased correctly!\n" f"Expected: {base_occupancy + file_size}, actual: {new_occupancy}") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", Filesystem) def test_ioclass_conditions_and(filesystem): """ Load config with IO class combining 5 conditions contradicting at least one other condition connected by AND operator. Check if every IO fulfilling one of the conditions is not classified. """ cache, core = prepare() Udev.disable() file_size = Size(random.randint(25, 50), Unit.MebiByte) file_size_bytes = int(file_size.get_value(Unit.Byte)) # directories OR condition ioclass_config.add_ioclass( ioclass_id=1, eviction_priority=1, allocation=True, rule=f"file_size:gt:{file_size_bytes}&file_size:lt:{file_size_bytes}&" f"file_size:ge:{file_size_bytes}&file_size:le:{file_size_bytes}&" f"file_size:eq:{file_size_bytes}", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) TestRun.LOGGER.info(f"Preparing {filesystem.name} filesystem " f"and mounting {core.system_path} at {mountpoint}") core.create_filesystem(filesystem) core.mount(mountpoint) sync() base_occupancy = cache.get_io_class_statistics(io_class_id=1).usage_stats.occupancy # Perform IO for size in [file_size, file_size + Size(1, Unit.MebiByte), file_size - Size(1, Unit.MebiByte)]: (Fio().create_command() .io_engine(IoEngine.libaio) .size(size) .read_write(ReadWrite.write) .target(f"{mountpoint}/test_file") .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=1).usage_stats.occupancy if new_occupancy != base_occupancy: TestRun.fail("Unexpected occupancy increase!\n" f"Expected: {base_occupancy}, actual: {new_occupancy}") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", Filesystem) def test_ioclass_effective_ioclass(filesystem): """ title: Effective IO class with multiple non-exclusive conditions description: | Test CAS ability to properly classify IO fulfilling multiple conditions based on IO class ids and presence of '&done' annotation in IO class rules pass_criteria: - In every iteration first IO is classified to the last in order IO class - In every iteration second IO is classified to the IO class with '&done' annotation """ with TestRun.LOGGER.step(f"Test prepare"): cache, core = prepare() Udev.disable() file_size = Size(10, Unit.Blocks4096) file_size_bytes = int(file_size.get_value(Unit.Byte)) test_dir = f"{mountpoint}/test" rules = ["direct", # rule contradicting other rules f"directory:{test_dir}", f"file_size:le:{2 * file_size_bytes}", f"file_size:ge:{file_size_bytes // 2}"] with TestRun.LOGGER.step(f"Preparing {filesystem.name} filesystem " f"and mounting {core.system_path} at {mountpoint}"): core.create_filesystem(filesystem) core.mount(mountpoint) fs_utils.create_directory(test_dir) sync() for i, permutation in TestRun.iteration(enumerate(permutations(range(1, 5)), start=1)): with TestRun.LOGGER.step("Load IO classes in order specified by permutation"): load_io_classes_in_permutation_order(rules, permutation, cache) io_class_id = 3 if rules[permutation.index(4)] == "direct" else 4 with TestRun.LOGGER.step("Perform IO fulfilling the non-contradicting conditions"): base_occupancy = cache.get_io_class_statistics( io_class_id=io_class_id).usage_stats.occupancy fio = (Fio().create_command() .io_engine(IoEngine.libaio) .size(file_size) .read_write(ReadWrite.write) .target(f"{test_dir}/test_file{i}")) fio.run() sync() with TestRun.LOGGER.step("Check if IO was properly classified " "(to the last non-contradicting IO class)"): new_occupancy = cache.get_io_class_statistics( io_class_id=io_class_id).usage_stats.occupancy if new_occupancy != base_occupancy + file_size: TestRun.LOGGER.error("Wrong IO classification!\n" f"Expected: {base_occupancy + file_size}, " f"actual: {new_occupancy}") with TestRun.LOGGER.step("Add '&done' to the second in order non-contradicting condition"): io_class_id = add_done_to_second_non_exclusive_condition(rules, permutation, cache) with TestRun.LOGGER.step("Repeat IO"): base_occupancy = cache.get_io_class_statistics( io_class_id=io_class_id).usage_stats.occupancy fio.run() sync() with TestRun.LOGGER.step("Check if IO was properly classified " "(to the IO class with '&done' annotation)"): new_occupancy = cache.get_io_class_statistics( io_class_id=io_class_id).usage_stats.occupancy if new_occupancy != base_occupancy + file_size: TestRun.LOGGER.error("Wrong IO classification!\n" f"Expected: {base_occupancy + file_size}, " f"actual: {new_occupancy}") def load_io_classes_in_permutation_order(rules, permutation, cache): ioclass_config.remove_ioclass_config(ioclass_config_path=ioclass_config_path) ioclass_config.create_ioclass_config( add_default_rule=False, ioclass_config_path=ioclass_config_path ) # To make test more precise all workload except of tested ioclass should be # put in pass-through mode ioclass_list = [IoClass.default(allocation=False)] for n in range(len(rules)): ioclass_list.append(IoClass(class_id=permutation[n], rule=rules[n])) IoClass.save_list_to_config_file(ioclass_list, add_default_rule=False, ioclass_config_path=ioclass_config_path) casadm.load_io_classes(cache.cache_id, file=ioclass_config_path) def add_done_to_second_non_exclusive_condition(rules, permutation, cache): non_exclusive_conditions = 0 second_class_id = 1 while True: idx = permutation.index(second_class_id) if rules[idx] != "direct": non_exclusive_conditions += 1 if non_exclusive_conditions == 2: break second_class_id += 1 fs_utils.replace_first_pattern_occurrence(ioclass_config_path, rules[idx], f"{rules[idx]}&done") sync() casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) return second_class_id

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import random from itertools import permutations import pytest from api.cas.ioclass_config import IoClass from storage_devices.disk import DiskType, DiskTypeSet, DiskTypeLowerThan from test_tools import fs_utils from test_tools.dd import Dd from test_tools.disk_utils import Filesystem from test_tools.fio.fio import Fio from test_tools.fio.fio_param import ReadWrite, IoEngine from test_utils.filesystem.file import File from test_utils.os_utils import sync, Udev from .io_class_common import * @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) def test_ioclass_lba(): cache, core = prepare() ioclass_id = 1 min_cached_lba = 56 max_cached_lba = 200 iterations = 100 dd_size = Size(1, Unit.Blocks512) dd_count = 1 ioclass_config.add_ioclass( ioclass_id=ioclass_id, eviction_priority=1, allocation=True, rule=f"lba:ge:{min_cached_lba}&lba:le:{max_cached_lba}&done", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) cache.flush_cache() dirty_count = 0 TestRun.LOGGER.info(f"Writing to one sector in each cache line from range.") for lba in range(min_cached_lba, max_cached_lba, 8): dd = ( Dd() .input("/dev/zero") .output(f"{core.system_path}") .count(dd_count) .block_size(dd_size) .seek(lba) ) dd.run() sync() dirty_count += 1 dirty = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.dirty if dirty.get_value(Unit.Blocks4096) != dirty_count: TestRun.LOGGER.error(f"LBA {lba} not cached") cache.flush_cache() TestRun.LOGGER.info(f"Writing to random sectors outside of cached range.") for i in range(iterations): rand_lba = random.randrange(2000) if min_cached_lba <= rand_lba <= max_cached_lba: continue dd = ( Dd() .input("/dev/zero") .output(f"{core.system_path}") .count(dd_count) .block_size(dd_size) .seek(rand_lba) ) dd.run() sync() dirty = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.dirty if dirty.get_value(Unit.Blocks4096) != 0: TestRun.LOGGER.error(f"Inappropriately cached lba: {rand_lba}") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) def test_ioclass_request_size(): cache, core = prepare() ioclass_id = 1 iterations = 100 ioclass_config.add_ioclass( ioclass_id=ioclass_id, eviction_priority=1, allocation=True, rule=f"request_size:ge:8192&request_size:le:16384&done", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) Udev.disable() TestRun.LOGGER.info( f"Check if requests with size within defined range are cached" ) cached_req_sizes = [Size(2, Unit.Blocks4096), Size(4, Unit.Blocks4096)] for i in range(iterations): cache.flush_cache() req_size = random.choice(cached_req_sizes) dd = ( Dd() .input("/dev/zero") .output(core.system_path) .count(1) .block_size(req_size) .oflag("direct") ) dd.run() dirty = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.dirty if dirty.get_value(Unit.Blocks4096) != req_size.value / Unit.Blocks4096.value: TestRun.fail("Incorrect number of dirty blocks!") cache.flush_cache() TestRun.LOGGER.info( f"Check if requests with size outside defined range are not cached" ) not_cached_req_sizes = [ Size(1, Unit.Blocks4096), Size(8, Unit.Blocks4096), Size(16, Unit.Blocks4096), ] for i in range(iterations): req_size = random.choice(not_cached_req_sizes) dd = ( Dd() .input("/dev/zero") .output(core.system_path) .count(1) .block_size(req_size) .oflag("direct") ) dd.run() dirty = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.dirty if dirty.get_value(Unit.Blocks4096) != 0: TestRun.fail("Dirty data present!") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", list(Filesystem) + [False]) def test_ioclass_direct(filesystem): cache, core = prepare() Udev.disable() ioclass_id = 1 io_size = Size(random.randint(1000, 2000), Unit.Blocks4096) ioclass_config.add_ioclass( ioclass_id=ioclass_id, eviction_priority=1, allocation=True, rule="direct", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) fio = ( Fio().create_command() .io_engine(IoEngine.libaio) .size(io_size) .offset(io_size) .read_write(ReadWrite.write) .target(f"{mountpoint}/tmp_file" if filesystem else core.system_path) ) if filesystem: TestRun.LOGGER.info( f"Preparing {filesystem.name} filesystem and mounting {core.system_path} at" f" {mountpoint}" ) core.create_filesystem(filesystem) core.mount(mountpoint) sync() else: TestRun.LOGGER.info("Testing on raw exported object") base_occupancy = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.occupancy TestRun.LOGGER.info(f"Buffered writes to {'file' if filesystem else 'device'}") fio.run() sync() new_occupancy = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.occupancy if new_occupancy != base_occupancy: TestRun.fail("Buffered writes were cached!\n" f"Expected: {base_occupancy}, actual: {new_occupancy}") TestRun.LOGGER.info(f"Direct writes to {'file' if filesystem else 'device'}") fio.direct() fio.run() sync() new_occupancy = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.occupancy if new_occupancy != base_occupancy + io_size: TestRun.fail("Wrong number of direct writes was cached!\n" f"Expected: {base_occupancy + io_size}, actual: {new_occupancy}") TestRun.LOGGER.info(f"Buffered reads from {'file' if filesystem else 'device'}") fio.remove_param("readwrite").remove_param("direct") fio.read_write(ReadWrite.read) fio.run() sync() new_occupancy = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.occupancy if new_occupancy != base_occupancy: TestRun.fail("Buffered reads did not cause reclassification!" f"Expected occupancy: {base_occupancy}, actual: {new_occupancy}") TestRun.LOGGER.info(f"Direct reads from {'file' if filesystem else 'device'}") fio.direct() fio.run() sync() new_occupancy = cache.get_io_class_statistics(io_class_id=ioclass_id).usage_stats.occupancy if new_occupancy != base_occupancy + io_size: TestRun.fail("Wrong number of direct reads was cached!\n" f"Expected: {base_occupancy + io_size}, actual: {new_occupancy}") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", Filesystem) def test_ioclass_metadata(filesystem): cache, core = prepare() Udev.disable() ioclass_id = random.randint(1, ioclass_config.MAX_IO_CLASS_ID) ioclass_config.add_ioclass( ioclass_id=ioclass_id, eviction_priority=1, allocation=True, rule="metadata&done", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) TestRun.LOGGER.info(f"Preparing {filesystem.name} filesystem " f"and mounting {core.system_path} at {mountpoint}") core.create_filesystem(filesystem) core.mount(mountpoint) sync() requests_to_metadata_before = cache.get_io_class_statistics( io_class_id=ioclass_id).request_stats.write TestRun.LOGGER.info("Creating 20 test files") files = [] for i in range(1, 21): file_path = f"{mountpoint}/test_file_{i}" dd = ( Dd() .input("/dev/urandom") .output(file_path) .count(random.randint(5, 50)) .block_size(Size(1, Unit.MebiByte)) .oflag("sync") ) dd.run() files.append(File(file_path)) TestRun.LOGGER.info("Checking requests to metadata") requests_to_metadata_after = cache.get_io_class_statistics( io_class_id=ioclass_id).request_stats.write if requests_to_metadata_after == requests_to_metadata_before: TestRun.fail("No requests to metadata while creating files!") requests_to_metadata_before = requests_to_metadata_after TestRun.LOGGER.info("Renaming all test files") for file in files: file.move(f"{file.full_path}_renamed") sync() TestRun.LOGGER.info("Checking requests to metadata") requests_to_metadata_after = cache.get_io_class_statistics( io_class_id=ioclass_id).request_stats.write if requests_to_metadata_after == requests_to_metadata_before: TestRun.fail("No requests to metadata while renaming files!") requests_to_metadata_before = requests_to_metadata_after test_dir_path = f"{mountpoint}/test_dir" TestRun.LOGGER.info(f"Creating directory {test_dir_path}") fs_utils.create_directory(path=test_dir_path) TestRun.LOGGER.info(f"Moving test files into {test_dir_path}") for file in files: file.move(test_dir_path) sync() TestRun.LOGGER.info("Checking requests to metadata") requests_to_metadata_after = cache.get_io_class_statistics( io_class_id=ioclass_id).request_stats.write if requests_to_metadata_after == requests_to_metadata_before: TestRun.fail("No requests to metadata while moving files!") TestRun.LOGGER.info(f"Removing {test_dir_path}") fs_utils.remove(path=test_dir_path, force=True, recursive=True) TestRun.LOGGER.info("Checking requests to metadata") requests_to_metadata_after = cache.get_io_class_statistics( io_class_id=ioclass_id).request_stats.write if requests_to_metadata_after == requests_to_metadata_before: TestRun.fail("No requests to metadata while deleting directory with files!") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", Filesystem) def test_ioclass_id_as_condition(filesystem): cache, core = prepare() Udev.disable() base_dir_path = f"{mountpoint}/base_dir" ioclass_file_size = Size(random.randint(25, 50), Unit.MebiByte) ioclass_file_size_bytes = int(ioclass_file_size.get_value(Unit.Byte)) ioclass_config.add_ioclass( ioclass_id=1, eviction_priority=1, allocation=True, rule=f"directory:{base_dir_path}", ioclass_config_path=ioclass_config_path, ) ioclass_config.add_ioclass( ioclass_id=2, eviction_priority=1, allocation=True, rule=f"file_size:eq:{ioclass_file_size_bytes}", ioclass_config_path=ioclass_config_path, ) ioclass_config.add_ioclass( ioclass_id=3, eviction_priority=1, allocation=True, rule="direct", ioclass_config_path=ioclass_config_path, ) ioclass_config.add_ioclass( ioclass_id=4, eviction_priority=1, allocation=True, rule="io_class:1|io_class:2", ioclass_config_path=ioclass_config_path, ) ioclass_config.add_ioclass( ioclass_id=5, eviction_priority=1, allocation=True, rule=f"io_class:4&file_size:eq:{ioclass_file_size_bytes}", ioclass_config_path=ioclass_config_path, ) ioclass_config.add_ioclass( ioclass_id=6, eviction_priority=1, allocation=True, rule="io_class:3", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) TestRun.LOGGER.info(f"Preparing {filesystem.name} filesystem " f"and mounting {core.system_path} at {mountpoint}") core.create_filesystem(filesystem) core.mount(mountpoint) fs_utils.create_directory(base_dir_path) sync() base_occupancy = cache.get_io_class_statistics(io_class_id=4).usage_stats.occupancy non_ioclass_file_size = Size(random.randrange(1, 25), Unit.MebiByte) (Fio().create_command() .io_engine(IoEngine.libaio) .size(non_ioclass_file_size) .read_write(ReadWrite.write) .target(f"{base_dir_path}/test_file_1") .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=4).usage_stats.occupancy if new_occupancy != base_occupancy + non_ioclass_file_size: TestRun.fail("Writes were not properly cached!\n" f"Expected: {base_occupancy + non_ioclass_file_size}, actual: {new_occupancy}") base_occupancy = cache.get_io_class_statistics(io_class_id=5).usage_stats.occupancy (Fio().create_command() .io_engine(IoEngine.libaio) .size(ioclass_file_size) .read_write(ReadWrite.write) .target(f"{mountpoint}/test_file_2") .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=5).usage_stats.occupancy if new_occupancy != base_occupancy + ioclass_file_size: TestRun.fail("Writes were not properly cached!\n" f"Expected: {base_occupancy + ioclass_file_size}, actual: {new_occupancy}") base_occupancy = new_occupancy (Fio().create_command() .io_engine(IoEngine.libaio) .size(ioclass_file_size) .read_write(ReadWrite.write) .target(f"{base_dir_path}/test_file_3") .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=5).usage_stats.occupancy if new_occupancy != base_occupancy + ioclass_file_size: TestRun.fail("Writes were not properly cached!\n" f"Expected: {base_occupancy + ioclass_file_size}, actual: {new_occupancy}") base_occupancy = cache.get_io_class_statistics(io_class_id=6).usage_stats.occupancy (Fio().create_command() .io_engine(IoEngine.libaio) .size(ioclass_file_size) .read_write(ReadWrite.write) .target(f"{base_dir_path}/test_file_3") .direct() .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=6).usage_stats.occupancy if new_occupancy != base_occupancy + ioclass_file_size: TestRun.fail("Writes were not properly cached!\n" f"Expected: {base_occupancy + ioclass_file_size}, actual: {new_occupancy}") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", Filesystem) def test_ioclass_conditions_or(filesystem): cache, core = prepare() Udev.disable() ioclass_config.add_ioclass( ioclass_id=1, eviction_priority=1, allocation=True, rule=f"directory:{mountpoint}/dir1|directory:{mountpoint}/dir2|directory:" f"{mountpoint}/dir3|directory:{mountpoint}/dir4|directory:{mountpoint}/dir5", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) TestRun.LOGGER.info(f"Preparing {filesystem.name} filesystem " f"and mounting {core.system_path} at {mountpoint}") core.create_filesystem(filesystem) core.mount(mountpoint) for i in range(1, 6): fs_utils.create_directory(f"{mountpoint}/dir{i}") sync() for i in range(1, 6): file_size = Size(random.randint(25, 50), Unit.MebiByte) base_occupancy = cache.get_io_class_statistics(io_class_id=1).usage_stats.occupancy (Fio().create_command() .io_engine(IoEngine.libaio) .size(file_size) .read_write(ReadWrite.write) .target(f"{mountpoint}/dir{i}/test_file") .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=1).usage_stats.occupancy if new_occupancy != base_occupancy + file_size: TestRun.fail("Occupancy has not increased correctly!\n" f"Expected: {base_occupancy + file_size}, actual: {new_occupancy}") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", Filesystem) def test_ioclass_conditions_and(filesystem): cache, core = prepare() Udev.disable() file_size = Size(random.randint(25, 50), Unit.MebiByte) file_size_bytes = int(file_size.get_value(Unit.Byte)) ioclass_config.add_ioclass( ioclass_id=1, eviction_priority=1, allocation=True, rule=f"file_size:gt:{file_size_bytes}&file_size:lt:{file_size_bytes}&" f"file_size:ge:{file_size_bytes}&file_size:le:{file_size_bytes}&" f"file_size:eq:{file_size_bytes}", ioclass_config_path=ioclass_config_path, ) casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) TestRun.LOGGER.info(f"Preparing {filesystem.name} filesystem " f"and mounting {core.system_path} at {mountpoint}") core.create_filesystem(filesystem) core.mount(mountpoint) sync() base_occupancy = cache.get_io_class_statistics(io_class_id=1).usage_stats.occupancy for size in [file_size, file_size + Size(1, Unit.MebiByte), file_size - Size(1, Unit.MebiByte)]: (Fio().create_command() .io_engine(IoEngine.libaio) .size(size) .read_write(ReadWrite.write) .target(f"{mountpoint}/test_file") .run()) sync() new_occupancy = cache.get_io_class_statistics(io_class_id=1).usage_stats.occupancy if new_occupancy != base_occupancy: TestRun.fail("Unexpected occupancy increase!\n" f"Expected: {base_occupancy}, actual: {new_occupancy}") @pytest.mark.require_disk("cache", DiskTypeSet([DiskType.optane, DiskType.nand])) @pytest.mark.require_disk("core", DiskTypeLowerThan("cache")) @pytest.mark.parametrizex("filesystem", Filesystem) def test_ioclass_effective_ioclass(filesystem): with TestRun.LOGGER.step(f"Test prepare"): cache, core = prepare() Udev.disable() file_size = Size(10, Unit.Blocks4096) file_size_bytes = int(file_size.get_value(Unit.Byte)) test_dir = f"{mountpoint}/test" rules = ["direct", f"directory:{test_dir}", f"file_size:le:{2 * file_size_bytes}", f"file_size:ge:{file_size_bytes // 2}"] with TestRun.LOGGER.step(f"Preparing {filesystem.name} filesystem " f"and mounting {core.system_path} at {mountpoint}"): core.create_filesystem(filesystem) core.mount(mountpoint) fs_utils.create_directory(test_dir) sync() for i, permutation in TestRun.iteration(enumerate(permutations(range(1, 5)), start=1)): with TestRun.LOGGER.step("Load IO classes in order specified by permutation"): load_io_classes_in_permutation_order(rules, permutation, cache) io_class_id = 3 if rules[permutation.index(4)] == "direct" else 4 with TestRun.LOGGER.step("Perform IO fulfilling the non-contradicting conditions"): base_occupancy = cache.get_io_class_statistics( io_class_id=io_class_id).usage_stats.occupancy fio = (Fio().create_command() .io_engine(IoEngine.libaio) .size(file_size) .read_write(ReadWrite.write) .target(f"{test_dir}/test_file{i}")) fio.run() sync() with TestRun.LOGGER.step("Check if IO was properly classified " "(to the last non-contradicting IO class)"): new_occupancy = cache.get_io_class_statistics( io_class_id=io_class_id).usage_stats.occupancy if new_occupancy != base_occupancy + file_size: TestRun.LOGGER.error("Wrong IO classification!\n" f"Expected: {base_occupancy + file_size}, " f"actual: {new_occupancy}") with TestRun.LOGGER.step("Add '&done' to the second in order non-contradicting condition"): io_class_id = add_done_to_second_non_exclusive_condition(rules, permutation, cache) with TestRun.LOGGER.step("Repeat IO"): base_occupancy = cache.get_io_class_statistics( io_class_id=io_class_id).usage_stats.occupancy fio.run() sync() with TestRun.LOGGER.step("Check if IO was properly classified " "(to the IO class with '&done' annotation)"): new_occupancy = cache.get_io_class_statistics( io_class_id=io_class_id).usage_stats.occupancy if new_occupancy != base_occupancy + file_size: TestRun.LOGGER.error("Wrong IO classification!\n" f"Expected: {base_occupancy + file_size}, " f"actual: {new_occupancy}") def load_io_classes_in_permutation_order(rules, permutation, cache): ioclass_config.remove_ioclass_config(ioclass_config_path=ioclass_config_path) ioclass_config.create_ioclass_config( add_default_rule=False, ioclass_config_path=ioclass_config_path ) ioclass_list = [IoClass.default(allocation=False)] for n in range(len(rules)): ioclass_list.append(IoClass(class_id=permutation[n], rule=rules[n])) IoClass.save_list_to_config_file(ioclass_list, add_default_rule=False, ioclass_config_path=ioclass_config_path) casadm.load_io_classes(cache.cache_id, file=ioclass_config_path) def add_done_to_second_non_exclusive_condition(rules, permutation, cache): non_exclusive_conditions = 0 second_class_id = 1 while True: idx = permutation.index(second_class_id) if rules[idx] != "direct": non_exclusive_conditions += 1 if non_exclusive_conditions == 2: break second_class_id += 1 fs_utils.replace_first_pattern_occurrence(ioclass_config_path, rules[idx], f"{rules[idx]}&done") sync() casadm.load_io_classes(cache_id=cache.cache_id, file=ioclass_config_path) return second_class_id

true

f72ae69658ad2a7325bb00e82f738fd441ad6552

1,684

py

Python

flask_controller/controller.py

AlexFence/FlaskController

efbd51a6970d407128f79876e8724b75fe6ec156

[ "MIT" ]

3

2020-10-19T08:18:51.000Z

2022-02-06T04:29:38.000Z

flask_controller/controller.py

TypicalFence/FlaskController

efbd51a6970d407128f79876e8724b75fe6ec156

[ "MIT" ]

null

flask_controller/controller.py

TypicalFence/FlaskController

efbd51a6970d407128f79876e8724b75fe6ec156

[ "MIT" ]

null

from abc import ABC def route(rule, **options): """Decorator for defining routes of FlaskController classes. Acts in the same way ass @app.route. Can be used for a class to set a base route too. Args: path (str): The path of the newly defined route options: refer to flasks docs for those, all of them can be used """ def decorator(f): f._route = (rule, options) return f return decorator class FlaskController(ABC): """Baseclass for the Controller Classes. Extend tis class and use it in conjunction with the route decoractor to define routes for your flask app. Use the register method to add your defined routes to a flask app. """ def __init__(self): super(FlaskController, self).__init__() def register(self, app): """Adds the routes of a Controller to a Flask instance. Args: app (Flask) """ members = dir(self) routes = [] for member in members: if hasattr(getattr(self, member), "_route"): if member is not "__class__": routes.append(member) self._register_routes(routes, app) def _register_routes(self, routes, app): for route in routes: func = getattr(self, route) real_route = self._generate_route(func._route[0]) options = func._route[1] app.add_url_rule(real_route, route + real_route, func, **options) def _generate_route(self, route): base_route = "" if hasattr(self, "_route"): base_route = self._route[0] return base_route + route

28.066667

78

0.604513

from abc import ABC def route(rule, **options): def decorator(f): f._route = (rule, options) return f return decorator class FlaskController(ABC): def __init__(self): super(FlaskController, self).__init__() def register(self, app): members = dir(self) routes = [] for member in members: if hasattr(getattr(self, member), "_route"): if member is not "__class__": routes.append(member) self._register_routes(routes, app) def _register_routes(self, routes, app): for route in routes: func = getattr(self, route) real_route = self._generate_route(func._route[0]) options = func._route[1] app.add_url_rule(real_route, route + real_route, func, **options) def _generate_route(self, route): base_route = "" if hasattr(self, "_route"): base_route = self._route[0] return base_route + route

true

f72ae6adfbd9100ea1e159819c5e0ed61df33f44

24,028

py

Python

windows_packages_gpu/torch/testing/_internal/jit_metaprogramming_utils.py

codeproject/DeepStack

d96368a3db1bc0266cb500ba3701d130834da0e6

[ "Apache-2.0" ]

353

2020-12-10T10:47:17.000Z

2022-03-31T23:08:29.000Z

windows_packages_gpu/torch/testing/_internal/jit_metaprogramming_utils.py

codeproject/DeepStack

d96368a3db1bc0266cb500ba3701d130834da0e6

[ "Apache-2.0" ]

80

2020-12-10T09:54:22.000Z

2022-03-30T22:08:45.000Z

windows_packages_gpu/torch/testing/_internal/jit_metaprogramming_utils.py

codeproject/DeepStack

d96368a3db1bc0266cb500ba3701d130834da0e6

[ "Apache-2.0" ]

63

2020-12-10T17:10:34.000Z

2022-03-28T16:27:07.000Z

# Torch from torch.jit.annotations import BroadcastingList2, BroadcastingList3 # noqa: F401 from torch.testing._internal.common_methods_invocations import non_differentiable, create_input, \ unpack_variables import torch.nn.functional as F import torch import torch.cuda import torch.jit import torch.jit._logging import torch.jit.frontend from torch.testing._internal.common_nn import module_tests, new_module_tests from copy import deepcopy import math # noqa: F401 # Testing utils from torch._six import inf torch.set_default_dtype(torch.double) L = 20 M = 10 S = 5 # NB: JIT script tests for all nn functional interfaces, script mode does # not support in_place operations yet, so no inplace operation tests added. # removed all the deprecated functions # # ( # method name, # input size/constructing fn, # args (tuple represents shape of a tensor arg), # test variant name(will be used at test name suffix, # 'inplace' skips grad tests), // optional # (True, nonfusible_nodes, fusible_nodes) for autodiff // optional # fn to determine if test should be skipped, // optional # fn mapping output to part that should be gradcheck'ed, // optional # kwargs for function, // optional # ) nn_functional_tests = [ ('conv1d', (S, S, S), ((S, S, S),)), ('conv2d', (S, S, S, S), ((S, S, S, S),)), ('conv3d', (S, S, S, S, S), ((S, S, S, S, S),)), ('conv_transpose1d', (S, S, S), ((S, S, S),)), ('conv_transpose2d', (S, S, S, S), ((S, S, S, S),)), ('conv_transpose3d', (S, S, S, S, S), ((S, S, S, S, S),)), ('conv_tbc', (S, S, S), ((S, S, S), (S,), 2)), ('avg_pool1d', (S, S, S), (3,)), ('avg_pool2d', (S, S, S, S), (3,), '', (True,)), ('avg_pool3d', (S, S, S, S, S), (3,)), ('fractional_max_pool2d', (S, S, S, S), (3, [2, 3],)), ('max_pool1d', (S, S, S), (2, 1)), ('max_pool1d', (S, S, S), (2, 1, 1, 1, False, True), 'with_indices'), ('max_pool2d', (S, S, S, S), (2, 1), '', (True, 'aten::max_pool2d_with_indices')), ('max_pool2d', (S, S, S, S), (2, 1, 1, 1, False, True), 'with_indices', (True, 'aten::max_pool2d_with_indices')), ('max_pool3d', (S, S, S, S, S), (2, 1)), ('max_unpool1d', torch.tensor([[[2., 4]]]), (torch.tensor([[[1, 3]]]), 2, 2, 0)), ('max_unpool2d', torch.tensor([[[[2., 4]]]]), (torch.tensor([[[[1, 3]]]]), 2, 2, 0)), ('max_unpool3d', torch.tensor([[[[[2., 4]]]]]), (torch.tensor([[[[[1, 3]]]]]), 2, 2, 0)), ('lp_pool1d', (S, S, S), (2., 3, 2,)), ('lp_pool2d', (S, S, S, S), (2., 3, 2,)), ('adaptive_max_pool1d', (S, S, S), (5,)), ('adaptive_max_pool2d', (S, S, S, S), ([5, 7],)), ('adaptive_max_pool3d', (S, S, S, S, S), ([3, 2, 2],)), ('adaptive_avg_pool1d', (S, S, S), (5,), '', (True,)), ('adaptive_avg_pool2d', (S, S, S, S), ([5, 7],), '', (True,)), ('adaptive_avg_pool3d', (S, S, S, S, S), ([3, 2, 2],), '', (True,)), ('dropout', (S, S, S), (0.5,), '', (True, ['aten::bernoulli_', 'aten::empty_like', 'aten::mul', 'aten::div'])), ('alpha_dropout', (S, S, S), (0.5,)), ('dropout2d', (S, S, S), (0.5,)), ('dropout3d', (S, S, S), (0.5,)), ('feature_alpha_dropout', (S, S, S), (0.5,)), ('threshold', (S, S, S), (0.1, 2.), '', (True,)), ('threshold', (S, S, S), (0.1, 2., True), 'inplace'), ('relu', (S, S, S), (), '', (True,)), ('relu', (S, S, S), (), 'inplace'), ('glu', (S - 1, S - 1, S - 1), (),), ('hardtanh', (S, S, S), (-0.5, 0.5),), ('hardtanh', (S, S, S), (-0.5, 0.5, True), 'inplace'), ('relu6', (S, S, S), (),), ('relu6', (S, S, S), (True), 'inplace'), ('elu', (S, S, S), (0.9,),), ('elu', (S, S, S), (0.9, True), 'inplace'), ('selu', (S, S, S), (),), ('selu', (S, S, S), (True), 'inplace'), ('celu', (S, S, S), (0.9,),), ('celu', (S, S, S), (0.9, True), 'inplace'), ('leaky_relu', (S, S, S), (0.02,),), ('leaky_relu', (S, S, S), (0.02,), 'inplace'), ('rrelu', (S, S), (0.1, 0.3, False),), ('rrelu', (S, S), (0.1, 0.3, False, True), 'inplace'), ('hardshrink', (S, S, S), (0.4,),), ('tanhshrink', (S, S, S), (),), ('softsign', (S, S, S), (),), ('softplus', (S, S, S), (),), ('softmin', (S, S, S), (0,),), ('softmax', (S, S, S), (0,), '', (True,)), ('softmax', (S, S, S), (0, 3, torch.double), 'with_all_args', (True,)), ('tanh', (S, S, S), (), '', (True,)), ('sigmoid', (S, S, S), (), '', (True,)), ('log_softmax', (S, S, S), (0,), '', (True,)), ('linear', (S, S), ((M, S),), '', (True, ['aten::t', 'aten::matmul'])), ('linear', (S, S), ((M, S), (M,)), 'addmm', (True, ['aten::add', 'aten::mm'])), ('bilinear', (S, S, S), ((S, S, M), torch.zeros(M, S, M),),), ('embedding', torch.tensor([[1, 2, 4, 5], [4, 3, 2, 5]]), (torch.rand(6, 3), ), '', (True,)), ('embedding_bag', torch.tensor([1, 2, 4, 2]), (torch.rand(5, 3), torch.tensor([0, 4]),),), ('batch_norm', (S, S), (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)), ), '', (False, 'aten::_batch_norm_impl_index')), ('instance_norm', (S, S, S), (non_differentiable(torch.zeros(S)), non_differentiable(torch.ones(S))),), ('layer_norm', (S, S, S, S), ([5],), '', (False, ['aten::contiguous', 'aten::_batch_norm_impl_index'])), ('layer_norm', (S, S, S, S), ([5], non_differentiable(torch.rand(S)),), 'with_only_weight', (False, ['aten::contiguous', 'aten::_batch_norm_impl_index'])), ('layer_norm', (S, S, S, S), ([5], None, non_differentiable(torch.rand(S)),), 'with_only_bias', (False, ['aten::contiguous', 'aten::_batch_norm_impl_index'])), ('layer_norm', (S, S, S, S), ([5], non_differentiable(torch.rand(S)), non_differentiable(torch.rand(S))), 'with_weight_and_bias', (False, ['aten::contiguous', 'aten::_batch_norm_impl_index', 'aten::addcmul'])), ('group_norm', (S, S, S), (1, torch.rand(5),),), ('local_response_norm', (S, S, S), (2, ),), ('nll_loss', F.log_softmax(torch.randn(3, 5), dim=0), (torch.tensor([1, 0, 4]),), '', (True, 'aten::nll_loss_forward')), ('poisson_nll_loss', torch.rand(S, 2), (torch.rand(S, 2),),), ('poisson_nll_loss', torch.rand(S, 2), (torch.rand(S, 2), True, True), 'full'), ('kl_div', F.log_softmax(torch.randn(S, 10), 1), (F.softmax(torch.randn(S, 10), 1),),), ('cross_entropy', (3, S), (torch.randint(S, (3,), dtype=torch.int64),),), ('binary_cross_entropy_with_logits', (3,), (torch.empty(3).random_(2), ),), ('smooth_l1_loss', (3, S), (non_differentiable(torch.rand(3, S)),),), ('l1_loss', (3, S), (non_differentiable(torch.rand(3, S)),),), ('mse_loss', (3, S), (non_differentiable(torch.rand(3, S)),),), ('smooth_l1_loss', (3, S), ((torch.rand(3, S)),), 'with_grad'), ('l1_loss', (3, S), ((torch.rand(3, S)),), 'with_grad'), ('mse_loss', (3, S), ((torch.rand(3, S)),), 'with_grad'), ('margin_ranking_loss', (3, S), ((3, S), (S,)),), ('hinge_embedding_loss', (3, S), (non_differentiable(torch.rand(3, S)),),), ('soft_margin_loss', (3, S), (non_differentiable(torch.rand(3, S)),),), ('multilabel_soft_margin_loss', (3, S), (non_differentiable(torch.rand(3, S)),),), ('cosine_embedding_loss', (S, S), ((S, S), non_differentiable(torch.rand(S,))),), ('pixel_shuffle', (1, 9, 4, 4), (3,),), ('affine_grid', (S, 2, 3), (torch.Size([S, 1, 7, 7]),),), ('pad', (3, 3, 4, 2), ([1, 1],),), ('pairwise_distance', (S, S), ((S, S),),), ('pdist', (S, S), (),), ('cosine_similarity', (S, S), ((S, S),),), ('triplet_margin_loss', (S, S), ((S, S), (S, S)),), ('normalize', (S, S, S), (),), ('unfold', (S, S, S, S), ([2, 3]),), ('fold', (1, 3 * 2 * 2, 12), ([4, 5], [2, 2]),), ('grid_sample', (S, S, S, S), (non_differentiable(torch.rand(S, S, S, 2)),),), ('gumbel_softmax', (S, S), (2.,), '', (True, ['aten::softmax', 'aten::add', 'aten::div'], ['aten::neg'])), ('gumbel_softmax', (S, S), (2., True,), 'hard', (True, ['aten::softmax', 'aten::add', 'aten::div'], ['aten::neg'])), ('multilabel_margin_loss', torch.tensor([[0.2, -0.2, 0.07]]), (torch.tensor([[0, 0, 1]]),),), ('multi_margin_loss', (S, S), (non_differentiable(torch.randint(S, (S, ), dtype=torch.int64)), 1, 1., non_differentiable(torch.randn(S))),), ('binary_cross_entropy', torch.randn(3, 2).sigmoid(), (non_differentiable(torch.rand(3, 2)), non_differentiable(torch.randn(3, 2))),), ('binary_cross_entropy', torch.randn(3, 2).sigmoid(), (non_differentiable(torch.rand(3, 2)), non_differentiable(torch.randn(3, 2)), None, None, 'mean'), 'size_average'), ('ctc_loss', torch.rand(S, S, S).log_softmax(2).detach().requires_grad_(), (torch.randint(1, S, (S, S), dtype=torch.long), torch.full((S,), S, dtype=torch.long), torch.randint(1, S, (S,), dtype=torch.long))), ('upsample', torch.randn(S, S, M, M), (None, 2.), 'with_scale'), ('upsample', torch.randn(S, S, M, M), (4,), 'with_size'), ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'nearest_4d'), ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'nearest_4d_with_scale'), ('interpolate', torch.randn(S, S, M, M), (4,), 'nearest_4d_with_size'), ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'area_4d'), ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'area_4d_with_scale'), ('interpolate', torch.randn(S, S, M, M), (4,), 'area_4d_with_size'), ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'bilinear_4d'), ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'bilinear_4d_with_scale'), ('interpolate', torch.randn(S, S, M, M), (4,), 'bilinear_4d_with_size'), ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'bicubic_4d'), ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'bicubic_4d_with_scale'), ('interpolate', torch.randn(S, S, M, M), (4,), 'bicubic_4d_with_size'), ('interpolate', torch.zeros(3, 3).view(1, 3, 3), (2,), 'nearest_3d'), ('interpolate', torch.randn(S, M, M), (None, 2.), 'nearest_3d_with_scale'), ('interpolate', torch.randn(S, M, M), (4,), 'nearest_3d_with_size'), ('interpolate', torch.zeros(3, 3).view(1, 3, 3), (2,), 'area_3d'), ('interpolate', torch.randn(S, M, M), (None, 2.), 'area_3d_with_scale'), ('interpolate', torch.randn(S, M, M), (4,), 'area_3d_with_size'), ('interpolate', torch.zeros(3, 3).view(1, 3, 3), (2,), 'linear_3d'), ('interpolate', torch.randn(S, M, M), (None, 2.), 'linear_3d_with_scale'), ('interpolate', torch.randn(S, M, M), (4,), 'linear_3d_with_size'), ('interpolate', torch.randn(S, M, M, M, M), (None, 2.), 'nearest_5d_with_scale'), ('interpolate', torch.randn(S, M, M, M, M), (4,), 'nearest_5d_with_size'), ('interpolate', torch.zeros(3, 3, 3).view(1, 1, 3, 3, 3), (2,), 'area_5d'), ('interpolate', torch.randn(S, M, M, M, M), (None, 2.), 'area_5d_with_scale'), ('interpolate', torch.randn(S, M, M, M, M), (4,), 'area_5d_with_size'), ('interpolate', torch.zeros(3, 3, 3).view(1, 1, 3, 3, 3), (2,), 'trilinear_5d'), ('interpolate', torch.randn(S, M, M, M, M), (None, 2.), 'trilinear_5d_with_scale'), ('interpolate', torch.randn(S, M, M, M, M), (4,), 'trilinear_5d_with_size'), ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2, None, 'nearest', None, False), 'nearest_4d_not_recompute_scale_factor'), ('interpolate', torch.randn(S, S, M, M), (4, None, 'nearest', None, False), 'nearest_4d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bilinear', None, False), 'bilinear_4d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, S, M, M), (4, None, 'bilinear', None, False), 'bilinear_4d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bicubic', None, False), 'bicubic_4d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, S, M, M), (4, None, 'bicubic', None, False), 'bicubic_4d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M), (None, 2., 'nearest', None, False), 'nearest_3d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M), (4, None, 'nearest', None, False), 'nearest_3d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M), (None, 2., 'linear', None, False), 'linear_3d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M), (4, None, 'linear', None, False), 'linear_3d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'nearest', None, False), 'nearest_5d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'nearest', None, False), 'nearest_5d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'trilinear', None, False), 'trilinear_5d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'trilinear', None, False), 'trilinear_5d_with_size_not_recompute_scale_factor'), ] script_template = ''' def the_method({}): return {} ''' def get_call(method_name, func_type, args, kwargs): kwargs_str = ', '.join([k + '=' + str(v) for k, v in kwargs.items()]) self_arg = args[0] if(func_type == 'method'): args = args[1:] argument_str = ', '.join(args) argument_str += ', ' if len(args) and len(kwargs) else '' argument_str += kwargs_str if func_type == 'functional': call = 'torch.{}({})'.format(method_name, argument_str) elif func_type == 'method': call = '{}.{}({})'.format(self_arg, method_name, argument_str) elif func_type == 'nn_functional': call = 'torch.nn.functional.{}({})'.format(method_name, argument_str) else: raise 'Unsupported function type' return call def get_constant(x): if x == inf: return 'math.inf' if x == -inf: return '-math.inf' return x def get_script_args(args): formals = [] tensors = [] actuals = [] for arg in args: if isinstance(arg, torch.Tensor): name = 'i{}'.format(len(formals)) formals.append(name) actuals.append(name) tensors.append(arg) elif isinstance(arg, str): actuals.append("'{}'".format(arg)) else: actuals.append(str(get_constant(arg))) return (formals, tensors, actuals) # create a script function from (name, func_type, output_process_fn), # and returns the compiled function and example inputs def gen_script_fn_and_args(method_name, func_type, *args, **kwargs): formals, tensors, actuals = get_script_args(args) call = get_call(method_name, func_type, actuals, kwargs) script = script_template.format(', '.join(formals), call) CU = torch.jit.CompilationUnit(script) return CU.the_method, tensors # create a script function from (name, func_type, output_process_fn), # returns a function takes in (args, kwargs) and runs the compiled function and # then applies the post process fn to the outputs def create_script_fn(self, method_name, func_type, output_process_fn): def script_fn(*args, **kwargs): fn, tensors = gen_script_fn_and_args(method_name, func_type, *args, **kwargs) self.assertExportImport(fn.graph, tensors) output = output_process_fn(fn(*tensors)) script_fn.last_graph = fn.graph_for(*tensors) return output return script_fn # make a new function where all non-tensor arguments in 'args' have been partially # applied, and all tensor arguments remain. # used to trace functions when some arguments are not tensors def partial_apply_nontensors(fn, args, **kwargs): source = ['t' if isinstance(arg, torch.Tensor) else 's' for arg in args] def new_fn(*tensors_): tensors = iter(tensors_) return fn(*(args[i] if s == 's' else next(tensors) for i, s in enumerate(source)), **kwargs) return new_fn, [arg for arg in args if isinstance(arg, torch.Tensor)] # create a trace function from input fn def create_traced_fn(self, fn): def traced_fn(*inputs, **kwargs): fn_tensors, inputs_tensors = partial_apply_nontensors(fn, inputs, **kwargs) # `check_trace` is set to False because check_trace is run with @no_grad # Also, `check_against_reference` already does all the checks # against python function traced = torch.jit.trace(fn_tensors, inputs_tensors, check_trace=False) self.assertExportImport(traced.graph, inputs_tensors) output = traced(*inputs_tensors) traced_fn.last_graph = traced.graph_for(*inputs_tensors) return output return traced_fn # known to be failing in script EXCLUDE_SCRIPT = { 'test_norm_fro_default', 'test_norm_fro_cpu', 'test_norm_nuc', 'test_norm_fro', 'test_norm_nuc_batched', # aten op has additional cudnn argument 'test_nn_unfold', # flaky test - TODO fix 'test_nn_ctc_loss', # unknown builtin op 'test_nn_fold', # jit doesn't support sparse tensors. 'test_to_sparse' } # generates a script function and set of example inputs # from a specified test in the format of nn_functional_tests def get_nn_functional_compiled_fn_and_inputs(name, self_size, args, variant_name='', *extra_args): test_name = 'test_nn_' + name if variant_name != '': test_name = test_name + '_' + variant_name no_grad = variant_name == 'inplace' self_variable = create_input((self_size,))[0][0] kwargs = None # need to record this because methods can change the size (e.g. unsqueeze) args_variable, kwargs_variable = create_input(args) self_tensor = deepcopy(self_variable.data) args_tensor = deepcopy(unpack_variables(args_variable)) f_args_variable = (self_variable,) + args_variable f_args_tensor = (self_tensor,) + args_tensor with torch.jit._disable_emit_hooks(): script_fn, inputs = gen_script_fn_and_args(name, "nn_functional", *f_args_variable) return script_fn, inputs # additional modules test # TODO: delete this list once we make all nn_tests work additional_module_tests = [ { 'module_name': 'Bilinear', 'constructor_args': (S, S, M), 'input_size': (S, S), 'extra_args': ((S, S),) }, { 'module_name': 'RNNCell', 'constructor_args': (S, S), 'input_size': (S, S), }, { 'module_name': 'LSTMCell', 'constructor_args': (S, S), 'input_size': (S, S), }, { 'module_name': 'GRUCell', 'constructor_args': (S, S), 'input_size': (S, S), }, { 'module_name': 'MultiheadAttention', 'constructor_args': (128, 8), 'input_size': (10, 8, 128), 'extra_args': (torch.randn(10, 8, 128), torch.randn(10, 8, 128)), 'slowTest': True }, { 'module_name': 'Transformer', 'constructor_args': (1, 1, 1, 1, 2), 'input_size': (3, 1, 1), 'extra_args': (torch.randn(1, 1, 1),), 'slowTest': True } ] EXCLUDE_SCRIPT_MODULES = { 'test_nn_AdaptiveAvgPool2d_tuple_none', 'test_nn_AdaptiveAvgPool3d_tuple_none', 'test_nn_AdaptiveMaxPool2d_tuple_none', 'test_nn_AdaptiveMaxPool3d_tuple_none', # Doesn't use future division, so this is not supported 'test_nn_CrossMapLRN2d', } script_method_template = ''' def forward({}): return {} ''' def create_script_module(self, nn_module, constructor_args, *args, **kwargs): def script_module(*args, **kwargs): formals, tensors, actuals = get_script_args(args) method_args = ', '.join(['self'] + actuals) call_args_str = ', '.join(actuals) call = "self.submodule({})".format(call_args_str) script = script_method_template.format(method_args, call) submodule_constants = [] if kwargs.get('is_constant'): submodule_constants = ['submodule'] # Create module to use the script method class TheModule(torch.jit.ScriptModule): __constants__ = submodule_constants def __init__(self): super(TheModule, self).__init__() self.submodule = nn_module(*constructor_args) def make_module(script): module = TheModule() # check __repr__ str(module) module.define(script) return module module = make_module(script) if self: self.assertExportImportModule(module, tensors) module(*args) create_script_module.last_graph = module.graph return module return script_module def get_nn_module_name_from_kwargs(**kwargs): if 'module_name' in kwargs: return kwargs['module_name'] elif 'fullname' in kwargs: return kwargs['fullname'] elif 'constructor' in kwargs: return kwargs['constructor'].__name__ def get_nn_mod_test_name(**kwargs): name = get_nn_module_name_from_kwargs(**kwargs) test_name = name if 'desc' in kwargs: test_name = "{}_{}".format(test_name, kwargs['desc']) return 'test_nn_{}'.format(test_name) def get_nn_module_class_from_kwargs(**kwargs): name = get_nn_module_name_from_kwargs(**kwargs) index = name.find("_") if index == -1: return name else: return name[0:name.find("_")] def try_get_nn_module_compiled_mod_and_inputs(*args, **kwargs): name = get_nn_module_name_from_kwargs(**kwargs) if 'desc' in kwargs and 'eval' in kwargs['desc']: # eval() is not supported, so skip these tests return test_name = name if 'desc' in kwargs: test_name = "{}_{}".format(test_name, kwargs['desc']) test_name = get_nn_mod_test_name(**kwargs) if test_name in EXCLUDE_SCRIPT_MODULES: return if 'constructor' in kwargs: nn_module = kwargs['constructor'] else: nn_module = getattr(torch.nn, name) if "FunctionalModule" in str(nn_module): return if 'constructor_args_fn' in kwargs: constructor_args = kwargs['constructor_args_fn']() else: constructor_args = kwargs.get('constructor_args', ()) # Set up inputs from tuple of sizes or constructor fn if 'input_fn' in kwargs: input = kwargs['input_fn']() else: input = (kwargs['input_size'],) # Extra parameters to forward() if 'extra_args' in kwargs: input = input + kwargs['extra_args'] if 'target_size' in kwargs: input = input + (kwargs['target_size'],) elif 'target_fn' in kwargs: if torch.is_tensor(input): input = (input,) input = input + (kwargs['target_fn'](),) args_variable, kwargs_variable = create_input(input) f_args_variable = deepcopy(unpack_variables(args_variable)) out_var = deepcopy(f_args_variable) args, mod = f_args_variable, create_script_module(None, nn_module, constructor_args, *f_args_variable)(*f_args_variable) return mod, out_var def get_all_nn_module_tests(): return module_tests + new_module_tests + additional_module_tests

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from torch.jit.annotations import BroadcastingList2, BroadcastingList3 from torch.testing._internal.common_methods_invocations import non_differentiable, create_input, \ unpack_variables import torch.nn.functional as F import torch import torch.cuda import torch.jit import torch.jit._logging import torch.jit.frontend from torch.testing._internal.common_nn import module_tests, new_module_tests from copy import deepcopy import math from torch._six import inf torch.set_default_dtype(torch.double) L = 20 M = 10 S = 5 # kwargs for function, // optional # ) nn_functional_tests = [ ('conv1d', (S, S, S), ((S, S, S),)), ('conv2d', (S, S, S, S), ((S, S, S, S),)), ('conv3d', (S, S, S, S, S), ((S, S, S, S, S),)), ('conv_transpose1d', (S, S, S), ((S, S, S),)), ('conv_transpose2d', (S, S, S, S), ((S, S, S, S),)), ('conv_transpose3d', (S, S, S, S, S), ((S, S, S, S, S),)), ('conv_tbc', (S, S, S), ((S, S, S), (S,), 2)), ('avg_pool1d', (S, S, S), (3,)), ('avg_pool2d', (S, S, S, S), (3,), '', (True,)), ('avg_pool3d', (S, S, S, S, S), (3,)), ('fractional_max_pool2d', (S, S, S, S), (3, [2, 3],)), ('max_pool1d', (S, S, S), (2, 1)), ('max_pool1d', (S, S, S), (2, 1, 1, 1, False, True), 'with_indices'), ('max_pool2d', (S, S, S, S), (2, 1), '', (True, 'aten::max_pool2d_with_indices')), ('max_pool2d', (S, S, S, S), (2, 1, 1, 1, False, True), 'with_indices', (True, 'aten::max_pool2d_with_indices')), ('max_pool3d', (S, S, S, S, S), (2, 1)), ('max_unpool1d', torch.tensor([[[2., 4]]]), (torch.tensor([[[1, 3]]]), 2, 2, 0)), ('max_unpool2d', torch.tensor([[[[2., 4]]]]), (torch.tensor([[[[1, 3]]]]), 2, 2, 0)), ('max_unpool3d', torch.tensor([[[[[2., 4]]]]]), (torch.tensor([[[[[1, 3]]]]]), 2, 2, 0)), ('lp_pool1d', (S, S, S), (2., 3, 2,)), ('lp_pool2d', (S, S, S, S), (2., 3, 2,)), ('adaptive_max_pool1d', (S, S, S), (5,)), ('adaptive_max_pool2d', (S, S, S, S), ([5, 7],)), ('adaptive_max_pool3d', (S, S, S, S, S), ([3, 2, 2],)), ('adaptive_avg_pool1d', (S, S, S), (5,), '', (True,)), ('adaptive_avg_pool2d', (S, S, S, S), ([5, 7],), '', (True,)), ('adaptive_avg_pool3d', (S, S, S, S, S), ([3, 2, 2],), '', (True,)), ('dropout', (S, S, S), (0.5,), '', (True, ['aten::bernoulli_', 'aten::empty_like', 'aten::mul', 'aten::div'])), ('alpha_dropout', (S, S, S), (0.5,)), ('dropout2d', (S, S, S), (0.5,)), ('dropout3d', (S, S, S), (0.5,)), ('feature_alpha_dropout', (S, S, S), (0.5,)), ('threshold', (S, S, S), (0.1, 2.), '', (True,)), ('threshold', (S, S, S), (0.1, 2., True), 'inplace'), ('relu', (S, S, S), (), '', (True,)), ('relu', (S, S, S), (), 'inplace'), ('glu', (S - 1, S - 1, S - 1), (),), ('hardtanh', (S, S, S), (-0.5, 0.5),), ('hardtanh', (S, S, S), (-0.5, 0.5, True), 'inplace'), ('relu6', (S, S, S), (),), ('relu6', (S, S, S), (True), 'inplace'), ('elu', (S, S, S), (0.9,),), ('elu', (S, S, S), (0.9, True), 'inplace'), ('selu', (S, S, S), (),), ('selu', (S, S, S), (True), 'inplace'), ('celu', (S, S, S), (0.9,),), ('celu', (S, S, S), (0.9, True), 'inplace'), ('leaky_relu', (S, S, S), (0.02,),), ('leaky_relu', (S, S, S), (0.02,), 'inplace'), ('rrelu', (S, S), (0.1, 0.3, False),), ('rrelu', (S, S), (0.1, 0.3, False, True), 'inplace'), ('hardshrink', (S, S, S), (0.4,),), ('tanhshrink', (S, S, S), (),), ('softsign', (S, S, S), (),), ('softplus', (S, S, S), (),), ('softmin', (S, S, S), (0,),), ('softmax', (S, S, S), (0,), '', (True,)), ('softmax', (S, S, S), (0, 3, torch.double), 'with_all_args', (True,)), ('tanh', (S, S, S), (), '', (True,)), ('sigmoid', (S, S, S), (), '', (True,)), ('log_softmax', (S, S, S), (0,), '', (True,)), ('linear', (S, S), ((M, S),), '', (True, ['aten::t', 'aten::matmul'])), ('linear', (S, S), ((M, S), (M,)), 'addmm', (True, ['aten::add', 'aten::mm'])), ('bilinear', (S, S, S), ((S, S, M), torch.zeros(M, S, M),),), ('embedding', torch.tensor([[1, 2, 4, 5], [4, 3, 2, 5]]), (torch.rand(6, 3), ), '', (True,)), ('embedding_bag', torch.tensor([1, 2, 4, 2]), (torch.rand(5, 3), torch.tensor([0, 4]),),), ('batch_norm', (S, S), 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3).view(1, 1, 3, 3), (2, None, 'nearest', None, False), 'nearest_4d_not_recompute_scale_factor'), ('interpolate', torch.randn(S, S, M, M), (4, None, 'nearest', None, False), 'nearest_4d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bilinear', None, False), 'bilinear_4d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, S, M, M), (4, None, 'bilinear', None, False), 'bilinear_4d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bicubic', None, False), 'bicubic_4d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, S, M, M), (4, None, 'bicubic', None, False), 'bicubic_4d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M), (None, 2., 'nearest', None, False), 'nearest_3d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M), (4, None, 'nearest', None, False), 'nearest_3d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M), (None, 2., 'linear', None, False), 'linear_3d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M), (4, None, 'linear', None, False), 'linear_3d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'nearest', None, False), 'nearest_5d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'nearest', None, False), 'nearest_5d_with_size_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'trilinear', None, False), 'trilinear_5d_with_scale_not_recompute_scale_factor'), ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'trilinear', None, False), 'trilinear_5d_with_size_not_recompute_scale_factor'), ] script_template = ''' def the_method({}): return {} ''' def get_call(method_name, func_type, args, kwargs): kwargs_str = ', '.join([k + '=' + str(v) for k, v in kwargs.items()]) self_arg = args[0] if(func_type == 'method'): args = args[1:] argument_str = ', '.join(args) argument_str += ', ' if len(args) and len(kwargs) else '' argument_str += kwargs_str if func_type == 'functional': call = 'torch.{}({})'.format(method_name, argument_str) elif func_type == 'method': call = '{}.{}({})'.format(self_arg, method_name, argument_str) elif func_type == 'nn_functional': call = 'torch.nn.functional.{}({})'.format(method_name, argument_str) else: raise 'Unsupported function type' return call def get_constant(x): if x == inf: return 'math.inf' if x == -inf: return '-math.inf' return x def get_script_args(args): formals = [] tensors = [] actuals = [] for arg in args: if isinstance(arg, torch.Tensor): name = 'i{}'.format(len(formals)) formals.append(name) actuals.append(name) tensors.append(arg) elif isinstance(arg, str): actuals.append("'{}'".format(arg)) else: actuals.append(str(get_constant(arg))) return (formals, tensors, actuals) # create a script function from (name, func_type, output_process_fn), # and returns the compiled function and example inputs def gen_script_fn_and_args(method_name, func_type, *args, **kwargs): formals, tensors, actuals = get_script_args(args) call = get_call(method_name, func_type, actuals, kwargs) script = script_template.format(', '.join(formals), call) CU = torch.jit.CompilationUnit(script) return CU.the_method, tensors # create a script function from (name, func_type, output_process_fn), # returns a function takes in (args, kwargs) and runs the compiled function and # then applies the post process fn to the outputs def create_script_fn(self, method_name, func_type, output_process_fn): def script_fn(*args, **kwargs): fn, tensors = gen_script_fn_and_args(method_name, func_type, *args, **kwargs) self.assertExportImport(fn.graph, tensors) output = output_process_fn(fn(*tensors)) script_fn.last_graph = fn.graph_for(*tensors) return output return script_fn # make a new function where all non-tensor arguments in 'args' have been partially # applied, and all tensor arguments remain. # used to trace functions when some arguments are not tensors def partial_apply_nontensors(fn, args, **kwargs): source = ['t' if isinstance(arg, torch.Tensor) else 's' for arg in args] def new_fn(*tensors_): tensors = iter(tensors_) return fn(*(args[i] if s == 's' else next(tensors) for i, s in enumerate(source)), **kwargs) return new_fn, [arg for arg in args if isinstance(arg, torch.Tensor)] # create a trace function from input fn def create_traced_fn(self, fn): def traced_fn(*inputs, **kwargs): fn_tensors, inputs_tensors = partial_apply_nontensors(fn, inputs, **kwargs) # `check_trace` is set to False because check_trace is run with @no_grad # Also, `check_against_reference` already does all the checks # against python function traced = torch.jit.trace(fn_tensors, inputs_tensors, check_trace=False) self.assertExportImport(traced.graph, inputs_tensors) output = traced(*inputs_tensors) traced_fn.last_graph = traced.graph_for(*inputs_tensors) return output return traced_fn # known to be failing in script EXCLUDE_SCRIPT = { 'test_norm_fro_default', 'test_norm_fro_cpu', 'test_norm_nuc', 'test_norm_fro', 'test_norm_nuc_batched', # aten op has additional cudnn argument 'test_nn_unfold', # flaky test - TODO fix 'test_nn_ctc_loss', # unknown builtin op 'test_nn_fold', # jit doesn't support sparse tensors. 'test_to_sparse' } def get_nn_functional_compiled_fn_and_inputs(name, self_size, args, variant_name='', *extra_args): test_name = 'test_nn_' + name if variant_name != '': test_name = test_name + '_' + variant_name no_grad = variant_name == 'inplace' self_variable = create_input((self_size,))[0][0] kwargs = None args_variable, kwargs_variable = create_input(args) self_tensor = deepcopy(self_variable.data) args_tensor = deepcopy(unpack_variables(args_variable)) f_args_variable = (self_variable,) + args_variable f_args_tensor = (self_tensor,) + args_tensor with torch.jit._disable_emit_hooks(): script_fn, inputs = gen_script_fn_and_args(name, "nn_functional", *f_args_variable) return script_fn, inputs additional_module_tests = [ { 'module_name': 'Bilinear', 'constructor_args': (S, S, M), 'input_size': (S, S), 'extra_args': ((S, S),) }, { 'module_name': 'RNNCell', 'constructor_args': (S, S), 'input_size': (S, S), }, { 'module_name': 'LSTMCell', 'constructor_args': (S, S), 'input_size': (S, S), }, { 'module_name': 'GRUCell', 'constructor_args': (S, S), 'input_size': (S, S), }, { 'module_name': 'MultiheadAttention', 'constructor_args': (128, 8), 'input_size': (10, 8, 128), 'extra_args': (torch.randn(10, 8, 128), torch.randn(10, 8, 128)), 'slowTest': True }, { 'module_name': 'Transformer', 'constructor_args': (1, 1, 1, 1, 2), 'input_size': (3, 1, 1), 'extra_args': (torch.randn(1, 1, 1),), 'slowTest': True } ] EXCLUDE_SCRIPT_MODULES = { 'test_nn_AdaptiveAvgPool2d_tuple_none', 'test_nn_AdaptiveAvgPool3d_tuple_none', 'test_nn_AdaptiveMaxPool2d_tuple_none', 'test_nn_AdaptiveMaxPool3d_tuple_none', 'test_nn_CrossMapLRN2d', } script_method_template = ''' def forward({}): return {} ''' def create_script_module(self, nn_module, constructor_args, *args, **kwargs): def script_module(*args, **kwargs): formals, tensors, actuals = get_script_args(args) method_args = ', '.join(['self'] + actuals) call_args_str = ', '.join(actuals) call = "self.submodule({})".format(call_args_str) script = script_method_template.format(method_args, call) submodule_constants = [] if kwargs.get('is_constant'): submodule_constants = ['submodule'] # Create module to use the script method class TheModule(torch.jit.ScriptModule): __constants__ = submodule_constants def __init__(self): super(TheModule, self).__init__() self.submodule = nn_module(*constructor_args) def make_module(script): module = TheModule() # check __repr__ str(module) module.define(script) return module module = make_module(script) if self: self.assertExportImportModule(module, tensors) module(*args) create_script_module.last_graph = module.graph return module return script_module def get_nn_module_name_from_kwargs(**kwargs): if 'module_name' in kwargs: return kwargs['module_name'] elif 'fullname' in kwargs: return kwargs['fullname'] elif 'constructor' in kwargs: return kwargs['constructor'].__name__ def get_nn_mod_test_name(**kwargs): name = get_nn_module_name_from_kwargs(**kwargs) test_name = name if 'desc' in kwargs: test_name = "{}_{}".format(test_name, kwargs['desc']) return 'test_nn_{}'.format(test_name) def get_nn_module_class_from_kwargs(**kwargs): name = get_nn_module_name_from_kwargs(**kwargs) index = name.find("_") if index == -1: return name else: return name[0:name.find("_")] def try_get_nn_module_compiled_mod_and_inputs(*args, **kwargs): name = get_nn_module_name_from_kwargs(**kwargs) if 'desc' in kwargs and 'eval' in kwargs['desc']: # eval() is not supported, so skip these tests return test_name = name if 'desc' in kwargs: test_name = "{}_{}".format(test_name, kwargs['desc']) test_name = get_nn_mod_test_name(**kwargs) if test_name in EXCLUDE_SCRIPT_MODULES: return if 'constructor' in kwargs: nn_module = kwargs['constructor'] else: nn_module = getattr(torch.nn, name) if "FunctionalModule" in str(nn_module): return if 'constructor_args_fn' in kwargs: constructor_args = kwargs['constructor_args_fn']() else: constructor_args = kwargs.get('constructor_args', ()) # Set up inputs from tuple of sizes or constructor fn if 'input_fn' in kwargs: input = kwargs['input_fn']() else: input = (kwargs['input_size'],) # Extra parameters to forward() if 'extra_args' in kwargs: input = input + kwargs['extra_args'] if 'target_size' in kwargs: input = input + (kwargs['target_size'],) elif 'target_fn' in kwargs: if torch.is_tensor(input): input = (input,) input = input + (kwargs['target_fn'](),) args_variable, kwargs_variable = create_input(input) f_args_variable = deepcopy(unpack_variables(args_variable)) out_var = deepcopy(f_args_variable) args, mod = f_args_variable, create_script_module(None, nn_module, constructor_args, *f_args_variable)(*f_args_variable) return mod, out_var def get_all_nn_module_tests(): return module_tests + new_module_tests + additional_module_tests

true

f72ae77f6af21241e139bcfcb73ffd4cb6993215

566

py

Python

setup.py

galperins4/python-client

c8b6ea1f33801254eb560429b2c775d10fe60273

[ "MIT" ]

1

2018-06-15T11:19:23.000Z

setup.py

galperins4/mirror-python-client

c8b6ea1f33801254eb560429b2c775d10fe60273

[ "MIT" ]

null

setup.py

galperins4/mirror-python-client

c8b6ea1f33801254eb560429b2c775d10fe60273

[ "MIT" ]

null

import sys import setuptools requires = [ 'requests>=2.19.1', 'backoff>=1.6.0', 'flatten_dict>=0.3.0' ] tests_require = [] extras_require = {} setuptools.setup( name='hedera-python-client', description='Python API client for Hedera Hashgraph.', version='0.0.1', author='TBD', author_email='TBD', url='https://github.com/galperins4/hedera-python-client', packages=setuptools.find_packages(exclude=['tests', 'tests.*']), install_requires=requires, extras_require=extras_require, tests_require=tests_require, )

20.214286

68

0.676678

import sys import setuptools requires = [ 'requests>=2.19.1', 'backoff>=1.6.0', 'flatten_dict>=0.3.0' ] tests_require = [] extras_require = {} setuptools.setup( name='hedera-python-client', description='Python API client for Hedera Hashgraph.', version='0.0.1', author='TBD', author_email='TBD', url='https://github.com/galperins4/hedera-python-client', packages=setuptools.find_packages(exclude=['tests', 'tests.*']), install_requires=requires, extras_require=extras_require, tests_require=tests_require, )

true

f72ae7e848291c51786e5d2a992f0c9c85761179

7,832

py

Python

plugins/modules/oci_object_storage_replication_policy_facts.py

sagar2938/oci-ansible-collection

5b8ce583a0d5d0aabf14494d61aea4649e18d1e6

[ "Apache-2.0" ]

null

plugins/modules/oci_object_storage_replication_policy_facts.py

sagar2938/oci-ansible-collection

5b8ce583a0d5d0aabf14494d61aea4649e18d1e6

[ "Apache-2.0" ]

null

plugins/modules/oci_object_storage_replication_policy_facts.py

sagar2938/oci-ansible-collection

5b8ce583a0d5d0aabf14494d61aea4649e18d1e6

[ "Apache-2.0" ]

null

#!/usr/bin/python # Copyright (c) 2020, 2021 Oracle and/or its affiliates. # This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Apache License v2.0 # See LICENSE.TXT for details. # GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_object_storage_replication_policy_facts short_description: Fetches details about one or multiple ReplicationPolicy resources in Oracle Cloud Infrastructure description: - Fetches details about one or multiple ReplicationPolicy resources in Oracle Cloud Infrastructure - List the replication policies associated with a bucket. - If I(replication_id) is specified, the details of a single ReplicationPolicy will be returned. version_added: "2.9.0" author: Oracle (@oracle) options: namespace_name: description: - The Object Storage namespace used for the request. type: str required: true bucket_name: description: - "The name of the bucket. Avoid entering confidential information. Example: `my-new-bucket1`" type: str required: true replication_id: description: - The ID of the replication policy. - Required to get a specific replication_policy. type: str aliases: ["id"] extends_documentation_fragment: [ oracle.oci.oracle, oracle.oci.oracle_name_option ] """ EXAMPLES = """ - name: Get a specific replication_policy oci_object_storage_replication_policy_facts: # required namespace_name: namespace_name_example bucket_name: my-new-bucket1 replication_id: "ocid1.replication.oc1..xxxxxxEXAMPLExxxxxx" - name: List replication_policies oci_object_storage_replication_policy_facts: # required namespace_name: namespace_name_example bucket_name: my-new-bucket1 """ RETURN = """ replication_policies: description: - List of ReplicationPolicy resources returned: on success type: complex contains: id: description: - The id of the replication policy. returned: on success type: str sample: "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx" name: description: - The name of the policy. returned: on success type: str sample: name_example destination_region_name: description: - "The destination region to replicate to, for example \\"us-ashburn-1\\"." returned: on success type: str sample: destination_region_name_example destination_bucket_name: description: - The bucket to replicate to in the destination region. Replication policy creation does not automatically create a destination bucket. Create the destination bucket before creating the policy. returned: on success type: str sample: destination_bucket_name_example time_created: description: - The date when the replication policy was created as per L(RFC 3339,https://tools.ietf.org/html/rfc3339). returned: on success type: str sample: "2013-10-20T19:20:30+01:00" time_last_sync: description: - Changes made to the source bucket before this time has been replicated. returned: on success type: str sample: "2013-10-20T19:20:30+01:00" status: description: - The replication status of the policy. If the status is CLIENT_ERROR, once the user fixes the issue described in the status message, the status will become ACTIVE. returned: on success type: str sample: ACTIVE status_message: description: - A human-readable description of the status. returned: on success type: str sample: status_message_example sample: [{ "id": "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx", "name": "name_example", "destination_region_name": "destination_region_name_example", "destination_bucket_name": "destination_bucket_name_example", "time_created": "2013-10-20T19:20:30+01:00", "time_last_sync": "2013-10-20T19:20:30+01:00", "status": "ACTIVE", "status_message": "status_message_example" }] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.object_storage import ObjectStorageClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class ReplicationPolicyFactsHelperGen(OCIResourceFactsHelperBase): """Supported operations: get, list""" def get_required_params_for_get(self): return [ "namespace_name", "bucket_name", "replication_id", ] def get_required_params_for_list(self): return [ "namespace_name", "bucket_name", ] def get_resource(self): return oci_common_utils.call_with_backoff( self.client.get_replication_policy, namespace_name=self.module.params.get("namespace_name"), bucket_name=self.module.params.get("bucket_name"), replication_id=self.module.params.get("replication_id"), ) def list_resources(self): optional_list_method_params = [ "name", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_list_method_params if self.module.params.get(param) is not None ) return oci_common_utils.list_all_resources( self.client.list_replication_policies, namespace_name=self.module.params.get("namespace_name"), bucket_name=self.module.params.get("bucket_name"), **optional_kwargs ) ReplicationPolicyFactsHelperCustom = get_custom_class( "ReplicationPolicyFactsHelperCustom" ) class ResourceFactsHelper( ReplicationPolicyFactsHelperCustom, ReplicationPolicyFactsHelperGen ): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update( dict( namespace_name=dict(type="str", required=True), bucket_name=dict(type="str", required=True), replication_id=dict(aliases=["id"], type="str"), name=dict(type="str"), ) ) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="replication_policy", service_client_class=ObjectStorageClient, namespace="object_storage", ) result = [] if resource_facts_helper.is_get(): result = [resource_facts_helper.get()] elif resource_facts_helper.is_list(): result = resource_facts_helper.list() else: resource_facts_helper.fail() module.exit_json(replication_policies=result) if __name__ == "__main__": main()

