xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

42e0d979edff9c7a998b5ec794cca8e78853323e

34

py

Python

services/dsrp-api/app/api/nominated_well_site/models/__init__.py

bcgov/dormant-site-reclamation-program

4710434174a204a292a3128d92c8daf1de2a65a6

[ "Apache-2.0" ]

null

services/dsrp-api/app/api/nominated_well_site/models/__init__.py

bcgov/dormant-site-reclamation-program

4710434174a204a292a3128d92c8daf1de2a65a6

[ "Apache-2.0" ]

9

2020-05-06T23:29:43.000Z

2022-03-14T22:58:17.000Z

services/dsrp-api/app/api/nominated_well_site/models/__init__.py

bcgov/dormant-site-reclamation-program

4710434174a204a292a3128d92c8daf1de2a65a6

[ "Apache-2.0" ]

3

2020-05-08T16:54:22.000Z

2021-01-27T17:28:49.000Z

from .nominated_well_site import *

34

0.852941

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34

5.4

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0.870968

0

1

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1

0

6

42f2881e36fcfe359f25396f5146f5b309a94e4a

21

py

Python

yippi/xml/__init__.py

KiTTYsh/yippi

c34ef48d2cdce2d0967cfdac6be2d747ad94bbb1

[ "MIT" ]

1

2018-08-01T21:48:22.000Z

yippi/xml/__init__.py

KiTTYsh/yippi

c34ef48d2cdce2d0967cfdac6be2d747ad94bbb1

[ "MIT" ]

null

yippi/xml/__init__.py

KiTTYsh/yippi

c34ef48d2cdce2d0967cfdac6be2d747ad94bbb1

[ "MIT" ]

null

from . import object

10.5

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null

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1

0

1

0

1

0

6

6e067a60c1a6756817292b3ccbba51d2c17c4ac8

222

py

Python

bottomline/account/views.py

mcm219/BottomLine

db82eef403c79bffa3864c4db6bc336632abaca5

[ "MIT" ]

null

bottomline/account/views.py

mcm219/BottomLine

db82eef403c79bffa3864c4db6bc336632abaca5

[ "MIT" ]

1

2021-06-14T02:20:40.000Z

bottomline/account/views.py

mcm219/BottomLine

db82eef403c79bffa3864c4db6bc336632abaca5

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpResponse from django.http import HttpRequest # Create your views here. def index(request): return HttpResponse("Welcome to BottomLine! (Account App)")

22.2

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null

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1

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6

28204b21a0cc1bb6d6a0ce1595f6a10fbcad95aa

1,656

py

Python

tests/test_cli.py

ggtr1138/yaml-to-sqlite

8ba02216ecad7c8a037086a2131f1dd4c9f1deb3

[ "Apache-2.0" ]

null

tests/test_cli.py

ggtr1138/yaml-to-sqlite

8ba02216ecad7c8a037086a2131f1dd4c9f1deb3

[ "Apache-2.0" ]

null

tests/test_cli.py

ggtr1138/yaml-to-sqlite

8ba02216ecad7c8a037086a2131f1dd4c9f1deb3

[ "Apache-2.0" ]

null

from click.testing import CliRunner from yaml_to_sqlite import cli import sqlite_utils import json TEST_YAML = """ - name: datasette-cluster-map url: https://github.com/simonw/datasette-cluster-map - name: datasette-vega url: https://github.com/simonw/datasette-vega nested_with_date: - title: Hello date: 2010-01-01 """ EXPECTED = [ { "name": "datasette-cluster-map", "url": "https://github.com/simonw/datasette-cluster-map", "nested_with_date": None, }, { "name": "datasette-vega", "url": "https://github.com/simonw/datasette-vega", "nested_with_date": json.dumps([{"title": "Hello", "date": "2010-01-01"}]), }, ] def test_without_pk(tmpdir): db_path = tmpdir / "db.db" assert ( 0 == CliRunner() .invoke(cli.cli, [str(db_path), "items", "-"], input=TEST_YAML) .exit_code ) db = sqlite_utils.Database(str(db_path)) assert EXPECTED == list(db["items"].rows) # Run it again should get double the rows CliRunner().invoke(cli.cli, [str(db_path), "items", "-"], input=TEST_YAML) assert EXPECTED + EXPECTED == list(db["items"].rows) def test_with_pk(tmpdir): db_path = tmpdir / "db.db" assert ( 0 == CliRunner() .invoke(cli.cli, [str(db_path), "items", "-", "--pk", "name"], input=TEST_YAML) .exit_code ) db = sqlite_utils.Database(str(db_path)) assert EXPECTED == list(db["items"].rows) # Run it again should get same number of rows CliRunner().invoke(cli.cli, [str(db_path), "items", "-"], input=TEST_YAML) assert EXPECTED == list(db["items"].rows)

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null

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null

0

6

2860cdc91b3b3aa265b287374d3fdedd81a1155b

101

py

Python

backend/apps/workorder/models/__init__.py

bopopescu/Journey

654eb66e0e2df59e916eff4c75b68b183f9b58b5

[ "MIT" ]

41

2019-01-02T09:36:54.000Z

2022-02-20T13:13:05.000Z

backend/apps/workorder/models/__init__.py

bopopescu/Journey

654eb66e0e2df59e916eff4c75b68b183f9b58b5

[ "MIT" ]

15

2019-09-30T05:40:20.000Z

2022-02-17T19:28:41.000Z

backend/apps/workorder/models/__init__.py

bopopescu/Journey

654eb66e0e2df59e916eff4c75b68b183f9b58b5

[ "MIT" ]

23

2019-02-18T10:50:10.000Z

2022-01-06T07:53:18.000Z

# -*- coding: UTF-8 -*- from .sqlorder import * from .approvalgroup import * from .autoorder import *

25.25

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101

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0.666667

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101

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true

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null

0

1

0

1

0

1

0

6

287640f9f6120a3e9f76493e6e1d631850dddd66

76

py

Python

conda/api.py

astrojuanlu/conda

badf048f5e8287250ef1940249a048f9bde08477

[ "BSD-3-Clause" ]

null

conda/api.py

astrojuanlu/conda

badf048f5e8287250ef1940249a048f9bde08477

[ "BSD-3-Clause" ]

null

conda/api.py

astrojuanlu/conda

badf048f5e8287250ef1940249a048f9bde08477

[ "BSD-3-Clause" ]

null

from .core.index import get_index get_index_ = get_index # suppress flake8

25.333333

41

0.802632

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76

4.75

0.583333

0.421053

0.385965

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0.144737

76

2

42

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false

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1

0

null

1

0

1

0

null

0

1

0

6

28a054bf9a49006df16a79f082eaa66355fc7d31

21

py

Python

Unidad 2/packages/extra/ugly/psi.py

angelxehg/utzac-ppy

fb88bcc661518bb35c08a102a67c20d0659f71db

[ "MIT" ]

null

Unidad 2/packages/extra/ugly/psi.py

angelxehg/utzac-ppy

fb88bcc661518bb35c08a102a67c20d0659f71db

[ "MIT" ]

null

Unidad 2/packages/extra/ugly/psi.py

angelxehg/utzac-ppy

fb88bcc661518bb35c08a102a67c20d0659f71db

[ "MIT" ]

null

def funP(): pass

7

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null

0

1

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null

0

1

0

6

956dc54f5ccbda8cffaa224db34c024543f1d2e1

1,359

py

Python

dtleads_api_helper/dtl_models/dtl_settingrestriction_models.py

Daniel-Timothy-Leads/dtleads-api-helper

700e201ab422b5239ef058e12d0cb0e7bcec6df9

[ "MIT" ]

null

dtleads_api_helper/dtl_models/dtl_settingrestriction_models.py

Daniel-Timothy-Leads/dtleads-api-helper

700e201ab422b5239ef058e12d0cb0e7bcec6df9

[ "MIT" ]

null

dtleads_api_helper/dtl_models/dtl_settingrestriction_models.py

Daniel-Timothy-Leads/dtleads-api-helper

700e201ab422b5239ef058e12d0cb0e7bcec6df9

[ "MIT" ]

null

# create setting restrictions class DtlSettingRestrictionsCreateModel: def __init__(self, name): # required fields self.name = name self.monday = [] self.tuesday = [] self.wednesday = [] self.thursday = [] self.friday = [] self.saturday = [] self.sunday = [] def get_json_object(self): return { 'restrictionName': self.name, 'monday': self.monday, 'tuesday': self.tuesday, 'wednesday': self.wednesday, 'thursday': self.thursday, 'friday': self.friday, 'saturday': self.saturday, 'sunday': self.sunday, } # update setting restrictions class DtlSettingRestrictionsPatchModel: def __init__(self): self.name = None self.monday = [] self.tuesday = [] self.wednesday = [] self.thursday = [] self.friday = [] self.saturday = [] self.sunday = [] def get_json_object(self): return { 'restrictionName': self.name, 'monday': self.monday, 'tuesday': self.tuesday, 'wednesday': self.wednesday, 'thursday': self.thursday, 'friday': self.friday, 'saturday': self.saturday, 'sunday': self.sunday, }

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null

0

1

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1

0

null

0

6

958630570337c742a51681777f07fbd331e3f82b

27

py

Python

fastai_xla_extensions/all.py

farizrahman4u/fastai_xla_extensions

c0d66fe7f8dcfb4eaf2358f5f95d613765d55492

[ "Apache-2.0" ]

1

2021-04-12T14:24:55.000Z

fastai_xla_extensions/all.py

farizrahman4u/fastai_xla_extensions

c0d66fe7f8dcfb4eaf2358f5f95d613765d55492

[ "Apache-2.0" ]

null

fastai_xla_extensions/all.py

farizrahman4u/fastai_xla_extensions

c0d66fe7f8dcfb4eaf2358f5f95d613765d55492

[ "Apache-2.0" ]

null

from .multi_core import *

9

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27

2

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13.5

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null

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1

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1

0

1

0

6

958703f585e74ec88ebcc968c0777fd51f9760ad

230

py

Python

pyNeuralEMPC/__init__.py

Enderdead/pyNeuralEMPC

032a3675b10389c10bf3e687633462b489b5f26f

[ "MIT" ]

2

2021-08-23T19:05:35.000Z

2022-02-24T20:32:04.000Z

pyNeuralEMPC/__init__.py

Enderdead/pyNeuralEMPC

032a3675b10389c10bf3e687633462b489b5f26f

[ "MIT" ]

null

pyNeuralEMPC/__init__.py

Enderdead/pyNeuralEMPC

032a3675b10389c10bf3e687633462b489b5f26f

[ "MIT" ]

null

__version__ = '0.0' from pyNeuralEMPC import model from pyNeuralEMPC import objective from pyNeuralEMPC import integrator from pyNeuralEMPC import optimizer from pyNeuralEMPC import constraints from pyNeuralEMPC import controller

28.75

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1

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6

9589a633c428373f332b33b2208836419aa9bf2e

74,305

py

Python

smooth_streams_proxy/http_server.py

kwaaak/SmoothStreamsProxy

36ec544fdbf66fa455144d05eac84570f8ac18c4

[ "MIT" ]

null

smooth_streams_proxy/http_server.py

kwaaak/SmoothStreamsProxy

36ec544fdbf66fa455144d05eac84570f8ac18c4

[ "MIT" ]

null

smooth_streams_proxy/http_server.py

kwaaak/SmoothStreamsProxy

36ec544fdbf66fa455144d05eac84570f8ac18c4

[ "MIT" ]

null

import base64 import json import logging.handlers import pprint import re import sys import traceback import urllib.parse import uuid from datetime import datetime from http.server import BaseHTTPRequestHandler from http.server import HTTPServer from threading import Thread import pytz import requests from tzlocal import get_localzone from .constants import VALID_SMOOTH_STREAMS_PROTOCOL_VALUES from .constants import VERSION from .enums import SmoothStreamsProxyRecordingStatus from .exceptions import DuplicateRecordingError from .exceptions import RecordingNotFoundError from .proxy import SmoothStreamsProxy from .recorder import SmoothStreamsProxyRecording from .utilities import SmoothStreamsProxyUtility from .validators import SmoothStreamsProxyCerberusValidator logger = logging.getLogger(__name__) class SmoothStreamsProxyHTTPRequestHandler(BaseHTTPRequestHandler): def _send_http_response(self, client_ip_address, client_uuid, path, response_status_code, response_headers, response_content, do_print_content=True): self.send_response(response_status_code) headers = [] if response_headers: for header_entry in sorted(response_headers): self.send_header(header_entry, response_headers[header_entry]) headers.append( '{0:32} => {1!s}'.format(header_entry, response_headers[header_entry])) self.end_headers() # noinspection PyUnresolvedReferences logger.trace( 'Response to {0}{1} for {2}\n' '[Status Code]\n=============\n{3}\n\n' '{4}' '{5}'.format(client_ip_address, '/{0}'.format(client_uuid) if client_uuid else '', path, response_status_code, '[Header]\n========\n{0}\n\n'.format('\n'.join(headers)) if headers else '', '[Content]\n=========\n{0:{1}}\n'.format( response_content if do_print_content else len(response_content), '' if do_print_content else ',') if response_content else '')) if response_content: try: self.wfile.write(bytes(response_content, 'utf-8')) except TypeError: self.wfile.write(response_content) # noinspection PyPep8Naming def do_DELETE(self): client_ip_address = self.client_address[0] requested_path_with_query_string = self.path requested_url_components = urllib.parse.urlparse(requested_path_with_query_string) requested_query_string_parameters = dict(urllib.parse.parse_qsl(requested_url_components.query)) requested_path_tokens = [requested_path_token.lower() for requested_path_token in requested_url_components.path[1:].split('/')] requested_path_tokens_length = len(requested_path_tokens) requested_path_not_found = False # noinspection PyBroadException try: logger.debug('{0} requested from {1}\n' 'Request type => {2}'.format(requested_path_with_query_string, client_ip_address, self.command)) if requested_path_tokens_length == 2 and \ requested_path_tokens[0] == 'recordings' and \ re.match('\A[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}\Z', requested_path_tokens[1]): content_length = int(self.headers.get('Content-Length', 0)) delete_request_body = self.rfile.read(content_length) if content_length else '' query_string_parameters_schema = {} query_string_parameters_validator = SmoothStreamsProxyCerberusValidator(query_string_parameters_schema) if delete_request_body: logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Error Title => Unsupported request body\n' 'Error Message => {2} recordings does not support a request body'.format( client_ip_address, requested_path_with_query_string, self.command)) delete_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.BAD_REQUEST), 'title': 'Unsupported request body', 'field': None, 'developer_message': '{0} recordings does not support a request body'.format( self.command), 'user_message': 'The request is badly formatted' } ] } delete_recordings_response_status_code = requests.codes.BAD_REQUEST elif not query_string_parameters_validator.validate(requested_query_string_parameters): logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Error Title => Unsupported query parameter{2}\n' 'Error Message => {3} recordings does not support [\'{4}\'] query parameter{2}'.format( client_ip_address, requested_path_with_query_string, 's' if len(query_string_parameters_validator.errors) > 1 else '', self.command, ', '.join(query_string_parameters_validator.errors))) delete_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.BAD_REQUEST), 'title': 'Unsupported query parameter{0}'.format( 's' if len(query_string_parameters_validator.errors) > 1 else ''), 'field': list(sorted(query_string_parameters_validator.errors)), 'developer_message': '{0} recordings does not support [\'{1}\'] query parameter' '{2}'.format( self.command, ', '.join(query_string_parameters_validator.errors), 's' if len(query_string_parameters_validator.errors) > 1 else ''), 'user_message': 'The request is badly formatted' } ] } delete_recordings_response_status_code = requests.codes.BAD_REQUEST else: recording_id = requested_url_components.path[len('/recordings/'):] try: recording = SmoothStreamsProxy.get_recording(recording_id) logger.debug( 'Attempting to {0} {1} recording\n' 'Channel name => {2}\n' 'Channel number => {3}\n' 'Program title => {4}\n' 'Start date & time => {5}\n' 'End date & time => {6}'.format( 'stop' if recording.status == SmoothStreamsProxyRecordingStatus.ACTIVE.value else 'delete', recording.status, recording.channel_name, recording.channel_number, recording.program_title, recording.start_date_time_in_utc.astimezone( get_localzone()).strftime('%Y-%m-%d %H:%M:%S'), recording.end_date_time_in_utc.astimezone( get_localzone()).strftime('%Y-%m-%d %H:%M:%S'))) if recording.status == SmoothStreamsProxyRecordingStatus.ACTIVE.value: try: SmoothStreamsProxy.stop_active_recording(recording) except KeyError: raise RecordingNotFoundError elif recording.status == SmoothStreamsProxyRecordingStatus.PERSISTED.value: try: SmoothStreamsProxy.delete_persisted_recording(recording) except OSError: raise RecordingNotFoundError elif recording.status == SmoothStreamsProxyRecordingStatus.SCHEDULED.value: try: SmoothStreamsProxy.delete_scheduled_recording(recording) except ValueError: raise RecordingNotFoundError logger.debug( '{0} {1} recording\n' 'Channel name => {2}\n' 'Channel number => {3}\n' 'Program title => {4}\n' 'Start date & time => {5}\n' 'End date & time => {6}'.format( 'Stopped' if recording.status == SmoothStreamsProxyRecordingStatus.ACTIVE.value else 'Deleted', recording.status, recording.channel_name, recording.channel_number, recording.program_title, recording.start_date_time_in_utc.astimezone( get_localzone()).strftime('%Y-%m-%d %H:%M:%S'), recording.end_date_time_in_utc.astimezone( get_localzone()).strftime('%Y-%m-%d %H:%M:%S'))) delete_recordings_response = { 'meta': { 'application': 'SmoothStreamsProxy', 'version': VERSION } } delete_recordings_response_status_code = requests.codes.OK except RecordingNotFoundError: logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Error Title => Resource not found\n' 'Error Message => Recording with ID {2} does not exist'.format( client_ip_address, requested_path_with_query_string, recording_id)) delete_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.NOT_FOUND), 'title': 'Resource not found', 'field': None, 'developer_message': 'Recording with ID {0} does not exist'.format(recording_id), 'user_message': 'Requested recording no longer exists' } ] } delete_recordings_response_status_code = requests.codes.NOT_FOUND json_api_response = json.dumps(delete_recordings_response, indent=4) self._send_http_response(client_ip_address, None, requested_path_with_query_string, delete_recordings_response_status_code, SmoothStreamsProxyUtility.construct_response_headers( json_api_response, 'application/vnd.api+json'), json_api_response) else: requested_path_not_found = True if requested_path_not_found: logger.error('HTTP error {0} encountered requesting {1} for {2}'.format( requests.codes.NOT_FOUND, requested_path_with_query_string, client_ip_address)) self._send_http_response(client_ip_address, requested_query_string_parameters.get('client_uuid', None), requested_path_with_query_string, requests.codes.NOT_FOUND, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) except Exception: (type_, value_, traceback_) = sys.exc_info() logger.error('\n'.join(traceback.format_exception(type_, value_, traceback_))) self._send_http_response(client_ip_address, requested_query_string_parameters.get('client_uuid', None), requested_path_with_query_string, requests.codes.INTERNAL_SERVER_ERROR, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) # noinspection PyPep8Naming def do_GET(self): client_ip_address = self.client_address[0] requested_path_with_query_string = self.path requested_url_components = urllib.parse.urlparse(requested_path_with_query_string) requested_query_string_parameters = dict(urllib.parse.parse_qsl(requested_url_components.query)) requested_path_tokens = [requested_path_token.lower() for requested_path_token in requested_url_components.path[1:].split('/')] requested_path_tokens_length = len(requested_path_tokens) requested_path_not_found = False # noinspection PyBroadException try: logger.debug('{0} requested from {1}\n' 'Request type => {2}'.format(requested_path_with_query_string, client_ip_address, self.command)) if requested_path_tokens[0] == 'live' and requested_path_tokens_length == 2: channel_number_parameter_value = requested_query_string_parameters.get('channel_number', None) client_uuid_parameter_value = requested_query_string_parameters.get('client_uuid', None) nimble_session_id_parameter_value = requested_query_string_parameters.get('nimblesessionid', None) number_of_days_parameter_value = requested_query_string_parameters.get('number_of_days', 1) protocol_parameter_value = requested_query_string_parameters.get('protocol', None) smooth_streams_hash_parameter_value = requested_query_string_parameters.get('wmsAuthSign', None) if protocol_parameter_value not in VALID_SMOOTH_STREAMS_PROTOCOL_VALUES: protocol_parameter_value = SmoothStreamsProxy.get_configuration_parameter('SMOOTH_STREAMS_PROTOCOL') if requested_path_tokens[1].endswith('.ts'): try: ts_file_content = SmoothStreamsProxy.download_ts_file(client_ip_address, requested_url_components.path, channel_number_parameter_value, client_uuid_parameter_value, nimble_session_id_parameter_value) self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, requests.codes.OK, SmoothStreamsProxyUtility.construct_response_headers(ts_file_content, 'video/m2ts'), ts_file_content, False) except requests.exceptions.HTTPError as e: self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, e.response.status_code, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) elif requested_path_tokens[1] == 'chunks.m3u8': nimble_session_id_parameter_value = SmoothStreamsProxy.map_nimble_session_id( client_ip_address, requested_url_components.path, channel_number_parameter_value, client_uuid_parameter_value, nimble_session_id_parameter_value, smooth_streams_hash_parameter_value) try: playlist_m3u8_content = SmoothStreamsProxy.download_chunks_m3u8( client_ip_address, requested_url_components.path, channel_number_parameter_value, client_uuid_parameter_value, nimble_session_id_parameter_value) self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, requests.codes.OK, SmoothStreamsProxyUtility.construct_response_headers( playlist_m3u8_content, 'application/vnd.apple.mpegurl'), playlist_m3u8_content) except requests.exceptions.HTTPError as e: self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, e.response.status_code, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) elif requested_path_tokens[1] == 'epg.xml': epg_file_name = 'xmltv{0}.xml.gz'.format(number_of_days_parameter_value) try: epg_xml_content = SmoothStreamsProxy.get_file_content(epg_file_name) self._send_http_response(client_ip_address, None, requested_path_with_query_string, requests.codes.OK, SmoothStreamsProxyUtility.construct_response_headers( epg_xml_content, 'application/xml'), epg_xml_content, do_print_content=False) except requests.exceptions.HTTPError as e: self._send_http_response(client_ip_address, None, requested_path_with_query_string, e.response.status_code, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) elif requested_path_tokens[1] == 'playlist.m3u8': do_generate_playlist_m3u8 = False if requested_query_string_parameters: if channel_number_parameter_value: logger.info('{0} requested from {1}/{2}'.format( SmoothStreamsProxy.get_channel_name(int(channel_number_parameter_value)), client_ip_address, client_uuid_parameter_value)) try: playlist_m3u8_content = SmoothStreamsProxy.download_playlist_m3u8( client_ip_address, requested_url_components.path, channel_number_parameter_value, client_uuid_parameter_value, protocol_parameter_value) self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, requests.codes.OK, SmoothStreamsProxyUtility.construct_response_headers( playlist_m3u8_content, 'application/vnd.apple.mpegurl'), playlist_m3u8_content) except requests.exceptions.HTTPError as e: self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, e.response.status_code, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) elif protocol_parameter_value: do_generate_playlist_m3u8 = True else: logger.error('{0} requested from {1}/{2} has an invalid query string'.format( requested_path_with_query_string, client_ip_address, client_uuid_parameter_value)) self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, requests.codes.BAD_REQUEST, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) else: do_generate_playlist_m3u8 = True if do_generate_playlist_m3u8: try: channels_json_content = SmoothStreamsProxy.get_file_content('channels.json') playlist_m3u8_content = SmoothStreamsProxy.generate_live_playlist_m3u8( client_ip_address, json.loads(channels_json_content), protocol_parameter_value) self._send_http_response(client_ip_address, None, requested_path_with_query_string, requests.codes.OK, SmoothStreamsProxyUtility.construct_response_headers( playlist_m3u8_content, 'application/vnd.apple.mpegurl'), playlist_m3u8_content) except requests.exceptions.HTTPError as e: self._send_http_response(client_ip_address, None, requested_path_with_query_string, e.response.status_code, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) else: requested_path_not_found = True elif requested_path_tokens[0] == 'recordings': content_length = int(self.headers.get('Content-Length', 0)) get_request_body = self.rfile.read(content_length) if content_length else '' if get_request_body: logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Error Title => Unsupported request body\n' 'Error Message => {2} recordings does not support a request body'.format( client_ip_address, requested_path_with_query_string, self.command)) get_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.BAD_REQUEST), 'title': 'Unsupported request body', 'field': None, 'developer_message': '{0} recordings does not support a request body'.format( self.command), 'user_message': 'The request is badly formatted' } ] } get_recordings_response_status_code = requests.codes.BAD_REQUEST elif requested_path_tokens_length == 1: query_string_parameters_schema = { 'status': { 'allowed': [SmoothStreamsProxyRecordingStatus.ACTIVE.value, SmoothStreamsProxyRecordingStatus.PERSISTED.value, SmoothStreamsProxyRecordingStatus.SCHEDULED.value], 'type': 'string' } } query_string_parameters_validator = SmoothStreamsProxyCerberusValidator( query_string_parameters_schema) if not query_string_parameters_validator.validate(requested_query_string_parameters): if [key for key in query_string_parameters_validator.errors if key != 'status']: logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Error Title => Unsupported query parameter{2}\n' 'Error Message => {3} recordings does not support [\'{4}\'] query parameter{2}'.format( client_ip_address, requested_path_with_query_string, 's' if len([error_key for error_key in query_string_parameters_validator.errors if error_key != 'status']) > 1 else '', self.command, ', '.join([error_key for error_key in query_string_parameters_validator.errors if error_key != 'status']))) get_recordings_response = {'errors': [ { 'status': '{0}'.format(requests.codes.BAD_REQUEST), 'title': 'Unsupported query parameter{0}'.format( 's' if len(query_string_parameters_validator.errors) > 1 else ''), 'field': list(sorted(query_string_parameters_validator.errors)), 'developer_message': '{0} recordings does not support [\'{1}\'] query parameter' '{2}'.format( self.command, ', '.join([error_key for error_key in query_string_parameters_validator.errors if error_key != 'status']), 's' if len( [error_key for error_key in query_string_parameters_validator.errors if error_key != 'status']) > 1 else ''), 'user_message': 'The request is badly formatted' } ]} get_recordings_response_status_code = requests.codes.BAD_REQUEST else: logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Error Title => Invalid query parameter value\n' 'Error Message => {2} recordings query parameter [\'status\'] value \'{3}\' ' 'is not supported'.format( client_ip_address, requested_path_with_query_string, self.command, requested_query_string_parameters['status'])) get_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.UNPROCESSABLE_ENTITY), 'title': 'Invalid query parameter value', 'field': ['status'], 'developer_message': '{0} recordings query parameter [\'status\'] value ' '\'{1}\' is not supported'.format( self.command, requested_query_string_parameters['status']), 'user_message': 'The request is badly formatted' } ] } get_recordings_response_status_code = requests.codes.UNPROCESSABLE_ENTITY else: get_recordings_response = { 'meta': { 'application': 'SmoothStreamsProxy', 'version': VERSION }, 'data': [] } status = requested_query_string_parameters.get('status', None) for recording in [recording for recording in SmoothStreamsProxy.get_recordings() if status is None or status == recording.status]: get_recordings_response['data'].append({ 'type': 'recordings', 'id': recording.id, 'attributes': { 'channel_name': recording.channel_name, 'channel_number': recording.channel_number, 'end_date_time_in_utc': '{0}'.format(recording.end_date_time_in_utc), 'program_title': recording.program_title, 'start_date_time_in_utc': '{0}'.format(recording.start_date_time_in_utc), 'status': recording.status } }) get_recordings_response_status_code = requests.codes.OK elif re.match('\A[0-9a-f]{8}-[0-9a-f]{4}-4[0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}\Z', requested_path_tokens[1]) and requested_path_tokens_length == 2: query_string_parameters_schema = {} query_string_parameters_validator = SmoothStreamsProxyCerberusValidator( query_string_parameters_schema) if not query_string_parameters_validator.validate(requested_query_string_parameters): logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Error Title => Unsupported query parameter{2}\n' 'Error Message => {3} recordings does not support [\'{4}\'] query parameter{2}'.format( client_ip_address, requested_path_with_query_string, 's' if len(query_string_parameters_validator.errors) > 1 else '', self.command, ', '.join(query_string_parameters_validator.errors))) get_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.BAD_REQUEST), 'title': 'Unsupported query parameter{0}'.format( 's' if len(query_string_parameters_validator.errors) > 1 else ''), 'field': list(sorted(query_string_parameters_validator.errors)), 'developer_message': '{0} recordings does not support [\'{1}\'] query parameter' '{2}'.format( self.command, ', '.join(query_string_parameters_validator.errors), 's' if len(query_string_parameters_validator.errors) > 1 else ''), 'user_message': 'The request is badly formatted' } ] } get_recordings_response_status_code = requests.codes.BAD_REQUEST else: recording_id = requested_path_tokens[1] try: recording = SmoothStreamsProxy.get_recording(recording_id) get_recordings_response = { 'meta': { 'application': 'SmoothStreamsProxy', 'version': VERSION }, 'data': { 'type': 'recordings', 'id': recording.id, 'attributes': { 'channel_name': recording.channel_name, 'channel_number': recording.channel_number, 'end_date_time_in_utc': '{0}'.format(recording.end_date_time_in_utc), 'program_title': recording.program_title, 'start_date_time_in_utc': '{0}'.format(recording.start_date_time_in_utc), 'status': recording.status } } } get_recordings_response_status_code = requests.codes.OK except RecordingNotFoundError: logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Error Title => Resource not found\n' 'Error Message => Recording with ID {2} does not exist'.format( client_ip_address, requested_path_with_query_string, recording_id)) get_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.NOT_FOUND), 'title': 'Resource not found', 'field': None, 'developer_message': 'Recording with ID {0} does not exist'.format( recording_id), 'user_message': 'Requested recording no longer exists' } ] } get_recordings_response_status_code = requests.codes.NOT_FOUND else: requested_path_not_found = True if not requested_path_not_found: # noinspection PyUnboundLocalVariable json_api_response = json.dumps(get_recordings_response, indent=4) # noinspection PyUnboundLocalVariable self._send_http_response(client_ip_address, None, requested_path_with_query_string, get_recordings_response_status_code, SmoothStreamsProxyUtility.construct_response_headers( json_api_response, 'application/vnd.api+json'), json_api_response) elif requested_path_tokens[0] == 'vod' and requested_path_tokens_length == 2: client_uuid_parameter_value = requested_query_string_parameters.get('client_uuid', None) program_title = requested_query_string_parameters.get('program_title', None) if requested_path_tokens[1].endswith('.ts'): ts_file_content = SmoothStreamsProxy.read_ts_file(requested_path_with_query_string, program_title) if ts_file_content: self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, requests.codes.OK, SmoothStreamsProxyUtility.construct_response_headers(ts_file_content, 'video/m2ts'), ts_file_content, False) else: self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, requests.codes.NOT_FOUND, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) elif requested_path_tokens[1] == 'playlist.m3u8': if requested_query_string_parameters: logger.info('{0} requested from {1}/{2}'.format( base64.urlsafe_b64decode(program_title.encode()).decode(), client_ip_address, client_uuid_parameter_value)) playlist_m3u8_content = SmoothStreamsProxy.read_vod_playlist_m3u8(program_title) if playlist_m3u8_content: self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, requests.codes.OK, SmoothStreamsProxyUtility.construct_response_headers( playlist_m3u8_content, 'application/vnd.apple.mpegurl'), playlist_m3u8_content) else: self._send_http_response(client_ip_address, client_uuid_parameter_value, requested_path_with_query_string, requests.codes.NOT_FOUND, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) else: playlist_m3u8_content = SmoothStreamsProxy.generate_vod_playlist_m3u8(client_ip_address) if playlist_m3u8_content: self._send_http_response(client_ip_address, None, requested_path_with_query_string, requests.codes.OK, SmoothStreamsProxyUtility.construct_response_headers( playlist_m3u8_content, 'application/vnd.apple.mpegurl'), playlist_m3u8_content) else: self._send_http_response(client_ip_address, None, requested_path_with_query_string, requests.codes.NOT_FOUND, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) else: requested_path_not_found = True else: requested_path_not_found = True if requested_path_not_found: logger.error('HTTP error {0} encountered requesting {1} for {2}'.format( requests.codes.NOT_FOUND, requested_path_with_query_string, client_ip_address)) self._send_http_response(client_ip_address, requested_query_string_parameters.get('client_uuid', None), requested_path_with_query_string, requests.codes.NOT_FOUND, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) except Exception: (status, value_, traceback_) = sys.exc_info() logger.error('\n'.join(traceback.format_exception(status, value_, traceback_))) self._send_http_response(client_ip_address, requested_query_string_parameters.get('client_uuid', None), requested_path_with_query_string, requests.codes.INTERNAL_SERVER_ERROR, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) # noinspection PyPep8Naming def do_OPTIONS(self): client_ip_address = self.client_address[0] requested_path_with_query_string = self.path requested_url_components = urllib.parse.urlparse(requested_path_with_query_string) requested_query_string_parameters = dict(urllib.parse.parse_qsl(requested_url_components.query)) self._send_http_response(client_ip_address, requested_query_string_parameters.get('client_uuid', None), requested_path_with_query_string, requests.codes.OK, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) # noinspection PyPep8Naming def do_POST(self): client_ip_address = self.client_address[0] requested_path_with_query_string = self.path requested_url_components = urllib.parse.urlparse(requested_path_with_query_string) requested_query_string_parameters = dict(urllib.parse.parse_qsl(requested_url_components.query)) requested_path_tokens = [requested_path_token.lower() for requested_path_token in requested_url_components.path[1:].split('/')] requested_path_tokens_length = len(requested_path_tokens) requested_path_not_found = False # noinspection PyBroadException try: logger.debug('{0} requested from {1}\n' 'Request type => {2}'.format(requested_path_with_query_string, client_ip_address, self.command)) if requested_path_tokens_length == 1 and requested_path_tokens[0] == 'recordings': content_length = int(self.headers.get('Content-Length', 0)) invalid_post_request_body = False try: post_request_body = json.loads(self.rfile.read(content_length)) if content_length else {} except json.JSONDecodeError: invalid_post_request_body = True query_string_parameters_schema = {} query_string_parameters_validator = SmoothStreamsProxyCerberusValidator(query_string_parameters_schema) post_request_body_schema = { 'data': { 'required': True, 'schema': { 'type': { 'allowed': ['recordings'], 'required': True, 'type': 'string' }, 'attributes': { 'required': True, 'schema': { 'channel_number': { 'is_channel_number_valid': True, 'required': True, 'type': 'string' }, 'end_date_time_in_utc': { 'is_end_date_time_after_start_date_time': 'start_date_time_in_utc', 'is_end_date_time_in_the_future': True, 'required': True, 'type': 'datetime_string' }, 'program_title': { 'required': True, 'type': 'string' }, 'start_date_time_in_utc': { 'required': True, 'type': 'datetime_string' } }, 'type': 'dict' } }, 'type': 'dict' } } post_request_body_validator = SmoothStreamsProxyCerberusValidator(post_request_body_schema) if invalid_post_request_body: logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Error Title => Invalid request body\n' 'Error Message => Request body is not a valid JSON document'.format( client_ip_address, requested_path_with_query_string)) post_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.BAD_REQUEST), 'title': 'Invalid request body', 'field': None, 'developer_message': 'Request body is not a valid JSON document'.format(self.command), 'user_message': 'The request is badly formatted' } ] } post_recordings_response_status_code = requests.codes.BAD_REQUEST elif not query_string_parameters_validator.validate(requested_query_string_parameters): logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Error Title => Unsupported query parameter{2}\n' 'Error Message => {3} recordings/{{id}} does not support [\'{4}\'] query parameter{2}'.format( client_ip_address, requested_path_with_query_string, 's' if len(query_string_parameters_validator.errors) > 1 else '', self.command, ', '.join(query_string_parameters_validator.errors))) post_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.BAD_REQUEST), 'title': 'Unsupported query parameter{0}'.format( 's' if len(query_string_parameters_validator.errors) > 1 else ''), 'field': list(sorted(query_string_parameters_validator.errors)), 'developer_message': '{0} recordings does not support [\'{1}\'] query parameter' '{2}'.format( self.command, ', '.join(query_string_parameters_validator.errors), 's' if len(query_string_parameters_validator.errors) > 1 else ''), 'user_message': 'The request is badly formatted' } ] } post_recordings_response_status_code = requests.codes.BAD_REQUEST elif not post_request_body_validator.validate(post_request_body): missing_required_fields = [match.group().replace( '\'', '') for match in re.finditer( '(\'[^{,\[]+\')(?=: \[\'required field\'\])', '{0}'.format(post_request_body_validator.errors))] included_unknown_fields = [match.group().replace( '\'', '') for match in re.finditer( '(\'[^{,\[]+\')(?=: \[\'unknown field\'\])', '{0}'.format(post_request_body_validator.errors))] incorrect_type_fields = [match.group().replace( '\'', '') for match in re.finditer( '(\'[^{,\[]+\')(?=: \[\'must be of (datetime_string|string) type\'\])', '{0}'.format(post_request_body_validator.errors))] invalid_type_value = [match.group().replace( '\'', '') for match in re.finditer( '(\'[^{,\[]+\')(?=: \[\'unallowed value .*\'\])', '{0}'.format(post_request_body_validator.errors))] invalid_channel_number = [match.group().replace( '\'', '') for match in re.finditer( '(\'[^{,\[]+\')(?=: \[\'must be between [0-9]{2} and [0-9]{2,4}\'\])', '{0}'.format(post_request_body_validator.errors))] invalid_end_date_time_in_the_future = [match.group().replace( '\'', '') for match in re.finditer( '(\'[^{,\[]+\')(?=: \[\'must be later than now\'\])', '{0}'.format(post_request_body_validator.errors))] invalid_end_date_time_after_start_date_time = [match.group().replace( '\'', '') for match in re.finditer( '(\'[^{,\[]+\')(?=: \[\'must be later than start_date_time_in_utc\'\])', '{0}'.format(post_request_body_validator.errors))] if missing_required_fields or included_unknown_fields: logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Post Data => {2}\n' 'Error Title => Invalid resource creation request\n' 'Error Message => Request body {3}'.format( client_ip_address, requested_path_with_query_string, pprint.pformat(post_request_body, indent=4), 'is missing mandatory field{0} {1}'.format( 's' if len(missing_required_fields) > 1 else '', missing_required_fields) if missing_required_fields else 'includes unknown field{0} {1}'.format( 's' if len(included_unknown_fields) > 1 else '', included_unknown_fields))) post_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.BAD_REQUEST), 'title': 'Invalid resource creation request', 'field': '{0}'.format(missing_required_fields if missing_required_fields else included_unknown_fields), 'developer_message': 'Request body {0}'.format( 'is missing mandatory field{0} {1}'.format( 's' if len(missing_required_fields) > 1 else '', missing_required_fields) if missing_required_fields else 'includes unknown field{0} {1}'.format( 's' if len(included_unknown_fields) > 1 else '', included_unknown_fields)), 'user_message': 'The request is badly formatted' } ] } post_recordings_response_status_code = requests.codes.BAD_REQUEST elif incorrect_type_fields or invalid_type_value or invalid_channel_number or \ invalid_end_date_time_in_the_future or invalid_end_date_time_after_start_date_time: field = None developer_message = None user_message = None if incorrect_type_fields: field = incorrect_type_fields developer_message = 'Request body includes field{0} with invalid type {1}'.format( 's' if len(incorrect_type_fields) > 1 else '', incorrect_type_fields) user_message = 'The request is badly formatted' elif invalid_type_value == ['type']: field = invalid_type_value developer_message = '[\'type\'] must be recordings' user_message = 'The request is badly formatted' elif invalid_channel_number == ['channel_number']: field = invalid_channel_number developer_message = '[\'channel_number\'] {0}'.format( post_request_body_validator.errors['data'][0]['attributes'][0]['channel_number'][0]) user_message = 'The requested channel does not exist' elif invalid_end_date_time_in_the_future == ['end_date_time_in_utc']: field = invalid_end_date_time_in_the_future developer_message = '[\'end_date_time_in_utc\'] must be later than now' user_message = 'The requested recording is in the past' elif invalid_end_date_time_after_start_date_time == ['end_date_time_in_utc']: field = invalid_end_date_time_after_start_date_time developer_message = '[\'end_date_time_in_utc\'] must be later than ' \ '[\'start_date_time_in_utc\']' user_message = 'The request is badly formatted' logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Post Data => {2}\n' 'Error Title => Invalid resource creation request\n' 'Error Message => {3}'.format( client_ip_address, requested_path_with_query_string, pprint.pformat(post_request_body, indent=4), developer_message)) post_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.UNPROCESSABLE_ENTITY), 'title': 'Invalid resource creation request', 'field': field, 'developer_message': '{0}'.format(developer_message), 'user_message': '{0}'.format(user_message) } ] } post_recordings_response_status_code = requests.codes.UNPROCESSABLE_ENTITY else: logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Post Data => {2}\n' 'Error Title => Invalid resource creation request\n' 'Error Message => Unexpected error'.format( client_ip_address, requested_path_with_query_string, pprint.pformat(post_request_body, indent=4))) post_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.UNPROCESSABLE_ENTITY), 'title': 'Invalid resource creation request', 'field': None, 'developer_message': 'Unexpected error', 'user_message': 'The request is badly formatted' } ] } post_recordings_response_status_code = requests.codes.UNPROCESSABLE_ENTITY else: channel_name = SmoothStreamsProxy.get_channel_name( int(post_request_body['data']['attributes']['channel_number'])) channel_number = post_request_body['data']['attributes']['channel_number'] end_date_time_in_utc = datetime.strptime( post_request_body['data']['attributes']['end_date_time_in_utc'], '%Y-%m-%d %H:%M:%S').replace(tzinfo=pytz.utc) id_ = '{0}'.format(uuid.uuid4()) program_title = post_request_body['data']['attributes']['program_title'] start_date_time_in_utc = datetime.strptime( post_request_body['data']['attributes']['start_date_time_in_utc'], '%Y-%m-%d %H:%M:%S').replace(tzinfo=pytz.utc) recording = SmoothStreamsProxyRecording(channel_name, channel_number, end_date_time_in_utc, id_, program_title, start_date_time_in_utc, SmoothStreamsProxyRecordingStatus.SCHEDULED.value) try: SmoothStreamsProxy.add_scheduled_recording(recording) logger.info( 'Scheduled recording\n' 'Channel name => {0}\n' 'Channel number => {1}\n' 'Program title => {2}\n' 'Start date & time => {3}\n' 'End date & time => {4}'.format(channel_name, channel_number, program_title, start_date_time_in_utc.astimezone( get_localzone()).strftime('%Y-%m-%d %H:%M:%S'), end_date_time_in_utc.astimezone( get_localzone()).strftime('%Y-%m-%d %H:%M:%S'))) post_recordings_response = { 'meta': { 'application': 'SmoothStreamsProxy', 'version': VERSION }, 'data': { 'type': 'recordings', 'id': id_, 'attributes': { 'channel_name': channel_name, 'channel_number': channel_number, 'end_date_time_in_utc': '{0}'.format(end_date_time_in_utc), 'program_title': program_title, 'start_date_time_in_utc': '{0}'.format(start_date_time_in_utc), 'status': 'scheduled' } } } post_recordings_response_status_code = requests.codes.CREATED except DuplicateRecordingError: logger.error( 'Error encountered processing request\n' 'Source IP => {0}\n' 'Requested path => {1}\n' 'Post Data => {2}\n' 'Error Title => Duplicate resource\n' 'Error Message => Recording already scheduled'.format( client_ip_address, requested_path_with_query_string, pprint.pformat(post_request_body, indent=4))) post_recordings_response = { 'errors': [ { 'status': '{0}'.format(requests.codes.CONFLICT), 'field': None, 'title': 'Duplicate resource', 'developer_message': 'Recording already scheduled', 'user_message': 'The recording is already scheduled' } ] } post_recordings_response_status_code = requests.codes.CONFLICT json_api_response = json.dumps(post_recordings_response, indent=4) self._send_http_response(client_ip_address, None, requested_path_with_query_string, post_recordings_response_status_code, SmoothStreamsProxyUtility.construct_response_headers( json_api_response, 'application/vnd.api+json'), json_api_response) else: requested_path_not_found = True if requested_path_not_found: logger.error('HTTP error {0} encountered requesting {1} for {2}'.format( requests.codes.NOT_FOUND, requested_path_with_query_string, client_ip_address)) self._send_http_response(client_ip_address, requested_query_string_parameters.get('client_uuid', None), requested_path_with_query_string, requests.codes.NOT_FOUND, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) except Exception: (type_, value_, traceback_) = sys.exc_info() logger.error('\n'.join(traceback.format_exception(type_, value_, traceback_))) self._send_http_response(client_ip_address, requested_query_string_parameters.get('client_uuid', None), requested_path_with_query_string, requests.codes.INTERNAL_SERVER_ERROR, SmoothStreamsProxyUtility.construct_response_headers(None, None), []) def log_message(self, format_, *args): return class SmoothStreamsProxyHTTPRequestHandlerThread(Thread): def __init__(self, server_address, server_socket): Thread.__init__(self) self.server_address = server_address self.server_socket = server_socket self.server_close = lambda self_: None self._smooth_streams_proxy_http_server = SmoothStreamsProxyHTTPServer(self.server_address, SmoothStreamsProxyHTTPRequestHandler, False) self.daemon = True self.start() def run(self): self._smooth_streams_proxy_http_server.socket = self.server_socket self._smooth_streams_proxy_http_server.server_bind = self.server_close self._smooth_streams_proxy_http_server.serve_forever() def stop(self): self._smooth_streams_proxy_http_server.shutdown() class SmoothStreamsProxyHTTPServer(HTTPServer): def __init__(self, server_address, request_handler, context): HTTPServer.__init__(self, server_address, request_handler, context)

