fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

790bc46c1ca684242c3375cd3c7dd7464b95ae8e

1,901

py

Python

tiktok_bot/models/comment.py

reliefs/tiktok_bot

30404c0cd9ae1d52eb5b8818fbf282af1f68ee7a

[ "MIT" ]

118

2019-10-22T07:56:34.000Z

2022-03-30T11:33:25.000Z

tiktok_bot/models/comment.py

reliefs/tiktok_bot

30404c0cd9ae1d52eb5b8818fbf282af1f68ee7a

[ "MIT" ]

14

2019-10-27T00:06:29.000Z

2020-12-30T09:10:43.000Z

tiktok_bot/models/comment.py

reliefs/tiktok_bot

30404c0cd9ae1d52eb5b8818fbf282af1f68ee7a

[ "MIT" ]

40

2019-10-27T15:46:58.000Z

2022-03-15T00:21:47.000Z

from typing import List, Optional from pydantic import BaseModel from typing_extensions import Literal from .request import BaseResponseData, CountOffsetParams, ListRequestParams, ListResponseData from .tag import Tag from .user import CommonUserDetails class Comment(BaseModel): # The ID of the post aweme_id: str # The ID of the comment cid: str # The timestamp in seconds when the comment was posted create_time: int # The number of times the comment has been liked digg_count: int # If this comment is replying to a comment, this array contains the original comment reply_comment: Optional[List["Comment"]] = None # If this comment is replying to a comment, the ID of that comment - "0" if not a reply reply_id: str # The status of the comment - 1 = published, 4 = published by you? status: int # The comment text text: str # Details about any tags in the comment text_extra: List[Tag] # Details about the author user: CommonUserDetails # 1 if the user likes the comment user_digged: Literal[0, 1] class ListCommentsRequest(ListRequestParams, CountOffsetParams): # The ID of the post to list comments for aweme_id: str # ??? - default is 2 comment_style: Optional[int] = None # ??? digged_cid = None # ??? insert_cids = None class ListCommentsResponse(ListResponseData, CountOffsetParams): comments: List[Comment] class PostCommentRequest(BaseModel): # The ID of the post to comment on aweme_id: str # The comment text text: str # The ID of the comment that is being replied to reply_id: Optional[str] = None # Details about any tags in the comment text_extra: List[Tag] # ??? is_self_see: Literal[0, 1] class PostCommentResponse(BaseResponseData): # The comment that was posted comment: Comment

22.630952

93

0.696476

from typing import List, Optional from pydantic import BaseModel from typing_extensions import Literal from .request import BaseResponseData, CountOffsetParams, ListRequestParams, ListResponseData from .tag import Tag from .user import CommonUserDetails class Comment(BaseModel): aweme_id: str cid: str create_time: int digg_count: int reply_comment: Optional[List["Comment"]] = None reply_id: str status: int text: str text_extra: List[Tag] user: CommonUserDetails user_digged: Literal[0, 1] class ListCommentsRequest(ListRequestParams, CountOffsetParams): aweme_id: str comment_style: Optional[int] = None digged_cid = None insert_cids = None class ListCommentsResponse(ListResponseData, CountOffsetParams): comments: List[Comment] class PostCommentRequest(BaseModel): aweme_id: str text: str reply_id: Optional[str] = None text_extra: List[Tag] is_self_see: Literal[0, 1] class PostCommentResponse(BaseResponseData): comment: Comment

true

790bc527f550caf6d6c3c54aa154d04198b28640

2,339

py

Python

src/playthrough-bot/models/alembic/env.py

Rain288/playthrough-bot

9ddefa336bf59cf8ab4a1de83ff4c4777195619b

[ "MIT" ]

1

2019-02-02T03:58:05.000Z

src/playthrough-bot/models/alembic/env.py

Rain288/playthrough-bot

9ddefa336bf59cf8ab4a1de83ff4c4777195619b

[ "MIT" ]

1

2019-02-02T03:58:05.000Z

2019-02-10T14:51:05.000Z

src/playthrough-bot/models/alembic/env.py

evangelos-ch/playthrough-bot

9ddefa336bf59cf8ab4a1de83ff4c4777195619b

[ "MIT" ]

null

# pylint: disable=no-member from logging.config import fileConfig from sqlalchemy import engine_from_config from sqlalchemy import pool from alembic import context from playthrough-bot.models import ModelBase, get_engine # this is the Alembic Config object, which provides # access to the values within the .ini file in use. config = context.config config.set_main_option("sqlalchemy.url", str(get_engine().url)) # Interpret the config file for Python logging. # This line sets up loggers basically. fileConfig(config.config_file_name) # add your model's MetaData object here # for 'autogenerate' support # from myapp import mymodel # target_metadata = mymodel.Base.metadata target_metadata = ModelBase.metadata # other values from the config, defined by the needs of env.py, # can be acquired: # my_important_option = config.get_main_option("my_important_option") # ... etc. def run_migrations_offline(): """Run migrations in 'offline' mode. This configures the context with just a URL and not an Engine, though an Engine is acceptable here as well. By skipping the Engine creation we don't even need a DBAPI to be available. Calls to context.execute() here emit the given string to the script output. """ url = config.get_main_option("sqlalchemy.url") context.configure( url=url, target_metadata=target_metadata, literal_binds=True, dialect_opts={"paramstyle": "named"}, ) with context.begin_transaction(): context.run_migrations() def run_migrations_online(): """Run migrations in 'online' mode. In this scenario we need to create an Engine and associate a connection with the context. """ connectable = engine_from_config( config.get_section(config.config_ini_section), prefix="sqlalchemy.", poolclass=pool.NullPool, ) with connectable.connect() as connection: context.configure( connection=connection, target_metadata=target_metadata, render_as_batch=config.get_main_option("sqlalchemy.url").startswith( "sqlite:" ), ) with context.begin_transaction(): context.run_migrations() if context.is_offline_mode(): run_migrations_offline() else: run_migrations_online()