32.633333

122

0.655388

from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_object_storage_replication_policy_facts short_description: Fetches details about one or multiple ReplicationPolicy resources in Oracle Cloud Infrastructure description: - Fetches details about one or multiple ReplicationPolicy resources in Oracle Cloud Infrastructure - List the replication policies associated with a bucket. - If I(replication_id) is specified, the details of a single ReplicationPolicy will be returned. version_added: "2.9.0" author: Oracle (@oracle) options: namespace_name: description: - The Object Storage namespace used for the request. type: str required: true bucket_name: description: - "The name of the bucket. Avoid entering confidential information. Example: `my-new-bucket1`" type: str required: true replication_id: description: - The ID of the replication policy. - Required to get a specific replication_policy. type: str aliases: ["id"] extends_documentation_fragment: [ oracle.oci.oracle, oracle.oci.oracle_name_option ] """ EXAMPLES = """ - name: Get a specific replication_policy oci_object_storage_replication_policy_facts: # required namespace_name: namespace_name_example bucket_name: my-new-bucket1 replication_id: "ocid1.replication.oc1..xxxxxxEXAMPLExxxxxx" - name: List replication_policies oci_object_storage_replication_policy_facts: # required namespace_name: namespace_name_example bucket_name: my-new-bucket1 """ RETURN = """ replication_policies: description: - List of ReplicationPolicy resources returned: on success type: complex contains: id: description: - The id of the replication policy. returned: on success type: str sample: "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx" name: description: - The name of the policy. returned: on success type: str sample: name_example destination_region_name: description: - "The destination region to replicate to, for example \\"us-ashburn-1\\"." returned: on success type: str sample: destination_region_name_example destination_bucket_name: description: - The bucket to replicate to in the destination region. Replication policy creation does not automatically create a destination bucket. Create the destination bucket before creating the policy. returned: on success type: str sample: destination_bucket_name_example time_created: description: - The date when the replication policy was created as per L(RFC 3339,https://tools.ietf.org/html/rfc3339). returned: on success type: str sample: "2013-10-20T19:20:30+01:00" time_last_sync: description: - Changes made to the source bucket before this time has been replicated. returned: on success type: str sample: "2013-10-20T19:20:30+01:00" status: description: - The replication status of the policy. If the status is CLIENT_ERROR, once the user fixes the issue described in the status message, the status will become ACTIVE. returned: on success type: str sample: ACTIVE status_message: description: - A human-readable description of the status. returned: on success type: str sample: status_message_example sample: [{ "id": "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx", "name": "name_example", "destination_region_name": "destination_region_name_example", "destination_bucket_name": "destination_bucket_name_example", "time_created": "2013-10-20T19:20:30+01:00", "time_last_sync": "2013-10-20T19:20:30+01:00", "status": "ACTIVE", "status_message": "status_message_example" }] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.object_storage import ObjectStorageClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class ReplicationPolicyFactsHelperGen(OCIResourceFactsHelperBase): def get_required_params_for_get(self): return [ "namespace_name", "bucket_name", "replication_id", ] def get_required_params_for_list(self): return [ "namespace_name", "bucket_name", ] def get_resource(self): return oci_common_utils.call_with_backoff( self.client.get_replication_policy, namespace_name=self.module.params.get("namespace_name"), bucket_name=self.module.params.get("bucket_name"), replication_id=self.module.params.get("replication_id"), ) def list_resources(self): optional_list_method_params = [ "name", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_list_method_params if self.module.params.get(param) is not None ) return oci_common_utils.list_all_resources( self.client.list_replication_policies, namespace_name=self.module.params.get("namespace_name"), bucket_name=self.module.params.get("bucket_name"), **optional_kwargs ) ReplicationPolicyFactsHelperCustom = get_custom_class( "ReplicationPolicyFactsHelperCustom" ) class ResourceFactsHelper( ReplicationPolicyFactsHelperCustom, ReplicationPolicyFactsHelperGen ): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update( dict( namespace_name=dict(type="str", required=True), bucket_name=dict(type="str", required=True), replication_id=dict(aliases=["id"], type="str"), name=dict(type="str"), ) ) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="replication_policy", service_client_class=ObjectStorageClient, namespace="object_storage", ) result = [] if resource_facts_helper.is_get(): result = [resource_facts_helper.get()] elif resource_facts_helper.is_list(): result = resource_facts_helper.list() else: resource_facts_helper.fail() module.exit_json(replication_policies=result) if __name__ == "__main__": main()

true

f72ae8822be3a2b344c2b3ee4a5a5f5d65da61a6

3,218

py

Python

NTP_Bot/msg_interpreter.py

PEI-I1/Nos_Tech_Problems

cf8b0b51285a912988a96cc96438f81c75fa45b7

[ "MIT" ]

null

NTP_Bot/msg_interpreter.py

PEI-I1/Nos_Tech_Problems

cf8b0b51285a912988a96cc96438f81c75fa45b7

[ "MIT" ]

14

2020-06-05T20:19:18.000Z

2021-09-22T18:18:23.000Z

NTP_Bot/msg_interpreter.py

PEI-I1/Nos_Tech_Problems

cf8b0b51285a912988a96cc96438f81c75fa45b7

[ "MIT" ]

null

#!/usr/bin/env python3 import tensorflow_hub as hub import numpy as np import tensorflow_text import json, re, os from threading import Thread from keywords import keywords embeddings = {} embed = None def loadModelData(): ''' Loads Tensorflow enconder and pre-encodes the problem data ''' global embed global embeddings embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/2") feature_types = ['Sintoma', 'Tipificacao_Nivel_1', 'Tipificacao_Nivel_2', 'Tipificacao_Nivel_3'] with open(os.getcwd() + '/input_options.json') as json_file: data = json.load(json_file) for typ in feature_types: embedProblemData(data, typ, embeddings) def embedProblemData(data, feature_type, embeddings): ''' Calculates embeddings for all the values of feature_type :param: data :param: feature type :param: dict that maps feature values to their embeddings ''' raw_features = [x for x in data[feature_type]] proc_features = [x.lower() for x in raw_features] feature_embeddings = embed(proc_features)["outputs"] for i in range(0, len(raw_features)): embeddings[raw_features[i]] = feature_embeddings[i] def replaceWithKeywords(line, keywords): ''' Replaces matches in line with a keyword :param: string to look for expressions :param: dictionary object that matches keywords with expressions :return: list of versions of the line with replaced expressions ''' keyworded_versions = [line] for keyword, matches in keywords.items(): keyworded_versions.extend([re.sub(match, keyword, line) for match in matches if re.search(match, line)]) return keyworded_versions def getFeatureSuggestion(line, keywords, ss_vals, ss_embeddings, category): ''' Calculates feature from category that is semantically closest to the one described in line :param: target :param: ''' ll = line.lower() line_versions = replaceWithKeywords(ll, keywords['common']) if category>0: line_versions.extend(replaceWithKeywords(ll, keywords['tip_'+str(category)])) sentence_embeddings = [embed(line_version)["outputs"] for line_version in line_versions] similarity_matrices = [list(np.inner(sent_emb, ss_embeddings)[0]) for sent_emb in sentence_embeddings] max_values = [max(similarity_matrice) for similarity_matrice in similarity_matrices] max_abs = max(max_values) similarity_matrix = similarity_matrices[max_values.index(max_abs)] sugestao = ss_vals[similarity_matrix.index(max_abs)] return sugestao, max_abs def extractProblemData(prob_desc, search_space, category): ''' Extracts the string in the search space that is semantically closest to the problem description :param: problem description :param: search space of the possible strings :param: search space category (simptome or typification) :return: closest string that belongs to search_space and confidence ''' ss_embeddings = [embeddings[ss_val] for ss_val in search_space] return getFeatureSuggestion(prob_desc, keywords, search_space, ss_embeddings, category)

37.858824

112

0.720945

import tensorflow_hub as hub import numpy as np import tensorflow_text import json, re, os from threading import Thread from keywords import keywords embeddings = {} embed = None def loadModelData(): global embed global embeddings embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/2") feature_types = ['Sintoma', 'Tipificacao_Nivel_1', 'Tipificacao_Nivel_2', 'Tipificacao_Nivel_3'] with open(os.getcwd() + '/input_options.json') as json_file: data = json.load(json_file) for typ in feature_types: embedProblemData(data, typ, embeddings) def embedProblemData(data, feature_type, embeddings): raw_features = [x for x in data[feature_type]] proc_features = [x.lower() for x in raw_features] feature_embeddings = embed(proc_features)["outputs"] for i in range(0, len(raw_features)): embeddings[raw_features[i]] = feature_embeddings[i] def replaceWithKeywords(line, keywords): keyworded_versions = [line] for keyword, matches in keywords.items(): keyworded_versions.extend([re.sub(match, keyword, line) for match in matches if re.search(match, line)]) return keyworded_versions def getFeatureSuggestion(line, keywords, ss_vals, ss_embeddings, category): ll = line.lower() line_versions = replaceWithKeywords(ll, keywords['common']) if category>0: line_versions.extend(replaceWithKeywords(ll, keywords['tip_'+str(category)])) sentence_embeddings = [embed(line_version)["outputs"] for line_version in line_versions] similarity_matrices = [list(np.inner(sent_emb, ss_embeddings)[0]) for sent_emb in sentence_embeddings] max_values = [max(similarity_matrice) for similarity_matrice in similarity_matrices] max_abs = max(max_values) similarity_matrix = similarity_matrices[max_values.index(max_abs)] sugestao = ss_vals[similarity_matrix.index(max_abs)] return sugestao, max_abs def extractProblemData(prob_desc, search_space, category): ss_embeddings = [embeddings[ss_val] for ss_val in search_space] return getFeatureSuggestion(prob_desc, keywords, search_space, ss_embeddings, category)

true

f72ae89046ac8b319ed71a62b07e68d530306531

3,901

py

Python

powerwatch/analysis/old_analysis_scripts/average_time_pw_uplug.py

nklugman/PlugWatch

4fbd2506a6808542fc5246e87d3c382761da1eaf

[ "MIT" ]

null

powerwatch/analysis/old_analysis_scripts/average_time_pw_uplug.py

nklugman/PlugWatch

4fbd2506a6808542fc5246e87d3c382761da1eaf

[ "MIT" ]

null

powerwatch/analysis/old_analysis_scripts/average_time_pw_uplug.py

nklugman/PlugWatch

4fbd2506a6808542fc5246e87d3c382761da1eaf

[ "MIT" ]

null

#!/usr/bin/env python from pyspark.sql import SparkSession from pyspark.sql.functions import col, window, asc, desc, lead, lag, udf, hour from pyspark.sql.functions import month, year, lit, when, collect_list, struct, mean, stddev, stddev_pop import pyspark.sql.functions as F from pyspark.sql.window import Window from pyspark.sql.types import FloatType, IntegerType, DateType, TimestampType from pyspark import SparkConf import yaml import datetime import os from math import isnan conf = SparkConf() conf.set("spark.jars", os.getenv("HOME") + "/.ivy2/jars/org.postgresql_postgresql-42.1.1.jar") conf.set("spark.executor.extrajavaoptions", "-Xmx15000m") conf.set("spark.executor.memory", "15g") conf.set("spark.driver.memory", "15g") conf.set("spark.storage.memoryFraction", "0") spark = SparkSession.builder \ .config(conf=conf) \ .master("local[4]") \ .appName("SAIDI Calculator") \ .getOrCreate() config = open('config.yaml') config = yaml.load(config) #connect to the database pw_df = spark.read.jdbc("jdbc:postgresql://timescale.lab11.eecs.umich.edu/powerwatch", "pw_dedupe", properties={"user": config['user'], "password": config['password'],"driver":"org.postgresql.Driver"}) #read the data that we care about pw_df = pw_df.select(pw_df['core_id'],pw_df['time'],pw_df['is_powered'],pw_df['product_id'],pw_df['millis'],pw_df['last_unplug_millis'],pw_df['last_plug_millis']) pw_df = pw_df.filter("product_id = 7008 OR product_id= 7009") #now we need to created a window function that looks at the leading lagging edge of is powered and detects transitions #then we can filter out all data that is not a transition def detectTransition(value1, value2): if(value1 == value2): return 0 else: return 1 udfDetectTransition = udf(detectTransition, IntegerType()) w = Window.partitionBy("core_id").orderBy(asc("time")) is_powered_lag = lag("is_powered",1).over(w) pw_df = pw_df.withColumn("transition", udfDetectTransition("is_powered",is_powered_lag)) #filter out all transitions pw_df = pw_df.filter("transition != 0") #now count each outage (really restoration) def countOutage(value1, value2, value3): if(value1 == False and value2 == True and value3 == True): return 1 else: return 0 udfCountTransition = udf(countOutage, IntegerType()) is_powered_lead = lead("is_powered",1).over(w) is_powered_lag = lag("is_powered",1).over(w) pw_df = pw_df.withColumn("outage", udfCountTransition("is_powered", is_powered_lead, is_powered_lag)) #now find all the exact outage and restore times using millis def timeCorrect(time, millis, unplugMillis): if(unplugMillis == 0 or millis == None or unplugMillis == None or isnan(millis) or isnan(unplugMillis)): return time elif unplugMillis > millis: return time else: return time - datetime.timedelta(microseconds = (int(millis)-int(unplugMillis))*1000) udftimeCorrect = udf(timeCorrect, TimestampType()) pw_df = pw_df.withColumn("outage_time", udftimeCorrect("time","millis","last_unplug_millis")) #now filter out everything that is not an outage. We should have a time and end_time for every outage pw_df = pw_df.filter("outage != 0") w = Window.orderBy(asc("outage_time")).rowsBetween(-1,1) pw_df = pw_df.withColumn("outage_window_list",collect_list(F.struct("outage_time","core_id")).over(w)) def filterOutage(time, imei, timeList): times = [] for i in timeList: if imei != i[1]: t = (i[0] - time).total_seconds() if(t > 0): times.append(t) if len(times) > 0: return min(times) return None udfFilterTransition = udf(filterOutage, FloatType()) pw_df = pw_df.withColumn("seconds_until_next_unplug", udfFilterTransition("outage_time","core_id","outage_window_list")) print(pw_df.stat.approxQuantile("seconds_until_next_unplug", [x*0.01 for x in range(0,100)], 0.0))

40.216495

162

0.722892

from pyspark.sql import SparkSession from pyspark.sql.functions import col, window, asc, desc, lead, lag, udf, hour from pyspark.sql.functions import month, year, lit, when, collect_list, struct, mean, stddev, stddev_pop import pyspark.sql.functions as F from pyspark.sql.window import Window from pyspark.sql.types import FloatType, IntegerType, DateType, TimestampType from pyspark import SparkConf import yaml import datetime import os from math import isnan conf = SparkConf() conf.set("spark.jars", os.getenv("HOME") + "/.ivy2/jars/org.postgresql_postgresql-42.1.1.jar") conf.set("spark.executor.extrajavaoptions", "-Xmx15000m") conf.set("spark.executor.memory", "15g") conf.set("spark.driver.memory", "15g") conf.set("spark.storage.memoryFraction", "0") spark = SparkSession.builder \ .config(conf=conf) \ .master("local[4]") \ .appName("SAIDI Calculator") \ .getOrCreate() config = open('config.yaml') config = yaml.load(config) pw_df = spark.read.jdbc("jdbc:postgresql://timescale.lab11.eecs.umich.edu/powerwatch", "pw_dedupe", properties={"user": config['user'], "password": config['password'],"driver":"org.postgresql.Driver"}) pw_df = pw_df.select(pw_df['core_id'],pw_df['time'],pw_df['is_powered'],pw_df['product_id'],pw_df['millis'],pw_df['last_unplug_millis'],pw_df['last_plug_millis']) pw_df = pw_df.filter("product_id = 7008 OR product_id= 7009") def detectTransition(value1, value2): if(value1 == value2): return 0 else: return 1 udfDetectTransition = udf(detectTransition, IntegerType()) w = Window.partitionBy("core_id").orderBy(asc("time")) is_powered_lag = lag("is_powered",1).over(w) pw_df = pw_df.withColumn("transition", udfDetectTransition("is_powered",is_powered_lag)) pw_df = pw_df.filter("transition != 0") def countOutage(value1, value2, value3): if(value1 == False and value2 == True and value3 == True): return 1 else: return 0 udfCountTransition = udf(countOutage, IntegerType()) is_powered_lead = lead("is_powered",1).over(w) is_powered_lag = lag("is_powered",1).over(w) pw_df = pw_df.withColumn("outage", udfCountTransition("is_powered", is_powered_lead, is_powered_lag)) def timeCorrect(time, millis, unplugMillis): if(unplugMillis == 0 or millis == None or unplugMillis == None or isnan(millis) or isnan(unplugMillis)): return time elif unplugMillis > millis: return time else: return time - datetime.timedelta(microseconds = (int(millis)-int(unplugMillis))*1000) udftimeCorrect = udf(timeCorrect, TimestampType()) pw_df = pw_df.withColumn("outage_time", udftimeCorrect("time","millis","last_unplug_millis")) pw_df = pw_df.filter("outage != 0") w = Window.orderBy(asc("outage_time")).rowsBetween(-1,1) pw_df = pw_df.withColumn("outage_window_list",collect_list(F.struct("outage_time","core_id")).over(w)) def filterOutage(time, imei, timeList): times = [] for i in timeList: if imei != i[1]: t = (i[0] - time).total_seconds() if(t > 0): times.append(t) if len(times) > 0: return min(times) return None udfFilterTransition = udf(filterOutage, FloatType()) pw_df = pw_df.withColumn("seconds_until_next_unplug", udfFilterTransition("outage_time","core_id","outage_window_list")) print(pw_df.stat.approxQuantile("seconds_until_next_unplug", [x*0.01 for x in range(0,100)], 0.0))

true

f72ae8f83fbcedd3eb02039ff2317a6935549fc8

5,975

py

Python

lightlab/equipment/visa_bases/driver_base.py

CharLee674/rvisa_lightlab

b43e36f3436b60c8c5f3088b4cb0896c5360aa4a

[ "MIT" ]

null

lightlab/equipment/visa_bases/driver_base.py

CharLee674/rvisa_lightlab

b43e36f3436b60c8c5f3088b4cb0896c5360aa4a

[ "MIT" ]

null

lightlab/equipment/visa_bases/driver_base.py

CharLee674/rvisa_lightlab

b43e36f3436b60c8c5f3088b4cb0896c5360aa4a

[ "MIT" ]

null

from abc import ABC, abstractmethod from contextlib import contextmanager import socket import time from lightlab import visalogger as logger from rvisa.util import from_ascii_block class InstrumentSessionBase(ABC): ''' Base class for Instrument sessions, to be inherited and specialized by VISAObject and PrologixGPIBObject''' @abstractmethod def spoll(self): pass @abstractmethod def LLO(self): pass @abstractmethod def LOC(self): pass @abstractmethod def open(self): pass @abstractmethod def close(self): pass @abstractmethod def write(self): pass @abstractmethod def query(self): pass @abstractmethod def wait(self): pass @abstractmethod def clear(self): pass @abstractmethod def query_raw_binary(self): pass def query_ascii_values(self, message, converter='f', separator=',', container=list): ''' Taken from pvisa.''' block = self.query(message) return from_ascii_block(block, converter, separator, container) def instrID(self): r"""Returns the \*IDN? string""" return self.query('*IDN?') @property @abstractmethod def timeout(self): pass @timeout.setter @abstractmethod def timeout(self, newTimeout): pass CR = '\r' LF = '\n' class TCPSocketConnection(object): ''' Opens a TCP socket connection, much like netcat. Usage: s = TCPSocketConnection('socket-server.school.edu', 1111) s.connect() # connects to socket and leaves it open s.send('command') # sends the command through the socket r = s.recv(1000) # receives a message of up to 1000 bytes s.disconnect() # shuts down connection ''' port = None #: socket server's port number _socket = None _termination = None def __init__(self, ip_address, port, timeout=2, termination=LF): """ Args: ip_address (str): hostname or ip address of the socket server port (int): socket server's port number timeout (float): timeout in seconds for establishing socket connection to socket server, default 2. """ self.timeout = timeout self.port = port self.ip_address = ip_address self._termination = termination def _send(self, socket, value): encoded_value = (('%s' % value) + self._termination).encode('ascii') sent = socket.sendall(encoded_value) return sent def _recv(self, socket, msg_length=2048): received_value = socket.recv(msg_length) return received_value.decode('ascii') def connect(self): ''' Connects to the socket and leaves the connection open. If already connected, does nothing. Returns: socket object. ''' if self._socket is None: s = socket.socket(socket.AF_INET, socket.SOCK_STREAM, socket.IPPROTO_TCP) try: logger.debug("Attempting new connection (timeout = %s)", str(self.timeout)) init_time = time.time() s.settimeout(self.timeout) s.connect((self.ip_address, self.port)) except socket.error: # avoiding shutdown to prevent sending any data to remote socket # https://stackoverflow.com/questions/13109899/does-socket-become-unusable-after-connect-fails # s.shutdown(socket.SHUT_WR) s.close() del s logger.error('Cannot connect to resource.') raise else: final_time = time.time() elapsed_time_ms = 1e3 * (final_time - init_time) logger.debug("Connected. Time elapsed: %s msec", '{:.2f}'.format(elapsed_time_ms)) self._socket = s return self._socket else: return self._socket def disconnect(self): ''' If connected, disconnects and kills the socket.''' if self._socket is not None: self._socket.shutdown(socket.SHUT_WR) self._socket.close() self._socket = None @contextmanager def connected(self): ''' Context manager for ensuring that the socket is connected while sending and receiving commands to remote socket. This is safe to use everywhere, even if the socket is previously connected. It can also be nested. This is useful to bundle multiple commands that you desire to be executed together in a single socket connection, for example: .. code-block:: python def query(self, query_msg, msg_length=2048): with self.connected(): self._send(self._socket, query_msg) recv = self._recv(self._socket, msg_length) return recv ''' previously_connected = (self._socket is not None) self.connect() try: yield self finally: if not previously_connected: self.disconnect() def startup(self): raise NotImplementedError def send(self, value): ''' Sends an ASCII string to the socket server. Auto-connects if necessary. Args: value (str): value to be sent ''' with self.connected(): sent = self._send(self._socket, value) return sent def recv(self, msg_length=2048): ''' Receives an ASCII string from the socket server. Auto-connects if necessary. Args: msg_length (int): maximum message length. ''' with self.connected(): recv = self._recv(self._socket, msg_length) return recv def query(self, query_msg, msg_length=2048): raise NotImplementedError

29.146341

110

0.594979

from abc import ABC, abstractmethod from contextlib import contextmanager import socket import time from lightlab import visalogger as logger from rvisa.util import from_ascii_block class InstrumentSessionBase(ABC): @abstractmethod def spoll(self): pass @abstractmethod def LLO(self): pass @abstractmethod def LOC(self): pass @abstractmethod def open(self): pass @abstractmethod def close(self): pass @abstractmethod def write(self): pass @abstractmethod def query(self): pass @abstractmethod def wait(self): pass @abstractmethod def clear(self): pass @abstractmethod def query_raw_binary(self): pass def query_ascii_values(self, message, converter='f', separator=',', container=list): block = self.query(message) return from_ascii_block(block, converter, separator, container) def instrID(self): return self.query('*IDN?') @property @abstractmethod def timeout(self): pass @timeout.setter @abstractmethod def timeout(self, newTimeout): pass CR = '\r' LF = '\n' class TCPSocketConnection(object): port = None _socket = None _termination = None def __init__(self, ip_address, port, timeout=2, termination=LF): self.timeout = timeout self.port = port self.ip_address = ip_address self._termination = termination def _send(self, socket, value): encoded_value = (('%s' % value) + self._termination).encode('ascii') sent = socket.sendall(encoded_value) return sent def _recv(self, socket, msg_length=2048): received_value = socket.recv(msg_length) return received_value.decode('ascii') def connect(self): if self._socket is None: s = socket.socket(socket.AF_INET, socket.SOCK_STREAM, socket.IPPROTO_TCP) try: logger.debug("Attempting new connection (timeout = %s)", str(self.timeout)) init_time = time.time() s.settimeout(self.timeout) s.connect((self.ip_address, self.port)) except socket.error: # avoiding shutdown to prevent sending any data to remote socket # https://stackoverflow.com/questions/13109899/does-socket-become-unusable-after-connect-fails # s.shutdown(socket.SHUT_WR) s.close() del s logger.error('Cannot connect to resource.') raise else: final_time = time.time() elapsed_time_ms = 1e3 * (final_time - init_time) logger.debug("Connected. Time elapsed: %s msec", '{:.2f}'.format(elapsed_time_ms)) self._socket = s return self._socket else: return self._socket def disconnect(self): if self._socket is not None: self._socket.shutdown(socket.SHUT_WR) self._socket.close() self._socket = None @contextmanager def connected(self): previously_connected = (self._socket is not None) self.connect() try: yield self finally: if not previously_connected: self.disconnect() def startup(self): raise NotImplementedError def send(self, value): with self.connected(): sent = self._send(self._socket, value) return sent def recv(self, msg_length=2048): with self.connected(): recv = self._recv(self._socket, msg_length) return recv def query(self, query_msg, msg_length=2048): raise NotImplementedError

true

f72ae943e83fcbed48d9e3f084fe924867622c96

2,382

py

Python

simple_ado/user.py

Bhaskers-Blu-Org2/simple_ado

bbfb1cd5d513cce0f606188e803db3dcf667cb75

[ "MIT" ]

null

simple_ado/user.py

Bhaskers-Blu-Org2/simple_ado

bbfb1cd5d513cce0f606188e803db3dcf667cb75

[ "MIT" ]

null

simple_ado/user.py

Bhaskers-Blu-Org2/simple_ado

bbfb1cd5d513cce0f606188e803db3dcf667cb75

[ "MIT" ]

1

2020-07-30T13:18:16.000Z

#!/usr/bin/env python3 # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. """ADO user API wrapper.""" import logging from typing import cast from simple_ado.base_client import ADOBaseClient from simple_ado.context import ADOContext from simple_ado.exceptions import ADOException from simple_ado.http_client import ADOHTTPClient from simple_ado.types import TeamFoundationId class ADOUserClient(ADOBaseClient): """Wrapper class around the ADO user APIs. :param context: The context information for the client :param http_client: The HTTP client to use for the client :param log: The logger to use """ def __init__( self, context: ADOContext, http_client: ADOHTTPClient, log: logging.Logger ) -> None: super().__init__(context, http_client, log.getChild("user")) def get_team_foundation_id(self, identity: str) -> TeamFoundationId: """Fetch the unique Team Foundation GUID for a given identity. :param str identity: The identity to fetch for (should be email for users and display name for groups) :returns: The team foundation ID :raises ADOException: If we can't get the identity from the response """ request_url = self.http_client.api_endpoint(is_default_collection=False, is_project=False) request_url += "/IdentityPicker/Identities?api-version=5.1-preview.1" body = { "query": identity, "identityTypes": ["user", "group"], "operationScopes": ["ims"], "properties": ["DisplayName", "Mail"], "filterByAncestorEntityIds": [], "filterByEntityIds": [], } response = self.http_client.post(request_url, json_data=body) response_data = self.http_client.decode_response(response) try: result = response_data["results"][0]["identities"][0] except: raise ADOException("Could not resolve identity: " + identity) if result["entityType"] == "User" and identity.lower() == result["mail"].lower(): return cast(TeamFoundationId, str(result["localId"])) if result["entityType"] == "Group" and identity.lower() == result["displayName"].lower(): return cast(TeamFoundationId, str(result["localId"])) raise ADOException("Could not resolve identity: " + identity)

35.552239

110

0.670025

import logging from typing import cast from simple_ado.base_client import ADOBaseClient from simple_ado.context import ADOContext from simple_ado.exceptions import ADOException from simple_ado.http_client import ADOHTTPClient from simple_ado.types import TeamFoundationId class ADOUserClient(ADOBaseClient): def __init__( self, context: ADOContext, http_client: ADOHTTPClient, log: logging.Logger ) -> None: super().__init__(context, http_client, log.getChild("user")) def get_team_foundation_id(self, identity: str) -> TeamFoundationId: request_url = self.http_client.api_endpoint(is_default_collection=False, is_project=False) request_url += "/IdentityPicker/Identities?api-version=5.1-preview.1" body = { "query": identity, "identityTypes": ["user", "group"], "operationScopes": ["ims"], "properties": ["DisplayName", "Mail"], "filterByAncestorEntityIds": [], "filterByEntityIds": [], } response = self.http_client.post(request_url, json_data=body) response_data = self.http_client.decode_response(response) try: result = response_data["results"][0]["identities"][0] except: raise ADOException("Could not resolve identity: " + identity) if result["entityType"] == "User" and identity.lower() == result["mail"].lower(): return cast(TeamFoundationId, str(result["localId"])) if result["entityType"] == "Group" and identity.lower() == result["displayName"].lower(): return cast(TeamFoundationId, str(result["localId"])) raise ADOException("Could not resolve identity: " + identity)

true

f72aea0d6cc0cce475a487b99abf5840a183729c

152

py

Python

controller/apps.py

skyrred/Gestion

c38c4d1fa229f5b0e0ef2667ff98864a28dc3241

[ "Apache-2.0" ]

1

2021-11-15T14:55:36.000Z

controller/apps.py

skyrred/Gestion

c38c4d1fa229f5b0e0ef2667ff98864a28dc3241

[ "Apache-2.0" ]

null

controller/apps.py

skyrred/Gestion

c38c4d1fa229f5b0e0ef2667ff98864a28dc3241

[ "Apache-2.0" ]

null

from django.apps import AppConfig class ControllerConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'controller'

21.714286

56

0.769737

from django.apps import AppConfig class ControllerConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'controller'

true

f72aeafc60f1c50f2b50e3c33dc739dfa7cb4e8a

1,675

py

Python

opening2d.py

Nobu575/AppItk

91de313115b753a6fb1ae67f53d4979580ef768b

[ "MIT" ]

null

opening2d.py

Nobu575/AppItk

91de313115b753a6fb1ae67f53d4979580ef768b

[ "MIT" ]

null

opening2d.py

Nobu575/AppItk

91de313115b753a6fb1ae67f53d4979580ef768b

[ "MIT" ]

null

import numpy as np import itk import matplotlib.pyplot as plt # Input file name input_filename = './jenga_g_150.png' # Set dimension Dimension = 2 # Read input image itk_image = itk.imread(input_filename) # Setting for input image (Grayscale) InputPixelType = itk.UC InputImageType = itk.Image[InputPixelType, Dimension] # Loading reader = itk.ImageFileReader[InputImageType].New() reader.SetFileName(input_filename) # Apply a filter: Thresholding thresholdFilter = itk.BinaryThresholdImageFilter[InputImageType,InputImageType].New() thresholdFilter.SetInput(reader.GetOutput()) thresholdFilter.SetUpperThreshold(200) thresholdFilter.SetOutsideValue(1) thresholdFilter.SetInsideValue(0) StructuringElementType = itk.FlatStructuringElement[Dimension] structuringElement = StructuringElementType.Ball(3) # Apply Opening (erosion and dilation) erodeFilter = itk.BinaryErodeImageFilter[InputImageType,InputImageType,StructuringElementType].New() erodeFilter.SetInput(thresholdFilter.GetOutput()) erodeFilter.SetKernel(structuringElement) erodeFilter.SetForegroundValue(1) dilateFilter = itk.BinaryDilateImageFilter[InputImageType,InputImageType,StructuringElementType].New() dilateFilter.SetInput(erodeFilter.GetOutput()) dilateFilter.SetKernel(structuringElement) dilateFilter.SetForegroundValue(1) dilateFilter.Update() # Plot the input and output images. plt.figure(figsize=(12, 4), dpi=50) plt.subplot(1,3,1),plt.title("original"),plt.imshow(itk_image, cmap="gray") plt.subplot(1,3,2),plt.title("threshold"),plt.imshow(thresholdFilter.GetOutput()) plt.subplot(1,3,3),plt.title("output"),plt.imshow(dilateFilter.GetOutput()) plt.savefig("./img/jenga_opening2d.png")

33.5

102

0.819104

import numpy as np import itk import matplotlib.pyplot as plt input_filename = './jenga_g_150.png' Dimension = 2 itk_image = itk.imread(input_filename) InputPixelType = itk.UC InputImageType = itk.Image[InputPixelType, Dimension] reader = itk.ImageFileReader[InputImageType].New() reader.SetFileName(input_filename) thresholdFilter = itk.BinaryThresholdImageFilter[InputImageType,InputImageType].New() thresholdFilter.SetInput(reader.GetOutput()) thresholdFilter.SetUpperThreshold(200) thresholdFilter.SetOutsideValue(1) thresholdFilter.SetInsideValue(0) StructuringElementType = itk.FlatStructuringElement[Dimension] structuringElement = StructuringElementType.Ball(3) erodeFilter = itk.BinaryErodeImageFilter[InputImageType,InputImageType,StructuringElementType].New() erodeFilter.SetInput(thresholdFilter.GetOutput()) erodeFilter.SetKernel(structuringElement) erodeFilter.SetForegroundValue(1) dilateFilter = itk.BinaryDilateImageFilter[InputImageType,InputImageType,StructuringElementType].New() dilateFilter.SetInput(erodeFilter.GetOutput()) dilateFilter.SetKernel(structuringElement) dilateFilter.SetForegroundValue(1) dilateFilter.Update() plt.figure(figsize=(12, 4), dpi=50) plt.subplot(1,3,1),plt.title("original"),plt.imshow(itk_image, cmap="gray") plt.subplot(1,3,2),plt.title("threshold"),plt.imshow(thresholdFilter.GetOutput()) plt.subplot(1,3,3),plt.title("output"),plt.imshow(dilateFilter.GetOutput()) plt.savefig("./img/jenga_opening2d.png")

true

f72aed1738f6ccb62f4bf6aeaaf1bcc63b40247b

2,587

py

Python

update.py

boost/bucket-antivirus-function

6eb93406e28f81a4c612f0dec29670451e0c5589

[ "Apache-2.0" ]

null

update.py

boost/bucket-antivirus-function

6eb93406e28f81a4c612f0dec29670451e0c5589

[ "Apache-2.0" ]

null

update.py

boost/bucket-antivirus-function

6eb93406e28f81a4c612f0dec29670451e0c5589

[ "Apache-2.0" ]

1

2020-07-16T12:47:24.000Z

# -*- coding: utf-8 -*- # Upside Travel, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import boto3 import clamav from common import AV_DEFINITION_PATH from common import AV_DEFINITION_S3_BUCKET from common import AV_DEFINITION_S3_PREFIX from common import CLAMAVLIB_PATH from common import get_timestamp import shutil def lambda_handler(event, context): s3 = boto3.resource("s3") s3_client = boto3.client("s3") print("Script starting at %s\n" % (get_timestamp())) for root, dirs, files in os.walk(AV_DEFINITION_PATH): for f in files: os.unlink(os.path.join(root, f)) for d in dirs: shutil.rmtree(os.path.join(root, d)) to_download = clamav.update_defs_from_s3( s3_client, AV_DEFINITION_S3_BUCKET, AV_DEFINITION_S3_PREFIX ) print("Skipping clamav definition download %s\n" % (get_timestamp())) # for download in to_download.values(): # s3_path = download["s3_path"] # local_path = download["local_path"] # print("Downloading definition file %s from s3://%s" % (local_path, s3_path)) # s3.Bucket(AV_DEFINITION_S3_BUCKET).download_file(s3_path, local_path) # print("Downloading definition file %s complete!" % (local_path)) clamav.update_defs_from_freshclam(AV_DEFINITION_PATH, CLAMAVLIB_PATH) # If main.cvd gets updated (very rare), we will need to force freshclam # to download the compressed version to keep file sizes down. # The existence of main.cud is the trigger to know this has happened. if os.path.exists(os.path.join(AV_DEFINITION_PATH, "main.cud")): os.remove(os.path.join(AV_DEFINITION_PATH, "main.cud")) if os.path.exists(os.path.join(AV_DEFINITION_PATH, "main.cvd")): os.remove(os.path.join(AV_DEFINITION_PATH, "main.cvd")) clamav.update_defs_from_freshclam(AV_DEFINITION_PATH, CLAMAVLIB_PATH) clamav.upload_defs_to_s3( s3_client, AV_DEFINITION_S3_BUCKET, AV_DEFINITION_S3_PREFIX, AV_DEFINITION_PATH ) print("Script finished at %s\n" % get_timestamp())

39.8

87

0.719366

import os import boto3 import clamav from common import AV_DEFINITION_PATH from common import AV_DEFINITION_S3_BUCKET from common import AV_DEFINITION_S3_PREFIX from common import CLAMAVLIB_PATH from common import get_timestamp import shutil def lambda_handler(event, context): s3 = boto3.resource("s3") s3_client = boto3.client("s3") print("Script starting at %s\n" % (get_timestamp())) for root, dirs, files in os.walk(AV_DEFINITION_PATH): for f in files: os.unlink(os.path.join(root, f)) for d in dirs: shutil.rmtree(os.path.join(root, d)) to_download = clamav.update_defs_from_s3( s3_client, AV_DEFINITION_S3_BUCKET, AV_DEFINITION_S3_PREFIX ) print("Skipping clamav definition download %s\n" % (get_timestamp())) clamav.update_defs_from_freshclam(AV_DEFINITION_PATH, CLAMAVLIB_PATH) if os.path.exists(os.path.join(AV_DEFINITION_PATH, "main.cud")): os.remove(os.path.join(AV_DEFINITION_PATH, "main.cud")) if os.path.exists(os.path.join(AV_DEFINITION_PATH, "main.cvd")): os.remove(os.path.join(AV_DEFINITION_PATH, "main.cvd")) clamav.update_defs_from_freshclam(AV_DEFINITION_PATH, CLAMAVLIB_PATH) clamav.upload_defs_to_s3( s3_client, AV_DEFINITION_S3_BUCKET, AV_DEFINITION_S3_PREFIX, AV_DEFINITION_PATH ) print("Script finished at %s\n" % get_timestamp())

true

f72aeddbd79707ad743350eba5e76f34ba47af5c

15,728

py

Python

ssd.py

tristanmooo/ssd_keras

e4be1dae086e91a81b020787f94560836379dc68

[ "MIT" ]

null

ssd.py

tristanmooo/ssd_keras

e4be1dae086e91a81b020787f94560836379dc68

[ "MIT" ]

null

ssd.py

tristanmooo/ssd_keras

e4be1dae086e91a81b020787f94560836379dc68

[ "MIT" ]

null

"""Keras implementation of SSD.""" import keras.backend as K from keras.layers import Activation from keras.layers import AtrousConvolution2D from keras.layers import Convolution2D from keras.layers import Dense from keras.layers import Flatten from keras.layers import GlobalAveragePooling2D from keras.layers import Input from keras.layers import MaxPooling2D from keras.layers import merge from keras.layers import Reshape from keras.layers import ZeroPadding2D from keras.models import Model from ssd_layers import Normalize from ssd_layers import PriorBox def SSD300(input_shape, num_classes=21): """SSD300 architecture. # Arguments input_shape: Shape of the input image, expected to be either (300, 300, 3) or (3, 300, 300)(not tested). num_classes: Number of classes including background. # References https://arxiv.org/abs/1512.02325 """ net = {} # Block 1 卷积层块 input_tensor = input_tensor = Input(shape=input_shape) img_size = (input_shape[1], input_shape[0]) net['input'] = input_tensor # 二维卷积层对二维输入进行滑动窗卷积 # keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, # dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', # bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, # kernel_constraint=None, bias_constraint=None) net['conv1_1'] = Convolution2D(64, 3, 3, # 64个过滤器；kernel_size:3，卷积窗口大小；strides:步长； activation='relu', # 激活函数：ReLU border_mode='same', # 过滤模式：same/valid name='conv1_1')(net['input']) net['conv1_2'] = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='conv1_2')(net['conv1_1']) # 对空间数据的最大池化 # keras.layers.MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None) # strides 默认为 None，为 None 时大小等于 net['pool1'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same', name='pool1')(net['conv1_2']) # Block 2 卷积层块 net['conv2_1'] = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='conv2_1')(net['pool1']) net['conv2_2'] = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='conv2_2')(net['conv2_1']) net['pool2'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same', name='pool2')(net['conv2_2']) # Block 3 卷积层块 net['conv3_1'] = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_1')(net['pool2']) net['conv3_2'] = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_2')(net['conv3_1']) net['conv3_3'] = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_3')(net['conv3_2']) net['pool3'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same', name='pool3')(net['conv3_3']) # Block 4 卷积层块 net['conv4_1'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_1')(net['pool3']) net['conv4_2'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_2')(net['conv4_1']) net['conv4_3'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_3')(net['conv4_2']) net['pool4'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same', name='pool4')(net['conv4_3']) # Block 5 卷积层块 net['conv5_1'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_1')(net['pool4']) net['conv5_2'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_2')(net['conv5_1']) net['conv5_3'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_3')(net['conv5_2']) net['pool5'] = MaxPooling2D((3, 3), strides=(1, 1), border_mode='same', name='pool5')(net['conv5_3']) # FC6 该层对二维输入进行Atrous卷积，也即膨胀卷积或带孔洞的卷积。 net['fc6'] = AtrousConvolution2D(1024, 3, 3, atrous_rate=(6, 6), activation='relu', border_mode='same', name='fc6')(net['pool5']) # x = Dropout(0.5, name='drop6')(x) # FC7 net['fc7'] = Convolution2D(1024, 1, 1, activation='relu', border_mode='same', name='fc7')(net['fc6']) # x = Dropout(0.5, name='drop7')(x) # Block 6 net['conv6_1'] = Convolution2D(256, 1, 1, activation='relu', border_mode='same', name='conv6_1')(net['fc7']) net['conv6_2'] = Convolution2D(512, 3, 3, subsample=(2, 2), activation='relu', border_mode='same', name='conv6_2')(net['conv6_1']) # Block 7 net['conv7_1'] = Convolution2D(128, 1, 1, activation='relu', border_mode='same', name='conv7_1')(net['conv6_2']) net['conv7_2'] = ZeroPadding2D()(net['conv7_1']) net['conv7_2'] = Convolution2D(256, 3, 3, subsample=(2, 2), activation='relu', border_mode='valid', name='conv7_2')(net['conv7_2']) # Block 8 net['conv8_1'] = Convolution2D(128, 1, 1, activation='relu', border_mode='same', name='conv8_1')(net['conv7_2']) net['conv8_2'] = Convolution2D(256, 3, 3, subsample=(2, 2), activation='relu', border_mode='same', name='conv8_2')(net['conv8_1']) # Last Pool net['pool6'] = GlobalAveragePooling2D(name='pool6')(net['conv8_2']) # Prediction from conv4_3 # keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, # beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', # beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None) # axis: 整数，需要标准化的轴（通常是特征轴） # 批量标准化层 (Ioffe and Szegedy, 2014)。在每一个批次的数据中标准化前一层的激活项，即，应用一个维持激活项平均值接近 0，标准差接近 1 的转换。 net['conv4_3_norm'] = Normalize(20, name='conv4_3_norm')(net['conv4_3']) num_priors = 3 x = Convolution2D(num_priors * 4, 3, 3, border_mode='same', name='conv4_3_norm_mbox_loc')(net['conv4_3_norm']) net['conv4_3_norm_mbox_loc'] = x flatten = Flatten(name='conv4_3_norm_mbox_loc_flat') net['conv4_3_norm_mbox_loc_flat'] = flatten(net['conv4_3_norm_mbox_loc']) name = 'conv4_3_norm_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same', name=name)(net['conv4_3_norm']) net['conv4_3_norm_mbox_conf'] = x flatten = Flatten(name='conv4_3_norm_mbox_conf_flat') net['conv4_3_norm_mbox_conf_flat'] = flatten(net['conv4_3_norm_mbox_conf']) priorbox = PriorBox(img_size, 30.0, aspect_ratios=[2], variances=[0.1, 0.1, 0.2, 0.2], name='conv4_3_norm_mbox_priorbox') net['conv4_3_norm_mbox_priorbox'] = priorbox(net['conv4_3_norm']) # Prediction from fc7 num_priors = 6 net['fc7_mbox_loc'] = Convolution2D(num_priors * 4, 3, 3, border_mode='same', name='fc7_mbox_loc')(net['fc7']) flatten = Flatten(name='fc7_mbox_loc_flat') net['fc7_mbox_loc_flat'] = flatten(net['fc7_mbox_loc']) name = 'fc7_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) net['fc7_mbox_conf'] = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same', name=name)(net['fc7']) flatten = Flatten(name='fc7_mbox_conf_flat') net['fc7_mbox_conf_flat'] = flatten(net['fc7_mbox_conf']) priorbox = PriorBox(img_size, 60.0, max_size=114.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='fc7_mbox_priorbox') net['fc7_mbox_priorbox'] = priorbox(net['fc7']) # Prediction from conv6_2 num_priors = 6 x = Convolution2D(num_priors * 4, 3, 3, border_mode='same', name='conv6_2_mbox_loc')(net['conv6_2']) net['conv6_2_mbox_loc'] = x flatten = Flatten(name='conv6_2_mbox_loc_flat') net['conv6_2_mbox_loc_flat'] = flatten(net['conv6_2_mbox_loc']) name = 'conv6_2_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same', name=name)(net['conv6_2']) net['conv6_2_mbox_conf'] = x flatten = Flatten(name='conv6_2_mbox_conf_flat') net['conv6_2_mbox_conf_flat'] = flatten(net['conv6_2_mbox_conf']) priorbox = PriorBox(img_size, 114.0, max_size=168.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='conv6_2_mbox_priorbox') net['conv6_2_mbox_priorbox'] = priorbox(net['conv6_2']) # Prediction from conv7_2 num_priors = 6 x = Convolution2D(num_priors * 4, 3, 3, border_mode='same', name='conv7_2_mbox_loc')(net['conv7_2']) net['conv7_2_mbox_loc'] = x flatten = Flatten(name='conv7_2_mbox_loc_flat') net['conv7_2_mbox_loc_flat'] = flatten(net['conv7_2_mbox_loc']) name = 'conv7_2_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same', name=name)(net['conv7_2']) net['conv7_2_mbox_conf'] = x flatten = Flatten(name='conv7_2_mbox_conf_flat') net['conv7_2_mbox_conf_flat'] = flatten(net['conv7_2_mbox_conf']) priorbox = PriorBox(img_size, 168.0, max_size=222.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='conv7_2_mbox_priorbox') net['conv7_2_mbox_priorbox'] = priorbox(net['conv7_2']) # Prediction from conv8_2 num_priors = 6 x = Convolution2D(num_priors * 4, 3, 3, border_mode='same', name='conv8_2_mbox_loc')(net['conv8_2']) net['conv8_2_mbox_loc'] = x flatten = Flatten(name='conv8_2_mbox_loc_flat') net['conv8_2_mbox_loc_flat'] = flatten(net['conv8_2_mbox_loc']) name = 'conv8_2_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same', name=name)(net['conv8_2']) net['conv8_2_mbox_conf'] = x flatten = Flatten(name='conv8_2_mbox_conf_flat') net['conv8_2_mbox_conf_flat'] = flatten(net['conv8_2_mbox_conf']) priorbox = PriorBox(img_size, 222.0, max_size=276.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='conv8_2_mbox_priorbox') net['conv8_2_mbox_priorbox'] = priorbox(net['conv8_2']) # Prediction from pool6 num_priors = 6 x = Dense(num_priors * 4, name='pool6_mbox_loc_flat')(net['pool6']) net['pool6_mbox_loc_flat'] = x name = 'pool6_mbox_conf_flat' if num_classes != 21: name += '_{}'.format(num_classes) x = Dense(num_priors * num_classes, name=name)(net['pool6']) net['pool6_mbox_conf_flat'] = x priorbox = PriorBox(img_size, 276.0, max_size=330.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='pool6_mbox_priorbox') if K.image_dim_ordering() == 'tf': target_shape = (1, 1, 256) else: target_shape = (256, 1, 1) net['pool6_reshaped'] = Reshape(target_shape, name='pool6_reshaped')(net['pool6']) net['pool6_mbox_priorbox'] = priorbox(net['pool6_reshaped']) # Gather all predictions net['mbox_loc'] = merge([net['conv4_3_norm_mbox_loc_flat'], net['fc7_mbox_loc_flat'], net['conv6_2_mbox_loc_flat'], net['conv7_2_mbox_loc_flat'], net['conv8_2_mbox_loc_flat'], net['pool6_mbox_loc_flat']], mode='concat', concat_axis=1, name='mbox_loc') net['mbox_conf'] = merge([net['conv4_3_norm_mbox_conf_flat'], net['fc7_mbox_conf_flat'], net['conv6_2_mbox_conf_flat'], net['conv7_2_mbox_conf_flat'], net['conv8_2_mbox_conf_flat'], net['pool6_mbox_conf_flat']], mode='concat', concat_axis=1, name='mbox_conf') net['mbox_priorbox'] = merge([net['conv4_3_norm_mbox_priorbox'], net['fc7_mbox_priorbox'], net['conv6_2_mbox_priorbox'], net['conv7_2_mbox_priorbox'], net['conv8_2_mbox_priorbox'], net['pool6_mbox_priorbox']], mode='concat', concat_axis=1, name='mbox_priorbox') if hasattr(net['mbox_loc'], '_keras_shape'): num_boxes = net['mbox_loc']._keras_shape[-1] // 4 elif hasattr(net['mbox_loc'], 'int_shape'): num_boxes = K.int_shape(net['mbox_loc'])[-1] // 4 net['mbox_loc'] = Reshape((num_boxes, 4), name='mbox_loc_final')(net['mbox_loc']) net['mbox_conf'] = Reshape((num_boxes, num_classes), name='mbox_conf_logits')(net['mbox_conf']) net['mbox_conf'] = Activation('softmax', name='mbox_conf_final')(net['mbox_conf']) net['predictions'] = merge([net['mbox_loc'], net['mbox_conf'], net['mbox_priorbox']], mode='concat', concat_axis=2, name='predictions') model = Model(net['input'], net['predictions']) return model

51.398693

127

0.532363

import keras.backend as K from keras.layers import Activation from keras.layers import AtrousConvolution2D from keras.layers import Convolution2D from keras.layers import Dense from keras.layers import Flatten from keras.layers import GlobalAveragePooling2D from keras.layers import Input from keras.layers import MaxPooling2D from keras.layers import merge from keras.layers import Reshape from keras.layers import ZeroPadding2D from keras.models import Model from ssd_layers import Normalize from ssd_layers import PriorBox def SSD300(input_shape, num_classes=21): net = {} input_tensor = input_tensor = Input(shape=input_shape) img_size = (input_shape[1], input_shape[0]) net['input'] = input_tensor net['conv1_1'] = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='conv1_1')(net['input']) net['conv1_2'] = Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='conv1_2')(net['conv1_1']) net['pool1'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same', name='pool1')(net['conv1_2']) net['conv2_1'] = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='conv2_1')(net['pool1']) net['conv2_2'] = Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='conv2_2')(net['conv2_1']) net['pool2'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same', name='pool2')(net['conv2_2']) net['conv3_1'] = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_1')(net['pool2']) net['conv3_2'] = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_2')(net['conv3_1']) net['conv3_3'] = Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='conv3_3')(net['conv3_2']) net['pool3'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same', name='pool3')(net['conv3_3']) net['conv4_1'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_1')(net['pool3']) net['conv4_2'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_2')(net['conv4_1']) net['conv4_3'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv4_3')(net['conv4_2']) net['pool4'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same', name='pool4')(net['conv4_3']) net['conv5_1'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_1')(net['pool4']) net['conv5_2'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_2')(net['conv5_1']) net['conv5_3'] = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='conv5_3')(net['conv5_2']) net['pool5'] = MaxPooling2D((3, 3), strides=(1, 1), border_mode='same', name='pool5')(net['conv5_3']) net['fc6'] = AtrousConvolution2D(1024, 3, 3, atrous_rate=(6, 6), activation='relu', border_mode='same', name='fc6')(net['pool5']) net['fc7'] = Convolution2D(1024, 1, 1, activation='relu', border_mode='same', name='fc7')(net['fc6']) net['conv6_1'] = Convolution2D(256, 1, 1, activation='relu', border_mode='same', name='conv6_1')(net['fc7']) net['conv6_2'] = Convolution2D(512, 3, 3, subsample=(2, 2), activation='relu', border_mode='same', name='conv6_2')(net['conv6_1']) net['conv7_1'] = Convolution2D(128, 1, 1, activation='relu', border_mode='same', name='conv7_1')(net['conv6_2']) net['conv7_2'] = ZeroPadding2D()(net['conv7_1']) net['conv7_2'] = Convolution2D(256, 3, 3, subsample=(2, 2), activation='relu', border_mode='valid', name='conv7_2')(net['conv7_2']) net['conv8_1'] = Convolution2D(128, 1, 1, activation='relu', border_mode='same', name='conv8_1')(net['conv7_2']) net['conv8_2'] = Convolution2D(256, 3, 3, subsample=(2, 2), activation='relu', border_mode='same', name='conv8_2')(net['conv8_1']) net['pool6'] = GlobalAveragePooling2D(name='pool6')(net['conv8_2']) net['conv4_3_norm'] = Normalize(20, name='conv4_3_norm')(net['conv4_3']) num_priors = 3 x = Convolution2D(num_priors * 4, 3, 3, border_mode='same', name='conv4_3_norm_mbox_loc')(net['conv4_3_norm']) net['conv4_3_norm_mbox_loc'] = x flatten = Flatten(name='conv4_3_norm_mbox_loc_flat') net['conv4_3_norm_mbox_loc_flat'] = flatten(net['conv4_3_norm_mbox_loc']) name = 'conv4_3_norm_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same', name=name)(net['conv4_3_norm']) net['conv4_3_norm_mbox_conf'] = x flatten = Flatten(name='conv4_3_norm_mbox_conf_flat') net['conv4_3_norm_mbox_conf_flat'] = flatten(net['conv4_3_norm_mbox_conf']) priorbox = PriorBox(img_size, 30.0, aspect_ratios=[2], variances=[0.1, 0.1, 0.2, 0.2], name='conv4_3_norm_mbox_priorbox') net['conv4_3_norm_mbox_priorbox'] = priorbox(net['conv4_3_norm']) num_priors = 6 net['fc7_mbox_loc'] = Convolution2D(num_priors * 4, 3, 3, border_mode='same', name='fc7_mbox_loc')(net['fc7']) flatten = Flatten(name='fc7_mbox_loc_flat') net['fc7_mbox_loc_flat'] = flatten(net['fc7_mbox_loc']) name = 'fc7_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) net['fc7_mbox_conf'] = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same', name=name)(net['fc7']) flatten = Flatten(name='fc7_mbox_conf_flat') net['fc7_mbox_conf_flat'] = flatten(net['fc7_mbox_conf']) priorbox = PriorBox(img_size, 60.0, max_size=114.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='fc7_mbox_priorbox') net['fc7_mbox_priorbox'] = priorbox(net['fc7']) num_priors = 6 x = Convolution2D(num_priors * 4, 3, 3, border_mode='same', name='conv6_2_mbox_loc')(net['conv6_2']) net['conv6_2_mbox_loc'] = x flatten = Flatten(name='conv6_2_mbox_loc_flat') net['conv6_2_mbox_loc_flat'] = flatten(net['conv6_2_mbox_loc']) name = 'conv6_2_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same', name=name)(net['conv6_2']) net['conv6_2_mbox_conf'] = x flatten = Flatten(name='conv6_2_mbox_conf_flat') net['conv6_2_mbox_conf_flat'] = flatten(net['conv6_2_mbox_conf']) priorbox = PriorBox(img_size, 114.0, max_size=168.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='conv6_2_mbox_priorbox') net['conv6_2_mbox_priorbox'] = priorbox(net['conv6_2']) num_priors = 6 x = Convolution2D(num_priors * 4, 3, 3, border_mode='same', name='conv7_2_mbox_loc')(net['conv7_2']) net['conv7_2_mbox_loc'] = x flatten = Flatten(name='conv7_2_mbox_loc_flat') net['conv7_2_mbox_loc_flat'] = flatten(net['conv7_2_mbox_loc']) name = 'conv7_2_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same', name=name)(net['conv7_2']) net['conv7_2_mbox_conf'] = x flatten = Flatten(name='conv7_2_mbox_conf_flat') net['conv7_2_mbox_conf_flat'] = flatten(net['conv7_2_mbox_conf']) priorbox = PriorBox(img_size, 168.0, max_size=222.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='conv7_2_mbox_priorbox') net['conv7_2_mbox_priorbox'] = priorbox(net['conv7_2']) num_priors = 6 x = Convolution2D(num_priors * 4, 3, 3, border_mode='same', name='conv8_2_mbox_loc')(net['conv8_2']) net['conv8_2_mbox_loc'] = x flatten = Flatten(name='conv8_2_mbox_loc_flat') net['conv8_2_mbox_loc_flat'] = flatten(net['conv8_2_mbox_loc']) name = 'conv8_2_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same', name=name)(net['conv8_2']) net['conv8_2_mbox_conf'] = x flatten = Flatten(name='conv8_2_mbox_conf_flat') net['conv8_2_mbox_conf_flat'] = flatten(net['conv8_2_mbox_conf']) priorbox = PriorBox(img_size, 222.0, max_size=276.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='conv8_2_mbox_priorbox') net['conv8_2_mbox_priorbox'] = priorbox(net['conv8_2']) num_priors = 6 x = Dense(num_priors * 4, name='pool6_mbox_loc_flat')(net['pool6']) net['pool6_mbox_loc_flat'] = x name = 'pool6_mbox_conf_flat' if num_classes != 21: name += '_{}'.format(num_classes) x = Dense(num_priors * num_classes, name=name)(net['pool6']) net['pool6_mbox_conf_flat'] = x priorbox = PriorBox(img_size, 276.0, max_size=330.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='pool6_mbox_priorbox') if K.image_dim_ordering() == 'tf': target_shape = (1, 1, 256) else: target_shape = (256, 1, 1) net['pool6_reshaped'] = Reshape(target_shape, name='pool6_reshaped')(net['pool6']) net['pool6_mbox_priorbox'] = priorbox(net['pool6_reshaped']) net['mbox_loc'] = merge([net['conv4_3_norm_mbox_loc_flat'], net['fc7_mbox_loc_flat'], net['conv6_2_mbox_loc_flat'], net['conv7_2_mbox_loc_flat'], net['conv8_2_mbox_loc_flat'], net['pool6_mbox_loc_flat']], mode='concat', concat_axis=1, name='mbox_loc') net['mbox_conf'] = merge([net['conv4_3_norm_mbox_conf_flat'], net['fc7_mbox_conf_flat'], net['conv6_2_mbox_conf_flat'], net['conv7_2_mbox_conf_flat'], net['conv8_2_mbox_conf_flat'], net['pool6_mbox_conf_flat']], mode='concat', concat_axis=1, name='mbox_conf') net['mbox_priorbox'] = merge([net['conv4_3_norm_mbox_priorbox'], net['fc7_mbox_priorbox'], net['conv6_2_mbox_priorbox'], net['conv7_2_mbox_priorbox'], net['conv8_2_mbox_priorbox'], net['pool6_mbox_priorbox']], mode='concat', concat_axis=1, name='mbox_priorbox') if hasattr(net['mbox_loc'], '_keras_shape'): num_boxes = net['mbox_loc']._keras_shape[-1] // 4 elif hasattr(net['mbox_loc'], 'int_shape'): num_boxes = K.int_shape(net['mbox_loc'])[-1] // 4 net['mbox_loc'] = Reshape((num_boxes, 4), name='mbox_loc_final')(net['mbox_loc']) net['mbox_conf'] = Reshape((num_boxes, num_classes), name='mbox_conf_logits')(net['mbox_conf']) net['mbox_conf'] = Activation('softmax', name='mbox_conf_final')(net['mbox_conf']) net['predictions'] = merge([net['mbox_loc'], net['mbox_conf'], net['mbox_priorbox']], mode='concat', concat_axis=2, name='predictions') model = Model(net['input'], net['predictions']) return model

true

f72aedf20d5a4dd130832d24767e5a8c5c2c559a

850

py

Python

test/record/parser/test_response_whois_nic_ve_property_nameservers_missing.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

[ "MIT" ]

null

test/record/parser/test_response_whois_nic_ve_property_nameservers_missing.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

[ "MIT" ]

null

test/record/parser/test_response_whois_nic_ve_property_nameservers_missing.py

huyphan/pyyawhois

77fb2f73a9c67989f1d41d98f37037406a69d136

[ "MIT" ]

null

# This file is autogenerated. Do not edit it manually. # If you want change the content of this file, edit # # spec/fixtures/responses/whois.nic.ve/property_nameservers_missing # # and regenerate the tests with the following script # # $ scripts/generate_tests.py # from nose.tools import * from dateutil.parser import parse as time_parse import yawhois class TestWhoisNicVePropertyNameserversMissing(object): def setUp(self): fixture_path = "spec/fixtures/responses/whois.nic.ve/property_nameservers_missing.txt" host = "whois.nic.ve" part = yawhois.record.Part(open(fixture_path, "r").read(), host) self.record = yawhois.record.Record(None, [part]) def test_nameservers(self): eq_(self.record.nameservers.__class__.__name__, 'list') eq_(self.record.nameservers, [])

31.481481

94

0.711765

from nose.tools import * from dateutil.parser import parse as time_parse import yawhois class TestWhoisNicVePropertyNameserversMissing(object): def setUp(self): fixture_path = "spec/fixtures/responses/whois.nic.ve/property_nameservers_missing.txt" host = "whois.nic.ve" part = yawhois.record.Part(open(fixture_path, "r").read(), host) self.record = yawhois.record.Record(None, [part]) def test_nameservers(self): eq_(self.record.nameservers.__class__.__name__, 'list') eq_(self.record.nameservers, [])

true

f72aee673e41aaa5710037678b883636f5df28d7

7,947

py

Python

src/python/pants/backend/python/lint/pylint/rules.py

danxmoran/pants

7fafd7d789747c9e6a266847a0ccce92c3fa0754

[ "Apache-2.0" ]

null

src/python/pants/backend/python/lint/pylint/rules.py

danxmoran/pants

7fafd7d789747c9e6a266847a0ccce92c3fa0754

[ "Apache-2.0" ]

22

2022-01-27T09:59:50.000Z

2022-03-30T07:06:49.000Z

src/python/pants/backend/python/lint/pylint/rules.py

danxmoran/pants

7fafd7d789747c9e6a266847a0ccce92c3fa0754

[ "Apache-2.0" ]

null

# Copyright 2020 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from __future__ import annotations from dataclasses import dataclass from typing import Tuple from pants.backend.python.lint.pylint.subsystem import ( Pylint, PylintFieldSet, PylintFirstPartyPlugins, ) from pants.backend.python.subsystems.setup import PythonSetup from pants.backend.python.util_rules import partition, pex_from_targets from pants.backend.python.util_rules.interpreter_constraints import InterpreterConstraints from pants.backend.python.util_rules.pex import ( Pex, PexRequest, VenvPex, VenvPexProcess, VenvPexRequest, ) from pants.backend.python.util_rules.pex_from_targets import RequirementsPexRequest from pants.backend.python.util_rules.python_sources import ( PythonSourceFiles, PythonSourceFilesRequest, ) from pants.core.goals.lint import REPORT_DIR, LintResult, LintResults, LintTargetsRequest from pants.core.util_rules.config_files import ConfigFiles, ConfigFilesRequest from pants.core.util_rules.source_files import SourceFiles, SourceFilesRequest from pants.engine.collection import Collection from pants.engine.fs import CreateDigest, Digest, Directory, MergeDigests, RemovePrefix from pants.engine.process import FallibleProcessResult from pants.engine.rules import Get, MultiGet, collect_rules, rule from pants.engine.target import CoarsenedTargets, Target from pants.engine.unions import UnionRule from pants.util.logging import LogLevel from pants.util.ordered_set import FrozenOrderedSet from pants.util.strutil import pluralize @dataclass(frozen=True) class PylintPartition: root_field_sets: FrozenOrderedSet[PylintFieldSet] closure: FrozenOrderedSet[Target] resolve_description: str | None interpreter_constraints: InterpreterConstraints def description(self) -> str: ics = str(sorted(str(c) for c in self.interpreter_constraints)) return f"{self.resolve_description}, {ics}" if self.resolve_description else ics class PylintPartitions(Collection[PylintPartition]): pass class PylintRequest(LintTargetsRequest): field_set_type = PylintFieldSet name = Pylint.options_scope def generate_argv(source_files: SourceFiles, pylint: Pylint) -> Tuple[str, ...]: args = [] if pylint.config is not None: args.append(f"--rcfile={pylint.config}") args.append("--jobs={pants_concurrency}") args.extend(pylint.args) args.extend(source_files.files) return tuple(args) @rule(level=LogLevel.DEBUG) async def pylint_lint_partition( partition: PylintPartition, pylint: Pylint, first_party_plugins: PylintFirstPartyPlugins ) -> LintResult: requirements_pex_get = Get( Pex, RequirementsPexRequest( (fs.address for fs in partition.root_field_sets), # NB: These constraints must be identical to the other PEXes. Otherwise, we risk using # a different version for the requirements than the other two PEXes, which can result # in a PEX runtime error about missing dependencies. hardcoded_interpreter_constraints=partition.interpreter_constraints, ), ) pylint_pex_get = Get( Pex, PexRequest, pylint.to_pex_request( interpreter_constraints=partition.interpreter_constraints, extra_requirements=first_party_plugins.requirement_strings, ), ) prepare_python_sources_get = Get(PythonSourceFiles, PythonSourceFilesRequest(partition.closure)) field_set_sources_get = Get( SourceFiles, SourceFilesRequest(fs.source for fs in partition.root_field_sets) ) # Ensure that the empty report dir exists. report_directory_digest_get = Get(Digest, CreateDigest([Directory(REPORT_DIR)])) ( pylint_pex, requirements_pex, prepared_python_sources, field_set_sources, report_directory, ) = await MultiGet( pylint_pex_get, requirements_pex_get, prepare_python_sources_get, field_set_sources_get, report_directory_digest_get, ) pylint_runner_pex, config_files = await MultiGet( Get( VenvPex, VenvPexRequest( PexRequest( output_filename="pylint_runner.pex", interpreter_constraints=partition.interpreter_constraints, main=pylint.main, internal_only=True, pex_path=[pylint_pex, requirements_pex], ), # TODO(John Sirois): Remove this (change to the default of symlinks) when we can # upgrade to a version of Pylint with https://github.com/PyCQA/pylint/issues/1470 # resolved. site_packages_copies=True, ), ), Get( ConfigFiles, ConfigFilesRequest, pylint.config_request(field_set_sources.snapshot.dirs) ), ) pythonpath = list(prepared_python_sources.source_roots) if first_party_plugins: pythonpath.append(first_party_plugins.PREFIX) input_digest = await Get( Digest, MergeDigests( ( config_files.snapshot.digest, first_party_plugins.sources_digest, prepared_python_sources.source_files.snapshot.digest, report_directory, ) ), ) result = await Get( FallibleProcessResult, VenvPexProcess( pylint_runner_pex, argv=generate_argv(field_set_sources, pylint), input_digest=input_digest, output_directories=(REPORT_DIR,), extra_env={"PEX_EXTRA_SYS_PATH": ":".join(pythonpath)}, concurrency_available=len(partition.root_field_sets), description=f"Run Pylint on {pluralize(len(partition.root_field_sets), 'file')}.", level=LogLevel.DEBUG, ), ) report = await Get(Digest, RemovePrefix(result.output_digest, REPORT_DIR)) return LintResult.from_fallible_process_result( result, partition_description=partition.description(), report=report, ) @rule(desc="Determine if necessary to partition Pylint input", level=LogLevel.DEBUG) async def pylint_determine_partitions( request: PylintRequest, python_setup: PythonSetup, first_party_plugins: PylintFirstPartyPlugins ) -> PylintPartitions: resolve_and_interpreter_constraints_to_coarsened_targets = ( await partition._by_interpreter_constraints_and_resolve(request.field_sets, python_setup) ) first_party_ics = InterpreterConstraints.create_from_compatibility_fields( first_party_plugins.interpreter_constraints_fields, python_setup ) return PylintPartitions( PylintPartition( FrozenOrderedSet(roots), FrozenOrderedSet(CoarsenedTargets(root_cts).closure()), resolve if len(python_setup.resolves) > 1 else None, InterpreterConstraints.merge((interpreter_constraints, first_party_ics)), ) for (resolve, interpreter_constraints), (roots, root_cts) in sorted( resolve_and_interpreter_constraints_to_coarsened_targets.items() ) ) @rule(desc="Lint using Pylint", level=LogLevel.DEBUG) async def pylint_lint(request: PylintRequest, pylint: Pylint) -> LintResults: if pylint.skip: return LintResults([], linter_name=request.name) partitions = await Get(PylintPartitions, PylintRequest, request) partitioned_results = await MultiGet( Get(LintResult, PylintPartition, partition) for partition in partitions ) return LintResults(partitioned_results, linter_name=request.name) def rules(): return [ *collect_rules(), UnionRule(LintTargetsRequest, PylintRequest), *pex_from_targets.rules(), ]

