Datasets:

PatrickHaller

/

the-stack-python-100k

like 0

283187b1acdcf513705c7326dc53d32846d75468

py

Python

image.py

harshkothari410/snn-image-segmentation

18fb28e8b2fee3d7583f6e62fd512ba90863c0ee

2016-04-17T21:11:41.000Z

2021-06-25T09:40:40.000Z

image.py

Arthas1121/snn-image-segmentation

18fb28e8b2fee3d7583f6e62fd512ba90863c0ee

image.py

Arthas1121/snn-image-segmentation

18fb28e8b2fee3d7583f6e62fd512ba90863c0ee

2016-04-17T19:14:41.000Z

2022-03-09T21:03:12.000Z

from PIL import Image def imageread(filename): file = Image.open(filename) pixel_values = list(file.getdata()) # Compute H and W w, h = file.size # compute pixel matrix pixel_mat = [[0 for x in xrange(w)] for x in xrange(h)] count = 0 for x in xrange(h): for y in xrange(w): # print type(pixel_values[count]) try: if len( pixel_values[count] ) > 1: pixel_mat[x][y] = pixel_values[count][0] #check whether is else: pixel_mat[x][y] = pixel_values[count] count+=1 except: pixel_mat[x][y] = pixel_values[count] count+=1 return pixel_mat, w, h def imagewrite(data, w, h): final_ans = [] count = 0 for x in xrange(h): for y in xrange(w): final_ans.append( data[x][y] ) count+=1 im = Image.new('1', (w,h)) # print im im.putdata(final_ans) im.show() def imagesave(data, w, h, name): final_ans = [] count = 0 for x in xrange(h): for y in xrange(w): final_ans.append( data[x][y] ) count+=1 im = Image.new('1', (w,h)) # print im im.putdata(final_ans) im.save(name+'.jpg')

862c998692f1850567159b1010a13f98027238a1

py

Python

nipype/interfaces/camino/tests/test_auto_TrackPICo.py

moloney/nipype

a7a9c85c79cb1412ba03406074f83200447ef50b

2017-02-17T08:54:26.000Z

2022-03-10T20:57:23.000Z

nipype/interfaces/camino/tests/test_auto_TrackPICo.py

moloney/nipype

a7a9c85c79cb1412ba03406074f83200447ef50b

2016-04-25T15:07:09.000Z

2016-04-25T15:07:09.000Z

nipype/interfaces/camino/tests/test_auto_TrackPICo.py

moloney/nipype

a7a9c85c79cb1412ba03406074f83200447ef50b

2017-09-23T16:22:00.000Z

2019-08-01T14:18:52.000Z

# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT from __future__ import unicode_literals from ..dti import TrackPICo def test_TrackPICo_inputs(): input_map = dict( anisfile=dict(argstr='-anisfile %s', ), anisthresh=dict(argstr='-anisthresh %f', ), args=dict(argstr='%s', ), curveinterval=dict( argstr='-curveinterval %f', requires=['curvethresh'], ), curvethresh=dict(argstr='-curvethresh %f', ), data_dims=dict( argstr='-datadims %s', units='voxels', ), environ=dict( nohash=True, usedefault=True, ), gzip=dict(argstr='-gzip', ), ignore_exception=dict( deprecated='1.0.0', nohash=True, usedefault=True, ), in_file=dict( argstr='-inputfile %s', position=1, ), inputdatatype=dict(argstr='-inputdatatype %s', ), inputmodel=dict( argstr='-inputmodel %s', usedefault=True, ), interpolator=dict(argstr='-interpolator %s', ), ipthresh=dict(argstr='-ipthresh %f', ), iterations=dict( argstr='-iterations %d', units='NA', ), maxcomponents=dict( argstr='-maxcomponents %d', units='NA', ), numpds=dict( argstr='-numpds %d', units='NA', ), out_file=dict( argstr='-outputfile %s', genfile=True, position=-1, ), output_root=dict( argstr='-outputroot %s', position=-1, ), outputtracts=dict(argstr='-outputtracts %s', ), pdf=dict(argstr='-pdf %s', ), seed_file=dict( argstr='-seedfile %s', position=2, ), stepsize=dict( argstr='-stepsize %f', requires=['tracker'], ), terminal_output=dict( deprecated='1.0.0', nohash=True, ), tracker=dict( argstr='-tracker %s', usedefault=True, ), voxel_dims=dict( argstr='-voxeldims %s', units='mm', ), ) inputs = TrackPICo.input_spec() for key, metadata in list(input_map.items()): for metakey, value in list(metadata.items()): assert getattr(inputs.traits()[key], metakey) == value def test_TrackPICo_outputs(): output_map = dict(tracked=dict(), ) outputs = TrackPICo.output_spec() for key, metadata in list(output_map.items()): for metakey, value in list(metadata.items()): assert getattr(outputs.traits()[key], metakey) == value

0e167608dba640e8e33ffb8e133f56b11ba0dc0a

py

Python

apps/project/business/board.py

rainydaygit/testtcloudserver

8037603efe4502726a4d794fb1fc0a3f3cc80137

2020-08-04T10:21:01.000Z

2022-03-23T08:31:29.000Z

apps/project/business/board.py

rainydaygit/testtcloudserver

8037603efe4502726a4d794fb1fc0a3f3cc80137

2021-01-07T06:17:05.000Z

2021-04-01T06:01:30.000Z

apps/project/business/board.py

rainydaygit/testtcloudserver

8037603efe4502726a4d794fb1fc0a3f3cc80137

2020-08-24T06:46:14.000Z

2022-03-25T13:23:27.000Z

import json import requests from flask import request, g, current_app from sqlalchemy import desc, func from sqlalchemy.orm import aliased from apps.auth.models.users import User from apps.project.models.issue import Issue from apps.project.models.modules import Module from apps.project.models.tasks import Task, TaskCase from apps.project.models.version import Version from apps.public.models.public import Config from library.api.transfer import transfer2json class BoardBusiness(object): @classmethod @transfer2json( '?id|!name|!description|!tmethod|!ttype|!status|!start_time|!end_time|!priority|!version_id|!version_name' '|!creator_id|!creator_name|!executor_id|!executor_name|!project_id', ispagination=True) def task_query(cls, projectid, userid, status, iscreator, page_size, page_index, title): # 0:创建,1:任务已删除,2:任务已完成 user_creator = aliased(User) user_executor = aliased(User) ret = Task.query.outerjoin( user_creator, user_creator.id == Task.creator).outerjoin( user_executor, user_executor.id == Task.executor).outerjoin( Version, Version.id == Task.version).add_columns( Task.id.label('id'), Task.name.label('name'), Task.description.label('description'), Task.tmethod.label('tmethod'), Task.ttype.label('ttype'), Task.status.label('status'), func.date_format(Task.start_time, "%Y-%m-%d").label('start_time'), func.date_format(Task.end_time, "%Y-%m-%d").label('end_time'), Task.priority.label('priority'), Task.project_id.label('project_id'), Version.id.label('version_id'), Version.title.label('version_name'), user_creator.id.label('creator_id'), user_creator.nickname.label('creator_name'), user_executor.id.label('executor_id'), user_executor.nickname.label('executor_name'), ) if projectid: ret = ret.filter(Task.project_id == projectid) if iscreator: ret = ret.filter(Task.creator == userid) else: ret = ret.filter(Task.executor == userid) if title not in ["", None]: ret = ret.filter(Task.name.like(f'%{title}%')) ret = ret.filter(Task.status.in_(status)) result = ret.order_by(desc(Task.id) ).limit(int(page_size)).offset(int(page_index - 1) * int(page_size)).all() count = ret.count() return result, count @classmethod @transfer2json( '?taskcaseid|!task_id|!executor_id|!executor_name|!handler_id|!handler_name|!exe_way|!cnumber|!ctype|!title|' '!description|!precondition|!step_result|!is_auto|!status|!comment|!module_id|!module_name|!project_id', ispagination=True ) def task_case_query(cls, projectid, userid, status, iscreator, page_size, page_index, title): # 0:case创建,1:case已删除,2:跳过,3:case执行通过,4:case执行不通过 user_executor = aliased(User) user_handler = aliased(User) ret = TaskCase.query.outerjoin( Module, TaskCase.module_id == Module.id).outerjoin( user_executor, user_executor.id == TaskCase.executor).outerjoin( user_handler, user_handler.id == TaskCase.handler).add_columns( TaskCase.id.label('taskcaseid'), TaskCase.task_id.label('task_id'), TaskCase.exe_way.label('exe_way'), TaskCase.cnumber.label('cnumber'), TaskCase.ctype.label('ctype'), TaskCase.title.label('title'), TaskCase.description.label('description'), TaskCase.precondition.label('precondition'), TaskCase.step_result.label('step_result'), TaskCase.is_auto.label('is_auto'), TaskCase.status.label('status'), TaskCase.comment.label('comment'), TaskCase.project_id.label('project_id'), Module.id.label('module_id'), Module.name.label('module_name'), user_executor.id.label('executor_id'), user_executor.nickname.label('executor_name'), user_handler.id.label('handler_id'), user_handler.nickname.label('handler_name'), ) if projectid: ret = ret.filter(TaskCase.project_id == projectid) if iscreator is 1: ret = ret.filter(TaskCase.handler == userid) else: ret = ret.filter(TaskCase.executor == userid) if title not in ["", None]: ret = ret.filter(TaskCase.title.like(f'%{title}%')) ret = ret.filter(TaskCase.status.in_(status)) result = ret.order_by(desc(TaskCase.id) ).limit(int(page_size)).offset(int(page_index - 1) * int(page_size)).all() count = ret.count() return result, count @classmethod @transfer2json('?id|!issue_number|!title|!handle_status|!description|!chance|!level|!priority|!stage' '|!version_id|!version_name|!creator_id|!creator_name|!handler_id|!handler_name|!project_id', ispagination=True ) def issue_query(cls, projectid, userid, status, iscreator, page_size, page_index, title): # 处理状态 {"1": "待办", "2": "处理中", "3": "测试中", "4": "已关闭", "5": "已拒绝", "6": "延时处理"} user_creator = aliased(User) user_handler = aliased(User) ret = Issue.query.outerjoin( user_creator, user_creator.id == Issue.creator).outerjoin( user_handler, user_handler.id == Issue.handler).outerjoin( Version, Version.id == Issue.version).add_columns( Issue.id.label('id'), Issue.issue_number.label('issue_number'), Issue.title.label('title'), Issue.handle_status.label('handle_status'), Issue.description.label('description'), Issue.chance.label('chance'), Issue.level.label('level'), Issue.priority.label('priority'), Issue.stage.label('stage'), Issue.project_id.label('project_id'), Version.id.label('version_id'), Version.title.label('version_name'), user_creator.id.label('creator_id'), user_creator.nickname.label('creator_name'), user_handler.id.label('handler_id'), user_handler.nickname.label('handler_name'), ) if projectid: ret = ret.filter(Issue.project_id == projectid) if iscreator: ret = ret.filter(Issue.creator == userid) else: ret = ret.filter(Issue.handler == userid) if title not in ["", None]: ret = ret.filter(Issue.title.like(f'%{title}%')) ret = ret.filter(Issue.handle_status.in_(status), Issue.status == Issue.ACTIVE) result = ret.order_by(desc(Issue.id) ).limit(int(page_size)).offset(int(page_index - 1) * int(page_size)).all() count = ret.count() return result, count @classmethod def board_config(cls): user_id = g.userid if g.userid else None board_config = Config.query.add_columns(Config.content.label('content')).filter(Config.module == 'board', Config.module_type == 1).first() board_config = json.loads(board_config.content) current_app.logger.info('board_config:' + str(board_config)) return user_id, board_config @classmethod def user_create(cls, page_size, page_index, r_type, title): project_id = request.args.get('projectid') user_id, board_config = cls.board_config() ret = None count = 0 if r_type == "task": ret, count = cls.task_query(project_id, user_id, board_config['create']['task'], 1, page_size, page_index, title) # task_case_ret = cls.task_case_query(projectid, user_id, board_config['create']['task_case'], 1) if r_type == "issue": ret, count = cls.issue_query(project_id, user_id, board_config['create']['issue'], 1, page_size, page_index, title) return ret, count @classmethod def user_unfinish(cls, page_size, page_index, r_type, title): project_id = request.args.get('projectid') user_id, board_config = cls.board_config() ret = None count = 0 if r_type == "task": ret, count = cls.task_query(project_id, user_id, board_config['unfinish']['task'], 0, page_size, page_index, title) if r_type == "task_case": ret, count = cls.task_case_query(project_id, user_id, board_config['unfinish']['task_case'], 1, page_size, page_index, title) if r_type == "issue": ret, count = cls.issue_query(project_id, user_id, board_config['unfinish']['issue'], 0, page_size, page_index, title) return ret, count @classmethod def user_finish(cls, page_size, page_index, r_type, title): project_id = request.args.get('projectid') user_id, board_config = cls.board_config() ret = None count = 0 if r_type == "task": ret, count = cls.task_query(project_id, user_id, board_config['finish']['task'], 0, page_size, page_index, title) if r_type == "task_case": ret, count = cls.task_case_query(project_id, user_id, board_config['finish']['task_case'], 1, page_size, page_index, title) if r_type == "issue": ret, count = cls.issue_query(project_id, user_id, board_config['finish']['issue'], 0, page_size, page_index, title) return ret, count @classmethod def stf_devices(cls): stf_devices = Config.query.add_columns(Config.content.label('content')).filter( Config.module == 'stf', Config.module_type == 1).first() stf_devices = json.loads(stf_devices.content) current_app.logger.info(json.dumps(stf_devices, ensure_ascii=False)) url = stf_devices['URL'] headers = stf_devices['headers'] ret = requests.get(url, headers=headers) ret = json.loads(ret.content) # logger.info(json.dumps(ret, ensure_ascii=False)) return ret

83d30c47ebde1323539e62d03f67b271652cf3be

py

Python

integration_testing/run_travis_tests.py

Glitchfix/mindsdb

e6c33d7085898c223030334962596ae8afa3fbd5

integration_testing/run_travis_tests.py

Glitchfix/mindsdb

e6c33d7085898c223030334962596ae8afa3fbd5

integration_testing/run_travis_tests.py

Glitchfix/mindsdb

e6c33d7085898c223030334962596ae8afa3fbd5

from data_generators import * import traceback import sys import os import itertools import logging from colorlog import ColoredFormatter import time import mindsdb from mindsdb import CONST #@TODO: Currently we use this isntead of randomly generated data since randomly generated data is not reliable enough # We tell mindsDB what we want to learn and from what data mdb = mindsdb.Predictor(name='home_rentals_price') mdb.learn( to_predict='rental_price', # the column we want to learn to predict given all the data in the file from_data="https://s3.eu-west-2.amazonaws.com/mindsdb-example-data/home_rentals.csv" # the path to the file where we can learn from, (note: can be url) ) prediction = mdb.predict(when={'sqft':300}) print(prediction[0]) amd = mdb.get_model_data('home_rentals_price') ''' types_that_work = ['int','float','date','datetime','timestamp','ascii'] logger = None def setup_testing_logger(): global logger formatter = ColoredFormatter( "%(log_color)s%(message)s", datefmt=None, reset=True, log_colors={ 'DEBUG': 'black,bg_white', 'INFO': 'blue,bg_white', 'WARNING': 'orange,bg_white', 'ERROR': 'red,bg_white', 'CRITICAL': 'red,bg_white', } ) logger = logging.getLogger('mindsdb_integration_testing') logger.handlers = [] handler = logging.StreamHandler() handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(logging.DEBUG) def run_tests(): logger.info('Starting one-label test') separator = ',' train_file_name = 'train_data.csv' test_file_name = 'test_data.csv' data_len = 8000 # Create the full dataset logger.debug(f'Creating one-labe test datasets and saving them to {train_file_name} and {test_file_name}, total dataset size will be {data_len} rows') try: features = generate_value_cols(types_that_work,data_len, separator) labels = [generate_labels_2(features, separator)] feature_headers = list(map(lambda col: col[0], features)) label_headers = list(map(lambda col: col[0], labels)) # Create the training dataset and save it to a file columns_train = list(map(lambda col: col[1:int(len(col)*3/4)], features)) columns_train.extend(list(map(lambda col: col[1:int(len(col)*3/4)], labels))) columns_to_file(columns_train, train_file_name, separator, headers=[*feature_headers,*label_headers]) # Create the testing dataset and save it to a file columns_test = list(map(lambda col: col[int(len(col)*3/4):], features)) columns_to_file(columns_test, test_file_name, separator, headers=feature_headers) logger.debug(f'Datasets generate and saved to files successfully') except: print(traceback.format_exc()) logger.error(f'Failed to generate datasets !') exit(1) # Train mdb = None try: mdb = mindsdb.Predictor(name='test_one_label_prediction') logger.debug(f'Succesfully create mindsdb Predictor') except: logger.error(f'Failed to create mindsdb Predictor') exit(1) try: mdb.learn(from_data=train_file_name, to_predict=label_headers) logger.info(f'--------------- Learning ran succesfully ---------------') mdb.learn(from_data=train_file_name, to_predict=label_headers, rebuild_model=False) logger.info(f'--------------- Additional learning ran succesfully ---------------') except: print(traceback.format_exc()) logger.error(f'Failed during the training !') exit(1) # Predict try: mdb = mindsdb.Predictor(name='test_one_label_prediction') logger.debug(f'Succesfully create mindsdb Predictor') except: print(traceback.format_exc()) logger.error(f'Failed to create mindsdb Predictor') exit(1) try: results = mdb.predict(when_data=test_file_name) for row in results: expect_columns = [label_headers[0] ,label_headers[0] + '_confidence'] for col in expect_columns: if col not in row: logger.error(f'Prediction failed to return expected column: {col}') logger.debug('Got row: {}'.format(row)) exit(1) logger.info(f'--------------- Predicting ran succesfully ---------------') # Print statements are in for debugging, remove later, but keep the funcion calls to make sure the interface is working models = mdb.get_models() amd = mdb.get_model_data('test_one_label_prediction') print(amd) except: print(traceback.format_exc()) logger.error(f'Failed whilst predicting') exit(1) logger.info('Travis CLI Tests ran succesfully !') setup_testing_logger() run_tests() '''

7a220662c532f6177643a5bc0c91a4955cdccfc8

py

Python

template_container_human/labels/slice_43.py

lkondratova/Brainplot

3c8a88c1995dedeaa5cbd88ee71499c7cf9c571d

template_container_human/labels/slice_43.py

lkondratova/Brainplot

3c8a88c1995dedeaa5cbd88ee71499c7cf9c571d

template_container_human/labels/slice_43.py

lkondratova/Brainplot

3c8a88c1995dedeaa5cbd88ee71499c7cf9c571d

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138), (111, 140), (112, 88), (112, 90), (112, 94), (112, 96), (112, 111), (112, 113), (112, 114), (112, 115), (112, 116), (112, 117), (112, 118), (112, 119), (112, 120), (112, 121), (112, 122), (112, 123), (112, 124), (112, 125), (112, 126), (112, 127), (112, 128), (112, 129), (112, 131), (112, 134), (112, 137), (112, 140), (113, 77), (113, 82), (113, 83), (113, 84), (113, 85), (113, 86), (113, 87), (113, 89), (113, 91), (113, 92), (113, 96), (113, 111), (113, 113), (113, 114), (113, 115), (113, 117), (113, 118), (113, 119), (113, 120), (113, 121), (113, 122), (113, 123), (113, 124), (113, 125), (113, 126), (113, 127), (113, 128), (113, 130), (113, 136), (113, 138), (113, 140), (114, 77), (114, 79), (114, 80), (114, 83), (114, 90), (114, 94), (114, 95), (114, 111), (114, 116), (114, 119), (114, 120), (114, 121), (114, 122), (114, 123), (114, 124), (114, 125), (114, 126), (114, 127), (114, 129), (114, 137), (115, 77), (115, 84), (115, 89), (115, 95), (115, 111), (115, 113), (115, 114), (115, 118), (115, 120), (115, 121), (115, 122), (115, 123), (115, 129), (115, 137), (116, 77), (116, 85), (116, 88), (116, 111), (116, 119), (116, 121), (116, 122), (116, 125), (116, 126), (116, 128), (117, 119), (117, 121), (117, 123), (118, 120), (118, 123), (119, 120), (119, 122), (120, 121), ) coordinates_FEDAB9 = ((126, 79), (126, 80), (127, 78), (127, 82), (128, 77), (128, 79), (128, 80), (128, 82), (129, 76), (129, 78), (129, 79), (129, 80), (129, 81), (129, 83), (130, 75), (130, 77), (130, 78), (130, 79), (130, 80), (130, 81), (130, 83), (131, 75), (131, 77), (131, 78), (131, 79), (131, 80), (131, 81), (131, 82), (131, 84), (132, 76), (132, 78), (132, 79), (132, 80), (132, 81), (132, 82), (132, 84), (133, 76), (133, 78), (133, 79), (133, 80), (133, 81), (133, 82), (133, 83), (133, 85), (134, 76), (134, 78), (134, 85), (135, 76), (135, 80), (135, 81), (135, 82), (135, 83), (135, 85), (136, 75), (136, 78), (137, 75), (137, 77), (137, 78), (138, 74), (138, 77), (139, 74), (139, 77), (140, 74), (140, 76), (140, 77), (140, 78), (140, 79), (140, 80), (140, 81), (140, 82), (140, 83), (140, 84), (140, 85), (140, 87), (141, 75), (141, 82), (141, 83), (141, 84), (141, 85), (141, 87), (141, 94), (141, 96), (141, 97), (141, 98), (141, 100), (142, 77), (142, 78), (142, 80), (142, 93), ) coordinates_D970D6 = ((123, 83), (124, 79), (124, 81), (124, 85), (124, 86), (124, 87), (124, 88), (124, 89), (124, 90), (125, 81), (125, 83), (125, 84), (125, 90), (126, 86), (126, 88), (126, 90), (127, 88), (127, 90), (128, 88), (128, 90), (129, 89), (130, 89), ) coordinates_01CED1 = ((143, 82), (143, 84), (143, 86), (143, 97), (143, 98), (143, 100), (144, 86), (144, 91), (144, 93), (144, 94), (144, 95), (144, 96), (144, 100), (145, 80), (145, 82), (145, 83), (145, 85), (145, 90), (145, 97), (145, 99), (146, 79), (146, 82), (146, 84), (146, 90), (146, 92), (146, 93), (146, 94), (146, 95), (146, 96), (146, 97), (146, 99), (147, 78), (147, 80), (147, 81), (147, 83), (147, 89), (147, 91), (147, 92), (147, 93), (147, 94), (147, 95), (147, 96), (147, 98), (148, 78), (148, 80), (148, 81), (148, 83), (148, 88), (148, 90), (148, 91), (148, 92), (148, 93), (148, 94), (148, 95), (148, 96), (148, 98), (149, 79), (149, 81), (149, 83), (149, 86), (149, 87), (149, 89), (149, 90), (149, 91), (149, 92), (149, 93), (149, 94), (149, 95), (149, 97), (150, 80), (150, 82), (150, 83), (150, 84), (150, 85), (150, 92), (150, 93), (150, 94), (150, 96), (151, 81), (151, 89), (151, 92), (151, 94), (151, 96), (152, 82), (152, 84), (152, 85), (152, 87), (152, 92), (152, 95), (153, 91), (153, 94), (153, 103), (153, 105), (154, 91), (154, 93), (154, 105), (155, 90), (155, 92), (155, 102), (155, 105), (155, 112), (156, 89), (156, 91), (156, 101), (156, 103), (156, 105), (156, 113), (157, 88), (157, 90), (157, 101), (157, 103), (157, 105), (157, 111), (157, 113), (158, 87), (158, 90), (158, 100), (158, 102), (158, 103), (158, 104), (158, 105), (158, 110), (158, 113), (159, 87), (159, 90), (159, 99), (159, 101), (159, 102), (159, 104), (159, 110), (159, 113), (160, 87), (160, 88), (160, 89), (160, 90), (160, 91), (160, 92), (160, 93), (160, 94), (160, 95), (160, 96), (160, 97), (160, 100), (160, 101), (160, 102), (160, 104), (160, 109), (160, 111), (160, 113), (161, 87), (161, 89), (161, 90), (161, 100), (161, 101), (161, 102), (161, 104), (161, 109), (161, 110), (161, 111), (161, 113), (162, 90), (162, 91), (162, 92), (162, 93), (162, 94), (162, 95), (162, 96), (162, 97), (162, 98), (162, 99), (162, 100), (162, 101), (162, 102), (162, 104), (162, 108), (162, 110), (162, 111), (162, 113), (163, 88), (163, 96), (163, 100), (163, 101), (163, 102), (163, 104), (163, 108), (163, 110), (163, 111), (163, 113), (164, 90), (164, 92), (164, 93), (164, 94), (164, 95), (164, 96), (164, 97), (164, 98), (164, 101), (164, 102), (164, 104), (164, 108), (164, 110), (164, 111), (164, 113), (165, 100), (165, 102), (165, 104), (165, 108), (165, 110), (165, 111), (165, 113), (166, 101), (166, 104), (166, 105), (166, 108), (166, 110), (166, 111), (166, 113), (167, 101), (167, 103), (167, 105), (167, 109), (167, 111), (167, 113), (168, 101), (168, 103), (168, 104), (168, 106), (168, 109), (168, 111), (168, 113), (169, 102), (169, 104), (169, 105), (169, 108), (169, 109), (169, 110), (169, 112), (170, 103), (170, 106), (170, 109), (170, 110), (170, 112), (171, 104), (171, 107), (171, 108), (171, 109), (171, 111), (172, 106), (172, 110), (173, 107), (173, 109), ) coordinates_FE3E96 = ((121, 99), (121, 100), (121, 101), (121, 102), (122, 94), (122, 95), (122, 96), (122, 97), (122, 98), (122, 104), (122, 105), (122, 132), (122, 133), (122, 134), (122, 136), (123, 93), (123, 99), (123, 100), (123, 101), (123, 102), (123, 103), (123, 107), (123, 108), (123, 109), (123, 110), (123, 111), (123, 112), (123, 113), (123, 114), (123, 115), (123, 116), (123, 117), (123, 119), (123, 124), (123, 126), (123, 127), (123, 128), (123, 129), (123, 130), (123, 131), (123, 136), (124, 93), (124, 95), (124, 97), (124, 100), (124, 101), (124, 102), (124, 103), (124, 104), (124, 105), (124, 106), (124, 118), (124, 125), (124, 132), (124, 136), (125, 92), (125, 94), (125, 96), (125, 99), (125, 101), (125, 102), (125, 103), (125, 104), (125, 105), (125, 106), (125, 107), (125, 108), (125, 111), (125, 112), (125, 117), (125, 126), (125, 128), (125, 129), (125, 130), (125, 131), (125, 134), (125, 136), (126, 92), (126, 95), (126, 100), (126, 102), (126, 103), (126, 104), (126, 105), (126, 106), (126, 107), (126, 110), (126, 113), (126, 115), (126, 127), (126, 132), (126, 136), (127, 92), (127, 100), (127, 102), (127, 103), (127, 104), (127, 105), (127, 106), (127, 108), (127, 128), (127, 131), (127, 135), (127, 136), (128, 92), (128, 94), (128, 101), (128, 103), (128, 104), (128, 105), (128, 107), (128, 130), (128, 135), (128, 136), (129, 92), (129, 93), (129, 102), (129, 104), (129, 105), (129, 107), (129, 135), (129, 136), (130, 92), (130, 102), (130, 104), (130, 105), (130, 107), (130, 135), (131, 103), (131, 105), (131, 107), (132, 103), (132, 107), (133, 104), (133, 106), ) coordinates_AF3060 = ((123, 146), (123, 148), (123, 149), (123, 151), (124, 145), (124, 151), (125, 145), (125, 147), (125, 151), (126, 146), (126, 149), (127, 146), (128, 145), (128, 147), (129, 144), (129, 146), (130, 143), (130, 145), (131, 143), (131, 144), (132, 143), ) coordinates_ACFF2F = ((128, 149), (128, 152), (129, 148), (129, 152), (130, 147), (130, 149), (130, 150), (130, 152), (131, 146), (131, 149), (131, 150), (131, 152), (132, 148), (132, 149), (132, 150), (132, 152), (133, 147), (133, 148), (133, 149), (133, 150), (133, 152), (134, 143), (134, 148), (134, 149), (134, 150), (134, 152), (135, 148), (135, 149), (136, 147), (136, 149), (136, 151), (137, 147), (137, 150), (138, 146), (138, 149), (139, 145), (139, 148), (140, 143), (140, 146), (140, 148), (141, 141), (141, 147), (142, 139), (142, 143), (142, 144), (142, 145), (142, 147), (143, 138), (143, 141), (143, 142), (143, 147), (144, 139), ) coordinates_FFDAB9 = ((109, 74), (109, 76), (109, 86), (109, 88), (110, 74), (110, 78), (110, 83), (110, 84), (110, 85), (111, 73), (111, 75), (111, 77), (111, 80), (111, 81), (111, 82), (111, 83), (111, 84), (111, 85), (111, 87), (112, 73), (112, 75), (112, 79), (113, 72), (113, 75), (114, 72), (114, 75), (115, 72), (115, 75), (116, 72), (116, 75), (117, 72), (117, 75), (118, 73), (118, 74), ) coordinates_DA70D6 = ((116, 80), (116, 82), (116, 83), (116, 90), (117, 79), (117, 84), (117, 89), (117, 90), (118, 80), (118, 81), (118, 82), (118, 85), (118, 88), (118, 90), (119, 76), (119, 78), (119, 83), (119, 84), (119, 87), (119, 90), (120, 79), (120, 80), (120, 81), (120, 82), (120, 90), (121, 84), (121, 85), (121, 86), (121, 87), (121, 88), (121, 90), ) coordinates_00CED1 = ((74, 101), (74, 103), (74, 104), (74, 105), (75, 99), (75, 107), (75, 108), (76, 98), (76, 101), (76, 102), (76, 103), (76, 104), (76, 105), (76, 106), (76, 108), (77, 97), (77, 99), (77, 100), (77, 101), (77, 102), (77, 103), (77, 106), (77, 107), (77, 109), (78, 98), (78, 100), (78, 101), (78, 102), (78, 103), (78, 104), (78, 105), (78, 106), (78, 107), (78, 109), (79, 98), (79, 100), (79, 101), (79, 103), (79, 107), (79, 110), (80, 92), (80, 93), (80, 99), (80, 101), (80, 103), (80, 107), (80, 110), (81, 91), (81, 95), (81, 98), (81, 100), (81, 101), (81, 103), (81, 107), (81, 110), (82, 90), (82, 93), (82, 94), (82, 97), (82, 98), (82, 99), (82, 100), (82, 101), (82, 103), (82, 107), (82, 109), (82, 111), (83, 90), (83, 92), (83, 93), (83, 94), (83, 95), (83, 98), (83, 99), (83, 100), (83, 101), (83, 103), (83, 107), (83, 108), (83, 111), (84, 89), (84, 91), (84, 93), (84, 94), (84, 95), (84, 96), (84, 97), (84, 98), (84, 99), (84, 100), (84, 101), (84, 103), (84, 108), (84, 110), (84, 112), (85, 89), (85, 94), (85, 95), (85, 96), (85, 97), (85, 98), (85, 99), (85, 100), (85, 102), (85, 108), (85, 110), (85, 111), (85, 113), (86, 88), (86, 90), (86, 94), (86, 95), (86, 96), (86, 97), (86, 98), (86, 99), (86, 101), (86, 108), (86, 110), (86, 111), (86, 113), (87, 88), (87, 90), (87, 94), (87, 96), (87, 97), (87, 98), (87, 100), (87, 109), (87, 111), (87, 113), (88, 86), (88, 88), (88, 90), (88, 94), (88, 96), (88, 97), (88, 99), (88, 109), (88, 112), (89, 84), (89, 89), (89, 95), (89, 98), (89, 109), (89, 112), (90, 82), (90, 85), (90, 86), (90, 88), (90, 95), (90, 97), (90, 110), (90, 112), (91, 84), (91, 85), (91, 87), (92, 81), (92, 83), (92, 84), (92, 85), (92, 87), (93, 80), (93, 82), (93, 83), (93, 84), (93, 85), (93, 86), (93, 87), (93, 88), (93, 89), (93, 91), (93, 100), (93, 102), (94, 79), (94, 81), (94, 82), (94, 83), (94, 84), (94, 85), (94, 86), (94, 87), (94, 93), (94, 99), (94, 103), (95, 79), (95, 81), (95, 82), (95, 83), (95, 84), (95, 85), (95, 86), (95, 87), (95, 88), (95, 89), (95, 90), (95, 91), (95, 94), (95, 100), (95, 101), (95, 103), (96, 78), (96, 80), (96, 81), (96, 82), (96, 83), (96, 84), (96, 85), (96, 86), (96, 87), (96, 88), (96, 89), (96, 90), (96, 91), (96, 92), (96, 93), (96, 96), (96, 99), (96, 100), (96, 101), (96, 102), (96, 104), (97, 78), (97, 80), (97, 81), (97, 82), (97, 83), (97, 84), (97, 85), (97, 86), (97, 87), (97, 88), (97, 89), (97, 90), (97, 91), (97, 92), (97, 93), (97, 94), (97, 95), (97, 97), (97, 98), (97, 99), (97, 103), (98, 78), (98, 80), (98, 81), (98, 82), (98, 83), (98, 84), (98, 85), (98, 86), (98, 92), (98, 93), (98, 94), (98, 95), (98, 96), (98, 97), (98, 101), (98, 102), (99, 79), (99, 81), (99, 82), (99, 83), (99, 84), (99, 87), (99, 88), (99, 89), (99, 90), (99, 91), (99, 98), (99, 99), (100, 80), (100, 82), (100, 85), (100, 86), (100, 92), (100, 93), (100, 94), (100, 95), (100, 96), (101, 80), (101, 82), (101, 84), (102, 79), (102, 82), (103, 77), (103, 80), (103, 82), (104, 79), (104, 80), (104, 82), (104, 86), (104, 88), (105, 77), (105, 79), (105, 80), (105, 81), (105, 82), (105, 83), (105, 84), (105, 85), (105, 89), (106, 78), (107, 79), (107, 81), (107, 85), (107, 86), (107, 88), (108, 83), (108, 84), ) coordinates_A120F0 = ((122, 138), (122, 140), (122, 141), (122, 143), (123, 138), (123, 144), (124, 138), (124, 140), (124, 143), (125, 138), (126, 138), (126, 140), (127, 138), (127, 140), (128, 138), (128, 139), (129, 138), (130, 137), ) coordinates_ADFF2F = ((100, 137), (100, 139), (100, 140), (100, 141), (100, 142), (100, 143), (100, 144), (100, 146), (101, 137), (101, 146), (102, 138), (102, 140), (102, 141), (102, 144), (102, 146), (103, 143), (103, 146), (104, 144), (104, 146), (105, 144), (105, 147), (106, 144), (106, 147), (107, 144), (107, 146), (107, 148), (108, 143), (108, 145), (108, 146), (108, 147), (108, 149), (109, 142), (109, 144), (109, 145), (109, 146), (109, 147), (109, 149), (110, 143), (110, 146), (110, 147), (110, 149), (111, 144), (111, 147), (111, 149), (112, 146), (112, 149), (113, 147), (113, 149), ) coordinates_A020F0 = ((114, 142), (115, 140), (115, 143), (116, 141), (116, 144), (117, 141), (117, 144), (118, 141), (118, 143), (118, 145), (119, 142), (119, 146), (120, 142), (120, 146), (121, 145), (121, 146), ) coordinates_B03060 = ((111, 142), (112, 142), (112, 143), (113, 143), (113, 144), (114, 144), (114, 146), (115, 145), (115, 147), (115, 148), (116, 146), (116, 149), (116, 150), (116, 152), (117, 147), (117, 152), (118, 147), (118, 149), (118, 150), (118, 152), (119, 148), (119, 152), (120, 148), (120, 150), (120, 152), (121, 152), ) coordinates_ACD8E6 = ((79, 137), (80, 136), (80, 138), (81, 135), (81, 137), (81, 139), (82, 134), (82, 136), (82, 137), (83, 133), (83, 136), (83, 137), (83, 138), (83, 140), (84, 133), (84, 135), (84, 136), (84, 137), (84, 138), (84, 139), (84, 141), (85, 132), (85, 134), (85, 138), (85, 139), (85, 141), (86, 132), (86, 133), (86, 138), (86, 139), (86, 140), (86, 142), (87, 131), (87, 134), (87, 138), (87, 140), (87, 142), (88, 130), (88, 133), (88, 138), (88, 140), (88, 141), (88, 143), (89, 129), (89, 132), (89, 138), (89, 143), (90, 129), (90, 131), (90, 139), (90, 141), (90, 142), (91, 128), (91, 131), (92, 130), (93, 127), (93, 130), (94, 127), (94, 130), (95, 127), (95, 129), (96, 127), (96, 128), ) coordinates_FF3E96 = ((109, 102), (109, 104), (109, 105), (109, 106), (109, 108), (110, 100), (110, 109), (111, 99), (111, 102), (111, 103), (111, 104), (111, 105), (111, 106), (111, 107), (111, 109), (112, 98), (112, 100), (112, 101), (112, 102), (112, 103), (112, 104), (112, 105), (112, 106), (112, 107), (112, 109), (113, 98), (113, 100), (113, 101), (113, 102), (113, 103), (113, 104), (113, 105), (113, 106), (113, 107), (113, 109), (114, 98), (114, 100), (114, 101), (114, 102), (114, 103), (114, 104), (114, 105), (114, 106), (114, 108), (114, 109), (114, 132), (114, 134), (115, 97), (115, 99), (115, 100), (115, 101), (115, 102), (115, 103), (115, 104), (115, 105), (115, 106), (115, 108), (115, 109), (115, 131), (115, 135), (116, 93), (116, 97), (116, 99), (116, 100), (116, 101), (116, 102), (116, 103), (116, 104), (116, 105), (116, 106), (116, 107), (116, 109), (116, 131), (116, 133), (116, 134), (116, 136), (117, 93), (117, 94), (117, 97), (117, 99), (117, 100), (117, 101), (117, 102), (117, 103), (117, 104), (117, 105), (117, 106), (117, 107), (117, 109), (117, 113), (117, 114), (117, 115), (117, 117), (117, 130), (117, 132), (117, 133), (117, 134), (117, 135), (117, 137), (117, 139), (118, 93), (118, 96), (118, 97), (118, 104), (118, 105), (118, 106), (118, 107), (118, 108), (118, 109), (118, 111), (118, 112), (118, 113), (118, 114), (118, 118), (118, 125), (118, 127), (118, 128), (118, 131), (118, 132), (118, 133), (118, 135), (118, 136), (118, 139), (119, 93), (119, 98), (119, 99), (119, 100), (119, 101), (119, 102), (119, 103), (119, 112), (119, 116), (119, 118), (119, 124), (119, 134), (120, 93), (120, 95), (120, 96), (120, 104), (120, 105), (120, 106), (120, 107), (120, 108), (120, 110), (120, 117), (120, 118), (120, 124), (120, 126), (120, 127), (120, 128), (120, 129), (120, 130), (120, 131), (120, 132), (120, 133), (120, 136), (120, 137), (120, 138), (120, 140), (121, 110), ) coordinates_7EFFD4 = ((153, 123), (153, 125), (154, 124), (154, 126), (155, 125), (155, 127), (156, 126), (156, 128), (157, 126), (157, 129), (158, 126), (158, 129), (159, 127), (159, 130), (160, 127), (160, 130), (161, 127), (161, 130), (162, 127), (162, 130), (163, 127), (163, 130), (164, 127), (164, 128), (164, 130), (165, 128), (165, 130), (165, 134), (166, 128), (166, 130), (166, 133), (166, 135), (167, 128), (167, 130), (167, 131), (167, 132), (167, 134), (167, 136), (168, 128), (168, 129), (168, 130), (168, 133), (168, 135), (169, 129), (169, 131), (169, 134), (170, 129), (170, 133), (171, 128), (171, 131), (172, 128), (172, 130), ) coordinates_B12222 = ((149, 127), (150, 127), (151, 130), (152, 127), (152, 131), (153, 128), (153, 131), (154, 129), (154, 132), (155, 130), (155, 133), (156, 130), (156, 134), (157, 131), (157, 135), (158, 133), (158, 136), (159, 134), (159, 137), (160, 134), (160, 136), (160, 139), (160, 140), (161, 134), (161, 136), (161, 137), (161, 139), (162, 134), (162, 136), (162, 137), (162, 139), (163, 134), (163, 138), (164, 135), (164, 138), (165, 137), ) coordinates_7FFFD4 = ((71, 124), (71, 126), (72, 110), (72, 112), (72, 113), (72, 114), (72, 120), (72, 122), (72, 127), (72, 128), (73, 109), (73, 116), (73, 117), (73, 118), (73, 119), (73, 124), (73, 125), (73, 126), (73, 128), (74, 109), (74, 111), (74, 112), (74, 113), (74, 114), (74, 115), (74, 121), (74, 122), (74, 123), (74, 124), (74, 125), (74, 127), (75, 110), (75, 112), (75, 113), (75, 114), (75, 115), (75, 116), (75, 117), (75, 119), (75, 124), (75, 126), (76, 111), (76, 113), (76, 114), (76, 115), (76, 116), (76, 117), (76, 119), (76, 124), (76, 126), (77, 111), (77, 113), (77, 114), (77, 115), (77, 116), (77, 118), (77, 124), (77, 125), (78, 112), (78, 114), (78, 115), (78, 116), (78, 118), (78, 125), (79, 112), (79, 114), (79, 115), (79, 116), (79, 117), (79, 118), (79, 125), (80, 112), (80, 114), (80, 115), (80, 117), (81, 112), (81, 114), (81, 115), (81, 117), (82, 113), (82, 115), (82, 117), (83, 114), (83, 117), (84, 115), (84, 117), (85, 116), (86, 116), (87, 115), (87, 116), (88, 115), (88, 116), (89, 114), (89, 115), (90, 114), ) coordinates_B22222 = ((74, 129), (75, 132), (76, 128), (76, 130), (76, 133), (77, 128), (77, 130), (77, 131), (77, 132), (78, 127), (78, 131), (78, 132), (78, 133), (78, 135), (79, 127), (79, 129), (79, 130), (79, 131), (79, 132), (79, 135), (80, 131), (80, 134), (81, 131), (81, 133), (82, 131), (82, 132), (83, 130), (83, 131), (84, 129), (85, 128), (85, 130), (86, 128), (86, 129), (87, 127), (88, 128), (89, 126), (89, 127), (90, 125), (90, 126), (91, 126), (92, 124), (92, 125), (93, 123), (93, 125), (94, 123), (94, 125), (95, 125), (96, 124), ) coordinates_499B3C = ((144, 144), (144, 145), (145, 136), (145, 141), (145, 142), (145, 143), (145, 146), (146, 136), (146, 140), (146, 143), (146, 144), (146, 146), (147, 137), (147, 139), (147, 143), (147, 144), (147, 146), (148, 130), (148, 132), (148, 142), (148, 143), (148, 144), (148, 146), (149, 133), (149, 141), (149, 143), (149, 145), (150, 131), (150, 134), (150, 141), (150, 143), (150, 145), (151, 132), (151, 135), (151, 141), (151, 144), (152, 133), (152, 136), (152, 141), (152, 144), (153, 134), (153, 137), (153, 140), (153, 143), (154, 135), (154, 137), (154, 140), (154, 141), (154, 143), (155, 136), (155, 140), (155, 142), (156, 137), (156, 139), (156, 140), (156, 142), (157, 138), (157, 141), (158, 139), (158, 141), ) coordinates_633263 = ((154, 114), (155, 115), (155, 123), (156, 115), (156, 123), (157, 115), (157, 123), (157, 124), (158, 115), (158, 116), (158, 123), (158, 124), (159, 115), (159, 116), (159, 123), (159, 124), (160, 115), (160, 123), (161, 115), (161, 117), (161, 123), (161, 125), (162, 116), (162, 117), (162, 122), (162, 125), (163, 115), (163, 116), (163, 122), (163, 125), (164, 115), (164, 116), (164, 122), (164, 125), (165, 115), (165, 116), (165, 122), (165, 125), (166, 115), (166, 116), (166, 122), (166, 124), (166, 126), (167, 115), (167, 122), (167, 124), (167, 126), (168, 115), (168, 122), (168, 124), (168, 126), (169, 115), (169, 122), (169, 124), (169, 126), (170, 114), (170, 115), (170, 121), (170, 122), (170, 123), (170, 124), (170, 126), (171, 115), (171, 121), (171, 123), (171, 124), (171, 126), (172, 113), (172, 115), (172, 116), (172, 117), (172, 118), (172, 119), (172, 122), (172, 123), (173, 113), (173, 115), (173, 121), (173, 124), (174, 113), (174, 121), (174, 122), (174, 124), (175, 115), (175, 116), (175, 117), (175, 118), (175, 119), (175, 120), ) coordinates_4A9B3C = ((92, 140), (92, 142), (92, 143), (92, 145), (93, 140), (93, 145), (94, 141), (94, 143), (94, 145), (95, 142), (95, 145), (96, 142), (96, 145), (97, 139), (97, 145), (98, 138), (98, 140), (98, 141), (98, 142), (98, 143), (98, 145), (98, 146), ) coordinates_218B22 = ((150, 156), (150, 158), (151, 155), (151, 159), (152, 154), (152, 156), (152, 157), (152, 159), (153, 152), (153, 155), (153, 156), (153, 157), (153, 158), (153, 160), (154, 150), (154, 154), (154, 155), (154, 156), (154, 160), (155, 149), (155, 152), (155, 153), (155, 154), (155, 155), (155, 159), (156, 148), (156, 150), (156, 151), (156, 152), (156, 153), (156, 154), (156, 155), (156, 156), (157, 148), (157, 150), (157, 151), (157, 152), (157, 153), (157, 155), (158, 148), (158, 150), (158, 151), (158, 152), (158, 153), (158, 155), (159, 147), (159, 149), (159, 150), (159, 151), (159, 152), (159, 153), (159, 154), (159, 155), (159, 156), (160, 147), (160, 149), (160, 150), (160, 155), (160, 158), (161, 147), (161, 151), (161, 152), (161, 153), (161, 154), (161, 158), (162, 147), (162, 149), (162, 150), (162, 155), (162, 157), ) coordinates_228B22 = ((78, 147), (78, 148), (79, 147), (79, 149), (80, 147), (80, 150), (81, 147), (81, 148), (81, 149), (81, 151), (82, 148), (82, 151), (83, 148), (83, 150), (83, 152), (84, 148), (84, 150), (84, 152), (85, 149), (85, 151), (85, 153), (86, 149), (86, 151), (86, 153), (87, 149), (87, 151), (87, 153), (88, 150), (88, 152), (88, 154), (89, 150), (89, 152), (89, 154), (90, 151), (90, 154), (91, 151), (91, 154), (92, 152), (92, 155), (93, 153), (93, 156), (94, 154), (94, 158), (95, 155), (95, 158), )

eb7c3fb12e03f2b24dcc584553fc30f0b1f73b73

py

Python

examples/Redfish/expand_data.py

andreaslangnevyjel/python-ilorest-library

cd40e5ed9dfd615074d34ec6bb929dc8ea04a797

2016-04-04T12:24:52.000Z

2022-03-28T11:35:46.000Z

examples/Redfish/expand_data.py

andreaslangnevyjel/python-ilorest-library

cd40e5ed9dfd615074d34ec6bb929dc8ea04a797

2016-04-02T04:22:29.000Z

2022-03-25T06:54:45.000Z

examples/Redfish/expand_data.py

andreaslangnevyjel/python-ilorest-library

cd40e5ed9dfd615074d34ec6bb929dc8ea04a797

2016-04-04T20:39:42.000Z

2021-11-13T06:53:41.000Z

# Copyright 2020 Hewlett Packard Enterprise Development LP # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # -*- coding: utf-8 -*- """ An example of expanding data responses """ import sys import json from redfish import RedfishClient from redfish.rest.v1 import ServerDownOrUnreachableError def expand_data(_redfishobj, expand_url="/redfish/v1/"): response = _redfishobj.get(expand_url) exp_response = _redfishobj.get(expand_url+'?$expand=.') sys.stdout.write('Standard response:\n') sys.stdout.write('\t'+str(response.dict)+'\n') sys.stdout.write('Expanded response:\n') sys.stdout.write('\t'+str(exp_response.dict)+'\n') if __name__ == "__main__": # When running on the server locally use the following commented values #SYSTEM_URL = None #LOGIN_ACCOUNT = None #LOGIN_PASSWORD = None # When running remotely connect using the secured (https://) address, # account name, and password to send https requests # SYSTEM_URL acceptable examples: # "https://10.0.0.100" # "https://ilo.hostname" SYSTEM_URL = "https://10.0.0.100" LOGIN_ACCOUNT = "admin" LOGIN_PASSWORD = "password" #url to be expanded EXPAND_URL = "/redfish/v1/systems/" try: # Create a Redfish client object REDFISHOBJ = RedfishClient(base_url=SYSTEM_URL, username=LOGIN_ACCOUNT, \ password=LOGIN_PASSWORD) # Login with the Redfish client REDFISHOBJ.login() except ServerDownOrUnreachableError as excp: sys.stderr.write("ERROR: server not reachable or does not support RedFish.\n") sys.exit() expand_data(REDFISHOBJ, EXPAND_URL) REDFISHOBJ.logout()

6989358b5828b06e1b53569a06aa7612e515fb30

py

Python

tensorflow_probability/python/math/linalg_test.py

timudk/probability

8bdbf1c0b0f801edaf342f4ffc9caf1cfd6f1103

tensorflow_probability/python/math/linalg_test.py

timudk/probability

8bdbf1c0b0f801edaf342f4ffc9caf1cfd6f1103

tensorflow_probability/python/math/linalg_test.py

timudk/probability

8bdbf1c0b0f801edaf342f4ffc9caf1cfd6f1103

# Copyright 2018 The TensorFlow Probability Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Tests for linear algebra.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function # Dependency imports from absl.testing import parameterized import hypothesis as hp from hypothesis import strategies as hps from hypothesis.extra import numpy as hpnp import numpy as np import tensorflow.compat.v1 as tf1 import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import test_util as tfp_test_util from tensorflow.python.framework import test_util # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top class _PinvTest(object): def expected_pinv(self, a, rcond): """Calls `np.linalg.pinv` but corrects its broken batch semantics.""" if a.ndim < 3: return np.linalg.pinv(a, rcond) if rcond is None: rcond = 10. * max(a.shape[-2], a.shape[-1]) * np.finfo(a.dtype).eps s = np.concatenate([a.shape[:-2], [a.shape[-1], a.shape[-2]]]) a_pinv = np.zeros(s, dtype=a.dtype) for i in np.ndindex(a.shape[:(a.ndim - 2)]): a_pinv[i] = np.linalg.pinv( a[i], rcond=rcond if isinstance(rcond, float) else rcond[i]) return a_pinv def test_symmetric(self): a_ = self.dtype([[1., .4, .5], [.4, .2, .25], [.5, .25, .35]]) a_ = np.stack([a_ + 1., a_], axis=0) # Batch of matrices. a = tf1.placeholder_with_default( a_, shape=a_.shape if self.use_static_shape else None) if self.use_default_rcond: rcond = None else: rcond = self.dtype([0., 0.01]) # Smallest 1 component is forced to zero. expected_a_pinv_ = self.expected_pinv(a_, rcond) a_pinv = tfp.math.pinv(a, rcond, validate_args=True) a_pinv_ = self.evaluate(a_pinv) self.assertAllClose(expected_a_pinv_, a_pinv_, atol=1e-5, rtol=1e-5) if not self.use_static_shape: return self.assertAllEqual(expected_a_pinv_.shape, a_pinv.shape) def test_nonsquare(self): a_ = self.dtype([[1., .4, .5, 1.], [.4, .2, .25, 2.], [.5, .25, .35, 3.]]) a_ = np.stack([a_ + 0.5, a_], axis=0) # Batch of matrices. a = tf1.placeholder_with_default( a_, shape=a_.shape if self.use_static_shape else None) if self.use_default_rcond: rcond = None else: # Smallest 2 components are forced to zero. rcond = self.dtype([0., 0.25]) expected_a_pinv_ = self.expected_pinv(a_, rcond) a_pinv = tfp.math.pinv(a, rcond, validate_args=True) a_pinv_ = self.evaluate(a_pinv) self.assertAllClose(expected_a_pinv_, a_pinv_, atol=1e-5, rtol=1e-4) if not self.use_static_shape: return self.assertAllEqual(expected_a_pinv_.shape, a_pinv.shape) @test_util.run_all_in_graph_and_eager_modes class PinvTestDynamic32DefaultRcond(tf.test.TestCase, _PinvTest): dtype = np.float32 use_static_shape = False use_default_rcond = True @test_util.run_all_in_graph_and_eager_modes class PinvTestStatic64DefaultRcond(tf.test.TestCase, _PinvTest): dtype = np.float64 use_static_shape = True use_default_rcond = True @test_util.run_all_in_graph_and_eager_modes class PinvTestDynamic32CustomtRcond(tf.test.TestCase, _PinvTest): dtype = np.float32 use_static_shape = False use_default_rcond = False @test_util.run_all_in_graph_and_eager_modes class PinvTestStatic64CustomRcond(tf.test.TestCase, _PinvTest): dtype = np.float64 use_static_shape = True use_default_rcond = False class _CholeskyExtend(tf.test.TestCase): def testCholeskyExtension(self): xs = np.random.random(7).astype(self.dtype)[:, tf.newaxis] xs = tf1.placeholder_with_default( xs, shape=xs.shape if self.use_static_shape else None) k = tfp.positive_semidefinite_kernels.MaternOneHalf() mat = k.matrix(xs, xs) chol = tf.linalg.cholesky(mat) ys = np.random.random(3).astype(self.dtype)[:, tf.newaxis] ys = tf1.placeholder_with_default( ys, shape=ys.shape if self.use_static_shape else None) xsys = tf.concat([xs, ys], 0) new_chol_expected = tf.linalg.cholesky(k.matrix(xsys, xsys)) new_chol = tfp.math.cholesky_concat(chol, k.matrix(xsys, ys)) self.assertAllClose(new_chol_expected, new_chol) @hp.given(hps.data()) @hp.settings(deadline=None, max_examples=10, derandomize=tfp_test_util.derandomize_hypothesis()) def testCholeskyExtensionRandomized(self, data): jitter = lambda n: tf.linalg.eye(n, dtype=self.dtype) * 1e-5 target_bs = data.draw(hpnp.array_shapes()) prev_bs, new_bs = data.draw(tfp_test_util.broadcasting_shapes(target_bs, 2)) ones = tf.TensorShape([1] * len(target_bs)) smallest_shared_shp = tuple(np.min( [tf.broadcast_static_shape(ones, shp).as_list() for shp in [prev_bs, new_bs]], axis=0)) z = data.draw(hps.integers(min_value=1, max_value=12)) n = data.draw(hps.integers(min_value=0, max_value=z - 1)) m = z - n np.random.seed(data.draw(hps.integers(min_value=0, max_value=2**32 - 1))) xs = np.random.uniform(size=smallest_shared_shp + (n,)) data.draw(hps.just(xs)) xs = (xs + np.zeros(prev_bs.as_list() + [n]))[..., np.newaxis] xs = xs.astype(self.dtype) xs = tf1.placeholder_with_default( xs, shape=xs.shape if self.use_static_shape else None) k = tfp.positive_semidefinite_kernels.MaternOneHalf() mat = k.matrix(xs, xs) + jitter(n) chol = tf.linalg.cholesky(mat) ys = np.random.uniform(size=smallest_shared_shp + (m,)) data.draw(hps.just(ys)) ys = (ys + np.zeros(new_bs.as_list() + [m]))[..., np.newaxis] ys = ys.astype(self.dtype) ys = tf1.placeholder_with_default( ys, shape=ys.shape if self.use_static_shape else None) xsys = tf.concat([xs + tf.zeros(target_bs + (n, 1), dtype=self.dtype), ys + tf.zeros(target_bs + (m, 1), dtype=self.dtype)], axis=-2) new_chol_expected = tf.linalg.cholesky(k.matrix(xsys, xsys) + jitter(z)) new_chol = tfp.math.cholesky_concat( chol, k.matrix(xsys, ys) + jitter(z)[:, n:]) self.assertAllClose(new_chol_expected, new_chol, rtol=1e-5, atol=1e-5) @test_util.run_all_in_graph_and_eager_modes class CholeskyExtend32Static(_CholeskyExtend): dtype = np.float32 use_static_shape = True @test_util.run_all_in_graph_and_eager_modes class CholeskyExtend64Dynamic(_CholeskyExtend): dtype = np.float64 use_static_shape = False del _CholeskyExtend class _PivotedCholesky(tf.test.TestCase, parameterized.TestCase): def _random_batch_psd(self, dim): matrix = np.random.random([2, dim, dim]) matrix = np.matmul(matrix, np.swapaxes(matrix, -2, -1)) matrix = (matrix + np.diag(np.arange(dim) * .1)).astype(self.dtype) masked_shape = ( matrix.shape if self.use_static_shape else [None] * len(matrix.shape)) matrix = tf1.placeholder_with_default(matrix, shape=masked_shape) return matrix def testPivotedCholesky(self): dim = 11 matrix = self._random_batch_psd(dim) true_diag = tf.linalg.diag_part(matrix) pchol = tfp.math.pivoted_cholesky(matrix, max_rank=1) mat = tf.matmul(pchol, pchol, transpose_b=True) diag_diff_prev = self.evaluate(tf.abs(tf.linalg.diag_part(mat) - true_diag)) diff_norm_prev = self.evaluate( tf.linalg.norm(tensor=mat - matrix, ord='fro', axis=[-1, -2])) for rank in range(2, dim + 1): # Specifying diag_rtol forces the full max_rank decomposition. pchol = tfp.math.pivoted_cholesky(matrix, max_rank=rank, diag_rtol=-1) zeros_per_col = dim - tf.math.count_nonzero(pchol, axis=-2) mat = tf.matmul(pchol, pchol, transpose_b=True) pchol_shp, diag_diff, diff_norm, zeros_per_col = self.evaluate([ tf.shape(pchol), tf.abs(tf.linalg.diag_part(mat) - true_diag), tf.linalg.norm(tensor=mat - matrix, ord='fro', axis=[-1, -2]), zeros_per_col ]) self.assertAllEqual([2, dim, rank], pchol_shp) self.assertAllEqual( np.ones([2, rank], dtype=np.bool), zeros_per_col >= np.arange(rank)) self.assertAllLessEqual(diag_diff - diag_diff_prev, np.finfo(self.dtype).resolution) self.assertAllLessEqual(diff_norm - diff_norm_prev, np.finfo(self.dtype).resolution) diag_diff_prev, diff_norm_prev = diag_diff, diff_norm def testGradient(self): dim = 11 matrix = self._random_batch_psd(dim) _, dmatrix = tfp.math.value_and_gradient( lambda matrix: tfp.math.pivoted_cholesky(matrix, max_rank=dim // 3), matrix) self.assertIsNotNone(dmatrix) self.assertAllGreater( tf.linalg.norm(tensor=dmatrix, ord='fro', axis=[-1, -2]), 0.) @test_util.enable_control_flow_v2 def testGradientTapeCFv2(self): dim = 11 matrix = self._random_batch_psd(dim) with tf.GradientTape() as tape: tape.watch(matrix) pchol = tfp.math.pivoted_cholesky(matrix, max_rank=dim // 3) dmatrix = tape.gradient( pchol, matrix, output_gradients=tf.ones_like(pchol) * .01) self.assertIsNotNone(dmatrix) self.assertAllGreater( tf.linalg.norm(tensor=dmatrix, ord='fro', axis=[-1, -2]), 0.) # pyformat: disable @parameterized.parameters( # Inputs are randomly shuffled arange->tril; outputs from gpytorch. ( np.array([ [7., 0, 0, 0, 0, 0], [9, 13, 0, 0, 0, 0], [4, 10, 6, 0, 0, 0], [18, 1, 2, 14, 0, 0], [5, 11, 20, 3, 17, 0], [19, 12, 16, 15, 8, 21] ]), np.array([ [3.4444, -1.3545, 4.084, 1.7674, -1.1789, 3.7562], [8.4685, 1.2821, 3.1179, 12.9197, 0.0000, 0.0000], [7.5621, 4.8603, 0.0634, 7.3942, 4.0637, 0.0000], [15.435, -4.8864, 16.2137, 0.0000, 0.0000, 0.0000], [18.8535, 22.103, 0.0000, 0.0000, 0.0000, 0.0000], [38.6135, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000] ])), ( np.array([ [1, 0, 0], [2, 3, 0], [4, 5, 6.] ]), np.array([ [0.4558, 0.3252, 0.8285], [2.6211, 2.4759, 0.0000], [8.7750, 0.0000, 0.0000] ])), ( np.array([ [6, 0, 0], [3, 2, 0], [4, 1, 5.] ]), np.array([ [3.7033, 4.7208, 0.0000], [2.1602, 2.1183, 1.9612], [6.4807, 0.0000, 0.0000] ]))) # pyformat: enable def testOracleExamples(self, mat, oracle_pchol): mat = np.matmul(mat, mat.T) for rank in range(1, mat.shape[-1] + 1): self.assertAllClose( oracle_pchol[..., :rank], tfp.math.pivoted_cholesky(mat, max_rank=rank, diag_rtol=-1), atol=1e-4) @test_util.run_all_in_graph_and_eager_modes class PivotedCholesky32Static(_PivotedCholesky): dtype = np.float32 use_static_shape = True @test_util.run_all_in_graph_and_eager_modes class PivotedCholesky64Dynamic(_PivotedCholesky): dtype = np.float64 use_static_shape = False del _PivotedCholesky def make_tensor_hiding_attributes(value, hide_shape, hide_value=True): if not hide_value: return tf.convert_to_tensor(value=value) shape = None if hide_shape else getattr(value, 'shape', None) return tf1.placeholder_with_default(value, shape=shape) class _LUReconstruct(object): dtype = np.float32 use_static_shape = True def test_non_batch(self): x_ = np.array( [[3, 4], [1, 2]], dtype=self.dtype) x = tf1.placeholder_with_default( x_, shape=x_.shape if self.use_static_shape else None) y = tfp.math.lu_reconstruct(*tf.linalg.lu(x), validate_args=True) y_ = self.evaluate(y) if self.use_static_shape: self.assertAllEqual(x_.shape, y.shape) self.assertAllClose(x_, y_, atol=0., rtol=1e-3) def test_batch(self): x_ = np.array( [ [[3, 4], [1, 2]], [[7, 8], [3, 4]], ], dtype=self.dtype) x = tf1.placeholder_with_default( x_, shape=x_.shape if self.use_static_shape else None) y = tfp.math.lu_reconstruct(*tf.linalg.lu(x), validate_args=True) y_ = self.evaluate(y) if self.use_static_shape: self.assertAllEqual(x_.shape, y.shape) self.assertAllClose(x_, y_, atol=0., rtol=1e-3) @test_util.run_all_in_graph_and_eager_modes class LUReconstructStatic(tf.test.TestCase, _LUReconstruct): use_static_shape = True @test_util.run_all_in_graph_and_eager_modes class LUReconstructDynamic(tf.test.TestCase, _LUReconstruct): use_static_shape = False class _LUMatrixInverse(object): dtype = np.float32 use_static_shape = True def test_non_batch(self): x_ = np.array([[1, 2], [3, 4]], dtype=self.dtype) x = tf1.placeholder_with_default( x_, shape=x_.shape if self.use_static_shape else None) y = tfp.math.lu_matrix_inverse(*tf.linalg.lu(x), validate_args=True) y_ = self.evaluate(y) if self.use_static_shape: self.assertAllEqual(x_.shape, y.shape) self.assertAllClose(np.linalg.inv(x_), y_, atol=0., rtol=1e-3) def test_batch(self): x_ = np.array( [ [[1, 2], [3, 4]], [[7, 8], [3, 4]], [[0.25, 0.5], [0.75, -2.]], ], dtype=self.dtype) x = tf1.placeholder_with_default( x_, shape=x_.shape if self.use_static_shape else None) y = tfp.math.lu_matrix_inverse(*tf.linalg.lu(x), validate_args=True) y_ = self.evaluate(y) if self.use_static_shape: self.assertAllEqual(x_.shape, y.shape) self.assertAllClose(np.linalg.inv(x_), y_, atol=0., rtol=1e-3) @test_util.run_all_in_graph_and_eager_modes class LUMatrixInverseStatic(tf.test.TestCase, _LUMatrixInverse): use_static_shape = True @test_util.run_all_in_graph_and_eager_modes class LUMatrixInverseDynamic(tf.test.TestCase, _LUMatrixInverse): use_static_shape = False class _LUSolve(object): dtype = np.float32 use_static_shape = True def test_non_batch(self): x_ = np.array( [[1, 2], [3, 4]], dtype=self.dtype) x = tf1.placeholder_with_default( x_, shape=x_.shape if self.use_static_shape else None) rhs_ = np.array([[1, 1]], dtype=self.dtype).T rhs = tf1.placeholder_with_default( rhs_, shape=rhs_.shape if self.use_static_shape else None) lower_upper, perm = tf.linalg.lu(x) y = tfp.math.lu_solve(lower_upper, perm, rhs, validate_args=True) y_, perm_ = self.evaluate([y, perm]) self.assertAllEqual([1, 0], perm_) expected_ = np.linalg.solve(x_, rhs_) if self.use_static_shape: self.assertAllEqual(expected_.shape, y.shape) self.assertAllClose(expected_, y_, atol=0., rtol=1e-3) def test_batch_broadcast(self): x_ = np.array( [ [[1, 2], [3, 4]], [[7, 8], [3, 4]], [[0.25, 0.5], [0.75, -2.]], ], dtype=self.dtype) x = tf1.placeholder_with_default( x_, shape=x_.shape if self.use_static_shape else None) rhs_ = np.array([[1, 1]], dtype=self.dtype).T rhs = tf1.placeholder_with_default( rhs_, shape=rhs_.shape if self.use_static_shape else None) lower_upper, perm = tf.linalg.lu(x) y = tfp.math.lu_solve(lower_upper, perm, rhs, validate_args=True) y_, perm_ = self.evaluate([y, perm]) self.assertAllEqual([[1, 0], [0, 1], [1, 0]], perm_) expected_ = np.linalg.solve(x_, rhs_[np.newaxis]) if self.use_static_shape: self.assertAllEqual(expected_.shape, y.shape) self.assertAllClose(expected_, y_, atol=0., rtol=1e-3) @test_util.run_all_in_graph_and_eager_modes class LUSolveStatic(tf.test.TestCase, _LUSolve): use_static_shape = True @test_util.run_all_in_graph_and_eager_modes class LUSolveDynamic(tf.test.TestCase, _LUSolve): use_static_shape = False class _SparseOrDenseMatmul(object): dtype = np.float32 use_static_shape = True use_sparse_tensor = False def _make_placeholder(self, x): return tf1.placeholder_with_default( x, shape=(x.shape if self.use_static_shape else None)) def _make_sparse_placeholder(self, x): indices_placeholder = self._make_placeholder(x.indices) values_placeholder = self._make_placeholder(x.values) if self.use_static_shape: dense_shape_placeholder = x.dense_shape else: dense_shape_placeholder = self._make_placeholder(x.dense_shape) return tf.SparseTensor( indices=indices_placeholder, values=values_placeholder, dense_shape=dense_shape_placeholder) def verify_sparse_dense_matmul(self, x_, y_): if self.use_sparse_tensor: x = self._make_sparse_placeholder(tfp.math.dense_to_sparse(x_)) else: x = self._make_placeholder(x_) y = self._make_placeholder(y_) z = tfp.math.sparse_or_dense_matmul(x, y) z_ = self.evaluate(z) if self.use_static_shape: batch_shape = x_.shape[:-2] self.assertAllEqual(z_.shape, batch_shape + (x_.shape[-2], y_.shape[-1])) self.assertAllClose(z_, np.matmul(x_, y_), atol=0., rtol=1e-3) def verify_sparse_dense_matvecmul(self, x_, y_): if self.use_sparse_tensor: x = self._make_sparse_placeholder(tfp.math.dense_to_sparse(x_)) else: x = self._make_placeholder(x_) y = self._make_placeholder(y_) z = tfp.math.sparse_or_dense_matvecmul(x, y) z_ = self.evaluate(z) if self.use_static_shape: batch_shape = x_.shape[:-2] self.assertAllEqual(z_.shape, batch_shape + (x_.shape[-2],)) self.assertAllClose( z_[..., np.newaxis], np.matmul(x_, y_[..., np.newaxis]), atol=0., rtol=1e-3) def test_non_batch_matmul(self): x_ = np.array([[3, 4, 0], [1, 0, 3]], dtype=self.dtype) y_ = np.array([[1, 0], [9, 0], [3, 1]], dtype=self.dtype) self.verify_sparse_dense_matmul(x_, y_) def test_non_batch_matvecmul(self): x_ = np.array([[3, 0, 5], [0, 2, 3]], dtype=self.dtype) y_ = np.array([1, 0, 9], dtype=self.dtype) self.verify_sparse_dense_matvecmul(x_, y_) def test_batch_matmul(self): x_ = np.array([ [[3, 4, 0], [1, 0, 3]], [[6, 0, 0], [0, 0, 0]], ], dtype=self.dtype) y_ = np.array([ [[1, 0], [9, 0], [3, 1]], [[2, 2], [5, 6], [0, 1]], ], dtype=self.dtype) self.verify_sparse_dense_matmul(x_, y_) def test_batch_matvecmul(self): x_ = np.array([ [[3, 0, 5], [0, 2, 3]], [[1, 1, 0], [6, 0, 0]], ], dtype=self.dtype) y_ = np.array([ [1, 0, 9], [0, 0, 2], ], dtype=self.dtype) self.verify_sparse_dense_matvecmul(x_, y_) @test_util.run_all_in_graph_and_eager_modes class SparseOrDenseMatmulStatic(tf.test.TestCase, _SparseOrDenseMatmul): use_static_shape = True @test_util.run_all_in_graph_and_eager_modes class SparseOrDenseMatmulDynamic(tf.test.TestCase, _SparseOrDenseMatmul): use_static_shape = False @test_util.run_all_in_graph_and_eager_modes class SparseOrDenseMatmulStaticSparse(tf.test.TestCase, _SparseOrDenseMatmul): use_static_shape = True use_sparse_tensor = True @test_util.run_all_in_graph_and_eager_modes class SparseOrDenseMatmulDynamicSparse(tf.test.TestCase, _SparseOrDenseMatmul): use_static_shape = False use_sparse_tensor = True class _MatrixRankTest(object): def test_batch_default_tolerance(self): x_ = np.array([[[2, 3, -2], # = row2+row3 [-1, 1, -2], [3, 2, 0]], [[0, 2, 0], # = 2*row2 [0, 1, 0], [0, 3, 0]], # = 3*row2 [[1, 0, 0], [0, 1, 0], [0, 0, 1]]], self.dtype) x = tf1.placeholder_with_default( x_, shape=x_.shape if self.use_static_shape else None) self.assertAllEqual([2, 1, 3], self.evaluate(tfp.math.matrix_rank(x))) def test_custom_tolerance_broadcasts(self): q = tf.linalg.qr(tf.random.uniform([3, 3], dtype=self.dtype))[0] e = tf.constant([0.1, 0.2, 0.3], dtype=self.dtype) a = tf.linalg.solve(q, tf.transpose(a=e * q), adjoint=True) self.assertAllEqual([3, 2, 1, 0], self.evaluate(tfp.math.matrix_rank( a, tol=[[0.09], [0.19], [0.29], [0.31]]))) def test_nonsquare(self): x_ = np.array([[[2, 3, -2, 2], # = row2+row3 [-1, 1, -2, 4], [3, 2, 0, -2]], [[0, 2, 0, 6], # = 2*row2 [0, 1, 0, 3], [0, 3, 0, 9]]], # = 3*row2 self.dtype) x = tf1.placeholder_with_default( x_, shape=x_.shape if self.use_static_shape else None) self.assertAllEqual([2, 1], self.evaluate(tfp.math.matrix_rank(x))) @test_util.run_all_in_graph_and_eager_modes class MatrixRankStatic32Test(tf.test.TestCase, _MatrixRankTest): dtype = np.float32 use_static_shape = True @test_util.run_all_in_graph_and_eager_modes class MatrixRankDynamic64Test(tf.test.TestCase, _MatrixRankTest): dtype = np.float64 use_static_shape = False @test_util.run_all_in_graph_and_eager_modes class FillTriangularTest(tf.test.TestCase): def _fill_triangular(self, x, upper=False): """Numpy implementation of `fill_triangular`.""" x = np.asarray(x) # Formula derived by solving for n: m = n(n+1)/2. m = np.int32(x.shape[-1]) n = np.sqrt(0.25 + 2. * m) - 0.5 if n != np.floor(n): raise ValueError('Invalid shape.') n = np.int32(n) # We can't do: `x[..., -(n**2-m):]` because this doesn't correctly handle # `m == n == 1`. Hence, we do absolute indexing. x_tail = x[..., (m - (n * n - m)):] y = np.concatenate( [x, x_tail[..., ::-1]] if upper else [x_tail, x[..., ::-1]], axis=-1) y = y.reshape(np.concatenate([ np.int32(x.shape[:-1]), np.int32([n, n]), ], axis=0)) return np.triu(y) if upper else np.tril(y) def _run_test(self, x_, use_deferred_shape=False, **kwargs): x_ = np.asarray(x_) static_shape = None if use_deferred_shape else x_.shape x_pl = tf1.placeholder_with_default(x_, shape=static_shape) # Add `zeros_like(x)` such that x's value and gradient are identical. We # do this so we can ensure each gradient value is mapped to the right # gradient location. (Not doing this means the gradient wrt `x` is simple # `ones_like(x)`.) # Note: # zeros_like_x_pl == zeros_like(x_pl) # gradient(zeros_like_x_pl, x_pl) == x_pl - 1 def _zeros_like(x): return x * tf.stop_gradient(x - 1.) - tf.stop_gradient(x * (x - 1.)) actual, grad_actual = tfp.math.value_and_gradient( lambda x: tfp.math.fill_triangular( # pylint: disable=g-long-lambda x + _zeros_like(x), **kwargs), x_pl) actual_, grad_actual_ = self.evaluate([actual, grad_actual]) expected = self._fill_triangular(x_, **kwargs) if use_deferred_shape and not tf.executing_eagerly(): self.assertEqual(None, actual.shape) else: self.assertAllEqual(expected.shape, actual.shape) self.assertAllClose(expected, actual_, rtol=1e-8, atol=1e-9) self.assertAllClose(x_, grad_actual_, rtol=1e-8, atol=1e-9) def testCorrectlyMakes1x1TriLower(self): self._run_test(np.random.randn(3, int(1*2/2))) def testCorrectlyMakesNoBatchTriLower(self): self._run_test(np.random.randn(int(4*5/2))) def testCorrectlyMakesBatchTriLower(self): self._run_test(np.random.randn(2, 3, int(3*4/2))) def testCorrectlyMakesBatchTriLowerUnknownShape(self): self._run_test(np.random.randn(2, 3, int(3*4/2)), use_deferred_shape=True) def testCorrectlyMakesBatch7x7TriLowerUnknownShape(self): self._run_test(np.random.randn(2, 3, int(7*8/2)), use_deferred_shape=True) def testCorrectlyMakesBatch7x7TriLower(self): self._run_test(np.random.randn(2, 3, int(7*8/2))) def testCorrectlyMakes1x1TriUpper(self): self._run_test(np.random.randn(3, int(1*2/2)), upper=True) def testCorrectlyMakesNoBatchTriUpper(self): self._run_test(np.random.randn(int(4*5/2)), upper=True) def testCorrectlyMakesBatchTriUpper(self): self._run_test(np.random.randn(2, 2, int(3*4/2)), upper=True) def testCorrectlyMakesBatchTriUpperUnknownShape(self): self._run_test(np.random.randn(2, 2, int(3*4/2)), use_deferred_shape=True, upper=True) def testCorrectlyMakesBatch7x7TriUpperUnknownShape(self): self._run_test(np.random.randn(2, 3, int(7*8/2)), use_deferred_shape=True, upper=True) def testCorrectlyMakesBatch7x7TriUpper(self): self._run_test(np.random.randn(2, 3, int(7*8/2)), upper=True) @test_util.run_all_in_graph_and_eager_modes class FillTriangularInverseTest(FillTriangularTest): def _run_test(self, x_, use_deferred_shape=False, **kwargs): x_ = np.asarray(x_) static_shape = None if use_deferred_shape else x_.shape x_pl = tf1.placeholder_with_default(x_, shape=static_shape) zeros_like_x_pl = (x_pl * tf.stop_gradient(x_pl - 1.) - tf.stop_gradient(x_pl * (x_pl - 1.))) x = x_pl + zeros_like_x_pl actual = tfp.math.fill_triangular(x, **kwargs) inverse_actual = tfp.math.fill_triangular_inverse(actual, **kwargs) inverse_actual_ = self.evaluate(inverse_actual) if use_deferred_shape and not tf.executing_eagerly(): self.assertEqual(None, inverse_actual.shape) else: self.assertAllEqual(x_.shape, inverse_actual.shape) self.assertAllEqual(x_, inverse_actual_) if __name__ == '__main__': tf.test.main()