26.885057

80

0.70714

from logging.config import fileConfig from sqlalchemy import engine_from_config from sqlalchemy import pool from alembic import context from playthrough-bot.models import ModelBase, get_engine config = context.config config.set_main_option("sqlalchemy.url", str(get_engine().url)) fileConfig(config.config_file_name) # for 'autogenerate' support # from myapp import mymodel # target_metadata = mymodel.Base.metadata target_metadata = ModelBase.metadata # other values from the config, defined by the needs of env.py, # can be acquired: # my_important_option = config.get_main_option("my_important_option") # ... etc. def run_migrations_offline(): """Run migrations in 'offline' mode. This configures the context with just a URL and not an Engine, though an Engine is acceptable here as well. By skipping the Engine creation we don't even need a DBAPI to be available. Calls to context.execute() here emit the given string to the script output. """ url = config.get_main_option("sqlalchemy.url") context.configure( url=url, target_metadata=target_metadata, literal_binds=True, dialect_opts={"paramstyle": "named"}, ) with context.begin_transaction(): context.run_migrations() def run_migrations_online(): """Run migrations in 'online' mode. In this scenario we need to create an Engine and associate a connection with the context. """ connectable = engine_from_config( config.get_section(config.config_ini_section), prefix="sqlalchemy.", poolclass=pool.NullPool, ) with connectable.connect() as connection: context.configure( connection=connection, target_metadata=target_metadata, render_as_batch=config.get_main_option("sqlalchemy.url").startswith( "sqlite:" ), ) with context.begin_transaction(): context.run_migrations() if context.is_offline_mode(): run_migrations_offline() else: run_migrations_online()

false

true

790bc552861a63a0b44e136084ced7fa30eddacf

120

py

Python

Regex/Capturing & Non-Capturing Groups.py

rafaelgreca/hackerrank-solutions

2be6c8fdd9b7f2ab3a678e7dcdc27e730edfaef3

[ "MIT" ]

2

2020-05-28T07:15:00.000Z

2020-07-21T08:34:06.000Z

Regex/Capturing & Non-Capturing Groups.py

rafaelgreca/hackerrank-solutions

2be6c8fdd9b7f2ab3a678e7dcdc27e730edfaef3

[ "MIT" ]

null

Regex/Capturing & Non-Capturing Groups.py

rafaelgreca/hackerrank-solutions

2be6c8fdd9b7f2ab3a678e7dcdc27e730edfaef3

[ "MIT" ]

null

Regex_Pattern = r'(ok){3,}' # Do not delete 'r'. import re print(str(bool(re.search(Regex_Pattern, input()))).lower())

24

59

0.666667

Regex_Pattern = r'(ok){3,}' import re print(str(bool(re.search(Regex_Pattern, input()))).lower())

true

790bc55d23e266c9aec44d33c5233dd226e9045f

746

py

Python

botblox_config/data_manager/erase.py

ararobotique/botblox-manager-software

64c5c893601ea62a7ac414023455e8c2da04816d

[ "MIT" ]

6

2021-04-18T21:30:17.000Z

2022-01-13T06:37:43.000Z

botblox_config/data_manager/erase.py

ararobotique/botblox-manager-software

64c5c893601ea62a7ac414023455e8c2da04816d

[ "MIT" ]

36

2020-12-16T12:29:24.000Z

2021-09-18T14:52:25.000Z

botblox_config/data_manager/erase.py

ararobotique/botblox-manager-software

64c5c893601ea62a7ac414023455e8c2da04816d

[ "MIT" ]

2

2021-04-08T20:27:48.000Z

2021-08-30T17:32:28.000Z

from argparse import Action, Namespace from typing import (List) from .switch_config import SwitchConfigCLI from ..switch import SwitchChip class EraseConfigCLI(SwitchConfigCLI): """ The "erase" action that removes all stored items from the EEPROM memory. """ def __init__(self, subparsers: Action, switch: SwitchChip) -> None: super().__init__(subparsers, switch) self._subparser = self._subparsers.add_parser( "erase", help="Erase all configuration", ) self._subparser.set_defaults(execute=self.apply) def apply(self, args: Namespace) -> SwitchConfigCLI: return self def create_configuration(self) -> List[List[int]]: return [[101, 0, 0, 0]]

28.692308

76

0.670241

from argparse import Action, Namespace from typing import (List) from .switch_config import SwitchConfigCLI from ..switch import SwitchChip class EraseConfigCLI(SwitchConfigCLI): def __init__(self, subparsers: Action, switch: SwitchChip) -> None: super().__init__(subparsers, switch) self._subparser = self._subparsers.add_parser( "erase", help="Erase all configuration", ) self._subparser.set_defaults(execute=self.apply) def apply(self, args: Namespace) -> SwitchConfigCLI: return self def create_configuration(self) -> List[List[int]]: return [[101, 0, 0, 0]]

true

790bc6fa7673a8d96d10c13fc38f867949e749dc

2,324

py

Python

ucscsdk/mometa/adaptor/AdaptorEthCompQueueProfile.py

parag-may4/ucscsdk

2ea762fa070330e3a4e2c21b46b157469555405b

[ "Apache-2.0" ]

null

ucscsdk/mometa/adaptor/AdaptorEthCompQueueProfile.py

parag-may4/ucscsdk

2ea762fa070330e3a4e2c21b46b157469555405b

[ "Apache-2.0" ]

null

ucscsdk/mometa/adaptor/AdaptorEthCompQueueProfile.py

parag-may4/ucscsdk

2ea762fa070330e3a4e2c21b46b157469555405b

[ "Apache-2.0" ]