35.959276

100

0.707059

from __future__ import annotations from dataclasses import dataclass from typing import Tuple from pants.backend.python.lint.pylint.subsystem import ( Pylint, PylintFieldSet, PylintFirstPartyPlugins, ) from pants.backend.python.subsystems.setup import PythonSetup from pants.backend.python.util_rules import partition, pex_from_targets from pants.backend.python.util_rules.interpreter_constraints import InterpreterConstraints from pants.backend.python.util_rules.pex import ( Pex, PexRequest, VenvPex, VenvPexProcess, VenvPexRequest, ) from pants.backend.python.util_rules.pex_from_targets import RequirementsPexRequest from pants.backend.python.util_rules.python_sources import ( PythonSourceFiles, PythonSourceFilesRequest, ) from pants.core.goals.lint import REPORT_DIR, LintResult, LintResults, LintTargetsRequest from pants.core.util_rules.config_files import ConfigFiles, ConfigFilesRequest from pants.core.util_rules.source_files import SourceFiles, SourceFilesRequest from pants.engine.collection import Collection from pants.engine.fs import CreateDigest, Digest, Directory, MergeDigests, RemovePrefix from pants.engine.process import FallibleProcessResult from pants.engine.rules import Get, MultiGet, collect_rules, rule from pants.engine.target import CoarsenedTargets, Target from pants.engine.unions import UnionRule from pants.util.logging import LogLevel from pants.util.ordered_set import FrozenOrderedSet from pants.util.strutil import pluralize @dataclass(frozen=True) class PylintPartition: root_field_sets: FrozenOrderedSet[PylintFieldSet] closure: FrozenOrderedSet[Target] resolve_description: str | None interpreter_constraints: InterpreterConstraints def description(self) -> str: ics = str(sorted(str(c) for c in self.interpreter_constraints)) return f"{self.resolve_description}, {ics}" if self.resolve_description else ics class PylintPartitions(Collection[PylintPartition]): pass class PylintRequest(LintTargetsRequest): field_set_type = PylintFieldSet name = Pylint.options_scope def generate_argv(source_files: SourceFiles, pylint: Pylint) -> Tuple[str, ...]: args = [] if pylint.config is not None: args.append(f"--rcfile={pylint.config}") args.append("--jobs={pants_concurrency}") args.extend(pylint.args) args.extend(source_files.files) return tuple(args) @rule(level=LogLevel.DEBUG) async def pylint_lint_partition( partition: PylintPartition, pylint: Pylint, first_party_plugins: PylintFirstPartyPlugins ) -> LintResult: requirements_pex_get = Get( Pex, RequirementsPexRequest( (fs.address for fs in partition.root_field_sets), hardcoded_interpreter_constraints=partition.interpreter_constraints, ), ) pylint_pex_get = Get( Pex, PexRequest, pylint.to_pex_request( interpreter_constraints=partition.interpreter_constraints, extra_requirements=first_party_plugins.requirement_strings, ), ) prepare_python_sources_get = Get(PythonSourceFiles, PythonSourceFilesRequest(partition.closure)) field_set_sources_get = Get( SourceFiles, SourceFilesRequest(fs.source for fs in partition.root_field_sets) ) report_directory_digest_get = Get(Digest, CreateDigest([Directory(REPORT_DIR)])) ( pylint_pex, requirements_pex, prepared_python_sources, field_set_sources, report_directory, ) = await MultiGet( pylint_pex_get, requirements_pex_get, prepare_python_sources_get, field_set_sources_get, report_directory_digest_get, ) pylint_runner_pex, config_files = await MultiGet( Get( VenvPex, VenvPexRequest( PexRequest( output_filename="pylint_runner.pex", interpreter_constraints=partition.interpreter_constraints, main=pylint.main, internal_only=True, pex_path=[pylint_pex, requirements_pex], ), site_packages_copies=True, ), ), Get( ConfigFiles, ConfigFilesRequest, pylint.config_request(field_set_sources.snapshot.dirs) ), ) pythonpath = list(prepared_python_sources.source_roots) if first_party_plugins: pythonpath.append(first_party_plugins.PREFIX) input_digest = await Get( Digest, MergeDigests( ( config_files.snapshot.digest, first_party_plugins.sources_digest, prepared_python_sources.source_files.snapshot.digest, report_directory, ) ), ) result = await Get( FallibleProcessResult, VenvPexProcess( pylint_runner_pex, argv=generate_argv(field_set_sources, pylint), input_digest=input_digest, output_directories=(REPORT_DIR,), extra_env={"PEX_EXTRA_SYS_PATH": ":".join(pythonpath)}, concurrency_available=len(partition.root_field_sets), description=f"Run Pylint on {pluralize(len(partition.root_field_sets), 'file')}.", level=LogLevel.DEBUG, ), ) report = await Get(Digest, RemovePrefix(result.output_digest, REPORT_DIR)) return LintResult.from_fallible_process_result( result, partition_description=partition.description(), report=report, ) @rule(desc="Determine if necessary to partition Pylint input", level=LogLevel.DEBUG) async def pylint_determine_partitions( request: PylintRequest, python_setup: PythonSetup, first_party_plugins: PylintFirstPartyPlugins ) -> PylintPartitions: resolve_and_interpreter_constraints_to_coarsened_targets = ( await partition._by_interpreter_constraints_and_resolve(request.field_sets, python_setup) ) first_party_ics = InterpreterConstraints.create_from_compatibility_fields( first_party_plugins.interpreter_constraints_fields, python_setup ) return PylintPartitions( PylintPartition( FrozenOrderedSet(roots), FrozenOrderedSet(CoarsenedTargets(root_cts).closure()), resolve if len(python_setup.resolves) > 1 else None, InterpreterConstraints.merge((interpreter_constraints, first_party_ics)), ) for (resolve, interpreter_constraints), (roots, root_cts) in sorted( resolve_and_interpreter_constraints_to_coarsened_targets.items() ) ) @rule(desc="Lint using Pylint", level=LogLevel.DEBUG) async def pylint_lint(request: PylintRequest, pylint: Pylint) -> LintResults: if pylint.skip: return LintResults([], linter_name=request.name) partitions = await Get(PylintPartitions, PylintRequest, request) partitioned_results = await MultiGet( Get(LintResult, PylintPartition, partition) for partition in partitions ) return LintResults(partitioned_results, linter_name=request.name) def rules(): return [ *collect_rules(), UnionRule(LintTargetsRequest, PylintRequest), *pex_from_targets.rules(), ]

true

f72aef2c46434bd7fee98942b7dd5f4091b26225

9,102

py

Python

homeassistant/components/philips_js/media_player.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

6

2020-07-18T16:33:25.000Z

2021-09-26T09:52:04.000Z

homeassistant/components/philips_js/media_player.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

47

2020-07-23T07:14:33.000Z

2022-03-31T06:01:46.000Z

homeassistant/components/philips_js/media_player.py

klauern/home-assistant-core

c18ba6aec0627e6afb6442c678edb5ff2bb17db6

[ "Apache-2.0" ]

5

2020-03-29T00:29:13.000Z

2021-09-06T20:58:40.000Z

"""Media Player component to integrate TVs exposing the Joint Space API.""" from datetime import timedelta import logging from haphilipsjs import PhilipsTV import voluptuous as vol from homeassistant.components.media_player import PLATFORM_SCHEMA, MediaPlayerEntity from homeassistant.components.media_player.const import ( MEDIA_TYPE_CHANNEL, SUPPORT_NEXT_TRACK, SUPPORT_PLAY_MEDIA, SUPPORT_PREVIOUS_TRACK, SUPPORT_SELECT_SOURCE, SUPPORT_TURN_OFF, SUPPORT_TURN_ON, SUPPORT_VOLUME_MUTE, SUPPORT_VOLUME_SET, SUPPORT_VOLUME_STEP, ) from homeassistant.const import ( CONF_API_VERSION, CONF_HOST, CONF_NAME, STATE_OFF, STATE_ON, ) import homeassistant.helpers.config_validation as cv from homeassistant.helpers.event import call_later, track_time_interval from homeassistant.helpers.script import Script _LOGGER = logging.getLogger(__name__) SUPPORT_PHILIPS_JS = ( SUPPORT_TURN_OFF | SUPPORT_VOLUME_STEP | SUPPORT_VOLUME_SET | SUPPORT_VOLUME_MUTE | SUPPORT_SELECT_SOURCE | SUPPORT_NEXT_TRACK | SUPPORT_PREVIOUS_TRACK | SUPPORT_PLAY_MEDIA ) CONF_ON_ACTION = "turn_on_action" DEFAULT_NAME = "Philips TV" DEFAULT_API_VERSION = "1" DEFAULT_SCAN_INTERVAL = 30 DELAY_ACTION_DEFAULT = 2.0 DELAY_ACTION_ON = 10.0 PREFIX_SEPARATOR = ": " PREFIX_SOURCE = "Input" PREFIX_CHANNEL = "Channel" PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend( { vol.Required(CONF_HOST): cv.string, vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string, vol.Optional(CONF_API_VERSION, default=DEFAULT_API_VERSION): cv.string, vol.Optional(CONF_ON_ACTION): cv.SCRIPT_SCHEMA, } ) def _inverted(data): return {v: k for k, v in data.items()} def setup_platform(hass, config, add_entities, discovery_info=None): """Set up the Philips TV platform.""" name = config.get(CONF_NAME) host = config.get(CONF_HOST) api_version = config.get(CONF_API_VERSION) turn_on_action = config.get(CONF_ON_ACTION) tvapi = PhilipsTV(host, api_version) on_script = Script(hass, turn_on_action) if turn_on_action else None add_entities([PhilipsTVMediaPlayer(tvapi, name, on_script)]) class PhilipsTVMediaPlayer(MediaPlayerEntity): """Representation of a Philips TV exposing the JointSpace API.""" def __init__(self, tv, name, on_script): """Initialize the Philips TV.""" self._tv = tv self._name = name self._sources = {} self._channels = {} self._on_script = on_script self._supports = SUPPORT_PHILIPS_JS if self._on_script: self._supports |= SUPPORT_TURN_ON self._update_task = None def _update_soon(self, delay): """Reschedule update task.""" if self._update_task: self._update_task() self._update_task = None self.schedule_update_ha_state(force_refresh=False) def update_forced(event_time): self.schedule_update_ha_state(force_refresh=True) def update_and_restart(event_time): update_forced(event_time) self._update_task = track_time_interval( self.hass, update_forced, timedelta(seconds=DEFAULT_SCAN_INTERVAL) ) call_later(self.hass, delay, update_and_restart) async def async_added_to_hass(self): """Start running updates once we are added to hass.""" await self.hass.async_add_executor_job(self._update_soon, 0) @property def name(self): """Return the device name.""" return self._name @property def should_poll(self): """Device should be polled.""" return False @property def supported_features(self): """Flag media player features that are supported.""" return self._supports @property def state(self): """Get the device state. An exception means OFF state.""" if self._tv.on: return STATE_ON return STATE_OFF @property def source(self): """Return the current input source.""" if self.media_content_type == MEDIA_TYPE_CHANNEL: name = self._channels.get(self._tv.channel_id) prefix = PREFIX_CHANNEL else: name = self._sources.get(self._tv.source_id) prefix = PREFIX_SOURCE if name is None: return None return prefix + PREFIX_SEPARATOR + name @property def source_list(self): """List of available input sources.""" complete = [] for source in self._sources.values(): complete.append(PREFIX_SOURCE + PREFIX_SEPARATOR + source) for channel in self._channels.values(): complete.append(PREFIX_CHANNEL + PREFIX_SEPARATOR + channel) return complete def select_source(self, source): """Set the input source.""" data = source.split(PREFIX_SEPARATOR, 1) if data[0] == PREFIX_SOURCE: source_id = _inverted(self._sources).get(data[1]) if source_id: self._tv.setSource(source_id) elif data[0] == PREFIX_CHANNEL: channel_id = _inverted(self._channels).get(data[1]) if channel_id: self._tv.setChannel(channel_id) self._update_soon(DELAY_ACTION_DEFAULT) @property def volume_level(self): """Volume level of the media player (0..1).""" return self._tv.volume @property def is_volume_muted(self): """Boolean if volume is currently muted.""" return self._tv.muted def turn_on(self): """Turn on the device.""" if self._on_script: self._on_script.run() self._update_soon(DELAY_ACTION_ON) def turn_off(self): """Turn off the device.""" self._tv.sendKey("Standby") self._tv.on = False self._update_soon(DELAY_ACTION_DEFAULT) def volume_up(self): """Send volume up command.""" self._tv.sendKey("VolumeUp") self._update_soon(DELAY_ACTION_DEFAULT) def volume_down(self): """Send volume down command.""" self._tv.sendKey("VolumeDown") self._update_soon(DELAY_ACTION_DEFAULT) def mute_volume(self, mute): """Send mute command.""" self._tv.setVolume(None, mute) self._update_soon(DELAY_ACTION_DEFAULT) def set_volume_level(self, volume): """Set volume level, range 0..1.""" self._tv.setVolume(volume, self._tv.muted) self._update_soon(DELAY_ACTION_DEFAULT) def media_previous_track(self): """Send rewind command.""" self._tv.sendKey("Previous") self._update_soon(DELAY_ACTION_DEFAULT) def media_next_track(self): """Send fast forward command.""" self._tv.sendKey("Next") self._update_soon(DELAY_ACTION_DEFAULT) @property def media_channel(self): """Get current channel if it's a channel.""" if self.media_content_type == MEDIA_TYPE_CHANNEL: return self._channels.get(self._tv.channel_id) return None @property def media_title(self): """Title of current playing media.""" if self.media_content_type == MEDIA_TYPE_CHANNEL: return self._channels.get(self._tv.channel_id) return self._sources.get(self._tv.source_id) @property def media_content_type(self): """Return content type of playing media.""" if self._tv.source_id == "tv" or self._tv.source_id == "11": return MEDIA_TYPE_CHANNEL if self._tv.source_id is None and self._tv.channels: return MEDIA_TYPE_CHANNEL return None @property def media_content_id(self): """Content type of current playing media.""" if self.media_content_type == MEDIA_TYPE_CHANNEL: return self._channels.get(self._tv.channel_id) return None @property def device_state_attributes(self): """Return the state attributes.""" return {"channel_list": list(self._channels.values())} def play_media(self, media_type, media_id, **kwargs): """Play a piece of media.""" _LOGGER.debug("Call play media type <%s>, Id <%s>", media_type, media_id) if media_type == MEDIA_TYPE_CHANNEL: channel_id = _inverted(self._channels).get(media_id) if channel_id: self._tv.setChannel(channel_id) self._update_soon(DELAY_ACTION_DEFAULT) else: _LOGGER.error("Unable to find channel <%s>", media_id) else: _LOGGER.error("Unsupported media type <%s>", media_type) def update(self): """Get the latest data and update device state.""" self._tv.update() self._sources = { srcid: source["name"] or f"Source {srcid}" for srcid, source in (self._tv.sources or {}).items() } self._channels = { chid: channel["name"] for chid, channel in (self._tv.channels or {}).items() }

30.854237

88

0.64777

from datetime import timedelta import logging from haphilipsjs import PhilipsTV import voluptuous as vol from homeassistant.components.media_player import PLATFORM_SCHEMA, MediaPlayerEntity from homeassistant.components.media_player.const import ( MEDIA_TYPE_CHANNEL, SUPPORT_NEXT_TRACK, SUPPORT_PLAY_MEDIA, SUPPORT_PREVIOUS_TRACK, SUPPORT_SELECT_SOURCE, SUPPORT_TURN_OFF, SUPPORT_TURN_ON, SUPPORT_VOLUME_MUTE, SUPPORT_VOLUME_SET, SUPPORT_VOLUME_STEP, ) from homeassistant.const import ( CONF_API_VERSION, CONF_HOST, CONF_NAME, STATE_OFF, STATE_ON, ) import homeassistant.helpers.config_validation as cv from homeassistant.helpers.event import call_later, track_time_interval from homeassistant.helpers.script import Script _LOGGER = logging.getLogger(__name__) SUPPORT_PHILIPS_JS = ( SUPPORT_TURN_OFF | SUPPORT_VOLUME_STEP | SUPPORT_VOLUME_SET | SUPPORT_VOLUME_MUTE | SUPPORT_SELECT_SOURCE | SUPPORT_NEXT_TRACK | SUPPORT_PREVIOUS_TRACK | SUPPORT_PLAY_MEDIA ) CONF_ON_ACTION = "turn_on_action" DEFAULT_NAME = "Philips TV" DEFAULT_API_VERSION = "1" DEFAULT_SCAN_INTERVAL = 30 DELAY_ACTION_DEFAULT = 2.0 DELAY_ACTION_ON = 10.0 PREFIX_SEPARATOR = ": " PREFIX_SOURCE = "Input" PREFIX_CHANNEL = "Channel" PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend( { vol.Required(CONF_HOST): cv.string, vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string, vol.Optional(CONF_API_VERSION, default=DEFAULT_API_VERSION): cv.string, vol.Optional(CONF_ON_ACTION): cv.SCRIPT_SCHEMA, } ) def _inverted(data): return {v: k for k, v in data.items()} def setup_platform(hass, config, add_entities, discovery_info=None): name = config.get(CONF_NAME) host = config.get(CONF_HOST) api_version = config.get(CONF_API_VERSION) turn_on_action = config.get(CONF_ON_ACTION) tvapi = PhilipsTV(host, api_version) on_script = Script(hass, turn_on_action) if turn_on_action else None add_entities([PhilipsTVMediaPlayer(tvapi, name, on_script)]) class PhilipsTVMediaPlayer(MediaPlayerEntity): def __init__(self, tv, name, on_script): self._tv = tv self._name = name self._sources = {} self._channels = {} self._on_script = on_script self._supports = SUPPORT_PHILIPS_JS if self._on_script: self._supports |= SUPPORT_TURN_ON self._update_task = None def _update_soon(self, delay): if self._update_task: self._update_task() self._update_task = None self.schedule_update_ha_state(force_refresh=False) def update_forced(event_time): self.schedule_update_ha_state(force_refresh=True) def update_and_restart(event_time): update_forced(event_time) self._update_task = track_time_interval( self.hass, update_forced, timedelta(seconds=DEFAULT_SCAN_INTERVAL) ) call_later(self.hass, delay, update_and_restart) async def async_added_to_hass(self): await self.hass.async_add_executor_job(self._update_soon, 0) @property def name(self): return self._name @property def should_poll(self): return False @property def supported_features(self): return self._supports @property def state(self): if self._tv.on: return STATE_ON return STATE_OFF @property def source(self): if self.media_content_type == MEDIA_TYPE_CHANNEL: name = self._channels.get(self._tv.channel_id) prefix = PREFIX_CHANNEL else: name = self._sources.get(self._tv.source_id) prefix = PREFIX_SOURCE if name is None: return None return prefix + PREFIX_SEPARATOR + name @property def source_list(self): complete = [] for source in self._sources.values(): complete.append(PREFIX_SOURCE + PREFIX_SEPARATOR + source) for channel in self._channels.values(): complete.append(PREFIX_CHANNEL + PREFIX_SEPARATOR + channel) return complete def select_source(self, source): data = source.split(PREFIX_SEPARATOR, 1) if data[0] == PREFIX_SOURCE: source_id = _inverted(self._sources).get(data[1]) if source_id: self._tv.setSource(source_id) elif data[0] == PREFIX_CHANNEL: channel_id = _inverted(self._channels).get(data[1]) if channel_id: self._tv.setChannel(channel_id) self._update_soon(DELAY_ACTION_DEFAULT) @property def volume_level(self): return self._tv.volume @property def is_volume_muted(self): return self._tv.muted def turn_on(self): if self._on_script: self._on_script.run() self._update_soon(DELAY_ACTION_ON) def turn_off(self): self._tv.sendKey("Standby") self._tv.on = False self._update_soon(DELAY_ACTION_DEFAULT) def volume_up(self): self._tv.sendKey("VolumeUp") self._update_soon(DELAY_ACTION_DEFAULT) def volume_down(self): self._tv.sendKey("VolumeDown") self._update_soon(DELAY_ACTION_DEFAULT) def mute_volume(self, mute): self._tv.setVolume(None, mute) self._update_soon(DELAY_ACTION_DEFAULT) def set_volume_level(self, volume): self._tv.setVolume(volume, self._tv.muted) self._update_soon(DELAY_ACTION_DEFAULT) def media_previous_track(self): self._tv.sendKey("Previous") self._update_soon(DELAY_ACTION_DEFAULT) def media_next_track(self): self._tv.sendKey("Next") self._update_soon(DELAY_ACTION_DEFAULT) @property def media_channel(self): if self.media_content_type == MEDIA_TYPE_CHANNEL: return self._channels.get(self._tv.channel_id) return None @property def media_title(self): if self.media_content_type == MEDIA_TYPE_CHANNEL: return self._channels.get(self._tv.channel_id) return self._sources.get(self._tv.source_id) @property def media_content_type(self): if self._tv.source_id == "tv" or self._tv.source_id == "11": return MEDIA_TYPE_CHANNEL if self._tv.source_id is None and self._tv.channels: return MEDIA_TYPE_CHANNEL return None @property def media_content_id(self): if self.media_content_type == MEDIA_TYPE_CHANNEL: return self._channels.get(self._tv.channel_id) return None @property def device_state_attributes(self): return {"channel_list": list(self._channels.values())} def play_media(self, media_type, media_id, **kwargs): _LOGGER.debug("Call play media type <%s>, Id <%s>", media_type, media_id) if media_type == MEDIA_TYPE_CHANNEL: channel_id = _inverted(self._channels).get(media_id) if channel_id: self._tv.setChannel(channel_id) self._update_soon(DELAY_ACTION_DEFAULT) else: _LOGGER.error("Unable to find channel <%s>", media_id) else: _LOGGER.error("Unsupported media type <%s>", media_type) def update(self): self._tv.update() self._sources = { srcid: source["name"] or f"Source {srcid}" for srcid, source in (self._tv.sources or {}).items() } self._channels = { chid: channel["name"] for chid, channel in (self._tv.channels or {}).items() }

true

f72aef7afd8a21811ad53f8b289714ccd5098693

8,333

py

Python

genie/assay.py

veo-ibd/Genie

735e3aa0dc71aab0c404fd0cb3a34c8e1d9784c2

[ "MIT" ]

null

genie/assay.py

veo-ibd/Genie

735e3aa0dc71aab0c404fd0cb3a34c8e1d9784c2

[ "MIT" ]

null

genie/assay.py

veo-ibd/Genie

735e3aa0dc71aab0c404fd0cb3a34c8e1d9784c2

[ "MIT" ]

1

2022-01-20T16:33:19.000Z

import os import logging import subprocess import yaml import pandas as pd from .example_filetype_format import FileTypeFormat from . import process_functions logger = logging.getLogger(__name__) class Assayinfo(FileTypeFormat): ''' Assay information file type ''' _fileType = "assayinfo" _process_kwargs = ["newPath", "databaseSynId"] def _validateFilename(self, filepath_list): assert os.path.basename(filepath_list[0]) == "assay_information.yaml" def process_steps(self, assay_info_df, newPath, databaseSynId): # databaseSynId = kwargs['databaseSynId'] # Must pass in a list process_assay_info_df = self._process(assay_info_df) col = ['SEQ_ASSAY_ID', 'is_paired_end', 'library_selection', 'library_strategy', 'platform', 'read_length', 'instrument_model', 'gene_padding', 'number_of_genes', 'variant_classifications', 'CENTER'] process_functions.updateData( self.syn, databaseSynId, process_assay_info_df, self.center, col=col, filterByColumn="CENTER", toDelete=True) process_assay_info_df.to_csv(newPath, sep="\t", index=False) return(newPath) def _process(self, df): ''' Processing function for Assay information - Standardizes SEQ_ASSAY_ID - Default 10 for gene_padding - Fills in variant_classifications Args: df: Assay information dataframe Returns: dataframe: Processed dataframe ''' seq_assay_ids = [ assay.upper().replace('_', '-') for assay in df['SEQ_ASSAY_ID']] df['SEQ_ASSAY_ID'] = seq_assay_ids if process_functions.checkColExist(df, "gene_padding"): df['gene_padding'] = df['gene_padding'].fillna(10) df['gene_padding'] = df['gene_padding'].astype(int) else: df['gene_padding'] = 10 if not process_functions.checkColExist(df, "variant_classifications"): df['variant_classifications'] = pd.np.nan df['CENTER'] = self.center return(df) def _get_dataframe(self, filepath_list): ''' Takes in yaml file, returns dataframe ''' filepath = filepath_list[0] try: with open(filepath, 'r') as yamlfile: # https://github.com/yaml/pyyaml/wiki/PyYAML-yaml.load(input)-Deprecation # Must add this because yaml load deprecation panel_info_dict = yaml.load(yamlfile, Loader=yaml.FullLoader) except Exception: raise ValueError( "assay_information.yaml: Can't read in your file. " "Please make sure the file is a correctly formatted yaml") assay_info_df = pd.DataFrame(panel_info_dict) assay_info_df = assay_info_df.transpose() assay_info_df['SEQ_ASSAY_ID'] = assay_info_df.index assay_info_df.reset_index(drop=True, inplace=True) return(assay_info_df) def _validate(self, assay_info_df): ''' Validates the values of assay information file Args: assay_info_df: assay information dataframe Returns: tuple: error and warning ''' total_error = "" warning = "" if process_functions.checkColExist(assay_info_df, "SEQ_ASSAY_ID"): all_seq_assays = assay_info_df.SEQ_ASSAY_ID.unique() if not all([assay.startswith(self.center) for assay in all_seq_assays]): total_error += \ "Assay_information.yaml: Please make sure your all your" +\ " SEQ_ASSAY_IDs start with your center abbreviation.\n" else: total_error += \ "Assay_information.yaml: Must have SEQ_ASSAY_ID column.\n" read_group_dict = process_functions.get_gdc_data_dictionary( "read_group") read_group_headers = read_group_dict['properties'] warn, error = process_functions.check_col_and_values( assay_info_df, 'is_paired_end', [True, False], filename="Assay_information.yaml", required=True) warning += warn total_error += error warn, error = process_functions.check_col_and_values( assay_info_df, 'library_selection', read_group_headers['library_selection']['enum'], filename="Assay_information.yaml", required=True) warning += warn total_error += error warn, error = process_functions.check_col_and_values( assay_info_df, 'library_strategy', read_group_headers['library_strategy']['enum'], filename="Assay_information.yaml", required=True) warning += warn total_error += error warn, error = process_functions.check_col_and_values( assay_info_df, 'platform', read_group_headers['platform']['enum'], filename="Assay_information.yaml", required=True) warning += warn total_error += error instrument_model = read_group_headers['instrument_model']['enum'] instrument_model.append(None) warn, error = process_functions.check_col_and_values( assay_info_df, 'instrument_model', instrument_model, filename="Assay_information.yaml", required=True) warning += warn total_error += error variant_classes = \ ['Splice_Site', 'Nonsense_Mutation', 'Frame_Shift_Del', 'Frame_Shift_Ins', 'Nonstop_Mutation', 'Translation_Start_Site', 'In_Frame_Ins', 'In_Frame_Del', 'Missense_Mutation', 'Intron', 'Splice_Region', 'Silent', 'RNA', "5'UTR", "3'UTR", 'IGR', "5'Flank", "3'Flank", None] warn, error = process_functions.check_col_and_values( assay_info_df, 'variant_classifications', variant_classes, filename="Assay_information.yaml", na_allowed=True) warning += warn total_error += error # if not process_functions.checkColExist( # assay_info_df, "target_capture_kit"): # total_error += ("Assay_information.yaml: " # "Must have target_capture_kit column.\n") if process_functions.checkColExist(assay_info_df, "read_length"): if not all([process_functions.checkInt(i) for i in assay_info_df["read_length"] if i is not None and not pd.isnull(i)]): total_error += \ ("Assay_information.yaml: " "Please double check your read_length. " "It must be an integer or null.\n") else: total_error += \ ("Assay_information.yaml: " "Must have read_length column.\n") if process_functions.checkColExist(assay_info_df, "number_of_genes"): if not all([process_functions.checkInt(i) for i in assay_info_df["number_of_genes"]]): total_error += \ ("Assay_information.yaml: " "Please double check your number_of_genes. " "It must be an integer.\n") else: total_error += \ ("Assay_information.yaml: " "Must have number_of_genes column.\n") if process_functions.checkColExist(assay_info_df, "gene_padding"): if not all([process_functions.checkInt(i) for i in assay_info_df["gene_padding"] if i is not None and not pd.isnull(i)]): total_error += \ ("Assay_information.yaml: " "Please double check your gene_padding. " "It must be an integer or blank.\n") else: warning += \ ("Assay_information.yaml: " "gene_padding is by default 10 if not specified.\n") return(total_error, warning)

36.388646

89

0.582503

import os import logging import subprocess import yaml import pandas as pd from .example_filetype_format import FileTypeFormat from . import process_functions logger = logging.getLogger(__name__) class Assayinfo(FileTypeFormat): _fileType = "assayinfo" _process_kwargs = ["newPath", "databaseSynId"] def _validateFilename(self, filepath_list): assert os.path.basename(filepath_list[0]) == "assay_information.yaml" def process_steps(self, assay_info_df, newPath, databaseSynId): process_assay_info_df = self._process(assay_info_df) col = ['SEQ_ASSAY_ID', 'is_paired_end', 'library_selection', 'library_strategy', 'platform', 'read_length', 'instrument_model', 'gene_padding', 'number_of_genes', 'variant_classifications', 'CENTER'] process_functions.updateData( self.syn, databaseSynId, process_assay_info_df, self.center, col=col, filterByColumn="CENTER", toDelete=True) process_assay_info_df.to_csv(newPath, sep="\t", index=False) return(newPath) def _process(self, df): seq_assay_ids = [ assay.upper().replace('_', '-') for assay in df['SEQ_ASSAY_ID']] df['SEQ_ASSAY_ID'] = seq_assay_ids if process_functions.checkColExist(df, "gene_padding"): df['gene_padding'] = df['gene_padding'].fillna(10) df['gene_padding'] = df['gene_padding'].astype(int) else: df['gene_padding'] = 10 if not process_functions.checkColExist(df, "variant_classifications"): df['variant_classifications'] = pd.np.nan df['CENTER'] = self.center return(df) def _get_dataframe(self, filepath_list): filepath = filepath_list[0] try: with open(filepath, 'r') as yamlfile: panel_info_dict = yaml.load(yamlfile, Loader=yaml.FullLoader) except Exception: raise ValueError( "assay_information.yaml: Can't read in your file. " "Please make sure the file is a correctly formatted yaml") assay_info_df = pd.DataFrame(panel_info_dict) assay_info_df = assay_info_df.transpose() assay_info_df['SEQ_ASSAY_ID'] = assay_info_df.index assay_info_df.reset_index(drop=True, inplace=True) return(assay_info_df) def _validate(self, assay_info_df): total_error = "" warning = "" if process_functions.checkColExist(assay_info_df, "SEQ_ASSAY_ID"): all_seq_assays = assay_info_df.SEQ_ASSAY_ID.unique() if not all([assay.startswith(self.center) for assay in all_seq_assays]): total_error += \ "Assay_information.yaml: Please make sure your all your" +\ " SEQ_ASSAY_IDs start with your center abbreviation.\n" else: total_error += \ "Assay_information.yaml: Must have SEQ_ASSAY_ID column.\n" read_group_dict = process_functions.get_gdc_data_dictionary( "read_group") read_group_headers = read_group_dict['properties'] warn, error = process_functions.check_col_and_values( assay_info_df, 'is_paired_end', [True, False], filename="Assay_information.yaml", required=True) warning += warn total_error += error warn, error = process_functions.check_col_and_values( assay_info_df, 'library_selection', read_group_headers['library_selection']['enum'], filename="Assay_information.yaml", required=True) warning += warn total_error += error warn, error = process_functions.check_col_and_values( assay_info_df, 'library_strategy', read_group_headers['library_strategy']['enum'], filename="Assay_information.yaml", required=True) warning += warn total_error += error warn, error = process_functions.check_col_and_values( assay_info_df, 'platform', read_group_headers['platform']['enum'], filename="Assay_information.yaml", required=True) warning += warn total_error += error instrument_model = read_group_headers['instrument_model']['enum'] instrument_model.append(None) warn, error = process_functions.check_col_and_values( assay_info_df, 'instrument_model', instrument_model, filename="Assay_information.yaml", required=True) warning += warn total_error += error variant_classes = \ ['Splice_Site', 'Nonsense_Mutation', 'Frame_Shift_Del', 'Frame_Shift_Ins', 'Nonstop_Mutation', 'Translation_Start_Site', 'In_Frame_Ins', 'In_Frame_Del', 'Missense_Mutation', 'Intron', 'Splice_Region', 'Silent', 'RNA', "5'UTR", "3'UTR", 'IGR', "5'Flank", "3'Flank", None] warn, error = process_functions.check_col_and_values( assay_info_df, 'variant_classifications', variant_classes, filename="Assay_information.yaml", na_allowed=True) warning += warn total_error += error # if not process_functions.checkColExist( # assay_info_df, "target_capture_kit"): # total_error += ("Assay_information.yaml: " # "Must have target_capture_kit column.\n") if process_functions.checkColExist(assay_info_df, "read_length"): if not all([process_functions.checkInt(i) for i in assay_info_df["read_length"] if i is not None and not pd.isnull(i)]): total_error += \ ("Assay_information.yaml: " "Please double check your read_length. " "It must be an integer or null.\n") else: total_error += \ ("Assay_information.yaml: " "Must have read_length column.\n") if process_functions.checkColExist(assay_info_df, "number_of_genes"): if not all([process_functions.checkInt(i) for i in assay_info_df["number_of_genes"]]): total_error += \ ("Assay_information.yaml: " "Please double check your number_of_genes. " "It must be an integer.\n") else: total_error += \ ("Assay_information.yaml: " "Must have number_of_genes column.\n") if process_functions.checkColExist(assay_info_df, "gene_padding"): if not all([process_functions.checkInt(i) for i in assay_info_df["gene_padding"] if i is not None and not pd.isnull(i)]): total_error += \ ("Assay_information.yaml: " "Please double check your gene_padding. " "It must be an integer or blank.\n") else: warning += \ ("Assay_information.yaml: " "gene_padding is by default 10 if not specified.\n") return(total_error, warning)

true

f72af06f509cb3b16be313e070fe087431a96b9c

1,550

py

Python

dlfairness/other/get_weight/alm.py

lin-tan/fairness-variance

7f6aee23160707ffe78f429e5d960022ea1c9fe4

[ "BSD-3-Clause" ]

null

dlfairness/other/get_weight/alm.py

lin-tan/fairness-variance

7f6aee23160707ffe78f429e5d960022ea1c9fe4

[ "BSD-3-Clause" ]

null

dlfairness/other/get_weight/alm.py

lin-tan/fairness-variance

7f6aee23160707ffe78f429e5d960022ea1c9fe4

[ "BSD-3-Clause" ]

null

import argparse import pandas as pd import json import pickle import numpy as np from pathlib import Path from scipy.special import softmax import shutil parser = argparse.ArgumentParser() parser.add_argument('--config', type=str) parser.add_argument('--raw_result_dir', type=str) parser.add_argument('--output_dir', type=str) args = parser.parse_args() with open(args.config, 'r') as f: config_json = json.load(f) for config in config_json: class_bias_result = [] for no_try in range(16): if (config['dataset'] != 'CelebA') or (not config['training_type'] in ['no-constraints', 'l2-penalty', 'fair-alm']): continue exp_result_path = Path( args.raw_result_dir, "{0}_{1}_{2}_{3}/{4}".format(config['network'], config['training_type'], config['dataset'], config['random_seed'], str(no_try))) p = Path(exp_result_path, 'checkpoint') ckpt_path = Path(p, 'ckpt_80.t7') if config['training_type'] == 'no-constraints': tech = 'A-Base' elif config['training_type'] == 'l2-penalty': tech = 'A-L2' elif config['training_type'] == 'fair-alm': tech = 'A-ALM' copy_path = Path(args.output_dir, tech, 'run_' + str(no_try).zfill(2) + '.pth') copy_path.parent.mkdir(parents=True, exist_ok=True) shutil.copy(ckpt_path, copy_path)

35.227273

124

0.570323

import argparse import pandas as pd import json import pickle import numpy as np from pathlib import Path from scipy.special import softmax import shutil parser = argparse.ArgumentParser() parser.add_argument('--config', type=str) parser.add_argument('--raw_result_dir', type=str) parser.add_argument('--output_dir', type=str) args = parser.parse_args() with open(args.config, 'r') as f: config_json = json.load(f) for config in config_json: class_bias_result = [] for no_try in range(16): if (config['dataset'] != 'CelebA') or (not config['training_type'] in ['no-constraints', 'l2-penalty', 'fair-alm']): continue exp_result_path = Path( args.raw_result_dir, "{0}_{1}_{2}_{3}/{4}".format(config['network'], config['training_type'], config['dataset'], config['random_seed'], str(no_try))) p = Path(exp_result_path, 'checkpoint') ckpt_path = Path(p, 'ckpt_80.t7') if config['training_type'] == 'no-constraints': tech = 'A-Base' elif config['training_type'] == 'l2-penalty': tech = 'A-L2' elif config['training_type'] == 'fair-alm': tech = 'A-ALM' copy_path = Path(args.output_dir, tech, 'run_' + str(no_try).zfill(2) + '.pth') copy_path.parent.mkdir(parents=True, exist_ok=True) shutil.copy(ckpt_path, copy_path)

true

f72af113f201219d494c2ae51b9d0c0fae085aeb

925

py

Python

Codefights/arcade/intro/level-7/33.stringsRearrangement/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codefights/arcade/intro/level-7/33.stringsRearrangement/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codefights/arcade/intro/level-7/33.stringsRearrangement/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python3 def diffOne(a, b): count = 0 for i in range(len(a)): if a[i] != b[i]: count += 1 if count == 2: return False return bool(count) def func(inputArray, curr): if len(inputArray) == 1: return diffOne(inputArray[0], curr) for i in range(len(inputArray)): if diffOne(inputArray[i], curr): inputArray[i], inputArray[-1] = inputArray[-1], inputArray[i] if func(inputArray[:-1], inputArray[-1]): return True inputArray[i], inputArray[-1] = inputArray[-1], inputArray[i] return False def stringsRearrangement(inputArray): for i in range(len(inputArray)): inputArray[i], inputArray[-1] = inputArray[-1], inputArray[i] if func(inputArray[:-1], inputArray[-1]): return True inputArray[i], inputArray[-1] = inputArray[-1], inputArray[i] return False

30.833333

73

0.572973

def diffOne(a, b): count = 0 for i in range(len(a)): if a[i] != b[i]: count += 1 if count == 2: return False return bool(count) def func(inputArray, curr): if len(inputArray) == 1: return diffOne(inputArray[0], curr) for i in range(len(inputArray)): if diffOne(inputArray[i], curr): inputArray[i], inputArray[-1] = inputArray[-1], inputArray[i] if func(inputArray[:-1], inputArray[-1]): return True inputArray[i], inputArray[-1] = inputArray[-1], inputArray[i] return False def stringsRearrangement(inputArray): for i in range(len(inputArray)): inputArray[i], inputArray[-1] = inputArray[-1], inputArray[i] if func(inputArray[:-1], inputArray[-1]): return True inputArray[i], inputArray[-1] = inputArray[-1], inputArray[i] return False

true

f72af114783cb0a76af49c20e78ca72551409642

1,378

py

Python

setup.py

jamesgregson/easy_image_io

4b5af29f3ccc37e4b10fbdc1e18d508ed04b882d

[ "MIT" ]

1

2017-08-17T11:59:45.000Z

setup.py

jamesgregson/easy_image_io

4b5af29f3ccc37e4b10fbdc1e18d508ed04b882d

[ "MIT" ]

null

setup.py

jamesgregson/easy_image_io

4b5af29f3ccc37e4b10fbdc1e18d508ed04b882d

[ "MIT" ]

null

from setuptools import setup, Extension import numpy import os import config def find(name, path): for root, dirs, files in os.walk(path): if name in files: return os.path.join(root, name) return ''; print('locating directories...') defines = [ ('MAJOR_VERSION',0),('MINOR_VERSION',1) ] include_dirs = [ numpy.get_include() ] libraries = [] library_dirs = [] print('checking for tiffio.h...') if find('tiffio.h', config.tiff_include_dir) != '': defines.append( ('cimg_use_tiff',1) ) include_dirs.append( config.tiff_include_dir ) libraries.append( 'tiff' ) library_dirs.append( config.tiff_library_dir ) print('checking for png.h...') if find('png.h', config.png_include_dir ) != '': defines.append( ('cimg_use_png',1) ) include_dirs.append( config.png_include_dir ) libraries.append( 'png' ) library_dirs.append( config.png_library_dir ) for lib in config.libs: libraries.append( lib ) print('Setting up extension...') easy_image_io = Extension('easy_image_io', define_macros=defines, sources=['easy_image_io.cpp'], include_dirs=include_dirs, library_dirs=library_dirs, libraries=libraries ) print('Building extension...') setup(name='easy_image_io', version='0.1', ext_modules=[ easy_image_io ] )

30.622222

74

0.650218

from setuptools import setup, Extension import numpy import os import config def find(name, path): for root, dirs, files in os.walk(path): if name in files: return os.path.join(root, name) return ''; print('locating directories...') defines = [ ('MAJOR_VERSION',0),('MINOR_VERSION',1) ] include_dirs = [ numpy.get_include() ] libraries = [] library_dirs = [] print('checking for tiffio.h...') if find('tiffio.h', config.tiff_include_dir) != '': defines.append( ('cimg_use_tiff',1) ) include_dirs.append( config.tiff_include_dir ) libraries.append( 'tiff' ) library_dirs.append( config.tiff_library_dir ) print('checking for png.h...') if find('png.h', config.png_include_dir ) != '': defines.append( ('cimg_use_png',1) ) include_dirs.append( config.png_include_dir ) libraries.append( 'png' ) library_dirs.append( config.png_library_dir ) for lib in config.libs: libraries.append( lib ) print('Setting up extension...') easy_image_io = Extension('easy_image_io', define_macros=defines, sources=['easy_image_io.cpp'], include_dirs=include_dirs, library_dirs=library_dirs, libraries=libraries ) print('Building extension...') setup(name='easy_image_io', version='0.1', ext_modules=[ easy_image_io ] )

true

f72af1e60284b4758cddcb59383f494df80a1a1a

148,700

py

Python

all/emojitations/data/hy.py

idleberg/sublime-emojitations

b2b4e8ce2c33ed0f6b8d6db6085e21da4e8d895b

[ "MIT" ]

6

2016-08-31T14:42:36.000Z

2021-09-05T23:55:47.000Z

all/emojitations/data/hy.py

idleberg/sublime-emojitations

b2b4e8ce2c33ed0f6b8d6db6085e21da4e8d895b

[ "MIT" ]

1

2016-10-20T10:52:06.000Z

2016-10-20T18:47:19.000Z

all/emojitations/data/hy.py

idleberg/sublime-emojitations

b2b4e8ce2c33ed0f6b8d6db6085e21da4e8d895b

[ "MIT" ]

5

2016-08-31T14:48:11.000Z

2021-09-05T23:55:33.000Z

from emojitations.emojitypes import EmojiAnnotations emoji = [ EmojiAnnotations(emoji='😀', codepoints=(128512,), name='ծիծաղող դեմք', slug='ծիծաղող_դեմք', annotations=frozenset({'դեմք', 'քմծիծաղել'})), EmojiAnnotations(emoji='😁', codepoints=(128513,), name='ծիծաղող դեմք ժպտացող աչքերով', slug='ծիծաղող_դեմք_ժպտացող_աչքերով', annotations=frozenset({'աչք', 'դեմք', 'ժպտալ', 'քմծիծաղել'})), EmojiAnnotations(emoji='😂', codepoints=(128514,), name='դեմք ուրախության արցունքներով', slug='դեմք_ուրախության_արցունքներով', annotations=frozenset({'ուրախություն', 'դեմք', 'ծիծաղել', 'արցունք'})), EmojiAnnotations(emoji='😃', codepoints=(128515,), name='ժպտացող դեմք բաց բերանով', slug='ժպտացող_դեմք_բաց_բերանով', annotations=frozenset({'բաց', 'դեմք', 'ժպտալ', 'բերան'})), EmojiAnnotations(emoji='😄', codepoints=(128516,), name='ժպտացող դեմք բաց բերանով և ժպտացող աչքերով', slug='ժպտացող_դեմք_բաց_բերանով_և_ժպտացող_աչքերով', annotations=frozenset({'բաց', 'աչք', 'դեմք', 'ժպտալ', 'բերան'})), EmojiAnnotations(emoji='😅', codepoints=(128517,), name='ժպտացող դեմք բաց բերանով և սառը քրտինքով', slug='ժպտացող_դեմք_բաց_բերանով_և_սառը_քրտինքով', annotations=frozenset({'բաց', 'սառը', 'դեմք', 'ժպտալ', 'քրտինք'})), EmojiAnnotations(emoji='😆', codepoints=(128518,), name='ժպտացող դեմք բաց բերանով և ամուր փակած աչքերով', slug='ժպտացող_դեմք_բաց_բերանով_և_ամուր_փակած_աչքերով', annotations=frozenset({'ժպտալ', 'գոհ', 'ծիծաղել', 'դեմք', 'բաց', 'բերան'})), EmojiAnnotations(emoji='😉', codepoints=(128521,), name='աչքով անող դեմք', slug='աչքով_անող_դեմք', annotations=frozenset({'դեմք', 'աչքով անել'})), EmojiAnnotations(emoji='😊', codepoints=(128522,), name='ժպտացող դեմք ժպտացող աչքերով', slug='ժպտացող_դեմք_ժպտացող_աչքերով', annotations=frozenset({'աչք', 'դեմք', 'ժպտալ', 'շիկնել'})), EmojiAnnotations(emoji='😋', codepoints=(128523,), name='համեղ ուտելիք վայելող դեմք', slug='համեղ_ուտելիք_վայելող_դեմք', annotations=frozenset({'դեմք', 'վեյելել', 'ժպտալ', 'համեղ', 'նյամ'})), EmojiAnnotations(emoji='😎', codepoints=(128526,), name='ժպտացող դեմք արևային ակնոցով', slug='ժպտացող_դեմք_արևային_ակնոցով', annotations=frozenset({'աչք', 'ակնոց', 'զիլ', 'ժպտալ', 'պայծառ', 'արևային ակնոց', 'դեմք', 'եղանակ', 'արև'})), EmojiAnnotations(emoji='😍', codepoints=(128525,), name='ժպտացող դեմք սրտաձև աչքերով', slug='ժպտացող_դեմք_սրտաձև_աչքերով', annotations=frozenset({'աչք', 'դեմք', 'սիրտ', 'ժպտալ', 'սեր'})), EmojiAnnotations(emoji='😘', codepoints=(128536,), name='համբույր ուղարկող դեմք', slug='համբույր_ուղարկող_դեմք', annotations=frozenset({'դեմք', 'սիրտ', 'համբուրել'})), EmojiAnnotations(emoji='😗', codepoints=(128535,), name='համբուրող դեմք', slug='համբուրող_դեմք', annotations=frozenset({'դեմք', 'համբույր'})), EmojiAnnotations(emoji='😙', codepoints=(128537,), name='համբուրող դեմք ժպտացող աչքերով', slug='համբուրող_դեմք_ժպտացող_աչքերով', annotations=frozenset({'աչք', 'դեմք', 'համբուրել', 'ժպտալ'})), EmojiAnnotations(emoji='😚', codepoints=(128538,), name='համբուրող դեմք փակ աչքերով', slug='համբուրող_դեմք_փակ_աչքերով', annotations=frozenset({'աչք', 'դեմք', 'փակ', 'համբուրել'})), EmojiAnnotations(emoji='☺', codepoints=(9786,), name='ժպտացող դեմք', slug='ժպտացող_դեմք', annotations=frozenset({'դեմք', 'ժպտալ', 'անկաշկանդ'})), EmojiAnnotations(emoji='\U0001f642', codepoints=(128578,), name='թեթևակի ժպտացող դեմք', slug='թեթևակի_ժպտացող_դեմք', annotations=frozenset({'դեմք', 'ժպտալ'})), EmojiAnnotations(emoji='\U0001f917', codepoints=(129303,), name='գրկող դեմք', slug='գրկող_դեմք', annotations=frozenset({'գրկախառնում', 'դեմք', 'գրկախառնվել'})), EmojiAnnotations(emoji='😇', codepoints=(128519,), name='ժպտացող դեմք լուսապսակով', slug='ժպտացող_դեմք_լուսապսակով', annotations=frozenset({'անմեղ', 'լուսապսակ', 'ժպտալ', 'հրեշտակ', 'դեմք', 'հեքիաթ', 'ֆանտազիա'})), EmojiAnnotations(emoji='\U0001f914', codepoints=(129300,), name='մտածող դեմք', slug='մտածող_դեմք', annotations=frozenset({'մտածող', 'դեմք'})), EmojiAnnotations(emoji='😐', codepoints=(128528,), name='չեզոք դեմք', slug='չեզոք_դեմք', annotations=frozenset({'դեմք', 'չեզոք', 'անվրդով'})), EmojiAnnotations(emoji='😑', codepoints=(128529,), name='անհույզ դեմք', slug='անհույզ_դեմք', annotations=frozenset({'դեմք', 'ոչինչ չարտահայտող', 'անարտահայտիչ', 'առանց էմոցիաների'})), EmojiAnnotations(emoji='😶', codepoints=(128566,), name='առանց բերանի դեմք', slug='առանց_բերանի_դեմք', annotations=frozenset({'դեմք', 'լուռ', 'բերան', 'հանգիստ'})), EmojiAnnotations(emoji='\U0001f644', codepoints=(128580,), name='պտտվող աչքերով դեմք', slug='պտտվող_աչքերով_դեմք', annotations=frozenset({'դեմք', 'աչքեր', 'պտտվող'})), EmojiAnnotations(emoji='😏', codepoints=(128527,), name='կեղծ ժպտացող դեմք', slug='կեղծ_ժպտացող_դեմք', annotations=frozenset({'դեմք', 'կեղծ ժպտալ'})), EmojiAnnotations(emoji='😣', codepoints=(128547,), name='համառող դեմք', slug='համառող_դեմք', annotations=frozenset({'դեմք', 'համառել'})), EmojiAnnotations(emoji='😥', codepoints=(128549,), name='հիասթափված; բայց թեթևացած դեմք', slug='հիասթափված;_բայց_թեթևացած_դեմք', annotations=frozenset({'դեմք', 'թեթևացած', 'հիասթափված'})), EmojiAnnotations(emoji='😮', codepoints=(128558,), name='բաց բերանով դեմք', slug='բաց_բերանով_դեմք', annotations=frozenset({'բաց', 'դեմք', 'բերան', 'համակրանք'})), EmojiAnnotations(emoji='\U0001f910', codepoints=(129296,), name='ճարմանդավոր բերանով դեմք', slug='ճարմանդավոր_բերանով_դեմք', annotations=frozenset({'դեմք', 'բերան', 'ճարմանդ'})), EmojiAnnotations(emoji='😯', codepoints=(128559,), name='սաստված դեմք', slug='սաստված_դեմք', annotations=frozenset({'զարմացած', 'դեմք', 'սաստված', 'ապշած'})), EmojiAnnotations(emoji='😪', codepoints=(128554,), name='քնատ դեմք', slug='քնատ_դեմք', annotations=frozenset({'քնել', 'դեմք'})), EmojiAnnotations(emoji='😫', codepoints=(128555,), name='հոգնած դեմք', slug='հոգնած_դեմք', annotations=frozenset({'դեմք', 'հոգնած'})), EmojiAnnotations(emoji='😴', codepoints=(128564,), name='քնած դեմք', slug='քնած_դեմք', annotations=frozenset({'քնել', 'դեմք', 'խռռ'})), EmojiAnnotations(emoji='😌', codepoints=(128524,), name='թեթևացած դեմք', slug='թեթևացած_դեմք', annotations=frozenset({'դեմք', 'թեթևացած'})), EmojiAnnotations(emoji='\U0001f913', codepoints=(129299,), name='գերազանցիկի դեմք', slug='գերազանցիկի_դեմք', annotations=frozenset({'դեմք', 'ցնդած', 'հիմար'})), EmojiAnnotations(emoji='😛', codepoints=(128539,), name='լեզու հանած դեմք', slug='լեզու_հանած_դեմք', annotations=frozenset({'դեմք', 'լեզու'})), EmojiAnnotations(emoji='😜', codepoints=(128540,), name='լեզու հանած և աչքով անող դեմք', slug='լեզու_հանած_և_աչքով_անող_դեմք', annotations=frozenset({'աչք', 'դեմք', 'կատակել', 'լեզու', 'աչքով անել'})), EmojiAnnotations(emoji='😝', codepoints=(128541,), name='լեզու հանած և ամուր փակած աչքերով դեմք', slug='լեզու_հանած_և_ամուր_փակած_աչքերով_դեմք', annotations=frozenset({'աչք', 'դեմք', 'սարսափելի', 'համ', 'լեզու'})), EmojiAnnotations(emoji='☹', codepoints=(9785,), name='խոժոռված դեմք', slug='խոժոռված_դեմք', annotations=frozenset({'դեմք', 'խոժոռված'})), EmojiAnnotations(emoji='\U0001f641', codepoints=(128577,), name='թեթևակի խոժոռված դեմք', slug='թեթևակի_խոժոռված_դեմք', annotations=frozenset({'դեմք', 'խոժոռված'})), EmojiAnnotations(emoji='😒', codepoints=(128530,), name='անտրամադիր դեմք', slug='անտրամադիր_դեմք', annotations=frozenset({'դեմք', 'անտրամադիր', 'դժբախտ'})), EmojiAnnotations(emoji='😓', codepoints=(128531,), name='սառը քրտինքով դեմք', slug='սառը_քրտինքով_դեմք', annotations=frozenset({'սառը', 'դեմք', 'քրտինք'})), EmojiAnnotations(emoji='😔', codepoints=(128532,), name='մտածկոտ դեմք', slug='մտածկոտ_դեմք', annotations=frozenset({'դեմք', 'մռայլված', 'մտածկոտ'})), EmojiAnnotations(emoji='😕', codepoints=(128533,), name='շփոթված դեմք', slug='շփոթված_դեմք', annotations=frozenset({'դեմք', 'շփոթված'})), EmojiAnnotations(emoji='😖', codepoints=(128534,), name='ցնցված դեմք', slug='ցնցված_դեմք', annotations=frozenset({'դեմք', 'ցնցված'})), EmojiAnnotations(emoji='\U0001f643', codepoints=(128579,), name='գլխնիվայր դեմք', slug='գլխնիվայր_դեմք', annotations=frozenset({'դեմք', 'գլխնիվայր'})), EmojiAnnotations(emoji='😷', codepoints=(128567,), name='բժշկական դիմակով դեմք', slug='բժշկական_դիմակով_դեմք', annotations=frozenset({'հիվանդ', 'բժիշկ', 'սառը', 'դեմք', 'բժշկական', 'դիմակ'})), EmojiAnnotations(emoji='\U0001f912', codepoints=(129298,), name='ջերմաչափով դեմք', slug='ջերմաչափով_դեմք', annotations=frozenset({'դեմք', 'հիվանդ', 'ջերմաչափ'})), EmojiAnnotations(emoji='\U0001f915', codepoints=(129301,), name='գլխակապով դեմք', slug='գլխակապով_դեմք', annotations=frozenset({'դեմք', 'վիրակապ', 'վնասվածք'})), EmojiAnnotations(emoji='\U0001f911', codepoints=(129297,), name='թղթադրամը բերանին դեմք', slug='թղթադրամը_բերանին_դեմք', annotations=frozenset({'դեմք', 'փող', 'բերան'})), EmojiAnnotations(emoji='😲', codepoints=(128562,), name='ապշահար դեմք', slug='ապշահար_դեմք', annotations=frozenset({'դեմք', 'ցնցված', 'ապշահար', 'ամբողջովին'})), EmojiAnnotations(emoji='😞', codepoints=(128542,), name='հիասթափված դեմք', 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EmojiAnnotations(emoji='🈂', codepoints=(127490,), name='կատականա սա', slug='կատականա_սա', annotations=frozenset({'ճապոնական', 'ճապոներեն'})), EmojiAnnotations(emoji='🈷', codepoints=(127543,), name='լուսին գաղափարագիր', slug='լուսին_գաղափարագիր', annotations=frozenset({'ճապոնական', 'ճապոներեն'})), EmojiAnnotations(emoji='🈶', codepoints=(127542,), name='գոյ գաղափարագիր', slug='գոյ_գաղափարագիր', annotations=frozenset({'ճապոնական', 'ճապոներեն'})), EmojiAnnotations(emoji='🈯', codepoints=(127535,), name='մատ գաղափարագիր', slug='մատ_գաղափարագիր', annotations=frozenset({'ճապոնական', 'ճապոներեն'})), EmojiAnnotations(emoji='🉐', codepoints=(127568,), name='առավելություն գաղափարագիր շրջանակի մեջ', slug='առավելություն_գաղափարագիր_շրջանակի_մեջ', annotations=frozenset({'ճապոնական', 'ճապոներեն'})), EmojiAnnotations(emoji='🈹', codepoints=(127545,), name='բաժանել գաղափարագիր', slug='բաժանել_գաղափարագիր', annotations=frozenset({'ճապոնական', 'ճապոներեն'})), EmojiAnnotations(emoji='🈚', codepoints=(127514,), name='ժխտում գաղափարագիր', slug='ժխտում_գաղափարագիր', annotations=frozenset({'ճապոնական', 'ճապոներեն'})), EmojiAnnotations(emoji='🈲', codepoints=(127538,), name='արգելել գաղափարագիր', slug='արգելել_գաղափարագիր', annotations=frozenset({'ճապոնական', 'ճապոներեն'})), EmojiAnnotations(emoji='🉑', codepoints=(127569,), name='ընդունել գաղափարագիր շրջանակի մեջ', slug='ընդունել_գաղափարագիր_շրջանակի_մեջ', annotations=frozenset({'չինարեն', 'չինական'})), EmojiAnnotations(emoji='🈸', codepoints=(127544,), name='կիրառել գաղափարագիր', slug='կիրառել_գաղափարագիր', annotations=frozenset({'չինարեն', 'չինական'})), EmojiAnnotations(emoji='🈴', codepoints=(127540,), name='միասին գաղափարագիր', slug='միասին_գաղափարագիր', annotations=frozenset({'չինարեն', 'չինական'})), EmojiAnnotations(emoji='🈳', codepoints=(127539,), name='դատարկ գաղափարագիր', slug='դատարկ_գաղափարագիր', annotations=frozenset({'չինարեն', 'չինական'})), EmojiAnnotations(emoji='㊗', codepoints=(12951,), name='շնորհավորել գաղափարագիր շրջանակի մեջ', slug='շնորհավորել_գաղափարագիր_շրջանակի_մեջ', annotations=frozenset({'շնորհավորանք', 'չինարեն', 'գաղափարագիր', 'չինական'})), EmojiAnnotations(emoji='㊙', codepoints=(12953,), name='գաղտնի գաղափարագիր շրջանակի մեջ', slug='գաղտնի_գաղափարագիր_շրջանակի__մեջ', annotations=frozenset({'գաղափարագիր', 'չինարեն', 'գաղտնիք', 'չինական'})), EmojiAnnotations(emoji='🈺', codepoints=(127546,), name='աշխատում է գաղափարագիր', slug='աշխատում_է_գաղափարագիր', annotations=frozenset({'չինարեն', 'չինական'})), EmojiAnnotations(emoji='🈵', codepoints=(127541,), name='լիություն գաղափարագիր', slug='լիություն_գաղափարագիր', annotations=frozenset({'չինարեն', 'չինական'})), EmojiAnnotations(emoji='▪', codepoints=(9642,), name='սև փոքր քառակուսի', slug='սև_փոքր_քառակուսի', annotations=frozenset({'երկրաչափական', 'քառակուսի'})), EmojiAnnotations(emoji='▫', codepoints=(9643,), name='սպիտակ փոքր քառակուսի', slug='սպիտակ_փոքր_քառակուսի', annotations=frozenset({'երկրաչափական', 'քառակուսի'})), EmojiAnnotations(emoji='◻', codepoints=(9723,), name='սպիտակ միջին չափի քառակուսի', slug='սպիտակ_միջին_չափի_քառակուսի', annotations=frozenset({'երկրաչափական', 'քառակուսի'})), EmojiAnnotations(emoji='◼', codepoints=(9724,), name='սև միջին չափի քառակուսի', slug='սև_միջին_չափի_քառակուսի', annotations=frozenset({'երկրաչափական', 'քառակուսի'})), EmojiAnnotations(emoji='◽', codepoints=(9725,), name='սպիտակ միջին-փոքր քառակուսի', slug='սպիտակ_միջին_փոքր_քառակուսի', annotations=frozenset({'երկրաչափական', 'քառակուսի'})), EmojiAnnotations(emoji='◾', codepoints=(9726,), name='սև միջին-փոքր քառակուսի', slug='սև_միջին_փոքր_քառակուսի', annotations=frozenset({'երկրաչափական', 'քառակուսի'})), EmojiAnnotations(emoji='⬛', codepoints=(11035,), name='սև մեծ քառակուսի', slug='սև_մեծ_քառակուսի', annotations=frozenset({'երկրաչափական', 'քառակուսի'})), EmojiAnnotations(emoji='⬜', codepoints=(11036,), name='սպիտակ մեծ քառակուսի', slug='սպիտակ_մեծ_քառակուսի', annotations=frozenset({'երկրաչափական', 'քառակուսի'})), EmojiAnnotations(emoji='🔶', codepoints=(128310,), name='նարնջագույն մեծ շեղանկյուն', slug='նարնջագույն_մեծ_շեղանկյուն', annotations=frozenset({'երկրաչափական', 'շեղանկյուն', 'նարնջագույն'})), EmojiAnnotations(emoji='🔷', codepoints=(128311,), name='կապույտ մեծ շեղանկյուն', slug='կապույտ_մեծ_շեղանկյուն', annotations=frozenset({'կապույտ', 'երկրաչափական', 'շեղանկյուն'})), EmojiAnnotations(emoji='🔸', codepoints=(128312,), name='նարնջագույն փոքր շեղանկյուն', slug='նարնջագույն_փոքր_շեղանկյուն', annotations=frozenset({'երկրաչափական', 'շեղանկյուն', 'նարնջագույն'})), EmojiAnnotations(emoji='🔹', codepoints=(128313,), name='կապույտ փոքր շեղանկյուն', slug='կապույտ_փոքր_շեղանկյուն', annotations=frozenset({'կապույտ', 'երկրաչափական', 'շեղանկյուն'})), EmojiAnnotations(emoji='🔺', codepoints=(128314,), name='կարմիր եռանկյունի ուղղված վերև', slug='կարմիր_եռանկյունի_ուղղված_վերև', annotations=frozenset({'երկրաչափական', 'կարմիր'})), EmojiAnnotations(emoji='🔻', codepoints=(128315,), name='կարմիր եռանկյունի ուղղված ներքև', slug='կարմիր_եռանկյունի_ուղղված_ներքև', annotations=frozenset({'ներքև', 'երկրաչափական', 'կարմիր'})), EmojiAnnotations(emoji='💠', codepoints=(128160,), name='կետով շեղանկյուն', slug='կետով_շեղանկյուն', annotations=frozenset({'երկրաչափական', 'կոմիքս', 'շեղանկյուն', 'ներսում'})), EmojiAnnotations(emoji='🔘', codepoints=(128280,), name='կետակոճակ', slug='կետակոճակ', annotations=frozenset({'կետ', 'կոճակ', 'երկրաչափական', 'ռադիո'})), EmojiAnnotations(emoji='🔲', codepoints=(128306,), name='սև քառակուսի կոճակ', slug='սև_քառակուսի_կոճակ', annotations=frozenset({'կոճակ', 'երկրաչափական', 'քառակուսի'})), EmojiAnnotations(emoji='🔳', codepoints=(128307,), name='սպիտակ քառակուսի կոճակ', slug='սպիտակ_քառակուսի_կոճակ', annotations=frozenset({'կոճակ', 'երկրաչափական', 'ուրվագծված', 'քառակուսի'})), EmojiAnnotations(emoji='⚪', codepoints=(9898,), name='սպիտակ շրջանակ', slug='սպիտակ_շրջանակ', annotations=frozenset({'երկրաչափական', 'շրջան'})), EmojiAnnotations(emoji='⚫', codepoints=(9899,), name='սև շրջանակ', slug='սև_շրջանակ', annotations=frozenset({'երկրաչափական', 'շրջան'})), EmojiAnnotations(emoji='🔴', codepoints=(128308,), name='կարմիր շրջանակ', slug='կարմիր_շրջանակ', annotations=frozenset({'երկրաչափական', 'կարմիր', 'շրջան'})), EmojiAnnotations(emoji='🔵', codepoints=(128309,), name='կապույտ շրջանակ', slug='կապույտ_շրջանակ', annotations=frozenset({'կապույտ', 'երկրաչափական', 'շրջան'})),]