bf628907100ed45f03c6ba2481f962223005d12b

py

Python

tests/functional/python_tests/cli_wallet/tests/009_get_open_orders.py

drov0/hive

747380ac6d1d621a99c94ccf3fd24bbece754a57

2020-03-20T02:13:12.000Z

2022-03-31T22:40:07.000Z

tests/functional/python_tests/cli_wallet/tests/009_get_open_orders.py

drov0/hive

747380ac6d1d621a99c94ccf3fd24bbece754a57

2020-03-20T03:09:16.000Z

2021-08-28T22:35:09.000Z

tests/functional/python_tests/cli_wallet/tests/009_get_open_orders.py

drov0/hive

747380ac6d1d621a99c94ccf3fd24bbece754a57

2020-03-20T01:53:05.000Z

2022-03-04T11:08:23.000Z

#!/usr/bin/python3 import time from utils.test_utils import * from utils.cmd_args import args from utils.cli_wallet import CliWallet from utils.logger import log, init_logger if __name__ == "__main__": with Test(__file__): with CliWallet( args ) as wallet: creator, user = make_user_for_tests(wallet) result_before = wallet.get_open_orders(user)['result'] assert(len(result_before) == 0) log.info( "testing buy order :10.000 TESTS for 1000.000 TBD created by user {}".format( user ) ) wallet.create_order(user, "1", "10.000 TESTS", "1000.000 TBD", "false", "9999", "true") result_sell = wallet.get_open_orders(user)['result'] assert(len(result_sell) == 1) assert(result_sell[0]['orderid'] == 1) assert(result_sell[0]['seller'] == user) assert(result_sell[0]['for_sale'] == 10000) assert(result_sell[0]['real_price'] == '100.00000000000000000') assert(result_sell[0]['sell_price']['base'] == '10.000 TESTS') assert(result_sell[0]['sell_price']['quote'] == '1000.000 TBD') assert(not result_sell[0]['rewarded']) log.info( "testing buy order :10.000 TBD for 1000.000 TESTS created by user {}".format( user ) ) wallet.create_order(user, "2", "10.000 TBD", "1000.000 TESTS", "false", "9999", "true") result_buy = wallet.get_open_orders(user)['result'] assert(len(result_buy) == 2) assert(result_buy[1]['orderid'] == 2) assert(result_buy[1]['seller'] == user) assert(result_buy[1]['for_sale'] == 10000) assert(result_buy[1]['real_price'] == '0.01000000000000000') assert(result_buy[1]['sell_price']['base'] == '10.000 TBD') assert(result_buy[1]['sell_price']['quote'] == '1000.000 TESTS') assert(not result_buy[1]['rewarded'])

29e2d85ba89fbd087f080618a4c9b26454eeac13

py

Python

flsim/utils/tests/test_training_time_estimator.py

JohnlNguyen/FLSim

a5ed7c0b84499cd9dbc5fe95f8bcb4ba8ab5a5cb

2021-12-09T18:05:09.000Z

2022-03-23T20:43:46.000Z

flsim/utils/tests/test_training_time_estimator.py

JohnlNguyen/FLSim

a5ed7c0b84499cd9dbc5fe95f8bcb4ba8ab5a5cb

2021-12-30T17:54:04.000Z

2022-03-23T17:23:00.000Z

flsim/utils/tests/test_training_time_estimator.py

JohnlNguyen/FLSim

a5ed7c0b84499cd9dbc5fe95f8bcb4ba8ab5a5cb

2021-12-09T19:55:22.000Z

2022-03-15T00:02:08.000Z

#!/usr/bin/env python3 # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. import torch from flsim.common.pytest_helper import assertEqual, assertAlmostEqual from flsim.utils.timing.training_duration_distribution import ( PerUserUniformDurationDistribution, PerUserUniformDurationDistributionConfig, PerUserHalfNormalDurationDistribution, PerUserHalfNormalDurationDistributionConfig, DurationDistributionFromListConfig, DurationDistributionFromList, DurationInfo, ) from flsim.utils.timing.training_time_estimator import ( get_training_time, AsyncTrainingTimeEstimator, SyncTrainingTimeEstimator, ) from omegaconf import OmegaConf class TestTrainingTimeEstimator: def test_time_from_list(self) -> None: """ Test training time from list Assuming UPR = 2 Sync would be the sum of slowest user between rounds round 1 user_1: duration = 4 user_2: duration = 3 round 2 user_3: duration = 2 user_4: duration = 1 total = 4 + 2 = 6 Async would be the user_1: duration = 4, start_time = 1 user_2: duration = 3, start_time = 1 user_3: duration = 2, start_time = 2 user_4: duration = 1, start_time = 3 users training @ time 1: user 1, user 2 users training @ time 3: user 2, user 3 users training @ time 4: user 3, user 4 users training @ time 5: user 4 finishes training """ training_events = [ DurationInfo(duration=4), DurationInfo(duration=3), DurationInfo(duration=2), DurationInfo(duration=1), ] async_start_times = [1, 1, 2, 3] sync_training_dist = DurationDistributionFromList( **OmegaConf.structured( DurationDistributionFromListConfig(training_events=training_events) ) ) async_training_dist = DurationDistributionFromList( **OmegaConf.structured( DurationDistributionFromListConfig(training_events=training_events) ) ) num_users = len(training_events) epochs = 1 users_per_round = 2 sync_estimator = SyncTrainingTimeEstimator( total_users=len(training_events), users_per_round=users_per_round, epochs=epochs, training_dist=sync_training_dist, ) async_estimator = AsyncTrainingTimeEstimator( total_users=num_users, users_per_round=users_per_round, epochs=epochs, training_dist=async_training_dist, start_times=async_start_times, ) async_time = async_estimator.training_time() sync_time = sync_estimator.training_time() assertEqual(sync_time, 6) assertEqual(async_time, 5) def test_uniform_training_time(self) -> None: """ Test uniform training time Sync and Async should have the same training time if UPR = 1 and duration_min close to duration_mean """ torch.manual_seed(0) num_users = 1000 epochs = 1 users_per_round = 1 duration_mean = 1.00 duration_min = 0.99999 training_dist = PerUserUniformDurationDistribution( **OmegaConf.structured( PerUserUniformDurationDistributionConfig( training_duration_mean=duration_mean, training_duration_min=duration_min, ) ) ) sync_time, async_time = get_training_time( num_users=num_users, users_per_round=users_per_round, epochs=epochs, training_dist=training_dist, ) assertAlmostEqual(sync_time, async_time, delta=1e-3) def test_per_user_half_normal(self) -> None: """ Test half normal training time Sync and Async should have the following training time sync_training_time = async_training_time = num_users * duration_min if UPR = 1 and duraton_std is close to 0 """ torch.manual_seed(0) num_users = 1000 epochs = 1 users_per_round = 1 duration_std = 1e-6 duration_min = 1.0 training_dist = PerUserHalfNormalDurationDistribution( **OmegaConf.structured( PerUserHalfNormalDurationDistributionConfig( training_duration_sd=duration_std, training_duration_min=duration_min, ) ) ) sync_time, async_time = get_training_time( num_users=num_users, users_per_round=users_per_round, epochs=epochs, training_dist=training_dist, ) assertAlmostEqual(sync_time, async_time, delta=1e-3) assertAlmostEqual(sync_time, num_users * duration_min, delta=1e-3) assertAlmostEqual(async_time, num_users * duration_min, delta=1e-3)

ce94faadb39823e06566cf7c720f348a448bb628

py

Python

nicos_demo/vsans1/setups/pressure.py

ebadkamil/nicos

0355a970d627aae170c93292f08f95759c97f3b5

2019-11-06T15:40:36.000Z

2022-01-01T16:23:00.000Z

nicos_demo/vsans1/setups/pressure.py

ebadkamil/nicos

0355a970d627aae170c93292f08f95759c97f3b5

2020-08-18T09:20:26.000Z

2022-02-01T11:07:14.000Z

nicos_demo/vsans1/setups/pressure.py

ISISComputingGroup/nicos

94cb4d172815919481f8c6ee686f21ebb76f2068

2020-01-11T10:52:30.000Z

2022-02-25T12:35:23.000Z

description = 'Vacuum sensors of detector and collimation tube' group = 'lowlevel' devices = dict( det_tube = device('nicos.devices.generic.ManualMove', description = 'pressure detector tube: Tube', abslimits = (0, 1000), fmtstr = '%.4G', pollinterval = 15, maxage = 60, lowlevel = True, unit = 'mbar', ), det_nose = device('nicos.devices.generic.ManualMove', description = 'pressure detector tube: Nose', abslimits = (0, 1000), fmtstr = '%.4G', pollinterval = 15, maxage = 60, lowlevel = True, unit = 'mbar', ), coll_tube = device('nicos.devices.generic.ManualMove', description = 'pressure collimation tube: Tube', abslimits = (0, 1000), fmtstr = '%.4G', pollinterval = 15, maxage = 60, lowlevel = True, unit = 'mbar', ), coll_nose = device('nicos.devices.generic.ManualMove', description = 'pressure collimation tube: Nose', abslimits = (0, 1000), fmtstr = '%.4G', pollinterval = 15, maxage = 60, lowlevel = True, unit = 'mbar', ), coll_pump = device('nicos.devices.generic.ManualMove', description = 'pressure collimation tube: Pump', abslimits = (0, 1000), fmtstr = '%.4G', pollinterval = 15, maxage = 60, lowlevel = True, unit = 'mbar', ), )

0433d8a6fe3adde21da874f20482a09af670d149

py

Python

neurolang/utils/testing/logic.py

hndgzkn/NeuroLang

a3178d47f80bc0941440d9bb09e06c2f217b9566

2021-01-07T02:00:22.000Z

2021-01-07T02:00:22.000Z

neurolang/utils/testing/logic.py

hndgzkn/NeuroLang

a3178d47f80bc0941440d9bb09e06c2f217b9566

2020-11-04T12:51:10.000Z

2022-03-30T13:42:26.000Z

neurolang/utils/testing/logic.py

hndgzkn/NeuroLang

a3178d47f80bc0941440d9bb09e06c2f217b9566

2020-11-04T13:59:35.000Z

2021-03-19T05:28:10.000Z

""" This module exposes utility functions for tests on logic expressions. It should not be used for any other purpose than testing. """ from ...expression_pattern_matching import add_match from ...expression_walker import ExpressionWalker from ...expressions import Definition, Expression from ...logic import NaryLogicOperator __all__ = [ "logic_exp_commutative_equal", ] class LogicCommutativeComparison(Definition): """ Comparison between two expressions that uses the commutativity property of some logic operators such as conjunctions and disjunctions. Parameters ---------- first : Expression First expression. second : Expression Second expression. """ def __init__(self, first, second): self.first = first self.second = second def __repr__(self): return "Compare\n\t{}\nwith\n\t{}".format( repr(self.first), repr(self.second) ) class LogicCommutativeComparator(ExpressionWalker): """ Compare logic expressions using the commutativity property of some logic operators such as conjunctions and disjunctions. """ @add_match( LogicCommutativeComparison(NaryLogicOperator, NaryLogicOperator) ) def nary_logic_operators(self, comp): """ Compare two n-ary logic operators by comparing their two sets of formulas. """ if not isinstance(comp.first, type(comp.second)) or not isinstance( comp.second, type(comp.first) ): return False return self._compare_set_of_formulas(comp.first, comp.second) @add_match(LogicCommutativeComparison(Expression, Expression)) def expressions(self, comp): args1 = comp.first.unapply() args2 = comp.second.unapply() if len(args1) != len(args2): return False for arg1, arg2 in zip(args1, args2): if not self._args_equal(arg1, arg2): return False return True def _args_equal(self, arg1, arg2): if isinstance(arg1, Expression) and isinstance(arg2, Expression): if not self.walk(LogicCommutativeComparison(arg1, arg2)): return False elif arg1 != arg2: return False return True def _compare_set_of_formulas(self, first, second): return all( any( self.walk(LogicCommutativeComparison(f1, f2)) for f2 in second.formulas ) for f1 in first.formulas ) def logic_exp_commutative_equal(exp1, exp2): """ Compare two expressions using the commutativity property of logic operators. The two expressions do not need to be purely equal if the order of the formulas of a commutative logic operator is not the same in the two expressions. Apart from commutative logic operators, the comparison between the two expressions remains the same as the equality comparison. Parameters ---------- exp1 : Expression First expression. exp2 : Expression Second expression. """ if not isinstance(exp1, Expression) or not isinstance(exp2, Expression): raise ValueError("Can only compare expressions") return LogicCommutativeComparator().walk( LogicCommutativeComparison(exp1, exp2) )

15e199b22e341cb7cab56a47709641d697da9e73

py

Python

tests/test_edn.py

ciena-blueplanet/pydatomic

6e49d5a4d9716392eaeb8647e1da21eb300d5380

2015-01-14T16:38:37.000Z

2022-02-24T10:54:53.000Z

tests/test_edn.py

ciena-blueplanet/pydatomic

6e49d5a4d9716392eaeb8647e1da21eb300d5380

tests/test_edn.py

ciena-blueplanet/pydatomic

6e49d5a4d9716392eaeb8647e1da21eb300d5380

2015-01-27T02:53:03.000Z

2021-12-06T11:30:24.000Z

# -*- coding: utf-8 -*- import unittest from datetime import datetime from uuid import UUID from pydatomic import edn class EdnParseTest(unittest.TestCase): def test_all_data(self): data = { '"helloworld"': "helloworld", "23": 23, "23.11": 23.11, "true": True, "false": False, "nil": None, ":hello": ":hello", r'"string\"ing"': 'string"ing', '"string\n"': 'string\n', '[:hello]':(":hello",), '-10.4':-10.4, '"你"': u'你', '\\€': u'€', "[1 2]": (1, 2), "#{true \"hello\" 12}": set([True, "hello", 12]), '#inst "2012-09-10T23:51:55.840-00:00"': datetime(2012, 9, 10, 23, 51, 55, 840000), "(\\a \\b \\c \\d)": ("a", "b", "c", "d"), "{:a 1 :b 2 :c 3 :d 4}": {":a":1, ":b":2, ":c":3,":d":4}, "[1 2 3,4]": (1,2,3,4), "{:a [1 2 3] :b #{23.1 43.1 33.1}}": {":a":(1, 2, 3), ":b":frozenset([23.1, 43.1, 33.1])}, "{:a 1 :b [32 32 43] :c 4}": {":a":1, ":b":(32,32,43), ":c":4}, "\\你": u"你", '#db/fn{:lang "clojure" :code "(map l)"}': {':lang':u'clojure', ':code':u'(map l)'}, "#_ {[#{}] #{[]}} [23[34][32][4]]": (23, (34,), (32,), (4,)), '(:graham/stratton true \n , "A string with \\n \\"s" true #uuid "f81d4fae7dec11d0a76500a0c91e6bf6")': ( u':graham/stratton', True, u'A string with \n "s', True, UUID('f81d4fae-7dec-11d0-a765-00a0c91e6bf6') ), '[\space \\\xE2\x82\xAC [true []] ;true\n[true #inst "2012-09-10T23:39:43.309-00:00" true ""]]': ( ' ', u'\u20ac', (True, ()), (True, datetime(2012, 9, 10, 23, 39, 43, 309000), True, '') ), ' {true false nil [true, ()] 6 {#{nil false} {nil \\newline} }}': { None: (True, ()), True: False, 6: {frozenset([False, None]): {None: '\n'}} }, '[#{6.22e-18, -3.1415, 1} true #graham #{"pie" "chips"} "work"]': ( frozenset([6.22e-18, -3.1415, 1]), True, u'work' ), '(\\a .5)': (u'a', 0.5), '(List #{[123 456 {}] {a 1 b 2 c ({}, [])}})': ( u'List', ((123, 456, {}), {u'a': 1, u'c': ({}, ()), u'b': 2}) ), } for k, v in data.items(): self.assertEqual(edn.loads(k), v) def test_malformed_data(self): '''Verify ValueError() exception raise on malformed data''' data = ["[1 2 3", "@EE", "[@nil tee]"] for d in data: self.assertRaises(ValueError, edn.loads, d) if __name__ == '__main__': unittest.main()

3ad157ffd25a76d559494e3b24db09b4d1ba2ef8

py

Python

ophelia/voicerooms/config_options.py

Bunnic/Ophelia

7a521ca8cef1e067b6e402db16911b554057ce0d

ophelia/voicerooms/config_options.py

Bunnic/Ophelia

7a521ca8cef1e067b6e402db16911b554057ce0d

ophelia/voicerooms/config_options.py

Bunnic/Ophelia

7a521ca8cef1e067b6e402db16911b554057ce0d

""" Voicerooms Configuration module. Contains the options required to set up a voiceroom generator. """ from typing import List from ophelia.output import ConfigItem, disp_str from ophelia.utils.discord_utils import ( extract_category_config, extract_text_config, extract_voice_config ) VOICEROOMS_GENERATOR_CONFIG: List[ConfigItem] = [] for category in ["voice_category", "text_category"]: VOICEROOMS_GENERATOR_CONFIG.append(ConfigItem( category, disp_str(f"voicerooms_generator_{category}"), extract_category_config )) for voice_channel in ["generator_channel", "sample_voice_channel"]: VOICEROOMS_GENERATOR_CONFIG.append(ConfigItem( voice_channel, disp_str(f"voicerooms_generator_{voice_channel}"), extract_voice_config )) for text_channel in ["sample_text_channel", "log_channel"]: VOICEROOMS_GENERATOR_CONFIG.append(ConfigItem( text_channel, disp_str(f"voicerooms_generator_{text_channel}"), extract_text_config ))

17d8273c73888cc04c224429611b598d929de315

py

Python

regression_test_utils/regression_test_utils.py

JivanAmara/test_utils

f077083ebdd8cbcd626ef98994c582cf585fde14

regression_test_utils/regression_test_utils.py

JivanAmara/test_utils

f077083ebdd8cbcd626ef98994c582cf585fde14

regression_test_utils/regression_test_utils.py

JivanAmara/test_utils

f077083ebdd8cbcd626ef98994c582cf585fde14

''' Created on Jul 29, 2015 @author: jivan ''' import jsonpickle, logging # PythonDecorators/my_decorator.py class log_test_case(object): """ @brief: Decorator to log input & output of a method as a jsonpickle'd tuple for easy test creation. Format of the tuple is (<method name>, <args (without self)>, <kwargs>, <result>) @author: Jivan @since: 2015-07-29 @change: 2015-08-03 by Jivan: Added class_name to initialization & logged output. """ def __init__(self, logger, class_name): self.logger = logger self.class_name = class_name def __call__(self, f): method_name = f.__name__ logger = self.logger def wrapped_f(*args, **kwargs): result = f(*args, **kwargs) if logger.getEffectiveLevel() <= logging.DEBUG: args_wo_instance = args[1:] tc = repr(jsonpickle.encode( (method_name, args_wo_instance, kwargs, result), keys=True ) ) logger.debug('Decorator TestCase for "{}.{}":\n\t{}'\ .format(self.class_name, method_name, tc)) return result return wrapped_f

095a8c4c739fb420c16da1e1ae8240d1d72e1c59

py

Python

Python/Assignments/week3.py

aquib-sh/DSA-C-PY

0cc9e874d5310762edd7b6c12dee07e351668c17

Python/Assignments/week3.py

aquib-sh/DSA-C-PY

0cc9e874d5310762edd7b6c12dee07e351668c17

Python/Assignments/week3.py

aquib-sh/DSA-C-PY

0cc9e874d5310762edd7b6c12dee07e351668c17

def remdup(li): length = len(li) holder = [] if length <= 1: return li for i in range(0, length): if i == length-1: holder.append(li[i]) else: if not li[i] in li[(i+1):]: holder.append(li[i]) return holder def splitsum(l): pos = 0 neg = 0 for i in range(0, len(l)): if l[i] < 0: neg += l[i]**3 else: pos += l[i]**2 return [pos, neg] def matrixflip(m, d): nm = [] if d == 'h': for elem in m: nm.append([elem[i] for i in range(len(elem)-1, -1, -1)]) if d == 'v': for i in range(len(m)-1, -1, -1): nm.append(m[i]) return nm

b0e650d33133e60c097f26b1e8671202dfc39782

py

Python

api/streamlit_experiments/s3.py

aws-samples/aws-open-data-analytics-notebooks

680e9689e1b0ceb047960662d220564ae3ecbddb

2019-05-09T20:02:13.000Z

2021-04-03T12:09:18.000Z

api/streamlit_experiments/s3.py

aws-samples/cloud-experiments

680e9689e1b0ceb047960662d220564ae3ecbddb

2021-05-15T21:14:28.000Z

2022-03-31T09:09:11.000Z

api/streamlit_experiments/s3.py

aws-samples/aws-open-data-analytics-notebooks

680e9689e1b0ceb047960662d220564ae3ecbddb

2019-05-20T00:48:04.000Z

2021-04-24T02:28:08.000Z

import streamlit as st import boto3 import botocore import pandas as pd import io s3_client = boto3.client('s3') s3_resource = boto3.resource('s3') def search_buckets(): search = st.text_input('Search S3 bucket in your account', '') response = s3_client.list_buckets() if search: buckets_found = 0 for bucket in response['Buckets']: if search: if search in bucket["Name"]: buckets_found = buckets_found + 1 st.write(f'{bucket["Name"]}') if buckets_found: st.success(f'Listing existing **{buckets_found}** buckets containing **{search}** string') else: st.warning(f'No matching buckets found containing **{search}** string') else: st.info('Provide string to search for listing buckets') def list_bucket_contents(): total_size_gb = 0 total_files = 0 match_size_gb = 0 match_files = 0 bucket = st.text_input('S3 bucket name (public bucket or private to your account)', '') bucket_resource = s3_resource.Bucket(bucket) match = st.text_input('(optional) Filter bucket contents with matching string', '') size_mb = st.text_input('(optional) Match files up to size in MB (0 for all sizes)', '0') if size_mb: size_mb = int(size_mb) else: size_mb = 0 if bucket: for key in bucket_resource.objects.all(): key_size_mb = key.size/1024/1024 total_size_gb += key_size_mb total_files += 1 list_check = False if not match: list_check = True elif match in key.key: list_check = True if list_check and not size_mb: match_files += 1 match_size_gb += key_size_mb st.write(f'{key.key} ({key_size_mb:3.0f}MB)') elif list_check and key_size_mb <= size_mb: match_files += 1 match_size_gb += key_size_mb st.write(f'{key.key} ({key_size_mb:3.0f}MB)') if match: st.info(f'Matched file size is **{match_size_gb/1024:3.1f}GB** with **{match_files}** files') st.success(f'Bucket **{bucket}** total size is **{total_size_gb/1024:3.1f}GB** with **{total_files}** files') else: st.info('Provide bucket name to list contents') def create_bucket(): bucket = st.text_input('S3 bucket name to create', '') if bucket: try: s3_client.create_bucket(Bucket=bucket) except botocore.exceptions.ClientError as e: st.error('Bucket **' + bucket + '** could not be created. ' + e.response['Error']['Message']) return st.success('The S3 bucket **' + bucket + '** successfully created or already exists in your account') else: st.info('Provide unique bucket name to create') def s3_select(): bucket = st.text_input('S3 bucket name', '') csv = st.text_input('CSV File path and name', '') st.write("Example: `SELECT * FROM s3object s LIMIT 5`") sql = st.text_area('SQL statement', '') if bucket and csv and sql: s3_select_results = s3_client.select_object_content( Bucket=bucket, Key=csv, Expression=sql, ExpressionType='SQL', InputSerialization={'CSV': {"FileHeaderInfo": "Use"}}, OutputSerialization={'JSON': {}}, ) for event in s3_select_results['Payload']: if 'Records' in event: df = pd.read_json(io.StringIO(event['Records']['Payload'].decode('utf-8')), lines=True) elif 'Stats' in event: st.write(f"Scanned: {int(event['Stats']['Details']['BytesScanned'])/1024/1024:5.2f}MB") st.write(f"Processed: {int(event['Stats']['Details']['BytesProcessed'])/1024/1024:5.2f}MB") st.write(f"Returned: {int(event['Stats']['Details']['BytesReturned'])/1024/1024:5.2f}MB") st.write(df) else: st.info('Provide S3 bucket, CSV file name, and SQL statement')

0e506a262abbfab83584566410dfe7ec665436a4

py

Python

tests/unit/bokeh/models/test_mappers.py

tcmetzger/bokeh

5daff21bfb7e10b69ff9aa2f35eb506777a38264

tests/unit/bokeh/models/test_mappers.py

tcmetzger/bokeh

5daff21bfb7e10b69ff9aa2f35eb506777a38264

tests/unit/bokeh/models/test_mappers.py

tcmetzger/bokeh

5daff21bfb7e10b69ff9aa2f35eb506777a38264

#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2020, Anaconda, Inc., and Bokeh Contributors. # All rights reserved. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Boilerplate #----------------------------------------------------------------------------- import pytest ; pytest #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Bokeh imports from _util_models import check_properties_existence from bokeh.palettes import Spectral6 # Module under test import bokeh.models.mappers as bmm # isort:skip #----------------------------------------------------------------------------- # Setup #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # General API #----------------------------------------------------------------------------- class Test_CategoricalColorMapper: def test_basic(self) -> None: mapper = bmm.CategoricalColorMapper() check_properties_existence(mapper, [ "factors", "palette", "start", "end", "nan_color"], ) def test_warning_with_short_palette(self, recwarn) -> None: bmm.CategoricalColorMapper(factors=["a", "b", "c"], palette=["red", "green"]) assert len(recwarn) == 1 def test_no_warning_with_long_palette(self, recwarn) -> None: bmm.CategoricalColorMapper(factors=["a", "b", "c"], palette=["red", "green", "orange", "blue"]) assert len(recwarn) == 0 def test_with_pandas_index(self, pd) -> None: fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] years = ['2015', '2016', '2017'] data = {'2015' : [2, 1, 4, 3, 2, 4], '2016' : [5, 3, 3, 2, 4, 6], '2017' : [3, 2, 4, 4, 5, 3]} df = pd.DataFrame(data, index=fruits) fruits = df.index years = df.columns m = bmm.CategoricalColorMapper(palette=Spectral6, factors=years, start=1, end=2) assert list(m.factors) == list(years) assert isinstance(m.factors, pd.Index) class Test_CategoricalPatternMapper: def test_basic(self) -> None: mapper = bmm.CategoricalPatternMapper() check_properties_existence(mapper, [ "factors", "patterns", "start", "end", "default_value"], ) class Test_CategoricalMarkerMapper: def test_basic(self) -> None: mapper = bmm.CategoricalMarkerMapper() check_properties_existence(mapper, [ "factors", "markers", "start", "end", "default_value"], ) class Test_LinearColorMapper: def test_basic(self) -> None: mapper = bmm.LinearColorMapper() check_properties_existence(mapper, [ "palette", "low", "high", "low_color", "high_color", "nan_color"], ) class Test_LogColorMapper: def test_basic(self) -> None: mapper = bmm.LogColorMapper() check_properties_existence(mapper, [ "palette", "low", "high", "low_color", "high_color", "nan_color"], ) #----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------

a4e32ef9c8adc091f8f4325ae63ce3419162c50b

py

Python

genedisco/evaluation/hitratio.py

genedisco/genedisco

26b7ce93b222fd80e914f2f2236969b356e7f701

2022-02-07T13:19:02.000Z

2022-03-25T03:38:15.000Z

genedisco/evaluation/hitratio.py

genedisco/genedisco

26b7ce93b222fd80e914f2f2236969b356e7f701

2022-02-05T19:12:30.000Z

2022-03-18T09:12:35.000Z

genedisco/evaluation/hitratio.py

genedisco/genedisco

26b7ce93b222fd80e914f2f2236969b356e7f701

2022-02-07T16:14:54.000Z

2022-03-18T22:26:31.000Z

""" Copyright (C) 2022 Arash Mehrjou, GlaxoSmithKline plc Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import os import pickle import numpy as np from typing import Optional, AnyStr from slingpy.evaluation.metrics.abstract_metric import AbstractMetric class HitRatio(AbstractMetric): """ A metric to measure the ratio of the top mover genes selected by the acquisition function. """ def get_abbreviation(self) -> AnyStr: return "HR" @staticmethod def evaluate(top_movers_filepath:AnyStr, super_dir_to_cycle_dirs: AnyStr) -> np.ndarray: with open(top_movers_filepath, "rb") as f: top_mover_indices = pickle.load(f) top_mover_set = set(top_mover_indices) num_top_hits = len(top_mover_indices) num_AL_cycles = get_num_AL_cycles(super_dir_to_cycle_dirs) selected_indices_per_cycle = get_cumulative_selected_indices( super_dir_to_cycle_dirs) cumulative_top_hit_ratio = [] for c in range(num_AL_cycles): selected_indices = selected_indices_per_cycle[c] num_of_hits = num_top_hits - len(top_mover_set - set(selected_indices)) cumulative_top_hit_ratio.append(num_of_hits/num_top_hits) return cumulative_top_hit_ratio[-1] # returns the top hit ratio of the current cycle def get_cumulative_selected_indices(super_dir_to_cycle_dirs: AnyStr): """ Get a list of selected indiced at cycles of active learning. Args: super_dir_to_cycle_dirs: The dir in which the cycle dirs are saved. seed: The seed of the experiment. Return a concatenated list of the saved selected indices so far. """ num_AL_cycles = get_num_AL_cycles(super_dir_to_cycle_dirs) selected_indices_per_cycles = [] for c in range(num_AL_cycles): filename = os.path.join(super_dir_to_cycle_dirs, "cycle_" + str(c), "selected_indices.pickle") with open(filename, "rb") as f: selected_indices = pickle.load(f) # selected_indices = [x.decode("utf-8") for x in selected_indices] # Uncomment this line if the stored Gene names are byte strings. selected_indices_per_cycles.append(selected_indices) return selected_indices_per_cycles def get_num_AL_cycles(super_dir_to_cycle_dirs: AnyStr): """Get the number of cycles stored in the provided dir. """ all_subdirs = list(os.walk(super_dir_to_cycle_dirs))[0][1] cycle_subdirs = [folder_name for folder_name in all_subdirs if folder_name.startswith("cycle")] num_AL_cycles = len(cycle_subdirs) return num_AL_cycles

12fe26a4af0f0a8758ed418b3d06127b37fa4ad8

py

Python

manager/projects/migrations/0016_auto_20201016_0326.py

jlbrewe/hub

c737669e6493ad17536eaa240bed3394b20c6b7d

2016-03-26T12:08:04.000Z

2021-12-24T14:48:32.000Z

manager/projects/migrations/0016_auto_20201016_0326.py

jlbrewe/hub

c737669e6493ad17536eaa240bed3394b20c6b7d

2016-03-23T04:56:50.000Z

2022-03-28T02:27:58.000Z

manager/projects/migrations/0016_auto_20201016_0326.py

jlbrewe/hub

c737669e6493ad17536eaa240bed3394b20c6b7d

2016-07-14T17:04:20.000Z

2021-07-01T16:19:09.000Z

# Generated by Django 3.1.2 on 2020-10-16 03:26 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('projects', '0015_auto_20201007_0337'), ] operations = [ migrations.RemoveField( model_name='googledrivesource', name='folder_id', ), migrations.AddField( model_name='googledrivesource', name='google_id', field=models.TextField(default='', help_text='The id of the file or folder.'), preserve_default=False, ), migrations.AddField( model_name='googledrivesource', name='kind', field=models.CharField(choices=[('file', 'File'), ('folder', 'Folder')], default='folder', help_text='The kind of Google Drive resource: file or folder.', max_length=16), preserve_default=False, ), ]

a201ac4aa8fba548a2db478ed74b26f9d6a8d17b

py

Python

mvpnet/train_3d.py

shnhrtkyk/mvpnet

cadf636749b5ee6e73e96ff68e4b32728088decd

2020-01-12T20:30:34.000Z

2022-03-15T06:37:09.000Z

mvpnet/train_3d.py

jtpils/mvpnet

cadf636749b5ee6e73e96ff68e4b32728088decd

2020-02-14T17:26:56.000Z

2021-08-30T07:54:47.000Z

mvpnet/train_3d.py

jtpils/mvpnet

cadf636749b5ee6e73e96ff68e4b32728088decd

2020-01-13T05:59:15.000Z

2021-11-02T03:00:22.000Z

#!/usr/bin/env python import os import os.path as osp import sys import argparse import logging import time import socket import warnings import open3d # import before torch import torch from torch import nn from torch.utils.tensorboard import SummaryWriter # Assume that the script is run at the root directory _ROOT_DIR = os.path.abspath(osp.dirname(__file__) + '/..') sys.path.insert(0, _ROOT_DIR) from common.solver.build import build_optimizer, build_scheduler from common.nn.freezer import Freezer from common.utils.checkpoint import CheckpointerV2 from common.utils.logger import setup_logger from common.utils.metric_logger import MetricLogger from common.utils.torch_util import set_random_seed from common.utils.sampler import IterationBasedBatchSampler from mvpnet.models.build import build_model_sem_seg_3d from mvpnet.data.build import build_dataloader def parse_args(): parser = argparse.ArgumentParser(description='PyTorch 3D Deep Learning Training') parser.add_argument( '--cfg', dest='config_file', default='', metavar='FILE', help='path to config file', type=str, ) parser.add_argument( 'opts', help='Modify config options using the command-line', default=None, nargs=argparse.REMAINDER, ) args = parser.parse_args() return args def train(cfg, output_dir='', run_name=''): # ---------------------------------------------------------------------------- # # Build models, optimizer, scheduler, checkpointer, etc. # It is recommended not to modify this section. # ---------------------------------------------------------------------------- # logger = logging.getLogger('mvpnet.train') # build model set_random_seed(cfg.RNG_SEED) model, loss_fn, train_metric, val_metric = build_model_sem_seg_3d(cfg) logger.info('Build model:\n{}'.format(str(model))) num_params = sum(param.numel() for param in model.parameters()) print('#Parameters: {:.2e}'.format(num_params)) num_gpus = torch.cuda.device_count() if num_gpus > 1: model = nn.DataParallel(model).cuda() elif num_gpus == 1: model = model.cuda() else: raise NotImplementedError('Not support cpu training now.') # build optimizer # model_cfg = cfg.MODEL[cfg.MODEL.TYPE] optimizer = build_optimizer(cfg, model) # build lr scheduler scheduler = build_scheduler(cfg, optimizer) # build checkpointer # Note that checkpointer will load state_dict of model, optimizer and scheduler. checkpointer = CheckpointerV2(model, optimizer=optimizer, scheduler=scheduler, save_dir=output_dir, logger=logger, max_to_keep=cfg.TRAIN.MAX_TO_KEEP) checkpoint_data = checkpointer.load(cfg.RESUME_PATH, resume=cfg.AUTO_RESUME, resume_states=cfg.RESUME_STATES) ckpt_period = cfg.TRAIN.CHECKPOINT_PERIOD # build freezer if cfg.TRAIN.FROZEN_PATTERNS: freezer = Freezer(model, cfg.TRAIN.FROZEN_PATTERNS) freezer.freeze(verbose=True) # sanity check else: freezer = None # build data loader # Reset the random seed again in case the initialization of models changes the random state. set_random_seed(cfg.RNG_SEED) train_dataloader = build_dataloader(cfg, mode='train') val_period = cfg.VAL.PERIOD val_dataloader = build_dataloader(cfg, mode='val') if val_period > 0 else None # build tensorboard logger (optionally by comment) if output_dir: tb_dir = osp.join(output_dir, 'tb.{:s}'.format(run_name)) summary_writier = SummaryWriter(tb_dir) else: summary_writier = None # ---------------------------------------------------------------------------- # # Train # Customization begins here. # ---------------------------------------------------------------------------- # max_iteration = cfg.SCHEDULER.MAX_ITERATION start_iteration = checkpoint_data.get('iteration', 0) best_metric_name = 'best_{}'.format(cfg.VAL.METRIC) best_metric = checkpoint_data.get(best_metric_name, None) logger.info('Start training from iteration {}'.format(start_iteration)) # add metrics if not isinstance(train_metric, (list, tuple)): train_metric = [train_metric] if not isinstance(val_metric, (list, tuple)): val_metric = [val_metric] train_metric_logger = MetricLogger(delimiter=' ') train_metric_logger.add_meters(train_metric) val_metric_logger = MetricLogger(delimiter=' ') val_metric_logger.add_meters(val_metric) # wrap the dataloader batch_sampler = train_dataloader.batch_sampler train_dataloader.batch_sampler = IterationBasedBatchSampler(batch_sampler, max_iteration, start_iteration) def setup_train(): # set training mode model.train() loss_fn.train() # freeze parameters/modules optionally if freezer is not None: freezer.freeze() # reset metric train_metric_logger.reset() def setup_validate(): # set evaluate mode model.eval() loss_fn.eval() # reset metric val_metric_logger.reset() setup_train() end = time.time() for iteration, data_batch in enumerate(train_dataloader, start_iteration): data_time = time.time() - end # copy data from cpu to gpu data_batch = {k: v.cuda(non_blocking=True) for k, v in data_batch.items()} # forward preds = model(data_batch) # update losses optimizer.zero_grad() loss_dict = loss_fn(preds, data_batch) total_loss = sum(loss_dict.values()) # It is slightly faster to update metrics and meters before backward with torch.no_grad(): train_metric_logger.update(loss=total_loss, **loss_dict) for metric in train_metric: metric.update_dict(preds, data_batch) # backward total_loss.backward() if cfg.OPTIMIZER.MAX_GRAD_NORM > 0: # CAUTION: built-in clip_grad_norm_ clips the total norm. nn.utils.clip_grad_norm_(model.parameters(), max_norm=cfg.OPTIMIZER.MAX_GRAD_NORM) optimizer.step() batch_time = time.time() - end train_metric_logger.update(time=batch_time, data=data_time) cur_iter = iteration + 1 # log if cur_iter == 1 or (cfg.TRAIN.LOG_PERIOD > 0 and cur_iter % cfg.TRAIN.LOG_PERIOD) == 0: logger.info( train_metric_logger.delimiter.join( [ 'iter: {iter:4d}', '{meters}', 'lr: {lr:.2e}', 'max mem: {memory:.0f}', ] ).format( iter=cur_iter, meters=str(train_metric_logger), lr=optimizer.param_groups[0]['lr'], memory=torch.cuda.max_memory_allocated() / (1024.0 ** 2), ) ) # summary if summary_writier is not None and cfg.TRAIN.SUMMARY_PERIOD > 0 and cur_iter % cfg.TRAIN.SUMMARY_PERIOD == 0: keywords = ('loss', 'acc', 'iou') for name, meter in train_metric_logger.meters.items(): if all(k not in name for k in keywords): continue summary_writier.add_scalar('train/' + name, meter.global_avg, global_step=cur_iter) # checkpoint if (ckpt_period > 0 and cur_iter % ckpt_period == 0) or cur_iter == max_iteration: checkpoint_data['iteration'] = cur_iter checkpoint_data[best_metric_name] = best_metric checkpointer.save('model_{:06d}'.format(cur_iter), **checkpoint_data) # ---------------------------------------------------------------------------- # # validate for one epoch # ---------------------------------------------------------------------------- # if val_period > 0 and (cur_iter % val_period == 0 or cur_iter == max_iteration): start_time_val = time.time() setup_validate() end = time.time() with torch.no_grad(): for iteration_val, data_batch in enumerate(val_dataloader): data_time = time.time() - end # copy data from cpu to gpu data_batch = {k: v.cuda(non_blocking=True) for k, v in data_batch.items()} # forward preds = model(data_batch) # update losses and metrics loss_dict = loss_fn(preds, data_batch) total_loss = sum(loss_dict.values()) # update metrics and meters val_metric_logger.update(loss=total_loss, **loss_dict) for metric in val_metric: metric.update_dict(preds, data_batch) batch_time = time.time() - end val_metric_logger.update(time=batch_time, data=data_time) end = time.time() if cfg.VAL.LOG_PERIOD > 0 and iteration_val % cfg.VAL.LOG_PERIOD == 0: logger.info( val_metric_logger.delimiter.join( [ 'iter: {iter:4d}', '{meters}', 'max mem: {memory:.0f}', ] ).format( iter=iteration, meters=str(val_metric_logger), memory=torch.cuda.max_memory_allocated() / (1024.0 ** 2), ) ) epoch_time_val = time.time() - start_time_val logger.info('Iteration[{}]-Val {} total_time: {:.2f}s'.format( cur_iter, val_metric_logger.summary_str, epoch_time_val)) # summary if summary_writier is not None: keywords = ('loss', 'acc', 'iou') for name, meter in val_metric_logger.meters.items(): if all(k not in name for k in keywords): continue summary_writier.add_scalar('val/' + name, meter.global_avg, global_step=cur_iter) # best validation if cfg.VAL.METRIC in val_metric_logger.meters: cur_metric = val_metric_logger.meters[cfg.VAL.METRIC].global_avg if best_metric is None \ or ('loss' not in cfg.VAL.METRIC and cur_metric > best_metric) \ or ('loss' in cfg.VAL.METRIC and cur_metric < best_metric): best_metric = cur_metric checkpoint_data['iteration'] = cur_iter checkpoint_data[best_metric_name] = best_metric checkpointer.save('model_best', tag=False, **checkpoint_data) # restore training setup_train() # since pytorch v1.1.0, lr_scheduler is called after optimization. if scheduler is not None: scheduler.step() end = time.time() logger.info('Best val-{} = {}'.format(cfg.VAL.METRIC, best_metric)) return model def main(): args = parse_args() # load the configuration # import on-the-fly to avoid overwriting cfg from common.config import purge_cfg from mvpnet.config.sem_seg_3d import cfg cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) purge_cfg(cfg) cfg.freeze() output_dir = cfg.OUTPUT_DIR # replace '@' with config path if output_dir: config_path = osp.splitext(args.config_file)[0] output_dir = output_dir.replace('@', config_path.replace('configs', 'outputs')) if osp.isdir(output_dir): warnings.warn('Output directory exists.') os.makedirs(output_dir, exist_ok=True) # run name timestamp = time.strftime('%m-%d_%H-%M-%S') hostname = socket.gethostname() run_name = '{:s}.{:s}'.format(timestamp, hostname) logger = setup_logger('mvpnet', output_dir, comment='train.{:s}'.format(run_name)) logger.info('{:d} GPUs available'.format(torch.cuda.device_count())) logger.info(args) from common.utils.misc import collect_env_info logger.info('Collecting env info (might take some time)\n' + collect_env_info()) logger.info('Loaded configuration file {:s}'.format(args.config_file)) logger.info('Running with config:\n{}'.format(cfg)) assert cfg.TASK == 'sem_seg_3d' train(cfg, output_dir, run_name) if __name__ == '__main__': main()

1b9c8afc1c1891eb64e0bd29e4e9910221cffe1d

py

Python

src/v5.1/resources/swagger_client/api/learning_standard_equivalence_associations_api.py

xmarcosx/edfi-notebook

0564ebdf1d0f45a9d25056e7e61369f0a837534d

2021-04-27T17:18:17.000Z

2021-04-27T19:14:39.000Z

src/v5.1/resources/swagger_client/api/learning_standard_equivalence_associations_api.py

xmarcosx/edfi-notebook

0564ebdf1d0f45a9d25056e7e61369f0a837534d

src/v5.1/resources/swagger_client/api/learning_standard_equivalence_associations_api.py

xmarcosx/edfi-notebook

0564ebdf1d0f45a9d25056e7e61369f0a837534d

2022-01-06T09:43:11.000Z

2022-01-06T09:43:11.000Z

# coding: utf-8 """ Ed-Fi Operational Data Store API The Ed-Fi ODS / API enables applications to read and write education data stored in an Ed-Fi ODS through a secure REST interface. *** > *Note: Consumers of ODS / API information should sanitize all data for display and storage. The ODS / API provides reasonable safeguards against cross-site scripting attacks and other malicious content, but the platform does not and cannot guarantee that the data it contains is free of all potentially harmful content.* *** # noqa: E501 OpenAPI spec version: 3 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from swagger_client.api_client import ApiClient class LearningStandardEquivalenceAssociationsApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def delete_learning_standard_equivalence_association_by_id(self, id, **kwargs): # noqa: E501 """Deletes an existing resource using the resource identifier. # noqa: E501 The DELETE operation is used to delete an existing resource by identifier. If the resource doesn't exist, an error will result (the resource will not be found). # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_learning_standard_equivalence_association_by_id(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: A resource identifier that uniquely identifies the resource. (required) :param str if_match: The ETag header value used to prevent the DELETE from removing a resource modified by another consumer. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_learning_standard_equivalence_association_by_id_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.delete_learning_standard_equivalence_association_by_id_with_http_info(id, **kwargs) # noqa: E501 return data def delete_learning_standard_equivalence_association_by_id_with_http_info(self, id, **kwargs): # noqa: E501 """Deletes an existing resource using the resource identifier. # noqa: E501 The DELETE operation is used to delete an existing resource by identifier. If the resource doesn't exist, an error will result (the resource will not be found). # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_learning_standard_equivalence_association_by_id_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: A resource identifier that uniquely identifies the resource. (required) :param str if_match: The ETag header value used to prevent the DELETE from removing a resource modified by another consumer. :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'if_match'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_learning_standard_equivalence_association_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in params or params['id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `id` when calling `delete_learning_standard_equivalence_association_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} if 'if_match' in params: header_params['If-Match'] = params['if_match'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['oauth2_client_credentials'] # noqa: E501 return self.api_client.call_api( '/ed-fi/learningStandardEquivalenceAssociations/{id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def deletes_learning_standard_equivalence_associations(self, **kwargs): # noqa: E501 """Retrieves deleted resources based on change version. # noqa: E501 The DELETES operation is used to retrieve deleted resources. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.deletes_learning_standard_equivalence_associations(async_req=True) >>> result = thread.get() :param async_req bool :param int offset: Indicates how many items should be skipped before returning results. :param int limit: Indicates the maximum number of items that should be returned in the results. :param int min_change_version: Used in synchronization to set sequence minimum ChangeVersion :param int max_change_version: Used in synchronization to set sequence maximum ChangeVersion :return: list[EdFiLearningStandardEquivalenceAssociation] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.deletes_learning_standard_equivalence_associations_with_http_info(**kwargs) # noqa: E501 else: (data) = self.deletes_learning_standard_equivalence_associations_with_http_info(**kwargs) # noqa: E501 return data def deletes_learning_standard_equivalence_associations_with_http_info(self, **kwargs): # noqa: E501 """Retrieves deleted resources based on change version. # noqa: E501 The DELETES operation is used to retrieve deleted resources. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.deletes_learning_standard_equivalence_associations_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param int offset: Indicates how many items should be skipped before returning results. :param int limit: Indicates the maximum number of items that should be returned in the results. :param int min_change_version: Used in synchronization to set sequence minimum ChangeVersion :param int max_change_version: Used in synchronization to set sequence maximum ChangeVersion :return: list[EdFiLearningStandardEquivalenceAssociation] If the method is called asynchronously, returns the request thread. """ all_params = ['offset', 'limit', 'min_change_version', 'max_change_version'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method deletes_learning_standard_equivalence_associations" % key ) params[key] = val del params['kwargs'] if self.api_client.client_side_validation and ('limit' in params and params['limit'] > 500): # noqa: E501 raise ValueError("Invalid value for parameter `limit` when calling `deletes_learning_standard_equivalence_associations`, must be a value less than or equal to `500`") # noqa: E501 if self.api_client.client_side_validation and ('limit' in params and params['limit'] < 0): # noqa: E501 raise ValueError("Invalid value for parameter `limit` when calling `deletes_learning_standard_equivalence_associations`, must be a value greater than or equal to `0`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'offset' in params: query_params.append(('offset', params['offset'])) # noqa: E501 if 'limit' in params: query_params.append(('limit', params['limit'])) # noqa: E501 if 'min_change_version' in params: query_params.append(('minChangeVersion', params['min_change_version'])) # noqa: E501 if 'max_change_version' in params: query_params.append(('maxChangeVersion', params['max_change_version'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['oauth2_client_credentials'] # noqa: E501 return self.api_client.call_api( '/ed-fi/learningStandardEquivalenceAssociations/deletes', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EdFiLearningStandardEquivalenceAssociation]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_learning_standard_equivalence_associations(self, **kwargs): # noqa: E501 """Retrieves specific resources using the resource's property values (using the \"Get\" pattern). # noqa: E501 This GET operation provides access to resources using the \"Get\" search pattern. The values of any properties of the resource that are specified will be used to return all matching results (if it exists). # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_learning_standard_equivalence_associations(async_req=True) >>> result = thread.get() :param async_req bool :param int offset: Indicates how many items should be skipped before returning results. :param int limit: Indicates the maximum number of items that should be returned in the results. :param int min_change_version: Used in synchronization to set sequence minimum ChangeVersion :param int max_change_version: Used in synchronization to set sequence maximum ChangeVersion :param bool total_count: Indicates if the total number of items available should be returned in the 'Total-Count' header of the response. If set to false, 'Total-Count' header will not be provided. :param str namespace: The namespace of the organization that has created and owns the association. :param str source_learning_standard_id: The identifier for the specific learning standard (e.g., 111.15.3.1.A). :param str target_learning_standard_id: The identifier for the specific learning standard (e.g., 111.15.3.1.A). :param str learning_standard_equivalence_strength_descriptor: A measure that indicates the strength or quality of the equivalence relationship. :param date effective_date: The date that the association is considered to be applicable or effective. :param str id: :param str learning_standard_equivalence_strength_description: Captures supplemental information on the relationship. Recommended for use only when the match is partial. :return: list[EdFiLearningStandardEquivalenceAssociation] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_learning_standard_equivalence_associations_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_learning_standard_equivalence_associations_with_http_info(**kwargs) # noqa: E501 return data def get_learning_standard_equivalence_associations_with_http_info(self, **kwargs): # noqa: E501 """Retrieves specific resources using the resource's property values (using the \"Get\" pattern). # noqa: E501 This GET operation provides access to resources using the \"Get\" search pattern. The values of any properties of the resource that are specified will be used to return all matching results (if it exists). # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_learning_standard_equivalence_associations_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param int offset: Indicates how many items should be skipped before returning results. :param int limit: Indicates the maximum number of items that should be returned in the results. :param int min_change_version: Used in synchronization to set sequence minimum ChangeVersion :param int max_change_version: Used in synchronization to set sequence maximum ChangeVersion :param bool total_count: Indicates if the total number of items available should be returned in the 'Total-Count' header of the response. If set to false, 'Total-Count' header will not be provided. :param str namespace: The namespace of the organization that has created and owns the association. :param str source_learning_standard_id: The identifier for the specific learning standard (e.g., 111.15.3.1.A). :param str target_learning_standard_id: The identifier for the specific learning standard (e.g., 111.15.3.1.A). :param str learning_standard_equivalence_strength_descriptor: A measure that indicates the strength or quality of the equivalence relationship. :param date effective_date: The date that the association is considered to be applicable or effective. :param str id: :param str learning_standard_equivalence_strength_description: Captures supplemental information on the relationship. Recommended for use only when the match is partial. :return: list[EdFiLearningStandardEquivalenceAssociation] If the method is called asynchronously, returns the request thread. """ all_params = ['offset', 'limit', 'min_change_version', 'max_change_version', 'total_count', 'namespace', 'source_learning_standard_id', 'target_learning_standard_id', 'learning_standard_equivalence_strength_descriptor', 'effective_date', 'id', 'learning_standard_equivalence_strength_description'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_learning_standard_equivalence_associations" % key ) params[key] = val del params['kwargs'] if self.api_client.client_side_validation and ('limit' in params and params['limit'] > 500): # noqa: E501 raise ValueError("Invalid value for parameter `limit` when calling `get_learning_standard_equivalence_associations`, must be a value less than or equal to `500`") # noqa: E501 if self.api_client.client_side_validation and ('limit' in params and params['limit'] < 0): # noqa: E501 raise ValueError("Invalid value for parameter `limit` when calling `get_learning_standard_equivalence_associations`, must be a value greater than or equal to `0`") # noqa: E501 if self.api_client.client_side_validation and ('namespace' in params and len(params['namespace']) > 255): raise ValueError("Invalid value for parameter `namespace` when calling `get_learning_standard_equivalence_associations`, length must be less than or equal to `255`") # noqa: E501 if self.api_client.client_side_validation and ('source_learning_standard_id' in params and len(params['source_learning_standard_id']) > 60): raise ValueError("Invalid value for parameter `source_learning_standard_id` when calling `get_learning_standard_equivalence_associations`, length must be less than or equal to `60`") # noqa: E501 if self.api_client.client_side_validation and ('target_learning_standard_id' in params and len(params['target_learning_standard_id']) > 60): raise ValueError("Invalid value for parameter `target_learning_standard_id` when calling `get_learning_standard_equivalence_associations`, length must be less than or equal to `60`") # noqa: E501 if self.api_client.client_side_validation and ('learning_standard_equivalence_strength_descriptor' in params and len(params['learning_standard_equivalence_strength_descriptor']) > 306): raise ValueError("Invalid value for parameter `learning_standard_equivalence_strength_descriptor` when calling `get_learning_standard_equivalence_associations`, length must be less than or equal to `306`") # noqa: E501 if self.api_client.client_side_validation and ('learning_standard_equivalence_strength_description' in params and len(params['learning_standard_equivalence_strength_description']) > 255): raise ValueError("Invalid value for parameter `learning_standard_equivalence_strength_description` when calling `get_learning_standard_equivalence_associations`, length must be less than or equal to `255`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'offset' in params: query_params.append(('offset', params['offset'])) # noqa: E501 if 'limit' in params: query_params.append(('limit', params['limit'])) # noqa: E501 if 'min_change_version' in params: query_params.append(('minChangeVersion', params['min_change_version'])) # noqa: E501 if 'max_change_version' in params: query_params.append(('maxChangeVersion', params['max_change_version'])) # noqa: E501 if 'total_count' in params: query_params.append(('totalCount', params['total_count'])) # noqa: E501 if 'namespace' in params: query_params.append(('namespace', params['namespace'])) # noqa: E501 if 'source_learning_standard_id' in params: query_params.append(('sourceLearningStandardId', params['source_learning_standard_id'])) # noqa: E501 if 'target_learning_standard_id' in params: query_params.append(('targetLearningStandardId', params['target_learning_standard_id'])) # noqa: E501 if 'learning_standard_equivalence_strength_descriptor' in params: query_params.append(('learningStandardEquivalenceStrengthDescriptor', params['learning_standard_equivalence_strength_descriptor'])) # noqa: E501 if 'effective_date' in params: query_params.append(('effectiveDate', params['effective_date'])) # noqa: E501 if 'id' in params: query_params.append(('id', params['id'])) # noqa: E501 if 'learning_standard_equivalence_strength_description' in params: query_params.append(('learningStandardEquivalenceStrengthDescription', params['learning_standard_equivalence_strength_description'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['oauth2_client_credentials'] # noqa: E501 return self.api_client.call_api( '/ed-fi/learningStandardEquivalenceAssociations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EdFiLearningStandardEquivalenceAssociation]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_learning_standard_equivalence_associations_by_id(self, id, **kwargs): # noqa: E501 """Retrieves a specific resource using the resource's identifier (using the \"Get By Id\" pattern). # noqa: E501 This GET operation retrieves a resource by the specified resource identifier. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_learning_standard_equivalence_associations_by_id(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: A resource identifier that uniquely identifies the resource. (required) :param str if_none_match: The previously returned ETag header value, used here to prevent the unnecessary data transfer of an unchanged resource. :return: EdFiLearningStandardEquivalenceAssociation If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_learning_standard_equivalence_associations_by_id_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.get_learning_standard_equivalence_associations_by_id_with_http_info(id, **kwargs) # noqa: E501 return data def get_learning_standard_equivalence_associations_by_id_with_http_info(self, id, **kwargs): # noqa: E501 """Retrieves a specific resource using the resource's identifier (using the \"Get By Id\" pattern). # noqa: E501 This GET operation retrieves a resource by the specified resource identifier. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_learning_standard_equivalence_associations_by_id_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: A resource identifier that uniquely identifies the resource. (required) :param str if_none_match: The previously returned ETag header value, used here to prevent the unnecessary data transfer of an unchanged resource. :return: EdFiLearningStandardEquivalenceAssociation If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'if_none_match'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_learning_standard_equivalence_associations_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in params or params['id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `id` when calling `get_learning_standard_equivalence_associations_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} if 'if_none_match' in params: header_params['If-None-Match'] = params['if_none_match'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['oauth2_client_credentials'] # noqa: E501 return self.api_client.call_api( '/ed-fi/learningStandardEquivalenceAssociations/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EdFiLearningStandardEquivalenceAssociation', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def post_learning_standard_equivalence_association(self, learning_standard_equivalence_association, **kwargs): # noqa: E501 """Creates or updates resources based on the natural key values of the supplied resource. # noqa: E501 The POST operation can be used to create or update resources. In database terms, this is often referred to as an \"upsert\" operation (insert + update). Clients should NOT include the resource \"id\" in the JSON body because it will result in an error (you must use a PUT operation to update a resource by \"id\"). The web service will identify whether the resource already exists based on the natural key values provided, and update or create the resource appropriately. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.post_learning_standard_equivalence_association(learning_standard_equivalence_association, async_req=True) >>> result = thread.get() :param async_req bool :param EdFiLearningStandardEquivalenceAssociation learning_standard_equivalence_association: The JSON representation of the \"learningStandardEquivalenceAssociation\" resource to be created or updated. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.post_learning_standard_equivalence_association_with_http_info(learning_standard_equivalence_association, **kwargs) # noqa: E501 else: (data) = self.post_learning_standard_equivalence_association_with_http_info(learning_standard_equivalence_association, **kwargs) # noqa: E501 return data def post_learning_standard_equivalence_association_with_http_info(self, learning_standard_equivalence_association, **kwargs): # noqa: E501 """Creates or updates resources based on the natural key values of the supplied resource. # noqa: E501 The POST operation can be used to create or update resources. In database terms, this is often referred to as an \"upsert\" operation (insert + update). Clients should NOT include the resource \"id\" in the JSON body because it will result in an error (you must use a PUT operation to update a resource by \"id\"). The web service will identify whether the resource already exists based on the natural key values provided, and update or create the resource appropriately. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.post_learning_standard_equivalence_association_with_http_info(learning_standard_equivalence_association, async_req=True) >>> result = thread.get() :param async_req bool :param EdFiLearningStandardEquivalenceAssociation learning_standard_equivalence_association: The JSON representation of the \"learningStandardEquivalenceAssociation\" resource to be created or updated. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['learning_standard_equivalence_association'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method post_learning_standard_equivalence_association" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'learning_standard_equivalence_association' is set if self.api_client.client_side_validation and ('learning_standard_equivalence_association' not in params or params['learning_standard_equivalence_association'] is None): # noqa: E501 raise ValueError("Missing the required parameter `learning_standard_equivalence_association` when calling `post_learning_standard_equivalence_association`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'learning_standard_equivalence_association' in params: body_params = params['learning_standard_equivalence_association'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['oauth2_client_credentials'] # noqa: E501 return self.api_client.call_api( '/ed-fi/learningStandardEquivalenceAssociations', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def put_learning_standard_equivalence_association(self, id, learning_standard_equivalence_association, **kwargs): # noqa: E501 """Updates or creates a resource based on the resource identifier. # noqa: E501 The PUT operation is used to update or create a resource by identifier. If the resource doesn't exist, the resource will be created using that identifier. Additionally, natural key values cannot be changed using this operation, and will not be modified in the database. If the resource \"id\" is provided in the JSON body, it will be ignored as well. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.put_learning_standard_equivalence_association(id, learning_standard_equivalence_association, async_req=True) >>> result = thread.get() :param async_req bool :param str id: A resource identifier that uniquely identifies the resource. (required) :param EdFiLearningStandardEquivalenceAssociation learning_standard_equivalence_association: The JSON representation of the \"learningStandardEquivalenceAssociation\" resource to be created or updated. (required) :param str if_match: The ETag header value used to prevent the PUT from updating a resource modified by another consumer. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.put_learning_standard_equivalence_association_with_http_info(id, learning_standard_equivalence_association, **kwargs) # noqa: E501 else: (data) = self.put_learning_standard_equivalence_association_with_http_info(id, learning_standard_equivalence_association, **kwargs) # noqa: E501 return data def put_learning_standard_equivalence_association_with_http_info(self, id, learning_standard_equivalence_association, **kwargs): # noqa: E501 """Updates or creates a resource based on the resource identifier. # noqa: E501 The PUT operation is used to update or create a resource by identifier. If the resource doesn't exist, the resource will be created using that identifier. Additionally, natural key values cannot be changed using this operation, and will not be modified in the database. If the resource \"id\" is provided in the JSON body, it will be ignored as well. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.put_learning_standard_equivalence_association_with_http_info(id, learning_standard_equivalence_association, async_req=True) >>> result = thread.get() :param async_req bool :param str id: A resource identifier that uniquely identifies the resource. (required) :param EdFiLearningStandardEquivalenceAssociation learning_standard_equivalence_association: The JSON representation of the \"learningStandardEquivalenceAssociation\" resource to be created or updated. (required) :param str if_match: The ETag header value used to prevent the PUT from updating a resource modified by another consumer. :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['id', 'learning_standard_equivalence_association', 'if_match'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method put_learning_standard_equivalence_association" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in params or params['id'] is None): # noqa: E501 raise ValueError("Missing the required parameter `id` when calling `put_learning_standard_equivalence_association`") # noqa: E501 # verify the required parameter 'learning_standard_equivalence_association' is set if self.api_client.client_side_validation and ('learning_standard_equivalence_association' not in params or params['learning_standard_equivalence_association'] is None): # noqa: E501 raise ValueError("Missing the required parameter `learning_standard_equivalence_association` when calling `put_learning_standard_equivalence_association`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} if 'if_match' in params: header_params['If-Match'] = params['if_match'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'learning_standard_equivalence_association' in params: body_params = params['learning_standard_equivalence_association'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['oauth2_client_credentials'] # noqa: E501 return self.api_client.call_api( '/ed-fi/learningStandardEquivalenceAssociations/{id}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