60.657143

120

0.428222

5,318

74,305

5.624107

0.058293

0.046708

0.048447

0.041927

0.82303

0.774984

0.753385

0.703367

0.674479

0.654786

0

0.0097

0.50469

74,305

1,224

121

60.706699

0.802956

0.004051

0

0.653846

0

0.001789

0.115511

0.009352

0

1

0.008945

false

0

0.022361

0.000894

0.034884

0.00805

0

null

0

1

0

1

0

1

0

null

0

6

9596b306c29a52323ebda6a5b805a61ca3af7453

45

py

Python

FPSim2/io/backends/__init__.py

adalke/FPSim2

23ddf388dd00657e595cf8244360e5c60dc11661

[ "MIT" ]

51

2019-01-24T14:23:01.000Z

2022-03-23T08:38:55.000Z

FPSim2/io/backends/__init__.py

adalke/FPSim2

23ddf388dd00657e595cf8244360e5c60dc11661

[ "MIT" ]

18

2019-01-18T16:38:37.000Z

2022-03-09T12:38:36.000Z

FPSim2/io/backends/__init__.py

adalke/FPSim2

23ddf388dd00657e595cf8244360e5c60dc11661

[ "MIT" ]

11

2019-01-30T01:17:51.000Z

2021-10-14T02:20:52.000Z

from .pytables import PyTablesStorageBackend

22.5

44

0.888889

4

45

10

1

0

0.088889

45

1

45

0.97561

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

255880a6a886714ffb3137bbe80dc34722297acd

159

py

Python

scsr_api/utils/sanitize.py

hiperlogic/scsr-api

d1c40d7b86b94c50c88833149c29f413e6d39843

[ "MIT" ]

1

2021-02-09T21:33:56.000Z

scsr_api/utils/sanitize.py

hiperlogic/scsr-api

d1c40d7b86b94c50c88833149c29f413e6d39843

[ "MIT" ]

null

scsr_api/utils/sanitize.py

hiperlogic/scsr-api

d1c40d7b86b94c50c88833149c29f413e6d39843

[ "MIT" ]

null

def sanitize_db(data): #TODO: Sanitize mongoDB Data. Just returns True and the data for now!. Returns false when not able to sanitize return True, data

53

114

0.748428

26

159

4.538462

0.730769

0

0.194969

159

3

115

53

0.921875

0.685535

0

0.333333

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

256e52f3a22aa73b7d32aa15da102272902b539d

45

py

Python

Supycap/cc_analysis/cccap/__init__.py

AdaYuanChen/Supercap

17d0302610a39e030911e46ba750cf15a1b9706f

[ "MIT" ]

7

2021-03-19T16:53:30.000Z

2022-03-08T23:06:29.000Z

Supycap/cc_analysis/cccap/__init__.py

AdaYuanChen/Supercap

17d0302610a39e030911e46ba750cf15a1b9706f

[ "MIT" ]

12

2020-08-28T04:22:12.000Z

2020-12-26T10:32:52.000Z

Supycap/cc_analysis/cccap/__init__.py

AdaYuanChen/Supercap

17d0302610a39e030911e46ba750cf15a1b9706f

[ "MIT" ]

1

2022-03-08T23:07:10.000Z

from .cc_cap import* from .utilities import*

22.5

23

0.777778

7

45

4.857143

0.714286

0

0.133333

45

2

23

22.5

0.871795

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c28a1c02fb723edafed49a21a2731f64d7dc4aa6

119

py

Python

app/api/v2/resources/food.py

misatifelix/fast-food-fast-api

083fee0993e8d2d0152b4cd13c1ab558d0ba9283

[ "MIT" ]

2

2018-09-26T16:55:50.000Z

2020-03-10T08:56:35.000Z

app/api/v2/resources/food.py

misatifelix/fast-food-fast-api

083fee0993e8d2d0152b4cd13c1ab558d0ba9283

[ "MIT" ]

1

2019-10-21T17:13:51.000Z

app/api/v2/resources/food.py

misatifelix/fast-food-fast-api

083fee0993e8d2d0152b4cd13c1ab558d0ba9283

[ "MIT" ]

null

from flask_restful import Resource class FoodResource(Resource): pass class FoodListResource(Resource): pass

14.875

34

0.781513

13

119

7.076923

0.692308

0.26087

0

0.168067

119

7

35

17

0.929293

0

0.4

0

1

0

true

0.4

0.2

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

c2b037e99788e44372c1e30b21417513e2b32d85

460

py

Python

Stack/Stack1.py

shaurtoonetwork/Data-Structures-Implemented-In-Python

31f770165d535547e6ab3973ca92944cd4d93e11

[ "Unlicense" ]

null

Stack/Stack1.py

shaurtoonetwork/Data-Structures-Implemented-In-Python

31f770165d535547e6ab3973ca92944cd4d93e11

[ "Unlicense" ]

null

Stack/Stack1.py

shaurtoonetwork/Data-Structures-Implemented-In-Python

31f770165d535547e6ab3973ca92944cd4d93e11

[ "Unlicense" ]

null

class Stack: def __init__(self): self.items=[] def push(self,item): self.items.append(item) def pop(self): return self.items.pop() def is_empty(self): return self.items == [] def peak(self): if not self.is_empty(): return self.items[-1] def get_stack(self): return self.items s=Stack() s.push("A") s.push(1) s.push(2) s.push(3) print(s.peak()) print(s.get_stack())

15.862069

33

0.556522

69

460

3.594203

0.333333

0.217742

0.241935

0.229839

0

0.012158

0.284783

460

29

34

15.862069

0.741641

0

0.002169

0

1

0.285714

false

0

0.142857

0.52381

0.095238

0

null

1

0

null

0

1

0

1

0

6

6c2505d0b901df4294008715b650ff4f230a3777

143

py

Python

AnonymounsLambdafunxtions.py

vidhurraj147/pythonexample

f595034cb6e4c317812f25d8d92501f62c2ccfee

[ "Apache-2.0" ]

null

AnonymounsLambdafunxtions.py

vidhurraj147/pythonexample

f595034cb6e4c317812f25d8d92501f62c2ccfee

[ "Apache-2.0" ]

null

AnonymounsLambdafunxtions.py

vidhurraj147/pythonexample

f595034cb6e4c317812f25d8d92501f62c2ccfee

[ "Apache-2.0" ]

null

sum = lambda arg1,arg2: arg1+arg2 mul = lambda arg1,arg2: arg1*arg2 print("The total is: ",sum(10,30)) print("Multiplication is: ",mul(50,40))

28.6

39

0.699301

25

143

4

0.56

0.32

0.28

0.36

0.44

0

0.126984

0.118881

143

5

39

28.6

0.666667

0

0.230769

0

1

0

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

6

6c3022da6aa672d7f5a967044beeb69847ea1754

31

py

Python

neuralpredictors/measures/__init__.py

kellirestivo/neuralpredictors

57205a90d2e3daa5f8746c6ef6170be9e35cb5f5

[ "MIT" ]

9

2020-11-26T18:22:32.000Z

2022-01-22T15:51:52.000Z

neuralpredictors/measures/__init__.py

kellirestivo/neuralpredictors

57205a90d2e3daa5f8746c6ef6170be9e35cb5f5

[ "MIT" ]

60

2020-10-21T15:32:28.000Z

2022-02-25T10:38:16.000Z

neuralpredictors/measures/__init__.py

mohammadbashiri/neuralpredictors

8e60c9ce91f83e3dcaa1b3dbe4422e1509ccbd5f

[ "MIT" ]

21

2020-10-21T09:29:17.000Z

2022-02-07T10:04:46.000Z

from .np_functions import corr

15.5

30

0.83871

5

31

5

1

0

0.129032

31

1

31

0.925926

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6c7f57d696af5d29ca31ab5412285ec021b3cf49

32

py

Python

run/__init__.py

Jignesh1996/s2w

065bbb97307254cc7ded3c84d40ac3c203d08899

[ "MIT" ]

null

run/__init__.py

Jignesh1996/s2w

065bbb97307254cc7ded3c84d40ac3c203d08899

[ "MIT" ]

null

run/__init__.py

Jignesh1996/s2w

065bbb97307254cc7ded3c84d40ac3c203d08899

[ "MIT" ]

null

from run.model import DumbModel

16

31

0.84375

5

32

5.4

1

0

0.125

32

1

32

0.964286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6c8d2ad99df01a983c9bfb68176b3714c0b567d2

28

py

Python

tests/__init__.py

SimpleArt/pyroot

1f1ac6a644999e86e4c3c83a5107cf2d34069c64

[ "MIT" ]

null

tests/__init__.py

SimpleArt/pyroot

1f1ac6a644999e86e4c3c83a5107cf2d34069c64

[ "MIT" ]

null

tests/__init__.py

SimpleArt/pyroot

1f1ac6a644999e86e4c3c83a5107cf2d34069c64

[ "MIT" ]

null

import tests._test as _test

14

27

0.821429

5

28

4.2

0.8

0

0.142857

28

1

28

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

66d33d91c1bee80370ef0eb5f6841c50a20c0463

267

py

Python

analyser_crystal.py

RobBosman-rwhb/sedea

7ff6caab247bc032ca350de7ba6e1db4f13dd338

[ "BSD-3-Clause" ]

null

analyser_crystal.py

RobBosman-rwhb/sedea

7ff6caab247bc032ca350de7ba6e1db4f13dd338

[ "BSD-3-Clause" ]

null

analyser_crystal.py

RobBosman-rwhb/sedea

7ff6caab247bc032ca350de7ba6e1db4f13dd338

[ "BSD-3-Clause" ]

null

class analyser_crystal: def __init__(self,base_indicies): self.base_indicies = base_indicies def set_harmonic_list(self,harmonic_list): self.harmonic_list = harmonic_list def get_harmonic_list(self): return self.harmonic_list

20.538462

46

0.722846

34

267

5.205882

0.382353

0.40678

0.271186

0.248588

0

0.213483

267

12

47

22.25

0.842857

0

1

0.428571

false

0

0.142857

0.714286

0

null

1

0

null

0

1

0

1

0

6

66e5d575e64634b40cd3366c11cc119267cc6509

261

py

Python

src/data_preparation/scripts/graph_generator/typeparsing/rewriterules/__init__.py

mir-am/typilus

d2c126f178c02cfcef9b0ce652c4b019c2462e09

[ "MIT" ]

41

2020-05-18T21:00:44.000Z

2022-01-26T23:06:58.000Z

src/data_preparation/scripts/graph_generator/typeparsing/rewriterules/__init__.py

fwangdo/typilus

69c377b4cd286fd3657708accf3b2f56a5da1e8d

[ "MIT" ]

7

2020-05-18T10:07:12.000Z

2021-09-28T12:17:37.000Z

codebleu/graph_generator/typeparsing/rewriterules/__init__.py

JetBrains-Research/metrics-evaluation

6e3696d11b9efcc7b4403f94b84651caed247649

[ "Apache-2.0" ]

12

2020-04-25T19:12:46.000Z

2022-02-17T08:49:24.000Z

from .rewriterule import RewriteRule from .removerecursivegenerics import RemoveRecursiveGenerics from .removestandalones import RemoveStandAlones from .removeunionwithanys import RemoveUnionWithAnys from .removegenericwithany import RemoveGenericWithAnys

43.5

61

0.885057

20

261

11.55

0.4

0

0.095785

261

5

62

52.2

0.978814

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

dd86cab99cbc38a625779038ecb8dbb7fec29903

133

py

Python

fun2.py

ofiro21-meet/meet2019y1lab7

b0f88d114c7c54dc866bb365e69598946e322aef

[ "MIT" ]

null

fun2.py

ofiro21-meet/meet2019y1lab7

b0f88d114c7c54dc866bb365e69598946e322aef

[ "MIT" ]

null

fun2.py

ofiro21-meet/meet2019y1lab7

b0f88d114c7c54dc866bb365e69598946e322aef

[ "MIT" ]

null

import turtle turtle.goto(0,0) def up(): print("you pressed the up key") turtle.onkey(up,"up") turtle.goto(0,0) turtle.listen()

14.777778

35

0.684211

24

133

3.791667

0.541667

0.21978

0.241758

0.263736

0

0.034783

0.135338

133

8

36

16.625

0.756522

0

0.285714

0

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1

0

null

0

1

0

6

6601cb2e1e4f3135f71d8589d4d8054567988e53

329

py

Python

markup/ParseError.py

MarkGotham/Taking-Form

6130a56d180aae36a7903e423078e287cfa92b55

[ "MIT" ]

7

2019-09-11T04:07:58.000Z

2022-02-24T07:43:11.000Z

markup/ParseError.py

MarkGotham/Taking-Form

6130a56d180aae36a7903e423078e287cfa92b55

[ "MIT" ]

5

2019-08-15T17:50:53.000Z

2020-04-27T08:35:58.000Z

markup/ParseError.py

MarkGotham/Taking-Form

6130a56d180aae36a7903e423078e287cfa92b55

[ "MIT" ]

3

2019-12-19T08:08:04.000Z

2022-01-07T21:51:56.000Z

# TODO tidy class ParseError(Exception): def __init__(self, annotationContent, bar): self.annotationContent = annotationContent self.bar = bar def __str__(self): return "Parse error | Bar " + str(self.bar) + " | " + self.annotationContent def __repr__(self): return self.__str__()

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null

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null

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1

0

1

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1

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6

66136ef6ef2556fb915721dd213cf4705e0c2601

38

py

Python

data_visualizer/__init__.py

RaczeQ/naive-bayes-classifier

c8adc960885118a13677e3c5ec4039b976810bee

[ "MIT" ]

null

data_visualizer/__init__.py

RaczeQ/naive-bayes-classifier

c8adc960885118a13677e3c5ec4039b976810bee

[ "MIT" ]

null

data_visualizer/__init__.py

RaczeQ/naive-bayes-classifier

c8adc960885118a13677e3c5ec4039b976810bee

[ "MIT" ]

null

from .data_visualizer import visualize

38

0.894737

5

38

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null

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1

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1

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1

0

6

66138c2f732231baa7f8c9795675e0d56e90b893

593

py

Python

Mild.py

tacoresearch/HotSauce

07c30130c5b1b97b72c9b1d0a25411c1ed897b81

[ "MIT" ]

1

2021-04-14T03:13:35.000Z

Mild.py

tacoresearch/HotSauce

07c30130c5b1b97b72c9b1d0a25411c1ed897b81

[ "MIT" ]

null

Mild.py

tacoresearch/HotSauce

07c30130c5b1b97b72c9b1d0a25411c1ed897b81

[ "MIT" ]

null

print('''\ _ \`*-. ) _`-. . : `. . : _ ' \ ; *` _. `*-._ `-.-' `-. ; ` `. :. . \ . \ . : .-' . ' `+.; ; ' : : ' | ; ;-. ; ' : :`-: _.`* ; txt me.*' / .*' ; .*`- +' `*' `*-* `*-* `*-*' 00111001 00110000 00111001 00110101 00110100 00110010 00111000 00110011 00110101 00110101 ''')

31.210526

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593

6.923077

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0.380952

0.645868

593

18

90

32.944444

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0

1

0

null

0

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6

66328c996e134ec40afa52e6ea5066407c3766fc

176

py

Python

reports/historical/__init__.py

CodeForAfrica/gmmp

d7ffe2dac16bd57e81bb3555ddea9df1fe7e9ebf

[ "Apache-2.0" ]

4

2020-01-05T09:14:19.000Z

2022-02-17T03:22:09.000Z

reports/historical/__init__.py

CodeForAfrica/gmmp

d7ffe2dac16bd57e81bb3555ddea9df1fe7e9ebf

[ "Apache-2.0" ]

68

2019-12-23T02:19:55.000Z

2021-04-23T06:13:36.000Z

reports/historical/__init__.py

CodeForAfrica/gmmp

d7ffe2dac16bd57e81bb3555ddea9df1fe7e9ebf

[ "Apache-2.0" ]

2

2020-11-07T12:23:21.000Z

2021-11-07T18:21:31.000Z

__all__ = ["historical", "canon"] # Preserve the current `from reports.historical import Historical, canon` syntax from .historical import Historical from .canon import canon

29.333333

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0.784091

21

176

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0.223881

0.38806

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0.130682

176

5

81

35.2

0.875817

0.443182

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0.666667

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0.666667

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null

1

0

1

0

null

0

1

0

1

0

6

b0acc85350260ea1ee5e2d8048c7fe9aa95dab0c

17,381

py

Python

model/calculator.py

AlexMout/OptionPricer

3c95a3758ab9a96027d40c8d7a23c3e4482ff221

[ "MIT" ]

1

2019-08-21T16:51:59.000Z

model/calculator.py

AlexMout/OptionPricer

3c95a3758ab9a96027d40c8d7a23c3e4482ff221

[ "MIT" ]

null

model/calculator.py

AlexMout/OptionPricer

3c95a3758ab9a96027d40c8d7a23c3e4482ff221

[ "MIT" ]

null

from model import graph_generator from scipy.stats import norm import math class BlackScholes: __oneDay = 1/365 # *********************** D1 & D2 ********************** @classmethod def __d1(cls,S,K,R,T,Vol): """Class method so that the method has the access to the other methods inside the class without being forced to call BlackScholes.My_function() but with cls.My_function()""" return (math.log(S/K)+(R+(Vol**2)/2)*T)/(Vol*math.sqrt(T)) @classmethod def __d2(cls,S,K,R,T,Vol): return cls.__d1(S,K,R,T,Vol)-Vol*math.sqrt(T) # *********************** PRICE FORMULAS ********************** @classmethod def call_price(cls,S,K,R,T,Vol): """Return the price of a vanilla call""" price = S*norm.cdf(cls.__d1(S,K,R,T,Vol))-K*math.exp(-R*T)*norm.cdf(cls.__d2(S,K,R,T,Vol)) return round(price,3) @classmethod def call_spread_price(cls,S,K1,K2,R,T,Vol,isbull): if isbull: return cls.call_price(S,K1,R,T,Vol)-cls.call_price(S,K2,R,T,Vol) return -cls.call_price(S, K1, R, T, Vol) + cls.call_price(S, K2, R, T, Vol) @classmethod def put_spread_price(cls,S,K1,K2,R,T,Vol,isbull): if isbull: return -cls.put_price(S,K2,R,T,Vol)+cls.put_price(S,K1,R,T,Vol) return cls.put_price(S,K2,R,T,Vol)-cls.put_price(S,K1,R,T,Vol) @classmethod def put_price(cls,S,K,R,T,Vol): """Return the price of a vanilla put""" price = -S*norm.cdf(-cls.__d1(S,K,R,T,Vol))+K*math.exp(-R*T)*norm.cdf(-cls.__d2(S,K,R,T,Vol)) return round(price,3) @classmethod def call_digital_price(cls,S,K,R,T,Vol): return round(math.exp(-R*T)*norm.cdf(cls.__d2(S,K,R,T,Vol)),3) @classmethod def put_digital_price(cls,S,K,R,T,Vol): return round(math.exp(-R*T)*norm.cdf(-cls.__d2(S,K,R,T,Vol)),3) @classmethod def straddle_price(cls,S,K,R,T,Vol): return cls.call_price(S,K,R,T,Vol)+cls.put_price(S,K,R,T,Vol) @classmethod def strangle_price(cls,S,K1,K2,R,T,Vol): return cls.put_price(S,K1,R,T,Vol)+cls.call_price(S,K2,R,T,Vol) @classmethod def risk_rev_price(cls,S,K1,K2,R,T,Vol): return -cls.put_price(S,K1,R,T,Vol)+cls.call_price(S,K2,R,T,Vol) @classmethod def calendar_price(cls,S,K,R,T1,T2,Vol): return -cls.call_price(S,K,R,T1,Vol)+cls.call_price(S,K,R,T2,Vol) # *********************** DELTA FORMULAS ********************** @classmethod def call_delta(cls,S,K,R,T,Vol): delta = norm.cdf(cls.__d1(S,K,R,T,Vol)) return round(delta,3) @classmethod def call_spread_delta(cls,S,K1,K2,R,T,Vol,isbull): if isbull: return cls.call_delta(S,K1,R,T,Vol) - cls.call_delta(S,K2,R,T,Vol) return -cls.call_delta(S,K1,R,T,Vol) + cls.call_delta(S,K2,R,T,Vol) @classmethod def put_delta(cls,S,K,R,T,Vol): delta = norm.cdf(cls.__d1(S,K,R,T,Vol))-1 return round(delta,3) @classmethod def put_spread_delta(cls,S,K1,K2,R,T,Vol,isbull): if isbull: return - cls.put_delta(S,K2,R,T,Vol) + cls.put_delta(S,K1,R,T,Vol) return cls.put_delta(S, K2, R, T, Vol) - cls.put_delta(S, K1, R, T, Vol) @classmethod def call_digital_delta(cls,S,K,R,T,Vol): return round((norm.pdf(cls.__d2(S,K,R,T,Vol))/(S*Vol*math.sqrt(T)))*math.exp(-R*T),3) @classmethod def put_digital_delta(cls,S,K,R,T,Vol): return round(-cls.call_digital_delta(S,K,R,T,Vol),3) @classmethod def straddle_delta(cls,S,K,R,T,Vol): return cls.call_delta(S,K,R,T,Vol)+cls.put_delta(S,K,R,T,Vol) @classmethod def strangle_delta(cls,S,K1,K2,R,T,Vol): return cls.put_delta(S,K1,R,T,Vol)+cls.call_delta(S,K2,R,T,Vol) @classmethod def risk_rev_delta(cls,S,K1,K2,R,T,Vol): return -cls.put_delta(S, K1, R, T, Vol) + cls.call_delta(S, K2, R, T, Vol) @classmethod def calendar_delta(cls,S,K,R,T1,T2,Vol): return -cls.call_delta(S,K,R,T1,Vol)+cls.call_delta(S,K,R,T2,Vol) # *********************** GAMMA FORMULAS ********************** @classmethod def gamma(cls,S,K,R,T,Vol): gamma = (1/(S*Vol*math.sqrt(T)))*norm.pdf(cls.__d1(S,K,R,T,Vol)) return round(gamma,3) @classmethod def call_spread_gamma(cls,S,K1,K2,R,T,Vol,isbull): if isbull: return cls.gamma(S,K1,R,T,Vol)-cls.gamma(S,K2,R,T,Vol) return -cls.gamma(S,K1,R,T,Vol)+cls.gamma(S,K2,R,T,Vol) @classmethod def put_spread_gamma(cls,S,K1,K2,R,T,Vol,isbull): if isbull: return - cls.gamma(S,K2,R,T,Vol) + cls.gamma(S,K1,R,T,Vol) return cls.gamma(S,K2,R,T,Vol) - cls.gamma(S,K1,R,T,Vol) @classmethod def call_digital_gamma(cls,S,K,R,T,Vol): return round((math.exp(-R*T)*norm.pdf(cls.__d2(S,K,R,T,Vol))*cls.__d1(S,K,R,T,Vol))/(S**2*Vol**2*T),3) @classmethod def put_digital_gamma(cls,S,K,R,T,Vol): return round(-cls.call_digital_gamma(S,K,R,T,Vol),3) @classmethod def straddle_gamma(cls,S,K,R,T,Vol): return 2*cls.gamma(S,K,R,T,Vol) @classmethod def strangle_gamma(cls,S,K1,K2,R,T,Vol): return cls.gamma(S,K1,R,T,Vol)+cls.gamma(S,K2,R,T,Vol) @classmethod def risk_rev_gamma(cls,S,K1,K2,R,T,Vol): return -cls.gamma(S, K1, R, T, Vol) + cls.gamma(S, K2, R, T, Vol) @classmethod def calendar_gamma(cls, S, K, R, T1, T2, Vol): return -cls.gamma(S, K, R, T1, Vol) + cls.gamma(S, K, R, T2, Vol) # *********************** VEGA FORMULAS ********************** @classmethod def vega(cls,S,K,R,T,Vol): vega = S*math.sqrt(Vol)*norm.pdf(cls.__d1(S,K,R,T,Vol)) return round(vega,3) @classmethod def call_spread_vega(cls,S,K1,K2,R,T,Vol,isbull): if isbull: return cls.vega(S,K1,R,T,Vol) - cls.vega(S,K2,R,T,Vol) return - cls.vega(S,K1,R,T,Vol) + cls.vega(S,K2,R,T,Vol) @classmethod def call_digital_vega(cls,S,K,R,T,Vol): return round(-math.exp(-R*T)*cls.__d1(S,K,R,T,Vol)*norm.pdf(cls.__d2(S,K,R,T,Vol))/Vol,3) @classmethod def put_spread_vega(cls,S,K1,K2,R,T,Vol,isbull): if isbull: return - cls.vega(S,K2,R,T,Vol) + cls.vega(S,K1,R,T,Vol) return cls.vega(S,K2,R,T,Vol) - cls.vega(S,K1,R,T,Vol) @classmethod def put_digital_vega(cls,S,K,R,T,Vol): return round(-cls.call_digital_vega(S,K,R,T,Vol),3) @classmethod def straddle_vega(cls,S,K,R,T,Vol): return 2*cls.vega(S,K,R,T,Vol) @classmethod def strangle_vega(cls,S,K1,K2,R,T,Vol): return cls.vega(S,K1,R,T,Vol)+cls.vega(S,K2,R,T,Vol) @classmethod def risk_rev_vega(cls, S, K1, K2, R, T, Vol): return -cls.vega(S, K1, R, T, Vol) + cls.vega(S, K2, R, T, Vol) @classmethod def calendar_vega(cls, S, K, R, T1, T2, Vol): return -cls.vega(S, K, R, T1, Vol) + cls.vega(S, K, R, T2, Vol) # *********************** THETA FORMULAS ********************** @classmethod def call_theta(cls,S,K,R,T,Vol): d2 = cls.__d2(S,K,R,T,Vol) theta = -(K*Vol*norm.pdf(d2)*math.exp(-R*T))/(2*math.sqrt(T)) - R*K*norm.cdf(d2)*math.exp(-R*T) return round(theta,3) @classmethod def call_spread_theta(cls,S,K1,K2,R,T,Vol,isbull): if isbull: return cls.call_theta(S,K1,R,T,Vol) - cls.call_theta(S,K2,R,T,Vol) return - cls.call_theta(S,K1,R,T,Vol) + cls.call_theta(S,K2,R,T,Vol) @classmethod def call_digital_theta(cls,S,K,R,T,Vol): disc_factor = math.exp(-R*T) d1 = cls.__d1(S,K,R,T,Vol) d2 = cls.__d2(S,K,R,T,Vol) return round(R*disc_factor*norm.cdf(d2)+disc_factor*norm.pdf(d2)*((d1/(2*T)) - (R/(Vol*math.sqrt(T)))),3) @classmethod def put_theta(cls,S,K,R,T,Vol): d1 = cls.__d1(S, K, R, T, Vol) d2 = cls.__d2(S, K, R, T, Vol) theta = R*K*math.exp(-R*T)*norm.cdf(-d2) - S*norm.pdf(d1)*Vol/(2*math.sqrt(T)) return round(theta,3) @classmethod def put_spread_theta(cls,S,K1,K2,R,T,Vol,isbull): if isbull: return -cls.put_theta(S,K2,R,T,Vol) + cls.put_theta(S,K1,R,T,Vol) return cls.put_theta(S,K2,R,T,Vol) - cls.put_theta(S,K1,R,T,Vol) @classmethod def put_digital_theta(cls,S,K,R,T,Vol): return round(-R*math.exp(-R*T) - cls.call_digital_theta(S,K,R,T,Vol),3) @classmethod def straddle_theta(cls,S,K,R,T,Vol): return cls.call_theta(S,K,R,T,Vol)+cls.put_theta(S,K,R,T,Vol) @classmethod def strangle_theta(cls,S,K1,K2,R,T,Vol): return cls.put_theta(S,K1,R,T,Vol)+cls.call_theta(S,K2,R,T,Vol) @classmethod def risk_rev_theta(cls, S, K1, K2, R, T, Vol): return -cls.put_theta(S, K1, R, T, Vol) + cls.call_theta(S, K2, R, T, Vol) @classmethod def calendar_theta(cls, S, K, R, T1, T2, Vol): return -cls.call_theta(S, K, R, T1, Vol) + cls.call_theta(S, K, R, T2, Vol) # *********************** PAYOFF GRAPH CALLERS ********************** @classmethod def payoff_lists(cls, S, K, R, T, Vol, call_price, put_price): """Return a tuple : (list of strikes, list of Y curve , list of title)""" return graph_generator.Plotter.get_payoff_lists((cls.call_price, cls.put_price) , K, R, T, Vol,cls.__oneDay, ["Call", "Put"], (call_price, put_price)) @classmethod def payoff_call_spread(cls,K1,K2,R,T,Vol,bull_price,bear_price): return graph_generator.Plotter.get_payoff_strategy((cls.call_spread_price,cls.call_spread_price,cls.call_spread_price,cls.call_spread_price), ((K1,K2,R,T,Vol,True),(K1,K2,R,cls.__oneDay,Vol,True),(K1,K2,R,T,Vol,False),(K1,K2,R,cls.__oneDay,Vol,False)), ["Bull Sp.","Bear Sp."], (bull_price,bull_price, bear_price,bear_price), (K1+K2)/2) @classmethod def payoff_put_spread(cls, K1, K2, R, T, Vol, bull_price, bear_price): return graph_generator.Plotter.get_payoff_strategy((cls.put_spread_price, cls.put_spread_price,cls.put_spread_price, cls.put_spread_price), ((K1, K2, R, T, Vol, True),(K1, K2, R, cls.__oneDay, Vol, True), (K1, K2, R, T, Vol, False),(K1, K2, R, cls.__oneDay, Vol, False)), ["Bull Spread","Bear Spread"], (bull_price,bull_price, bear_price,bear_price), (K1 + K2) / 2) @classmethod def payoff_digital_graph(cls, S, K, R, T, Vol, call_price, put_price): return graph_generator.Plotter.get_payoff_lists((cls.call_digital_price, cls.put_digital_price) , K, R, T, Vol,cls.__oneDay, ["Digital Call", "Digital Put"], (call_price, put_price)) @classmethod def straddle_payoff_graph(cls,S,K,R,T,Vol,straddle_price): return graph_generator.Plotter.get_payoff_lists((cls.straddle_price,) , K, R, T, Vol,cls.__oneDay, ["Straddle"], (straddle_price,)) @classmethod def strangle_payoff_graph(cls,S,K1,K2,R,T,Vol,price): return graph_generator.Plotter.get_payoff_strategy([cls.strangle_price,cls.strangle_price] , [[K1,K2, R, T, Vol],[K1,K2, R, cls.__oneDay, Vol]], ["Strangle"], [price,price],(K1+K2)/2) @classmethod def risk_rev_payoff_graph(cls, S, K1, K2, R, T, Vol,price): return graph_generator.Plotter.get_payoff_strategy([cls.risk_rev_price,cls.risk_rev_price] , [[K1, K2, R, T, Vol],[K1, K2, R, cls.__oneDay, Vol]], ["Risk Reversal"], [price,price], (K1 + K2) / 2) @classmethod def calendar_payoff_graph(cls, S, K, R, T1, T2, Vol,price): return graph_generator.Plotter.get_payoff_strategy([cls.calendar_price,cls.calendar_price], [[K, R, T1, T2, Vol],[K, R, cls.__oneDay, T2-T1+cls.__oneDay, Vol]], ["Calendar Spread"], [price,price], K) # *********************** GREEKS GRAPH CALLERS ********************** @classmethod def greeks_graph(cls,S,K,R,T,Vol): list_greeks = (cls.gamma,cls.gamma,cls.call_delta,cls.put_delta,cls.call_theta,cls.put_theta,cls.vega,cls.vega) list_title = ["Call Gamma","Put Gamma","Call Delta","Put Delta","Call Theta","Put Theta","Call Vega","Put Vega"] return graph_generator.Plotter.get_graph_greeks(list_greeks,K,R,T,Vol,cls.__oneDay,list_title) @classmethod def greeks_digital_graph(cls,S,K,R,T,Vol): list_greeks = ( cls.call_digital_gamma, cls.put_digital_gamma, cls.call_digital_delta, cls.put_digital_delta, cls.call_digital_theta, cls.put_digital_theta, cls.call_digital_vega, cls.put_digital_vega) list_title = [ "Digital Call Gamma", "Digital Put Gamma", "Digital Call Delta", "Digital Put Delta", "Digital Call Theta", "Digital Put Theta","Digital Call Vega", "Digital Put Vega"] return graph_generator.Plotter.get_graph_greeks(list_greeks, K, R, T, Vol,cls.__oneDay, list_title) @classmethod def call_spread_greeks_graph(cls,K1,K2,R,T,Vol): list_greeks = ( cls.call_spread_gamma, cls.call_spread_gamma, cls.call_spread_delta, cls.call_spread_delta, cls.call_spread_theta, cls.call_spread_theta, cls.call_spread_vega, cls.call_spread_vega) list_title = [ "Bull Gamma", "Bear Gamma", "Bull Delta", "Bear Delta", "Bull Theta", "Bear Theta" , "Bull Vega", "Bear Vega"] return graph_generator.Plotter.get_graph_strategy_greeks(list_greeks, ((K1,K2,R,T,Vol,True),(K1,K2,R,cls.__oneDay,Vol,True),(K1,K2,R,T,Vol,False),(K1,K2,R,cls.__oneDay,Vol,False)), list_title,(K1+K2)/2) @classmethod def put_spread_greeks_graph(cls,K1,K2,R,T,Vol): list_greeks = ( cls.put_spread_delta, cls.put_spread_delta, cls.put_spread_gamma, cls.put_spread_gamma, cls.put_spread_vega, cls.put_spread_vega, cls.put_spread_theta, cls.put_spread_theta) list_title = [ "Bull Gamma", "Bear Gamma", "Bull Delta", "Bear Delta", "Bull Theta", "Bear Theta" ,"Bull Vega", "Bear Vega"] return graph_generator.Plotter.get_graph_strategy_greeks(list_greeks, ( (K1, K2, R, T, Vol, True),(K1, K2, R, cls.__oneDay, Vol, True), (K1, K2, R, T, Vol, False),(K1, K2, R, cls.__oneDay, Vol, False)), list_title, (K1 + K2) / 2) @classmethod def straddle_greeks_graph(cls, S, K, R, T, Vol): list_greeks = (cls.straddle_gamma, cls.straddle_delta, cls.straddle_theta, cls.straddle_vega) list_title = ["Gamma","Delta", "Theta", "Vega"] return graph_generator.Plotter.get_graph_strategy_greeks_one_leg(list_greeks, ((K, R, T, Vol),(K, R, cls.__oneDay, Vol)), list_title, K) @classmethod def strangle_greeks_graph(cls, S, K1, K2, R, T, Vol): list_greeks = (cls.strangle_gamma,cls.strangle_delta, cls.strangle_theta, cls.strangle_vega) list_title = ["Gamma", "Delta", "Theta", "Vega"] return graph_generator.Plotter.get_graph_strategy_greeks_one_leg(list_greeks, ((K1, K2, R, T, Vol),(K1, K2, R, cls.__oneDay, Vol)), list_title, (K1 + K2) / 2) @classmethod def risk_rev_greeks_graph(cls, S, K1, K2, R, T, Vol): list_greeks = (cls.risk_rev_gamma,cls.risk_rev_delta, cls.risk_rev_theta,cls.risk_rev_vega) list_title = ["Gamma", "Delta","Theta","Vega"] return graph_generator.Plotter.get_graph_strategy_greeks_one_leg(list_greeks, ((K1, K2, R, T, Vol),(K1, K2, R, cls.__oneDay, Vol)), list_title, (K1 + K2) / 2) @classmethod def calendar_greeks_graph(cls, S, K, R, T1, T2, Vol): list_greeks = (cls.calendar_gamma, cls.calendar_delta, cls.calendar_theta, cls.calendar_vega) list_title = ["Gamma", "Delta", "Theta", "Vega"] return graph_generator.Plotter.get_graph_strategy_greeks_one_leg(list_greeks, ((K, R, T1, T2, Vol),(K, R, cls.__oneDay, T2-T1+cls.__oneDay, Vol)), list_title,K)

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6

b0d1900eda941ab3792629dcb955a38a809ed2de

3,413

py

Python

src/encoded/tests/test_types_file.py

procha2/regulome-encoded

327a097ebb539d1b4770145a598de08b579234f9

[ "MIT" ]

null

src/encoded/tests/test_types_file.py

procha2/regulome-encoded

327a097ebb539d1b4770145a598de08b579234f9

[ "MIT" ]

38

2019-03-22T14:11:51.000Z

2022-03-30T23:56:09.000Z

src/encoded/tests/test_types_file.py

procha2/regulome-encoded

327a097ebb539d1b4770145a598de08b579234f9

[ "MIT" ]

2

2020-10-01T11:48:07.000Z

2021-02-23T06:33:15.000Z

import pytest from encoded.types.file import File from moto import ( mock_sts, mock_s3 ) @pytest.fixture def file_with_external_sheet(file, root): file_item = root.get_by_uuid(file['uuid']) properties = file_item.upgrade_properties() file_item.update( properties, sheets={ 'external': { 'service': 's3', 'key': 'xyz.bed', 'bucket': 'test_file_bucket', } } ) return file @mock_sts @mock_s3 @pytest.mark.parametrize("file_status", [ status for status in File.public_s3_statuses ]) def test_public_file_has_cloud_metadata(testapp, file_with_external_sheet, file_status): testapp.patch_json(file_with_external_sheet['@id'], {'status': file_status}) res = testapp.get(file_with_external_sheet['@id']) assert 'cloud_metadata' in res.json cm = res.json['cloud_metadata'] assert 'test_file_bucket' in cm['url'] assert 'xyz.bed' in cm['url'] assert cm['md5sum_base64'] == '1B2M2Y8AsgTpgAmY7PhCfg==' assert cm['file_size'] == 34 def test_public_restricted_file_does_not_have_cloud_metadata(testapp, file_with_external_sheet): testapp.patch_json( file_with_external_sheet['@id'], { 'status': 'released', 'restricted': True } ) res = testapp.get(file_with_external_sheet['@id']) assert 'cloud_metadata' not in res.json @pytest.mark.parametrize("file_status", [ status for status in File.private_s3_statuses if status != 'replaced' ]) def test_private_file_does_not_have_cloud_metadata(testapp, file_with_external_sheet, file_status): testapp.patch_json(file_with_external_sheet['@id'], {'status': file_status}) res = testapp.get(file_with_external_sheet['@id']) assert 'cloud_metadata' not in res.json def test_public_file_with_no_external_sheet_no_cloud_metadata(testapp, file): testapp.patch_json(file['@id'], {'status': 'released'}) res = testapp.get(file['@id']) assert 'cloud_metadata' not in res.json @pytest.mark.parametrize("file_status", [ status for status in File.public_s3_statuses ]) def test_public_file_has_s3_uri(testapp, file_with_external_sheet, file_status): testapp.patch_json(file_with_external_sheet['@id'], {'status': file_status}) res = testapp.get(file_with_external_sheet['@id']) assert 's3_uri' in res.json assert res.json['s3_uri'] == 's3://test_file_bucket/xyz.bed' @pytest.mark.parametrize("file_status", [ status for status in File.private_s3_statuses if status != 'replaced' ]) def test_private_file_does_not_have_s3_uri(testapp, file_with_external_sheet, file_status): testapp.patch_json(file_with_external_sheet['@id'], {'status': file_status}) res = testapp.get(file_with_external_sheet['@id']) assert 's3_uri' not in res.json def test_public_file_no_external_sheet_no_s3_uri(testapp, file): testapp.patch_json(file['@id'], {'status': 'released'}) res = testapp.get(file['@id']) assert 's3_uri' not in res.json def test_public_restricted_file_does_not_have_s3_uri(testapp, file_with_external_sheet): testapp.patch_json( file_with_external_sheet['@id'], { 'status': 'released', 'restricted': True, } ) res = testapp.get(file_with_external_sheet['@id']) assert 's3_uri' not in res.json