154.573805

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from emojitations.emojitypes import EmojiAnnotations emoji = [ EmojiAnnotations(emoji='😀', codepoints=(128512,), name='ծիծաղող դեմք', slug='ծիծաղող_դեմք', annotations=frozenset({'դեմք', 'քմծիծաղել'})), EmojiAnnotations(emoji='😁', codepoints=(128513,), name='ծիծաղող դեմք ժպտացող աչքերով', slug='ծիծաղող_դեմք_ժպտացող_աչքերով', annotations=frozenset({'աչք', 'դեմք', 'ժպտալ', 'քմծիծաղել'})), EmojiAnnotations(emoji='😂', codepoints=(128514,), name='դեմք ուրախության արցունքներով', slug='դեմք_ուրախության_արցունքներով', annotations=frozenset({'ուրախություն', 'դեմք', 'ծիծաղել', 'արցունք'})), EmojiAnnotations(emoji='😃', codepoints=(128515,), name='ժպտացող դեմք բաց բերանով', slug='ժպտացող_դեմք_բաց_բերանով', annotations=frozenset({'բաց', 'դեմք', 'ժպտալ', 'բերան'})), EmojiAnnotations(emoji='😄', codepoints=(128516,), name='ժպտացող դեմք բաց բերանով և ժպտացող աչքերով', slug='ժպտացող_դեմք_բաց_բերանով_և_ժպտացող_աչքերով', annotations=frozenset({'բաց', 'աչք', 'դեմք', 'ժպտալ', 'բերան'})), EmojiAnnotations(emoji='😅', codepoints=(128517,), name='ժպտացող դեմք բաց բերանով և սառը քրտինքով', slug='ժպտացող_դեմք_բաց_բերանով_և_սառը_քրտինքով', annotations=frozenset({'բաց', 'սառը', 'դեմք', 'ժպտալ', 'քրտինք'})), EmojiAnnotations(emoji='😆', codepoints=(128518,), name='ժպտացող դեմք բաց բերանով և ամուր փակած աչքերով', slug='ժպտացող_դեմք_բաց_բերանով_և_ամուր_փակած_աչքերով', annotations=frozenset({'ժպտալ', 'գոհ', 'ծիծաղել', 'դեմք', 'բաց', 'բերան'})), EmojiAnnotations(emoji='😉', codepoints=(128521,), name='աչքով անող դեմք', slug='աչքով_անող_դեմք', annotations=frozenset({'դեմք', 'աչքով անել'})), EmojiAnnotations(emoji='😊', codepoints=(128522,), name='ժպտացող դեմք ժպտացող աչքերով', slug='ժպտացող_դեմք_ժպտացող_աչքերով', annotations=frozenset({'աչք', 'դեմք', 'ժպտալ', 'շիկնել'})), EmojiAnnotations(emoji='😋', codepoints=(128523,), name='համեղ ուտելիք վայելող դեմք', slug='համեղ_ուտելիք_վայելող_դեմք', annotations=frozenset({'դեմք', 'վեյելել', 'ժպտալ', 'համեղ', 'նյամ'})), EmojiAnnotations(emoji='😎', codepoints=(128526,), name='ժպտացող դեմք արևային ակնոցով', slug='ժպտացող_դեմք_արևային_ակնոցով', annotations=frozenset({'աչք', 'ակնոց', 'զիլ', 'ժպտալ', 'պայծառ', 'արևային ակնոց', 'դեմք', 'եղանակ', 'արև'})), EmojiAnnotations(emoji='😍', codepoints=(128525,), name='ժպտացող դեմք սրտաձև աչքերով', slug='ժպտացող_դեմք_սրտաձև_աչքերով', annotations=frozenset({'աչք', 'դեմք', 'սիրտ', 'ժպտալ', 'սեր'})), EmojiAnnotations(emoji='😘', codepoints=(128536,), name='համբույր ուղարկող դեմք', slug='համբույր_ուղարկող_դեմք', annotations=frozenset({'դեմք', 'սիրտ', 'համբուրել'})), EmojiAnnotations(emoji='😗', codepoints=(128535,), name='համբուրող դեմք', slug='համբուրող_դեմք', annotations=frozenset({'դեմք', 'համբույր'})), EmojiAnnotations(emoji='😙', codepoints=(128537,), name='համբուրող դեմք ժպտացող աչքերով', slug='համբուրող_դեմք_ժպտացող_աչքերով', annotations=frozenset({'աչք', 'դեմք', 'համբուրել', 'ժպտալ'})), EmojiAnnotations(emoji='😚', codepoints=(128538,), name='համբուրող դեմք փակ 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spec/API_specification/array_api/elementwise_functions.py

oleksandr-pavlyk/array-api

34aa9251bec8e53d8e7f4330f0b2b6221b3f6dcb

[ "MIT" ]

null

spec/API_specification/array_api/elementwise_functions.py

oleksandr-pavlyk/array-api

34aa9251bec8e53d8e7f4330f0b2b6221b3f6dcb

[ "MIT" ]

null

spec/API_specification/array_api/elementwise_functions.py

oleksandr-pavlyk/array-api

34aa9251bec8e53d8e7f4330f0b2b6221b3f6dcb

[ "MIT" ]

null

from ._types import array def abs(x: array, /) -> array: """ Calculates the absolute value for each element ``x_i`` of the input array ``x`` (i.e., the element-wise result has the same magnitude as the respective element in ``x`` but has positive sign). .. note:: For signed integer data types, the absolute value of the minimum representable integer is implementation-dependent. **Special Cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``-0``, the result is ``+0``. - If ``x_i`` is ``-infinity``, the result is ``+infinity``. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing the absolute value of each element in ``x``. The returned array must have the same data type as ``x``. """ def acos(x: array, /) -> array: """ Calculates an implementation-dependent approximation of the principal value of the inverse cosine, having domain ``[-1, +1]`` and codomain ``[+0, +π]``, for each element ``x_i`` of the input array ``x``. Each element-wise result is expressed in radians. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is greater than ``1``, the result is ``NaN``. - If ``x_i`` is less than ``-1``, the result is ``NaN``. - If ``x_i`` is ``1``, the result is ``+0``. Parameters ---------- x: array input array. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the inverse cosine of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def acosh(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the inverse hyperbolic cosine, having domain ``[+1, +infinity]`` and codomain ``[+0, +infinity]``, for each element ``x_i`` of the input array ``x``. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is less than ``1``, the result is ``NaN``. - If ``x_i`` is ``1``, the result is ``+0``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. Parameters ---------- x: array input array whose elements each represent the area of a hyperbolic sector. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the inverse hyperbolic cosine of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def add(x1: array, x2: array, /) -> array: """ Calculates the sum for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. **Special cases** For floating-point operands, - If either ``x1_i`` or ``x2_i`` is ``NaN``, the result is ``NaN``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is ``-infinity``, the result is ``NaN``. - If ``x1_i`` is ``-infinity`` and ``x2_i`` is ``+infinity``, the result is ``NaN``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is ``+infinity``, the result is ``+infinity``. - If ``x1_i`` is ``-infinity`` and ``x2_i`` is ``-infinity``, the result is ``-infinity``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is a finite number, the result is ``+infinity``. - If ``x1_i`` is ``-infinity`` and ``x2_i`` is a finite number, the result is ``-infinity``. - If ``x1_i`` is a finite number and ``x2_i`` is ``+infinity``, the result is ``+infinity``. - If ``x1_i`` is a finite number and ``x2_i`` is ``-infinity``, the result is ``-infinity``. - If ``x1_i`` is ``-0`` and ``x2_i`` is ``-0``, the result is ``-0``. - If ``x1_i`` is ``-0`` and ``x2_i`` is ``+0``, the result is ``+0``. - If ``x1_i`` is ``+0`` and ``x2_i`` is ``-0``, the result is ``+0``. - If ``x1_i`` is ``+0`` and ``x2_i`` is ``+0``, the result is ``+0``. - If ``x1_i`` is either ``+0`` or ``-0`` and ``x2_i`` is a nonzero finite number, the result is ``x2_i``. - If ``x1_i`` is a nonzero finite number and ``x2_i`` is either ``+0`` or ``-0``, the result is ``x1_i``. - If ``x1_i`` is a nonzero finite number and ``x2_i`` is ``-x1_i``, the result is ``+0``. - In the remaining cases, when neither ``infinity``, ``+0``, ``-0``, nor a ``NaN`` is involved, and the operands have the same mathematical sign or have different magnitudes, the sum must be computed and rounded to the nearest representable value according to IEEE 754-2019 and a supported round mode. If the magnitude is too large to represent, the operation overflows and the result is an `infinity` of appropriate mathematical sign. .. note:: Floating-point addition is a commutative operation, but not always associative. Parameters ---------- x1: array first input array. Should have a real-valued data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise sums. The returned array must have a data type determined by :ref:`type-promotion`. """ def asin(x: array, /) -> array: """ Calculates an implementation-dependent approximation of the principal value of the inverse sine, having domain ``[-1, +1]`` and codomain ``[-π/2, +π/2]`` for each element ``x_i`` of the input array ``x``. Each element-wise result is expressed in radians. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is greater than ``1``, the result is ``NaN``. - If ``x_i`` is less than ``-1``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. Parameters ---------- x: array input array. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the inverse sine of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def asinh(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the inverse hyperbolic sine, having domain ``[-infinity, +infinity]`` and codomain ``[-infinity, +infinity]``, for each element ``x_i`` in the input array ``x``. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. - If ``x_i`` is ``-infinity``, the result is ``-infinity``. Parameters ---------- x: array input array whose elements each represent the area of a hyperbolic sector. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the inverse hyperbolic sine of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def atan(x: array, /) -> array: """ Calculates an implementation-dependent approximation of the principal value of the inverse tangent, having domain ``[-infinity, +infinity]`` and codomain ``[-π/2, +π/2]``, for each element ``x_i`` of the input array ``x``. Each element-wise result is expressed in radians. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``+infinity``, the result is an implementation-dependent approximation to ``+π/2``. - If ``x_i`` is ``-infinity``, the result is an implementation-dependent approximation to ``-π/2``. Parameters ---------- x: array input array. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the inverse tangent of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def atan2(x1: array, x2: array, /) -> array: """ Calculates an implementation-dependent approximation of the inverse tangent of the quotient ``x1/x2``, having domain ``[-infinity, +infinity] x [-infinity, +infinity]`` (where the ``x`` notation denotes the set of ordered pairs of elements ``(x1_i, x2_i)``) and codomain ``[-π, +π]``, for each pair of elements ``(x1_i, x2_i)`` of the input arrays ``x1`` and ``x2``, respectively. Each element-wise result is expressed in radians. The mathematical signs of ``x1_i`` and ``x2_i`` determine the quadrant of each element-wise result. The quadrant (i.e., branch) is chosen such that each element-wise result is the signed angle in radians between the ray ending at the origin and passing through the point ``(1,0)`` and the ray ending at the origin and passing through the point ``(x2_i, x1_i)``. .. note:: Note the role reversal: the "y-coordinate" is the first function parameter; the "x-coordinate" is the second function parameter. The parameter order is intentional and traditional for the two-argument inverse tangent function where the y-coordinate argument is first and the x-coordinate argument is second. By IEEE 754 convention, the inverse tangent of the quotient ``x1/x2`` is defined for ``x2_i`` equal to positive or negative zero and for either or both of ``x1_i`` and ``x2_i`` equal to positive or negative ``infinity``. **Special cases** For floating-point operands, - If either ``x1_i`` or ``x2_i`` is ``NaN``, the result is ``NaN``. - If ``x1_i`` is greater than ``0`` and ``x2_i`` is ``+0``, the result is an implementation-dependent approximation to ``+π/2``. - If ``x1_i`` is greater than ``0`` and ``x2_i`` is ``-0``, the result is an implementation-dependent approximation to ``+π/2``. - If ``x1_i`` is ``+0`` and ``x2_i`` is greater than ``0``, the result is ``+0``. - If ``x1_i`` is ``+0`` and ``x2_i`` is ``+0``, the result is ``+0``. - If ``x1_i`` is ``+0`` and ``x2_i`` is ``-0``, the result is an implementation-dependent approximation to ``+π``. - If ``x1_i`` is ``+0`` and ``x2_i`` is less than ``0``, the result is an implementation-dependent approximation to ``+π``. - If ``x1_i`` is ``-0`` and ``x2_i`` is greater than ``0``, the result is ``-0``. - If ``x1_i`` is ``-0`` and ``x2_i`` is ``+0``, the result is ``-0``. - If ``x1_i`` is ``-0`` and ``x2_i`` is ``-0``, the result is an implementation-dependent approximation to ``-π``. - If ``x1_i`` is ``-0`` and ``x2_i`` is less than ``0``, the result is an implementation-dependent approximation to ``-π``. - If ``x1_i`` is less than ``0`` and ``x2_i`` is ``+0``, the result is an implementation-dependent approximation to ``-π/2``. - If ``x1_i`` is less than ``0`` and ``x2_i`` is ``-0``, the result is an implementation-dependent approximation to ``-π/2``. - If ``x1_i`` is greater than ``0``, ``x1_i`` is a finite number, and ``x2_i`` is ``+infinity``, the result is ``+0``. - If ``x1_i`` is greater than ``0``, ``x1_i`` is a finite number, and ``x2_i`` is ``-infinity``, the result is an implementation-dependent approximation to ``+π``. - If ``x1_i`` is less than ``0``, ``x1_i`` is a finite number, and ``x2_i`` is ``+infinity``, the result is ``-0``. - If ``x1_i`` is less than ``0``, ``x1_i`` is a finite number, and ``x2_i`` is ``-infinity``, the result is an implementation-dependent approximation to ``-π``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is finite, the result is an implementation-dependent approximation to ``+π/2``. - If ``x1_i`` is ``-infinity`` and ``x2_i`` is finite, the result is an implementation-dependent approximation to ``-π/2``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is ``+infinity``, the result is an implementation-dependent approximation to ``+π/4``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is ``-infinity``, the result is an implementation-dependent approximation to ``+3π/4``. - If ``x1_i`` is ``-infinity`` and ``x2_i`` is ``+infinity``, the result is an implementation-dependent approximation to ``-π/4``. - If ``x1_i`` is ``-infinity`` and ``x2_i`` is ``-infinity``, the result is an implementation-dependent approximation to ``-3π/4``. Parameters ---------- x1: array input array corresponding to the y-coordinates. Should have a real-valued floating-point data type. x2: array input array corresponding to the x-coordinates. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued floating-point data type. Returns ------- out: array an array containing the inverse tangent of the quotient ``x1/x2``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def atanh(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the inverse hyperbolic tangent, having domain ``[-1, +1]`` and codomain ``[-infinity, +infinity]``, for each element ``x_i`` of the input array ``x``. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is less than ``-1``, the result is ``NaN``. - If ``x_i`` is greater than ``1``, the result is ``NaN``. - If ``x_i`` is ``-1``, the result is ``-infinity``. - If ``x_i`` is ``+1``, the result is ``+infinity``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. Parameters ---------- x: array input array whose elements each represent the area of a hyperbolic sector. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the inverse hyperbolic tangent of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def bitwise_and(x1: array, x2: array, /) -> array: """ Computes the bitwise AND of the underlying binary representation of each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. Parameters ---------- x1: array first input array. Should have an integer or boolean data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have an integer or boolean data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type determined by :ref:`type-promotion`. """ def bitwise_left_shift(x1: array, x2: array, /) -> array: """ Shifts the bits of each element ``x1_i`` of the input array ``x1`` to the left by appending ``x2_i`` (i.e., the respective element in the input array ``x2``) zeros to the right of ``x1_i``. Parameters ---------- x1: array first input array. Should have an integer data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have an integer data type. Each element must be greater than or equal to ``0``. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type determined by :ref:`type-promotion`. """ def bitwise_invert(x: array, /) -> array: """ Inverts (flips) each bit for each element ``x_i`` of the input array ``x``. Parameters ---------- x: array input array. Should have an integer or boolean data type. Returns ------- out: array an array containing the element-wise results. The returned array must have the same data type as ``x``. """ def bitwise_or(x1: array, x2: array, /) -> array: """ Computes the bitwise OR of the underlying binary representation of each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. Parameters ---------- x1: array first input array. Should have an integer or boolean data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have an integer or boolean data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type determined by :ref:`type-promotion`. """ def bitwise_right_shift(x1: array, x2: array, /) -> array: """ Shifts the bits of each element ``x1_i`` of the input array ``x1`` to the right according to the respective element ``x2_i`` of the input array ``x2``. .. note:: This operation must be an arithmetic shift (i.e., sign-propagating) and thus equivalent to floor division by a power of two. Parameters ---------- x1: array first input array. Should have an integer data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have an integer data type. Each element must be greater than or equal to ``0``. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type determined by :ref:`type-promotion`. """ def bitwise_xor(x1: array, x2: array, /) -> array: """ Computes the bitwise XOR of the underlying binary representation of each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. Parameters ---------- x1: array first input array. Should have an integer or boolean data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have an integer or boolean data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type determined by :ref:`type-promotion`. """ def ceil(x: array, /) -> array: """ Rounds each element ``x_i`` of the input array ``x`` to the smallest (i.e., closest to ``-infinity``) integer-valued number that is not less than ``x_i``. **Special cases** - If ``x_i`` is already integer-valued, the result is ``x_i``. For floating-point operands, - If ``x_i`` is ``+infinity``, the result is ``+infinity``. - If ``x_i`` is ``-infinity``, the result is ``-infinity``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``NaN``, the result is ``NaN``. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing the rounded result for each element in ``x``. The returned array must have the same data type as ``x``. """ def cos(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the cosine, having domain ``(-infinity, +infinity)`` and codomain ``[-1, +1]``, for each element ``x_i`` of the input array ``x``. Each element ``x_i`` is assumed to be expressed in radians. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``1``. - If ``x_i`` is ``-0``, the result is ``1``. - If ``x_i`` is ``+infinity``, the result is ``NaN``. - If ``x_i`` is ``-infinity``, the result is ``NaN``. Parameters ---------- x: array input array whose elements are each expressed in radians. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the cosine of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def cosh(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the hyperbolic cosine, having domain ``[-infinity, +infinity]`` and codomain ``[-infinity, +infinity]``, for each element ``x_i`` in the input array ``x``. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``1``. - If ``x_i`` is ``-0``, the result is ``1``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. - If ``x_i`` is ``-infinity``, the result is ``+infinity``. Parameters ---------- x: array input array whose elements each represent a hyperbolic angle. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the hyperbolic cosine of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def divide(x1: array, x2: array, /) -> array: """ Calculates the division for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. .. note:: If one or both of the input arrays have integer data types, the result is implementation-dependent, as type promotion between data type "kinds" (e.g., integer versus floating-point) is unspecified. Specification-compliant libraries may choose to raise an error or return an array containing the element-wise results. If an array is returned, the array must have a real-valued floating-point data type. **Special cases** For floating-point operands, - If either ``x1_i`` or ``x2_i`` is ``NaN``, the result is ``NaN``. - If ``x1_i`` is either ``+infinity`` or ``-infinity`` and ``x2_i`` is either ``+infinity`` or ``-infinity``, the result is ``NaN``. - If ``x1_i`` is either ``+0`` or ``-0`` and ``x2_i`` is either ``+0`` or ``-0``, the result is ``NaN``. - If ``x1_i`` is ``+0`` and ``x2_i`` is greater than ``0``, the result is ``+0``. - If ``x1_i`` is ``-0`` and ``x2_i`` is greater than ``0``, the result is ``-0``. - If ``x1_i`` is ``+0`` and ``x2_i`` is less than ``0``, the result is ``-0``. - If ``x1_i`` is ``-0`` and ``x2_i`` is less than ``0``, the result is ``+0``. - If ``x1_i`` is greater than ``0`` and ``x2_i`` is ``+0``, the result is ``+infinity``. - If ``x1_i`` is greater than ``0`` and ``x2_i`` is ``-0``, the result is ``-infinity``. - If ``x1_i`` is less than ``0`` and ``x2_i`` is ``+0``, the result is ``-infinity``. - If ``x1_i`` is less than ``0`` and ``x2_i`` is ``-0``, the result is ``+infinity``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is a positive (i.e., greater than ``0``) finite number, the result is ``+infinity``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is a negative (i.e., less than ``0``) finite number, the result is ``-infinity``. - If ``x1_i`` is ``-infinity`` and ``x2_i`` is a positive (i.e., greater than ``0``) finite number, the result is ``-infinity``. - If ``x1_i`` is ``-infinity`` and ``x2_i`` is a negative (i.e., less than ``0``) finite number, the result is ``+infinity``. - If ``x1_i`` is a positive (i.e., greater than ``0``) finite number and ``x2_i`` is ``+infinity``, the result is ``+0``. - If ``x1_i`` is a positive (i.e., greater than ``0``) finite number and ``x2_i`` is ``-infinity``, the result is ``-0``. - If ``x1_i`` is a negative (i.e., less than ``0``) finite number and ``x2_i`` is ``+infinity``, the result is ``-0``. - If ``x1_i`` is a negative (i.e., less than ``0``) finite number and ``x2_i`` is ``-infinity``, the result is ``+0``. - If ``x1_i`` and ``x2_i`` have the same mathematical sign and are both nonzero finite numbers, the result has a positive mathematical sign. - If ``x1_i`` and ``x2_i`` have different mathematical signs and are both nonzero finite numbers, the result has a negative mathematical sign. - In the remaining cases, where neither ``-infinity``, ``+0``, ``-0``, nor ``NaN`` is involved, the quotient must be computed and rounded to the nearest representable value according to IEEE 754-2019 and a supported rounding mode. If the magnitude is too large to represent, the operation overflows and the result is an ``infinity`` of appropriate mathematical sign. If the magnitude is too small to represent, the operation underflows and the result is a zero of appropriate mathematical sign. Parameters ---------- x1: array dividend input array. Should have a real-valued data type. x2: array divisor input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def equal(x1: array, x2: array, /) -> array: """ Computes the truth value of ``x1_i == x2_i`` for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. Parameters ---------- x1: array first input array. May have any data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). May have any data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type of ``bool``. """ def exp(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the exponential function, having domain ``[-infinity, +infinity]`` and codomain ``[+0, +infinity]``, for each element ``x_i`` of the input array ``x`` (``e`` raised to the power of ``x_i``, where ``e`` is the base of the natural logarithm). **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``1``. - If ``x_i`` is ``-0``, the result is ``1``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. - If ``x_i`` is ``-infinity``, the result is ``+0``. Parameters ---------- x: array input array. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the evaluated exponential function result for each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def expm1(x: array, /) -> array: """ Calculates an implementation-dependent approximation to ``exp(x)-1``, having domain ``[-infinity, +infinity]`` and codomain ``[-1, +infinity]``, for each element ``x_i`` of the input array ``x``. .. note:: The purpose of this function is to calculate ``exp(x)-1.0`` more accurately when `x` is close to zero. Accordingly, conforming implementations should avoid implementing this function as simply ``exp(x)-1.0``. See FDLIBM, or some other IEEE 754-2019 compliant mathematical library, for a potential reference implementation. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. - If ``x_i`` is ``-infinity``, the result is ``-1``. Parameters ---------- x: array input array. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the evaluated result for each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def floor(x: array, /) -> array: """ Rounds each element ``x_i`` of the input array ``x`` to the greatest (i.e., closest to ``+infinity``) integer-valued number that is not greater than ``x_i``. **Special cases** - If ``x_i`` is already integer-valued, the result is ``x_i``. For floating-point operands, - If ``x_i`` is ``+infinity``, the result is ``+infinity``. - If ``x_i`` is ``-infinity``, the result is ``-infinity``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``NaN``, the result is ``NaN``. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing the rounded result for each element in ``x``. The returned array must have the same data type as ``x``. """ def floor_divide(x1: array, x2: array, /) -> array: """ Rounds the result of dividing each element ``x1_i`` of the input array ``x1`` by the respective element ``x2_i`` of the input array ``x2`` to the greatest (i.e., closest to `+infinity`) integer-value number that is not greater than the division result. .. note:: For input arrays which promote to an integer data type, the result of division by zero is unspecified and thus implementation-defined. **Special cases** .. note:: Floor division was introduced in Python via `PEP 238 <https://www.python.org/dev/peps/pep-0238/>`_ with the goal to disambiguate "true division" (i.e., computing an approximation to the mathematical operation of division) from "floor division" (i.e., rounding the result of division toward negative infinity). The former was computed when one of the operands was a ``float``, while the latter was computed when both operands were ``int``\s. Overloading the ``/`` operator to support both behaviors led to subtle numerical bugs when integers are possible, but not expected. To resolve this ambiguity, ``/`` was designated for true division, and ``//`` was designated for floor division. Semantically, floor division was `defined <https://www.python.org/dev/peps/pep-0238/#semantics-of-floor-division>`_ as equivalent to ``a // b == floor(a/b)``; however, special floating-point cases were left ill-defined. Accordingly, floor division is not implemented consistently across array libraries for some of the special cases documented below. Namely, when one of the operands is ``infinity``, libraries may diverge with some choosing to strictly follow ``floor(a/b)`` and others choosing to pair ``//`` with ``%`` according to the relation ``b = a % b + b * (a // b)``. The special cases leading to divergent behavior are documented below. This specification prefers floor division to match ``floor(divide(x1, x2))`` in order to avoid surprising and unexpected results; however, array libraries may choose to more strictly follow Python behavior. For floating-point operands, - If either ``x1_i`` or ``x2_i`` is ``NaN``, the result is ``NaN``. - If ``x1_i`` is either ``+infinity`` or ``-infinity`` and ``x2_i`` is either ``+infinity`` or ``-infinity``, the result is ``NaN``. - If ``x1_i`` is either ``+0`` or ``-0`` and ``x2_i`` is either ``+0`` or ``-0``, the result is ``NaN``. - If ``x1_i`` is ``+0`` and ``x2_i`` is greater than ``0``, the result is ``+0``. - If ``x1_i`` is ``-0`` and ``x2_i`` is greater than ``0``, the result is ``-0``. - If ``x1_i`` is ``+0`` and ``x2_i`` is less than ``0``, the result is ``-0``. - If ``x1_i`` is ``-0`` and ``x2_i`` is less than ``0``, the result is ``+0``. - If ``x1_i`` is greater than ``0`` and ``x2_i`` is ``+0``, the result is ``+infinity``. - If ``x1_i`` is greater than ``0`` and ``x2_i`` is ``-0``, the result is ``-infinity``. - If ``x1_i`` is less than ``0`` and ``x2_i`` is ``+0``, the result is ``-infinity``. - If ``x1_i`` is less than ``0`` and ``x2_i`` is ``-0``, the result is ``+infinity``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is a positive (i.e., greater than ``0``) finite number, the result is ``+infinity``. (**note**: libraries may return ``NaN`` to match Python behavior.) - If ``x1_i`` is ``+infinity`` and ``x2_i`` is a negative (i.e., less than ``0``) finite number, the result is ``-infinity``. (**note**: libraries may return ``NaN`` to match Python behavior.) - If ``x1_i`` is ``-infinity`` and ``x2_i`` is a positive (i.e., greater than ``0``) finite number, the result is ``-infinity``. (**note**: libraries may return ``NaN`` to match Python behavior.) - If ``x1_i`` is ``-infinity`` and ``x2_i`` is a negative (i.e., less than ``0``) finite number, the result is ``+infinity``. (**note**: libraries may return ``NaN`` to match Python behavior.) - If ``x1_i`` is a positive (i.e., greater than ``0``) finite number and ``x2_i`` is ``+infinity``, the result is ``+0``. - If ``x1_i`` is a positive (i.e., greater than ``0``) finite number and ``x2_i`` is ``-infinity``, the result is ``-0``. (**note**: libraries may return ``-1.0`` to match Python behavior.) - If ``x1_i`` is a negative (i.e., less than ``0``) finite number and ``x2_i`` is ``+infinity``, the result is ``-0``. (**note**: libraries may return ``-1.0`` to match Python behavior.) - If ``x1_i`` is a negative (i.e., less than ``0``) finite number and ``x2_i`` is ``-infinity``, the result is ``+0``. - If ``x1_i`` and ``x2_i`` have the same mathematical sign and are both nonzero finite numbers, the result has a positive mathematical sign. - If ``x1_i`` and ``x2_i`` have different mathematical signs and are both nonzero finite numbers, the result has a negative mathematical sign. - In the remaining cases, where neither ``-infinity``, ``+0``, ``-0``, nor ``NaN`` is involved, the quotient must be computed and rounded to the greatest (i.e., closest to `+infinity`) representable integer-value number that is not greater than the division result. If the magnitude is too large to represent, the operation overflows and the result is an ``infinity`` of appropriate mathematical sign. If the magnitude is too small to represent, the operation underflows and the result is a zero of appropriate mathematical sign. Parameters ---------- x1: array dividend input array. Should have a real-valued data type. x2: array divisor input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type determined by :ref:`type-promotion`. """ def greater(x1: array, x2: array, /) -> array: """ Computes the truth value of ``x1_i > x2_i`` for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. Parameters ---------- x1: array first input array. Should have a real-valued data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type of ``bool``. """ def greater_equal(x1: array, x2: array, /) -> array: """ Computes the truth value of ``x1_i >= x2_i`` for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. Parameters ---------- x1: array first input array. Should have a real-valued data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type of ``bool``. """ def isfinite(x: array, /) -> array: """ Tests each element ``x_i`` of the input array ``x`` to determine if finite (i.e., not ``NaN`` and not equal to positive or negative infinity). Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing test results. An element ``out_i`` is ``True`` if ``x_i`` is finite and ``False`` otherwise. The returned array must have a data type of ``bool``. """ def isinf(x: array, /) -> array: """ Tests each element ``x_i`` of the input array ``x`` to determine if equal to positive or negative infinity. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing test results. An element ``out_i`` is ``True`` if ``x_i`` is either positive or negative infinity and ``False`` otherwise. The returned array must have a data type of ``bool``. """ def isnan(x: array, /) -> array: """ Tests each element ``x_i`` of the input array ``x`` to determine whether the element is ``NaN``. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing test results. An element ``out_i`` is ``True`` if ``x_i`` is ``NaN`` and ``False`` otherwise. The returned array should have a data type of ``bool``. """ def less(x1: array, x2: array, /) -> array: """ Computes the truth value of ``x1_i < x2_i`` for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. Parameters ---------- x1: array first input array. Should have a real-valued data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type of ``bool``. """ def less_equal(x1: array, x2: array, /) -> array: """ Computes the truth value of ``x1_i <= x2_i`` for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. Parameters ---------- x1: array first input array. Should have a real-valued data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type of ``bool``. """ def log(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the natural (base ``e``) logarithm, having domain ``[0, +infinity]`` and codomain ``[-infinity, +infinity]``, for each element ``x_i`` of the input array ``x``. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is less than ``0``, the result is ``NaN``. - If ``x_i`` is either ``+0`` or ``-0``, the result is ``-infinity``. - If ``x_i`` is ``1``, the result is ``+0``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. Parameters ---------- x: array input array. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the evaluated natural logarithm for each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def log1p(x: array, /) -> array: """ Calculates an implementation-dependent approximation to ``log(1+x)``, where ``log`` refers to the natural (base ``e``) logarithm, having domain ``[-1, +infinity]`` and codomain ``[-infinity, +infinity]``, for each element ``x_i`` of the input array ``x``. .. note:: The purpose of this function is to calculate ``log(1+x)`` more accurately when `x` is close to zero. Accordingly, conforming implementations should avoid implementing this function as simply ``log(1+x)``. See FDLIBM, or some other IEEE 754-2019 compliant mathematical library, for a potential reference implementation. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is less than ``-1``, the result is ``NaN``. - If ``x_i`` is ``-1``, the result is ``-infinity``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. Parameters ---------- x: array input array. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the evaluated result for each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def log2(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the base ``2`` logarithm, having domain ``[0, +infinity]`` and codomain ``[-infinity, +infinity]``, for each element ``x_i`` of the input array ``x``. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is less than ``0``, the result is ``NaN``. - If ``x_i`` is either ``+0`` or ``-0``, the result is ``-infinity``. - If ``x_i`` is ``1``, the result is ``+0``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. Parameters ---------- x: array input array. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the evaluated base ``2`` logarithm for each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def log10(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the base ``10`` logarithm, having domain ``[0, +infinity]`` and codomain ``[-infinity, +infinity]``, for each element ``x_i`` of the input array ``x``. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is less than ``0``, the result is ``NaN``. - If ``x_i`` is either ``+0`` or ``-0``, the result is ``-infinity``. - If ``x_i`` is ``1``, the result is ``+0``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. Parameters ---------- x: array input array. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the evaluated base ``10`` logarithm for each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def logaddexp(x1: array, x2: array, /) -> array: """ Calculates the logarithm of the sum of exponentiations ``log(exp(x1) + exp(x2))`` for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. **Special cases** For floating-point operands, - If either ``x1_i`` or ``x2_i`` is ``NaN``, the result is ``NaN``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is not ``NaN``, the result is ``+infinity``. - If ``x1_i`` is not ``NaN`` and ``x2_i`` is ``+infinity``, the result is ``+infinity``. Parameters ---------- x1: array first input array. Should have a real-valued floating-point data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued floating-point data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def logical_and(x1: array, x2: array, /) -> array: """ Computes the logical AND for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. .. note:: While this specification recommends that this function only accept input arrays having a boolean data type, specification-compliant array libraries may choose to accept input arrays having real-valued data types. If non-boolean data types are supported, zeros must be considered the equivalent of ``False``, while non-zeros must be considered the equivalent of ``True``. Parameters ---------- x1: array first input array. Should have a boolean data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a boolean data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type of `bool`. """ def logical_not(x: array, /) -> array: """ Computes the logical NOT for each element ``x_i`` of the input array ``x``. .. note:: While this specification recommends that this function only accept input arrays having a boolean data type, specification-compliant array libraries may choose to accept input arrays having real-valued data types. If non-boolean data types are supported, zeros must be considered the equivalent of ``False``, while non-zeros must be considered the equivalent of ``True``. Parameters ---------- x: array input array. Should have a boolean data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type of ``bool``. """ def logical_or(x1: array, x2: array, /) -> array: """ Computes the logical OR for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. .. note:: While this specification recommends that this function only accept input arrays having a boolean data type, specification-compliant array libraries may choose to accept input arrays having real-valued data types. If non-boolean data types are supported, zeros must be considered the equivalent of ``False``, while non-zeros must be considered the equivalent of ``True``. Parameters ---------- x1: array first input array. Should have a boolean data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a boolean data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type of ``bool``. """ def logical_xor(x1: array, x2: array, /) -> array: """ Computes the logical XOR for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. .. note:: While this specification recommends that this function only accept input arrays having a boolean data type, specification-compliant array libraries may choose to accept input arrays having real-valued data types. If non-boolean data types are supported, zeros must be considered the equivalent of ``False``, while non-zeros must be considered the equivalent of ``True``. Parameters ---------- x1: array first input array. Should have a boolean data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a boolean data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type of ``bool``. """ def multiply(x1: array, x2: array, /) -> array: """ Calculates the product for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. **Special cases** For floating-point operands, - If either ``x1_i`` or ``x2_i`` is ``NaN``, the result is ``NaN``. - If ``x1_i`` is either ``+infinity`` or ``-infinity`` and ``x2_i`` is either ``+0`` or ``-0``, the result is ``NaN``. - If ``x1_i`` is either ``+0`` or ``-0`` and ``x2_i`` is either ``+infinity`` or ``-infinity``, the result is ``NaN``. - If ``x1_i`` and ``x2_i`` have the same mathematical sign, the result has a positive mathematical sign, unless the result is ``NaN``. If the result is ``NaN``, the "sign" of ``NaN`` is implementation-defined. - If ``x1_i`` and ``x2_i`` have different mathematical signs, the result has a negative mathematical sign, unless the result is ``NaN``. If the result is ``NaN``, the "sign" of ``NaN`` is implementation-defined. - If ``x1_i`` is either ``+infinity`` or ``-infinity`` and ``x2_i`` is either ``+infinity`` or ``-infinity``, the result is a signed infinity with the mathematical sign determined by the rule already stated above. - If ``x1_i`` is either ``+infinity`` or ``-infinity`` and ``x2_i`` is a nonzero finite number, the result is a signed infinity with the mathematical sign determined by the rule already stated above. - If ``x1_i`` is a nonzero finite number and ``x2_i`` is either ``+infinity`` or ``-infinity``, the result is a signed infinity with the mathematical sign determined by the rule already stated above. - In the remaining cases, where neither ``infinity`` nor ``NaN`` is involved, the product must be computed and rounded to the nearest representable value according to IEEE 754-2019 and a supported rounding mode. If the magnitude is too large to represent, the result is an `infinity` of appropriate mathematical sign. If the magnitude is too small to represent, the result is a zero of appropriate mathematical sign. .. note:: Floating-point multiplication is not always associative due to finite precision. Parameters ---------- x1: array first input array. Should have a real-valued data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise products. The returned array must have a data type determined by :ref:`type-promotion`. """ def negative(x: array, /) -> array: """ Computes the numerical negative of each element ``x_i`` (i.e., ``y_i = -x_i``) of the input array ``x``. .. note:: For signed integer data types, the numerical negative of the minimum representable integer is implementation-dependent. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing the evaluated result for each element in ``x``. The returned array must have a data type determined by :ref:`type-promotion`. """ def not_equal(x1: array, x2: array, /) -> array: """ Computes the truth value of ``x1_i != x2_i`` for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. Parameters ---------- x1: array first input array. May have any data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Returns ------- out: array an array containing the element-wise results. The returned array must have a data type of ``bool``. """ def positive(x: array, /) -> array: """ Computes the numerical positive of each element ``x_i`` (i.e., ``y_i = +x_i``) of the input array ``x``. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing the evaluated result for each element in ``x``. The returned array must have the same data type as ``x``. """ def pow(x1: array, x2: array, /) -> array: """ Calculates an implementation-dependent approximation of exponentiation by raising each element ``x1_i`` (the base) of the input array ``x1`` to the power of ``x2_i`` (the exponent), where ``x2_i`` is the corresponding element of the input array ``x2``. .. note:: If both ``x1`` and ``x2`` have integer data types, the result of ``pow`` when ``x2_i`` is negative (i.e., less than zero) is unspecified and thus implementation-dependent. If ``x1`` has an integer data type and ``x2`` has a real-valued floating-point data type, behavior is implementation-dependent (type promotion between data type "kinds" (integer versus floating-point) is unspecified). **Special cases** For floating-point operands, - If ``x1_i`` is not equal to ``1`` and ``x2_i`` is ``NaN``, the result is ``NaN``. - If ``x2_i`` is ``+0``, the result is ``1``, even if ``x1_i`` is ``NaN``. - If ``x2_i`` is ``-0``, the result is ``1``, even if ``x1_i`` is ``NaN``. - If ``x1_i`` is ``NaN`` and ``x2_i`` is not equal to ``0``, the result is ``NaN``. - If ``abs(x1_i)`` is greater than ``1`` and ``x2_i`` is ``+infinity``, the result is ``+infinity``. - If ``abs(x1_i)`` is greater than ``1`` and ``x2_i`` is ``-infinity``, the result is ``+0``. - If ``abs(x1_i)`` is ``1`` and ``x2_i`` is ``+infinity``, the result is ``1``. - If ``abs(x1_i)`` is ``1`` and ``x2_i`` is ``-infinity``, the result is ``1``. - If ``x1_i`` is ``1`` and ``x2_i`` is not ``NaN``, the result is ``1``. - If ``abs(x1_i)`` is less than ``1`` and ``x2_i`` is ``+infinity``, the result is ``+0``. - If ``abs(x1_i)`` is less than ``1`` and ``x2_i`` is ``-infinity``, the result is ``+infinity``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is greater than ``0``, the result is ``+infinity``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is less than ``0``, the result is ``+0``. - If ``x1_i`` is ``-infinity``, ``x2_i`` is greater than ``0``, and ``x2_i`` is an odd integer value, the result is ``-infinity``. - If ``x1_i`` is ``-infinity``, ``x2_i`` is greater than ``0``, and ``x2_i`` is not an odd integer value, the result is ``+infinity``. - If ``x1_i`` is ``-infinity``, ``x2_i`` is less than ``0``, and ``x2_i`` is an odd integer value, the result is ``-0``. - If ``x1_i`` is ``-infinity``, ``x2_i`` is less than ``0``, and ``x2_i`` is not an odd integer value, the result is ``+0``. - If ``x1_i`` is ``+0`` and ``x2_i`` is greater than ``0``, the result is ``+0``. - If ``x1_i`` is ``+0`` and ``x2_i`` is less than ``0``, the result is ``+infinity``. - If ``x1_i`` is ``-0``, ``x2_i`` is greater than ``0``, and ``x2_i`` is an odd integer value, the result is ``-0``. - If ``x1_i`` is ``-0``, ``x2_i`` is greater than ``0``, and ``x2_i`` is not an odd integer value, the result is ``+0``. - If ``x1_i`` is ``-0``, ``x2_i`` is less than ``0``, and ``x2_i`` is an odd integer value, the result is ``-infinity``. - If ``x1_i`` is ``-0``, ``x2_i`` is less than ``0``, and ``x2_i`` is not an odd integer value, the result is ``+infinity``. - If ``x1_i`` is less than ``0``, ``x1_i`` is a finite number, ``x2_i`` is a finite number, and ``x2_i`` is not an integer value, the result is ``NaN``. Parameters ---------- x1: array first input array whose elements correspond to the exponentiation base. Should have a real-valued data type. x2: array second input array whose elements correspond to the exponentiation exponent. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise results. The returned array must have a data type determined by :ref:`type-promotion`. """ def remainder(x1: array, x2: array, /) -> array: """ Returns the remainder of division for each element ``x1_i`` of the input array ``x1`` and the respective element ``x2_i`` of the input array ``x2``. .. note:: This function is equivalent to the Python modulus operator ``x1_i % x2_i``. .. note:: For input arrays which promote to an integer data type, the result of division by zero is unspecified and thus implementation-defined. **Special cases** .. note:: In general, similar to Python's ``%`` operator, this function is **not** recommended for floating-point operands as semantics do not follow IEEE 754. That this function is specified to accept floating-point operands is primarily for reasons of backward compatibility. For floating-point operands, - If either ``x1_i`` or ``x2_i`` is ``NaN``, the result is ``NaN``. - If ``x1_i`` is either ``+infinity`` or ``-infinity`` and ``x2_i`` is either ``+infinity`` or ``-infinity``, the result is ``NaN``. - If ``x1_i`` is either ``+0`` or ``-0`` and ``x2_i`` is either ``+0`` or ``-0``, the result is ``NaN``. - If ``x1_i`` is ``+0`` and ``x2_i`` is greater than ``0``, the result is ``+0``. - If ``x1_i`` is ``-0`` and ``x2_i`` is greater than ``0``, the result is ``+0``. - If ``x1_i`` is ``+0`` and ``x2_i`` is less than ``0``, the result is ``-0``. - If ``x1_i`` is ``-0`` and ``x2_i`` is less than ``0``, the result is ``-0``. - If ``x1_i`` is greater than ``0`` and ``x2_i`` is ``+0``, the result is ``NaN``. - If ``x1_i`` is greater than ``0`` and ``x2_i`` is ``-0``, the result is ``NaN``. - If ``x1_i`` is less than ``0`` and ``x2_i`` is ``+0``, the result is ``NaN``. - If ``x1_i`` is less than ``0`` and ``x2_i`` is ``-0``, the result is ``NaN``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is a positive (i.e., greater than ``0``) finite number, the result is ``NaN``. - If ``x1_i`` is ``+infinity`` and ``x2_i`` is a negative (i.e., less than ``0``) finite number, the result is ``NaN``. - If ``x1_i`` is ``-infinity`` and ``x2_i`` is a positive (i.e., greater than ``0``) finite number, the result is ``NaN``. - If ``x1_i`` is ``-infinity`` and ``x2_i`` is a negative (i.e., less than ``0``) finite number, the result is ``NaN``. - If ``x1_i`` is a positive (i.e., greater than ``0``) finite number and ``x2_i`` is ``+infinity``, the result is ``x1_i``. (**note**: this result matches Python behavior.) - If ``x1_i`` is a positive (i.e., greater than ``0``) finite number and ``x2_i`` is ``-infinity``, the result is ``x2_i``. (**note**: this result matches Python behavior.) - If ``x1_i`` is a negative (i.e., less than ``0``) finite number and ``x2_i`` is ``+infinity``, the result is ``x2_i``. (**note**: this results matches Python behavior.) - If ``x1_i`` is a negative (i.e., less than ``0``) finite number and ``x2_i`` is ``-infinity``, the result is ``x1_i``. (**note**: this result matches Python behavior.) - In the remaining cases, the result must match that of the Python ``%`` operator. Parameters ---------- x1: array dividend input array. Should have a real-valued data type. x2: array divisor input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise results. Each element-wise result must have the same sign as the respective element ``x2_i``. The returned array must have a data type determined by :ref:`type-promotion`. """ def round(x: array, /) -> array: """ Rounds each element ``x_i`` of the input array ``x`` to the nearest integer-valued number. **Special cases** - If ``x_i`` is already integer-valued, the result is ``x_i``. For floating-point operands, - If ``x_i`` is ``+infinity``, the result is ``+infinity``. - If ``x_i`` is ``-infinity``, the result is ``-infinity``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``NaN``, the result is ``NaN``. - If two integers are equally close to ``x_i``, the result is the even integer closest to ``x_i``. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing the rounded result for each element in ``x``. The returned array must have the same data type as ``x``. """ def sign(x: array, /) -> array: """ Returns an indication of the sign of a number for each element ``x_i`` of the input array ``x``. **Special cases** - If ``x_i`` is less than ``0``, the result is ``-1``. - If ``x_i`` is either ``-0`` or ``+0``, the result is ``0``. - If ``x_i`` is greater than ``0``, the result is ``+1``. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing the evaluated result for each element in ``x``. The returned array must have the same data type as ``x``. """ def sin(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the sine, having domain ``(-infinity, +infinity)`` and codomain ``[-1, +1]``, for each element ``x_i`` of the input array ``x``. Each element ``x_i`` is assumed to be expressed in radians. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is either ``+infinity`` or ``-infinity``, the result is ``NaN``. Parameters ---------- x: array input array whose elements are each expressed in radians. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the sine of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def sinh(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the hyperbolic sine, having domain ``[-infinity, +infinity]`` and codomain ``[-infinity, +infinity]``, for each element ``x_i`` of the input array ``x``. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. - If ``x_i`` is ``-infinity``, the result is ``-infinity``. Parameters ---------- x: array input array whose elements each represent a hyperbolic angle. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the hyperbolic sine of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def square(x: array, /) -> array: """ Squares (``x_i * x_i``) each element ``x_i`` of the input array ``x``. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing the evaluated result for each element in ``x``. The returned array must have a data type determined by :ref:`type-promotion`. """ def sqrt(x: array, /) -> array: """ Calculates the square root, having domain ``[0, +infinity]`` and codomain ``[0, +infinity]``, for each element ``x_i`` of the input array ``x``. After rounding, each result must be indistinguishable from the infinitely precise result (as required by IEEE 754). **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is less than ``0``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``+infinity``, the result is ``+infinity``. Parameters ---------- x: array input array. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the square root of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def subtract(x1: array, x2: array, /) -> array: """ Calculates the difference for each element ``x1_i`` of the input array ``x1`` with the respective element ``x2_i`` of the input array ``x2``. The result of ``x1_i - x2_i`` must be the same as ``x1_i + (-x2_i)`` and must be governed by the same floating-point rules as addition (see :meth:`add`). Parameters ---------- x1: array first input array. Should have a real-valued data type. x2: array second input array. Must be compatible with ``x1`` (see :ref:`broadcasting`). Should have a real-valued data type. Returns ------- out: array an array containing the element-wise differences. The returned array must have a data type determined by :ref:`type-promotion`. """ def tan(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the tangent, having domain ``(-infinity, +infinity)`` and codomain ``(-infinity, +infinity)``, for each element ``x_i`` of the input array ``x``. Each element ``x_i`` is assumed to be expressed in radians. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is either ``+infinity`` or ``-infinity``, the result is ``NaN``. Parameters ---------- x: array input array whose elements are expressed in radians. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the tangent of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def tanh(x: array, /) -> array: """ Calculates an implementation-dependent approximation to the hyperbolic tangent, having domain ``[-infinity, +infinity]`` and codomain ``[-1, +1]``, for each element ``x_i`` of the input array ``x``. **Special cases** For floating-point operands, - If ``x_i`` is ``NaN``, the result is ``NaN``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``+infinity``, the result is ``+1``. - If ``x_i`` is ``-infinity``, the result is ``-1``. Parameters ---------- x: array input array whose elements each represent a hyperbolic angle. Should have a real-valued floating-point data type. Returns ------- out: array an array containing the hyperbolic tangent of each element in ``x``. The returned array must have a real-valued floating-point data type determined by :ref:`type-promotion`. """ def trunc(x: array, /) -> array: """ Rounds each element ``x_i`` of the input array ``x`` to the integer-valued number that is closest to but no greater than ``x_i``. **Special cases** - If ``x_i`` is already integer-valued, the result is ``x_i``. For floating-point operands, - If ``x_i`` is ``+infinity``, the result is ``+infinity``. - If ``x_i`` is ``-infinity``, the result is ``-infinity``. - If ``x_i`` is ``+0``, the result is ``+0``. - If ``x_i`` is ``-0``, the result is ``-0``. - If ``x_i`` is ``NaN``, the result is ``NaN``. Parameters ---------- x: array input array. Should have a real-valued data type. Returns ------- out: array an array containing the rounded result for each element in ``x``. The returned array must have the same data type as ``x``. """ __all__ = ['abs', 'acos', 'acosh', 'add', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'bitwise_and', 'bitwise_left_shift', 'bitwise_invert', 'bitwise_or', 'bitwise_right_shift', 'bitwise_xor', 'ceil', 'cos', 'cosh', 'divide', 'equal', 'exp', 'expm1', 'floor', 'floor_divide', 'greater', 'greater_equal', 'isfinite', 'isinf', 'isnan', 'less', 'less_equal', 'log', 'log1p', 'log2', 'log10', 'logaddexp', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'multiply', 'negative', 'not_equal', 'positive', 'pow', 'remainder', 'round', 'sign', 'sin', 'sinh', 'square', 'sqrt', 'subtract', 'tan', 'tanh', 'trunc']

49.407035

614

0.621527

from ._types import array def abs(x: array, /) -> array: def acos(x: array, /) -> array: def acosh(x: array, /) -> array: def add(x1: array, x2: array, /) -> array: def asin(x: array, /) -> array: def asinh(x: array, /) -> array: def atan(x: array, /) -> array: def atan2(x1: array, x2: array, /) -> array: def atanh(x: array, /) -> array: def bitwise_and(x1: array, x2: array, /) -> array: def bitwise_left_shift(x1: array, x2: array, /) -> array: def bitwise_invert(x: array, /) -> array: def bitwise_or(x1: array, x2: array, /) -> array: def bitwise_right_shift(x1: array, x2: array, /) -> array: def bitwise_xor(x1: array, x2: array, /) -> array: def ceil(x: array, /) -> array: def cos(x: array, /) -> array: def cosh(x: array, /) -> array: def divide(x1: array, x2: array, /) -> array: def equal(x1: array, x2: array, /) -> array: def exp(x: array, /) -> array: def expm1(x: array, /) -> array: def floor(x: array, /) -> array: def floor_divide(x1: array, x2: array, /) -> array: def greater(x1: array, x2: array, /) -> array: def greater_equal(x1: array, x2: array, /) -> array: def isfinite(x: array, /) -> array: def isinf(x: array, /) -> array: def isnan(x: array, /) -> array: def less(x1: array, x2: array, /) -> array: def less_equal(x1: array, x2: array, /) -> array: def log(x: array, /) -> array: def log1p(x: array, /) -> array: def log2(x: array, /) -> array: def log10(x: array, /) -> array: def logaddexp(x1: array, x2: array, /) -> array: def logical_and(x1: array, x2: array, /) -> array: def logical_not(x: array, /) -> array: def logical_or(x1: array, x2: array, /) -> array: def logical_xor(x1: array, x2: array, /) -> array: def multiply(x1: array, x2: array, /) -> array: def negative(x: array, /) -> array: def not_equal(x1: array, x2: array, /) -> array: def positive(x: array, /) -> array: def pow(x1: array, x2: array, /) -> array: def remainder(x1: array, x2: array, /) -> array: def round(x: array, /) -> array: def sign(x: array, /) -> array: def sin(x: array, /) -> array: def sinh(x: array, /) -> array: def square(x: array, /) -> array: def sqrt(x: array, /) -> array: def subtract(x1: array, x2: array, /) -> array: def tan(x: array, /) -> array: def tanh(x: array, /) -> array: def trunc(x: array, /) -> array: __all__ = ['abs', 'acos', 'acosh', 'add', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'bitwise_and', 'bitwise_left_shift', 'bitwise_invert', 'bitwise_or', 'bitwise_right_shift', 'bitwise_xor', 'ceil', 'cos', 'cosh', 'divide', 'equal', 'exp', 'expm1', 'floor', 'floor_divide', 'greater', 'greater_equal', 'isfinite', 'isinf', 'isnan', 'less', 'less_equal', 'log', 'log1p', 'log2', 'log10', 'logaddexp', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'multiply', 'negative', 'not_equal', 'positive', 'pow', 'remainder', 'round', 'sign', 'sin', 'sinh', 'square', 'sqrt', 'subtract', 'tan', 'tanh', 'trunc']

true

f72af3b77a6c41b7fa62f8cf773835380670f57a

130

py

Python

cwlkernel/__main__.py

codacy-badger/CWLJNIKernel

89c830d2ab300f3775e4e49cfc2d0fe894170f5e

[ "Apache-2.0" ]

null

cwlkernel/__main__.py

codacy-badger/CWLJNIKernel

89c830d2ab300f3775e4e49cfc2d0fe894170f5e

[ "Apache-2.0" ]

null

cwlkernel/__main__.py

codacy-badger/CWLJNIKernel

89c830d2ab300f3775e4e49cfc2d0fe894170f5e

[ "Apache-2.0" ]

null

from ipykernel.kernelapp import IPKernelApp from .CWLKernel import CWLKernel IPKernelApp.launch_instance(kernel_class=CWLKernel)

26

51

0.876923

from ipykernel.kernelapp import IPKernelApp from .CWLKernel import CWLKernel IPKernelApp.launch_instance(kernel_class=CWLKernel)

true

f72af4bbb77cd40f08c0addf4a50faf422264aa8

7,875

py

Python

TorchRay/torchray/benchmark/evaluate_imagenet_gradcam_energy_inside_bbox.py

UMBCvision/Consistent-Explanations-by-Contrastive-Learning

589ff89cbcc96a1d8bd8d5b7bd7a785448ed2de3

[ "MIT" ]

null

TorchRay/torchray/benchmark/evaluate_imagenet_gradcam_energy_inside_bbox.py

UMBCvision/Consistent-Explanations-by-Contrastive-Learning

589ff89cbcc96a1d8bd8d5b7bd7a785448ed2de3

[ "MIT" ]

null

TorchRay/torchray/benchmark/evaluate_imagenet_gradcam_energy_inside_bbox.py

UMBCvision/Consistent-Explanations-by-Contrastive-Learning

589ff89cbcc96a1d8bd8d5b7bd7a785448ed2de3

[ "MIT" ]

null

import argparse import time import numpy as np import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim import torch.utils.data.distributed import torchvision.transforms as transforms import resnet_multigpu_cgc as resnet import cv2 import datasets as pointing_datasets """ Here, we evaluate the content heatmap (Grad-CAM heatmap within object bounding box) on the imagenet dataset. """ model_names = ['resnet18', 'resnet50'] parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') parser.add_argument('data', metavar='DIR', help='path to dataset') parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', choices=model_names, help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet18)') parser.add_argument('-j', '--workers', default=16, type=int, metavar='N', help='number of data loading workers (default: 16)') parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N', help='mini-batch size (default: 96)') parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model') parser.add_argument('-g', '--num-gpus', default=1, type=int, metavar='N', help='number of GPUs to match (default: 4)') parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') parser.add_argument('--input_resize', default=224, type=int, metavar='N', help='Resize for smallest side of input (default: 224)') def main(): global args args = parser.parse_args() if args.pretrained: print("=> using pre-trained model '{}'".format(args.arch)) if args.arch.startswith('resnet'): model = resnet.__dict__[args.arch](pretrained=True) else: assert False, 'Unsupported architecture: {}'.format(args.arch) else: print("=> creating model '{}'".format(args.arch)) if args.arch.startswith('resnet'): model = resnet.__dict__[args.arch]() model = torch.nn.DataParallel(model).cuda() if args.resume: print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume) model.load_state_dict(checkpoint['state_dict']) if (not args.resume) and (not args.pretrained): assert False, "Please specify either the pre-trained model or checkpoint for evaluation" cudnn.benchmark = True normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Here, we don't resize the images. We feed the full image and use AdaptivePooling before FC. # We will resize Gradcam heatmap to image size and compare the actual bbox co-ordinates val_dataset = pointing_datasets.ImageNetDetection(args.data, transform=transforms.Compose([ transforms.Resize(args.input_resize), transforms.ToTensor(), normalize, ])) # we set batch size=1 since we are loading full resolution images. val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=1, shuffle=False, num_workers=args.workers, pin_memory=True) validate_multi(val_loader, val_dataset, model) def validate_multi(val_loader, val_dataset, model): batch_time = AverageMeter() heatmap_inside_bbox = AverageMeter() # switch to evaluate mode model.eval() end = time.time() for i, (images, annotation, targets) in enumerate(val_loader): images = images.cuda(non_blocking=True) targets = targets.cuda(non_blocking=True) # we assume batch size == 1 and unwrap the first elem of every list in annotation object annotation = unwrap_dict(annotation) image_size = val_dataset.as_image_size(annotation) output, feats = model(images, vanilla_with_feats=True) output_gradcam = compute_gradcam(output, feats, targets) output_gradcam_np = output_gradcam.data.cpu().numpy()[0] # since we have batch size==1 resized_output_gradcam = cv2.resize(output_gradcam_np, image_size) spatial_sum = resized_output_gradcam.sum() if spatial_sum <= 0: # We ignore images with zero Grad-CAM continue # resized_output_gradcam is now normalized and can be considered as probabilities resized_output_gradcam = resized_output_gradcam / spatial_sum mask = pointing_datasets.imagenet_as_mask(annotation, targets[0].item()) mask = mask.type(torch.ByteTensor) mask = mask.cpu().data.numpy() gcam_inside_gt_mask = mask * resized_output_gradcam # Now we sum the heatmap inside the object bounding box total_gcam_inside_gt_mask = gcam_inside_gt_mask.sum() heatmap_inside_bbox.update(total_gcam_inside_gt_mask) if i % 1000 == 0: print('\nResults after {} examples: '.format(i+1)) print('Curr % of heatmap inside bbox: {:.4f} ({:.4f})'.format(heatmap_inside_bbox.val * 100, heatmap_inside_bbox.avg * 100)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() print('\nFinal Results - ') print('\n\n% of heatmap inside bbox: {:.4f}'.format(heatmap_inside_bbox.avg * 100)) return def compute_gradcam(output, feats, target): """ Compute the gradcam for the top predicted category :param output: :param feats: :param target: :return: """ eps = 1e-8 relu = nn.ReLU(inplace=True) target = target.cpu().numpy() one_hot = np.zeros((output.shape[0], output.shape[-1]), dtype=np.float32) indices_range = np.arange(output.shape[0]) one_hot[indices_range, target[indices_range]] = 1 one_hot = torch.from_numpy(one_hot) one_hot.requires_grad = True # Compute the Grad-CAM for the original image one_hot_cuda = torch.sum(one_hot.cuda() * output) dy_dz1, = torch.autograd.grad(one_hot_cuda, feats, grad_outputs=torch.ones(one_hot_cuda.size()).cuda(), retain_graph=True, create_graph=True) # Changing to dot product of grad and features to preserve grad spatial locations gcam512_1 = dy_dz1 * feats gradcam = gcam512_1.sum(dim=1) gradcam = relu(gradcam) spatial_sum1 = gradcam.sum(dim=[1, 2]).unsqueeze(-1).unsqueeze(-1) gradcam = (gradcam / (spatial_sum1 + eps)) + eps return gradcam def unwrap_dict(dict_object): new_dict = {} for k, v in dict_object.items(): if k == 'object': new_v_list = [] for elem in v: new_v_list.append(unwrap_dict(elem)) new_dict[k] = new_v_list continue if isinstance(v, dict): new_v = unwrap_dict(v) elif isinstance(v, list) and len(v) == 1: new_v = v[0] else: new_v = v new_dict[k] = new_v return new_dict class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count if __name__ == '__main__': main()

36.971831

112

0.616381

import argparse import time import numpy as np import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim import torch.utils.data.distributed import torchvision.transforms as transforms import resnet_multigpu_cgc as resnet import cv2 import datasets as pointing_datasets model_names = ['resnet18', 'resnet50'] parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') parser.add_argument('data', metavar='DIR', help='path to dataset') parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', choices=model_names, help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet18)') parser.add_argument('-j', '--workers', default=16, type=int, metavar='N', help='number of data loading workers (default: 16)') parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N', help='mini-batch size (default: 96)') parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model') parser.add_argument('-g', '--num-gpus', default=1, type=int, metavar='N', help='number of GPUs to match (default: 4)') parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') parser.add_argument('--input_resize', default=224, type=int, metavar='N', help='Resize for smallest side of input (default: 224)') def main(): global args args = parser.parse_args() if args.pretrained: print("=> using pre-trained model '{}'".format(args.arch)) if args.arch.startswith('resnet'): model = resnet.__dict__[args.arch](pretrained=True) else: assert False, 'Unsupported architecture: {}'.format(args.arch) else: print("=> creating model '{}'".format(args.arch)) if args.arch.startswith('resnet'): model = resnet.__dict__[args.arch]() model = torch.nn.DataParallel(model).cuda() if args.resume: print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume) model.load_state_dict(checkpoint['state_dict']) if (not args.resume) and (not args.pretrained): assert False, "Please specify either the pre-trained model or checkpoint for evaluation" cudnn.benchmark = True normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # We will resize Gradcam heatmap to image size and compare the actual bbox co-ordinates val_dataset = pointing_datasets.ImageNetDetection(args.data, transform=transforms.Compose([ transforms.Resize(args.input_resize), transforms.ToTensor(), normalize, ])) # we set batch size=1 since we are loading full resolution images. val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=1, shuffle=False, num_workers=args.workers, pin_memory=True) validate_multi(val_loader, val_dataset, model) def validate_multi(val_loader, val_dataset, model): batch_time = AverageMeter() heatmap_inside_bbox = AverageMeter() # switch to evaluate mode model.eval() end = time.time() for i, (images, annotation, targets) in enumerate(val_loader): images = images.cuda(non_blocking=True) targets = targets.cuda(non_blocking=True) # we assume batch size == 1 and unwrap the first elem of every list in annotation object annotation = unwrap_dict(annotation) image_size = val_dataset.as_image_size(annotation) output, feats = model(images, vanilla_with_feats=True) output_gradcam = compute_gradcam(output, feats, targets) output_gradcam_np = output_gradcam.data.cpu().numpy()[0] # since we have batch size==1 resized_output_gradcam = cv2.resize(output_gradcam_np, image_size) spatial_sum = resized_output_gradcam.sum() if spatial_sum <= 0: # We ignore images with zero Grad-CAM continue # resized_output_gradcam is now normalized and can be considered as probabilities resized_output_gradcam = resized_output_gradcam / spatial_sum mask = pointing_datasets.imagenet_as_mask(annotation, targets[0].item()) mask = mask.type(torch.ByteTensor) mask = mask.cpu().data.numpy() gcam_inside_gt_mask = mask * resized_output_gradcam # Now we sum the heatmap inside the object bounding box total_gcam_inside_gt_mask = gcam_inside_gt_mask.sum() heatmap_inside_bbox.update(total_gcam_inside_gt_mask) if i % 1000 == 0: print('\nResults after {} examples: '.format(i+1)) print('Curr % of heatmap inside bbox: {:.4f} ({:.4f})'.format(heatmap_inside_bbox.val * 100, heatmap_inside_bbox.avg * 100)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() print('\nFinal Results - ') print('\n\n% of heatmap inside bbox: {:.4f}'.format(heatmap_inside_bbox.avg * 100)) return def compute_gradcam(output, feats, target): eps = 1e-8 relu = nn.ReLU(inplace=True) target = target.cpu().numpy() one_hot = np.zeros((output.shape[0], output.shape[-1]), dtype=np.float32) indices_range = np.arange(output.shape[0]) one_hot[indices_range, target[indices_range]] = 1 one_hot = torch.from_numpy(one_hot) one_hot.requires_grad = True # Compute the Grad-CAM for the original image one_hot_cuda = torch.sum(one_hot.cuda() * output) dy_dz1, = torch.autograd.grad(one_hot_cuda, feats, grad_outputs=torch.ones(one_hot_cuda.size()).cuda(), retain_graph=True, create_graph=True) # Changing to dot product of grad and features to preserve grad spatial locations gcam512_1 = dy_dz1 * feats gradcam = gcam512_1.sum(dim=1) gradcam = relu(gradcam) spatial_sum1 = gradcam.sum(dim=[1, 2]).unsqueeze(-1).unsqueeze(-1) gradcam = (gradcam / (spatial_sum1 + eps)) + eps return gradcam def unwrap_dict(dict_object): new_dict = {} for k, v in dict_object.items(): if k == 'object': new_v_list = [] for elem in v: new_v_list.append(unwrap_dict(elem)) new_dict[k] = new_v_list continue if isinstance(v, dict): new_v = unwrap_dict(v) elif isinstance(v, list) and len(v) == 1: new_v = v[0] else: new_v = v new_dict[k] = new_v return new_dict class AverageMeter(object): def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count if __name__ == '__main__': main()

true

f72af5153bd9f88566e3d6863c7c6bad63faba5c

558

py

Python

var_global_local.py

Spy142/python_lesson_4

1539576301c2bf61be803be7846c9278f350a0f3

[ "MIT" ]

null

var_global_local.py

Spy142/python_lesson_4

1539576301c2bf61be803be7846c9278f350a0f3

[ "MIT" ]

null

var_global_local.py

Spy142/python_lesson_4

1539576301c2bf61be803be7846c9278f350a0f3

[ "MIT" ]

1

2020-09-09T09:27:06.000Z

global_var = 10 def function_example(local_var_1, local_var_2): print(local_var_1, local_var_2, global_var) function_example(11, 12) def function_example_1(local_var_1, local_var_2): global global_var global_var = 20 print(local_var_1, local_var_2, global_var, id(global_var)) function_example_1(11, 12) print(global_var, id(global_var)) # nonlocal def counter(): num = 0 def plus_one(): nonlocal num num+=1 return num return plus_one count = counter() print(count) print(count()) print(count())

16.909091

63

0.702509

global_var = 10 def function_example(local_var_1, local_var_2): print(local_var_1, local_var_2, global_var) function_example(11, 12) def function_example_1(local_var_1, local_var_2): global global_var global_var = 20 print(local_var_1, local_var_2, global_var, id(global_var)) function_example_1(11, 12) print(global_var, id(global_var)) def counter(): num = 0 def plus_one(): nonlocal num num+=1 return num return plus_one count = counter() print(count) print(count()) print(count())

true

f72af6a3f7871c38684b0e461069b71876226a9b

157

py

Python

tests/model_control/detailed/transf_None/model_control_one_enabled_None_MovingAverage_Seasonal_Second_MLP.py

shaido987/pyaf

b9afd089557bed6b90b246d3712c481ae26a1957

[ "BSD-3-Clause" ]

377

2016-10-13T20:52:44.000Z

2022-03-29T18:04:14.000Z

tests/model_control/detailed/transf_None/model_control_one_enabled_None_MovingAverage_Seasonal_Second_MLP.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

160

2016-10-13T16:11:53.000Z

2022-03-28T04:21:34.000Z

tests/model_control/detailed/transf_None/model_control_one_enabled_None_MovingAverage_Seasonal_Second_MLP.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

63

2017-03-09T14:51:18.000Z

2022-03-27T20:52:57.000Z

import tests.model_control.test_ozone_custom_models_enabled as testmod testmod.build_model( ['None'] , ['MovingAverage'] , ['Seasonal_Second'] , ['MLP'] );

39.25

84

0.751592

import tests.model_control.test_ozone_custom_models_enabled as testmod testmod.build_model( ['None'] , ['MovingAverage'] , ['Seasonal_Second'] , ['MLP'] );

true

f72af6e888f158710810a4b5ed837ab592f4f7f4

3,251

py

Python

tests/toolkit/utils.py

Devtography/ibpy_native

e3e2a406a8db9bb338953be6dc195b8099379acb

[ "Apache-2.0" ]

6

2020-07-09T20:55:41.000Z

2022-01-22T15:43:29.000Z

tests/toolkit/utils.py

Devtography/ibpy_native

e3e2a406a8db9bb338953be6dc195b8099379acb

[ "Apache-2.0" ]

1

2021-02-28T13:37:43.000Z

tests/toolkit/utils.py

Devtography/ibpy_native

e3e2a406a8db9bb338953be6dc195b8099379acb

[ "Apache-2.0" ]

5

2020-05-24T19:15:06.000Z

2022-01-22T15:43:35.000Z

"""Utilities for making unittests easier to write.""" # pylint: disable=protected-access import asyncio import os import queue from typing import Dict, List, Optional, Union from ibapi import wrapper from ibpy_native import error from ibpy_native import models from ibpy_native.interfaces import delegates from ibpy_native.interfaces import listeners from ibpy_native.utils import finishable_queue as fq #region - General utils def async_test(fn): # pylint: disable=invalid-name """Decorator for testing the async functions.""" def fn_wrapper(*args, **kwargs): loop = asyncio.new_event_loop() return loop.run_until_complete(fn(*args, **kwargs)) return fn_wrapper #endregion - General utils #region - ibpy_native specific # Constants IB_HOST: str = os.getenv("IB_HOST", "127.0.0.1") IB_PORT: int = int(os.getenv("IB_PORT", "4002")) IB_CLIENT_ID: int = int(os.getenv("IB_CLIENT_ID", "1001")) IB_ACC_ID: str = os.getenv("IB_ACC_ID", "") class MockConnectionListener(listeners.ConnectionListener): """Mock connection listener.""" def __init__(self): self.connected: Optional[bool] = None def on_connected(self): self.connected = True def on_disconnected(self): self.connected = False class MockNotificationListener(listeners.NotificationListener): """Mock notification listener.""" def __init__(self): self.msg_code = -1 self.msg = "" def on_notify(self, msg_code: int, msg: str): """Mock callback implementation.""" self.msg_code = msg_code self.msg = msg class MockAccountsManagementDelegate(delegates.AccountsManagementDelegate): """Mock accounts delegate""" def __init__(self): self._account_list: Dict[str, models.Account] = {} self._account_updates_queue: fq.FinishableQueue = fq.FinishableQueue( queue_to_finish=queue.Queue() ) @property def accounts(self) -> Dict[str, models.Account]: return self._account_list @property def account_updates_queue(self) -> fq.FinishableQueue: return self._account_updates_queue def on_account_list_update(self, account_list: List[str]): for account_id in account_list: self._account_list[account_id] = models.Account(account_id) async def sub_account_updates(self, account: models.Account): pass async def unsub_account_updates(self): pass def on_disconnected(self): pass class MockLiveTicksListener(listeners.LiveTicksListener): """Mock notification listener""" def __init__(self): self.ticks: List[Union[wrapper.HistoricalTick, wrapper.HistoricalTickBidAsk, wrapper.HistoricalTickLast]] = [] self.finished = False def on_tick_receive(self, req_id: int, tick: Union[wrapper.HistoricalTick, wrapper.HistoricalTickBidAsk, wrapper.HistoricalTickLast,]): self.ticks.append(tick) def on_finish(self, req_id: int): self.finished = True def on_err(self, err: error.IBError): raise err #endregion - ibpy_native specific

30.669811

77

0.671486

import asyncio import os import queue from typing import Dict, List, Optional, Union from ibapi import wrapper from ibpy_native import error from ibpy_native import models from ibpy_native.interfaces import delegates from ibpy_native.interfaces import listeners from ibpy_native.utils import finishable_queue as fq def async_test(fn): def fn_wrapper(*args, **kwargs): loop = asyncio.new_event_loop() return loop.run_until_complete(fn(*args, **kwargs)) return fn_wrapper IB_HOST: str = os.getenv("IB_HOST", "127.0.0.1") IB_PORT: int = int(os.getenv("IB_PORT", "4002")) IB_CLIENT_ID: int = int(os.getenv("IB_CLIENT_ID", "1001")) IB_ACC_ID: str = os.getenv("IB_ACC_ID", "") class MockConnectionListener(listeners.ConnectionListener): def __init__(self): self.connected: Optional[bool] = None def on_connected(self): self.connected = True def on_disconnected(self): self.connected = False class MockNotificationListener(listeners.NotificationListener): def __init__(self): self.msg_code = -1 self.msg = "" def on_notify(self, msg_code: int, msg: str): self.msg_code = msg_code self.msg = msg class MockAccountsManagementDelegate(delegates.AccountsManagementDelegate): def __init__(self): self._account_list: Dict[str, models.Account] = {} self._account_updates_queue: fq.FinishableQueue = fq.FinishableQueue( queue_to_finish=queue.Queue() ) @property def accounts(self) -> Dict[str, models.Account]: return self._account_list @property def account_updates_queue(self) -> fq.FinishableQueue: return self._account_updates_queue def on_account_list_update(self, account_list: List[str]): for account_id in account_list: self._account_list[account_id] = models.Account(account_id) async def sub_account_updates(self, account: models.Account): pass async def unsub_account_updates(self): pass def on_disconnected(self): pass class MockLiveTicksListener(listeners.LiveTicksListener): def __init__(self): self.ticks: List[Union[wrapper.HistoricalTick, wrapper.HistoricalTickBidAsk, wrapper.HistoricalTickLast]] = [] self.finished = False def on_tick_receive(self, req_id: int, tick: Union[wrapper.HistoricalTick, wrapper.HistoricalTickBidAsk, wrapper.HistoricalTickLast,]): self.ticks.append(tick) def on_finish(self, req_id: int): self.finished = True def on_err(self, err: error.IBError): raise err

true

f72af7d6e7b04db16a0baa10f553c130371e0a1e

1,561

py

Python

__scraping__/comics.panini.it - scrapy/main-itemloader.py

whitmans-max/python-examples

881a8f23f0eebc76816a0078e19951893f0daaaa

[ "MIT" ]

140

2017-02-21T22:49:04.000Z

2022-03-22T17:51:58.000Z

__scraping__/comics.panini.it - scrapy/main-itemloader.py

whitmans-max/python-examples

881a8f23f0eebc76816a0078e19951893f0daaaa

[ "MIT" ]

5

2017-12-02T19:55:00.000Z

2021-09-22T23:18:39.000Z

__scraping__/comics.panini.it - scrapy/main-itemloader.py

whitmans-max/python-examples

881a8f23f0eebc76816a0078e19951893f0daaaa

[ "MIT" ]