7ac505b3efb2ee5e29189a3312c2a89ae79a9876

py

Python

docker/app/conf/application.py

sled30/python-sample-app-test

35a8a1669023dfde7dfc14e6f6cba6926fb1d610

docker/app/conf/application.py

sled30/python-sample-app-test

35a8a1669023dfde7dfc14e6f6cba6926fb1d610

docker/app/conf/application.py

sled30/python-sample-app-test

35a8a1669023dfde7dfc14e6f6cba6926fb1d610

from api import api as application if __name__ == "__main__": application.run(host='0.0.0.0') # api.run(host='0.0.0.0')

8c9d5afe40d4c85f1596803c4f4e1fd94937bfcc

py

Python

sfi_www/urls.py

sfikrakow/www

ec4e1451849863749d2dc977b8a91c7767e75a1a

2020-04-27T22:51:14.000Z

2020-12-03T13:08:49.000Z

sfi_www/urls.py

sfikrakow/www

ec4e1451849863749d2dc977b8a91c7767e75a1a

2021-04-02T22:31:11.000Z

2021-04-02T22:31:12.000Z

sfi_www/urls.py

sfikrakow/www

ec4e1451849863749d2dc977b8a91c7767e75a1a

2020-04-28T07:08:25.000Z

2021-04-16T09:49:08.000Z

from django.conf import settings from django.conf.urls import include from django.conf.urls.i18n import i18n_patterns from django.contrib import admin from django.urls import path from wagtail.admin import urls as wagtailadmin_urls from wagtail.contrib.sitemaps.views import sitemap from wagtail.core import urls as wagtail_urls from wagtail.documents import urls as wagtaildocs_urls from agenda.views import EditionPodcastFeedView from common.views import sitemap_index from forms.views import ContactFormView urlpatterns = [ path('oidc/', include('mozilla_django_oidc.urls')), path('django-admin/', admin.site.urls), path('admin/', include(wagtailadmin_urls)), path('documents/', include(wagtaildocs_urls)), path('contact_form/', ContactFormView.as_view()), path('feeds/podcasts/<slug:slug>/feed.rss', EditionPodcastFeedView(), name='feeds_podcast'), path('sitemap.xml', sitemap_index) ] if settings.DEBUG: from django.conf.urls.static import static from django.contrib.staticfiles.urls import staticfiles_urlpatterns # Serve static and media files from development server urlpatterns += staticfiles_urlpatterns() urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) urlpatterns = urlpatterns + i18n_patterns( path('sitemap.xml', sitemap), # For anything not caught by a more specific rule above, hand over to # Wagtail's page serving mechanism. This should be the last pattern in # the list: path("", include(wagtail_urls)), )

c996895a7b918da169eb2f22d7284c727253bfc5

py

Python

safe/settings.py

MaryMbugua/Safe

2aaa4760cfa96aafc4d37233fe7b4df584e2ed79

safe/settings.py

MaryMbugua/Safe

2aaa4760cfa96aafc4d37233fe7b4df584e2ed79

safe/settings.py

MaryMbugua/Safe

2aaa4760cfa96aafc4d37233fe7b4df584e2ed79

""" Django settings for safe project. Generated by 'django-admin startproject' using Django 1.11. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import os import dj_database_url from decouple import config # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = config('SECRET_KEY') # SECURITY WARNING: don't run with debug turned on in production! DEBUG = config('DEBUG',default=False,cast=bool) ALLOWED_HOSTS = ['*'] # Application definition INSTALLED_APPS = [ 'hood', 'bootstrap3', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'safe.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', 'django.template.context_processors.media', ], }, }, ] WSGI_APPLICATION = 'safe.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'NAME': 'hoodwatch', 'USER': 'nish', 'PASSWORD': 'Nish', } } db_from_env = dj_database_url.config(conn_max_age=500) DATABASES['default'].update(db_from_env) # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Africa/Nairobi' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [ os.path.join(BASE_DIR, "static") ] STATIC_ROOT = os.path.join(BASE_DIR, "staticfiles") STATICFILES_STORAGE = 'whitenoise.django.GzipManifestStaticFilesStorage' MEDIA_URL = '/media/' MEDIA_ROOT = os.path.join(BASE_DIR, 'media')

e7e96d5bcbaa7b76e47b48a67006f7044bcad3c8

py

Python

cvpods/modeling/meta_arch/detr.py

reinforcementdriving/cvpods

32d98b74745020be035a0e20337ad934201615c4

2021-04-24T17:01:29.000Z

2021-04-24T17:01:29.000Z

cvpods/modeling/meta_arch/detr.py

wondervictor/cvpods

614a975e5425bbaeb66bbd1ffca552d633ba89ca

cvpods/modeling/meta_arch/detr.py

wondervictor/cvpods

614a975e5425bbaeb66bbd1ffca552d633ba89ca

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright (c) BaseDetection, Inc. and its affiliates. All Rights Reserved """ DETR model and criterion classes. """ import torch import torch.nn.functional as F from torch import nn from cvpods.layers import ShapeSpec, position_encoding_dict from cvpods.modeling.backbone import Transformer from cvpods.modeling.matcher import HungarianMatcher from cvpods.structures import Boxes, ImageList, Instances from cvpods.structures import boxes as box_ops from cvpods.structures.boxes import generalized_box_iou from cvpods.utils import comm from cvpods.utils.metrics import accuracy class DETR(nn.Module): def __init__(self, cfg): super(DETR, self).__init__() self.device = torch.device(cfg.MODEL.DEVICE) # Build Backbone self.backbone = cfg.build_backbone( cfg, input_shape=ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN)) ) # Build Transformer self.transformer = Transformer(cfg) self.aux_loss = not cfg.MODEL.DETR.NO_AUX_LOSS self.num_classes = cfg.MODEL.DETR.NUM_CLASSES self.num_queries = cfg.MODEL.DETR.NUM_QUERIES hidden_dim = self.transformer.d_model # Build FFN self.class_embed = nn.Linear(hidden_dim, self.num_classes + 1) self.bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3) # Build Object Queries self.query_embed = nn.Embedding(self.num_queries, hidden_dim) backbone_out_shapes = self.backbone.output_shape()["res5"] self.input_proj = nn.Conv2d(backbone_out_shapes.channels, hidden_dim, kernel_size=1) self.position_embedding = position_encoding_dict[cfg.MODEL.DETR.POSITION_EMBEDDING]( num_pos_feats=hidden_dim // 2, temperature=cfg.MODEL.DETR.TEMPERATURE, normalize=True if cfg.MODEL.DETR.POSITION_EMBEDDING == "sine" else False, scale=None, ) self.weight_dict = { "loss_ce": cfg.MODEL.DETR.CLASS_LOSS_COEFF, "loss_bbox": cfg.MODEL.DETR.BBOX_LOSS_COEFF, "loss_giou": cfg.MODEL.DETR.GIOU_LOSS_COEFF, } if self.aux_loss: self.aux_weight_dict = {} for i in range(cfg.MODEL.DETR.TRANSFORMER.NUM_DEC_LAYERS - 1): self.aux_weight_dict.update({k + f"_{i}": v for k, v in self.weight_dict.items()}) self.weight_dict.update(self.aux_weight_dict) losses = ["labels", "boxes", "cardinality"] matcher = HungarianMatcher( cost_class=cfg.MODEL.DETR.COST_CLASS, cost_bbox=cfg.MODEL.DETR.COST_BBOX, cost_giou=cfg.MODEL.DETR.COST_GIOU, ) self.criterion = SetCriterion( self.num_classes, matcher=matcher, weight_dict=self.weight_dict, eos_coef=cfg.MODEL.DETR.EOS_COEFF, losses=losses, ) self.post_processors = {"bbox": PostProcess()} pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(3, 1, 1) pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(3, 1, 1) if not cfg.MODEL.RESNETS.STRIDE_IN_1X1: # Custom or torch pretrain weights self.normalizer = lambda x: (x / 255.0 - pixel_mean) / pixel_std else: # MSRA pretrain weights self.normalizer = lambda x: (x - pixel_mean) / pixel_std self.to(self.device) def forward(self, batched_inputs): """ Args: batched_inputs: a list, batched outputs of :class:`DatasetMapper` . Each item in the list contains the inputs for one image. For now, each item in the list is a dict that contains: * image: Tensor, image in (C, H, W) format. * instances: Instances Other information that's included in the original dicts, such as: * "height", "width" (int): the output resolution of the model, used in inference. See :meth:`postprocess` for details. Returns: dict[str: Tensor]: mapping from a named loss to a tensor storing the loss. Used during training only. """ images = self.preprocess_image(batched_inputs) B, C, H, W = images.tensor.shape device = images.tensor.device mask = torch.ones((B, H, W), dtype=torch.bool, device=device) for img_shape, m in zip(images.image_sizes, mask): m[: img_shape[0], : img_shape[1]] = False src = self.backbone(images.tensor)["res5"] mask = F.interpolate(mask[None].float(), size=src.shape[-2:]).bool()[0] pos = self.position_embedding(src, mask) hs = self.transformer(self.input_proj(src), mask, self.query_embed.weight, pos)[0] outputs_class = self.class_embed(hs) outputs_coord = self.bbox_embed(hs).sigmoid() out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord[-1]} if self.training: targets = self.convert_anno_format(batched_inputs) if self.aux_loss: out["aux_outputs"] = [ {"pred_logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1]) ] loss_dict = self.criterion(out, targets) for k, v in loss_dict.items(): loss_dict[k] = v * self.weight_dict[k] if k in self.weight_dict else v return loss_dict else: target_sizes = torch.stack( [ torch.tensor([ bi.get("height", img_size[0]), bi.get("width", img_size[1])], device=self.device) for bi, img_size in zip(batched_inputs, images.image_sizes) ] ) res = self.post_processors["bbox"](out, target_sizes) processed_results = [] # for results_per_image, input_per_image, image_size in zip( for results_per_image, _, image_size in zip(res, batched_inputs, images.image_sizes): result = Instances(image_size) result.pred_boxes = Boxes(results_per_image["boxes"].float()) result.scores = results_per_image["scores"].float() result.pred_classes = results_per_image["labels"] processed_results.append({"instances": result}) return processed_results def preprocess_image(self, batched_inputs): """ Normalize, pad and batch the input images. """ images = [x["image"].float().to(self.device) for x in batched_inputs] images = [self.normalizer(img) for img in images] images = ImageList.from_tensors(images, self.backbone.size_divisibility) return images def convert_anno_format(self, batched_inputs): targets = [] for bi in batched_inputs: target = {} h, w = bi["image"].shape[-2:] boxes = box_ops.box_xyxy_to_cxcywh( bi["instances"].gt_boxes.tensor / torch.tensor([w, h, w, h], dtype=torch.float32) ) target["boxes"] = boxes.to(self.device) target["area"] = bi["instances"].gt_boxes.area().to(self.device) target["labels"] = bi["instances"].gt_classes.to(self.device) if hasattr(bi["instances"], "gt_masks"): target["masks"] = bi["instances"].gt_masks target["iscrowd"] = torch.zeros_like(target["labels"], device=self.device) target["orig_size"] = torch.tensor([bi["height"], bi["width"]], device=self.device) target["size"] = torch.tensor([h, w], device=self.device) target["image_id"] = torch.tensor(bi["image_id"], device=self.device) targets.append(target) return targets class SetCriterion(nn.Module): """ This class computes the loss for DETR. The process happens in two steps: 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched ground-truth / prediction (supervise class and box) """ def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses): """ Create the criterion. Parameters: num_classes: number of object categories, omitting the special no-object category matcher: module able to compute a matching between targets and proposals weight_dict: dict containing as key the names of the losses and as values their relative weight. eos_coef: relative classification weight applied to the no-object category losses: list of all the losses to be applied. See get_loss for list of available losses. """ super().__init__() self.num_classes = num_classes self.matcher = matcher self.weight_dict = weight_dict self.eos_coef = eos_coef self.losses = losses empty_weight = torch.ones(self.num_classes + 1) empty_weight[-1] = self.eos_coef self.register_buffer("empty_weight", empty_weight) def loss_labels(self, outputs, targets, indices, num_boxes, log=True): """Classification loss (NLL) targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] """ assert "pred_logits" in outputs del num_boxes src_logits = outputs["pred_logits"] idx = self._get_src_permutation_idx(indices) target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) target_classes = torch.full( src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device ) target_classes[idx] = target_classes_o loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight) losses = {"loss_ce": loss_ce} if log: # TODO this should probably be a separate loss, not hacked in this one here losses["class_error"] = 100 - accuracy(src_logits[idx], target_classes_o)[0] return losses @torch.no_grad() def loss_cardinality(self, outputs, targets, indices, num_boxes): """ Compute the cardinality error, ie the absolute error in the number of predicted non-empty boxes. This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients """ del indices del num_boxes pred_logits = outputs["pred_logits"] device = pred_logits.device tgt_lengths = torch.as_tensor([len(v["labels"]) for v in targets], device=device) # Count the number of predictions that are NOT "no-object" (which is the last class) card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1) card_err = F.l1_loss(card_pred.float(), tgt_lengths.float()) losses = {"cardinality_error": card_err} return losses def loss_boxes(self, outputs, targets, indices, num_boxes): """ Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4] The target boxes are expected in format (center_x, center_y, h, w), normalized by the image size. """ assert "pred_boxes" in outputs idx = self._get_src_permutation_idx(indices) src_boxes = outputs["pred_boxes"][idx] target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction="none") losses = {} losses["loss_bbox"] = loss_bbox.sum() / num_boxes loss_giou = 1 - torch.diag( generalized_box_iou( box_ops.box_cxcywh_to_xyxy(src_boxes), box_ops.box_cxcywh_to_xyxy(target_boxes) ) ) losses["loss_giou"] = loss_giou.sum() / num_boxes return losses def _get_src_permutation_idx(self, indices): # permute predictions following indices batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) src_idx = torch.cat([src for (src, _) in indices]) return batch_idx, src_idx def _get_tgt_permutation_idx(self, indices): # permute targets following indices batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) tgt_idx = torch.cat([tgt for (_, tgt) in indices]) return batch_idx, tgt_idx def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs): loss_map = { "labels": self.loss_labels, "cardinality": self.loss_cardinality, "boxes": self.loss_boxes, } assert loss in loss_map, f"do you really want to compute {loss} loss?" return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs) def forward(self, outputs, targets): """ This performs the loss computation. Parameters: outputs: dict of tensors, see the output specification of the model for the format targets: list of dicts, such that len(targets) == batch_size. The expected keys in each dict depends on the losses applied, see each loss' doc """ outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"} # Retrieve the matching between the outputs of the last layer and the targets indices = self.matcher(outputs_without_aux, targets) # Compute the average number of target boxes accross all nodes, for normalization purposes num_boxes = sum(len(t["labels"]) for t in targets) num_boxes = torch.as_tensor( [num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device ) if comm.get_world_size() > 1: torch.distributed.all_reduce(num_boxes) num_boxes = torch.clamp(num_boxes / comm.get_world_size(), min=1).item() # Compute all the requested losses losses = {} for loss in self.losses: losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes)) # In case of auxiliary losses, we repeat this process with the output of # each intermediate layer. if "aux_outputs" in outputs: for i, aux_outputs in enumerate(outputs["aux_outputs"]): indices = self.matcher(aux_outputs, targets) for loss in self.losses: if loss == "masks": # Intermediate masks losses are too costly to compute, we ignore them. continue kwargs = {} if loss == "labels": # Logging is enabled only for the last layer kwargs = {"log": False} l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes, **kwargs) l_dict = {k + f"_{i}": v for k, v in l_dict.items()} losses.update(l_dict) return losses class PostProcess(nn.Module): """ This module converts the model's output into the format expected by the coco api""" @torch.no_grad() def forward(self, outputs, target_sizes): """ Perform the computation Parameters: outputs: raw outputs of the model target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch For evaluation, this must be the original image size (before any data augmentation) For visualization, this should be the image size after data augment, but before padding """ out_logits, out_bbox = outputs["pred_logits"], outputs["pred_boxes"] assert len(out_logits) == len(target_sizes) assert target_sizes.shape[1] == 2 prob = F.softmax(out_logits, -1) scores, labels = prob[..., :-1].max(-1) # convert to [x0, y0, x1, y1] format boxes = box_ops.box_cxcywh_to_xyxy(out_bbox) # and from relative [0, 1] to absolute [0, height] coordinates img_h, img_w = target_sizes.unbind(1) scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) boxes = boxes * scale_fct[:, None, :] results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)] return results class MLP(nn.Module): """ Very simple multi-layer perceptron (also called FFN)""" def __init__(self, input_dim, hidden_dim, output_dim, num_layers): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) self.layers = nn.ModuleList( nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) ) def forward(self, x): for i, layer in enumerate(self.layers): x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x

8f0c08070cdc926d8829459beaa4ca7be716f1a5

py

Python

Tools/remove_car_from_sun2012.py

n8886919/YOLO

3726a8819d7880e03f4a7e056751ad10a850201b

2019-03-27T05:18:28.000Z

2022-03-22T04:30:17.000Z

Tools/remove_car_from_sun2012.py

n8886919/YOLO

3726a8819d7880e03f4a7e056751ad10a850201b

2019-08-27T07:48:51.000Z

2022-01-13T01:08:26.000Z

Tools/remove_car_from_sun2012.py

n8886919/YOLO

3726a8819d7880e03f4a7e056751ad10a850201b

2019-03-27T15:12:54.000Z

2022-03-18T21:30:14.000Z

def remove_car_from_sun2012(): from shutil import copyfile import xml.etree.cElementTree as ET bg_root = '/media/nolan/HDD1/sun2012pascalformat' sun_img_path = os.path.join(bg_root, 'JPEGImages') sun_anno_path = os.path.join(bg_root, 'Annotations') counter = 0 for img in os.listdir(sun_img_path): detected = False img_name = (img.split('.')[0]).split('/')[-1] img_xml_path = os.path.join(sun_anno_path, (img_name+'.xml')) try: img_xml = ET.ElementTree(file=img_xml_path) root = img_xml.getroot() for child in root: if child.tag == 'object': for sub_child in child: if sub_child.tag == 'name': text = sub_child.text if ('car' in text or 'van' in text or 'truck' in text): detected = True break if detected: break except Exception as e: pass if not detected: counter += 1 src = os.path.join(sun_img_path, img) dst = os.path.join('/media/nolan/9fc64877-3935-46df-9ad0-c601733f5888/sun2012', img) copyfile(src, dst) print(counter)

d8cfc5273e137c985b9a50691494c4aea99e27f0

py

Python

template/iv.py

houzeyu2683/PythonCrawlerTemplate

701d371789fc81eb8ed052e9e8dd0e83ed847580

template/iv.py

houzeyu2683/PythonCrawlerTemplate

701d371789fc81eb8ed052e9e8dd0e83ed847580

template/iv.py

houzeyu2683/PythonCrawlerTemplate

701d371789fc81eb8ed052e9e8dd0e83ed847580

## The packages. from selenium import webdriver import pandas, os, time, tqdm import re import time ## The goal. ''' 從 ptt 的股票版搜尋文章，時間由新至舊，將搜尋的文章擷取，輸出成表格。 ''' ## ## The arguments. platform = 'dcard' board = 'mood' site = "https://www.dcard.tw/f/mood" number = 20 folder = "LOG/IV" confirmation = False ## ## Initial process. os.makedirs(folder) if not os.path.isdir(folder) else None option = webdriver.chrome.options.Options() option.binary_location = "/usr/bin/google-chrome" driver = webdriver.Chrome(options=option, executable_path='driver/chrome') driver.set_window_size(1920/5, 1080/2) driver.get(site) driver.find_element_by_css_selector(".btn-big").click() if confirmation else None document = { "platform":platform, "board":board, "title":[], "link":[], "author":[], "date":[], "content":[], "comment":[] } ## Relax a second. time.sleep(1) ## ## Get title and link. for n in range(1, number+1): document['title'] += [re.sub("#", "", i.text) for i in driver.find_elements_by_css_selector('.cUGTXH')] document['link'] += [i.get_attribute('href') for i in driver.find_elements_by_xpath('//h2[@class="tgn9uw-2 jWUdzO"]/a')] driver.execute_script("var q=document.documentElement.scrollTop={}".format(n * 10000)) time.sleep(1) pass ## ## Get other information base on link. for l in tqdm.tqdm(document['link']): driver.get(l) time.sleep(5) try: document['date'] += [driver.find_element_by_css_selector(".boQZzA+ .boQZzA").text] document['author'] += [driver.find_element_by_xpath("//div[@class='s3d701-2 kBmYXB']").text] document['content'] += [driver.find_element_by_xpath("//div[@class='phqjxq-0 fQNVmg']").text] document['comment'] += ['\n\n'.join([i.text for i in driver.find_elements_by_xpath("//div[@class='sc-71lpws-1 hcbtbx-0 kxmuAN cCOVWi']")])] pass except: document['date'] += [None] document['author'] += [None] document['content'] += [None] document['comment'] += [None] pass pass driver.close() ## ## Convert to table. table = { "data":pandas.DataFrame(document), "location":os.path.join(folder, "{} {} {}.csv".format(platform, board, re.sub(" ", "-", time.ctime()))) } table['data'].to_csv(table['location'], index=False, encoding="utf_8_sig") pass

e8fb23e00c52d8542897df8937fa3b60bad7b2ad

py

Python

src/datatools/columns_rename.py

ingwersen-erik/dev-datatools

907a8a3ec68e06b757918618c2c292deef9bf2a3

src/datatools/columns_rename.py

ingwersen-erik/dev-datatools

907a8a3ec68e06b757918618c2c292deef9bf2a3

src/datatools/columns_rename.py

ingwersen-erik/dev-datatools

907a8a3ec68e06b757918618c2c292deef9bf2a3

# # MIT License # # Copyright (c) 2021 Erik Ingwersen # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR ABOUT THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from __future__ import annotations import logging from typing import Optional import pandas as pd def rename_back( df: pd.DataFrame, attr_column_map: str | None = "column_map", errors: str | None = "ignore", ) -> pd.DataFrame: """ Rename columns back to their original names. Function tries to do that, by relying on a potentially saved attribute, that contains a dictionary with the original and their new names. Parameters ---------- df : pd.DataFrame The dataframe to rename. attr_column_map: str, optional The attribute name that suposedly stores the column map. By default, "column_map" errors : str {'ignore', 'raise'}, optional The error handling strategy to use when old column names are not inside the dataframe attributes. By default, 'ignore' is used. Returns ------- pd.DataFrame The dataframe with the columns renamed, when attribute :param:`column_map` exists. Raises ------ AttributeError When attribute :param:`column_map` does not exist. Examples -------- >>> _df = pd.DataFrame( ... { ... 'order_material': ['A', 'B', 'C'], ... 'site': ['X', 'Y', 'Z'], ... pd.to_datetime('2021-10-10'): [0, 10, 0], ... } ... ) >>> _df.attrs['column_map'] = {'order_material': 'material'} >>> rename_back(_df) material site 2021-10-10 00:00:00 0 A X 0 1 B Y 10 2 C Z 0 """ column_map = df.attrs.get(attr_column_map) if column_map is None and errors == "raise": raise AttributeError( f"Tried to rename columns, but no {attr_column_map} attribute found" ) if column_map: logging.info("Renaming columns back to original names") column_map = {v: k for k, v in column_map.items()} df = df.rename(columns=column_map) return df def fmt_colnames(_df: pd.DataFrame) -> pd.DataFrame: """ Beautifies the column names of a given dataframe. Formatting Options ------------------ * Convert column names to uppercase * Replaces underscores with spaces "_" -> " " * Converts any datetime columns to dates Parameters ---------- _df : pd.DataFrame The dataframe to rename the columns for. Returns ------- pd.DataFrame The dataframe with renamed columns. Examples -------- >>> # noinspection PyShadowingNames >>> _df = pd.DataFrame( ... { ... 'order_material': ['A', 'B', 'C'], ... 'site': ['X', 'Y', 'Z'], ... '2021/10/10': [0, 10, 0] ... } ... ) >>> fmt_colnames(_df) ORDER MATERIAL SITE 2021-10-10 0 A X 0 1 B Y 10 2 C Z 0 """ df_original_names = rename_back(_df) if df_original_names is not None: return df_original_names return _df.rename( columns={ original_column: pd.to_datetime(original_column, errors="ignore").strftime( "%Y-%m-%d" ) for original_column in _df.columns if isinstance( pd.to_datetime(original_column, errors="ignore"), pd.Timestamp ) } ).rename( columns={ original_column: str(original_column).upper().replace("_", " ") for original_column in _df.columns } )

c70c54385ad033648389d9d18f5d3407ce091306

py

Python

urls.py

giovanniherdigein/my_first_django

ed547cf8802951a6af17c0683a642548e025935f

urls.py

giovanniherdigein/my_first_django

ed547cf8802951a6af17c0683a642548e025935f

urls.py

giovanniherdigein/my_first_django

ed547cf8802951a6af17c0683a642548e025935f

from django.urls import path from . import views app_name ='crudsite' urlpatterns=[ path('',views.index,name='index'), path('create_item',views.createItem,name= 'create_item'), path('update_item/<int:item_id>/',views.updateItem,name='update_item'), path('delete_item/<int:item_id>/',views.deleteItem,name='delete_item'), ]

71d0705f6135c102dc41859770354b804739b3ce

py

Python

venv/Lib/site-packages/pyrogram/raw/functions/account/get_content_settings.py

D1ne2021/jjhhhjj

a090da30983b3ef276dfe4cef2ded4526f36002a

2021-12-13T07:09:55.000Z

2022-01-12T12:15:20.000Z

venv/Lib/site-packages/pyrogram/raw/functions/account/get_content_settings.py

hoangkiet1906/Botcie_ver1

c133b915edde06dac690a7dc6ca160f6792fc4c8

venv/Lib/site-packages/pyrogram/raw/functions/account/get_content_settings.py

hoangkiet1906/Botcie_ver1

c133b915edde06dac690a7dc6ca160f6792fc4c8

# Pyrogram - Telegram MTProto API Client Library for Python # Copyright (C) 2017-2021 Dan <https://github.com/delivrance> # # This file is part of Pyrogram. # # Pyrogram is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published # by the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Pyrogram is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with Pyrogram. If not, see <http://www.gnu.org/licenses/>. from io import BytesIO from pyrogram.raw.core.primitives import Int, Long, Int128, Int256, Bool, Bytes, String, Double, Vector from pyrogram.raw.core import TLObject from pyrogram import raw from typing import List, Union, Any # # # # # # # # # # # # # # # # # # # # # # # # # !!! WARNING !!! # # This is a generated file! # # All changes made in this file will be lost! # # # # # # # # # # # # # # # # # # # # # # # # # class GetContentSettings(TLObject): # type: ignore """Telegram API method. Details: - Layer: ``126`` - ID: ``0x8b9b4dae`` **No parameters required.** Returns: :obj:`account.ContentSettings <pyrogram.raw.base.account.ContentSettings>` """ __slots__: List[str] = [] ID = 0x8b9b4dae QUALNAME = "functions.account.GetContentSettings" def __init__(self) -> None: pass @staticmethod def read(data: BytesIO, *args: Any) -> "GetContentSettings": # No flags return GetContentSettings() def write(self) -> bytes: data = BytesIO() data.write(Int(self.ID, False)) # No flags return data.getvalue()

01fe3f40b6829f7fd22099cd602aafa49135bd95

py

Python

doc/make.py

raspbian-packages/pandas

fb33806b5286deb327b2e0fa96aedf25a6ed563f

doc/make.py

raspbian-packages/pandas

fb33806b5286deb327b2e0fa96aedf25a6ed563f

doc/make.py

raspbian-packages/pandas

fb33806b5286deb327b2e0fa96aedf25a6ed563f

#!/usr/bin/env python """ Python script for building documentation. To build the docs you must have all optional dependencies for pandas installed. See the installation instructions for a list of these. <del>Note: currently latex builds do not work because of table formats that are not supported in the latex generation.</del> 2014-01-30: Latex has some issues but 'latex_forced' works ok for 0.13.0-400 or so Usage ----- python make.py clean python make.py html """ from __future__ import print_function import io import glob # noqa import os import shutil import sys from contextlib import contextmanager import sphinx # noqa import argparse import jinja2 # noqa # Debian's debian/rules overrides it to point to correct built pandas # os.environ['PYTHONPATH'] = '..' SPHINX_BUILD = 'sphinxbuild' def upload_dev(user='pandas'): 'push a copy to the pydata dev directory' if os.system('cd build/html; rsync -avz . {0}@pandas.pydata.org' ':/usr/share/nginx/pandas/pandas-docs/dev/ -essh'.format(user)): raise SystemExit('Upload to Pydata Dev failed') def upload_dev_pdf(user='pandas'): 'push a copy to the pydata dev directory' if os.system('cd build/latex; scp pandas.pdf {0}@pandas.pydata.org' ':/usr/share/nginx/pandas/pandas-docs/dev/'.format(user)): raise SystemExit('PDF upload to Pydata Dev failed') def upload_stable(user='pandas'): 'push a copy to the pydata stable directory' if os.system('cd build/html; rsync -avz . {0}@pandas.pydata.org' ':/usr/share/nginx/pandas/pandas-docs/stable/ -essh'.format(user)): raise SystemExit('Upload to stable failed') def upload_stable_pdf(user='pandas'): 'push a copy to the pydata dev directory' if os.system('cd build/latex; scp pandas.pdf {0}@pandas.pydata.org' ':/usr/share/nginx/pandas/pandas-docs/stable/'.format(user)): raise SystemExit('PDF upload to stable failed') def upload_prev(ver, doc_root='./', user='pandas'): 'push a copy of older release to appropriate version directory' local_dir = doc_root + 'build/html' remote_dir = '/usr/share/nginx/pandas/pandas-docs/version/%s/' % ver cmd = 'cd %s; rsync -avz . %[email protected]:%s -essh' cmd = cmd % (local_dir, user, remote_dir) print(cmd) if os.system(cmd): raise SystemExit( 'Upload to %s from %s failed' % (remote_dir, local_dir)) local_dir = doc_root + 'build/latex' pdf_cmd = 'cd %s; scp pandas.pdf %[email protected]:%s' pdf_cmd = pdf_cmd % (local_dir, user, remote_dir) if os.system(pdf_cmd): raise SystemExit('Upload PDF to %s from %s failed' % (ver, doc_root)) def build_pandas(): os.chdir('..') os.system('python setup.py clean') os.system('python setup.py build_ext --inplace') os.chdir('doc') def build_prev(ver): if os.system('git checkout v%s' % ver) != 1: os.chdir('..') os.system('python setup.py clean') os.system('python setup.py build_ext --inplace') os.chdir('doc') os.system('python make.py clean') os.system('python make.py html') os.system('python make.py latex') os.system('git checkout master') def clean(): if os.path.exists('build'): shutil.rmtree('build') if os.path.exists('source/generated'): shutil.rmtree('source/generated') @contextmanager def cleanup_nb(nb): try: yield finally: try: os.remove(nb + '.executed') except OSError: pass def get_kernel(): """Find the kernel name for your python version""" return 'python%s' % sys.version_info.major def execute_nb(src, dst, allow_errors=False, timeout=1000, kernel_name=''): """ Execute notebook in `src` and write the output to `dst` Parameters ---------- src, dst: str path to notebook allow_errors: bool timeout: int kernel_name: str defualts to value set in notebook metadata Returns ------- dst: str """ import nbformat from nbconvert.preprocessors import ExecutePreprocessor with io.open(src, encoding='utf-8') as f: nb = nbformat.read(f, as_version=4) ep = ExecutePreprocessor(allow_errors=allow_errors, timeout=timeout, kernel_name=kernel_name) ep.preprocess(nb, resources={}) with io.open(dst, 'wt', encoding='utf-8') as f: nbformat.write(nb, f) return dst def convert_nb(src, dst, to='html', template_file='basic'): """ Convert a notebook `src`. Parameters ---------- src, dst: str filepaths to: {'rst', 'html'} format to export to template_file: str name of template file to use. Default 'basic' """ from nbconvert import HTMLExporter, RSTExporter dispatch = {'rst': RSTExporter, 'html': HTMLExporter} exporter = dispatch[to.lower()](template_file=template_file) (body, resources) = exporter.from_filename(src) with io.open(dst, 'wt', encoding='utf-8') as f: f.write(body) return dst def html(): check_build() notebooks = [ 'source/html-styling.ipynb', ] for nb in notebooks: with cleanup_nb(nb): try: print("Converting %s" % nb) kernel_name = get_kernel() executed = execute_nb(nb, nb + '.executed', allow_errors=True, kernel_name=kernel_name) convert_nb(executed, nb.rstrip('.ipynb') + '.html') except (ImportError, IndexError) as e: print(e) print("Failed to convert %s" % nb) if os.system('sphinx-build -P -b html -d build/doctrees ' 'source build/html'): raise SystemExit("Building HTML failed.") try: # remove stale file os.system('rm source/html-styling.html') os.system('cd build; rm -f html/pandas.zip;') except: pass def zip_html(): try: print("\nZipping up HTML docs...") # just in case the wonky build box doesn't have zip # don't fail this. os.system('cd build; rm -f html/pandas.zip; zip html/pandas.zip -r -q html/* ') print("\n") except: pass def latex(): check_build() if sys.platform != 'win32': # LaTeX format. if os.system('sphinx-build -b latex -d build/doctrees ' 'source build/latex'): raise SystemExit("Building LaTeX failed.") # Produce pdf. os.chdir('build/latex') # Call the makefile produced by sphinx... if os.system('make'): print("Rendering LaTeX failed.") print("You may still be able to get a usable PDF file by going into 'build/latex'") print("and executing 'pdflatex pandas.tex' for the requisite number of passes.") print("Or using the 'latex_forced' target") raise SystemExit os.chdir('../..') else: print('latex build has not been tested on windows') def latex_forced(): check_build() if sys.platform != 'win32': # LaTeX format. if os.system('sphinx-build -b latex -d build/doctrees ' 'source build/latex'): raise SystemExit("Building LaTeX failed.") # Produce pdf. os.chdir('build/latex') # Manually call pdflatex, 3 passes should ensure latex fixes up # all the required cross-references and such. os.system('pdflatex -interaction=nonstopmode pandas.tex') os.system('pdflatex -interaction=nonstopmode pandas.tex') os.system('pdflatex -interaction=nonstopmode pandas.tex') raise SystemExit("You should check the file 'build/latex/pandas.pdf' for problems.") os.chdir('../..') else: print('latex build has not been tested on windows') def check_build(): build_dirs = [ 'build', 'build/doctrees', 'build/html', 'build/latex', 'build/plots', 'build/_static', 'build/_templates'] for d in build_dirs: try: os.mkdir(d) except OSError: pass def all(): # clean() html() def auto_dev_build(debug=False): msg = '' try: step = 'clean' clean() step = 'html' html() step = 'upload dev' upload_dev() if not debug: sendmail(step) step = 'latex' latex() step = 'upload pdf' upload_dev_pdf() if not debug: sendmail(step) except (Exception, SystemExit) as inst: msg = str(inst) + '\n' sendmail(step, '[ERROR] ' + msg) def sendmail(step=None, err_msg=None): from_name, to_name = _get_config() if step is None: step = '' if err_msg is None or '[ERROR]' not in err_msg: msgstr = 'Daily docs %s completed successfully' % step subject = "DOC: %s successful" % step else: msgstr = err_msg subject = "DOC: %s failed" % step import smtplib from email.MIMEText import MIMEText msg = MIMEText(msgstr) msg['Subject'] = subject msg['From'] = from_name msg['To'] = to_name server_str, port, login, pwd = _get_credentials() server = smtplib.SMTP(server_str, port) server.ehlo() server.starttls() server.ehlo() server.login(login, pwd) try: server.sendmail(from_name, to_name, msg.as_string()) finally: server.close() def _get_dir(subdir=None): import getpass USERNAME = getpass.getuser() if sys.platform == 'darwin': HOME = '/Users/%s' % USERNAME else: HOME = '/home/%s' % USERNAME if subdir is None: subdir = '/code/scripts/config' conf_dir = '%s/%s' % (HOME, subdir) return conf_dir def _get_credentials(): tmp_dir = _get_dir() cred = '%s/credentials' % tmp_dir with open(cred, 'r') as fh: server, port, un, domain = fh.read().split(',') port = int(port) login = un + '@' + domain + '.com' import base64 with open('%s/cron_email_pwd' % tmp_dir, 'r') as fh: pwd = base64.b64decode(fh.read()) return server, port, login, pwd def _get_config(): tmp_dir = _get_dir() with open('%s/addresses' % tmp_dir, 'r') as fh: from_name, to_name = fh.read().split(',') return from_name, to_name funcd = { 'html': html, 'zip_html': zip_html, 'upload_dev': upload_dev, 'upload_stable': upload_stable, 'upload_dev_pdf': upload_dev_pdf, 'upload_stable_pdf': upload_stable_pdf, 'latex': latex, 'latex_forced': latex_forced, 'clean': clean, 'auto_dev': auto_dev_build, 'auto_debug': lambda: auto_dev_build(True), 'build_pandas': build_pandas, 'all': all, } small_docs = False # current_dir = os.getcwd() # os.chdir(os.path.dirname(os.path.join(current_dir, __file__))) import argparse argparser = argparse.ArgumentParser(description=""" pandas documentation builder """.strip()) # argparser.add_argument('-arg_name', '--arg_name', # metavar='label for arg help', # type=str|etc, # nargs='N|*|?|+|argparse.REMAINDER', # required=False, # #choices='abc', # help='help string', # action='store|store_true') # args = argparser.parse_args() #print args.accumulate(args.integers) def generate_index(api=True, single=False, **kwds): from jinja2 import Template with open("source/index.rst.template") as f: t = Template(f.read()) with open("source/index.rst","w") as f: f.write(t.render(api=api,single=single,**kwds)) import argparse argparser = argparse.ArgumentParser(description="pandas documentation builder", epilog="Targets : %s" % funcd.keys()) argparser.add_argument('--no-api', default=False, help='Ommit api and autosummary', action='store_true') argparser.add_argument('--single', metavar='FILENAME', type=str, default=False, help='filename of section to compile, e.g. "indexing"') argparser.add_argument('--user', type=str, default=False, help='Username to connect to the pydata server') def main(): args, unknown = argparser.parse_known_args() sys.argv = [sys.argv[0]] + unknown if args.single: args.single = os.path.basename(args.single).split(".rst")[0] if 'clean' in unknown: args.single=False generate_index(api=not args.no_api and not args.single, single=args.single) if len(sys.argv) > 2: ftype = sys.argv[1] ver = sys.argv[2] if ftype == 'build_previous': build_prev(ver, user=args.user) if ftype == 'upload_previous': upload_prev(ver, user=args.user) elif len(sys.argv) == 2: for arg in sys.argv[1:]: func = funcd.get(arg) if func is None: raise SystemExit('Do not know how to handle %s; valid args are %s' % ( arg, list(funcd.keys()))) if args.user: func(user=args.user) else: func() else: small_docs = False all() # os.chdir(current_dir) if __name__ == '__main__': import sys sys.exit(main())

e1071060a680e49b4bfc307f8c45f59ed083707d

py

Python

the_index/objects/index.py

lastmeta/index_credit

fd85111341d996d678c1a5ac94832904288e3e48

the_index/objects/index.py

lastmeta/index_credit

fd85111341d996d678c1a5ac94832904288e3e48

the_index/objects/index.py

lastmeta/index_credit

fd85111341d996d678c1a5ac94832904288e3e48

class User(): def __init__(self, credit: float, vote: str, against: int): self.credit = credit self.vote = vote self.against = against self.trades = {} def __repr__(self): return ( f'\n credit {self.credit}' f'\n vote {self.vote}' f'\n against {self.against}' f'\n trades {self.trades}') def trade(self, asset, amount): self.trades[asset] = amount def complete_trades(self, credit): self.trades = {} self.credit += credit class Index(): def __init__(self, users: list, assets: dict, rates: dict): self.users = users self.assets = assets self.rates = rates self.mana = {} self.weight = {} def __repr__(self): return ( f'\nusers {self.users}' f'\nassets {self.assets}' f'\nrates {self.rates}' f'\nmana {self.mana}' f'\nweight {self.weight}' f'\nvalue {self.value()}') def value(self): return {k: v * self.rates[k] for k, v in self.assets.items()} def clear_mana(self): self.mana = {k: 0 for k, v in self.mana.items()} def generate_ideal_allocation(self, mana_total): values = self.value() values_total = sum([v for v in values.values()]) self.weight = { # I thought you had to weight it according to how large the asset is, but I guess not... # k: (self.mana[k] / mana_total) * (v / values_total) k: (self.mana[k] / mana_total) for k, v in values.items()} return {k: v + (v * self.weight[k]) for k, v in values.items()} def apply_trades(self): ''' assumes only valid trades exist ''' for user in self.users: credit_for_user = 0 print(self.assets, user.credit) for k, amount in user.trades.items(): self.assets[k] += amount credit_for_user += (amount * self.rates[k]) user.complete_trades(credit_for_user) print(self.assets, user.credit) def negotiate_allocations(self, ideal, trade, mana_total): print('self.weight', self.weight) for k, value in trade.items(): print( f'\n{k}: {value} + abs({ideal[k]} - {value}) * {self.weight[k]}', f'\n{k}: {value} + {abs(ideal[k] - value)} * {self.weight[k]}', f'\n{k}: {value} + {abs(ideal[k] - value) * self.weight[k]}' f'\n{k}: {value + abs(ideal[k] - value) * self.weight[k]}') return { k: value + abs(ideal[k] - value) * self.weight[k] for k, value in trade.items()} def rate_translation(self, negotiation): ''' if the negotiation was our value and our asset counts is what it is, what would the rates have to be? ''' for k, v in negotiation.items(): print(k, 'rate:', self.rates[k], 'v:', v, 'count:', self.assets[k], '()', v / self.assets[k]) self.rates[k] = v / self.assets[k] return None def round(self): ''' 1. take allocation of value from last round 2. generate mana, tally up what it was spent on (in demo you can only vote for one asset: all mana is excess mana) 3. generate ideal allocation 4. tally up and apply trades 5. calculate achieved allocation via trading 6. modify achieved allocation according to mana spent 7. translate allocation into an effect on rates and apply ''' self.clear_mana() mana_total = 0 for user in self.users: self.mana[user.vote] = user.credit * user.against mana_total += user.credit ideal = self.generate_ideal_allocation(mana_total) print('ideal', ideal) self.apply_trades() trade = self.value() print('trade', trade) negotiation = self.negotiate_allocations(ideal, trade, mana_total) print('negotiation', negotiation) self.rate_translation(negotiation) def run(): users = [ User(credit=1, vote='btc', against=1), User(credit=2, vote='eth', against=1), User(credit=3, vote='xmr', against=-1)] assets = {'btc': 1, 'eth': 2, 'xmr': 3} rates = {'btc': 1, 'eth': 2, 'xmr': 3} index = Index(users, assets, rates) index.round() users[0].trade('btc', 1) index.round() users[2].trade('xmr', -3) index.round() users[1].trade('eth', -1)