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0

1

0

null

0

6

b0f11f4aa9f639bf4bc29fe5d34f6b9c6f0278e5

509

py

Python

test/test_pytest.py

alexsander2902ariel/python_3_exercicio

a9a2480a7820637c9821352c58c626261074481c

[ "MIT" ]

1

2022-03-23T20:24:37.000Z

test/test_pytest.py

alexsander2902ariel/python_3_exercicio

a9a2480a7820637c9821352c58c626261074481c

[ "MIT" ]

null

test/test_pytest.py

alexsander2902ariel/python_3_exercicio

a9a2480a7820637c9821352c58c626261074481c

[ "MIT" ]

null

from main import sum_numbers_sequence def test_check_sum_1(): assert sum_numbers_sequence([0,1,2,3,5,8]) == 19 def test_check_sum_2(): assert sum_numbers_sequence([.1,.2,.3,.4]) == 1 from main import div_numbers_sequence def test_check_div_1(): assert div_numbers_sequence(10,5) == 2 def test_check_div_2(): assert div_numbers_sequence(.5,.1) == 5 def test_check_sum_3(): assert sum_numbers_sequence([.1,.2]) == .3 def test_check_div_3(): assert div_numbers_sequence(.3,.1) == 3

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6

b000a77b0101412e1791aa30c828d71893d7ac74

124

py

Python

src/__main__.py

doublechiang/qsmcmd

63e31390de020472c6ff4284cbe2d2c5147cb13d

[ "MIT" ]

1

2021-05-07T09:57:01.000Z

src/__main__.py

doublechiang/qsmcmd

63e31390de020472c6ff4284cbe2d2c5147cb13d

[ "MIT" ]

30

2017-08-24T21:21:03.000Z

2021-01-21T19:32:36.000Z

src/__main__.py

doublechiang/qsmcmd

63e31390de020472c6ff4284cbe2d2c5147cb13d

[ "MIT" ]

null

import os,sys import logging import qsmcli.qsmcli logging.basicConfig(level=logging.WARNING) qsmcli.qsmcli.Qsmcli().run()

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c6d7fb3eb3765031f31999433b5a0232bc1ed3b8

78

py

Python

scripts/init/__init__.py

mathias-sm/mne-bids-pipeline

55a8d7c7ca5a254ff7b9af84b818b164692667d5

[ "BSD-3-Clause" ]

null

scripts/init/__init__.py

mathias-sm/mne-bids-pipeline

55a8d7c7ca5a254ff7b9af84b818b164692667d5

[ "BSD-3-Clause" ]

null

scripts/init/__init__.py

mathias-sm/mne-bids-pipeline

55a8d7c7ca5a254ff7b9af84b818b164692667d5

[ "BSD-3-Clause" ]

null

from . import _00_init_derivatives_dir SCRIPTS = (_00_init_derivatives_dir,)

19.5

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1

0

null

0

1

0

6

c6d881e39bad2394624329a0e0f2df39e1781ee1

30

py

Python

test1/login.py

luoning1206/test

74740e00a38876af14d80669c5283f0993954d9a

[ "MIT" ]

null

test1/login.py

luoning1206/test

74740e00a38876af14d80669c5283f0993954d9a

[ "MIT" ]

null

test1/login.py

luoning1206/test

74740e00a38876af14d80669c5283f0993954d9a

[ "MIT" ]

null

a = 10 b = 100 c = 30 d = 40

5

7

0.433333

8

30

1.625

1

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0.529412

0.433333

30

5

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0.235294

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null

0

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1

0

1

0

null

0

6

059e200d48ce3465f893a07f0a067812ebab2d7d

33

py

Python

api/db/__init__.py

FlipsideCrypto/flip

a0b27ec2dffbba42d3a907767bbae0fc6ec1bcbb

[ "MIT" ]

null

api/db/__init__.py

FlipsideCrypto/flip

a0b27ec2dffbba42d3a907767bbae0fc6ec1bcbb

[ "MIT" ]

null

api/db/__init__.py

FlipsideCrypto/flip

a0b27ec2dffbba42d3a907767bbae0fc6ec1bcbb

[ "MIT" ]

1

2022-02-02T10:23:21.000Z

from .session import SessionLocal

33

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null

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1

0

1

0

1

0

6

05a1578a73c7349ca1fc8647a1564524e8d96d3b

33

py

Python

coursescheduler/util/__init__.py

zHxng/ClassScheduler

2f6f0b40811af05ebd2a3fc5038864de4ba96509

[ "MIT" ]

1

2019-01-19T05:14:08.000Z

coursescheduler/util/__init__.py

zHxng/CourseScheduler

2f6f0b40811af05ebd2a3fc5038864de4ba96509

[ "MIT" ]

null

coursescheduler/util/__init__.py

zHxng/CourseScheduler

2f6f0b40811af05ebd2a3fc5038864de4ba96509

[ "MIT" ]

null

from .structures import uwcourse

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null

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1

0

1

0

1

0

6

05a771f4a979f96a42b17ab9fd79a00b99999963

33,467

py

Python

pdf417dict.py

Hassoo7/pdf417-decoder

907bc367e9803a4a6406e3c4cbcf9456134172a8

[ "MIT" ]

36

2015-01-29T02:43:56.000Z

2022-01-27T20:50:47.000Z

pdf417dict.py

Hassoo7/pdf417-decoder

907bc367e9803a4a6406e3c4cbcf9456134172a8

[ "MIT" ]

5

2017-02-22T01:59:45.000Z

2020-08-06T16:33:13.000Z

pdf417dict.py

Hassoo7/pdf417-decoder

907bc367e9803a4a6406e3c4cbcf9456134172a8

[ "MIT" ]

18

2017-09-14T07:16:37.000Z

2021-05-01T10:27:15.000Z

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'21131423', '11131514', '11131613', '11132126', '21132134', '31132142', '11132225', '21132233', '31132241', '11132324', '21132332', '11132423', '11132522', '11133134', '21133142', '11133233', '21133241', '11133332', '11134142', '21141125', '31141133', '41141141', '11141216', '21141224', '31141232', '11141315', '21141323', '31141331', '11141414', '21141422', '11141513', '21141521', '11142125', '21142133', '31142141', '11142224', '21142232', '11142323', '21142331', '11142422', '11142521', '21143141', '11143331', '11151116', '21151124', '31151132', '11151215', '21151223', '31151231', '11151314', '21151322', '11151413', '21151421', '11151512', '11152124', '11152223', '11152322', '11161115', '31161131', '21161222', '21161321', '11161511', '32111135', '42111143', '52111151', '22111226', '32111234', '42111242', '22111325', '32111333', '42111341', '12111416', '22111424', '12111515', '22112135', '32112143', '42112151', '12112226', '22112234', '32112242', '12112325', '22112333', '12112424', '12112523', '12113135', '22113143', '32113151', '12113234', '22113242', '12113333', '12113432', '12114143', '22114151', '12114242', '12115151', '31211126', '41211134', '51211142', '31211225', '41211233', '51211241', '21211316', '31211324', '41211332', '21211415', '31211423', '41211431', '21211514', '31211522', '22121126', '32121134', '42121142', '21212126', '22121225', '32121233', '42121241', '21212225', '31212233', '41212241', '11212316', '12121415', '22121423', '32121431', '11212415', '21212423', '11212514', '12122126', '22122134', '32122142', '11213126', '12122225', '22122233', '32122241', '11213225', '21213233', '31213241', '11213324', '12122423', '11213423', '12123134', '22123142', '11214134', '12123233', '22123241', '11214233', '21214241', '11214332', '12124142', '11215142', '12124241', '11215241', '31221125', '41221133', '51221141', '21221216', '31221224', '41221232', '21221315', '31221323', '41221331', '21221414', '31221422', '21221513', '21221612', '22131125', '32131133', '42131141', '21222125', '22131224', '32131232', '11222216', '12131315', '31222232', '32131331', '11222315', '12131414', '22131422', '11222414', '21222422', '22131521', '12131612', '12132125', '22132133', '32132141', '11223125', '12132224', '22132232', '11223224', '21223232', '22132331', '11223323', '12132422', '12132521', '12133133', '22133141', '11224133', '12133232', '11224232', '12133331', '11224331', '11225141', '21231116', '31231124', '41231132', '21231215', '31231223', '41231231', '21231314', '31231322', '21231413', '31231421', '21231512', '21231611', '12141116', '22141124', '32141132', '11232116', '12141215', '22141223', '32141231', '11232215', '21232223', '31232231', '11232314', '12141413', '22141421', '11232413', '21232421', '11232512', '12142124', '22142132', '11233124', '12142223', '22142231', '11233223', '21233231', '11233322', '12142421', '11233421', '11234132', '11234231', '21241115', '31241123', '41241131', '21241214', '31241222', '21241313', '31241321', '21241412', '21241511', '12151115', '22151123', '32151131', '11242115', '12151214', '22151222', '11242214', '21242222', '22151321', '11242313', '12151412', '11242412', '12151511', '12152123', '11243123', '11243222', '11243321', '31251122', '31251221', '21251411', '22161122', '12161213', '11252213', '11252312', '11252411', '23111126', '33111134', '43111142', '23111225', '33111233', '13111316', '23111324', '33111332', '13111415', '23111423', '13111514', '13111613', '13112126', '23112134', '33112142', '13112225', '23112233', '33112241', '13112324', '23112332', '13112423', '13112522', '13113134', '23113142', '13113233', '23113241', '13113332', '13114142', '13114241', '32211125', '42211133', '52211141', '22211216', '32211224', '42211232', '22211315', '32211323', '42211331', '22211414', '32211422', '22211513', '32211521', '23121125', '33121133', '43121141', '22212125', '23121224', '33121232', '12212216', '13121315', '32212232', '33121331', '12212315', '22212323', '23121422', '12212414', '13121513', '12212513', '13122125', '23122133', '33122141', '12213125', '13122224', '32213141', '12213224', '22213232', '23122331', '12213323', '13122422', '12213422', '13123133', '23123141', '12214133', '13123232', '12214232', '13123331', '13124141', '12215141', '31311116', '41311124', '51311132', '31311215', '41311223', '51311231', '31311314', '41311322', '31311413', '41311421', '31311512', '22221116', '32221124', '42221132', '21312116', '22221215', '41312132', '42221231', '21312215', '31312223', '41312231', '21312314', '22221413', '32221421', '21312413', '31312421', '22221611', '13131116', '23131124', '33131132', '12222116', '13131215', '23131223', '33131231', '11313116', '12222215', '22222223', '32222231', '11313215', '21313223', '31313231', '23131421', '11313314', '12222413', '22222421', '11313413', '13131611', '13132124', '23132132', '12223124', '13132223', '23132231', '11314124', '12223223', '22223231', '11314223', '21314231', '13132421', '12223421', '13133132', '12224132', '13133231', '11315132', '12224231', '31321115', '41321123', '51321131', '31321214', '41321222', '31321313', '41321321', '31321412', '31321511', '22231115', '32231123', '42231131', '21322115', '22231214', '41322131', '21322214', '31322222', '32231321', '21322313', '22231412', '21322412', '22231511', '21322511', '13141115', '23141123', '33141131', '12232115', '13141214', '23141222', '11323115', '12232214', '22232222', '23141321', '11323214', '21323222', '13141412', '11323313', '12232412', '13141511', '12232511', '13142123', '23142131', '12233123', '13142222', '11324123', '12233222', '13142321', '11324222', '12233321', '13143131', '11325131', '31331114', '41331122', '31331213', '41331221', '31331312', '31331411', '22241114', '32241122', '21332114', '22241213', '32241221', '21332213', '31332221', '21332312', '22241411', '21332411', '13151114', '23151122', '12242114', '13151213', '23151221', '11333114', '12242213', '22242221', '11333213', '21333221', '13151411', '11333312', '12242411', '11333411', '12243122', '11334122', '11334221', '41341121', '31341311', '32251121', '22251212', '22251311', '13161113', '12252113', '11343113', '13161311', '12252311', '24111125', '14111216', '24111224', '14111315', '24111323', '34111331', '14111414', '24111422', '14111513', '24111521', '14112125', '24112133', '34112141', '14112224', '24112232', '14112323', '24112331', '14112422', '14112521', '14113133', '24113141', '14113232', '14113331', '14114141', '23211116', '33211124', '43211132', '23211215', '33211223', '23211314', '33211322', '23211413', '33211421', '23211512', '14121116', '24121124', '34121132', '13212116', '14121215', '33212132', '34121231', '13212215', '23212223', '33212231', '13212314', '14121413', '24121421', '13212413', '23212421', '14121611', '14122124', '24122132', '13213124', '14122223', '24122231', '13213223', '23213231', '13213322', '14122421', '14123132', '13214132', '14123231', '13214231', '32311115', '42311123', '52311131', '32311214', '42311222', '32311313', '42311321', '32311412', '32311511', '23221115', '33221123', '22312115', '23221214', '33221222', '22312214', '32312222', '33221321', '22312313', '23221412', '22312412', '23221511', '22312511', '14131115', '24131123', '13222115', '14131214', '33222131', '12313115', '13222214', '23222222', '24131321', '12313214', '22313222', '14131412', '12313313', '13222412', '14131511', '13222511', '14132123', '24132131', '13223123', '14132222', '12314123', '13223222', '14132321', '12314222', '13223321', '14133131', '13224131', '12315131', '41411114', '51411122', '41411213', '51411221', '41411312', '41411411', '32321114', '42321122', '31412114', '41412122', '42321221', '31412213', '41412221', '31412312', '32321411', '31412411', '23231114', '33231122', '22322114', '23231213', '33231221', '21413114', '22322213', '32322221', '21413213', '31413221', '23231411', '21413312', '22322411', '21413411', '14141114', '24141122', '13232114', '14141213', '24141221', '12323114', '13232213', '23232221', '11414114', '12323213', '22323221', '14141411', '11414213', '21414221', '13232411', '11414312', '14142122', '13233122', '14142221', '12324122', '13233221', '11415122', '12324221', '11415221', '41421113', '51421121', '41421212', '41421311', '32331113', '42331121', '31422113', '41422121', '31422212', '32331311', '31422311', '23241113', '33241121', '22332113', '23241212', '21423113', '22332212', '23241311', '21423212', '22332311', '21423311', '14151113', '24151121', '13242113', '23242121', '12333113', '13242212', '14151311', '11424113', '12333212', '13242311', '11424212', '12333311', '11424311', '13243121', '11425121', '41431211', '31432112', '31432211', '22342112', '21433112', '21433211', '13252112', '12343112', '11434112', '11434211', '15111116', '15111215', '25111223', '15111314', '15111413', '15111512', '15112124', '15112223', '15112322', '15112421', '15113132', '15113231', '24211115', '24211214', '34211222', '24211313', '34211321', '24211412', '24211511', '15121115', '25121123', '14212115', '24212123', '25121222', '14212214', '24212222', '14212313', '24212321', '14212412', '15121511', '14212511', '15122123', '25122131', '14213123', '24213131', '14213222', '15122321', '14213321', '15123131', '14214131', '33311114', '33311213', '33311312', '33311411', '24221114', '23312114', '33312122', '34221221', '23312213', '33312221', '23312312', '24221411', '23312411', '15131114', '14222114', '15131213', '25131221', '13313114', '14222213', '15131312', '13313213', '14222312', '15131411', '13313312', '14222411', '15132122', '14223122', '15132221', '13314122', '14223221', '13314221', '42411113', '42411212', '42411311', '33321113', '32412113', '42412121', '32412212', '33321311', '32412311', '24231113', '34231121', '23322113', '33322121', '22413113', '23322212', '24231311', '22413212', '23322311', '22413311', '15141113', '25141121', '14232113', '24232121', '13323113', '14232212', '15141311', '12414113', '13323212', '14232311', '12414212', '13323311', '15142121', '14233121', '13324121', '12415121', '51511112', '51511211', '42421112', '41512112', '42421211', '41512211', '33331112', '32422112', '33331211', '31513112', '32422211', '31513211', '24241112', '23332112', '24241211', '22423112', '23332211', '21514112'], ['51111125', '61111133', '41111216', '51111224', '61111232', '41111315', '51111323', '61111331', '41111414', '51111422', '41111513', '51111521', '41111612', '41112125', '51112133', '61112141', '31112216', '41112224', '51112232', '31112315', '41112323', '51112331', '31112414', '41112422', '31112513', '41112521', '31112612', '31113125', '41113133', '51113141', '21113216', '31113224', '41113232', '21113315', '31113323', '41113331', '21113414', '31113422', '21113513', '31113521', '21113612', '21114125', '31114133', '41114141', '11114216', '21114224', '31114232', '11114315', '21114323', '31114331', '11114414', '21114422', '11114513', '21114521', '11115125', '21115133', '31115141', '11115224', '21115232', '11115323', '21115331', '11115422', '11116133', '21116141', '11116232', '11116331', '41121116', '51121124', '61121132', '41121215', '51121223', '61121231', '41121314', '51121322', '41121413', '51121421', '41121512', '41121611', '31122116', '41122124', '51122132', '31122215', '41122223', '51122231', '31122314', '41122322', '31122413', '41122421', '31122512', '31122611', '21123116', '31123124', '41123132', '21123215', '31123223', '41123231', '21123314', '31123322', '21123413', '31123421', '21123512', '21123611', '11124116', '21124124', '31124132', '11124215', '21124223', '31124231', '11124314', '21124322', '11124413', '21124421', '11124512', '11125124', '21125132', '11125223', '21125231', '11125322', '11125421', '11126132', '11126231', '41131115', '51131123', '61131131', '41131214', '51131222', '41131313', '51131321', '41131412', '41131511', '31132115', '41132123', '51132131', '31132214', '41132222', '31132313', '41132321', '31132412', '31132511', '21133115', '31133123', '41133131', '21133214', '31133222', '21133313', '31133321', '21133412', '21133511', '11134115', '21134123', '31134131', '11134214', '21134222', '11134313', '21134321', '11134412', '11134511', '11135123', '21135131', '11135222', '11135321', '11136131', '41141114', '51141122', '41141213', '51141221', '41141312', '41141411', '31142114', '41142122', '31142213', '41142221', '31142312', '31142411', '21143114', '31143122', '21143213', '31143221', '21143312', '21143411', '11144114', '21144122', '11144213', '21144221', '11144312', '11144411', '11145122', '11145221', '41151113', '51151121', '41151212', '41151311', '31152113', '41152121', '31152212', '31152311', '21153113', '31153121', '21153212', '21153311', '11154113', '21154121', '11154212', '11154311', '41161112', '41161211', '31162112', '31162211', '21163112', '21163211', '42111116', '52111124', '62111132', '42111215', '52111223', '62111231', '42111314', '52111322', '42111413', '52111421', '42111512', '42111611', '32112116', '42112124', '52112132', '32112215', '42112223', '52112231', '32112314', '42112322', '32112413', '42112421', '32112512', '32112611', '22113116', '32113124', '42113132', '22113215', '32113223', '42113231', '22113314', '32113322', '22113413', '32113421', '22113512', '22113611', '12114116', '22114124', '32114132', '12114215', '22114223', '32114231', '12114314', '22114322', '12114413', '22114421', '12114512', '12115124', '22115132', '12115223', '22115231', '12115322', '12115421', '12116132', '12116231', '51211115', '61211123', '11211164', '51211214', '61211222', '11211263', '51211313', '61211321', '11211362', '51211412', '51211511', '42121115', '52121123', '62121131', '41212115', '42121214', '61212131', '41212214', '51212222', '52121321', '41212313', '42121412', '41212412', '42121511', '41212511', '32122115', '42122123', '52122131', '31213115', '32122214', '42122222', '31213214', '41213222', '42122321', '31213313', '32122412', '31213412', '32122511', '31213511', '22123115', '32123123', '42123131', '21214115', '22123214', '32123222', '21214214', '31214222', '32123321', '21214313', '22123412', '21214412', '22123511', '21214511', '12124115', '22124123', '32124131', '11215115', '12124214', '22124222', '11215214', '21215222', '22124321', '11215313', '12124412', '11215412', '12124511', '12125123', '22125131', '11216123', '12125222', '11216222', '12125321', '11216321', '12126131', '51221114', '61221122', '11221163', '51221213', '61221221', '11221262', '51221312', '11221361', '51221411', '42131114', '52131122', '41222114', '42131213', '52131221', '41222213', '51222221', '41222312', '42131411', '41222411', '32132114', '42132122', '31223114', '32132213', '42132221', '31223213', '41223221', '31223312', '32132411', '31223411', '22133114', '32133122', '21224114', '22133213', '32133221', '21224213', '31224221', '21224312', '22133411', '21224411', '12134114', '22134122', '11225114', '12134213', '22134221', '11225213', '21225221', '11225312', '12134411', '11225411', '12135122', '11226122', '12135221', '11226221', '51231113', '61231121', '11231162', '51231212', '11231261', 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py

Python

indy_node/test/rs_schema/test_rs_send_schema.py

eric-erki/indy-node

7313dd4f948b059b01f992548bf253bde646b432

[ "Apache-2.0" ]

null

indy_node/test/rs_schema/test_rs_send_schema.py

eric-erki/indy-node

7313dd4f948b059b01f992548bf253bde646b432

[ "Apache-2.0" ]

null

indy_node/test/rs_schema/test_rs_send_schema.py

eric-erki/indy-node

7313dd4f948b059b01f992548bf253bde646b432

[ "Apache-2.0" ]

null

import json import pytest from indy_common.authorize.auth_constraints import AuthConstraintForbidden from indy_common.types import SetRsSchemaDataField from indy_node.test.api.helper import sdk_write_rs_schema_and_check, build_rs_schema_request from indy_node.test.rs_schema.templates import TEST_1 from plenum.common.exceptions import RequestRejectedException from indy_node.test.api.helper import req_id _reqId = req_id() def test_send_rs_schema_multiple_attrib(looper, sdk_pool_handle, sdk_wallet_endorser): _, identifier = sdk_wallet_endorser authors_did, name, version, type = identifier, "ISO18023_Drivers_License", "1.1", "8" _id = identifier + ':' + type + ':' + name + ':' + version schema = TEST_1 schema['@id'] = _id request_json = build_rs_schema_request(identifier, schema, name, version) sdk_write_rs_schema_and_check(looper, sdk_pool_handle, sdk_wallet_endorser, request_json) def test_send_rs_schema_one_attrib(looper, sdk_pool_handle, sdk_wallet_endorser): _, identifier = sdk_wallet_endorser authors_did, name, version, type = identifier, "ISO18023_Drivers_License", "1.2", "8" _id = identifier + ':' + type + ':' + name + ':' + version schema = {'@id': _id, '@type': "0od"} request_json = build_rs_schema_request(identifier, schema, name, version) sdk_write_rs_schema_and_check(looper, sdk_pool_handle, sdk_wallet_endorser, request_json) def test_can_not_send_same_rs_schema(looper, sdk_pool_handle, sdk_wallet_endorser): _, identifier = sdk_wallet_endorser authors_did, name, version, type = identifier, "ISO18023_Drivers_License", "1.3", "8" _id = identifier + ':' + type + ':' + name + ':' + version schema = {'@id': _id, '@type': "0od"} request_json = build_rs_schema_request(identifier, schema, name, version) sdk_write_rs_schema_and_check(looper, sdk_pool_handle, sdk_wallet_endorser, request_json) with pytest.raises(RequestRejectedException, match=str(AuthConstraintForbidden())): request_json = build_rs_schema_request(identifier, schema, name, version) sdk_write_rs_schema_and_check(looper, sdk_pool_handle, sdk_wallet_endorser, request_json) def test_can_not_send_rs_schema_missing_id(looper, sdk_pool_handle, sdk_wallet_endorser): _, identifier = sdk_wallet_endorser authors_did, name, version, type = identifier, "ISO18023_Drivers_License", "1.3", "8" # _id = identifier + ':' + type + ':' + name + ':' + version schema = {'@type': "0od"} request_json = build_rs_schema_request(identifier, schema, name, version) with pytest.raises(Exception) as ex_info: sdk_write_rs_schema_and_check(looper, sdk_pool_handle, sdk_wallet_endorser, request_json) ex_info.match( "validation error" ) def test_can_not_send_rs_schema_missing_type(looper, sdk_pool_handle, sdk_wallet_endorser): _, identifier = sdk_wallet_endorser authors_did, name, version, type = identifier, "ISO18023_Drivers_License", "1.3", "8" _id = identifier + ':' + type + ':' + name + ':' + version schema = {'@id': _id} request_json = build_rs_schema_request(identifier, schema, name, version) with pytest.raises(Exception) as ex_info: sdk_write_rs_schema_and_check(looper, sdk_pool_handle, sdk_wallet_endorser, request_json) ex_info.match( "validation error" ) def test_can_not_send_rs_schema_missing_meta_type(looper, sdk_pool_handle, sdk_wallet_endorser): _, identifier = sdk_wallet_endorser authors_did, name, version, type = identifier, "ISO18023_Drivers_License", "1.3", "8" _id = identifier + ':' + type + ':' + name + ':' + version schema = {'@id': _id} txn_dict = { 'operation': { 'type': "201", 'meta': { #'type': "sch", 'name': name, 'version': version }, 'data': { 'schema': schema } }, "identifier": identifier, "reqId": next(_reqId), "protocolVersion": 2 } request_json = json.dumps(txn_dict) with pytest.raises(Exception) as ex_info: sdk_write_rs_schema_and_check(looper, sdk_pool_handle, sdk_wallet_endorser, request_json) ex_info.match("validation error") def test_can_not_send_rs_schema_invalid_meta_type(looper, sdk_pool_handle, sdk_wallet_endorser): _, identifier = sdk_wallet_endorser authors_did, name, version, type = identifier, "ISO18023_Drivers_License", "1.3", "8" _id = identifier + ':' + type + ':' + name + ':' + version schema = {'@id': _id} txn_dict = { 'operation': { 'type': "201", 'meta': { 'type': "Allen", 'name': name, 'version': version }, 'data': { 'schema': schema } }, "identifier": identifier, "reqId": next(_reqId), "protocolVersion": 2 } request_json = json.dumps(txn_dict) with pytest.raises(Exception) as ex_info: sdk_write_rs_schema_and_check(looper, sdk_pool_handle, sdk_wallet_endorser, request_json) ex_info.match( "validation error" ) def test_rs_schema_over_maximum_size(): attribs = {} for i in range(131072 + 1): attribs['attrib' + str(i)] = str(i) schema = SetRsSchemaDataField() with pytest.raises(Exception) as ex_info: schema.validate({ "schema": attribs}) ex_info.match('length of rs_schema is {}; should be <= {}'.format(131073, 131072)) def test_rs_schema_empty_failure(): schema = SetRsSchemaDataField() with pytest.raises(Exception) as ex_info: schema.validate({ "schema": {}}) ex_info.match('validation error')

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6

af1d7c615833113b38219a590bac3b615e21d00b

41

py

Python

kattis/Echo Echo Echo.py

jaredliw/python-question-bank

9c8c246623d8d171f875700b57772df0afcbdcdf

[ "MIT" ]

1

2021-04-08T07:49:15.000Z

kattis/Echo Echo Echo.py

jaredliw/leetcode-solutions

9c8c246623d8d171f875700b57772df0afcbdcdf

[ "MIT" ]

null

kattis/Echo Echo Echo.py

jaredliw/leetcode-solutions

9c8c246623d8d171f875700b57772df0afcbdcdf

[ "MIT" ]

1

2022-01-23T02:12:24.000Z

# CPU: 0.05 s print((input() + " ") * 3)

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6

af2c509df092085d2daeef325150606b7cd06662

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py

Python

remodet_repository_wdh_part/Projects/PyLib/NetLib/GoogleNet.py

UrwLee/Remo_experience

a59d5b9d6d009524672e415c77d056bc9dd88c72

[ "MIT" ]

null

remodet_repository_wdh_part/Projects/PyLib/NetLib/GoogleNet.py

UrwLee/Remo_experience

a59d5b9d6d009524672e415c77d056bc9dd88c72

[ "MIT" ]

null

remodet_repository_wdh_part/Projects/PyLib/NetLib/GoogleNet.py

UrwLee/Remo_experience

a59d5b9d6d009524672e415c77d056bc9dd88c72

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os import caffe from caffe import layers as L from caffe import params as P from caffe.proto import caffe_pb2 import sys sys.dont_write_bytecode = True from ConvBNLayer import * # Create IP def InceptionTower(net, from_layer, tower_name, layer_params): use_scale = False for param in layer_params: tower_layer = '{}/{}'.format(tower_name, param['name']) del param['name'] if 'pool' in tower_layer: net[tower_layer] = L.Pooling(net[from_layer], **param) else: ConvBNUnitLayer(net, from_layer, tower_layer, use_bn=True, use_relu=True, use_scale=use_scale, **param) from_layer = tower_layer return net[from_layer] # Create GoogleNet Inception V3 def Google_IP_V3_Net(net, from_layer="data", output_pred=False): # scale is fixed to 1, thus we ignore it. use_scale = False out_layer = 'conv' ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True, num_output=32, kernel_size=3, pad=0, stride=2, use_scale=use_scale) from_layer = out_layer out_layer = 'conv_1' ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True, num_output=32, kernel_size=3, pad=0, stride=1, use_scale=use_scale) from_layer = out_layer out_layer = 'conv_2' ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True, num_output=64, kernel_size=3, pad=1, stride=1, use_scale=use_scale) from_layer = out_layer out_layer = 'pool' net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.MAX, kernel_size=3, stride=2, pad=0) from_layer = out_layer out_layer = 'conv_3' ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True, num_output=80, kernel_size=1, pad=0, stride=1, use_scale=use_scale) from_layer = out_layer out_layer = 'conv_4' ConvBNUnitLayer(net, from_layer, out_layer, use_bn=True, use_relu=True, num_output=192, kernel_size=3, pad=0, stride=1, use_scale=use_scale) from_layer = out_layer out_layer = 'pool_1' net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.MAX, kernel_size=3, stride=2, pad=0) from_layer = out_layer # inceptions with 1x1, 3x3, 5x5 convolutions for inception_id in xrange(0, 3): if inception_id == 0: out_layer = 'mixed' tower_2_conv_num_output = 32 else: out_layer = 'mixed_{}'.format(inception_id) tower_2_conv_num_output = 64 towers = [] tower_name = '{}'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=64, kernel_size=1, pad=0, stride=1), ]) towers.append(tower) tower_name = '{}/tower'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=48, kernel_size=1, pad=0, stride=1), dict(name='conv_1', num_output=64, kernel_size=5, pad=2, stride=1), ]) towers.append(tower) tower_name = '{}/tower_1'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=64, kernel_size=1, pad=0, stride=1), dict(name='conv_1', num_output=96, kernel_size=3, pad=1, stride=1), dict(name='conv_2', num_output=96, kernel_size=3, pad=1, stride=1), ]) towers.append(tower) tower_name = '{}/tower_2'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='pool', pool=P.Pooling.AVE, kernel_size=3, pad=1, stride=1), dict(name='conv', num_output=tower_2_conv_num_output, kernel_size=1, pad=0, stride=1), ]) towers.append(tower) out_layer = '{}/join'.format(out_layer) net[out_layer] = L.Concat(*towers, axis=1) from_layer = out_layer # inceptions with 1x1, 3x3(in sequence) convolutions out_layer = 'mixed_3' towers = [] tower_name = '{}'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=384, kernel_size=3, pad=0, stride=2), ]) towers.append(tower) tower_name = '{}/tower'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=64, kernel_size=1, pad=0, stride=1), dict(name='conv_1', num_output=96, kernel_size=3, pad=1, stride=1), dict(name='conv_2', num_output=96, kernel_size=3, pad=0, stride=2), ]) towers.append(tower) tower_name = '{}'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='pool', pool=P.Pooling.MAX, kernel_size=3, pad=0, stride=2), ]) towers.append(tower) out_layer = '{}/join'.format(out_layer) net[out_layer] = L.Concat(*towers, axis=1) from_layer = out_layer # inceptions with 1x1, 7x1, 1x7 convolutions for inception_id in xrange(4, 8): if inception_id == 4: num_output = 128 elif inception_id == 5 or inception_id == 6: num_output = 160 elif inception_id == 7: num_output = 192 out_layer = 'mixed_{}'.format(inception_id) towers = [] tower_name = '{}'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1), ]) towers.append(tower) tower_name = '{}/tower'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=num_output, kernel_size=1, pad=0, stride=1), dict(name='conv_1', num_output=num_output, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]), dict(name='conv_2', num_output=192, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]), ]) towers.append(tower) tower_name = '{}/tower_1'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=num_output, kernel_size=1, pad=0, stride=1), dict(name='conv_1', num_output=num_output, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]), dict(name='conv_2', num_output=num_output, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]), dict(name='conv_3', num_output=num_output, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]), dict(name='conv_4', num_output=192, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]), ]) towers.append(tower) tower_name = '{}/tower_2'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='pool', pool=P.Pooling.AVE, kernel_size=3, pad=1, stride=1), dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1), ]) towers.append(tower) out_layer = '{}/join'.format(out_layer) net[out_layer] = L.Concat(*towers, axis=1) from_layer = out_layer # inceptions with 1x1, 3x3, 1x7, 7x1 filters out_layer = 'mixed_8' towers = [] tower_name = '{}/tower'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1), dict(name='conv_1', num_output=320, kernel_size=3, pad=0, stride=2), ]) towers.append(tower) tower_name = '{}/tower_1'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1), dict(name='conv_1', num_output=192, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]), dict(name='conv_2', num_output=192, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]), dict(name='conv_3', num_output=192, kernel_size=3, pad=0, stride=2), ]) towers.append(tower) tower_name = '{}'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='pool', pool=P.Pooling.MAX, kernel_size=3, pad=0, stride=2), ]) towers.append(tower) out_layer = '{}/join'.format(out_layer) net[out_layer] = L.Concat(*towers, axis=1) from_layer = out_layer for inception_id in xrange(9, 11): num_output = 384 num_output2 = 448 if inception_id == 9: pool = P.Pooling.AVE else: pool = P.Pooling.MAX out_layer = 'mixed_{}'.format(inception_id) towers = [] tower_name = '{}'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=320, kernel_size=1, pad=0, stride=1), ]) towers.append(tower) tower_name = '{}/tower'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=num_output, kernel_size=1, pad=0, stride=1), ]) subtowers = [] subtower_name = '{}/mixed'.format(tower_name) subtower = InceptionTower(net, '{}/conv'.format(tower_name), subtower_name, [ dict(name='conv', num_output=num_output, kernel_size=[1, 3], pad=[0, 1], stride=[1, 1]), ]) subtowers.append(subtower) subtower = InceptionTower(net, '{}/conv'.format(tower_name), subtower_name, [ dict(name='conv_1', num_output=num_output, kernel_size=[3, 1], pad=[1, 0], stride=[1, 1]), ]) subtowers.append(subtower) net[subtower_name] = L.Concat(*subtowers, axis=1) towers.append(net[subtower_name]) tower_name = '{}/tower_1'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='conv', num_output=num_output2, kernel_size=1, pad=0, stride=1), dict(name='conv_1', num_output=num_output, kernel_size=3, pad=1, stride=1), ]) subtowers = [] subtower_name = '{}/mixed'.format(tower_name) subtower = InceptionTower(net, '{}/conv_1'.format(tower_name), subtower_name, [ dict(name='conv', num_output=num_output, kernel_size=[1, 3], pad=[0, 1], stride=[1, 1]), ]) subtowers.append(subtower) subtower = InceptionTower(net, '{}/conv_1'.format(tower_name), subtower_name, [ dict(name='conv_1', num_output=num_output, kernel_size=[3, 1], pad=[1, 0], stride=[1, 1]), ]) subtowers.append(subtower) net[subtower_name] = L.Concat(*subtowers, axis=1) towers.append(net[subtower_name]) tower_name = '{}/tower_2'.format(out_layer) tower = InceptionTower(net, from_layer, tower_name, [ dict(name='pool', pool=pool, kernel_size=3, pad=1, stride=1), dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1), ]) towers.append(tower) out_layer = '{}/join'.format(out_layer) net[out_layer] = L.Concat(*towers, axis=1) from_layer = out_layer if output_pred: net.pool_3 = L.Pooling(net[from_layer], pool=P.Pooling.AVE, kernel_size=8, pad=0, stride=1) net.softmax = L.InnerProduct(net.pool_3, num_output=1008) net.softmax_prob = L.Softmax(net.softmax) return net

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6

af9167f9c03d34f1a29cf08d0575c3442bd947c0

235

py

Python

pandana/core/__init__.py

HEPonHPC/pandana

8ee68071892f2a34b54a09ac54033f5d14d42019

[ "Apache-2.0" ]

2

2021-04-23T19:36:57.000Z

2021-06-30T15:57:35.000Z

pandana/core/__init__.py

HEPonHPC/pandana

8ee68071892f2a34b54a09ac54033f5d14d42019

[ "Apache-2.0" ]

null

pandana/core/__init__.py

HEPonHPC/pandana

8ee68071892f2a34b54a09ac54033f5d14d42019

[ "Apache-2.0" ]

null

from pandana.core import * from pandana.core.loader import Loader from pandana.core.var import Var from pandana.core.cut import Cut from pandana.core.spectrum import Spectrum,FilledSpectrum from pandana.core.datagroup import DataGroup

33.571429

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0

1

0

1

0

6

bb9c013f0a20eba847240aa5d5fe7240cea9d309

121

py

Python

haystack/ranker/__init__.py

adithyaur99/haystack

6db9e7eed48520d7e8aeb061a3cc1d1a4b542ab0

[ "Apache-2.0" ]

1

2021-08-08T19:03:56.000Z

haystack/ranker/__init__.py

adithyaur99/haystack

6db9e7eed48520d7e8aeb061a3cc1d1a4b542ab0

[ "Apache-2.0" ]

null

haystack/ranker/__init__.py

adithyaur99/haystack

6db9e7eed48520d7e8aeb061a3cc1d1a4b542ab0

[ "Apache-2.0" ]

null

from haystack.ranker.farm import FARMRanker from haystack.ranker.sentence_transformers import SentenceTransformersRanker

40.333333

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121

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0

1

0

1

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6

bbb123d3ba6333224faefaca20d0d96f597bb779

72

py

Python

py_battlescribe/bs_reference/link.py

akabbeke/py_battlescribe

7f96d44295d46810268e666394e3e3238a6f2c61

[ "MIT" ]

1

2021-11-17T22:00:21.000Z

py_battlescribe/bs_reference/link.py

akabbeke/py_battlescribe

7f96d44295d46810268e666394e3e3238a6f2c61

[ "MIT" ]

null

py_battlescribe/bs_reference/link.py

akabbeke/py_battlescribe

7f96d44295d46810268e666394e3e3238a6f2c61

[ "MIT" ]

null

from . import BSReference class BSReferenceLink(BSReference): pass

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null

0

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bbf9d05c8ce38ee353081306bccff45c7ee751ed

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py

Python

continual_learning/methods/task_incremental/multi_task/gg/single_task/__init__.py

jaryP/ContinualAI

7d9b7614066d219ebd72049692da23ad6ec132b0

[ "MIT" ]

null

continual_learning/methods/task_incremental/multi_task/gg/single_task/__init__.py

jaryP/ContinualAI

7d9b7614066d219ebd72049692da23ad6ec132b0

[ "MIT" ]

null

continual_learning/methods/task_incremental/multi_task/gg/single_task/__init__.py

jaryP/ContinualAI

7d9b7614066d219ebd72049692da23ad6ec132b0

[ "MIT" ]

null

from .Single_Task import SingleTask

18

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a5416a74240bbe593292bd72f189a9716052f735

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py

Python

0x04-python-more_data_structures/101-square_matrix_map.py

calypsobronte/holbertonschool-higher_level_programming

c39c060d8473976fa475d22fffba5cb4329c9965

[ "MIT" ]

null

0x04-python-more_data_structures/101-square_matrix_map.py

calypsobronte/holbertonschool-higher_level_programming

c39c060d8473976fa475d22fffba5cb4329c9965

[ "MIT" ]

null

0x04-python-more_data_structures/101-square_matrix_map.py

calypsobronte/holbertonschool-higher_level_programming

c39c060d8473976fa475d22fffba5cb4329c9965

[ "MIT" ]

null

#!/usr/bin/python3 def square_matrix_map(matrix=[]): return list(map(lambda n1: list(map(lambda n2: n2 ** 2, n1)), matrix))

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a5535d0eb375b9f44e4b25fd52a18ba26438f5cc

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py

Python

tests/molecular/molecules/molecule/fixtures/cage/three_plus_four/__init__.py

stevenbennett96/stk

6e5af87625b83e0bfc7243bc42d8c7a860cbeb76

[ "MIT" ]

21

2018-04-12T16:25:24.000Z

2022-02-14T23:05:43.000Z

tests/molecular/molecules/molecule/fixtures/cage/three_plus_four/__init__.py

stevenbennett96/stk

6e5af87625b83e0bfc7243bc42d8c7a860cbeb76

[ "MIT" ]

8

2019-03-19T12:36:36.000Z

2020-11-11T12:46:00.000Z

tests/molecular/molecules/molecule/fixtures/cage/three_plus_four/__init__.py

stevenbennett96/stk

6e5af87625b83e0bfc7243bc42d8c7a860cbeb76

[ "MIT" ]