79

2017-01-25T10:53:33.000Z

2022-03-11T16:13:57.000Z

#!/usr/bin/env python3 # date: 2019.08.06 # https://stackoverflow.com/questions/57366488/how-to-pass-the-single-link-in-a-nested-url-scrape import scrapy from scrapy.loader import ItemLoader from scrapy.loader.processors import MapCompose def clean(text): text = text.replace('\xa0', ' ') text = text.strip().split('\n') text = ' '.join(x.strip() for x in text) return text class ComicscraperItem(scrapy.Item): title = scrapy.Field(input_processor=MapCompose(clean)) link = scrapy.Field() price = scrapy.Field(input_processor=MapCompose(clean)) class PaniniSpider(scrapy.Spider): name = "spiderP" start_urls = ["http://comics.panini.it/store/pub_ita_it/magazines.html"] def parse(self, response): for sel in response.xpath("//div[@class='list-group']//h3/a"): l = ItemLoader(item=ComicscraperItem(), selector=sel) l.add_xpath('title', './text()') l.add_xpath('link', './@href') request = scrapy.Request(sel.xpath('./@href').extract_first(), callback=self.parse_isbn, dont_filter=True) request.meta['l'] = l yield request def parse_isbn(self, response): l = response.meta['l'] l.add_value('price', response.xpath("//p[@class='special-price']//span/text()").get()) return l.load_item() from scrapy.crawler import CrawlerProcess c = CrawlerProcess({ 'USER_AGENT': 'Mozilla/5.0', 'FEED_FORMAT': 'csv', # csv, json, xml 'FEED_URI': 'output.csv', # }) c.crawl(PaniniSpider) c.start()

31.22

118

0.643177

import scrapy from scrapy.loader import ItemLoader from scrapy.loader.processors import MapCompose def clean(text): text = text.replace('\xa0', ' ') text = text.strip().split('\n') text = ' '.join(x.strip() for x in text) return text class ComicscraperItem(scrapy.Item): title = scrapy.Field(input_processor=MapCompose(clean)) link = scrapy.Field() price = scrapy.Field(input_processor=MapCompose(clean)) class PaniniSpider(scrapy.Spider): name = "spiderP" start_urls = ["http://comics.panini.it/store/pub_ita_it/magazines.html"] def parse(self, response): for sel in response.xpath("//div[@class='list-group']//h3/a"): l = ItemLoader(item=ComicscraperItem(), selector=sel) l.add_xpath('title', './text()') l.add_xpath('link', './@href') request = scrapy.Request(sel.xpath('./@href').extract_first(), callback=self.parse_isbn, dont_filter=True) request.meta['l'] = l yield request def parse_isbn(self, response): l = response.meta['l'] l.add_value('price', response.xpath("//p[@class='special-price']//span/text()").get()) return l.load_item() from scrapy.crawler import CrawlerProcess c = CrawlerProcess({ 'USER_AGENT': 'Mozilla/5.0', 'FEED_FORMAT': 'csv', 'FEED_URI': 'output.csv', }) c.crawl(PaniniSpider) c.start()

true

f72af7e4a722a6457a4e5bb9862634b05fb4b74c

3,915

py

Python

sendSMSSkillLambda/package/ask_sdk_model/interfaces/geolocation/altitude.py

shneydor/aws-alexa-lambda-workshop

0fa6b7067b04fc85c46b9ce1c2cc04554ed5baf4

[ "Apache-2.0" ]

null

sendSMSSkillLambda/package/ask_sdk_model/interfaces/geolocation/altitude.py

shneydor/aws-alexa-lambda-workshop

0fa6b7067b04fc85c46b9ce1c2cc04554ed5baf4

[ "Apache-2.0" ]

null

sendSMSSkillLambda/package/ask_sdk_model/interfaces/geolocation/altitude.py

shneydor/aws-alexa-lambda-workshop

0fa6b7067b04fc85c46b9ce1c2cc04554ed5baf4

[ "Apache-2.0" ]

1

2019-10-11T17:15:20.000Z

# coding: utf-8 # # Copyright 2019 Amazon.com, Inc. or its affiliates. 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. A copy of the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file 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 pprint import re # noqa: F401 import six import typing from enum import Enum if typing.TYPE_CHECKING: from typing import Dict, List, Optional, Union from datetime import datetime class Altitude(object): """ An object containing the altitude information of the device. :param altitude_in_meters: A double representing the altitude of the device in meters. :type altitude_in_meters: (optional) float :param accuracy_in_meters: A double representing the accuracy of the altitude measurement in meters. :type accuracy_in_meters: (optional) float """ deserialized_types = { 'altitude_in_meters': 'float', 'accuracy_in_meters': 'float' } # type: Dict attribute_map = { 'altitude_in_meters': 'altitudeInMeters', 'accuracy_in_meters': 'accuracyInMeters' } # type: Dict def __init__(self, altitude_in_meters=None, accuracy_in_meters=None): # type: (Optional[float], Optional[float]) -> None """An object containing the altitude information of the device. :param altitude_in_meters: A double representing the altitude of the device in meters. :type altitude_in_meters: (optional) float :param accuracy_in_meters: A double representing the accuracy of the altitude measurement in meters. :type accuracy_in_meters: (optional) float """ self.__discriminator_value = None # type: str self.altitude_in_meters = altitude_in_meters self.accuracy_in_meters = accuracy_in_meters def to_dict(self): # type: () -> Dict[str, object] """Returns the model properties as a dict""" result = {} # type: Dict for attr, _ in six.iteritems(self.deserialized_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x.value if isinstance(x, Enum) else x, value )) elif isinstance(value, Enum): result[attr] = value.value elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else (item[0], item[1].value) if isinstance(item[1], Enum) else item, value.items() )) else: result[attr] = value return result def to_str(self): # type: () -> str """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): # type: () -> str """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): # type: (object) -> bool """Returns true if both objects are equal""" if not isinstance(other, Altitude): return False return self.__dict__ == other.__dict__ def __ne__(self, other): # type: (object) -> bool """Returns true if both objects are not equal""" return not self == other

34.043478

108

0.61507

import pprint import re import six import typing from enum import Enum if typing.TYPE_CHECKING: from typing import Dict, List, Optional, Union from datetime import datetime class Altitude(object): deserialized_types = { 'altitude_in_meters': 'float', 'accuracy_in_meters': 'float' } attribute_map = { 'altitude_in_meters': 'altitudeInMeters', 'accuracy_in_meters': 'accuracyInMeters' } def __init__(self, altitude_in_meters=None, accuracy_in_meters=None): self.__discriminator_value = None self.altitude_in_meters = altitude_in_meters self.accuracy_in_meters = accuracy_in_meters def to_dict(self): result = {} for attr, _ in six.iteritems(self.deserialized_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x.value if isinstance(x, Enum) else x, value )) elif isinstance(value, Enum): result[attr] = value.value elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else (item[0], item[1].value) if isinstance(item[1], Enum) else item, value.items() )) else: result[attr] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, Altitude): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f72af7f234a3a7aaf0e57fc752f62d4dd0d648af

38

py

Python

frontend/GUI/ROOT_AND_MAIN/USER_WINDOW/USER_FRAME/callbacks.py

Lucianofc138/smart_scheduler_usm

0ac50d71cfd1947b889a9551c31a3a67ecabfb88

[ "MIT" ]

null

frontend/GUI/ROOT_AND_MAIN/USER_WINDOW/USER_FRAME/callbacks.py

Lucianofc138/smart_scheduler_usm

0ac50d71cfd1947b889a9551c31a3a67ecabfb88

[ "MIT" ]

null

frontend/GUI/ROOT_AND_MAIN/USER_WINDOW/USER_FRAME/callbacks.py

Lucianofc138/smart_scheduler_usm

0ac50d71cfd1947b889a9551c31a3a67ecabfb88

[ "MIT" ]

null

def new_user(user_stringvar): pass

19

29

0.763158

def new_user(user_stringvar): pass

true

f72af8f3d31d026bd4517c8b3a0509701311dff5

4,016

py

Python

netmiko/exercise4.py

Tes3awy/DevNet-DC

03b4c7dc82221943bc25d0ab9d74ee2697fcc34c

[ "MIT" ]

null

netmiko/exercise4.py

Tes3awy/DevNet-DC

03b4c7dc82221943bc25d0ab9d74ee2697fcc34c

[ "MIT" ]

null

netmiko/exercise4.py

Tes3awy/DevNet-DC

03b4c7dc82221943bc25d0ab9d74ee2697fcc34c

[ "MIT" ]

null

# Export Nexus device show interface brief command output to # an Excel file import json import xlsxwriter from netmiko import ConnectHandler # Devices to SSH into devices = [ { "device_type": "cisco_nxos", "ip": "sbx-nxos-mgmt.cisco.com", "username": "admin", "password": "Admin_1234!", "port": 8181, "fast_cli": False, "session_log": "nxos-exercise4.log", }, { "device_type": "cisco_nxos", "ip": "192.168.90.46", "username": "admin", "password": "P@ssw0rd", "fast_cli": False, "session_log": "nxos-exercise4-1.log", "verbose": True, }, { "device_type": "cisco_nxos", "ip": "192.168.90.47", "username": "admin", "password": "P@ssw0rd", "fast_cli": False, "session_log": "nxos-exercise4-2.log", "verbose": True, }, ] # Create an Excel file with xlsxwriter.Workbook(filename="Ex4-Nexus-Interfaces-Brief.xlsx") as workbook: # Loop over each device for device in devices: # Connect to each device with ConnectHandler(**device) as net_connect: # Parse hostname of each device hostname = net_connect.send_command( command_string="show hostname", use_textfsm=True )[0]["hostname"] # Parse show interface brief of each device intfs = net_connect.send_command( command_string="show interface brief", use_textfsm=True ) # Export interfaces to a JSON file for readability (Comment out if you don't need it) with open(file=f"{hostname}-intfs-brief.json", mode="w") as outfile: json.dump(obj=intfs, fp=outfile, indent=4, sort_keys=True) # Create worksheets with the hostname of each device worksheet = workbook.add_worksheet(f"{hostname} Interface Brief") # Auto Filter for header line worksheet.autofilter("A1:L1") # Freeze top row and very left column only worksheet.freeze_panes(1, 1) # Header line header_line = { "A1": "Interface Name", # 1 "B1": "IP Address", # 2 "C1": "Interface Type", # 3 "D1": "Mode", # 4 "E1": "VLAN", # 5 "F1": "Port-Channel", # 6 "G1": "Speed", # 7 "H1": "Status", # 8 "I1": "MTU", # 9 "J1": "VRF", # 10 "K1": "Reason", # 11 "L1": "Description", # 12 } # Format header line text header_line_frmt = workbook.add_format( { "bold": True, "align": "center", "valign": "vcenter", "bg_color": "#0058a0", "font_color": "#FFFFFF", } ) # Write header line for key, value in header_line.items(): worksheet.write(key, value, header_line_frmt) # Initial Values for row and col row = 1 col = 0 # Place data according to header line for intf in intfs: worksheet.write(row, col + 0, intf["interface"]) # Interface Name worksheet.write(row, col + 1, intf["ip"]) # IP worksheet.write(row, col + 2, intf["type"]) # Type worksheet.write(row, col + 3, intf["mode"]) # Mode worksheet.write(row, col + 4, intf["vlan"]) # VLAN worksheet.write(row, col + 5, intf["portch"]) # Port-Channel worksheet.write(row, col + 6, intf["speed"]) # Speed worksheet.write(row, col + 7, intf["status"]) # Status worksheet.write(row, col + 8, intf["mtu"]) # MTU worksheet.write(row, col + 9, intf["vrf"]) # VRF worksheet.write(row, col + 10, intf["reason"]) # Reason worksheet.write(row, col + 11, intf["description"]) # Description # Jump to next row row += 1 print("Done")

34.033898

93

0.528884

import json import xlsxwriter from netmiko import ConnectHandler devices = [ { "device_type": "cisco_nxos", "ip": "sbx-nxos-mgmt.cisco.com", "username": "admin", "password": "Admin_1234!", "port": 8181, "fast_cli": False, "session_log": "nxos-exercise4.log", }, { "device_type": "cisco_nxos", "ip": "192.168.90.46", "username": "admin", "password": "P@ssw0rd", "fast_cli": False, "session_log": "nxos-exercise4-1.log", "verbose": True, }, { "device_type": "cisco_nxos", "ip": "192.168.90.47", "username": "admin", "password": "P@ssw0rd", "fast_cli": False, "session_log": "nxos-exercise4-2.log", "verbose": True, }, ] with xlsxwriter.Workbook(filename="Ex4-Nexus-Interfaces-Brief.xlsx") as workbook: for device in devices: with ConnectHandler(**device) as net_connect: hostname = net_connect.send_command( command_string="show hostname", use_textfsm=True )[0]["hostname"] intfs = net_connect.send_command( command_string="show interface brief", use_textfsm=True ) with open(file=f"{hostname}-intfs-brief.json", mode="w") as outfile: json.dump(obj=intfs, fp=outfile, indent=4, sort_keys=True) # Create worksheets with the hostname of each device worksheet = workbook.add_worksheet(f"{hostname} Interface Brief") # Auto Filter for header line worksheet.autofilter("A1:L1") # Freeze top row and very left column only worksheet.freeze_panes(1, 1) # Header line header_line = { "A1": "Interface Name", # 1 "B1": "IP Address", # 2 "C1": "Interface Type", # 3 "D1": "Mode", # 4 "E1": "VLAN", # 5 "F1": "Port-Channel", # 6 "G1": "Speed", # 7 "H1": "Status", # 8 "I1": "MTU", # 9 "J1": "VRF", # 10 "K1": "Reason", # 11 "L1": "Description", # 12 } # Format header line text header_line_frmt = workbook.add_format( { "bold": True, "align": "center", "valign": "vcenter", "bg_color": "#0058a0", "font_color": "#FFFFFF", } ) # Write header line for key, value in header_line.items(): worksheet.write(key, value, header_line_frmt) # Initial Values for row and col row = 1 col = 0 # Place data according to header line for intf in intfs: worksheet.write(row, col + 0, intf["interface"]) # Interface Name worksheet.write(row, col + 1, intf["ip"]) # IP worksheet.write(row, col + 2, intf["type"]) # Type worksheet.write(row, col + 3, intf["mode"]) # Mode worksheet.write(row, col + 4, intf["vlan"]) # VLAN worksheet.write(row, col + 5, intf["portch"]) # Port-Channel worksheet.write(row, col + 6, intf["speed"]) # Speed worksheet.write(row, col + 7, intf["status"]) # Status worksheet.write(row, col + 8, intf["mtu"]) # MTU worksheet.write(row, col + 9, intf["vrf"]) # VRF worksheet.write(row, col + 10, intf["reason"]) # Reason worksheet.write(row, col + 11, intf["description"]) # Description # Jump to next row row += 1 print("Done")

true

f72afbb1ae862f6cc33248e2ecf5c95000d6017c

7,390

py

Python

server/opendp_apps/dataset/dataset_formatter.py

opendifferentialprivacy/opendp-ux

2669602d0a65f6a83d9e9916cbf753c38fd64c94

[ "MIT" ]

null

server/opendp_apps/dataset/dataset_formatter.py

opendifferentialprivacy/opendp-ux

2669602d0a65f6a83d9e9916cbf753c38fd64c94

[ "MIT" ]

82

2020-08-06T17:11:12.000Z

2021-02-07T21:01:05.000Z

server/opendp_apps/dataset/dataset_formatter.py

opendifferentialprivacy/opendp-ux

2669602d0a65f6a83d9e9916cbf753c38fd64c94

[ "MIT" ]

2

2020-10-16T22:03:24.000Z

2020-11-15T22:45:19.000Z

""" Format a DataSetInfo for use in a JSON Release """ import json from opendp_apps.dataset.models import DataSetInfo from opendp_apps.dataset import static_vals as dstatic from opendp_apps.model_helpers.basic_err_check import BasicErrCheck from opendp_apps.model_helpers.basic_response import ok_resp, err_resp, BasicResponse class DataSetFormatter(BasicErrCheck): def __init__(self, dataset_info: DataSetInfo): """Init with a DataSetInfo object""" assert isinstance(dataset_info, DataSetInfo), '"dataset_info" must be a DataSetInfo instance.' self.dataset = dataset_info self.formatted_info = {} self.run_formatter() def run_formatter(self): """ Format the dataset info """ if self.dataset.source == DataSetInfo.SourceChoices.UserUpload: self.dataset = self.dataset.uploadfileinfo # Get the UploadFileInfo object self.format_user_upload() elif self.dataset.source == DataSetInfo.SourceChoices.Dataverse: self.dataset = self.dataset.dataversefileinfo # Get the DataverseFileInfo object self.format_dataverse_dataset() else: self.add_err_msg('Unknown dataset type: {self.dataset.source}') return def get_formatted_info(self, as_json=False): """ Return the formatted data """ assert self.has_error() is False,\ "Do not call this method before checking if \".has_error()\" is False" if as_json: return json.dumps(self.formatted_info, indent=4) return self.formatted_info def format_user_upload(self): """Format UserUpload dataset""" if self.has_error(): return ds_dict = { 'type': self.dataset.source, 'name': self.dataset.name, 'creator': self.dataset.creator, 'created': self.dataset.created, } self.formatted_info = ds_dict def format_dataverse_dataset(self): """Format UserUpload dataset""" if self.has_error(): return # Pull citation from self.dataset.dataset_schema_info # citation_info = self.get_citation_from_dataset_schema_or_None() if citation_info.success: citation = citation_info.data else: self.add_err_msg(citation_info.message) return # Pull name from self.dataset.dataset_schema_info # name_info = self.get_name_from_dataset_schema() if name_info.success: ds_name = name_info.data else: self.add_err_msg(name_info.message) return # Format info in self.dataset.file_schema_info # file_info = self.get_file_info() if file_info.success: file_dict = file_info.data else: self.add_err_msg(file_info.message) return ds_dict = { 'type': self.dataset.source, 'name': self.dataset.name, "citation": citation, "doi": self.dataset.dataset_doi, "identifier": self.get_dataset_identifier_or_none(), 'release_deposit_info': { "deposited": False, # if True, add: "release_url": "some-url" # update with https://github.com/opendp/dpcreator/issues/34 # "release_urls": { # "release_json": "http://dataverse.edu/some.json", # "release_pdf": "http://dataverse.edu/some.pdf" # } }, 'installation': { "name": self.dataset.dv_installation.name, "url": self.dataset.dv_installation.dataverse_url }, "file_information": file_dict } self.formatted_info = ds_dict def get_name_from_dataset_schema(self) -> BasicResponse: """ Return the "name" text from self.dataset_schema_info (a bit ugly...) Trying to return string from: self.dataset.dataset_schema_info['name'] """ if self.has_error(): # Shouldn't happen... return err_resp(self.get_err_msg()) if not self.dataset.dataset_schema_info: return err_resp('".dataset_schema_info" is empty') if not 'name' in self.dataset.dataset_schema_info: return err_resp('"name" not found in ".dataset_schema_info" not found') ds_name = self.dataset.dataset_schema_info['name'] if not ds_name: return err_resp('"name" within ".dataset_schema_info" is empty') return ok_resp(ds_name) def get_dataset_identifier_or_none(self): """Return the identifer within dataset_schema_info['identifer']""" if '@id' in self.dataset.dataset_schema_info['@id']: return elf.dataset.dataset_schema_info['@id'] return None def get_citation_from_dataset_schema_or_None(self): """ Return the citation text from self.dataset_schema_info (a bit ugly...) Trying to return string from: self.dataset.dataset_schema_info['citation'][0] """ if self.has_error(): # Shouldn't happen... return err_resp(self.get_err_msg()) if not self.dataset.dataset_schema_info: return err_resp('".dataset_schema_info" is empty') if not 'citation' in self.dataset.dataset_schema_info: return ok_resp(None) # If the citation key is found, then do error checking.... if (not self.dataset.dataset_schema_info['citation']) or \ (not isinstance(self.dataset.dataset_schema_info['citation'], list)): return err_resp('"citation" within ".dataset_schema_info" is empty or not a list') if not 'text' in self.dataset.dataset_schema_info['citation'][0]: return err_resp('"[\'citation\'][0][\'text\']" not found in ".dataset_schema_info"') return ok_resp(self.dataset.dataset_schema_info['citation'][0]['text']) def get_file_info(self): """ Return information from the "DataverseFileInfo.file_schema_info" field Ideal: { "name": "crisis.tab" "identifier": "https://doi.org/10.7910/DVN/OLD7MB/ZI4N3J", "fileFormat": "text/tab-separated-values", } """ if self.has_error(): # Shouldn't happen! return err_resp(self.get_err_msg()) if not self.dataset.file_schema_info: return err_resp('".file_schema_info" is empty') file_dict = {} if 'name' in self.dataset.file_schema_info: file_dict['name'] = self.dataset.file_schema_info['name'] else: return err_resp('"name" not found in ".file_schema_info" not found') if 'identifier' in self.dataset.file_schema_info: file_dict['identifier'] = self.dataset.file_schema_info['identifier'] else: file_dict['identifier'] = None if 'fileFormat' in self.dataset.file_schema_info: file_dict['fileFormat'] = self.dataset.file_schema_info['fileFormat'] else: file_dict['fileFormat'] = None return ok_resp(file_dict)

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import json from opendp_apps.dataset.models import DataSetInfo from opendp_apps.dataset import static_vals as dstatic from opendp_apps.model_helpers.basic_err_check import BasicErrCheck from opendp_apps.model_helpers.basic_response import ok_resp, err_resp, BasicResponse class DataSetFormatter(BasicErrCheck): def __init__(self, dataset_info: DataSetInfo): assert isinstance(dataset_info, DataSetInfo), '"dataset_info" must be a DataSetInfo instance.' self.dataset = dataset_info self.formatted_info = {} self.run_formatter() def run_formatter(self): if self.dataset.source == DataSetInfo.SourceChoices.UserUpload: self.dataset = self.dataset.uploadfileinfo self.format_user_upload() elif self.dataset.source == DataSetInfo.SourceChoices.Dataverse: self.dataset = self.dataset.dataversefileinfo self.format_dataverse_dataset() else: self.add_err_msg('Unknown dataset type: {self.dataset.source}') return def get_formatted_info(self, as_json=False): assert self.has_error() is False,\ "Do not call this method before checking if \".has_error()\" is False" if as_json: return json.dumps(self.formatted_info, indent=4) return self.formatted_info def format_user_upload(self): if self.has_error(): return ds_dict = { 'type': self.dataset.source, 'name': self.dataset.name, 'creator': self.dataset.creator, 'created': self.dataset.created, } self.formatted_info = ds_dict def format_dataverse_dataset(self): if self.has_error(): return citation_info = self.get_citation_from_dataset_schema_or_None() if citation_info.success: citation = citation_info.data else: self.add_err_msg(citation_info.message) return name_info = self.get_name_from_dataset_schema() if name_info.success: ds_name = name_info.data else: self.add_err_msg(name_info.message) return file_info = self.get_file_info() if file_info.success: file_dict = file_info.data else: self.add_err_msg(file_info.message) return ds_dict = { 'type': self.dataset.source, 'name': self.dataset.name, "citation": citation, "doi": self.dataset.dataset_doi, "identifier": self.get_dataset_identifier_or_none(), 'release_deposit_info': { "deposited": False, }, 'installation': { "name": self.dataset.dv_installation.name, "url": self.dataset.dv_installation.dataverse_url }, "file_information": file_dict } self.formatted_info = ds_dict def get_name_from_dataset_schema(self) -> BasicResponse: if self.has_error(): return err_resp(self.get_err_msg()) if not self.dataset.dataset_schema_info: return err_resp('".dataset_schema_info" is empty') if not 'name' in self.dataset.dataset_schema_info: return err_resp('"name" not found in ".dataset_schema_info" not found') ds_name = self.dataset.dataset_schema_info['name'] if not ds_name: return err_resp('"name" within ".dataset_schema_info" is empty') return ok_resp(ds_name) def get_dataset_identifier_or_none(self): if '@id' in self.dataset.dataset_schema_info['@id']: return elf.dataset.dataset_schema_info['@id'] return None def get_citation_from_dataset_schema_or_None(self): if self.has_error(): # Shouldn't happen... return err_resp(self.get_err_msg()) if not self.dataset.dataset_schema_info: return err_resp('".dataset_schema_info" is empty') if not 'citation' in self.dataset.dataset_schema_info: return ok_resp(None) if (not self.dataset.dataset_schema_info['citation']) or \ (not isinstance(self.dataset.dataset_schema_info['citation'], list)): return err_resp('"citation" within ".dataset_schema_info" is empty or not a list') if not 'text' in self.dataset.dataset_schema_info['citation'][0]: return err_resp('"[\'citation\'][0][\'text\']" not found in ".dataset_schema_info"') return ok_resp(self.dataset.dataset_schema_info['citation'][0]['text']) def get_file_info(self): if self.has_error(): return err_resp(self.get_err_msg()) if not self.dataset.file_schema_info: return err_resp('".file_schema_info" is empty') file_dict = {} if 'name' in self.dataset.file_schema_info: file_dict['name'] = self.dataset.file_schema_info['name'] else: return err_resp('"name" not found in ".file_schema_info" not found') if 'identifier' in self.dataset.file_schema_info: file_dict['identifier'] = self.dataset.file_schema_info['identifier'] else: file_dict['identifier'] = None if 'fileFormat' in self.dataset.file_schema_info: file_dict['fileFormat'] = self.dataset.file_schema_info['fileFormat'] else: file_dict['fileFormat'] = None return ok_resp(file_dict)

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py

Python

04_test.py

500kg/learn2branch

693d6f68def3ce290a0f5f289820e708019c019a

[ "MIT" ]

248

2019-01-10T21:58:46.000Z

2022-03-30T07:55:34.000Z

04_test.py

500kg/learn2branch

693d6f68def3ce290a0f5f289820e708019c019a

[ "MIT" ]

17

2018-10-09T19:17:25.000Z

2022-02-27T07:33:11.000Z

04_test.py

500kg/learn2branch

693d6f68def3ce290a0f5f289820e708019c019a

[ "MIT" ]

66

2019-06-08T12:18:43.000Z

2022-03-29T07:44:18.000Z

import os import sys import importlib import argparse import csv import numpy as np import time import pickle import pathlib import gzip import tensorflow as tf import tensorflow.contrib.eager as tfe import svmrank import utilities from utilities_tf import load_batch_gcnn def load_batch_flat(sample_files, feats_type, augment_feats, normalize_feats): cand_features = [] cand_choices = [] cand_scoress = [] for i, filename in enumerate(sample_files): cand_states, cand_scores, cand_choice = utilities.load_flat_samples(filename, feats_type, 'scores', augment_feats, normalize_feats) cand_features.append(cand_states) cand_choices.append(cand_choice) cand_scoress.append(cand_scores) n_cands_per_sample = [v.shape[0] for v in cand_features] cand_features = np.concatenate(cand_features, axis=0).astype(np.float32, copy=False) cand_choices = np.asarray(cand_choices).astype(np.int32, copy=False) cand_scoress = np.concatenate(cand_scoress, axis=0).astype(np.float32, copy=False) n_cands_per_sample = np.asarray(n_cands_per_sample).astype(np.int32, copy=False) return cand_features, n_cands_per_sample, cand_choices, cand_scoress def padding(output, n_vars_per_sample, fill=-1e8): n_vars_max = tf.reduce_max(n_vars_per_sample) output = tf.split( value=output, num_or_size_splits=n_vars_per_sample, axis=1, ) output = tf.concat([ tf.pad( x, paddings=[[0, 0], [0, n_vars_max - tf.shape(x)[1]]], mode='CONSTANT', constant_values=fill) for x in output ], axis=0) return output def process(policy, dataloader, top_k): mean_kacc = np.zeros(len(top_k)) n_samples_processed = 0 for batch in dataloader: if policy['type'] == 'gcnn': c, ei, ev, v, n_cs, n_vs, n_cands, cands, best_cands, cand_scores = batch pred_scores = policy['model']((c, ei, ev, v, tf.reduce_sum(n_cs, keepdims=True), tf.reduce_sum(n_vs, keepdims=True)), tf.convert_to_tensor(False)) # filter candidate variables pred_scores = tf.expand_dims(tf.gather(tf.squeeze(pred_scores, 0), cands), 0) elif policy['type'] == 'ml-competitor': cand_feats, n_cands, best_cands, cand_scores = batch # move to numpy cand_feats = cand_feats.numpy() n_cands = n_cands.numpy() # feature normalization cand_feats = (cand_feats - policy['feat_shift']) / policy['feat_scale'] pred_scores = policy['model'].predict(cand_feats) # move back to TF pred_scores = tf.convert_to_tensor(pred_scores.reshape((1, -1)), dtype=tf.float32) # padding pred_scores = padding(pred_scores, n_cands) true_scores = padding(tf.reshape(cand_scores, (1, -1)), n_cands) true_bestscore = tf.reduce_max(true_scores, axis=-1, keepdims=True) assert all(true_bestscore.numpy() == np.take_along_axis(true_scores.numpy(), best_cands.numpy().reshape((-1, 1)), axis=1)) kacc = [] for k in top_k: pred_top_k = tf.nn.top_k(pred_scores, k=k)[1].numpy() pred_top_k_true_scores = np.take_along_axis(true_scores.numpy(), pred_top_k, axis=1) kacc.append(np.mean(np.any(pred_top_k_true_scores == true_bestscore.numpy(), axis=1))) kacc = np.asarray(kacc) batch_size = int(n_cands.shape[0]) mean_kacc += kacc * batch_size n_samples_processed += batch_size mean_kacc /= n_samples_processed return mean_kacc if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( 'problem', help='MILP instance type to process.', choices=['setcover', 'cauctions', 'facilities', 'indset'], ) parser.add_argument( '-g', '--gpu', help='CUDA GPU id (-1 for CPU).', type=int, default=0, ) args = parser.parse_args() print(f"problem: {args.problem}") print(f"gpu: {args.gpu}") os.makedirs("results", exist_ok=True) result_file = f"results/{args.problem}_validation_{time.strftime('%Y%m%d-%H%M%S')}.csv" seeds = [0, 1, 2, 3, 4] gcnn_models = ['baseline'] other_models = ['extratrees_gcnn_agg', 'lambdamart_khalil', 'svmrank_khalil'] test_batch_size = 128 top_k = [1, 3, 5, 10] problem_folders = { 'setcover': 'setcover/500r_1000c_0.05d', 'cauctions': 'cauctions/100_500', 'facilities': 'facilities/100_100_5', 'indset': 'indset/500_4', } problem_folder = problem_folders[args.problem] if args.problem == 'setcover': gcnn_models += ['mean_convolution', 'no_prenorm'] result_file = f"results/{args.problem}_test_{time.strftime('%Y%m%d-%H%M%S')}" result_file = result_file + '.csv' os.makedirs('results', exist_ok=True) ### TENSORFLOW SETUP ### if args.gpu == -1: os.environ['CUDA_VISIBLE_DEVICES'] = '' else: os.environ['CUDA_VISIBLE_DEVICES'] = f'{args.gpu}' config = tf.ConfigProto() config.gpu_options.allow_growth = True tf.enable_eager_execution(config) tf.executing_eagerly() test_files = list(pathlib.Path(f"data/samples/{problem_folder}/test").glob('sample_*.pkl')) test_files = [str(x) for x in test_files] print(f"{len(test_files)} test samples") evaluated_policies = [['gcnn', model] for model in gcnn_models] + \ [['ml-competitor', model] for model in other_models] fieldnames = [ 'policy', 'seed', ] + [ f'acc@{k}' for k in top_k ] with open(result_file, 'w', newline='') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writeheader() for policy_type, policy_name in evaluated_policies: print(f"{policy_type}:{policy_name}...") for seed in seeds: rng = np.random.RandomState(seed) tf.set_random_seed(rng.randint(np.iinfo(int).max)) policy = {} policy['name'] = policy_name policy['type'] = policy_type if policy['type'] == 'gcnn': # load model sys.path.insert(0, os.path.abspath(f"models/{policy['name']}")) import model importlib.reload(model) del sys.path[0] policy['model'] = model.GCNPolicy() policy['model'].restore_state(f"trained_models/{args.problem}/{policy['name']}/{seed}/best_params.pkl") policy['model'].call = tfe.defun(policy['model'].call, input_signature=policy['model'].input_signature) policy['batch_datatypes'] = [tf.float32, tf.int32, tf.float32, tf.float32, tf.int32, tf.int32, tf.int32, tf.int32, tf.int32, tf.float32] policy['batch_fun'] = load_batch_gcnn else: # load feature normalization parameters try: with open(f"trained_models/{args.problem}/{policy['name']}/{seed}/normalization.pkl", 'rb') as f: policy['feat_shift'], policy['feat_scale'] = pickle.load(f) except: policy['feat_shift'], policy['feat_scale'] = 0, 1 # load model if policy_name.startswith('svmrank'): policy['model'] = svmrank.Model().read(f"trained_models/{args.problem}/{policy['name']}/{seed}/model.txt") else: with open(f"trained_models/{args.problem}/{policy['name']}/{seed}/model.pkl", 'rb') as f: policy['model'] = pickle.load(f) # load feature specifications with open(f"trained_models/{args.problem}/{policy['name']}/{seed}/feat_specs.pkl", 'rb') as f: feat_specs = pickle.load(f) policy['batch_datatypes'] = [tf.float32, tf.int32, tf.int32, tf.float32] policy['batch_fun'] = lambda x: load_batch_flat(x, feat_specs['type'], feat_specs['augment'], feat_specs['qbnorm']) test_data = tf.data.Dataset.from_tensor_slices(test_files) test_data = test_data.batch(test_batch_size) test_data = test_data.map(lambda x: tf.py_func( policy['batch_fun'], [x], policy['batch_datatypes'])) test_data = test_data.prefetch(2) test_kacc = process(policy, test_data, top_k) print(f" {seed} " + " ".join([f"acc@{k}: {100*acc:4.1f}" for k, acc in zip(top_k, test_kacc)])) writer.writerow({ **{ 'policy': f"{policy['type']}:{policy['name']}", 'seed': seed, }, **{ f'acc@{k}': test_kacc[i] for i, k in enumerate(top_k) }, }) csvfile.flush()

37.060484

158

0.586878

import os import sys import importlib import argparse import csv import numpy as np import time import pickle import pathlib import gzip import tensorflow as tf import tensorflow.contrib.eager as tfe import svmrank import utilities from utilities_tf import load_batch_gcnn def load_batch_flat(sample_files, feats_type, augment_feats, normalize_feats): cand_features = [] cand_choices = [] cand_scoress = [] for i, filename in enumerate(sample_files): cand_states, cand_scores, cand_choice = utilities.load_flat_samples(filename, feats_type, 'scores', augment_feats, normalize_feats) cand_features.append(cand_states) cand_choices.append(cand_choice) cand_scoress.append(cand_scores) n_cands_per_sample = [v.shape[0] for v in cand_features] cand_features = np.concatenate(cand_features, axis=0).astype(np.float32, copy=False) cand_choices = np.asarray(cand_choices).astype(np.int32, copy=False) cand_scoress = np.concatenate(cand_scoress, axis=0).astype(np.float32, copy=False) n_cands_per_sample = np.asarray(n_cands_per_sample).astype(np.int32, copy=False) return cand_features, n_cands_per_sample, cand_choices, cand_scoress def padding(output, n_vars_per_sample, fill=-1e8): n_vars_max = tf.reduce_max(n_vars_per_sample) output = tf.split( value=output, num_or_size_splits=n_vars_per_sample, axis=1, ) output = tf.concat([ tf.pad( x, paddings=[[0, 0], [0, n_vars_max - tf.shape(x)[1]]], mode='CONSTANT', constant_values=fill) for x in output ], axis=0) return output def process(policy, dataloader, top_k): mean_kacc = np.zeros(len(top_k)) n_samples_processed = 0 for batch in dataloader: if policy['type'] == 'gcnn': c, ei, ev, v, n_cs, n_vs, n_cands, cands, best_cands, cand_scores = batch pred_scores = policy['model']((c, ei, ev, v, tf.reduce_sum(n_cs, keepdims=True), tf.reduce_sum(n_vs, keepdims=True)), tf.convert_to_tensor(False)) pred_scores = tf.expand_dims(tf.gather(tf.squeeze(pred_scores, 0), cands), 0) elif policy['type'] == 'ml-competitor': cand_feats, n_cands, best_cands, cand_scores = batch cand_feats = cand_feats.numpy() n_cands = n_cands.numpy() cand_feats = (cand_feats - policy['feat_shift']) / policy['feat_scale'] pred_scores = policy['model'].predict(cand_feats) pred_scores = tf.convert_to_tensor(pred_scores.reshape((1, -1)), dtype=tf.float32) pred_scores = padding(pred_scores, n_cands) true_scores = padding(tf.reshape(cand_scores, (1, -1)), n_cands) true_bestscore = tf.reduce_max(true_scores, axis=-1, keepdims=True) assert all(true_bestscore.numpy() == np.take_along_axis(true_scores.numpy(), best_cands.numpy().reshape((-1, 1)), axis=1)) kacc = [] for k in top_k: pred_top_k = tf.nn.top_k(pred_scores, k=k)[1].numpy() pred_top_k_true_scores = np.take_along_axis(true_scores.numpy(), pred_top_k, axis=1) kacc.append(np.mean(np.any(pred_top_k_true_scores == true_bestscore.numpy(), axis=1))) kacc = np.asarray(kacc) batch_size = int(n_cands.shape[0]) mean_kacc += kacc * batch_size n_samples_processed += batch_size mean_kacc /= n_samples_processed return mean_kacc if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( 'problem', help='MILP instance type to process.', choices=['setcover', 'cauctions', 'facilities', 'indset'], ) parser.add_argument( '-g', '--gpu', help='CUDA GPU id (-1 for CPU).', type=int, default=0, ) args = parser.parse_args() print(f"problem: {args.problem}") print(f"gpu: {args.gpu}") os.makedirs("results", exist_ok=True) result_file = f"results/{args.problem}_validation_{time.strftime('%Y%m%d-%H%M%S')}.csv" seeds = [0, 1, 2, 3, 4] gcnn_models = ['baseline'] other_models = ['extratrees_gcnn_agg', 'lambdamart_khalil', 'svmrank_khalil'] test_batch_size = 128 top_k = [1, 3, 5, 10] problem_folders = { 'setcover': 'setcover/500r_1000c_0.05d', 'cauctions': 'cauctions/100_500', 'facilities': 'facilities/100_100_5', 'indset': 'indset/500_4', } problem_folder = problem_folders[args.problem] if args.problem == 'setcover': gcnn_models += ['mean_convolution', 'no_prenorm'] result_file = f"results/{args.problem}_test_{time.strftime('%Y%m%d-%H%M%S')}" result_file = result_file + '.csv' os.makedirs('results', exist_ok=True) SIBLE_DEVICES'] = '' else: os.environ['CUDA_VISIBLE_DEVICES'] = f'{args.gpu}' config = tf.ConfigProto() config.gpu_options.allow_growth = True tf.enable_eager_execution(config) tf.executing_eagerly() test_files = list(pathlib.Path(f"data/samples/{problem_folder}/test").glob('sample_*.pkl')) test_files = [str(x) for x in test_files] print(f"{len(test_files)} test samples") evaluated_policies = [['gcnn', model] for model in gcnn_models] + \ [['ml-competitor', model] for model in other_models] fieldnames = [ 'policy', 'seed', ] + [ f'acc@{k}' for k in top_k ] with open(result_file, 'w', newline='') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writeheader() for policy_type, policy_name in evaluated_policies: print(f"{policy_type}:{policy_name}...") for seed in seeds: rng = np.random.RandomState(seed) tf.set_random_seed(rng.randint(np.iinfo(int).max)) policy = {} policy['name'] = policy_name policy['type'] = policy_type if policy['type'] == 'gcnn': sys.path.insert(0, os.path.abspath(f"models/{policy['name']}")) import model importlib.reload(model) del sys.path[0] policy['model'] = model.GCNPolicy() policy['model'].restore_state(f"trained_models/{args.problem}/{policy['name']}/{seed}/best_params.pkl") policy['model'].call = tfe.defun(policy['model'].call, input_signature=policy['model'].input_signature) policy['batch_datatypes'] = [tf.float32, tf.int32, tf.float32, tf.float32, tf.int32, tf.int32, tf.int32, tf.int32, tf.int32, tf.float32] policy['batch_fun'] = load_batch_gcnn else: try: with open(f"trained_models/{args.problem}/{policy['name']}/{seed}/normalization.pkl", 'rb') as f: policy['feat_shift'], policy['feat_scale'] = pickle.load(f) except: policy['feat_shift'], policy['feat_scale'] = 0, 1 if policy_name.startswith('svmrank'): policy['model'] = svmrank.Model().read(f"trained_models/{args.problem}/{policy['name']}/{seed}/model.txt") else: with open(f"trained_models/{args.problem}/{policy['name']}/{seed}/model.pkl", 'rb') as f: policy['model'] = pickle.load(f) with open(f"trained_models/{args.problem}/{policy['name']}/{seed}/feat_specs.pkl", 'rb') as f: feat_specs = pickle.load(f) policy['batch_datatypes'] = [tf.float32, tf.int32, tf.int32, tf.float32] policy['batch_fun'] = lambda x: load_batch_flat(x, feat_specs['type'], feat_specs['augment'], feat_specs['qbnorm']) test_data = tf.data.Dataset.from_tensor_slices(test_files) test_data = test_data.batch(test_batch_size) test_data = test_data.map(lambda x: tf.py_func( policy['batch_fun'], [x], policy['batch_datatypes'])) test_data = test_data.prefetch(2) test_kacc = process(policy, test_data, top_k) print(f" {seed} " + " ".join([f"acc@{k}: {100*acc:4.1f}" for k, acc in zip(top_k, test_kacc)])) writer.writerow({ **{ 'policy': f"{policy['type']}:{policy['name']}", 'seed': seed, }, **{ f'acc@{k}': test_kacc[i] for i, k in enumerate(top_k) }, }) csvfile.flush()

true

f72afdb37d0bc3631c2708300be0110723f46ee0

4,090

py

Python

src/python/pants/ivy/ivy_subsystem.py

SergeKireev/pants

cd92c65aeb3dfdcee3e0946f2b68a301ef2f4541

[ "Apache-2.0" ]

1

2020-08-26T03:30:31.000Z

src/python/pants/ivy/ivy_subsystem.py

SergeKireev/pants

cd92c65aeb3dfdcee3e0946f2b68a301ef2f4541

[ "Apache-2.0" ]

1

2021-09-02T21:06:31.000Z

src/python/pants/ivy/ivy_subsystem.py

SergeKireev/pants

cd92c65aeb3dfdcee3e0946f2b68a301ef2f4541

[ "Apache-2.0" ]

null

# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). import os import urllib from pants.java.distribution.distribution import DistributionLocator from pants.subsystem.subsystem import Subsystem class IvySubsystem(Subsystem): """Common configuration items for ivy tasks. :API: public """ options_scope = 'ivy' _DEFAULT_VERSION = '2.4.0' _DEFAULT_URL = ('https://repo1.maven.org/maven2/' 'org/apache/ivy/ivy/' '{version}/ivy-{version}.jar'.format(version=_DEFAULT_VERSION)) @classmethod def register_options(cls, register): super().register_options(register) register('--http-proxy', advanced=True, help='Specify a proxy URL for http requests.') register('--https-proxy', advanced=True, help='Specify a proxy URL for https requests.') register('--bootstrap-jar-url', advanced=True, default=cls._DEFAULT_URL, help='Location to download a bootstrap version of Ivy.') register('--bootstrap-fetch-timeout-secs', type=int, advanced=True, default=10, help='Timeout the fetch if the connection is idle for longer than this value.') register('--ivy-profile', advanced=True, default=cls._DEFAULT_VERSION, help='The version of ivy to fetch.') register('--cache-dir', advanced=True, default=os.path.expanduser('~/.ivy2/pants'), help='The default directory used for both the Ivy resolution and repository caches.' 'If you want to isolate the resolution cache from the repository cache, we ' 'recommend setting both the --resolution-cache-dir and --repository-cache-dir ' 'instead of using --cache-dir') register('--resolution-cache-dir', advanced=True, help='Directory to store Ivy resolution artifacts.') register('--repository-cache-dir', advanced=True, help='Directory to store Ivy repository artifacts.') register('--ivy-settings', advanced=True, help='Location of XML configuration file for Ivy settings.') register('--bootstrap-ivy-settings', advanced=True, help='Bootstrap Ivy XML configuration file.') @classmethod def subsystem_dependencies(cls): return super().subsystem_dependencies() + (DistributionLocator,) def http_proxy(self): """Set ivy to use an http proxy. Expects a string of the form http://<host>:<port> """ if os.getenv('HTTP_PROXY'): return os.getenv('HTTP_PROXY') if os.getenv('http_proxy'): return os.getenv('http_proxy') return self.get_options().http_proxy def https_proxy(self): """Set ivy to use an https proxy. Expects a string of the form http://<host>:<port> """ if os.getenv('HTTPS_PROXY'): return os.getenv('HTTPS_PROXY') if os.getenv('https_proxy'): return os.getenv('https_proxy') return self.get_options().https_proxy def extra_jvm_options(self): extra_options = [] http_proxy = self.http_proxy() if http_proxy: host, port = self._parse_proxy_string(http_proxy) extra_options.extend([ "-Dhttp.proxyHost={}".format(host), "-Dhttp.proxyPort={}".format(port), ]) https_proxy = self.https_proxy() if https_proxy: host, port = self._parse_proxy_string(https_proxy) extra_options.extend([ "-Dhttps.proxyHost={}".format(host), "-Dhttps.proxyPort={}".format(port), ]) return extra_options def _parse_proxy_string(self, proxy_string): parse_result = urllib.parse.urlparse(proxy_string) return parse_result.hostname, parse_result.port def resolution_cache_dir(self): if self.get_options().resolution_cache_dir: return self.get_options().resolution_cache_dir else: return self.get_options().cache_dir def repository_cache_dir(self): if self.get_options().repository_cache_dir: return self.get_options().repository_cache_dir else: return self.get_options().cache_dir

37.181818

97

0.674817

import os import urllib from pants.java.distribution.distribution import DistributionLocator from pants.subsystem.subsystem import Subsystem class IvySubsystem(Subsystem): options_scope = 'ivy' _DEFAULT_VERSION = '2.4.0' _DEFAULT_URL = ('https://repo1.maven.org/maven2/' 'org/apache/ivy/ivy/' '{version}/ivy-{version}.jar'.format(version=_DEFAULT_VERSION)) @classmethod def register_options(cls, register): super().register_options(register) register('--http-proxy', advanced=True, help='Specify a proxy URL for http requests.') register('--https-proxy', advanced=True, help='Specify a proxy URL for https requests.') register('--bootstrap-jar-url', advanced=True, default=cls._DEFAULT_URL, help='Location to download a bootstrap version of Ivy.') register('--bootstrap-fetch-timeout-secs', type=int, advanced=True, default=10, help='Timeout the fetch if the connection is idle for longer than this value.') register('--ivy-profile', advanced=True, default=cls._DEFAULT_VERSION, help='The version of ivy to fetch.') register('--cache-dir', advanced=True, default=os.path.expanduser('~/.ivy2/pants'), help='The default directory used for both the Ivy resolution and repository caches.' 'If you want to isolate the resolution cache from the repository cache, we ' 'recommend setting both the --resolution-cache-dir and --repository-cache-dir ' 'instead of using --cache-dir') register('--resolution-cache-dir', advanced=True, help='Directory to store Ivy resolution artifacts.') register('--repository-cache-dir', advanced=True, help='Directory to store Ivy repository artifacts.') register('--ivy-settings', advanced=True, help='Location of XML configuration file for Ivy settings.') register('--bootstrap-ivy-settings', advanced=True, help='Bootstrap Ivy XML configuration file.') @classmethod def subsystem_dependencies(cls): return super().subsystem_dependencies() + (DistributionLocator,) def http_proxy(self): if os.getenv('HTTP_PROXY'): return os.getenv('HTTP_PROXY') if os.getenv('http_proxy'): return os.getenv('http_proxy') return self.get_options().http_proxy def https_proxy(self): if os.getenv('HTTPS_PROXY'): return os.getenv('HTTPS_PROXY') if os.getenv('https_proxy'): return os.getenv('https_proxy') return self.get_options().https_proxy def extra_jvm_options(self): extra_options = [] http_proxy = self.http_proxy() if http_proxy: host, port = self._parse_proxy_string(http_proxy) extra_options.extend([ "-Dhttp.proxyHost={}".format(host), "-Dhttp.proxyPort={}".format(port), ]) https_proxy = self.https_proxy() if https_proxy: host, port = self._parse_proxy_string(https_proxy) extra_options.extend([ "-Dhttps.proxyHost={}".format(host), "-Dhttps.proxyPort={}".format(port), ]) return extra_options def _parse_proxy_string(self, proxy_string): parse_result = urllib.parse.urlparse(proxy_string) return parse_result.hostname, parse_result.port def resolution_cache_dir(self): if self.get_options().resolution_cache_dir: return self.get_options().resolution_cache_dir else: return self.get_options().cache_dir def repository_cache_dir(self): if self.get_options().repository_cache_dir: return self.get_options().repository_cache_dir else: return self.get_options().cache_dir

true

f72afdfc03221196ea9ceaf1098c9e1569cc1366

808

py

Python

sampling/text.py

YoannDupont/corpus-sampling

20fd993bc967fd499e88444d882472ba7598c197

[ "MIT" ]

null

sampling/text.py

YoannDupont/corpus-sampling

20fd993bc967fd499e88444d882472ba7598c197

[ "MIT" ]

null

sampling/text.py

YoannDupont/corpus-sampling

20fd993bc967fd499e88444d882472ba7598c197

[ "MIT" ]

null

from pathlib import Path import nltk from nltk.tokenize import sent_tokenize tokenizer = nltk.RegexpTokenizer(r"([A-Z][A-Z0-9.]+|[0-9]+[,.][0-9]+|[cdjlmnst]'|qu'|[\w'-]+|\S)") class Sentence: def __init__(self, text, nth): self.text = text self.nth = nth def __len__(self): return len(tokenizer.tokenize(self.text)) @property def id(self): return self.nth def contains_pos(self, postag): return False def count_pos(self, postag): return 0 def read_corpus(path): corpus = [] with open(path) as input_stream: content = input_stream.read() sents = [item.replace("\n", " ") for item in sent_tokenize(content)] for nth, sent in enumerate(sents): corpus.append(Sentence(sent, nth)) return corpus

22.444444

98

0.62005

from pathlib import Path import nltk from nltk.tokenize import sent_tokenize tokenizer = nltk.RegexpTokenizer(r"([A-Z][A-Z0-9.]+|[0-9]+[,.][0-9]+|[cdjlmnst]'|qu'|[\w'-]+|\S)") class Sentence: def __init__(self, text, nth): self.text = text self.nth = nth def __len__(self): return len(tokenizer.tokenize(self.text)) @property def id(self): return self.nth def contains_pos(self, postag): return False def count_pos(self, postag): return 0 def read_corpus(path): corpus = [] with open(path) as input_stream: content = input_stream.read() sents = [item.replace("\n", " ") for item in sent_tokenize(content)] for nth, sent in enumerate(sents): corpus.append(Sentence(sent, nth)) return corpus

true

f72aff11df732c260aca806b126e282388a93204

4,897

py

Python

seahub/api2/authentication.py

saukrIppl/newsea

0fd5ab2ade9a8fb16b1e7b43ba13dac32eb39603

[ "Apache-2.0" ]

2

2017-06-21T09:46:55.000Z

2018-05-30T10:07:32.000Z

seahub/api2/authentication.py

saukrIppl/newsea

0fd5ab2ade9a8fb16b1e7b43ba13dac32eb39603

[ "Apache-2.0" ]

null

seahub/api2/authentication.py

saukrIppl/newsea

0fd5ab2ade9a8fb16b1e7b43ba13dac32eb39603

[ "Apache-2.0" ]

1

2020-10-01T04:11:41.000Z

import datetime import logging from rest_framework import status from rest_framework.authentication import BaseAuthentication from rest_framework.exceptions import APIException import seaserv from seahub.base.accounts import User from seahub.constants import GUEST_USER from seahub.api2.models import Token, TokenV2 from seahub.api2.utils import get_client_ip from seahub.utils import within_time_range try: from seahub.settings import MULTI_TENANCY except ImportError: MULTI_TENANCY = False logger = logging.getLogger(__name__) HEADER_CLIENT_VERSION = 'HTTP_X_SEAFILE_CLIENT_VERSION' HEADER_PLATFORM_VERSION = 'HTTP_X_SEAFILE_PLATFORM_VERSION' class AuthenticationFailed(APIException): status_code = status.HTTP_401_UNAUTHORIZED default_detail = 'Incorrect authentication credentials.' def __init__(self, detail=None): self.detail = detail or self.default_detail class TokenAuthentication(BaseAuthentication): """ Simple token based authentication. Clients should authenticate by passing the token key in the "Authorization" HTTP header, prepended with the string "Token ". For example: Authorization: Token 401f7ac837da42b97f613d789819ff93537bee6a A custom token model may be used, but must have the following properties. * key -- The string identifying the token * user -- The user to which the token belongs """ def authenticate(self, request): auth = request.META.get('HTTP_AUTHORIZATION', '').split() if not auth or auth[0].lower() != 'token': return None if len(auth) == 1: msg = 'Invalid token header. No credentials provided.' raise AuthenticationFailed(msg) elif len(auth) > 2: msg = 'Invalid token header. Token string should not contain spaces.' raise AuthenticationFailed(msg) key = auth[1] ret = self.authenticate_v2(request, key) if ret: return ret return self.authenticate_v1(request, key) def _populate_user_permissions(self, user): """Disable some operations if ``user`` is a guest. """ if user.role == GUEST_USER: user.permissions.can_add_repo = lambda: False user.permissions.can_add_group = lambda: False user.permissions.can_view_org = lambda: False user.permissions.can_use_global_address_book = lambda: False user.permissions.can_generate_shared_link = lambda: False def authenticate_v1(self, request, key): try: token = Token.objects.get(key=key) except Token.DoesNotExist: raise AuthenticationFailed('Invalid token') try: user = User.objects.get(email=token.user) except User.DoesNotExist: raise AuthenticationFailed('User inactive or deleted') if MULTI_TENANCY: orgs = seaserv.get_orgs_by_user(token.user) if orgs: user.org = orgs[0] self._populate_user_permissions(user) if user.is_active: return (user, token) def authenticate_v2(self, request, key): try: token = TokenV2.objects.get(key=key) except TokenV2.DoesNotExist: return None # Continue authentication in token v1 try: user = User.objects.get(email=token.user) except User.DoesNotExist: raise AuthenticationFailed('User inactive or deleted') if MULTI_TENANCY: orgs = seaserv.get_orgs_by_user(token.user) if orgs: user.org = orgs[0] self._populate_user_permissions(user) if user.is_active: need_save = False # We update the device's last_login_ip, client_version, platform_version if changed ip = get_client_ip(request) if ip and ip != token.last_login_ip: token.last_login_ip = ip need_save = True client_version = request.META.get(HEADER_CLIENT_VERSION, '') if client_version and client_version != token.client_version: token.client_version = client_version need_save = True platform_version = request.META.get(HEADER_PLATFORM_VERSION, '') if platform_version and platform_version != token.platform_version: token.platform_version = platform_version need_save = True if not within_time_range(token.last_accessed, datetime.datetime.now(), 10 * 60): # We only need 10min precision for the last_accessed field need_save = True if need_save: try: token.save() except: logger.exception('error when save token v2:') return (user, token)

33.772414

95

0.647131

import datetime import logging from rest_framework import status from rest_framework.authentication import BaseAuthentication from rest_framework.exceptions import APIException import seaserv from seahub.base.accounts import User from seahub.constants import GUEST_USER from seahub.api2.models import Token, TokenV2 from seahub.api2.utils import get_client_ip from seahub.utils import within_time_range try: from seahub.settings import MULTI_TENANCY except ImportError: MULTI_TENANCY = False logger = logging.getLogger(__name__) HEADER_CLIENT_VERSION = 'HTTP_X_SEAFILE_CLIENT_VERSION' HEADER_PLATFORM_VERSION = 'HTTP_X_SEAFILE_PLATFORM_VERSION' class AuthenticationFailed(APIException): status_code = status.HTTP_401_UNAUTHORIZED default_detail = 'Incorrect authentication credentials.' def __init__(self, detail=None): self.detail = detail or self.default_detail class TokenAuthentication(BaseAuthentication): def authenticate(self, request): auth = request.META.get('HTTP_AUTHORIZATION', '').split() if not auth or auth[0].lower() != 'token': return None if len(auth) == 1: msg = 'Invalid token header. No credentials provided.' raise AuthenticationFailed(msg) elif len(auth) > 2: msg = 'Invalid token header. Token string should not contain spaces.' raise AuthenticationFailed(msg) key = auth[1] ret = self.authenticate_v2(request, key) if ret: return ret return self.authenticate_v1(request, key) def _populate_user_permissions(self, user): if user.role == GUEST_USER: user.permissions.can_add_repo = lambda: False user.permissions.can_add_group = lambda: False user.permissions.can_view_org = lambda: False user.permissions.can_use_global_address_book = lambda: False user.permissions.can_generate_shared_link = lambda: False def authenticate_v1(self, request, key): try: token = Token.objects.get(key=key) except Token.DoesNotExist: raise AuthenticationFailed('Invalid token') try: user = User.objects.get(email=token.user) except User.DoesNotExist: raise AuthenticationFailed('User inactive or deleted') if MULTI_TENANCY: orgs = seaserv.get_orgs_by_user(token.user) if orgs: user.org = orgs[0] self._populate_user_permissions(user) if user.is_active: return (user, token) def authenticate_v2(self, request, key): try: token = TokenV2.objects.get(key=key) except TokenV2.DoesNotExist: return None try: user = User.objects.get(email=token.user) except User.DoesNotExist: raise AuthenticationFailed('User inactive or deleted') if MULTI_TENANCY: orgs = seaserv.get_orgs_by_user(token.user) if orgs: user.org = orgs[0] self._populate_user_permissions(user) if user.is_active: need_save = False ip = get_client_ip(request) if ip and ip != token.last_login_ip: token.last_login_ip = ip need_save = True client_version = request.META.get(HEADER_CLIENT_VERSION, '') if client_version and client_version != token.client_version: token.client_version = client_version need_save = True platform_version = request.META.get(HEADER_PLATFORM_VERSION, '') if platform_version and platform_version != token.platform_version: token.platform_version = platform_version need_save = True if not within_time_range(token.last_accessed, datetime.datetime.now(), 10 * 60): # We only need 10min precision for the last_accessed field need_save = True if need_save: try: token.save() except: logger.exception('error when save token v2:') return (user, token)

true

f72affbaf63edad2e1efdfe81604b7c4734c0339

405

py

Python

setup.py

mstroud/python-matrix-gfyrslf

0375bfb12d1cd50611f01101917d2cd2123543e4

[ "MIT" ]

null

setup.py

mstroud/python-matrix-gfyrslf

0375bfb12d1cd50611f01101917d2cd2123543e4

[ "MIT" ]

null

setup.py

mstroud/python-matrix-gfyrslf

0375bfb12d1cd50611f01101917d2cd2123543e4

[ "MIT" ]

null

from distutils.core import setup DESC='A simple, extensible chatbot for Matrix' setup( name='python-matrix-gfyrslf', version='0.1', author='Matt Stroud', author_email='see github', url='https://github.com/mstroud/python-matrix-gfyrslf', packages=['python-matrix-gfyrslf'], install_requires=['matrix_client'], license='MIT', summary=DESC, long_description=DESC, )

23.823529

59

0.688889

from distutils.core import setup DESC='A simple, extensible chatbot for Matrix' setup( name='python-matrix-gfyrslf', version='0.1', author='Matt Stroud', author_email='see github', url='https://github.com/mstroud/python-matrix-gfyrslf', packages=['python-matrix-gfyrslf'], install_requires=['matrix_client'], license='MIT', summary=DESC, long_description=DESC, )

true

f72b00a5286e87e05ac8c588aa0072278e0c0565

30

py

Python

bot/__init__.py

Sc2-AI-Cup/example-bot-workerrush

6a4ddcc4c22018bcd64d07ba405b7ef13ed634f2

[ "MIT" ]

null

bot/__init__.py

Sc2-AI-Cup/example-bot-workerrush

6a4ddcc4c22018bcd64d07ba405b7ef13ed634f2

[ "MIT" ]

null

bot/__init__.py

Sc2-AI-Cup/example-bot-workerrush

6a4ddcc4c22018bcd64d07ba405b7ef13ed634f2

[ "MIT" ]

null

from .bot import WorkerRushBot

30

0.866667

from .bot import WorkerRushBot

true

f72b00c52fc98e9202a373c7817029e4bb84f7b4

8,185

py

Python

controllers.py

Yoshiyuki-Su/FastAPITodo

d9efcc2793eb5191f70923eb669eb9a1a3fcc427

[ "MIT" ]

null

controllers.py

Yoshiyuki-Su/FastAPITodo

d9efcc2793eb5191f70923eb669eb9a1a3fcc427

[ "MIT" ]

6

2020-11-23T14:38:55.000Z

2021-01-10T16:55:57.000Z

controllers.py

Yoshiyuki-Su/FastAPITodo

d9efcc2793eb5191f70923eb669eb9a1a3fcc427

[ "MIT" ]

null

from fastapi import FastAPI, Depends, Form from fastapi.security import HTTPBasic, HTTPBasicCredentials from starlette.templating import Jinja2Templates from starlette.requests import Request from starlette.responses import RedirectResponse from datetime import datetime, timedelta import db import hashlib from mycalendar import MyCalendar import re from auth import auth from models import User, Task app = FastAPI( title='FastAPIでつくるToDoアプリケーション', description='FastAPIチュートリアル：FastAPI(とstarlette)でシンプルなToDoアプリの作成', version='0.0.1' ) security = HTTPBasic() templates = Jinja2Templates(directory="templates") jinja_env = templates.env pattern = re.compile(r'\w{4,20}') # 任意の4~20の英数字を示す正規表現 pattern_pw = re.compile(r'\w{6,20}') # 任意の6~20の英数字を示す正規表現 pattern_mail = re.compile(r'^\w+([-+.]\w+)*@\w+([-.]\w+)*\.\w+([-.]\w+)*$') # e-mailの正規表現 def index(request: Request): return templates.TemplateResponse('index.html', {'request': request}) def admin(request: Request, credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() task = db.session.query(Task).filter(Task.user_id == user.id).all() db.session.close() """ [new] 今日の日付と来週の日付""" today = datetime.now() next_w = today + timedelta(days=7) # １週間後の日付 """ [new] カレンダー関連 """ # カレンダーをHTML形式で取得 cal = MyCalendar(username, {t.deadline.strftime('%Y%m%d'): t.done for t in task}) # 予定がある日付をキーとして渡す cal = cal.formatyear(today.year, 4) # カレンダーをHTMLで取得 # 直近のタスクだけでいいので、リストを書き換える task = [t for t in task if today <= t.deadline <= next_w] links = [t.deadline.strftime('/todo/'+username+'/%Y/%m/%d') for t in task] # 直近の予定リンク return templates.TemplateResponse('admin.html', {'request': request, 'user': user, 'task': task, 'links': links, 'calender': cal}) async def register(request: Request): if request.method == 'GET': return templates.TemplateResponse('register.html', {'request': request, 'username': '', 'error': []}) if request.method == 'POST': data = await request.form() username = data.get('username') password = data.get('password') password_tmp = data.get('password_tmp') mail = data.get('mail') error = [] tmp_user = db.session.query(User).filter(User.username == username).first() if tmp_user is not None: error.append('同じユーザ名のユーザが存在します。') if password != password_tmp: error.append('入力したパスワードが一致しません。') if pattern.match(username) is None: error.append('ユーザ名は4~20文字の半角英数字にしてください。') if pattern_pw.match(password) is None: error.append('パスワードは6~20文字の半角英数字にしてください。') if pattern_mail.match(mail) is None: error.append('正しくメールアドレスを入力してください。') # エラーがあれば登録ページへ戻す if error: return templates.TemplateResponse('register.html', {'request': request, 'username': username, 'error': error}) # 問題がなければユーザ登録 user = User(username, password, mail) db.session.add(user) db.session.commit() db.session.close() return templates.TemplateResponse('complete.html', {'request': request, 'username': username}) def detail(request: Request, username, year, month, day, credentials: HTTPBasicCredentials = Depends(security)): username_tmp = auth(credentials) if username_tmp != username: # もし他のユーザが訪問してきたらはじく return RedirectResponse('/') # ログインユーザを取得 user = db.session.query(User).filter(User.username == username).first() # ログインユーザのタスクを取得 task = db.session.query(Task).filter(Task.user_id == user.id).all() db.session.close() # 該当の日付と一致するものだけのリストにする theday = f'{year}{month.zfill(2)}{day.zfill(2)}' # 月日は0埋めする task = [t for t in task if t.deadline.strftime('%Y%m%d') == theday] return templates.TemplateResponse('detail.html', {'request': request, 'username': username, 'task': task, 'year': year, 'month': month, 'day': day}) async def done(request: Request, credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) # ユーザ情報を取得 user = db.session.query(User).filter(User.username == username).first() # ログインユーザのタスクを取得 task = db.session.query(Task).filter(Task.user_id == user.id).all() # フォームで受け取ったタスクの終了判定を見て内容を変更する data = await request.form() t_dones = data.getlist('done[]') # リストとして取得 for t in task: if str(t.id) in t_dones: # もしIDが一致すれば "終了した予定" とする t.done = True db.session.commit() # update!! db.session.close() return RedirectResponse('/admin') async def add(request: Request, credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() # フォームからデータを取得 data = await request.form() print(data) year = int(data['year']) month = int(data['month']) day = int(data['day']) hour = int(data['hour']) minute = int(data['minute']) deadline = datetime(year=year, month=month, day=day, hour=hour, minute=minute) # 新しくタスクを生成しコミット task = Task(user.id, data['content'], deadline) db.session.add(task) db.session.commit() db.session.close() return RedirectResponse('/admin') def delete(request: Request, t_id, credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() task = db.session.query(Task).filter(Task.id == t_id).first() # もしユーザIDが異なれば削除せずリダイレクト if task.user_id != user.id: return RedirectResponse('/admin') # 削除してコミット db.session.delete(task) db.session.commit() db.session.close() return RedirectResponse('/admin') def get(request: Request, credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() task = db.session.query(Task).filter(Task.user_id == user.id).all() db.session.close() # JSONフォーマット task = [{ 'id': t.id, 'content': t.content, 'deadline': t.deadline.strftime('%Y-%m-%d %H:%M:%S'), 'published': t.date.strftime('%Y-%m-%d %H:%M:%S'), 'done': t.done, } for t in task] return task async def insert(request: Request, content: str = Form(...), deadline: str = Form(...), credentials: HTTPBasicCredentials = Depends(security)): """ タスクを追加してJSONで新規タスクを返す。「deadline」は%Y-%m-%d_%H:%M:%S (e.g. 2019-11-03_12:30:00)の形式 """ username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() task = Task(user.id, content, datetime.strptime(deadline, '%Y-%m-%d_%H:%M:%S')) db.session.add(task) db.session.commit() # テーブルから新しく追加したタスクを取得する task = db.session.query(Task).all()[-1] db.session.close() # 新規タスクをJSONで返す return { 'id': task.id, 'content': task.content, 'deadline': task.deadline.strftime('%Y-%m-%d %H:%M:%S'), 'published': task.date.strftime('%Y-%m-%d %H:%M:%S'), 'done': task.done, }

32.871486

94

0.579475

from fastapi import FastAPI, Depends, Form from fastapi.security import HTTPBasic, HTTPBasicCredentials from starlette.templating import Jinja2Templates from starlette.requests import Request from starlette.responses import RedirectResponse from datetime import datetime, timedelta import db import hashlib from mycalendar import MyCalendar import re from auth import auth from models import User, Task app = FastAPI( title='FastAPIでつくるToDoアプリケーション', description='FastAPIチュートリアル：FastAPI(とstarlette)でシンプルなToDoアプリの作成', version='0.0.1' ) security = HTTPBasic() templates = Jinja2Templates(directory="templates") jinja_env = templates.env pattern = re.compile(r'\w{4,20}') pattern_pw = re.compile(r'\w{6,20}') pattern_mail = re.compile(r'^\w+([-+.]\w+)*@\w+([-.]\w+)*\.\w+([-.]\w+)*$') def index(request: Request): return templates.TemplateResponse('index.html', {'request': request}) def admin(request: Request, credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() task = db.session.query(Task).filter(Task.user_id == user.id).all() db.session.close() today = datetime.now() next_w = today + timedelta(days=7) cal = MyCalendar(username, {t.deadline.strftime('%Y%m%d'): t.done for t in task}) cal = cal.formatyear(today.year, 4) task = [t for t in task if today <= t.deadline <= next_w] links = [t.deadline.strftime('/todo/'+username+'/%Y/%m/%d') for t in task] return templates.TemplateResponse('admin.html', {'request': request, 'user': user, 'task': task, 'links': links, 'calender': cal}) async def register(request: Request): if request.method == 'GET': return templates.TemplateResponse('register.html', {'request': request, 'username': '', 'error': []}) if request.method == 'POST': data = await request.form() username = data.get('username') password = data.get('password') password_tmp = data.get('password_tmp') mail = data.get('mail') error = [] tmp_user = db.session.query(User).filter(User.username == username).first() if tmp_user is not None: error.append('同じユーザ名のユーザが存在します。') if password != password_tmp: error.append('入力したパスワードが一致しません。') if pattern.match(username) is None: error.append('ユーザ名は4~20文字の半角英数字にしてください。') if pattern_pw.match(password) is None: error.append('パスワードは6~20文字の半角英数字にしてください。') if pattern_mail.match(mail) is None: error.append('正しくメールアドレスを入力してください。') if error: return templates.TemplateResponse('register.html', {'request': request, 'username': username, 'error': error}) user = User(username, password, mail) db.session.add(user) db.session.commit() db.session.close() return templates.TemplateResponse('complete.html', {'request': request, 'username': username}) def detail(request: Request, username, year, month, day, credentials: HTTPBasicCredentials = Depends(security)): username_tmp = auth(credentials) if username_tmp != username: return RedirectResponse('/') user = db.session.query(User).filter(User.username == username).first() task = db.session.query(Task).filter(Task.user_id == user.id).all() db.session.close() theday = f'{year}{month.zfill(2)}{day.zfill(2)}' task = [t for t in task if t.deadline.strftime('%Y%m%d') == theday] return templates.TemplateResponse('detail.html', {'request': request, 'username': username, 'task': task, 'year': year, 'month': month, 'day': day}) async def done(request: Request, credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() task = db.session.query(Task).filter(Task.user_id == user.id).all() data = await request.form() t_dones = data.getlist('done[]') for t in task: if str(t.id) in t_dones: t.done = True db.session.commit() db.session.close() return RedirectResponse('/admin') async def add(request: Request, credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() data = await request.form() print(data) year = int(data['year']) month = int(data['month']) day = int(data['day']) hour = int(data['hour']) minute = int(data['minute']) deadline = datetime(year=year, month=month, day=day, hour=hour, minute=minute) task = Task(user.id, data['content'], deadline) db.session.add(task) db.session.commit() db.session.close() return RedirectResponse('/admin') def delete(request: Request, t_id, credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() task = db.session.query(Task).filter(Task.id == t_id).first() if task.user_id != user.id: return RedirectResponse('/admin') db.session.delete(task) db.session.commit() db.session.close() return RedirectResponse('/admin') def get(request: Request, credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() task = db.session.query(Task).filter(Task.user_id == user.id).all() db.session.close() task = [{ 'id': t.id, 'content': t.content, 'deadline': t.deadline.strftime('%Y-%m-%d %H:%M:%S'), 'published': t.date.strftime('%Y-%m-%d %H:%M:%S'), 'done': t.done, } for t in task] return task async def insert(request: Request, content: str = Form(...), deadline: str = Form(...), credentials: HTTPBasicCredentials = Depends(security)): username = auth(credentials) user = db.session.query(User).filter(User.username == username).first() task = Task(user.id, content, datetime.strptime(deadline, '%Y-%m-%d_%H:%M:%S')) db.session.add(task) db.session.commit() task = db.session.query(Task).all()[-1] db.session.close() return { 'id': task.id, 'content': task.content, 'deadline': task.deadline.strftime('%Y-%m-%d %H:%M:%S'), 'published': task.date.strftime('%Y-%m-%d %H:%M:%S'), 'done': task.done, }

true

f72b01644b9c24e4ff1dde34645ffd6b1aec9355

2,765

py

Python

Contrib/LEF/ClusterFps.py

kazuyaujihara/rdkit

06027dcd05674787b61f27ba46ec0d42a6037540

[ "BSD-3-Clause" ]