null

"""This module contains the general information for AdaptorEthCompQueueProfile ManagedObject.""" from ...ucscmo import ManagedObject from ...ucsccoremeta import UcscVersion, MoPropertyMeta, MoMeta from ...ucscmeta import VersionMeta class AdaptorEthCompQueueProfileConsts(): pass class AdaptorEthCompQueueProfile(ManagedObject): """This is AdaptorEthCompQueueProfile class.""" consts = AdaptorEthCompQueueProfileConsts() naming_props = set([]) mo_meta = MoMeta("AdaptorEthCompQueueProfile", "adaptorEthCompQueueProfile", "eth-comp-q", VersionMeta.Version111a, "InputOutput", 0x1f, [], ["admin", "ls-config-policy", "ls-network", "ls-server-policy"], [u'adaptorHostEthIfProfile', u'adaptorUsnicConnDef'], [], ["Get", "Set"]) prop_meta = { "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version111a, MoPropertyMeta.INTERNAL, None, None, None, r"""((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}""", [], []), "count": MoPropertyMeta("count", "count", "ushort", VersionMeta.Version111a, MoPropertyMeta.READ_WRITE, 0x2, None, None, None, [], ["1-2000"]), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []), "ring_size": MoPropertyMeta("ring_size", "ringSize", "ushort", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], ["1-1"]), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, 0x8, 0, 256, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version111a, MoPropertyMeta.READ_WRITE, 0x10, None, None, r"""((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}""", [], []), } prop_map = { "childAction": "child_action", "count": "count", "dn": "dn", "ringSize": "ring_size", "rn": "rn", "status": "status", } def __init__(self, parent_mo_or_dn, **kwargs): self._dirty_mask = 0 self.child_action = None self.count = None self.ring_size = None self.status = None ManagedObject.__init__(self, "AdaptorEthCompQueueProfile", parent_mo_or_dn, **kwargs)

49.446809

283

0.669105

from ...ucscmo import ManagedObject from ...ucsccoremeta import UcscVersion, MoPropertyMeta, MoMeta from ...ucscmeta import VersionMeta class AdaptorEthCompQueueProfileConsts(): pass class AdaptorEthCompQueueProfile(ManagedObject): consts = AdaptorEthCompQueueProfileConsts() naming_props = set([]) mo_meta = MoMeta("AdaptorEthCompQueueProfile", "adaptorEthCompQueueProfile", "eth-comp-q", VersionMeta.Version111a, "InputOutput", 0x1f, [], ["admin", "ls-config-policy", "ls-network", "ls-server-policy"], [u'adaptorHostEthIfProfile', u'adaptorUsnicConnDef'], [], ["Get", "Set"]) prop_meta = { "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version111a, MoPropertyMeta.INTERNAL, None, None, None, r"""((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}""", [], []), "count": MoPropertyMeta("count", "count", "ushort", VersionMeta.Version111a, MoPropertyMeta.READ_WRITE, 0x2, None, None, None, [], ["1-2000"]), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []), "ring_size": MoPropertyMeta("ring_size", "ringSize", "ushort", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, None, None, None, None, [], ["1-1"]), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, 0x8, 0, 256, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version111a, MoPropertyMeta.READ_WRITE, 0x10, None, None, r"""((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}""", [], []), } prop_map = { "childAction": "child_action", "count": "count", "dn": "dn", "ringSize": "ring_size", "rn": "rn", "status": "status", } def __init__(self, parent_mo_or_dn, **kwargs): self._dirty_mask = 0 self.child_action = None self.count = None self.ring_size = None self.status = None ManagedObject.__init__(self, "AdaptorEthCompQueueProfile", parent_mo_or_dn, **kwargs)

true

790bc74afceb54e8f3e9d007005e22897ef55221

6,404

py

Python

mycroft/skills/common_query_skill.py

assistent-cat/mycroft-core

6f8bae6ba136c9dd66ca47aaadd75e214d006190

[ "Apache-2.0" ]

2

2021-04-05T22:28:37.000Z

2021-06-16T00:24:41.000Z

mycroft/skills/common_query_skill.py

assistent-cat/mycroft-core

6f8bae6ba136c9dd66ca47aaadd75e214d006190

[ "Apache-2.0" ]

4

2021-06-08T20:55:12.000Z

2022-03-12T00:15:06.000Z

mycroft/skills/common_query_skill.py

assistent-cat/mycroft-core

6f8bae6ba136c9dd66ca47aaadd75e214d006190

[ "Apache-2.0" ]