5

2018-08-07T13:00:16.000Z

2021-11-01T00:55:10.000Z

from .six_plus_eight import * # noqa

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3c0286cc6f6371ef5bb0810953d6993db348bd7d

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py

Python

dist/Basilisk/fswAlgorithms/thrFiringRemainder/__init__.py

ian-cooke/basilisk_mag

a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14

[ "0BSD" ]

null

dist/Basilisk/fswAlgorithms/thrFiringRemainder/__init__.py

ian-cooke/basilisk_mag

a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14

[ "0BSD" ]

1

2019-03-13T20:52:22.000Z

dist/Basilisk/fswAlgorithms/thrFiringRemainder/__init__.py

ian-cooke/basilisk_mag

a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14

[ "0BSD" ]

null

# This __init__.py file for the thrFiringRemainder package is automatically generated by the build system from thrFiringRemainder import *

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1

0

6

3c0f3e37d3e94e6aa8b542162ee4dd14ea0ff39f

113

py

Python

project/project/configs/mailgun.py

hiraqdev/base-django

4df57f356905274b26af57af8328f015d6c680a4

[ "MIT" ]

1

2018-03-19T05:21:53.000Z

project/project/configs/mailgun.py

hiraq/base-django

4df57f356905274b26af57af8328f015d6c680a4

[ "MIT" ]

6

2020-06-05T20:17:33.000Z

2022-03-11T23:45:44.000Z

project/project/configs/mailgun.py

hiraq/base-django

4df57f356905274b26af57af8328f015d6c680a4

[ "MIT" ]

null

import os MAILGUN_API_KEY = os.environ.get('MAILGUN_API_KEY') MAILGUN_DOMAIN = os.environ.get('MAILGUN_DOMAIN')

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0

6

3c1d03b06261b841507065bce6d00cb53b94e3e3

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py

Python

src/backend/framework/__init__.py

TestomatProject/sOrTES

b147a64a256fb53afee741c87f8670d95b7e3e8b

[ "MIT" ]

null

src/backend/framework/__init__.py

TestomatProject/sOrTES

b147a64a256fb53afee741c87f8670d95b7e3e8b

[ "MIT" ]

null

src/backend/framework/__init__.py

TestomatProject/sOrTES

b147a64a256fb53afee741c87f8670d95b7e3e8b

[ "MIT" ]

null

from .Handler import Handler

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3c92f5990584b8b73bae9b9721e4adb1307bd046

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py

Python

src/mkdv/tools/hdl/__init__.py

fvutils/sim-mk

271b4374a21785ab1b22fac333e423b5febb6a81

[ "Apache-2.0" ]

null

src/mkdv/tools/hdl/__init__.py

fvutils/sim-mk

271b4374a21785ab1b22fac333e423b5febb6a81

[ "Apache-2.0" ]

null

src/mkdv/tools/hdl/__init__.py

fvutils/sim-mk

271b4374a21785ab1b22fac333e423b5febb6a81

[ "Apache-2.0" ]

null

from .mkdv_plugin_tool_questa import * from .hdl_tool_questa import *

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6

b1f6f48b28249b16820b134d868eb67dfe8a2f0c

2,374

py

Python

code/loader.py

unique-chan/YeLU

e70c1e7ab8504ff8d22a33b681d0538a0f6e5745

[ "MIT" ]

1

2021-07-01T16:00:54.000Z

code/loader.py

unique-chan/YeLU

e70c1e7ab8504ff8d22a33b681d0538a0f6e5745

[ "MIT" ]

null

code/loader.py

unique-chan/YeLU

e70c1e7ab8504ff8d22a33b681d0538a0f6e5745

[ "MIT" ]

null

from tensorflow import keras def train_test(args, train='train', test='test'): train_dir, test_dir =\ '{}/{}'.format(args.data, train), '{}/{}'.format(args.data, test) train_dataset = keras.preprocessing.image_dataset_from_directory(directory=train_dir, batch_size=args.batch_size, image_size=(args.height, args.width), shuffle=True) test_dataset = keras.preprocessing.image_dataset_from_directory(directory=test_dir, batch_size=args.batch_size, image_size=(args.height, args.width), shuffle=False) return train_dataset, test_dataset def train_valid_test(args, train='train', valid='valid', test='test'): train_dir, valid_dir, test_dir =\ '{}/{}'.format(args.data, train), '{}/{}'.format(args.data, valid), '{}/{}'.format(args.data, test) train_dataset = keras.preprocessing.image_dataset_from_directory(directory=train_dir, batch_size=args.batch_size, image_size=(args.height, args.width), shuffle=True) valid_dataset = keras.preprocessing.image_dataset_from_directory(directory=valid_dir, batch_size=args.batch_size, image_size=(args.height, args.width), shuffle=False) test_dataset = keras.preprocessing.image_dataset_from_directory(directory=test_dir, batch_size=args.batch_size, image_size=(args.height, args.width), shuffle=False) return train_dataset, valid_dataset, test_dataset

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null

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6

592248d03856b99ad1e0425fdfd6f851707eda31

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py

Python

vic/drivers/python/vic/driver.py

lingyunan0510/VIC

dbc00a813b5df5a88027d1dc57a7805e9a464436

[ "MIT" ]

1

2022-01-18T01:23:47.000Z

vic/drivers/python/vic/driver.py

yusheng-wang/VIC

8f6cc0661bdc67c4f6caabdd4dcd0b8782517435

[ "MIT" ]

null

vic/drivers/python/vic/driver.py

yusheng-wang/VIC

8f6cc0661bdc67c4f6caabdd4dcd0b8782517435

[ "MIT" ]

null

""" @section DESCRIPTION Python driver for VIC """ from .vic import lib def vic_init(): pass def vic_run(): pass def vic_final(): pass

7.95

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null

0

1

0

1

0

6

3ccf0c900445f61bfe6e24c74097b9833058f636

125

py

Python

sdtv3/__init__.py

ankraft/SDTTool

aec305052d191d1659851b7615c67c79d269064d

[ "Apache-2.0" ]

2

2018-05-14T16:00:23.000Z

2018-12-26T14:02:51.000Z

sdtv3/__init__.py

ankraft/SDTTool

aec305052d191d1659851b7615c67c79d269064d

[ "Apache-2.0" ]

null

sdtv3/__init__.py

ankraft/SDTTool

aec305052d191d1659851b7615c67c79d269064d

[ "Apache-2.0" ]

2

2016-09-05T09:24:41.000Z

2020-06-23T14:05:45.000Z

from sdtv3.SDT3PrintOneM2MXSD import print3OneM2MXSD from sdtv3.SDT3Parser import SDT3Parser from sdtv3.SDT3Classes import *

31.25

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0

true

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1

0

1

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1

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6

3ce8ca12ae47b78ebbb7b310ef195a05e1a615c0

7,630

py

Python

tests/core/service/pagination_test.py

symphony-elias/symphony-bdk-python

0d1cd94a9982e3687ea52c49acdb5f942ecd9bec

[ "Apache-2.0" ]

17

2018-09-06T09:58:18.000Z

2021-07-13T12:54:20.000Z

tests/core/service/pagination_test.py

symphony-elias/symphony-bdk-python

0d1cd94a9982e3687ea52c49acdb5f942ecd9bec

[ "Apache-2.0" ]

59

2018-11-21T15:17:57.000Z

2021-08-03T10:00:43.000Z

tests/core/service/pagination_test.py

symphony-elias/symphony-bdk-python

0d1cd94a9982e3687ea52c49acdb5f942ecd9bec

[ "Apache-2.0" ]

37

2018-09-01T03:07:48.000Z

2021-07-06T10:21:50.000Z

from unittest.mock import AsyncMock, call import pytest from symphony.bdk.core.service.pagination import offset_based_pagination, cursor_based_pagination AFTER = "after" CHUNK_SIZE = 2 class TestOffsetBasedPagination: @staticmethod async def assert_generator_produces(func_responses, max_number, expected_output, expected_calls): mock_func = AsyncMock() mock_func.side_effect = func_responses assert [x async for x in offset_based_pagination(mock_func, CHUNK_SIZE, max_number)] == expected_output mock_func.assert_has_awaits(expected_calls) @pytest.mark.asyncio async def test_empty_answer(self): await self.assert_generator_produces(func_responses=[[]], max_number=None, expected_output=[], expected_calls=[call(0, CHUNK_SIZE)]) @pytest.mark.asyncio async def test_answer_less_than_one_chunk(self): await self.assert_generator_produces(func_responses=[["one"]], max_number=None, expected_output=["one"], expected_calls=[call(0, CHUNK_SIZE)]) @pytest.mark.asyncio async def test_answer_same_length_than_one_chunk(self): await self.assert_generator_produces(func_responses=[["one", "two"], []], max_number=None, expected_output=["one", "two"], expected_calls=[call(0, CHUNK_SIZE), call(CHUNK_SIZE, CHUNK_SIZE)]) @pytest.mark.asyncio async def test_answer_more_than_one_chunk_less_than_two_chunks(self): await self.assert_generator_produces(func_responses=[["one", "two"], ["three"]], max_number=None, expected_output=["one", "two", "three"], expected_calls=[call(0, CHUNK_SIZE), call(CHUNK_SIZE, CHUNK_SIZE)]) @pytest.mark.asyncio async def test_answer_two_chunks(self): await self.assert_generator_produces(func_responses=[["one", "two"], ["three", "four"], []], max_number=None, expected_output=["one", "two", "three", "four"], expected_calls=[call(0, CHUNK_SIZE), call(CHUNK_SIZE, CHUNK_SIZE), call(2 * CHUNK_SIZE, CHUNK_SIZE)]) @pytest.mark.asyncio async def test_negative_max_number(self): await self.assert_generator_produces(func_responses=[[]], max_number=-1, expected_output=[], expected_calls=[]) @pytest.mark.asyncio async def test_zero_max_number(self): await self.assert_generator_produces(func_responses=[[]], max_number=0, expected_output=[], expected_calls=[]) @pytest.mark.asyncio async def test_max_number_less_than_one_chunk(self): await self.assert_generator_produces(func_responses=[["one", "two"]], max_number=1, expected_output=["one"], expected_calls=[call(0, CHUNK_SIZE)]) @pytest.mark.asyncio async def test_max_number_equals_one_chunk(self): await self.assert_generator_produces(func_responses=[["one", "two"]], max_number=CHUNK_SIZE, expected_output=["one", "two"], expected_calls=[call(0, CHUNK_SIZE)]) @pytest.mark.asyncio async def test_max_number_equals_more_than_one_chunk(self): await self.assert_generator_produces(func_responses=[["one", "two"], ["three", "four"]], max_number=3, expected_output=["one", "two", "three"], expected_calls=[call(0, CHUNK_SIZE), call(CHUNK_SIZE, CHUNK_SIZE)]) @pytest.mark.asyncio async def test_func_returns_none(self): await self.assert_generator_produces(func_responses=[None], max_number=None, expected_output=[], expected_calls=[call(0, CHUNK_SIZE)]) @pytest.mark.asyncio async def test_func_second_chunk_returns_none(self): await self.assert_generator_produces(func_responses=[["one", "two"], None], max_number=None, expected_output=["one", "two"], expected_calls=[call(0, CHUNK_SIZE), call(CHUNK_SIZE, CHUNK_SIZE)]) class TestCursorBasedPagination: @pytest.mark.asyncio async def test_answer_none(self): mock_func = AsyncMock() mock_func.side_effect = [(None, None)] assert [x async for x in cursor_based_pagination(mock_func, CHUNK_SIZE)] == [] mock_func.assert_has_awaits([call(CHUNK_SIZE, None)]) @pytest.mark.asyncio async def test_empty_answer(self): mock_func = AsyncMock() mock_func.side_effect = [([], None)] assert [x async for x in cursor_based_pagination(mock_func, CHUNK_SIZE)] == [] mock_func.assert_has_awaits([call(CHUNK_SIZE, None)]) @pytest.mark.asyncio async def test_answer_only_one_chunk(self): mock_func = AsyncMock() mock_func.side_effect = [(["one"], None)] assert [x async for x in cursor_based_pagination(mock_func, CHUNK_SIZE)] == ["one"] mock_func.assert_has_awaits([call(CHUNK_SIZE, None)]) @pytest.mark.asyncio async def test_answer_two_chunks(self): mock_func = AsyncMock() mock_func.side_effect = [(["one", "two"], AFTER), (["three"], None)] assert [x async for x in cursor_based_pagination(mock_func, CHUNK_SIZE)] == ["one", "two", "three"] mock_func.assert_has_awaits([call(CHUNK_SIZE, None), call(CHUNK_SIZE, AFTER)]) @pytest.mark.asyncio async def test_negative_max_number(self): mock_func = AsyncMock() assert [x async for x in cursor_based_pagination(mock_func, CHUNK_SIZE, -1)] == [] mock_func.assert_not_awaited() @pytest.mark.asyncio async def test_zero_max_number(self): mock_func = AsyncMock() assert [x async for x in cursor_based_pagination(mock_func, CHUNK_SIZE, 0)] == [] mock_func.assert_not_awaited() @pytest.mark.asyncio async def test_max_number_less_than_one_chunk(self): mock_func = AsyncMock() mock_func.side_effect = [(["one", "two"], AFTER)] assert [x async for x in cursor_based_pagination(mock_func, CHUNK_SIZE, 1)] == ["one"] mock_func.assert_has_awaits([call(CHUNK_SIZE, None)]) @pytest.mark.asyncio async def test_max_number_equals_one_chunk(self): mock_func = AsyncMock() mock_func.side_effect = [(["one", "two"], AFTER)] assert [x async for x in cursor_based_pagination(mock_func, CHUNK_SIZE, 2)] == ["one", "two"] mock_func.assert_has_awaits([call(CHUNK_SIZE, None)]) @pytest.mark.asyncio async def test_max_number_equals_more_than_one_chunk(self): mock_func = AsyncMock() mock_func.side_effect = [(["one", "two"], AFTER), (["three", "four"], "after_two")] assert [x async for x in cursor_based_pagination(mock_func, CHUNK_SIZE, 3)] == ["one", "two", "three"] mock_func.assert_has_awaits([call(CHUNK_SIZE, None), call(CHUNK_SIZE, AFTER)])

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0.039683

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null

0

1

0

null

0

6

3cfe3b02873e6361a789a0dad624902cbc189c25

19,284

py

Python

pybind/nos/v7_1_0/interface_vlan/interface/ve/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/nos/v7_1_0/interface_vlan/interface/ve/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/nos/v7_1_0/interface_vlan/interface/ve/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ import ip import ipv6 import attach class ve(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-interface - based on the path /interface-vlan/interface/ve. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: The list of ve interfaces in the managed device. Each row represents a ve interface. User can create/delete an entry in to this list. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__gve_name','__global_ve_shutdown','__ip','__ipv6','__attach',) _yang_name = 've' _rest_name = 'Ve' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__ip = YANGDynClass(base=ip.ip, is_container='container', presence=False, yang_name="ip", rest_name="ip", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'The Internet Protocol (IP).', u'cli-incomplete-no': None, u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='container', is_config=True) self.__global_ve_shutdown = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="global-ve-shutdown", rest_name="shutdown", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Shutdown the selected interface', u'cli-show-no': None, u'alt-name': u'shutdown'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='empty', is_config=True) self.__attach = YANGDynClass(base=attach.attach, is_container='container', presence=False, yang_name="attach", rest_name="attach", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure attachments', u'cli-suppress-no': None}}, namespace='urn:brocade.com:mgmt:brocade-anycast-gateway', defining_module='brocade-anycast-gateway', yang_type='container', is_config=True) self.__gve_name = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..8191']}), is_leaf=True, yang_name="gve-name", rest_name="gve-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-suppress-range': None, u'cli-custom-range': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='ve-type', is_config=True) self.__ipv6 = YANGDynClass(base=ipv6.ipv6, is_container='container', presence=False, yang_name="ipv6", rest_name="ipv6", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'The Internet Protocol version 6(IPv6).', u'cli-incomplete-no': None, u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='container', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'interface-vlan', u'interface', u've'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'interface', u'Ve'] def _get_gve_name(self): """ Getter method for gve_name, mapped from YANG variable /interface_vlan/interface/ve/gve_name (ve-type) """ return self.__gve_name def _set_gve_name(self, v, load=False): """ Setter method for gve_name, mapped from YANG variable /interface_vlan/interface/ve/gve_name (ve-type) If this variable is read-only (config: false) in the source YANG file, then _set_gve_name is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_gve_name() directly. """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError("Cannot set keys directly when" + " within an instantiated list") if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..8191']}), is_leaf=True, yang_name="gve-name", rest_name="gve-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-suppress-range': None, u'cli-custom-range': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='ve-type', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """gve_name must be of a type compatible with ve-type""", 'defined-type': "brocade-interface:ve-type", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..8191']}), is_leaf=True, yang_name="gve-name", rest_name="gve-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-suppress-range': None, u'cli-custom-range': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='ve-type', is_config=True)""", }) self.__gve_name = t if hasattr(self, '_set'): self._set() def _unset_gve_name(self): self.__gve_name = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..8191']}), is_leaf=True, yang_name="gve-name", rest_name="gve-name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-suppress-range': None, u'cli-custom-range': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='ve-type', is_config=True) def _get_global_ve_shutdown(self): """ Getter method for global_ve_shutdown, mapped from YANG variable /interface_vlan/interface/ve/global_ve_shutdown (empty) """ return self.__global_ve_shutdown def _set_global_ve_shutdown(self, v, load=False): """ Setter method for global_ve_shutdown, mapped from YANG variable /interface_vlan/interface/ve/global_ve_shutdown (empty) If this variable is read-only (config: false) in the source YANG file, then _set_global_ve_shutdown is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_global_ve_shutdown() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="global-ve-shutdown", rest_name="shutdown", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Shutdown the selected interface', u'cli-show-no': None, u'alt-name': u'shutdown'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='empty', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """global_ve_shutdown must be of a type compatible with empty""", 'defined-type': "empty", 'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="global-ve-shutdown", rest_name="shutdown", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Shutdown the selected interface', u'cli-show-no': None, u'alt-name': u'shutdown'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='empty', is_config=True)""", }) self.__global_ve_shutdown = t if hasattr(self, '_set'): self._set() def _unset_global_ve_shutdown(self): self.__global_ve_shutdown = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="global-ve-shutdown", rest_name="shutdown", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'cli-full-command': None, u'info': u'Shutdown the selected interface', u'cli-show-no': None, u'alt-name': u'shutdown'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='empty', is_config=True) def _get_ip(self): """ Getter method for ip, mapped from YANG variable /interface_vlan/interface/ve/ip (container) YANG Description: The IP configurations for an interface. """ return self.__ip def _set_ip(self, v, load=False): """ Setter method for ip, mapped from YANG variable /interface_vlan/interface/ve/ip (container) If this variable is read-only (config: false) in the source YANG file, then _set_ip is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ip() directly. YANG Description: The IP configurations for an interface. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=ip.ip, is_container='container', presence=False, yang_name="ip", rest_name="ip", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'The Internet Protocol (IP).', u'cli-incomplete-no': None, u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """ip must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=ip.ip, is_container='container', presence=False, yang_name="ip", rest_name="ip", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'The Internet Protocol (IP).', u'cli-incomplete-no': None, u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='container', is_config=True)""", }) self.__ip = t if hasattr(self, '_set'): self._set() def _unset_ip(self): self.__ip = YANGDynClass(base=ip.ip, is_container='container', presence=False, yang_name="ip", rest_name="ip", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'The Internet Protocol (IP).', u'cli-incomplete-no': None, u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='container', is_config=True) def _get_ipv6(self): """ Getter method for ipv6, mapped from YANG variable /interface_vlan/interface/ve/ipv6 (container) YANG Description: The IPv6 configurations for an interface. """ return self.__ipv6 def _set_ipv6(self, v, load=False): """ Setter method for ipv6, mapped from YANG variable /interface_vlan/interface/ve/ipv6 (container) If this variable is read-only (config: false) in the source YANG file, then _set_ipv6 is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_ipv6() directly. YANG Description: The IPv6 configurations for an interface. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=ipv6.ipv6, is_container='container', presence=False, yang_name="ipv6", rest_name="ipv6", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'The Internet Protocol version 6(IPv6).', u'cli-incomplete-no': None, u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """ipv6 must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=ipv6.ipv6, is_container='container', presence=False, yang_name="ipv6", rest_name="ipv6", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'The Internet Protocol version 6(IPv6).', u'cli-incomplete-no': None, u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='container', is_config=True)""", }) self.__ipv6 = t if hasattr(self, '_set'): self._set() def _unset_ipv6(self): self.__ipv6 = YANGDynClass(base=ipv6.ipv6, is_container='container', presence=False, yang_name="ipv6", rest_name="ipv6", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'The Internet Protocol version 6(IPv6).', u'cli-incomplete-no': None, u'sort-priority': u'RUNNCFG_INTERFACE_LEVEL_IP_CONFIG', u'cli-incomplete-command': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='container', is_config=True) def _get_attach(self): """ Getter method for attach, mapped from YANG variable /interface_vlan/interface/ve/attach (container) """ return self.__attach def _set_attach(self, v, load=False): """ Setter method for attach, mapped from YANG variable /interface_vlan/interface/ve/attach (container) If this variable is read-only (config: false) in the source YANG file, then _set_attach is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_attach() directly. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=attach.attach, is_container='container', presence=False, yang_name="attach", rest_name="attach", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure attachments', u'cli-suppress-no': None}}, namespace='urn:brocade.com:mgmt:brocade-anycast-gateway', defining_module='brocade-anycast-gateway', yang_type='container', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """attach must be of a type compatible with container""", 'defined-type': "container", 'generated-type': """YANGDynClass(base=attach.attach, is_container='container', presence=False, yang_name="attach", rest_name="attach", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure attachments', u'cli-suppress-no': None}}, namespace='urn:brocade.com:mgmt:brocade-anycast-gateway', defining_module='brocade-anycast-gateway', yang_type='container', is_config=True)""", }) self.__attach = t if hasattr(self, '_set'): self._set() def _unset_attach(self): self.__attach = YANGDynClass(base=attach.attach, is_container='container', presence=False, yang_name="attach", rest_name="attach", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Configure attachments', u'cli-suppress-no': None}}, namespace='urn:brocade.com:mgmt:brocade-anycast-gateway', defining_module='brocade-anycast-gateway', yang_type='container', is_config=True) gve_name = __builtin__.property(_get_gve_name, _set_gve_name) global_ve_shutdown = __builtin__.property(_get_global_ve_shutdown, _set_global_ve_shutdown) ip = __builtin__.property(_get_ip, _set_ip) ipv6 = __builtin__.property(_get_ipv6, _set_ipv6) attach = __builtin__.property(_get_attach, _set_attach) _pyangbind_elements = {'gve_name': gve_name, 'global_ve_shutdown': global_ve_shutdown, 'ip': ip, 'ipv6': ipv6, 'attach': attach, }

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59aad792b475c45e9c60c5e194d322418730b886

10,583

py

Python

scripts/codalab/make_table_1.py

felixwzh/control-tasks

43b5e8acfefc20aea0e776064239790fe9948910

[ "Apache-2.0" ]

24

2019-09-10T18:51:04.000Z

2022-02-24T09:20:32.000Z

scripts/codalab/make_table_1.py

felixwzh/control-tasks

43b5e8acfefc20aea0e776064239790fe9948910

[ "Apache-2.0" ]

2

2019-12-15T02:21:06.000Z

2021-03-25T23:15:50.000Z

scripts/codalab/make_table_1.py

felixwzh/control-tasks

43b5e8acfefc20aea0e776064239790fe9948910

[ "Apache-2.0" ]

8

2019-09-10T18:51:08.000Z

2021-11-11T03:33:56.000Z

import json import sys # Start of table code print('\\begin{tabular}{l c c c | c c c}') print('\\toprule') print('\\bf Probe & \\bf PoS & Ctl & \\bf Select. & \\bf Dep & Ctl & \\bf Select.\\\\') print('\\midrule') ### Probes with default hyperparams print('\\multicolumn{7}{c}{Probes with ``Default'' Hyperparameters}') print('\\vspace{3pt}\\\\') pos_dropout_results = json.load(open('pos-codalab/summarize_pos_dropout/results.json')) dep_dropout_results = json.load(open('dep-codalab/summarize_dep_dropout/results.json')) pos_linear_default_acc = pos_dropout_results["dropout"]["0"]['0hid'][0] pos_linear_default_ctl = pos_dropout_results["dropout"]["1"]['0hid'][0] pos_linear_default_select = pos_linear_default_acc - pos_linear_default_ctl #pos_linear_default_select = (1-pos_linear_default_ctl)/ (1-pos_linear_default_acc) dep_bilinear_default_acc = dep_dropout_results["dropout"]["0"]['bilinear'][0] dep_bilinear_default_ctl = dep_dropout_results["dropout"]["1"]['bilinear'][0] dep_bilinear_default_select = dep_bilinear_default_acc - dep_bilinear_default_ctl #dep_bilinear_default_select = (1-dep_bilinear_default_ctl)/(1- dep_bilinear_default_acc) pos_1hid_default_acc = pos_dropout_results["dropout"]["0"]['1hid'][0] pos_1hid_default_ctl = pos_dropout_results["dropout"]["1"]['1hid'][0] pos_1hid_default_select = pos_1hid_default_acc - pos_1hid_default_ctl #pos_1hid_default_select = (1-pos_1hid_default_ctl)/(1- pos_1hid_default_acc) dep_1hid_default_acc = dep_dropout_results["dropout"]["0"]['1hid'][0] dep_1hid_default_ctl = dep_dropout_results["dropout"]["1"]['1hid'][0] dep_1hid_default_select = dep_1hid_default_acc - dep_1hid_default_ctl #dep_1hid_default_select = (1-dep_1hid_default_ctl)/(1-dep_1hid_default_acc) pos_2hid_default_acc = pos_dropout_results["dropout"]["0"]['2hid'][0] pos_2hid_default_ctl = pos_dropout_results["dropout"]["1"]['2hid'][0] pos_2hid_default_select = pos_2hid_default_acc - pos_2hid_default_ctl #pos_2hid_default_select = (1-pos_2hid_default_ctl) / (1-pos_2hid_default_acc) dep_2hid_default_acc = dep_dropout_results["dropout"]["0"]['2hid'][0] dep_2hid_default_ctl = dep_dropout_results["dropout"]["1"]['2hid'][0] dep_2hid_default_select = dep_2hid_default_acc - dep_2hid_default_ctl #dep_2hid_default_select = (1-dep_2hid_default_ctl) / (1-dep_2hid_default_acc) default_linear_tex_line = [pos_linear_default_acc, pos_linear_default_ctl, pos_linear_default_select] default_linear_tex_line = ' & '.join(['Linear'] + ['${0:.1f}$'.format(100*x) for x in default_linear_tex_line] + ['-', '-', '-']) + '\\\\' print(default_linear_tex_line) default_bilinear_tex_line = [dep_bilinear_default_acc, dep_bilinear_default_ctl, dep_bilinear_default_select] default_bilinear_tex_line = ' & '.join(['Bilinear', '-', '-', '-']+ ['${0:.1f}$'.format(100*x) for x in default_bilinear_tex_line]) + '\\\\' print(default_bilinear_tex_line) default_1hid_tex_line = [pos_1hid_default_acc, pos_1hid_default_ctl, pos_1hid_default_select] + [dep_1hid_default_acc, dep_1hid_default_ctl, dep_1hid_default_select] default_1hid_tex_line = ' & '.join(['MLP-1'] + ['${0:.1f}$'.format(100*x) for x in default_1hid_tex_line]) + '\\\\' print(default_1hid_tex_line) default_2hid_tex_line = [pos_2hid_default_acc, pos_2hid_default_ctl, pos_2hid_default_select] + [dep_2hid_default_acc, dep_2hid_default_ctl, dep_2hid_default_select] default_2hid_tex_line = ' & '.join(['MLP-2'] +['${0:.1f}$'.format(100*x) for x in default_2hid_tex_line]) + '\\\\' print(default_2hid_tex_line) print('\\midrule') ### Probes with .4 dropout print('\\multicolumn{7}{c}{Probes with $0.4$ Dropout}') print('\\vspace{3pt}\\\\') pos_dropout_results = json.load(open('pos-codalab/summarize_pos_dropout/results.json')) dep_dropout_results = json.load(open('dep-codalab/summarize_dep_dropout/results.json')) pos_linear_default_acc = pos_dropout_results["dropout"]["0"]['0hid'][2] pos_linear_default_ctl = pos_dropout_results["dropout"]["1"]['0hid'][2] pos_linear_default_select = pos_linear_default_acc - pos_linear_default_ctl #pos_linear_default_select = (1-pos_linear_default_ctl)/ (1-pos_linear_default_acc) dep_bilinear_default_acc = dep_dropout_results["dropout"]["0"]['bilinear'][2] dep_bilinear_default_ctl = dep_dropout_results["dropout"]["1"]['bilinear'][2] dep_bilinear_default_select = dep_bilinear_default_acc - dep_bilinear_default_ctl #dep_bilinear_default_select = (1-dep_bilinear_default_ctl)/(1- dep_bilinear_default_acc) pos_1hid_default_acc = pos_dropout_results["dropout"]["0"]['1hid'][2] pos_1hid_default_ctl = pos_dropout_results["dropout"]["1"]['1hid'][2] pos_1hid_default_select = pos_1hid_default_acc - pos_1hid_default_ctl #pos_1hid_default_select = (1-pos_1hid_default_ctl)/(1- pos_1hid_default_acc) dep_1hid_default_acc = dep_dropout_results["dropout"]["0"]['1hid'][2] dep_1hid_default_ctl = dep_dropout_results["dropout"]["1"]['1hid'][2] dep_1hid_default_select = dep_1hid_default_acc - dep_1hid_default_ctl #dep_1hid_default_select = (1-dep_1hid_default_ctl)/(1-dep_1hid_default_acc) pos_2hid_default_acc = pos_dropout_results["dropout"]["0"]['2hid'][2] pos_2hid_default_ctl = pos_dropout_results["dropout"]["1"]['2hid'][2] pos_2hid_default_select = pos_2hid_default_acc - pos_2hid_default_ctl #pos_2hid_default_select = (1-pos_2hid_default_ctl) / (1-pos_2hid_default_acc) dep_2hid_default_acc = dep_dropout_results["dropout"]["0"]['2hid'][2] dep_2hid_default_ctl = dep_dropout_results["dropout"]["1"]['2hid'][2] dep_2hid_default_select = dep_2hid_default_acc - dep_2hid_default_ctl #dep_2hid_default_select = (1-dep_2hid_default_ctl) / (1-dep_2hid_default_acc) default_linear_tex_line = [pos_linear_default_acc, pos_linear_default_ctl, pos_linear_default_select] default_linear_tex_line = ' & '.join(['Linear'] + ['${0:.1f}$'.format(100*x) for x in default_linear_tex_line] + ['-', '-', '-']) + '\\\\' print(default_linear_tex_line) default_bilinear_tex_line = [dep_bilinear_default_acc, dep_bilinear_default_ctl, dep_bilinear_default_select] default_bilinear_tex_line = ' & '.join(['Bilinear', '-', '-', '-']+ ['${0:.1f}$'.format(100*x) for x in default_bilinear_tex_line]) + '\\\\' print(default_bilinear_tex_line) default_1hid_tex_line = [pos_1hid_default_acc, pos_1hid_default_ctl, pos_1hid_default_select] + [dep_1hid_default_acc, dep_1hid_default_ctl, dep_1hid_default_select] default_1hid_tex_line = ' & '.join(['MLP-1'] +['${0:.1f}$'.format(100*x) for x in default_1hid_tex_line]) + '\\\\' print(default_1hid_tex_line) default_2hid_tex_line = [pos_2hid_default_acc, pos_2hid_default_ctl, pos_2hid_default_select] + [dep_2hid_default_acc, dep_2hid_default_ctl, dep_2hid_default_select] default_2hid_tex_line = ' & '.join(['MLP-2'] +['${0:.1f}$'.format(100*x) for x in default_2hid_tex_line]) + '\\\\' print(default_2hid_tex_line) print('\\midrule') ### Probes designed with control tasks print('\\multicolumn{7}{c}{Probes with Control Tasks }') print('\\vspace{3pt}\\\\') pos_rank_results = json.load(open('pos-codalab/summarize_pos_rank/results.json')) dep_wd_results = json.load(open('dep-codalab/summarize_dep_wd/results.json')) pos_linear_default_acc = pos_rank_results["rank"]["0"]['0hid'][2] # Rank=10 pos_linear_default_ctl = pos_rank_results["rank"]["1"]['0hid'][2] # Rank=10 pos_linear_default_select = pos_linear_default_acc - pos_linear_default_ctl print(pos_rank_results['hyperparameter_options'][2], 'rank=10',file=sys.stderr) #pos_linear_default_select = (1-pos_linear_default_ctl)/ (1-pos_linear_default_acc) dep_bilinear_default_acc = dep_wd_results["wd"]["0"]['bilinear'][1] # wd=.01 dep_bilinear_default_ctl = dep_wd_results["wd"]["1"]['bilinear'][1] # wd=.01 print(dep_wd_results['hyperparameter_options'][1], 'wd=.01',file=sys.stderr) dep_bilinear_default_select = dep_bilinear_default_acc - dep_bilinear_default_ctl #dep_bilinear_default_select = (1-dep_bilinear_default_ctl)/(1- dep_bilinear_default_acc) pos_1hid_default_acc = pos_rank_results["rank"]["0"]['1hid'][3] # rank=45 pos_1hid_default_ctl = pos_rank_results["rank"]["1"]['1hid'][3] # rank=45 pos_1hid_default_select = pos_1hid_default_acc - pos_1hid_default_ctl print(pos_rank_results['hyperparameter_options'][3], 'rank=45',file=sys.stderr) #pos_1hid_default_select = (1-pos_1hid_default_ctl)/(1- pos_1hid_default_acc) dep_1hid_default_acc = dep_wd_results["wd"]["0"]['1hid'][2] # wd=.1 dep_1hid_default_ctl = dep_wd_results["wd"]["1"]['1hid'][2] # wd=.1 dep_1hid_default_select = dep_1hid_default_acc - dep_1hid_default_ctl print(dep_wd_results['hyperparameter_options'][2], 'wd=0.1',file=sys.stderr) #dep_1hid_default_select = (1-dep_1hid_default_ctl)/(1-dep_1hid_default_acc) pos_2hid_default_acc = pos_rank_results["rank"]["0"]['2hid'][3] # rank=45 pos_2hid_default_ctl = pos_rank_results["rank"]["1"]['2hid'][3] # rank=45 pos_2hid_default_select = pos_2hid_default_acc - pos_2hid_default_ctl print(pos_rank_results['hyperparameter_options'][3], 'rank=45',file=sys.stderr) #pos_2hid_default_select = (1-pos_2hid_default_ctl) / (1-pos_2hid_default_acc) dep_2hid_default_acc = dep_wd_results["wd"]["0"]['2hid'][2] # wd=.1 dep_2hid_default_ctl = dep_wd_results["wd"]["1"]['2hid'][2] # wd=.1 dep_2hid_default_select = dep_2hid_default_acc - dep_2hid_default_ctl print(dep_wd_results['hyperparameter_options'][2], 'wd=0.1',file=sys.stderr) #dep_2hid_default_select = (1-dep_2hid_default_ctl) / (1-dep_2hid_default_acc) default_linear_tex_line = [pos_linear_default_acc, pos_linear_default_ctl, pos_linear_default_select] default_linear_tex_line = ' & '.join(['Linear'] + ['${0:.1f}$'.format(100*x) for x in default_linear_tex_line] + ['-', '-', '-']) + '\\\\' print(default_linear_tex_line) default_bilinear_tex_line = [dep_bilinear_default_acc, dep_bilinear_default_ctl, dep_bilinear_default_select] default_bilinear_tex_line = ' & '.join(['Bilinear', '-', '-', '-']+ ['${0:.1f}$'.format(100*x) for x in default_bilinear_tex_line]) + '\\\\' print(default_bilinear_tex_line) default_1hid_tex_line = [pos_1hid_default_acc, pos_1hid_default_ctl, pos_1hid_default_select] + [dep_1hid_default_acc, dep_1hid_default_ctl, dep_1hid_default_select] default_1hid_tex_line = ' & '.join(['MLP-1'] +['${0:.1f}$'.format(100*x) for x in default_1hid_tex_line]) + '\\\\' print(default_1hid_tex_line) default_2hid_tex_line = [pos_2hid_default_acc, pos_2hid_default_ctl, pos_2hid_default_select] + [dep_2hid_default_acc, dep_2hid_default_ctl, dep_2hid_default_select] default_2hid_tex_line = ' & '.join(['MLP-2'] +['${0:.1f}$'.format(100*x) for x in default_2hid_tex_line]) + '\\\\' print(default_2hid_tex_line) print('\\bottomrule') print('\\end{tabular}')

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null

0

6

59c2006019f68e02e1584c8e3584e5c4fe60e7f6

2,167

py

Python

examples/maml/load_assistive_gym.py

hzyjerry/pytorch-meta

e63aa57984ec80d6e78f45a228232f0424b06bca

[ "MIT" ]

null

examples/maml/load_assistive_gym.py

hzyjerry/pytorch-meta

e63aa57984ec80d6e78f45a228232f0424b06bca

[ "MIT" ]

null

examples/maml/load_assistive_gym.py

hzyjerry/pytorch-meta

e63aa57984ec80d6e78f45a228232f0424b06bca

[ "MIT" ]

null

from torchmeta.datasets.helpers import omniglot from torchmeta.toy.helpers import sinusoid, behaviour from torchmeta.utils.data import BatchMetaDataLoader # dataset = omniglot("data", ways=5, shots=5, test_shots=15, meta_train=True, download=True) # dataloader = BatchMetaDataLoader(dataset, batch_size=16, num_workers=4) # for batch in dataloader: # train_inputs, train_targets = batch["train"] # print('Train inputs shape: {0}'.format(train_inputs.shape)) # (16, 25, 1, 28, 28) # print('Train targets shape: {0}'.format(train_targets.shape)) # (16, 25) # test_inputs, test_targets = batch["test"] # print('Test inputs shape: {0}'.format(test_inputs.shape)) # (16, 75, 1, 28, 28) # print('Test targets shape: {0}'.format(test_targets.shape)) # (16, 75) # dataset = sinusoid(shots=1000, test_shots=100) # dataloader = BatchMetaDataLoader(dataset, batch_size=16, num_workers=4) # for batch in dataloader: # train_inputs, train_targets = batch['train'] # print('Train inputs shape: {0}'.format(train_inputs.shape)) # (16, 25, 1, 28, 28) # print('Train targets shape: {0}'.format(train_targets.shape)) # (16, 25) # test_inputs, test_targets = batch["test"] # print('Test inputs shape: {0}'.format(test_inputs.shape)) # (16, 75, 1, 28, 28) # print('Test targets shape: {0}'.format(test_targets.shape)) # (16, 75) inputs = [ "new_models/210430/dataset/BedBathingJacoHuman-v0217_0-v1-human-coop-robot-coop_10k", "new_models/210430/dataset/BedBathingJacoHuman-v0217_0-v1-human-coop-robot-coop_10k" ] dataset = behaviour(inputs, shots=1000, test_shots=200) dataloader = BatchMetaDataLoader(dataset, batch_size=16, num_workers=4) for batch in dataloader: train_inputs, train_targets = batch['train'] print('Train inputs shape: {0}'.format(train_inputs.shape)) # (16, 25, 1, 28, 28) print('Train targets shape: {0}'.format(train_targets.shape)) # (16, 25) test_inputs, test_targets = batch["test"] print('Test inputs shape: {0}'.format(test_inputs.shape)) # (16, 75, 1, 28, 28) print('Test targets shape: {0}'.format(test_targets.shape)) # (16, 75)

46.106383

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2,167

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null

0

1

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null

0

6

59cfdff668a017643486cbbf5e53cb0598bfad57

184

py

Python

code_search/src/code_search/do_fns/__init__.py

AlexRogalskiy/kubeflow-examples

2d6a784b6206cb9d692de622b91f23f6f965d44c

[ "Apache-2.0" ]

8

2018-05-28T02:13:40.000Z

2022-01-15T05:06:49.000Z

code_search/src/code_search/do_fns/__init__.py

katacoda/kubeflow-examples

2d6a784b6206cb9d692de622b91f23f6f965d44c

[ "Apache-2.0" ]

2

2022-01-06T13:28:33.000Z

2022-01-06T13:28:51.000Z

code_search/src/code_search/do_fns/__init__.py

AlexRogalskiy/kubeflow-examples

2d6a784b6206cb9d692de622b91f23f6f965d44c

[ "Apache-2.0" ]

6

2018-11-05T14:12:54.000Z

2022-02-22T10:56:06.000Z

from code_search.do_fns.github_files import ExtractFuncInfo from code_search.do_fns.github_files import TokenizeCodeDocstring from code_search.do_fns.github_files import SplitRepoPath

46

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0.902174

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184

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0

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null

0

1

0

1

0

6

ab80f9cae183663424b857598ba953b393c633a0

621

py

Python

trojsten/special/installed_apps.py

MvonK/web

b701a6ea8fb6f0bdfb720e66d0a430db13db8bff

[ "MIT" ]

null

trojsten/special/installed_apps.py

MvonK/web

b701a6ea8fb6f0bdfb720e66d0a430db13db8bff

[ "MIT" ]

null

trojsten/special/installed_apps.py

MvonK/web

b701a6ea8fb6f0bdfb720e66d0a430db13db8bff

[ "MIT" ]

null

INSTALLED_APPS = ( "trojsten.special.plugin_ksp_32_1_1", "trojsten.special.plugin_ksp_32_2_1", "trojsten.special.plugin_prask_1_2_1", "trojsten.special.plugin_prask_1_2_3", "trojsten.special.plugin_prask_2_1_3", "trojsten.special.plugin_prask_2_2_3", "trojsten.special.plugin_prask_2_3_3", "trojsten.special.plugin_prask_2_4_1", "trojsten.special.plugin_prask_2_4_3", "trojsten.special.plugin_prask_3_3_3", "trojsten.special.plugin_prask_5_1_1", "trojsten.special.plugin_prask_5_1_2", "trojsten.special.plugin_prask_7_1_1", "trojsten.special.plugin_prask_7_2_1", )