1,609

2015-01-05T02:41:13.000Z

2022-03-30T21:57:24.000Z

Contrib/LEF/ClusterFps.py

kazuyaujihara/rdkit

06027dcd05674787b61f27ba46ec0d42a6037540

[ "BSD-3-Clause" ]

3,412

2015-01-06T12:13:33.000Z

2022-03-31T17:25:41.000Z

Contrib/LEF/ClusterFps.py

kazuyaujihara/rdkit

06027dcd05674787b61f27ba46ec0d42a6037540

[ "BSD-3-Clause" ]

811

2015-01-11T03:33:48.000Z

2022-03-28T11:57:49.000Z

# # Copyright (c) 2009, Novartis Institutes for BioMedical Research Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # * Neither the name of Novartis Institutes for BioMedical Research Inc. # nor the names of its contributors may be used to endorse or promote # products derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # 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. # # Created by Greg Landrum and Anna Vulpetti, March 2009 from rdkit.ML.Cluster import Butina from rdkit import DataStructs import sys, pickle # sims is the list of similarity thresholds used to generate clusters sims = [.9, .8, .7, .6] smis = [] uniq = [] uFps = [] for fileN in sys.argv[1:]: inF = file(sys.argv[1], 'r') cols = pickle.load(inF) fps = pickle.load(inF) for row in fps: nm, smi, fp = row[:3] if smi not in smis: try: fpIdx = uFps.index(fp) except ValueError: fpIdx = len(uFps) uFps.append(fp) uniq.append([fp, nm, smi, 'FP_%d' % fpIdx] + row[3:]) smis.append(smi) def distFunc(a, b): return 1. - DataStructs.DiceSimilarity(a[0], b[0]) for sim in sims: clusters = Butina.ClusterData(uniq, len(uniq), 1. - sim, False, distFunc) print('Sim: %.2f, nClusters: %d' % (sim, len(clusters)), file=sys.stderr) for i, cluster in enumerate(clusters): for pt in cluster: uniq[pt].append(str(i + 1)) cols.append('cluster_thresh_%d' % (int(100 * sim))) print(' '.join(cols)) for row in uniq: print(' '.join(row[1:]))

37.364865

86

0.707052

from rdkit.ML.Cluster import Butina from rdkit import DataStructs import sys, pickle sims = [.9, .8, .7, .6] smis = [] uniq = [] uFps = [] for fileN in sys.argv[1:]: inF = file(sys.argv[1], 'r') cols = pickle.load(inF) fps = pickle.load(inF) for row in fps: nm, smi, fp = row[:3] if smi not in smis: try: fpIdx = uFps.index(fp) except ValueError: fpIdx = len(uFps) uFps.append(fp) uniq.append([fp, nm, smi, 'FP_%d' % fpIdx] + row[3:]) smis.append(smi) def distFunc(a, b): return 1. - DataStructs.DiceSimilarity(a[0], b[0]) for sim in sims: clusters = Butina.ClusterData(uniq, len(uniq), 1. - sim, False, distFunc) print('Sim: %.2f, nClusters: %d' % (sim, len(clusters)), file=sys.stderr) for i, cluster in enumerate(clusters): for pt in cluster: uniq[pt].append(str(i + 1)) cols.append('cluster_thresh_%d' % (int(100 * sim))) print(' '.join(cols)) for row in uniq: print(' '.join(row[1:]))

true

f72b01a7f0fb8665343e290a8c45dfabc5c03f99

801

py

Python

predictability_utils/utils/helpers.py

marpyr/forecast_predictability

2285b37e20095ae6f67533595bcb0580882924a2

[ "MIT" ]

2

2020-10-23T08:58:18.000Z

2021-05-03T17:30:03.000Z

predictability_utils/utils/helpers.py

marpyr/forecast_predictability

2285b37e20095ae6f67533595bcb0580882924a2

[ "MIT" ]

null

predictability_utils/utils/helpers.py

marpyr/forecast_predictability

2285b37e20095ae6f67533595bcb0580882924a2

[ "MIT" ]

1

2020-10-23T09:07:19.000Z

import numpy as np def compute_anomaly_corrs(out_true, out_pred): anomaly_corrs = np.zeros(out_pred.shape[1]) for i in range(anomaly_corrs.size): anomaly_corrs[i] = np.corrcoef(out_pred[:,i], out_true[:,i])[0,1] return anomaly_corrs def split_train_data(train_months, test_months, train_years, test_years): def make_idx(months, years): # based on simple broadcasting return np.asarray(months).reshape(-1,1)+(12*np.asarray(years).flatten()) idx_source_train = make_idx(train_months, train_years) idx_target_train = make_idx(test_months, train_years) idx_source_test = make_idx(train_months, test_years) idx_target_test = make_idx(test_months, test_years) return idx_source_train, idx_target_train, idx_source_test, idx_target_test

36.409091

80

0.740325

import numpy as np def compute_anomaly_corrs(out_true, out_pred): anomaly_corrs = np.zeros(out_pred.shape[1]) for i in range(anomaly_corrs.size): anomaly_corrs[i] = np.corrcoef(out_pred[:,i], out_true[:,i])[0,1] return anomaly_corrs def split_train_data(train_months, test_months, train_years, test_years): def make_idx(months, years): return np.asarray(months).reshape(-1,1)+(12*np.asarray(years).flatten()) idx_source_train = make_idx(train_months, train_years) idx_target_train = make_idx(test_months, train_years) idx_source_test = make_idx(train_months, test_years) idx_target_test = make_idx(test_months, test_years) return idx_source_train, idx_target_train, idx_source_test, idx_target_test

true

f72b01c050db440e10771a348c74c4d89b91660f

19,971

py

Python

dfvfs/lib/gzipfile.py

dfjxs/dfvfs

a4154b07bb08c3c86afa2847f3224189dd80c138

[ "Apache-2.0" ]

176

2015-01-02T13:55:39.000Z

2022-03-12T11:44:37.000Z

dfvfs/lib/gzipfile.py

dfjxs/dfvfs

a4154b07bb08c3c86afa2847f3224189dd80c138

[ "Apache-2.0" ]

495

2015-01-13T06:47:06.000Z

2022-03-12T11:07:03.000Z

dfvfs/lib/gzipfile.py

dfjxs/dfvfs

a4154b07bb08c3c86afa2847f3224189dd80c138

[ "Apache-2.0" ]

62

2015-02-23T08:19:38.000Z

2022-03-18T06:01:22.000Z

# -*- coding: utf-8 -*- """Gzip compressed stream file.""" # Note: do not rename file to gzip.py this can cause the exception: # AttributeError: 'module' object has no attribute 'GzipFile' # when using pip. import collections import os from dtfabric.runtime import fabric as dtfabric_fabric from dfvfs.compression import zlib_decompressor from dfvfs.lib import data_format from dfvfs.lib import errors class _GzipDecompressorState(object): """Deflate decompressor wrapper for reading a gzip member. This class encapsulates the state of a deflate decompression object, as well as the location of the decompressor's source data. Attributes: uncompressed_offset (int): offset into the uncompressed data in a gzip member last emitted by the state object. """ _MAXIMUM_READ_SIZE = 16 * 1024 * 1024 def __init__(self, stream_start): """Initializes a gzip member decompressor wrapper. Args: stream_start (int): offset to the compressed stream within the containing file object. """ self._compressed_data = b'' self._decompressor = zlib_decompressor.DeflateDecompressor() self._last_read = stream_start self.uncompressed_offset = 0 def Read(self, file_object): """Reads the next uncompressed data from the gzip stream. Args: file_object (FileIO): file object that contains the compressed stream. Returns: bytes: next uncompressed data from the compressed stream. """ file_object.seek(self._last_read, os.SEEK_SET) read_data = file_object.read(self._MAXIMUM_READ_SIZE) self._last_read = file_object.get_offset() compressed_data = b''.join([self._compressed_data, read_data]) decompressed_data, remaining_compressed_data = ( self._decompressor.Decompress(compressed_data)) self._compressed_data = remaining_compressed_data self.uncompressed_offset += len(decompressed_data) return decompressed_data def GetUnusedData(self): """Retrieves any bytes past the end of the compressed data. See https://docs.python.org/2/library/zlib.html#zlib.Decompress.unused_data Unused data can be any bytes after a Deflate compressed block (or chunk). Returns: bytes: data past the end of the compressed data, if any has been read from the gzip file. """ return self._decompressor.unused_data class GzipMember(data_format.DataFormat): """Gzip member. Gzip files have no index of members, so each member must be read sequentially before metadata and random seeks are possible. This class provides caching of gzip member data during the initial read of each member. Attributes: comment (str): comment stored in the member. member_end_offset (int): offset to the end of the member in the parent file object. member_start_offset (int): offset to the start of the member in the parent file object. operating_system (int): type of file system on which the compression took place. original_filename (str): original filename of the uncompressed file. uncompressed_data_offset (int): offset of the start of the uncompressed data in this member relative to the whole gzip file's uncompressed data. uncompressed_data_size (int): total size of the data in this gzip member after decompression. """ _DATA_TYPE_FABRIC_DEFINITION_FILE = os.path.join( os.path.dirname(__file__), 'gzipfile.yaml') with open(_DATA_TYPE_FABRIC_DEFINITION_FILE, 'rb') as file_object: _DATA_TYPE_FABRIC_DEFINITION = file_object.read() _DATA_TYPE_FABRIC = dtfabric_fabric.DataTypeFabric( yaml_definition=_DATA_TYPE_FABRIC_DEFINITION) _MEMBER_HEADER = _DATA_TYPE_FABRIC.CreateDataTypeMap( 'gzip_member_header') _MEMBER_HEADER_SIZE = _MEMBER_HEADER.GetByteSize() _MEMBER_FOOTER = _DATA_TYPE_FABRIC.CreateDataTypeMap( 'gzip_member_footer') _MEMBER_FOOTER_SIZE = _MEMBER_FOOTER.GetByteSize() _UINT16LE = _DATA_TYPE_FABRIC.CreateDataTypeMap('uint16le') _UINT16LE_SIZE = _UINT16LE.GetByteSize() _CSTRING = _DATA_TYPE_FABRIC.CreateDataTypeMap('cstring') _GZIP_SIGNATURE = 0x8b1f _COMPRESSION_METHOD_DEFLATE = 8 _FLAG_FTEXT = 0x01 _FLAG_FHCRC = 0x02 _FLAG_FEXTRA = 0x04 _FLAG_FNAME = 0x08 _FLAG_FCOMMENT = 0x10 # The maximum size of the uncompressed data cache. _UNCOMPRESSED_DATA_CACHE_SIZE = 2 * 1024 * 1024 def __init__( self, file_object, member_start_offset, uncompressed_data_offset): """Initializes a gzip member. Args: file_object (FileIO): file-like object, containing the gzip member. member_start_offset (int): offset to the beginning of the gzip member in the containing file. uncompressed_data_offset (int): offset of the start of the uncompressed data in this member relative to the whole gzip file's uncompressed data. """ self._cache = b'' # End offset of the cached uncompressed data of the member. self._cache_end_offset = None # Start offset of the cached uncompressed data of the member. self._cache_start_offset = None self.comment = None self.modification_time = None self.operating_system = None self.original_filename = None file_size = file_object.get_size() file_object.seek(member_start_offset, os.SEEK_SET) self._ReadMemberHeader(file_object) data_offset = 0 uncompressed_data_size = 0 compressed_data_offset = file_object.get_offset() decompressor_state = _GzipDecompressorState(compressed_data_offset) # Read the member data to determine the uncompressed data size and # the offset of the member footer. file_offset = compressed_data_offset while file_offset < file_size: data_offset += uncompressed_data_size decompressed_data = decompressor_state.Read(file_object) uncompressed_data_size += len(decompressed_data) # Note that unused data will be set when the decompressor reads beyond # the end of the compressed data stream. unused_data = decompressor_state.GetUnusedData() if unused_data: file_object.seek(-len(unused_data), os.SEEK_CUR) file_offset = file_object.get_offset() break file_offset = file_object.get_offset() # Do not read the the last member footer if it is missing, which is # a common corruption scenario. if file_offset < file_size: self._ReadStructure( file_object, file_offset, self._MEMBER_FOOTER_SIZE, self._MEMBER_FOOTER, 'member footer') member_end_offset = file_object.get_offset() # Initialize the member with data. self._file_object = file_object self._file_object.seek(member_start_offset, os.SEEK_SET) # Cache uncompressed data of gzip files that fit entirely in the cache. if (data_offset == 0 and uncompressed_data_size < self._UNCOMPRESSED_DATA_CACHE_SIZE): self._cache = decompressed_data self._cache_start_offset = 0 self._cache_end_offset = uncompressed_data_size # Offset to the beginning of the compressed data in the file object. self._compressed_data_start = compressed_data_offset self._decompressor_state = _GzipDecompressorState(compressed_data_offset) # Offset to the start of the member in the parent file object. self.member_start_offset = member_start_offset # Offset to the end of the member in the parent file object. self.member_end_offset = member_end_offset # Total size of the data in this gzip member after decompression. self.uncompressed_data_size = uncompressed_data_size # Offset of the start of the uncompressed data in this member relative to # the whole gzip file's uncompressed data. self.uncompressed_data_offset = uncompressed_data_offset def _GetCacheSize(self): """Determines the size of the uncompressed cached data. Returns: int: number of cached bytes. """ if None in (self._cache_start_offset, self._cache_end_offset): return 0 return self._cache_end_offset - self._cache_start_offset def _IsCacheFull(self): """Checks whether the uncompressed data cache is full. Returns: bool: True if the cache is full. """ return self._GetCacheSize() >= self._UNCOMPRESSED_DATA_CACHE_SIZE def _LoadDataIntoCache(self, file_object, minimum_offset): """Reads and decompresses the data in the member. This function already loads as much data as possible in the cache, up to UNCOMPRESSED_DATA_CACHE_SIZE bytes. Args: file_object (FileIO): file-like object. minimum_offset (int): offset into this member's uncompressed data at which the cache should start. """ # Decompression can only be performed from beginning to end of the stream. # So, if data before the current position of the decompressor in the stream # is required, it's necessary to throw away the current decompression # state and start again. if minimum_offset < self._decompressor_state.uncompressed_offset: self._ResetDecompressorState() cache_is_full = self._IsCacheFull() while not cache_is_full: decompressed_data = self._decompressor_state.Read(file_object) # Note that decompressed_data will be empty if there is no data left # to read and decompress. if not decompressed_data: break decompressed_data_length = len(decompressed_data) decompressed_end_offset = self._decompressor_state.uncompressed_offset decompressed_start_offset = ( decompressed_end_offset - decompressed_data_length) data_to_add = decompressed_data added_data_start_offset = decompressed_start_offset if decompressed_start_offset < minimum_offset: data_to_add = None if decompressed_start_offset < minimum_offset < decompressed_end_offset: data_add_offset = decompressed_end_offset - minimum_offset data_to_add = decompressed_data[-data_add_offset:] added_data_start_offset = decompressed_end_offset - data_add_offset if data_to_add and not cache_is_full: self._cache = b''.join([self._cache, data_to_add]) if self._cache_start_offset is None: self._cache_start_offset = added_data_start_offset if self._cache_end_offset is None: self._cache_end_offset = self._cache_start_offset + len(data_to_add) else: self._cache_end_offset += len(data_to_add) cache_is_full = self._IsCacheFull() # If there's no more data in the member, the unused_data value is # populated in the decompressor. When this situation arises, we rewind # to the end of the compressed_data section. unused_data = self._decompressor_state.GetUnusedData() if unused_data: seek_offset = -len(unused_data) file_object.seek(seek_offset, os.SEEK_CUR) self._ResetDecompressorState() break def _ReadMemberHeader(self, file_object): """Reads a member header. Args: file_object (FileIO): file-like object to read from. Raises: FileFormatError: if the member header cannot be read. """ file_offset = file_object.get_offset() member_header = self._ReadStructure( file_object, file_offset, self._MEMBER_HEADER_SIZE, self._MEMBER_HEADER, 'member header') if member_header.signature != self._GZIP_SIGNATURE: raise errors.FileFormatError( 'Unsupported signature: 0x{0:04x}.'.format(member_header.signature)) if member_header.compression_method != self._COMPRESSION_METHOD_DEFLATE: raise errors.FileFormatError( 'Unsupported compression method: {0:d}.'.format( member_header.compression_method)) self.modification_time = member_header.modification_time self.operating_system = member_header.operating_system if member_header.flags & self._FLAG_FEXTRA: file_offset = file_object.get_offset() extra_field_data_size = self._ReadStructure( file_object, file_offset, self._UINT16LE_SIZE, self._UINT16LE, 'extra field data size') file_object.seek(extra_field_data_size, os.SEEK_CUR) if member_header.flags & self._FLAG_FNAME: file_offset = file_object.get_offset() string_value = self._ReadString( file_object, file_offset, self._CSTRING, 'original filename') self.original_filename = string_value.rstrip('\x00') if member_header.flags & self._FLAG_FCOMMENT: file_offset = file_object.get_offset() string_value = self._ReadString( file_object, file_offset, self._CSTRING, 'comment') self.comment = string_value.rstrip('\x00') if member_header.flags & self._FLAG_FHCRC: file_object.read(2) def _ResetDecompressorState(self): """Resets the state of the internal decompression object.""" self._decompressor_state = _GzipDecompressorState( self._compressed_data_start) def FlushCache(self): """Empties the cache that holds cached decompressed data.""" self._cache = b'' self._cache_start_offset = None self._cache_end_offset = None self._ResetDecompressorState() def ReadAtOffset(self, offset, size=None): """Reads a byte string from the gzip member at the specified offset. The function will read a byte string of the specified size or all of the remaining data if no size was specified. Args: offset (int): offset within the uncompressed data in this member to read from. size (Optional[int]): maximum number of bytes to read, where None represents all remaining data, to a maximum of the uncompressed cache size. Returns: bytes: data read. Raises: IOError: if the read failed. ValueError: if a negative read size or offset is specified. """ if size is not None and size < 0: raise ValueError('Invalid size value {0!s}'.format(size)) if offset < 0: raise ValueError('Invalid offset value {0!s}'.format(offset)) if size == 0 or offset >= self.uncompressed_data_size: return b'' if self._cache_start_offset is None: self._LoadDataIntoCache(self._file_object, offset) if offset > self._cache_end_offset or offset < self._cache_start_offset: self.FlushCache() self._LoadDataIntoCache(self._file_object, offset) cache_offset = offset - self._cache_start_offset if not size: return self._cache[cache_offset:] data_end_offset = cache_offset + size if data_end_offset > self._cache_end_offset: return self._cache[cache_offset:] return self._cache[cache_offset:data_end_offset] class GzipCompressedStream(object): """File-like object of a gzip compressed stream (file). The gzip file format is defined in RFC1952: http://www.zlib.org/rfc-gzip.html Attributes: uncompressed_data_size (int): total size of the decompressed data stored in the gzip file. """ def __init__(self): """Initializes a file-like object.""" super(GzipCompressedStream, self).__init__() self._compressed_data_size = -1 self._current_offset = 0 self._file_object = None self._members_by_end_offset = collections.OrderedDict() self.uncompressed_data_size = 0 @property def members(self): """list(GzipMember): members in the gzip file.""" return list(self._members_by_end_offset.values()) def _GetMemberForOffset(self, offset): """Finds the member whose data includes the provided offset. Args: offset (int): offset in the uncompressed data to find the containing member for. Returns: GzipMember: gzip file member or None if not available. Raises: ValueError: if the provided offset is outside of the bounds of the uncompressed data. """ if offset < 0 or offset >= self.uncompressed_data_size: raise ValueError('Offset {0:d} is larger than file size {1:d}.'.format( offset, self.uncompressed_data_size)) for end_offset, member in self._members_by_end_offset.items(): if offset < end_offset: return member return None def Open(self, file_object): """Opens the file-like object defined by path specification. Args: file_object (FileIO): file-like object that contains the gzip compressed stream. Raises: IOError: if the file-like object could not be opened. OSError: if the file-like object could not be opened. """ file_size = file_object.get_size() file_object.seek(0, os.SEEK_SET) uncompressed_data_offset = 0 next_member_offset = 0 while next_member_offset < file_size: member = GzipMember( file_object, next_member_offset, uncompressed_data_offset) uncompressed_data_offset = ( uncompressed_data_offset + member.uncompressed_data_size) self._members_by_end_offset[uncompressed_data_offset] = member self.uncompressed_data_size += member.uncompressed_data_size next_member_offset = member.member_end_offset self._file_object = file_object # Note: that the following functions do not follow the style guide # because they are part of the file-like object interface. # pylint: disable=invalid-name def close(self): """Closes the file-like object.""" self._members_by_end_offset = [] if self._file_object: self._file_object = None def read(self, size=None): """Reads a byte string from the gzip file at the current offset. The function will read a byte string up to the specified size or all of the remaining data if no size was specified. Args: size (Optional[int]): number of bytes to read, where None is all remaining data. Returns: bytes: data read. Raises: IOError: if the read failed. OSError: if the read failed. """ data = b'' while ((size and len(data) < size) and self._current_offset < self.uncompressed_data_size): member = self._GetMemberForOffset(self._current_offset) member_offset = self._current_offset - member.uncompressed_data_offset data_read = member.ReadAtOffset(member_offset, size) if not data_read: break self._current_offset += len(data_read) data = b''.join([data, data_read]) return data def seek(self, offset, whence=os.SEEK_SET): """Seeks to an offset within the file-like object. Args: offset (int): offset to seek to. whence (Optional(int)): value that indicates whether offset is an absolute or relative position within the file. Raises: IOError: if the seek failed or the file has not been opened. OSError: if the seek failed or the file has not been opened. """ if not self._file_object: raise IOError('Not opened.') if whence == os.SEEK_CUR: offset += self._current_offset elif whence == os.SEEK_END: offset += self.uncompressed_data_size elif whence != os.SEEK_SET: raise IOError('Unsupported whence.') if offset < 0: raise IOError('Invalid offset value less than zero.') self._current_offset = offset def get_offset(self): """Retrieves the current offset into the file-like object. Returns: int: current offset into the file-like object. Raises: IOError: if the file-like object has not been opened. OSError: if the file-like object has not been opened. """ if not self._file_object: raise IOError('Not opened.') return self._current_offset def get_size(self): """Retrieves the size of the file-like object. Returns: int: size of the file-like object data. Raises: IOError: if the file-like object has not been opened. OSError: if the file-like object has not been opened. """ if not self._file_object: raise IOError('Not opened.') return self.uncompressed_data_size

33.452261

80

0.714286

import collections import os from dtfabric.runtime import fabric as dtfabric_fabric from dfvfs.compression import zlib_decompressor from dfvfs.lib import data_format from dfvfs.lib import errors class _GzipDecompressorState(object): _MAXIMUM_READ_SIZE = 16 * 1024 * 1024 def __init__(self, stream_start): self._compressed_data = b'' self._decompressor = zlib_decompressor.DeflateDecompressor() self._last_read = stream_start self.uncompressed_offset = 0 def Read(self, file_object): file_object.seek(self._last_read, os.SEEK_SET) read_data = file_object.read(self._MAXIMUM_READ_SIZE) self._last_read = file_object.get_offset() compressed_data = b''.join([self._compressed_data, read_data]) decompressed_data, remaining_compressed_data = ( self._decompressor.Decompress(compressed_data)) self._compressed_data = remaining_compressed_data self.uncompressed_offset += len(decompressed_data) return decompressed_data def GetUnusedData(self): return self._decompressor.unused_data class GzipMember(data_format.DataFormat): _DATA_TYPE_FABRIC_DEFINITION_FILE = os.path.join( os.path.dirname(__file__), 'gzipfile.yaml') with open(_DATA_TYPE_FABRIC_DEFINITION_FILE, 'rb') as file_object: _DATA_TYPE_FABRIC_DEFINITION = file_object.read() _DATA_TYPE_FABRIC = dtfabric_fabric.DataTypeFabric( yaml_definition=_DATA_TYPE_FABRIC_DEFINITION) _MEMBER_HEADER = _DATA_TYPE_FABRIC.CreateDataTypeMap( 'gzip_member_header') _MEMBER_HEADER_SIZE = _MEMBER_HEADER.GetByteSize() _MEMBER_FOOTER = _DATA_TYPE_FABRIC.CreateDataTypeMap( 'gzip_member_footer') _MEMBER_FOOTER_SIZE = _MEMBER_FOOTER.GetByteSize() _UINT16LE = _DATA_TYPE_FABRIC.CreateDataTypeMap('uint16le') _UINT16LE_SIZE = _UINT16LE.GetByteSize() _CSTRING = _DATA_TYPE_FABRIC.CreateDataTypeMap('cstring') _GZIP_SIGNATURE = 0x8b1f _COMPRESSION_METHOD_DEFLATE = 8 _FLAG_FTEXT = 0x01 _FLAG_FHCRC = 0x02 _FLAG_FEXTRA = 0x04 _FLAG_FNAME = 0x08 _FLAG_FCOMMENT = 0x10 _UNCOMPRESSED_DATA_CACHE_SIZE = 2 * 1024 * 1024 def __init__( self, file_object, member_start_offset, uncompressed_data_offset): self._cache = b'' self._cache_end_offset = None self._cache_start_offset = None self.comment = None self.modification_time = None self.operating_system = None self.original_filename = None file_size = file_object.get_size() file_object.seek(member_start_offset, os.SEEK_SET) self._ReadMemberHeader(file_object) data_offset = 0 uncompressed_data_size = 0 compressed_data_offset = file_object.get_offset() decompressor_state = _GzipDecompressorState(compressed_data_offset) file_offset = compressed_data_offset while file_offset < file_size: data_offset += uncompressed_data_size decompressed_data = decompressor_state.Read(file_object) uncompressed_data_size += len(decompressed_data) unused_data = decompressor_state.GetUnusedData() if unused_data: file_object.seek(-len(unused_data), os.SEEK_CUR) file_offset = file_object.get_offset() break file_offset = file_object.get_offset() if file_offset < file_size: self._ReadStructure( file_object, file_offset, self._MEMBER_FOOTER_SIZE, self._MEMBER_FOOTER, 'member footer') member_end_offset = file_object.get_offset() self._file_object = file_object self._file_object.seek(member_start_offset, os.SEEK_SET) if (data_offset == 0 and uncompressed_data_size < self._UNCOMPRESSED_DATA_CACHE_SIZE): self._cache = decompressed_data self._cache_start_offset = 0 self._cache_end_offset = uncompressed_data_size self._compressed_data_start = compressed_data_offset self._decompressor_state = _GzipDecompressorState(compressed_data_offset) self.member_start_offset = member_start_offset self.member_end_offset = member_end_offset self.uncompressed_data_size = uncompressed_data_size self.uncompressed_data_offset = uncompressed_data_offset def _GetCacheSize(self): if None in (self._cache_start_offset, self._cache_end_offset): return 0 return self._cache_end_offset - self._cache_start_offset def _IsCacheFull(self): return self._GetCacheSize() >= self._UNCOMPRESSED_DATA_CACHE_SIZE def _LoadDataIntoCache(self, file_object, minimum_offset): # Decompression can only be performed from beginning to end of the stream. # So, if data before the current position of the decompressor in the stream # is required, it's necessary to throw away the current decompression if minimum_offset < self._decompressor_state.uncompressed_offset: self._ResetDecompressorState() cache_is_full = self._IsCacheFull() while not cache_is_full: decompressed_data = self._decompressor_state.Read(file_object) if not decompressed_data: break decompressed_data_length = len(decompressed_data) decompressed_end_offset = self._decompressor_state.uncompressed_offset decompressed_start_offset = ( decompressed_end_offset - decompressed_data_length) data_to_add = decompressed_data added_data_start_offset = decompressed_start_offset if decompressed_start_offset < minimum_offset: data_to_add = None if decompressed_start_offset < minimum_offset < decompressed_end_offset: data_add_offset = decompressed_end_offset - minimum_offset data_to_add = decompressed_data[-data_add_offset:] added_data_start_offset = decompressed_end_offset - data_add_offset if data_to_add and not cache_is_full: self._cache = b''.join([self._cache, data_to_add]) if self._cache_start_offset is None: self._cache_start_offset = added_data_start_offset if self._cache_end_offset is None: self._cache_end_offset = self._cache_start_offset + len(data_to_add) else: self._cache_end_offset += len(data_to_add) cache_is_full = self._IsCacheFull() # populated in the decompressor. When this situation arises, we rewind # to the end of the compressed_data section. unused_data = self._decompressor_state.GetUnusedData() if unused_data: seek_offset = -len(unused_data) file_object.seek(seek_offset, os.SEEK_CUR) self._ResetDecompressorState() break def _ReadMemberHeader(self, file_object): file_offset = file_object.get_offset() member_header = self._ReadStructure( file_object, file_offset, self._MEMBER_HEADER_SIZE, self._MEMBER_HEADER, 'member header') if member_header.signature != self._GZIP_SIGNATURE: raise errors.FileFormatError( 'Unsupported signature: 0x{0:04x}.'.format(member_header.signature)) if member_header.compression_method != self._COMPRESSION_METHOD_DEFLATE: raise errors.FileFormatError( 'Unsupported compression method: {0:d}.'.format( member_header.compression_method)) self.modification_time = member_header.modification_time self.operating_system = member_header.operating_system if member_header.flags & self._FLAG_FEXTRA: file_offset = file_object.get_offset() extra_field_data_size = self._ReadStructure( file_object, file_offset, self._UINT16LE_SIZE, self._UINT16LE, 'extra field data size') file_object.seek(extra_field_data_size, os.SEEK_CUR) if member_header.flags & self._FLAG_FNAME: file_offset = file_object.get_offset() string_value = self._ReadString( file_object, file_offset, self._CSTRING, 'original filename') self.original_filename = string_value.rstrip('\x00') if member_header.flags & self._FLAG_FCOMMENT: file_offset = file_object.get_offset() string_value = self._ReadString( file_object, file_offset, self._CSTRING, 'comment') self.comment = string_value.rstrip('\x00') if member_header.flags & self._FLAG_FHCRC: file_object.read(2) def _ResetDecompressorState(self): self._decompressor_state = _GzipDecompressorState( self._compressed_data_start) def FlushCache(self): self._cache = b'' self._cache_start_offset = None self._cache_end_offset = None self._ResetDecompressorState() def ReadAtOffset(self, offset, size=None): if size is not None and size < 0: raise ValueError('Invalid size value {0!s}'.format(size)) if offset < 0: raise ValueError('Invalid offset value {0!s}'.format(offset)) if size == 0 or offset >= self.uncompressed_data_size: return b'' if self._cache_start_offset is None: self._LoadDataIntoCache(self._file_object, offset) if offset > self._cache_end_offset or offset < self._cache_start_offset: self.FlushCache() self._LoadDataIntoCache(self._file_object, offset) cache_offset = offset - self._cache_start_offset if not size: return self._cache[cache_offset:] data_end_offset = cache_offset + size if data_end_offset > self._cache_end_offset: return self._cache[cache_offset:] return self._cache[cache_offset:data_end_offset] class GzipCompressedStream(object): def __init__(self): super(GzipCompressedStream, self).__init__() self._compressed_data_size = -1 self._current_offset = 0 self._file_object = None self._members_by_end_offset = collections.OrderedDict() self.uncompressed_data_size = 0 @property def members(self): return list(self._members_by_end_offset.values()) def _GetMemberForOffset(self, offset): if offset < 0 or offset >= self.uncompressed_data_size: raise ValueError('Offset {0:d} is larger than file size {1:d}.'.format( offset, self.uncompressed_data_size)) for end_offset, member in self._members_by_end_offset.items(): if offset < end_offset: return member return None def Open(self, file_object): file_size = file_object.get_size() file_object.seek(0, os.SEEK_SET) uncompressed_data_offset = 0 next_member_offset = 0 while next_member_offset < file_size: member = GzipMember( file_object, next_member_offset, uncompressed_data_offset) uncompressed_data_offset = ( uncompressed_data_offset + member.uncompressed_data_size) self._members_by_end_offset[uncompressed_data_offset] = member self.uncompressed_data_size += member.uncompressed_data_size next_member_offset = member.member_end_offset self._file_object = file_object # Note: that the following functions do not follow the style guide # because they are part of the file-like object interface. # pylint: disable=invalid-name def close(self): self._members_by_end_offset = [] if self._file_object: self._file_object = None def read(self, size=None): data = b'' while ((size and len(data) < size) and self._current_offset < self.uncompressed_data_size): member = self._GetMemberForOffset(self._current_offset) member_offset = self._current_offset - member.uncompressed_data_offset data_read = member.ReadAtOffset(member_offset, size) if not data_read: break self._current_offset += len(data_read) data = b''.join([data, data_read]) return data def seek(self, offset, whence=os.SEEK_SET): if not self._file_object: raise IOError('Not opened.') if whence == os.SEEK_CUR: offset += self._current_offset elif whence == os.SEEK_END: offset += self.uncompressed_data_size elif whence != os.SEEK_SET: raise IOError('Unsupported whence.') if offset < 0: raise IOError('Invalid offset value less than zero.') self._current_offset = offset def get_offset(self): if not self._file_object: raise IOError('Not opened.') return self._current_offset def get_size(self): if not self._file_object: raise IOError('Not opened.') return self.uncompressed_data_size

true

f72b027333bbe2d8bc09150e018d4e2a3f9db7df

11,472

py

Python

vspk/v4_0/nustaticroute.py

mohaimenhasan/vspk-python

4c7b297427048340b250cc3c74d9214dc0d4bde1

[ "BSD-3-Clause" ]

null

vspk/v4_0/nustaticroute.py

mohaimenhasan/vspk-python

4c7b297427048340b250cc3c74d9214dc0d4bde1

[ "BSD-3-Clause" ]

null

vspk/v4_0/nustaticroute.py

mohaimenhasan/vspk-python

4c7b297427048340b250cc3c74d9214dc0d4bde1

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # # Copyright (c) 2015, Alcatel-Lucent Inc, 2017 Nokia # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. from .fetchers import NUMetadatasFetcher from .fetchers import NUGlobalMetadatasFetcher from .fetchers import NUEventLogsFetcher from bambou import NURESTObject class NUStaticRoute(NURESTObject): """ Represents a StaticRoute in the VSD Notes: Static routes allow end users to define how traffic is routed through the dVRS in addition to the routes learned by VSC through VM activation. By using static routes, end users can define for example that all traffic with a destination address towards a specific subnet must be forwarded to a specific VM attached in the dVRS and this VM could be a firewall """ __rest_name__ = "staticroute" __resource_name__ = "staticroutes" ## Constants CONST_ENTITY_SCOPE_GLOBAL = "GLOBAL" CONST_TYPE_OVERLAY = "OVERLAY" CONST_ENTITY_SCOPE_ENTERPRISE = "ENTERPRISE" CONST_IP_TYPE_IPV6 = "IPV6" CONST_IP_TYPE_IPV4 = "IPV4" CONST_TYPE_EXIT_DOMAIN = "EXIT_DOMAIN" CONST_IP_TYPE_DUALSTACK = "DUALSTACK" def __init__(self, **kwargs): """ Initializes a StaticRoute instance Notes: You can specify all parameters while calling this methods. A special argument named `data` will enable you to load the object from a Python dictionary Examples: >>> staticroute = NUStaticRoute(id=u'xxxx-xxx-xxx-xxx', name=u'StaticRoute') >>> staticroute = NUStaticRoute(data=my_dict) """ super(NUStaticRoute, self).__init__() # Read/Write Attributes self._ip_type = None self._ipv6_address = None self._last_updated_by = None self._address = None self._netmask = None self._next_hop_ip = None self._entity_scope = None self._route_distinguisher = None self._external_id = None self._type = None self.expose_attribute(local_name="ip_type", remote_name="IPType", attribute_type=str, is_required=False, is_unique=False, choices=[u'DUALSTACK', u'IPV4', u'IPV6']) self.expose_attribute(local_name="ipv6_address", remote_name="IPv6Address", attribute_type=str, is_required=False, is_unique=False) self.expose_attribute(local_name="last_updated_by", remote_name="lastUpdatedBy", attribute_type=str, is_required=False, is_unique=False) self.expose_attribute(local_name="address", remote_name="address", attribute_type=str, is_required=True, is_unique=False) self.expose_attribute(local_name="netmask", remote_name="netmask", attribute_type=str, is_required=True, is_unique=False) self.expose_attribute(local_name="next_hop_ip", remote_name="nextHopIp", attribute_type=str, is_required=True, is_unique=False) self.expose_attribute(local_name="entity_scope", remote_name="entityScope", attribute_type=str, is_required=False, is_unique=False, choices=[u'ENTERPRISE', u'GLOBAL']) self.expose_attribute(local_name="route_distinguisher", remote_name="routeDistinguisher", attribute_type=str, is_required=False, is_unique=False) self.expose_attribute(local_name="external_id", remote_name="externalID", attribute_type=str, is_required=False, is_unique=True) self.expose_attribute(local_name="type", remote_name="type", attribute_type=str, is_required=False, is_unique=False, choices=[u'EXIT_DOMAIN', u'OVERLAY']) # Fetchers self.metadatas = NUMetadatasFetcher.fetcher_with_object(parent_object=self, relationship="child") self.global_metadatas = NUGlobalMetadatasFetcher.fetcher_with_object(parent_object=self, relationship="child") self.event_logs = NUEventLogsFetcher.fetcher_with_object(parent_object=self, relationship="child") self._compute_args(**kwargs) # Properties @property def ip_type(self): """ Get ip_type value. Notes: IPv4 or IPv6 This attribute is named `IPType` in VSD API. """ return self._ip_type @ip_type.setter def ip_type(self, value): """ Set ip_type value. Notes: IPv4 or IPv6 This attribute is named `IPType` in VSD API. """ self._ip_type = value @property def ipv6_address(self): """ Get ipv6_address value. Notes: IPv6 address of the route This attribute is named `IPv6Address` in VSD API. """ return self._ipv6_address @ipv6_address.setter def ipv6_address(self, value): """ Set ipv6_address value. Notes: IPv6 address of the route This attribute is named `IPv6Address` in VSD API. """ self._ipv6_address = value @property def last_updated_by(self): """ Get last_updated_by value. Notes: ID of the user who last updated the object. This attribute is named `lastUpdatedBy` in VSD API. """ return self._last_updated_by @last_updated_by.setter def last_updated_by(self, value): """ Set last_updated_by value. Notes: ID of the user who last updated the object. This attribute is named `lastUpdatedBy` in VSD API. """ self._last_updated_by = value @property def address(self): """ Get address value. Notes: IP address of the route """ return self._address @address.setter def address(self, value): """ Set address value. Notes: IP address of the route """ self._address = value @property def netmask(self): """ Get netmask value. Notes: Netmask associated with the route """ return self._netmask @netmask.setter def netmask(self, value): """ Set netmask value. Notes: Netmask associated with the route """ self._netmask = value @property def next_hop_ip(self): """ Get next_hop_ip value. Notes: IP address of the next hop. This must be a VM attached to the dVRS This attribute is named `nextHopIp` in VSD API. """ return self._next_hop_ip @next_hop_ip.setter def next_hop_ip(self, value): """ Set next_hop_ip value. Notes: IP address of the next hop. This must be a VM attached to the dVRS This attribute is named `nextHopIp` in VSD API. """ self._next_hop_ip = value @property def entity_scope(self): """ Get entity_scope value. Notes: Specify if scope of entity is Data center or Enterprise level This attribute is named `entityScope` in VSD API. """ return self._entity_scope @entity_scope.setter def entity_scope(self, value): """ Set entity_scope value. Notes: Specify if scope of entity is Data center or Enterprise level This attribute is named `entityScope` in VSD API. """ self._entity_scope = value @property def route_distinguisher(self): """ Get route_distinguisher value. Notes: Route distinguisher associated with the nexthop. System generates this identifier automatically This attribute is named `routeDistinguisher` in VSD API. """ return self._route_distinguisher @route_distinguisher.setter def route_distinguisher(self, value): """ Set route_distinguisher value. Notes: Route distinguisher associated with the nexthop. System generates this identifier automatically This attribute is named `routeDistinguisher` in VSD API. """ self._route_distinguisher = value @property def external_id(self): """ Get external_id value. Notes: External object ID. Used for integration with third party systems This attribute is named `externalID` in VSD API. """ return self._external_id @external_id.setter def external_id(self, value): """ Set external_id value. Notes: External object ID. Used for integration with third party systems This attribute is named `externalID` in VSD API. """ self._external_id = value @property def type(self): """ Get type value. Notes: Type flag for static-route provisioning for exit-domain (break-to-underlay) prefixes. """ return self._type @type.setter def type(self, value): """ Set type value. Notes: Type flag for static-route provisioning for exit-domain (break-to-underlay) prefixes. """ self._type = value

29.720207

369

0.602772

from .fetchers import NUMetadatasFetcher from .fetchers import NUGlobalMetadatasFetcher from .fetchers import NUEventLogsFetcher from bambou import NURESTObject class NUStaticRoute(NURESTObject): __rest_name__ = "staticroute" __resource_name__ = "staticroutes" ONST_ENTITY_SCOPE_GLOBAL = "GLOBAL" CONST_TYPE_OVERLAY = "OVERLAY" CONST_ENTITY_SCOPE_ENTERPRISE = "ENTERPRISE" CONST_IP_TYPE_IPV6 = "IPV6" CONST_IP_TYPE_IPV4 = "IPV4" CONST_TYPE_EXIT_DOMAIN = "EXIT_DOMAIN" CONST_IP_TYPE_DUALSTACK = "DUALSTACK" def __init__(self, **kwargs): super(NUStaticRoute, self).__init__() self._ip_type = None self._ipv6_address = None self._last_updated_by = None self._address = None self._netmask = None self._next_hop_ip = None self._entity_scope = None self._route_distinguisher = None self._external_id = None self._type = None self.expose_attribute(local_name="ip_type", remote_name="IPType", attribute_type=str, is_required=False, is_unique=False, choices=[u'DUALSTACK', u'IPV4', u'IPV6']) self.expose_attribute(local_name="ipv6_address", remote_name="IPv6Address", attribute_type=str, is_required=False, is_unique=False) self.expose_attribute(local_name="last_updated_by", remote_name="lastUpdatedBy", attribute_type=str, is_required=False, is_unique=False) self.expose_attribute(local_name="address", remote_name="address", attribute_type=str, is_required=True, is_unique=False) self.expose_attribute(local_name="netmask", remote_name="netmask", attribute_type=str, is_required=True, is_unique=False) self.expose_attribute(local_name="next_hop_ip", remote_name="nextHopIp", attribute_type=str, is_required=True, is_unique=False) self.expose_attribute(local_name="entity_scope", remote_name="entityScope", attribute_type=str, is_required=False, is_unique=False, choices=[u'ENTERPRISE', u'GLOBAL']) self.expose_attribute(local_name="route_distinguisher", remote_name="routeDistinguisher", attribute_type=str, is_required=False, is_unique=False) self.expose_attribute(local_name="external_id", remote_name="externalID", attribute_type=str, is_required=False, is_unique=True) self.expose_attribute(local_name="type", remote_name="type", attribute_type=str, is_required=False, is_unique=False, choices=[u'EXIT_DOMAIN', u'OVERLAY']) self.metadatas = NUMetadatasFetcher.fetcher_with_object(parent_object=self, relationship="child") self.global_metadatas = NUGlobalMetadatasFetcher.fetcher_with_object(parent_object=self, relationship="child") self.event_logs = NUEventLogsFetcher.fetcher_with_object(parent_object=self, relationship="child") self._compute_args(**kwargs) @property def ip_type(self): return self._ip_type @ip_type.setter def ip_type(self, value): self._ip_type = value @property def ipv6_address(self): return self._ipv6_address @ipv6_address.setter def ipv6_address(self, value): self._ipv6_address = value @property def last_updated_by(self): return self._last_updated_by @last_updated_by.setter def last_updated_by(self, value): self._last_updated_by = value @property def address(self): return self._address @address.setter def address(self, value): self._address = value @property def netmask(self): return self._netmask @netmask.setter def netmask(self, value): self._netmask = value @property def next_hop_ip(self): return self._next_hop_ip @next_hop_ip.setter def next_hop_ip(self, value): self._next_hop_ip = value @property def entity_scope(self): return self._entity_scope @entity_scope.setter def entity_scope(self, value): self._entity_scope = value @property def route_distinguisher(self): return self._route_distinguisher @route_distinguisher.setter def route_distinguisher(self, value): self._route_distinguisher = value @property def external_id(self): return self._external_id @external_id.setter def external_id(self, value): self._external_id = value @property def type(self): return self._type @type.setter def type(self, value): self._type = value

true

f72b045654dc44f3155f6d877133a3202b759449

5,054

py

Python

python-lib/dku_error_analysis_mpp/dku_error_visualizer.py

dataiku/dss-plugin-model-error-analysis

4c0f42a5c0aa1710005db3d81ca9bd9d7f829e6b

[ "Apache-2.0" ]

null

python-lib/dku_error_analysis_mpp/dku_error_visualizer.py

dataiku/dss-plugin-model-error-analysis

4c0f42a5c0aa1710005db3d81ca9bd9d7f829e6b

[ "Apache-2.0" ]

2

2021-09-29T15:08:25.000Z

2022-01-13T11:20:58.000Z

python-lib/dku_error_analysis_mpp/dku_error_visualizer.py

dataiku/dss-plugin-model-error-analysis

4c0f42a5c0aa1710005db3d81ca9bd9d7f829e6b

[ "Apache-2.0" ]

1

2021-09-10T12:25:08.000Z

# -*- coding: utf-8 -*- import numpy as np from graphviz import Source import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt from dku_error_analysis_mpp.dku_error_analyzer import DkuErrorAnalyzer from mealy import _BaseErrorVisualizer, ErrorAnalyzerConstants from dku_error_analysis_utils import safe_str, format_float import logging logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO, format='Error Analysis Plugin | %(levelname)s - %(message)s') plt.rc('font', family="sans-serif") SMALL_SIZE, MEDIUM_SIZE, BIGGER_SIZE = 8, 10, 12 plt.rc('axes', titlesize=BIGGER_SIZE, labelsize=MEDIUM_SIZE) plt.rc('xtick', labelsize=SMALL_SIZE) plt.rc('ytick', labelsize=SMALL_SIZE) plt.rc('legend', fontsize=SMALL_SIZE) plt.rc("hatch", color="white", linewidth=4) class DkuErrorVisualizer(_BaseErrorVisualizer): """ ErrorVisualizer provides visual utilities to analyze the error classifier in ErrorAnalyzer and DkuErrorAnalyzer. """ def __init__(self, error_analyzer): if not isinstance(error_analyzer, DkuErrorAnalyzer): raise TypeError('You need to input a DkuErrorAnalyzer object.') super(DkuErrorVisualizer, self).__init__(error_analyzer) self._tree = error_analyzer.tree def plot_error_tree(self, size=(50, 50)): """ Plot the graph of the decision tree Args: size (tuple): Size of the output plot as (width, length), in inches. """ return Source(self._tree.to_dot_string(size)) def plot_feature_distributions_on_leaves(self, leaf_selector=None, top_k_features=ErrorAnalyzerConstants.TOP_K_FEATURES, show_global=True, show_class=False, rank_leaves_by="total_error_fraction", nr_bins=10, figsize=(15, 10)): """ Return plot of error node feature distribution and compare to global baseline """ leaf_nodes = self._get_ranked_leaf_ids(leaf_selector, rank_leaves_by) ranked_features = self._tree.ranked_features[:top_k_features] nr_leaves, nr_features = len(leaf_nodes), len(ranked_features) logger.info("{} lea{} selected: {}".format(nr_leaves, "f" if nr_leaves == 1 else "ves", leaf_nodes)) logger.info("{} feature distribution{} plotted: {}".format(nr_features, "" if nr_features == 1 else "s", [f["name"] for f in ranked_features])) for leaf_id in leaf_nodes: leaf = self._tree.get_node(leaf_id) suptitle = 'Leaf {} ({}: {}'.format(leaf.id, leaf.probabilities[0][0], format_float(leaf.probabilities[0][1], 3)) suptitle += ', {}: {})'.format(leaf.probabilities[1][0], format_float(leaf.probabilities[1][1], 3)) for feature in ranked_features: feature_name = feature["name"] leaf_stats = self._tree.get_stats(leaf.id, feature_name, nr_bins) feature_is_numerical = feature["numerical"] bins = leaf_stats["bin_edge"] if feature_is_numerical else leaf_stats["bin_value"] if show_global: root_samples = self._tree.get_node(0).samples[0] root_stats = self._tree.get_stats(0, feature_name, nr_bins, bins) # TODO: optimize if show_class: root_hist_data = {} for class_value, bar_heights in root_stats["target_distrib"].items(): root_hist_data[class_value] = np.array(bar_heights)/root_samples else: root_hist_data, root_prediction = {}, self._tree.get_node(0).prediction root_hist_data[root_prediction] = np.array(root_stats["count"])/root_samples else: root_hist_data = None if bins: leaf_hist_data = {} if show_class: for class_value, bar_heights in leaf_stats["target_distrib"].items(): leaf_hist_data[class_value] = np.array(bar_heights)/leaf.samples[0] else: leaf_hist_data = {leaf.prediction: np.array(leaf_stats["count"])/leaf.samples[0]} else: leaf_hist_data = None logger.info("No values for the feature {} at the leaf {}".format(feature_name, leaf.id)) if show_global: bins = root_stats["bin_edge"] if feature_is_numerical else root_stats["bin_value"] x_ticks = range(len(bins)) _BaseErrorVisualizer._add_new_plot(figsize, bins, x_ticks, feature_name, suptitle) _BaseErrorVisualizer._plot_feature_distribution(x_ticks, feature_is_numerical, leaf_hist_data, root_hist_data) plt.show()

49.54902

149

0.609616

import numpy as np from graphviz import Source import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt from dku_error_analysis_mpp.dku_error_analyzer import DkuErrorAnalyzer from mealy import _BaseErrorVisualizer, ErrorAnalyzerConstants from dku_error_analysis_utils import safe_str, format_float import logging logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO, format='Error Analysis Plugin | %(levelname)s - %(message)s') plt.rc('font', family="sans-serif") SMALL_SIZE, MEDIUM_SIZE, BIGGER_SIZE = 8, 10, 12 plt.rc('axes', titlesize=BIGGER_SIZE, labelsize=MEDIUM_SIZE) plt.rc('xtick', labelsize=SMALL_SIZE) plt.rc('ytick', labelsize=SMALL_SIZE) plt.rc('legend', fontsize=SMALL_SIZE) plt.rc("hatch", color="white", linewidth=4) class DkuErrorVisualizer(_BaseErrorVisualizer): def __init__(self, error_analyzer): if not isinstance(error_analyzer, DkuErrorAnalyzer): raise TypeError('You need to input a DkuErrorAnalyzer object.') super(DkuErrorVisualizer, self).__init__(error_analyzer) self._tree = error_analyzer.tree def plot_error_tree(self, size=(50, 50)): return Source(self._tree.to_dot_string(size)) def plot_feature_distributions_on_leaves(self, leaf_selector=None, top_k_features=ErrorAnalyzerConstants.TOP_K_FEATURES, show_global=True, show_class=False, rank_leaves_by="total_error_fraction", nr_bins=10, figsize=(15, 10)): leaf_nodes = self._get_ranked_leaf_ids(leaf_selector, rank_leaves_by) ranked_features = self._tree.ranked_features[:top_k_features] nr_leaves, nr_features = len(leaf_nodes), len(ranked_features) logger.info("{} lea{} selected: {}".format(nr_leaves, "f" if nr_leaves == 1 else "ves", leaf_nodes)) logger.info("{} feature distribution{} plotted: {}".format(nr_features, "" if nr_features == 1 else "s", [f["name"] for f in ranked_features])) for leaf_id in leaf_nodes: leaf = self._tree.get_node(leaf_id) suptitle = 'Leaf {} ({}: {}'.format(leaf.id, leaf.probabilities[0][0], format_float(leaf.probabilities[0][1], 3)) suptitle += ', {}: {})'.format(leaf.probabilities[1][0], format_float(leaf.probabilities[1][1], 3)) for feature in ranked_features: feature_name = feature["name"] leaf_stats = self._tree.get_stats(leaf.id, feature_name, nr_bins) feature_is_numerical = feature["numerical"] bins = leaf_stats["bin_edge"] if feature_is_numerical else leaf_stats["bin_value"] if show_global: root_samples = self._tree.get_node(0).samples[0] root_stats = self._tree.get_stats(0, feature_name, nr_bins, bins) if show_class: root_hist_data = {} for class_value, bar_heights in root_stats["target_distrib"].items(): root_hist_data[class_value] = np.array(bar_heights)/root_samples else: root_hist_data, root_prediction = {}, self._tree.get_node(0).prediction root_hist_data[root_prediction] = np.array(root_stats["count"])/root_samples else: root_hist_data = None if bins: leaf_hist_data = {} if show_class: for class_value, bar_heights in leaf_stats["target_distrib"].items(): leaf_hist_data[class_value] = np.array(bar_heights)/leaf.samples[0] else: leaf_hist_data = {leaf.prediction: np.array(leaf_stats["count"])/leaf.samples[0]} else: leaf_hist_data = None logger.info("No values for the feature {} at the leaf {}".format(feature_name, leaf.id)) if show_global: bins = root_stats["bin_edge"] if feature_is_numerical else root_stats["bin_value"] x_ticks = range(len(bins)) _BaseErrorVisualizer._add_new_plot(figsize, bins, x_ticks, feature_name, suptitle) _BaseErrorVisualizer._plot_feature_distribution(x_ticks, feature_is_numerical, leaf_hist_data, root_hist_data) plt.show()

true

f72b04ab534d3991395505fbd9524526beed8f88

5,288

py

Python

seahub/api2/endpoints/draft_reviewer.py

odontomachus/seahub

5b6f2153921da21a473d9ff20ce443d40efc93ab

[ "Apache-2.0" ]

null

seahub/api2/endpoints/draft_reviewer.py

odontomachus/seahub

5b6f2153921da21a473d9ff20ce443d40efc93ab

[ "Apache-2.0" ]

6

2019-12-13T09:55:45.000Z

2022-03-11T23:47:29.000Z

seahub/api2/endpoints/draft_reviewer.py

odontomachus/seahub

5b6f2153921da21a473d9ff20ce443d40efc93ab

[ "Apache-2.0" ]

1

2019-05-16T06:58:16.000Z

# Copyright (c) 2012-2016 Seafile Ltd. import posixpath from rest_framework import status from rest_framework.authentication import SessionAuthentication from rest_framework.permissions import IsAuthenticated from rest_framework.response import Response from rest_framework.views import APIView from django.utils.translation import ugettext as _ from seaserv import seafile_api from seahub.api2.authentication import TokenAuthentication from seahub.api2.throttling import UserRateThrottle from seahub.api2.utils import api_error, user_to_dict from seahub.base.templatetags.seahub_tags import email2nickname from seahub.base.accounts import User from seahub.tags.models import FileUUIDMap from seahub.views import check_folder_permission from seahub.utils import is_valid_username from seahub.drafts.models import Draft, DraftReviewer from seahub.drafts.signals import request_reviewer_successful class DraftReviewerView(APIView): authentication_classes = (TokenAuthentication, SessionAuthentication) permission_classes = (IsAuthenticated, ) throttle_classes = (UserRateThrottle, ) def get(self, request, pk, format=None): try: d = Draft.objects.get(pk=pk) except Draft.DoesNotExist: return api_error(status.HTTP_404_NOT_FOUND, 'Draft %s not found' % pk) # format user result try: avatar_size = int(request.GET.get('avatar_size', 32)) except ValueError: avatar_size = 32 # get reviewer list reviewers = [] for x in d.draftreviewer_set.all(): reviewer = user_to_dict(x.reviewer, request=request, avatar_size=avatar_size) reviewers.append(reviewer) return Response({'reviewers': reviewers}) def post(self, request, pk, format=None): """add draft reviewer """ try: d = Draft.objects.get(pk=pk) except Draft.DoesNotExist: return api_error(status.HTTP_404_NOT_FOUND, 'Draft %s not found' % pk) result = {} result['failed'] = [] result['success'] = [] reviewers = request.data.getlist('reviewer') for reviewer in reviewers: if not is_valid_username(reviewer): result['failed'].append({ 'email': reviewer, 'error_msg': _(u'username invalid.') }) continue try: User.objects.get(email=reviewer) except User.DoesNotExist: result['failed'].append({ 'email': reviewer, 'error_msg': _(u'User %s not found.') % reviewer }) continue # can't share to owner if reviewer == d.username: error_msg = 'Draft can not be asked owner to review.' result['failed'].append({ 'email': reviewer, 'error_msg': error_msg }) continue uuid = FileUUIDMap.objects.get_fileuuidmap_by_uuid(d.origin_file_uuid) origin_file_path = posixpath.join(uuid.parent_path, uuid.filename) # check perm if seafile_api.check_permission_by_path(d.origin_repo_id, origin_file_path, reviewer) != 'rw': error_msg = _(u'Permission denied.') result['failed'].append({ 'email': reviewer, 'error_msg': error_msg }) continue if DraftReviewer.objects.filter(draft=d, reviewer=reviewer): error_msg = u'Reviewer %s has existed.' % reviewer result['failed'].append({ 'email': reviewer, 'error_msg': error_msg }) continue result['success'].append({ "user_info": { "name": reviewer, "nickname": email2nickname(reviewer) } }) DraftReviewer.objects.add(reviewer, d) request_reviewer_successful.send(sender=None, from_user=request.user.username, to_user=reviewer, draft_id=d.id) return Response(result) def delete(self, request, pk): """Delete a reviewer """ try: d = Draft.objects.get(pk=pk) except Draft.DoesNotExist: return api_error(status.HTTP_404_NOT_FOUND, 'Draft %s not found' % pk) perm = check_folder_permission(request, d.origin_repo_id, '/') if perm is None: error_msg = 'Permission denied.' return api_error(status.HTTP_403_FORBIDDEN, error_msg) reviewer = request.GET.get('username') if reviewer is None: return api_error(status.HTTP_400_BAD_REQUEST, 'Email %s invalid.' % reviewer) try: reviewer = DraftReviewer.objects.get(reviewer=reviewer, draft=d) except DraftReviewer.DoesNotExist: return Response(status.HTTP_200_OK) reviewer.delete() return Response(status.HTTP_200_OK)

34.562092

106

0.587368

import posixpath from rest_framework import status from rest_framework.authentication import SessionAuthentication from rest_framework.permissions import IsAuthenticated from rest_framework.response import Response from rest_framework.views import APIView from django.utils.translation import ugettext as _ from seaserv import seafile_api from seahub.api2.authentication import TokenAuthentication from seahub.api2.throttling import UserRateThrottle from seahub.api2.utils import api_error, user_to_dict from seahub.base.templatetags.seahub_tags import email2nickname from seahub.base.accounts import User from seahub.tags.models import FileUUIDMap from seahub.views import check_folder_permission from seahub.utils import is_valid_username from seahub.drafts.models import Draft, DraftReviewer from seahub.drafts.signals import request_reviewer_successful class DraftReviewerView(APIView): authentication_classes = (TokenAuthentication, SessionAuthentication) permission_classes = (IsAuthenticated, ) throttle_classes = (UserRateThrottle, ) def get(self, request, pk, format=None): try: d = Draft.objects.get(pk=pk) except Draft.DoesNotExist: return api_error(status.HTTP_404_NOT_FOUND, 'Draft %s not found' % pk) try: avatar_size = int(request.GET.get('avatar_size', 32)) except ValueError: avatar_size = 32 reviewers = [] for x in d.draftreviewer_set.all(): reviewer = user_to_dict(x.reviewer, request=request, avatar_size=avatar_size) reviewers.append(reviewer) return Response({'reviewers': reviewers}) def post(self, request, pk, format=None): try: d = Draft.objects.get(pk=pk) except Draft.DoesNotExist: return api_error(status.HTTP_404_NOT_FOUND, 'Draft %s not found' % pk) result = {} result['failed'] = [] result['success'] = [] reviewers = request.data.getlist('reviewer') for reviewer in reviewers: if not is_valid_username(reviewer): result['failed'].append({ 'email': reviewer, 'error_msg': _(u'username invalid.') }) continue try: User.objects.get(email=reviewer) except User.DoesNotExist: result['failed'].append({ 'email': reviewer, 'error_msg': _(u'User %s not found.') % reviewer }) continue if reviewer == d.username: error_msg = 'Draft can not be asked owner to review.' result['failed'].append({ 'email': reviewer, 'error_msg': error_msg }) continue uuid = FileUUIDMap.objects.get_fileuuidmap_by_uuid(d.origin_file_uuid) origin_file_path = posixpath.join(uuid.parent_path, uuid.filename) # check perm if seafile_api.check_permission_by_path(d.origin_repo_id, origin_file_path, reviewer) != 'rw': error_msg = _(u'Permission denied.') result['failed'].append({ 'email': reviewer, 'error_msg': error_msg }) continue if DraftReviewer.objects.filter(draft=d, reviewer=reviewer): error_msg = u'Reviewer %s has existed.' % reviewer result['failed'].append({ 'email': reviewer, 'error_msg': error_msg }) continue result['success'].append({ "user_info": { "name": reviewer, "nickname": email2nickname(reviewer) } }) DraftReviewer.objects.add(reviewer, d) request_reviewer_successful.send(sender=None, from_user=request.user.username, to_user=reviewer, draft_id=d.id) return Response(result) def delete(self, request, pk): try: d = Draft.objects.get(pk=pk) except Draft.DoesNotExist: return api_error(status.HTTP_404_NOT_FOUND, 'Draft %s not found' % pk) perm = check_folder_permission(request, d.origin_repo_id, '/') if perm is None: error_msg = 'Permission denied.' return api_error(status.HTTP_403_FORBIDDEN, error_msg) reviewer = request.GET.get('username') if reviewer is None: return api_error(status.HTTP_400_BAD_REQUEST, 'Email %s invalid.' % reviewer) try: reviewer = DraftReviewer.objects.get(reviewer=reviewer, draft=d) except DraftReviewer.DoesNotExist: return Response(status.HTTP_200_OK) reviewer.delete() return Response(status.HTTP_200_OK)

true

f72b04c22d26af35d88e3f843c7d2b7c9e606c26

120

py

Python

module_2/lab2_1_1_7.py

dzooli/pcep_prepare

ddf34991a2d6ef2cfe3bda706ec333e9caa2aea5

[ "MIT" ]

null

module_2/lab2_1_1_7.py

dzooli/pcep_prepare

ddf34991a2d6ef2cfe3bda706ec333e9caa2aea5

[ "MIT" ]

null

module_2/lab2_1_1_7.py

dzooli/pcep_prepare

ddf34991a2d6ef2cfe3bda706ec333e9caa2aea5

[ "MIT" ]

null

print("Hello, Python!") print("Zoltan") #print(Zoltan) #print "Zoltan" print('Zoltan') print(''' Alma on the tree ''' )

10

23

0.65

print("Hello, Python!") print("Zoltan") print('Zoltan') print(''' Alma on the tree ''' )

true

f72b058123386b2f12effdfae7010abf516ca956

13,314

py

Python

Lib/json/__init__.py

Hadron/python

73137f499ed658169f49273eee46845e3b53e800

[ "PSF-2.0" ]

null

Lib/json/__init__.py

Hadron/python

73137f499ed658169f49273eee46845e3b53e800

[ "PSF-2.0" ]

null

Lib/json/__init__.py

Hadron/python

73137f499ed658169f49273eee46845e3b53e800

[ "PSF-2.0" ]

null

r"""JSON (JavaScript Object Notation) <http://json.org> is a subset of JavaScript syntax (ECMA-262 3rd edition) used as a lightweight data interchange format. :mod:`json` exposes an API familiar to users of the standard library :mod:`marshal` and :mod:`pickle` modules. It is derived from a version of the externally maintained simplejson library. Encoding basic Python object hierarchies:: >>> import json >>> json.dumps(['foo', {'bar': ('baz', None, 1.0, 2)}]) '["foo", {"bar": ["baz", null, 1.0, 2]}]' >>> print(json.dumps("\"foo\bar")) "\"foo\bar" >>> print(json.dumps('\u1234')) "\u1234" >>> print(json.dumps('\\')) "\\" >>> print(json.dumps({"c": 0, "b": 0, "a": 0}, sort_keys=True)) {"a": 0, "b": 0, "c": 0} >>> from io import StringIO >>> io = StringIO() >>> json.dump(['streaming API'], io) >>> io.getvalue() '["streaming API"]' Compact encoding:: >>> import json >>> from collections import OrderedDict >>> mydict = OrderedDict([('4', 5), ('6', 7)]) >>> json.dumps([1,2,3,mydict], separators=(',', ':')) '[1,2,3,{"4":5,"6":7}]' Pretty printing:: >>> import json >>> print(json.dumps({'4': 5, '6': 7}, sort_keys=True, indent=4)) { "4": 5, "6": 7 } Decoding JSON:: >>> import json >>> obj = ['foo', {'bar': ['baz', None, 1.0, 2]}] >>> json.loads('["foo", {"bar":["baz", null, 1.0, 2]}]') == obj True >>> json.loads('"\\"foo\\bar"') == '"foo\x08ar' True >>> from io import StringIO >>> io = StringIO('["streaming API"]') >>> json.load(io)[0] == 'streaming API' True Specializing JSON object decoding:: >>> import json >>> def as_complex(dct): ... if '__complex__' in dct: ... return complex(dct['real'], dct['imag']) ... return dct ... >>> json.loads('{"__complex__": true, "real": 1, "imag": 2}', ... object_hook=as_complex) (1+2j) >>> from decimal import Decimal >>> json.loads('1.1', parse_float=Decimal) == Decimal('1.1') True Specializing JSON object encoding:: >>> import json >>> def encode_complex(obj): ... if isinstance(obj, complex): ... return [obj.real, obj.imag] ... raise TypeError(repr(o) + " is not JSON serializable") ... >>> json.dumps(2 + 1j, default=encode_complex) '[2.0, 1.0]' >>> json.JSONEncoder(default=encode_complex).encode(2 + 1j) '[2.0, 1.0]' >>> ''.join(json.JSONEncoder(default=encode_complex).iterencode(2 + 1j)) '[2.0, 1.0]' Using json.tool from the shell to validate and pretty-print:: $ echo '{"json":"obj"}' | python -m json.tool { "json": "obj" } $ echo '{ 1.2:3.4}' | python -m json.tool Expecting property name enclosed in double quotes: line 1 column 3 (char 2) """ __version__ = '2.0.9' __all__ = [ 'dump', 'dumps', 'load', 'loads', 'JSONDecoder', 'JSONDecodeError', 'JSONEncoder', ] __author__ = 'Bob Ippolito <bob@redivi.com>' from .decoder import JSONDecoder, JSONDecodeError from .encoder import JSONEncoder _default_encoder = JSONEncoder( skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, indent=None, separators=None, default=None, ) def dump(obj, fp, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw): """Serialize ``obj`` as a JSON formatted stream to ``fp`` (a ``.write()``-supporting file-like object). If ``skipkeys`` is true then ``dict`` keys that are not basic types (``str``, ``int``, ``float``, ``bool``, ``None``) will be skipped instead of raising a ``TypeError``. If ``ensure_ascii`` is false, then the strings written to ``fp`` can contain non-ASCII characters if they appear in strings contained in ``obj``. Otherwise, all such characters are escaped in JSON strings. If ``check_circular`` is false, then the circular reference check for container types will be skipped and a circular reference will result in an ``OverflowError`` (or worse). If ``allow_nan`` is false, then it will be a ``ValueError`` to serialize out of range ``float`` values (``nan``, ``inf``, ``-inf``) in strict compliance of the JSON specification, instead of using the JavaScript equivalents (``NaN``, ``Infinity``, ``-Infinity``). If ``indent`` is a non-negative integer, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0 will only insert newlines. ``None`` is the most compact representation. If specified, ``separators`` should be an ``(item_separator, key_separator)`` tuple. The default is ``(', ', ': ')`` if *indent* is ``None`` and ``(',', ': ')`` otherwise. To get the most compact JSON representation, you should specify ``(',', ':')`` to eliminate whitespace. ``default(obj)`` is a function that should return a serializable version of obj or raise TypeError. The default simply raises TypeError. If *sort_keys* is true (default: ``False``), then the output of dictionaries will be sorted by key. To use a custom ``JSONEncoder`` subclass (e.g. one that overrides the ``.default()`` method to serialize additional types), specify it with the ``cls`` kwarg; otherwise ``JSONEncoder`` is used. """ # cached encoder if (not skipkeys and ensure_ascii and check_circular and allow_nan and cls is None and indent is None and separators is None and default is None and not sort_keys and not kw): iterable = _default_encoder.iterencode(obj) else: if cls is None: cls = JSONEncoder iterable = cls(skipkeys=skipkeys, ensure_ascii=ensure_ascii, check_circular=check_circular, allow_nan=allow_nan, indent=indent, separators=separators, default=default, sort_keys=sort_keys, **kw).iterencode(obj) # could accelerate with writelines in some versions of Python, at # a debuggability cost for chunk in iterable: fp.write(chunk) def dumps(obj, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw): """Serialize ``obj`` to a JSON formatted ``str``. If ``skipkeys`` is true then ``dict`` keys that are not basic types (``str``, ``int``, ``float``, ``bool``, ``None``) will be skipped instead of raising a ``TypeError``. If ``ensure_ascii`` is false, then the return value can contain non-ASCII characters if they appear in strings contained in ``obj``. Otherwise, all such characters are escaped in JSON strings. If ``check_circular`` is false, then the circular reference check for container types will be skipped and a circular reference will result in an ``OverflowError`` (or worse). If ``allow_nan`` is false, then it will be a ``ValueError`` to serialize out of range ``float`` values (``nan``, ``inf``, ``-inf``) in strict compliance of the JSON specification, instead of using the JavaScript equivalents (``NaN``, ``Infinity``, ``-Infinity``). If ``indent`` is a non-negative integer, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0 will only insert newlines. ``None`` is the most compact representation. If specified, ``separators`` should be an ``(item_separator, key_separator)`` tuple. The default is ``(', ', ': ')`` if *indent* is ``None`` and ``(',', ': ')`` otherwise. To get the most compact JSON representation, you should specify ``(',', ':')`` to eliminate whitespace. ``default(obj)`` is a function that should return a serializable version of obj or raise TypeError. The default simply raises TypeError. If *sort_keys* is true (default: ``False``), then the output of dictionaries will be sorted by key. To use a custom ``JSONEncoder`` subclass (e.g. one that overrides the ``.default()`` method to serialize additional types), specify it with the ``cls`` kwarg; otherwise ``JSONEncoder`` is used. """ # cached encoder if (not skipkeys and ensure_ascii and check_circular and allow_nan and cls is None and indent is None and separators is None and default is None and not sort_keys and not kw): return _default_encoder.encode(obj) if cls is None: cls = JSONEncoder return cls( skipkeys=skipkeys, ensure_ascii=ensure_ascii, check_circular=check_circular, allow_nan=allow_nan, indent=indent, separators=separators, default=default, sort_keys=sort_keys, **kw).encode(obj) _default_decoder = JSONDecoder(object_hook=None, object_pairs_hook=None) def load(fp, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw): """Deserialize ``fp`` (a ``.read()``-supporting file-like object containing a JSON document) to a Python object. ``object_hook`` is an optional function that will be called with the result of any object literal decode (a ``dict``). The return value of ``object_hook`` will be used instead of the ``dict``. This feature can be used to implement custom decoders (e.g. JSON-RPC class hinting). ``object_pairs_hook`` is an optional function that will be called with the result of any object literal decoded with an ordered list of pairs. The return value of ``object_pairs_hook`` will be used instead of the ``dict``. This feature can be used to implement custom decoders that rely on the order that the key and value pairs are decoded (for example, collections.OrderedDict will remember the order of insertion). If ``object_hook`` is also defined, the ``object_pairs_hook`` takes priority. To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` kwarg; otherwise ``JSONDecoder`` is used. """ return loads(fp.read(), cls=cls, object_hook=object_hook, parse_float=parse_float, parse_int=parse_int, parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) def loads(s, encoding=None, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw): """Deserialize ``s`` (a ``str`` instance containing a JSON document) to a Python object. ``object_hook`` is an optional function that will be called with the result of any object literal decode (a ``dict``). The return value of ``object_hook`` will be used instead of the ``dict``. This feature can be used to implement custom decoders (e.g. JSON-RPC class hinting). ``object_pairs_hook`` is an optional function that will be called with the result of any object literal decoded with an ordered list of pairs. The return value of ``object_pairs_hook`` will be used instead of the ``dict``. This feature can be used to implement custom decoders that rely on the order that the key and value pairs are decoded (for example, collections.OrderedDict will remember the order of insertion). If ``object_hook`` is also defined, the ``object_pairs_hook`` takes priority. ``parse_float``, if specified, will be called with the string of every JSON float to be decoded. By default this is equivalent to float(num_str). This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal). ``parse_int``, if specified, will be called with the string of every JSON int to be decoded. By default this is equivalent to int(num_str). This can be used to use another datatype or parser for JSON integers (e.g. float). ``parse_constant``, if specified, will be called with one of the following strings: -Infinity, Infinity, NaN, null, true, false. This can be used to raise an exception if invalid JSON numbers are encountered. To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` kwarg; otherwise ``JSONDecoder`` is used. The ``encoding`` argument is ignored and deprecated. """ if not isinstance(s, str): raise TypeError('the JSON object must be str, not {!r}'.format( s.__class__.__name__)) if s.startswith(u'\ufeff'): raise JSONDecodeError("Unexpected UTF-8 BOM (decode using utf-8-sig)", s, 0) if (cls is None and object_hook is None and parse_int is None and parse_float is None and parse_constant is None and object_pairs_hook is None and not kw): return _default_decoder.decode(s) if cls is None: cls = JSONDecoder if object_hook is not None: kw['object_hook'] = object_hook if object_pairs_hook is not None: kw['object_pairs_hook'] = object_pairs_hook if parse_float is not None: kw['parse_float'] = parse_float if parse_int is not None: kw['parse_int'] = parse_int if parse_constant is not None: kw['parse_constant'] = parse_constant return cls(**kw).decode(s)