2

2020-09-28T01:38:34.000Z

2020-12-03T03:14:32.000Z

# Copyright 2018 Mycroft AI Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from enum import IntEnum from abc import ABC, abstractmethod from .mycroft_skill import MycroftSkill class CQSMatchLevel(IntEnum): EXACT = 1 # Skill could find a specific answer for the question CATEGORY = 2 # Skill could find an answer from a category in the query GENERAL = 3 # The query could be processed as a general quer # Copy of CQSMatchLevel to use if the skill returns visual media CQSVisualMatchLevel = IntEnum('CQSVisualMatchLevel', [e.name for e in CQSMatchLevel]) def is_CQSVisualMatchLevel(match_level): return isinstance(match_level, type(CQSVisualMatchLevel.EXACT)) VISUAL_DEVICES = ['mycroft_mark_2'] def handles_visuals(platform): return platform in VISUAL_DEVICES class CommonQuerySkill(MycroftSkill, ABC): """Question answering skills should be based on this class. The skill author needs to implement `CQS_match_query_phrase` returning an answer and can optionally implement `CQS_action` to perform additional actions if the skill's answer is selected. This class works in conjunction with skill-query which collects answers from several skills presenting the best one available. """ def __init__(self, name=None, bus=None): super().__init__(name, bus) def bind(self, bus): """Overrides the default bind method of MycroftSkill. This registers messagebus handlers for the skill during startup but is nothing the skill author needs to consider. """ if bus: super().bind(bus) self.add_event('question:query', self.__handle_question_query) self.add_event('question:action', self.__handle_query_action) def __handle_question_query(self, message): search_phrase = message.data["phrase"] # First, notify the requestor that we are attempting to handle # (this extends a timeout while this skill looks for a match) self.bus.emit(message.response({"phrase": search_phrase, "skill_id": self.skill_id, "searching": True})) # Now invoke the CQS handler to let the skill perform its search result = self.CQS_match_query_phrase(search_phrase) if result: match = result[0] level = result[1] answer = result[2] callback = result[3] if len(result) > 3 else None confidence = self.__calc_confidence(match, search_phrase, level) self.bus.emit(message.response({"phrase": search_phrase, "skill_id": self.skill_id, "answer": answer, "callback_data": callback, "conf": confidence})) else: # Signal we are done (can't handle it) self.bus.emit(message.response({"phrase": search_phrase, "skill_id": self.skill_id, "searching": False})) def __calc_confidence(self, match, phrase, level): # Assume the more of the words that get consumed, the better the match consumed_pct = len(match.split()) / len(phrase.split()) if consumed_pct > 1.0: consumed_pct = 1.0 # Add bonus if match has visuals and the device supports them. platform = self.config_core.get('enclosure', {}).get('platform') if is_CQSVisualMatchLevel(level) and handles_visuals(platform): bonus = 0.1 else: bonus = 0 if int(level) == int(CQSMatchLevel.EXACT): return 0.9 + (consumed_pct / 10) + bonus elif int(level) == int(CQSMatchLevel.CATEGORY): return 0.6 + (consumed_pct / 10) + bonus elif int(level) == int(CQSMatchLevel.GENERAL): return 0.5 + (consumed_pct / 10) + bonus else: return 0.0 # should never happen def __handle_query_action(self, message): """Message handler for question:action. Extracts phrase and data from message forward this to the skills CQS_action method. """ if message.data["skill_id"] != self.skill_id: # Not for this skill! return phrase = message.data["phrase"] data = message.data.get("callback_data") # Invoke derived class to provide playback data self.CQS_action(phrase, data) @abstractmethod def CQS_match_query_phrase(self, phrase): """Analyze phrase to see if it is a play-able phrase with this skill. Needs to be implemented by the skill. Arguments: phrase (str): User phrase, "What is an aardwark" Returns: (match, CQSMatchLevel[, callback_data]) or None: Tuple containing a string with the appropriate matching phrase, the PlayMatch type, and optionally data to return in the callback if the match is selected. """ # Derived classes must implement this, e.g. return None def CQS_action(self, phrase, data): """Take additional action IF the skill is selected. The speech is handled by the common query but if the chosen skill wants to display media, set a context or prepare for sending information info over e-mail this can be implemented here. Args: phrase (str): User phrase uttered after "Play", e.g. "some music" data (dict): Callback data specified in match_query_phrase() """ # Derived classes may implement this if they use additional media # or wish to set context after being called. pass

39.288344

78

0.626327

from enum import IntEnum from abc import ABC, abstractmethod from .mycroft_skill import MycroftSkill class CQSMatchLevel(IntEnum): EXACT = 1 CATEGORY = 2 GENERAL = 3 CQSVisualMatchLevel = IntEnum('CQSVisualMatchLevel', [e.name for e in CQSMatchLevel]) def is_CQSVisualMatchLevel(match_level): return isinstance(match_level, type(CQSVisualMatchLevel.EXACT)) VISUAL_DEVICES = ['mycroft_mark_2'] def handles_visuals(platform): return platform in VISUAL_DEVICES class CommonQuerySkill(MycroftSkill, ABC): def __init__(self, name=None, bus=None): super().__init__(name, bus) def bind(self, bus): if bus: super().bind(bus) self.add_event('question:query', self.__handle_question_query) self.add_event('question:action', self.__handle_query_action) def __handle_question_query(self, message): search_phrase = message.data["phrase"] self.bus.emit(message.response({"phrase": search_phrase, "skill_id": self.skill_id, "searching": True})) result = self.CQS_match_query_phrase(search_phrase) if result: match = result[0] level = result[1] answer = result[2] callback = result[3] if len(result) > 3 else None confidence = self.__calc_confidence(match, search_phrase, level) self.bus.emit(message.response({"phrase": search_phrase, "skill_id": self.skill_id, "answer": answer, "callback_data": callback, "conf": confidence})) else: self.bus.emit(message.response({"phrase": search_phrase, "skill_id": self.skill_id, "searching": False})) def __calc_confidence(self, match, phrase, level): # Assume the more of the words that get consumed, the better the match consumed_pct = len(match.split()) / len(phrase.split()) if consumed_pct > 1.0: consumed_pct = 1.0 # Add bonus if match has visuals and the device supports them. platform = self.config_core.get('enclosure', {}).get('platform') if is_CQSVisualMatchLevel(level) and handles_visuals(platform): bonus = 0.1 else: bonus = 0 if int(level) == int(CQSMatchLevel.EXACT): return 0.9 + (consumed_pct / 10) + bonus elif int(level) == int(CQSMatchLevel.CATEGORY): return 0.6 + (consumed_pct / 10) + bonus elif int(level) == int(CQSMatchLevel.GENERAL): return 0.5 + (consumed_pct / 10) + bonus else: return 0.0 # should never happen def __handle_query_action(self, message): if message.data["skill_id"] != self.skill_id: # Not for this skill! return phrase = message.data["phrase"] data = message.data.get("callback_data") # Invoke derived class to provide playback data self.CQS_action(phrase, data) @abstractmethod def CQS_match_query_phrase(self, phrase): # Derived classes must implement this, e.g. return None def CQS_action(self, phrase, data): # Derived classes may implement this if they use additional media # or wish to set context after being called. pass