36.529412

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null

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6

abbc81f5dcbc99c5a71612839b7d7fdc2e49d30d

194

py

Python

prime/index_ambiguity/__init__.py

dperl-sol/cctbx_project

b9e390221a2bc4fd00b9122e97c3b79c632c6664

[ "BSD-3-Clause-LBNL" ]

155

2016-11-23T12:52:16.000Z

2022-03-31T15:35:44.000Z

prime/index_ambiguity/__init__.py

dperl-sol/cctbx_project

b9e390221a2bc4fd00b9122e97c3b79c632c6664

[ "BSD-3-Clause-LBNL" ]

590

2016-12-10T11:31:18.000Z

2022-03-30T23:10:09.000Z

prime/index_ambiguity/__init__.py

dperl-sol/cctbx_project

b9e390221a2bc4fd00b9122e97c3b79c632c6664

[ "BSD-3-Clause-LBNL" ]

115

2016-11-15T08:17:28.000Z

2022-02-09T15:30:14.000Z

from __future__ import absolute_import, division, print_function import boost_adaptbx.boost.python as bp ext = bp.import_ext("prime_index_ambiguity_ext") from prime_index_ambiguity_ext import *

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0

1

0

6

abe24e5ec681efc5e21236fad626c9f33534f282

93

py

Python

Sea/adapter/system/ViewProviderFrequency.py

FRidh/Sea

b474e93a449570a9ba3b915c4d80f814feee2545

[ "BSD-3-Clause" ]

2

2015-07-02T13:34:09.000Z

2015-09-28T09:07:52.000Z

Sea/adapter/system/ViewProviderFrequency.py

FRidh/Sea

b474e93a449570a9ba3b915c4d80f814feee2545

[ "BSD-3-Clause" ]

null

Sea/adapter/system/ViewProviderFrequency.py

FRidh/Sea

b474e93a449570a9ba3b915c4d80f814feee2545

[ "BSD-3-Clause" ]

1

2022-01-22T03:01:54.000Z

from ..base import ViewProviderBase class ViewProviderFrequency(ViewProviderBase): pass

18.6

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null

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null

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1

0

1

0

6

e62e475ff614d259171bb64711798393e4959192

221

py

Python

gym_radio_scheduler/envs/src/__init__.py

vidits-kth/gym-radio-scheduler

be072423dd584ce927c59068398c5d5446a40b50

[ "MIT" ]

2

2018-12-05T08:32:21.000Z

2021-04-12T14:24:42.000Z

gym_radio_scheduler/envs/src/__init__.py

vidits-kth/gym-radio-scheduler

be072423dd584ce927c59068398c5d5446a40b50

[ "MIT" ]

null

gym_radio_scheduler/envs/src/__init__.py

vidits-kth/gym-radio-scheduler

be072423dd584ce927c59068398c5d5446a40b50

[ "MIT" ]

2

2018-12-05T08:32:29.000Z

2019-02-05T19:44:50.000Z

from .baseband_processing import * from .buffer_manipulation import * from .channel_quality_index import * from .radio_channel import * from .plot_utils import * from .postprocessing import * from .preprocessing import *

27.625

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null

0

1

0

1

0

1

0

6

e635d24ab59f67f281f48b901edfaeb79c018010

33

py

Python

systemconfig/validators/__init__.py

PyFlux/PyFlux

8abae10261e276bf4942aed8d54ef3b5498754ca

[ "Apache-2.0" ]

null

systemconfig/validators/__init__.py

PyFlux/PyFlux

8abae10261e276bf4942aed8d54ef3b5498754ca

[ "Apache-2.0" ]

10

2020-03-24T17:09:56.000Z

2021-12-13T20:00:15.000Z

systemconfig/validators/__init__.py

PyFlux/PyFlux-Django-Html

8abae10261e276bf4942aed8d54ef3b5498754ca

[ "Apache-2.0" ]

null

from .common_validators import *

16.5

32

0.818182

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6.5

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null

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0

6

0558f54750f8f207ea6bc6d711f8336917f5f3f1

103

py

Python

Math/2588_곱셈/2588_곱셈.py

7dudtj/BOJ_myCode

37d105590a7963e2232102b3098fea3c3504b96f

[ "MIT" ]

1

2022-03-30T15:50:47.000Z

Math/2588_곱셈/2588_곱셈.py

7dudtj/BOJ_myCode

37d105590a7963e2232102b3098fea3c3504b96f

[ "MIT" ]

null

Math/2588_곱셈/2588_곱셈.py

7dudtj/BOJ_myCode

37d105590a7963e2232102b3098fea3c3504b96f

[ "MIT" ]

1

2021-07-20T07:11:06.000Z

a = int(input()) b = input() print(a*int(b[2])) print(a*int(b[1])) print(a*int(b[0])) print(a*int(b))

12.875

18

0.563107

24

103

2.416667

0.333333

0.344828

0.62069

0.689655

0

0.032609

0.106796

103

7

19

14.714286

0.597826

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false

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0

null

1

0

1

0

null

0

1

0

6

055a9cd3a1b6c0e55307db02649ba47818204b28

171

py

Python

terrascript/docker/d.py

GarnerCorp/python-terrascript

ec6c2d9114dcd3cb955dd46069f8ba487e320a8c

[ "BSD-2-Clause" ]

null

terrascript/docker/d.py

GarnerCorp/python-terrascript

ec6c2d9114dcd3cb955dd46069f8ba487e320a8c

[ "BSD-2-Clause" ]

null

terrascript/docker/d.py

GarnerCorp/python-terrascript

ec6c2d9114dcd3cb955dd46069f8ba487e320a8c

[ "BSD-2-Clause" ]

1

2018-11-15T16:23:05.000Z

from terrascript import _data class docker_registry_image(_data): pass registry_image = docker_registry_image class docker_network(_data): pass network = docker_network

21.375

40

0.847953

23

171

5.869565

0.434783

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0.281481

0

0.105263

171

7

41

24.428571

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1

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false

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null

0

1

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1

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6

056c75d725f082693f267817bd747e927ca5f37f

49

py

Python

lib/__init__.py

MrFlynn/wordfilter

8ff8c5796f7badfa7e49821862c75f0cdaa0d705

[ "MIT" ]

196

2015-01-17T01:34:56.000Z

2021-12-27T17:49:49.000Z

lib/__init__.py

MrFlynn/wordfilter

8ff8c5796f7badfa7e49821862c75f0cdaa0d705

[ "MIT" ]

27

2015-02-17T16:44:18.000Z

2021-03-18T23:35:30.000Z

lib/__init__.py

MrFlynn/wordfilter

8ff8c5796f7badfa7e49821862c75f0cdaa0d705

[ "MIT" ]

61

2015-02-14T20:31:39.000Z

2022-03-11T16:12:49.000Z

from .wordfilter import Wordfilter # noqa: F401

24.5

48

0.77551

6

49

6.333333

0.833333

0

0.073171

0.163265

49

1

49

0.853659

0.204082

0

1

0

true

0

1

0

1

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1

0

null

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1

0

null

0

1

0

1

0

1

0

6

554c698600ccfd11986d830dc58a46d8d1fa05f9

121

py

Python

ctools/worker/actor/env_manager/__init__.py

XinyuJing/DI-star

b573a5462e3d0ab72298c767eb945742e36fa6d8

[ "Apache-2.0" ]

267

2021-07-08T02:18:08.000Z

2022-03-02T11:37:33.000Z

ctools/worker/actor/env_manager/__init__.py

XinyuJing/DI-star

b573a5462e3d0ab72298c767eb945742e36fa6d8

[ "Apache-2.0" ]

5

2021-07-15T22:55:22.000Z

2022-01-11T15:28:10.000Z

ctools/worker/actor/env_manager/__init__.py

XinyuJing/DI-star

b573a5462e3d0ab72298c767eb945742e36fa6d8

[ "Apache-2.0" ]

35

2021-07-08T08:01:51.000Z

2022-02-10T07:00:24.000Z

from .base_env_manager import BaseEnvManager from .vec_env_manager import SubprocessEnvManager, SyncSubprocessEnvManager

40.333333

75

0.900826

13

121

8.076923

0.692308

0.190476

0.304762

0

0.07438

121

2

76

60.5

0.9375

0

1

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true

0

1

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1

0

1

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null

0

1

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1

0

null

0

1

0

1

0

1

0

6

554f00c725263132750b45f44d609043670cceeb

38

py

Python

project/management/__init__.py

danielbraga/hcap

a3ca0d6963cff19ed6ec0436cce84e2b41615454

[ "MIT" ]

null

project/management/__init__.py

danielbraga/hcap

a3ca0d6963cff19ed6ec0436cce84e2b41615454

[ "MIT" ]

null

project/management/__init__.py

danielbraga/hcap

a3ca0d6963cff19ed6ec0436cce84e2b41615454

[ "MIT" ]

null

from .base_command import BaseCommand

19

37

0.868421

5

38

6.4

1

0

0.105263

38

1

38

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5564e1dc1f9a981c6cf9dafb405226bea5a817d9

42

py

Python

credentialdigger/generator/__init__.py

Soontao/credential-digger

365eedca3eaec201503441046ba0c37937db69e1

[ "Apache-2.0" ]

null

credentialdigger/generator/__init__.py

Soontao/credential-digger

365eedca3eaec201503441046ba0c37937db69e1

[ "Apache-2.0" ]

null

credentialdigger/generator/__init__.py

Soontao/credential-digger

365eedca3eaec201503441046ba0c37937db69e1

[ "Apache-2.0" ]

null

from .generator import ExtractorGenerator

21

41

0.880952

4

42

9.25

1

0

0.095238

42

1

42

0.973684

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

55a1b77fabc90c335aed81f7099b60e389312f5e

108

py

Python

src/pyoffice/outlook/windows/dasl/linker/__init__.py

qq809326636/pyoffice

a3c036ef82f6b0438c1e38a7675eb1f06c61144d

[ "MIT" ]

7

2020-06-19T03:11:48.000Z

2020-11-18T06:14:21.000Z

src/pyoffice/outlook/windows/dasl/linker/__init__.py

qq809326636/pyoffice

a3c036ef82f6b0438c1e38a7675eb1f06c61144d

[ "MIT" ]

null

src/pyoffice/outlook/windows/dasl/linker/__init__.py

qq809326636/pyoffice

a3c036ef82f6b0438c1e38a7675eb1f06c61144d

[ "MIT" ]

null

from .AndLinker import * from .BaseLinker import * from .OrLinker import * from .LinkerFactory import *

21.6

29

0.740741

12

108

6.666667

0.5

0.375

0

0.185185

108

4

30

27

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e96e7440f3030faabd8b6bf17f1a110d81467741

45

py

Python

TypewriterPrint/__init__.py

radroid/typerwriter-effect

586b7949b42e125950b991304006a198449f82fc

[ "MIT" ]

1

2020-07-24T06:39:52.000Z

TypewriterPrint/__init__.py

radroid/typerwriter-effect

586b7949b42e125950b991304006a198449f82fc

[ "MIT" ]

null

TypewriterPrint/__init__.py

radroid/typerwriter-effect

586b7949b42e125950b991304006a198449f82fc

[ "MIT" ]

null

from .TypewriterPrint import TypewriterPrint

22.5

44

0.888889

4

45

10

0.75

0

0.088889

45

1

45

0.97561

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e9b493c87220e3433e993f1361b0cd4f8e227d82

278

py

Python

ultrafastultrafast/__init__.py

peterarose/UF2

cfc2c6625467945e12ac08bd267f79b6741e567f

[ "MIT" ]

2

2020-02-28T15:36:42.000Z

2021-07-26T21:27:54.000Z

ultrafastultrafast/__init__.py

peterarose/UF2

cfc2c6625467945e12ac08bd267f79b6741e567f

[ "MIT" ]

null

ultrafastultrafast/__init__.py

peterarose/UF2

cfc2c6625467945e12ac08bd267f79b6741e567f

[ "MIT" ]

null

from ultrafastultrafast.core import Wavepackets from ultrafastultrafast.RK_core import RK_Wavepackets from ultrafastultrafast.dipole_pruning import DipolePruning import ultrafastultrafast.signals as signals import ultrafastultrafast.vibronic_eigenstates as vibronic_eigenstates

46.333333

70

0.910072

30

278

8.266667

0.433333

0.266129

0

0.071942

278

5

71

55.6

0.96124

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e9b673b550b41271f020c929f82aa249eef22c8a

172

py

Python

mikenet/__init__.py

michael2017le/mikenet

88bde76c529110529b7b9b3293bf710b4e288f2c

[ "MIT" ]

null

mikenet/__init__.py

michael2017le/mikenet

88bde76c529110529b7b9b3293bf710b4e288f2c

[ "MIT" ]

null

mikenet/__init__.py

michael2017le/mikenet

88bde76c529110529b7b9b3293bf710b4e288f2c

[ "MIT" ]

null

from mikenet.loss import MSE from mikenet.layers import Linear, Tanh, ReLU from mikenet.optim import SGD from mikenet.nn import Sequential from mikenet.train import train

24.571429

45

0.825581

27

172

5.259259

0.518519

0.387324

0

0.133721

172

6

46

28.666667

0.95302

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

e9c8308a92b780784f20f4e3af18397e40920811

50,574

py

Python

LossJLearn/linear_model/_stochastic_gradient.py

LossJ/Statistical-Machine-Learning

c70fd82ee287f4902d8607ec459e52b0a301d6a2

[ "MIT" ]

null

LossJLearn/linear_model/_stochastic_gradient.py

LossJ/Statistical-Machine-Learning

c70fd82ee287f4902d8607ec459e52b0a301d6a2

[ "MIT" ]

1

2020-09-26T07:57:23.000Z

LossJLearn/linear_model/_stochastic_gradient.py

LossJ/Statistical-Machine-Learning

c70fd82ee287f4902d8607ec459e52b0a301d6a2

[ "MIT" ]

null

import time import copy from ._base import NumpyBaseLinearRegressor, TFBaseLinearRegressor, TorchBaseLinearRegressor from ..utils.translator import sec2time from ..datasets._generator import TorchBaseDataset import numpy as np import tensorflow as tf from tensorflow import keras import torch import torch.nn.functional as F from torch.utils.data import DataLoader, random_split class NumpySGDBaseEstimator: """SGD Estimator Base class with numpy. Attributes: _X_train:feature data for training. A np.ndarray matrix of (n_samples, n_features) shape, data type must be continuous value type. _y_train:label data for training. A np.ndarray array of (n_samples, ) shape, data type must be continuous value. coef_: coef of linear regressor. A np.ndarray matrix of (n_features, ) shape. intercept_: intercept of regressor. A np.ndarray integer if intercept_ is not None else None. alpha: the regularize rate. A float number and must be greater than 0, default = 0.001. save_param_list: if save param of the train process. A bool value, default = True. coef_list: list of coef param from the train process, every coef is a np.ndarray of (n_features, ) shape. intercept_list: list of intercept param from the train process, every intercept is a np.ndarray float number. learning_rate: learning rate. A positive float number, default = 0.001. epochs: epochs. A positive int number, default = 10. batch_size: batch size. A positive int number, default = 32. early_stopping: if early stopping when loss don't reduce again. A bool value, default = True. patient: Number of epochs that do not reduce loss continuously, patient only takes effect when early_stopping is True. A positive int number, default = 5. toc: The threshold that symbolizes loss no longer decreases, toc only takes effect when early_stopping is True. A float number, default = 0.001 random_state: random seed. A positive int number if random_state is not None else None, default = None. regularize: regularize. A str value in {"l1", "l2"} if regularize is not None else None, default = None. best_loss: best loss of the train process. A np.ndarray float number. best_coef: best coef of the train process. A np.ndarray array of (n_features, 1) shape. best_intercept_: best intercept of the train process. A np.ndarray number. train_loss: list of train loss from the train process. every loss is a np.ndarray float number. valid_loss: list of valid loss from the train process. every loss is a np.ndarray float number. n_iter: the actual iteration of train process. A int number, initial = 0. save_best_model: if save the best model params as the final model. A bool value, defalut = True. """ def __init__( self, loss="mse", alpha=0.0001, fit_intercept=True, save_param_list=True, learning_rate=0.0001, epochs=10, batch_size=32, print_step=1, early_stopping=True, patient=5, toc=0.0001, random_state=None, regularize=None, shuffle=True, save_best_model=True ): """NumpySGDBaseEstimator initial method. Args: loss: A str in {"mse"}, default = "mse" alpha: the regularize rate. A float number and must be greater than 0, default = 0.001. fit_intercept: if fit intercept. A bool value, default = True. save_param_list: if save param of the train process. A bool value, default = True. learning_rate: learning rate. A positive float number, default = 0.001. epochs: epochs. A positive int number, default = 10. batch_size: batch size. A positive int number, default = 32. early_stopping: if early stopping when loss don't reduce again. A bool value, default = True patient: Number of epochs that do not reduce loss continuously, patient only takes effect when early_stopping is True. A positive int number, default = 5. toc: The threshold that symbolizes loss no longer decreases, toc only takes effect when early_stopping is True. A float number, default = 0.001 random_state: random seed. A positive int number if random_state is not None else None, default = None. regularize: regularize. A str value in {"l2"} if regularize is not None else None, default = None. shuffle: if shuffle the train data. A bool value, default = True. save_best_model: if save the best model params as the final model. A bool value, defalut = True. Raises: AssertionError: Some parameters do not match. """ ( loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, print_step, early_stopping, patient, toc, random_state, regularize, shuffle, save_best_model ) = self._init_validation( loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, print_step, early_stopping, patient, toc, random_state, regularize, shuffle, save_best_model ) self.random_state = None if random_state: self.random_state = random_state np.random.seed(self.random_state) loss_func_dict = {"mse": self._mse} loss_gradient_func_dict = {"mse": self._mse_gradient} self._loss_func = loss_func_dict[loss] self._gradient_func = loss_gradient_func_dict[loss] self.alpha = alpha self.intercept_ = None if fit_intercept: self.intercept_ = np.random.randn() self.save_param_list = save_param_list self.learning_rate = learning_rate self.epochs = epochs self.batch_size = batch_size self._print_step = print_step self.early_stopping = early_stopping self.patient = patient self.toc = toc self.regularize = regularize self.shuffle = shuffle self.save_best_model = save_best_model self._X_train = None self._y_train = None self.n_iter_ = 0 self.coef_ = None self.best_loss = float("inf") self.best_coef_ = None self.best_intercept_ = None self.best_epoch = 1 self.coef_list = [] self.intercept_list = [] self.valid_loss = [] self.train_loss = [] def fit(self, X_train, y_train, validation_data=None): """train model methed. Args: X_train: A np.ndarray matrix of (n_samples, n_features) shape, data type must be continuous value type. y_train: A np.ndarray array of (n_samples, ) shape, data type must be continuous value type. validation: the validation data for validate the model. A tuple like (X_valid, y_valid) , the shape of X_valid and y_valid is like X_train and y_train. Default = None. """ self._X_train, self._y_train = self._fit_validation(X_train, y_train) X_valid, y_valid = self._validation_data_valid(validation_data) if self.coef_ is None: self.coef_ = np.random.randn(self._X_train.shape[1]) current_patient = 0 last_valid_loss = None self.coef_list.append(copy.deepcopy(self.coef_)) self.intercept_list.append(self.intercept_) for epoch in range(self.epochs): # train model self._fit_train() # validate model valid_mean_loss = self._fit_valid(X_valid, y_valid, epoch) self.n_iter_ += 1 # early stopping if self.early_stopping and epoch != 0: if last_valid_loss - valid_mean_loss < self.toc: current_patient += 1 else: current_patient = 0 if current_patient >= self.patient: break last_valid_loss = valid_mean_loss if self.save_best_model: self.coef_ = self.best_coef_ self.intercept_ = self.best_intercept_ self._final_print() return self def _final_print(self): print( f"Actual iter epoch: {self.n_iter_}, best epoch: {self.best_epoch}, " f"best loss: {self.best_loss}, best coef: {self.best_coef_}, " f"best intercept: {self.best_intercept_}" ) def _fit_train(self): train_data = self._batch_generator(self._X_train, self._y_train, self.shuffle) for X_batch, y_batch in train_data: y_pred = self.predict(X_batch, _miss_valid=True) coef_gradient, intercept_gradient = self._gradient_func( y_batch, y_pred, X_batch ) self.coef_ -= self.learning_rate * coef_gradient if self.intercept_: self.intercept_ -= self.learning_rate * intercept_gradient train_loss_last_batch = self._loss_func(y_batch, y_pred) self.train_loss.append(train_loss_last_batch) def _fit_valid(self, X_valid, y_valid, epoch): valid_data = self._batch_generator(X_valid, y_valid, self.shuffle) valid_sum_loss = 0 for X_valid_batch, y_valid_batch in valid_data: valid_batch_pred = self.predict(X_valid_batch) loss = self._loss_func(y_valid_batch, valid_batch_pred) valid_sum_loss += loss valid_mean_loss = valid_sum_loss / (X_valid.shape[0] // self.batch_size) if valid_mean_loss < self.best_loss: self.best_loss = valid_mean_loss self.best_coef_ = self.coef_ self.best_intercept_ = self.intercept_ self.best_epoch = epoch + 1 if self.save_param_list: self.coef_list.append(copy.deepcopy(self.coef_)) self.intercept_list.append(self.intercept_) if (epoch + 1) % self._print_step == 0: print(f"Epoch {epoch + 1}: valid_data loss: {valid_mean_loss}") self.valid_loss.append(valid_mean_loss) return valid_mean_loss def _validation_data_valid(self, validation_data): if validation_data: assert isinstance(validation_data, tuple) and len(validation_data) == 2 X_valid, y_valid = validation_data X_valid, y_valid = self._fit_validation(X_valid, y_valid) else: X_train_len = int(self._X_train.shape[0] * 0.25) X_valid = self._X_train[X_train_len:] y_valid = self._y_train[X_train_len:] self._X_train = self._X_train[:X_train_len] self._y_train = self._y_train[:X_train_len] return X_valid, y_valid def _batch_generator(self, X_data, y_data, shuffle=True): step = 0 steps_per_epoch = X_data.shape[0] // self.batch_size while steps_per_epoch > step: if shuffle: index = np.random.choice(X_data.shape[0], self.batch_size) else: index = np.arange(self.batch_size * step, self.batch_size * (step + 1)) yield X_data[index], y_data[index] step += 1 def _regularize_gradient(self, coef_gradient): if self.regularize is None: return coef_gradient elif self.regularize == "l2": return coef_gradient + 2 * self.alpha * self.coef_ def _init_validation( self, loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, print_step, early_stopping, patient, toc, random_state, regularize, shuffle, save_best_model ): loss_key_set = {"mse", "cross_entropy"} assert loss in loss_key_set assert isinstance(alpha, (int, float)) assert 0 < alpha assert isinstance(fit_intercept, bool) assert isinstance(save_param_list, bool) assert isinstance(learning_rate, (int, float)) assert 0 < learning_rate assert isinstance(epochs, int) and epochs >= 1 assert isinstance(batch_size, int) and batch_size >= 1 assert isinstance(print_step, int) and print_step >= 1 assert isinstance(early_stopping, bool) assert isinstance(patient, int) and patient >= 2 assert isinstance(toc, (int, float)) and toc > 0.0 assert (random_state is None) or isinstance(random_state, int) regularize_key_set = {None, "l2"} assert regularize in regularize_key_set assert isinstance(shuffle, bool) assert isinstance(save_best_model, bool) return ( loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, print_step, early_stopping, patient, toc, random_state, regularize, shuffle, save_best_model ) class NumpySGDRegressor(NumpySGDBaseEstimator, NumpyBaseLinearRegressor): """SGD Regressor model with numpy, explicitly inherits from NumpyBaseLinearRegression and NumpySGDBaseEstimator already. Attributes: _X_train:feature data for training. A np.ndarray matrix of (n_samples, n_features) shape, data type must be continuous value type. _y_train:label data for training. A np.ndarray array of (n_samples, ) shape, data type must be continuous value. coef_: coef of linear regressor. A np.ndarray matrix of (n_features, ) shape. intercept_: intercept of regressor. A np.ndarray integer if intercept_ is not None else None. alpha: the regularize rate. A float number and must be greater than 0, default = 0.001. save_param_list: if save param of the train process. A bool value, default = True. coef_list: list of coef param from the train process, every coef is a np.ndarray of (n_features, ) shape. intercept_list: list of intercept param from the train process, every intercept is a np.ndarray float number. learning_rate: learning rate. A positive float number, default = 0.001. epochs: epochs. A positive int number, default = 10. batch_size: batch size. A positive int number, default = 32. early_stopping: if early stopping when loss don't reduce again. A bool value, default = True. patient: Number of epochs that do not reduce loss continuously, patient only takes effect when early_stopping is True. A positive int number, default = 5. toc: The threshold that symbolizes loss no longer decreases, toc only takes effect when early_stopping is True. A float number, default = 0.001 random_state: random seed. A positive int number if random_state is not None else None, default = None. regularize: regularize. A str value in {"l1", "l2"} if regularize is not None else None, default = None. best_loss: best loss of the train process. A np.ndarray float number. best_coef: best coef of the train process. A np.ndarray array of (n_features, 1) shape. best_intercept_: best intercept of the train process. A np.ndarray number. train_loss: list of train loss from the train process. every loss is a np.ndarray float number. valid_loss: list of valid loss from the train process. every loss is a np.ndarray float number. n_iter: the actual iteration of train process. A int number, initial = 0. """ def __init__( self, loss="mse", alpha=0.0001, fit_intercept=True, save_param_list=True, learning_rate=0.0001, epochs=10, batch_size=32, print_step=1, early_stopping=True, patient=5, toc=0.0001, random_state=None, regularize=None, shuffle=True ): """NumpySGDRegressor initial method. Args: loss: A str in {"mse"}, default = "mse" alpha: the regularize rate. A float number and must be greater than 0, default = 0.001. fit_intercept: if fit intercept. A bool value, default = True. save_param_list: if save param of the train process. A bool value, default = True. learning_rate: learning rate. A positive float number, default = 0.001. epochs: epochs. A positive int number, default = 10. batch_size: batch size. A positive int number, default = 32. early_stopping: if early stopping when loss don't reduce again. A bool value, default = True patient: Number of epochs that do not reduce loss continuously, patient only takes effect when early_stopping is True. A positive int number, default = 5. toc: The threshold that symbolizes loss no longer decreases, toc only takes effect when early_stopping is True. A float number, default = 0.001 random_state: random seed. A positive int number if random_state is not None else None, default = None. regularize: regularize. A str value in {"l2"} if regularize is not None else None, default = None. shuffle: if shuffle the train data. A bool value, default = True. save_best_model: if save the best model params as the final model. A bool value, defalut = True Raises: AssertionError: Some parameters do not match. """ super().__init__( loss=loss, alpha=alpha, fit_intercept=fit_intercept, save_param_list=save_param_list, learning_rate=learning_rate, epochs=epochs, batch_size=batch_size, print_step=print_step, early_stopping=early_stopping, patient=patient, toc=toc, random_state=random_state, regularize=regularize, shuffle=shuffle ) def _mse_gradient(self, y_true, y_pred, x): difference_y = y_pred - y_true intercept_gradient = None if self.intercept_: intercept_gradient = np.sum(difference_y) * 2 / self.batch_size coef_gradient = ( np.sum(difference_y.reshape([-1, 1]) * x, axis=0) * 2 / self.batch_size ) coef_gradient = self._regularize_gradient(coef_gradient) return coef_gradient, intercept_gradient def _mse(self, y, pred): return np.sum(np.square(y - pred)) / y.shape[0] class TFSGDRegressor(TFBaseLinearRegressor): """Linear SGD regressor class with tensorflow, explicitly inherits from TFBaseLinearRegressor already. Attributes: _X_train:feature data for training. A tf.Tensor matrix of (n_samples, n_features) shape, data type must be continuous value type. _y_train:label data for training. A tf.Tensor array of (n_samples, ) shape, data type must be continuous value. coef_: coef of linear regressor. A tf.Tensor matrix of (n_features, 1) shape. intercept_: intercept of regressor. A tf.Tensor integer if intercept_ is not None else None. alpha: the regularize rate. A float number and must be greater than 0, default = 0.001. save_param_list: if save param of the train process. A bool value, default = True. coef_list: list of coef param from the train process, every coef is a np.ndarray of (n_features, ) shape. intercept_list: list of intercept param from the train process, every intercept is a np.ndarray float number. learning_rate: learning rate. A positive float number, default = 0.001. epochs: epochs. A positive int number, default = 10. batch_size: batch size. A positive int number, default = 32. early_stopping: if early stopping when loss don't reduce again. A bool value, default = True. patient: Number of epochs that do not reduce loss continuously, patient only takes effect when early_stopping is True. A positive int number, default = 5. toc: The threshold that symbolizes loss no longer decreases, toc only takes effect when early_stopping is True. A float number, default = 0.001 random_state: random seed. A positive int number if random_state is not None else None, default = None. regularize: regularize. A str value in {"l1", "l2"} if regularize is not None else None, default = None. best_loss: best loss of the train process. A np.ndarray float number. best_coef: best coef of the train process. A tf.Tensor array of (n_features, 1) shape. best_intercept_: best intercept of the train process. A tf.Tensor number. train_loss: list of train loss from the train process. every loss is a np.ndarray float number. valid_loss: list of valid loss from the train process. every loss is a np.ndarray float number. n_iter: the actual iteration of train process. A int number, initial = 0. save_best_model: if save the best model params as the final model. A bool value, defalut = True. """ def __init__( self, loss="mse", alpha=0.001, fit_intercept=True, save_param_list=True, learning_rate=0.001, epochs=10, batch_size=32, early_stopping=True, patient=5, toc=0.001, random_state=None, regularize=None, save_best_model=True ): """TFSGDRegressor initial method. Args: loss: A str in {"mse"}, default = "mse" alpha: the regularize rate. A float number and must be greater than 0, default = 0.001. fit_intercept: if fit intercept. A bool value, default = True. save_param_list: if save param of the train process. A bool value, default = True. learning_rate: learning rate. A positive float number, default = 0.001. epochs: epochs. A positive int number, default = 10. batch_size: batch size. A positive int number, default = 32. early_stopping: if early stopping when loss don't reduce again. A bool value, default = True patient: Number of epochs that do not reduce loss continuously, patient only takes effect when early_stopping is True. A positive int number, default = 5. toc: The threshold that symbolizes loss no longer decreases, toc only takes effect when early_stopping is True. A float number, default = 0.001 random_state: random seed. A positive int number if random_state is not None else None, default = None. regularize: regularize. A str value in {"l1", "l2"} if regularize is not None else None, default = None. save_best_model: if save the best model params as the final model. A bool value, defalut = True Raises: AssertionError: Some parameters do not match. """ ( loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, early_stopping, patient, toc, random_state, regularize, save_best_model ) = self._init_validation( loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, early_stopping, patient, toc, random_state, regularize, save_best_model ) self.random_state = random_state if isinstance(self.random_state, int): tf.random.set_seed(self.random_state) loss_func_dict = {"mse": keras.losses.mean_squared_error} self._loss_func = loss_func_dict[loss] metric_dict = {"mse": keras.metrics.MeanSquaredError} self._metric = metric_dict[loss]() self.alpha = alpha self.intercept_ = None if fit_intercept: self.intercept_ = tf.Variable(tf.random.normal([])) self.save_param_list = save_param_list self.coef_list = [] self.intercept_list = [] self.learning_rate = learning_rate self.epochs = epochs self.batch_size = batch_size self.early_stopping = early_stopping self.patient = patient self.toc = toc self.regularize = regularize self._regularizer = lambda: 0 if self.regularize: reg_dict = {"l1": keras.regularizers.l1(l1=self.alpha), "l2": keras.regularizers.l2(l2=self.alpha)} self._regularizer = reg_dict[self.regularize] self.save_best_model = save_best_model self._X_train = None self._y_train = None self.coef_ = None self._optimizer = keras.optimizers.SGD(learning_rate=self.learning_rate) self.best_loss = tf.constant(float("inf")).numpy() self.best_coef_ = None self.best_intercept_ = None self.train_loss = [] self.valid_loss = [] self.n_iter = 0 def fit(self, X_train, y_train, validation=None): """train model methed. Args: X_train: A np.ndarray matrix of (n_samples, n_features) shape, data type must be continuous value type. y_train: A np.ndarray array of (n_samples, ) shape, data type must be continuous value type. validation: the validation data for validate the model. A tuple like (X_valid, y_valid) , the shape of X_valid and y_valid is like X_train and y_train. Default = None. Returns: return self object. """ self._X_train, self._y_train = self._fit_validation(X_train, y_train) X_train, y_train = self._X_train, self._y_train if self.coef_ is None: self.coef_ = tf.Variable(tf.random.normal(shape=[self._X_train.shape[1], 1])) X_valid, y_valid, X_train, y_train = self._validation_valid(validation, X_train, y_train) steps_per_epoch = self._X_train.shape[0] // self.batch_size if self.early_stopping: current_patient = 0 last_val_loss = 0 for epoch in range(self.epochs): # 1. train train_data = self._batch_generator(X_train, y_train) epoch_time = 0 print(f"Epoch {epoch + 1}/{self.epochs}") self._metric.reset_states() for step, (X_train_batch, y_train_batch) in enumerate(train_data): start = time.time() self._fit_step(X_train_batch, y_train_batch) epoch_time, mean_step_time, train_loss = self._step_print(steps_per_epoch, step, epoch_time, start) # 2. valid val_loss = self._epoch_valid_and_print(X_valid, y_valid, epoch_time, mean_step_time, steps_per_epoch) # 3. save train process self._save_train_process(val_loss, train_loss) self.n_iter += 1 # 4. early stopping if self.early_stopping: if epoch != 0: if last_val_loss - val_loss < self.toc: current_patient += 1 else: current_patient = 0 if current_patient >= self.patient: break last_val_loss = val_loss if self.save_best_model: self._save_best_params() return self def _save_best_params(self): self.coef_ = copy.deepcopy(self.best_coef_) if self.intercept_ is not None: self.intercept_ = copy.deepcopy(self.best_intercept_) def _save_train_process(self, val_loss, train_loss): if val_loss < self.best_loss: self.best_loss = copy.deepcopy(val_loss) self.best_coef_ = copy.deepcopy(self.coef_) if self.intercept_ is not None: self.best_intercept_ = copy.deepcopy(self.intercept_) if self.save_param_list: self.coef_list.append(self.coef_.numpy().reshape([-1])) if self.intercept_ is not None: self.intercept_list.append(self.intercept_.numpy()) self.train_loss.append(train_loss) self.valid_loss.append(val_loss) def _validation_valid(self, validation, X_train, y_train): if validation is None: n_samples = int(self._X_train.shape[0] * 0.8) idx = tf.random.shuffle(tf.range(self._X_train.shape[0])) X_train = tf.gather(self._X_train, indices=idx[:n_samples]) y_train = tf.gather(self._y_train, indices=idx[:n_samples]) X_valid = tf.gather(self._X_train, indices=idx[n_samples:]) y_valid = tf.gather(self._y_train, indices=idx[n_samples:]) else: X_valid, y_valid = self._fit_validation(*validation) return X_valid, y_valid, X_train, y_train @tf.function def _call(self, X): y = tf.matmul(X, self.coef_) if self.intercept_ is not None: y = tf.add(y, self.intercept_) return tf.reshape(y, shape=[-1]) def _batch_generator(self, X, y, shuffle=True): dataset = tf.data.Dataset.from_tensor_slices((X, y)) if shuffle: dataset = dataset.shuffle(buffer_size=self._X_train.shape[0]) dataset = dataset.batch(self.batch_size) return dataset def _init_validation( self, loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, early_stopping, patient, toc, random_state, regularize, save_best_model, ): assert loss in {"mse"} assert isinstance(alpha, (int, float)) and 0 < alpha assert isinstance(fit_intercept, bool) assert isinstance(save_param_list, bool) assert isinstance(learning_rate, (int, float)) and 0 < learning_rate <= 1.0 assert isinstance(epochs, int) and epochs >= 1 assert isinstance(batch_size, int) and batch_size >= 1 assert isinstance(early_stopping, bool) assert isinstance(patient, int) and patient >= 2 assert isinstance(toc, (int, float)) and 0 < toc assert isinstance(random_state, (type(None), int)) if isinstance(random_state, int): assert random_state >= 0 assert regularize in {"l2", "l1", None} assert isinstance(save_best_model, bool) return ( loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, early_stopping, patient, toc, random_state, regularize, save_best_model ) def _fit_step(self, X_train_batch, y_train_batch): # 1.open a tape and calculate loss under the tape with tf.GradientTape() as tape: y_pred_batch = self._call(X_train_batch) loss = self._loss_func(y_train_batch, y_pred_batch) if self.regularize: loss += self._regularizer(self.coef_) if self.intercept_ is not None: # 2.use tape to calculate gradients by loss coef_grad, intercept_grad = tape.gradient( loss, [self.coef_, self.intercept_] ) # 3.use optimizer to update params by gradients # self.coef_.assign_sub(coef_grad * self.learning_rate) # self.intercept_.assign_sub(intercept_grad * self.learning_rate) self._optimizer.apply_gradients( [(coef_grad, self.coef_), (intercept_grad, self.intercept_)] ) else: coef_grad = tape.gradient(loss, self.coef_) self._optimizer.apply_gradients([(coef_grad, self.coef_)]) # 4.use metric to calculate the mean loss for output self._metric(y_train_batch, y_pred_batch) def _step_print(self, steps_per_epoch, step, epoch_time, start): steps_str_len = len(str(steps_per_epoch)) done_count = int((step + 1) / steps_per_epoch * 30) done_str = "=" * done_count to_do_str = "." * (30 - 1 - done_count) end = time.time() step_time = end - start epoch_time += step_time mean_step_time = epoch_time / (step + 1) remain_time = (steps_per_epoch - (step + 1)) * mean_step_time remain_time = sec2time(remain_time) print( f"\r{step + 1:{steps_str_len}}/{steps_per_epoch} [{done_str}>{to_do_str}] - ETA: {remain_time} " f"- loss: {self._metric.result().numpy():.4f}", end="", ) return epoch_time, mean_step_time, self._metric.result().numpy() def _epoch_valid_and_print(self, X_valid, y_valid, epoch_time, mean_step_time, steps_per_epoch): valid_data = self._batch_generator(X_valid, y_valid, shuffle=False) train_mean_loss = self._metric.result().numpy() self._metric.reset_states() for valid_step, (X_valid_batch, y_valid_batch) in enumerate(valid_data): y_valid_pred = self._call(X_valid_batch) self._metric(y_valid_batch, y_valid_pred) epoch_time = sec2time(epoch_time) mean_step_time = sec2time(mean_step_time) print( f"\r{steps_per_epoch}/{steps_per_epoch} [{'=' * 30}] - {epoch_time} {mean_step_time}/step - " f"loss: {train_mean_loss:.4f} - val_loss: {self._metric.result().numpy():.4f}" ) return self._metric.result().numpy() class TorchSGDRegressor(TorchBaseLinearRegressor): """Linear SGD regressor class with tensorflow, explicitly inherits from TFBaseLinearRegressor already. Attributes: _X_train:feature data for training. A torch.Tensor matrix of (n_samples, n_features) shape, data type must be continuous value type. _y_train:label data for training. A torch.Tensor array of (n_samples, ) shape, data type must be continuous value. coef_: coef of linear regressor. A torch.Tensor matrix of (n_features, 1) shape. intercept_: intercept of regressor. A torch.Tensor integer if intercept_ is not None else None. alpha: the regularize rate. A float number and must be greater than 0, default = 0.001. save_param_list: if save param of the train process. A bool value, default = True. coef_list: list of coef param from the train process, every coef is a np.ndarray of (n_features, ) shape. intercept_list: list of intercept param from the train process, every intercept is a np.ndarray float number. learning_rate: learning rate. A positive float number, default = 0.001. epochs: epochs. A positive int number, default = 10. batch_size: batch size. A positive int number, default = 32. early_stopping: if early stopping when loss don't reduce again. A bool value, default = True. patient: Number of epochs that do not reduce loss continuously, patient only takes effect when early_stopping is True. A positive int number, default = 5. toc: The threshold that symbolizes loss no longer decreases, toc only takes effect when early_stopping is True. A float number, default = 0.001 random_state: random seed. A positive int number if random_state is not None else None, default = None. regularize: regularize. A str value in {"l1", "l2"} if regularize is not None else None, default = None. best_loss: best loss of the train process. A np.ndarray float number. best_coef: best coef of the train process. A torch.Tensor array of (n_features, 1) shape. best_intercept_: best intercept of the train process. A torch.Tensor number. train_loss: list of train loss from the train process. every loss is a np.ndarray float number. valid_loss: list of valid loss from the train process. every loss is a np.ndarray float number. n_iter: the actual iteration of train process. A int number, initial = 0. save_best_model: if save the best model params as the final model. A bool value, defalut = True. """ def __init__( self, loss="mse", alpha=0.001, fit_intercept=True, save_param_list=True, learning_rate=0.001, epochs=10, batch_size=32, early_stopping=True, patient=5, toc=0.001, random_state=None, regularize=None, save_best_model=True ): """TorchSGDRegressor initial method. Args: loss: A str in {"mse"}, default = "mse" alpha: the regularize rate. A float number and must be greater than 0, default = 0.001. fit_intercept: if fit intercept. A bool value, default = True. save_param_list: if save param of the train process. A bool value, default = True. learning_rate: learning rate. A positive float number, default = 0.001. epochs: epochs. A positive int number, default = 10. batch_size: batch size. A positive int number, default = 32. early_stopping: if early stopping when loss don't reduce again. A bool value, default = True patient: Number of epochs that do not reduce loss continuously, patient only takes effect when early_stopping is True. A positive int number, default = 5. toc: The threshold that symbolizes loss no longer decreases, toc only takes effect when early_stopping is True. A float number, default = 0.001 random_state: random seed. A positive int number if random_state is not None else None, default = None. regularize: regularize. A str value in {"l1", "l2"} if regularize is not None else None, default = None. save_best_model: if save the best model params as the final model. A bool value, defalut = True Raises: AssertionError: Some parameters do not match. """ ( loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, early_stopping, patient, toc, random_state, regularize, save_best_model ) = self._init_validation( loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, early_stopping, patient, toc, random_state, regularize, save_best_model ) self.random_state = random_state if isinstance(self.random_state, int): torch.manual_seed(self.random_state) loss_func_dict = {"mse": F.mse_loss} self._loss_func = loss_func_dict[loss] self.alpha = alpha self.intercept_ = None if fit_intercept: self.intercept_ = torch.normal(mean=0.0, std=1.0, size=[]).requires_grad_() self.save_param_list = save_param_list self.coef_list = [] self.intercept_list = [] self.learning_rate = learning_rate self.epochs = epochs self.batch_size = batch_size self.early_stopping = early_stopping self.patient = patient self.toc = toc self.regularize = regularize self._regularizer = lambda: 0 if self.regularize: reg_dict = {"l1": self._l1_term, "l2": self._l2_term} self._regularizer = reg_dict[self.regularize] self.save_best_model = save_best_model self._X_train = None self._y_train = None self.coef_ = None self._optimizer = None self.best_loss = torch.tensor(float("inf")).item() self.best_coef_ = None self.best_intercept_ = None self.train_loss = [] self.valid_loss = [] self.n_iter = 0 def _l1_term(self, w): return self.alpha * torch.sum(torch.abs(w)) def _l2_term(self, w): return self.alpha * torch.sum(torch.square(w)) def fit(self, X_train, y_train, validation=None): """train model methed. Args: X_train: A np.ndarray matrix of (n_samples, n_features) shape, data type must be continuous value type. y_train: A np.ndarray array of (n_samples, ) shape, data type must be continuous value type. validation: the validation data for validate the model. A tuple like (X_valid, y_valid) , the shape of X_valid and y_valid is like X_train and y_train. Default = None. Returns: return self object. """ self._X_train, self._y_train = self._fit_validation(X_train, y_train) X_train, y_train = self._X_train, self._y_train if self.coef_ is None: self.coef_ = torch.normal(mean=0.0, std=1.0, size=(self._X_train.shape[1],)).requires_grad_() if self._optimizer is None: params = [self.coef_] if self.intercept_ is None else [self.coef_, self.intercept_] self._optimizer = torch.optim.SGD(params=params, lr=self.learning_rate) train_dataset, valid_dataset = self._validation_valid(validation, X_train, y_train) steps_per_epoch = self._X_train.shape[0] // self.batch_size if self.early_stopping: current_patient = 0 last_val_loss = 0 valid_loader = DataLoader(valid_dataset, batch_size=self.batch_size, shuffle=False) for epoch in range(self.epochs): # 1. train train_loader = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True) epoch_time = 0 print(f"Epoch {epoch + 1}/{self.epochs}") train_sum_loss = 0 for step, (X_train_batch, y_train_batch) in enumerate(train_loader): start = time.time() train_sum_loss = self._fit_step(X_train_batch, y_train_batch, train_sum_loss) train_mean_loss = train_sum_loss / (step + 1) epoch_time, mean_step_time = self._step_print(steps_per_epoch, step, epoch_time, start, train_mean_loss) # 2. valid val_loss = self._epoch_valid_and_print(epoch_time, mean_step_time, steps_per_epoch, train_mean_loss, valid_loader) # 3. save train process self._save_train_process(val_loss, train_mean_loss) self.n_iter += 1 # 4. early stopping if self.early_stopping: if epoch != 0: if last_val_loss - val_loss < self.toc: current_patient += 1 else: current_patient = 0 if current_patient >= self.patient: break last_val_loss = val_loss if self.save_best_model: self._save_best_params() return self def _save_best_params(self): self.coef_ = copy.deepcopy(self.best_coef_) if self.intercept_ is not None: self.intercept_ = copy.deepcopy(self.best_intercept_) def _save_train_process(self, val_loss, train_loss): if val_loss < self.best_loss: self.best_loss = val_loss.item() self.best_coef_ = copy.deepcopy(self.coef_) if self.intercept_ is not None: self.best_intercept_ = copy.deepcopy(self.intercept_) if self.save_param_list: self.coef_list.append(copy.deepcopy(self.coef_.detach().numpy())) if self.intercept_ is not None: self.intercept_list.append(copy.deepcopy(self.intercept_.item())) self.train_loss.append(train_loss) self.valid_loss.append(val_loss) def _validation_valid(self, validation, X_train, y_train): dataset = TorchBaseDataset(X_train, y_train) if validation is None: n_samples = int(self._X_train.shape[0] * 0.8) train_dataset, valid_dataset = random_split( dataset=dataset, lengths=[n_samples, self._X_train.shape[0] - n_samples]) else: X_valid, y_valid = self._fit_validation(*validation) train_dataset = dataset valid_dataset = TorchBaseDataset(X_valid, y_valid) return train_dataset, valid_dataset def _call(self, X): y = torch.matmul(X, self.coef_) if self.intercept_ is not None: y = torch.add(y, self.intercept_) return y def _init_validation( self, loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, early_stopping, patient, toc, random_state, regularize, save_best_model, ): assert loss in {"mse"} assert isinstance(alpha, (int, float)) and 0 < alpha assert isinstance(fit_intercept, bool) assert isinstance(save_param_list, bool) assert isinstance(learning_rate, float) and 0 < learning_rate <= 1.0 assert isinstance(epochs, int) and epochs >= 1 assert isinstance(batch_size, int) and batch_size >= 1 assert isinstance(early_stopping, bool) assert isinstance(patient, int) and patient >= 2 assert isinstance(toc, (int, float)) and 0 < toc assert isinstance(random_state, (type(None), int)) if isinstance(random_state, int): assert random_state >= 0 assert regularize in {"l2", "l1", None} assert isinstance(save_best_model, bool) return ( loss, alpha, fit_intercept, save_param_list, learning_rate, epochs, batch_size, early_stopping, patient, toc, random_state, regularize, save_best_model ) def _fit_step(self, X_train_batch, y_train_batch, train_sum_loss): # 1.calculate loss y_pred_batch = self._call(X_train_batch) loss = self._loss_func(y_train_batch, y_pred_batch) if self.regularize: loss += self._regularizer(self.coef_) # optimizer clean gradient self._optimizer.zero_grad() # 2.calculate gradients by loss loss.backward() # 3.use optimizer to update params by gradients self._optimizer.step() # 4.use metric to calculate the mean loss for output train_sum_loss += loss return train_sum_loss def _step_print(self, steps_per_epoch, step, epoch_time, start, train_mean_loss): steps_str_len = len(str(steps_per_epoch)) done_count = int((step + 1) / steps_per_epoch * 30) done_str = "=" * done_count to_do_str = "." * (30 - 1 - done_count) end = time.time() step_time = end - start epoch_time += step_time mean_step_time = epoch_time / (step + 1) remain_time = (steps_per_epoch - (step + 1)) * mean_step_time remain_time = sec2time(remain_time) print( f"\r{step + 1:{steps_str_len}}/{steps_per_epoch} [{done_str}>{to_do_str}] - ETA: {remain_time} - loss: {train_mean_loss:.4f}", end="", ) return epoch_time, mean_step_time def _epoch_valid_and_print(self, epoch_time, mean_step_time, steps_per_epoch, train_mean_loss, valid_loader): valid_mean_loss = 0 for valid_step, (X_valid_batch, y_valid_batch) in enumerate(valid_loader): y_valid_pred = self._call(X_valid_batch) loss = self._loss_func(y_valid_batch, y_valid_pred) valid_mean_loss += loss valid_mean_loss /= (valid_step + 1) epoch_time = sec2time(epoch_time) mean_step_time = sec2time(mean_step_time) print( f"\r{steps_per_epoch}/{steps_per_epoch} [{'=' * 30}] - {epoch_time} {mean_step_time}/step - loss: {train_mean_loss:.4f} - val_loss: {valid_mean_loss:.4f}" ) return valid_mean_loss