83fc082b545106d02622de20f2083e8a7562f96c

py

Python

venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/chardet/jisfreq.py

xiegudong45/typeidea

db6504a232d120d6ffa185730bd35b9b9ecffa6c

2015-01-01T00:15:34.000Z

2022-03-31T22:57:03.000Z

env/Lib/site-packages/pip/_vendor/chardet/jisfreq.py

aammjian/cotton

f72b814f795f79a4054688e465c8b0ae5560f3b7

2015-01-01T13:03:16.000Z

2022-03-31T17:40:27.000Z

lib/python2.7/site-packages/pip/_vendor/chardet/jisfreq.py

anish03/weather-dash

d517fa9da9028d1fc5d8fd71d77cee829ddee87b

2015-01-01T08:41:17.000Z

2022-03-31T16:12:52.000Z

######################## BEGIN LICENSE BLOCK ######################## # The Original Code is Mozilla Communicator client code. # # The Initial Developer of the Original Code is # Netscape Communications Corporation. # Portions created by the Initial Developer are Copyright (C) 1998 # the Initial Developer. All Rights Reserved. # # Contributor(s): # Mark Pilgrim - port to Python # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this library; if not, write to the Free Software # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA # 02110-1301 USA ######################### END LICENSE BLOCK ######################### # Sampling from about 20M text materials include literature and computer technology # # Japanese frequency table, applied to both S-JIS and EUC-JP # They are sorted in order. # 128 --> 0.77094 # 256 --> 0.85710 # 512 --> 0.92635 # 1024 --> 0.97130 # 2048 --> 0.99431 # # Ideal Distribution Ratio = 0.92635 / (1-0.92635) = 12.58 # Random Distribution Ration = 512 / (2965+62+83+86-512) = 0.191 # # Typical Distribution Ratio, 25% of IDR JIS_TYPICAL_DISTRIBUTION_RATIO = 3.0 # Char to FreqOrder table , JIS_TABLE_SIZE = 4368 JIS_CHAR_TO_FREQ_ORDER = ( 40, 1, 6, 182, 152, 180, 295,2127, 285, 381,3295,4304,3068,4606,3165,3510, # 16 3511,1822,2785,4607,1193,2226,5070,4608, 171,2996,1247, 18, 179,5071, 856,1661, # 32 1262,5072, 619, 127,3431,3512,3230,1899,1700, 232, 228,1294,1298, 284, 283,2041, # 48 2042,1061,1062, 48, 49, 44, 45, 433, 434,1040,1041, 996, 787,2997,1255,4305, # 64 2108,4609,1684,1648,5073,5074,5075,5076,5077,5078,3687,5079,4610,5080,3927,3928, # 80 5081,3296,3432, 290,2285,1471,2187,5082,2580,2825,1303,2140,1739,1445,2691,3375, # 96 1691,3297,4306,4307,4611, 452,3376,1182,2713,3688,3069,4308,5083,5084,5085,5086, # 112 5087,5088,5089,5090,5091,5092,5093,5094,5095,5096,5097,5098,5099,5100,5101,5102, # 128 5103,5104,5105,5106,5107,5108,5109,5110,5111,5112,4097,5113,5114,5115,5116,5117, # 144 5118,5119,5120,5121,5122,5123,5124,5125,5126,5127,5128,5129,5130,5131,5132,5133, # 160 5134,5135,5136,5137,5138,5139,5140,5141,5142,5143,5144,5145,5146,5147,5148,5149, # 176 5150,5151,5152,4612,5153,5154,5155,5156,5157,5158,5159,5160,5161,5162,5163,5164, # 192 5165,5166,5167,5168,5169,5170,5171,5172,5173,5174,5175,1472, 598, 618, 820,1205, # 208 1309,1412,1858,1307,1692,5176,5177,5178,5179,5180,5181,5182,1142,1452,1234,1172, # 224 1875,2043,2149,1793,1382,2973, 925,2404,1067,1241, 960,1377,2935,1491, 919,1217, # 240 1865,2030,1406,1499,2749,4098,5183,5184,5185,5186,5187,5188,2561,4099,3117,1804, # 256 2049,3689,4309,3513,1663,5189,3166,3118,3298,1587,1561,3433,5190,3119,1625,2998, # 272 3299,4613,1766,3690,2786,4614,5191,5192,5193,5194,2161, 26,3377, 2,3929, 20, # 288 3691, 47,4100, 50, 17, 16, 35, 268, 27, 243, 42, 155, 24, 154, 29, 184, # 304 4, 91, 14, 92, 53, 396, 33, 289, 9, 37, 64, 620, 21, 39, 321, 5, # 320 12, 11, 52, 13, 3, 208, 138, 0, 7, 60, 526, 141, 151,1069, 181, 275, # 336 1591, 83, 132,1475, 126, 331, 829, 15, 69, 160, 59, 22, 157, 55,1079, 312, # 352 109, 38, 23, 25, 10, 19, 79,5195, 61, 382,1124, 8, 30,5196,5197,5198, # 368 5199,5200,5201,5202,5203,5204,5205,5206, 89, 62, 74, 34,2416, 112, 139, 196, # 384 271, 149, 84, 607, 131, 765, 46, 88, 153, 683, 76, 874, 101, 258, 57, 80, # 400 32, 364, 121,1508, 169,1547, 68, 235, 145,2999, 41, 360,3027, 70, 63, 31, # 416 43, 259, 262,1383, 99, 533, 194, 66, 93, 846, 217, 192, 56, 106, 58, 565, # 432 280, 272, 311, 256, 146, 82, 308, 71, 100, 128, 214, 655, 110, 261, 104,1140, # 448 54, 51, 36, 87, 67,3070, 185,2618,2936,2020, 28,1066,2390,2059,5207,5208, # 464 5209,5210,5211,5212,5213,5214,5215,5216,4615,5217,5218,5219,5220,5221,5222,5223, # 480 5224,5225,5226,5227,5228,5229,5230,5231,5232,5233,5234,5235,5236,3514,5237,5238, # 496 5239,5240,5241,5242,5243,5244,2297,2031,4616,4310,3692,5245,3071,5246,3598,5247, # 512 4617,3231,3515,5248,4101,4311,4618,3808,4312,4102,5249,4103,4104,3599,5250,5251, # 528 5252,5253,5254,5255,5256,5257,5258,5259,5260,5261,5262,5263,5264,5265,5266,5267, # 544 5268,5269,5270,5271,5272,5273,5274,5275,5276,5277,5278,5279,5280,5281,5282,5283, # 560 5284,5285,5286,5287,5288,5289,5290,5291,5292,5293,5294,5295,5296,5297,5298,5299, # 576 5300,5301,5302,5303,5304,5305,5306,5307,5308,5309,5310,5311,5312,5313,5314,5315, # 592 5316,5317,5318,5319,5320,5321,5322,5323,5324,5325,5326,5327,5328,5329,5330,5331, # 608 5332,5333,5334,5335,5336,5337,5338,5339,5340,5341,5342,5343,5344,5345,5346,5347, # 624 5348,5349,5350,5351,5352,5353,5354,5355,5356,5357,5358,5359,5360,5361,5362,5363, # 640 5364,5365,5366,5367,5368,5369,5370,5371,5372,5373,5374,5375,5376,5377,5378,5379, # 656 5380,5381, 363, 642,2787,2878,2788,2789,2316,3232,2317,3434,2011, 165,1942,3930, # 672 3931,3932,3933,5382,4619,5383,4620,5384,5385,5386,5387,5388,5389,5390,5391,5392, # 688 5393,5394,5395,5396,5397,5398,5399,5400,5401,5402,5403,5404,5405,5406,5407,5408, # 704 5409,5410,5411,5412,5413,5414,5415,5416,5417,5418,5419,5420,5421,5422,5423,5424, # 720 5425,5426,5427,5428,5429,5430,5431,5432,5433,5434,5435,5436,5437,5438,5439,5440, # 736 5441,5442,5443,5444,5445,5446,5447,5448,5449,5450,5451,5452,5453,5454,5455,5456, # 752 5457,5458,5459,5460,5461,5462,5463,5464,5465,5466,5467,5468,5469,5470,5471,5472, # 768 5473,5474,5475,5476,5477,5478,5479,5480,5481,5482,5483,5484,5485,5486,5487,5488, # 784 5489,5490,5491,5492,5493,5494,5495,5496,5497,5498,5499,5500,5501,5502,5503,5504, # 800 5505,5506,5507,5508,5509,5510,5511,5512,5513,5514,5515,5516,5517,5518,5519,5520, # 816 5521,5522,5523,5524,5525,5526,5527,5528,5529,5530,5531,5532,5533,5534,5535,5536, # 832 5537,5538,5539,5540,5541,5542,5543,5544,5545,5546,5547,5548,5549,5550,5551,5552, # 848 5553,5554,5555,5556,5557,5558,5559,5560,5561,5562,5563,5564,5565,5566,5567,5568, # 864 5569,5570,5571,5572,5573,5574,5575,5576,5577,5578,5579,5580,5581,5582,5583,5584, # 880 5585,5586,5587,5588,5589,5590,5591,5592,5593,5594,5595,5596,5597,5598,5599,5600, # 896 5601,5602,5603,5604,5605,5606,5607,5608,5609,5610,5611,5612,5613,5614,5615,5616, # 912 5617,5618,5619,5620,5621,5622,5623,5624,5625,5626,5627,5628,5629,5630,5631,5632, # 928 5633,5634,5635,5636,5637,5638,5639,5640,5641,5642,5643,5644,5645,5646,5647,5648, # 944 5649,5650,5651,5652,5653,5654,5655,5656,5657,5658,5659,5660,5661,5662,5663,5664, # 960 5665,5666,5667,5668,5669,5670,5671,5672,5673,5674,5675,5676,5677,5678,5679,5680, # 976 5681,5682,5683,5684,5685,5686,5687,5688,5689,5690,5691,5692,5693,5694,5695,5696, # 992 5697,5698,5699,5700,5701,5702,5703,5704,5705,5706,5707,5708,5709,5710,5711,5712, # 1008 5713,5714,5715,5716,5717,5718,5719,5720,5721,5722,5723,5724,5725,5726,5727,5728, # 1024 5729,5730,5731,5732,5733,5734,5735,5736,5737,5738,5739,5740,5741,5742,5743,5744, # 1040 5745,5746,5747,5748,5749,5750,5751,5752,5753,5754,5755,5756,5757,5758,5759,5760, # 1056 5761,5762,5763,5764,5765,5766,5767,5768,5769,5770,5771,5772,5773,5774,5775,5776, # 1072 5777,5778,5779,5780,5781,5782,5783,5784,5785,5786,5787,5788,5789,5790,5791,5792, # 1088 5793,5794,5795,5796,5797,5798,5799,5800,5801,5802,5803,5804,5805,5806,5807,5808, # 1104 5809,5810,5811,5812,5813,5814,5815,5816,5817,5818,5819,5820,5821,5822,5823,5824, # 1120 5825,5826,5827,5828,5829,5830,5831,5832,5833,5834,5835,5836,5837,5838,5839,5840, # 1136 5841,5842,5843,5844,5845,5846,5847,5848,5849,5850,5851,5852,5853,5854,5855,5856, # 1152 5857,5858,5859,5860,5861,5862,5863,5864,5865,5866,5867,5868,5869,5870,5871,5872, # 1168 5873,5874,5875,5876,5877,5878,5879,5880,5881,5882,5883,5884,5885,5886,5887,5888, # 1184 5889,5890,5891,5892,5893,5894,5895,5896,5897,5898,5899,5900,5901,5902,5903,5904, # 1200 5905,5906,5907,5908,5909,5910,5911,5912,5913,5914,5915,5916,5917,5918,5919,5920, # 1216 5921,5922,5923,5924,5925,5926,5927,5928,5929,5930,5931,5932,5933,5934,5935,5936, # 1232 5937,5938,5939,5940,5941,5942,5943,5944,5945,5946,5947,5948,5949,5950,5951,5952, # 1248 5953,5954,5955,5956,5957,5958,5959,5960,5961,5962,5963,5964,5965,5966,5967,5968, # 1264 5969,5970,5971,5972,5973,5974,5975,5976,5977,5978,5979,5980,5981,5982,5983,5984, # 1280 5985,5986,5987,5988,5989,5990,5991,5992,5993,5994,5995,5996,5997,5998,5999,6000, # 1296 6001,6002,6003,6004,6005,6006,6007,6008,6009,6010,6011,6012,6013,6014,6015,6016, # 1312 6017,6018,6019,6020,6021,6022,6023,6024,6025,6026,6027,6028,6029,6030,6031,6032, # 1328 6033,6034,6035,6036,6037,6038,6039,6040,6041,6042,6043,6044,6045,6046,6047,6048, # 1344 6049,6050,6051,6052,6053,6054,6055,6056,6057,6058,6059,6060,6061,6062,6063,6064, # 1360 6065,6066,6067,6068,6069,6070,6071,6072,6073,6074,6075,6076,6077,6078,6079,6080, # 1376 6081,6082,6083,6084,6085,6086,6087,6088,6089,6090,6091,6092,6093,6094,6095,6096, # 1392 6097,6098,6099,6100,6101,6102,6103,6104,6105,6106,6107,6108,6109,6110,6111,6112, # 1408 6113,6114,2044,2060,4621, 997,1235, 473,1186,4622, 920,3378,6115,6116, 379,1108, # 1424 4313,2657,2735,3934,6117,3809, 636,3233, 573,1026,3693,3435,2974,3300,2298,4105, # 1440 854,2937,2463, 393,2581,2417, 539, 752,1280,2750,2480, 140,1161, 440, 708,1569, # 1456 665,2497,1746,1291,1523,3000, 164,1603, 847,1331, 537,1997, 486, 508,1693,2418, # 1472 1970,2227, 878,1220, 299,1030, 969, 652,2751, 624,1137,3301,2619, 65,3302,2045, # 1488 1761,1859,3120,1930,3694,3516, 663,1767, 852, 835,3695, 269, 767,2826,2339,1305, # 1504 896,1150, 770,1616,6118, 506,1502,2075,1012,2519, 775,2520,2975,2340,2938,4314, # 1520 3028,2086,1224,1943,2286,6119,3072,4315,2240,1273,1987,3935,1557, 175, 597, 985, # 1536 3517,2419,2521,1416,3029, 585, 938,1931,1007,1052,1932,1685,6120,3379,4316,4623, # 1552 804, 599,3121,1333,2128,2539,1159,1554,2032,3810, 687,2033,2904, 952, 675,1467, # 1568 3436,6121,2241,1096,1786,2440,1543,1924, 980,1813,2228, 781,2692,1879, 728,1918, # 1584 3696,4624, 548,1950,4625,1809,1088,1356,3303,2522,1944, 502, 972, 373, 513,2827, # 1600 586,2377,2391,1003,1976,1631,6122,2464,1084, 648,1776,4626,2141, 324, 962,2012, # 1616 2177,2076,1384, 742,2178,1448,1173,1810, 222, 102, 301, 445, 125,2420, 662,2498, # 1632 277, 200,1476,1165,1068, 224,2562,1378,1446, 450,1880, 659, 791, 582,4627,2939, # 1648 3936,1516,1274, 555,2099,3697,1020,1389,1526,3380,1762,1723,1787,2229, 412,2114, # 1664 1900,2392,3518, 512,2597, 427,1925,2341,3122,1653,1686,2465,2499, 697, 330, 273, # 1680 380,2162, 951, 832, 780, 991,1301,3073, 965,2270,3519, 668,2523,2636,1286, 535, # 1696 1407, 518, 671, 957,2658,2378, 267, 611,2197,3030,6123, 248,2299, 967,1799,2356, # 1712 850,1418,3437,1876,1256,1480,2828,1718,6124,6125,1755,1664,2405,6126,4628,2879, # 1728 2829, 499,2179, 676,4629, 557,2329,2214,2090, 325,3234, 464, 811,3001, 992,2342, # 1744 2481,1232,1469, 303,2242, 466,1070,2163, 603,1777,2091,4630,2752,4631,2714, 322, # 1760 2659,1964,1768, 481,2188,1463,2330,2857,3600,2092,3031,2421,4632,2318,2070,1849, # 1776 2598,4633,1302,2254,1668,1701,2422,3811,2905,3032,3123,2046,4106,1763,1694,4634, # 1792 1604, 943,1724,1454, 917, 868,2215,1169,2940, 552,1145,1800,1228,1823,1955, 316, # 1808 1080,2510, 361,1807,2830,4107,2660,3381,1346,1423,1134,4108,6127, 541,1263,1229, # 1824 1148,2540, 545, 465,1833,2880,3438,1901,3074,2482, 816,3937, 713,1788,2500, 122, # 1840 1575, 195,1451,2501,1111,6128, 859, 374,1225,2243,2483,4317, 390,1033,3439,3075, # 1856 2524,1687, 266, 793,1440,2599, 946, 779, 802, 507, 897,1081, 528,2189,1292, 711, # 1872 1866,1725,1167,1640, 753, 398,2661,1053, 246, 348,4318, 137,1024,3440,1600,2077, # 1888 2129, 825,4319, 698, 238, 521, 187,2300,1157,2423,1641,1605,1464,1610,1097,2541, # 1904 1260,1436, 759,2255,1814,2150, 705,3235, 409,2563,3304, 561,3033,2005,2564, 726, # 1920 1956,2343,3698,4109, 949,3812,3813,3520,1669, 653,1379,2525, 881,2198, 632,2256, # 1936 1027, 778,1074, 733,1957, 514,1481,2466, 554,2180, 702,3938,1606,1017,1398,6129, # 1952 1380,3521, 921, 993,1313, 594, 449,1489,1617,1166, 768,1426,1360, 495,1794,3601, # 1968 1177,3602,1170,4320,2344, 476, 425,3167,4635,3168,1424, 401,2662,1171,3382,1998, # 1984 1089,4110, 477,3169, 474,6130,1909, 596,2831,1842, 494, 693,1051,1028,1207,3076, # 2000 606,2115, 727,2790,1473,1115, 743,3522, 630, 805,1532,4321,2021, 366,1057, 838, # 2016 684,1114,2142,4322,2050,1492,1892,1808,2271,3814,2424,1971,1447,1373,3305,1090, # 2032 1536,3939,3523,3306,1455,2199, 336, 369,2331,1035, 584,2393, 902, 718,2600,6131, # 2048 2753, 463,2151,1149,1611,2467, 715,1308,3124,1268, 343,1413,3236,1517,1347,2663, # 2064 2093,3940,2022,1131,1553,2100,2941,1427,3441,2942,1323,2484,6132,1980, 872,2368, # 2080 2441,2943, 320,2369,2116,1082, 679,1933,3941,2791,3815, 625,1143,2023, 422,2200, # 2096 3816,6133, 730,1695, 356,2257,1626,2301,2858,2637,1627,1778, 937, 883,2906,2693, # 2112 3002,1769,1086, 400,1063,1325,3307,2792,4111,3077, 456,2345,1046, 747,6134,1524, # 2128 884,1094,3383,1474,2164,1059, 974,1688,2181,2258,1047, 345,1665,1187, 358, 875, # 2144 3170, 305, 660,3524,2190,1334,1135,3171,1540,1649,2542,1527, 927, 968,2793, 885, # 2160 1972,1850, 482, 500,2638,1218,1109,1085,2543,1654,2034, 876, 78,2287,1482,1277, # 2176 861,1675,1083,1779, 724,2754, 454, 397,1132,1612,2332, 893, 672,1237, 257,2259, # 2192 2370, 135,3384, 337,2244, 547, 352, 340, 709,2485,1400, 788,1138,2511, 540, 772, # 2208 1682,2260,2272,2544,2013,1843,1902,4636,1999,1562,2288,4637,2201,1403,1533, 407, # 2224 576,3308,1254,2071, 978,3385, 170, 136,1201,3125,2664,3172,2394, 213, 912, 873, # 2240 3603,1713,2202, 699,3604,3699, 813,3442, 493, 531,1054, 468,2907,1483, 304, 281, # 2256 4112,1726,1252,2094, 339,2319,2130,2639, 756,1563,2944, 748, 571,2976,1588,2425, # 2272 2715,1851,1460,2426,1528,1392,1973,3237, 288,3309, 685,3386, 296, 892,2716,2216, # 2288 1570,2245, 722,1747,2217, 905,3238,1103,6135,1893,1441,1965, 251,1805,2371,3700, # 2304 2601,1919,1078, 75,2182,1509,1592,1270,2640,4638,2152,6136,3310,3817, 524, 706, # 2320 1075, 292,3818,1756,2602, 317, 98,3173,3605,3525,1844,2218,3819,2502, 814, 567, # 2336 385,2908,1534,6137, 534,1642,3239, 797,6138,1670,1529, 953,4323, 188,1071, 538, # 2352 178, 729,3240,2109,1226,1374,2000,2357,2977, 731,2468,1116,2014,2051,6139,1261, # 2368 1593, 803,2859,2736,3443, 556, 682, 823,1541,6140,1369,2289,1706,2794, 845, 462, # 2384 2603,2665,1361, 387, 162,2358,1740, 739,1770,1720,1304,1401,3241,1049, 627,1571, # 2400 2427,3526,1877,3942,1852,1500, 431,1910,1503, 677, 297,2795, 286,1433,1038,1198, # 2416 2290,1133,1596,4113,4639,2469,1510,1484,3943,6141,2442, 108, 712,4640,2372, 866, # 2432 3701,2755,3242,1348, 834,1945,1408,3527,2395,3243,1811, 824, 994,1179,2110,1548, # 2448 1453, 790,3003, 690,4324,4325,2832,2909,3820,1860,3821, 225,1748, 310, 346,1780, # 2464 2470, 821,1993,2717,2796, 828, 877,3528,2860,2471,1702,2165,2910,2486,1789, 453, # 2480 359,2291,1676, 73,1164,1461,1127,3311, 421, 604, 314,1037, 589, 116,2487, 737, # 2496 837,1180, 111, 244, 735,6142,2261,1861,1362, 986, 523, 418, 581,2666,3822, 103, # 2512 855, 503,1414,1867,2488,1091, 657,1597, 979, 605,1316,4641,1021,2443,2078,2001, # 2528 1209, 96, 587,2166,1032, 260,1072,2153, 173, 94, 226,3244, 819,2006,4642,4114, # 2544 2203, 231,1744, 782, 97,2667, 786,3387, 887, 391, 442,2219,4326,1425,6143,2694, # 2560 633,1544,1202, 483,2015, 592,2052,1958,2472,1655, 419, 129,4327,3444,3312,1714, # 2576 1257,3078,4328,1518,1098, 865,1310,1019,1885,1512,1734, 469,2444, 148, 773, 436, # 2592 1815,1868,1128,1055,4329,1245,2756,3445,2154,1934,1039,4643, 579,1238, 932,2320, # 2608 353, 205, 801, 115,2428, 944,2321,1881, 399,2565,1211, 678, 766,3944, 335,2101, # 2624 1459,1781,1402,3945,2737,2131,1010, 844, 981,1326,1013, 550,1816,1545,2620,1335, # 2640 1008, 371,2881, 936,1419,1613,3529,1456,1395,2273,1834,2604,1317,2738,2503, 416, # 2656 1643,4330, 806,1126, 229, 591,3946,1314,1981,1576,1837,1666, 347,1790, 977,3313, # 2672 764,2861,1853, 688,2429,1920,1462, 77, 595, 415,2002,3034, 798,1192,4115,6144, # 2688 2978,4331,3035,2695,2582,2072,2566, 430,2430,1727, 842,1396,3947,3702, 613, 377, # 2704 278, 236,1417,3388,3314,3174, 757,1869, 107,3530,6145,1194, 623,2262, 207,1253, # 2720 2167,3446,3948, 492,1117,1935, 536,1838,2757,1246,4332, 696,2095,2406,1393,1572, # 2736 3175,1782, 583, 190, 253,1390,2230, 830,3126,3389, 934,3245,1703,1749,2979,1870, # 2752 2545,1656,2204, 869,2346,4116,3176,1817, 496,1764,4644, 942,1504, 404,1903,1122, # 2768 1580,3606,2945,1022, 515, 372,1735, 955,2431,3036,6146,2797,1110,2302,2798, 617, # 2784 6147, 441, 762,1771,3447,3607,3608,1904, 840,3037, 86, 939,1385, 572,1370,2445, # 2800 1336, 114,3703, 898, 294, 203,3315, 703,1583,2274, 429, 961,4333,1854,1951,3390, # 2816 2373,3704,4334,1318,1381, 966,1911,2322,1006,1155, 309, 989, 458,2718,1795,1372, # 2832 1203, 252,1689,1363,3177, 517,1936, 168,1490, 562, 193,3823,1042,4117,1835, 551, # 2848 470,4645, 395, 489,3448,1871,1465,2583,2641, 417,1493, 279,1295, 511,1236,1119, # 2864 72,1231,1982,1812,3004, 871,1564, 984,3449,1667,2696,2096,4646,2347,2833,1673, # 2880 3609, 695,3246,2668, 807,1183,4647, 890, 388,2333,1801,1457,2911,1765,1477,1031, # 2896 3316,3317,1278,3391,2799,2292,2526, 163,3450,4335,2669,1404,1802,6148,2323,2407, # 2912 1584,1728,1494,1824,1269, 298, 909,3318,1034,1632, 375, 776,1683,2061, 291, 210, # 2928 1123, 809,1249,1002,2642,3038, 206,1011,2132, 144, 975, 882,1565, 342, 667, 754, # 2944 1442,2143,1299,2303,2062, 447, 626,2205,1221,2739,2912,1144,1214,2206,2584, 760, # 2960 1715, 614, 950,1281,2670,2621, 810, 577,1287,2546,4648, 242,2168, 250,2643, 691, # 2976 123,2644, 647, 313,1029, 689,1357,2946,1650, 216, 771,1339,1306, 808,2063, 549, # 2992 913,1371,2913,2914,6149,1466,1092,1174,1196,1311,2605,2396,1783,1796,3079, 406, # 3008 2671,2117,3949,4649, 487,1825,2220,6150,2915, 448,2348,1073,6151,2397,1707, 130, # 3024 900,1598, 329, 176,1959,2527,1620,6152,2275,4336,3319,1983,2191,3705,3610,2155, # 3040 3706,1912,1513,1614,6153,1988, 646, 392,2304,1589,3320,3039,1826,1239,1352,1340, # 3056 2916, 505,2567,1709,1437,2408,2547, 906,6154,2672, 384,1458,1594,1100,1329, 710, # 3072 423,3531,2064,2231,2622,1989,2673,1087,1882, 333, 841,3005,1296,2882,2379, 580, # 3088 1937,1827,1293,2585, 601, 574, 249,1772,4118,2079,1120, 645, 901,1176,1690, 795, # 3104 2207, 478,1434, 516,1190,1530, 761,2080, 930,1264, 355, 435,1552, 644,1791, 987, # 3120 220,1364,1163,1121,1538, 306,2169,1327,1222, 546,2645, 218, 241, 610,1704,3321, # 3136 1984,1839,1966,2528, 451,6155,2586,3707,2568, 907,3178, 254,2947, 186,1845,4650, # 3152 745, 432,1757, 428,1633, 888,2246,2221,2489,3611,2118,1258,1265, 956,3127,1784, # 3168 4337,2490, 319, 510, 119, 457,3612, 274,2035,2007,4651,1409,3128, 970,2758, 590, # 3184 2800, 661,2247,4652,2008,3950,1420,1549,3080,3322,3951,1651,1375,2111, 485,2491, # 3200 1429,1156,6156,2548,2183,1495, 831,1840,2529,2446, 501,1657, 307,1894,3247,1341, # 3216 666, 899,2156,1539,2549,1559, 886, 349,2208,3081,2305,1736,3824,2170,2759,1014, # 3232 1913,1386, 542,1397,2948, 490, 368, 716, 362, 159, 282,2569,1129,1658,1288,1750, # 3248 2674, 276, 649,2016, 751,1496, 658,1818,1284,1862,2209,2087,2512,3451, 622,2834, # 3264 376, 117,1060,2053,1208,1721,1101,1443, 247,1250,3179,1792,3952,2760,2398,3953, # 3280 6157,2144,3708, 446,2432,1151,2570,3452,2447,2761,2835,1210,2448,3082, 424,2222, # 3296 1251,2449,2119,2836, 504,1581,4338, 602, 817, 857,3825,2349,2306, 357,3826,1470, # 3312 1883,2883, 255, 958, 929,2917,3248, 302,4653,1050,1271,1751,2307,1952,1430,2697, # 3328 2719,2359, 354,3180, 777, 158,2036,4339,1659,4340,4654,2308,2949,2248,1146,2232, # 3344 3532,2720,1696,2623,3827,6158,3129,1550,2698,1485,1297,1428, 637, 931,2721,2145, # 3360 914,2550,2587, 81,2450, 612, 827,2646,1242,4655,1118,2884, 472,1855,3181,3533, # 3376 3534, 569,1353,2699,1244,1758,2588,4119,2009,2762,2171,3709,1312,1531,6159,1152, # 3392 1938, 134,1830, 471,3710,2276,1112,1535,3323,3453,3535, 982,1337,2950, 488, 826, # 3408 674,1058,1628,4120,2017, 522,2399, 211, 568,1367,3454, 350, 293,1872,1139,3249, # 3424 1399,1946,3006,1300,2360,3324, 588, 736,6160,2606, 744, 669,3536,3828,6161,1358, # 3440 199, 723, 848, 933, 851,1939,1505,1514,1338,1618,1831,4656,1634,3613, 443,2740, # 3456 3829, 717,1947, 491,1914,6162,2551,1542,4121,1025,6163,1099,1223, 198,3040,2722, # 3472 370, 410,1905,2589, 998,1248,3182,2380, 519,1449,4122,1710, 947, 928,1153,4341, # 3488 2277, 344,2624,1511, 615, 105, 161,1212,1076,1960,3130,2054,1926,1175,1906,2473, # 3504 414,1873,2801,6164,2309, 315,1319,3325, 318,2018,2146,2157, 963, 631, 223,4342, # 3520 4343,2675, 479,3711,1197,2625,3712,2676,2361,6165,4344,4123,6166,2451,3183,1886, # 3536 2184,1674,1330,1711,1635,1506, 799, 219,3250,3083,3954,1677,3713,3326,2081,3614, # 3552 1652,2073,4657,1147,3041,1752, 643,1961, 147,1974,3955,6167,1716,2037, 918,3007, # 3568 1994, 120,1537, 118, 609,3184,4345, 740,3455,1219, 332,1615,3830,6168,1621,2980, # 3584 1582, 783, 212, 553,2350,3714,1349,2433,2082,4124, 889,6169,2310,1275,1410, 973, # 3600 166,1320,3456,1797,1215,3185,2885,1846,2590,2763,4658, 629, 822,3008, 763, 940, # 3616 1990,2862, 439,2409,1566,1240,1622, 926,1282,1907,2764, 654,2210,1607, 327,1130, # 3632 3956,1678,1623,6170,2434,2192, 686, 608,3831,3715, 903,3957,3042,6171,2741,1522, # 3648 1915,1105,1555,2552,1359, 323,3251,4346,3457, 738,1354,2553,2311,2334,1828,2003, # 3664 3832,1753,2351,1227,6172,1887,4125,1478,6173,2410,1874,1712,1847, 520,1204,2607, # 3680 264,4659, 836,2677,2102, 600,4660,3833,2278,3084,6174,4347,3615,1342, 640, 532, # 3696 543,2608,1888,2400,2591,1009,4348,1497, 341,1737,3616,2723,1394, 529,3252,1321, # 3712 983,4661,1515,2120, 971,2592, 924, 287,1662,3186,4349,2700,4350,1519, 908,1948, # 3728 2452, 156, 796,1629,1486,2223,2055, 694,4126,1259,1036,3392,1213,2249,2742,1889, # 3744 1230,3958,1015, 910, 408, 559,3617,4662, 746, 725, 935,4663,3959,3009,1289, 563, # 3760 867,4664,3960,1567,2981,2038,2626, 988,2263,2381,4351, 143,2374, 704,1895,6175, # 3776 1188,3716,2088, 673,3085,2362,4352, 484,1608,1921,2765,2918, 215, 904,3618,3537, # 3792 894, 509, 976,3043,2701,3961,4353,2837,2982, 498,6176,6177,1102,3538,1332,3393, # 3808 1487,1636,1637, 233, 245,3962, 383, 650, 995,3044, 460,1520,1206,2352, 749,3327, # 3824 530, 700, 389,1438,1560,1773,3963,2264, 719,2951,2724,3834, 870,1832,1644,1000, # 3840 839,2474,3717, 197,1630,3394, 365,2886,3964,1285,2133, 734, 922, 818,1106, 732, # 3856 480,2083,1774,3458, 923,2279,1350, 221,3086, 85,2233,2234,3835,1585,3010,2147, # 3872 1387,1705,2382,1619,2475, 133, 239,2802,1991,1016,2084,2383, 411,2838,1113, 651, # 3888 1985,1160,3328, 990,1863,3087,1048,1276,2647, 265,2627,1599,3253,2056, 150, 638, # 3904 2019, 656, 853, 326,1479, 680,1439,4354,1001,1759, 413,3459,3395,2492,1431, 459, # 3920 4355,1125,3329,2265,1953,1450,2065,2863, 849, 351,2678,3131,3254,3255,1104,1577, # 3936 227,1351,1645,2453,2193,1421,2887, 812,2121, 634, 95,2435, 201,2312,4665,1646, # 3952 1671,2743,1601,2554,2702,2648,2280,1315,1366,2089,3132,1573,3718,3965,1729,1189, # 3968 328,2679,1077,1940,1136, 558,1283, 964,1195, 621,2074,1199,1743,3460,3619,1896, # 3984 1916,1890,3836,2952,1154,2112,1064, 862, 378,3011,2066,2113,2803,1568,2839,6178, # 4000 3088,2919,1941,1660,2004,1992,2194, 142, 707,1590,1708,1624,1922,1023,1836,1233, # 4016 1004,2313, 789, 741,3620,6179,1609,2411,1200,4127,3719,3720,4666,2057,3721, 593, # 4032 2840, 367,2920,1878,6180,3461,1521, 628,1168, 692,2211,2649, 300, 720,2067,2571, # 4048 2953,3396, 959,2504,3966,3539,3462,1977, 701,6181, 954,1043, 800, 681, 183,3722, # 4064 1803,1730,3540,4128,2103, 815,2314, 174, 467, 230,2454,1093,2134, 755,3541,3397, # 4080 1141,1162,6182,1738,2039, 270,3256,2513,1005,1647,2185,3837, 858,1679,1897,1719, # 4096 2954,2324,1806, 402, 670, 167,4129,1498,2158,2104, 750,6183, 915, 189,1680,1551, # 4112 455,4356,1501,2455, 405,1095,2955, 338,1586,1266,1819, 570, 641,1324, 237,1556, # 4128 2650,1388,3723,6184,1368,2384,1343,1978,3089,2436, 879,3724, 792,1191, 758,3012, # 4144 1411,2135,1322,4357, 240,4667,1848,3725,1574,6185, 420,3045,1546,1391, 714,4358, # 4160 1967, 941,1864, 863, 664, 426, 560,1731,2680,1785,2864,1949,2363, 403,3330,1415, # 4176 1279,2136,1697,2335, 204, 721,2097,3838, 90,6186,2085,2505, 191,3967, 124,2148, # 4192 1376,1798,1178,1107,1898,1405, 860,4359,1243,1272,2375,2983,1558,2456,1638, 113, # 4208 3621, 578,1923,2609, 880, 386,4130, 784,2186,2266,1422,2956,2172,1722, 497, 263, # 4224 2514,1267,2412,2610, 177,2703,3542, 774,1927,1344, 616,1432,1595,1018, 172,4360, # 4240 2325, 911,4361, 438,1468,3622, 794,3968,2024,2173,1681,1829,2957, 945, 895,3090, # 4256 575,2212,2476, 475,2401,2681, 785,2744,1745,2293,2555,1975,3133,2865, 394,4668, # 4272 3839, 635,4131, 639, 202,1507,2195,2766,1345,1435,2572,3726,1908,1184,1181,2457, # 4288 3727,3134,4362, 843,2611, 437, 916,4669, 234, 769,1884,3046,3047,3623, 833,6187, # 4304 1639,2250,2402,1355,1185,2010,2047, 999, 525,1732,1290,1488,2612, 948,1578,3728, # 4320 2413,2477,1216,2725,2159, 334,3840,1328,3624,2921,1525,4132, 564,1056, 891,4363, # 4336 1444,1698,2385,2251,3729,1365,2281,2235,1717,6188, 864,3841,2515, 444, 527,2767, # 4352 2922,3625, 544, 461,6189, 566, 209,2437,3398,2098,1065,2068,3331,3626,3257,2137, # 4368 #last 512 )

e851f983570ccecaa86411e210398fa509c5ee74

py

Python

enas/controller.py

dnddnjs/pytorch-vision

d432b467774f838bef37372d6cff3576c6559803

2018-10-14T12:13:54.000Z

2021-12-12T17:48:35.000Z

enas/controller.py

dnddnjs/pytorch-vision

d432b467774f838bef37372d6cff3576c6559803

2018-10-11T01:29:39.000Z

2019-05-29T23:44:49.000Z

enas/controller.py

dnddnjs/pytorch-vision

d432b467774f838bef37372d6cff3576c6559803

2018-11-14T14:01:16.000Z

2021-12-07T00:17:41.000Z

import torch import torch.nn as nn import torch.nn.functional as F import numpy as np device = 'cuda' if torch.cuda.is_available() else 'cpu' class Controller(nn.Module): def __init__(self): super(Controller, self).__init__() # constants self.num_nodes = 7 self.lstm_size = 64 self.tanh_constant = 1.10 self.op_tanh_reduce = 2.5 self.additional_bias = torch.Tensor([0.25, 0.25, -0.25, -0.25, -0.25]).to(device) # layers self.embed_first = nn.Embedding(num_embeddings=1, embedding_dim=self.lstm_size) self.embed_ops = nn.Embedding(num_embeddings=5, embedding_dim=self.lstm_size) self.lstm = nn.LSTMCell(input_size=self.lstm_size, hidden_size=self.lstm_size, bias=False) self.init_hidden(batch_size=1) # fully-connected layers for index of previous cell outputs self.fc_index_prev = nn.Linear(in_features=self.lstm_size, out_features=self.lstm_size, bias=False) self.fc_index_curr = nn.Linear(in_features=self.lstm_size, out_features=self.lstm_size, bias=False) self.fc_index_out = nn.Linear(in_features=self.lstm_size, out_features=1, bias=False) # fully-connected layer for 5 operations self.fc_ops = nn.Linear(in_features=self.lstm_size, out_features=5) # init parameters self.init_parameters() def init_parameters(self): torch.nn.init.xavier_uniform_(self.embed_first.weight) torch.nn.init.xavier_uniform_(self.embed_ops.weight) torch.nn.init.xavier_uniform_(self.lstm.weight_hh) torch.nn.init.xavier_uniform_(self.lstm.weight_ih) self.fc_ops.bias.data = torch.Tensor([10, 10, 0, 0, 0]) def init_hidden(self, batch_size): self.hx = torch.zeros(batch_size, self.lstm_size).to(device) self.cx = torch.zeros(batch_size, self.lstm_size).to(device) # prev_lstm_outputs is a placeholder for saving previous cell's lstm output # The linear transformation of lstm output is saved at prev_fc_outputs. def sample_cell(self, arc_seq, entropy_list, log_prob_list, use_additional_bias): inputs = torch.zeros(1).long().to(device) inputs = self.embed_first(inputs) # lstm should have a dynamic size of output for indices of previous layer. # so save previous lstm outputs and fc outputs as a list prev_lstm_outputs, prev_fc_outputs = list(), list() for node_id in range(2): hidden = (self.hx, self.cx) self.hx, self.cx = self.lstm(inputs, hidden) prev_lstm_outputs.append(torch.zeros_like(self.hx)) prev_fc_outputs.append(self.fc_index_prev(self.hx.clone())) for node_id in range(2, self.num_nodes): # sample 2 indices to select input of the node for i in range(2): hidden = (self.hx, self.cx) self.hx, self.cx = self.lstm(inputs, hidden) # todo: need to be fixed logits = self.fc_index_curr(self.hx) query = torch.cat(prev_fc_outputs) query = torch.tanh(query + logits) query = self.fc_index_out(query) logits = query.view(query.size(-1), -1) logits = self.tanh_constant * torch.tanh(logits) probs = F.softmax(logits, dim=-1) log_prob = F.log_softmax(logits, dim=-1) action = torch.multinomial(probs, 1)[0] arc_seq.append(action) selected_log_prob = log_prob[:, action.long()] entropy = -(log_prob * probs).sum(1, keepdim=False) entropy_list.append(entropy) log_prob_list.append(selected_log_prob) # next input for lstm is the output of selected previous node index inputs = prev_lstm_outputs[action] # sample 2 operations for computation for i in range(2): hidden = (self.hx, self.cx) self.hx, self.cx = self.lstm(inputs, hidden) logits = self.fc_ops(self.hx) logits = (self.tanh_constant / self.op_tanh_reduce) * torch.tanh(logits) if use_additional_bias: logits += self.additional_bias probs = F.softmax(logits, dim=-1) log_prob = F.log_softmax(logits, dim=-1) action = torch.multinomial(probs, 1)[0] arc_seq.append(action) selected_log_prob = log_prob[:, action.long()] entropy = -(log_prob * probs).sum(1, keepdim=False) entropy_list.append(entropy) log_prob_list.append(selected_log_prob) inputs = self.embed_ops(action) hidden = (self.hx, self.cx) self.hx, self.cx = self.lstm(inputs, hidden) prev_lstm_outputs.append(self.hx.clone()) prev_fc_outputs.append(self.fc_index_prev(self.hx.clone())) inputs = torch.zeros(1).long().to(device) inputs = self.embed_first(inputs) return arc_seq, entropy_list, log_prob_list # sample child model specifications # this is micro controller so sample architecture for 2 cells(normal, reduction) def sample_child(self): # for each node, there is 4 indices for constructing architecture of the node. # 2 previous node indices and 2 operation indices normal_arc, reduction_arc = [], [] # entropy and log prob is for the training of controller entropy_list, log_prob_list = [], [] # sample normal architecture outputs = self.sample_cell(normal_arc, entropy_list, log_prob_list, True) normal_arc, entropy_list, log_prob_list = outputs # sample reduction architecture outputs = self.sample_cell(reduction_arc, entropy_list, log_prob_list, True) reduction_arc, entropy_list, log_prob_list = outputs return normal_arc, reduction_arc, entropy_list, log_prob_list

792410a8878c6963d6cd8112b58e1a46e3656b36

py

Python

Hotails/asgi.py

ErezCohenn/Beyond-07-team-1

37eed5bf1b0902b21f7c824acfd25634c40270db

2022-03-03T12:03:17.000Z

2022-03-03T12:03:17.000Z

Hotails/asgi.py

ErezCohenn/Beyond-07-team-1

37eed5bf1b0902b21f7c824acfd25634c40270db

2022-03-07T14:14:48.000Z

2022-03-31T18:37:52.000Z

Hotails/asgi.py

ErezCohenn/Beyond-07-team-1

37eed5bf1b0902b21f7c824acfd25634c40270db

2022-02-28T18:55:09.000Z

2022-03-06T08:04:40.000Z

""" ASGI config for Hotails project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/4.0/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Hotails.settings') application = get_asgi_application()

ed213be000f725f5747faae6289523d918b77a47

py

Python

foo/lc/leetcode/editor/cn/[144]Binary Tree Preorder Traversal.py

JaeCoding/keepPying

5bf07b34a6c63e9a6bd2b39c17149adb2dc59570

2020-02-24T15:15:55.000Z

2020-02-24T15:15:55.000Z

foo/lc/leetcode/editor/cn/[144]Binary Tree Preorder Traversal.py

JaeCoding/keepPying

5bf07b34a6c63e9a6bd2b39c17149adb2dc59570

foo/lc/leetcode/editor/cn/[144]Binary Tree Preorder Traversal.py

JaeCoding/keepPying

5bf07b34a6c63e9a6bd2b39c17149adb2dc59570

# Given the root of a binary tree, return the preorder traversal of its nodes' v # alues. # # # Example 1: # # # Input: root = [1,null,2,3] # Output: [1,2,3] # # # Example 2: # # # Input: root = [] # Output: [] # # # Example 3: # # # Input: root = [1] # Output: [1] # # # Example 4: # # # Input: root = [1,2] # Output: [1,2] # # # Example 5: # # # Input: root = [1,null,2] # Output: [1,2] # # # # Constraints: # # # The number of nodes in the tree is in the range [0, 100]. # -100 <= Node.val <= 100 # # # # # Follow up: # # Recursive solution is trivial, could you do it iteratively? # # # Related Topics 栈 树 # 👍 426 👎 0 # leetcode submit region begin(Prohibit modification and deletion) # Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right from typing import List from foo.lc.leetcode.editor.TreeNode import TreeNode from foo.lc.leetcode.editor.TreeUtil import TreeUtil class Solution: def preorderTraversal(self, root: TreeNode) -> List[int]: result = [] def pre(node: TreeNode): if not node: return result.append(node.val) pre(node.left) pre(node.right) pre(root) return result def preorderTraversal2(self, root: TreeNode) -> List[int]: result = [] stack = [] if root: stack.append(root) while stack: node = stack.pop() result.append(node.val) if node.right: stack.append(node.right) if node.left: stack.append(node.left) return result # root = TreeUtil.creat_tree([1,2,3,4,5,6,7]) root = TreeUtil.creat_tree([]) a = Solution().preorderTraversal2(root) print(a) # leetcode submit region end(Prohibit modification and deletion)

5419578dbc621b84a223b528e23753aaa12b74cb

py

Python

ConvNN.py

pierremtb/modded-MNIST-digit-classification

18f55fb00ca6a436712707ab1a21eafaf384553d

ConvNN.py

pierremtb/modded-MNIST-digit-classification

18f55fb00ca6a436712707ab1a21eafaf384553d

ConvNN.py

pierremtb/modded-MNIST-digit-classification

18f55fb00ca6a436712707ab1a21eafaf384553d

# Michael Segev # Pierre Jacquier # Albert Faucher # Group 70 # COMP 551 MP3 # March 18 2019 import torch import torch.nn as nn import torch.optim import torch.nn.functional as F import matplotlib.pyplot as plt import math from helpers import * class ConvNN(torch.nn.Module): def __init__(self): super(ConvNN, self).__init__() # call the inherited class constructor print("Model: ConvNN") # define the architecture of the neural network self.conv1 = nn.Sequential( nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5), # output is 60x60 nn.BatchNorm2d(32), nn.ReLU(True), nn.MaxPool2d(2, 2) # output is 30x30 ) self.conv2 = nn.Sequential( nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5), # output is 26x26 nn.BatchNorm2d(64), nn.ReLU(True), nn.MaxPool2d(2, 2) # output is 13x13 ) self.linear1 = nn.Sequential( torch.nn.Linear(64*13*13, 1000), nn.ReLU(True) ) self.linear2 = nn.Sequential( torch.nn.Linear(1000, 200), nn.ReLU(True) ) self.linear3 = torch.nn.Linear(200, 10) self.losses = [] self.accuracies = [] self.val_accuracies = [] self.loss_LPF = 2.3 self.criterion = None self.optimizer = None def init_optimizer(self): # loss function # self.criterion = torch.nn.MSELoss(reduction='sum') self.criterion = torch.nn.CrossEntropyLoss() # optimizer lr = 1e-2 print("Learning rate: {}".format(lr)) # self.optimizer = torch.optim.Adam(self.parameters(), lr=lr) self.optimizer = torch.optim.SGD(self.parameters(), lr=lr, momentum=0.9) def forward(self, x): h = self.conv1(x) h = self.conv2(h) h = h.reshape(h.size(0), -1) h = self.linear1(h) h = self.linear2(h) y_pred = self.linear3(h) return y_pred def train_batch(self, x, y): # Forward pass: Compute predicted y by passing x to the model y_pred = self(x) # Compute and print loss loss = self.criterion(y_pred, y) self.losses.append(float(loss.data.item())) # Record accuracy total = y.size(0) _, predicted = torch.max(y_pred.data, 1) correct = (predicted == y).sum().item() acc = correct / total self.accuracies.append(acc) # Reset gradients to zero, perform a backward pass, and update the weights. self.optimizer.zero_grad() loss.backward() self.optimizer.step() return loss, acc def train_all_batches(self, x, y, batch_size, num_epochs, loss_target, device, x_val=[], y_val=[], val_skip=0): # figure out how many batches we can make num_batches = int(y.shape[0] / batch_size) last_batch_size = batch_size print("Number of batches = {}".format(num_batches)) if y.shape[0] % batch_size != 0: num_batches += 1 last_batch_size = y.shape[0] % batch_size for epoch in range(num_epochs): if self.loss_LPF < loss_target: print("reached loss target, ending early!") break for batch_num in range(num_batches): # slice tensors according into requested batch if batch_num == num_batches - 1: # last batch logic! # print("Last batch!") current_batch_size = last_batch_size else: current_batch_size = batch_size x_batch = torch.tensor( x[batch_num * current_batch_size:batch_num * current_batch_size + current_batch_size], dtype=torch.float32, requires_grad=True, device=device) y_batch = torch.tensor( y[batch_num * current_batch_size:batch_num * current_batch_size + current_batch_size], dtype=torch.long, requires_grad=False, device=device) loss, acc = self.train_batch(x_batch, y_batch) self.loss_LPF = 0.01 * float(loss.data.item()) + 0.99*self.loss_LPF val_acc = 0 if batch_num % ((val_skip + 1) * 40) == 0 and len(x_val) == len(y_val) and len(x_val) > 0: val_acc = validate_data(self, x_val, y_val, device) self.val_accuracies.append(val_acc) if batch_num % 40 == 0: toPrint = "Epoch: {}, Loss: {}, Acc: {}%".format(epoch, self.loss_LPF, round(acc * 100, 3)) if (val_acc > 0): toPrint += ", ValAcc: {}%".format(round(val_acc * 100, 3)) print(toPrint) def plot_loss(self): plt.title('Loss over time') plt.xlabel('Epoch') plt.ylabel('Loss') plt.plot(self.losses) plt.show() def plot_acc(self): plt.title('Accuracy over time') plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.plot(self.accuracies) plt.plot(self.val_accuracies) plt.show()

065e304b12cabb5b6c6f2e24116af47fd95a0efd

py

Python

src/files.py

agtoever/twixtbot-ui

366d7bef33fdbaa260ea8b3330fa9ab29ad05f03

src/files.py

agtoever/twixtbot-ui

366d7bef33fdbaa260ea8b3330fa9ab29ad05f03

2021-11-10T20:13:45.000Z

2022-01-12T07:38:04.000Z

src/files.py

agtoever/twixtbot-ui

366d7bef33fdbaa260ea8b3330fa9ab29ad05f03

import PySimpleGUI as sg from backend import twixt import layout as lt import string def str2twixt(move): """ Converts one move string to a twixt backend class move. Handles both T1-style coordinates (e.g.: 'd5', 'f18'') as well as tsgf- style coordinates (e.g.: 'fg', 'bi') as well as special strings ('swap' and 'resign'). It can handle letter in upper as well as lowercase. Args: move: string with a move Returns: twixt.SWAP or twixt.RESIGN or twixt.Point Raises ValueError if the move_str can't be parsed in any valid format Examples: >>> str2twixt('b3') b3 >>> str2twixt('i18') i18 >>> str2twixt('fj') f10 >>> str2twixt('swap') 'swap' >>> str2twixt('resign') 'resign' >>> str2twixt('123') ValueError: Can't parse move: '123' >>> str2twixt('invalid') ValueError: Can't parse move: 'invalid' """ # Handle swap and resign if move.lower() == twixt.SWAP.lower(): return twixt.SWAP elif move.lower() == twixt.RESIGN.lower(): return twixt.RESIGN # Handle T1-style moves elif move[0] in string.ascii_letters and move[-1] in string.digits: return twixt.Point(move) # Handle tsgf-stype moves elif len(move) == 2 and all(c in string.ascii_letters for c in move): return twixt.Point(move[0] + str(ord(move[1].lower()) - ord('a') + 1)) # Can't handle move. Throw exception raise ValueError(f"Can't parse move: '{move}'") def parse_t1_file(content): """Returns (players, moves) from a list of strings from a T1 file Args: content: list of strings: content from a T1 file Returns: tuple: (list: players as strings, list: twixt moves) Raises: ValueError: if players or moves data can't be interpreted Examples: >>> content = [ '# File created by T1j', '# T1j is a program to play TwixT ([email protected])', '1 # version of file-format', 'Player# Name of Player 1', 'Computer# Name of Player 2', '24# y-size of board', '24# x-size of board', 'H# player 1 human or computer', 'C# player 2 human or computer', '1# starting player (1 plays top-down)', 'V# Direction of letters', 'N# pierule?', 'N# game already over?', 'L10', 'L17', 'Q15', 'Q8', 'S12', 'P11', 'O14', 'P19', 'V18', 'U15', 'V16', 'T17', 'U14', 'V17', 'W16', 'W15', 'F16', 'L19', 'F20', 'I14', 'F12', 'X13', 'G14', 'G8', 'I9', 'J9', 'J7', 'E9', 'G10', 'N18', 'J3', 'G20', 'G18', 'E21'] >>> parse_t1_file(content) (['Player', 'Computer'], [l10, l17, q15, q8, s12, p11, o14, p19, v18, u15, v16, t17, u14, v17, w16, w15, f16, l19, f20, i14, f12, x13, g14, g8, i9, j9, j7, e9, g10, n18, j3, g20, g18, e21]) """ MOVES_STARTLINE = 13 PLAYER_LINES = [3, 4] COMMENT_CHAR = '#' try: players = [content[linenr].split(COMMENT_CHAR)[0] for linenr in PLAYER_LINES] except Exception: raise ValueError("Can't read player names from T1 file") try: moves = [str2twixt(move) for move in content[MOVES_STARTLINE:] if len(move) > 0] except Exception: # Just pass on the exception from str2twixt raise return players, moves def parse_tsgf_file(content): """Returns (players, moves) from a list of strings from a tsgf file Args: content: list of strings: content from a tsgf file Returns: tuple: (list: players as strings, list: twixt moves) Raises: ValueError: if players or moves data can't be interpreted Examples: >>> content = [ ('(;FF[4]EV[twixt.ld.DEFAULT]PB[agtoever]PW[Jan Krabbenbos]SZ[24]' 'SO[https://www.littlegolem.net];b[pl];r[ps];b[pr];r[rt];b[ot];' 'r[po];b[pn];r[qq];b[op];r[pg];b[nh];r[oj];b[oi];r[qi];b[nk];' 'r[nf];b[mf])')] >>> parse_tsgf_file(content) (['agtoever', 'Jan Krabbenbos'], [p12, p19, p18, r20, o20, p15, p14, q17, o16, p7, n8, o10, o9, q9, n11, n6, m6]) """ PLAYERS_STR = ('PB', 'PW') TURN_STR = ('r[', 'b[') FIELD_SEP = ';' if len(content) > 1: raise ValueError('Found more than 1 line in a tsgf file.') try: player_idx = [content[0].find(key) for key in PLAYERS_STR] players = [content[0][idx + 3:content[0].find(']', idx)] for idx in player_idx] except Exception: raise ValueError("Can't read player names from tsgf file") try: raw_moves = [field[2:field.find('|') if '|' in field else field.find(']')] for field in content[0].split(FIELD_SEP) if field[:2] in TURN_STR] moves = list(map(str2twixt, raw_moves)) except Exception: # Just pass on the exception from str2twixt raise return players, moves def get_game(curent_cross_lines_setting=False): """Returns (players, moves) from a file, chosen by the user Shows a file-open dialog to the user. The chosen file is read and parsed into players and moves. If the file is a tsgf file, the user is asked if the setting to allow crossing lines should be enabled, because Little Golem plays with corring lines allowed by default. The resulting player name list and moves list is returned. Finally a boolean is returned, which indicates if crossing lines should be set to enabled (True) or if it should be left in the current state (False). Exceptions that occur while opening and/or parsing the file are handled within this function. Args: curent_cross_lines_setting (bool): current setting for crossing lines Returns: tuple: (list: players as strings, list: twixt moves, bool: enable_crossing_lines) """ RETURN_ON_FAILURE = None, None, False # Get filename file_name = sg.PopupGetFile('Choose file', file_types=( ("All Files", "*.*"), ("T1j Files", "*.T1"), ("Little Golem Files", "*.tsgf")), no_window=True, keep_on_top=True) if file_name is None or file_name == "": return RETURN_ON_FAILURE # Open file try: with open(file_name, "tr") as f: content = list(map(lambda s: s.strip(), f.readlines())) except Exception: sg.popup_ok(f"Can't open {file_name} as a valid Twixt file.") return RETURN_ON_FAILURE # Parse file try: if file_name[-2:].upper() == 'T1': players, moves = parse_t1_file(content) return players, moves, False elif file_name[-4:].lower() == 'tsgf': enable_crossing_lines = False if not curent_cross_lines_setting: enable_crossing_lines = sg.popup_yes_no( "You have opened a .tsgf file, which propably comes " "from LittleGolem. By default, LittleGolem allows " "crossing lines. You don't have crossing lines enabled. " "Do you want to enable crossing lines?", title='Enable crossing lines?') == "Yes" players, moves = parse_tsgf_file(content) return players, moves, enable_crossing_lines else: lt.popup("Didn't recognize the filename extension.") except Exception as e: sg.popup_ok(f"Error '{e}' while opening file {file_name}") return RETURN_ON_FAILURE def save_game(players=['Player1', 'Player2'], moves=[''], board_size=24, game_over=False): """ Saves a Twixt game to T1 file, chosen by the user Shows a file-save dialog to the user. The twixt game given by the function parameters are saved to the file. Only .T1 file format is currently supported. Exceptions that occur while saving the file are handled within this function. Args: players: list of two strings with player names moves: list of twixt moves board_size: int with board size (defaults to 24) game_over: boolean, true if the game is over (defaults to False) Returns: None """ # Get filename file_name = sg.PopupGetFile('Choose file', file_types=( ("T1j Files", "*.T1"),), no_window=True, save_as=True, keep_on_top=True) if file_name is None or file_name == "": return # Build file contents try: content = [ '# File created by twixtbot-ui', ('# twixtbot-ui is a program to play TwixtT ' '(https://github.com/stevens68/twixtbot-ui)'), '1 # version of file-format', str(players[0]) + ' # Name of player 1', str(players[1]) + ' # Name of player 2', str(board_size) + ' # y-size of board', str(board_size) + ' # x-size of board', 'H # player 1 human or computer', 'H # player 2 human or computer', '1 # starting player (1 plays top-down)', 'V # direction of letters', 'Y # pierule?', ('Y' if game_over else 'N') + ' # game already over?' ] content += [str(m).upper() for m in moves] except Exception as e: sg.popup_ok('Could not create file contents. Game is NOT saved!\n' f'Python error: {e}') return # Write file try: with open(file_name, "tw") as f: f.write('\n'.join(content)) except Exception: sg.popup_ok(f"Can't write {file_name}. Game is NOT saved!") return sg.popup_ok(f'Game saved successfully as {file_name}') return

358435ab6938a9d40de3109dec9caa90f34c2864

py

Python

jdcloud_sdk/services/disk/client/DiskClient.py

jdcloud-apigateway/jdcloud-sdk-python

0886769bcf1fb92128a065ff0f4695be099571cc

2018-04-19T09:53:56.000Z

2022-01-27T06:05:48.000Z

jdcloud_sdk/services/disk/client/DiskClient.py

jdcloud-apigateway/jdcloud-sdk-python

0886769bcf1fb92128a065ff0f4695be099571cc

2018-09-11T05:39:54.000Z

2021-07-02T12:38:02.000Z

jdcloud_sdk/services/disk/client/DiskClient.py

jdcloud-apigateway/jdcloud-sdk-python

0886769bcf1fb92128a065ff0f4695be099571cc

2018-04-20T05:29:16.000Z

2022-02-17T09:10:05.000Z

# coding=utf8 # Copyright 2018 JDCLOUD.COM # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # NOTE: This class is auto generated by the jdcloud code generator program. from jdcloud_sdk.core.jdcloudclient import JDCloudClient from jdcloud_sdk.core.config import Config class DiskClient(JDCloudClient): def __init__(self, credential, config=None, logger=None): if config is None: config = Config('disk.jdcloud-api.com') super(DiskClient, self).__init__(credential, config, 'disk', '0.12.7', logger)

e99aa428e4ebfd014ec1d6c28b062b86d539e43b

py

Python

Vision/AnalyzeFormV2/AnalyzeForm/__init__.py

iii-PaulCridland/azure-search-power-skills

bbc5848c32b3bd6f2c8942693d854563e0cee708

2019-06-12T19:24:34.000Z

2022-03-08T18:39:40.000Z

Vision/AnalyzeFormV2/AnalyzeForm/__init__.py

iii-PaulCridland/azure-search-power-skills

bbc5848c32b3bd6f2c8942693d854563e0cee708

2019-07-15T22:04:23.000Z

2022-03-04T18:35:57.000Z

Vision/AnalyzeFormV2/AnalyzeForm/__init__.py

iii-PaulCridland/azure-search-power-skills

bbc5848c32b3bd6f2c8942693d854563e0cee708

2019-06-28T20:56:21.000Z

2022-03-30T17:17:24.000Z

import logging import json import os import logging import pathlib from azure.core.exceptions import ResourceNotFoundError from azure.ai.formrecognizer import FormRecognizerClient from azure.ai.formrecognizer import FormTrainingClient from azure.core.credentials import AzureKeyCredential import azure.functions as func def main(req: func.HttpRequest) -> func.HttpResponse: logging.info('Invoked AnalyzeForm Skill.') try: body = json.dumps(req.get_json()) if body: # For testing uncomment the following line to log the incoming request #logging.info(body) result = compose_response(body) return func.HttpResponse(result, mimetype="application/json") else: return func.HttpResponse( "The body of the request could not be parsed", status_code=400 ) except ValueError: return func.HttpResponse( "The body of the request could not be parsed", status_code=400 ) except KeyError: return func.HttpResponse( "Skill configuration error. Endpoint, key and model_id required.", status_code=400 ) except AssertionError as error: return func.HttpResponse( "Request format is not a valid custom skill input", status_code=400 ) def compose_response(json_data): body = json.loads(json_data) assert ('values' in body), "request does not implement the custom skill interface" values = body['values'] # Prepare the Output before the loop results = {} results["values"] = [] mappings = None with open(pathlib.Path(__file__).parent / 'field_mappings.json') as file: mappings = json.loads(file.read()) endpoint = os.environ["FORMS_RECOGNIZER_ENDPOINT"] key = os.environ["FORMS_RECOGNIZER_KEY"] model_id = os.environ["FORMS_RECOGNIZER_MODEL_ID"] form_recognizer_client = FormRecognizerClient(endpoint, AzureKeyCredential(key)) for value in values: output_record = transform_value(value, mappings, form_recognizer_client, model_id) if output_record != None: results["values"].append(output_record) break return json.dumps(results, ensure_ascii=False) ## Perform an operation on a record def transform_value(value, mappings, form_recognizer_client,model_id): try: recordId = value['recordId'] except AssertionError as error: return None try: assert ('data' in value), "'data' field is required." data = value['data'] formUrl = data['formUrl'] formSasToken = data ['formSasToken'] formUrl = formUrl + formSasToken poller = form_recognizer_client.begin_recognize_custom_forms_from_url( model_id=model_id, form_url=formUrl) result = poller.result() recognized = {} for recognized_form in result: print("Form type: {}".format(recognized_form.form_type)) for name, field in recognized_form.fields.items(): label = field.label_data.text if field.label_data else name for (k, v) in mappings.items(): if(label == k): recognized[v] = field.value except AssertionError as error: return ( { "recordId": recordId, "errors": [ { "message": "Error:" + error.args[0] } ] }) except Exception as error: return ( { "recordId": recordId, "errors": [ { "message": "Error:" + str(error) } ] }) return ({ "recordId": recordId, "data": { "recognized": recognized } })

8eb19cb5bb6e480425577d4672af1bfd07e3f193

py

Python

tests/artificial/transf_BoxCox/trend_LinearTrend/cycle_7/ar_12/test_artificial_1024_BoxCox_LinearTrend_7_12_20.py

shaido987/pyaf

b9afd089557bed6b90b246d3712c481ae26a1957

2016-10-13T20:52:44.000Z

2022-03-29T18:04:14.000Z

tests/artificial/transf_BoxCox/trend_LinearTrend/cycle_7/ar_12/test_artificial_1024_BoxCox_LinearTrend_7_12_20.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

2016-10-13T16:11:53.000Z

2022-03-28T04:21:34.000Z

tests/artificial/transf_BoxCox/trend_LinearTrend/cycle_7/ar_12/test_artificial_1024_BoxCox_LinearTrend_7_12_20.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

2017-03-09T14:51:18.000Z

2022-03-27T20:52:57.000Z

import pyaf.Bench.TS_datasets as tsds import tests.artificial.process_artificial_dataset as art art.process_dataset(N = 1024 , FREQ = 'D', seed = 0, trendtype = "LinearTrend", cycle_length = 7, transform = "BoxCox", sigma = 0.0, exog_count = 20, ar_order = 12);

972ed5804f9aeebbf33d60956e0968a1325d4660

py

Python

aiotdlib/api/types/sessions.py

mostafa-arshadi/aiotdlib

59f430a65dfb424fc69d471a0d7bcd77ad7acf08

2021-05-04T10:41:41.000Z

2022-03-30T13:48:05.000Z

aiotdlib/api/types/sessions.py

mostafa-arshadi/aiotdlib

59f430a65dfb424fc69d471a0d7bcd77ad7acf08

2021-07-17T19:54:51.000Z

2022-02-26T06:50:00.000Z

aiotdlib/api/types/sessions.py

mostafa-arshadi/aiotdlib

59f430a65dfb424fc69d471a0d7bcd77ad7acf08

2021-09-22T21:27:11.000Z

2022-02-20T02:33:19.000Z

# =============================================================================== # # # # This file has been generated automatically!! Do not change this manually! # # # # =============================================================================== # from __future__ import annotations from pydantic import Field from .session import Session from ..base_object import BaseObject class Sessions(BaseObject): """ Contains a list of sessions :param sessions: List of sessions :type sessions: :class:`list[Session]` :param inactive_session_ttl_days: Number of days of inactivity before sessions will automatically be terminated; 1-366 days :type inactive_session_ttl_days: :class:`int` """ ID: str = Field("sessions", alias="@type") sessions: list[Session] inactive_session_ttl_days: int @staticmethod def read(q: dict) -> Sessions: return Sessions.construct(**q)

24b4d02aa4b0856bf30f24067c64833ea23cd95a

py

Python

training_site/manage.py

janvorac/guess-the-number

89ba9b70525b6b6d11541372ade6e0d1a48a5543

training_site/manage.py

janvorac/guess-the-number

89ba9b70525b6b6d11541372ade6e0d1a48a5543

2022-01-25T08:44:26.000Z

2022-02-02T09:07:38.000Z

training_site/manage.py

janvorac/guess-the-number

89ba9b70525b6b6d11541372ade6e0d1a48a5543

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): """Run administrative tasks.""" os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'training_site.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

88f14a49d414c0b69f5c82bdfa2f989bb01c57bf

py

Python

plugin/references.py

Narretz/LSP

28ecbb2221956781222fcf2aaa8ceb54c6a34f35

plugin/references.py

Narretz/LSP

28ecbb2221956781222fcf2aaa8ceb54c6a34f35

plugin/references.py

Narretz/LSP

28ecbb2221956781222fcf2aaa8ceb54c6a34f35

import os import sublime import linecache from .core.documents import is_at_word, get_position, get_document_position from .core.panels import ensure_panel from .core.protocol import Request, Point from .core.registry import LspTextCommand, windows from .core.settings import PLUGIN_NAME, settings from .core.url import uri_to_filename from .core.views import get_line try: from typing import List, Dict, Optional, Callable, Tuple from mypy_extensions import TypedDict assert List and Dict and Optional and Callable and Tuple and TypedDict ReferenceDict = TypedDict('ReferenceDict', {'uri': str, 'range': dict}) except ImportError: pass def ensure_references_panel(window: sublime.Window) -> 'Optional[sublime.View]': return ensure_panel(window, "references", r"^\s*\S\s+(\S.*):$", r"^\s+([0-9]+):?([0-9]+).*$", "Packages/" + PLUGIN_NAME + "/Syntaxes/References.sublime-syntax") class LspSymbolReferencesCommand(LspTextCommand): def __init__(self, view: sublime.View) -> None: super().__init__(view) self.reflist = [] # type: List[List[str]] self.word_region = None # type: Optional[sublime.Region] self.word = "" self.base_dir = None # type: Optional[str] def is_enabled(self, event: 'Optional[dict]' = None) -> bool: if self.has_client_with_capability('referencesProvider'): return is_at_word(self.view, event) return False def run(self, edit: sublime.Edit, event: 'Optional[dict]' = None) -> None: client = self.client_with_capability('referencesProvider') file_path = self.view.file_name() if client and file_path: pos = get_position(self.view, event) window = self.view.window() self.word_region = self.view.word(pos) self.word = self.view.substr(self.word_region) # use relative paths if file on the same root. base_dir = windows.lookup(window).get_project_path() if base_dir: if os.path.commonprefix([base_dir, file_path]): self.base_dir = base_dir document_position = get_document_position(self.view, pos) if document_position: document_position['context'] = { "includeDeclaration": False } request = Request.references(document_position) client.send_request( request, lambda response: self.handle_response(response, pos)) def handle_response(self, response: 'Optional[List[ReferenceDict]]', pos: int) -> None: window = self.view.window() if response is None: response = [] if window: references_count = len(response) # return if there are no references if references_count < 1: window.run_command("hide_panel", {"panel": "output.references"}) window.status_message("No references found") return references_by_file = self._group_references_by_file(response) if settings.show_references_in_quick_panel: self.show_quick_panel(references_by_file) else: self.show_references_panel(references_by_file) def show_quick_panel(self, references_by_file: 'Dict[str, List[Tuple[Point, str]]]') -> None: selected_index = -1 current_file_path = self.view.file_name() for file_path, references in references_by_file.items(): for reference in references: point, line = reference item = ['{}:{}:{}'.format(self.get_relative_path(file_path), point.row + 1, point.col + 1), line] self.reflist.append(item) # pre-select a reference in the current file. if current_file_path == file_path and selected_index == -1: selected_index = len(self.reflist) - 1 flags = sublime.KEEP_OPEN_ON_FOCUS_LOST if settings.quick_panel_monospace_font: flags |= sublime.MONOSPACE_FONT window = self.view.window() if window: window.show_quick_panel( self.reflist, self.on_ref_choice, flags, selected_index, self.on_ref_highlight ) def on_ref_choice(self, index: int) -> None: self.open_ref_index(index) def on_ref_highlight(self, index: int) -> None: self.open_ref_index(index, transient=True) def open_ref_index(self, index: int, transient: bool=False) -> None: if index != -1: flags = sublime.ENCODED_POSITION | sublime.TRANSIENT if transient else sublime.ENCODED_POSITION window = self.view.window() if window: window.open_file(self.get_selected_file_path(index), flags) def show_references_panel(self, references_by_file: 'Dict[str, List[Tuple[Point, str]]]') -> None: window = self.view.window() if window: panel = ensure_references_panel(window) if not panel: return text = '' references_count = 0 for file, references in references_by_file.items(): text += '◌ {}:\n'.format(self.get_relative_path(file)) for reference in references: references_count += 1 point, line = reference text += '\t{:>8}:{:<4} {}\n'.format(point.row + 1, point.col + 1, line) # append a new line after each file name text += '\n' base_dir = windows.lookup(window).get_project_path() panel.settings().set("result_base_dir", base_dir) panel.set_read_only(False) panel.run_command("lsp_clear_panel") window.run_command("show_panel", {"panel": "output.references"}) panel.run_command('append', { 'characters': "{} references for '{}'\n\n{}".format(references_count, self.word, text), 'force': True, 'scroll_to_end': False }) # highlight all word occurrences regions = panel.find_all(r"\b{}\b".format(self.word)) panel.add_regions('ReferenceHighlight', regions, 'comment', flags=sublime.DRAW_OUTLINED) panel.set_read_only(True) def get_selected_file_path(self, index: int) -> str: return self.get_full_path(self.reflist[index][0]) def get_relative_path(self, file_path: str) -> str: if self.base_dir: return os.path.relpath(file_path, self.base_dir) else: return file_path def get_full_path(self, file_path: str) -> str: if self.base_dir: return os.path.join(self.base_dir, file_path) return file_path def want_event(self) -> bool: return True def _group_references_by_file(self, references: 'List[ReferenceDict]' ) -> 'Dict[str, List[Tuple[Point, str]]]': """ Return a dictionary that groups references by the file it belongs. """ grouped_references = {} # type: Dict[str, List[Tuple[Point, str]]] for reference in references: file_path = uri_to_filename(reference["uri"]) point = Point.from_lsp(reference['range']['start']) # get line of the reference, to showcase its use reference_line = get_line(self.view.window(), file_path, point.row) if grouped_references.get(file_path) is None: grouped_references[file_path] = [] grouped_references[file_path].append((point, reference_line)) # we don't want to cache the line, we always want to get fresh data linecache.clearcache() return grouped_references

8035a3de023aa6e3d5ea47b099145fa484f4830c

py

Python

src/encoded/batch_download.py

beta-cell-network/beta-cell-nw

093b078fdb7932ebfcbc0715aeeb2261eda3ee52

2018-01-04T22:31:08.000Z

2021-07-15T17:39:16.000Z

src/encoded/batch_download.py

beta-cell-network/beta-cell-nw

093b078fdb7932ebfcbc0715aeeb2261eda3ee52

2017-10-31T23:47:47.000Z

2022-01-10T00:12:42.000Z

src/encoded/batch_download.py

beta-cell-network/beta-cell-nw

093b078fdb7932ebfcbc0715aeeb2261eda3ee52

2017-09-14T00:57:07.000Z

2021-07-27T23:41:14.000Z

from collections import OrderedDict from pyramid.compat import bytes_ from pyramid.httpexceptions import HTTPBadRequest from pyramid.view import view_config from pyramid.response import Response from snovault import TYPES from snovault.util import simple_path_ids from urllib.parse import ( parse_qs, urlencode, ) from .search import iter_search_results from .search import list_visible_columns_for_schemas import csv import io import json import datetime import logging import re log = logging.getLogger(__name__) currenttime = datetime.datetime.now() def includeme(config): config.add_route('batch_download', '/batch_download/{search_params}') config.add_route('metadata', '/metadata/{search_params}/{tsv}') config.add_route('peak_download', '/peak_download/{search_params}/{tsv}') config.add_route('report_download', '/report.tsv') config.scan(__name__) # includes concatenated properties _tsv_mapping = OrderedDict([ ('File accession', ['files.title']), ('File format', ['files.file_type']), ('Output type', ['files.output_type']), ('Experiment accession', ['accession']), ('Annotation accession', ['accession']), ('Assay', ['assay_term_name']), ('Annotation', ['annotation_type']), ('Biosample term id', ['biosample_term_id']), ('Biosample term name', ['biosample_term_name']), ('Biosample type', ['biosample_type']), ('Biosample life stage', ['replicates.library.biosample.life_stage']), ('Biosample sex', ['replicates.library.biosample.sex']), ('Biosample Age', ['replicates.library.biosample.age', 'replicates.library.biosample.age_units']), ('Biosample organism', ['replicates.library.biosample.organism.scientific_name']), ('Biosample treatments', ['replicates.library.biosample.treatments.treatment_term_name']), ('Biosample subcellular fraction term name', ['replicates.library.biosample.subcellular_fraction_term_name']), ('Biosample phase', ['replicates.library.biosample.phase']), ('Biosample synchronization stage', ['replicates.library.biosample.fly_synchronization_stage', 'replicates.library.biosample.worm_synchronization_stage', 'replicates.library.biosample.post_synchronization_time', 'replicates.library.biosample.post_synchronization_time_units']), ('Experiment target', ['target.name']), ('Antibody accession', ['replicates.antibody.accession']), ('Library made from', ['replicates.library.nucleic_acid_term_name']), ('Library depleted in', ['replicates.library.depleted_in_term_name']), ('Library extraction method', ['replicates.library.extraction_method']), ('Library lysis method', ['replicates.library.lysis_method']), ('Library crosslinking method', ['replicates.library.crosslinking_method']), ('Library strand specific', ['replicates.library.strand_specificity']), ('Experiment date released', ['date_released']), ('Project', ['award.project']), ('RBNS protein concentration', ['files.replicate.rbns_protein_concentration', 'files.replicate.rbns_protein_concentration_units']), ('Library fragmentation method', ['files.replicate.library.fragmentation_method']), ('Library size range', ['files.replicate.library.size_range']), ('Biological replicate(s)', ['files.biological_replicates']), ('Technical replicate', ['files.replicate.technical_replicate_number']), ('Read length', ['files.read_length']), ('Mapped read length', ['files.mapped_read_length']), ('Run type', ['files.run_type']), ('Paired end', ['files.paired_end']), ('Paired with', ['files.paired_with']), ('Derived from', ['files.derived_from']), ('Size', ['files.file_size']), ('Lab', ['files.lab.title']), ('md5sum', ['files.md5sum']), ('dbxrefs', ['files.dbxrefs']), ('file_format', ['files.file_format']), ('File download URL', ['files.href']), ('Assembly', ['files.assembly']), ('Platform', ['files.platform.title']), ('Controlled by', ['files.controlled_by']), ('File Status', ['files.status']) ]) _audit_mapping = OrderedDict([ ('Audit WARNING', ['audit.WARNING.path', 'audit.WARNING.category', 'audit.WARNING.detail']), ('Audit INTERNAL_ACTION', ['audit.INTERNAL_ACTION.path', 'audit.INTERNAL_ACTION.category', 'audit.INTERNAL_ACTION.detail']), ('Audit NOT_COMPLIANT', ['audit.NOT_COMPLIANT.path', 'audit.NOT_COMPLIANT.category', 'audit.NOT_COMPLIANT.detail']), ('Audit ERROR', ['audit.ERROR.path', 'audit.ERROR.category', 'audit.ERROR.detail']) ]) def get_file_uuids(result_dict): file_uuids = [] for item in result_dict['@graph']: for file in item['files']: file_uuids.append(file['uuid']) return list(set(file_uuids)) def get_biosample_accessions(file_json, experiment_json): for f in experiment_json['files']: if file_json['uuid'] == f['uuid']: accession = f.get('replicate', {}).get('library', {}).get('biosample', {}).get('accession') if accession: return accession accessions = [] for replicate in experiment_json.get('replicates', []): accession = replicate['library']['biosample']['accession'] accessions.append(accession) return ', '.join(list(set(accessions))) def get_peak_metadata_links(request): if request.matchdict.get('search_params'): search_params = request.matchdict['search_params'] else: search_params = request.query_string peak_metadata_tsv_link = '{host_url}/peak_metadata/{search_params}/peak_metadata.tsv'.format( host_url=request.host_url, search_params=search_params ) peak_metadata_json_link = '{host_url}/peak_metadata/{search_params}/peak_metadata.json'.format( host_url=request.host_url, search_params=search_params ) return [peak_metadata_tsv_link, peak_metadata_json_link] def make_cell(header_column, row, exp_data_row): temp = [] for column in _tsv_mapping[header_column]: c_value = [] for value in simple_path_ids(row, column): if str(value) not in c_value: c_value.append(str(value)) if column == 'replicates.library.biosample.post_synchronization_time' and len(temp): if len(c_value): temp[0] = temp[0] + ' + ' + c_value[0] elif len(temp): if len(c_value): temp = [x + ' ' + c_value[0] for x in temp] else: temp = c_value exp_data_row.append(', '.join(list(set(temp)))) def make_audit_cell(header_column, experiment_json, file_json): categories = [] paths = [] for column in _audit_mapping[header_column]: for value in simple_path_ids(experiment_json, column): if 'path' in column: paths.append(value) elif 'category' in column: categories.append(value) data = [] for i, path in enumerate(paths): if '/files/' in path and file_json.get('title', '') not in path: # Skip file audits that does't belong to the file continue else: data.append(categories[i]) return ', '.join(list(set(data))) @view_config(route_name='peak_download', request_method='GET') def peak_download(context, request): param_list = parse_qs(request.matchdict['search_params']) param_list['field'] = [] header = ['annotation_type', 'coordinates', 'biosample.accession', 'file.accession', 'annotation.accession'] param_list['limit'] = ['all'] path = '/variant-search/?{}&{}'.format(urlencode(param_list, True),'referrer=peak_download') results = request.embed(path, as_user=True) uuids_in_results = get_file_uuids(results) rows = [] json_doc = {} for row in results['peaks']: if row['_id'] in uuids_in_results: file_json = request.embed(row['_id']) annotation_json = request.embed(file_json['dataset']) for hit in row['inner_hits']['positions']['hits']['hits']: data_row = [] chrom = '{}'.format(row['_index']) assembly = '{}'.format(row['_type']) coordinates = '{}:{}-{}'.format(row['_index'], hit['_source']['start'], hit['_source']['end']) file_accession = file_json['accession'] annotation_accession = annotation_json['accession'] annotation = annotation_json['annotation_type'] biosample_term = annotation_json['biosample_term_name'] data_row.extend([annotation, biosample_term, coordinates, file_accession, annotation_accession]) rows.append(data_row) fout = io.StringIO() writer = csv.writer(fout, delimiter='\t') writer.writerow(header) writer.writerows(rows) return Response( content_type='text/tsv', body=fout.getvalue(), content_disposition='attachment;filename="%s"' % 'peak_metadata.tsv' ) @view_config(route_name='metadata', request_method='GET') def metadata_tsv(context, request): param_list = parse_qs(request.matchdict['search_params']) if 'referrer' in param_list: search_path = '/{}/'.format(param_list.pop('referrer')[0]) else: search_path = '/search/' param_list['field'] = [] header = [] file_attributes = [] for prop in _tsv_mapping: header.append(prop) param_list['field'] = param_list['field'] + _tsv_mapping[prop] if _tsv_mapping[prop][0].startswith('files'): file_attributes = file_attributes + [_tsv_mapping[prop][0]] param_list['limit'] = ['all'] path = '{}?{}'.format(search_path, urlencode(param_list, True)) results = request.embed(path, as_user=True) rows = [] for experiment_json in results['@graph']: #log.warn(results['@graph']) for f in experiment_json.get('files', []): exp_data_row = [] for column in header: if not _tsv_mapping[column][0].startswith('files'): make_cell(column, experiment_json, exp_data_row) f_attributes = ['files.title', 'files.file_type', 'files.output_type'] for f in experiment_json['files']: if 'files.file_type' in param_list: if f['file_type'] not in param_list['files.file_type']: continue f['href'] = request.host_url + f['href'] f_row = [] for attr in f_attributes: f_row.append(f[attr[6:]]) data_row = f_row + exp_data_row for prop in file_attributes: if prop in f_attributes: continue path = prop[6:] temp = [] for value in simple_path_ids(f, path): temp.append(str(value)) if prop == 'files.replicate.rbns_protein_concentration': if 'replicate' in f and 'rbns_protein_concentration_units' in f['replicate']: temp[0] = temp[0] + ' ' + f['replicate']['rbns_protein_concentration_units'] if prop in ['files.paired_with', 'files.derived_from']: # chopping of path to just accession if len(temp): new_values = [t[7:-1] for t in temp] temp = new_values data = list(set(temp)) data.sort() data_row.append(', '.join(data)) audit_info = [make_audit_cell(audit_type, experiment_json, f) for audit_type in _audit_mapping] data_row.extend(audit_info) rows.append(data_row) fout = io.StringIO() writer = csv.writer(fout, delimiter='\t') header.extend([prop for prop in _audit_mapping]) writer.writerow(header) writer.writerows(rows) return Response( content_type='text/tsv', body=fout.getvalue(), content_disposition='attachment;filename="%s"' % 'metadata.tsv' ) @view_config(route_name='batch_download', request_method='GET') def batch_download(context, request): # adding extra params to get required columns param_list = parse_qs(request.matchdict['search_params']) param_list['field'] = ['files.href', 'files.file_type', 'files'] param_list['limit'] = ['all'] path = '/search/?%s' % urlencode(param_list, True) results = request.embed(path, as_user=True) metadata_link = '{host_url}/metadata/{search_params}/metadata.tsv'.format( host_url=request.host_url, search_params=request.matchdict['search_params'] ) files = [metadata_link] if 'files.file_type' in param_list: for exp in results['@graph']: for f in exp.get('files', []): if f['file_type'] in param_list['files.file_type']: files.append('{host_url}{href}'.format( host_url=request.host_url, href=f['href'] )) else: for exp in results['@graph']: for f in exp.get('files', []): files.append('{host_url}{href}'.format( host_url=request.host_url, href=f['href'] )) return Response( content_type='text/plain', body='\n'.join(files), content_disposition='attachment; filename="%s"' % 'files.txt' ) def lookup_column_value(value, path): nodes = [value] names = path.split('.') for name in names: nextnodes = [] for node in nodes: if name not in node: continue value = node[name] if isinstance(value, list): nextnodes.extend(value) else: nextnodes.append(value) nodes = nextnodes if not nodes: return '' # if we ended with an embedded object, show the @id if nodes and hasattr(nodes[0], '__contains__') and '@id' in nodes[0]: nodes = [node['@id'] for node in nodes] seen = set() deduped_nodes = [] for n in nodes: if isinstance(n, dict): n = str(n) if n not in seen: deduped_nodes.append(n) return u','.join(u'{}'.format(n) for n in deduped_nodes) def format_row(columns): """Format a list of text columns as a tab-separated byte string.""" return b'\t'.join([bytes_(c, 'utf-8') for c in columns]) + b'\r\n' @view_config(route_name='report_download', request_method='GET') def report_download(context, request): types = request.params.getall('type') if len(types) != 1: msg = 'Report view requires specifying a single type.' raise HTTPBadRequest(explanation=msg) # Make sure we get all results request.GET['limit'] = 'all' type = types[0] schemas = [request.registry[TYPES][type].schema] columns = list_visible_columns_for_schemas(request, schemas) type = type.replace("'", '') def format_header(seq): newheader="%s\t%s%s?%s\r\n" % (currenttime, request.host_url, '/report/', request.query_string) return(bytes(newheader, 'utf-8')) # Work around Excel bug; can't open single column TSV with 'ID' header if len(columns) == 1 and '@id' in columns: columns['@id']['title'] = 'id' header = [column.get('title') or field for field, column in columns.items()] def generate_rows(): yield format_header(header) yield format_row(header) for item in iter_search_results(context, request): values = [lookup_column_value(item, path) for path in columns] yield format_row(values) # Stream response using chunked encoding. request.response.content_type = 'text/tsv' request.response.content_disposition = 'attachment;filename="%s"' % '%(doctype)s Report %(yyyy)s/%(mm)s/%(dd)s.tsv' % {'yyyy': currenttime.year, 'mm': currenttime.month, 'dd': currenttime.day, 'doctype': type} #change file name request.response.app_iter = generate_rows() return request.response