39.981982

81

0.653372

__version__ = '2.0.9' __all__ = [ 'dump', 'dumps', 'load', 'loads', 'JSONDecoder', 'JSONDecodeError', 'JSONEncoder', ] __author__ = 'Bob Ippolito <bob@redivi.com>' from .decoder import JSONDecoder, JSONDecodeError from .encoder import JSONEncoder _default_encoder = JSONEncoder( skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, indent=None, separators=None, default=None, ) def dump(obj, fp, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw): if (not skipkeys and ensure_ascii and check_circular and allow_nan and cls is None and indent is None and separators is None and default is None and not sort_keys and not kw): iterable = _default_encoder.iterencode(obj) else: if cls is None: cls = JSONEncoder iterable = cls(skipkeys=skipkeys, ensure_ascii=ensure_ascii, check_circular=check_circular, allow_nan=allow_nan, indent=indent, separators=separators, default=default, sort_keys=sort_keys, **kw).iterencode(obj) for chunk in iterable: fp.write(chunk) def dumps(obj, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw): if (not skipkeys and ensure_ascii and check_circular and allow_nan and cls is None and indent is None and separators is None and default is None and not sort_keys and not kw): return _default_encoder.encode(obj) if cls is None: cls = JSONEncoder return cls( skipkeys=skipkeys, ensure_ascii=ensure_ascii, check_circular=check_circular, allow_nan=allow_nan, indent=indent, separators=separators, default=default, sort_keys=sort_keys, **kw).encode(obj) _default_decoder = JSONDecoder(object_hook=None, object_pairs_hook=None) def load(fp, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw): return loads(fp.read(), cls=cls, object_hook=object_hook, parse_float=parse_float, parse_int=parse_int, parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) def loads(s, encoding=None, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw): if not isinstance(s, str): raise TypeError('the JSON object must be str, not {!r}'.format( s.__class__.__name__)) if s.startswith(u'\ufeff'): raise JSONDecodeError("Unexpected UTF-8 BOM (decode using utf-8-sig)", s, 0) if (cls is None and object_hook is None and parse_int is None and parse_float is None and parse_constant is None and object_pairs_hook is None and not kw): return _default_decoder.decode(s) if cls is None: cls = JSONDecoder if object_hook is not None: kw['object_hook'] = object_hook if object_pairs_hook is not None: kw['object_pairs_hook'] = object_pairs_hook if parse_float is not None: kw['parse_float'] = parse_float if parse_int is not None: kw['parse_int'] = parse_int if parse_constant is not None: kw['parse_constant'] = parse_constant return cls(**kw).decode(s)

true

f72b05a1e16676d1178d4682bdc7c44175562994

3,192

py

Python

scripts/loadelastic-aurora.py

dbmi-pitt/aurora-meta

a0d3d3963fce2639081cb55715b5357cd0e21902

[ "Apache-2.0" ]

null

scripts/loadelastic-aurora.py

dbmi-pitt/aurora-meta

a0d3d3963fce2639081cb55715b5357cd0e21902

[ "Apache-2.0" ]

null

scripts/loadelastic-aurora.py

dbmi-pitt/aurora-meta

a0d3d3963fce2639081cb55715b5357cd0e21902

[ "Apache-2.0" ]

null

import requests, json, os import argparse import pandas as pd import ijson import time # Elasticsearch python libs from elasticsearch import Elasticsearch from elasticsearch import helpers directory = "" indexName = "aurora-meta2" typeName = "patient" THRESHOLD = 10000 # this regulates how much data gets loaded then is processed in a bulk group PK = "ID" json_root = "item" errors = [] def loadit(): es = Elasticsearch([{'host': 'localhost', 'port': '9200'}]) for filename in os.listdir(directory): if filename.endswith(".json"): json_filename = directory+filename print("Loading " + json_filename) with open(json_filename, 'r') as input_file: i = 1 batchCtr = 1 bulk_action = [] bulkCount = 0 ij = ijson.items(input_file, json_root) print(ij) for rec in ij: print(rec) #pk = rec['clin'][PK] pk = rec['clin'][PK] print(pk) bulk = { "_index" : indexName, #"_type" : typeName, "_id" : pk, "_source" : rec, } bulk_action.append(bulk) i = i + 1 batchCtr = batchCtr + 1 if batchCtr > THRESHOLD: try: #print(bulk_action) bulkCount = bulkCount + batchCtr rtn_status = helpers.bulk(es, bulk_action) if rtn_status: print(rtn_status) #print ('Imported data ' + str(bulkCount-1) + ' successfully from ' + json_filename) batchCtr = 1 bulk_action = [] except Exception as ex: print ("Loading failed for " + json_filename) errors.append(json_filename) print ('Error:' + str(ex)) #print ("Loading failed!") #pass if i < THRESHOLD: try: rtn_status = helpers.bulk(es, bulk_action) if rtn_status: print(rtn_status) #print ('Imported data ' + str(i-1) + ' successfully from ' + json_filename) batchCtr = 1 bulk_action = [] except Exception as ex: print ('Error:' + str(ex)) print ("Loading failed for " + json_filename) errors.append(json_filename) #pass if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("-d", required=True, help="dir path to json file(s)") parser.add_argument("-thres", help="set the batch threshold") parser.add_argument("-i", help="set the index name") parser.add_argument("-t", help="set the type") parser.add_argument("-pk", help="primary key of the record, default 'ID'") parser.add_argument("-r", help="json root node, default 'item', passing 'NOROOT' will ignore the root item") args = parser.parse_args() print("Args:") print(args) if args.d: directory = args.d if directory[-1] != '/': directory = directory + '/' if args.thres: THRESHOLD = int(args.thres) print ("Batch threshold: " + str(THRESHOLD)) print(type(THRESHOLD)) if args.i: indexName = args.i if args.t: typeName = args.t if args.pk: PK = args.pk if args.r: if args.r == "NOROOT": json_root = "" # ignore the root else: json_root = args.r start = time.time() loadit() end = time.time() print("Elapsed time: {}".format((end-start))) if len(errors) > 0: print("The following files failed:") print(errors)

25.95122

109

0.628446

import requests, json, os import argparse import pandas as pd import ijson import time from elasticsearch import Elasticsearch from elasticsearch import helpers directory = "" indexName = "aurora-meta2" typeName = "patient" THRESHOLD = 10000 PK = "ID" json_root = "item" errors = [] def loadit(): es = Elasticsearch([{'host': 'localhost', 'port': '9200'}]) for filename in os.listdir(directory): if filename.endswith(".json"): json_filename = directory+filename print("Loading " + json_filename) with open(json_filename, 'r') as input_file: i = 1 batchCtr = 1 bulk_action = [] bulkCount = 0 ij = ijson.items(input_file, json_root) print(ij) for rec in ij: print(rec) pk = rec['clin'][PK] print(pk) bulk = { "_index" : indexName, "_id" : pk, "_source" : rec, } bulk_action.append(bulk) i = i + 1 batchCtr = batchCtr + 1 if batchCtr > THRESHOLD: try: bulkCount = bulkCount + batchCtr rtn_status = helpers.bulk(es, bulk_action) if rtn_status: print(rtn_status) batchCtr = 1 bulk_action = [] except Exception as ex: print ("Loading failed for " + json_filename) errors.append(json_filename) print ('Error:' + str(ex)) if i < THRESHOLD: try: rtn_status = helpers.bulk(es, bulk_action) if rtn_status: print(rtn_status) batchCtr = 1 bulk_action = [] except Exception as ex: print ('Error:' + str(ex)) print ("Loading failed for " + json_filename) errors.append(json_filename) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("-d", required=True, help="dir path to json file(s)") parser.add_argument("-thres", help="set the batch threshold") parser.add_argument("-i", help="set the index name") parser.add_argument("-t", help="set the type") parser.add_argument("-pk", help="primary key of the record, default 'ID'") parser.add_argument("-r", help="json root node, default 'item', passing 'NOROOT' will ignore the root item") args = parser.parse_args() print("Args:") print(args) if args.d: directory = args.d if directory[-1] != '/': directory = directory + '/' if args.thres: THRESHOLD = int(args.thres) print ("Batch threshold: " + str(THRESHOLD)) print(type(THRESHOLD)) if args.i: indexName = args.i if args.t: typeName = args.t if args.pk: PK = args.pk if args.r: if args.r == "NOROOT": json_root = "" else: json_root = args.r start = time.time() loadit() end = time.time() print("Elapsed time: {}".format((end-start))) if len(errors) > 0: print("The following files failed:") print(errors)

true

f72b05a397836379cf15a5545dc470a6f2762a91

5,781

py

Python

smoke/data/build.py

SmallMunich/Smoke

591a03bdb5cad962999914c9a97c7a8bed9e529b

[ "MIT" ]

2

2022-03-08T02:54:57.000Z

2022-03-10T09:09:40.000Z

smoke/data/build.py

SmallMunich/Smoke

591a03bdb5cad962999914c9a97c7a8bed9e529b

[ "MIT" ]

null

smoke/data/build.py

SmallMunich/Smoke

591a03bdb5cad962999914c9a97c7a8bed9e529b

[ "MIT" ]

null

import logging import copy import bisect import numpy as np import torch.utils.data from smoke.utils.comm import get_world_size from smoke.utils.imports import import_file from smoke.utils.envs import seed_all_rng from . import datasets as D from . import samplers from .transforms import build_transforms from .collate_batch import BatchCollator def build_dataset(cfg, transforms, dataset_catalog, is_train=True): ''' Args: dataset_list (list[str]): Contains the names of the datasets. transforms (callable): transforms to apply to each (image, target) sample dataset_catalog (DatasetCatalog): contains the information on how to construct a dataset. is_train (bool): whether to setup the dataset for training or testing Returns: ''' dataset_list = cfg.DATASETS.TRAIN if is_train else cfg.DATASETS.TEST if not isinstance(dataset_list, (list, tuple)): raise RuntimeError( "dataset_list should be a list of strings, got {}".format(dataset_list) ) datasets = [] for dataset_name in dataset_list: data = dataset_catalog.get(dataset_name) factory = getattr(D, data["factory"]) args = data["args"] args["cfg"] = cfg args["is_train"] = is_train args["transforms"] = transforms # make dataset from factory dataset = factory(**args) datasets.append(dataset) # for testing, return a list of datasets if not is_train: return datasets # for training, concatenate all datasets into a single one dataset = datasets[0] if len(datasets) > 1: dataset = D.ConcatDataset(datasets) return [dataset] def make_data_loader(cfg, is_train=True): num_gpus = get_world_size() if is_train: images_per_batch = cfg.SOLVER.IMS_PER_BATCH assert images_per_batch % num_gpus == 0, \ "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of GPUs ({}) used." \ .format(images_per_batch, num_gpus) images_per_gpu = images_per_batch // num_gpus else: images_per_batch = cfg.TEST.IMS_PER_BATCH assert images_per_batch % num_gpus == 0, \ "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of GPUs ({}) used." \ .format(images_per_batch, num_gpus) images_per_gpu = images_per_batch // num_gpus # if images_per_gpu > 1: # logger = logging.getLogger(__name__) # logger.warning( # "When using more than one image per GPU you may encounter " # "an out-of-memory (OOM) error if your GPU does not have " # "sufficient memory. If this happens, you can reduce " # "SOLVER.IMS_PER_BATCH (for training) or " # "TEST.IMS_PER_BATCH (for inference). For training, you must " # "also adjust the learning rate and schedule length according " # "to the linear scaling rule. See for example: " # "https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14" # ) # group images which have similar aspect ratio. In this case, we only # group in two cases: those with width / height > 1, and the other way around, # but the code supports more general grouping strategy aspect_grouping = [1] if cfg.DATALOADER.ASPECT_RATIO_GROUPING else [] path_catalog = import_file( "smoke.config.paths_catalog", cfg.PATHS_CATALOG, True ) DatasetCatalog = path_catalog.DatasetCatalog transforms = build_transforms(cfg, is_train) datasets = build_dataset(cfg, transforms, DatasetCatalog, is_train) data_loaders = [] for dataset in datasets: sampler = samplers.TrainingSampler(len(dataset)) batch_sampler = torch.utils.data.sampler.BatchSampler( sampler, images_per_gpu, drop_last=True ) collator = BatchCollator(cfg.DATALOADER.SIZE_DIVISIBILITY) num_workers = cfg.DATALOADER.NUM_WORKERS # import pdb; pdb.set_trace() data_loader = torch.utils.data.DataLoader( dataset, num_workers=num_workers, batch_sampler=batch_sampler, collate_fn=collator, worker_init_fn=worker_init_reset_seed, ) data_loaders.append(data_loader) if is_train: # during training, a single (possibly concatenated) data_loader is returned assert len(data_loaders) == 1 return data_loaders[0] return data_loaders def build_test_loader(cfg, is_train=False): path_catalog = import_file( "smoke.config.paths_catalog", cfg.PATHS_CATALOG, True ) DatasetCatalog = path_catalog.DatasetCatalog transforms = build_transforms(cfg, is_train) datasets = build_dataset(cfg, transforms, DatasetCatalog, is_train) data_loaders = [] for dataset in datasets: sampler = samplers.InferenceSampler(len(dataset)) batch_sampler = torch.utils.data.sampler.BatchSampler( sampler, 1, drop_last=False ) collator = BatchCollator(cfg.DATALOADER.SIZE_DIVISIBILITY) num_workers = cfg.DATALOADER.NUM_WORKERS data_loader = torch.utils.data.DataLoader( dataset, num_workers=num_workers, batch_sampler=batch_sampler, collate_fn=collator, ) data_loaders.append(data_loader) # Origin is data_loader, Now I think this should be data_loaders return data_loader def trivial_batch_collator(batch): """ A batch collator that does nothing. """ return batch def worker_init_reset_seed(worker_id): seed_all_rng(np.random.randint(2 ** 31) + worker_id)

34.825301

145

0.669088

import logging import copy import bisect import numpy as np import torch.utils.data from smoke.utils.comm import get_world_size from smoke.utils.imports import import_file from smoke.utils.envs import seed_all_rng from . import datasets as D from . import samplers from .transforms import build_transforms from .collate_batch import BatchCollator def build_dataset(cfg, transforms, dataset_catalog, is_train=True): dataset_list = cfg.DATASETS.TRAIN if is_train else cfg.DATASETS.TEST if not isinstance(dataset_list, (list, tuple)): raise RuntimeError( "dataset_list should be a list of strings, got {}".format(dataset_list) ) datasets = [] for dataset_name in dataset_list: data = dataset_catalog.get(dataset_name) factory = getattr(D, data["factory"]) args = data["args"] args["cfg"] = cfg args["is_train"] = is_train args["transforms"] = transforms dataset = factory(**args) datasets.append(dataset) if not is_train: return datasets dataset = datasets[0] if len(datasets) > 1: dataset = D.ConcatDataset(datasets) return [dataset] def make_data_loader(cfg, is_train=True): num_gpus = get_world_size() if is_train: images_per_batch = cfg.SOLVER.IMS_PER_BATCH assert images_per_batch % num_gpus == 0, \ "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of GPUs ({}) used." \ .format(images_per_batch, num_gpus) images_per_gpu = images_per_batch // num_gpus else: images_per_batch = cfg.TEST.IMS_PER_BATCH assert images_per_batch % num_gpus == 0, \ "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of GPUs ({}) used." \ .format(images_per_batch, num_gpus) images_per_gpu = images_per_batch // num_gpus aspect_grouping = [1] if cfg.DATALOADER.ASPECT_RATIO_GROUPING else [] path_catalog = import_file( "smoke.config.paths_catalog", cfg.PATHS_CATALOG, True ) DatasetCatalog = path_catalog.DatasetCatalog transforms = build_transforms(cfg, is_train) datasets = build_dataset(cfg, transforms, DatasetCatalog, is_train) data_loaders = [] for dataset in datasets: sampler = samplers.TrainingSampler(len(dataset)) batch_sampler = torch.utils.data.sampler.BatchSampler( sampler, images_per_gpu, drop_last=True ) collator = BatchCollator(cfg.DATALOADER.SIZE_DIVISIBILITY) num_workers = cfg.DATALOADER.NUM_WORKERS data_loader = torch.utils.data.DataLoader( dataset, num_workers=num_workers, batch_sampler=batch_sampler, collate_fn=collator, worker_init_fn=worker_init_reset_seed, ) data_loaders.append(data_loader) if is_train: assert len(data_loaders) == 1 return data_loaders[0] return data_loaders def build_test_loader(cfg, is_train=False): path_catalog = import_file( "smoke.config.paths_catalog", cfg.PATHS_CATALOG, True ) DatasetCatalog = path_catalog.DatasetCatalog transforms = build_transforms(cfg, is_train) datasets = build_dataset(cfg, transforms, DatasetCatalog, is_train) data_loaders = [] for dataset in datasets: sampler = samplers.InferenceSampler(len(dataset)) batch_sampler = torch.utils.data.sampler.BatchSampler( sampler, 1, drop_last=False ) collator = BatchCollator(cfg.DATALOADER.SIZE_DIVISIBILITY) num_workers = cfg.DATALOADER.NUM_WORKERS data_loader = torch.utils.data.DataLoader( dataset, num_workers=num_workers, batch_sampler=batch_sampler, collate_fn=collator, ) data_loaders.append(data_loader) return data_loader def trivial_batch_collator(batch): return batch def worker_init_reset_seed(worker_id): seed_all_rng(np.random.randint(2 ** 31) + worker_id)

true

f72b0684f170d3fddc3fc47d05fff76101d188b3

1,072

py

Python

i3wsgroups/cli.py

damani42/i3-workspace-groups

13fe8e22e829166eb22df031b4c39f3501dfb362

[ "MIT" ]

null

i3wsgroups/cli.py

damani42/i3-workspace-groups

13fe8e22e829166eb22df031b4c39f3501dfb362

[ "MIT" ]

null

i3wsgroups/cli.py

damani42/i3-workspace-groups

13fe8e22e829166eb22df031b4c39f3501dfb362

[ "MIT" ]

null

import argparse def add_common_args(parser: argparse.ArgumentParser): parser.add_argument( '--dry-run', action='store_true', default=False, help='If true, will not actually do any changes to i3 workspaces.') parser.add_argument( '--log-level', choices=('debug', 'info', 'warning', 'error', 'critical'), default='warning', help='Logging level for stderr and syslog.') def add_workspace_naming_args(parser: argparse.ArgumentParser) -> None: parser.add_argument( '--window-icons-all-groups', action='store_true', default=False, help='If true, will add the icons of the open windows to workspaces' ' in all groups, and not just the active group. Also implies ' '--window-icons.') parser.add_argument( '--renumber-workspaces', action='store_true', default=False, help='If true, will renumber workspaces in every groups so that they ' 'are in numerical order, similar to tmux\'s renumber-windows option.')

34.580645

78

0.636194

import argparse def add_common_args(parser: argparse.ArgumentParser): parser.add_argument( '--dry-run', action='store_true', default=False, help='If true, will not actually do any changes to i3 workspaces.') parser.add_argument( '--log-level', choices=('debug', 'info', 'warning', 'error', 'critical'), default='warning', help='Logging level for stderr and syslog.') def add_workspace_naming_args(parser: argparse.ArgumentParser) -> None: parser.add_argument( '--window-icons-all-groups', action='store_true', default=False, help='If true, will add the icons of the open windows to workspaces' ' in all groups, and not just the active group. Also implies ' '--window-icons.') parser.add_argument( '--renumber-workspaces', action='store_true', default=False, help='If true, will renumber workspaces in every groups so that they ' 'are in numerical order, similar to tmux\'s renumber-windows option.')

true

f72b073f2c249ce06aea52ce2b03bad057fb64ac

10,626

py

Python

src/neqsim/process/processTools.py

kwafafoa/neqsimpython

2a540297552b39dac2666bbfb7c76eda0f5779db

[ "Apache-2.0" ]

null

src/neqsim/process/processTools.py

kwafafoa/neqsimpython

2a540297552b39dac2666bbfb7c76eda0f5779db

[ "Apache-2.0" ]

null

src/neqsim/process/processTools.py

kwafafoa/neqsimpython

2a540297552b39dac2666bbfb7c76eda0f5779db

[ "Apache-2.0" ]

null

import jpype import jpype.imports from jpype.types import * from neqsim.neqsimpython import neqsim processoperations = neqsim.processSimulation.processSystem.ProcessSystem() def stream(thermoSystem, name="stream ?", t=0, p=0): if t != 0: thermoSystem.setTemperature(t) if p != 0: thermoSystem.setPressure(p) stream = neqsim.processSimulation.processEquipment.stream.Stream(thermoSystem) stream.setName(name) processoperations.add(stream) return stream def neqstream(thermoSystem, name="stream ?", t=0, p=0): if t != 0: thermoSystem.setTemperature(t) if p != 0: thermoSystem.setPressure(p) stream = neqsim.processSimulation.processEquipment.stream.NeqStream(thermoSystem) stream.setName(name) processoperations.add(stream) return stream def recycle(teststream, name="recycle ?"): recycle1 = neqsim.processSimulation.processEquipment.util.Recycle() recycle1.addStream(teststream) processoperations.add(recycle1) return recycle1 def saturator(teststream, name="water saturator"): streamsaturator = neqsim.processSimulation.processEquipment.util.StreamSaturatorUtil(teststream) processoperations.add(streamsaturator) return streamsaturator def glycoldehydrationlmodule(teststream, name="TEG process"): dehydrationlmodule = neqsim.processSimulation.processSystem.processModules.GlycolDehydrationlModule() dehydrationlmodule.setName(name) dehydrationlmodule.addInputStream("gasStreamToAbsorber", teststream) processoperations.add(dehydrationlmodule) return dehydrationlmodule def openprocess(filename): processoperations = neqsim.processSimulation.processSystem.ProcessSystem.open(filename) return processoperations def separator(teststream, name="separator ?"): separator = neqsim.processSimulation.processEquipment.separator.Separator(teststream) separator.setName(name) processoperations.add(separator) return separator def GORfitter(teststream, name="GOR fitter ?"): GORfitter1 = neqsim.processSimulation.processEquipment.util.GORfitter(name, teststream) GORfitter1.setName(name) processoperations.add(GORfitter1) return GORfitter1 def simpleTEGAbsorber(name="TEG absorber ?"): absorber = neqsim.processSimulation.processEquipment.absorber.SimpleTEGAbsorber() absorber.setName(name) processoperations.add(absorber) return absorber def waterStripperColumn(name="water stripper ?"): stripper = neqsim.processSimulation.processEquipment.absorber.WaterStripperColumn() stripper.setName(name) processoperations.add(stripper) return stripper def gasscrubber(teststream, name="scrubber ?"): separator = neqsim.processSimulation.processEquipment.separator.GasScrubber(teststream) separator.setName(name) processoperations.add(separator) return separator def separator3phase(teststream, name="separator ?"): separator = neqsim.processSimulation.processEquipment.separator.ThreePhaseSeparator(teststream) separator.setName(name) processoperations.add(separator) return separator def valve(teststream, p=1.0, name="valve ?"): valve = neqsim.processSimulation.processEquipment.valve.ThrottlingValve(teststream) valve.setOutletPressure(p) valve.setName(name) processoperations.add(valve) return valve def recycle2(name="recycle ?"): recyc = neqsim.processSimulation.processEquipment.util.Recycle(name) processoperations.add(recyc) return recyc def calculator(name="calculator ?"): calc2 = neqsim.processSimulation.processEquipment.util.Calculator(name) processoperations.add(calc2) return calc2 def setpoint(name1, unit1, name2, unit2): setp = neqsim.processSimulation.processEquipment.util.SetPoint(name1, unit1, name2, unit2) processoperations.add(setp) return setp def filters(teststream): filter2 = neqsim.processSimulation.processEquipment.filter.Filter(teststream) processoperations.add(filter2) return filter2 def compressor(teststream, pres=10.0, name="compressor ?"): compressor = neqsim.processSimulation.processEquipment.compressor.Compressor(teststream) compressor.setOutletPressure(pres) compressor.setName(name) processoperations.add(compressor) return compressor def compressorChart(compressor, curveConditions, speed, flow, head, polyEff ): compressor.getCompressorChart().setCurves(JDouble[:](curveConditions), JDouble[:](speed), JDouble[:][:](flow), JDouble[:][:](head), JDouble[:][:](polyEff)) def compressorSurgeCurve(compressor, curveConditions, surgeflow, surgehead): compressor.getCompressorChart().getSurgeCurve().setCurve(JDouble[:](curveConditions), JDouble[:](surgeflow), JDouble[:](surgehead)) def compressorStoneWallCurve(compressor, curveConditions, stoneWallflow, stoneWallHead): compressor.getCompressorChart().getStoneWallCurve().setCurve(JDouble[:](curveConditions), JDouble[:](stoneWallflow), JDouble[:](stoneWallHead)) def pump(teststream, p=1.0, name="pump ?"): pump = neqsim.processSimulation.processEquipment.pump.Pump(teststream) pump.setOutletPressure(p) pump.setName(name) processoperations.add(pump) return pump def expander(teststream, p, name="expander ?"): expander = neqsim.processSimulation.processEquipment.expander.Expander(teststream) expander.setOutletPressure(p) expander.setName(name) processoperations.add(expander) return expander def mixer(name=""): mixer = neqsim.processSimulation.processEquipment.mixer.StaticMixer() mixer.setName(name) processoperations.add(mixer) return mixer def phasemixer(name=""): mixer = neqsim.processSimulation.processEquipment.mixer.StaticPhaseMixer() mixer.setName(name) processoperations.add(mixer) return mixer def nequnit(teststream, equipment="pipeline", flowpattern="stratified", numberOfNodes=100): neqUn = neqsim.processSimulation.processEquipment.util.NeqSimUnit(teststream, equipment, flowpattern) neqUn.setNumberOfNodes(numberOfNodes) processoperations.add(neqUn) return neqUn def splitter(teststream, splitfactors, name=""): splitter = neqsim.processSimulation.processEquipment.splitter.Splitter(teststream) splitter.setSplitNumber(len(splitfactors)) splitter.setSplitFactors(JDouble[:](splitfactors)) splitter.setName(name) processoperations.add(splitter) return splitter def heater(teststream, name=""): heater = neqsim.processSimulation.processEquipment.heatExchanger.Heater(teststream) heater.setName(name) processoperations.add(heater) return heater def simplereservoir(fluid, name="Reservoir 1", gasvolume=10.0 * 1e7, oilvolume=120.0 * 1e6, watervolume=10.0e6): reserv = neqsim.processSimulation.processEquipment.reservoir.SimpleReservoir(name) reserv.setReservoirFluid(fluid, gasvolume, oilvolume, watervolume) processoperations.add(reserv) return reserv def cooler(teststream, name=""): cooler = neqsim.processSimulation.processEquipment.heatExchanger.Cooler(teststream) cooler.setName(name) processoperations.add(cooler) return cooler def heatExchanger(stream1, stream2=None, name=""): if stream2==None: heater = neqsim.processSimulation.processEquipment.heatExchanger.HeatExchanger(stream1) else: heater = neqsim.processSimulation.processEquipment.heatExchanger.HeatExchanger(stream1, stream2) heater.setName(name) processoperations.add(heater) return heater def distillationColumn(trays=5, reboil=True, condenser=True, name="destColumn"): distillationColumn = neqsim.processSimulation.processEquipment.distillation.DistillationColumn(trays, reboil, condenser) distillationColumn.setName(name) processoperations.add(distillationColumn) return distillationColumn def neqheater(teststream, name=""): neqheater = neqsim.processSimulation.processEquipment.heatExchanger.NeqHeater(teststream) neqheater.setName(name) processoperations.add(neqheater) return neqheater def twophasepipe(teststream, position, diameter, height, outTemp, rough): pipe = neqsim.processSimulation.processEquipment.pipeline.TwoPhasePipeLine(teststream) pipe.setOutputFileName("c:/tempNew20.nc") pipe.setInitialFlowPattern("annular") numberOfLegs = len(position) - 1 numberOfNodesInLeg = 60 pipe.setNumberOfLegs(numberOfLegs) pipe.setNumberOfNodesInLeg(numberOfNodesInLeg) pipe.setLegPositions(position) pipe.setHeightProfile(height) pipe.setPipeDiameters(diameter) pipe.setPipeWallRoughness(rough) pipe.setOuterTemperatures(outTemp) pipe.setEquilibriumMassTransfer(0) pipe.setEquilibriumHeatTransfer(1) processoperations.add(pipe) return pipe def pipe(teststream, length, deltaElevation, diameter, rough): pipe = neqsim.processSimulation.processEquipment.pipeline.AdiabaticPipe(teststream) pipe.setDiameter(diameter) pipe.setLength(length) pipe.setPipeWallRoughness(rough) pipe.setInletElevation(0.0) pipe.setOutletElevation(deltaElevation) processoperations.add(pipe) return pipe def pipeline(teststream, position, diameter, height, outTemp, rough, outerHeatTransferCoefficients, pipeWallHeatTransferCoefficients, numberOfNodesInLeg = 50): pipe = neqsim.processSimulation.processEquipment.pipeline.OnePhasePipeLine(teststream) pipe.setOutputFileName("c:/tempNew20.nc") numberOfLegs = len(position) - 1 pipe.setNumberOfLegs(numberOfLegs) pipe.setNumberOfNodesInLeg(numberOfNodesInLeg) pipe.setLegPositions(JDouble[:](position)) pipe.setHeightProfile(JDouble[:](height)) pipe.setPipeDiameters(JDouble[:](diameter)) pipe.setPipeWallRoughness(JDouble[:](rough)) pipe.setPipeOuterHeatTransferCoefficients(JDouble[:](outerHeatTransferCoefficients)) pipe.setPipeWallHeatTransferCoefficients(JDouble[:](pipeWallHeatTransferCoefficients)) pipe.setOuterTemperatures(JDouble[:](outTemp)) processoperations.add(pipe) return pipe def clear(): processoperations.clearAll() def run(): processoperations.run() def clearProcess(): processoperations.clearAll() def runProcess(): processoperations.run() def runProcessAsThread(process): Thread = jpype.JPackage('java.lang.Thread') threadProcess = Thread(process) threadProcess.run() return threadProcess def getProcess(): return processoperations def runtrans(): processoperations.runTransient() def view(): processoperations.displayResult() def viewProcess(): processoperations.displayResult()

36.768166

159

0.769245

import jpype import jpype.imports from jpype.types import * from neqsim.neqsimpython import neqsim processoperations = neqsim.processSimulation.processSystem.ProcessSystem() def stream(thermoSystem, name="stream ?", t=0, p=0): if t != 0: thermoSystem.setTemperature(t) if p != 0: thermoSystem.setPressure(p) stream = neqsim.processSimulation.processEquipment.stream.Stream(thermoSystem) stream.setName(name) processoperations.add(stream) return stream def neqstream(thermoSystem, name="stream ?", t=0, p=0): if t != 0: thermoSystem.setTemperature(t) if p != 0: thermoSystem.setPressure(p) stream = neqsim.processSimulation.processEquipment.stream.NeqStream(thermoSystem) stream.setName(name) processoperations.add(stream) return stream def recycle(teststream, name="recycle ?"): recycle1 = neqsim.processSimulation.processEquipment.util.Recycle() recycle1.addStream(teststream) processoperations.add(recycle1) return recycle1 def saturator(teststream, name="water saturator"): streamsaturator = neqsim.processSimulation.processEquipment.util.StreamSaturatorUtil(teststream) processoperations.add(streamsaturator) return streamsaturator def glycoldehydrationlmodule(teststream, name="TEG process"): dehydrationlmodule = neqsim.processSimulation.processSystem.processModules.GlycolDehydrationlModule() dehydrationlmodule.setName(name) dehydrationlmodule.addInputStream("gasStreamToAbsorber", teststream) processoperations.add(dehydrationlmodule) return dehydrationlmodule def openprocess(filename): processoperations = neqsim.processSimulation.processSystem.ProcessSystem.open(filename) return processoperations def separator(teststream, name="separator ?"): separator = neqsim.processSimulation.processEquipment.separator.Separator(teststream) separator.setName(name) processoperations.add(separator) return separator def GORfitter(teststream, name="GOR fitter ?"): GORfitter1 = neqsim.processSimulation.processEquipment.util.GORfitter(name, teststream) GORfitter1.setName(name) processoperations.add(GORfitter1) return GORfitter1 def simpleTEGAbsorber(name="TEG absorber ?"): absorber = neqsim.processSimulation.processEquipment.absorber.SimpleTEGAbsorber() absorber.setName(name) processoperations.add(absorber) return absorber def waterStripperColumn(name="water stripper ?"): stripper = neqsim.processSimulation.processEquipment.absorber.WaterStripperColumn() stripper.setName(name) processoperations.add(stripper) return stripper def gasscrubber(teststream, name="scrubber ?"): separator = neqsim.processSimulation.processEquipment.separator.GasScrubber(teststream) separator.setName(name) processoperations.add(separator) return separator def separator3phase(teststream, name="separator ?"): separator = neqsim.processSimulation.processEquipment.separator.ThreePhaseSeparator(teststream) separator.setName(name) processoperations.add(separator) return separator def valve(teststream, p=1.0, name="valve ?"): valve = neqsim.processSimulation.processEquipment.valve.ThrottlingValve(teststream) valve.setOutletPressure(p) valve.setName(name) processoperations.add(valve) return valve def recycle2(name="recycle ?"): recyc = neqsim.processSimulation.processEquipment.util.Recycle(name) processoperations.add(recyc) return recyc def calculator(name="calculator ?"): calc2 = neqsim.processSimulation.processEquipment.util.Calculator(name) processoperations.add(calc2) return calc2 def setpoint(name1, unit1, name2, unit2): setp = neqsim.processSimulation.processEquipment.util.SetPoint(name1, unit1, name2, unit2) processoperations.add(setp) return setp def filters(teststream): filter2 = neqsim.processSimulation.processEquipment.filter.Filter(teststream) processoperations.add(filter2) return filter2 def compressor(teststream, pres=10.0, name="compressor ?"): compressor = neqsim.processSimulation.processEquipment.compressor.Compressor(teststream) compressor.setOutletPressure(pres) compressor.setName(name) processoperations.add(compressor) return compressor def compressorChart(compressor, curveConditions, speed, flow, head, polyEff ): compressor.getCompressorChart().setCurves(JDouble[:](curveConditions), JDouble[:](speed), JDouble[:][:](flow), JDouble[:][:](head), JDouble[:][:](polyEff)) def compressorSurgeCurve(compressor, curveConditions, surgeflow, surgehead): compressor.getCompressorChart().getSurgeCurve().setCurve(JDouble[:](curveConditions), JDouble[:](surgeflow), JDouble[:](surgehead)) def compressorStoneWallCurve(compressor, curveConditions, stoneWallflow, stoneWallHead): compressor.getCompressorChart().getStoneWallCurve().setCurve(JDouble[:](curveConditions), JDouble[:](stoneWallflow), JDouble[:](stoneWallHead)) def pump(teststream, p=1.0, name="pump ?"): pump = neqsim.processSimulation.processEquipment.pump.Pump(teststream) pump.setOutletPressure(p) pump.setName(name) processoperations.add(pump) return pump def expander(teststream, p, name="expander ?"): expander = neqsim.processSimulation.processEquipment.expander.Expander(teststream) expander.setOutletPressure(p) expander.setName(name) processoperations.add(expander) return expander def mixer(name=""): mixer = neqsim.processSimulation.processEquipment.mixer.StaticMixer() mixer.setName(name) processoperations.add(mixer) return mixer def phasemixer(name=""): mixer = neqsim.processSimulation.processEquipment.mixer.StaticPhaseMixer() mixer.setName(name) processoperations.add(mixer) return mixer def nequnit(teststream, equipment="pipeline", flowpattern="stratified", numberOfNodes=100): neqUn = neqsim.processSimulation.processEquipment.util.NeqSimUnit(teststream, equipment, flowpattern) neqUn.setNumberOfNodes(numberOfNodes) processoperations.add(neqUn) return neqUn def splitter(teststream, splitfactors, name=""): splitter = neqsim.processSimulation.processEquipment.splitter.Splitter(teststream) splitter.setSplitNumber(len(splitfactors)) splitter.setSplitFactors(JDouble[:](splitfactors)) splitter.setName(name) processoperations.add(splitter) return splitter def heater(teststream, name=""): heater = neqsim.processSimulation.processEquipment.heatExchanger.Heater(teststream) heater.setName(name) processoperations.add(heater) return heater def simplereservoir(fluid, name="Reservoir 1", gasvolume=10.0 * 1e7, oilvolume=120.0 * 1e6, watervolume=10.0e6): reserv = neqsim.processSimulation.processEquipment.reservoir.SimpleReservoir(name) reserv.setReservoirFluid(fluid, gasvolume, oilvolume, watervolume) processoperations.add(reserv) return reserv def cooler(teststream, name=""): cooler = neqsim.processSimulation.processEquipment.heatExchanger.Cooler(teststream) cooler.setName(name) processoperations.add(cooler) return cooler def heatExchanger(stream1, stream2=None, name=""): if stream2==None: heater = neqsim.processSimulation.processEquipment.heatExchanger.HeatExchanger(stream1) else: heater = neqsim.processSimulation.processEquipment.heatExchanger.HeatExchanger(stream1, stream2) heater.setName(name) processoperations.add(heater) return heater def distillationColumn(trays=5, reboil=True, condenser=True, name="destColumn"): distillationColumn = neqsim.processSimulation.processEquipment.distillation.DistillationColumn(trays, reboil, condenser) distillationColumn.setName(name) processoperations.add(distillationColumn) return distillationColumn def neqheater(teststream, name=""): neqheater = neqsim.processSimulation.processEquipment.heatExchanger.NeqHeater(teststream) neqheater.setName(name) processoperations.add(neqheater) return neqheater def twophasepipe(teststream, position, diameter, height, outTemp, rough): pipe = neqsim.processSimulation.processEquipment.pipeline.TwoPhasePipeLine(teststream) pipe.setOutputFileName("c:/tempNew20.nc") pipe.setInitialFlowPattern("annular") numberOfLegs = len(position) - 1 numberOfNodesInLeg = 60 pipe.setNumberOfLegs(numberOfLegs) pipe.setNumberOfNodesInLeg(numberOfNodesInLeg) pipe.setLegPositions(position) pipe.setHeightProfile(height) pipe.setPipeDiameters(diameter) pipe.setPipeWallRoughness(rough) pipe.setOuterTemperatures(outTemp) pipe.setEquilibriumMassTransfer(0) pipe.setEquilibriumHeatTransfer(1) processoperations.add(pipe) return pipe def pipe(teststream, length, deltaElevation, diameter, rough): pipe = neqsim.processSimulation.processEquipment.pipeline.AdiabaticPipe(teststream) pipe.setDiameter(diameter) pipe.setLength(length) pipe.setPipeWallRoughness(rough) pipe.setInletElevation(0.0) pipe.setOutletElevation(deltaElevation) processoperations.add(pipe) return pipe def pipeline(teststream, position, diameter, height, outTemp, rough, outerHeatTransferCoefficients, pipeWallHeatTransferCoefficients, numberOfNodesInLeg = 50): pipe = neqsim.processSimulation.processEquipment.pipeline.OnePhasePipeLine(teststream) pipe.setOutputFileName("c:/tempNew20.nc") numberOfLegs = len(position) - 1 pipe.setNumberOfLegs(numberOfLegs) pipe.setNumberOfNodesInLeg(numberOfNodesInLeg) pipe.setLegPositions(JDouble[:](position)) pipe.setHeightProfile(JDouble[:](height)) pipe.setPipeDiameters(JDouble[:](diameter)) pipe.setPipeWallRoughness(JDouble[:](rough)) pipe.setPipeOuterHeatTransferCoefficients(JDouble[:](outerHeatTransferCoefficients)) pipe.setPipeWallHeatTransferCoefficients(JDouble[:](pipeWallHeatTransferCoefficients)) pipe.setOuterTemperatures(JDouble[:](outTemp)) processoperations.add(pipe) return pipe def clear(): processoperations.clearAll() def run(): processoperations.run() def clearProcess(): processoperations.clearAll() def runProcess(): processoperations.run() def runProcessAsThread(process): Thread = jpype.JPackage('java.lang.Thread') threadProcess = Thread(process) threadProcess.run() return threadProcess def getProcess(): return processoperations def runtrans(): processoperations.runTransient() def view(): processoperations.displayResult() def viewProcess(): processoperations.displayResult()

true

f72b0759efafb83d0661f521221014ba2f8d3aab

7,021

py

Python

tests/graph/test_floyd_warshall.py

aalekhpatel07/retworkx

ae93fcab17d55bc259476c65a677221b4177870a

[ "Apache-2.0" ]

1

2021-11-29T23:15:07.000Z

tests/graph/test_floyd_warshall.py

aalekhpatel07/retworkx

ae93fcab17d55bc259476c65a677221b4177870a

[ "Apache-2.0" ]

40

2020-08-31T06:09:06.000Z

2022-03-18T19:02:34.000Z

tests/graph/test_floyd_warshall.py

aalekhpatel07/retworkx

ae93fcab17d55bc259476c65a677221b4177870a

[ "Apache-2.0" ]

null

# 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 unittest import numpy import retworkx class TestFloydWarshall(unittest.TestCase): parallel_threshold = 300 def test_vs_dijkstra_all_pairs(self): graph = retworkx.PyGraph() a = graph.add_node("A") b = graph.add_node("B") c = graph.add_node("C") d = graph.add_node("D") e = graph.add_node("E") f = graph.add_node("F") edge_list = [ (a, b, 7), (c, a, 9), (a, d, 14), (b, c, 10), (d, c, 2), (d, e, 9), (b, f, 15), (c, f, 11), (e, f, 6), ] graph.add_edges_from(edge_list) dijkstra_lengths = retworkx.graph_all_pairs_dijkstra_path_lengths( graph, float ) expected = {k: {**v, k: 0.0} for k, v in dijkstra_lengths.items()} result = retworkx.graph_floyd_warshall( graph, float, parallel_threshold=self.parallel_threshold ) self.assertEqual(result, expected) def test_vs_dijkstra_all_pairs_with_node_removal(self): graph = retworkx.PyGraph() a = graph.add_node("A") b = graph.add_node("B") c = graph.add_node("C") d = graph.add_node("D") e = graph.add_node("E") f = graph.add_node("F") edge_list = [ (a, b, 7), (c, a, 9), (a, d, 14), (b, c, 10), (d, c, 2), (d, e, 9), (b, f, 15), (c, f, 11), (e, f, 6), ] graph.add_edges_from(edge_list) graph.remove_node(d) dijkstra_lengths = retworkx.graph_all_pairs_dijkstra_path_lengths( graph, float ) expected = {k: {**v, k: 0.0} for k, v in dijkstra_lengths.items()} result = retworkx.graph_floyd_warshall( graph, float, parallel_threshold=self.parallel_threshold ) self.assertEqual(result, expected) def test_floyd_warshall_empty_graph(self): graph = retworkx.PyGraph() self.assertEqual({}, retworkx.graph_floyd_warshall(graph, float)) def test_floyd_warshall_graph_no_edges(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(1000))) expected = {x: {} for x in range(1000)} self.assertEqual( expected, retworkx.graph_floyd_warshall(graph, float), ) def test_floyd_warshall_numpy_three_edges(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(6))) weights = [2, 12, 1, 5, 1] graph.add_edges_from([(i, i + 1, weights[i]) for i in range(5)]) graph.add_edge(5, 0, 10) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: x, parallel_threshold=self.parallel_threshold ) self.assertEqual(dist[0, 3], 15) self.assertEqual(dist[3, 0], 15) def test_weighted_numpy_two_edges(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(8))) graph.add_edges_from( [ (0, 1, 2), (1, 2, 2), (2, 3, 1), (3, 4, 1), (4, 5, 1), (5, 6, 1), (6, 7, 1), (7, 0, 1), ] ) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: x, parallel_threshold=self.parallel_threshold ) self.assertEqual(dist[0, 2], 4) self.assertEqual(dist[2, 0], 4) def test_weighted_numpy_negative_cycle(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(4))) graph.add_edges_from( [ (0, 1, 1), (1, 2, -1), (2, 3, -1), (3, 0, -1), ] ) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: x, parallel_threshold=self.parallel_threshold ) self.assertTrue(numpy.all(numpy.diag(dist) < 0)) def test_floyd_warshall_numpy_cycle(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(7))) graph.add_edges_from_no_data( [(0, 1), (0, 6), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)] ) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: 1, parallel_threshold=self.parallel_threshold ) self.assertEqual(dist[0, 3], 3) self.assertEqual(dist[0, 4], 3) def test_numpy_no_edges(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(4))) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: x, parallel_threshold=self.parallel_threshold ) expected = numpy.full((4, 4), numpy.inf) numpy.fill_diagonal(expected, 0) self.assertTrue(numpy.array_equal(dist, expected)) def test_floyd_warshall_numpy_graph_cycle_with_removals(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(8))) graph.remove_node(0) graph.add_edges_from_no_data( [(1, 2), (1, 7), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7)] ) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: 1, parallel_threshold=self.parallel_threshold ) self.assertEqual(dist[0, 3], 3) self.assertEqual(dist[0, 4], 3) def test_floyd_warshall_numpy_graph_cycle_no_weight_fn(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(8))) graph.remove_node(0) graph.add_edges_from_no_data( [(1, 2), (1, 7), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7)] ) dist = retworkx.graph_floyd_warshall_numpy(graph) self.assertEqual(dist[0, 3], 3) self.assertEqual(dist[0, 4], 3) def test_floyd_warshall_numpy_graph_cycle_default_weight(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(8))) graph.remove_node(0) graph.add_edges_from_no_data( [(1, 2), (1, 7), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7)] ) dist = retworkx.graph_floyd_warshall_numpy( graph, default_weight=2, parallel_threshold=self.parallel_threshold ) self.assertEqual(dist[0, 3], 6) self.assertEqual(dist[0, 4], 6) class TestParallelFloydWarshall(TestFloydWarshall): parallel_threshold = 0

32.808411

79

0.565874

import unittest import numpy import retworkx class TestFloydWarshall(unittest.TestCase): parallel_threshold = 300 def test_vs_dijkstra_all_pairs(self): graph = retworkx.PyGraph() a = graph.add_node("A") b = graph.add_node("B") c = graph.add_node("C") d = graph.add_node("D") e = graph.add_node("E") f = graph.add_node("F") edge_list = [ (a, b, 7), (c, a, 9), (a, d, 14), (b, c, 10), (d, c, 2), (d, e, 9), (b, f, 15), (c, f, 11), (e, f, 6), ] graph.add_edges_from(edge_list) dijkstra_lengths = retworkx.graph_all_pairs_dijkstra_path_lengths( graph, float ) expected = {k: {**v, k: 0.0} for k, v in dijkstra_lengths.items()} result = retworkx.graph_floyd_warshall( graph, float, parallel_threshold=self.parallel_threshold ) self.assertEqual(result, expected) def test_vs_dijkstra_all_pairs_with_node_removal(self): graph = retworkx.PyGraph() a = graph.add_node("A") b = graph.add_node("B") c = graph.add_node("C") d = graph.add_node("D") e = graph.add_node("E") f = graph.add_node("F") edge_list = [ (a, b, 7), (c, a, 9), (a, d, 14), (b, c, 10), (d, c, 2), (d, e, 9), (b, f, 15), (c, f, 11), (e, f, 6), ] graph.add_edges_from(edge_list) graph.remove_node(d) dijkstra_lengths = retworkx.graph_all_pairs_dijkstra_path_lengths( graph, float ) expected = {k: {**v, k: 0.0} for k, v in dijkstra_lengths.items()} result = retworkx.graph_floyd_warshall( graph, float, parallel_threshold=self.parallel_threshold ) self.assertEqual(result, expected) def test_floyd_warshall_empty_graph(self): graph = retworkx.PyGraph() self.assertEqual({}, retworkx.graph_floyd_warshall(graph, float)) def test_floyd_warshall_graph_no_edges(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(1000))) expected = {x: {} for x in range(1000)} self.assertEqual( expected, retworkx.graph_floyd_warshall(graph, float), ) def test_floyd_warshall_numpy_three_edges(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(6))) weights = [2, 12, 1, 5, 1] graph.add_edges_from([(i, i + 1, weights[i]) for i in range(5)]) graph.add_edge(5, 0, 10) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: x, parallel_threshold=self.parallel_threshold ) self.assertEqual(dist[0, 3], 15) self.assertEqual(dist[3, 0], 15) def test_weighted_numpy_two_edges(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(8))) graph.add_edges_from( [ (0, 1, 2), (1, 2, 2), (2, 3, 1), (3, 4, 1), (4, 5, 1), (5, 6, 1), (6, 7, 1), (7, 0, 1), ] ) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: x, parallel_threshold=self.parallel_threshold ) self.assertEqual(dist[0, 2], 4) self.assertEqual(dist[2, 0], 4) def test_weighted_numpy_negative_cycle(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(4))) graph.add_edges_from( [ (0, 1, 1), (1, 2, -1), (2, 3, -1), (3, 0, -1), ] ) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: x, parallel_threshold=self.parallel_threshold ) self.assertTrue(numpy.all(numpy.diag(dist) < 0)) def test_floyd_warshall_numpy_cycle(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(7))) graph.add_edges_from_no_data( [(0, 1), (0, 6), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)] ) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: 1, parallel_threshold=self.parallel_threshold ) self.assertEqual(dist[0, 3], 3) self.assertEqual(dist[0, 4], 3) def test_numpy_no_edges(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(4))) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: x, parallel_threshold=self.parallel_threshold ) expected = numpy.full((4, 4), numpy.inf) numpy.fill_diagonal(expected, 0) self.assertTrue(numpy.array_equal(dist, expected)) def test_floyd_warshall_numpy_graph_cycle_with_removals(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(8))) graph.remove_node(0) graph.add_edges_from_no_data( [(1, 2), (1, 7), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7)] ) dist = retworkx.graph_floyd_warshall_numpy( graph, lambda x: 1, parallel_threshold=self.parallel_threshold ) self.assertEqual(dist[0, 3], 3) self.assertEqual(dist[0, 4], 3) def test_floyd_warshall_numpy_graph_cycle_no_weight_fn(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(8))) graph.remove_node(0) graph.add_edges_from_no_data( [(1, 2), (1, 7), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7)] ) dist = retworkx.graph_floyd_warshall_numpy(graph) self.assertEqual(dist[0, 3], 3) self.assertEqual(dist[0, 4], 3) def test_floyd_warshall_numpy_graph_cycle_default_weight(self): graph = retworkx.PyGraph() graph.add_nodes_from(list(range(8))) graph.remove_node(0) graph.add_edges_from_no_data( [(1, 2), (1, 7), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7)] ) dist = retworkx.graph_floyd_warshall_numpy( graph, default_weight=2, parallel_threshold=self.parallel_threshold ) self.assertEqual(dist[0, 3], 6) self.assertEqual(dist[0, 4], 6) class TestParallelFloydWarshall(TestFloydWarshall): parallel_threshold = 0

true

f72b08276373a7b8064dc7eb363bb32779d3d0ce

9,830

py

Python

anima/ui/widgets/task_dashboard.py

MehmetErer/anima

f92ae599b5a4c181fc8e131a9ccdde537e635303

[ "MIT" ]

101

2015-02-08T22:20:11.000Z

2022-03-21T18:56:42.000Z

anima/ui/widgets/task_dashboard.py

MehmetErer/anima

f92ae599b5a4c181fc8e131a9ccdde537e635303

[ "MIT" ]

23

2016-11-30T08:33:21.000Z

2021-01-26T12:11:12.000Z

anima/ui/widgets/task_dashboard.py

MehmetErer/anima

f92ae599b5a4c181fc8e131a9ccdde537e635303

[ "MIT" ]

27

2015-01-03T06:49:45.000Z

2021-12-28T03:30:54.000Z

# -*- coding: utf-8 -*- from anima.ui.lib import QtCore, QtWidgets class TaskDashboardWidget(QtWidgets.QWidget): """A widget that displays task related information """ def __init__(self, task=None, parent=None, **kwargs): self._task = None self.parent = parent super(TaskDashboardWidget, self).__init__(parent=parent) # storage for UI stuff self.vertical_layout = None self.widget_label = None self.task_thumbnail_widget = None self.schedule_info_form_layout = None self.task_detail_widget = None self.task_timing_widget = None self.description_label = None self.description_field = None self.description_field_is_updating = False self.responsible_info_widget = None self.resource_info_widget = None self.task_versions_usage_info_widget = None self.watch_task_button = None self.fix_task_status_button = None self.task_status_label = None self.task_progress = None self.task_notes_widget = None self._setup_ui() self.task = task def _setup_ui(self): """create the UI widgets """ # we need a main layout # may be a vertical one # or a form layout self.vertical_layout = QtWidgets.QVBoxLayout(self) # ------------------------- # Dialog Label and buttons horizontal_layout3 = QtWidgets.QHBoxLayout() self.vertical_layout.addLayout(horizontal_layout3) self.widget_label = QtWidgets.QLabel(self) self.widget_label.setStyleSheet( "color: rgb(71, 143, 202);\nfont: 18pt;" ) horizontal_layout3.addWidget(self.widget_label) horizontal_layout3.addStretch(1) # Add Watch Task button self.watch_task_button = QtWidgets.QPushButton(self) self.watch_task_button.setMaximumWidth(24) self.watch_task_button.setMaximumHeight(24) self.watch_task_button.setText("W") self.watch_task_button.setToolTip("Watch Task") self.fix_task_status_button = QtWidgets.QPushButton(self) self.fix_task_status_button.setMaximumWidth(24) self.fix_task_status_button.setMaximumHeight(24) self.fix_task_status_button.setText("F") self.fix_task_status_button.setToolTip("Fix Task Status") horizontal_layout3.addWidget(self.watch_task_button) horizontal_layout3.addWidget(self.fix_task_status_button) QtCore.QObject.connect( self.fix_task_status_button, QtCore.SIGNAL("clicked()"), self.fix_task_status ) # Add Status Label vertical_layout3 = QtWidgets.QVBoxLayout() from anima.ui.widgets.task_status_label import TaskStatusLabel self.task_status_label = TaskStatusLabel(task=self.task) self.task_status_label.setMaximumHeight(12) vertical_layout3.addWidget(self.task_status_label) # Add ProgressBar self.task_progress = QtWidgets.QProgressBar(self) self.task_progress.setMinimum(0) self.task_progress.setMaximum(100) self.task_progress.setValue(50) self.task_progress.setAlignment(QtCore.Qt.AlignCenter) self.task_progress.setMaximumHeight(12) self.task_progress.setStyleSheet(""" QProgressBar::chunk { background-color: #3add36; width: 1px; } """) vertical_layout3.addWidget(self.task_progress) # set items closer to each other vertical_layout3.setSpacing(0) horizontal_layout3.addLayout(vertical_layout3) # Add divider line = QtWidgets.QFrame(self) line.setFrameShape(QtWidgets.QFrame.HLine) line.setFrameShadow(QtWidgets.QFrame.Sunken) self.vertical_layout.addWidget(line) horizontal_layout1 = QtWidgets.QHBoxLayout() self.vertical_layout.addLayout(horizontal_layout1) vertical_layout1 = QtWidgets.QVBoxLayout() vertical_layout2 = QtWidgets.QVBoxLayout() horizontal_layout1.addLayout(vertical_layout1) horizontal_layout1.addLayout(vertical_layout2) # -------------------------- # Horizontal Layout for thumbnail and detail widgets horizontal_layout2 = QtWidgets.QHBoxLayout() vertical_layout1.addLayout(horizontal_layout2) # -------------------------- # Task Thumbnail from anima.ui.widgets.entity_thumbnail import EntityThumbnailWidget self.task_thumbnail_widget = EntityThumbnailWidget(task=self.task, parent=self) horizontal_layout2.addWidget(self.task_thumbnail_widget) # -------------------------- # Task Detail Info from anima.ui.widgets.task_detail import TaskDetailWidget self.task_detail_widget = TaskDetailWidget(task=self.task, parent=self) horizontal_layout2.addWidget(self.task_detail_widget) # -------------------------- # Task Timing Info from anima.ui.widgets.task_timing import TaskTimingInfoWidget self.task_timing_widget = TaskTimingInfoWidget(task=self.task, parent=self) horizontal_layout2.addWidget(self.task_timing_widget) # add stretcher # horizontal_layout2.addStretch(1) # -------------------------- # Description field self.description_label = QtWidgets.QLabel(self) self.description_label.setStyleSheet(""" background-color: gray; color: white; font-weight: bold; padding: 0.5em; """) self.description_label.setText("Description") self.description_field = QtWidgets.QTextEdit(self) self.description_field.setAcceptRichText(True) vertical_layout1.addWidget(self.description_label) vertical_layout1.addWidget(self.description_field) # add stretcher vertical_layout1.addStretch(1) # connect signal self.description_field.textChanged.connect(self.update_description) # --------------------------- # Responsible Info from anima.ui.widgets.responsible_info import ResponsibleInfoWidget self.responsible_info_widget = ResponsibleInfoWidget( task=self.task, parent=self ) vertical_layout2.addWidget(self.responsible_info_widget) # --------------------------- # Resource Info from anima.ui.widgets.resource_info import ResourceInfoWidget self.resource_info_widget = ResourceInfoWidget( task=self.task, parent=self ) vertical_layout2.addWidget(self.resource_info_widget) # --------------------------- # Task Versions Usage Info from anima.ui.widgets.task_version_usage_info import \ TaskVersionUsageInfoWidget self.task_versions_usage_info_widget = TaskVersionUsageInfoWidget( task=self.task, parent=self ) vertical_layout2.addWidget(self.task_versions_usage_info_widget) vertical_layout2.addStretch(1) horizontal_layout1.setStretch(0, 2) horizontal_layout1.setStretch(1, 1) # --------------------------- # Task Notes from anima.ui.widgets.entity_notes import EntityNotesWidgets self.task_notes_widget = EntityNotesWidgets(entity=self.task, parent=self) self.vertical_layout.addWidget(self.task_notes_widget) @property def task(self): """getter for the _task attribute """ return self._task @task.setter def task(self, task): """setter for the task attribute """ from stalker import Task if isinstance(task, Task): self._task = task else: self._task = None # self.description_label = None # self.description_field = None # self.responsible_info_widget = None # self.resource_info_widget = None # self.task_versions_usage_info_widget = None # self.watch_task_button = None # self.fix_task_status_button = None # self.task_progress = None if self._task: self.description_field_is_updating = True self.description_field.setText(self._task.description) self.description_field_is_updating = False self.task_progress.setValue(self._task.percent_complete) else: self.description_field_is_updating = True self.description_field.setText('') self.description_field_is_updating = False self.task_progress.setValue(0) self.widget_label.setText(self._task.name if self._task else 'Task Name') self.task_thumbnail_widget.task = self._task self.task_detail_widget.task = self._task self.task_timing_widget.task = self._task self.task_status_label.task = self._task self.task_notes_widget.task = self._task def fix_task_status(self): """fix current task status """ from stalker import Task assert isinstance(self.task, Task) from anima import utils utils.fix_task_statuses(self.task) utils.fix_task_computed_time(self.task) from stalker.db.session import DBSession DBSession.add(self.task) DBSession.commit() def update_description(self): """runs when description field has changed """ if self.description_field_is_updating: return self.description_field_is_updating = True self.task.description = self.description_field.toPlainText() from stalker.db.session import DBSession DBSession.add(self.task) DBSession.commit() self.description_field_is_updating = False

35.487365

87

0.649135

from anima.ui.lib import QtCore, QtWidgets class TaskDashboardWidget(QtWidgets.QWidget): def __init__(self, task=None, parent=None, **kwargs): self._task = None self.parent = parent super(TaskDashboardWidget, self).__init__(parent=parent) self.vertical_layout = None self.widget_label = None self.task_thumbnail_widget = None self.schedule_info_form_layout = None self.task_detail_widget = None self.task_timing_widget = None self.description_label = None self.description_field = None self.description_field_is_updating = False self.responsible_info_widget = None self.resource_info_widget = None self.task_versions_usage_info_widget = None self.watch_task_button = None self.fix_task_status_button = None self.task_status_label = None self.task_progress = None self.task_notes_widget = None self._setup_ui() self.task = task def _setup_ui(self): self.vertical_layout = QtWidgets.QVBoxLayout(self) horizontal_layout3 = QtWidgets.QHBoxLayout() self.vertical_layout.addLayout(horizontal_layout3) self.widget_label = QtWidgets.QLabel(self) self.widget_label.setStyleSheet( "color: rgb(71, 143, 202);\nfont: 18pt;" ) horizontal_layout3.addWidget(self.widget_label) horizontal_layout3.addStretch(1) self.watch_task_button = QtWidgets.QPushButton(self) self.watch_task_button.setMaximumWidth(24) self.watch_task_button.setMaximumHeight(24) self.watch_task_button.setText("W") self.watch_task_button.setToolTip("Watch Task") self.fix_task_status_button = QtWidgets.QPushButton(self) self.fix_task_status_button.setMaximumWidth(24) self.fix_task_status_button.setMaximumHeight(24) self.fix_task_status_button.setText("F") self.fix_task_status_button.setToolTip("Fix Task Status") horizontal_layout3.addWidget(self.watch_task_button) horizontal_layout3.addWidget(self.fix_task_status_button) QtCore.QObject.connect( self.fix_task_status_button, QtCore.SIGNAL("clicked()"), self.fix_task_status ) vertical_layout3 = QtWidgets.QVBoxLayout() from anima.ui.widgets.task_status_label import TaskStatusLabel self.task_status_label = TaskStatusLabel(task=self.task) self.task_status_label.setMaximumHeight(12) vertical_layout3.addWidget(self.task_status_label) self.task_progress = QtWidgets.QProgressBar(self) self.task_progress.setMinimum(0) self.task_progress.setMaximum(100) self.task_progress.setValue(50) self.task_progress.setAlignment(QtCore.Qt.AlignCenter) self.task_progress.setMaximumHeight(12) self.task_progress.setStyleSheet(""" QProgressBar::chunk { background-color: #3add36; width: 1px; } """) vertical_layout3.addWidget(self.task_progress) vertical_layout3.setSpacing(0) horizontal_layout3.addLayout(vertical_layout3) line = QtWidgets.QFrame(self) line.setFrameShape(QtWidgets.QFrame.HLine) line.setFrameShadow(QtWidgets.QFrame.Sunken) self.vertical_layout.addWidget(line) horizontal_layout1 = QtWidgets.QHBoxLayout() self.vertical_layout.addLayout(horizontal_layout1) vertical_layout1 = QtWidgets.QVBoxLayout() vertical_layout2 = QtWidgets.QVBoxLayout() horizontal_layout1.addLayout(vertical_layout1) horizontal_layout1.addLayout(vertical_layout2) horizontal_layout2 = QtWidgets.QHBoxLayout() vertical_layout1.addLayout(horizontal_layout2) from anima.ui.widgets.entity_thumbnail import EntityThumbnailWidget self.task_thumbnail_widget = EntityThumbnailWidget(task=self.task, parent=self) horizontal_layout2.addWidget(self.task_thumbnail_widget) from anima.ui.widgets.task_detail import TaskDetailWidget self.task_detail_widget = TaskDetailWidget(task=self.task, parent=self) horizontal_layout2.addWidget(self.task_detail_widget) from anima.ui.widgets.task_timing import TaskTimingInfoWidget self.task_timing_widget = TaskTimingInfoWidget(task=self.task, parent=self) horizontal_layout2.addWidget(self.task_timing_widget) self.description_label = QtWidgets.QLabel(self) self.description_label.setStyleSheet(""" background-color: gray; color: white; font-weight: bold; padding: 0.5em; """) self.description_label.setText("Description") self.description_field = QtWidgets.QTextEdit(self) self.description_field.setAcceptRichText(True) vertical_layout1.addWidget(self.description_label) vertical_layout1.addWidget(self.description_field) vertical_layout1.addStretch(1) self.description_field.textChanged.connect(self.update_description) from anima.ui.widgets.responsible_info import ResponsibleInfoWidget self.responsible_info_widget = ResponsibleInfoWidget( task=self.task, parent=self ) vertical_layout2.addWidget(self.responsible_info_widget) from anima.ui.widgets.resource_info import ResourceInfoWidget self.resource_info_widget = ResourceInfoWidget( task=self.task, parent=self ) vertical_layout2.addWidget(self.resource_info_widget) from anima.ui.widgets.task_version_usage_info import \ TaskVersionUsageInfoWidget self.task_versions_usage_info_widget = TaskVersionUsageInfoWidget( task=self.task, parent=self ) vertical_layout2.addWidget(self.task_versions_usage_info_widget) vertical_layout2.addStretch(1) horizontal_layout1.setStretch(0, 2) horizontal_layout1.setStretch(1, 1) from anima.ui.widgets.entity_notes import EntityNotesWidgets self.task_notes_widget = EntityNotesWidgets(entity=self.task, parent=self) self.vertical_layout.addWidget(self.task_notes_widget) @property def task(self): return self._task @task.setter def task(self, task): from stalker import Task if isinstance(task, Task): self._task = task else: self._task = None if self._task: self.description_field_is_updating = True self.description_field.setText(self._task.description) self.description_field_is_updating = False self.task_progress.setValue(self._task.percent_complete) else: self.description_field_is_updating = True self.description_field.setText('') self.description_field_is_updating = False self.task_progress.setValue(0) self.widget_label.setText(self._task.name if self._task else 'Task Name') self.task_thumbnail_widget.task = self._task self.task_detail_widget.task = self._task self.task_timing_widget.task = self._task self.task_status_label.task = self._task self.task_notes_widget.task = self._task def fix_task_status(self): from stalker import Task assert isinstance(self.task, Task) from anima import utils utils.fix_task_statuses(self.task) utils.fix_task_computed_time(self.task) from stalker.db.session import DBSession DBSession.add(self.task) DBSession.commit() def update_description(self): if self.description_field_is_updating: return self.description_field_is_updating = True self.task.description = self.description_field.toPlainText() from stalker.db.session import DBSession DBSession.add(self.task) DBSession.commit() self.description_field_is_updating = False

true

f72b08b59e5cb86bba78fc94a90a6d1fa03c18e3

6,363

py

Python

lsdr/envs/analysis.py

melfm/lsdr

36b0a85e970fdcaae828eeff6c147432aa767c93

[ "MIT" ]