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0.60102

6,385

50,574

4.519969

0.043226

0.03018

0.013167

0.019958

0.842516

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null

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e9cc139a0dc9da580cde5e301ff0b5f8450366b0

225

py

Python

chainladder/__init__.py

AragondaJyosna/chainladder-python

45f51365279d6a30eac6d74f5d3ea492d7b7e1d8

[ "MIT" ]

1

2019-03-03T06:01:26.000Z

chainladder/__init__.py

AragondaJyosna/chainladder-python

45f51365279d6a30eac6d74f5d3ea492d7b7e1d8

[ "MIT" ]

null

chainladder/__init__.py

AragondaJyosna/chainladder-python

45f51365279d6a30eac6d74f5d3ea492d7b7e1d8

[ "MIT" ]

null

from chainladder.utils import * from chainladder.core import * from chainladder.development import * from chainladder.tails import * from chainladder.methods import * from chainladder.workflow import * __version__ = '0.2.6'

25

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756b092e792b370d5eb0aea8f078b43c276cba0f

131

py

Python

moon/test_main.py

m3d/osgar_archive_2020

556b534e59f8aa9b6c8055e2785c8ae75a1a0a0e

[ "MIT" ]

null

moon/test_main.py

m3d/osgar_archive_2020

556b534e59f8aa9b6c8055e2785c8ae75a1a0a0e

[ "MIT" ]

null

moon/test_main.py

m3d/osgar_archive_2020

556b534e59f8aa9b6c8055e2785c8ae75a1a0a0e

[ "MIT" ]

1

2022-01-02T04:06:01.000Z

import unittest from unittest.mock import MagicMock class MoonMainTest(unittest.TestCase): pass # vim: expandtab sw=4 ts=4

13.1

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f9337f303796a54830341038887f8c94e8376c53

43

py

Python

discord_handler/__init__.py

Tim232/DiscordHandler

c72a9131a55ad429f4f90d86340df432cd0494dd

[ "MIT" ]

16

2019-01-14T03:44:37.000Z

2022-01-29T12:55:00.000Z

discord_handler/__init__.py

Tim232/DiscordHandler

c72a9131a55ad429f4f90d86340df432cd0494dd

[ "MIT" ]

4

2020-08-11T06:16:33.000Z

2022-02-07T20:17:54.000Z

discord_handler/__init__.py

Tim232/DiscordHandler

c72a9131a55ad429f4f90d86340df432cd0494dd

[ "MIT" ]

9

2017-06-09T07:16:39.000Z

2022-02-07T20:18:02.000Z

from .DiscordHandler import DiscordHandler

21.5

42

0.883721

4

43

9.5

0.75

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0.974359

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1

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1

0

1

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6

f93883f976bb6411969fe408641d2dcd05ce9d41

311

py

Python

yterModule.py

light-technology/line-bot-multifunction

8d913b278f48069d701fa06a7130abcaeede2ae1

[ "MIT" ]

1

2021-11-14T13:47:48.000Z

yterModule.py

inctoolsproject/mult

cd1eb2f46f17329ca5dda8a331369da86596832b

[ "MIT" ]

null

yterModule.py

inctoolsproject/mult

cd1eb2f46f17329ca5dda8a331369da86596832b

[ "MIT" ]

1

2021-11-01T07:39:16.000Z

# -*- coding: utf-8 -*- from linepy import * #################################################### from liff.ttypes import * #################################################### from ang.ttypes import * #################################################### if __name__ == "__main__": print('此為模組檔案僅供導入使用')

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1

0

1

0

1

0

6

f987e6a38cb69b6eaad4333396f4d42f18db27ca

165

py

Python

raven/projects/Scripts/Pu238Calc.py

arfc/2019-12-bigdata-npps

ebf03664c1d96541956d317f3a305323cf76c23d

[ "CC-BY-4.0" ]

null

raven/projects/Scripts/Pu238Calc.py

arfc/2019-12-bigdata-npps

ebf03664c1d96541956d317f3a305323cf76c23d

[ "CC-BY-4.0" ]

2

2019-10-26T14:32:13.000Z

2019-12-17T17:48:05.000Z

raven/projects/Scripts/Pu238Calc.py

arfc/2019-12-bigdata-npps

ebf03664c1d96541956d317f3a305323cf76c23d

[ "CC-BY-4.0" ]

3

2019-10-25T18:50:31.000Z

2020-06-23T04:17:28.000Z

import MassFractionCalc def evaluate(self): return MassFractionCalc.return_value('Pu238',self.salt_type,self.fuel_type,self.U235F4_mole_frac,self.UF4_mole_frac)

41.25

120

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165

5.5

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0.121212

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0.051613

0.060606

165

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null

0

1

0

1

0

6

f9bc0d12b49a382706e3f6db4d52c5da6e2fe189

37

py

Python

augmentation/__init__.py

mlvc-lab/AIChallenge_4th_Round1

2a7cd64254540a5779bc3d9accdb21ddaa38aa51

[ "MIT" ]

18

2020-12-23T06:06:41.000Z

2020-12-24T04:34:57.000Z

augmentation/__init__.py

mlvc-lab/AIChallenge_4th_Round1

2a7cd64254540a5779bc3d9accdb21ddaa38aa51

[ "MIT" ]

null

augmentation/__init__.py

mlvc-lab/AIChallenge_4th_Round1

2a7cd64254540a5779bc3d9accdb21ddaa38aa51

[ "MIT" ]

null

from .randaugment import RandAugment

18.5

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null

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1

0

1

0

1

0

6

ddb03f02e8b098cc6b0ea0ce10a706d7fc8d89b6

313

py

Python

usbdrive/_file.py

ihrigb/stagebuzzer

dbce1c5fa59a6f22e74d84ccc96d4d1a28a5b680

[ "Apache-2.0" ]

null

usbdrive/_file.py

ihrigb/stagebuzzer

dbce1c5fa59a6f22e74d84ccc96d4d1a28a5b680

[ "Apache-2.0" ]

null

usbdrive/_file.py

ihrigb/stagebuzzer

dbce1c5fa59a6f22e74d84ccc96d4d1a28a5b680

[ "Apache-2.0" ]

null

class File: def is_directory(self) -> bool: pass def get_children(self, extension: str = None) -> list: pass def get_absolute_path(self) -> str: pass def get_name(self) -> str: pass def exists(self) -> bool: pass def parent(self): pass

16.473684

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0

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313

18

59

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null

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1

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1

0

1

0

6

ddb399aa5fdaee31666962e6291bc3fcdc453637

1,617

py

Python

130 Count Inversions/Count_Inversions_test.py

Iftakharpy/AlgoExpert-Questions

f4aef449bfe0ee651d84a92487c3b3bedb3aa739

[ "Apache-2.0" ]

3

2021-11-19T07:32:27.000Z

2022-03-22T13:46:27.000Z

130 Count Inversions/Count_Inversions_test.py

Iftakharpy/AlgoExpert-Questions

f4aef449bfe0ee651d84a92487c3b3bedb3aa739

[ "Apache-2.0" ]

null

130 Count Inversions/Count_Inversions_test.py

Iftakharpy/AlgoExpert-Questions

f4aef449bfe0ee651d84a92487c3b3bedb3aa739

[ "Apache-2.0" ]

5

2022-01-02T11:51:12.000Z

2022-03-22T13:53:32.000Z

from Count_Inversions import countInversions def test_countInversions_case_1(): assert countInversions(array=[2, 3, 3, 1, 9, 5, 6]) == 5 def test_countInversions_case_2(): assert countInversions(array=[]) == 0 def test_countInversions_case_3(): assert countInversions(array=[1, 2, 3, 4, 5, 6, -1]) == 6 def test_countInversions_case_4(): assert countInversions(array=[0, 2, 4, 5, 76]) == 0 def test_countInversions_case_5(): assert countInversions(array=[54, 1, 2, 3, 4]) == 4 def test_countInversions_case_6(): assert countInversions(array=[1, 10, 2, 8, 3, 7, 4, 6, 5]) == 16 def test_countInversions_case_7(): assert countInversions(array=[2, -18]) == 1 def test_countInversions_case_8(): assert countInversions(array=[15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]) == 105 def test_countInversions_case_9(): assert countInversions(array=[5, -1, 2, -4, 3, 4, 19, 87, 762, -8, 0]) == 23 def test_countInversions_case_10(): assert countInversions(array=[1, 1, 1, 1, 1, 1, 1, 1]) == 0 def test_countInversions_case_11(): assert countInversions(array=[1, 1, 1, 1, 0, 1, 1, 1]) == 4 def test_countInversions_case_12(): assert countInversions(array=[2, 2, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3]) == 16 def test_countInversions_case_13(): assert countInversions(array=[3, 1, 2]) == 2 def test_countInversions_case_14(): assert countInversions(array=[3, 2, 1, 1]) == 5 def test_countInversions_case_15(): assert countInversions(array=[10, 7, 2, 3, 1, -9, -86, -862, 234, 312, 3421, 23, 0, 2, 1, 2]) == 62

33.6875

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0.31

0.064

0

0.138249

0.194805

1,617

47

105

34.404255

0.6298

0

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1

0.483871

true

0

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0

0.516129

0

null

0

1

0

null

0

1

0

1

0

1

0

6

fb2e0783067758ff1958c4844710030c6ab3350e

105

py

Python

basemodels/pydantic/manifest/__init__.py

hhio618/hmt-basemodels

be1f7c8c968d86ac9b7feb16cfcde6b6d9b905e3

[ "MIT" ]

3

2020-09-08T15:03:31.000Z

2021-06-30T19:00:45.000Z

basemodels/pydantic/manifest/__init__.py

hhio618/hmt-basemodels

be1f7c8c968d86ac9b7feb16cfcde6b6d9b905e3

[ "MIT" ]

43

2019-02-28T17:43:42.000Z

2022-02-13T11:37:08.000Z

basemodels/pydantic/manifest/__init__.py

hhio618/hmt-basemodels

be1f7c8c968d86ac9b7feb16cfcde6b6d9b905e3

[ "MIT" ]

5

2019-05-09T15:58:07.000Z

2020-12-09T23:24:24.000Z

from .manifest import Manifest, NestedManifest, RequestConfig, TaskData, Webhook, validate_manifest_uris

52.5

104

0.857143

11

105

8

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0

0.085714

105

1

105

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1

0

1

0

1

0

6

fb340ab2b81c4f53d01db5328ed5bdc5764487fb

113

py

Python

lambda/local_exec.py

jossM/manga_scraping

f6cad0ee3ca33ad2083a9f67be5ca29b2dafc8ba

[ "MIT" ]

3

2018-11-05T08:16:13.000Z

2019-03-04T13:35:53.000Z

lambda/local_exec.py

jossM/manga_scraping

f6cad0ee3ca33ad2083a9f67be5ca29b2dafc8ba

[ "MIT" ]

7

2019-01-06T14:49:31.000Z

2021-12-13T20:44:48.000Z

lambda/local_exec.py

jossM/manga_scraping

f6cad0ee3ca33ad2083a9f67be5ca29b2dafc8ba

[ "MIT" ]

null

from main import handle_scheduled_scraping if __name__ == '__main__': handle_scheduled_scraping(None, None)

22.6

42

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null

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1

0

6

5515dbcb7ffdcfd87455bf8db39bc6b69d0a15eb

27

py

Python

src/euler_python_package/euler_python/medium/p330.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

src/euler_python_package/euler_python/medium/p330.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

src/euler_python_package/euler_python/medium/p330.py

wilsonify/euler

5214b776175e6d76a7c6d8915d0e062d189d9b79

[ "MIT" ]

null

def problem330(): pass

9

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null

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6

9b8adbcdda0c9cf0a9a12e3289f24a3de8296ac4

67

py

Python

gerardo/__init__.py

kevinywlui/gerardo

71d8846a248401f635166b420e09c164475ba53b

[ "MIT" ]

1

2019-08-28T23:34:17.000Z

gerardo/__init__.py

kevinywlui/gerardo

71d8846a248401f635166b420e09c164475ba53b

[ "MIT" ]

null

gerardo/__init__.py

kevinywlui/gerardo

71d8846a248401f635166b420e09c164475ba53b

[ "MIT" ]

null

from .psql_insert import psql_handler, psql_insert, psql_mp_insert

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null

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1

0

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0

6

9b98bbd2dd5d8244493c2f8b694f935d10550918

12,042

py

Python

tests/test_signals.py

david-a-joy/multilineage-organoid

9b9848cfa5ee0d051b2a9645f9ffd8b9423beec8

[ "BSD-3-Clause" ]

2

2020-08-13T18:09:53.000Z

2021-12-31T22:36:07.000Z

tests/test_signals.py

david-a-joy/multilineage-organoid

9b9848cfa5ee0d051b2a9645f9ffd8b9423beec8

[ "BSD-3-Clause" ]

null

tests/test_signals.py

david-a-joy/multilineage-organoid

9b9848cfa5ee0d051b2a9645f9ffd8b9423beec8

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # Stdlib import unittest from helpers import FileSystemTestCase, BASEDIR # 3rd party import numpy as np # Our own imports from multilineage_organoid import signals # Tests class TestFilterDatafile(FileSystemTestCase): def test_everything_works_multilineage(self): infile = BASEDIR / 'data' / 'Exp7_d80_MultilineageOrganoid_pacing_1hz.csv' outfile = self.tempdir / 'out.csv' plotfile = self.tempdir / 'plot.png' exp_traces = 4 res = signals.filter_datafile(infile=infile, outfile=outfile, plotfile=plotfile, plot_types='all') self.assertTrue(outfile.is_file()) for i in range(1, exp_traces+1): self.assertTrue((plotfile.parent / f'{plotfile.stem}_{i:02d}.png').is_file()) self.assertEqual(len(res), exp_traces) def test_everything_works_microtissue(self): infile = BASEDIR / 'data' / 'Exp7_d80_ConventionalMicrotissue_pacing_1hz.csv' outfile = self.tempdir / 'out.csv' plotfile = self.tempdir / 'plot.png' exp_traces = 2 res = signals.filter_datafile(infile=infile, outfile=outfile, plotfile=plotfile, plot_types='all') self.assertTrue(outfile.is_file()) for i in range(1, exp_traces+1): self.assertTrue((plotfile.parent / f'{plotfile.stem}_{i:02d}.png').is_file()) self.assertEqual(len(res), exp_traces) class TestFindKeyTimes(unittest.TestCase): def test_finds_times_at_boundary(self): timeline = np.array([0, 1, 2, 3, 4, 5]) values = np.array([1, 3, 5, 7, 9, 11]) key_times = signals.find_key_times(timeline, values, [0, 100], direction='up') exp_times = [0, 5] np.testing.assert_allclose(key_times, exp_times) timeline = np.array([0, 1, 2, 3, 4, 5]) values = np.array([13, 11, 9, 8, 6, 4]) key_times = signals.find_key_times(timeline, values, [0, 100], direction='down') exp_times = [5, 0] np.testing.assert_allclose(key_times, exp_times) def test_finds_times_in_the_middle(self): timeline = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) + 15 values = np.array([1, 3, 5, 7, 9, 11, 13, 15, 17]) key_times = signals.find_key_times(timeline, values, [25, 50, 75], direction='up') exp_times = [2, 4, 6] np.testing.assert_allclose(key_times, exp_times) timeline = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) + 15 values = np.array([17, 15, 13, 11, 9, 7, 5, 3, 1]) key_times = signals.find_key_times(timeline, values, [25, 50, 75], direction='down') exp_times = [6, 4, 2] np.testing.assert_allclose(key_times, exp_times) def test_finds_times_linear_interpolated(self): timeline = np.array([0, 2, 8, 9]) + 13 values = np.array([1, 5, 15, 17]) key_times = signals.find_key_times(timeline, values, [25, 50, 75], direction='up') # 25% from (1 to 17) is 5, so t = 2.0 # 50% from (1 to 17) is 9, so t = (9-5)/(15-5)*(8-2) + 2 = 4.4 # 75% from (1 to 17) is 13, so t = (13-5)/(15-5)*(8-2) + 2 = 6.8 exp_times = [2, 4.4, 6.8] np.testing.assert_allclose(key_times, exp_times) timeline = np.array([0, 2, 8, 9]) + 13 values = np.array([17, 15, 5, 1]) key_times = signals.find_key_times(timeline, values, [25, 50, 75], direction='down') # 25% from (17 to 1) is 5, so t = 8.0 # 50% from (17 to 1) is 9, so t = (15-9)/(15-5)*(8-2) + 2 = 5.6 # 75% from (17 to 1) is 13, so t = (15-13)/(15-5)*(8-2) + 2 = 3.2 exp_times = [8, 5.6, 3.2] np.testing.assert_allclose(key_times, exp_times) class TestCalcStatsAroundPeak(unittest.TestCase): def test_stats_for_single_flat_line(self): time = np.linspace(0, 2*np.pi, 100) signal = np.zeros_like(time) peaks = (0, 25, 100) res = signals.calc_stats_around_peak(time, signal, peaks) exp = { 'peak_value': 0.0, 'peak_index': 25, 'peak_start_index': 25, 'peak_end_index': 25, 'total_wave_time': 0.0, } for key, val in exp.items(): assert round(res[key], 2) == round(exp[key], 2), key def test_stats_for_single_line_up(self): time = np.linspace(0, 2*np.pi, 100) signal = time * 0.5 peaks = (0, 25, 100) res = signals.calc_stats_around_peak(time, signal, peaks) exp = { 'peak_value': 0.79, 'peak_index': 25, 'peak_start_index': 1, 'peak_end_index': 25, 'total_wave_time': 1.52, } for key, val in exp.items(): assert round(res[key], 2) == round(exp[key], 2), key def test_stats_for_single_line_down(self): time = np.linspace(0, 2*np.pi, 100) signal = time * -0.5 peaks = (0, 25, 100) res = signals.calc_stats_around_peak(time, signal, peaks) exp = { 'peak_value': -0.79, 'peak_index': 25, 'peak_start_index': 25, 'peak_end_index': 96, 'total_wave_time': 4.51, } for key, val in exp.items(): assert round(res[key], 2) == round(exp[key], 2), key def test_stats_for_sawtooth(self): time = np.array([0, 1, 2, 3, 4, 5, 6, 7]) signal = np.array([0, 0, 2, 4, 3, 2, 1, 0]) peaks = (0, 3, 7) res = signals.calc_stats_around_peak(time, signal, peaks) exp = { 'peak_value': 4, 'peak_index': 3, 'peak_start_index': 1, 'peak_end_index': 7, 'total_wave_time': 6, } for key, val in exp.items(): assert round(res[key], 2) == round(exp[key], 2), key def test_stats_for_single_peak(self): time = np.linspace(0, 2*np.pi, 100) signal = np.sin(time) peaks = (0, 25, 100) res = signals.calc_stats_around_peak(time, signal, peaks) exp = { 'peak_value': 1.0, 'peak_index': 25, 'peak_start_index': 0, 'peak_end_index': 68, 'total_wave_time': 4.32, } for key, val in exp.items(): assert round(res[key], 2) == round(exp[key], 2), key def test_stats_for_double_peak(self): time = np.linspace(0, 4*np.pi, 200) signal = np.sin(time) peaks = (25, 125, 200) res = signals.calc_stats_around_peak(time, signal, peaks) exp = { 'peak_value': 1.0, 'peak_index': 125, 'peak_start_index': 81, 'peak_end_index': 167, 'total_wave_time': 5.43, } for key, val in exp.items(): assert round(res[key], 2) == round(exp[key], 2), key def test_stats_for_double_peak_offset(self): time = np.linspace(0, 4*np.pi, 200) signal = np.sin(time) + 4.0 peaks = (25, 125, 200) res = signals.calc_stats_around_peak(time, signal, peaks) exp = { 'peak_value': 5.0, 'peak_index': 125, 'peak_start_index': 81, 'peak_end_index': 167, 'total_wave_time': 5.43, } for key, val in exp.items(): assert round(res[key], 2) == round(exp[key], 2), key class TestRefineSignalPeaks(unittest.TestCase): def test_refines_empty_list(self): time = np.linspace(0, 2*np.pi, 100) signal = np.sin(time) res = signals.refine_signal_peaks(time, signal, []) assert res == [] def test_refines_single_peak(self): time = np.linspace(0, 2*np.pi, 100) signal = np.sin(time) res = signals.refine_signal_peaks(time, signal, [25]) assert len(res) == 1 res = res[0] exp = { 'peak_value': 1.0, 'peak_index': 25, 'peak_start_index': 0, 'peak_end_index': 68, 'total_wave_time': 4.32, } for key, val in exp.items(): assert round(res[key], 2) == round(exp[key], 2), key def test_refines_single_peak_bad_annotation(self): time = np.linspace(0, 2*np.pi, 100) signal = np.sin(time) res = signals.refine_signal_peaks(time, signal, [25, 50]) assert len(res) == 1 res = res[0] exp = { 'peak_value': 1.0, 'peak_index': 25, 'peak_start_index': 0, 'peak_end_index': 68, 'total_wave_time': 4.32, } for key, val in exp.items(): assert round(res[key], 2) == round(exp[key], 2), key def test_refines_multiple_peaks_bad_annotations(self): time = np.linspace(0, 4*np.pi, 200) signal = np.sin(time) res = signals.refine_signal_peaks(time, signal, [25, 50, 75, 125, 150]) exp = [ { 'peak_value': 1.0, 'peak_index': 25, 'peak_start_index': 0, 'peak_end_index': 68, 'total_wave_time': 4.29, }, { 'peak_value': 1.0, 'peak_index': 125, 'peak_start_index': 81, 'peak_end_index': 167, 'total_wave_time': 5.43, }, ] assert len(res) == len(exp) for res_stats, exp_stats in zip(res, exp): for key, val in exp_stats.items(): assert round(res_stats[key], 2) == round(exp_stats[key], 2), key def test_refines_multiple_peaks_bad_annotations_numpy_arrays(self): time = np.linspace(0, 4*np.pi, 200) signal = np.sin(time) res = signals.refine_signal_peaks(time, signal, [np.array([[25]]), 50, np.array([75]), 125, 150]) exp = [ { 'peak_value': 1.0, 'peak_index': 25, 'peak_start_index': 0, 'peak_end_index': 68, 'total_wave_time': 4.29, }, { 'peak_value': 1.0, 'peak_index': 125, 'peak_start_index': 81, 'peak_end_index': 167, 'total_wave_time': 5.43, }, ] assert len(res) == len(exp) for res_stats, exp_stats in zip(res, exp): for key, val in exp_stats.items(): assert round(res_stats[key], 2) == round(exp_stats[key], 2), key class TestCalcVelocityStats(unittest.TestCase): def test_not_enough_data(self): time = np.linspace(0, 10, 2) signal = 2 * time mean, std, max = signals.calc_velocity_stats(time, signal) self.assertTrue(np.isnan(mean)) self.assertTrue(np.isnan(std)) self.assertTrue(np.isnan(max)) def test_works_up(self): time = np.linspace(0, 10, 10) signal = 2 * time mean, std, max = signals.calc_velocity_stats(time, signal, direction='up', time_scale=1.0) self.assertAlmostEqual(mean, 2.0) self.assertAlmostEqual(std, 0.0) self.assertAlmostEqual(max, 2.0) def test_works_down(self): time = np.linspace(0, 10, 10) signal = -2 * time mean, std, max = signals.calc_velocity_stats(time, signal, direction='down', time_scale=1.0) self.assertAlmostEqual(mean, -2.0) self.assertAlmostEqual(std, 0.0) self.assertAlmostEqual(max, -2.0) def test_wrong_direction(self): time = np.linspace(0, 10, 10) signal = -2 * time mean, std, max = signals.calc_velocity_stats(time, signal, direction='up', time_scale=1.0) self.assertTrue(np.isnan(mean)) self.assertTrue(np.isnan(std)) self.assertTrue(np.isnan(max))