418078d4e3be94880f3339f61052ffce4d274fcc

py

Python

batproject/accountapp/admin.py

JaL11/BAT

ed4bccef3c70ec01064ebd0c26933853d4f95355

2020-07-16T14:29:55.000Z

2020-07-16T14:29:55.000Z

batproject/accountapp/admin.py

JaL11/BAT

ed4bccef3c70ec01064ebd0c26933853d4f95355

2020-06-04T14:41:18.000Z

2020-07-29T18:06:14.000Z

batproject/accountapp/admin.py

JaL11/BAT

ed4bccef3c70ec01064ebd0c26933853d4f95355

2020-06-06T13:12:35.000Z

2020-08-28T20:25:51.000Z

from django.contrib import admin # Register your models here. from .models import User admin.site.register(User)

5b802f71e281f98036456fc7a5675e9deac9b681

py

Python

example/ibc/main.py

kingli-crypto/chainlibpy

8511c08c3bdb7de9cf58254a804ca329188a1dd8

example/ibc/main.py

kingli-crypto/chainlibpy

8511c08c3bdb7de9cf58254a804ca329188a1dd8

example/ibc/main.py

kingli-crypto/chainlibpy

8511c08c3bdb7de9cf58254a804ca329188a1dd8

import json import subprocess import time from pathlib import Path import requests import yaml from pystarport.cluster import (ClusterCLI, find_account, init_cluster, interact, start_cluster) from pystarport.ports import api_port from chainlibpy import Transaction, Wallet from chainlibpy.amino import Coin, StdFee, TimeoutHeight from chainlibpy.amino.message import IbcMsgTransfer class Runner(): ''' we use pystarport to create the IBC env need to install hermes: https://github.com/informalsystems/ibc-rs/releases ''' def __init__(self, data_root=Path("/tmp/data"), config_file="config.yaml"): self.data_root = data_root self.config_file = config_file @property def cluster(self): config = yaml.safe_load(open(self.config_file)) clis = {} for key in config: if key == "relayer": continue chain_id = key clis[chain_id] = ClusterCLI(self.data_root, chain_id=chain_id) return clis def url_base(self, chain_id, index=0): cli = self.cluster[chain_id] port = cli.base_port(index) return "http://127.0.0.1:{}".format(api_port(port)) def get_balance(self, chain_id, index, address): url_base = self.url_base(chain_id, index) url_balance = f"{url_base}/cosmos/bank/v1beta1/balances/{address}" response = requests.get(url_balance) balance = int(response.json()["balances"][0]["amount"]) return balance def get_account_info(self, chain_id, index, address): url_base = self.url_base(chain_id, index) url_account = f"{url_base}/cosmos/auth/v1beta1/accounts/{address}" response = requests.get(url_account) account_info = response.json()["account"] account_num = int(account_info["account_number"]) sequence = int(account_info["sequence"]) return account_num, sequence def send_tx(self, chain_id, index, data): url_base = self.url_base(chain_id, index) url = f"{url_base}/txs" response = requests.post(url, json=data) return response def init_relayer(self): relayer = ["hermes", "-j", "-c", self.data_root / "relayer.toml"] subprocess.run( relayer + [ "create", "channel", "ibc-0", "ibc-1", "--port-a", "transfer", "--port-b", "transfer", ], check=True, ) # start relaying self.cluster["ibc-0"].supervisor.startProcess("relayer-demo") @property def relayer_channels(self): # all clusters share the same root data directory relayer = ["hermes", "-j", "-c", self.data_root / "relayer.toml"] rsp = json.loads(subprocess.check_output(relayer + ["query", "channels", "ibc-0"])) src_channel = rsp["result"][0]["channel_id"] rsp = json.loads(subprocess.check_output(relayer + ["query", "channels", "ibc-1"])) dst_channel = rsp["result"][0]["channel_id"] return src_channel, dst_channel def start(self): ''' after start the tasks, you can use `supervisorctl -c task.ini` to see the status of each program ''' data_path = "/tmp/dadta" interact(f"rm -r {data_path}; mkdir -p {data_path}", ignore_error=True) data_dir = Path("/tmp/data") init_cluster(data_dir, "config.yaml", 26650) start_cluster(data_dir) time.sleep(10) self.init_relayer() def test_ibc(): r = Runner() # r.start() # time.sleep(10) seed_0 = find_account(r.data_root, "ibc-0", "relayer")["mnemonic"] seed_1 = find_account(r.data_root, "ibc-1", "relayer")["mnemonic"] wallet_0 = Wallet(seed_0) wallet_1 = Wallet(seed_1) addr_0 = wallet_0.address addr_1 = wallet_1.address src_channel, dst_channel = r.relayer_channels # do a transfer from ibc-0 to ibc-1 print("transfer ibc0 -> ibc1") account_num, sequence = r.get_account_info("ibc-0", 0, addr_0) fee = StdFee("300000", [Coin("100000")]) tx = Transaction( wallet=wallet_0, account_num=account_num, sequence=sequence, chain_id="ibc-0", fee=fee, ) amount = Coin("10000") target_version = 1 timeout_height = TimeoutHeight(str(target_version), "10000000000") msg = IbcMsgTransfer( source_port="transfer", source_channel=src_channel, sender=addr_0, receiver=addr_1, coin=amount, packet_timeout_height=timeout_height, packet_timeout_timestamp="0", absolute_timeouts=True, ) tx.add_msg(msg) signed_tx = tx.get_pushable() response = r.send_tx("ibc-0", 0, signed_tx) if not response.ok: raise Exception(response.reason) else: result = response.json() print("send tx result:", result) if result.get("code"): raise Exception(result["raw_log"]) # get the balance after sync time.sleep(5) # get the ibc-0 balance balance_0 = r.get_balance("ibc-0", 0, addr_0) print("balance 0 after transfer: ", balance_0) balance_1 = r.get_balance("ibc-1", 0, addr_1) print("balance 1 after transfer: ", balance_1) # do a transfer from ibc-1 to ibc-0 print("transfer ibc1 -> ibc0") account_num, sequence = r.get_account_info("ibc-1", 0, addr_1) tx = Transaction( wallet=wallet_1, account_num=account_num, sequence=sequence, chain_id="ibc-1", ) amount = Coin("10000", f"transfer/{dst_channel}/basecro") target_version = 0 timeout_height = TimeoutHeight(str(target_version), "10000000000") msg = IbcMsgTransfer( source_port="transfer", source_channel=dst_channel, sender=addr_1, receiver=addr_0, coin=amount, packet_timeout_height=timeout_height, packet_timeout_timestamp="0", absolute_timeouts=True, ) tx.add_msg(msg) signed_tx = tx.get_pushable() response = r.send_tx("ibc-1", 0, signed_tx) if not response.ok: raise Exception(response.reason) else: result = response.json() print("send tx result:", result) if result.get("code"): raise Exception(result["raw_log"]) # get the balance after sync time.sleep(50) # get the ibc-0 balance balance_0 = r.get_balance("ibc-0", 0, addr_0) print("balance 0 after transfer: ", balance_0) balance_1 = r.get_balance("ibc-1", 0, addr_1) print("balance 1 after transfer: ", balance_1) if __name__ == "__main__": test_ibc()

a84fa6edf2b9189f7afa5bdd83be2921d2a76042

py

Python

yap/controllers/front.py

AFPy/Yap

542fd1b679cedf1772c3ce0948d1fa40390a288e

yap/controllers/front.py

AFPy/Yap

542fd1b679cedf1772c3ce0948d1fa40390a288e

yap/controllers/front.py

AFPy/Yap

542fd1b679cedf1772c3ce0948d1fa40390a288e

# -*- encoding: utf-8 -*- # (C) Copyright 2008 Tarek Ziadé <[email protected]> # import logging import os from os.path import join, dirname import shutil from lxml import etree import time import datetime from pylons import config from sgmllib import SGMLParser from yap.lib.base import * from atomisator.main.config import AtomisatorConfig TITLESIZE = 70 MAXSIZE = 150 log = logging.getLogger(__name__) root = os.path.split(os.path.dirname(__file__))[0] PUBLIC_RSS = os.path.realpath(join(root, 'public', 'rss.xml')) CONFIG = join(dirname(root), 'atomisator.cfg') class Html2Txt(SGMLParser): def reset(self): SGMLParser.reset(self) self.pieces = [] def handle_data(self, text): self.pieces.append(text) def handle_entityref(self, ref): if ref == 'amp': self.pieces.append("&") def output(self): return ' '.join(self.pieces).replace('<br/>', '') class FrontController(BaseController): def index(self): parser = AtomisatorConfig(CONFIG) # getting parameters for the rss output rss = dict(parser.outputs)['rss'] # getting the target xml file rss_file = rss[0] xml = os.path.realpath(rss_file) if not os.path.exists(xml): xml = os.path.realpath(join(root, rss_file)) if not os.path.exists(xml): raise ValueError('File %s not found' % xml) # if not under public, we need to copy it to public/rss.xml if xml != PUBLIC_RSS: shutil.copyfile(xml, PUBLIC_RSS) doc = etree.XML(open(xml).read()) items = doc.xpath('/rss/channel/item') def _date(value): d = time.strptime(value.split('.')[0], '%Y-%m-%d %H:%M:%S') d = datetime.datetime(*d[:6]) return d.strftime('%d/%m/%Y') def _extract(entry): if entry.tag == 'pubDate': return entry.tag, _date(entry.text) if entry.tag == 'title': if len(entry.text) > TITLESIZE: return 'title', entry.text[:TITLESIZE] + '...' return 'title', entry.text return entry.tag, entry.text items = [dict([_extract(x) for x in e.getchildren()]) for e in items] # building an extract def _extract(html, title): if isinstance(html, unicode): try: html = html.decode('utf8') except: html = str(type(html)) parser = Html2Txt() parser.reset() parser.feed(html) parser.close() res = parser.output().strip() size = MAXSIZE - len(title) if size < 0: return '' return res[:size] + '...' for i in items: i['extract'] = _extract(i['description'], i['title']) c.entries = items c.title = doc.xpath('/rss/channel/title')[0].text return render('/front.mako')

7bb34199a716aba79a1f5d9cc7cfd915e382ddfa

py

Python

app/main/views.py

edithamadi/pitch_one

40c8d1c67c77e483b29bd326721dde7f4a20120d

app/main/views.py

edithamadi/pitch_one

40c8d1c67c77e483b29bd326721dde7f4a20120d

app/main/views.py

edithamadi/pitch_one

40c8d1c67c77e483b29bd326721dde7f4a20120d

from flask import render_template,request,redirect,url_for,abort,flash from . import main from flask_login import login_required,current_user from ..models import User,Pitch,Comment from .forms import UpdateProfile,PitchForm,CommentForm from .. import db,photos # import markdown2 # Views @main.route('/') def index(): ''' View root page function that returns the index page and its data ''' title = 'Pitch Application' pitch = Pitch.query.all() # categories = Category.get_categories() return render_template('index.html',title = title, Pitch = pitch) @main.route('/pitch/new', methods=['GET','POST']) @login_required def new_pitch(): form=PitchForm() if form.validate_on_submit(): pitches=Pitch(category=form.category.data,pitch_content=form.content.data) db.session.add(pitches) db.session.commit() flash('pitch created') pitches=Pitch.query.all() return render_template('pitch.html',form=form, pitch=pitches) @main.route('/category/<int:id>') def category(id): category = PitchCategory.query.get(id) category_name = PitchCategory.query.get(category_name) if category is None: abort(404) pitch_in_category = Pitch.get_pitch(id) return render_template('category.html' ,category= category, pitch= pitch_in_category) @main.route('/pitch/comments/new/<int:id>',methods = ['GET','POST']) @login_required def new_comment(id): form = CommentForm() if form.validate_on_submit(): new_comment = Comment(pitch_id =id,data=form.comment.data) new_comment.save_comment() return redirect(url_for('main.new_pitch')) return render_template('ncomment.html', form=form) @main.route('/comments/<int:id>') def single_comment(id): comment=Comment.query.get(id) if comment is None: abort(404) return render_template('new_comment.html') @main.route('/view/comment/<int:id>') def view_comments(id): ''' Function that shows the comments of a particular pitch ''' comments = Comment.get_comments(id) return render_template('viewcomment.html',comments = comments, id=id) @main.route('/user/<uname>') def profile(uname): user = User.query.filter_by(username = uname).first() if user is None: abort(404) return render_template("profile/profile.html",user = user) @main.route('/user/<uname>/update',methods = ['GET','POST']) @login_required def update_profile(uname): user = User.query.filter_by(username = uname).first() if user is None: abort(404) form = UpdateProfile() if form.validate_on_submit(): user.bio = form.bio.data db.session.add(user) db.session.commit() return redirect(url_for('.profile',uname=user.username)) return render_template('profile/update.html',form =form) @main.route('/user/<uname>/update/pic',methods= ['POST']) @login_required def update_pic(uname): user = User.query.filter_by(username = uname).first() if 'photo' in request.files: filename = photos.save(request.files['photo']) path = f'photos/{filename}' user.profile_pic_path = path db.session.commit() return redirect(url_for('main.profile',uname=uname))

6e8229ddd4deceb52130dfea853f3d4016ccf290

py

Python

genRandInputs.py

deehzee/affine-charform

e03d43972e28e2a364bd54b6bd7b95e77bf2e9d2

2019-04-13T03:50:22.000Z

2021-03-02T12:34:47.000Z

genRandInputs.py

deehzee/affine-charform

e03d43972e28e2a364bd54b6bd7b95e77bf2e9d2

genRandInputs.py

deehzee/affine-charform

e03d43972e28e2a364bd54b6bd7b95e77bf2e9d2

# genRandInput.py - Generate ranodom input def random_inputs(N = 5, maxvarn = 4, maxs = 3): # N = size of each sample size for each kind # maxvarn = maximum variation for n # maxs = maximum value for s_i # X = type (A, B, C, D, E or F) # n = subscript # r = superscript # S = specialization # in $X_n^{(r)}$ # k = number of nodes (GCM A is (k x k) matrix) import random sfrom = range(maxs + 1) # # Aff-1: r=1 # r = 1 # Type $A_n^{(1)}$ X = "A" nfrom = range(1, maxvarn + 1) for _ in range(N): n = random.choice(nfrom) k = n + 1 S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # Type $B_n^{(1)}$ X = "B" nfrom = range(3, maxvarn + 3) for _ in range(N): n = random.choice(nfrom) k = n + 1 S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # Type $C_n^{(1)}$ X = "C" nfrom = range(2, maxvarn + 2) for _ in range(N): n = random.choice(nfrom) k = n + 1 S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # Type $D_n^{(1)}$ X = "D" nfrom = range(4, maxvarn + 4) for _ in range(N): n = random.choice(nfrom) k = n + 1 S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # Type $E_n^{(1)}$ X = "E" nfrom = [6, 7, 8] for _ in range(N): n = random.choice(nfrom) k = n + 1 S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # Type $F_n^{(1)}$ X, n = "F", 4 k = n + 1 for _ in range(N): S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # Type $G_n^{(1)}$ X, n = "G", 2 k = n + 1 for _ in range(N): S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # # Aff-2 # r = 2 # Type $A_n^{(2)}: X = "A" ## n is even nfrom = range(2, 2 + 2*maxvarn, 2) for _ in range(N): n = random.choice(nfrom) k = n/2 + 1 S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) ## n is odd nfrom = range(5, 5 + 2*maxvarn, 2) for _ in range(N): n = random.choice(nfrom) k = (n + 1)/2 + 1 S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # Type $D_n^{(2)} X = "D" nfrom = range(3, 3 + maxvarn) for _ in range(N): n = random.choice(nfrom) k = n S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # Type $E_n^{(2)}$ X, n = "E", 6 k = n - 1 for _ in range(N): S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # # Aff-3 # r = 3 # Type $D_n^{(3)} X, n = "D", 4 k = n - 1 for _ in range(N): S = [random.choice(sfrom) for i in range(k)] print(X, n, r, S) # End of random_inputs(...) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser( description='Generate random inputs.' ) parser.add_argument( '-N', metavar='NRAND', help='the number of inputs per test cases (default 5)', action='store', #dest='N', type=int, default=5, ) parser.add_argument( '-n', '--varn', metavar='VARN', help="the variability range for the parameter 'n' \ (default 4)", action='store', type=int, default=4, ) parser.add_argument( '-s', '--maxs', metavar='MAXS', help="the max value for each 's_i's (default 3)", action='store', type=int, default=3, ) parser.add_argument( '-m', '--message', metavar='HDR_MSG', help='the header message at the top', action='store', type=str, default=None, ) args = parser.parse_args(); # print args if args.message: print '# {}'.format(args.message) random_inputs(args.N, args.varn, args.maxs)

d5aadadfed1c29d40d8d3576096584dcc5489b49

py

Python

monai/handlers/checkpoint_saver.py

dylanbuchi/MONAI

1651f1b003b0ffae8b615d191952ad65ad091277

2019-10-16T23:53:16.000Z

2022-03-31T20:58:24.000Z

monai/handlers/checkpoint_saver.py

dylanbuchi/MONAI

1651f1b003b0ffae8b615d191952ad65ad091277

2020-01-10T16:23:44.000Z

2022-03-31T22:14:53.000Z

monai/handlers/checkpoint_saver.py

dylanbuchi/MONAI

1651f1b003b0ffae8b615d191952ad65ad091277

2020-01-14T19:18:01.000Z

2022-03-31T14:06:14.000Z

# Copyright 2020 - 2021 MONAI Consortium # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import warnings from typing import TYPE_CHECKING, Dict, Mapping, Optional from monai.config import IgniteInfo from monai.utils import min_version, optional_import Events, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Events") Checkpoint, _ = optional_import("ignite.handlers", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Checkpoint") if TYPE_CHECKING: from ignite.engine import Engine from ignite.handlers import DiskSaver else: Engine, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine") DiskSaver, _ = optional_import("ignite.handlers", IgniteInfo.OPT_IMPORT_VERSION, min_version, "DiskSaver") class CheckpointSaver: """ CheckpointSaver acts as an Ignite handler to save checkpoint data into files. It supports to save according to metrics result, epoch number, iteration number and last model or exception. Args: save_dir: the target directory to save the checkpoints. save_dict: source objects that save to the checkpoint. examples:: {'network': net, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler} name: identifier of logging.logger to use, if None, defaulting to ``engine.logger``. file_prefix: prefix for the filenames to which objects will be saved. save_final: whether to save checkpoint or session at final iteration or exception. If checkpoints are to be saved when an exception is raised, put this handler before `StatsHandler` in the handler list, because the logic with Ignite can only trigger the first attached handler for `EXCEPTION_RAISED` event. final_filename: set a fixed filename to save the final model if `save_final=True`. If None, default to `checkpoint_final_iteration=N.pt`. save_key_metric: whether to save checkpoint or session when the value of key_metric is higher than all the previous values during training.keep 4 decimal places of metric, checkpoint name is: {file_prefix}_key_metric=0.XXXX.pth. key_metric_name: the name of key_metric in ignite metrics dictionary. If None, use `engine.state.key_metric` instead. key_metric_n_saved: save top N checkpoints or sessions, sorted by the value of key metric in descending order. key_metric_filename: set a fixed filename to set the best metric model, if not None, `key_metric_n_saved` should be 1 and only keep the best metric model. key_metric_save_state: whether to save the tracking list of key metric in the checkpoint file. if `True`, then will save an object in the checkpoint file with key `checkpointer` to be consistent with the `include_self` arg of `Checkpoint` in ignite: https://pytorch.org/ignite/v0.4.5/generated/ignite.handlers.checkpoint.Checkpoint.html. typically, it's used to resume training and compare current metric with previous N values. key_metric_greater_or_equal: if `True`, the latest equally scored model is stored. Otherwise, save the the first equally scored model. default to `False`. key_metric_negative_sign: whether adding a negative sign to the metric score to compare metrics, because for error-like metrics, smaller is better(objects with larger score are retained). default to `False`. epoch_level: save checkpoint during training for every N epochs or every N iterations. `True` is epoch level, `False` is iteration level. save_interval: save checkpoint every N epochs, default is 0 to save no checkpoint. n_saved: save latest N checkpoints of epoch level or iteration level, 'None' is to save all. Note: CheckpointHandler can be used during training, validation or evaluation. example of saved files: - checkpoint_iteration=400.pt - checkpoint_iteration=800.pt - checkpoint_epoch=1.pt - checkpoint_final_iteration=1000.pt - checkpoint_key_metric=0.9387.pt """ def __init__( self, save_dir: str, save_dict: Dict, name: Optional[str] = None, file_prefix: str = "", save_final: bool = False, final_filename: Optional[str] = None, save_key_metric: bool = False, key_metric_name: Optional[str] = None, key_metric_n_saved: int = 1, key_metric_filename: Optional[str] = None, key_metric_save_state: bool = False, key_metric_greater_or_equal: bool = False, key_metric_negative_sign: bool = False, epoch_level: bool = True, save_interval: int = 0, n_saved: Optional[int] = None, ) -> None: if save_dir is None: raise AssertionError("must provide directory to save the checkpoints.") self.save_dir = save_dir if not (save_dict is not None and len(save_dict) > 0): raise AssertionError("must provide source objects to save.") self.save_dict = save_dict self.logger = logging.getLogger(name) self.epoch_level = epoch_level self.save_interval = save_interval self._final_checkpoint = self._key_metric_checkpoint = self._interval_checkpoint = None self._name = name class _DiskSaver(DiskSaver): """ Enhance the DiskSaver to support fixed filename. """ def __init__(self, dirname: str, filename: Optional[str] = None): # set `atomic=False` as `atomic=True` only gives read/write permission to the user who saved the file, # without group/others read permission super().__init__(dirname=dirname, require_empty=False, atomic=False) self.filename = filename def __call__(self, checkpoint: Mapping, filename: str, metadata: Optional[Mapping] = None) -> None: if self.filename is not None: filename = self.filename super().__call__(checkpoint=checkpoint, filename=filename, metadata=metadata) def remove(self, filename: str) -> None: if self.filename is not None: filename = self.filename super().remove(filename=filename) if save_final: def _final_func(engine: Engine): return engine.state.iteration self._final_checkpoint = Checkpoint( to_save=self.save_dict, save_handler=_DiskSaver(dirname=self.save_dir, filename=final_filename), filename_prefix=file_prefix, score_function=_final_func, score_name="final_iteration", ) if save_key_metric: def _score_func(engine: Engine): if isinstance(key_metric_name, str): metric_name = key_metric_name elif hasattr(engine.state, "key_metric_name"): metric_name = engine.state.key_metric_name # type: ignore else: raise ValueError( f"Incompatible values: save_key_metric=True and key_metric_name={key_metric_name}." ) return (-1 if key_metric_negative_sign else 1) * engine.state.metrics[metric_name] if key_metric_filename is not None and key_metric_n_saved > 1: raise ValueError("if using fixed filename to save the best metric model, we should only save 1 model.") self._key_metric_checkpoint = Checkpoint( to_save=self.save_dict, save_handler=_DiskSaver(dirname=self.save_dir, filename=key_metric_filename), filename_prefix=file_prefix, score_function=_score_func, score_name="key_metric", n_saved=key_metric_n_saved, include_self=key_metric_save_state, greater_or_equal=key_metric_greater_or_equal, ) if save_interval > 0: def _interval_func(engine: Engine): return engine.state.epoch if self.epoch_level else engine.state.iteration self._interval_checkpoint = Checkpoint( to_save=self.save_dict, save_handler=_DiskSaver(dirname=self.save_dir), filename_prefix=file_prefix, score_function=_interval_func, score_name="epoch" if self.epoch_level else "iteration", n_saved=n_saved, ) def load_state_dict(self, state_dict: Dict) -> None: """ Utility to resume the internal state of key metric tracking list if configured to save checkpoints based on the key metric value. Note to set `key_metric_save_state=True` when saving the previous checkpoint. Example:: CheckpointSaver( ... save_key_metric=True, key_metric_save_state=True, # config to also save the state of this saver ).attach(engine) engine.run(...) # resumed training with a new CheckpointSaver saver = CheckpointSaver(save_key_metric=True, ...) # load the previous key metric tracking list into saver CheckpointLoader("/test/model.pt"), {"checkpointer": saver}).attach(engine) """ if self._key_metric_checkpoint is not None: self._key_metric_checkpoint.load_state_dict(state_dict) else: warnings.warn("no key metric checkpoint saver to resume the key metric tracking list.") def attach(self, engine: Engine) -> None: """ Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. """ if self._name is None: self.logger = engine.logger if self._final_checkpoint is not None: engine.add_event_handler(Events.COMPLETED, self.completed) engine.add_event_handler(Events.EXCEPTION_RAISED, self.exception_raised) if self._key_metric_checkpoint is not None: engine.add_event_handler(Events.EPOCH_COMPLETED, self.metrics_completed) if self._interval_checkpoint is not None: if self.epoch_level: engine.add_event_handler(Events.EPOCH_COMPLETED(every=self.save_interval), self.interval_completed) else: engine.add_event_handler(Events.ITERATION_COMPLETED(every=self.save_interval), self.interval_completed) def _delete_previous_final_ckpt(self): saved = self._final_checkpoint._saved if len(saved) > 0: item = saved.pop(0) self._final_checkpoint.save_handler.remove(item.filename) self.logger.info(f"Deleted previous saved final checkpoint: {item.filename}") def completed(self, engine: Engine) -> None: """Callback for train or validation/evaluation completed Event. Save final checkpoint if configure save_final is True. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. """ if not callable(self._final_checkpoint): raise AssertionError("Error: _final_checkpoint function not specified.") # delete previous saved final checkpoint if existing self._delete_previous_final_ckpt() self._final_checkpoint(engine) if self.logger is None: raise AssertionError if not hasattr(self.logger, "info"): raise AssertionError("Error, provided logger has not info attribute.") self.logger.info(f"Train completed, saved final checkpoint: {self._final_checkpoint.last_checkpoint}") def exception_raised(self, engine: Engine, e: Exception) -> None: """Callback for train or validation/evaluation exception raised Event. Save current data as final checkpoint if configure save_final is True. This callback may be skipped because the logic with Ignite can only trigger the first attached handler for `EXCEPTION_RAISED` event. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. e: the exception caught in Ignite during engine.run(). """ if not callable(self._final_checkpoint): raise AssertionError("Error: _final_checkpoint function not specified.") # delete previous saved final checkpoint if existing self._delete_previous_final_ckpt() self._final_checkpoint(engine) if self.logger is None: raise AssertionError if not hasattr(self.logger, "info"): raise AssertionError("Error, provided logger has not info attribute.") self.logger.info(f"Exception raised, saved the last checkpoint: {self._final_checkpoint.last_checkpoint}") raise e def metrics_completed(self, engine: Engine) -> None: """Callback to compare metrics and save models in train or validation when epoch completed. Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. """ if not callable(self._key_metric_checkpoint): raise AssertionError("Error: _key_metric_checkpoint function not specified.") self._key_metric_checkpoint(engine) def interval_completed(self, engine: Engine) -> None: """Callback for train epoch/iteration completed Event. Save checkpoint if configure save_interval = N Args: engine: Ignite Engine, it can be a trainer, validator or evaluator. """ if not callable(self._interval_checkpoint): raise AssertionError("Error: _interval_checkpoint function not specified.") self._interval_checkpoint(engine) if self.logger is None: raise AssertionError if not hasattr(self.logger, "info"): raise AssertionError("Error, provided logger has not info attribute.") if self.epoch_level: self.logger.info(f"Saved checkpoint at epoch: {engine.state.epoch}") else: self.logger.info(f"Saved checkpoint at iteration: {engine.state.iteration}")

d514c63a18be319ee21a400e322a3d9cc8f5b7de

py

Python

plotly/widgets/graph_widget.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/widgets/graph_widget.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/widgets/graph_widget.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

""" Module to allow Plotly graphs to interact with IPython widgets. """ import uuid from collections import deque import pkgutil from requests.compat import json as _json # TODO: protected imports? import ipywidgets as widgets from traitlets import Unicode from IPython.display import Javascript, display import plotly.plotly.plotly as py from plotly import utils, tools from plotly.graph_objs import Figure # Load JS widget code # No officially recommended way to do this in any other way # http://mail.scipy.org/pipermail/ipython-dev/2014-April/013835.html js_widget_code = pkgutil.get_data('plotly', 'package_data/graphWidget.js' ).decode('utf-8') display(Javascript(js_widget_code)) __all__ = None class GraphWidget(widgets.DOMWidget): """An interactive Plotly graph widget for use in IPython Notebooks. """ _view_name = Unicode('GraphView', sync=True) _view_module = Unicode('graphWidget', sync=True) _message = Unicode(sync=True) _graph_url = Unicode(sync=True) _new_url = Unicode(sync=True) _filename = '' _flags = { 'save_pending': False } # TODO: URL for offline enterprise def __init__(self, graph_url='https://plot.ly/~playground/7', **kwargs): """Initialize a plotly graph widget Args: graph_url: The url of a Plotly graph Example: ``` GraphWidget('https://plot.ly/~chris/3375') ``` """ super(GraphWidget, self).__init__(**kwargs) # TODO: Validate graph_url self._graph_url = graph_url self._listener_set = set() self._event_handlers = { 'click': widgets.CallbackDispatcher(), 'hover': widgets.CallbackDispatcher(), 'zoom': widgets.CallbackDispatcher() } self._graphId = '' self.on_msg(self._handle_msg) # messages to the iframe client need to wait for the # iframe to communicate that it is ready # unfortunately, this two-way blocking communication # isn't possible # (https://github.com/ipython/ipython/wiki/IPEP-21:-Widget-Messages#caveats) # so we'll just cue up messages until they're ready to be sent self._clientMessages = deque() @property def url(self): return self._new_url or '' def _handle_msg(self, message): """Handle a msg from the front-end. Args: content (dict): Content of the msg. """ content = message['content']['data']['content'] if content.get('event', '') == 'pong': self._graphId = content['graphId'] # ready to recieve - pop out all of the items in the deque while self._clientMessages: _message = self._clientMessages.popleft() _message['graphId'] = self._graphId _message = _json.dumps(_message) self._message = _message if content.get('event', '') in ['click', 'hover', 'zoom']: # De-nest the message if content['event'] == 'click' or content['event'] == 'hover': message = content['message']['points'] elif content['event'] == 'zoom': message = content['message']['ranges'] self._event_handlers[content['event']](self, message) if content.get('event', '') == 'getAttributes': self._attributes = content.get('response', {}) # there might be a save pending, use the plotly module to save if self._flags['save_pending']: self._flags['save_pending'] = False url = py.plot(self._attributes, auto_open=False, filename=self._filename, validate=False) self._new_url = url self._fade_to('slow', 1) def _handle_registration(self, event_type, callback, remove): self._event_handlers[event_type].register_callback(callback, remove=remove) event_callbacks = self._event_handlers[event_type].callbacks if (len(event_callbacks) and event_type not in self._listener_set): self._listener_set.add(event_type) message = {'task': 'listen', 'events': list(self._listener_set)} self._handle_outgoing_message(message) def _handle_outgoing_message(self, message): if self._graphId == '': self._clientMessages.append(message) else: message['graphId'] = self._graphId message['uid'] = str(uuid.uuid4()) self._message = _json.dumps(message, cls=utils.PlotlyJSONEncoder) def on_click(self, callback, remove=False): """ Assign a callback to click events propagated by clicking on point(s) in the Plotly graph. Args: callback (function): Callback function this is called on click events with the signature: callback(widget, hover_obj) -> None Args: widget (GraphWidget): The current instance of the graph widget that this callback is assigned to. click_obj (dict): a nested dict that describes which point(s) were clicked on. click_obj example: [ { 'curveNumber': 1, 'pointNumber': 2, 'x': 4, 'y': 14 } ] remove (bool, optional): If False, attach the callback. If True, remove the callback. Defaults to False. Returns: None Example: ``` from IPython.display import display def message_handler(widget, msg): display(widget._graph_url) display(msg) g = GraphWidget('https://plot.ly/~chris/3375') display(g) g.on_click(message_handler) ``` """ self._handle_registration('click', callback, remove) def on_hover(self, callback, remove=False): """ Assign a callback to hover events propagated by hovering over points in the Plotly graph. Args: callback (function): Callback function this is called on hover events with the signature: callback(widget, hover_obj) -> None Args: widget (GraphWidget): The current instance of the graph widget that this callback is assigned to. hover_obj (dict): a nested dict that describes which point(s) was hovered over. hover_obj example: [ { 'curveNumber': 1, 'pointNumber': 2, 'x': 4, 'y': 14 } ] remove (bool, optional): If False, attach the callback. If True, remove the callback. Defaults to False. Returns: None Example: ``` from IPython.display import display def message_handler(widget, hover_msg): display(widget._graph_url) display(hover_msg) g = GraphWidget('https://plot.ly/~chris/3375') display(g) g.on_hover(message_handler) ``` """ self._handle_registration('hover', callback, remove) def on_zoom(self, callback, remove=False): """ Assign a callback to zoom events propagated by zooming in regions in the Plotly graph. Args: callback (function): Callback function this is called on zoom events with the signature: callback(widget, ranges) -> None Args: widget (GraphWidget): The current instance of the graph widget that this callback is assigned to. ranges (dict): A description of the region that was zoomed into. ranges example: { 'x': [1.8399058038561549, 2.16443359662], 'y': [4.640902872777017, 7.855677154582] } remove (bool, optional): If False, attach the callback. If True, remove the callback. Defaults to False. Returns: None Example: ``` from IPython.display import display def message_handler(widget, ranges): display(widget._graph_url) display(ranges) g = GraphWidget('https://plot.ly/~chris/3375') display(g) g.on_zoom(message_handler) ``` """ self._handle_registration('zoom', callback, remove) def plot(self, figure_or_data, validate=True): """Plot figure_or_data in the Plotly graph widget. Args: figure_or_data (dict, list, or plotly.graph_obj object): The standard Plotly graph object that describes Plotly graphs as used in `plotly.plotly.plot`. See examples of the figure_or_data in https://plot.ly/python/ Returns: None Example 1 - Graph a scatter plot: ``` from plotly.graph_objs import Scatter g = GraphWidget() g.plot([Scatter(x=[1, 2, 3], y=[10, 15, 13])]) ``` Example 2 - Graph a scatter plot with a title: ``` from plotly.graph_objs import Scatter, Figure, Data fig = Figure( data = Data([ Scatter(x=[1, 2, 3], y=[20, 15, 13]) ]), layout = Layout(title='Experimental Data') ) g = GraphWidget() g.plot(fig) ``` Example 3 - Clear a graph widget ``` from plotly.graph_objs import Scatter, Figure g = GraphWidget() g.plot([Scatter(x=[1, 2, 3], y=[10, 15, 13])]) # Now clear it g.plot({}) # alternatively, g.plot(Figure()) ``` """ if figure_or_data == {} or figure_or_data == Figure(): validate = False figure = tools.return_figure_from_figure_or_data(figure_or_data, validate) message = { 'task': 'newPlot', 'data': figure.get('data', []), 'layout': figure.get('layout', {}), 'graphId': self._graphId } self._handle_outgoing_message(message) def restyle(self, update, indices=None): """Update the style of existing traces in the Plotly graph. Args: update (dict): dict where keys are the graph attribute strings and values are the value of the graph attribute. To update graph objects that are nested, like a marker's color, combine the keys with a period, e.g. `marker.color`. To replace an entire nested object, like `marker`, set the value to the object. See Example 2 below. To update an attribute of multiple traces, set the value to an list of values. If the list is shorter than the number of traces, the values will wrap around. Note: this means that for values that are naturally an array, like `x` or `colorscale`, you need to wrap the value in an extra array, i.e. {'colorscale': [[[0, 'red'], [1, 'green']]]} You can also supply values to different traces with the indices argument. See all of the graph attributes in our reference documentation here: https://plot.ly/python/reference or by calling `help` on graph objects in `plotly.graph_objs`. indices (list, optional): Specify which traces to apply the update dict to. Negative indices are supported. If indices are not given, the update will apply to *all* traces. Examples: Initialization - Start each example below with this setup: ``` from plotly.widgets import GraphWidget from IPython.display import display graph = GraphWidget() display(graph) ``` Example 1 - Set `marker.color` to red in every trace in the graph ``` graph.restyle({'marker.color': 'red'}) ``` Example 2 - Replace `marker` with {'color': 'red'} ``` graph.restyle({'marker': {'color': red'}}) ``` Example 3 - Set `marker.color` to red in the first trace of the graph ``` graph.restyle({'marker.color': 'red'}, indices=[0]) ``` Example 4 - Set `marker.color` of all of the traces to alternating sequences of red and green ``` graph.restyle({'marker.color': ['red', 'green']}) ``` Example 5 - Set just `marker.color` of the first two traces to red and green ``` graph.restyle({'marker.color': ['red', 'green']}, indices=[0, 1]) ``` Example 6 - Set multiple attributes of all of the traces ``` graph.restyle({ 'marker.color': 'red', 'line.color': 'green' }) ``` Example 7 - Update the data of the first trace ``` graph.restyle({ 'x': [[1, 2, 3]], 'y': [[10, 20, 30]], }, indices=[0]) ``` Example 8 - Update the data of the first two traces ``` graph.restyle({ 'x': [[1, 2, 3], [1, 2, 4]], 'y': [[10, 20, 30], [5, 8, 14]], }, indices=[0, 1]) ``` """ # TODO: Add flat traces to graph_objs message = { 'task': 'restyle', 'update': update, 'graphId': self._graphId } if indices: message['indices'] = indices self._handle_outgoing_message(message) def relayout(self, layout): """Update the layout of the Plotly graph. Args: layout (dict): dict where keys are the graph attribute strings and values are the value of the graph attribute. To update graph objects that are nested, like the title of an axis, combine the keys with a period e.g. `xaxis.title`. To set a value of an element in an array, like an axis's range, use brackets, e.g. 'xaxis.range[0]'. To replace an entire nested object, just specify the value to the sub-object. See example 4 below. See all of the layout attributes in our reference documentation https://plot.ly/python/reference/#Layout Or by calling `help` on `plotly.graph_objs.Layout` Examples - Start each example below with this setup: Initialization: ``` from plotly.widgets import GraphWidget from IPython.display import display graph = GraphWidget('https://plot.ly/~chris/3979') display(graph) ``` Example 1 - Update the title ``` graph.relayout({'title': 'Experimental results'}) ``` Example 2 - Update the xaxis range ``` graph.relayout({'xaxis.range': [-1, 6]}) ``` Example 3 - Update the first element of the xaxis range ``` graph.relayout({'xaxis.range[0]': -3}) ``` Example 4 - Replace the entire xaxis object ``` graph.relayout({'xaxis': {'title': 'Experimental results'}}) ``` """ # TODO: Add flat layout to graph_objs message = { 'task': 'relayout', 'update': layout, 'graphId': self._graphId } self._handle_outgoing_message(message) def hover(self, *hover_objs): """Show hover labels over the points specified in hover_obj. Hover labels are the labels that normally appear when the mouse hovers over points in the plotly graph. Args: hover_objs (tuple of dicts): Specifies which points to place hover labels over. The location of the hover labels is described by a dict with keys and'xval' and/or 'yval' or 'curveNumber' and 'pointNumber' and optional keys 'hovermode' and 'subplot' 'xval' and 'yval' specify the (x, y) coordinates to place the label. 'xval' and 'yval need to be close to a point drawn in a graph. 'curveNumber' and 'pointNumber' specify the trace number and the index theof the point in that trace respectively. 'subplot' describes which axes to the coordinates refer to. By default, it is equal to 'xy'. For example, to specify the second x-axis and the third y-axis, set 'subplot' to 'x2y3' 'hovermode' is either 'closest', 'x', or 'y'. When set to 'x', all data sharing the same 'x' coordinate will be shown on screen with corresponding trace labels. When set to 'y' all data sharing the same 'y' coordinates will be shown on the screen with corresponding trace labels. When set to 'closest', information about the data point closest to where the viewer is hovering will appear. Note: If 'hovermode' is 'x', only 'xval' needs to be set. If 'hovermode' is 'y', only 'yval' needs to be set. If 'hovermode' is 'closest', 'xval' and 'yval' both need to be set. Note: 'hovermode' can be toggled by the user in the graph toolbar. Note: It is not currently possible to apply multiple hover labels to points on different axes. Note: `hover` can only be called with multiple dicts if 'curveNumber' and 'pointNumber' are the keys of the dicts Examples: Initialization - Start each example below with this setup: ``` from plotly.widgets import GraphWidget from IPython.display import display graph = GraphWidget('https://plot.ly/~chris/3979') display(graph) ``` Example 1 - Apply a label to the (x, y) point (3, 2) ``` graph.hover({'xval': 3, 'yval': 2, 'hovermode': 'closest'}) ``` Example 2 -Apply a labels to all the points with the x coordinate 3 ``` graph.hover({'xval': 3, 'hovermode': 'x'}) ``` Example 3 - Apply a label to the first point of the first trace and the second point of the second trace. ``` graph.hover({'curveNumber': 0, 'pointNumber': 0}, {'curveNumber': 1, 'pointNumber': 1}) ``` """ # TODO: Add to graph objects if len(hover_objs) == 1: hover_objs = hover_objs[0] message = { 'task': 'hover', 'selection': hover_objs, 'graphId': self._graphId } self._handle_outgoing_message(message) def add_traces(self, traces, new_indices=None): """ Add new data traces to a graph. If `new_indices` isn't specified, they are simply appended. Args: traces (dict or list of dicts, or class of plotly.graph_objs):trace new_indices (list[int]|None), optional: The final indices the added traces should occupy in the graph. Examples: Initialization - Start each example below with this setup: ``` from plotly.widgets import GraphWidget from plotly.graph_objs import Scatter from IPython.display import display graph = GraphWidget('https://plot.ly/~chris/3979') display(graph) ``` Example 1 - Add a scatter/line trace to the graph ``` graph.add_traces(Scatter(x = [1, 2, 3], y = [5, 4, 5])) ``` Example 2 - Add a scatter trace and set it to to be the second trace. This will appear as the second item in the legend. ``` graph.add_traces(Scatter(x = [1, 2, 3], y = [5, 6, 5]), new_indices=[1]) ``` Example 3 - Add multiple traces to the graph ``` graph.add_traces([ Scatter(x = [1, 2, 3], y = [5, 6, 5]), Scatter(x = [1, 2.5, 3], y = [5, 8, 5]) ]) ``` """ # TODO: Validate traces with graph_objs message = { 'task': 'addTraces', 'traces': traces, 'graphId': self._graphId } if new_indices is not None: message['newIndices'] = new_indices self._handle_outgoing_message(message) def delete_traces(self, indices): """Delete data traces from a graph. Args: indices (list[int]): The indices of the traces to be removed Example - Delete the 2nd trace: ``` from plotly.widgets import GraphWidget from IPython.display import display graph = GraphWidget('https://plot.ly/~chris/3979') display(graph) graph.delete_traces([1]) ``` """ message = { 'task': 'deleteTraces', 'indices': indices, 'graphId': self._graphId } self._handle_outgoing_message(message) def reorder_traces(self, current_indices, new_indices=None): """Reorder the traces in a graph. The order of the traces determines the order of the legend entries and the layering of the objects drawn in the graph, i.e. the first trace is drawn first and the second trace is drawn on top of the first trace. Args: current_indices (list[int]): The index of the traces to reorder. new_indices (list[int], optional): The index of the traces specified by `current_indices` after ordering. If None, then move the traces to the end. Examples: Example 1 - Move the first trace to the second to last position, the second trace to the last position ``` graph.move_traces([0, 1]) ``` Example 2 - Move the first trace to the second position, the second trace to the first position. ``` graph.move_traces([0], [1]) ``` """ message = { 'task': 'moveTraces', 'currentIndices': current_indices, 'graphId': self._graphId } if new_indices is not None: message['newIndices'] = new_indices self._handle_outgoing_message(message) def save(self, ignore_defaults=False, filename=''): """ Save a copy of the current state of the widget in plotly. :param (bool) ignore_defaults: Auto-fill in unspecified figure keys? :param (str) filename: Name of the file on plotly. """ self._flags['save_pending'] = True self._filename = filename message = {'task': 'getAttributes', 'ignoreDefaults': ignore_defaults} self._handle_outgoing_message(message) self._fade_to('slow', 0.1) def extend_traces(self, update, indices=(0,), max_points=None): """ Append data points to existing traces in the Plotly graph. Args: update (dict): dict where keys are the graph attribute strings and values are arrays of arrays with values to extend. Each array in the array will extend a trace. Valid keys include: 'x', 'y', 'text, 'marker.color', 'marker.size', 'marker.symbol', 'marker.line.color', 'marker.line.width' indices (list, int): Specify which traces to apply the `update` dict to. If indices are not given, the update will apply to the traces in order. max_points (int or dict, optional): If specified, then only show the `max_points` most recent points in the graph. This is useful to prevent traces from becoming too large (and slow) or for creating "windowed" graphs in monitoring applications. To set max_points to different values for each trace or attribute, set max_points to a dict mapping keys to max_points values. See the examples below. Examples: Initialization - Start each example below with this setup: ``` from plotly.widgets import GraphWidget from IPython.display import display graph = GraphWidget() graph.plot([ {'x': [], 'y': []}, {'x': [], 'y': []} ]) display(graph) ``` Example 1 - Extend the first trace with x and y data ``` graph.extend_traces({'x': [[1, 2, 3]], 'y': [[10, 20, 30]]}, indices=[0]) ``` Example 2 - Extend the second trace with x and y data ``` graph.extend_traces({'x': [[1, 2, 3]], 'y': [[10, 20, 30]]}, indices=[1]) ``` Example 3 - Extend the first two traces with x and y data ``` graph.extend_traces({ 'x': [[1, 2, 3], [2, 3, 4]], 'y': [[10, 20, 30], [3, 4, 3]] }, indices=[0, 1]) ``` Example 4 - Extend the first trace with x and y data and limit the length of data in that trace to 50 points. ``` graph.extend_traces({ 'x': [range(100)], 'y': [range(100)] }, indices=[0, 1], max_points=50) ``` Example 5 - Extend the first and second trace with x and y data and limit the length of data in the first trace to 25 points and the second trace to 50 points. ``` new_points = range(100) graph.extend_traces({ 'x': [new_points, new_points], 'y': [new_points, new_points] }, indices=[0, 1], max_points={ 'x': [25, 50], 'y': [25, 50] } ) ``` Example 6 - Update other attributes, like marker colors and sizes and text ``` # Initialize a plot with some empty attributes graph.plot([{ 'x': [], 'y': [], 'text': [], 'marker': { 'size': [], 'color': [] } }]) # Append some data into those attributes graph.extend_traces({ 'x': [[1, 2, 3]], 'y': [[10, 20, 30]], 'text': [['A', 'B', 'C']], 'marker.size': [[10, 15, 20]], 'marker.color': [['blue', 'red', 'orange']] }, indices=[0]) ``` Example 7 - Live-update a graph over a few seconds ``` import time graph.plot([{'x': [], 'y': []}]) for i in range(10): graph.extend_traces({ 'x': [[i]], 'y': [[i]] }, indices=[0]) time.sleep(0.5) ``` """ message = { 'task': 'extendTraces', 'update': update, 'graphId': self._graphId, 'indices': indices } if max_points is not None: message['maxPoints'] = max_points self._handle_outgoing_message(message) def _fade_to(self, duration, opacity): """ Change the opacity to give a visual signal to users. """ message = {'fadeTo': True, 'duration': duration, 'opacity': opacity} self._handle_outgoing_message(message)

603daef8bd50008fb784efa6d1e8320da21c3393

py

Python

apps/greencheck/api/asn_viewset.py

denning/admin-portal

34a8e9f07f88c85c01ae1255517d889fb7951ba5

2020-11-23T22:47:26.000Z

2022-01-28T16:26:50.000Z

apps/greencheck/api/asn_viewset.py

denning/admin-portal

34a8e9f07f88c85c01ae1255517d889fb7951ba5

2020-05-17T20:25:50.000Z

2022-03-29T18:11:50.000Z

apps/greencheck/api/asn_viewset.py

denning/admin-portal

34a8e9f07f88c85c01ae1255517d889fb7951ba5

2020-11-30T00:13:45.000Z

2021-06-11T13:42:04.000Z

import logging from rest_framework import mixins, viewsets from rest_framework.authentication import BasicAuthentication, SessionAuthentication from rest_framework_csv import renderers as drf_csv_rndr # noqa from django.utils.decorators import method_decorator from drf_yasg.utils import swagger_auto_schema # noqa from ..models import GreencheckASN from ..serializers import GreenASNSerializer from .permissions import BelongsToHostingProvider logger = logging.getLogger(__name__) ASN_API_LIST_DESCRIPTION = """ List the AS Networks associated with this provider. Returns a list of AS Networks registered with the provider. """ # noqa ASN_API_CREATE_DESCRIPTION = """ Register a new AS Networks for the hosting provider associated with this user. Once an ASN is registered, it can take a short while before checks against the new IP range show as green. """ # noqa ASN_API_DESTROY_DESCRIPTION = """ Removes the association of the AS Network with the corresponding id from this hosting provider. As with POSTing a new AS Network, there can be a delay until the change propogates. """ ASN_API_RETRIEVE_DESCRIPTION = """ Fetch the AS Network for the corresponding id provided. """ @method_decorator( name="list", decorator=swagger_auto_schema( operation_description=ASN_API_LIST_DESCRIPTION, tags=["AS Network"] ), ) @method_decorator( name="create", decorator=swagger_auto_schema( operation_description=ASN_API_CREATE_DESCRIPTION, tags=["AS Network"] ), ) @method_decorator( name="retrieve", decorator=swagger_auto_schema( operation_description=ASN_API_RETRIEVE_DESCRIPTION, tags=["AS Network"] ), ) @method_decorator( name="destroy", decorator=swagger_auto_schema( operation_description=ASN_API_DESTROY_DESCRIPTION, tags=["AS Network"] ), ) class ASNViewSet( mixins.CreateModelMixin, mixins.DestroyModelMixin, mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet, ): """ This viewset automatically provides `list` and `retrieve` actions. We don't want ASNs to be editable once created, as they're often linked to an request to approve it. So, we expose 'create', 'destroy' and 'list' methods. Similarly, 'delete' does not delete a range, but instead it marks the IP range as inactive. """ serializer_class = GreenASNSerializer queryset = GreencheckASN.objects.all() authentication_classes = [SessionAuthentication, BasicAuthentication] permission_classes = [BelongsToHostingProvider] def filter_queryset(self, queryset): """ Because our viewset takes care of pagination and the rest all we change is what is returned when we filter the queryset for a given user. http://www.cdrf.co/3.9/rest_framework.viewsets/ModelViewSet.html#list """ user = self.request.user if user is not None: provider = self.request.user.hostingprovider if provider is not None: return provider.greencheckasn_set.filter(active=True) return [] def perform_destroy(self, instance): """ Overriding this one function means that the rest of our destroy method works as expected. """ instance.active = False instance.save()

5b785bbb99ef2241514135101ebd7a3f34ce3490

py

Python

apps/contrib/utils/email.py

jimialex/django-wise-template-mysql

78b7281ba5cdd1e89a165b217e1b200fdba0135b

2020-04-11T20:11:48.000Z

2021-03-16T23:58:01.000Z

apps/contrib/utils/email.py

jimialex/django-wise-template-mysql

78b7281ba5cdd1e89a165b217e1b200fdba0135b

2020-04-11T20:17:56.000Z

2021-06-16T19:18:29.000Z

apps/contrib/utils/email.py

jimialex/django-wise-template-mysql

78b7281ba5cdd1e89a165b217e1b200fdba0135b

2020-10-10T14:07:37.000Z

2020-10-10T14:07:37.000Z

# -*- coding: utf-8 -*- from django.conf import settings from django.core.mail import EmailMultiAlternatives def send_email(subject, to, text_body, html_body): """Helps to send and email.""" email = EmailMultiAlternatives( subject=subject, from_email=settings.DEFAULT_FROM_EMAIL, to=to, body=text_body, ) email.attach_alternative(html_body, 'text/html') email.send()

2a19f5b9566d1435bd3209f6b8d2621391a42acd

py

Python

libcloud/compute/drivers/softlayer.py

ggreer/libcloud

a391ccdc0d068d37cb906a703f1494af50d83c8f

2021-06-14T11:11:39.000Z

2021-06-14T11:11:39.000Z

libcloud/compute/drivers/softlayer.py

ggreer/libcloud

a391ccdc0d068d37cb906a703f1494af50d83c8f

libcloud/compute/drivers/softlayer.py

ggreer/libcloud

a391ccdc0d068d37cb906a703f1494af50d83c8f

# Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Softlayer driver """ import sys import time import libcloud from libcloud.utils.py3 import xmlrpclib from libcloud.common.types import InvalidCredsError, LibcloudError from libcloud.compute.types import Provider, NodeState from libcloud.compute.base import NodeDriver, Node, NodeLocation, NodeSize, NodeImage DATACENTERS = { 'sea01': {'country': 'US'}, 'wdc01': {'country': 'US'}, 'dal01': {'country': 'US'} } NODE_STATE_MAP = { 'RUNNING': NodeState.RUNNING, 'HALTED': NodeState.TERMINATED, 'PAUSED': NodeState.TERMINATED, } DEFAULT_PACKAGE = 46 SL_IMAGES = [ {'id': 1684, 'name': 'CentOS 5 - Minimal Install (32 bit)'}, {'id': 1685, 'name': 'CentOS 5 - Minimal Install (64 bit)'}, {'id': 1686, 'name': 'CentOS 5 - LAMP Install (32 bit)'}, {'id': 1687, 'name': 'CentOS 5 - LAMP Install (64 bit)'}, {'id': 1688, 'name': 'Red Hat Enterprise Linux 5 - Minimal Install (32 bit)'}, {'id': 1689, 'name': 'Red Hat Enterprise Linux 5 - Minimal Install (64 bit)'}, {'id': 1690, 'name': 'Red Hat Enterprise Linux 5 - LAMP Install (32 bit)'}, {'id': 1691, 'name': 'Red Hat Enterprise Linux 5 - LAMP Install (64 bit)'}, {'id': 1692, 'name': 'Ubuntu Linux 8 LTS Hardy Heron - Minimal Install (32 bit)'}, {'id': 1693, 'name': 'Ubuntu Linux 8 LTS Hardy Heron - Minimal Install (64 bit)'}, {'id': 1694, 'name': 'Ubuntu Linux 8 LTS Hardy Heron - LAMP Install (32 bit)'}, {'id': 1695, 'name': 'Ubuntu Linux 8 LTS Hardy Heron - LAMP Install (64 bit)'}, {'id': 1696, 'name': 'Debian GNU/Linux 5.0 Lenny/Stable - Minimal Install (32 bit)'}, {'id': 1697, 'name': 'Debian GNU/Linux 5.0 Lenny/Stable - Minimal Install (64 bit)'}, {'id': 1698, 'name': 'Debian GNU/Linux 5.0 Lenny/Stable - LAMP Install (32 bit)'}, {'id': 1699, 'name': 'Debian GNU/Linux 5.0 Lenny/Stable - LAMP Install (64 bit)'}, {'id': 1700, 'name': 'Windows Server 2003 Standard SP2 with R2 (32 bit)'}, {'id': 1701, 'name': 'Windows Server 2003 Standard SP2 with R2 (64 bit)'}, {'id': 1703, 'name': 'Windows Server 2003 Enterprise SP2 with R2 (64 bit)'}, {'id': 1705, 'name': 'Windows Server 2008 Standard Edition (64bit)'}, {'id': 1715, 'name': 'Windows Server 2003 Datacenter SP2 (64 bit)'}, {'id': 1716, 'name': 'Windows Server 2003 Datacenter SP2 (32 bit)'}, {'id': 1742, 'name': 'Windows Server 2008 Standard Edition SP2 (32bit)'}, {'id': 1752, 'name': 'Windows Server 2008 Standard Edition SP2 (64bit)'}, {'id': 1756, 'name': 'Windows Server 2008 Enterprise Edition SP2 (32bit)'}, {'id': 1761, 'name': 'Windows Server 2008 Enterprise Edition SP2 (64bit)'}, {'id': 1766, 'name': 'Windows Server 2008 Datacenter Edition SP2 (32bit)'}, {'id': 1770, 'name': 'Windows Server 2008 Datacenter Edition SP2 (64bit)'}, {'id': 1857, 'name': 'Windows Server 2008 R2 Standard Edition (64bit)'}, {'id': 1860, 'name': 'Windows Server 2008 R2 Enterprise Edition (64bit)'}, {'id': 1863, 'name': 'Windows Server 2008 R2 Datacenter Edition (64bit)'}, ] """ The following code snippet will print out all available "prices" mask = { 'items': '' } res = self.connection.request( "SoftLayer_Product_Package", "getObject", res, id=46, object_mask=mask ) from pprint import pprint; pprint(res) """ SL_TEMPLATES = { 'sl1': { 'imagedata': { 'name': '2 x 2.0 GHz, 1GB ram, 100GB', 'ram': 1024, 'disk': 100, 'bandwidth': None }, 'prices':[ {'id': 1644}, # 1 GB {'id': 1639}, # 100 GB (SAN) {'id': 1963}, # Private 2 x 2.0 GHz Cores {'id': 21}, # 1 IP Address {'id': 55}, # Host Ping {'id': 58}, # Automated Notification {'id': 1800}, # 0 GB Bandwidth {'id': 57}, # Email and Ticket {'id': 274}, # 1000 Mbps Public & Private Networks {'id': 905}, # Reboot / Remote Console {'id': 418}, # Nessus Vulnerability Assessment & Reporting {'id': 420}, # Unlimited SSL VPN Users & 1 PPTP VPN User per account ], }, 'sl2': { 'imagedata': { 'name': '2 x 2.0 GHz, 4GB ram, 350GB', 'ram': 4096, 'disk': 350, 'bandwidth': None }, 'prices': [ {'id': 1646}, # 4 GB {'id': 1639}, # 100 GB (SAN) - This is the only available "First Disk" {'id': 1638}, # 250 GB (SAN) {'id': 1963}, # Private 2 x 2.0 GHz Cores {'id': 21}, # 1 IP Address {'id': 55}, # Host Ping {'id': 58}, # Automated Notification {'id': 1800}, # 0 GB Bandwidth {'id': 57}, # Email and Ticket {'id': 274}, # 1000 Mbps Public & Private Networks {'id': 905}, # Reboot / Remote Console {'id': 418}, # Nessus Vulnerability Assessment & Reporting {'id': 420}, # Unlimited SSL VPN Users & 1 PPTP VPN User per account ], } } class SoftLayerException(LibcloudError): """ Exception class for SoftLayer driver """ pass class SoftLayerSafeTransport(xmlrpclib.SafeTransport): pass class SoftLayerTransport(xmlrpclib.Transport): pass class SoftLayerProxy(xmlrpclib.ServerProxy): transportCls = (SoftLayerTransport, SoftLayerSafeTransport) API_PREFIX = 'https://api.softlayer.com/xmlrpc/v3/' def __init__(self, service, user_agent, verbose=0): cls = self.transportCls[0] if SoftLayerProxy.API_PREFIX[:8] == "https://": cls = self.transportCls[1] t = cls(use_datetime=0) t.user_agent = user_agent xmlrpclib.ServerProxy.__init__( self, uri="%s/%s" % (SoftLayerProxy.API_PREFIX, service), transport=t, verbose=verbose ) class SoftLayerConnection(object): """ Connection class for the SoftLayer driver """ proxyCls = SoftLayerProxy driver = None def __init__(self, user, key): self.user = user self.key = key self.ua = [] def request(self, service, method, *args, **kwargs): sl = self.proxyCls(service, self._user_agent()) headers = {} headers.update(self._get_auth_headers()) headers.update(self._get_init_params(service, kwargs.get('id'))) headers.update(self._get_object_mask(service, kwargs.get('object_mask'))) params = [{'headers': headers}] + list(args) try: return getattr(sl, method)(*params) except xmlrpclib.Fault: e = sys.exc_info()[1] if e.faultCode == "SoftLayer_Account": raise InvalidCredsError(e.faultString) raise SoftLayerException(e) def _user_agent(self): return 'libcloud/%s (%s)%s' % ( libcloud.__version__, self.driver.name, "".join([" (%s)" % x for x in self.ua])) def user_agent_append(self, s): self.ua.append(s) def _get_auth_headers(self): return { 'authenticate': { 'username': self.user, 'apiKey': self.key } } def _get_init_params(self, service, id): if id is not None: return { '%sInitParameters' % service: {'id': id} } else: return {} def _get_object_mask(self, service, mask): if mask is not None: return { '%sObjectMask' % service: {'mask': mask} } else: return {} class SoftLayerNodeDriver(NodeDriver): """ SoftLayer node driver Extra node attributes: - password: root password - hourlyRecurringFee: hourly price (if applicable) - recurringFee : flat rate (if applicable) - recurringMonths : The number of months in which the recurringFee will be incurred. """ connectionCls = SoftLayerConnection name = 'SoftLayer' type = Provider.SOFTLAYER features = {"create_node": ["generates_password"]} def __init__(self, key, secret=None, secure=False): self.key = key self.secret = secret self.connection = self.connectionCls(key, secret) self.connection.driver = self def _to_node(self, host): try: password = host['softwareComponents'][0]['passwords'][0]['password'] except (IndexError, KeyError): password = None hourlyRecurringFee = host.get('billingItem', {}).get('hourlyRecurringFee', 0) recurringFee = host.get('billingItem', {}).get('recurringFee', 0) recurringMonths = host.get('billingItem', {}).get('recurringMonths', 0) return Node( id=host['id'], name=host['hostname'], state=NODE_STATE_MAP.get( host['powerState']['keyName'], NodeState.UNKNOWN ), public_ips=[host['primaryIpAddress']], private_ips=[host['primaryBackendIpAddress']], driver=self, extra={ 'password': password, 'hourlyRecurringFee': hourlyRecurringFee, 'recurringFee': recurringFee, 'recurringMonths': recurringMonths, } ) def _to_nodes(self, hosts): return [self._to_node(h) for h in hosts] def destroy_node(self, node): billing_item = self.connection.request( "SoftLayer_Virtual_Guest", "getBillingItem", id=node.id ) if billing_item: res = self.connection.request( "SoftLayer_Billing_Item", "cancelService", id=billing_item['id'] ) return res else: return False def _get_order_information(self, order_id, timeout=1200, check_interval=5): mask = { 'orderTopLevelItems': { 'billingItem': { 'resource': { 'softwareComponents': { 'passwords': '' }, 'powerState': '', } }, } } for i in range(0, timeout, check_interval): try: res = self.connection.request( "SoftLayer_Billing_Order", "getObject", id=order_id, object_mask=mask ) item = res['orderTopLevelItems'][0]['billingItem']['resource'] if item['softwareComponents'][0]['passwords']: return item except (KeyError, IndexError): pass time.sleep(check_interval) return None def create_node(self, **kwargs): """Create a new SoftLayer node See L{NodeDriver.create_node} for more keyword args. @keyword ex_domain: e.g. libcloud.org @type ex_domain: C{string} """ name = kwargs['name'] image = kwargs['image'] size = kwargs['size'] domain = kwargs.get('ex_domain') location = kwargs['location'] if domain == None: if name.find(".") != -1: domain = name[name.find('.')+1:] if domain == None: # TODO: domain is a required argument for the Sofylayer API, but it # it shouldn't be. domain = "exmaple.com" res = {'prices': SL_TEMPLATES[size.id]['prices']} res['packageId'] = DEFAULT_PACKAGE res['prices'].append({'id': image.id}) # Add OS to order res['location'] = location.id res['complexType'] = 'SoftLayer_Container_Product_Order_Virtual_Guest' res['quantity'] = 1 res['useHourlyPricing'] = True res['virtualGuests'] = [ { 'hostname': name, 'domain': domain } ] res = self.connection.request( "SoftLayer_Product_Order", "placeOrder", res ) order_id = res['orderId'] raw_node = self._get_order_information(order_id) return self._to_node(raw_node) def _to_image(self, img): return NodeImage( id=img['id'], name=img['name'], driver=self.connection.driver ) def list_images(self, location=None): return [self._to_image(i) for i in SL_IMAGES] def _to_size(self, id, size): return NodeSize( id=id, name=size['name'], ram=size['ram'], disk=size['disk'], bandwidth=size['bandwidth'], price=None, driver=self.connection.driver, ) def list_sizes(self, location=None): return [self._to_size(id, s['imagedata']) for id, s in list(SL_TEMPLATES.items())] def _to_loc(self, loc): return NodeLocation( id=loc['id'], name=loc['name'], country=DATACENTERS[loc['name']]['country'], driver=self ) def list_locations(self): res = self.connection.request( "SoftLayer_Location_Datacenter", "getDatacenters" ) # checking "in DATACENTERS", because some of the locations returned by getDatacenters are not useable. return [self._to_loc(l) for l in res if l['name'] in DATACENTERS] def list_nodes(self): mask = { 'virtualGuests': { 'powerState': '', 'softwareComponents': { 'passwords': '' }, 'billingItem': '', }, } res = self.connection.request( "SoftLayer_Account", "getVirtualGuests", object_mask=mask ) nodes = self._to_nodes(res) return nodes def reboot_node(self, node): res = self.connection.request( "SoftLayer_Virtual_Guest", "rebootHard", id=node.id ) return res