3

2019-09-20T19:10:50.000Z

2021-12-30T02:55:21.000Z

lsdr/envs/analysis.py

melfm/lsdr

36b0a85e970fdcaae828eeff6c147432aa767c93

[ "MIT" ]

null

lsdr/envs/analysis.py

melfm/lsdr

36b0a85e970fdcaae828eeff6c147432aa767c93

[ "MIT" ]

1

2020-08-01T21:28:12.000Z

import numpy as np import torch import matplotlib.pyplot as plt import os import math import scipy.stats as stats import lsdr.envs.environment_sampler as env_sampler from enum import IntEnum ############################ # Optimization Loss Opt ############################ class Objectives(IntEnum): REWARDS = 1 KL_OPT = 2 REW_AND_KL = 3 def reward_function(x): return np.exp(-(x-20)**2) def reward_function_v2(x): return np.sin(np.sqrt(x**2)) def calculate_reward(x): return reward_function(x) def setup_distributions(): ############################## # Initial distribution configs ############################## test_params = [ np.array([-30.0, 50.0]) ] # This can be modified for the initial distributions # to be different. ranges = np.asarray(test_params) mean = ranges.mean(-1) covar = (((ranges[:, 1] - ranges[:, 0])**2.0) / 12.0) * np.eye( ranges.shape[0]) mu_train, L_train = mean, np.linalg.cholesky(covar) dist_params = [mu_train, L_train] sampler = env_sampler.init_env_sampler( 'hopper', seed=0, experiment_id='test_kl_div_loss_0', init_dist_params=dist_params, dist_type='gaussian', test_dist_params=None) ############################ # Train Distribution ############################ p_train = sampler.train_dist ############################ # Test Distribution ############################ ranges = np.asarray(test_params) mean = ranges.mean(-1) covar = (((ranges[:, 1] - ranges[:, 0])**2.0) / 12.0) * np.eye( ranges.shape[0]) mu_test, L_test = mean, np.linalg.cholesky(covar) mu_test = torch.tensor(mu_test) L_test = torch.tensor(L_test) mu_test = mu_test.float().detach().requires_grad_(False) L_test = L_test.float().detach().requires_grad_(False) p_test = torch.distributions.MultivariateNormal(mu_test, scale_tril=L_test) train_mean = p_train.mean.detach() train_std = (p_train._unbroadcasted_scale_tril).diag().detach() test_mean = p_test.mean.detach() test_std = (p_test._unbroadcasted_scale_tril).diag().detach() print('Initial Distributions') print('Train Distribution Mean ', train_mean) print('Train Distribution STD ', train_std) print('Test Distribution Mean ', test_mean) print('Test Distribution STD ', test_std) ############################ # Plot Initial Distribution ############################ plot_distrs(train_mean, train_std, test_mean, test_std, plot_name='initial_train_distr') return sampler, p_train, p_test def plot_distrs(train_mean, train_var, test_mean, test_var, plot_name='distributions'): plt.figure() mu = train_mean variance = train_var sigma = math.sqrt(variance) x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100) plt.plot(x, stats.norm.pdf(x, mu, sigma), color='green', label='$p_{\phi}(z)$', linestyle='-.') mu = test_mean variance = test_var sigma = math.sqrt(variance) x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100) plt.plot(x, stats.norm.pdf(x, mu, sigma), color='red', label='$p(z)$') rew_func_range = np.arange(-20, 50, 1) plt.plot(rew_func_range, calculate_reward(rew_func_range), color='orange', label='$R(\Theta, z)$') plt.legend(loc='upper left') res_dir = 'grad_analysis' if not os.path.exists(res_dir): os.makedirs(res_dir) plotname = res_dir + '/' + plot_name + '.png' plt.savefig(plotname) def optimize_distribution(sampler, p_train, p_test, objective_opt): epochs, n_samples = 10000, 1000 alpha = 1e-5 opt = torch.optim.Adam(sampler.params, 1e-2) mu_grads = [] var_grads = [] def store_mu_grad_rew(grad): mu_grads.append(np.copy(grad)) def store_tril_grad_rew(grad): var_grads.append(np.copy(grad)) for _ in range(epochs): opt.zero_grad() #################### # Sample from p_test #################### z = p_test.sample(torch.Size([n_samples])) contexts = p_train.sample(torch.Size([n_samples])) ################ # Eval Log probs ################ log_p_train = p_train.log_prob(z) log_p_test = p_test.log_prob(z) ################ # Calculate KL ################ kl_samples = log_p_test - log_p_train kl_loss = kl_samples.mean(0) ####################### # Calculate Reward term ####################### log_probs_context = p_train.log_prob(contexts) reward_loss = (calculate_reward(contexts) * log_probs_context).mean(0) if objective_opt == Objectives.REWARDS: # For this to converge to the reward function, # need to change `z` sampling to be from train # distribution. total_loss = - reward_loss elif objective_opt == Objectives.KL_OPT: total_loss = kl_loss elif objective_opt == Objectives.REW_AND_KL: total_loss = (-(reward_loss) + (alpha*kl_loss)) else: raise ValueError('Invalid op') total_loss.mean().backward() opt.step() train_mean = p_train.mean.detach() train_std = (p_train._unbroadcasted_scale_tril).diag().detach() test_mean = p_test.mean.detach() test_std = (p_test._unbroadcasted_scale_tril).diag().detach() print('Updated Distributions') print('######################') print('Train Distribution Mean ', train_mean) print('Train Distribution STD ', train_std) print('Test Distribution Mean ', test_mean) print('Test Distribution STD ', test_std) plot_distrs(train_mean, train_std, test_mean, test_std, plot_name='final_distributions') if __name__ == '__main__': sampler, p_train, p_test = setup_distributions() # objective_opt = Objectives.REWARDS # objective_opt = Objectives.KL_OPT objective_opt = Objectives.REW_AND_KL optimize_distribution(sampler, p_train, p_test, objective_opt)

28.28

78

0.573157

import numpy as np import torch import matplotlib.pyplot as plt import os import math import scipy.stats as stats import lsdr.envs.environment_sampler as env_sampler from enum import IntEnum objective_opt)

true

f72b09030b2c9ba7bc22260ba632e1a45e870da9

1,020

py

Python

examples/pitz_daily/pitz_daily_runner.py

ImperialCollegeLondon/al_cfd_benchmark

03b51d7e7d4def804e2ac18084deee8401636851

[ "MIT" ]

6

2020-09-27T00:14:48.000Z

2021-11-23T03:35:09.000Z

examples/pitz_daily/pitz_daily_runner.py

ImperialCollegeLondon/al_cfd_benchmark

03b51d7e7d4def804e2ac18084deee8401636851

[ "MIT" ]

null

examples/pitz_daily/pitz_daily_runner.py

ImperialCollegeLondon/al_cfd_benchmark

03b51d7e7d4def804e2ac18084deee8401636851

[ "MIT" ]

2

2020-09-27T17:40:33.000Z

2021-12-13T02:31:49.000Z

# -*- coding: utf-8 -*- """Pitz Daily This case uses the pitzDaily example from the OpenFOAM tutorials and varies two parameters: Reynolds number and height of the inlet. It returns the pressure difference between inlet and outlet. """ import numpy as np from active_learning_cfd.cfd_case import CFDCase import os class PitzDaily(CFDCase): mesher = "blockMesh" solver = "simpleFoam" template = "pitzDaily" parameter_names = ("reynolds", "entryHeight") output_list = (("deltaP", "subtract$p$ = (.+)"),) def __call__(self, parameters): assert len(parameters) == len(self.parameter_names) parameter_dict = dict(zip(self.parameter_names, parameters)) parameter_dict["reynolds"] = np.power(10, parameter_dict["reynolds"]) self.solve(parameter_dict) return self.results["deltaP"] if __name__ == "__main__": case = PitzDaily() reynolds = 50800.0 entryHeight = 25.4 print("deltaP = {}".format(case([np.log10(reynolds), entryHeight])))

28.333333

77

0.683333

import numpy as np from active_learning_cfd.cfd_case import CFDCase import os class PitzDaily(CFDCase): mesher = "blockMesh" solver = "simpleFoam" template = "pitzDaily" parameter_names = ("reynolds", "entryHeight") output_list = (("deltaP", "subtract$p$ = (.+)"),) def __call__(self, parameters): assert len(parameters) == len(self.parameter_names) parameter_dict = dict(zip(self.parameter_names, parameters)) parameter_dict["reynolds"] = np.power(10, parameter_dict["reynolds"]) self.solve(parameter_dict) return self.results["deltaP"] if __name__ == "__main__": case = PitzDaily() reynolds = 50800.0 entryHeight = 25.4 print("deltaP = {}".format(case([np.log10(reynolds), entryHeight])))

true

f72b091c4068f3540061214d903965fad918e1a4

5,557

py

Python

cogdl/oag/dual_position_bert_model.py

li-ziang/cogdl

60022d3334e3abae2d2a505e6e049a26acf10f39

[ "MIT" ]

6

2020-07-09T02:48:41.000Z

2021-06-16T09:04:14.000Z

cogdl/oag/dual_position_bert_model.py

li-ziang/cogdl

60022d3334e3abae2d2a505e6e049a26acf10f39

[ "MIT" ]

null

cogdl/oag/dual_position_bert_model.py

li-ziang/cogdl

60022d3334e3abae2d2a505e6e049a26acf10f39

[ "MIT" ]

1

2020-05-19T11:45:45.000Z

import torch from torch import nn from torch.nn import CrossEntropyLoss import logging from .bert_model import BertPreTrainedModel, BertPreTrainingHeads, BertModel, BertEncoder, BertPooler, BertLayerNorm logger = logging.getLogger(__name__) class DualPositionBertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super(DualPositionBertEmbeddings, self).__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.position_embeddings_second = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids, token_type_ids, position_ids, position_ids_second): if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) words_embeddings = self.word_embeddings(input_ids) position_embeddings = self.position_embeddings(position_ids) position_embeddings_second = self.position_embeddings(position_ids_second) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = words_embeddings + position_embeddings + position_embeddings_second + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class DualPositionBertModel(BertModel): def __init__(self, config): super(DualPositionBertModel, self).__init__(config) self.embeddings = DualPositionBertEmbeddings(config) self.encoder = BertEncoder(config) self.pooler = BertPooler(config) self.apply(self.init_bert_weights) logger.info("Init BERT pretrain model") def forward( self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True, checkpoint_activations=False, position_ids=None, position_ids_second=None, ): if attention_mask is None: attention_mask = torch.ones_like(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) if len(attention_mask.shape) == 2: extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) elif len(attention_mask.shape) == 3: extended_attention_mask = attention_mask.unsqueeze(1) else: raise Exception("invalid attention mask shape! shape: %s" % (attention_mask.shape)) extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 embedding_output = self.embeddings(input_ids, token_type_ids, position_ids, position_ids_second) encoded_layers = self.encoder( embedding_output, extended_attention_mask, output_all_encoded_layers=output_all_encoded_layers, checkpoint_activations=checkpoint_activations, ) sequence_output = encoded_layers[-1] pooled_output = self.pooler(sequence_output) if not output_all_encoded_layers: encoded_layers = encoded_layers[-1] return encoded_layers, pooled_output class DualPositionBertForPreTrainingPreLN(BertPreTrainedModel): """BERT model with pre-training heads and dual position Params: config: a BertConfig class instance with the configuration to build a new model. """ def __init__(self, config): super(DualPositionBertForPreTrainingPreLN, self).__init__(config) self.bert = DualPositionBertModel(config) self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight) self.apply(self.init_bert_weights) def forward( self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, position_ids=None, position_ids_second=None, log=True, ): sequence_output, pooled_output = self.bert( input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, output_all_encoded_layers=False, checkpoint_activations=False, position_ids=position_ids, position_ids_second=position_ids_second, ) if masked_lm_labels is not None: # filter out all masked labels. masked_token_indexes = torch.nonzero((masked_lm_labels + 1).view(-1)).view(-1) prediction_scores, _ = self.cls(sequence_output, pooled_output, masked_token_indexes) target = torch.index_select(masked_lm_labels.view(-1), 0, masked_token_indexes) loss_fct = CrossEntropyLoss(ignore_index=-1) masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), target) return masked_lm_loss else: prediction_scores, _ = self.cls(sequence_output, pooled_output) return prediction_scores

41.781955

119

0.703617

import torch from torch import nn from torch.nn import CrossEntropyLoss import logging from .bert_model import BertPreTrainedModel, BertPreTrainingHeads, BertModel, BertEncoder, BertPooler, BertLayerNorm logger = logging.getLogger(__name__) class DualPositionBertEmbeddings(nn.Module): def __init__(self, config): super(DualPositionBertEmbeddings, self).__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.position_embeddings_second = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids, token_type_ids, position_ids, position_ids_second): if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) words_embeddings = self.word_embeddings(input_ids) position_embeddings = self.position_embeddings(position_ids) position_embeddings_second = self.position_embeddings(position_ids_second) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = words_embeddings + position_embeddings + position_embeddings_second + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class DualPositionBertModel(BertModel): def __init__(self, config): super(DualPositionBertModel, self).__init__(config) self.embeddings = DualPositionBertEmbeddings(config) self.encoder = BertEncoder(config) self.pooler = BertPooler(config) self.apply(self.init_bert_weights) logger.info("Init BERT pretrain model") def forward( self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True, checkpoint_activations=False, position_ids=None, position_ids_second=None, ): if attention_mask is None: attention_mask = torch.ones_like(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) if len(attention_mask.shape) == 2: extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) elif len(attention_mask.shape) == 3: extended_attention_mask = attention_mask.unsqueeze(1) else: raise Exception("invalid attention mask shape! shape: %s" % (attention_mask.shape)) extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 embedding_output = self.embeddings(input_ids, token_type_ids, position_ids, position_ids_second) encoded_layers = self.encoder( embedding_output, extended_attention_mask, output_all_encoded_layers=output_all_encoded_layers, checkpoint_activations=checkpoint_activations, ) sequence_output = encoded_layers[-1] pooled_output = self.pooler(sequence_output) if not output_all_encoded_layers: encoded_layers = encoded_layers[-1] return encoded_layers, pooled_output class DualPositionBertForPreTrainingPreLN(BertPreTrainedModel): def __init__(self, config): super(DualPositionBertForPreTrainingPreLN, self).__init__(config) self.bert = DualPositionBertModel(config) self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight) self.apply(self.init_bert_weights) def forward( self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, position_ids=None, position_ids_second=None, log=True, ): sequence_output, pooled_output = self.bert( input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, output_all_encoded_layers=False, checkpoint_activations=False, position_ids=position_ids, position_ids_second=position_ids_second, ) if masked_lm_labels is not None: masked_token_indexes = torch.nonzero((masked_lm_labels + 1).view(-1)).view(-1) prediction_scores, _ = self.cls(sequence_output, pooled_output, masked_token_indexes) target = torch.index_select(masked_lm_labels.view(-1), 0, masked_token_indexes) loss_fct = CrossEntropyLoss(ignore_index=-1) masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), target) return masked_lm_loss else: prediction_scores, _ = self.cls(sequence_output, pooled_output) return prediction_scores

true

f72b094590d5184ffbaf3cd4a122b4c8a53db388

7,097

py

Python

sdk/containerregistry/azure-mgmt-containerregistry/azure/mgmt/containerregistry/v2020_11_01_preview/_container_registry_management_client.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

[ "MIT" ]

1

2021-09-07T18:39:05.000Z

sdk/containerregistry/azure-mgmt-containerregistry/azure/mgmt/containerregistry/v2020_11_01_preview/_container_registry_management_client.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

[ "MIT" ]

null

sdk/containerregistry/azure-mgmt-containerregistry/azure/mgmt/containerregistry/v2020_11_01_preview/_container_registry_management_client.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

[ "MIT" ]

1

2022-03-04T06:21:56.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from copy import deepcopy from typing import Any, Optional, TYPE_CHECKING from azure.core.rest import HttpRequest, HttpResponse from azure.mgmt.core import ARMPipelineClient from msrest import Deserializer, Serializer from . import models from ._configuration import ContainerRegistryManagementClientConfiguration from .operations import ConnectedRegistriesOperations, ExportPipelinesOperations, ImportPipelinesOperations, Operations, PipelineRunsOperations, PrivateEndpointConnectionsOperations, RegistriesOperations, ReplicationsOperations, ScopeMapsOperations, TokensOperations, WebhooksOperations if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from azure.core.credentials import TokenCredential class ContainerRegistryManagementClient: """ContainerRegistryManagementClient. :ivar connected_registries: ConnectedRegistriesOperations operations :vartype connected_registries: azure.mgmt.containerregistry.v2020_11_01_preview.operations.ConnectedRegistriesOperations :ivar export_pipelines: ExportPipelinesOperations operations :vartype export_pipelines: azure.mgmt.containerregistry.v2020_11_01_preview.operations.ExportPipelinesOperations :ivar registries: RegistriesOperations operations :vartype registries: azure.mgmt.containerregistry.v2020_11_01_preview.operations.RegistriesOperations :ivar import_pipelines: ImportPipelinesOperations operations :vartype import_pipelines: azure.mgmt.containerregistry.v2020_11_01_preview.operations.ImportPipelinesOperations :ivar operations: Operations operations :vartype operations: azure.mgmt.containerregistry.v2020_11_01_preview.operations.Operations :ivar pipeline_runs: PipelineRunsOperations operations :vartype pipeline_runs: azure.mgmt.containerregistry.v2020_11_01_preview.operations.PipelineRunsOperations :ivar private_endpoint_connections: PrivateEndpointConnectionsOperations operations :vartype private_endpoint_connections: azure.mgmt.containerregistry.v2020_11_01_preview.operations.PrivateEndpointConnectionsOperations :ivar replications: ReplicationsOperations operations :vartype replications: azure.mgmt.containerregistry.v2020_11_01_preview.operations.ReplicationsOperations :ivar scope_maps: ScopeMapsOperations operations :vartype scope_maps: azure.mgmt.containerregistry.v2020_11_01_preview.operations.ScopeMapsOperations :ivar tokens: TokensOperations operations :vartype tokens: azure.mgmt.containerregistry.v2020_11_01_preview.operations.TokensOperations :ivar webhooks: WebhooksOperations operations :vartype webhooks: azure.mgmt.containerregistry.v2020_11_01_preview.operations.WebhooksOperations :param credential: Credential needed for the client to connect to Azure. :type credential: ~azure.core.credentials.TokenCredential :param subscription_id: The Microsoft Azure subscription ID. :type subscription_id: str :param base_url: Service URL. Default value is 'https://management.azure.com'. :type base_url: str :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. """ def __init__( self, credential: "TokenCredential", subscription_id: str, base_url: str = "https://management.azure.com", **kwargs: Any ) -> None: self._config = ContainerRegistryManagementClientConfiguration(credential=credential, subscription_id=subscription_id, **kwargs) self._client = ARMPipelineClient(base_url=base_url, config=self._config, **kwargs) client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} self._serialize = Serializer(client_models) self._deserialize = Deserializer(client_models) self._serialize.client_side_validation = False self.connected_registries = ConnectedRegistriesOperations(self._client, self._config, self._serialize, self._deserialize) self.export_pipelines = ExportPipelinesOperations(self._client, self._config, self._serialize, self._deserialize) self.registries = RegistriesOperations(self._client, self._config, self._serialize, self._deserialize) self.import_pipelines = ImportPipelinesOperations(self._client, self._config, self._serialize, self._deserialize) self.operations = Operations(self._client, self._config, self._serialize, self._deserialize) self.pipeline_runs = PipelineRunsOperations(self._client, self._config, self._serialize, self._deserialize) self.private_endpoint_connections = PrivateEndpointConnectionsOperations(self._client, self._config, self._serialize, self._deserialize) self.replications = ReplicationsOperations(self._client, self._config, self._serialize, self._deserialize) self.scope_maps = ScopeMapsOperations(self._client, self._config, self._serialize, self._deserialize) self.tokens = TokensOperations(self._client, self._config, self._serialize, self._deserialize) self.webhooks = WebhooksOperations(self._client, self._config, self._serialize, self._deserialize) def _send_request( self, request, # type: HttpRequest **kwargs: Any ) -> HttpResponse: """Runs the network request through the client's chained policies. >>> from azure.core.rest import HttpRequest >>> request = HttpRequest("GET", "https://www.example.org/") <HttpRequest [GET], url: 'https://www.example.org/'> >>> response = client._send_request(request) <HttpResponse: 200 OK> For more information on this code flow, see https://aka.ms/azsdk/python/protocol/quickstart :param request: The network request you want to make. Required. :type request: ~azure.core.rest.HttpRequest :keyword bool stream: Whether the response payload will be streamed. Defaults to False. :return: The response of your network call. Does not do error handling on your response. :rtype: ~azure.core.rest.HttpResponse """ request_copy = deepcopy(request) request_copy.url = self._client.format_url(request_copy.url) return self._client.send_request(request_copy, **kwargs) def close(self): # type: () -> None self._client.close() def __enter__(self): # type: () -> ContainerRegistryManagementClient self._client.__enter__() return self def __exit__(self, *exc_details): # type: (Any) -> None self._client.__exit__(*exc_details)

53.360902

286

0.748908

from copy import deepcopy from typing import Any, Optional, TYPE_CHECKING from azure.core.rest import HttpRequest, HttpResponse from azure.mgmt.core import ARMPipelineClient from msrest import Deserializer, Serializer from . import models from ._configuration import ContainerRegistryManagementClientConfiguration from .operations import ConnectedRegistriesOperations, ExportPipelinesOperations, ImportPipelinesOperations, Operations, PipelineRunsOperations, PrivateEndpointConnectionsOperations, RegistriesOperations, ReplicationsOperations, ScopeMapsOperations, TokensOperations, WebhooksOperations if TYPE_CHECKING: from azure.core.credentials import TokenCredential class ContainerRegistryManagementClient: def __init__( self, credential: "TokenCredential", subscription_id: str, base_url: str = "https://management.azure.com", **kwargs: Any ) -> None: self._config = ContainerRegistryManagementClientConfiguration(credential=credential, subscription_id=subscription_id, **kwargs) self._client = ARMPipelineClient(base_url=base_url, config=self._config, **kwargs) client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} self._serialize = Serializer(client_models) self._deserialize = Deserializer(client_models) self._serialize.client_side_validation = False self.connected_registries = ConnectedRegistriesOperations(self._client, self._config, self._serialize, self._deserialize) self.export_pipelines = ExportPipelinesOperations(self._client, self._config, self._serialize, self._deserialize) self.registries = RegistriesOperations(self._client, self._config, self._serialize, self._deserialize) self.import_pipelines = ImportPipelinesOperations(self._client, self._config, self._serialize, self._deserialize) self.operations = Operations(self._client, self._config, self._serialize, self._deserialize) self.pipeline_runs = PipelineRunsOperations(self._client, self._config, self._serialize, self._deserialize) self.private_endpoint_connections = PrivateEndpointConnectionsOperations(self._client, self._config, self._serialize, self._deserialize) self.replications = ReplicationsOperations(self._client, self._config, self._serialize, self._deserialize) self.scope_maps = ScopeMapsOperations(self._client, self._config, self._serialize, self._deserialize) self.tokens = TokensOperations(self._client, self._config, self._serialize, self._deserialize) self.webhooks = WebhooksOperations(self._client, self._config, self._serialize, self._deserialize) def _send_request( self, request, **kwargs: Any ) -> HttpResponse: request_copy = deepcopy(request) request_copy.url = self._client.format_url(request_copy.url) return self._client.send_request(request_copy, **kwargs) def close(self): self._client.close() def __enter__(self): self._client.__enter__() return self def __exit__(self, *exc_details): self._client.__exit__(*exc_details)

true

f72b097de1b2982d94f31803515377aa94536b9a

1,869

py

Python

authentik/stages/deny/tests.py

BeryJu/passbook

350f0d836580f4411524614f361a76c4f27b8a2d

[ "MIT" ]

15

2020-01-05T09:09:57.000Z

2020-11-28T05:27:39.000Z

authentik/stages/deny/tests.py

BeryJu/passbook

350f0d836580f4411524614f361a76c4f27b8a2d

[ "MIT" ]

302

2020-01-21T08:03:59.000Z

2020-12-04T05:04:57.000Z

authentik/stages/deny/tests.py

BeryJu/passbook

350f0d836580f4411524614f361a76c4f27b8a2d

[ "MIT" ]

3

2020-03-04T08:21:59.000Z

2020-08-01T20:37:18.000Z

"""deny tests""" from django.urls import reverse from authentik.core.tests.utils import create_test_admin_user, create_test_flow from authentik.flows.markers import StageMarker from authentik.flows.models import FlowDesignation, FlowStageBinding from authentik.flows.planner import FlowPlan from authentik.flows.tests import FlowTestCase from authentik.flows.views.executor import SESSION_KEY_PLAN from authentik.stages.deny.models import DenyStage class TestUserDenyStage(FlowTestCase): """Deny tests""" def setUp(self): super().setUp() self.user = create_test_admin_user() self.flow = create_test_flow(FlowDesignation.AUTHENTICATION) self.stage = DenyStage.objects.create(name="logout") self.binding = FlowStageBinding.objects.create(target=self.flow, stage=self.stage, order=2) def test_valid_get(self): """Test with a valid pending user and backend""" plan = FlowPlan(flow_pk=self.flow.pk.hex, bindings=[self.binding], markers=[StageMarker()]) session = self.client.session session[SESSION_KEY_PLAN] = plan session.save() response = self.client.get( reverse("authentik_api:flow-executor", kwargs={"flow_slug": self.flow.slug}) ) self.assertStageResponse(response, self.flow, component="ak-stage-access-denied") def test_valid_post(self): """Test with a valid pending user and backend""" plan = FlowPlan(flow_pk=self.flow.pk.hex, bindings=[self.binding], markers=[StageMarker()]) session = self.client.session session[SESSION_KEY_PLAN] = plan session.save() response = self.client.post( reverse("authentik_api:flow-executor", kwargs={"flow_slug": self.flow.slug}) ) self.assertStageResponse(response, self.flow, component="ak-stage-access-denied")

38.9375

99

0.70626

from django.urls import reverse from authentik.core.tests.utils import create_test_admin_user, create_test_flow from authentik.flows.markers import StageMarker from authentik.flows.models import FlowDesignation, FlowStageBinding from authentik.flows.planner import FlowPlan from authentik.flows.tests import FlowTestCase from authentik.flows.views.executor import SESSION_KEY_PLAN from authentik.stages.deny.models import DenyStage class TestUserDenyStage(FlowTestCase): def setUp(self): super().setUp() self.user = create_test_admin_user() self.flow = create_test_flow(FlowDesignation.AUTHENTICATION) self.stage = DenyStage.objects.create(name="logout") self.binding = FlowStageBinding.objects.create(target=self.flow, stage=self.stage, order=2) def test_valid_get(self): plan = FlowPlan(flow_pk=self.flow.pk.hex, bindings=[self.binding], markers=[StageMarker()]) session = self.client.session session[SESSION_KEY_PLAN] = plan session.save() response = self.client.get( reverse("authentik_api:flow-executor", kwargs={"flow_slug": self.flow.slug}) ) self.assertStageResponse(response, self.flow, component="ak-stage-access-denied") def test_valid_post(self): plan = FlowPlan(flow_pk=self.flow.pk.hex, bindings=[self.binding], markers=[StageMarker()]) session = self.client.session session[SESSION_KEY_PLAN] = plan session.save() response = self.client.post( reverse("authentik_api:flow-executor", kwargs={"flow_slug": self.flow.slug}) ) self.assertStageResponse(response, self.flow, component="ak-stage-access-denied")

true

f72b09d34e7b78c00c0b504b76cded6aa3b45a39

1,425

py

Python

models/vasilyev2020/src/score.py

leoribeiro/repro

7dc2ad611925542b4deb62fd1e30761ba56a7f60

[ "Apache-2.0" ]

15

2021-07-28T19:52:03.000Z

2022-03-28T15:55:17.000Z

models/vasilyev2020/src/score.py

leoribeiro/repro

7dc2ad611925542b4deb62fd1e30761ba56a7f60

[ "Apache-2.0" ]

3

2021-11-19T17:09:34.000Z

2022-02-14T19:40:48.000Z

models/vasilyev2020/src/score.py

leoribeiro/repro

7dc2ad611925542b4deb62fd1e30761ba56a7f60

[ "Apache-2.0" ]

null

import argparse import json import os from blanc import BlancHelp, BlancTune def main(args): kwargs = json.loads(args.kwargs) device = "cpu" if args.device == -1 else "cuda" if args.type == "tune": blanc = BlancTune(device=device, random_seed=args.random_seed, **kwargs) elif args.type == "help": blanc = BlancHelp(device=device, **kwargs) else: raise Exception(f"Unknown BLANC type: {args.type}") documents = [] summaries_list = [] with open(args.input_file, "r") as f: for line in f: data = json.loads(line) documents.append(data["document"]) summaries_list.append(data["summaries"]) scores_list = blanc.eval_summaries_for_docs(documents, summaries_list) dirname = os.path.dirname(args.output_file) if dirname: os.makedirs(dirname, exist_ok=True) with open(args.output_file, "w") as out: out.write(json.dumps(scores_list)) if __name__ == "__main__": argp = argparse.ArgumentParser() argp.add_argument("--input-file", required=True) argp.add_argument("--type", required=True, choices=["help", "tune"]) argp.add_argument("--device", required=True, type=int) argp.add_argument("--random-seed", required=True, type=int) argp.add_argument("--kwargs", required=True) argp.add_argument("--output-file", required=True) args = argp.parse_args() main(args)

31.666667

80

0.655439

import argparse import json import os from blanc import BlancHelp, BlancTune def main(args): kwargs = json.loads(args.kwargs) device = "cpu" if args.device == -1 else "cuda" if args.type == "tune": blanc = BlancTune(device=device, random_seed=args.random_seed, **kwargs) elif args.type == "help": blanc = BlancHelp(device=device, **kwargs) else: raise Exception(f"Unknown BLANC type: {args.type}") documents = [] summaries_list = [] with open(args.input_file, "r") as f: for line in f: data = json.loads(line) documents.append(data["document"]) summaries_list.append(data["summaries"]) scores_list = blanc.eval_summaries_for_docs(documents, summaries_list) dirname = os.path.dirname(args.output_file) if dirname: os.makedirs(dirname, exist_ok=True) with open(args.output_file, "w") as out: out.write(json.dumps(scores_list)) if __name__ == "__main__": argp = argparse.ArgumentParser() argp.add_argument("--input-file", required=True) argp.add_argument("--type", required=True, choices=["help", "tune"]) argp.add_argument("--device", required=True, type=int) argp.add_argument("--random-seed", required=True, type=int) argp.add_argument("--kwargs", required=True) argp.add_argument("--output-file", required=True) args = argp.parse_args() main(args)

true

f72b0a2e2db8a201933a779f2d9eaf3fc70eda33

9,937

py

Python

python/tvm/tensor_graph/testing/relay_examples/lenet.py

QinHan-Erin/AMOS

634bf48edf4015e4a69a8c32d49b96bce2b5f16f

[ "Apache-2.0" ]

22

2022-03-18T07:29:31.000Z

2022-03-23T14:54:32.000Z

python/tvm/tensor_graph/testing/relay_examples/lenet.py

QinHan-Erin/AMOS

634bf48edf4015e4a69a8c32d49b96bce2b5f16f

[ "Apache-2.0" ]

null

python/tvm/tensor_graph/testing/relay_examples/lenet.py

QinHan-Erin/AMOS

634bf48edf4015e4a69a8c32d49b96bce2b5f16f

[ "Apache-2.0" ]

2

2022-03-18T08:26:34.000Z

2022-03-20T06:02:48.000Z

import tvm import numpy as np from tvm import relay from tvm.relay.testing import run_infer_type, gradient def get_lenet(batch_size, num_classes=10, image_shape=(1, 28, 28), dtype="float32"): """Get lenet funciton Parameters ---------- batch_size : int The batch size used in the model num_classes : int, optional Number of claseses image_shape : tuple, optional The input image shape dtype : str, optional The data type Returns ------- net : relay.Function The dataflow. """ data_shape = (batch_size,) + image_shape data = relay.TensorType(data_shape, dtype=dtype) data = relay.var("data", data) conv_w1 = relay.var('c1.weight') c1 = relay.nn.conv2d(data=data, weight=conv_w1, channels=6, kernel_size=(5, 5), strides=(1, 1), padding=(2, 2)) conv_b1 = relay.var('c1.bias', dtype=dtype) c1 = relay.nn.bias_add(c1, conv_b1, axis=-1) act_c1 = relay.nn.relu(data=c1) # Max-pooling # [64, 6, 14, 14] conv_w2 = relay.var('c2.weight', dtype=dtype) conv_b2 = relay.var('c2.bias', dtype=dtype) p1 = relay.nn.conv2d(data=act_c1, weight=conv_w2, channels=6, kernel_size=(2, 2), strides=(2, 2), padding=(0, 0)) p1 = relay.nn.bias_add(p1, conv_b2, axis=-1) # Convolution conv_w3 = relay.var('c3.weight', dtype=dtype) conv_b3 = relay.var('c3.bias', dtype=dtype) c2 = relay.nn.conv2d(data=p1, weight=conv_w3, channels=6, kernel_size=(5, 5), strides=(1, 1), padding=(0, 0)) c2 = relay.nn.bias_add(c2, conv_b3, axis=-1) # [64, 6, 28, 28]conv2d(p1, 16, (5, 5), (1, 1), (0, 0), 'c2') # [64, 16, 10, 10] act_c2 = relay.nn.relu(data=c2) # Max-pooling # [64, 16, 5, 5] conv_w4 = relay.var('c4.weight', dtype=dtype) conv_b4 = relay.var('c4.bias', dtype=dtype) p2 = relay.nn.conv2d(data=act_c2, weight=conv_w4, channels=6, kernel_size=(2, 2), strides=(2, 2), padding=(0, 0)) p2 = relay.nn.bias_add(p2, conv_b4, axis=-1) # reshape r1 = relay.nn.batch_flatten(data=p2) w1 = relay.var('fc1.weight', dtype=dtype) b1 = relay.var('fc1.bias', dtype=dtype) fc1 = relay.nn.dense(data=r1, weight=w1, units=128) fc1 = relay.nn.bias_add(fc1, b1, axis=-1) act1 = relay.nn.relu(data=fc1) w2 = relay.var('fc2.weight', dtype=dtype) b2 = relay.var('fc2.bias', dtype=dtype) fc2 = relay.nn.dense(data=act1, weight=w2, units=64) fc2 = relay.nn.bias_add(fc2, b2, axis=-1) act2 = relay.nn.relu(data=fc2) w3 = relay.var('fc3.weight', dtype=dtype) b3 = relay.var('fc3.bias', dtype=dtype) fc3 = relay.nn.dense(data=act2, weight=w3, units=num_classes) fc3 = relay.nn.bias_add(fc3, b3, axis=-1) lenet = relay.nn.softmax(data=fc3) argu_list = [conv_w1, conv_b1, conv_w2, conv_b2, w1, b1, w2, b2, w3, b3] return relay.Function(relay.analysis.free_vars(lenet), lenet), argu_list def make_sgd_update_net(loss_function, var, lr=0.002, scale=1.0, wd=0.0, clip=None): type_loss_function = run_infer_type(loss_function) grad_func = run_infer_type(gradient(type_loss_function)) grads = relay.TupleWrapper(relay.TupleGetItem(grad_func.body, 1), len(loss_function.params)) useful_grad = [] type_var = [] for var_item in var: for index, value_item in enumerate(type_loss_function.params): if var_item.name_hint == value_item.name_hint: useful_grad.append(grads[index]) type_var.append(value_item) break else: raise("can't get required params from loss function, internal error") updates = [] for i, v in enumerate(type_var): g = useful_grad[i] g = relay.multiply(g, relay.const(scale, "float32")) if clip is not None: g = relay.clip(g, a_min=-1 * clip, a_max=clip) g = relay.subtract(v, relay.multiply(relay.const(lr, "float32"), relay.add(g, relay.multiply(relay.const(wd, "float32"), v)))) updates.append(g) sgd_body = relay.Tuple(updates) return relay.Function(relay.analysis.free_vars(sgd_body), sgd_body) def make_adam_update_net(loss_function, var, lr=0.001, beta1=0.9, beta2=0.99, scale=1.0, wd=0.0, clip=None, name="adam", dtype='float32'): type_loss_function = run_infer_type(loss_function) grad_func = run_infer_type(gradient(type_loss_function)) grads = relay.TupleWrapper(relay.TupleGetItem(grad_func.body, 1), len(loss_function.params)) useful_grad = [] type_var = [] for var_item in var: for index, value_item in enumerate(type_loss_function.params): if var_item.name_hint == value_item.name_hint: useful_grad.append(grads[index]) type_var.append(value_item) break else: raise("can't get required params from loss function, internal error") print(type_var) updates = [] m = [] t = relay.zeros(shape=[1], dtype=dtype) epsilon = 1e-04 const_1 = relay.const(1, dtype=dtype) const_beta1 = relay.const(beta1, dtype=dtype) const_beta2 = relay.const(beta2, dtype=dtype) for i, va in enumerate(type_var): m.append(relay.zeros_like(va)) update_t = relay.add(t, const_1) rate = relay.divide(relay.sqrt(relay.subtract(const_1, relay.power(const_beta2, update_t))), relay.subtract(const_1, relay.power(const_beta1, update_t))) lr_t = relay.multiply(relay.const(lr, dtype=dtype), rate) for var, g, m in zip(type_var, useful_grad, m): update_m = relay.add(relay.multiply(const_beta1, m), relay.multiply(relay.subtract(const_1, const_beta1), g)) update_v = relay.add(relay.multiply(const_beta2, m), relay.multiply(relay.subtract(const_1, const_beta2), relay.multiply(g, g))) update_var = relay.subtract(var, relay.divide(relay.multiply(lr_t, update_m), relay.add(relay.sqrt(update_v), relay.const(epsilon, dtype="float32")))) updates.append(update_var) adam_body = relay.Tuple(updates) return relay.Function(relay.analysis.free_vars(adam_body), adam_body) def mse_loss(lenet_function, target): sub = relay.subtract(lenet_function.body, target) loss_body = relay.sum(relay.multiply(sub, sub)) return relay.Function(relay.analysis.free_vars(loss_body), loss_body) # return sum((predict - target)**2) / 2.0 def cross_entropy_loss(lenet_function, target): loss_body = relay.negative(relay.sum(relay.multiply(relay.log(relay.add(lenet_function.body, relay.const(1e-5, dtype="float32"))), target))) return relay.Function(relay.analysis.free_vars(loss_body), loss_body) def make_loss_net(lenet_function, target, optim="CROSS"): """Get loss funtion for lenet Parameters ---------- lenet_function : relay.Function target : relay.Expr optim : str, optional loss_function strategy, "CROSS" or "MSE" Returns ------- net : relay.Function The dataflow. """ if optim == "CROSS": return cross_entropy_loss(lenet_function, target) if optim == "MSE": return mse_loss(lenet_function, target) raise("unknown optim, use 'CROSS' or 'MSE'.") def make_grad_net(loss_function): """Get updated funtion for lenet Parameters ---------- loss_function : relay.Function Returns ------- net : relay.Function The dataflow. """ type_loss_function = run_infer_type(loss_function) grad_func = run_infer_type(gradient(type_loss_function)) return grad_func def make_update_net(loss_function, weights, optim="SGD"): """Get updated funtion for lenet Parameters ---------- loss_function : relay.Function weights : [relay.var] vars to compute gradient optim : str, optional updated_function strategy, "ADAM" or "SGD" Returns ------- net : relay.Function The dataflow. """ if optim == "ADAM": return make_adam_update_net(loss_function, weights) if optim == "SGD": return make_sgd_update_net(loss_function, weights) raise("unknown optim, use 'ADAM' or 'SGD'.") def create_workload(net, initializer=None, seed=0): """Helper function to create benchmark image classification workload. Parameters ---------- net : tvm.relay.Function The selected function of the network. initializer : Initializer The initializer used seed : int The seed used in initialization. Returns ------- mod : tvm.IRModule The created relay module. params : dict of str to NDArray The parameters. """ mod = tvm.IRModule.from_expr(net) mod = relay.transform.InferType()(mod) shape_dict = { v.name_hint : v.checked_type for v in mod["main"].params} np.random.seed(seed) initializer = initializer if initializer else Xavier() params = {} for k, v in shape_dict.items(): # modify here, skip "label" as well if k == "data" or k == "label": continue init_value = np.zeros(v.concrete_shape).astype(v.dtype) initializer(k, init_value) params[k] = tvm.nd.array(init_value, ctx=tvm.cpu(0)) return mod, params

36.399267

138

0.600986

import tvm import numpy as np from tvm import relay from tvm.relay.testing import run_infer_type, gradient def get_lenet(batch_size, num_classes=10, image_shape=(1, 28, 28), dtype="float32"): data_shape = (batch_size,) + image_shape data = relay.TensorType(data_shape, dtype=dtype) data = relay.var("data", data) conv_w1 = relay.var('c1.weight') c1 = relay.nn.conv2d(data=data, weight=conv_w1, channels=6, kernel_size=(5, 5), strides=(1, 1), padding=(2, 2)) conv_b1 = relay.var('c1.bias', dtype=dtype) c1 = relay.nn.bias_add(c1, conv_b1, axis=-1) act_c1 = relay.nn.relu(data=c1) conv_w2 = relay.var('c2.weight', dtype=dtype) conv_b2 = relay.var('c2.bias', dtype=dtype) p1 = relay.nn.conv2d(data=act_c1, weight=conv_w2, channels=6, kernel_size=(2, 2), strides=(2, 2), padding=(0, 0)) p1 = relay.nn.bias_add(p1, conv_b2, axis=-1) conv_w3 = relay.var('c3.weight', dtype=dtype) conv_b3 = relay.var('c3.bias', dtype=dtype) c2 = relay.nn.conv2d(data=p1, weight=conv_w3, channels=6, kernel_size=(5, 5), strides=(1, 1), padding=(0, 0)) c2 = relay.nn.bias_add(c2, conv_b3, axis=-1) y.nn.relu(data=c2) conv_w4 = relay.var('c4.weight', dtype=dtype) conv_b4 = relay.var('c4.bias', dtype=dtype) p2 = relay.nn.conv2d(data=act_c2, weight=conv_w4, channels=6, kernel_size=(2, 2), strides=(2, 2), padding=(0, 0)) p2 = relay.nn.bias_add(p2, conv_b4, axis=-1) r1 = relay.nn.batch_flatten(data=p2) w1 = relay.var('fc1.weight', dtype=dtype) b1 = relay.var('fc1.bias', dtype=dtype) fc1 = relay.nn.dense(data=r1, weight=w1, units=128) fc1 = relay.nn.bias_add(fc1, b1, axis=-1) act1 = relay.nn.relu(data=fc1) w2 = relay.var('fc2.weight', dtype=dtype) b2 = relay.var('fc2.bias', dtype=dtype) fc2 = relay.nn.dense(data=act1, weight=w2, units=64) fc2 = relay.nn.bias_add(fc2, b2, axis=-1) act2 = relay.nn.relu(data=fc2) w3 = relay.var('fc3.weight', dtype=dtype) b3 = relay.var('fc3.bias', dtype=dtype) fc3 = relay.nn.dense(data=act2, weight=w3, units=num_classes) fc3 = relay.nn.bias_add(fc3, b3, axis=-1) lenet = relay.nn.softmax(data=fc3) argu_list = [conv_w1, conv_b1, conv_w2, conv_b2, w1, b1, w2, b2, w3, b3] return relay.Function(relay.analysis.free_vars(lenet), lenet), argu_list def make_sgd_update_net(loss_function, var, lr=0.002, scale=1.0, wd=0.0, clip=None): type_loss_function = run_infer_type(loss_function) grad_func = run_infer_type(gradient(type_loss_function)) grads = relay.TupleWrapper(relay.TupleGetItem(grad_func.body, 1), len(loss_function.params)) useful_grad = [] type_var = [] for var_item in var: for index, value_item in enumerate(type_loss_function.params): if var_item.name_hint == value_item.name_hint: useful_grad.append(grads[index]) type_var.append(value_item) break else: raise("can't get required params from loss function, internal error") updates = [] for i, v in enumerate(type_var): g = useful_grad[i] g = relay.multiply(g, relay.const(scale, "float32")) if clip is not None: g = relay.clip(g, a_min=-1 * clip, a_max=clip) g = relay.subtract(v, relay.multiply(relay.const(lr, "float32"), relay.add(g, relay.multiply(relay.const(wd, "float32"), v)))) updates.append(g) sgd_body = relay.Tuple(updates) return relay.Function(relay.analysis.free_vars(sgd_body), sgd_body) def make_adam_update_net(loss_function, var, lr=0.001, beta1=0.9, beta2=0.99, scale=1.0, wd=0.0, clip=None, name="adam", dtype='float32'): type_loss_function = run_infer_type(loss_function) grad_func = run_infer_type(gradient(type_loss_function)) grads = relay.TupleWrapper(relay.TupleGetItem(grad_func.body, 1), len(loss_function.params)) useful_grad = [] type_var = [] for var_item in var: for index, value_item in enumerate(type_loss_function.params): if var_item.name_hint == value_item.name_hint: useful_grad.append(grads[index]) type_var.append(value_item) break else: raise("can't get required params from loss function, internal error") print(type_var) updates = [] m = [] t = relay.zeros(shape=[1], dtype=dtype) epsilon = 1e-04 const_1 = relay.const(1, dtype=dtype) const_beta1 = relay.const(beta1, dtype=dtype) const_beta2 = relay.const(beta2, dtype=dtype) for i, va in enumerate(type_var): m.append(relay.zeros_like(va)) update_t = relay.add(t, const_1) rate = relay.divide(relay.sqrt(relay.subtract(const_1, relay.power(const_beta2, update_t))), relay.subtract(const_1, relay.power(const_beta1, update_t))) lr_t = relay.multiply(relay.const(lr, dtype=dtype), rate) for var, g, m in zip(type_var, useful_grad, m): update_m = relay.add(relay.multiply(const_beta1, m), relay.multiply(relay.subtract(const_1, const_beta1), g)) update_v = relay.add(relay.multiply(const_beta2, m), relay.multiply(relay.subtract(const_1, const_beta2), relay.multiply(g, g))) update_var = relay.subtract(var, relay.divide(relay.multiply(lr_t, update_m), relay.add(relay.sqrt(update_v), relay.const(epsilon, dtype="float32")))) updates.append(update_var) adam_body = relay.Tuple(updates) return relay.Function(relay.analysis.free_vars(adam_body), adam_body) def mse_loss(lenet_function, target): sub = relay.subtract(lenet_function.body, target) loss_body = relay.sum(relay.multiply(sub, sub)) return relay.Function(relay.analysis.free_vars(loss_body), loss_body) def cross_entropy_loss(lenet_function, target): loss_body = relay.negative(relay.sum(relay.multiply(relay.log(relay.add(lenet_function.body, relay.const(1e-5, dtype="float32"))), target))) return relay.Function(relay.analysis.free_vars(loss_body), loss_body) def make_loss_net(lenet_function, target, optim="CROSS"): if optim == "CROSS": return cross_entropy_loss(lenet_function, target) if optim == "MSE": return mse_loss(lenet_function, target) raise("unknown optim, use 'CROSS' or 'MSE'.") def make_grad_net(loss_function): type_loss_function = run_infer_type(loss_function) grad_func = run_infer_type(gradient(type_loss_function)) return grad_func def make_update_net(loss_function, weights, optim="SGD"): if optim == "ADAM": return make_adam_update_net(loss_function, weights) if optim == "SGD": return make_sgd_update_net(loss_function, weights) raise("unknown optim, use 'ADAM' or 'SGD'.") def create_workload(net, initializer=None, seed=0): mod = tvm.IRModule.from_expr(net) mod = relay.transform.InferType()(mod) shape_dict = { v.name_hint : v.checked_type for v in mod["main"].params} np.random.seed(seed) initializer = initializer if initializer else Xavier() params = {} for k, v in shape_dict.items(): if k == "data" or k == "label": continue init_value = np.zeros(v.concrete_shape).astype(v.dtype) initializer(k, init_value) params[k] = tvm.nd.array(init_value, ctx=tvm.cpu(0)) return mod, params

true

f72b0a4f41647e949ba4e6202d2c7f3980d53dab

575

py

Python

M5_assgmnt.py

AVNEETK99/FANTASY-CRICKET-LEAGUE

17fc188e48a51c6f3937a9965f1edcead2a8d0b8

[ "CC0-1.0" ]

23

2018-07-18T10:47:12.000Z

2021-07-31T21:53:17.000Z

M5_assgmnt.py

RupinSamria/Summer-Training-Python-development

4fa38344d6aa71581b004c16eddeec22f9f739f4

[ "CC0-1.0" ]

3

2018-11-18T07:11:05.000Z

2020-04-30T20:16:51.000Z

M5_assgmnt.py

RupinSamria/Summer-Training-Python-development

4fa38344d6aa71581b004c16eddeec22f9f739f4

[ "CC0-1.0" ]

53

2018-10-04T05:49:30.000Z

2021-12-12T15:52:17.000Z

import sqlite3 mystore=sqlite3.connect('bookstores.db') mycursor=mystore.cursor() sql=''' create table book (id integer primary key not null,title text(20), author text(20),price real);''' mycursor.execute(sql) sql='''insert into book values(1,'think java','rhooney',550.0);''' mycursor.execute(sql) mystore.commit() sql='''insert into book values(2,'think python','allen',450.0);''' mycursor.execute(sql) mystore.commit() sql='''insert into book values(3,'think c++','booty',375.0);''' mycursor.execute(sql) mystore.commit() mystore.close()

21.296296

75

0.683478

import sqlite3 mystore=sqlite3.connect('bookstores.db') mycursor=mystore.cursor() sql=''' create table book (id integer primary key not null,title text(20), author text(20),price real);''' mycursor.execute(sql) sql='''insert into book values(1,'think java','rhooney',550.0);''' mycursor.execute(sql) mystore.commit() sql='''insert into book values(2,'think python','allen',450.0);''' mycursor.execute(sql) mystore.commit() sql='''insert into book values(3,'think c++','booty',375.0);''' mycursor.execute(sql) mystore.commit() mystore.close()

true

f72b0a5531db17b2a97a3179af5c86bd986dd358

12,137

py

Python

test/data_join/test_data_block_dumper.py

chen1i/fedlearner

981514dadbd0aa49ae87d185dd247d310e35605c

[ "Apache-2.0" ]

null

test/data_join/test_data_block_dumper.py

chen1i/fedlearner

981514dadbd0aa49ae87d185dd247d310e35605c

[ "Apache-2.0" ]

null

test/data_join/test_data_block_dumper.py

chen1i/fedlearner

981514dadbd0aa49ae87d185dd247d310e35605c

[ "Apache-2.0" ]

null

# Copyright 2020 The FedLearner 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. # coding: utf-8 import unittest import os import tensorflow.compat.v1 as tf tf.enable_eager_execution() from google.protobuf import text_format, timestamp_pb2 import tensorflow_io from tensorflow.compat.v1 import gfile from fedlearner.common import db_client from fedlearner.common import common_pb2 as common_pb from fedlearner.common import data_join_service_pb2 as dj_pb from fedlearner.data_join import ( data_block_manager, common, data_block_dumper, raw_data_manifest_manager, raw_data_visitor, visitor ) from fedlearner.data_join.data_block_manager import DataBlockBuilder from fedlearner.data_join.raw_data_iter_impl.tf_record_iter import TfExampleItem class TestDataBlockDumper(unittest.TestCase): def setUp(self): data_source_f = common_pb.DataSource() data_source_f.data_source_meta.name = "milestone" data_source_f.data_source_meta.partition_num = 1 data_source_f.output_base_dir = "./output-f" self.data_source_f = data_source_f if gfile.Exists(self.data_source_f.output_base_dir): gfile.DeleteRecursively(self.data_source_f.output_base_dir) data_source_l = common_pb.DataSource() data_source_l.data_source_meta.name = "milestone" data_source_l.data_source_meta.partition_num = 1 data_source_l.output_base_dir = "./output-l" self.raw_data_dir_l = "./raw_data-l" self.data_source_l = data_source_l if gfile.Exists(self.data_source_l.output_base_dir): gfile.DeleteRecursively(self.data_source_l.output_base_dir) if gfile.Exists(self.raw_data_dir_l): gfile.DeleteRecursively(self.raw_data_dir_l) self.kvstore = db_client.DBClient('etcd', True) self.kvstore.delete_prefix(common.data_source_kvstore_base_dir(self.data_source_l.data_source_meta.name)) self.manifest_manager = raw_data_manifest_manager.RawDataManifestManager( self.kvstore, self.data_source_l) def generate_follower_data_block(self): dbm = data_block_manager.DataBlockManager(self.data_source_f, 0) self.assertEqual(dbm.get_dumped_data_block_count(), 0) self.assertEqual(dbm.get_lastest_data_block_meta(), None) leader_index = 0 follower_index = 65536 self.dumped_metas = [] for i in range(5): builder = DataBlockBuilder( common.data_source_data_block_dir(self.data_source_f), self.data_source_f.data_source_meta.name, 0, i, dj_pb.WriterOptions(output_writer='TF_RECORD'), None ) builder.set_data_block_manager(dbm) for j in range(1024): feat = {} example_id = '{}'.format(i * 1024 + j).encode() feat['example_id'] = tf.train.Feature( bytes_list=tf.train.BytesList(value=[example_id])) event_time = 150000000 + i * 1024 + j feat['event_time'] = tf.train.Feature( int64_list=tf.train.Int64List(value=[event_time])) feat['leader_index'] = tf.train.Feature( int64_list=tf.train.Int64List(value=[leader_index])) feat['follower_index'] = tf.train.Feature( int64_list=tf.train.Int64List(value=[follower_index])) example = tf.train.Example(features=tf.train.Features(feature=feat)) builder.append_item(TfExampleItem(example.SerializeToString()), leader_index, follower_index) leader_index += 3 follower_index += 1 meta = builder.finish_data_block() self.dumped_metas.append(meta) self.leader_start_index = 0 self.leader_end_index = leader_index self.assertEqual(dbm.get_dumped_data_block_count(), 5) for (idx, meta) in enumerate(self.dumped_metas): self.assertEqual(dbm.get_data_block_meta_by_index(idx), meta) def generate_leader_raw_data(self): dbm = data_block_manager.DataBlockManager(self.data_source_l, 0) raw_data_dir = os.path.join(self.raw_data_dir_l, common.partition_repr(0)) if gfile.Exists(raw_data_dir): gfile.DeleteRecursively(raw_data_dir) gfile.MakeDirs(raw_data_dir) rdm = raw_data_visitor.RawDataManager(self.kvstore, self.data_source_l, 0) block_index = 0 builder = DataBlockBuilder( self.raw_data_dir_l, self.data_source_l.data_source_meta.name, 0, block_index, dj_pb.WriterOptions(output_writer='TF_RECORD'), None ) process_index = 0 start_index = 0 for i in range(0, self.leader_end_index + 3): if (i > 0 and i % 2048 == 0) or (i == self.leader_end_index + 2): meta = builder.finish_data_block() if meta is not None: ofname = common.encode_data_block_fname( self.data_source_l.data_source_meta.name, meta ) fpath = os.path.join(raw_data_dir, ofname) self.manifest_manager.add_raw_data( 0, [dj_pb.RawDataMeta(file_path=fpath, timestamp=timestamp_pb2.Timestamp(seconds=3))], False) process_index += 1 start_index += len(meta.example_ids) block_index += 1 builder = DataBlockBuilder( self.raw_data_dir_l, self.data_source_l.data_source_meta.name, 0, block_index, dj_pb.WriterOptions(output_writer='TF_RECORD'), None ) feat = {} pt = i + 1 << 30 if i % 3 == 0: pt = i // 3 example_id = '{}'.format(pt).encode() feat['example_id'] = tf.train.Feature( bytes_list=tf.train.BytesList(value=[example_id])) event_time = 150000000 + pt feat['event_time'] = tf.train.Feature( int64_list=tf.train.Int64List(value=[event_time])) example = tf.train.Example(features=tf.train.Features(feature=feat)) builder.append_item(TfExampleItem(example.SerializeToString()), i, i) fpaths = [os.path.join(raw_data_dir, f) for f in gfile.ListDirectory(raw_data_dir) if not gfile.IsDirectory(os.path.join(raw_data_dir, f))] for fpath in fpaths: if not fpath.endswith(common.DataBlockSuffix): gfile.Remove(fpath) def test_data_block_dumper(self): self.generate_follower_data_block() self.generate_leader_raw_data() dbd = data_block_dumper.DataBlockDumperManager( self.kvstore, self.data_source_l, 0, dj_pb.RawDataOptions(raw_data_iter='TF_RECORD', read_ahead_size=1<<20, read_batch_size=128), dj_pb.WriterOptions(output_writer='TF_RECORD') ) self.assertEqual(dbd.get_next_data_block_index(), 0) for (idx, meta) in enumerate(self.dumped_metas): success, next_index = dbd.add_synced_data_block_meta(meta) self.assertTrue(success) self.assertEqual(next_index, idx + 1) self.assertTrue(dbd.need_dump()) self.assertEqual(dbd.get_next_data_block_index(), len(self.dumped_metas)) with dbd.make_data_block_dumper() as dumper: dumper() dbm_f = data_block_manager.DataBlockManager(self.data_source_f, 0) dbm_l = data_block_manager.DataBlockManager(self.data_source_l, 0) self.assertEqual(dbm_f.get_dumped_data_block_count(), len(self.dumped_metas)) self.assertEqual(dbm_f.get_dumped_data_block_count(), dbm_l.get_dumped_data_block_count()) for (idx, meta) in enumerate(self.dumped_metas): self.assertEqual(meta.data_block_index, idx) self.assertEqual(dbm_l.get_data_block_meta_by_index(idx), meta) self.assertEqual(dbm_f.get_data_block_meta_by_index(idx), meta) meta_fpth_l = os.path.join( common.data_source_data_block_dir(self.data_source_l), common.partition_repr(0), common.encode_data_block_meta_fname( self.data_source_l.data_source_meta.name, 0, meta.data_block_index ) ) mitr = tf.io.tf_record_iterator(meta_fpth_l) meta_l = text_format.Parse(next(mitr), dj_pb.DataBlockMeta()) self.assertEqual(meta_l, meta) meta_fpth_f = os.path.join( common.data_source_data_block_dir(self.data_source_f), common.partition_repr(0), common.encode_data_block_meta_fname( self.data_source_f.data_source_meta.name, 0, meta.data_block_index ) ) mitr = tf.io.tf_record_iterator(meta_fpth_f) meta_f = text_format.Parse(next(mitr), dj_pb.DataBlockMeta()) self.assertEqual(meta_f, meta) data_fpth_l = os.path.join( common.data_source_data_block_dir(self.data_source_l), common.partition_repr(0), common.encode_data_block_fname( self.data_source_l.data_source_meta.name, meta_l ) ) for (iidx, record) in enumerate(tf.io.tf_record_iterator(data_fpth_l)): example = tf.train.Example() example.ParseFromString(record) feat = example.features.feature self.assertEqual(feat['example_id'].bytes_list.value[0], meta.example_ids[iidx]) self.assertEqual(len(meta.example_ids), iidx + 1) data_fpth_f = os.path.join( common.data_source_data_block_dir(self.data_source_f), common.partition_repr(0), common.encode_data_block_fname( self.data_source_l.data_source_meta.name, meta_f ) ) for (iidx, record) in enumerate(tf.io.tf_record_iterator(data_fpth_f)): example = tf.train.Example() example.ParseFromString(record) feat = example.features.feature self.assertEqual(feat['example_id'].bytes_list.value[0], meta.example_ids[iidx]) self.assertEqual(len(meta.example_ids), iidx +1) def tearDown(self): if gfile.Exists(self.data_source_f.output_base_dir): gfile.DeleteRecursively(self.data_source_f.output_base_dir) if gfile.Exists(self.data_source_l.output_base_dir): gfile.DeleteRecursively(self.data_source_l.output_base_dir) if gfile.Exists(self.raw_data_dir_l): gfile.DeleteRecursively(self.raw_data_dir_l) self.kvstore.delete_prefix(common.data_source_kvstore_base_dir(self.data_source_l.data_source_meta.name)) if __name__ == '__main__': unittest.main()

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0.616215

import unittest import os import tensorflow.compat.v1 as tf tf.enable_eager_execution() from google.protobuf import text_format, timestamp_pb2 import tensorflow_io from tensorflow.compat.v1 import gfile from fedlearner.common import db_client from fedlearner.common import common_pb2 as common_pb from fedlearner.common import data_join_service_pb2 as dj_pb from fedlearner.data_join import ( data_block_manager, common, data_block_dumper, raw_data_manifest_manager, raw_data_visitor, visitor ) from fedlearner.data_join.data_block_manager import DataBlockBuilder from fedlearner.data_join.raw_data_iter_impl.tf_record_iter import TfExampleItem class TestDataBlockDumper(unittest.TestCase): def setUp(self): data_source_f = common_pb.DataSource() data_source_f.data_source_meta.name = "milestone" data_source_f.data_source_meta.partition_num = 1 data_source_f.output_base_dir = "./output-f" self.data_source_f = data_source_f if gfile.Exists(self.data_source_f.output_base_dir): gfile.DeleteRecursively(self.data_source_f.output_base_dir) data_source_l = common_pb.DataSource() data_source_l.data_source_meta.name = "milestone" data_source_l.data_source_meta.partition_num = 1 data_source_l.output_base_dir = "./output-l" self.raw_data_dir_l = "./raw_data-l" self.data_source_l = data_source_l if gfile.Exists(self.data_source_l.output_base_dir): gfile.DeleteRecursively(self.data_source_l.output_base_dir) if gfile.Exists(self.raw_data_dir_l): gfile.DeleteRecursively(self.raw_data_dir_l) self.kvstore = db_client.DBClient('etcd', True) self.kvstore.delete_prefix(common.data_source_kvstore_base_dir(self.data_source_l.data_source_meta.name)) self.manifest_manager = raw_data_manifest_manager.RawDataManifestManager( self.kvstore, self.data_source_l) def generate_follower_data_block(self): dbm = data_block_manager.DataBlockManager(self.data_source_f, 0) self.assertEqual(dbm.get_dumped_data_block_count(), 0) self.assertEqual(dbm.get_lastest_data_block_meta(), None) leader_index = 0 follower_index = 65536 self.dumped_metas = [] for i in range(5): builder = DataBlockBuilder( common.data_source_data_block_dir(self.data_source_f), self.data_source_f.data_source_meta.name, 0, i, dj_pb.WriterOptions(output_writer='TF_RECORD'), None ) builder.set_data_block_manager(dbm) for j in range(1024): feat = {} example_id = '{}'.format(i * 1024 + j).encode() feat['example_id'] = tf.train.Feature( bytes_list=tf.train.BytesList(value=[example_id])) event_time = 150000000 + i * 1024 + j feat['event_time'] = tf.train.Feature( int64_list=tf.train.Int64List(value=[event_time])) feat['leader_index'] = tf.train.Feature( int64_list=tf.train.Int64List(value=[leader_index])) feat['follower_index'] = tf.train.Feature( int64_list=tf.train.Int64List(value=[follower_index])) example = tf.train.Example(features=tf.train.Features(feature=feat)) builder.append_item(TfExampleItem(example.SerializeToString()), leader_index, follower_index) leader_index += 3 follower_index += 1 meta = builder.finish_data_block() self.dumped_metas.append(meta) self.leader_start_index = 0 self.leader_end_index = leader_index self.assertEqual(dbm.get_dumped_data_block_count(), 5) for (idx, meta) in enumerate(self.dumped_metas): self.assertEqual(dbm.get_data_block_meta_by_index(idx), meta) def generate_leader_raw_data(self): dbm = data_block_manager.DataBlockManager(self.data_source_l, 0) raw_data_dir = os.path.join(self.raw_data_dir_l, common.partition_repr(0)) if gfile.Exists(raw_data_dir): gfile.DeleteRecursively(raw_data_dir) gfile.MakeDirs(raw_data_dir) rdm = raw_data_visitor.RawDataManager(self.kvstore, self.data_source_l, 0) block_index = 0 builder = DataBlockBuilder( self.raw_data_dir_l, self.data_source_l.data_source_meta.name, 0, block_index, dj_pb.WriterOptions(output_writer='TF_RECORD'), None ) process_index = 0 start_index = 0 for i in range(0, self.leader_end_index + 3): if (i > 0 and i % 2048 == 0) or (i == self.leader_end_index + 2): meta = builder.finish_data_block() if meta is not None: ofname = common.encode_data_block_fname( self.data_source_l.data_source_meta.name, meta ) fpath = os.path.join(raw_data_dir, ofname) self.manifest_manager.add_raw_data( 0, [dj_pb.RawDataMeta(file_path=fpath, timestamp=timestamp_pb2.Timestamp(seconds=3))], False) process_index += 1 start_index += len(meta.example_ids) block_index += 1 builder = DataBlockBuilder( self.raw_data_dir_l, self.data_source_l.data_source_meta.name, 0, block_index, dj_pb.WriterOptions(output_writer='TF_RECORD'), None ) feat = {} pt = i + 1 << 30 if i % 3 == 0: pt = i // 3 example_id = '{}'.format(pt).encode() feat['example_id'] = tf.train.Feature( bytes_list=tf.train.BytesList(value=[example_id])) event_time = 150000000 + pt feat['event_time'] = tf.train.Feature( int64_list=tf.train.Int64List(value=[event_time])) example = tf.train.Example(features=tf.train.Features(feature=feat)) builder.append_item(TfExampleItem(example.SerializeToString()), i, i) fpaths = [os.path.join(raw_data_dir, f) for f in gfile.ListDirectory(raw_data_dir) if not gfile.IsDirectory(os.path.join(raw_data_dir, f))] for fpath in fpaths: if not fpath.endswith(common.DataBlockSuffix): gfile.Remove(fpath) def test_data_block_dumper(self): self.generate_follower_data_block() self.generate_leader_raw_data() dbd = data_block_dumper.DataBlockDumperManager( self.kvstore, self.data_source_l, 0, dj_pb.RawDataOptions(raw_data_iter='TF_RECORD', read_ahead_size=1<<20, read_batch_size=128), dj_pb.WriterOptions(output_writer='TF_RECORD') ) self.assertEqual(dbd.get_next_data_block_index(), 0) for (idx, meta) in enumerate(self.dumped_metas): success, next_index = dbd.add_synced_data_block_meta(meta) self.assertTrue(success) self.assertEqual(next_index, idx + 1) self.assertTrue(dbd.need_dump()) self.assertEqual(dbd.get_next_data_block_index(), len(self.dumped_metas)) with dbd.make_data_block_dumper() as dumper: dumper() dbm_f = data_block_manager.DataBlockManager(self.data_source_f, 0) dbm_l = data_block_manager.DataBlockManager(self.data_source_l, 0) self.assertEqual(dbm_f.get_dumped_data_block_count(), len(self.dumped_metas)) self.assertEqual(dbm_f.get_dumped_data_block_count(), dbm_l.get_dumped_data_block_count()) for (idx, meta) in enumerate(self.dumped_metas): self.assertEqual(meta.data_block_index, idx) self.assertEqual(dbm_l.get_data_block_meta_by_index(idx), meta) self.assertEqual(dbm_f.get_data_block_meta_by_index(idx), meta) meta_fpth_l = os.path.join( common.data_source_data_block_dir(self.data_source_l), common.partition_repr(0), common.encode_data_block_meta_fname( self.data_source_l.data_source_meta.name, 0, meta.data_block_index ) ) mitr = tf.io.tf_record_iterator(meta_fpth_l) meta_l = text_format.Parse(next(mitr), dj_pb.DataBlockMeta()) self.assertEqual(meta_l, meta) meta_fpth_f = os.path.join( common.data_source_data_block_dir(self.data_source_f), common.partition_repr(0), common.encode_data_block_meta_fname( self.data_source_f.data_source_meta.name, 0, meta.data_block_index ) ) mitr = tf.io.tf_record_iterator(meta_fpth_f) meta_f = text_format.Parse(next(mitr), dj_pb.DataBlockMeta()) self.assertEqual(meta_f, meta) data_fpth_l = os.path.join( common.data_source_data_block_dir(self.data_source_l), common.partition_repr(0), common.encode_data_block_fname( self.data_source_l.data_source_meta.name, meta_l ) ) for (iidx, record) in enumerate(tf.io.tf_record_iterator(data_fpth_l)): example = tf.train.Example() example.ParseFromString(record) feat = example.features.feature self.assertEqual(feat['example_id'].bytes_list.value[0], meta.example_ids[iidx]) self.assertEqual(len(meta.example_ids), iidx + 1) data_fpth_f = os.path.join( common.data_source_data_block_dir(self.data_source_f), common.partition_repr(0), common.encode_data_block_fname( self.data_source_l.data_source_meta.name, meta_f ) ) for (iidx, record) in enumerate(tf.io.tf_record_iterator(data_fpth_f)): example = tf.train.Example() example.ParseFromString(record) feat = example.features.feature self.assertEqual(feat['example_id'].bytes_list.value[0], meta.example_ids[iidx]) self.assertEqual(len(meta.example_ids), iidx +1) def tearDown(self): if gfile.Exists(self.data_source_f.output_base_dir): gfile.DeleteRecursively(self.data_source_f.output_base_dir) if gfile.Exists(self.data_source_l.output_base_dir): gfile.DeleteRecursively(self.data_source_l.output_base_dir) if gfile.Exists(self.raw_data_dir_l): gfile.DeleteRecursively(self.raw_data_dir_l) self.kvstore.delete_prefix(common.data_source_kvstore_base_dir(self.data_source_l.data_source_meta.name)) if __name__ == '__main__': unittest.main()

true