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6

9bacde7d55fd9c361c6844122ef5f212ad0f6870

261,060

py

Python

instances/passenger_demand/pas-20210422-1717-int16e/79.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int16e/79.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int16e/79.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 30651 passenger_arriving = ( (10, 7, 7, 7, 6, 3, 5, 3, 0, 1, 0, 1, 0, 8, 5, 3, 3, 11, 4, 6, 0, 2, 2, 1, 0, 0), # 0 (7, 10, 12, 5, 12, 6, 5, 1, 0, 3, 1, 1, 0, 12, 8, 8, 3, 12, 6, 3, 2, 1, 0, 2, 2, 0), # 1 (10, 6, 14, 2, 8, 3, 4, 2, 6, 3, 0, 0, 0, 13, 8, 10, 4, 11, 6, 2, 2, 4, 3, 4, 0, 0), # 2 (10, 15, 5, 7, 8, 1, 3, 3, 6, 1, 0, 2, 0, 10, 4, 5, 7, 9, 3, 3, 0, 1, 5, 2, 0, 0), # 3 (6, 10, 8, 6, 8, 3, 2, 5, 2, 3, 1, 1, 0, 2, 11, 7, 5, 10, 5, 1, 3, 3, 3, 0, 1, 0), # 4 (14, 15, 13, 14, 11, 8, 5, 5, 2, 1, 0, 1, 0, 9, 9, 7, 7, 9, 8, 9, 3, 5, 2, 0, 1, 0), # 5 (6, 11, 10, 6, 4, 4, 1, 6, 9, 2, 0, 1, 0, 13, 10, 7, 7, 8, 10, 5, 1, 7, 3, 1, 0, 0), # 6 (10, 9, 13, 11, 10, 3, 7, 2, 6, 1, 1, 0, 0, 14, 8, 7, 8, 10, 10, 6, 4, 5, 5, 3, 1, 0), # 7 (12, 15, 9, 14, 9, 6, 2, 6, 3, 1, 0, 2, 0, 14, 9, 9, 5, 8, 9, 4, 3, 1, 6, 0, 4, 0), # 8 (6, 11, 9, 18, 6, 4, 1, 6, 3, 0, 0, 0, 0, 15, 12, 12, 13, 12, 5, 2, 5, 8, 2, 2, 0, 0), # 9 (8, 3, 16, 15, 9, 4, 5, 5, 4, 2, 1, 0, 0, 11, 11, 10, 7, 15, 8, 2, 4, 3, 2, 1, 2, 0), # 10 (12, 12, 11, 7, 9, 3, 8, 4, 6, 4, 3, 2, 0, 19, 9, 6, 8, 14, 9, 6, 2, 8, 5, 6, 0, 0), # 11 (14, 9, 11, 12, 7, 4, 5, 4, 8, 1, 1, 2, 0, 10, 8, 9, 10, 12, 8, 4, 6, 8, 3, 2, 0, 0), # 12 (11, 6, 17, 14, 9, 7, 6, 11, 7, 4, 0, 2, 0, 16, 9, 10, 10, 10, 8, 10, 4, 6, 7, 2, 2, 0), # 13 (12, 19, 12, 13, 14, 3, 10, 6, 5, 2, 3, 1, 0, 18, 10, 7, 7, 14, 8, 7, 3, 6, 2, 0, 2, 0), # 14 (14, 17, 10, 8, 11, 5, 11, 10, 9, 2, 2, 2, 0, 15, 11, 7, 10, 6, 4, 2, 2, 7, 4, 2, 0, 0), # 15 (18, 13, 16, 20, 17, 7, 7, 8, 4, 6, 3, 1, 0, 14, 19, 13, 3, 12, 9, 5, 4, 6, 5, 0, 1, 0), # 16 (16, 22, 11, 16, 12, 3, 8, 5, 7, 2, 0, 1, 0, 10, 7, 7, 10, 7, 9, 5, 5, 12, 5, 0, 0, 0), # 17 (17, 21, 15, 12, 9, 9, 5, 4, 7, 6, 1, 4, 0, 18, 14, 14, 9, 13, 13, 8, 4, 13, 6, 4, 1, 0), # 18 (17, 19, 11, 16, 12, 8, 6, 5, 3, 2, 3, 0, 0, 16, 10, 13, 8, 18, 5, 8, 9, 6, 3, 3, 3, 0), # 19 (11, 14, 8, 9, 7, 7, 10, 5, 5, 2, 2, 1, 0, 20, 14, 10, 11, 13, 3, 2, 3, 5, 3, 5, 2, 0), # 20 (17, 16, 15, 18, 11, 7, 5, 6, 2, 4, 2, 1, 0, 15, 12, 19, 11, 11, 6, 3, 4, 7, 9, 1, 2, 0), # 21 (12, 17, 9, 18, 12, 4, 9, 6, 7, 5, 2, 1, 0, 7, 21, 5, 8, 14, 6, 5, 4, 7, 3, 5, 4, 0), # 22 (9, 20, 14, 15, 10, 2, 5, 6, 6, 3, 3, 1, 0, 19, 15, 10, 7, 12, 9, 4, 6, 5, 6, 4, 1, 0), # 23 (11, 22, 13, 18, 13, 3, 7, 8, 5, 1, 2, 0, 0, 26, 10, 7, 8, 12, 5, 5, 3, 3, 4, 2, 1, 0), # 24 (13, 15, 15, 12, 15, 2, 7, 6, 6, 1, 2, 0, 0, 13, 14, 15, 13, 13, 11, 4, 5, 6, 4, 1, 3, 0), # 25 (14, 16, 11, 12, 14, 5, 11, 2, 8, 6, 2, 1, 0, 21, 11, 14, 13, 14, 10, 6, 2, 4, 6, 6, 3, 0), # 26 (14, 9, 20, 14, 6, 7, 3, 9, 9, 3, 1, 0, 0, 14, 9, 10, 16, 10, 3, 1, 2, 6, 9, 2, 1, 0), # 27 (11, 17, 11, 13, 13, 7, 6, 7, 3, 2, 1, 2, 0, 19, 10, 8, 7, 8, 7, 10, 3, 1, 1, 2, 3, 0), # 28 (14, 16, 12, 16, 4, 5, 2, 5, 7, 3, 6, 4, 0, 16, 8, 12, 12, 17, 7, 6, 6, 8, 5, 5, 3, 0), # 29 (16, 16, 13, 12, 8, 7, 5, 7, 7, 3, 4, 1, 0, 12, 8, 13, 11, 4, 6, 5, 1, 6, 8, 3, 3, 0), # 30 (18, 16, 19, 11, 17, 8, 8, 7, 3, 2, 2, 0, 0, 17, 18, 12, 8, 8, 12, 7, 5, 4, 0, 3, 2, 0), # 31 (22, 12, 12, 17, 19, 3, 5, 7, 6, 3, 0, 0, 0, 14, 18, 12, 9, 11, 4, 7, 6, 10, 5, 1, 0, 0), # 32 (12, 19, 17, 12, 8, 4, 6, 6, 5, 0, 2, 0, 0, 18, 10, 9, 7, 13, 12, 3, 5, 5, 3, 3, 0, 0), # 33 (16, 12, 14, 21, 13, 7, 11, 3, 5, 2, 9, 1, 0, 15, 16, 10, 14, 14, 10, 8, 9, 5, 4, 2, 2, 0), # 34 (17, 13, 17, 20, 11, 6, 8, 7, 4, 2, 4, 0, 0, 12, 15, 13, 11, 13, 6, 6, 2, 4, 6, 1, 2, 0), # 35 (13, 11, 12, 11, 15, 6, 5, 8, 11, 3, 1, 2, 0, 16, 16, 18, 8, 14, 8, 11, 5, 6, 2, 5, 3, 0), # 36 (13, 17, 21, 7, 3, 5, 12, 5, 8, 1, 5, 1, 0, 20, 16, 7, 9, 13, 10, 6, 3, 5, 1, 3, 3, 0), # 37 (16, 18, 15, 20, 16, 7, 11, 5, 3, 2, 0, 1, 0, 22, 18, 11, 9, 9, 6, 6, 2, 8, 5, 1, 1, 0), # 38 (18, 22, 9, 11, 27, 3, 3, 8, 13, 2, 4, 3, 0, 19, 17, 10, 6, 15, 6, 10, 3, 11, 5, 3, 4, 0), # 39 (16, 16, 15, 8, 8, 10, 5, 2, 7, 6, 4, 0, 0, 21, 12, 8, 15, 11, 9, 7, 10, 8, 9, 1, 1, 0), # 40 (17, 14, 11, 15, 11, 6, 5, 5, 5, 3, 2, 2, 0, 12, 18, 11, 9, 13, 7, 10, 6, 11, 4, 2, 3, 0), # 41 (20, 14, 17, 19, 12, 8, 10, 9, 4, 5, 1, 5, 0, 18, 13, 10, 13, 9, 4, 8, 2, 3, 5, 0, 2, 0), # 42 (19, 11, 11, 16, 12, 5, 5, 9, 6, 1, 1, 3, 0, 17, 23, 13, 7, 9, 7, 6, 6, 6, 7, 4, 2, 0), # 43 (11, 23, 20, 14, 19, 4, 8, 5, 2, 3, 2, 0, 0, 13, 11, 8, 10, 11, 4, 6, 3, 8, 7, 1, 1, 0), # 44 (18, 14, 16, 17, 14, 6, 11, 8, 5, 6, 2, 1, 0, 14, 17, 11, 9, 16, 5, 9, 7, 9, 4, 3, 0, 0), # 45 (20, 18, 14, 13, 7, 4, 6, 4, 6, 1, 2, 1, 0, 18, 17, 11, 12, 12, 10, 7, 4, 12, 5, 2, 1, 0), # 46 (16, 21, 10, 11, 13, 1, 8, 6, 7, 1, 5, 0, 0, 16, 21, 11, 12, 16, 11, 10, 3, 7, 1, 3, 2, 0), # 47 (14, 18, 9, 8, 5, 8, 10, 3, 5, 1, 1, 3, 0, 9, 15, 10, 10, 20, 10, 2, 5, 5, 5, 3, 1, 0), # 48 (9, 15, 12, 12, 17, 8, 8, 9, 5, 1, 3, 3, 0, 15, 7, 10, 12, 16, 10, 7, 4, 4, 9, 4, 1, 0), # 49 (18, 18, 12, 14, 19, 5, 2, 9, 8, 2, 2, 1, 0, 18, 12, 15, 14, 13, 6, 7, 3, 7, 5, 2, 4, 0), # 50 (12, 17, 16, 24, 11, 6, 4, 3, 3, 4, 3, 1, 0, 15, 17, 11, 15, 12, 6, 5, 8, 11, 2, 2, 1, 0), # 51 (14, 21, 9, 15, 12, 4, 11, 5, 2, 2, 4, 1, 0, 17, 17, 12, 13, 15, 10, 9, 1, 9, 4, 6, 0, 0), # 52 (18, 14, 9, 17, 16, 8, 5, 3, 4, 4, 2, 0, 0, 11, 14, 5, 10, 11, 3, 4, 2, 9, 5, 2, 1, 0), # 53 (14, 14, 16, 14, 20, 3, 9, 6, 2, 4, 2, 3, 0, 17, 10, 8, 11, 9, 9, 3, 5, 6, 3, 0, 1, 0), # 54 (21, 7, 14, 13, 10, 5, 3, 2, 3, 3, 1, 2, 0, 13, 13, 12, 8, 11, 9, 1, 3, 7, 4, 3, 1, 0), # 55 (19, 19, 15, 20, 17, 7, 6, 4, 7, 2, 1, 1, 0, 11, 10, 5, 5, 14, 9, 3, 0, 8, 2, 5, 2, 0), # 56 (11, 16, 16, 21, 15, 8, 2, 3, 6, 2, 2, 1, 0, 18, 7, 8, 10, 11, 4, 4, 2, 6, 2, 4, 2, 0), # 57 (8, 19, 11, 14, 14, 6, 5, 2, 12, 3, 3, 3, 0, 14, 9, 10, 8, 18, 4, 6, 4, 6, 3, 5, 3, 0), # 58 (18, 16, 20, 18, 9, 10, 7, 7, 7, 0, 2, 1, 0, 18, 15, 11, 7, 11, 8, 5, 1, 8, 4, 4, 2, 0), # 59 (9, 19, 16, 24, 13, 8, 6, 4, 6, 2, 3, 0, 0, 9, 12, 25, 10, 12, 5, 5, 1, 4, 3, 4, 0, 0), # 60 (22, 11, 12, 16, 12, 3, 6, 3, 7, 3, 1, 2, 0, 10, 11, 13, 6, 12, 3, 6, 4, 7, 2, 2, 4, 0), # 61 (23, 10, 17, 12, 14, 8, 7, 5, 8, 5, 1, 0, 0, 15, 12, 13, 4, 9, 4, 4, 2, 9, 4, 2, 1, 0), # 62 (16, 17, 11, 25, 16, 1, 3, 5, 7, 4, 1, 0, 0, 15, 12, 8, 14, 10, 6, 4, 2, 4, 3, 0, 1, 0), # 63 (17, 17, 17, 12, 12, 8, 5, 2, 6, 3, 3, 0, 0, 17, 16, 11, 12, 11, 7, 3, 4, 6, 2, 2, 0, 0), # 64 (13, 19, 16, 10, 14, 8, 5, 2, 6, 1, 4, 1, 0, 19, 22, 9, 9, 9, 7, 4, 5, 4, 2, 1, 1, 0), # 65 (16, 19, 15, 15, 9, 6, 4, 5, 11, 2, 0, 2, 0, 17, 18, 14, 9, 17, 8, 6, 6, 5, 10, 3, 0, 0), # 66 (15, 14, 14, 20, 12, 6, 6, 2, 4, 4, 0, 2, 0, 12, 11, 15, 10, 14, 7, 6, 8, 5, 4, 5, 1, 0), # 67 (16, 13, 10, 13, 15, 10, 8, 2, 10, 6, 2, 2, 0, 21, 21, 7, 7, 10, 5, 5, 8, 7, 5, 3, 0, 0), # 68 (14, 13, 14, 10, 12, 7, 3, 5, 6, 3, 2, 2, 0, 12, 11, 8, 11, 7, 5, 6, 6, 4, 3, 1, 2, 0), # 69 (17, 10, 12, 16, 10, 8, 11, 3, 7, 6, 1, 1, 0, 28, 9, 13, 9, 10, 3, 10, 4, 9, 4, 3, 1, 0), # 70 (13, 16, 19, 14, 12, 5, 5, 6, 9, 1, 2, 0, 0, 20, 14, 7, 7, 10, 8, 5, 4, 7, 5, 1, 1, 0), # 71 (17, 13, 18, 14, 19, 8, 3, 3, 10, 2, 3, 0, 0, 15, 15, 12, 8, 11, 4, 5, 4, 3, 3, 8, 0, 0), # 72 (19, 17, 13, 10, 11, 5, 10, 3, 4, 2, 1, 4, 0, 15, 15, 9, 12, 17, 4, 10, 4, 4, 2, 5, 1, 0), # 73 (15, 12, 15, 20, 12, 6, 5, 4, 7, 2, 5, 1, 0, 13, 10, 10, 8, 13, 8, 12, 4, 7, 4, 4, 0, 0), # 74 (13, 12, 11, 16, 10, 8, 7, 2, 4, 3, 2, 0, 0, 18, 13, 17, 11, 19, 7, 9, 4, 7, 2, 6, 3, 0), # 75 (15, 10, 20, 18, 8, 6, 5, 6, 9, 5, 3, 3, 0, 21, 8, 7, 9, 17, 4, 5, 8, 5, 2, 1, 4, 0), # 76 (17, 16, 10, 20, 9, 5, 5, 8, 15, 1, 1, 2, 0, 10, 11, 19, 6, 11, 9, 4, 1, 5, 4, 5, 1, 0), # 77 (12, 10, 8, 12, 17, 5, 6, 4, 7, 4, 1, 1, 0, 21, 10, 11, 6, 12, 8, 5, 3, 4, 4, 3, 1, 0), # 78 (19, 16, 12, 13, 8, 8, 5, 5, 5, 4, 3, 0, 0, 10, 16, 10, 7, 11, 6, 5, 3, 7, 2, 2, 4, 0), # 79 (27, 16, 7, 7, 7, 3, 5, 3, 2, 2, 1, 1, 0, 14, 13, 17, 3, 15, 6, 4, 6, 3, 5, 3, 4, 0), # 80 (15, 18, 17, 18, 13, 5, 5, 5, 4, 3, 1, 1, 0, 12, 12, 12, 4, 10, 10, 5, 3, 7, 4, 4, 0, 0), # 81 (13, 10, 19, 15, 10, 11, 6, 5, 4, 1, 2, 1, 0, 15, 15, 12, 6, 18, 9, 6, 7, 6, 4, 1, 4, 0), # 82 (12, 14, 21, 16, 14, 7, 2, 3, 4, 3, 3, 0, 0, 17, 12, 6, 5, 15, 6, 5, 1, 5, 5, 4, 0, 0), # 83 (21, 16, 12, 9, 16, 7, 4, 5, 5, 2, 1, 0, 0, 25, 19, 8, 9, 10, 7, 6, 6, 6, 4, 1, 2, 0), # 84 (12, 17, 10, 8, 12, 5, 5, 6, 3, 0, 4, 5, 0, 19, 12, 14, 9, 21, 7, 10, 4, 11, 4, 4, 1, 0), # 85 (20, 14, 12, 13, 10, 6, 5, 4, 9, 2, 4, 4, 0, 12, 14, 17, 6, 12, 4, 5, 4, 6, 7, 1, 0, 0), # 86 (15, 13, 21, 13, 11, 11, 5, 2, 5, 1, 1, 0, 0, 14, 11, 12, 13, 18, 5, 4, 8, 5, 2, 2, 1, 0), # 87 (16, 15, 8, 11, 11, 5, 6, 6, 5, 3, 1, 0, 0, 19, 7, 7, 9, 14, 11, 4, 2, 7, 4, 0, 1, 0), # 88 (15, 14, 17, 21, 11, 7, 6, 2, 5, 4, 2, 1, 0, 22, 11, 10, 8, 16, 5, 8, 2, 7, 1, 6, 2, 0), # 89 (16, 12, 10, 12, 12, 6, 5, 2, 4, 0, 4, 3, 0, 22, 14, 5, 8, 15, 5, 6, 4, 7, 8, 0, 1, 0), # 90 (12, 10, 17, 12, 9, 13, 4, 1, 6, 1, 1, 0, 0, 18, 14, 15, 7, 15, 10, 2, 4, 6, 1, 4, 1, 0), # 91 (17, 10, 11, 20, 8, 4, 5, 4, 2, 1, 2, 2, 0, 23, 22, 11, 7, 11, 4, 4, 6, 4, 3, 2, 1, 0), # 92 (14, 14, 6, 14, 16, 8, 3, 7, 2, 2, 2, 3, 0, 12, 9, 10, 10, 9, 5, 5, 2, 9, 3, 4, 1, 0), # 93 (14, 7, 13, 10, 9, 8, 2, 3, 7, 1, 1, 0, 0, 20, 13, 10, 3, 14, 3, 4, 7, 1, 3, 3, 0, 0), # 94 (20, 7, 12, 14, 13, 2, 5, 8, 4, 3, 1, 1, 0, 17, 9, 11, 11, 11, 5, 3, 4, 5, 2, 2, 2, 0), # 95 (18, 7, 16, 10, 12, 7, 10, 5, 11, 6, 2, 3, 0, 20, 13, 13, 10, 11, 5, 7, 4, 7, 6, 1, 1, 0), # 96 (14, 10, 9, 17, 8, 4, 3, 3, 3, 4, 1, 3, 0, 6, 8, 8, 2, 14, 7, 3, 1, 10, 5, 3, 2, 0), # 97 (10, 15, 14, 21, 13, 6, 6, 5, 11, 0, 5, 1, 0, 19, 16, 9, 5, 17, 9, 12, 6, 6, 6, 4, 1, 0), # 98 (15, 24, 12, 17, 16, 2, 11, 3, 3, 1, 1, 0, 0, 11, 11, 13, 7, 11, 6, 10, 2, 7, 5, 4, 3, 0), # 99 (11, 14, 10, 11, 6, 6, 8, 6, 6, 1, 0, 3, 0, 14, 4, 5, 7, 12, 7, 7, 8, 3, 5, 0, 2, 0), # 100 (12, 17, 14, 11, 7, 5, 2, 3, 8, 4, 3, 2, 0, 18, 15, 8, 8, 10, 7, 4, 4, 8, 2, 2, 1, 0), # 101 (13, 8, 11, 15, 16, 4, 6, 5, 9, 5, 5, 2, 0, 14, 8, 10, 5, 9, 8, 5, 4, 5, 1, 6, 0, 0), # 102 (11, 8, 11, 11, 12, 5, 7, 8, 4, 2, 1, 3, 0, 13, 7, 9, 4, 9, 4, 5, 4, 9, 5, 3, 1, 0), # 103 (21, 11, 11, 16, 8, 4, 4, 3, 4, 1, 2, 1, 0, 14, 17, 9, 6, 20, 10, 4, 6, 6, 6, 5, 0, 0), # 104 (12, 13, 12, 12, 18, 3, 4, 5, 6, 3, 1, 2, 0, 22, 6, 10, 12, 11, 3, 4, 4, 7, 3, 4, 2, 0), # 105 (27, 7, 14, 16, 13, 12, 5, 2, 5, 2, 3, 3, 0, 12, 18, 9, 5, 9, 4, 2, 6, 8, 4, 3, 0, 0), # 106 (15, 9, 11, 15, 9, 4, 5, 4, 2, 4, 1, 0, 0, 17, 14, 10, 3, 15, 4, 5, 1, 5, 4, 1, 1, 0), # 107 (11, 9, 7, 19, 14, 7, 4, 3, 6, 3, 0, 0, 0, 12, 15, 10, 9, 14, 4, 7, 3, 4, 4, 3, 0, 0), # 108 (15, 17, 17, 3, 19, 4, 4, 6, 12, 0, 2, 1, 0, 19, 13, 5, 8, 13, 7, 4, 4, 8, 3, 4, 4, 0), # 109 (22, 11, 12, 11, 8, 6, 7, 2, 4, 3, 3, 2, 0, 18, 13, 9, 7, 9, 4, 7, 5, 4, 4, 4, 0, 0), # 110 (25, 8, 10, 9, 11, 4, 9, 5, 8, 1, 3, 2, 0, 10, 19, 12, 8, 13, 5, 6, 1, 8, 4, 0, 1, 0), # 111 (11, 10, 12, 17, 13, 3, 4, 2, 5, 2, 4, 4, 0, 14, 16, 14, 8, 11, 4, 8, 2, 4, 3, 3, 0, 0), # 112 (9, 7, 11, 16, 13, 5, 6, 2, 7, 2, 2, 2, 0, 13, 11, 7, 5, 17, 12, 4, 3, 4, 4, 4, 4, 0), # 113 (13, 13, 14, 13, 11, 4, 10, 5, 5, 2, 3, 1, 0, 13, 9, 10, 6, 9, 5, 4, 4, 5, 5, 4, 0, 0), # 114 (14, 13, 7, 12, 12, 4, 8, 0, 5, 2, 3, 1, 0, 18, 13, 9, 7, 21, 5, 4, 6, 8, 5, 1, 0, 0), # 115 (16, 10, 17, 18, 10, 6, 5, 2, 7, 1, 3, 1, 0, 13, 7, 9, 5, 13, 10, 1, 3, 4, 4, 2, 0, 0), # 116 (16, 15, 9, 15, 2, 8, 3, 5, 11, 2, 2, 0, 0, 16, 14, 6, 8, 19, 6, 5, 2, 1, 4, 5, 3, 0), # 117 (20, 8, 14, 22, 8, 4, 4, 5, 5, 3, 4, 2, 0, 13, 11, 14, 7, 5, 4, 5, 3, 10, 8, 3, 0, 0), # 118 (18, 14, 9, 11, 10, 6, 2, 2, 3, 2, 1, 1, 0, 13, 12, 11, 13, 13, 6, 3, 5, 4, 6, 4, 0, 0), # 119 (10, 9, 12, 13, 10, 7, 5, 3, 4, 0, 1, 2, 0, 13, 14, 11, 9, 11, 4, 6, 4, 1, 3, 1, 3, 0), # 120 (22, 15, 12, 10, 15, 5, 4, 2, 4, 1, 2, 3, 0, 22, 18, 9, 4, 12, 6, 3, 3, 9, 6, 1, 1, 0), # 121 (15, 13, 11, 11, 14, 3, 8, 3, 7, 2, 0, 1, 0, 12, 18, 9, 4, 14, 7, 10, 5, 3, 6, 3, 0, 0), # 122 (10, 7, 5, 10, 12, 5, 4, 7, 7, 2, 3, 2, 0, 21, 8, 8, 7, 11, 6, 6, 6, 4, 3, 3, 3, 0), # 123 (13, 8, 14, 18, 12, 3, 3, 4, 4, 4, 2, 1, 0, 12, 11, 7, 8, 15, 7, 3, 4, 4, 5, 4, 0, 0), # 124 (10, 9, 10, 8, 10, 8, 3, 7, 7, 5, 1, 0, 0, 24, 9, 9, 5, 8, 6, 4, 1, 7, 4, 0, 0, 0), # 125 (12, 7, 14, 12, 9, 6, 3, 5, 8, 2, 1, 0, 0, 9, 14, 10, 5, 7, 6, 4, 7, 7, 5, 4, 0, 0), # 126 (14, 6, 13, 11, 10, 6, 3, 1, 5, 2, 1, 2, 0, 15, 6, 5, 5, 10, 8, 0, 0, 8, 2, 3, 1, 0), # 127 (11, 9, 7, 11, 8, 3, 6, 1, 9, 3, 0, 0, 0, 15, 10, 10, 5, 14, 10, 7, 5, 6, 2, 1, 0, 0), # 128 (12, 10, 14, 13, 16, 3, 2, 4, 6, 2, 2, 2, 0, 19, 10, 8, 7, 11, 3, 6, 1, 3, 3, 4, 1, 0), # 129 (21, 14, 10, 12, 10, 5, 1, 3, 5, 2, 2, 3, 0, 17, 13, 9, 4, 10, 5, 1, 1, 5, 7, 2, 1, 0), # 130 (16, 13, 13, 11, 7, 2, 9, 6, 6, 2, 2, 2, 0, 10, 8, 12, 6, 8, 6, 3, 6, 3, 5, 6, 3, 0), # 131 (16, 8, 8, 10, 9, 8, 4, 6, 5, 1, 2, 1, 0, 15, 9, 10, 5, 15, 6, 5, 3, 4, 5, 2, 1, 0), # 132 (11, 11, 17, 11, 15, 3, 3, 5, 2, 1, 1, 0, 0, 8, 12, 13, 4, 15, 12, 9, 2, 2, 5, 4, 0, 0), # 133 (14, 5, 14, 11, 13, 4, 6, 7, 5, 0, 3, 3, 0, 17, 16, 5, 1, 10, 5, 6, 4, 4, 5, 2, 1, 0), # 134 (15, 16, 10, 9, 12, 1, 2, 1, 4, 0, 2, 1, 0, 9, 13, 9, 10, 9, 4, 7, 2, 10, 4, 1, 1, 0), # 135 (16, 14, 13, 8, 13, 10, 4, 4, 5, 1, 0, 2, 0, 13, 15, 5, 8, 8, 8, 6, 2, 8, 2, 4, 1, 0), # 136 (25, 10, 13, 6, 10, 8, 5, 3, 10, 0, 4, 2, 0, 10, 11, 9, 6, 15, 6, 10, 4, 6, 5, 3, 3, 0), # 137 (13, 11, 17, 7, 9, 1, 3, 3, 7, 3, 2, 0, 0, 19, 14, 7, 5, 16, 3, 2, 4, 5, 1, 0, 1, 0), # 138 (22, 9, 11, 14, 15, 5, 4, 3, 4, 4, 4, 2, 0, 21, 12, 7, 7, 16, 3, 4, 8, 6, 7, 4, 1, 0), # 139 (17, 8, 13, 9, 13, 10, 5, 2, 5, 1, 0, 0, 0, 15, 10, 8, 5, 6, 7, 5, 4, 3, 2, 0, 0, 0), # 140 (11, 6, 7, 12, 11, 6, 2, 6, 8, 4, 5, 2, 0, 16, 3, 7, 4, 12, 8, 6, 4, 4, 1, 2, 0, 0), # 141 (8, 12, 13, 6, 17, 3, 6, 4, 6, 0, 1, 0, 0, 13, 10, 9, 4, 10, 5, 7, 5, 6, 2, 3, 2, 0), # 142 (10, 6, 11, 16, 10, 5, 3, 4, 4, 0, 1, 0, 0, 15, 12, 8, 5, 10, 1, 0, 6, 8, 5, 3, 2, 0), # 143 (13, 9, 13, 18, 11, 6, 13, 5, 2, 1, 3, 2, 0, 7, 20, 8, 7, 4, 5, 4, 5, 4, 5, 1, 0, 0), # 144 (9, 8, 13, 6, 12, 5, 1, 3, 2, 2, 1, 0, 0, 13, 13, 9, 6, 11, 4, 11, 4, 7, 5, 4, 0, 0), # 145 (7, 7, 9, 9, 13, 3, 5, 3, 3, 1, 2, 1, 0, 14, 10, 6, 6, 11, 7, 6, 4, 6, 6, 5, 0, 0), # 146 (10, 14, 12, 10, 6, 9, 5, 4, 4, 3, 0, 1, 0, 6, 9, 9, 9, 16, 4, 1, 2, 5, 4, 7, 1, 0), # 147 (20, 10, 10, 15, 17, 7, 0, 6, 3, 5, 0, 2, 0, 7, 9, 11, 5, 17, 8, 1, 7, 4, 2, 2, 0, 0), # 148 (9, 6, 22, 7, 9, 6, 1, 6, 7, 3, 1, 1, 0, 14, 17, 8, 4, 14, 5, 5, 6, 8, 4, 2, 0, 0), # 149 (19, 6, 5, 13, 11, 5, 6, 6, 3, 3, 1, 1, 0, 15, 11, 9, 5, 9, 5, 2, 4, 4, 2, 2, 0, 0), # 150 (11, 3, 10, 14, 12, 4, 4, 1, 5, 1, 3, 0, 0, 19, 10, 7, 5, 11, 7, 4, 6, 6, 3, 0, 1, 0), # 151 (11, 16, 16, 7, 10, 5, 2, 2, 3, 0, 2, 2, 0, 21, 8, 8, 10, 10, 5, 8, 5, 6, 5, 0, 0, 0), # 152 (11, 6, 18, 13, 7, 8, 4, 3, 1, 3, 5, 0, 0, 13, 23, 4, 5, 12, 7, 3, 6, 2, 1, 2, 2, 0), # 153 (11, 4, 15, 8, 8, 4, 4, 6, 6, 2, 2, 0, 0, 18, 7, 11, 3, 12, 6, 3, 7, 5, 4, 1, 0, 0), # 154 (8, 9, 8, 15, 11, 5, 2, 6, 10, 1, 3, 2, 0, 12, 11, 10, 4, 13, 5, 5, 3, 5, 2, 5, 1, 0), # 155 (11, 8, 13, 15, 11, 4, 5, 5, 8, 2, 0, 1, 0, 17, 4, 5, 6, 8, 7, 6, 3, 3, 4, 3, 1, 0), # 156 (9, 10, 10, 12, 14, 5, 4, 6, 8, 6, 2, 3, 0, 16, 11, 10, 10, 10, 2, 3, 3, 6, 6, 2, 2, 0), # 157 (14, 14, 10, 9, 11, 4, 6, 7, 2, 1, 2, 1, 0, 9, 10, 7, 5, 14, 1, 7, 7, 5, 7, 3, 0, 0), # 158 (12, 5, 17, 14, 10, 5, 9, 6, 4, 4, 1, 2, 0, 9, 12, 5, 8, 10, 4, 4, 2, 4, 5, 2, 0, 0), # 159 (11, 7, 19, 13, 5, 5, 1, 3, 7, 2, 3, 0, 0, 13, 6, 4, 2, 11, 3, 1, 4, 5, 3, 1, 3, 0), # 160 (15, 8, 9, 17, 12, 4, 4, 4, 4, 2, 1, 1, 0, 8, 11, 11, 3, 9, 3, 3, 3, 4, 5, 3, 0, 0), # 161 (18, 10, 9, 7, 8, 3, 4, 3, 6, 4, 1, 1, 0, 10, 17, 4, 8, 10, 6, 5, 1, 4, 1, 1, 0, 0), # 162 (13, 12, 8, 10, 7, 4, 2, 6, 1, 2, 0, 1, 0, 14, 5, 7, 6, 11, 3, 4, 4, 7, 3, 1, 0, 0), # 163 (7, 9, 16, 8, 7, 7, 3, 6, 4, 2, 0, 2, 0, 10, 2, 7, 6, 6, 3, 3, 5, 4, 1, 6, 0, 0), # 164 (10, 9, 7, 9, 9, 3, 0, 0, 3, 2, 1, 1, 0, 18, 6, 5, 8, 7, 3, 1, 5, 6, 2, 0, 1, 0), # 165 (11, 8, 9, 7, 12, 3, 4, 4, 3, 4, 2, 1, 0, 8, 10, 9, 4, 11, 3, 3, 4, 5, 5, 0, 0, 0), # 166 (7, 11, 7, 13, 16, 4, 2, 2, 7, 1, 1, 1, 0, 11, 8, 4, 3, 5, 3, 2, 3, 1, 5, 2, 0, 0), # 167 (9, 6, 12, 8, 5, 2, 2, 3, 2, 1, 0, 0, 0, 16, 16, 5, 3, 7, 5, 3, 2, 3, 4, 2, 0, 0), # 168 (8, 6, 10, 10, 5, 4, 2, 1, 2, 4, 1, 3, 0, 9, 10, 6, 2, 12, 5, 5, 1, 3, 6, 5, 1, 0), # 169 (9, 8, 10, 11, 8, 3, 2, 2, 10, 0, 3, 0, 0, 7, 7, 11, 5, 5, 6, 3, 3, 4, 4, 5, 1, 0), # 170 (10, 2, 7, 19, 3, 3, 0, 2, 6, 0, 3, 0, 0, 8, 6, 8, 2, 9, 10, 3, 0, 3, 2, 3, 0, 0), # 171 (11, 4, 9, 8, 10, 0, 3, 1, 5, 2, 1, 0, 0, 4, 6, 6, 2, 7, 2, 0, 4, 3, 3, 4, 0, 0), # 172 (6, 3, 10, 16, 11, 2, 2, 2, 9, 1, 2, 0, 0, 6, 3, 6, 1, 11, 4, 2, 2, 5, 2, 2, 0, 0), # 173 (11, 5, 8, 8, 6, 3, 4, 1, 6, 1, 0, 0, 0, 10, 5, 6, 6, 8, 6, 4, 1, 8, 2, 1, 0, 0), # 174 (10, 6, 5, 6, 4, 5, 2, 5, 3, 3, 2, 1, 0, 7, 6, 6, 3, 7, 4, 4, 5, 3, 4, 2, 0, 0), # 175 (7, 8, 3, 5, 5, 5, 3, 0, 3, 1, 0, 1, 0, 7, 5, 1, 1, 6, 3, 1, 0, 3, 0, 0, 0, 0), # 176 (4, 5, 4, 2, 2, 3, 2, 2, 2, 0, 2, 0, 0, 7, 7, 9, 7, 7, 5, 3, 2, 1, 2, 1, 0, 0), # 177 (8, 3, 3, 10, 4, 3, 1, 4, 3, 0, 0, 0, 0, 7, 5, 0, 4, 6, 4, 1, 2, 2, 3, 0, 1, 0), # 178 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179 ) station_arriving_intensity = ( (8.033384925394829, 8.840461695509067, 8.33805316738001, 9.943468438181492, 8.887496972175379, 5.021847891259743, 6.6336569845982645, 7.445081876767077, 9.744158499468812, 6.332824024835792, 6.728424262216965, 7.836664125289878, 8.134208340125381), # 0 (8.566923443231959, 9.424097110631614, 8.888554546128244, 10.600230805242587, 9.475984539958779, 5.353573734468089, 7.07115030602191, 7.9352219566491335, 10.387592522132655, 6.75036910764344, 7.172953817529811, 8.353946657302968, 8.671666635903767), # 1 (9.09875681436757, 10.005416273425567, 9.436867656875862, 11.254380327463672, 10.062340757999591, 5.683976183219912, 7.506909612737127, 8.423400396647072, 11.028458891004078, 7.166262040032874, 7.615717038042101, 8.869172243284888, 9.206983725135505), # 2 (9.6268124690345, 10.582112803098315, 9.980817390911767, 11.903322252051318, 10.644258681603043, 6.011744996136181, 7.939205826636729, 8.907681851991212, 11.664216257473749, 7.578852317481889, 8.054957458923813, 9.380297095888738, 9.738036490006762), # 3 (10.149017837465571, 11.15188031885724, 10.518228639524859, 12.544461826212112, 11.219431366074389, 6.335569931837869, 8.366309869613534, 9.386130977911865, 12.292323272932332, 7.986489435468286, 8.48891861534492, 9.885277427767623, 10.262701812703709), # 4 (10.663300349893618, 11.712412439909741, 11.04692629400403, 13.17520429715263, 11.785551866718848, 6.654140748945943, 8.786492663560358, 9.856812429639348, 12.910238588770495, 8.387522889469862, 8.915844042475412, 10.382069451574637, 10.778856575412524), # 5 (11.167587436551466, 12.261402785463202, 11.564735245638186, 13.792954912079445, 12.34031323884167, 6.9661472060813825, 9.19802513037002, 10.317790862403982, 13.515420856378904, 8.780302174964413, 9.333977275485251, 10.868629379962893, 11.284377660319372), # 6 (11.65980652767195, 12.79654497472501, 12.069480385716217, 14.39511891819914, 12.881408537748086, 7.270279061865153, 9.599178191935335, 10.767130931436084, 14.105328727148231, 9.16317678742974, 9.74156184954443, 11.342913425585486, 11.777141949610431), # 7 (12.137885053487896, 13.31553262690256, 12.558986605527034, 14.979101562718284, 13.406530818743338, 7.565226074918224, 9.988222770149116, 11.20289729196596, 14.67742085246913, 9.53449622234364, 10.136841299822914, 11.802877801095525, 12.255026325471867), # 8 (12.599750444232136, 13.816059361203237, 13.031078796359527, 15.54230809284347, 13.913373137132655, 7.849678003861574, 10.363429786904192, 11.623154599223941, 15.229155883732279, 9.892609975183907, 10.518059161490685, 12.246478719146102, 12.71590767008986), # 9 (13.043330130137491, 14.295818796834425, 13.483581849502599, 16.08214375578126, 14.399628548221282, 8.122324607316171, 10.723070164093368, 12.025967508440338, 15.757992472328343, 10.235867541428343, 10.883458969717719, 12.671672392390324, 13.157662865650577), # 10 (13.466551541436809, 14.752504553003531, 13.914320656245145, 16.596013798738237, 14.862990107314454, 8.38185564390299, 11.065414823609466, 12.409400674845465, 16.26138926964799, 10.56261841655475, 11.231284259673998, 13.076415033481297, 13.57816879434018), # 11 (13.8673421083629, 15.183810248917917, 14.321120107876064, 17.08132346892098, 15.301150869717404, 8.626960872242991, 11.388734687345298, 12.771518753669634, 16.736804927081888, 10.871212096040916, 11.559778566529495, 13.45866285507211, 13.975302338344855), # 12 (14.243629261148602, 15.587429503784993, 14.701805095684259, 17.53547801353607, 15.711803890735363, 8.856330050957158, 11.69130067719369, 13.11038640014317, 17.181698096020693, 11.159998075364648, 11.86718542545419, 13.816372069815873, 14.346940379850777), # 13 (14.593340430026746, 15.961055936812143, 15.054200510958635, 17.95588267979007, 16.092642225673583, 9.068652938666455, 11.971383715047459, 13.424068269496395, 17.593527427855076, 11.427325850003735, 12.151748371618055, 14.147498890365696, 14.690959801044102), # 14 (14.914403045230168, 16.30238316720675, 15.376131244988068, 18.339942714889578, 16.441358929837293, 9.26261929399186, 12.227254722799401, 13.71062901695961, 17.96975157397571, 11.671544915435986, 12.411710940191071, 14.449999529374674, 15.00523748411101), # 15 (15.204744536991681, 16.609104814176213, 15.66542218906148, 18.685063366041145, 16.755647058531732, 9.436918875554335, 12.457184622342362, 13.968133297763139, 18.307829185773258, 11.891004767139194, 12.64531666634322, 14.721830199495905, 15.287650311237673), # 16 (15.46229233554412, 16.878914496927916, 15.919898234467764, 18.98864988045138, 17.033199667062142, 9.590241441974857, 12.659444335569138, 14.19464576713731, 18.605218914638375, 12.084054900591148, 12.850809085244478, 14.960947113382488, 15.536075164610265), # 17 (15.684973871120327, 17.10950583466924, 16.137384272495808, 19.248107505326846, 17.271709810733743, 9.721276751874406, 12.832304784372562, 14.388231080312417, 18.859379411961754, 12.249044811269659, 13.026431732064815, 15.165306483687544, 15.748388926414954), # 18 (15.870716573953118, 17.29857244660759, 16.315705194434525, 19.460841487874106, 17.468870544851786, 9.828714563873934, 12.974036890645431, 14.546953892518793, 19.067769329134048, 12.384323994652526, 13.170428141974206, 15.332864523064154, 15.922468478837914), # 19 (16.01744787427533, 17.44380795195034, 16.452685891572806, 19.624257075299766, 17.62237492472151, 9.91124463659443, 13.08291157628058, 14.668878858986748, 19.22784731754592, 12.488241946217535, 13.28104185014264, 15.461577444165426, 16.05619070406532), # 20 (16.123095202319785, 17.542905969904893, 16.54615125519955, 19.73575951481038, 17.729916005648143, 9.967556728656858, 13.157199763170816, 14.752070634946598, 19.337072028588036, 12.559148161442488, 13.356516391740096, 15.54940145964447, 16.147432484283325), # 21 (16.18558598831933, 17.59356011967863, 16.593926176603656, 19.79275405361254, 17.78918684293692, 9.996340598682188, 13.19517237320896, 14.794593875628664, 19.392902113651065, 12.595392135805188, 13.395095301936545, 15.594292782154383, 16.194070701678125), # 22 (16.208629381348224, 17.599557750342935, 16.599877091906723, 19.799889300411525, 17.804371289652156, 10.0, 13.199686403614942, 14.79919012345679, 19.399881975308645, 12.599667636031093, 13.399932859458785, 15.599836122542294, 16.2), # 23 (16.225619860854646, 17.59605925925926, 16.598903703703705, 19.799011111111113, 17.812972181783763, 10.0, 13.197206100217867, 14.7928, 19.398946666666667, 12.59704098765432, 13.39939932659933, 15.598538271604937, 16.2), # 24 (16.242251568338528, 17.589163237311386, 16.59698216735254, 19.797273662551444, 17.821383912951205, 10.0, 13.192318244170096, 14.78024691358025, 19.3970987654321, 12.591870141746686, 13.39834143908218, 15.595976223136716, 16.2), # 25 (16.258523230476854, 17.578975034293556, 16.594138820301787, 19.79469670781893, 17.82960618947377, 10.0, 13.185098749293955, 14.76176790123457, 19.39436197530864, 12.58424113397348, 13.396768774161368, 15.592185093735715, 16.2), # 26 (16.27443357394662, 17.5656, 16.5904, 19.7913, 17.837638717670742, 10.0, 13.175623529411766, 14.7376, 19.39076, 12.57424, 13.39469090909091, 15.587200000000003, 16.2), # 27 (16.2899813254248, 17.549143484224967, 16.585792043895747, 19.787103292181072, 17.845481203861443, 10.0, 13.163968498345842, 14.707980246913582, 19.386316543209876, 12.561952775491541, 13.39211742112483, 15.581056058527665, 16.2), # 28 (16.3051652115884, 17.52971083676269, 16.580341289437587, 19.78212633744856, 17.853133354365152, 10.0, 13.150209569918506, 14.673145679012345, 19.381055308641976, 12.547465496113398, 13.389057887517147, 15.57378838591678, 16.2), # 29 (16.319983959114396, 17.50740740740741, 16.574074074074073, 19.77638888888889, 17.860594875501178, 10.0, 13.13442265795207, 14.633333333333333, 19.375, 12.530864197530866, 13.385521885521886, 15.56543209876543, 16.2), # 30 (16.334436294679772, 17.482338545953365, 16.567016735253773, 19.76991069958848, 17.867865473588814, 10.0, 13.116683676268863, 14.588780246913581, 19.368174320987656, 12.512234915409238, 13.381518992393067, 15.556022313671699, 16.2), # 31 (16.34852094496153, 17.45460960219479, 16.55919561042524, 19.762711522633747, 17.874944854947355, 10.0, 13.097068538691198, 14.539723456790126, 19.360601975308644, 12.49166368541381, 13.377058785384712, 15.545594147233656, 16.2), # 32 (16.362236636636634, 17.424325925925924, 16.55063703703704, 19.75481111111111, 17.8818327258961, 10.0, 13.075653159041394, 14.486400000000001, 19.352306666666667, 12.469236543209878, 13.372150841750841, 15.534182716049381, 16.2), # 33 (16.375582096382097, 17.391592866941014, 16.541367352537723, 19.746229218106997, 17.888528792754347, 10.0, 13.052513451141776, 14.429046913580246, 19.343312098765438, 12.445039524462736, 13.36680473874548, 15.521823136716964, 16.2), # 34 (16.388556050874893, 17.356515775034293, 16.53141289437586, 19.736985596707818, 17.895032761841392, 10.0, 13.027725328814654, 14.367901234567903, 19.333641975308645, 12.419158664837678, 13.361030053622645, 15.508550525834478, 16.2), # 35 (16.40115722679201, 17.3192, 16.5208, 19.7271, 17.901344339476537, 10.0, 13.001364705882352, 14.303200000000002, 19.32332, 12.391680000000001, 13.354836363636364, 15.494400000000002, 16.2), # 36 (16.41338435081044, 17.27975089163237, 16.50955500685871, 19.71659218106996, 17.907463231979076, 10.0, 12.97350749616719, 14.23518024691358, 19.31236987654321, 12.362689565615, 13.348233246040657, 15.479406675811616, 16.2), # 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171 (8.368132617954185, 6.4046935697385114, 9.172262799671339, 10.350556506331834, 10.436031551469046, 5.940340991572694, 5.245000635792105, 6.392289979928433, 11.524502245889417, 5.468338059902528, 6.473691907130711, 7.779846990771154, 9.278900437035686), # 172 (8.014157008649567, 6.127367976959108, 8.79478640929394, 9.919958648940762, 10.005465534181923, 5.69854509327084, 5.02083474097464, 6.132810303439398, 11.058643142861477, 5.238281777739651, 6.202292391830685, 7.45544655480756, 8.89554760994954), # 173 (7.6537353977365505, 5.845959979214909, 8.408855084423363, 9.480431056994465, 9.565421708371947, 5.450918750474696, 4.792940404121401, 5.866965223669057, 10.581049127367942, 5.003850823863915, 5.9255744015838845, 7.124440155807469, 8.503844970445494), # 174 (7.288123984259929, 5.561387209281111, 8.015917784018413, 9.033576718083788, 9.11754095998411, 5.198418476994606, 4.562088457368093, 5.595789317371834, 10.09359824321745, 4.765874484015079, 5.644527029332911, 6.788027200329303, 8.105247397697292), # 175 (6.91857896726451, 5.274567299932917, 7.617423467037885, 8.58099861979956, 8.663464174963408, 4.942000786640907, 4.329049732850424, 5.3203171613021585, 9.598168534218628, 4.525182043932907, 5.360139368020368, 6.447407094931487, 7.701209770878679), # 176 (6.546356545795092, 4.986417883945522, 7.214821092440582, 8.124299749732613, 8.204832239254838, 4.682622193223941, 4.094595062704101, 5.0415833322144525, 9.096638044180112, 4.282602789357159, 5.073400510588858, 6.103779246172446, 7.2931869691634), # 177 (6.172712918896475, 4.697856594094126, 6.809559619185302, 7.665083095473786, 7.743286038803382, 4.421239210554052, 3.859495279064828, 4.760622406863145, 8.590884816910537, 4.0389660060276, 4.78529954998098, 5.758343060610604, 6.882633871725203), # 178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_arriving_acc = ( (10, 7, 7, 7, 6, 3, 5, 3, 0, 1, 0, 1, 0, 8, 5, 3, 3, 11, 4, 6, 0, 2, 2, 1, 0, 0), # 0 (17, 17, 19, 12, 18, 9, 10, 4, 0, 4, 1, 2, 0, 20, 13, 11, 6, 23, 10, 9, 2, 3, 2, 3, 2, 0), # 1 (27, 23, 33, 14, 26, 12, 14, 6, 6, 7, 1, 2, 0, 33, 21, 21, 10, 34, 16, 11, 4, 7, 5, 7, 2, 0), # 2 (37, 38, 38, 21, 34, 13, 17, 9, 12, 8, 1, 4, 0, 43, 25, 26, 17, 43, 19, 14, 4, 8, 10, 9, 2, 0), # 3 (43, 48, 46, 27, 42, 16, 19, 14, 14, 11, 2, 5, 0, 45, 36, 33, 22, 53, 24, 15, 7, 11, 13, 9, 3, 0), # 4 (57, 63, 59, 41, 53, 24, 24, 19, 16, 12, 2, 6, 0, 54, 45, 40, 29, 62, 32, 24, 10, 16, 15, 9, 4, 0), # 5 (63, 74, 69, 47, 57, 28, 25, 25, 25, 14, 2, 7, 0, 67, 55, 47, 36, 70, 42, 29, 11, 23, 18, 10, 4, 0), # 6 (73, 83, 82, 58, 67, 31, 32, 27, 31, 15, 3, 7, 0, 81, 63, 54, 44, 80, 52, 35, 15, 28, 23, 13, 5, 0), # 7 (85, 98, 91, 72, 76, 37, 34, 33, 34, 16, 3, 9, 0, 95, 72, 63, 49, 88, 61, 39, 18, 29, 29, 13, 9, 0), # 8 (91, 109, 100, 90, 82, 41, 35, 39, 37, 16, 3, 9, 0, 110, 84, 75, 62, 100, 66, 41, 23, 37, 31, 15, 9, 0), # 9 (99, 112, 116, 105, 91, 45, 40, 44, 41, 18, 4, 9, 0, 121, 95, 85, 69, 115, 74, 43, 27, 40, 33, 16, 11, 0), # 10 (111, 124, 127, 112, 100, 48, 48, 48, 47, 22, 7, 11, 0, 140, 104, 91, 77, 129, 83, 49, 29, 48, 38, 22, 11, 0), # 11 (125, 133, 138, 124, 107, 52, 53, 52, 55, 23, 8, 13, 0, 150, 112, 100, 87, 141, 91, 53, 35, 56, 41, 24, 11, 0), # 12 (136, 139, 155, 138, 116, 59, 59, 63, 62, 27, 8, 15, 0, 166, 121, 110, 97, 151, 99, 63, 39, 62, 48, 26, 13, 0), # 13 (148, 158, 167, 151, 130, 62, 69, 69, 67, 29, 11, 16, 0, 184, 131, 117, 104, 165, 107, 70, 42, 68, 50, 26, 15, 0), # 14 (162, 175, 177, 159, 141, 67, 80, 79, 76, 31, 13, 18, 0, 199, 142, 124, 114, 171, 111, 72, 44, 75, 54, 28, 15, 0), # 15 (180, 188, 193, 179, 158, 74, 87, 87, 80, 37, 16, 19, 0, 213, 161, 137, 117, 183, 120, 77, 48, 81, 59, 28, 16, 0), # 16 (196, 210, 204, 195, 170, 77, 95, 92, 87, 39, 16, 20, 0, 223, 168, 144, 127, 190, 129, 82, 53, 93, 64, 28, 16, 0), # 17 (213, 231, 219, 207, 179, 86, 100, 96, 94, 45, 17, 24, 0, 241, 182, 158, 136, 203, 142, 90, 57, 106, 70, 32, 17, 0), # 18 (230, 250, 230, 223, 191, 94, 106, 101, 97, 47, 20, 24, 0, 257, 192, 171, 144, 221, 147, 98, 66, 112, 73, 35, 20, 0), # 19 (241, 264, 238, 232, 198, 101, 116, 106, 102, 49, 22, 25, 0, 277, 206, 181, 155, 234, 150, 100, 69, 117, 76, 40, 22, 0), # 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28 (356, 412, 358, 368, 296, 143, 171, 161, 155, 77, 43, 35, 0, 427, 316, 281, 250, 345, 214, 144, 104, 164, 123, 68, 43, 0), # 29 (372, 428, 371, 380, 304, 150, 176, 168, 162, 80, 47, 36, 0, 439, 324, 294, 261, 349, 220, 149, 105, 170, 131, 71, 46, 0), # 30 (390, 444, 390, 391, 321, 158, 184, 175, 165, 82, 49, 36, 0, 456, 342, 306, 269, 357, 232, 156, 110, 174, 131, 74, 48, 0), # 31 (412, 456, 402, 408, 340, 161, 189, 182, 171, 85, 49, 36, 0, 470, 360, 318, 278, 368, 236, 163, 116, 184, 136, 75, 48, 0), # 32 (424, 475, 419, 420, 348, 165, 195, 188, 176, 85, 51, 36, 0, 488, 370, 327, 285, 381, 248, 166, 121, 189, 139, 78, 48, 0), # 33 (440, 487, 433, 441, 361, 172, 206, 191, 181, 87, 60, 37, 0, 503, 386, 337, 299, 395, 258, 174, 130, 194, 143, 80, 50, 0), # 34 (457, 500, 450, 461, 372, 178, 214, 198, 185, 89, 64, 37, 0, 515, 401, 350, 310, 408, 264, 180, 132, 198, 149, 81, 52, 0), # 35 (470, 511, 462, 472, 387, 184, 219, 206, 196, 92, 65, 39, 0, 531, 417, 368, 318, 422, 272, 191, 137, 204, 151, 86, 55, 0), # 36 (483, 528, 483, 479, 390, 189, 231, 211, 204, 93, 70, 40, 0, 551, 433, 375, 327, 435, 282, 197, 140, 209, 152, 89, 58, 0), # 37 (499, 546, 498, 499, 406, 196, 242, 216, 207, 95, 70, 41, 0, 573, 451, 386, 336, 444, 288, 203, 142, 217, 157, 90, 59, 0), # 38 (517, 568, 507, 510, 433, 199, 245, 224, 220, 97, 74, 44, 0, 592, 468, 396, 342, 459, 294, 213, 145, 228, 162, 93, 63, 0), # 39 (533, 584, 522, 518, 441, 209, 250, 226, 227, 103, 78, 44, 0, 613, 480, 404, 357, 470, 303, 220, 155, 236, 171, 94, 64, 0), # 40 (550, 598, 533, 533, 452, 215, 255, 231, 232, 106, 80, 46, 0, 625, 498, 415, 366, 483, 310, 230, 161, 247, 175, 96, 67, 0), # 41 (570, 612, 550, 552, 464, 223, 265, 240, 236, 111, 81, 51, 0, 643, 511, 425, 379, 492, 314, 238, 163, 250, 180, 96, 69, 0), # 42 (589, 623, 561, 568, 476, 228, 270, 249, 242, 112, 82, 54, 0, 660, 534, 438, 386, 501, 321, 244, 169, 256, 187, 100, 71, 0), # 43 (600, 646, 581, 582, 495, 232, 278, 254, 244, 115, 84, 54, 0, 673, 545, 446, 396, 512, 325, 250, 172, 264, 194, 101, 72, 0), # 44 (618, 660, 597, 599, 509, 238, 289, 262, 249, 121, 86, 55, 0, 687, 562, 457, 405, 528, 330, 259, 179, 273, 198, 104, 72, 0), # 45 (638, 678, 611, 612, 516, 242, 295, 266, 255, 122, 88, 56, 0, 705, 579, 468, 417, 540, 340, 266, 183, 285, 203, 106, 73, 0), # 46 (654, 699, 621, 623, 529, 243, 303, 272, 262, 123, 93, 56, 0, 721, 600, 479, 429, 556, 351, 276, 186, 292, 204, 109, 75, 0), # 47 (668, 717, 630, 631, 534, 251, 313, 275, 267, 124, 94, 59, 0, 730, 615, 489, 439, 576, 361, 278, 191, 297, 209, 112, 76, 0), # 48 (677, 732, 642, 643, 551, 259, 321, 284, 272, 125, 97, 62, 0, 745, 622, 499, 451, 592, 371, 285, 195, 301, 218, 116, 77, 0), # 49 (695, 750, 654, 657, 570, 264, 323, 293, 280, 127, 99, 63, 0, 763, 634, 514, 465, 605, 377, 292, 198, 308, 223, 118, 81, 0), # 50 (707, 767, 670, 681, 581, 270, 327, 296, 283, 131, 102, 64, 0, 778, 651, 525, 480, 617, 383, 297, 206, 319, 225, 120, 82, 0), # 51 (721, 788, 679, 696, 593, 274, 338, 301, 285, 133, 106, 65, 0, 795, 668, 537, 493, 632, 393, 306, 207, 328, 229, 126, 82, 0), # 52 (739, 802, 688, 713, 609, 282, 343, 304, 289, 137, 108, 65, 0, 806, 682, 542, 503, 643, 396, 310, 209, 337, 234, 128, 83, 0), # 53 (753, 816, 704, 727, 629, 285, 352, 310, 291, 141, 110, 68, 0, 823, 692, 550, 514, 652, 405, 313, 214, 343, 237, 128, 84, 0), # 54 (774, 823, 718, 740, 639, 290, 355, 312, 294, 144, 111, 70, 0, 836, 705, 562, 522, 663, 414, 314, 217, 350, 241, 131, 85, 0), # 55 (793, 842, 733, 760, 656, 297, 361, 316, 301, 146, 112, 71, 0, 847, 715, 567, 527, 677, 423, 317, 217, 358, 243, 136, 87, 0), # 56 (804, 858, 749, 781, 671, 305, 363, 319, 307, 148, 114, 72, 0, 865, 722, 575, 537, 688, 427, 321, 219, 364, 245, 140, 89, 0), # 57 (812, 877, 760, 795, 685, 311, 368, 321, 319, 151, 117, 75, 0, 879, 731, 585, 545, 706, 431, 327, 223, 370, 248, 145, 92, 0), # 58 (830, 893, 780, 813, 694, 321, 375, 328, 326, 151, 119, 76, 0, 897, 746, 596, 552, 717, 439, 332, 224, 378, 252, 149, 94, 0), # 59 (839, 912, 796, 837, 707, 329, 381, 332, 332, 153, 122, 76, 0, 906, 758, 621, 562, 729, 444, 337, 225, 382, 255, 153, 94, 0), # 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177 (2490, 2163, 2205, 2305, 1982, 952, 912, 784, 989, 411, 337, 224, 0, 2598, 2112, 1686, 1317, 2098, 1110, 902, 686, 1011, 705, 464, 208, 0), # 178 (2490, 2163, 2205, 2305, 1982, 952, 912, 784, 989, 411, 337, 224, 0, 2598, 2112, 1686, 1317, 2098, 1110, 902, 686, 1011, 705, 464, 208, 0), # 179 ) passenger_arriving_rate = ( (8.033384925394829, 8.103756554216645, 6.9483776394833425, 7.45760132863612, 5.924997981450252, 2.9294112699015167, 3.3168284922991322, 3.102117448652949, 3.2480528331562706, 1.5832060062089484, 1.1214040437028276, 0.6530553437741565, 0.0, 8.134208340125381, 7.183608781515721, 5.607020218514138, 4.749618018626844, 6.496105666312541, 4.342964428114128, 3.3168284922991322, 2.0924366213582264, 2.962498990725126, 2.4858671095453735, 1.3896755278966686, 0.7367051412924223, 0.0), # 0 (8.566923443231959, 8.638755684745645, 7.407128788440204, 7.95017310393194, 6.317323026639185, 3.122918011773052, 3.535575153010955, 3.306342481937139, 3.462530840710885, 1.6875922769108604, 1.1954923029216353, 0.6961622214419141, 0.0, 8.671666635903767, 7.657784435861053, 5.9774615146081755, 5.06277683073258, 6.92506168142177, 4.628879474711995, 3.535575153010955, 2.230655722695037, 3.1586615133195926, 2.650057701310647, 1.4814257576880407, 0.7853414258859679, 0.0), # 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166 (9.997353673783238, 7.056470952695688, 9.06842264216894, 9.235121406959813, 8.259509001324778, 4.099891735511655, 3.14146217729654, 3.1491654694787847, 4.537347513581013, 1.6309935985330861, 1.2860490177396875, 0.7717317079580612, 0.0, 11.019496615116793, 8.489048787538673, 6.430245088698436, 4.892980795599257, 9.074695027162026, 4.408831657270299, 3.14146217729654, 2.928494096794039, 4.129754500662389, 3.0783738023199385, 1.8136845284337881, 0.6414973593359717, 0.0), # 167 (9.694451058192634, 6.833584299395522, 8.807199864227664, 8.963693128895455, 8.020466544326124, 3.9842190871177325, 3.0444815759950434, 3.0607762991595733, 4.411009552390856, 1.5820576891575493, 1.247720560001835, 0.7489500810910016, 0.0, 10.69971603772634, 8.238450892001017, 6.2386028000091756, 4.746173067472647, 8.822019104781711, 4.285086818823403, 3.0444815759950434, 2.8458707765126663, 4.010233272163062, 2.987897709631819, 1.7614399728455332, 0.6212349363086839, 0.0), # 168 (9.378821445769624, 6.602749624657969, 8.53289397854708, 8.67955634644906, 7.769635928495594, 3.8623555141553156, 2.9437096733363934, 2.9675795092347634, 4.277629880697781, 1.5309915046790952, 1.2076814456540184, 0.7251182045715564, 0.0, 10.364311252049257, 7.976300250287119, 6.038407228270092, 4.592974514037284, 8.555259761395561, 4.154611312928669, 2.9437096733363934, 2.7588253672537966, 3.884817964247797, 2.8931854488163538, 1.706578795709416, 0.6002499658779973, 0.0), # 169 (9.051721035559014, 6.3648080918602945, 8.24671245092618, 8.383913300313743, 7.508111077796515, 3.7348589830137664, 2.8395318853884426, 2.870006173352032, 4.137834513104661, 1.4780023665326634, 1.1660965235200045, 0.7003360289462612, 0.0, 10.014737137259289, 7.7036963184088725, 5.830482617600023, 4.43400709959799, 8.275669026209322, 4.018008642692845, 2.8395318853884426, 2.6677564164384044, 3.7540555388982577, 2.7946377667712485, 1.649342490185236, 0.5786189174418451, 0.0), # 170 (8.7144060266056, 6.12060086437976, 7.949862747163971, 8.077966231182643, 7.23698591619222, 3.602287460082452, 2.7323336282190445, 2.7684873651590554, 3.992249464214377, 1.4232975961531957, 1.1231306424235596, 0.6747035047616515, 0.0, 9.652448572530185, 7.421738552378166, 5.615653212117798, 4.269892788459586, 7.984498928428754, 3.8758823112226777, 2.7323336282190445, 2.5730624714874657, 3.61849295809611, 2.692655410394215, 1.5899725494327943, 0.5564182603981601, 0.0), # 171 (8.368132617954185, 5.870969105593635, 7.643552333059449, 7.762917379748876, 6.9573543676460305, 3.4651989117507385, 2.6225003178960526, 2.663454158303514, 3.8415007486298056, 1.3670845149756323, 1.0789486511884518, 0.648320582564263, 0.0, 9.278900437035686, 7.1315264082068905, 5.3947432559422595, 4.101253544926896, 7.683001497259611, 3.7288358216249198, 2.6225003178960526, 2.475142079821956, 3.4786771838230153, 2.587639126582959, 1.52871046661189, 0.5337244641448761, 0.0), # 172 (8.014157008649567, 5.616753978879182, 7.328988674411616, 7.439968986705571, 6.6703103561212815, 3.3241513044079904, 2.51041737048732, 2.5553376264330825, 3.6862143809538255, 1.309570444434913, 1.0337153986384477, 0.62128721290063, 0.0, 8.89554760994954, 6.83415934190693, 5.168576993192238, 3.9287113333047383, 7.372428761907651, 3.5774726770063157, 2.51041737048732, 2.37439378886285, 3.3351551780606408, 2.479989662235191, 1.4657977348823235, 0.5106139980799257, 0.0), # 173 (7.6537353977365505, 5.358796647613667, 7.00737923701947, 7.110323292745849, 6.376947805581297, 3.179702604443573, 2.3964702020607005, 2.4445688431954404, 3.527016375789314, 1.250962705965979, 0.9875957335973142, 0.5937033463172892, 0.0, 8.503844970445494, 6.53073680949018, 4.93797866798657, 3.7528881178979363, 7.054032751578628, 3.4223963804736166, 2.3964702020607005, 2.2712161460311235, 3.1884739027906486, 2.370107764248617, 1.401475847403894, 0.4871633316012425, 0.0), # 174 (7.288123984259929, 5.097938275174352, 6.679931486682011, 6.7751825385628415, 6.078360639989406, 3.0324107782468537, 2.2810442286840464, 2.331578882238264, 3.36453274773915, 1.19146862100377, 0.9407545048888186, 0.5656689333607753, 0.0, 8.105247397697292, 6.222358266968527, 4.703772524444093, 3.574405863011309, 6.7290654954783, 3.26421043513357, 2.2810442286840464, 2.1660076987477526, 3.039180319994703, 2.2583941795209475, 1.3359862973364023, 0.46344893410675936, 0.0), # 175 (6.91857896726451, 4.835020024938507, 6.347852889198238, 6.435748964849671, 5.775642783308939, 2.882833792207196, 2.164524866425212, 2.216798817209233, 3.199389511406209, 1.131295510983227, 0.8933565613367281, 0.537283924577624, 0.0, 7.701209770878679, 5.910123170353863, 4.46678280668364, 3.39388653294968, 6.398779022812418, 3.103518344092926, 2.164524866425212, 2.0591669944337117, 2.8878213916544695, 2.1452496549498905, 1.2695705778396478, 0.4395472749944098, 0.0), # 176 (6.546356545795092, 4.570883060283395, 6.012350910367152, 6.093224812299459, 5.469888159503225, 2.731529612713966, 2.0472975313520503, 2.100659721756022, 3.0322126813933705, 1.07065069733929, 0.8455667517648098, 0.5086482705143706, 0.0, 7.2931869691634, 5.595130975658075, 4.227833758824048, 3.211952092017869, 6.064425362786741, 2.9409236104584306, 2.0472975313520503, 1.9510925805099755, 2.7349440797516125, 2.0310749374331536, 1.2024701820734305, 0.4155348236621269, 0.0), # 177 (6.172712918896475, 4.306368544586282, 5.6746330159877525, 5.74881232160534, 5.162190692535588, 2.5790562061565305, 1.929747639532414, 1.9835926695263104, 2.863628272303512, 1.0097415015069002, 0.7975499249968301, 0.4798619217175504, 0.0, 6.882633871725203, 5.278481138893053, 3.98774962498415, 3.0292245045207, 5.727256544607024, 2.7770297373368344, 1.929747639532414, 1.8421830043975218, 2.581095346267794, 1.916270773868447, 1.1349266031975505, 0.3914880495078438, 0.0), # 178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_allighting_rate = ( (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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13 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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160 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 161 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 162 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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169 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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175 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 8991598675325360468762009371570610170 #index for seed sequence child child_seed_index = ( 1, # 0 78, # 1 )