11ca3433648652b74a631296db32a4b9a39b19fc

py

Python

matrix/common/etl/transformers/cell_expression.py

ambrosejcarr/matrix-service

f61252d79941fa962240e27062682c9676f07e95

matrix/common/etl/transformers/cell_expression.py

ambrosejcarr/matrix-service

f61252d79941fa962240e27062682c9676f07e95

matrix/common/etl/transformers/cell_expression.py

ambrosejcarr/matrix-service

f61252d79941fa962240e27062682c9676f07e95

import csv import glob import gzip import hashlib import json import os import pathlib import typing import numpy import scipy.io import zarr from . import MetadataToPsvTransformer from matrix.common.aws.redshift_handler import TableName from matrix.common.etl.dcp_zarr_store import DCPZarrStore from matrix.common.exceptions import MatrixException from matrix.common.logging import Logging logger = Logging.get_logger(__name__) class CellExpressionTransformer(MetadataToPsvTransformer): """Reads SS2 and 10X bundles and writes out rows for expression and cell tables in PSV format.""" # The minimum UMI count in the emptydrops result required to include a # putative cell in the matrix service. emptydrops_min_count = 100 def __init__(self, staging_dir): super(CellExpressionTransformer, self).__init__(staging_dir) def _write_rows_to_psvs(self, *args: typing.Tuple): for arg in args: table = arg[0] rows = arg[1] bundle_dir = arg[2] out_dir = os.path.join(self.output_dir, table.value) os.makedirs(out_dir, exist_ok=True) out_file_path = os.path.join( self.output_dir, table.value, f"{os.path.split(os.path.normpath(bundle_dir))[-1]}.{table.value}.data.gz") with gzip.open(out_file_path, 'w') as out_file: out_file.writelines((row.encode() for row in rows)) def _parse_from_metadatas(self, bundle_dir, bundle_manifest_path): protocol_id = json.load( open(os.path.join(bundle_dir, "analysis_protocol_0.json")))['protocol_core']['protocol_id'] if protocol_id.startswith("smartseq2"): cell_lines, expression_lines = self._parse_ss2_bundle(bundle_dir, bundle_manifest_path) elif protocol_id.startswith("optimus"): cell_lines, expression_lines = self._parse_optimus_bundle(bundle_dir, bundle_manifest_path) elif protocol_id.startswith("cellranger"): cell_lines, expression_lines = self._parse_cellranger_bundle(bundle_dir, bundle_manifest_path) else: raise MatrixException(400, f"Failed to parse cell and expression metadata. " f"Unsupported analysis protocol {protocol_id}.") return (TableName.CELL, cell_lines, bundle_dir), (TableName.EXPRESSION, expression_lines, bundle_dir) def _parse_ss2_bundle(self, bundle_dir, bundle_manifest_path): """ Parses SS2 analysis files into PSV rows for cell and expression Redshift tables. """ # Get the keys associated with this cell, except for cellkey keys = self._parse_keys(bundle_dir) cell_key = json.load(open( os.path.join(bundle_dir, "cell_suspension_0.json")))['provenance']['document_id'] # Read in isoform and gene expression values isoforms_path = glob.glob(os.path.join(bundle_dir, "*.isoforms.results"))[0] isoforms_values = {} with open(isoforms_path) as iso_file: reader = csv.DictReader(iso_file, delimiter='\t') for row in reader: transcript_id = row['transcript_id'].split('.')[0] isoforms_values[transcript_id] = { 'TPM': float(row['TPM']) + isoforms_values.get(transcript_id, {}).get('TPM', 0), 'Count': float(row['expected_count']) + isoforms_values.get(transcript_id, {}).get('Count', 0) } genes_path = glob.glob(os.path.join(bundle_dir, "*.genes.results"))[0] genes_values = {} with open(genes_path) as genes_file: reader = csv.DictReader(genes_file, delimiter='\t') for row in reader: gene_id = row['gene_id'].split('.')[0] genes_values[gene_id] = { 'TPM': float(row['TPM']) + genes_values.get(gene_id, {}).get('TPM', 0), 'Count': float(row['expected_count']) + genes_values.get(gene_id, {}).get('Count', 0) } genes_detected = sum((1 for k in genes_values.values() if k["Count"] > 0)) file_uuid = [f for f in json.load(open(bundle_manifest_path))["files"] if f["name"].endswith(".genes.results")][0]["uuid"] file_version = [f for f in json.load(open(bundle_manifest_path))["files"] if f["name"].endswith(".genes.results")][0]["version"] cell_lines = ['|'.join([ cell_key, cell_key, keys["project_key"], keys["specimen_key"], keys["library_key"], keys["analysis_key"], file_uuid, file_version, "", str(genes_detected), "", ""]) + '\n'] expression_lines = [] for transcript_id, expr_values in isoforms_values.items(): if expr_values["Count"] == 0: continue for expr_type in ["TPM", "Count"]: expression_line = '|'.join( [cell_key, transcript_id, expr_type, str(expr_values[expr_type])]) + '\n' expression_lines.append(expression_line) for gene_id, expr_values in genes_values.items(): if expr_values["Count"] == 0: continue for expr_type in ["TPM", "Count"]: expression_line = '|'.join( [cell_key, gene_id, expr_type, str(expr_values[expr_type])]) + '\n' expression_lines.append(expression_line) return cell_lines, expression_lines def _parse_cellranger_bundle(self, bundle_dir, bundle_manifest_path): """ Parses cellranger analysis files into PSV rows for cell and expression Redshift tables. """ keys = self._parse_keys(bundle_dir) cell_suspension_id = json.load(open( os.path.join(bundle_dir, "cell_suspension_0.json")))['provenance']['document_id'] matrix = scipy.io.mmread(os.path.join(bundle_dir, "matrix.mtx")) genes = [g.split("\t")[0].split(".", 1)[0] for g in open(os.path.join(bundle_dir, "genes.tsv")).readlines()] barcodes = [b.strip() for b in open(os.path.join(bundle_dir, "barcodes.tsv")).readlines()] # columns are cells, rows are genes expression_lines = [] cell_lines = set() cell_gene_counts = {} cell_to_barcode = {} for i, j, v in zip(matrix.row, matrix.col, matrix.data): barcode = barcodes[j] gene = genes[i] # Just make up a cell id bundle_uuid = pathlib.Path(bundle_dir).parts[-1] cell_key = self._generate_10x_cell_key(bundle_uuid, barcode) cell_to_barcode[cell_key] = barcode if cell_key not in cell_gene_counts: cell_gene_counts[cell_key] = {} cell_gene_counts[cell_key][gene] = cell_gene_counts[cell_key].get(gene, 0) + v file_uuid = [f for f in json.load(open(bundle_manifest_path))["files"] if f["name"].endswith("matrix.mtx")][0]["uuid"] file_version = [f for f in json.load(open(bundle_manifest_path))["files"] if f["name"].endswith("matrix.mtx")][0]["version"] for cell_key, gene_count_dict in cell_gene_counts.items(): for gene, count in gene_count_dict.items(): expression_line = '|'.join( [cell_key, gene, "Count", str(count)]) + '\n' expression_lines.append(expression_line) gene_count = len(gene_count_dict) cell_line = '|'.join( [cell_key, cell_suspension_id, keys["project_key"], keys["specimen_key"], keys["library_key"], keys["analysis_key"], file_uuid, file_version, cell_to_barcode[cell_key], str(gene_count), "", ""]) + '\n' cell_lines.add(cell_line) return cell_lines, expression_lines def _parse_optimus_bundle(self, bundle_dir, bundle_manifest_path): """ Parses optimus analysis files into PSV rows for cell and expression Redshift tables. """ keys = self._parse_keys(bundle_dir) file_uuid = [f for f in json.load(open(bundle_manifest_path))["files"] if f["name"].endswith(".zattrs")][0]["uuid"] file_version = [f for f in json.load(open(bundle_manifest_path))["files"] if f["name"].endswith(".zattrs")][0]["version"] emptydrops_result = {} with open(os.path.join(bundle_dir, "empty_drops_result.csv")) as emptydrops_file: reader = csv.DictReader(emptydrops_file) for row in reader: emptydrops_result[row["CellId"]] = {"total_umi_count": int(row["Total"]), "is_cell": row["IsCell"] == "TRUE"} # read expression matrix from zarr store = DCPZarrStore(bundle_dir=bundle_dir) root = zarr.group(store=store) n_cells = root.expression_matrix.cell_id.shape[0] chunk_size = root.expression_matrix.cell_id.chunks[0] n_chunks = root.expression_matrix.cell_id.nchunks cell_lines = set() expression_lines = [] logger.info(f"Optimus bundle has {n_cells} cells and {n_chunks} chunks.") for i in range(n_chunks): self._parse_optimus_chunk( keys=keys, file_uuid=file_uuid, file_version=file_version, root=root, start_row=chunk_size * i, end_row=(i + 1) * chunk_size if (i + 1) * chunk_size < n_cells else n_cells, cell_lines=cell_lines, expression_lines=expression_lines, emptydrops_result=emptydrops_result ) return cell_lines, expression_lines def _parse_optimus_chunk(self, keys: dict, file_uuid: str, file_version: str, root: zarr.Group, start_row: int, end_row: int, cell_lines: set, expression_lines: list, emptydrops_result: dict): """ Parses a chunk of a zarr group containing an expression matrix into cell and expression PSV lines. Modifies cell_lines and expression_lines. :param keys: Metadata keys generated by _parse_keys :param file_uuid: UUID of the file used for joining with rest of HCA metadata :param file_version: Version of the file used for joining with rest of HCA metadata :param root: Zarr group of the full expression matrix :param start_row: Start row of the chunk :param end_row: End row of the chunk :param cell_lines: Output cell PSV lines :param expression_lines: Output expression PSV lines :param emptydrops_result: Dict from cell barcode to UMI count and emptydrops call """ logger.info(f"Parsing rows {start_row} to {end_row}.") chunk_size = end_row - start_row expr_values = root.expression_matrix.expression[start_row:end_row] barcodes = root.expression_matrix.cell_id[start_row:end_row] gene_ids = numpy.array([g.split(".")[0] for g in root.expression_matrix.gene_id]) for i in range(chunk_size): if emptydrops_result[barcodes[i]]["total_umi_count"] < self.emptydrops_min_count: continue cell_key = self._generate_10x_cell_key(keys["bundle_uuid"], barcodes[i]) gene_count = numpy.count_nonzero(expr_values[i]) cell_line = '|'.join( [cell_key, keys["cell_suspension_key"], keys["project_key"], keys["specimen_key"], keys["library_key"], keys["analysis_key"], file_uuid, file_version, barcodes[i], str(gene_count), str(emptydrops_result[barcodes[i]]["total_umi_count"]), 't' if emptydrops_result[barcodes[i]]["is_cell"] else 'f'] ) + '\n' cell_lines.add(cell_line) cell_expr_values = expr_values[i] nonzero_gene_ids = gene_ids[cell_expr_values != 0] nonzero_cevs = cell_expr_values[cell_expr_values != 0] for j in range(nonzero_gene_ids.shape[0]): expression_line = '|'.join( [cell_key, nonzero_gene_ids[j], "Count", str(nonzero_cevs[j])] ) + '\n' expression_lines.append(expression_line) def _generate_10x_cell_key(self, bundle_uuid, barcode): """ Generate a unique hash for a cell. :param bundle_uuid: Bundle UUID the cell belongs to :param barcode: 10X cell barcode :return: MD5 hash """ h = hashlib.md5() h.update(bundle_uuid.encode()) h.update(barcode.encode()) return h.hexdigest() def _parse_keys(self, bundle_dir): p = pathlib.Path(bundle_dir) bundle_uuid = pathlib.Path(bundle_dir).parts[-1] cs_path = p.joinpath("cell_suspension_0.json") cs_key = json.load(open(cs_path))['provenance']['document_id'] project_path = p.joinpath("project_0.json") project_key = json.load(open(project_path))["provenance"]["document_id"] ap_path = p.joinpath("analysis_protocol_0.json") ap_key = json.load(open(ap_path))["provenance"]["document_id"] specimen_paths = list(p.glob("specimen_from_organism_*.json")) specimen_keys = [json.load(open(p))['provenance']['document_id'] for p in specimen_paths] specimen_key = sorted(specimen_keys)[0] library_paths = list(p.glob("library_preparation_protocol_*.json")) library_keys = [json.load(open(p))['provenance']['document_id'] for p in library_paths] library_key = sorted(library_keys)[0] return { "bundle_uuid": bundle_uuid, "cell_suspension_key": cs_key, "project_key": project_key, "specimen_key": specimen_key, "library_key": library_key, "analysis_key": ap_key }

fba6143b49d5a5da2085ca9b92b550ec03205161

py

Python

task1/multiboxloss.py

manhph2211/Receipt-data-extraction

bd9ea74fbe8b8cbeddf201c7ea05d9a85cd38ac5

2021-02-04T13:25:51.000Z

2021-08-18T02:15:46.000Z

task1/multiboxloss.py

manhph2211/Receipt-data-extraction

bd9ea74fbe8b8cbeddf201c7ea05d9a85cd38ac5

task1/multiboxloss.py

manhph2211/Receipt-data-extraction

bd9ea74fbe8b8cbeddf201c7ea05d9a85cd38ac5

# Jaccard: # Hard negative mining: negative default box = 3times positve default bos # Loss in regression task: MSE ->F.SmoothL1Loss # Loss in classification (multi class): F.CrossEntropy import torch import torch.nn as nn import torch.nn.functional as F from utils import match class MultiBoxLoss(nn.Module): def __init__(self, jaccard_threshold=0.5, neg_pos=3, device="cpu"):# means negative_defautbox_num=neg_pos * positive_one super(MultiBoxLoss, self).__init__() self.jaccard_threshold = jaccard_threshold self.neg_pos = neg_pos self.device = device def forward(self, predictions, targets): loc_data, conf_data, dbox_list = predictions # (batch_num, num_dbox, num_classes) -- loc_data num_batch = loc_data.size(0) num_dbox = loc_data.size(1) # 8732 num_classes = conf_data.size(2) # make 2 empty tensors :) conf_t_label = torch.LongTensor(num_batch, num_dbox).to(self.device) loc_t = torch.Tensor(num_batch, num_dbox, 4).to(self.device) # every dbox has 4 (x_min...,y_max) for idx in range(num_batch): truths = targets[idx][:, :-1].to(self.device) # (xmin, ymin, xmax, ymax) BBox labels = targets[idx][:, -1].to(self.device) # label dbox = dbox_list.to(self.device) variances = [0.1, 0.2] match(self.jaccard_threshold, truths, dbox, variances, labels, loc_t, conf_t_label, idx) #--> conf_t_label # SmoothL1Loss pos_mask = conf_t_label > 0 # positive # loc_data(num_batch, 8732, 4) pos_idx = pos_mask.unsqueeze(pos_mask.dim()).expand_as(loc_data) # positive dbox, loc_data loc_p = loc_data[pos_idx].view(-1, 4) #print(loc_p.shape) loc_t = loc_t[pos_idx].view(-1, 4) #print(loc_t.shape) loss_loc = F.smooth_l1_loss(loc_p, loc_t, reduction="sum") # loss_conf1 # CrossEntropy batch_conf = conf_data.view(-1, num_classes) # (num_batch*num_box, num_classes) loss_conf = F.cross_entropy(batch_conf, conf_t_label.view(-1), reduction="none") # hard negative mining num_pos = pos_mask.long().sum(1, keepdim=True) loss_conf = loss_conf.view(num_batch, -1) # torch.size([num_batch, 8732]) _, loss_idx = loss_conf.sort(1, descending=True) _, idx_rank = loss_idx.sort(1) # idx_rank chính là thông số để biết được độ lớn loss nằm ở vị trí bao nhiêu num_neg = torch.clamp(num_pos * self.neg_pos, max=num_dbox) neg_mask = idx_rank < (num_neg).expand_as(idx_rank) # (num_batch, 8732) -> (num_batch, 8732, 2) pos_idx_mask = pos_mask.unsqueeze(2).expand_as(conf_data) neg_idx_mask = neg_mask.unsqueeze(2).expand_as(conf_data) conf_t_pre = conf_data[(pos_idx_mask + neg_idx_mask).gt(0)].view(-1, num_classes) conf_t_label_ = conf_t_label[(pos_mask + neg_mask).gt(0)] loss_conf = F.cross_entropy(conf_t_pre, conf_t_label_, reduction="sum") # total loss = loss_loc + loss_conf N = num_pos.sum() loss_loc = loss_loc / N loss_conf = loss_conf / N return loss_loc, loss_conf

aa498abe99973fbe01588788e477d78243873a9f

py

Python

contrib/seeds/makeseeds.py

emberce/Phore

ff388a81e8c9546eb01ab754ada86e560c63ea6e

2018-12-02T18:47:54.000Z

2018-12-02T18:47:54.000Z

contrib/seeds/makeseeds.py

emberce/Phore

ff388a81e8c9546eb01ab754ada86e560c63ea6e

2018-06-17T19:33:23.000Z

2018-06-17T19:33:23.000Z

contrib/seeds/makeseeds.py

emberce/Phore

ff388a81e8c9546eb01ab754ada86e560c63ea6e

2018-04-01T03:11:02.000Z

2018-05-23T17:27:52.000Z

#!/usr/bin/env python3 # Copyright (c) 2013-2017 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # # Generate seeds.txt from Pieter's DNS seeder # NSEEDS=512 MAX_SEEDS_PER_ASN=2 MIN_BLOCKS = 615801 # These are hosts that have been observed to be behaving strangely (e.g. # aggressively connecting to every node). SUSPICIOUS_HOSTS = { "" } import re import sys import dns.resolver import collections PATTERN_IPV4 = re.compile(r"^((\d{1,3})\.(\d{1,3})\.(\d{1,3})\.(\d{1,3})):(\d+)$") PATTERN_IPV6 = re.compile(r"^\[([0-9a-z:]+)\]:(\d+)$") PATTERN_ONION = re.compile(r"^([abcdefghijklmnopqrstuvwxyz234567]{16}\.onion):(\d+)$") PATTERN_AGENT = re.compile(r"^(/AtheneumCore:2.2.(0|1|99)/)$") def parseline(line): sline = line.split() if len(sline) < 11: return None m = PATTERN_IPV4.match(sline[0]) sortkey = None ip = None if m is None: m = PATTERN_IPV6.match(sline[0]) if m is None: m = PATTERN_ONION.match(sline[0]) if m is None: return None else: net = 'onion' ipstr = sortkey = m.group(1) port = int(m.group(2)) else: net = 'ipv6' if m.group(1) in ['::']: # Not interested in localhost return None ipstr = m.group(1) sortkey = ipstr # XXX parse IPv6 into number, could use name_to_ipv6 from generate-seeds port = int(m.group(2)) else: # Do IPv4 sanity check ip = 0 for i in range(0,4): if int(m.group(i+2)) < 0 or int(m.group(i+2)) > 255: return None ip = ip + (int(m.group(i+2)) << (8*(3-i))) if ip == 0: return None net = 'ipv4' sortkey = ip ipstr = m.group(1) port = int(m.group(6)) # Skip bad results. if sline[1] == 0: return None # Extract uptime %. uptime30 = float(sline[7][:-1]) # Extract Unix timestamp of last success. lastsuccess = int(sline[2]) # Extract protocol version. version = int(sline[10]) # Extract user agent. if len(sline) > 11: agent = sline[11][1:] + sline[12][:-1] else: agent = sline[11][1:-1] # Extract service flags. service = int(sline[9], 16) # Extract blocks. blocks = int(sline[8]) # Construct result. return { 'net': net, 'ip': ipstr, 'port': port, 'ipnum': ip, 'uptime': uptime30, 'lastsuccess': lastsuccess, 'version': version, 'agent': agent, 'service': service, 'blocks': blocks, 'sortkey': sortkey, } def filtermultiport(ips): '''Filter out hosts with more nodes per IP''' hist = collections.defaultdict(list) for ip in ips: hist[ip['sortkey']].append(ip) return [value[0] for (key,value) in list(hist.items()) if len(value)==1] # Based on Greg Maxwell's seed_filter.py def filterbyasn(ips, max_per_asn, max_total): # Sift out ips by type ips_ipv4 = [ip for ip in ips if ip['net'] == 'ipv4'] ips_ipv6 = [ip for ip in ips if ip['net'] == 'ipv6'] ips_onion = [ip for ip in ips if ip['net'] == 'onion'] # Filter IPv4 by ASN result = [] asn_count = {} for ip in ips_ipv4: if len(result) == max_total: break try: asn = int([x.to_text() for x in dns.resolver.query('.'.join(reversed(ip['ip'].split('.'))) + '.origin.asn.cymru.com', 'TXT').response.answer][0].split('\"')[1].split(' ')[0]) if asn not in asn_count: asn_count[asn] = 0 if asn_count[asn] == max_per_asn: continue asn_count[asn] += 1 result.append(ip) except: sys.stderr.write('ERR: Could not resolve ASN for "' + ip['ip'] + '"\n') # TODO: filter IPv6 by ASN # Add back non-IPv4 result.extend(ips_ipv6) result.extend(ips_onion) return result def main(): lines = sys.stdin.readlines() ips = [parseline(line) for line in lines] # Skip entries with valid address. ips = [ip for ip in ips if ip is not None] # Skip entries from suspicious hosts. ips = [ip for ip in ips if ip['ip'] not in SUSPICIOUS_HOSTS] # Enforce minimal number of blocks. ips = [ip for ip in ips if ip['blocks'] >= MIN_BLOCKS] # Require service bit 1. ips = [ip for ip in ips if (ip['service'] & 1) == 1] # Require at least 50% 30-day uptime. ips = [ip for ip in ips if ip['uptime'] > 50] # Require a known and recent user agent. ips = [ip for ip in ips if PATTERN_AGENT.match(re.sub(' ', '-', ip['agent']))] # Sort by availability (and use last success as tie breaker) ips.sort(key=lambda x: (x['uptime'], x['lastsuccess'], x['ip']), reverse=True) # Filter out hosts with multiple bitcoin ports, these are likely abusive ips = filtermultiport(ips) # Look up ASNs and limit results, both per ASN and globally. ips = filterbyasn(ips, MAX_SEEDS_PER_ASN, NSEEDS) # Sort the results by IP address (for deterministic output). ips.sort(key=lambda x: (x['net'], x['sortkey'])) for ip in ips: if ip['net'] == 'ipv6': print('[%s]:%i' % (ip['ip'], ip['port'])) else: print('%s:%i' % (ip['ip'], ip['port'])) if __name__ == '__main__': main()

84209ca2520b89d631856afb67be5aa4a2cd16a4

py

Python

Lib/test/test_concurrent_futures.py

Horcruxes/cpython

1cbaa505d007e11c4a1f0d2073d72b6c02c7147c

2021-07-25T14:23:35.000Z

2022-03-31T00:17:30.000Z

Lib/test/test_concurrent_futures.py

Horcruxes/cpython

1cbaa505d007e11c4a1f0d2073d72b6c02c7147c

2020-02-26T01:08:34.000Z

2022-03-01T15:00:59.000Z

Lib/test/test_concurrent_futures.py

Horcruxes/cpython

1cbaa505d007e11c4a1f0d2073d72b6c02c7147c

2021-09-30T11:22:32.000Z

2022-02-17T11:19:14.000Z

from test import support from test.support import import_helper from test.support import threading_helper # Skip tests if _multiprocessing wasn't built. import_helper.import_module('_multiprocessing') from test.support import hashlib_helper from test.support.script_helper import assert_python_ok import contextlib import itertools import logging from logging.handlers import QueueHandler import os import queue import sys import threading import time import unittest import weakref from pickle import PicklingError from concurrent import futures from concurrent.futures._base import ( PENDING, RUNNING, CANCELLED, CANCELLED_AND_NOTIFIED, FINISHED, Future, BrokenExecutor) from concurrent.futures.process import BrokenProcessPool, _check_system_limits from multiprocessing import get_context import multiprocessing.process import multiprocessing.util def create_future(state=PENDING, exception=None, result=None): f = Future() f._state = state f._exception = exception f._result = result return f PENDING_FUTURE = create_future(state=PENDING) RUNNING_FUTURE = create_future(state=RUNNING) CANCELLED_FUTURE = create_future(state=CANCELLED) CANCELLED_AND_NOTIFIED_FUTURE = create_future(state=CANCELLED_AND_NOTIFIED) EXCEPTION_FUTURE = create_future(state=FINISHED, exception=OSError()) SUCCESSFUL_FUTURE = create_future(state=FINISHED, result=42) INITIALIZER_STATUS = 'uninitialized' def mul(x, y): return x * y def capture(*args, **kwargs): return args, kwargs def sleep_and_raise(t): time.sleep(t) raise Exception('this is an exception') def sleep_and_print(t, msg): time.sleep(t) print(msg) sys.stdout.flush() def init(x): global INITIALIZER_STATUS INITIALIZER_STATUS = x def get_init_status(): return INITIALIZER_STATUS def init_fail(log_queue=None): if log_queue is not None: logger = logging.getLogger('concurrent.futures') logger.addHandler(QueueHandler(log_queue)) logger.setLevel('CRITICAL') logger.propagate = False time.sleep(0.1) # let some futures be scheduled raise ValueError('error in initializer') class MyObject(object): def my_method(self): pass class EventfulGCObj(): def __init__(self, mgr): self.event = mgr.Event() def __del__(self): self.event.set() def make_dummy_object(_): return MyObject() class BaseTestCase(unittest.TestCase): def setUp(self): self._thread_key = threading_helper.threading_setup() def tearDown(self): support.reap_children() threading_helper.threading_cleanup(*self._thread_key) class ExecutorMixin: worker_count = 5 executor_kwargs = {} def setUp(self): super().setUp() self.t1 = time.monotonic() if hasattr(self, "ctx"): self.executor = self.executor_type( max_workers=self.worker_count, mp_context=self.get_context(), **self.executor_kwargs) else: self.executor = self.executor_type( max_workers=self.worker_count, **self.executor_kwargs) self._prime_executor() def tearDown(self): self.executor.shutdown(wait=True) self.executor = None dt = time.monotonic() - self.t1 if support.verbose: print("%.2fs" % dt, end=' ') self.assertLess(dt, 300, "synchronization issue: test lasted too long") super().tearDown() def get_context(self): return get_context(self.ctx) def _prime_executor(self): # Make sure that the executor is ready to do work before running the # tests. This should reduce the probability of timeouts in the tests. futures = [self.executor.submit(time.sleep, 0.1) for _ in range(self.worker_count)] for f in futures: f.result() class ThreadPoolMixin(ExecutorMixin): executor_type = futures.ThreadPoolExecutor class ProcessPoolForkMixin(ExecutorMixin): executor_type = futures.ProcessPoolExecutor ctx = "fork" def get_context(self): try: _check_system_limits() except NotImplementedError: self.skipTest("ProcessPoolExecutor unavailable on this system") if sys.platform == "win32": self.skipTest("require unix system") return super().get_context() class ProcessPoolSpawnMixin(ExecutorMixin): executor_type = futures.ProcessPoolExecutor ctx = "spawn" def get_context(self): try: _check_system_limits() except NotImplementedError: self.skipTest("ProcessPoolExecutor unavailable on this system") return super().get_context() class ProcessPoolForkserverMixin(ExecutorMixin): executor_type = futures.ProcessPoolExecutor ctx = "forkserver" def get_context(self): try: _check_system_limits() except NotImplementedError: self.skipTest("ProcessPoolExecutor unavailable on this system") if sys.platform == "win32": self.skipTest("require unix system") return super().get_context() def create_executor_tests(mixin, bases=(BaseTestCase,), executor_mixins=(ThreadPoolMixin, ProcessPoolForkMixin, ProcessPoolForkserverMixin, ProcessPoolSpawnMixin)): def strip_mixin(name): if name.endswith(('Mixin', 'Tests')): return name[:-5] elif name.endswith('Test'): return name[:-4] else: return name for exe in executor_mixins: name = ("%s%sTest" % (strip_mixin(exe.__name__), strip_mixin(mixin.__name__))) cls = type(name, (mixin,) + (exe,) + bases, {}) globals()[name] = cls class InitializerMixin(ExecutorMixin): worker_count = 2 def setUp(self): global INITIALIZER_STATUS INITIALIZER_STATUS = 'uninitialized' self.executor_kwargs = dict(initializer=init, initargs=('initialized',)) super().setUp() def test_initializer(self): futures = [self.executor.submit(get_init_status) for _ in range(self.worker_count)] for f in futures: self.assertEqual(f.result(), 'initialized') class FailingInitializerMixin(ExecutorMixin): worker_count = 2 def setUp(self): if hasattr(self, "ctx"): # Pass a queue to redirect the child's logging output self.mp_context = self.get_context() self.log_queue = self.mp_context.Queue() self.executor_kwargs = dict(initializer=init_fail, initargs=(self.log_queue,)) else: # In a thread pool, the child shares our logging setup # (see _assert_logged()) self.mp_context = None self.log_queue = None self.executor_kwargs = dict(initializer=init_fail) super().setUp() def test_initializer(self): with self._assert_logged('ValueError: error in initializer'): try: future = self.executor.submit(get_init_status) except BrokenExecutor: # Perhaps the executor is already broken pass else: with self.assertRaises(BrokenExecutor): future.result() # At some point, the executor should break t1 = time.monotonic() while not self.executor._broken: if time.monotonic() - t1 > 5: self.fail("executor not broken after 5 s.") time.sleep(0.01) # ... and from this point submit() is guaranteed to fail with self.assertRaises(BrokenExecutor): self.executor.submit(get_init_status) def _prime_executor(self): pass @contextlib.contextmanager def _assert_logged(self, msg): if self.log_queue is not None: yield output = [] try: while True: output.append(self.log_queue.get_nowait().getMessage()) except queue.Empty: pass else: with self.assertLogs('concurrent.futures', 'CRITICAL') as cm: yield output = cm.output self.assertTrue(any(msg in line for line in output), output) create_executor_tests(InitializerMixin) create_executor_tests(FailingInitializerMixin) class ExecutorShutdownTest: def test_run_after_shutdown(self): self.executor.shutdown() self.assertRaises(RuntimeError, self.executor.submit, pow, 2, 5) def test_interpreter_shutdown(self): # Test the atexit hook for shutdown of worker threads and processes rc, out, err = assert_python_ok('-c', """if 1: from concurrent.futures import {executor_type} from time import sleep from test.test_concurrent_futures import sleep_and_print if __name__ == "__main__": context = '{context}' if context == "": t = {executor_type}(5) else: from multiprocessing import get_context context = get_context(context) t = {executor_type}(5, mp_context=context) t.submit(sleep_and_print, 1.0, "apple") """.format(executor_type=self.executor_type.__name__, context=getattr(self, "ctx", ""))) # Errors in atexit hooks don't change the process exit code, check # stderr manually. self.assertFalse(err) self.assertEqual(out.strip(), b"apple") def test_submit_after_interpreter_shutdown(self): # Test the atexit hook for shutdown of worker threads and processes rc, out, err = assert_python_ok('-c', """if 1: import atexit @atexit.register def run_last(): try: t.submit(id, None) except RuntimeError: print("runtime-error") raise from concurrent.futures import {executor_type} if __name__ == "__main__": context = '{context}' if not context: t = {executor_type}(5) else: from multiprocessing import get_context context = get_context(context) t = {executor_type}(5, mp_context=context) t.submit(id, 42).result() """.format(executor_type=self.executor_type.__name__, context=getattr(self, "ctx", ""))) # Errors in atexit hooks don't change the process exit code, check # stderr manually. self.assertIn("RuntimeError: cannot schedule new futures", err.decode()) self.assertEqual(out.strip(), b"runtime-error") def test_hang_issue12364(self): fs = [self.executor.submit(time.sleep, 0.1) for _ in range(50)] self.executor.shutdown() for f in fs: f.result() def test_cancel_futures(self): executor = self.executor_type(max_workers=3) fs = [executor.submit(time.sleep, .1) for _ in range(50)] executor.shutdown(cancel_futures=True) # We can't guarantee the exact number of cancellations, but we can # guarantee that *some* were cancelled. With setting max_workers to 3, # most of the submitted futures should have been cancelled. cancelled = [fut for fut in fs if fut.cancelled()] self.assertTrue(len(cancelled) >= 35, msg=f"{len(cancelled)=}") # Ensure the other futures were able to finish. # Use "not fut.cancelled()" instead of "fut.done()" to include futures # that may have been left in a pending state. others = [fut for fut in fs if not fut.cancelled()] for fut in others: self.assertTrue(fut.done(), msg=f"{fut._state=}") self.assertIsNone(fut.exception()) # Similar to the number of cancelled futures, we can't guarantee the # exact number that completed. But, we can guarantee that at least # one finished. self.assertTrue(len(others) > 0, msg=f"{len(others)=}") def test_hang_issue39205(self): """shutdown(wait=False) doesn't hang at exit with running futures. See https://bugs.python.org/issue39205. """ if self.executor_type == futures.ProcessPoolExecutor: raise unittest.SkipTest( "Hangs due to https://bugs.python.org/issue39205") rc, out, err = assert_python_ok('-c', """if True: from concurrent.futures import {executor_type} from test.test_concurrent_futures import sleep_and_print if __name__ == "__main__": t = {executor_type}(max_workers=3) t.submit(sleep_and_print, 1.0, "apple") t.shutdown(wait=False) """.format(executor_type=self.executor_type.__name__)) self.assertFalse(err) self.assertEqual(out.strip(), b"apple") class ThreadPoolShutdownTest(ThreadPoolMixin, ExecutorShutdownTest, BaseTestCase): def _prime_executor(self): pass def test_threads_terminate(self): def acquire_lock(lock): lock.acquire() sem = threading.Semaphore(0) for i in range(3): self.executor.submit(acquire_lock, sem) self.assertEqual(len(self.executor._threads), 3) for i in range(3): sem.release() self.executor.shutdown() for t in self.executor._threads: t.join() def test_context_manager_shutdown(self): with futures.ThreadPoolExecutor(max_workers=5) as e: executor = e self.assertEqual(list(e.map(abs, range(-5, 5))), [5, 4, 3, 2, 1, 0, 1, 2, 3, 4]) for t in executor._threads: t.join() def test_del_shutdown(self): executor = futures.ThreadPoolExecutor(max_workers=5) res = executor.map(abs, range(-5, 5)) threads = executor._threads del executor for t in threads: t.join() # Make sure the results were all computed before the # executor got shutdown. assert all([r == abs(v) for r, v in zip(res, range(-5, 5))]) def test_shutdown_no_wait(self): # Ensure that the executor cleans up the threads when calling # shutdown with wait=False executor = futures.ThreadPoolExecutor(max_workers=5) res = executor.map(abs, range(-5, 5)) threads = executor._threads executor.shutdown(wait=False) for t in threads: t.join() # Make sure the results were all computed before the # executor got shutdown. assert all([r == abs(v) for r, v in zip(res, range(-5, 5))]) def test_thread_names_assigned(self): executor = futures.ThreadPoolExecutor( max_workers=5, thread_name_prefix='SpecialPool') executor.map(abs, range(-5, 5)) threads = executor._threads del executor support.gc_collect() # For PyPy or other GCs. for t in threads: self.assertRegex(t.name, r'^SpecialPool_[0-4]$') t.join() def test_thread_names_default(self): executor = futures.ThreadPoolExecutor(max_workers=5) executor.map(abs, range(-5, 5)) threads = executor._threads del executor support.gc_collect() # For PyPy or other GCs. for t in threads: # Ensure that our default name is reasonably sane and unique when # no thread_name_prefix was supplied. self.assertRegex(t.name, r'ThreadPoolExecutor-\d+_[0-4]$') t.join() def test_cancel_futures_wait_false(self): # Can only be reliably tested for TPE, since PPE often hangs with # `wait=False` (even without *cancel_futures*). rc, out, err = assert_python_ok('-c', """if True: from concurrent.futures import ThreadPoolExecutor from test.test_concurrent_futures import sleep_and_print if __name__ == "__main__": t = ThreadPoolExecutor() t.submit(sleep_and_print, .1, "apple") t.shutdown(wait=False, cancel_futures=True) """.format(executor_type=self.executor_type.__name__)) # Errors in atexit hooks don't change the process exit code, check # stderr manually. self.assertFalse(err) self.assertEqual(out.strip(), b"apple") class ProcessPoolShutdownTest(ExecutorShutdownTest): def _prime_executor(self): pass def test_processes_terminate(self): def acquire_lock(lock): lock.acquire() mp_context = get_context() sem = mp_context.Semaphore(0) for _ in range(3): self.executor.submit(acquire_lock, sem) self.assertEqual(len(self.executor._processes), 3) for _ in range(3): sem.release() processes = self.executor._processes self.executor.shutdown() for p in processes.values(): p.join() def test_context_manager_shutdown(self): with futures.ProcessPoolExecutor(max_workers=5) as e: processes = e._processes self.assertEqual(list(e.map(abs, range(-5, 5))), [5, 4, 3, 2, 1, 0, 1, 2, 3, 4]) for p in processes.values(): p.join() def test_del_shutdown(self): executor = futures.ProcessPoolExecutor(max_workers=5) res = executor.map(abs, range(-5, 5)) executor_manager_thread = executor._executor_manager_thread processes = executor._processes call_queue = executor._call_queue executor_manager_thread = executor._executor_manager_thread del executor support.gc_collect() # For PyPy or other GCs. # Make sure that all the executor resources were properly cleaned by # the shutdown process executor_manager_thread.join() for p in processes.values(): p.join() call_queue.join_thread() # Make sure the results were all computed before the # executor got shutdown. assert all([r == abs(v) for r, v in zip(res, range(-5, 5))]) def test_shutdown_no_wait(self): # Ensure that the executor cleans up the processes when calling # shutdown with wait=False executor = futures.ProcessPoolExecutor(max_workers=5) res = executor.map(abs, range(-5, 5)) processes = executor._processes call_queue = executor._call_queue executor_manager_thread = executor._executor_manager_thread executor.shutdown(wait=False) # Make sure that all the executor resources were properly cleaned by # the shutdown process executor_manager_thread.join() for p in processes.values(): p.join() call_queue.join_thread() # Make sure the results were all computed before the executor got # shutdown. assert all([r == abs(v) for r, v in zip(res, range(-5, 5))]) create_executor_tests(ProcessPoolShutdownTest, executor_mixins=(ProcessPoolForkMixin, ProcessPoolForkserverMixin, ProcessPoolSpawnMixin)) class WaitTests: def test_first_completed(self): future1 = self.executor.submit(mul, 21, 2) future2 = self.executor.submit(time.sleep, 1.5) done, not_done = futures.wait( [CANCELLED_FUTURE, future1, future2], return_when=futures.FIRST_COMPLETED) self.assertEqual(set([future1]), done) self.assertEqual(set([CANCELLED_FUTURE, future2]), not_done) def test_first_completed_some_already_completed(self): future1 = self.executor.submit(time.sleep, 1.5) finished, pending = futures.wait( [CANCELLED_AND_NOTIFIED_FUTURE, SUCCESSFUL_FUTURE, future1], return_when=futures.FIRST_COMPLETED) self.assertEqual( set([CANCELLED_AND_NOTIFIED_FUTURE, SUCCESSFUL_FUTURE]), finished) self.assertEqual(set([future1]), pending) def test_first_exception(self): future1 = self.executor.submit(mul, 2, 21) future2 = self.executor.submit(sleep_and_raise, 1.5) future3 = self.executor.submit(time.sleep, 3) finished, pending = futures.wait( [future1, future2, future3], return_when=futures.FIRST_EXCEPTION) self.assertEqual(set([future1, future2]), finished) self.assertEqual(set([future3]), pending) def test_first_exception_some_already_complete(self): future1 = self.executor.submit(divmod, 21, 0) future2 = self.executor.submit(time.sleep, 1.5) finished, pending = futures.wait( [SUCCESSFUL_FUTURE, CANCELLED_FUTURE, CANCELLED_AND_NOTIFIED_FUTURE, future1, future2], return_when=futures.FIRST_EXCEPTION) self.assertEqual(set([SUCCESSFUL_FUTURE, CANCELLED_AND_NOTIFIED_FUTURE, future1]), finished) self.assertEqual(set([CANCELLED_FUTURE, future2]), pending) def test_first_exception_one_already_failed(self): future1 = self.executor.submit(time.sleep, 2) finished, pending = futures.wait( [EXCEPTION_FUTURE, future1], return_when=futures.FIRST_EXCEPTION) self.assertEqual(set([EXCEPTION_FUTURE]), finished) self.assertEqual(set([future1]), pending) def test_all_completed(self): future1 = self.executor.submit(divmod, 2, 0) future2 = self.executor.submit(mul, 2, 21) finished, pending = futures.wait( [SUCCESSFUL_FUTURE, CANCELLED_AND_NOTIFIED_FUTURE, EXCEPTION_FUTURE, future1, future2], return_when=futures.ALL_COMPLETED) self.assertEqual(set([SUCCESSFUL_FUTURE, CANCELLED_AND_NOTIFIED_FUTURE, EXCEPTION_FUTURE, future1, future2]), finished) self.assertEqual(set(), pending) def test_timeout(self): future1 = self.executor.submit(mul, 6, 7) future2 = self.executor.submit(time.sleep, 6) finished, pending = futures.wait( [CANCELLED_AND_NOTIFIED_FUTURE, EXCEPTION_FUTURE, SUCCESSFUL_FUTURE, future1, future2], timeout=5, return_when=futures.ALL_COMPLETED) self.assertEqual(set([CANCELLED_AND_NOTIFIED_FUTURE, EXCEPTION_FUTURE, SUCCESSFUL_FUTURE, future1]), finished) self.assertEqual(set([future2]), pending) class ThreadPoolWaitTests(ThreadPoolMixin, WaitTests, BaseTestCase): def test_pending_calls_race(self): # Issue #14406: multi-threaded race condition when waiting on all # futures. event = threading.Event() def future_func(): event.wait() oldswitchinterval = sys.getswitchinterval() sys.setswitchinterval(1e-6) try: fs = {self.executor.submit(future_func) for i in range(100)} event.set() futures.wait(fs, return_when=futures.ALL_COMPLETED) finally: sys.setswitchinterval(oldswitchinterval) create_executor_tests(WaitTests, executor_mixins=(ProcessPoolForkMixin, ProcessPoolForkserverMixin, ProcessPoolSpawnMixin)) class AsCompletedTests: # TODO([email protected]): Should have a test with a non-zero timeout. def test_no_timeout(self): future1 = self.executor.submit(mul, 2, 21) future2 = self.executor.submit(mul, 7, 6) completed = set(futures.as_completed( [CANCELLED_AND_NOTIFIED_FUTURE, EXCEPTION_FUTURE, SUCCESSFUL_FUTURE, future1, future2])) self.assertEqual(set( [CANCELLED_AND_NOTIFIED_FUTURE, EXCEPTION_FUTURE, SUCCESSFUL_FUTURE, future1, future2]), completed) def test_zero_timeout(self): future1 = self.executor.submit(time.sleep, 2) completed_futures = set() try: for future in futures.as_completed( [CANCELLED_AND_NOTIFIED_FUTURE, EXCEPTION_FUTURE, SUCCESSFUL_FUTURE, future1], timeout=0): completed_futures.add(future) except futures.TimeoutError: pass self.assertEqual(set([CANCELLED_AND_NOTIFIED_FUTURE, EXCEPTION_FUTURE, SUCCESSFUL_FUTURE]), completed_futures) def test_duplicate_futures(self): # Issue 20367. Duplicate futures should not raise exceptions or give # duplicate responses. # Issue #31641: accept arbitrary iterables. future1 = self.executor.submit(time.sleep, 2) completed = [ f for f in futures.as_completed(itertools.repeat(future1, 3)) ] self.assertEqual(len(completed), 1) def test_free_reference_yielded_future(self): # Issue #14406: Generator should not keep references # to finished futures. futures_list = [Future() for _ in range(8)] futures_list.append(create_future(state=CANCELLED_AND_NOTIFIED)) futures_list.append(create_future(state=FINISHED, result=42)) with self.assertRaises(futures.TimeoutError): for future in futures.as_completed(futures_list, timeout=0): futures_list.remove(future) wr = weakref.ref(future) del future support.gc_collect() # For PyPy or other GCs. self.assertIsNone(wr()) futures_list[0].set_result("test") for future in futures.as_completed(futures_list): futures_list.remove(future) wr = weakref.ref(future) del future support.gc_collect() # For PyPy or other GCs. self.assertIsNone(wr()) if futures_list: futures_list[0].set_result("test") def test_correct_timeout_exception_msg(self): futures_list = [CANCELLED_AND_NOTIFIED_FUTURE, PENDING_FUTURE, RUNNING_FUTURE, SUCCESSFUL_FUTURE] with self.assertRaises(futures.TimeoutError) as cm: list(futures.as_completed(futures_list, timeout=0)) self.assertEqual(str(cm.exception), '2 (of 4) futures unfinished') create_executor_tests(AsCompletedTests) class ExecutorTest: # Executor.shutdown() and context manager usage is tested by # ExecutorShutdownTest. def test_submit(self): future = self.executor.submit(pow, 2, 8) self.assertEqual(256, future.result()) def test_submit_keyword(self): future = self.executor.submit(mul, 2, y=8) self.assertEqual(16, future.result()) future = self.executor.submit(capture, 1, self=2, fn=3) self.assertEqual(future.result(), ((1,), {'self': 2, 'fn': 3})) with self.assertRaises(TypeError): self.executor.submit(fn=capture, arg=1) with self.assertRaises(TypeError): self.executor.submit(arg=1) def test_map(self): self.assertEqual( list(self.executor.map(pow, range(10), range(10))), list(map(pow, range(10), range(10)))) self.assertEqual( list(self.executor.map(pow, range(10), range(10), chunksize=3)), list(map(pow, range(10), range(10)))) def test_map_exception(self): i = self.executor.map(divmod, [1, 1, 1, 1], [2, 3, 0, 5]) self.assertEqual(i.__next__(), (0, 1)) self.assertEqual(i.__next__(), (0, 1)) self.assertRaises(ZeroDivisionError, i.__next__) def test_map_timeout(self): results = [] try: for i in self.executor.map(time.sleep, [0, 0, 6], timeout=5): results.append(i) except futures.TimeoutError: pass else: self.fail('expected TimeoutError') self.assertEqual([None, None], results) def test_shutdown_race_issue12456(self): # Issue #12456: race condition at shutdown where trying to post a # sentinel in the call queue blocks (the queue is full while processes # have exited). self.executor.map(str, [2] * (self.worker_count + 1)) self.executor.shutdown() @support.cpython_only def test_no_stale_references(self): # Issue #16284: check that the executors don't unnecessarily hang onto # references. my_object = MyObject() my_object_collected = threading.Event() my_object_callback = weakref.ref( my_object, lambda obj: my_object_collected.set()) # Deliberately discarding the future. self.executor.submit(my_object.my_method) del my_object collected = my_object_collected.wait(timeout=support.SHORT_TIMEOUT) self.assertTrue(collected, "Stale reference not collected within timeout.") def test_max_workers_negative(self): for number in (0, -1): with self.assertRaisesRegex(ValueError, "max_workers must be greater " "than 0"): self.executor_type(max_workers=number) def test_free_reference(self): # Issue #14406: Result iterator should not keep an internal # reference to result objects. for obj in self.executor.map(make_dummy_object, range(10)): wr = weakref.ref(obj) del obj support.gc_collect() # For PyPy or other GCs. self.assertIsNone(wr()) class ThreadPoolExecutorTest(ThreadPoolMixin, ExecutorTest, BaseTestCase): def test_map_submits_without_iteration(self): """Tests verifying issue 11777.""" finished = [] def record_finished(n): finished.append(n) self.executor.map(record_finished, range(10)) self.executor.shutdown(wait=True) self.assertCountEqual(finished, range(10)) def test_default_workers(self): executor = self.executor_type() expected = min(32, (os.cpu_count() or 1) + 4) self.assertEqual(executor._max_workers, expected) def test_saturation(self): executor = self.executor_type(4) def acquire_lock(lock): lock.acquire() sem = threading.Semaphore(0) for i in range(15 * executor._max_workers): executor.submit(acquire_lock, sem) self.assertEqual(len(executor._threads), executor._max_workers) for i in range(15 * executor._max_workers): sem.release() executor.shutdown(wait=True) def test_idle_thread_reuse(self): executor = self.executor_type() executor.submit(mul, 21, 2).result() executor.submit(mul, 6, 7).result() executor.submit(mul, 3, 14).result() self.assertEqual(len(executor._threads), 1) executor.shutdown(wait=True) @unittest.skipUnless(hasattr(os, 'register_at_fork'), 'need os.register_at_fork') def test_hang_global_shutdown_lock(self): # bpo-45021: _global_shutdown_lock should be reinitialized in the child # process, otherwise it will never exit def submit(pool): pool.submit(submit, pool) with futures.ThreadPoolExecutor(1) as pool: pool.submit(submit, pool) for _ in range(50): with futures.ProcessPoolExecutor(1, mp_context=get_context('fork')) as workers: workers.submit(tuple) class ProcessPoolExecutorTest(ExecutorTest): @unittest.skipUnless(sys.platform=='win32', 'Windows-only process limit') def test_max_workers_too_large(self): with self.assertRaisesRegex(ValueError, "max_workers must be <= 61"): futures.ProcessPoolExecutor(max_workers=62) def test_killed_child(self): # When a child process is abruptly terminated, the whole pool gets # "broken". futures = [self.executor.submit(time.sleep, 3)] # Get one of the processes, and terminate (kill) it p = next(iter(self.executor._processes.values())) p.terminate() for fut in futures: self.assertRaises(BrokenProcessPool, fut.result) # Submitting other jobs fails as well. self.assertRaises(BrokenProcessPool, self.executor.submit, pow, 2, 8) def test_map_chunksize(self): def bad_map(): list(self.executor.map(pow, range(40), range(40), chunksize=-1)) ref = list(map(pow, range(40), range(40))) self.assertEqual( list(self.executor.map(pow, range(40), range(40), chunksize=6)), ref) self.assertEqual( list(self.executor.map(pow, range(40), range(40), chunksize=50)), ref) self.assertEqual( list(self.executor.map(pow, range(40), range(40), chunksize=40)), ref) self.assertRaises(ValueError, bad_map) @classmethod def _test_traceback(cls): raise RuntimeError(123) # some comment def test_traceback(self): # We want ensure that the traceback from the child process is # contained in the traceback raised in the main process. future = self.executor.submit(self._test_traceback) with self.assertRaises(Exception) as cm: future.result() exc = cm.exception self.assertIs(type(exc), RuntimeError) self.assertEqual(exc.args, (123,)) cause = exc.__cause__ self.assertIs(type(cause), futures.process._RemoteTraceback) self.assertIn('raise RuntimeError(123) # some comment', cause.tb) with support.captured_stderr() as f1: try: raise exc except RuntimeError: sys.excepthook(*sys.exc_info()) self.assertIn('raise RuntimeError(123) # some comment', f1.getvalue()) @hashlib_helper.requires_hashdigest('md5') def test_ressources_gced_in_workers(self): # Ensure that argument for a job are correctly gc-ed after the job # is finished mgr = get_context(self.ctx).Manager() obj = EventfulGCObj(mgr) future = self.executor.submit(id, obj) future.result() self.assertTrue(obj.event.wait(timeout=1)) # explicitly destroy the object to ensure that EventfulGCObj.__del__() # is called while manager is still running. obj = None support.gc_collect() mgr.shutdown() mgr.join() def test_saturation(self): executor = self.executor_type(4) mp_context = get_context() sem = mp_context.Semaphore(0) job_count = 15 * executor._max_workers try: for _ in range(job_count): executor.submit(sem.acquire) self.assertEqual(len(executor._processes), executor._max_workers) for _ in range(job_count): sem.release() finally: executor.shutdown() def test_idle_process_reuse_one(self): executor = self.executor_type(4) executor.submit(mul, 21, 2).result() executor.submit(mul, 6, 7).result() executor.submit(mul, 3, 14).result() self.assertEqual(len(executor._processes), 1) executor.shutdown() def test_idle_process_reuse_multiple(self): executor = self.executor_type(4) executor.submit(mul, 12, 7).result() executor.submit(mul, 33, 25) executor.submit(mul, 25, 26).result() executor.submit(mul, 18, 29) self.assertLessEqual(len(executor._processes), 2) executor.shutdown() create_executor_tests(ProcessPoolExecutorTest, executor_mixins=(ProcessPoolForkMixin, ProcessPoolForkserverMixin, ProcessPoolSpawnMixin)) def _crash(delay=None): """Induces a segfault.""" if delay: time.sleep(delay) import faulthandler faulthandler.disable() faulthandler._sigsegv() def _exit(): """Induces a sys exit with exitcode 1.""" sys.exit(1) def _raise_error(Err): """Function that raises an Exception in process.""" raise Err() def _raise_error_ignore_stderr(Err): """Function that raises an Exception in process and ignores stderr.""" import io sys.stderr = io.StringIO() raise Err() def _return_instance(cls): """Function that returns a instance of cls.""" return cls() class CrashAtPickle(object): """Bad object that triggers a segfault at pickling time.""" def __reduce__(self): _crash() class CrashAtUnpickle(object): """Bad object that triggers a segfault at unpickling time.""" def __reduce__(self): return _crash, () class ExitAtPickle(object): """Bad object that triggers a process exit at pickling time.""" def __reduce__(self): _exit() class ExitAtUnpickle(object): """Bad object that triggers a process exit at unpickling time.""" def __reduce__(self): return _exit, () class ErrorAtPickle(object): """Bad object that triggers an error at pickling time.""" def __reduce__(self): from pickle import PicklingError raise PicklingError("Error in pickle") class ErrorAtUnpickle(object): """Bad object that triggers an error at unpickling time.""" def __reduce__(self): from pickle import UnpicklingError return _raise_error_ignore_stderr, (UnpicklingError, ) class ExecutorDeadlockTest: TIMEOUT = support.SHORT_TIMEOUT def _fail_on_deadlock(self, executor): # If we did not recover before TIMEOUT seconds, consider that the # executor is in a deadlock state and forcefully clean all its # composants. import faulthandler from tempfile import TemporaryFile with TemporaryFile(mode="w+") as f: faulthandler.dump_traceback(file=f) f.seek(0) tb = f.read() for p in executor._processes.values(): p.terminate() # This should be safe to call executor.shutdown here as all possible # deadlocks should have been broken. executor.shutdown(wait=True) print(f"\nTraceback:\n {tb}", file=sys.__stderr__) self.fail(f"Executor deadlock:\n\n{tb}") def _check_crash(self, error, func, *args, ignore_stderr=False): # test for deadlock caused by crashes in a pool self.executor.shutdown(wait=True) executor = self.executor_type( max_workers=2, mp_context=get_context(self.ctx)) res = executor.submit(func, *args) if ignore_stderr: cm = support.captured_stderr() else: cm = contextlib.nullcontext() try: with self.assertRaises(error): with cm: res.result(timeout=self.TIMEOUT) except futures.TimeoutError: # If we did not recover before TIMEOUT seconds, # consider that the executor is in a deadlock state self._fail_on_deadlock(executor) executor.shutdown(wait=True) def test_error_at_task_pickle(self): # Check problem occurring while pickling a task in # the task_handler thread self._check_crash(PicklingError, id, ErrorAtPickle()) def test_exit_at_task_unpickle(self): # Check problem occurring while unpickling a task on workers self._check_crash(BrokenProcessPool, id, ExitAtUnpickle()) def test_error_at_task_unpickle(self): # Check problem occurring while unpickling a task on workers self._check_crash(BrokenProcessPool, id, ErrorAtUnpickle()) def test_crash_at_task_unpickle(self): # Check problem occurring while unpickling a task on workers self._check_crash(BrokenProcessPool, id, CrashAtUnpickle()) def test_crash_during_func_exec_on_worker(self): # Check problem occurring during func execution on workers self._check_crash(BrokenProcessPool, _crash) def test_exit_during_func_exec_on_worker(self): # Check problem occurring during func execution on workers self._check_crash(SystemExit, _exit) def test_error_during_func_exec_on_worker(self): # Check problem occurring during func execution on workers self._check_crash(RuntimeError, _raise_error, RuntimeError) def test_crash_during_result_pickle_on_worker(self): # Check problem occurring while pickling a task result # on workers self._check_crash(BrokenProcessPool, _return_instance, CrashAtPickle) def test_exit_during_result_pickle_on_worker(self): # Check problem occurring while pickling a task result # on workers self._check_crash(SystemExit, _return_instance, ExitAtPickle) def test_error_during_result_pickle_on_worker(self): # Check problem occurring while pickling a task result # on workers self._check_crash(PicklingError, _return_instance, ErrorAtPickle) def test_error_during_result_unpickle_in_result_handler(self): # Check problem occurring while unpickling a task in # the result_handler thread self._check_crash(BrokenProcessPool, _return_instance, ErrorAtUnpickle, ignore_stderr=True) def test_exit_during_result_unpickle_in_result_handler(self): # Check problem occurring while unpickling a task in # the result_handler thread self._check_crash(BrokenProcessPool, _return_instance, ExitAtUnpickle) def test_shutdown_deadlock(self): # Test that the pool calling shutdown do not cause deadlock # if a worker fails after the shutdown call. self.executor.shutdown(wait=True) with self.executor_type(max_workers=2, mp_context=get_context(self.ctx)) as executor: self.executor = executor # Allow clean up in fail_on_deadlock f = executor.submit(_crash, delay=.1) executor.shutdown(wait=True) with self.assertRaises(BrokenProcessPool): f.result() def test_shutdown_deadlock_pickle(self): # Test that the pool calling shutdown with wait=False does not cause # a deadlock if a task fails at pickle after the shutdown call. # Reported in bpo-39104. self.executor.shutdown(wait=True) with self.executor_type(max_workers=2, mp_context=get_context(self.ctx)) as executor: self.executor = executor # Allow clean up in fail_on_deadlock # Start the executor and get the executor_manager_thread to collect # the threads and avoid dangling thread that should be cleaned up # asynchronously. executor.submit(id, 42).result() executor_manager = executor._executor_manager_thread # Submit a task that fails at pickle and shutdown the executor # without waiting f = executor.submit(id, ErrorAtPickle()) executor.shutdown(wait=False) with self.assertRaises(PicklingError): f.result() # Make sure the executor is eventually shutdown and do not leave # dangling threads executor_manager.join() create_executor_tests(ExecutorDeadlockTest, executor_mixins=(ProcessPoolForkMixin, ProcessPoolForkserverMixin, ProcessPoolSpawnMixin)) class FutureTests(BaseTestCase): def test_done_callback_with_result(self): callback_result = None def fn(callback_future): nonlocal callback_result callback_result = callback_future.result() f = Future() f.add_done_callback(fn) f.set_result(5) self.assertEqual(5, callback_result) def test_done_callback_with_exception(self): callback_exception = None def fn(callback_future): nonlocal callback_exception callback_exception = callback_future.exception() f = Future() f.add_done_callback(fn) f.set_exception(Exception('test')) self.assertEqual(('test',), callback_exception.args) def test_done_callback_with_cancel(self): was_cancelled = None def fn(callback_future): nonlocal was_cancelled was_cancelled = callback_future.cancelled() f = Future() f.add_done_callback(fn) self.assertTrue(f.cancel()) self.assertTrue(was_cancelled) def test_done_callback_raises(self): with support.captured_stderr() as stderr: raising_was_called = False fn_was_called = False def raising_fn(callback_future): nonlocal raising_was_called raising_was_called = True raise Exception('doh!') def fn(callback_future): nonlocal fn_was_called fn_was_called = True f = Future() f.add_done_callback(raising_fn) f.add_done_callback(fn) f.set_result(5) self.assertTrue(raising_was_called) self.assertTrue(fn_was_called) self.assertIn('Exception: doh!', stderr.getvalue()) def test_done_callback_already_successful(self): callback_result = None def fn(callback_future): nonlocal callback_result callback_result = callback_future.result() f = Future() f.set_result(5) f.add_done_callback(fn) self.assertEqual(5, callback_result) def test_done_callback_already_failed(self): callback_exception = None def fn(callback_future): nonlocal callback_exception callback_exception = callback_future.exception() f = Future() f.set_exception(Exception('test')) f.add_done_callback(fn) self.assertEqual(('test',), callback_exception.args) def test_done_callback_already_cancelled(self): was_cancelled = None def fn(callback_future): nonlocal was_cancelled was_cancelled = callback_future.cancelled() f = Future() self.assertTrue(f.cancel()) f.add_done_callback(fn) self.assertTrue(was_cancelled) def test_done_callback_raises_already_succeeded(self): with support.captured_stderr() as stderr: def raising_fn(callback_future): raise Exception('doh!') f = Future() # Set the result first to simulate a future that runs instantly, # effectively allowing the callback to be run immediately. f.set_result(5) f.add_done_callback(raising_fn) self.assertIn('exception calling callback for', stderr.getvalue()) self.assertIn('doh!', stderr.getvalue()) def test_repr(self): self.assertRegex(repr(PENDING_FUTURE), '<Future at 0x[0-9a-f]+ state=pending>') self.assertRegex(repr(RUNNING_FUTURE), '<Future at 0x[0-9a-f]+ state=running>') self.assertRegex(repr(CANCELLED_FUTURE), '<Future at 0x[0-9a-f]+ state=cancelled>') self.assertRegex(repr(CANCELLED_AND_NOTIFIED_FUTURE), '<Future at 0x[0-9a-f]+ state=cancelled>') self.assertRegex( repr(EXCEPTION_FUTURE), '<Future at 0x[0-9a-f]+ state=finished raised OSError>') self.assertRegex( repr(SUCCESSFUL_FUTURE), '<Future at 0x[0-9a-f]+ state=finished returned int>') def test_cancel(self): f1 = create_future(state=PENDING) f2 = create_future(state=RUNNING) f3 = create_future(state=CANCELLED) f4 = create_future(state=CANCELLED_AND_NOTIFIED) f5 = create_future(state=FINISHED, exception=OSError()) f6 = create_future(state=FINISHED, result=5) self.assertTrue(f1.cancel()) self.assertEqual(f1._state, CANCELLED) self.assertFalse(f2.cancel()) self.assertEqual(f2._state, RUNNING) self.assertTrue(f3.cancel()) self.assertEqual(f3._state, CANCELLED) self.assertTrue(f4.cancel()) self.assertEqual(f4._state, CANCELLED_AND_NOTIFIED) self.assertFalse(f5.cancel()) self.assertEqual(f5._state, FINISHED) self.assertFalse(f6.cancel()) self.assertEqual(f6._state, FINISHED) def test_cancelled(self): self.assertFalse(PENDING_FUTURE.cancelled()) self.assertFalse(RUNNING_FUTURE.cancelled()) self.assertTrue(CANCELLED_FUTURE.cancelled()) self.assertTrue(CANCELLED_AND_NOTIFIED_FUTURE.cancelled()) self.assertFalse(EXCEPTION_FUTURE.cancelled()) self.assertFalse(SUCCESSFUL_FUTURE.cancelled()) def test_done(self): self.assertFalse(PENDING_FUTURE.done()) self.assertFalse(RUNNING_FUTURE.done()) self.assertTrue(CANCELLED_FUTURE.done()) self.assertTrue(CANCELLED_AND_NOTIFIED_FUTURE.done()) self.assertTrue(EXCEPTION_FUTURE.done()) self.assertTrue(SUCCESSFUL_FUTURE.done()) def test_running(self): self.assertFalse(PENDING_FUTURE.running()) self.assertTrue(RUNNING_FUTURE.running()) self.assertFalse(CANCELLED_FUTURE.running()) self.assertFalse(CANCELLED_AND_NOTIFIED_FUTURE.running()) self.assertFalse(EXCEPTION_FUTURE.running()) self.assertFalse(SUCCESSFUL_FUTURE.running()) def test_result_with_timeout(self): self.assertRaises(futures.TimeoutError, PENDING_FUTURE.result, timeout=0) self.assertRaises(futures.TimeoutError, RUNNING_FUTURE.result, timeout=0) self.assertRaises(futures.CancelledError, CANCELLED_FUTURE.result, timeout=0) self.assertRaises(futures.CancelledError, CANCELLED_AND_NOTIFIED_FUTURE.result, timeout=0) self.assertRaises(OSError, EXCEPTION_FUTURE.result, timeout=0) self.assertEqual(SUCCESSFUL_FUTURE.result(timeout=0), 42) def test_result_with_success(self): # TODO([email protected]): This test is timing dependent. def notification(): # Wait until the main thread is waiting for the result. time.sleep(1) f1.set_result(42) f1 = create_future(state=PENDING) t = threading.Thread(target=notification) t.start() self.assertEqual(f1.result(timeout=5), 42) t.join() def test_result_with_cancel(self): # TODO([email protected]): This test is timing dependent. def notification(): # Wait until the main thread is waiting for the result. time.sleep(1) f1.cancel() f1 = create_future(state=PENDING) t = threading.Thread(target=notification) t.start() self.assertRaises(futures.CancelledError, f1.result, timeout=support.SHORT_TIMEOUT) t.join() def test_exception_with_timeout(self): self.assertRaises(futures.TimeoutError, PENDING_FUTURE.exception, timeout=0) self.assertRaises(futures.TimeoutError, RUNNING_FUTURE.exception, timeout=0) self.assertRaises(futures.CancelledError, CANCELLED_FUTURE.exception, timeout=0) self.assertRaises(futures.CancelledError, CANCELLED_AND_NOTIFIED_FUTURE.exception, timeout=0) self.assertTrue(isinstance(EXCEPTION_FUTURE.exception(timeout=0), OSError)) self.assertEqual(SUCCESSFUL_FUTURE.exception(timeout=0), None) def test_exception_with_success(self): def notification(): # Wait until the main thread is waiting for the exception. time.sleep(1) with f1._condition: f1._state = FINISHED f1._exception = OSError() f1._condition.notify_all() f1 = create_future(state=PENDING) t = threading.Thread(target=notification) t.start() self.assertTrue(isinstance(f1.exception(timeout=support.SHORT_TIMEOUT), OSError)) t.join() def test_multiple_set_result(self): f = create_future(state=PENDING) f.set_result(1) with self.assertRaisesRegex( futures.InvalidStateError, 'FINISHED: <Future at 0x[0-9a-f]+ ' 'state=finished returned int>' ): f.set_result(2) self.assertTrue(f.done()) self.assertEqual(f.result(), 1) def test_multiple_set_exception(self): f = create_future(state=PENDING) e = ValueError() f.set_exception(e) with self.assertRaisesRegex( futures.InvalidStateError, 'FINISHED: <Future at 0x[0-9a-f]+ ' 'state=finished raised ValueError>' ): f.set_exception(Exception()) self.assertEqual(f.exception(), e) def setUpModule(): unittest.addModuleCleanup(multiprocessing.util._cleanup_tests) thread_info = threading_helper.threading_setup() unittest.addModuleCleanup(threading_helper.threading_cleanup, *thread_info) if __name__ == "__main__": unittest.main()