true

790bc912b9632f19ea762af1efef5229f787d170

27,416

py

Python

evaluate_3dpw_mine.py

akashsengupta1997/GraphCMR

0b8b05be4f711995ba50e414effbde98b6b11c5b

[ "BSD-3-Clause" ]

null

evaluate_3dpw_mine.py

akashsengupta1997/GraphCMR

0b8b05be4f711995ba50e414effbde98b6b11c5b

[ "BSD-3-Clause" ]

null

evaluate_3dpw_mine.py

akashsengupta1997/GraphCMR

0b8b05be4f711995ba50e414effbde98b6b11c5b

[ "BSD-3-Clause" ]

null

import os import numpy as np import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt import torch from torch.utils.data import DataLoader from tqdm import tqdm import argparse import cv2 import config from utils import Mesh from models import CMR from models.smpl_from_lib import SMPL from utils.pose_utils import compute_similarity_transform_batch, \ scale_and_translation_transform_batch from utils.cam_utils import orthographic_project_torch, undo_keypoint_normalisation from datasets.my_3dpw_eval_dataset import PW3DEvalDataset def evaluate_3dpw(model, eval_dataset, metrics, device, vis_save_path, num_workers=4, pin_memory=True, vis_every_n_batches=1000): eval_dataloader = DataLoader(eval_dataset, batch_size=1, shuffle=False, drop_last=True, num_workers=num_workers, pin_memory=pin_memory) smpl = SMPL(config.SMPL_MODEL_DIR, batch_size=1) smpl_male = SMPL(config.SMPL_MODEL_DIR, batch_size=1, gender='male') smpl_female = SMPL(config.SMPL_MODEL_DIR, batch_size=1, gender='female') smpl.to(device) smpl_male.to(device) smpl_female.to(device) J_regressor = torch.from_numpy(np.load(config.JOINT_REGRESSOR_H36M)).float() J_regressor_batch = J_regressor[None, :].to(device) if 'pve' in metrics: pve_smpl_sum = 0.0 pve_graph_sum = 0.0 pve_smpl_per_frame = [] pve_graph_per_frame = [] if 'pve_scale_corrected' in metrics: pve_scale_corrected_smpl_sum = 0.0 pve_scale_corrected_graph_sum = 0.0 pve_scale_corrected_smpl_per_frame = [] pve_scale_corrected_graph_per_frame = [] if 'pve_pa' in metrics: pve_pa_smpl_sum = 0.0 pve_pa_graph_sum = 0.0 pve_pa_smpl_per_frame = [] pve_pa_graph_per_frame = [] if 'pve-t' in metrics: pvet_sum = 0.0 pvet_per_frame = [] if 'pve-t_scale_corrected' in metrics: pvet_scale_corrected_sum = 0.0 pvet_scale_corrected_per_frame = [] if 'mpjpe' in metrics: mpjpe_smpl_sum = 0.0 mpjpe_graph_sum = 0.0 mpjpe_smpl_per_frame = [] mpjpe_graph_per_frame = [] if 'mpjpe_scale_corrected' in metrics: mpjpe_scale_corrected_smpl_sum = 0.0 mpjpe_scale_corrected_graph_sum = 0.0 mpjpe_scale_corrected_smpl_per_frame = [] mpjpe_scale_corrected_graph_per_frame = [] if 'j3d_rec_err' in metrics: j3d_rec_err_smpl_sum = 0.0 j3d_rec_err_graph_sum = 0.0 j3d_rec_err_smpl_per_frame = [] j3d_rec_err_graph_per_frame = [] if 'pve_2d' in metrics: pve_2d_smpl_sum = 0.0 pve_2d_graph_sum = 0.0 if 'pve_2d_scale_corrected' in metrics: pve_2d_scale_corrected_smpl_sum = 0.0 pve_2d_scale_corrected_graph_sum = 0.0 if 'pve_2d_pa' in metrics: pve_2d_pa_smpl_sum = 0.0 pve_2d_pa_graph_sum = 0.0 num_samples = 0 num_vertices = 6890 num_joints3d = 14 model.eval() for batch_num, samples_batch in enumerate(tqdm(eval_dataloader)): # ------------------------------- TARGETS and INPUTS ------------------------------- input = samples_batch['input'] input = input.to(device) target_pose = samples_batch['pose'].to(device) target_shape = samples_batch['shape'].to(device) target_gender = samples_batch['gender'][0] if target_gender == 'm': target_smpl_output = smpl_male(body_pose=target_pose[:, 3:], global_orient=target_pose[:, :3], betas=target_shape) target_vertices = target_smpl_output.vertices target_reposed_smpl_output = smpl_male(betas=target_shape) target_reposed_vertices = target_reposed_smpl_output.vertices target_joints_h36m = torch.matmul(J_regressor_batch, target_vertices) target_joints_h36mlsp = target_joints_h36m[:, config.H36M_TO_J14, :] elif target_gender == 'f': target_smpl_output = smpl_female(body_pose=target_pose[:, 3:], global_orient=target_pose[:, :3], betas=target_shape) target_vertices = target_smpl_output.vertices target_reposed_smpl_output = smpl_female(betas=target_shape) target_reposed_vertices = target_reposed_smpl_output.vertices target_joints_h36m = torch.matmul(J_regressor_batch, target_vertices) target_joints_h36mlsp = target_joints_h36m[:, config.H36M_TO_J14, :] # ------------------------------- PREDICTIONS ------------------------------- pred_vertices, pred_vertices_smpl, pred_camera, pred_rotmat, pred_betas = model(input) pred_vertices_projected2d = orthographic_project_torch(pred_vertices, pred_camera) pred_vertices_projected2d = undo_keypoint_normalisation(pred_vertices_projected2d, input.shape[-1]) pred_vertices_smpl_projected2d = orthographic_project_torch(pred_vertices_smpl, pred_camera) pred_vertices_smpl_projected2d = undo_keypoint_normalisation(pred_vertices_smpl_projected2d, input.shape[-1]) pred_reposed_smpl_output = smpl(betas=pred_betas) pred_reposed_vertices = pred_reposed_smpl_output.vertices pred_joints_h36m = torch.matmul(J_regressor_batch, pred_vertices) pred_joints_h36mlsp = pred_joints_h36m[:, config.H36M_TO_J14, :] pred_joints_smpl_h36m = torch.matmul(J_regressor_batch, pred_vertices_smpl) pred_joints_smpl_h36mlsp = pred_joints_smpl_h36m[:, config.H36M_TO_J14, :] # Numpy-fying target_vertices = target_vertices.cpu().detach().numpy() target_reposed_vertices = target_reposed_vertices.cpu().detach().numpy() target_joints_h36mlsp = target_joints_h36mlsp.cpu().detach().numpy() pred_vertices = pred_vertices.cpu().detach().numpy() pred_vertices_smpl = pred_vertices_smpl.cpu().detach().numpy() pred_vertices_projected2d = pred_vertices_projected2d.cpu().detach().numpy() pred_vertices_smpl_projected2d = pred_vertices_smpl_projected2d.cpu().detach().numpy() pred_reposed_vertices = pred_reposed_vertices.cpu().detach().numpy() pred_joints_h36mlsp = pred_joints_h36mlsp.cpu().detach().numpy() pred_joints_smpl_h36mlsp = pred_joints_smpl_h36mlsp.cpu().detach().numpy() # ------------------------------- METRICS ------------------------------- if 'pve' in metrics: pve_smpl_batch = np.linalg.norm(pred_vertices_smpl - target_vertices, axis=-1) # (1, 6890) pve_graph_batch = np.linalg.norm(pred_vertices - target_vertices, axis=-1) pve_smpl_sum += np.sum(pve_smpl_batch) # scalar pve_graph_sum += np.sum(pve_graph_batch) pve_smpl_per_frame.append(np.mean(pve_smpl_batch, axis=-1)) pve_graph_per_frame.append(np.mean(pve_graph_batch, axis=-1)) # Scale and translation correction if 'pve_scale_corrected' in metrics: pred_vertices_smpl_sc = scale_and_translation_transform_batch(pred_vertices_smpl, target_vertices) pred_vertices_sc = scale_and_translation_transform_batch(pred_vertices, target_vertices) pve_sc_smpl_batch = np.linalg.norm(pred_vertices_smpl_sc - target_vertices, axis=-1) # (1, 6890) pve_sc_graph_batch = np.linalg.norm(pred_vertices_sc - target_vertices, axis=-1) # (1, 6890) pve_scale_corrected_smpl_sum += np.sum(pve_sc_smpl_batch) # scalar pve_scale_corrected_graph_sum += np.sum(pve_sc_graph_batch) # scalar pve_scale_corrected_smpl_per_frame.append(np.mean(pve_sc_smpl_batch, axis=-1)) pve_scale_corrected_graph_per_frame.append(np.mean(pve_sc_graph_batch, axis=-1)) # Procrustes analysis if 'pve_pa' in metrics: pred_vertices_smpl_pa = compute_similarity_transform_batch(pred_vertices_smpl, target_vertices) pred_vertices_pa = compute_similarity_transform_batch(pred_vertices, target_vertices) pve_pa_smpl_batch = np.linalg.norm(pred_vertices_smpl_pa - target_vertices, axis=-1) # (1, 6890) pve_pa_graph_batch = np.linalg.norm(pred_vertices_pa - target_vertices, axis=-1) # (1, 6890) pve_pa_smpl_sum += np.sum(pve_pa_smpl_batch) # scalar pve_pa_graph_sum += np.sum(pve_pa_graph_batch) # scalar pve_pa_smpl_per_frame.append(np.mean(pve_pa_smpl_batch, axis=-1)) pve_pa_graph_per_frame.append(np.mean(pve_pa_graph_batch, axis=-1)) if 'pve-t' in metrics: pvet_batch = np.linalg.norm(pred_reposed_vertices - target_reposed_vertices, axis=-1) pvet_sum += np.sum(pvet_batch) pvet_per_frame.append(np.mean(pvet_batch, axis=-1)) # Scale and translation correction if 'pve-t_scale_corrected' in metrics: pred_reposed_vertices_sc = scale_and_translation_transform_batch(pred_reposed_vertices, target_reposed_vertices) pvet_scale_corrected_batch = np.linalg.norm(pred_reposed_vertices_sc - target_reposed_vertices, axis=-1) # (bs, 6890) pvet_scale_corrected_sum += np.sum(pvet_scale_corrected_batch) # scalar pvet_scale_corrected_per_frame.append(np.mean(pvet_scale_corrected_batch, axis=-1)) if 'mpjpe' in metrics: mpjpe_smpl_batch = np.linalg.norm(pred_joints_smpl_h36mlsp - target_joints_h36mlsp, axis=-1) # (bs, 14) mpjpe_graph_batch = np.linalg.norm(pred_joints_h36mlsp - target_joints_h36mlsp, axis=-1) # (bs, 14) mpjpe_smpl_sum += np.sum(mpjpe_smpl_batch) mpjpe_graph_sum += np.sum(mpjpe_graph_batch) mpjpe_smpl_per_frame.append(np.mean(mpjpe_smpl_batch, axis=-1)) mpjpe_graph_per_frame.append(np.mean(mpjpe_graph_batch, axis=-1)) # Scale and translation correction if 'mpjpe_scale_corrected' in metrics: pred_joints_smpl_h36mlsp_sc = scale_and_translation_transform_batch(pred_joints_smpl_h36mlsp, target_joints_h36mlsp) pred_joints_h36mlsp_sc = scale_and_translation_transform_batch(pred_joints_h36mlsp, target_joints_h36mlsp) mpjpe_scale_corrected_smpl_batch = np.linalg.norm(pred_joints_smpl_h36mlsp_sc - target_joints_h36mlsp, axis=-1) # (bs, 14) mpjpe_scale_corrected_graph_batch = np.linalg.norm(pred_joints_h36mlsp_sc - target_joints_h36mlsp, axis=-1) # (bs, 14) mpjpe_scale_corrected_smpl_sum += np.sum(mpjpe_scale_corrected_smpl_batch) mpjpe_scale_corrected_graph_sum += np.sum(mpjpe_scale_corrected_graph_batch) mpjpe_scale_corrected_smpl_per_frame.append(np.mean(mpjpe_scale_corrected_smpl_batch, axis=-1)) mpjpe_scale_corrected_graph_per_frame.append(np.mean(mpjpe_scale_corrected_graph_batch, axis=-1)) # Procrustes analysis if 'j3d_rec_err' in metrics: pred_joints_smpl_h36mlsp_pa = compute_similarity_transform_batch(pred_joints_smpl_h36mlsp, target_joints_h36mlsp) pred_joints_h36mlsp_pa = compute_similarity_transform_batch(pred_joints_h36mlsp, target_joints_h36mlsp) j3d_rec_err_smpl_batch = np.linalg.norm(pred_joints_smpl_h36mlsp_pa - target_joints_h36mlsp, axis=-1) # (bs, 14) j3d_rec_err_graph_batch = np.linalg.norm(pred_joints_h36mlsp_pa - target_joints_h36mlsp, axis=-1) # (bs, 14) j3d_rec_err_smpl_sum += np.sum(j3d_rec_err_smpl_batch) j3d_rec_err_graph_sum += np.sum(j3d_rec_err_graph_batch) j3d_rec_err_smpl_per_frame.append(np.mean(j3d_rec_err_smpl_batch, axis=-1)) j3d_rec_err_graph_per_frame.append(np.mean(j3d_rec_err_graph_batch, axis=-1)) if 'pve_2d' in metrics: pred_vertices_smpl_2d = pred_vertices_smpl[:, :, :2] pred_vertices_2d = pred_vertices[:, :, :2] target_vertices_2d = target_vertices[:, :, :2] pve_2d_smpl_batch = np.linalg.norm(pred_vertices_smpl_2d - target_vertices_2d, axis=-1) # (bs, 6890) pve_2d_graph_batch = np.linalg.norm(pred_vertices_2d - target_vertices_2d, axis=-1) # (bs, 6890) pve_2d_smpl_sum += np.sum(pve_2d_smpl_batch) pve_2d_graph_sum += np.sum(pve_2d_graph_batch) # Scale and translation correction if 'pve_2d_scale_corrected' in metrics: pred_vertices_smpl_sc = scale_and_translation_transform_batch(pred_vertices_smpl, target_vertices) pred_vertices_sc = scale_and_translation_transform_batch(pred_vertices, target_vertices) pred_vertices_smpl_2d_sc = pred_vertices_smpl_sc[:, :, :2] pred_vertices_2d_sc = pred_vertices_sc[:, :, :2] target_vertices_2d = target_vertices[:, :, :2] pve_2d_sc_smpl_batch = np.linalg.norm(pred_vertices_smpl_2d_sc - target_vertices_2d, axis=-1) # (bs, 6890) pve_2d_sc_graph_batch = np.linalg.norm(pred_vertices_2d_sc - target_vertices_2d, axis=-1) # (bs, 6890) pve_2d_scale_corrected_smpl_sum += np.sum(pve_2d_sc_smpl_batch) pve_2d_scale_corrected_graph_sum += np.sum(pve_2d_sc_graph_batch) # Procrustes analysis if 'pve_2d_pa' in metrics: pred_vertices_smpl_pa = compute_similarity_transform_batch(pred_vertices_smpl, target_vertices) pred_vertices_pa = compute_similarity_transform_batch(pred_vertices, target_vertices) pred_vertices_smpl_2d_pa = pred_vertices_smpl_pa[:, :, :2] pred_vertices_2d_pa = pred_vertices_pa[:, :, :2] target_vertices_2d = target_vertices[:, :, :2] pve_2d_pa_smpl_batch = np.linalg.norm(pred_vertices_smpl_2d_pa - target_vertices_2d, axis=-1) # (bs, 6890) pve_2d_pa_graph_batch = np.linalg.norm(pred_vertices_2d_pa - target_vertices_2d, axis=-1) # (bs, 6890) pve_2d_pa_smpl_sum += np.sum(pve_2d_pa_smpl_batch) pve_2d_pa_graph_sum += np.sum(pve_2d_pa_graph_batch) num_samples += target_pose.shape[0] # ------------------------------- VISUALISE ------------------------------- if vis_every_n_batches is not None: if batch_num % vis_every_n_batches == 0: vis_imgs = samples_batch['vis_img'].numpy() vis_imgs = np.transpose(vis_imgs, [0, 2, 3, 1]) fnames = samples_batch['fname'] plt.figure(figsize=(16, 12)) plt.subplot(341) plt.imshow(vis_imgs[0]) plt.subplot(342) plt.imshow(vis_imgs[0]) plt.scatter(pred_vertices_projected2d[0, :, 0], pred_vertices_projected2d[0, :, 1], s=0.1, c='r') plt.subplot(343) plt.imshow(vis_imgs[0]) plt.scatter(pred_vertices_smpl_projected2d[0, :, 0], pred_vertices_smpl_projected2d[0, :, 1], s=0.1, c='r') plt.subplot(345) plt.scatter(target_vertices[0, :, 0], target_vertices[0, :, 1], s=0.1, c='b') plt.scatter(pred_vertices[0, :, 0], pred_vertices[0, :, 1], s=0.1, c='r') plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(346) plt.scatter(target_vertices[0, :, 0], target_vertices[0, :, 1], s=0.1, c='b') plt.scatter(pred_vertices_smpl[0, :, 0], pred_vertices_smpl[0, :, 1], s=0.1, c='r') plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(347) plt.scatter(target_vertices[0, :, 0], target_vertices[0, :, 1], s=0.1, c='b') plt.scatter(pred_vertices_pa[0, :, 