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32d955f2fa3d65d510cd46e0c2eb99557b17a24f

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py

Python

rlgym/gamelaunch/__init__.py

Rolv-Arild/rocket-league-gym

f1200c161fdd4f720c52b0f962907298587102a5

[ "Apache-2.0" ]

null

rlgym/gamelaunch/__init__.py

Rolv-Arild/rocket-league-gym

f1200c161fdd4f720c52b0f962907298587102a5

[ "Apache-2.0" ]

null

rlgym/gamelaunch/__init__.py

Rolv-Arild/rocket-league-gym

f1200c161fdd4f720c52b0f962907298587102a5

[ "Apache-2.0" ]

null

from .launch import launch_rocket_league, run_injector from .paging import page_rocket_league

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32dbd174f320e97230a93b723f0a2d11afbab324

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py

Python

src/key.py

imartinezl/dBizi

73a4d9076aa098be5a7e34ca23ada71400ff4cd9

[ "MIT" ]

null

src/key.py

imartinezl/dBizi

73a4d9076aa098be5a7e34ca23ada71400ff4cd9

[ "MIT" ]

null

src/key.py

imartinezl/dBizi

73a4d9076aa098be5a7e34ca23ada71400ff4cd9

[ "MIT" ]

null

conn_string = "host='192.168.1.205' dbname='dBici' user='postgres' password='root'" key_gh = 'a61d7bca-ca1f-4c32-bf38-e5855d64a884'

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6

bd1b5e58bf49e61bd3579f060210387f22e632b7

29

py

Python

python.py

sozinscomments/github-slideshow

7eda780ccfd0e64febe754398a4e1487f82e3d9c

[ "MIT" ]

null

python.py

sozinscomments/github-slideshow

7eda780ccfd0e64febe754398a4e1487f82e3d9c

[ "MIT" ]

3

2021-06-24T22:31:02.000Z

2021-07-05T21:23:12.000Z

python.py

sozinscomments/github-slideshow

7eda780ccfd0e64febe754398a4e1487f82e3d9c

[ "MIT" ]

null

print("how does this work?")

14.5

28

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1

29

0.8

0

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0

1

0

1

0

null

0

1

0

1

0

6

95050500889b9b100498dd5097c26b6d24f73a12

137

py

Python

src/zabbix_enums/specific/z50/__init__.py

szuro/zabbix-enums

f2ef3b9ea630f678c336d4fc58b5401771a0e4d1

[ "MIT" ]

1

2022-02-07T01:21:34.000Z

src/zabbix_enums/specific/z54/__init__.py

szuro/zabbix-enums

f2ef3b9ea630f678c336d4fc58b5401771a0e4d1

[ "MIT" ]

null

src/zabbix_enums/specific/z54/__init__.py

szuro/zabbix-enums

f2ef3b9ea630f678c336d4fc58b5401771a0e4d1

[ "MIT" ]

null

from .audit_log import * from .dashboard import * from .item import * from .lld import * from .script import * from .user_macro import *

19.571429

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0.737226

20

137

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137

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0

1

0

1

0

1

0

6

95216c2454a125c002882a46224592ca854549ba

6,729

py

Python

localutils/changedetect.py

maxmouchet/rtt

a1694f0ecaf418149d7eb6076da2623d3c253a73

[ "MIT" ]

5

2018-01-12T19:31:08.000Z

2021-04-10T03:01:56.000Z

localutils/changedetect.py

maxmouchet/rtt

a1694f0ecaf418149d7eb6076da2623d3c253a73

[ "MIT" ]

1

2019-03-31T06:57:11.000Z

localutils/changedetect.py

maxmouchet/rtt

a1694f0ecaf418149d7eb6076da2623d3c253a73

[ "MIT" ]

4

2018-01-12T19:31:51.000Z

2021-04-10T03:02:06.000Z

""" changedetect.py provides tools for detecting changes in RTT time series """ import numpy as np import logging from rpy2.rinterface import RRuntimeError from rpy2.robjects.packages import importr from rpy2.robjects.vectors import IntVector, FloatVector changepoint = importr('changepoint') changepoint_np = importr('changepoint.np') def cpt_normal(x, penalty="MBIC", minseglen=2): """changepoint detection with Normal distribution as test statistic Args: x (list of numeric type): timeseries to be handled penalty (string): possible choices "None", "SIC", "BIC", "MBIC", "AIC", "Hannan-Quinn" Returns: list of int: beginning of new segment in python index, that is starting from 0; the actually return from R changepoint detection is the last index of a segment. since the R indexing starts from 1, the return naturally become the beginning of segment. """ x = [i if i > 0 else 1e3 for i in x] return [int(i) for i in changepoint.cpts(changepoint.cpt_meanvar(FloatVector(x), test_stat='Normal', method='PELT', penalty=penalty, minseglen=minseglen))] def cpt_np(x, penalty="MBIC", minseglen=2): """changepoint detection with non-parametric method, empirical distribution is the only choice now Args: x (list of numeric type): timeseries to be handled penalty (string): possible choices "None", "SIC", "BIC", "MBIC", "AIC", "Hannan-Quinn" Returns: list of int: beginning of new segment in python index, that is starting from 0; the actually return from R changepoint detection is the last index of a segment. since the R indexing starts from 1, the return naturally become the beginning of segment. """ x = [i if i > 0 else 1e3 for i in x] return [int(i) for i in changepoint.cpts(changepoint_np.cpt_np(FloatVector(x), penalty=penalty, minseglen=minseglen))] def cpt_poisson(x, penalty="MBIC", minseglen=2): """changepoint detection with Poisson distribution as test statistic Baseline equaling the smallest non-negative value is remove; negative value is set to a very large RTT, 1e3. Args: x (list of numeric type): timeseries to be handled penalty (string): possible choices "None", "SIC", "BIC", "MBIC", "AIC", "Hannan-Quinn" Returns: list of int: beginning of new segment in python index, that is starting from 0; the actually return from R changepoint detection is the last index of a segment. since the R indexing starts from 1, the return naturally become the beginning of segment. """ x = np.rint(x) try: base = np.min([i for i in x if i > 0]) except ValueError: # if no positive number if x, set base to 0 base = 0 x = [i-base if i > 0 else 1e3 for i in x] return [int(i) for i in changepoint.cpts(changepoint.cpt_meanvar(IntVector(x), test_stat='Poisson', method='PELT', penalty=penalty, minseglen=minseglen))] def cpt_poisson_naive(x, penalty="MBIC", minseglen=2): """changepoint detection with Poisson distribution as test statistic negative value is set to a very large RTT, 1e3. Args: x (list of numeric type): timeseries to be handled penalty (string): possible choices "None", "SIC", "BIC", "MBIC", "AIC", "Hannan-Quinn" Returns: list of int: beginning of new segment in python index, that is starting from 0; the actually return from R changepoint detection is the last index of a segment. since the R indexing starts from 1, the return naturally become the beginning of segment. """ x = np.rint(x) x = [i if i > 0 else 1e3 for i in x] return [int(i) for i in changepoint.cpts(changepoint.cpt_meanvar(IntVector(x), test_stat='Poisson', method='PELT', penalty=penalty, minseglen=minseglen))] def cpt_exp(x, penalty='MBIC', minseglen=2): """changepoint detection with Exponential distribution as test statistic non-negative value is required negative value is set to a very large RTT, 1e3. Args: x (list of numeric type): timeseries to be handled penalty (string): possible choices "None", "SIC", "BIC", "MBIC", "AIC", "Hannan-Quinn" Returns: list of int: beginning of new segment in python index, that is starting from 0; the actually return from R changepoint detection is the last index of a segment. since the R indexing starts from 1, the return naturally become the beginning of segment. """ try: base = np.min([i for i in x if i > 0]) except ValueError: # if no positive number if x, set base to 0 base = 0 x = [i-base if i > 0 else 1e3 for i in x] return [int(i) for i in changepoint.cpts(changepoint.cpt_meanvar(FloatVector(x), test_stat='Exponential', method='PELT', penalty=penalty, minseglen=minseglen))] def cpt_gamma(x, penalty='MBIC', minseglen=2, shape=100): """changepoint detection with Gamma distribution as test statistic positive value is required negative value is set to a very large RTT, 1e3. Args: x (list of numeric type): timeseries to be handled penalty (string): possible choices "None", "SIC", "BIC", "MBIC", "AIC", "Hannan-Quinn" Returns: list of int: beginning of new segment in python index, that is starting from 0; the actually return from R changepoint detection is the last index of a segment. since the R indexing starts from 1, the return naturally become the beginning of segment. """ try: base = np.min([i for i in x if i > 0]) except ValueError: # if no positive number if x, set base to 0 base = 0 x = [(i-base + 0.1) if i > 0 else 1e3 for i in x] return [int(i) for i in changepoint.cpts(changepoint.cpt_meanvar(FloatVector(x), test_stat='Gamma', method='PELT', penalty=penalty, minseglen=minseglen, shape=shape))]

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0

null

0

1

0

null

0

6

20fdac08644e47cbbe220d85d04c38025c18074e

174

py

Python

website/views.py

farzan-mortez/mySite_Resume

e41f35e0edabd19afde2cd9ce350d7e96be8cbc9

[ "MIT" ]

null

website/views.py

farzan-mortez/mySite_Resume

e41f35e0edabd19afde2cd9ce350d7e96be8cbc9

[ "MIT" ]

null

website/views.py

farzan-mortez/mySite_Resume

e41f35e0edabd19afde2cd9ce350d7e96be8cbc9

[ "MIT" ]

null

from django.shortcuts import render # Create your views here. from django.http import HttpRequest def index_view(request): return render(request, 'website/index.html')

21.75

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6

1f01bb70fc4e4a37911032788e7dc012c39c718b

1,937

py

Python

test/test_process_vcf.py

linyc74/covid_variant

b6cc17487dec83b8afe3514af60c22a832a967c5

[ "MIT" ]

1

2021-06-09T08:02:01.000Z

test/test_process_vcf.py

linyc74/covid_variant

b6cc17487dec83b8afe3514af60c22a832a967c5

[ "MIT" ]

null

test/test_process_vcf.py

linyc74/covid_variant

b6cc17487dec83b8afe3514af60c22a832a967c5

[ "MIT" ]

null

import pandas as pd from covid_variant.process_vcf import ProcessVcf, RemoveConflictVariants, VcfDfToCdsEditDf, ReadVcf from .setup import TestCase class TestProcessVcf(TestCase): def setUp(self): self.set_up(py_path=__file__) def tearDown(self): self.tear_down() def test_main(self): actual = ProcessVcf(self.settings).main(vcf=f'{self.indir}/{self.__class__.__name__}_in.vcf') expected = pd.read_csv(f'{self.indir}/{self.__class__.__name__}_out.csv') self.assertDataFrameEqual(expected, actual) class TestReadVcf(TestCase): def setUp(self): self.set_up(py_path=__file__) def tearDown(self): self.tear_down() def test_main(self): actual = ReadVcf(self.settings).main(vcf=f'{self.indir}/{self.__class__.__name__}_in.vcf') expected = pd.read_csv(f'{self.indir}/{self.__class__.__name__}_out.csv') self.assertDataFrameEqual(expected, actual) class TestRemoveConflictVariants(TestCase): def setUp(self): self.set_up(py_path=__file__) def tearDown(self): self.tear_down() def test_main(self): indf = pd.read_csv(f'{self.indir}/{self.__class__.__name__}_in.csv') actual = RemoveConflictVariants(self.settings).main(indf=indf) expected = pd.read_csv(f'{self.indir}/{self.__class__.__name__}_out.csv') self.assertDataFrameEqual(expected, actual) class TestVcfDfToCdsEditEf(TestCase): def setUp(self): self.set_up(py_path=__file__) def tearDown(self): self.tear_down() def test_main(self): vcf_df = pd.read_csv(f'{self.indir}/{self.__class__.__name__}_in.csv') actual = VcfDfToCdsEditDf(self.settings).main(vcf_df=vcf_df) expected = pd.read_csv(f'{self.indir}/{self.__class__.__name__}_out.csv') self.assertDataFrameEqual(expected, actual)

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false

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0.073171

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0.463415

0

null

0

1

0

null

0

1

0

6

1f3bba5233c6c69577bcf099ee8e22206b83a981

155

py

Python

thirdparty/cpython/test_cpython.py

lfaraone/grouper

7df5eda8003a0b4a9ba7f0dcb044ae1e4710b171

[ "Apache-2.0" ]

null

thirdparty/cpython/test_cpython.py

lfaraone/grouper

7df5eda8003a0b4a9ba7f0dcb044ae1e4710b171

[ "Apache-2.0" ]

1

2016-02-18T18:55:29.000Z

thirdparty/cpython/test_cpython.py

lfaraone/grouper

7df5eda8003a0b4a9ba7f0dcb044ae1e4710b171

[ "Apache-2.0" ]

null

# A very simple test that cpython works at all import platform print platform.python_implementation() assert platform.python_implementation() == 'CPython'

31

52

0.812903

20

155

6.2

0.75

0.225806

0.451613

0

0.116129

155

4

53

38.75

0.905109

0.283871

0

0.06422

0

0.333333

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null

0.333333

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null

1

0

1

0

null

0

1

0

1

0

6

1f6f5fba9a48c5a85093606bb8bdbfa85b62d642

100

py

Python

Server/app/blueprints.py

callsign-viper/LOM-PlanA

2a5e585843ad57245c26a1dc18ce15be716b931e

[ "MIT" ]

4

2018-08-07T08:06:23.000Z

2018-11-15T00:08:20.000Z

Server/app/blueprints.py

callsign-viper/LOM-PlanA

2a5e585843ad57245c26a1dc18ce15be716b931e

[ "MIT" ]

16

2018-10-02T12:55:15.000Z

2018-10-20T12:36:35.000Z

Server/app/blueprints.py

Mean-t/Mean.t-Backend

31c27cee618d53894c147ac98ec957528fd691be

[ "MIT" ]

null

from flask import Blueprint api_v1_blueprint = Blueprint('api_v1', __name__, url_prefix='/api/v1')

25

70

0.78

15

100

4.666667

0.6

0.214286

0.4

0

0.033333

0.1

100

3

71

33.333333

0.744444

0

0.13

0

1

0

false

0

0.5

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

6

2f1951d20928452a0bfaacf59c129a3ead02d646

45

py

Python

src/pyfme/models/__init__.py

gaofeng2020/PyFME

26b76f0622a8dca0e24eb477a6fb4a8b2aa604d7

[ "MIT" ]

199

2015-12-29T19:49:42.000Z

2022-03-19T14:31:24.000Z

src/pyfme/models/__init__.py

gaofeng2020/PyFME

26b76f0622a8dca0e24eb477a6fb4a8b2aa604d7

[ "MIT" ]

126

2015-09-23T11:15:42.000Z

2020-07-29T12:27:22.000Z

src/pyfme/models/__init__.py

gaofeng2020/PyFME

26b76f0622a8dca0e24eb477a6fb4a8b2aa604d7

[ "MIT" ]

93

2015-12-26T13:02:29.000Z

2022-03-19T14:31:13.000Z

from .euler_flat_earth import EulerFlatEarth

22.5

44

0.888889

6

45

6.333333

1

0

0.088889

45

1

45

0.926829

0

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true

0

1

0

1

0

1

0

null

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null

0

1

0

1

0

1

0

6

c84a0e329b2b2492b10ec8bf74d6362a3dc8d187

80

py

Python

flask_unchained/forms/__init__.py

achiang/flask-unchained

12788a6e618904a25ff2b571eb05ff1dc8f1840f

[ "MIT" ]

69

2018-10-10T01:59:11.000Z

2022-03-29T17:29:30.000Z

flask_unchained/forms/__init__.py

achiang/flask-unchained

12788a6e618904a25ff2b571eb05ff1dc8f1840f

[ "MIT" ]

18

2018-11-17T12:42:02.000Z

2021-05-22T18:45:27.000Z

flask_unchained/forms/__init__.py

achiang/flask-unchained

12788a6e618904a25ff2b571eb05ff1dc8f1840f

[ "MIT" ]

7

2018-10-12T16:20:25.000Z

2021-10-06T12:18:21.000Z

from .flask_form import FlaskForm from . import fields from . import validators

20

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0.15

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0

null

0

1

0

1

0

1

0

6

c0bc39bdd4e752ec3281c536d84cf0316c786973

15,652

py

Python

test/test_coverage_scenarios.py

jackdewinter/pyscan

05ea9bff0aaf4d53aa401c51526bb847accec56a

[ "MIT" ]

1

2021-01-14T17:39:18.000Z

test/test_coverage_scenarios.py

jackdewinter/pyscan

05ea9bff0aaf4d53aa401c51526bb847accec56a

[ "MIT" ]

17

2020-08-15T23:27:28.000Z

2022-02-20T18:23:49.000Z

test/test_coverage_scenarios.py

jackdewinter/pyscan

05ea9bff0aaf4d53aa401c51526bb847accec56a

[ "MIT" ]

null

""" Tests to cover scenarios around the coverage measuring and reporting. """ import os from shutil import copyfile from test.patch_builtin_open import PatchBuiltinOpen from test.test_scenarios import ( COBERTURA_COMMAND_LINE_FLAG, COVERAGE_SUMMARY_FILE_NAME, ONLY_CHANGES_COMMAND_LINE_FLAG, PUBLISH_COMMAND_LINE_FLAG, PUBLISH_DIRECTORY, REPORT_DIRECTORY, MainlineExecutor, get_coverage_file_name, setup_directories, ) def compose_coverage_summary_file(): """ Create a test coverage file for a sample report. """ return """{ "projectName": "project_summarizer", "reportSource": "pytest", "branchLevel": { "totalMeasured": 4, "totalCovered": 2 }, "lineLevel": { "totalMeasured": 15, "totalCovered": 10 } } """ def test_summarize_simple_cobertura_report( create_publish_directory=False, temporary_work_directory=None ): """ Test the summarizing of a simple cobertura report with no previous summary. """ # Arrange executor = MainlineExecutor() temporary_work_directory, report_directory, publish_directory = setup_directories( create_publish_directory=create_publish_directory, temporary_work_directory=temporary_work_directory, ) cobertura_coverage_file = os.path.join( temporary_work_directory.name, get_coverage_file_name() ) copyfile( os.path.join(executor.resource_directory, get_coverage_file_name()), cobertura_coverage_file, ) summary_coverage_file = os.path.join(report_directory, COVERAGE_SUMMARY_FILE_NAME) suppplied_arguments = [COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file] expected_output = """ Test Coverage Summary --------------------- TYPE COVERED MEASURED PERCENTAGE Instructions -- -- ----- Lines 10 (+10) 15 (+15) 66.67 (+66.67) Branches 2 ( +2) 4 ( +4) 50.00 (+50.00) Complexity -- -- ----- Methods -- -- ----- Classes -- -- ----- """ expected_error = "" expected_return_code = 0 expected_test_coverage_file = compose_coverage_summary_file() # Act execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) execute_results.assert_resultant_file( summary_coverage_file, expected_test_coverage_file ) return ( executor, temporary_work_directory, publish_directory, cobertura_coverage_file, ) def test_summarize_cobertura_report_with_bad_source(): """ Test to make sure that summarizing a test coverage file that does not exist. """ # Arrange executor = MainlineExecutor() temporary_work_directory, _, _ = setup_directories() cobertura_coverage_file = os.path.join( temporary_work_directory.name, get_coverage_file_name() ) assert not os.path.exists(cobertura_coverage_file) suppplied_arguments = [COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file] expected_output = ( f"Project test coverage file '{cobertura_coverage_file}' does not exist.\n" ) expected_error = "" expected_return_code = 1 # Act execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) def test_summarize_cobertura_report_with_source_as_directory(): """ Test to make sure that summarizing a test coverage file that is not a file. """ # Arrange executor = MainlineExecutor() temporary_work_directory, _, _ = setup_directories() cobertura_coverage_file = os.path.join( temporary_work_directory.name, get_coverage_file_name() ) os.makedirs(cobertura_coverage_file) suppplied_arguments = [COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file] expected_output = ( f"Project test coverage file '{cobertura_coverage_file}' is not a file.\n" ) expected_error = "" expected_return_code = 1 # Act execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) def test_summarize_simple_cobertura_report_and_publish( temporary_work_directory=None, check_file_contents=True ): """ Test the summarizing of a simple cobertura report, then publishing that report. NOTE: This function is in this module because of the other tests in this module that rely on it. Moving it to the test_publish_scenarios module would create a circular reference. """ # Arrange ( executor, temporary_work_directory, publish_directory, cobertura_coverage_file, ) = test_summarize_simple_cobertura_report( temporary_work_directory=temporary_work_directory ) summary_coverage_file = os.path.join(publish_directory, COVERAGE_SUMMARY_FILE_NAME) suppplied_arguments = [PUBLISH_COMMAND_LINE_FLAG] expected_output = ( f"Publish directory '{PUBLISH_DIRECTORY}' does not exist. Creating." ) expected_error = "" expected_return_code = 0 expected_test_coverage_file = compose_coverage_summary_file() # Act execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) if check_file_contents: execute_results.assert_resultant_file( summary_coverage_file, expected_test_coverage_file ) return ( executor, temporary_work_directory, publish_directory, cobertura_coverage_file, ) def test_summarize_simple_cobertura_report_and_publish_and_summarize_again( temporary_work_directory=None, check_file_contents=True ): """ Test the summarizing of a cobertura report, publishing, and then comparing again. """ # Arrange ( executor, temporary_work_directory, _, cobertura_coverage_file, ) = test_summarize_simple_cobertura_report_and_publish( temporary_work_directory=temporary_work_directory, check_file_contents=check_file_contents, ) suppplied_arguments = [COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file] expected_output = """ Test Coverage Summary --------------------- TYPE COVERED MEASURED PERCENTAGE Instructions -- -- ----- Lines 10 15 66.67 Branches 2 4 50.00 Complexity -- -- ----- Methods -- -- ----- Classes -- -- ----- """ expected_error = "" expected_return_code = 0 # Act execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) def test_summarize_simple_cobertura_report_and_publish_and_summarize_again_only_changes( temporary_work_directory=None, check_file_contents=True ): """ Test the summarizing of a cobertura report, publishing, and then comparing again with the only changes flat set. """ # Arrange ( executor, temporary_work_directory, _, cobertura_coverage_file, ) = test_summarize_simple_cobertura_report_and_publish( temporary_work_directory=temporary_work_directory, check_file_contents=check_file_contents, ) suppplied_arguments = [ ONLY_CHANGES_COMMAND_LINE_FLAG, COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file, ] expected_output = """ Test Coverage Summary --------------------- Test coverage has not changed since last published test coverage. """ expected_error = "" expected_return_code = 0 # Act execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) def test_summarize_bad_xml_test_coverage(): """ Test the summarizing of cobertura results with a bad coverage file. """ # Arrange executor = MainlineExecutor() temporary_work_directory, _, _ = setup_directories() cobertura_coverage_file = os.path.join( temporary_work_directory.name, get_coverage_file_name() ) copyfile( os.path.join(executor.resource_directory, "coverage-bad.xml"), cobertura_coverage_file, ) suppplied_arguments = [COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file] expected_output = ( f"Project test coverage file '{cobertura_coverage_file}' is not a " + "proper Cobertura-format test coverage file.\n" ) expected_error = "" expected_return_code = 1 # Act execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) def test_summarize_bad_test_coverage(): """ Test the summarizing of cobertura results with a bad coverage file. """ # Arrange executor = MainlineExecutor() temporary_work_directory, _, _ = setup_directories() cobertura_coverage_file = os.path.join( temporary_work_directory.name, get_coverage_file_name() ) copyfile( os.path.join(executor.resource_directory, "coverage-bad.txt"), cobertura_coverage_file, ) suppplied_arguments = [COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file] expected_output = f"Project test coverage file '{cobertura_coverage_file}' is not a valid test coverage file.\n" expected_error = "" expected_return_code = 1 # Act execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) def test_summarize_bad_report_directory(): """ Test the summarizing of cobertura results with a bad report directory. """ # Arrange executor = MainlineExecutor() temporary_work_directory, _, _ = setup_directories(create_report_directory=False) cobertura_coverage_file = os.path.join( temporary_work_directory.name, get_coverage_file_name() ) copyfile( os.path.join(executor.resource_directory, get_coverage_file_name()), cobertura_coverage_file, ) suppplied_arguments = [COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file] expected_output = f"Summary output path '{REPORT_DIRECTORY}' does not exist." expected_error = "" expected_return_code = 1 # Act execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) def test_summarize_invalid_published_summary_file(): """ Test the summarizing of cobertura results with a bad report directory. """ # Arrange executor = MainlineExecutor() temporary_work_directory, _, publish_directory = setup_directories( create_publish_directory=True ) cobertura_coverage_file = os.path.join( temporary_work_directory.name, get_coverage_file_name() ) copyfile( os.path.join(executor.resource_directory, get_coverage_file_name()), cobertura_coverage_file, ) summary_coverage_file = os.path.join(publish_directory, COVERAGE_SUMMARY_FILE_NAME) with open(summary_coverage_file, "w", encoding="utf-8") as outfile: outfile.write("this is not a json file") suppplied_arguments = [COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file] file_name = os.path.join(PUBLISH_DIRECTORY, COVERAGE_SUMMARY_FILE_NAME) expected_output = ( f"Previous coverage summary file '{file_name}' is not " + "a valid JSON file (Expecting value: line 1 column 1 (char 0))." ) expected_error = "" expected_return_code = 1 # Act execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) def test_summarize_simple_cobertura_report_and_publish_and_summarize_with_error_on_publish_read(): """ Test a summarize when trying to load a summary file from a previous run and getting an error when trying to write the summary report. """ # Arrange ( executor, temporary_work_directory, publish_directory, cobertura_coverage_file, ) = test_summarize_simple_cobertura_report_and_publish() suppplied_arguments = [COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file] file_name = os.path.join(PUBLISH_DIRECTORY, COVERAGE_SUMMARY_FILE_NAME) expected_output = ( f"Previous coverage summary file '{file_name}' was not loaded (None).\n" ) expected_error = "" expected_return_code = 1 summary_coverage_file = os.path.join(publish_directory, COVERAGE_SUMMARY_FILE_NAME) # Act try: pbo = PatchBuiltinOpen() pbo.register_exception(summary_coverage_file, "r") pbo.start() execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) finally: pbo.stop() # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code ) def test_summarize_simple_cobertura_report_with_error_on_report_write(): """ Test a summarize with an error when trying to write the summary report. """ # Arrange executor = MainlineExecutor() temporary_work_directory, report_directory, _ = setup_directories() cobertura_coverage_file = os.path.join( temporary_work_directory.name, get_coverage_file_name() ) copyfile( os.path.join(executor.resource_directory, get_coverage_file_name()), cobertura_coverage_file, ) summary_coverage_file = os.path.join(report_directory, COVERAGE_SUMMARY_FILE_NAME) suppplied_arguments = [COBERTURA_COMMAND_LINE_FLAG, cobertura_coverage_file] expected_output = ( f"Project test coverage summary file '{os.path.abspath(summary_coverage_file)}' " + "was not written (None).\n" ) expected_error = "" expected_return_code = 1 # Act try: pbo = PatchBuiltinOpen() pbo.register_exception(os.path.abspath(summary_coverage_file), "w") pbo.start() execute_results = executor.invoke_main( arguments=suppplied_arguments, cwd=temporary_work_directory.name ) finally: pbo.stop() # Assert execute_results.assert_results( expected_output, expected_error, expected_return_code )

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plotData.py

doterkuile/soccerapi

b2968297fdd151e154ed6aee0c2f84e9f3ec82fc

[ "MIT" ]

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plotData.py

doterkuile/soccerapi

b2968297fdd151e154ed6aee0c2f84e9f3ec82fc

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null

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doterkuile/soccerapi

b2968297fdd151e154ed6aee0c2f84e9f3ec82fc

[ "MIT" ]

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