6452c8a81d8e93d9fd2794db2306cbf9b018a064

py

Python

setup.py

Naveenaidu/moban

5a4d41546a99f66b39e7deb3e216c9238fa3b07b

setup.py

Naveenaidu/moban

5a4d41546a99f66b39e7deb3e216c9238fa3b07b

setup.py

Naveenaidu/moban

5a4d41546a99f66b39e7deb3e216c9238fa3b07b

#!/usr/bin/env python3 # Template by pypi-mobans import os import sys import codecs from shutil import rmtree from setuptools import Command, setup, find_packages PY2 = sys.version_info[0] == 2 PY26 = PY2 and sys.version_info[1] < 7 NAME = 'moban' AUTHOR = 'C. W.' VERSION = '0.3.10' EMAIL = '[email protected]' LICENSE = 'MIT' ENTRY_POINTS = { 'console_scripts': [ 'moban = moban.main:main' ], } DESCRIPTION = ( 'Yet another jinja2 cli command for static text generation' ) URL = 'https://github.com/moremoban/moban' DOWNLOAD_URL = '%s/archive/0.3.10.tar.gz' % URL FILES = ['README.rst', 'CONTRIBUTORS.rst', 'CHANGELOG.rst'] KEYWORDS = [ 'python', 'jinja2', 'moban', ] CLASSIFIERS = [ 'Topic :: Software Development :: Libraries', 'Programming Language :: Python', 'Intended Audience :: Developers', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', ] INSTALL_REQUIRES = [ 'ruamel.yaml==0.15.87', 'jinja2>=2.7.1', 'lml>=0.0.9', 'appdirs==1.4.3', 'crayons', 'GitPython==2.1.11', 'git-url-parse', ] SETUP_COMMANDS = {} PACKAGES = find_packages(exclude=['ez_setup', 'examples', 'tests']) EXTRAS_REQUIRE = { } # You do not need to read beyond this line PUBLISH_COMMAND = '{0} setup.py sdist bdist_wheel upload -r pypi'.format( sys.executable) GS_COMMAND = ('gs moban v0.3.10 ' + "Find 0.3.10 in changelog for more details") NO_GS_MESSAGE = ('Automatic github release is disabled. ' + 'Please install gease to enable it.') UPLOAD_FAILED_MSG = ( 'Upload failed. please run "%s" yourself.' % PUBLISH_COMMAND) HERE = os.path.abspath(os.path.dirname(__file__)) class PublishCommand(Command): """Support setup.py upload.""" description = 'Build and publish the package on github and pypi' user_options = [] @staticmethod def status(s): """Prints things in bold.""" print('\033[1m{0}\033[0m'.format(s)) def initialize_options(self): pass def finalize_options(self): pass def run(self): try: self.status('Removing previous builds...') rmtree(os.path.join(HERE, 'dist')) rmtree(os.path.join(HERE, 'build')) rmtree(os.path.join(HERE, 'moban.egg-info')) except OSError: pass self.status('Building Source and Wheel (universal) distribution...') run_status = True if has_gease(): run_status = os.system(GS_COMMAND) == 0 else: self.status(NO_GS_MESSAGE) if run_status: if os.system(PUBLISH_COMMAND) != 0: self.status(UPLOAD_FAILED_MSG % PUBLISH_COMMAND) sys.exit() SETUP_COMMANDS.update({ 'publish': PublishCommand }) def has_gease(): """ test if github release command is installed visit http://github.com/moremoban/gease for more info """ try: import gease # noqa return True except ImportError: return False def read_files(*files): """Read files into setup""" text = "" for single_file in files: content = read(single_file) text = text + content + "\n" return text def read(afile): """Read a file into setup""" the_relative_file = os.path.join(HERE, afile) with codecs.open(the_relative_file, 'r', 'utf-8') as opened_file: content = filter_out_test_code(opened_file) content = "".join(list(content)) return content def filter_out_test_code(file_handle): found_test_code = False for line in file_handle.readlines(): if line.startswith('.. testcode:'): found_test_code = True continue if found_test_code is True: if line.startswith(' '): continue else: empty_line = line.strip() if len(empty_line) == 0: continue else: found_test_code = False yield line else: for keyword in ['|version|', '|today|']: if keyword in line: break else: yield line if __name__ == '__main__': setup( name=NAME, author=AUTHOR, version=VERSION, author_email=EMAIL, description=DESCRIPTION, url=URL, download_url=DOWNLOAD_URL, long_description=read_files(*FILES), license=LICENSE, keywords=KEYWORDS, extras_require=EXTRAS_REQUIRE, tests_require=['nose'], install_requires=INSTALL_REQUIRES, packages=PACKAGES, include_package_data=True, zip_safe=False, entry_points=ENTRY_POINTS, classifiers=CLASSIFIERS, cmdclass=SETUP_COMMANDS )

7005212e52d344aec7a9101eb4ae457a5712bc37

py

Python

Backend/venv/Lib/site-packages/google/cloud/firestore_admin_v1/services/firestore_admin/__init__.py

homer65/fellowcoder

89433e1d44db64d9aa64a8603a7a38bcd220f035

2021-09-17T10:55:14.000Z

2021-09-17T10:55:38.000Z

Backend/venv/Lib/site-packages/google/cloud/firestore_admin_v1/services/firestore_admin/__init__.py

homer65/fellowcoder

89433e1d44db64d9aa64a8603a7a38bcd220f035

2020-04-01T07:31:01.000Z

2020-11-30T07:03:51.000Z

Backend/venv/Lib/site-packages/google/cloud/firestore_admin_v1/services/firestore_admin/__init__.py

homer65/fellowcoder

89433e1d44db64d9aa64a8603a7a38bcd220f035

2020-10-04T12:11:36.000Z

2020-10-04T12:11:36.000Z

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from .client import FirestoreAdminClient from .async_client import FirestoreAdminAsyncClient __all__ = ( "FirestoreAdminClient", "FirestoreAdminAsyncClient", )

748d557c125cd1f5269c44ca3909cbb4b4f55b20

py

Python

bin/attrib_to_categorical.py

arup-group/london-pop-synth

38e56230d440d49ddb2e2841d46a5cbaab260c35

2020-11-25T06:56:43.000Z

2020-11-25T06:56:43.000Z

bin/attrib_to_categorical.py

arup-group/london-pop-synth

38e56230d440d49ddb2e2841d46a5cbaab260c35

bin/attrib_to_categorical.py

arup-group/london-pop-synth

38e56230d440d49ddb2e2841d46a5cbaab260c35

import xml.etree.ElementTree as ET import os import argparse import numpy as np import pandas as pd """ Helper CLI for converting raw attribute data (one hot encoded) into classes """ def get_args(): """ Gets command line args. Includes a number of defaults for input and output paths. :return: argparse arguments object """ data_path = os.path.join('data', 'plans', 'HHsPerson2016.csv') # keep = ['recID', 'bname', 'Freq16'] # categories = {'hsize': ['hsize1', 'hsize2', 'hsize3', 'hsize4'], # 'car': ['car0', 'car1', 'car2'], # 'inc': ['inc12', 'inc34', 'inc56', 'inc7p'], # 'hstr': ['hstr1', 'hstr2', 'hstr3'], # 'gender': ['male', 'female'], # 'age': ['age5', 'age11', 'age18p', 'age30', 'age65', 'age65p'], # 'race': ['white', 'mixed', 'india', 'pakbag', 'asian', 'black'], # 'license': ['pdlcar', 'pdlnone'], # 'job': ['ft', 'pt', 'edu', 'retired'], # 'occ': ['occ1', 'occ2', 'occ3', 'occ4', 'occ5', 'occ6', 'occ7', 'occ8'] # } keep = ['thid', 'tpid', 'hincome', 'age', 'Borough', 'Freq16'] # columns to keep as is categories = { 'day': ['mon', 'tues', 'wed', 'thur', 'fri', 'sat', 'sun'], 'hsize': ['hsize1', 'hsize2', 'hsize3', 'hsize4', 'hsize5', 'hsize6p'], 'car': ['car0', 'car1', 'car2', 'car2p'], 'hstr': ['hstr1', 'hstr2', 'hstr3', 'hstr4', 'hstr5', 'hstr6'], 'gender': ['male', 'female'], 'age': ['age5', 'age11', 'age16', 'age18', 'age30', 'age65', 'age65p'], 'race': ['white', 'mixed', 'india', 'pakbag', 'asian', 'black'], 'license': ['pdlcar', 'pdlnone'], 'job': ['ft', 'pt', 'student', 'retired'], 'occ': ['occ1', 'occ2', 'occ3', 'occ4', 'occ5', 'occ6', 'occ7', 'occ8'], } parser = argparse.ArgumentParser() parser.add_argument('--att', '-I', default=data_path, type=str, help="Population attributes path (.csv)") parser.add_argument('--out', '-O', default=None, type=str, help="Population attributes path (.csv)") parser.add_argument('--verbose', '-V', action='store_true') arguments = parser.parse_args() arguments.categories = categories arguments.keep = keep if not arguments.out: name = os.path.basename(arguments.att).split('.')[0] + '_cat.csv' arguments.out = os.path.join('outputs', name) print("\t> pop attribute inputs from: {}".format(arguments.att)) print("\t> output to: {}".format(arguments.out)) print("\t> conversion using:") for name, columns in arguments.categories.items(): print("{}: {}".format(name, columns)) return arguments def get_category(row, columns): if sum(row) == 0: return 'unknown' for column in columns: if row[column] == 1: return column def to_categorical(args): # convert csv to categorical format attributes_raw = pd.read_csv(args.att) attributes_categorical = attributes_raw.loc[:, args.keep] for category, columns in args.categories.items(): for column in columns: assert column in attributes_raw.columns, '{} header not found in input data headers'.format(column) # select input columns for category cols = attributes_raw[columns] # extract category from boolean cat = cols.apply(get_category, args=(columns,), axis=1) # cat = pd.Series(cols.columns[np.where(cols != 0)[1]]) # this is fast but can't deal with missing 1 # add to new df attributes_categorical[category] = cat return attributes_categorical if __name__ == '__main__': args = get_args() print('converting data...') df = to_categorical(args) print('saving to disk as {}'.format(args.out)) df.to_csv(args.out, index=False) print('done') # # tree = ET.parse('tfl_pop_transformer/test1.xml') # root = tree.getroot() # print(len(root)) # for child in root: # print(child.attrib['id'])

608c2f9766a40c5d38a4b87b69c1d73a80494e81

py

Python

SprintMgr/Model.py

esitarski/CrossMgr

de33b5ed662556ec659e6e2910f5fd0f88f25fa0

2015-02-26T01:26:10.000Z

2022-03-25T15:46:55.000Z

SprintMgr/Model.py

esitarski/CrossMgr

de33b5ed662556ec659e6e2910f5fd0f88f25fa0

2015-12-09T04:24:30.000Z

2022-02-18T16:39:28.000Z

SprintMgr/Model.py

esitarski/CrossMgr

de33b5ed662556ec659e6e2910f5fd0f88f25fa0

2015-04-23T07:37:13.000Z

2020-01-22T17:47:16.000Z

import sys import copy import random import datetime import traceback from operator import attrgetter from collections import defaultdict import Utils QualifyingTimeDefault = 99*60*60 Sprint200mQualificationCompetitionTime = 60.0 SprintFinalCompetitionTime = 3*60.0 KeirinCompetitionTime = 5*60.0 class Rider: status = '' seeding_rank = 0 uci_points = 0 fields = { 'bib', 'first_name', 'last_name', 'team', 'team_code', 'uci_id', 'qualifying_time', 'uci_points', 'seeding_rank', 'status' } extended_fields = fields | {'full_name', 'bib_full_name', 'uci_points_text', 'short_name', 'long_name'} def __init__( self, bib, first_name = '', last_name = '', team = '', team_code = '', uci_id = '', qualifying_time = QualifyingTimeDefault, uci_points = 0, seeding_rank = 0, status = '' ): self.bib = int(bib) self.first_name = first_name self.last_name = last_name self.team = team self.team_code = team_code self.uci_id = uci_id self.qualifying_time = float(qualifying_time) self.iSeeding = 0 # Actual value in the seeding list. self.uci_points = float(uci_points or 0) self.seeding_rank = int(seeding_rank or 0) self.status = status named_aliases = ( (('bib#','bib_#','#','num','bibnum','bib_num',), 'bib'), (('name','rider_name'), 'full_name'), (('first','fname','firstname','rider_first'), 'first_name'), (('last','lname','lastname','rider_last',), 'last_name'), (('uciid','rider_uciid',), 'uci_id'), (('ucipoints','points','riderucipoints','rider_ucipoints',), 'uci_points'), (('qualifying','time','rider_time',), 'qualifying_time'), (('rank', 'seedingrank',), 'seeding_rank'), (('rider_team',), 'team'), (('teamcode',), 'team_code'), (('rider_status',), 'status'), ) aliases = {} for names, key in named_aliases: aliases[key] = key for n in names: aliases[n] = key @staticmethod def GetHeaderNameMap( headers ): header_map = {} for col, h in enumerate(headers): h = h.strip().replace(' ', '_').lower() h = Rider.aliases.get( h, h ) if h in Rider.extended_fields: header_map[h] = col return header_map def isOpen( self ): return self.last_name == 'OPEN' def copyDataFields( self, r ): if r == self: return fields = ('first_name', 'last_name', 'team', 'team_code', 'uci_id', 'uci_points', 'seeding_rank') for attr in fields: setattr( self, attr, getattr(r, attr) ) return self def key( self ): return tuple( getattr(self, a) for a in ('bib', 'first_name', 'last_name', 'team', 'team_code', 'uci_id', 'qualifying_time', 'uci_points', 'seeding_rank', ) ) @staticmethod def getKeyQualifying( isKeirin ): if isKeirin: return attrgetter('status', 'iSeeding') else: return attrgetter('status', 'qualifying_time', 'iSeeding') @property def qualifying_time_text( self ): return Utils.SecondsToStr(self.qualifying_time) if self.qualifying_time < QualifyingTimeDefault else '' @property def uci_points_text( self ): return '{:.2f}'.format(self.uci_points) if self.uci_points else '' @property def full_name( self ): return ', '.join( n for n in (self.last_name.upper(), self.first_name) if n ) @property def bib_full_name( self ): return '({}) {}'.format( self.bib, self.full_name ) if self.bib else self.full_name @property def short_name( self ): if self.last_name and self.first_name: return '{}, {}.'.format(self.last_name.upper(), self.first_name[:1]) return self.last_name.upper() if self.last_name else self.first_name @property def bib_short_name( self ): return '{} {}'.format(self.bib, self.short_name) @property def long_name( self ): n = self.full_name return '{} ({})'.format(n, self.team) if self.team else n def __repr__( self ): return '{}'.format(self.bib) #------------------------------------------------------------------------------------------------ class State: def __init__( self ): self.labels = {} # Riders bound to competition labels. self.noncontinue = {} self.OpenRider = Rider( bib=0, last_name='OPEN', qualifying_time=QualifyingTimeDefault + 1.0 ) def setQualifyingInfo( self, riders, competition ): riders = sorted( (r for r in riders if r.status != 'DNQ'), key = Rider.getKeyQualifying(competition.isKeirin), )[:competition.starters] self.labels = { 'N{}'.format(i):rider for i, rider in enumerate(riders,1) } # Initialize extra open spaces to make sure we have enough starters. self.labels.update( {'N{}'.format(i):self.OpenRider for i in range(len(riders)+1, 128)} ) self.OpenRider.qualifying_time = QualifyingTimeDefault + 1.0 def inContention( self, label ): return self.labels.get(label, None) != self.OpenRider and label not in self.noncontinue def canReassignStarters( self ): ''' Check if no competitions have started and we can reasign starters. ''' return all( label.startswith('N') for label in self.labels.keys() ) def __repr__( self ): st = [(k,v) for k,v in self.labels.items() if not str(v).startswith('0')] return ','.join('{}:{}'.format(k,v) for k,v in st) if st else '<<< no state >>>' #------------------------------------------------------------------------------------------------ class Start: placesTimestamp = None # Timestamp when places were modified. finishCode = { 'Inside': 1, 'DNF': 2, 'DNS': 3, 'DQ': 4, } warning = set() def __init__( self, event, lastStart ): self.event = event self.lastStart = lastStart self.startPositions = [] self.finishPositions = [] # id, including finishers, DNF and DNS. self.continuingPositions = [] # id, including finishers - no DNF and DNS. self.places = {} # In the format of places[composition] = place, place in 1, 2, 3, 4, etc. self.times = {} # In the format of times[1] = winner's time, times[2] = runner up's time, etc. self.relegated = set() # Rider assigned a relegated position in this heat. self.inside = [] # Rider required to take inside position on next start. self.noncontinue = {} # In the format of noncontinue[composition] = reason self.restartRequired = False self.canDrawLots = False remainingComposition = self.getRemainingComposition() if not lastStart: self.heat = 1 self.firstStartInHeat = True self.startPositions = [c for c in remainingComposition] random.shuffle( self.startPositions ) self.canDrawLots = True else: if lastStart.restartRequired: self.firstStartInHeat = False self.heat = lastStart.heat self.startPositions = [r for r in lastStart.inside] + \ [c for c in lastStart.startPositions if c not in lastStart.inside] self.canDrawLots = False else: self.heat = lastStart.heat + 1 self.firstStartInHeat = True if self.heat == 2: # Find the non-restarted start of the heat. s = lastStart while s and not s.firstStartInHeat: s = s.lastStart self.startPositions = [r for r in lastStart.inside] + \ [c for c in reversed(s.startPositions) if c not in lastStart.inside] self.canDrawLots = False elif self.heat == 3: if lastStart.inside: # Don't randomize the start positions again if the last run had a relegation. self.startPositions = [r for r in lastStart.inside] + \ [c for c in lastStart.startPositions if c not in lastStart.inside] self.canDrawLots = False else: # Randomize the start positions again. self.startPositions = [c for c in remainingComposition] random.shuffle( self.startPositions ) self.canDrawLots = True else: assert False, 'Cannot have more than 3 heats' state = event.competition.state self.startPositions = [c for c in self.startPositions if state.inContention(c)] if self.event.competition.isMTB: self.startPositions.sort( key=lambda c: state.labels[c].bib ) def isHanging( self ): ''' Check if there are no results, and this is not a restart. If so, this start was interrupted and needs to be removed. ''' if self.restartRequired: return False if self.places: return False return True def setStartPositions( self, startSequence ): ''' startPositions is of the form [(bib, status), (bib, status), ...] ''' state = self.event.competition.state remainingComposition = self.getRemainingComposition() bibToId = dict( (state.labels[c].bib, c) for c in remainingComposition ) startIdPosition = { id : i+1000 for i, id in enumerate(self.startPositions) } for p, (bib, status) in enumerate(startSequence): id = bibToId[int(bib)] startIdPosition[id] = p if status: self.noncontinue[id] = status else: self.noncontinue.pop(id, None) self.startPositions = [id for p, id in sorted((p, id) for id, p in startIdPosition.items())] def setPlaces( self, places ): ''' places is of the form [(bib, status, warning, relegation), (bib, status, warning, relegation), ...] ''' state = self.event.competition.state remainingComposition = self.getRemainingComposition() bibToId = { state.labels[c].bib: c for c in remainingComposition } self.noncontinue = {} self.warning = set() self.places = {} self.finishPositions = [] # Correct for status information. finishCode = self.finishCode statusPlaceId = [] place = 0 for bib, status, warning, relegation in places: id = bibToId[int(bib)] if finishCode.get(status,0) >= 2: self.noncontinue[id] = status if status == 'Inside': self.addInside( id ) if finishCode.get(status,0) <= 3: place += 1 statusPlaceId.append( (finishCode.get(status,0), place, id) ) if ('{}'.format(warning)[:1] or '0') in '1TtYy': self.addWarning( id ) if ('{}'.format(relegation)[:1] or '0') in '1TtYy': self.addRelegation( id ) statusPlaceId.sort() self.places = { id : i+1 for i, (finishCode, place, id) in enumerate(statusPlaceId) if id not in self.noncontinue } self.finishPositions = [ id for (finishCode, place, id) in statusPlaceId ] self.continuingPositions = [ id for (finishCode, place, id) in statusPlaceId if id not in self.noncontinue ] self.placesTimestamp = datetime.datetime.now() def resetPlaces( self ): # Fix up data from previous versions. if hasattr(self, 'finishPositions'): return # Based on the known places and noncontinue status, set the places again so that the # additional data structures get initialized. state = self.event.competition.state OpenRider = state.OpenRider bibStatus = [] for pos, id in sorted( (pos, id) for pos, id in self.places.items() ): try: bibStatus.append( (state.labels[id].bib, '') ) except KeyError: pass for id, status in self.noncontinue.items(): bibStatus.append( (state.labels[id].bib, status) ) self.setPlaces( bibStatus ) def setTimes( self, times ): ''' times is of the form [(pos, t), (pos, t), ...] - missing pos have no time ''' self.times = dict( times ) def addRelegation( self, id ): if isinstance(self.relegated, list): self.relegated = set( self.relegated ) self.relegated.add( id ) def addInside( self, id ): self.inside.append( id ) def addWarning( self, id ): self.warning.add( id ) def getRemainingComposition( self ): state = self.event.competition.state return [c for c in self.event.composition if state.inContention(c)] #------------------------------------------------------------------------------------------------ class Event: def __init__( self, rule, heatsMax=1 ): assert '->' in rule, 'Rule must contain ->' self.rule = rule.upper() rule = rule.replace( '->', ' -> ').replace('-',' ') fields = rule.split() iSep = fields.index( '>' ) # Event transformation. self.composition = fields[:iSep] self.winner = fields[iSep+1] # Winner of competition. self.others = fields[iSep+2:] # Other non-winners. # If "others" are incomplete, assume classification by TT time. self.others.extend( ['TT'] * (len(self.composition)-1 - len(self.others)) ) assert len(self.composition) == len(self.others) + 1, 'Rule outputs cannot exceed inputs.' self.heatsMax = heatsMax # Number of heats required to decide the outcome. # Convenience fields and are set by the competition. self.competition = None self.system = None # State of the Event. self.finishRiders, self.finishRiderPlace, self.finishRiderRank = [], {}, {} self.starts = [] self.compositionRiders = [] # Input riders. @property def competitionTime( self ): if self.competition.isSprint: if self.competition.isKeirin: return KeirinCompetitionTime else: return (1 if self.heatsMax == 1 else 1.5) * SprintFinalCompetitionTime return None @property def isSemiFinal( self ): try: return self.competition.isMTB and self.system == self.competition.systems[-2] except IndexError: return False @property def isFinal( self ): try: return self.competition.isMTB and self.system == self.competition.systems[-1] except IndexError: return False @property def isSmallFinal( self ): try: return self.competition.isMTB and self.system == self.competition.systems[-1] and self == self.system.events[-2] except IndexError: return False @property def isBigFinal( self ): try: return self.competition.isMTB and self.system == self.competition.systems[-1] and self == self.system.events[-1] except IndexError: return False @property def output( self ): return [self.winner] + self.others def getHeat( self ): heats = sum( 1 for s in self.starts if not s.restartRequired ) return min(heats, self.heatsMax) def getHeatPlaces( self, heat ): state = self.competition.state remainingComposition = [c for c in self.composition if state.inContention(c)] heatCur = 0 for start in self.starts: if start.restartRequired: continue heatCur += 1 if heatCur != heat: continue placeStatus = start.noncontinue.copy() for c in remainingComposition: if c not in placeStatus: placeStatus[c] = str(start.places.get(c, '')) heatPlaces = [placeStatus.get(c, '') for c in remainingComposition] heatPlaces = ['Win' if p == '1' else '-' for p in heatPlaces] return heatPlaces return [''] * len(remainingComposition) def __repr__( self ): state = self.competition.state remainingComposition = [c for c in self.composition if state.inContention(c)] remainingOthers = self.others[:len(remainingComposition)-1] def labName( id ): return '{}={:-12s}'.format(id, state.labels[id].full_name) if id in state.labels else '{}'.format(id) s = '{}, Heat {}/{} Start {}: {} => {} {}'.format( self.system.name, self.getHeat(), self.heatsMax, len(self.starts), ' '.join(labName(c) for c in remainingComposition), labName(self.winner), ' '.join(labName(c) for c in remainingOthers) ) return s @property def multi_line_name( self ): return '{}\nHeat {}/{}'.format(self.system.name, self.getHeat(), self.heatsMax) @property def multi_line_bibs( self ): state = self.competition.state remainingComposition = [c for c in self.composition if state.inContention(c)] return '\n'.join((str(state.labels[c].bib)) for c in remainingComposition) @property def multi_line_rider_names( self ): state = self.competition.state remainingComposition = [c for c in self.composition if state.inContention(c)] return '\n'.join(state.labels[c].full_name for c in remainingComposition) @property def multi_line_rider_teams( self ): state = self.competition.state remainingComposition = [c for c in self.composition if state.inContention(c)] return '\n'.join(state.labels[c].team for c in remainingComposition) @property def multi_line_inlabels( self ): state = self.competition.state remainingComposition = [c for c in self.composition if state.inContention(c)] return '\n'.join( remainingComposition ) @property def multi_line_outlabels( self ): state = self.competition.state remainingComposition = [c for c in self.composition if state.inContention(c)] outlabels = [self.winner] outlabels.extend( self.others[0:len(remainingComposition)-1] ) return '\n'.join( outlabels ) def getRepr( self ): return self.__repr__() def getStart( self ): if not self.canStart(): return None self.starts.append( Start(self, self.starts[-1] if self.starts else None) ) return self.starts[-1] def isFinished( self ): return self.winner in self.competition.state def canStart( self ): state = self.competition.state return ( all(c in state.labels for c in self.composition) and any(state.inContention(c) for c in self.composition) and self.winner not in state.labels ) def setFinishRiders( self, places ): finishCode = Start.finishCode state = self.competition.state OpenRider = state.OpenRider noncontinue = state.noncontinue infoSort = [] for place, id in enumerate(places): rider = state.labels.get(id, OpenRider) infoSort.append( (finishCode.get(noncontinue.get(id,''),0), place, rider.qualifying_time, rider, noncontinue.get(id,'')) ) infoSort.sort() self.finishRiders = [rider for state, place, qualifying_time, rider, nc in infoSort] self.finishRiderRank = { rider: p+1 for p, (state, place, qualifying_time, rider, nc) in enumerate(infoSort) } self.finishRiderPlace = { rider: nc if nc else p+1 for p, (state, place, qualifying_time, rider, nc) in enumerate(infoSort) } def getCompositionRiders( self, places ): state = self.competition.state OpenRider = state.OpenRider return [state.labels.get(p,OpenRider) for p in places] def propagate( self ): if not self.canStart(): #print( ', '.join(self.composition), 'Cannot start or already finished - nothing to propagate' ) return False state = self.competition.state # Update all non-continuing riders into the competition state. for s in self.starts: s.resetPlaces() state.noncontinue.update( s.noncontinue ) self.finishRiders, self.finishRiderPlace = [], {} self.compositionRiders = self.getCompositionRiders(self.composition) # Check for default winner(s). availableStarters = [c for c in self.composition if c not in state.noncontinue] # Single sprint case. if len(availableStarters) == 1: # Set the default winner. state.labels[self.winner] = state.labels[availableStarters[0]] self.setFinishRiders(self.composition) # Mark the "others" as open riders. for o in self.others: state.labels[o] = state.OpenRider return True # Check if we have a rider with a majority of wins in the heats. winCount = defaultdict( int ) for s in self.starts: if s.restartRequired: continue winnerId = s.continuingPositions[0] winCount[winnerId] += 1 if winCount[winnerId] < self.heatsMax - 1: continue # We have a winner of the event. Propagate the results. state.labels[self.winner] = state.labels[winnerId] for o, c in zip(self.others, s.continuingPositions[1:]): state.labels[o] = state.labels[c] # Set any extra others to "OpenRider". for o in self.others[len(s.continuingPositions)-1:]: state.labels[o] = state.OpenRider # Create the list of finish positions to match the event finish. self.setFinishRiders( s.finishPositions if self.heatsMax == 1 else s.continuingPositions ) return True return False #------------------------------------------------------------------------------------------------ class Competition: def __init__( self, name, systems ): self.name = name self.systems = systems self.state = State() # Check that there are no repeated labels in the spec. inLabels = set() outLabels = set() self.starters = 0 starterLabels = set() self.isMTB = 'MTB' in name self.isSprint = not self.isMTB self.isKeirin = self.isSprint and 'Keirin' in name byeEvents = set() byeOutcomeMap = {} ttCount = 0 for iSystem, system in enumerate(self.systems): rrCount = 0 for e in system.events: e.competition = self e.system = system # Assign unique outcomes for eliminated riders. if not self.isMTB: # If Track, non-winners in each round are ranked based on qualifying time only. for i, other in enumerate(e.others): if other == 'TT': ttCount += 1 e.others[i] = '{}TT'.format(ttCount) else: # If MTB, the non-winners get credit for the round and finish position. for i, other in enumerate(e.others): if other == 'TT': rrCount += 1 e.others[i] = '{}RR_{}_{}'.format(rrCount, iSystem+1, i+2) # Label is nnRR_ro_fo where nn=unique#, ro=round, fo=finishOrder print( 'Event:', ' - '.join(e.composition), ' -> ', e.winner, e.others ) for c in e.composition: assert c not in inLabels, '{}-{} c={}, outLabels={}'.format(e.competition.name, e.system.name, c, ','.join( sorted(outLabels) )) inLabels.add( c ) if c.startswith('N'): self.starters += 1 assert c[1:].isdigit(), '{}-{} Non-numeric starter reference "{}"'.format(e.competition.name, e.system.name, c) starterLabels.add( int(c[1:]) ) else: assert c in outLabels, '{}-{} Rule uses undefined input label "{}"'.format(e.competition.name, e.system.name, c) assert e.winner not in outLabels, '{}-{} winner: {}, outLabels={}'.format( e.competition.name, e.system.name, e.winner, ','.join( sorted(outLabels) )) outLabels.add( e.winner ) for c in e.others: assert c not in outLabels, '{}-{} other label: {} is already in outLabels={}'.format( e.competition.name, e.system.name, c, ','.join( outLabels )) outLabels.add( c ) assert len(outLabels) <= len(inLabels), '{}-{} len(outLabels)={} exceeds len(inLabels)={}\n {}\n {}'.format( e.competition.name, e.system.name, len(outLabels), len(inLabels), ','.join(inLabels), ','.join(outLabels) ) # Check if this is a bye Event. # We handle this by deleting the event and substituting the output value as the input in subsequent events. if not e.others: byeEvents.add( e ) byeOutcomeMap[e.winner] = e.composition[0] assert self.starters != 0, '{}-{} No starters. Check for missing N values'.format( e.competition.name, e.system.name ) assert self.starters == len(starterLabels), '{}-{} Starters reused in input'.format( e.competition.name, e.system.name ) assert self.starters == max( s for s in starterLabels), '{}-{} Starter references are not sequential'.format( e.competition.name, e.system.name ) # Process Bye events (substitute outcome into composition of subsequent events, delete bye event). # Assign indexes to each component for sorting purposes. for j, system in enumerate(self.systems): system.i = j system.events = [e for e in system.events if e not in byeEvents] for k, event in enumerate(system.events): event.i = k event.composition = [byeOutcomeMap.get(c,c) for c in event.composition] def getRelegationsWarnings( self, bib, eventCur, before=False ): relegations = 0 warnings = 0 for system, event in self.allEvents(): if before and event == eventCur: break for id in event.composition: try: if self.state.labels[id].bib == bib: for start in event.starts: if id in start.relegated: relegations += 1 if id in start.warning: warnings += 1 except KeyError: pass if event == eventCur: break return relegations, warnings def getRelegationsWarningsStr( self, bib, eventCur, before=False ): relegations, warnings = self.getRelegationsWarnings(bib, eventCur, before) s = [] if warnings: s.append( '{} {}'.format(warnings, 'Warn') ) if relegations: s.append( '{} {}'.format(relegations, 'Rel') ) return ','.join( s ) def canReassignStarters( self ): return self.state.canReassignStarters() def allEvents( self ): for system in self.systems: for event in system.events: yield system, event @property def heatsMax( self ): return max( event.heatsMax for system, event in self.allEvents() ) @property def competitionTime( self ): return None if not self.isSprint else sum( event.competitionTime for system, event in self.allEvents() ) def reset( self ): for system, event in self.allEvents(): for start in event.starts: start.resetPlaces() def __repr__( self ): out = ['***** {}'.format(self.name)] for s, e in self.allEvents(): out.append( ' '.join( [s.name, '[{}]'.format(','.join(e.composition)), ' --> ', '[{}]'.format(','.join(e.output))] ) ) return '\n'.join( out ) def fixHangingStarts( self ): for s, e in self.allEvents(): while e.starts and e.starts[-1].isHanging(): del e.starts[-1] def getCanStart( self ): return [(s, e) for s, e in self.allEvents() if e.canStart()] def propagate( self ): while True: success = False for s, e in self.allEvents(): success |= e.propagate() if not success: break labels = self.state.labels return [ labels.get('{}R'.format(r+1), None) for r in range(self.starters) ] def getRiderStates( self ): riderState = defaultdict( set ) for id, reason in self.state.noncontinue.items(): riderState[reason].add( self.state.labels[id] ) DQs = riderState['DQ'] DNSs = set( e for e in riderState['DNS'] if e not in DQs ) DNFs = set( e for e in riderState['DNF'] if e not in DNSs and e not in DQs ) return DQs, DNSs, DNFs def getResults( self ): DQs, DNSs, DNFs = self.getRiderStates() semiFinalRound, smallFinalRound, bigFinalRound = 60, 61, 62 riders = { rider for label, rider in self.state.labels.items() if label.startswith('N') } Finisher, DNF, DNS, DQ = 1, 2, 3, 4 riderStatus = { rider: (DQ if rider in DQs else DNS if rider in DNSs else DNF if rider in DNFs else Finisher) for rider in riders } statusText = { Finisher: 'Finisher', DNF: 'DNF', DNS: 'DNS', DQ: 'DQ', } if not self.isMTB: # Rank the rest of the riders based on their results in the competition. results = [None] * self.starters for i in range(self.starters): try: results[i] = self.state.labels['{}R'.format(i+1)] except KeyError: pass # Rank the remaining riders based on qualifying time (TT). iTT = self.starters tts = [rider for label, rider in self.state.labels.items() if label.endswith('TT')] tts.sort( key = lambda r: r.qualifying_time, reverse = True ) # Sort these in reverse as we assign them in from most to least. for rider in tts: iTT -= 1 results[iTT] = rider results = [('Finisher', r) for r in results if not r or not r.isOpen()] # Purge unfillable spots from the results. for r in (DNFs | DNSs | DQs): try: results.remove( (statusText[Finisher], None) ) except ValueError: break # Add the unclassifiable riders. for classification, s in (('DNF',DNFs), ('DNS',DNSs), ('DQ', DQs)): for r in sorted(s, key = lambda r: r.qualifying_time): results.append( (classification, r) ) # Purge empty results, except at the top. try: i = next( j for j, r in enumerate(results) if r[1] ) # Find first non-empty result. if i != 0: results[i:] = [r for r in results[i:] if r[1]] except StopIteration: pass # Assign classification for all finishers. results = [(p+1 if classification == 'Finisher' else classification, rider) for p, (classification, rider) in enumerate(results)] DNFs = set() DNSs = set() else: # Rank the rest of the riders based on their results also considering the result of their last round. abnormalFinishers = set() compResults = [] for system in self.systems: for event in system.events: # Get the round of the event. round = 1 if event.isSemiFinal: round = semiFinalRound elif event.isSmallFinal: round = smallFinalRound elif event.isBigFinal: round = bigFinalRound else: for id in event.output: if 'RR' in id: round = int(id.split('_')[-2]) break # Rank the finishers. rank = 0 for i, id in enumerate(event.output): try: rider = event.finishRiders[i] except IndexError: rider = None if rider in DQs: continue if id.endswith('R'): rank = int(id[:-1]) isFinish = True else: try: rank = int(id.split('_')[-1]) except ValueError: rank = i + 1 isFinish = ('RR' in id) if (isFinish and riderStatus.get(rider,1) == 1) or (round >= 1 and riderStatus.get(rider,1) != 1): if riderStatus.get(rider,1) != 1: abnormalFinishers.add( rider ) status = riderStatus.get(rider,1) statTxt = statusText[Finisher] if status != DQ and round > 1 else statusText[status] compResults.append( ( -round, status, rank, rider.qualifying_time if rider else sys.float_info.max, rider.seeding_rank if rider else 9999999, rider.bib if rider else 999999, random.random(), # Some insurance so that the sort does not fail. statusText[status], rider ) ) try: compResults.sort() except Exception as e: print( '****', self.name ) raise e results = [rr[-2:] for rr in compResults] # Adjust the available finisher positions for the abnormal finishes. for i in range(len(abnormalFinishers)): try: results.remove( (statusText[Finisher], None) ) except ValueError: break # Purge empty results, except at the top. try: i = next( j for j, r in enumerate(results) if r[1] ) # Find first non-empty result. if i != 0: results[i:] = [r for r in results[i:] if r[1]] except StopIteration: pass # Investigate later - should not have to do this! already_seen = set() results_non_duplicated = [] for classification, rider in results: if not rider or rider not in already_seen: already_seen.add( rider ) results_non_duplicated.append( (classification, rider) ) results = results_non_duplicated # Assign classification for all finishers. results = [(p+1 if classification == 'Finisher' or rider in abnormalFinishers else classification, rider) for p, (classification, rider) in enumerate(results)] DNFs = set() DNSs = set() return ( results, sorted(DNFs, key = lambda r: (r.qualifying_time, -r.uci_points, r.iSeeding)), sorted(DQs, key = lambda r: (r.qualifying_time, -r.uci_points, r.iSeeding)) ) class System: def __init__( self, name, events ): self.name = name self.events = events @property def competitionTime( self ): try: return sum( event.competitionTime for event in self.events ) except TypeError: return None class Model: communique_start = 100 modifier = 0 def __init__( self ): self.competition_name = 'My Competition' self.date = datetime.date.today() self.category = 'My Category' self.track = 'My Track' self.organizer = 'My Organizer' self.chief_official = 'My Chief Official' self.competition = None self.riders = [] self.changed = False self.showResults = 0 self.communique_number = {} @property def competitionTime( self ): try: return self.competition.competitionTime + self.qualifyingCompetitionTime except TypeError: return None @property def qualifyingCompetitionTime( self ): return None if self.competition.isMTB else len(self.riders) * Sprint200mQualificationCompetitionTime @property def isKeirin( self ): return self.competition and self.competition.isKeirin def getProperties( self ): return { a : getattr(self, a) for a in ('competition_name', 'date', 'category', 'track', 'organizer', 'chief_official') } def setProperties( self, properties ): for a, v in properties.items(): setattr(self, a, v) def updateSeeding( self ): for iSeeding, rider in enumerate(self.riders, 1): rider.iSeeding = iSeeding def getDNQs( self ): riders = sorted( self.riders, key = lambda r: r.keyQualifying() ) return riders[self.competition.starters:] def setQualifyingInfo( self ): self.updateSeeding() self.competition.state.setQualifyingInfo( self.riders, self.competition ) def canReassignStarters( self ): return self.competition.state.canReassignStarters() def setChanged( self, changed=True ): self.changed = changed def setCompetition( self, competitionNew, modifier=0 ): if self.competition.name == competitionNew.name and self.modifier == modifier: return stateSave = self.competition.state self.competition = copy.deepcopy( competitionNew ) self.competition.state = stateSave self.modifier = modifier if modifier: for system, event in self.competition.allEvents(): if modifier == 3: event.heatsMax = 1 elif modifier == 2: if '1/4' in system.name or '1/2' in system.name: event.heatsMax = 1 elif modifier == 1: if '1/4' in system.name: event.heatsMax = 1 self.setChanged( True ) model = Model()

215b72e4debb86de578af98cf8b75ba4b2979e70

py

Python

flexmeasures/data/services/forecasting.py

FlexMeasures/flexmeasures

a4367976d37ac5721b8eb3ce8a2414595e52c678

2021-12-18T10:41:10.000Z

2022-03-29T23:00:29.000Z

flexmeasures/data/services/forecasting.py

FlexMeasures/flexmeasures

a4367976d37ac5721b8eb3ce8a2414595e52c678

2021-12-07T08:51:15.000Z

2022-03-31T13:28:48.000Z

flexmeasures/data/services/forecasting.py

FlexMeasures/flexmeasures

a4367976d37ac5721b8eb3ce8a2414595e52c678

2022-01-18T04:45:48.000Z

2022-03-14T09:48:22.000Z

from datetime import datetime, timedelta from typing import List from flask import current_app import click from rq import get_current_job from rq.job import Job from timetomodel.forecasting import make_rolling_forecasts import timely_beliefs as tb from flexmeasures.data import db from flexmeasures.data.models.forecasting import lookup_model_specs_configurator from flexmeasures.data.models.forecasting.exceptions import InvalidHorizonException from flexmeasures.data.models.time_series import Sensor, TimedBelief from flexmeasures.data.models.forecasting.utils import ( get_query_window, check_data_availability, ) from flexmeasures.data.utils import get_data_source, save_to_db from flexmeasures.utils.time_utils import ( as_server_time, server_now, forecast_horizons_for, supported_horizons, ) """ The life cycle of a forecasting job: 1. A forecasting job is born in create_forecasting_jobs. 2. It is run in make_rolling_viewpoint_forecasts or make_fixed_viewpoint_forecasts, which write results to the db. This is also where model specs are configured and a possible fallback model is stored for step 3. 3. If an error occurs (and the worker is configured accordingly), handle_forecasting_exception comes in. This might re-enqueue the job or try a different model (which creates a new job). """ # TODO: we could also monitor the failed queue and re-enqueue jobs who had missing data # (and maybe failed less than three times so far) class MisconfiguredForecastingJobException(Exception): pass def create_forecasting_jobs( sensor_id: int, start_of_roll: datetime, end_of_roll: datetime, resolution: timedelta = None, horizons: List[timedelta] = None, model_search_term="linear-OLS", custom_model_params: dict = None, enqueue: bool = True, ) -> List[Job]: """Create forecasting jobs by rolling through a time window, for a number of given forecast horizons. Start and end of the forecasting jobs are equal to the time window (start_of_roll, end_of_roll) plus the horizon. For example (with shorthand notation): start_of_roll = 3pm end_of_roll = 5pm resolution = 15min horizons = [1h, 6h, 1d] This creates the following 3 jobs: 1) forecast each quarter-hour from 4pm to 6pm, i.e. the 1h forecast 2) forecast each quarter-hour from 9pm to 11pm, i.e. the 6h forecast 3) forecast each quarter-hour from 3pm to 5pm the next day, i.e. the 1d forecast If not given, relevant horizons are derived from the resolution of the posted data. The job needs a model configurator, for which you can supply a model search term. If omitted, the current default model configuration will be used. It's possible to customize model parameters, but this feature is (currently) meant to only be used by tests, so that model behaviour can be adapted to test conditions. If used outside of testing, an exception is raised. if enqueue is True (default), the jobs are put on the redis queue. Returns the redis-queue forecasting jobs which were created. """ if not current_app.testing and custom_model_params is not None: raise MisconfiguredForecastingJobException( "Model parameters can only be customized during testing." ) if horizons is None: if resolution is None: raise MisconfiguredForecastingJobException( "Cannot create forecasting jobs - set either horizons or resolution." ) horizons = forecast_horizons_for(resolution) jobs: List[Job] = [] for horizon in horizons: job = Job.create( make_rolling_viewpoint_forecasts, kwargs=dict( sensor_id=sensor_id, horizon=horizon, start=start_of_roll + horizon, end=end_of_roll + horizon, custom_model_params=custom_model_params, ), connection=current_app.queues["forecasting"].connection, ttl=int( current_app.config.get( "FLEXMEASURES_JOB_TTL", timedelta(-1) ).total_seconds() ), ) job.meta["model_search_term"] = model_search_term job.save_meta() jobs.append(job) if enqueue: current_app.queues["forecasting"].enqueue_job(job) return jobs def make_fixed_viewpoint_forecasts( sensor_id: int, horizon: timedelta, start: datetime, end: datetime, custom_model_params: dict = None, ) -> int: """Build forecasting model specs, make fixed-viewpoint forecasts, and save the forecasts made. Each individual forecast is a belief about a time interval. Fixed-viewpoint forecasts share the same belief time. See the timely-beliefs lib for relevant terminology. """ # todo: implement fixed-viewpoint forecasts raise NotImplementedError def make_rolling_viewpoint_forecasts( sensor_id: int, horizon: timedelta, start: datetime, end: datetime, custom_model_params: dict = None, ) -> int: """Build forecasting model specs, make rolling-viewpoint forecasts, and save the forecasts made. Each individual forecast is a belief about a time interval. Rolling-viewpoint forecasts share the same belief horizon (the duration between belief time and knowledge time). Model specs are also retrained in a rolling fashion, but with its own frequency set in custom_model_params. See the timely-beliefs lib for relevant terminology. Parameters ---------- :param sensor_id: int To identify which sensor to forecast :param horizon: timedelta duration between the end of each interval and the time at which the belief about that interval is formed :param start: datetime start of forecast period, i.e. start time of the first interval to be forecast :param end: datetime end of forecast period, i.e end time of the last interval to be forecast :param custom_model_params: dict pass in params which will be passed to the model specs configurator, e.g. outcome_var_transformation, only advisable to be used for testing. :returns: int the number of forecasts made """ # https://docs.sqlalchemy.org/en/13/faq/connections.html#how-do-i-use-engines-connections-sessions-with-python-multiprocessing-or-os-fork db.engine.dispose() rq_job = get_current_job() # find out which model to run, fall back to latest recommended model_search_term = rq_job.meta.get("model_search_term", "linear-OLS") # find sensor sensor = Sensor.query.filter_by(id=sensor_id).one_or_none() click.echo( "Running Forecasting Job %s: %s for %s on model '%s', from %s to %s" % (rq_job.id, sensor, horizon, model_search_term, start, end) ) if hasattr(sensor, "market_type"): ex_post_horizon = None # Todo: until we sorted out the ex_post_horizon, use all available price data else: ex_post_horizon = timedelta(hours=0) # Make model specs model_configurator = lookup_model_specs_configurator(model_search_term) model_specs, model_identifier, fallback_model_search_term = model_configurator( sensor=sensor, forecast_start=as_server_time(start), forecast_end=as_server_time(end), forecast_horizon=horizon, ex_post_horizon=ex_post_horizon, custom_model_params=custom_model_params, ) model_specs.creation_time = server_now() rq_job.meta["model_identifier"] = model_identifier rq_job.meta["fallback_model_search_term"] = fallback_model_search_term rq_job.save() # before we run the model, check if horizon is okay and enough data is available if horizon not in supported_horizons(): raise InvalidHorizonException( "Invalid horizon on job %s: %s" % (rq_job.id, horizon) ) query_window = get_query_window( model_specs.start_of_training, end, [lag * model_specs.frequency for lag in model_specs.lags], ) check_data_availability( sensor, TimedBelief, start, end, query_window, horizon, ) data_source = get_data_source( data_source_name="Seita (%s)" % rq_job.meta.get("model_identifier", "unknown model"), data_source_type="forecasting script", ) forecasts, model_state = make_rolling_forecasts( start=as_server_time(start), end=as_server_time(end), model_specs=model_specs, ) click.echo("Job %s made %d forecasts." % (rq_job.id, len(forecasts))) ts_value_forecasts = [ TimedBelief( event_start=dt, belief_horizon=horizon, event_value=value, sensor=sensor, source=data_source, ) for dt, value in forecasts.items() ] bdf = tb.BeliefsDataFrame(ts_value_forecasts) save_to_db(bdf) db.session.commit() return len(forecasts) def handle_forecasting_exception(job, exc_type, exc_value, traceback): """ Decide if we can do something about this failure: * Try a different model * Re-queue at a later time (using rq_scheduler) """ click.echo("HANDLING RQ WORKER EXCEPTION: %s:%s\n" % (exc_type, exc_value)) if "failures" not in job.meta: job.meta["failures"] = 1 else: job.meta["failures"] = job.meta["failures"] + 1 job.save_meta() # We might use this to decide if we want to re-queue a failed job # if job.meta['failures'] < 3: # job.queue.failures.requeue(job) # TODO: use this to add more meta information? # if exc_type == NotEnoughDataException: if "fallback_model_search_term" in job.meta: if job.meta["fallback_model_search_term"] is not None: new_job = Job.create( make_rolling_viewpoint_forecasts, args=job.args, kwargs=job.kwargs, connection=current_app.queues["forecasting"].connection, ) new_job.meta["model_search_term"] = job.meta["fallback_model_search_term"] new_job.save_meta() current_app.queues["forecasting"].enqueue_job(new_job) def num_forecasts(start: datetime, end: datetime, resolution: timedelta) -> int: """Compute how many forecasts a job needs to make, given a resolution""" return (end - start) // resolution

488e77b23ededf6682d54cf9c6cd3333931c6978

py

Python

solutions/Course Schedule II/solution.py

nilax97/leetcode-solutions

d3c12f2b289662d199510e0431e177bbf3cda121

2021-06-06T22:03:15.000Z

2021-06-08T08:49:04.000Z

solutions/Course Schedule II/solution.py

nilax97/leetcode-solutions

d3c12f2b289662d199510e0431e177bbf3cda121

solutions/Course Schedule II/solution.py

nilax97/leetcode-solutions

d3c12f2b289662d199510e0431e177bbf3cda121

class Solution: def findOrder(self, numCourses: int, prerequisites: List[List[int]]) -> List[int]: order = [] valid = [1] * numCourses val = dict() for x in prerequisites: if x[0] in val: val[x[0]].append(x[1]) else: val[x[0]] = [x[1]] valid[x[0]] = 0 for i in range(numCourses): if valid[i] == 1: order.append(i) while(True): change = 0 for i in range(numCourses): random = 0 if valid[i] == 1: continue for x in val[i]: if valid[x] == 0: random = 1 break if random == 0: change += 1 valid[i] = 1 order.append(i) if change == 0: break for x in valid: if x==0: return [] return order

e4a6ec94830031686a58be56fd37245202151992

py

Python

devilry/devilry_compressionutil/backend_registry.py

aless80/devilry-django

416c262e75170d5662542f15e2d7fecf5ab84730

2015-01-18T22:56:23.000Z

2020-11-10T21:28:27.000Z

devilry/devilry_compressionutil/backend_registry.py

aless80/devilry-django

416c262e75170d5662542f15e2d7fecf5ab84730

2015-01-06T16:10:18.000Z

2022-03-16T11:10:50.000Z

devilry/devilry_compressionutil/backend_registry.py

aless80/devilry-django

416c262e75170d5662542f15e2d7fecf5ab84730

2015-04-06T06:18:43.000Z

2021-02-24T12:28:30.000Z

from ievv_opensource.utils.singleton import Singleton class DuplicateBackendTypeError(Exception): """ Exception raised when trying to add multiple :class:`.~devilry.devilry_ziputil.backends.PythonZipFileBackend` with same ID. """ class Registry(Singleton): """ Registry for subclasses of :class:`~devilry.devilry_ziputil.backends.backends_base.PythonZipFileBackend`. """ def __init__(self): super(Registry, self).__init__() self._backendclasses = {} def __get_class_path(self): """ Get class path. Returns: Classpath. """ return '{}.{}'.format(self.__module__, self.__class__.__name__) def add(self, backend): """ Add a backend class. Args: backend: backend class. """ if backend.backend_id in self._backendclasses: raise DuplicateBackendTypeError('Duplicate backend id in {}: {}'.format( self.__get_class_path(), backend.backend_id )) self._backendclasses[backend.backend_id] = backend def get(self, backend_id): """ Get backend class. Args: backend_id: ID of backend class. Returns: :class:`~devilry.devilry_ziputil.backends.backends_base.PythonZipFileBackend` subclass or ``None``. """ try: backend_class = self._backendclasses[backend_id] except KeyError: return None return backend_class class MockableRegistry(Registry): """ A non-singleton version of :class:`.Registry` for tests. """ def __init__(self): self._instance = None super(MockableRegistry, self).__init__() @classmethod def make_mockregistry(cls, *backend_classes): """ Create a mocked instance of Registry. Args: *backend_classes: Backends to add. Returns: MockableRegistry: An object of this class with the ``backend_classes`` registered. """ mockregistry = cls() for backend_class in backend_classes: mockregistry.add(backend_class) return mockregistry

05a7a92177c638b16e713c15b994403bf349ee36

py

Python

platform/gsutil/gslib/tests/test_ls.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

platform/gsutil/gslib/tests/test_ls.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

platform/gsutil/gslib/tests/test_ls.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

2020-11-04T03:08:21.000Z

2020-11-05T08:14:41.000Z

# -*- coding: utf-8 -*- # Copyright 2013 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for ls command.""" from __future__ import absolute_import import posixpath import re import subprocess import sys import gslib from gslib.cs_api_map import ApiSelector import gslib.tests.testcase as testcase from gslib.tests.testcase.integration_testcase import SkipForS3 from gslib.tests.util import ObjectToURI as suri from gslib.tests.util import SetBotoConfigForTest from gslib.tests.util import TEST_ENCRYPTION_CONTENT1 from gslib.tests.util import TEST_ENCRYPTION_CONTENT1_CRC32C from gslib.tests.util import TEST_ENCRYPTION_CONTENT1_MD5 from gslib.tests.util import TEST_ENCRYPTION_CONTENT2 from gslib.tests.util import TEST_ENCRYPTION_CONTENT2_CRC32C from gslib.tests.util import TEST_ENCRYPTION_CONTENT2_MD5 from gslib.tests.util import TEST_ENCRYPTION_CONTENT3 from gslib.tests.util import TEST_ENCRYPTION_CONTENT3_CRC32C from gslib.tests.util import TEST_ENCRYPTION_CONTENT3_MD5 from gslib.tests.util import TEST_ENCRYPTION_CONTENT4 from gslib.tests.util import TEST_ENCRYPTION_CONTENT4_CRC32C from gslib.tests.util import TEST_ENCRYPTION_CONTENT4_MD5 from gslib.tests.util import TEST_ENCRYPTION_CONTENT5 from gslib.tests.util import TEST_ENCRYPTION_CONTENT5_CRC32C from gslib.tests.util import TEST_ENCRYPTION_CONTENT5_MD5 from gslib.tests.util import TEST_ENCRYPTION_KEY1 from gslib.tests.util import TEST_ENCRYPTION_KEY1_SHA256_B64 from gslib.tests.util import TEST_ENCRYPTION_KEY2 from gslib.tests.util import TEST_ENCRYPTION_KEY2_SHA256_B64 from gslib.tests.util import TEST_ENCRYPTION_KEY3 from gslib.tests.util import TEST_ENCRYPTION_KEY3_SHA256_B64 from gslib.tests.util import TEST_ENCRYPTION_KEY4 from gslib.tests.util import TEST_ENCRYPTION_KEY4_SHA256_B64 from gslib.tests.util import unittest from gslib.util import IS_WINDOWS from gslib.util import Retry from gslib.util import UTF8 class TestLs(testcase.GsUtilIntegrationTestCase): """Integration tests for ls command.""" def test_blank_ls(self): self.RunGsUtil(['ls']) def test_empty_bucket(self): bucket_uri = self.CreateBucket() self.AssertNObjectsInBucket(bucket_uri, 0) def test_empty_bucket_with_b(self): bucket_uri = self.CreateBucket() # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check1(): stdout = self.RunGsUtil(['ls', '-b', suri(bucket_uri)], return_stdout=True) self.assertEqual('%s/\n' % suri(bucket_uri), stdout) _Check1() def test_bucket_with_Lb(self): """Tests ls -Lb.""" bucket_uri = self.CreateBucket() # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check1(): stdout = self.RunGsUtil(['ls', '-Lb', suri(bucket_uri)], return_stdout=True) self.assertIn(suri(bucket_uri), stdout) self.assertNotIn('TOTAL:', stdout) _Check1() def test_bucket_with_lb(self): """Tests ls -lb.""" bucket_uri = self.CreateBucket() # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check1(): stdout = self.RunGsUtil(['ls', '-lb', suri(bucket_uri)], return_stdout=True) self.assertIn(suri(bucket_uri), stdout) self.assertNotIn('TOTAL:', stdout) _Check1() def test_bucket_list_wildcard(self): """Tests listing multiple buckets with a wildcard.""" random_prefix = self.MakeRandomTestString() bucket1_name = self.MakeTempName('bucket', prefix=random_prefix) bucket2_name = self.MakeTempName('bucket', prefix=random_prefix) bucket1_uri = self.CreateBucket(bucket_name=bucket1_name) bucket2_uri = self.CreateBucket(bucket_name=bucket2_name) # This just double checks that the common prefix of the two buckets is what # we think it should be (based on implementation detail of CreateBucket). # We want to be careful when setting a wildcard on buckets to make sure we # don't step outside the test buckets to affect other buckets. common_prefix = posixpath.commonprefix([suri(bucket1_uri), suri(bucket2_uri)]) self.assertTrue(common_prefix.startswith( '%s://%sgsutil-test-test_bucket_list_wildcard-bucket-' % (self.default_provider, random_prefix))) wildcard = '%s*' % common_prefix # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check1(): stdout = self.RunGsUtil(['ls', '-b', wildcard], return_stdout=True) expected = set([suri(bucket1_uri) + '/', suri(bucket2_uri) + '/']) actual = set(stdout.split()) self.assertEqual(expected, actual) _Check1() def test_nonexistent_bucket_with_ls(self): """Tests a bucket that is known not to exist.""" stderr = self.RunGsUtil( ['ls', '-lb', 'gs://%s' % self.nonexistent_bucket_name], return_stderr=True, expected_status=1) self.assertIn('404', stderr) stderr = self.RunGsUtil( ['ls', '-Lb', 'gs://%s' % self.nonexistent_bucket_name], return_stderr=True, expected_status=1) self.assertIn('404', stderr) stderr = self.RunGsUtil( ['ls', '-b', 'gs://%s' % self.nonexistent_bucket_name], return_stderr=True, expected_status=1) self.assertIn('404', stderr) def test_list_missing_object(self): """Tests listing a non-existent object.""" bucket_uri = self.CreateBucket() stderr = self.RunGsUtil(['ls', suri(bucket_uri, 'missing')], return_stderr=True, expected_status=1) self.assertIn('matched no objects', stderr) def test_with_one_object(self): bucket_uri = self.CreateBucket() obj_uri = self.CreateObject(bucket_uri=bucket_uri, contents='foo') # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check1(): stdout = self.RunGsUtil(['ls', suri(bucket_uri)], return_stdout=True) self.assertEqual('%s\n' % obj_uri, stdout) _Check1() def test_subdir(self): """Tests listing a bucket subdirectory.""" bucket_uri = self.CreateBucket(test_objects=1) k1_uri = bucket_uri.clone_replace_name('foo') k1_uri.set_contents_from_string('baz') k2_uri = bucket_uri.clone_replace_name('dir/foo') k2_uri.set_contents_from_string('bar') # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check1(): stdout = self.RunGsUtil(['ls', '%s/dir' % suri(bucket_uri)], return_stdout=True) self.assertEqual('%s\n' % suri(k2_uri), stdout) stdout = self.RunGsUtil(['ls', suri(k1_uri)], return_stdout=True) self.assertEqual('%s\n' % suri(k1_uri), stdout) _Check1() def test_subdir_nocontents(self): """Tests listing a bucket subdirectory using -d. Result will display subdirectory names instead of contents. Uses a wildcard to show multiple matching subdirectories. """ bucket_uri = self.CreateBucket(test_objects=1) k1_uri = bucket_uri.clone_replace_name('foo') k1_uri.set_contents_from_string('baz') k2_uri = bucket_uri.clone_replace_name('dir/foo') k2_uri.set_contents_from_string('bar') k3_uri = bucket_uri.clone_replace_name('dir/foo2') k3_uri.set_contents_from_string('foo') k4_uri = bucket_uri.clone_replace_name('dir2/foo3') k4_uri.set_contents_from_string('foo2') # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check1(): stdout = self.RunGsUtil(['ls', '-d', '%s/dir*' % suri(bucket_uri)], return_stdout=True) self.assertEqual('%s/dir/\n%s/dir2/\n' % (suri(bucket_uri), suri(bucket_uri)), stdout) stdout = self.RunGsUtil(['ls', suri(k1_uri)], return_stdout=True) self.assertEqual('%s\n' % suri(k1_uri), stdout) _Check1() def test_versioning(self): """Tests listing a versioned bucket.""" bucket1_uri = self.CreateBucket(test_objects=1) bucket2_uri = self.CreateVersionedBucket(test_objects=1) self.AssertNObjectsInBucket(bucket1_uri, 1, versioned=True) bucket_list = list(bucket1_uri.list_bucket()) objuri = [bucket1_uri.clone_replace_key(key).versionless_uri for key in bucket_list][0] self.RunGsUtil(['cp', objuri, suri(bucket2_uri)]) self.RunGsUtil(['cp', objuri, suri(bucket2_uri)]) # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check2(): stdout = self.RunGsUtil(['ls', '-a', suri(bucket2_uri)], return_stdout=True) self.assertNumLines(stdout, 3) stdout = self.RunGsUtil(['ls', '-la', suri(bucket2_uri)], return_stdout=True) self.assertIn('%s#' % bucket2_uri.clone_replace_name(bucket_list[0].name), stdout) self.assertIn('metageneration=', stdout) _Check2() def test_etag(self): """Tests that listing an object with an etag.""" bucket_uri = self.CreateBucket() obj_uri = self.CreateObject(bucket_uri=bucket_uri, contents='foo') # TODO: When testcase setup can use JSON, match against the exact JSON # etag. etag = obj_uri.get_key().etag.strip('"\'') # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check1(): stdout = self.RunGsUtil(['ls', '-l', suri(bucket_uri)], return_stdout=True) if self.test_api == ApiSelector.XML: self.assertNotIn(etag, stdout) else: self.assertNotIn('etag=', stdout) _Check1() def _Check2(): stdout = self.RunGsUtil(['ls', '-le', suri(bucket_uri)], return_stdout=True) if self.test_api == ApiSelector.XML: self.assertIn(etag, stdout) else: self.assertIn('etag=', stdout) _Check2() def _Check3(): stdout = self.RunGsUtil(['ls', '-ale', suri(bucket_uri)], return_stdout=True) if self.test_api == ApiSelector.XML: self.assertIn(etag, stdout) else: self.assertIn('etag=', stdout) _Check3() @SkipForS3('S3 bucket configuration values are not supported via ls.') def test_location(self): """Tests listing a bucket with location constraint.""" bucket_uri = self.CreateBucket() bucket_suri = suri(bucket_uri) # No location info stdout = self.RunGsUtil(['ls', '-lb', bucket_suri], return_stdout=True) self.assertNotIn('Location constraint', stdout) # Default location constraint is US stdout = self.RunGsUtil(['ls', '-Lb', bucket_suri], return_stdout=True) self.assertIn('Location constraint:\t\tUS', stdout) @SkipForS3('S3 bucket configuration values are not supported via ls.') def test_logging(self): """Tests listing a bucket with logging config.""" bucket_uri = self.CreateBucket() bucket_suri = suri(bucket_uri) # No logging info stdout = self.RunGsUtil(['ls', '-lb', bucket_suri], return_stdout=True) self.assertNotIn('Logging configuration', stdout) # Logging configuration is absent by default stdout = self.RunGsUtil(['ls', '-Lb', bucket_suri], return_stdout=True) self.assertIn('Logging configuration:\t\tNone', stdout) # Enable and check self.RunGsUtil(['logging', 'set', 'on', '-b', bucket_suri, bucket_suri]) stdout = self.RunGsUtil(['ls', '-Lb', bucket_suri], return_stdout=True) self.assertIn('Logging configuration:\t\tPresent', stdout) # Disable and check self.RunGsUtil(['logging', 'set', 'off', bucket_suri]) stdout = self.RunGsUtil(['ls', '-Lb', bucket_suri], return_stdout=True) self.assertIn('Logging configuration:\t\tNone', stdout) @SkipForS3('S3 bucket configuration values are not supported via ls.') def test_web(self): """Tests listing a bucket with website config.""" bucket_uri = self.CreateBucket() bucket_suri = suri(bucket_uri) # No website configuration stdout = self.RunGsUtil(['ls', '-lb', bucket_suri], return_stdout=True) self.assertNotIn('Website configuration', stdout) # Website configuration is absent by default stdout = self.RunGsUtil(['ls', '-Lb', bucket_suri], return_stdout=True) self.assertIn('Website configuration:\t\tNone', stdout) # Initialize and check self.RunGsUtil(['web', 'set', '-m', 'google.com', bucket_suri]) stdout = self.RunGsUtil(['ls', '-Lb', bucket_suri], return_stdout=True) self.assertIn('Website configuration:\t\tPresent', stdout) # Clear and check self.RunGsUtil(['web', 'set', bucket_suri]) stdout = self.RunGsUtil(['ls', '-Lb', bucket_suri], return_stdout=True) self.assertIn('Website configuration:\t\tNone', stdout) def test_list_sizes(self): """Tests various size listing options.""" bucket_uri = self.CreateBucket() self.CreateObject(bucket_uri=bucket_uri, contents='x' * 2048) # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check1(): stdout = self.RunGsUtil(['ls', '-l', suri(bucket_uri)], return_stdout=True) self.assertIn('2048', stdout) _Check1() # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check2(): stdout = self.RunGsUtil(['ls', '-L', suri(bucket_uri)], return_stdout=True) self.assertIn('2048', stdout) _Check2() # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check3(): stdout = self.RunGsUtil(['ls', '-al', suri(bucket_uri)], return_stdout=True) self.assertIn('2048', stdout) _Check3() # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check4(): stdout = self.RunGsUtil(['ls', '-lh', suri(bucket_uri)], return_stdout=True) self.assertIn('2 KiB', stdout) _Check4() # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check5(): stdout = self.RunGsUtil(['ls', '-alh', suri(bucket_uri)], return_stdout=True) self.assertIn('2 KiB', stdout) _Check5() @unittest.skipIf(IS_WINDOWS, 'Unicode handling on Windows requires mods to site-packages') def test_list_unicode_filename(self): """Tests listing an object with a unicode filename.""" # Note: This test fails on Windows (command.exe). I was able to get ls to # output Unicode filenames correctly by hacking the UniStream class code # shown at # http://stackoverflow.com/questions/878972/windows-cmd-encoding-change-causes-python-crash/3259271 # into the start of gslib/commands/ls.py, along with no-op flush and # isastream functions (as an experiment). However, even with that change, # the current test still fails, since it also needs to run that # stdout/stderr-replacement code. That UniStream class replacement really # needs to be added to the site-packages on Windows python. object_name = u'Аудиоархив' object_name_bytes = object_name.encode(UTF8) bucket_uri = self.CreateVersionedBucket() key_uri = self.CreateObject(bucket_uri=bucket_uri, contents='foo', object_name=object_name) self.AssertNObjectsInBucket(bucket_uri, 1, versioned=True) stdout = self.RunGsUtil(['ls', '-ael', suri(key_uri)], return_stdout=True) self.assertIn(object_name_bytes, stdout) if self.default_provider == 'gs': self.assertIn(str(key_uri.generation), stdout) self.assertIn( 'metageneration=%s' % key_uri.get_key().metageneration, stdout) if self.test_api == ApiSelector.XML: self.assertIn(key_uri.get_key().etag.strip('"\''), stdout) else: # TODO: When testcase setup can use JSON, match against the exact JSON # etag. self.assertIn('etag=', stdout) elif self.default_provider == 's3': self.assertIn(key_uri.version_id, stdout) self.assertIn(key_uri.get_key().etag.strip('"\''), stdout) def test_list_acl(self): """Tests that long listing includes an ACL.""" key_uri = self.CreateObject(contents='foo') stdout = self.RunGsUtil(['ls', '-L', suri(key_uri)], return_stdout=True) self.assertIn('ACL:', stdout) self.assertNotIn('ACCESS DENIED', stdout) def test_list_gzip_content_length(self): """Tests listing a gzipped object.""" file_size = 10000 file_contents = 'x' * file_size fpath = self.CreateTempFile(contents=file_contents, file_name='foo.txt') key_uri = self.CreateObject() self.RunGsUtil(['cp', '-z', 'txt', suri(fpath), suri(key_uri)]) # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check1(): stdout = self.RunGsUtil(['ls', '-L', suri(key_uri)], return_stdout=True) self.assertRegexpMatches(stdout, r'Content-Encoding:\s+gzip') find_content_length_re = r'Content-Length:\s+(?P<num>\d)' self.assertRegexpMatches(stdout, find_content_length_re) m = re.search(find_content_length_re, stdout) content_length = int(m.group('num')) self.assertGreater(content_length, 0) self.assertLess(content_length, file_size) _Check1() def test_output_chopped(self): """Tests that gsutil still succeeds with a truncated stdout.""" bucket_uri = self.CreateBucket(test_objects=2) # Run Python with the -u flag so output is not buffered. gsutil_cmd = [ sys.executable, '-u', gslib.GSUTIL_PATH, 'ls', suri(bucket_uri)] # Set bufsize to 0 to make sure output is not buffered. p = subprocess.Popen(gsutil_cmd, stdout=subprocess.PIPE, bufsize=0) # Immediately close the stdout pipe so that gsutil gets a broken pipe error. p.stdout.close() p.wait() # Make sure it still exited cleanly. self.assertEqual(p.returncode, 0) def test_recursive_list_trailing_slash(self): """Tests listing an object with a trailing slash.""" bucket_uri = self.CreateBucket() self.CreateObject(bucket_uri=bucket_uri, object_name='/', contents='foo') self.AssertNObjectsInBucket(bucket_uri, 1) stdout = self.RunGsUtil(['ls', '-R', suri(bucket_uri)], return_stdout=True) # Note: The suri function normalizes the URI, so the double slash gets # removed. self.assertIn(suri(bucket_uri) + '/', stdout) def test_recursive_list_trailing_two_slash(self): """Tests listing an object with two trailing slashes.""" bucket_uri = self.CreateBucket() self.CreateObject(bucket_uri=bucket_uri, object_name='//', contents='foo') self.AssertNObjectsInBucket(bucket_uri, 1) stdout = self.RunGsUtil(['ls', '-R', suri(bucket_uri)], return_stdout=True) # Note: The suri function normalizes the URI, so the double slash gets # removed. self.assertIn(suri(bucket_uri) + '//', stdout) def test_wildcard_prefix(self): """Tests that an object name with a wildcard does not infinite loop.""" bucket_uri = self.CreateBucket() wildcard_folder_object = 'wildcard*/' object_matching_folder = 'wildcard10/foo' self.CreateObject(bucket_uri=bucket_uri, object_name=wildcard_folder_object, contents='foo') self.CreateObject(bucket_uri=bucket_uri, object_name=object_matching_folder, contents='foo') self.AssertNObjectsInBucket(bucket_uri, 2) stderr = self.RunGsUtil(['ls', suri(bucket_uri, 'wildcard*')], return_stderr=True, expected_status=1) self.assertIn('Cloud folder %s%s contains a wildcard' % (suri(bucket_uri), '/wildcard*/'), stderr) # Listing with a flat wildcard should still succeed. # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _Check(): stdout = self.RunGsUtil(['ls', '-l', suri(bucket_uri, '**')], return_stdout=True) self.assertNumLines(stdout, 3) # 2 object lines, one summary line. _Check() @SkipForS3('S3 anonymous access is not supported.') def test_get_object_without_list_bucket_permission(self): # Bucket is not publicly readable by default. bucket_uri = self.CreateBucket() object_uri = self.CreateObject(bucket_uri=bucket_uri, object_name='permitted', contents='foo') # Set this object to be publicly readable. self.RunGsUtil(['acl', 'set', 'public-read', suri(object_uri)]) # Drop credentials. with self.SetAnonymousBotoCreds(): stdout = self.RunGsUtil(['ls', '-L', suri(object_uri)], return_stdout=True) self.assertIn(suri(object_uri), stdout) @SkipForS3('S3 customer-supplied encryption keys are not supported.') def test_list_encrypted_object(self): if self.test_api == ApiSelector.XML: return unittest.skip( 'gsutil does not support encryption with the XML API') object_uri = self.CreateObject(object_name='foo', contents=TEST_ENCRYPTION_CONTENT1, encryption_key=TEST_ENCRYPTION_KEY1) # Listing object with key should return unencrypted hashes. with SetBotoConfigForTest([ ('GSUtil', 'encryption_key', TEST_ENCRYPTION_KEY1)]): # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _ListExpectDecrypted(): stdout = self.RunGsUtil(['ls', '-L', suri(object_uri)], return_stdout=True) self.assertIn(TEST_ENCRYPTION_CONTENT1_MD5, stdout) self.assertIn(TEST_ENCRYPTION_CONTENT1_CRC32C, stdout) self.assertIn(TEST_ENCRYPTION_KEY1_SHA256_B64, stdout) _ListExpectDecrypted() # Listing object without a key should return encrypted hashes. # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _ListExpectEncrypted(): stdout = self.RunGsUtil(['ls', '-L', suri(object_uri)], return_stdout=True) self.assertNotIn(TEST_ENCRYPTION_CONTENT1_MD5, stdout) self.assertNotIn(TEST_ENCRYPTION_CONTENT1_CRC32C, stdout) self.assertIn('encrypted', stdout) self.assertIn(TEST_ENCRYPTION_KEY1_SHA256_B64, stdout) _ListExpectEncrypted() # Listing object with a non-matching key should return encrypted hashes. with SetBotoConfigForTest([ ('GSUtil', 'encryption_key', TEST_ENCRYPTION_KEY2)]): _ListExpectEncrypted() @SkipForS3('S3 customer-supplied encryption keys are not supported.') def test_list_mixed_encryption(self): """Tests listing objects with various encryption interactions.""" bucket_uri = self.CreateBucket() self.CreateObject( bucket_uri=bucket_uri, object_name='foo', contents=TEST_ENCRYPTION_CONTENT1, encryption_key=TEST_ENCRYPTION_KEY1) self.CreateObject( bucket_uri=bucket_uri, object_name='foo2', contents=TEST_ENCRYPTION_CONTENT2, encryption_key=TEST_ENCRYPTION_KEY2) self.CreateObject( bucket_uri=bucket_uri, object_name='foo3', contents=TEST_ENCRYPTION_CONTENT3, encryption_key=TEST_ENCRYPTION_KEY3) self.CreateObject( bucket_uri=bucket_uri, object_name='foo4', contents=TEST_ENCRYPTION_CONTENT4, encryption_key=TEST_ENCRYPTION_KEY4) self.CreateObject( bucket_uri=bucket_uri, object_name='foo5', contents=TEST_ENCRYPTION_CONTENT5) # List 5 objects, one encrypted with each of four keys, and one # unencrypted. Supplying keys [1,3,4] should result in four unencrypted # listings and one encrypted listing (for key 2). with SetBotoConfigForTest([ ('GSUtil', 'encryption_key', TEST_ENCRYPTION_KEY1), ('GSUtil', 'decryption_key1', TEST_ENCRYPTION_KEY3), ('GSUtil', 'decryption_key2', TEST_ENCRYPTION_KEY4) ]): # Use @Retry as hedge against bucket listing eventual consistency. @Retry(AssertionError, tries=3, timeout_secs=1) def _ListExpectMixed(): """Validates object listing.""" stdout = self.RunGsUtil(['ls', '-L', suri(bucket_uri)], return_stdout=True) self.assertIn(TEST_ENCRYPTION_CONTENT1_MD5, stdout) self.assertIn(TEST_ENCRYPTION_CONTENT1_CRC32C, stdout) self.assertIn(TEST_ENCRYPTION_KEY1_SHA256_B64, stdout) self.assertNotIn(TEST_ENCRYPTION_CONTENT2_MD5, stdout) self.assertNotIn(TEST_ENCRYPTION_CONTENT2_CRC32C, stdout) self.assertIn('encrypted', stdout) self.assertIn(TEST_ENCRYPTION_KEY2_SHA256_B64, stdout) self.assertIn(TEST_ENCRYPTION_CONTENT3_MD5, stdout) self.assertIn(TEST_ENCRYPTION_CONTENT3_CRC32C, stdout) self.assertIn(TEST_ENCRYPTION_KEY3_SHA256_B64, stdout) self.assertIn(TEST_ENCRYPTION_CONTENT4_MD5, stdout) self.assertIn(TEST_ENCRYPTION_CONTENT4_CRC32C, stdout) self.assertIn(TEST_ENCRYPTION_KEY4_SHA256_B64, stdout) self.assertIn(TEST_ENCRYPTION_CONTENT5_MD5, stdout) self.assertIn(TEST_ENCRYPTION_CONTENT5_CRC32C, stdout) _ListExpectMixed()

2b94621993cb2121bde7c9ee5c6588cca9078f30

py

Python

src/tarski/fstrips/visitors.py

phoeft670/tarski

7d955e535fbbca012bfd1a12402b97febc6b35b9

2018-11-26T20:31:04.000Z

2021-12-29T11:08:40.000Z

src/tarski/fstrips/visitors.py

phoeft670/tarski

7d955e535fbbca012bfd1a12402b97febc6b35b9

2018-06-07T13:10:01.000Z

2022-03-11T11:54:00.000Z

src/tarski/fstrips/visitors.py

phoeft670/tarski

7d955e535fbbca012bfd1a12402b97febc6b35b9

2018-11-01T22:44:39.000Z

2022-02-28T04:57:15.000Z

""" Visitors implementing diverse aspects of FSTRIPS problems translation, analysis and compilation. """ from typing import Set from ..syntax.symrefs import TermReference from ..syntax.temporal import ltl from ..syntax.formulas import CompoundFormula, Atom, QuantifiedFormula from ..syntax.terms import CompoundTerm from ..syntax import symref class FluentHeuristic: action_effects = 1 precondition = 2 constraint = 3 class FluentSymbolCollector: """ This visitor collects CompoundTerms which are candidates to become state variables. """ def __init__(self, lang, fluents, statics, mode: FluentHeuristic): self.mode = mode self.lang = lang self.fluents = fluents self.statics = statics self.under_next = False self.visited = set() # type: Set[TermReference] def reset(self): self.visited = set() def _visit_action_effect_formula(self, phi): if isinstance(phi, CompoundFormula): _ = [self.visit(f) for f in phi.subformulas] elif isinstance(phi, QuantifiedFormula): self.visit(phi.formula) elif isinstance(phi, Atom): if not phi.predicate.builtin: self.fluents.add(symref(phi)) else: _ = [self.visit(f) for f in phi.subterms] elif isinstance(phi, CompoundTerm): # print("Compound Term: {}, {}, {}".format(str(phi), phi.symbol, phi.symbol.builtin)) if not phi.symbol.builtin: self.fluents.add(symref(phi)) else: _ = [self.visit(f) for f in phi.subterms] def _visit_constraint_formula(self, phi): if isinstance(phi, ltl.TemporalCompoundFormula) and phi.connective == ltl.TemporalConnective.X: old_value = self.under_next self.under_next = True _ = [self.visit(f) for f in phi.subformulas] self.under_next = old_value elif isinstance(phi, CompoundFormula): old_visited = self.visited.copy() _ = [self.visit(f) for f in phi.subformulas] delta = self.visited - old_visited # print('Fluents: {}'.format([str(x) for x in self.fluents])) # print('Delta: {}'.format([str(x) for x in delta])) if any(f in self.fluents for f in delta): # print("Fluency propagates") for f in delta: self.fluents.add(f) elif isinstance(phi, QuantifiedFormula): self.visit(phi.formula) elif isinstance(phi, Atom): if not phi.predicate.builtin: self.visited.add(symref(phi)) if self.under_next: if not phi.predicate.builtin: self.fluents.add(symref(phi)) else: self.statics.add(symref(phi)) _ = [self.visit(f) for f in phi.subterms] elif isinstance(phi, CompoundTerm): if not phi.symbol.builtin: self.visited.add(symref(phi)) if self.under_next: if not phi.symbol.builtin: self.fluents.add(symref(phi)) else: self.statics.add(symref(phi)) _ = [self.visit(f) for f in phi.subterms] def _visit_precondition_formula(self, phi): if isinstance(phi, CompoundFormula): _ = [self.visit(f) for f in phi.subformulas] elif isinstance(phi, QuantifiedFormula): self.visit(phi.formula) elif isinstance(phi, Atom): self.statics.add(symref(phi)) elif isinstance(phi, CompoundTerm): self.statics.add(symref(phi)) def visit(self, phi): """ Visitor method to sort atoms and terms into the "fluent" and "static" categories. Note that a given symbol can be in both sets, this means that it gets "votes" as static and fluent... the post_process() method is meant to settle the issue (and potentially allow for more ellaborate/clever heuristics). NB: at the moment we're trawling all (possibly lifted) sub-expressions, this is intentional. """ if self.mode == FluentHeuristic.action_effects: self._visit_action_effect_formula(phi) elif self.mode == FluentHeuristic.constraint: self._visit_constraint_formula(phi) else: assert self.mode == FluentHeuristic.precondition self._visit_precondition_formula(phi)

3ed76ba2b7d42660492afdccca06d4721336bbb4

py

Python

trainer.py

AZdet/causal-infogan

146b647863a27542ad4a1a01ddb033cdcab9843d

trainer.py

AZdet/causal-infogan

146b647863a27542ad4a1a01ddb033cdcab9843d

trainer.py

AZdet/causal-infogan

146b647863a27542ad4a1a01ddb033cdcab9843d

2020-02-15T19:17:24.000Z

2020-02-15T19:17:24.000Z

import numpy as np import csv import os import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import torchvision.datasets as dset import torchvision.transforms as transforms from tqdm import tqdm from torch.autograd import Variable from torch.utils.data import DataLoader from torchvision.utils import save_image from tensorboard_logger import configure, log_value from collections import OrderedDict from vpa.planning import plan_traj_astar, discretize, undiscretize # from dataset import ImagePairs from vpa.gcp_datasets import h36m, maze, maze1000, sawyer from vpa.utils import plot_img, from_numpy_to_var, print_array, write_number_on_images, write_stats_from_var from vpa.logger import Logger from vpa.eval import EvalPSNR class Trainer: def __init__(self, G, D, Q, T, P, **kwargs): # Models self.G = G self.D = D self.Q = Q self.T = T self.P = P self.classifier = kwargs['classifier'] self.fcn = kwargs.get('fcn', None) # Weights self.lr_g = kwargs['lr_g'] self.lr_d = kwargs['lr_d'] self.infow = kwargs['infow'] self.transw = kwargs['transw'] # Training hyperparameters self.batch_size = 16 self.n_epochs = kwargs['n_epochs'] self.c_dim = kwargs['cont_code_dim'] self.rand_z_dim = kwargs['random_noise_dim'] self.channel_dim = kwargs['channel_dim'] self.latent_dim = self.c_dim + self.rand_z_dim self.k = kwargs['k'] self.gray = kwargs['gray'] # Planning hyperparameters self.planner = getattr(self, kwargs['planner']) self.traj_eval_copies = kwargs['traj_eval_copies'] self.planning_epoch = kwargs['planning_epoch'] self.plan_length = kwargs['plan_length'] self.discretization_bins = 20 self.n_closest_iters = kwargs['n_closest_iters'] # Make directories self.data_dir = kwargs['data_dir'] self.planning_data_dir = kwargs['planning_data_dir'] self.out_dir = kwargs['out_dir'] if not os.path.exists(self.out_dir): os.makedirs(self.out_dir) # TF logger. self.logger = None self.configure_logger() self.log_dict = OrderedDict() # Evaluation self.test_sample_size = 12 self.test_num_codes = max(20, self.c_dim + 1) self.test_size = self.test_sample_size * self.test_num_codes self.eval_input = self._eval_noise() if kwargs['dataset'] == 'h36m': self.dataset = h36m.Dataset elif kwargs['dataset'] == 'maze': self.dataset = maze.Dataset elif kwargs['dataset'] == 'maze1000': self.dataset = maze1000.Dataset elif kwargs['dataset'] == 'sawyer': self.dataset = sawyer.Dataset def configure_logger(self): self.logger = Logger(os.path.join(self.out_dir, "log")) configure(os.path.join(self.out_dir, "log"), flush_secs=5) def _noise_sample(self, z, bs): c = self.P.sample(bs) c_next = self.T(c) z.data.normal_(0, 1) return z, c, c_next def _eval_noise(self): ''' :return: z (sample_size x num_codes x z_dim), c (sample_size x num_codes x z_dim) ''' more_codes = self.test_num_codes - (self.c_dim + 1) # c = Variable(torch.cuda.FloatTensor([[j<i for j in range(self.disc_c_dim)] for i in range(min(self.test_num_codes, self.disc_c_dim+1))])) c = Variable(torch.cuda.FloatTensor( [[j < i for j in range(self.c_dim)] for i in range(min(self.test_num_codes, self.c_dim + 1))])) * ( self.P.unif_range[1] - self.P.unif_range[0]) + self.P.unif_range[0] if more_codes > 0: c = torch.cat([c, self.P.sample(more_codes)], 0) self.eval_c = c z = Variable(torch.FloatTensor(self.test_sample_size, self.rand_z_dim).normal_(0, 1).cuda()) plot_img(c.t().detach().cpu(), os.path.join(self.out_dir, 'gen', 'eval_code.png'), vrange=self.P.unif_range) return z[:, None, :].repeat(1, self.test_num_codes, 1).view(-1, self.rand_z_dim), \ c.repeat(1, 1, self.test_sample_size).permute(2, 0, 1).contiguous().view(-1, self.c_dim) def get_c_next(self, epoch): c_next = self.T(self.eval_c) plot_img(c_next.t().detach().cpu(), os.path.join(self.out_dir, 'gen', 'eval_code_next_%d.png' % epoch), vrange=self.P.unif_range) return c_next.repeat(1, 1, self.test_sample_size).permute(2, 0, 1).contiguous().view(-1, self.c_dim) def apply_fcn_mse(self, img): o = self.fcn(Variable(img).cuda()).detach() return torch.clamp(2 * (o - 0.5), -1 + 1e-3, 1 - 1e-3) # return torch.clamp(2.6*(o - 0.5), -1 + 1e-3, 1 - 1e-3) def preprocess_function(self, state): return discretize(state, self.discretization_bins, self.P.unif_range) def discriminator_function(self, obs, obs_next): out = self.classifier(obs, obs_next) return out.detach().cpu().numpy() def discriminator_function_np(self, obs, obs_next): return self.discriminator_function(from_numpy_to_var(obs), from_numpy_to_var(obs_next)) def continuous_transition_function(self, c_): c_ = undiscretize(c_, self.discretization_bins, self.P.unif_range) c_next_ = self.T(from_numpy_to_var(c_)).data.cpu().numpy() c_next_ = np.clip(c_next_, self.P.unif_range[0] + 1e-6, self.P.unif_range[1] - 1e-6) c_next_d = discretize(c_next_, self.discretization_bins, self.P.unif_range) return c_next_d def conditional_generator_function(self, c_, c_next_, obs): ''' This doesn't do anything. ''' c_ = undiscretize(c_, self.discretization_bins, self.P.unif_range) c_next_ = undiscretize(c_next_, self.discretization_bins, self.P.unif_range) z_ = from_numpy_to_var(np.random.randn(c_.shape[0], self.rand_z_dim)) _, next_observation = self.G(z_, from_numpy_to_var(c_), from_numpy_to_var(c_next_)) return next_observation.data.cpu().numpy() def train(self): # Set up training. criterionD = nn.BCELoss().cuda() optimD = optim.Adam([{'params': self.D.parameters()}], lr=self.lr_d, betas=(0.5, 0.999)) optimG = optim.Adam([{'params': self.G.parameters()}, {'params': self.Q.parameters()}, {'params': self.T.parameters()}], lr=self.lr_g, betas=(0.5, 0.999)) ############################################ # Load rope dataset and apply transformations rope_path = os.path.realpath(self.data_dir) trans = [ transforms.Resize(64), transforms.CenterCrop(64), transforms.ToTensor(), # transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ] if not self.fcn: # If fcn it will do the transformation to gray # and normalize in the loop. trans.append(transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))) if self.gray: # Apply grayscale transformation. trans.append(lambda x: x.mean(dim=0)[None, :, :]) trans_comp = transforms.Compose(trans) # Image 1 and image 2 are k steps apart. dataset = self.dataset(phase='train', mode='train') self.plan_length = dataset.spec.max_seq_len - 3 dataloader = torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, shuffle=True, num_workers=2, drop_last=True) ############################################ # Load eval plan dataset planning_data_dir = self.planning_data_dir dataset_plan = self.dataset(phase='val', mode='plan') data_plan_loader = torch.utils.data.DataLoader(dataset_plan, batch_size=1, shuffle=False, num_workers=4, drop_last=True) # dataset_start = self.dataset(phase='val', mode='start') # dataset_goal = self.dataset(phase='val', mode='goal') # data_start_loader = torch.utils.data.DataLoader(dataset_start, # batch_size=1, # shuffle=False, # num_workers=1, # drop_last=True) # data_goal_loader = torch.utils.data.DataLoader(dataset_goal, # batch_size=1, # shuffle=False, # num_workers=1, # drop_last=True) ############################################ real_o = Variable(torch.FloatTensor(self.batch_size, 3, dataset.img_sz, dataset.img_sz).cuda(), requires_grad=False) real_o_next = Variable(torch.FloatTensor(self.batch_size, 3, dataset.img_sz, dataset.img_sz).cuda(), requires_grad=False) label = Variable(torch.FloatTensor(self.batch_size).cuda(), requires_grad=False) z = Variable(torch.FloatTensor(self.batch_size, self.rand_z_dim).cuda(), requires_grad=False) for epoch in range(self.n_epochs + 1): self.G.train() self.D.train() self.Q.train() self.T.train() for num_iters, batch_data in tqdm(enumerate(dataloader, 0)): # break # Real data o, _ = batch_data[0] o_next, _ = batch_data[1] bs = o.size(0) real_o.data.resize_(o.size()) real_o_next.data.resize_(o_next.size()) label.data.resize_(bs) real_o.data.copy_(o) real_o_next.data.copy_(o_next) if self.fcn: real_o = self.apply_fcn_mse(o) real_o_next = self.apply_fcn_mse(o_next) if real_o.abs().max() > 1: import ipdb; ipdb.set_trace() assert real_o.abs().max() <= 1 if epoch == 0: break ############################################ # D Loss (Update D) optimD.zero_grad() # Real data probs_real = self.D(real_o, real_o_next) label.data.fill_(1) loss_real = criterionD(probs_real, label) loss_real.backward() # Fake data z, c, c_next = self._noise_sample(z, bs) fake_o, fake_o_next = self.G(z, c, c_next) probs_fake = self.D(fake_o.detach(), fake_o_next.detach()) label.data.fill_(0) loss_fake = criterionD(probs_fake, label) loss_fake.backward() D_loss = loss_real + loss_fake optimD.step() ############################################ # G loss (Update G) optimG.zero_grad() probs_fake_2 = self.D(fake_o, fake_o_next) label.data.fill_(1) G_loss = criterionD(probs_fake_2, label) # Q loss (Update G, T, Q) ent_loss = -self.P.log_prob(c).mean(0) crossent_loss = -self.Q.log_prob(fake_o, c).mean(0) crossent_loss_next = -self.Q.log_prob(fake_o_next, c_next).mean(0) # trans_prob = self.T.get_prob(Variable(torch.eye(self.dis_c_dim).cuda())) ent_loss_next = -self.T.log_prob(c, None, c_next).mean(0) mi_loss = crossent_loss - ent_loss mi_loss_next = crossent_loss_next - ent_loss_next Q_loss = mi_loss + mi_loss_next # T loss (Update T) Q_c_given_x, Q_c_given_x_var = (i.detach() for i in self.Q.forward(real_o)) t_mu, t_variance = self.T.get_mu_and_var(c) t_diff = t_mu - c # Keep the variance small. # TODO: add loss on t_diff T_loss = (t_variance ** 2).sum(1).mean(0) (G_loss + self.infow * Q_loss + self.transw * T_loss).backward() optimG.step() ############################################# # Logging (iteration) if num_iters % 100 == 0: self.log_dict['Dloss'] = D_loss.item() self.log_dict['Gloss'] = G_loss.item() self.log_dict['Qloss'] = Q_loss.item() self.log_dict['Tloss'] = T_loss.item() self.log_dict['mi_loss'] = mi_loss.item() self.log_dict['mi_loss_next'] = mi_loss_next.item() self.log_dict['ent_loss'] = ent_loss.item() self.log_dict['ent_loss_next'] = ent_loss_next.item() self.log_dict['crossent_loss'] = crossent_loss.item() self.log_dict['crossent_loss_next'] = crossent_loss_next.item() self.log_dict['D(real)'] = probs_real.data.mean() self.log_dict['D(fake)_before'] = probs_fake.data.mean() self.log_dict['D(fake)_after'] = probs_fake_2.data.mean() write_stats_from_var(self.log_dict, Q_c_given_x, 'Q_c_given_real_x_mu') write_stats_from_var(self.log_dict, Q_c_given_x, 'Q_c_given_real_x_mu', idx=0) write_stats_from_var(self.log_dict, Q_c_given_x_var, 'Q_c_given_real_x_variance') write_stats_from_var(self.log_dict, Q_c_given_x_var, 'Q_c_given_real_x_variance', idx=0) write_stats_from_var(self.log_dict, t_mu, 't_mu') write_stats_from_var(self.log_dict, t_mu, 't_mu', idx=0) write_stats_from_var(self.log_dict, t_diff, 't_diff') write_stats_from_var(self.log_dict, t_diff, 't_diff', idx=0) write_stats_from_var(self.log_dict, t_variance, 't_variance') write_stats_from_var(self.log_dict, t_variance, 't_variance', idx=0) print('\n#######################' '\nEpoch/Iter:%d/%d; ' '\nDloss: %.3f; ' '\nGloss: %.3f; ' '\nQloss: %.3f, %.3f; ' '\nT_loss: %.3f; ' '\nEnt: %.3f, %.3f; ' '\nCross Ent: %.3f, %.3f; ' '\nD(x): %.3f; ' '\nD(G(z)): b %.3f, a %.3f;' '\n0_Q_c_given_rand_x_mean: %.3f' '\n0_Q_c_given_rand_x_std: %.3f' '\n0_Q_c_given_fixed_x_std: %.3f' '\nt_diff_abs_mean: %.3f' '\nt_std_mean: %.3f' % (epoch, num_iters, D_loss.item(), G_loss.item(), mi_loss.item(), mi_loss_next.item(), T_loss.item(), ent_loss.item(), ent_loss_next.item(), crossent_loss.item(), crossent_loss_next.item(), probs_real.data.mean(), probs_fake.data.mean(), probs_fake_2.data.mean(), Q_c_given_x[:, 0].cpu().numpy().mean(), Q_c_given_x[:, 0].cpu().numpy().std(), np.sqrt(Q_c_given_x_var[:, 0].cpu().numpy().mean()), t_diff.data.abs().mean(), t_variance.data.sqrt().mean(), )) ############################################# # Start evaluation from here. self.G.eval() self.D.eval() self.Q.eval() self.T.eval() ############################################# # Save images # Plot fake data x_save, x_next_save = self.G(*self.eval_input, self.get_c_next(epoch)) save_image(x_save.data, os.path.join(self.out_dir, 'gen', 'curr_samples_%03d.png' % epoch), nrow=self.test_num_codes, normalize=True) save_image(x_next_save.data, os.path.join(self.out_dir, 'gen', 'next_samples_%03d.png' % epoch), nrow=self.test_num_codes, normalize=True) save_image((x_save - x_next_save).data, os.path.join(self.out_dir, 'gen', 'diff_samples_%03d.png' % epoch), nrow=self.test_num_codes, normalize=True) # Plot real data. if epoch % 10 == 0: save_image(real_o.data, os.path.join(self.out_dir, 'real', 'real_samples_%d.png' % epoch), nrow=self.test_num_codes, normalize=True) save_image(real_o_next.data, os.path.join(self.out_dir, 'real', 'real_samples_next_%d.png' % epoch), nrow=self.test_num_codes, normalize=True) ############################################# # Save parameters if epoch % 5 == 0: if not os.path.exists('%s/var' % self.out_dir): os.makedirs('%s/var' % self.out_dir) for i in [self.G, self.D, self.Q, self.T]: torch.save(i.state_dict(), os.path.join(self.out_dir, 'var', '%s_%d' % (i.__class__.__name__, epoch, ))) ############################################# # Logging (epoch) for k, v in self.log_dict.items(): log_value(k, v, epoch) if epoch > 0: # tf logger # log_value('avg|x_next - x|', (x_next_save.data - x_save.data).abs().mean(dim=0).sum(), epoch + 1) # self.logger.histo_summary("Q_c_given_x", Q_c_given_x.data.cpu().numpy().reshape(-1), step=epoch) # self.logger.histo_summary("Q_c0_given_x", Q_c_given_x[:, 0].data.cpu().numpy(), step=epoch) # self.logger.histo_summary("Q_c_given_x_var", Q_c_given_x_var.cpu().numpy().reshape(-1), step=epoch) # self.logger.histo_summary("Q_c0_given_x_var", Q_c_given_x_var[:, 0].data.cpu().numpy(), step=epoch) # csv log with open(os.path.join(self.out_dir, 'progress.csv'), 'a') as csv_file: writer = csv.writer(csv_file) if epoch == 1: writer.writerow(["epoch"] + list(self.log_dict.keys())) writer.writerow(["%.3f" % _tmp for _tmp in [epoch] + list(self.log_dict.values())]) ############################################# # Do planning? if self.plan_length <= 0 or epoch not in self.planning_epoch: continue print("\n#######################" "\nPlanning") ############################################# # Showing plans on real images using best code. # Min l2 distance from start and goal real images. evaluator = EvalPSNR(2) plans = [] datas = [] for i, data in enumerate(data_plan_loader): plan = self.plan_hack(i, data[:, 0], data[:, -1], epoch, 'L2', data.shape[1] - 3, save=False) evaluator(plan[None].cpu().numpy(), data.cpu().numpy()) print(evaluator.PSNR(), evaluator.SSIM()) # if i < 4: # self.make_gif(torch.cat([data[0], plan.cpu()], dim=2), i, epoch) plans.append(plan.cpu()) datas.append(data[0]) if i == 3: for i in range(4): datas[i] = np.concatenate( [datas[i], np.zeros([100 - datas[i].shape[0]] + list(datas[i].shape[1:]))], 0) for i in range(4): plans[i] = np.concatenate([plans[i], torch.zeros([100 - plans[i].shape[0]] + list(plans[i].shape[1:]))], 0) data = np.concatenate(datas, 3) plan = np.concatenate(plans, 3) self.make_gif(torch.from_numpy(np.concatenate([data, plan], 2)), i, epoch, fps=4) import pdb; pdb.set_trace() print(('Test: [{0}/{1}]\t' 'PSNR {PSNR:.3f}' 'SSIM {SSIM:.3f}'.format( i, len(data_plan_loader), PSNR=evaluator.PSNR(), SSIM=evaluator.SSIM()))) # # Min classifier distance from start and goal real images. # self.plan_hack(data_start_loader, # data_goal_loader, # epoch, # 'classifier') ############################################# # Visual Planning def plan_hack(self, i, start_img, goal_img, epoch, metric, length, save=True, # TODO implement keep_best=1): """ Generate visual plans from starts to goals. First, find the closest codes for starts and goals. Then, generate the plans in the latent space. Finally, map the latent plans to visual plans and use the classifier to pick the top K. The start image is fixed. The goal image is loaded from data_goal_loader. :param data_start_loader: :param data_goal_loader: :param epoch: :param metric: :param keep_best: :return: """ all_confidences = [] c_start = None est_start_obs = None # for start_img in data_start_loader: if self.fcn: start_obs = self.apply_fcn_mse(start_img) else: start_obs = Variable(start_img).cuda() pt_start = os.path.join(self.out_dir, 'plans', 'c_min_start_%s_%i.pt' % (metric, i)) if os.path.exists(pt_start) and save: z_start, c_start, _, est_start_obs = torch.load(pt_start) else: z_start, c_start, _, est_start_obs = self.closest_code(start_obs, 400, False, metric, 1) if save: torch.save([z_start, c_start, _, est_start_obs], pt_start) # Hacky for now try: c_start = Variable(c_start) est_start_obs = Variable(est_start_obs) except RuntimeError: pass # for i, goal_img in enumerate(data_goal_loader, 0): if self.fcn: goal_obs = self.apply_fcn_mse(goal_img) else: goal_obs = Variable(goal_img).cuda() pt_goal = os.path.join(self.out_dir, 'plans', 'c_min_goal_%s_%d_epoch_%d.pt' % (metric, i, epoch)) if os.path.exists(pt_goal) and save: z_goal, _, c_goal, est_goal_obs = torch.load(pt_goal) else: z_goal, _, c_goal, est_goal_obs = self.closest_code(goal_obs, 400, True, metric, 1) if save: torch.save([z_goal, _, c_goal, est_goal_obs], pt_goal) # Hacky for now try: c_goal = Variable(c_goal) est_goal_obs = Variable(est_goal_obs) except RuntimeError: pass # Plan using c_start and c_goal. rollout = self.planner(c_start.repeat(self.traj_eval_copies, 1), c_goal.repeat(self.traj_eval_copies, 1), length, start_obs=start_obs, goal_obs=goal_obs) # Insert real start and goal. rollout.insert(0, est_start_obs.repeat(self.traj_eval_copies, 1, 1, 1)) rollout.append(est_goal_obs.repeat(self.traj_eval_copies, 1, 1, 1)) rollout_best_k, confidences = self.get_best_k(rollout, 1) rollout_data = torch.stack(rollout_best_k, dim=0) masks = - np.ones([rollout_data.size()[0], keep_best, self.channel_dim] + list(start_img.shape[-2:]), dtype=np.float32) # write_number_on_images(masks, confidences) pd = torch.max(rollout_data, from_numpy_to_var(masks))\ .permute(1, 0, 2, 3, 4).contiguous().view([-1, self.channel_dim] + list(start_img.shape[-2:])) # confidences.T has size keep_best x rollout length all_confidences.append(confidences.T[-1][:-1]) if save: save_image(pd.data, os.path.join(self.out_dir, 'plans', '%s_min_%s_%d_epoch_%d.png' % (self.planner.__name__, metric, i, epoch)), nrow=int(pd.size()[0] / keep_best), normalize=True) self.save_img(start_obs, os.path.join(self.out_dir, 'plans/im0.png')) # After the loop # all_confidences = np.stack(all_confidences) # print((all_confidences[:, 0] > 0.9).sum(), (all_confidences[:, -1] > 0.9).sum()) # import pickle as pkl # with open(os.path.join(self.out_dir, 'all_confidences.pkl'), 'wb') as f: # pkl.dump(all_confidences, f) # import matplotlib.pyplot as plt # plt.boxplot([all_confidences.mean(1), all_confidences[all_confidences[:, -1] > 0.9].mean(1)]) # plt.savefig(os.path.join(self.out_dir, 'boxplot.png')) return pd def save_img(self, img, name): import skimage skimage.io.imsave(name, img.detach().cpu().numpy()[0].transpose((1, 2, 0))) def make_gif(self, plan, i, epoch, fps): from recursive_planning.infra.utils.create_gif_lib import npy_to_gif, npy_to_mp4 filename = self.out_dir + '/plans/gif_{}_{}'.format(i, epoch) x = plan.detach().cpu().numpy() npy_to_gif(list(((x.transpose([0, 2, 3, 1]) + 1) * 127.5).astype(np.uint8)), filename, fps=fps) # def plan(self, # dataloader, # epoch, # metric, # keep_best=10): # """ # Generate visual plans from starts to goals. # First, find the closest codes for starts and goals. # Then, generate the plans in the latent space. # Finally, map the latent plans to visual plans and use the classifier to pick the top K. # The start image is loaded from data_start_loader. The goal image is loaded from data_goal_loader. # :param data_start_loader: # :param data_goal_loader: # :param epoch: # :param metric: # :param keep_best: # :return: # """ # # for i, pair in enumerate(dataloader, 0): # if self.fcn: # start_obs = self.apply_fcn_mse(pair[0][0]) # goal_obs = self.apply_fcn_mse(pair[1][0]) # # # Compute c_start and c_goal # pt_path = os.path.join(self.out_dir, 'plans', 'c_min_%s_%d_epoch_%d.pt' % (metric, i, epoch)) # if os.path.exists(pt_path): # c_start, c_goal, est_start_obs, est_goal_obs = torch.load(pt_path) # else: # _, c_start, _, est_start_obs = self.closest_code(start_obs, # 400, # False, # metric, 1) # _, _, c_goal, est_goal_obs = self.closest_code(goal_obs, # 400, # True, # metric, 1) # # _, c_start, _, est_start_obs = self.closest_code(start_obs, # # self.traj_eval_copies, # # False, # # metric, 0) # # _, _, c_goal, est_goal_obs = self.closest_code(goal_obs, # # self.traj_eval_copies, # # True, # # metric, 0) # torch.save([c_start, c_goal, est_start_obs, est_goal_obs], pt_path) # # # Plan using c_start and c_goal. # rollout = self.planner(c_start.repeat(self.traj_eval_copies, 1), # c_goal.repeat(self.traj_eval_copies, 1), # start_obs=start_obs, # goal_obs=goal_obs) # # # Insert closest start and goal. # rollout.insert(0, est_start_obs.repeat(self.traj_eval_copies, 1, 1, 1)) # rollout.append(est_goal_obs.repeat(self.traj_eval_copies, 1, 1, 1)) # # # Insert real start and goal. # rollout.insert(0, start_obs.repeat(self.traj_eval_copies, 1, 1, 1)) # rollout.append(goal_obs.repeat(self.traj_eval_copies, 1, 1, 1)) # # rollout_best_k, confidences = self.get_best_k(rollout, keep_best) # rollout_data = torch.stack(rollout_best_k, dim=0) # # masks = - np.ones((rollout_data.size()[0], keep_best, self.channel_dim, 64, 64), # dtype=np.float32) # write_number_on_images(masks, confidences) # # # save_image(torch.max(rollout_data, from_numpy_to_var(masks)).view(-1, self.channel_dim, 64, 64).data, # # os.path.join(self.out_dir, 'plans', '%s_min_%s_%d_epoch_%d.png' # # % (self.planner.__name__, metric, i, epoch)), # # nrow=keep_best, # # normalize=True) # # pd = torch.max(rollout_data, from_numpy_to_var(masks)).permute(1, 0, 2, 3, 4).contiguous().view(-1, self.channel_dim, 64, 64) # # save_image(pd.data, # os.path.join(self.out_dir, 'plans', '%s_min_%s_%d_epoch_%d.png' # % (self.planner.__name__, metric, i, epoch)), # nrow=int(pd.size()[0] / keep_best), # normalize=True) def get_best_k(self, rollout, keep_best=10): """ Evaluate confidence using discriminator. :param rollout: (list) n x (torch) channel size x W x H :param keep_best: get the best keep_best scores. :return: rollout list size n x (torch) keep_best x channel size x W x H, confidence np size n-1 x keep_best """ confidences = [self.D(rollout[i], rollout[i + 1]).reshape(-1).detach().cpu().numpy() for i in range(len(rollout) - 1)] np_confidences = np.array(confidences) # take minimum confidence along trajectory min_confidences = np.mean(np_confidences, axis=0) # sort according to confidence sort_ind = np.argsort(min_confidences, axis=0) rollout = [r[sort_ind[-keep_best:]] for r in rollout] # confidences = [c[sort_ind[-keep_best:]] for c in confidences] np_confidences = np.concatenate([np_confidences[:, sort_ind[-keep_best:]], np.zeros((1, keep_best))], 0) return rollout, np_confidences def closest_code(self, obs, n_trials, use_second, metric, regress_bs, verbose=True): """ Get the code that generates an image with closest distance to obs. :param obs: 1 x channel_dim x img_W x img_H :param n_trials: number of copies to search :param use_second: bool, to measure distance using the second image :param metric: str, choose either l2 or D to measure distance :param regress_bs: int, regression batch size when 0 do just sampling. :return: the best noise and codes """ if metric == 'L2': f = lambda x, y: ((x - y) ** 2).view(n_trials, -1).sum(1) elif metric == 'classifier': f = lambda x, y: - self.classifier(x, y).view(-1) + ((x - y) ** 2).view(n_trials, -1).sum(1) / 10 else: assert metric == 'D' # turned max into min using minus. f = lambda x, y: - self.D(x, y).view(-1) if regress_bs: z_var = Variable(0.1 * torch.randn(n_trials, self.rand_z_dim).cuda(), requires_grad=True) c_var = Variable(0.1 * torch.randn(n_trials, self.c_dim).cuda(), requires_grad=True) # c_var = Variable(self.Q.forward_soft(self.FE(obs.repeat(n_trials, 1, 1, 1))).data, requires_grad=True) optimizer = optim.Adam([c_var, z_var], lr=1e-2) n_iters = self.n_closest_iters for i in range(n_iters): optimizer.zero_grad() if self.planner == self.astar_plan: c = F.tanh(c_var.repeat(regress_bs, 1)) else: c = c_var.repeat(regress_bs, 1) _z = z_var.repeat(regress_bs, 1) c_next = self.T(c) o, o_next = self.G(_z, c, c_next) if use_second: out = o_next else: out = o dist = f(obs.repeat(n_trials * regress_bs, 1, 1, 1), out).sum(0) / regress_bs if i % 100 == 0: print("\t Closest code (%d/%d): %.3f" % (i, n_iters, dist)) dist.backward() optimizer.step() _z = z_var.detach() if self.planner == self.astar_plan: c = F.tanh(c_var.detach()) else: c = c_var.detach() else: _z = Variable(torch.randn(n_trials, self.rand_z_dim)).cuda() c = self.Q.forward_soft(self.FE(obs)).repeat(n_trials, 1) # Select best c and c_next from different initializations. if self.planner == self.astar_plan: c_next = torch.clamp(self.T(c), -1 + 1e-3, 1 - 1e-3) else: c_next = self.T(c) o, o_next = self.G(_z, c, c_next) if use_second: out = o_next else: out = o dist = f(obs.repeat(n_trials, 1, 1, 1), out) min_dist, min_idx = dist.min(0) if verbose: # import ipdb; ipdb.set_trace() print("\t best_c: %s" % print_array(c[min_idx.item()].data)) print("\t best_c_next: %s" % print_array(c_next[min_idx.item()].data)) print('\t %s measure: %.3f' % (metric, min_dist)) return _z[min_idx].detach(), c[min_idx].detach(), c_next[min_idx].detach(), out[min_idx].detach() def simple_plan(self, c_start, c_goal, length, verbose=True, **kwargs): """ Generate a plan in observation space given start and goal states via interpolation. :param c_start: bs x c_dim :param c_goal: bs x c_dim :return: rollout: horizon x bs x channel_dim x img_W x img_H """ with torch.no_grad(): rollout = [] _z = Variable(torch.randn(c_start.size()[0], self.rand_z_dim)).cuda() for t in range(length): c = c_start + (c_goal - c_start) * t / length c_next = c_start + (c_goal - c_start) * (t + 1) / length # _z = Variable(torch.randn(c.size()[0], self.rand_z_dim)).cuda() _cur_img, _next_img = self.G(_z, c, c_next) if t == 0: rollout.append(_cur_img) next_img = _next_img rollout.append(next_img) if verbose: # import ipdb; ipdb.set_trace() print("\t c_%d: %s" % (t, print_array(c[0].data))) # print("\t Transition var: %s" % print_array(self.T.get_var(c_start[0, None]).data[0])) # print("\t Direction: %s" % print_array((c_goal-c_start).data[0]/self.planning_horizon)) return rollout def astar_plan(self, c_start, c_goal, verbose=True, **kwargs): """ Generate a plan in observation space given start and goal states via A* search. :param c_start: bs x c_dim :param c_goal: bs x c_dim :return: rollout: horizon x bs x channel_dim x img_W x img_H """ with torch.no_grad(): rollout = [] # _z = Variable(torch.randn(c_start.size()[0], self.rand_z_dim)).cuda() bs = c_start.size()[0] traj = plan_traj_astar( kwargs['start_obs'], kwargs['goal_obs'], start_state=c_start[0].data.cpu().numpy(), goal_state=c_goal[0].data.cpu().numpy(), transition_function=self.continuous_transition_function, preprocess_function=self.preprocess_function, discriminator_function=self.discriminator_function_np, generator_function=self.conditional_generator_function) for t, disc in enumerate(traj[:-1]): state = undiscretize(disc.state, self.discretization_bins, self.P.unif_range) state_next = undiscretize(traj[t + 1].state, self.discretization_bins, self.P.unif_range) c = from_numpy_to_var(state).repeat(bs, 1) c_next = from_numpy_to_var(state_next).repeat(bs, 1) _z = Variable(torch.randn(c.size()[0], self.rand_z_dim)).cuda() _cur_img, _next_img = self.G(_z, c, c_next) if t == 0: rollout.append(_cur_img) next_img = _next_img rollout.append(next_img) if verbose: # import ipdb; ipdb.set_trace() print("\t c_%d: %s" % (t, print_array(c[0].data))) return rollout

bead85c2db00d94e176879ac706fbcf051df380e

py

Python

wiretap/__init__.py

CBSDigital/Hiero-Wiretap

c1326382b3c8b111ad682ccaec22990b409aff08

2016-11-02T16:27:38.000Z

2018-05-08T06:27:29.000Z

wiretap/__init__.py

CBSDigital/Hiero-Wiretap

c1326382b3c8b111ad682ccaec22990b409aff08

wiretap/__init__.py

CBSDigital/Hiero-Wiretap

c1326382b3c8b111ad682ccaec22990b409aff08

"""Loads the platform-appropriate Wiretap Python bindings. @details The Wiretap Python extension requires the Boost.Python and dynamic Wiretap libraries that were both compiled for the current platform and Python version. @note Autodesk refers to "Wiretap" in their SDK Documentation and product materials, while using the convention "WireTap" in their API. @note For autocompletion in Eclipse, add the folder containing the proper version of libwiretapPythonClientAPI that was compiled for the currently selected interpreter to the PYTHONPATH - External Libraries list. """ import os.path import platform import sys LIBNAME = 'Wiretap' def GetLibraryDirPath(): osAbbrev = { 'Windows': 'win', 'Microsoft': 'win', 'Darwin': 'osx', 'Linux': 'linux' } systemOS = platform.system() if sys.maxsize <= 2**32: arch = 32 else: arch = 64 # Check whether the OS is in the abbreviation table try: platformFolder = osAbbrev[systemOS] + str(arch) except KeyError: msg = ("The {0} Python bindings are not available on the {1} " "operating system.").format(LIBNAME, systemOS) return '', msg # Check whether there is a folder for the platform pkgPath = os.path.dirname(__file__) curPath = os.path.join(pkgPath, platformFolder) if not os.path.isdir(curPath): msg = ( "The {0} Python bindings have not yet been compiled for {1} " "{2}-bit." ).format(LIBNAME, systemOS, arch) return '', msg # Check whether there is a folder for the current Python version pythonFolder = 'py{0}{1}'.format(*sys.version_info[0:2]) pythonVersion = '{0}.{1}'.format(*sys.version_info[0:2]) curPath = os.path.join(curPath, pythonFolder) if not os.path.isdir(curPath): msg = ( "The {0} Python bindings have not yet been compiled for " "Python {1} on {2} {3}-bit." ).format(LIBNAME, pythonVersion, systemOS, arch) return '', msg return curPath, '' # TO DO: handle unsupported platform __libDirPath, __errors = GetLibraryDirPath() if __libDirPath: if __libDirPath not in sys.path: sys.path.append(__libDirPath) else: raise ImportError(__errors) from libwiretapPythonClientAPI import * class WireTapException(Exception): pass # Define here other classes/functions that are dependent on Wiretap Python API

ddd435c9593af3c813a55e095a7dd14fa5b59ae1

py

Python

pyserum/market/types.py

dpguoming/pyserum

d0938e9149de67093808c4d89fee1eb6f75fcd52

2020-08-30T03:50:10.000Z

2022-03-24T02:52:18.000Z

pyserum/market/types.py

dpguoming/pyserum

d0938e9149de67093808c4d89fee1eb6f75fcd52

2020-08-30T04:38:30.000Z

2022-03-23T10:22:11.000Z

pyserum/market/types.py

dpguoming/pyserum

d0938e9149de67093808c4d89fee1eb6f75fcd52

2020-09-29T13:58:20.000Z

2022-03-07T09:49:47.000Z

from __future__ import annotations from typing import NamedTuple from solana.publickey import PublicKey from .._layouts.account_flags import ACCOUNT_FLAGS_LAYOUT from ..enums import Side class AccountFlags(NamedTuple): initialized: bool = False """""" market: bool = False """""" open_orders: bool = False """""" request_queue: bool = False """""" event_queue: bool = False """""" bids: bool = False """""" asks: bool = False """""" @staticmethod def from_bytes(buffer: bytes) -> AccountFlags: con = ACCOUNT_FLAGS_LAYOUT.parse(buffer) return AccountFlags( initialized=con.initialized, market=con.market, open_orders=con.open_orders, request_queue=con.request_queue, event_queue=con.event_queue, bids=con.bids, asks=con.asks, ) class FilledOrder(NamedTuple): order_id: int """""" side: Side """""" price: float """""" size: float """""" fee_cost: int """""" class OrderInfo(NamedTuple): price: float """""" size: float """""" price_lots: int """""" size_lots: int """""" class Order(NamedTuple): order_id: int """""" client_id: int """""" open_order_address: PublicKey """""" open_order_slot: int """""" fee_tier: int """""" info: OrderInfo """""" side: Side """""" class ReuqestFlags(NamedTuple): new_order: bool cancel_order: bool bid: bool post_only: bool ioc: bool class Request(NamedTuple): request_flags: ReuqestFlags """""" open_order_slot: int """""" fee_tier: int """""" max_base_size_or_cancel_id: int """""" native_quote_quantity_locked: int """""" order_id: int """""" open_orders: PublicKey """""" client_order_id: int """""" class EventFlags(NamedTuple): fill: bool out: bool bid: bool maker: bool class Event(NamedTuple): event_flags: EventFlags """""" open_order_slot: int """""" fee_tier: int """""" native_quantity_released: int """""" native_quantity_paid: int """""" native_fee_or_rebate: int """""" order_id: int """""" public_key: PublicKey """""" client_order_id: int """""" class MarketInfo(NamedTuple): name: str """""" address: PublicKey """""" program_id: PublicKey """""" class TokenInfo(NamedTuple): name: str """""" address: PublicKey """"""

6003e3cc03352ecb5590280b920b34917d2d7722

py

Python

7-4.py

demogest/Python_Practice

6d074d694e761e0ba390252fbeb1092bb21d26e1

7-4.py

demogest/Python_Practice

6d074d694e761e0ba390252fbeb1092bb21d26e1

7-4.py

demogest/Python_Practice

6d074d694e761e0ba390252fbeb1092bb21d26e1

from tkinter import * # 导入ttk from tkinter import ttk from tkinter import colorchooser import math class App: def __init__(self, master): self.master = master # 保存设置初始的边框宽度 self.width = IntVar() self.width.set(1) # 保存设置初始的边框颜色 self.outline = 'black' # 保存设置初始的填充颜色 self.fill = None # 记录拖动时前一个点的x、y坐标 self.prevx = self.prevy = -10 # 记录拖动开始的第一个点的x、y坐标 self.firstx = self.firsty = -10 # 记录拖动右键来移动图形时前一个点的x、y坐标 self.mv_prevx = self.mv_prevy = -10 # item_type记录要绘制哪种图形 self.item_type = 0 self.points = [] self.init_widgets() self.temp_item = None self.temp_items = [] # 初始化选中的图形项 self.choose_item = None # 创建界面组件 def init_widgets(self): self.cv = Canvas(root, background='white') self.cv.pack(fill=BOTH, expand=True) # 为鼠标左键拖动事件、鼠标左键释放事件绑定处理函数 self.cv.bind('<B1-Motion>', self.drag_handler) self.cv.bind('<ButtonRelease-1>', self.release_handler) # 为鼠标左键双击事件绑定处理函数 self.cv.bind('<Double-1>', self.double_handler) f = ttk.Frame(self.master) f.pack(fill=X) self.bns = [] # 采用循环创建多个按钮，用于绘制不同的图形 for i, lb in enumerate(('直线', '矩形', '椭圆', '多边形', '铅笔')): bn = Button(f, text=lb, command=lambda i=i: self.choose_type(i)) bn.pack(side=LEFT, ipadx=8, ipady=5, padx=5) self.bns.append(bn) # 默认选中直线 self.bns[self.item_type]['relief'] = SUNKEN ttk.Button(f, text='边框颜色', command=self.choose_outline).pack(side=LEFT, ipadx=8, ipady=5, padx=5) ttk.Button(f, text='填充颜色', command=self.choose_fill).pack(side=LEFT, ipadx=8, ipady=5, padx=5) om = ttk.OptionMenu(f, self.width, # 绑定变量 '1', # 设置初始选中值 '0', # 以下多个值用于设置菜单项 '1', '2', '3', '4', '5', '6', '7', '8', command=None) om.pack(side=LEFT, ipadx=8, ipady=5, padx=5) def choose_type(self, i): # 将所有按钮恢复默认状态 for b in self.bns: b['relief'] = RAISED # 将当前按钮设置选中样式 self.bns[i]['relief'] = SUNKEN # 设置要绘制的图形 self.item_type = i # 处理选择边框颜色的方法 def choose_outline(self): # 弹出颜色选择对话框 select_color = colorchooser.askcolor(parent=self.master, title="请选择边框颜色", color=self.outline) if select_color is not None: self.outline = select_color[1] # 处理选择填充颜色的方法 def choose_fill(self): # 弹出颜色选择对话框 select_color = colorchooser.askcolor(parent=self.master, title="请选择填充颜色", color=self.fill) if select_color is not None: self.fill = select_color[1] else: self.fill = None def drag_handler(self, event): # 如果是绘制直线 if self.item_type == 0: # 如果第一个点不存在（self.firstx 和 self.firsty都小于0） if self.firstx < -1 and self.firsty < -1: self.firstx, self.firsty = event.x, event.y # 删除上一次绘制的虚线图形 if self.temp_item is not None: self.cv.delete(self.temp_item) # 重新绘制虚线 self.temp_item = self.cv.create_line(self.firstx, self.firsty, event.x, event.y, dash=2) # 如果是绘制矩形或椭圆 if self.item_type == 1: # 如果第一个点不存在（self.firstx 和 self.firsty都小于0） if self.firstx < -1 and self.firsty < -1: self.firstx, self.firsty = event.x, event.y # 删除上一次绘制的虚线图形 if self.temp_item is not None: self.cv.delete(self.temp_item) leftx, lefty = min(self.firstx, event.x), min(self.firsty, event.y) rightx, righty = max(self.firstx, event.x), max(self.firsty, event.y) # 重新绘制虚线选择框 self.temp_item = self.cv.create_rectangle(leftx, lefty, rightx, righty, dash=2) if self.item_type == 2: # 如果第一个点不存在（self.firstx 和 self.firsty都小于0） if self.firstx < -1 and self.firsty < -1: self.firstx, self.firsty = event.x, event.y # 删除上一次绘制的虚线图形 if self.temp_item is not None: self.cv.delete(self.temp_item) radius = math.sqrt((event.x-self.firstx)**2+(event.y-self.firsty)**2) leftx, lefty = self.firstx-radius,self.firsty-radius rightx, righty = event.x + radius,event.y+radius self.temp_item = self.cv.create_oval(leftx, lefty, rightx, righty, outline=self.outline, fill=self.fill, width=self.width.get(),dash=2) if self.item_type == 3: self.draw_polygon = True # 如果第一个点不存在（self.firstx 和 self.firsty都小于0） if self.firstx < -1 and self.firsty < -1: self.firstx, self.firsty = event.x, event.y # 删除上一次绘制的虚线图形 if self.temp_item is not None: self.cv.delete(self.temp_item) # 重新绘制虚线 self.temp_item = self.cv.create_line(self.firstx, self.firsty, event.x, event.y, dash=2) if self.item_type == 4: # 如果前一个点存在（self.prevx 和 self.prevy都大于0） if self.prevx > 0 and self.prevy > 0: self.cv.create_line(self.prevx, self.prevy, event.x, event.y, fill=self.outline, width=self.width.get()) self.prevx, self.prevy = event.x, event.y def item_bind(self, t): # 为鼠标右键拖动事件绑定处理函数 self.cv.tag_bind(t, '<B3-Motion>', self.move) # 为鼠标右键释放事件绑定处理函数 self.cv.tag_bind(t, '<ButtonRelease-3>', self.move_end) def release_handler(self, event): # 删除临时绘制的虚线图形项 if self.temp_item is not None: # 如果不是绘制多边形 if self.item_type != 3: self.cv.delete(self.temp_item) # 如果绘制多边形，将之前绘制的虚线先保存下来，以便后面删除它们 else: self.temp_items.append(self.temp_item) self.temp_item = None # 如果是绘制直线 if self.item_type == 0: # 如果第一个点存在（self.firstx 和 self.firsty都大于0） if self.firstx > 0 and self.firsty > 0: # 绘制实际的直线 t = self.cv.create_line(self.firstx, self.firsty, event.x, event.y, fill=self.outline, width=self.width.get()) # 为鼠标左键单击事件绑定处理函数，用于选择被单击的图形项 self.cv.tag_bind(t, '<Button-1>', lambda event=event, t=t: self.choose_item_handler(event, t)) self.item_bind(t) # 如果是绘制矩形或椭圆 if self.item_type == 1 or self.item_type == 2: # 如果第一个点存在（self.firstx 和 self.firsty都大于0） if self.firstx > 0 and self.firsty > 0: leftx, lefty = min(self.firstx, event.x), min(self.firsty, event.y) rightx, righty = max(self.firstx, event.x), max(self.firsty, event.y) if self.item_type == 1: # 绘制实际的矩形 t = self.cv.create_rectangle(leftx, lefty, rightx, righty, outline=self.outline, fill=self.fill, width=self.width.get()) if self.item_type == 2: # 绘制实际的椭圆 t = self.cv.create_oval(leftx, lefty, rightx, righty, outline=self.outline, fill=self.fill, width=self.width.get()) # 为鼠标左键单击事件绑定处理函数，用于选择被单击的图形项 self.cv.tag_bind(t, '<Button-1>', lambda event=event, t=t: self.choose_item_handler(event, t)) self.item_bind(t) if self.item_type != 3: self.prevx = self.prevy = -10 self.firstx = self.firsty = -10 # 如果正在绘制多边形 elif (self.draw_polygon): # 将第一个点添加到列表中 self.points.append((self.firstx, self.firsty)) self.firstx, self.firsty = event.x, event.y def double_handler(self, event): # 只处理绘制多边形的情形 if self.item_type == 3: t = self.cv.create_polygon(*self.points, outline=self.outline, fill="" if self.fill is None else self.fill, width=self.width.get()) # 为鼠标左键单击事件绑定处理函数，用于选择被单击的图形项 self.cv.tag_bind(t, '<Button-1>', lambda event=event, t=t: self.choose_item_handler(event, t)) self.item_bind(t) # 清空所有保存的点数据 self.points.clear() # 将self.firstx = self.firsty设置为-10，停止绘制 self.firstx = self.firsty = -10 # 删除所有临时的虚线框 for it in self.temp_items: self.cv.delete(it) self.temp_items.clear() self.draw_polygon = False # 根据传入的参数t来选中对应的图形项 def choose_item_handler(self, event, t): # 使用self.choose_item保存当前选中项 self.choose_item = t # 定义移动图形项的方法 def move(self, event): # 如果被选中图形项不为空，才可以执行移动 if self.choose_item is not None: # 如果前一个点存在（self.mv_prevx 和 self.mv_prevy都大于0） if self.mv_prevx > 0 and self.mv_prevy > 0: # 移动选中的图形项 self.cv.move(self.choose_item, event.x - self.mv_prevx, event.y - self.mv_prevy) self.mv_prevx, self.mv_prevy = event.x, event.y # 结束移动的方法 def move_end(self, event): self.mv_prevx = self.mv_prevy = -10 def delete_item(self, event): # 如果被选中的item不为空，删除被选中的图形项 if self.choose_item is not None: self.cv.delete(self.choose_item) root = Tk() root.title("绘图工具") root.geometry('800x680') app = App(root) root.bind('<Delete>', app.delete_item) root.mainloop()

0d0f76f8520cc734ed25f6a5d0a551e54e8a9bb7

py

Python

datasets/fbms.py

Schmiddo/d2conv3d

9b330be56f0dfb9657a63e3fb3394ab36b35a67b

2021-11-16T04:20:32.000Z

2022-03-10T12:07:13.000Z

datasets/fbms.py

Schmiddo/d2conv3d

9b330be56f0dfb9657a63e3fb3394ab36b35a67b

2022-02-23T14:25:47.000Z

2022-02-23T14:25:47.000Z

datasets/fbms.py

Schmiddo/d2conv3d

9b330be56f0dfb9657a63e3fb3394ab36b35a67b

2022-02-12T07:39:10.000Z

2022-02-12T07:39:10.000Z

import glob import os.path as osp import random as rand import numpy as np from imageio import imread from datasets.base_dataset import VideoSegmentationDataset, INFO, IMAGES_, TARGETS from utils.registry import register @register("dataset") class FBMS(VideoSegmentationDataset): def __init__(self, root, mode="train", resize_mode=None, resize_shape=None, clip_size=8, max_tw=16): if mode not in ("train", "val", "test"): raise ValueError(f"'mode' should be either train, val, or test but is {mode}") self.image_dir = osp.join(root, "Trainingset" if mode == "train" else "Testset") self.mask_dir = osp.join(root, "inst", "train" if mode == "train" else "test") self.num_frames = {} super(FBMS, self).__init__(root, mode, resize_mode, resize_shape, clip_size, max_tw) def read_target(self, sample): masks = [] shape = sample["info"]["shape"] for t in sample["targets"]: if osp.exists(t): mask = imread(t, pilmode="P") else: mask = np.zeros(shape).astype(np.uint8) masks.append(mask) return {"mask": np.stack(masks)[..., None]} def _get_support_indices(self, index, video): temporal_window = self.max_temporal_gap if self.is_train() else self.tw start_index = max(0, index - temporal_window//2) stop_index = min(self.num_frames[video], index + temporal_window//2) indices = list(range(start_index, stop_index)) if self.is_train(): # TODO: sample without replacement? indices = sorted(rand.choices(indices, k=self.tw)) else: missing_frames = self.tw - len(indices) if min(indices) == 0: indices = indices + missing_frames * [start_index] else: indices = missing_frames * [stop_index-1] + indices return indices def _create_sample(self, video, img_list, mask_list, support_indices): sample = { IMAGES_: [img_list[s] for s in support_indices], TARGETS: [mask_list[s] for s in support_indices], INFO: { "support_indices": support_indices, "video": video, "num_frames": self.num_frames[video], "gt_frames": np.array([osp.exists(mask_list[s]) for s in support_indices]), } } return sample def create_sample_list(self): self.videos = [osp.basename(v) for v in glob.glob(self.image_dir + "/*")] if len(self.videos) == 0: raise ValueError(f"Image directory {self.image_dir} is empty") for video in self.videos: img_list = sorted(glob.glob(osp.join(self.image_dir, video, "*.jpg"))) mask_list = [ osp.join(self.mask_dir, video, (osp.basename(img)[:-4] + ".png")) for img in img_list ] num_frames = len(img_list) self.num_frames[video] = num_frames for i, img in enumerate(img_list): support_indices = self._get_support_indices(i, video) sample = self._create_sample(video, img_list, mask_list, support_indices) self.raw_samples.append(sample) self.samples = self.raw_samples @register("dataset") class FBMSInfer(FBMS): def __init__(self, root, mode="val", resize_mode=None, resize_shape=None, clip_size=8): super(FBMSInfer, self).__init__(root, mode, resize_mode, resize_shape, clip_size, clip_size) def create_sample_list(self): self.videos = [osp.basename(v) for v in glob.glob(self.image_dir + "/*")] if len(self.videos) == 0: raise ValueError(f"Image directory {self.image_dir} is empty") for video in self.videos: img_list = sorted(glob.glob(osp.join(self.image_dir, video, "*.jpg"))) mask_list = [ osp.join(self.mask_dir, video, (osp.basename(img)[:-4] + ".png")) for img in img_list ] num_frames = len(img_list) self.num_frames[video] = num_frames support_indices = list(range(num_frames)) sample = self._create_sample(video, img_list, mask_list, support_indices) self.raw_samples.append(sample) self.samples = self.raw_samples

5d03d9f9863b7a0f7e49e0c815959c550a9eadd8

py

Python

biothings/hub/datarelease/releasenote.py

sirloon/biothings.api

8a981fa2151e368d0ca76aaf226eb565d794d4fb

biothings/hub/datarelease/releasenote.py

sirloon/biothings.api

8a981fa2151e368d0ca76aaf226eb565d794d4fb

biothings/hub/datarelease/releasenote.py

sirloon/biothings.api

8a981fa2151e368d0ca76aaf226eb565d794d4fb