0], pred_vertices_pa[0, :, 1], s=0.1, c='r') plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(348) plt.scatter(target_vertices[0, :, 0], target_vertices[0, :, 1], s=0.1, c='b') plt.scatter(pred_vertices_smpl_pa[0, :, 0], pred_vertices_smpl_pa[0, :, 1], s=0.1, c='r') plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(349) plt.scatter(target_reposed_vertices[0, :, 0], target_reposed_vertices[0, :, 1], s=0.1, c='b') plt.scatter(pred_reposed_vertices_sc[0, :, 0], pred_reposed_vertices_sc[0, :, 1], s=0.1, c='r') plt.gca().set_aspect('equal', adjustable='box') plt.subplot(3, 4, 10) for j in range(num_joints3d): plt.scatter(pred_joints_h36mlsp[0, j, 0], pred_joints_h36mlsp[0, j, 1], c='r') plt.scatter(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], c='b') plt.text(pred_joints_h36mlsp[0, j, 0], pred_joints_h36mlsp[0, j, 1], s=str(j)) plt.text(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], s=str(j)) plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(3, 4, 11) for j in range(num_joints3d): plt.scatter(pred_joints_h36mlsp_pa[0, j, 0], pred_joints_h36mlsp_pa[0, j, 1], c='r') plt.scatter(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], c='b') plt.text(pred_joints_h36mlsp_pa[0, j, 0], pred_joints_h36mlsp_pa[0, j, 1], s=str(j)) plt.text(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], s=str(j)) plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(3, 4, 12) for j in range(num_joints3d): plt.scatter(pred_joints_smpl_h36mlsp_pa[0, j, 0], pred_joints_smpl_h36mlsp_pa[0, j, 1], c='r') plt.scatter(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], c='b') plt.text(pred_joints_smpl_h36mlsp_pa[0, j, 0], pred_joints_smpl_h36mlsp_pa[0, j, 1], s=str(j)) plt.text(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], s=str(j)) plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') # plt.show() save_fig_path = os.path.join(vis_save_path, fnames[0]) plt.savefig(save_fig_path, bbox_inches='tight') plt.close() if 'pve' in metrics: pve_smpl = pve_smpl_sum / (num_samples * num_vertices) print('PVE SMPL: {:.5f}'.format(pve_smpl)) pve_graph = pve_graph_sum / (num_samples * num_vertices) print('PVE GRAPH: {:.5f}'.format(pve_graph)) pve_smpl_per_frame = np.concatenate(pve_smpl_per_frame, axis=0) pve_graph_per_frame = np.concatenate(pve_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'pve_per_frame.npy'), pve_smpl_per_frame) np.save(os.path.join(save_path, 'pve_graph_per_frame.npy'), pve_graph_per_frame) if 'pve_scale_corrected' in metrics: pve_sc_smpl = pve_scale_corrected_smpl_sum / (num_samples * num_vertices) print('PVE SC SMPL: {:.5f}'.format(pve_sc_smpl)) pve_sc_graph = pve_scale_corrected_graph_sum / (num_samples * num_vertices) print('PVE SC GRAPH: {:.5f}'.format(pve_sc_graph)) pve_scale_corrected_smpl_per_frame = np.concatenate(pve_scale_corrected_smpl_per_frame, axis=0) pve_scale_corrected_graph_per_frame = np.concatenate(pve_scale_corrected_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'pve_scale_corrected_per_frame.npy'), pve_scale_corrected_smpl_per_frame) np.save(os.path.join(save_path, 'pve_scale_corrected_graph_per_frame.npy'), pve_scale_corrected_graph_per_frame) if 'pve_pa' in metrics: pve_pa_smpl = pve_pa_smpl_sum / (num_samples * num_vertices) print('PVE PA SMPL: {:.5f}'.format(pve_pa_smpl)) pve_pa_graph = pve_pa_graph_sum / (num_samples * num_vertices) print('PVE PA GRAPH: {:.5f}'.format(pve_pa_graph)) pve_pa_smpl_per_frame = np.concatenate(pve_pa_smpl_per_frame, axis=0) pve_pa_graph_per_frame = np.concatenate(pve_pa_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'pve_pa_per_frame.npy'), pve_pa_smpl_per_frame) np.save(os.path.join(save_path, 'pve_pa_graph_per_frame.npy'), pve_pa_graph_per_frame) if 'pve-t' in metrics: pvet = pvet_sum / (num_samples * num_vertices) print('PVE-T: {:.5f}'.format(pvet)) pvet_per_frame = np.concatenate(pvet_per_frame, axis=0) np.save(os.path.join(save_path, 'pvet_per_frame.npy'), pvet_per_frame) if 'pve-t_scale_corrected' in metrics: pvet_sc = pvet_scale_corrected_sum / (num_samples * num_vertices) print('PVE-T SC: {:.5f}'.format(pvet_sc)) pvet_scale_corrected_per_frame = np.concatenate(pvet_scale_corrected_per_frame, axis=0) np.save(os.path.join(save_path, 'pvet_scale_corrected_per_frame.npy'), pvet_scale_corrected_per_frame) if 'mpjpe' in metrics: mpjpe_smpl = mpjpe_smpl_sum / (num_samples * num_joints3d) print('MPJPE SMPL: {:.5f}'.format(mpjpe_smpl)) mpjpe_graph = mpjpe_graph_sum / (num_samples * num_joints3d) print('MPJPE GRAPH: {:.5f}'.format(mpjpe_graph)) mpjpe_smpl_per_frame = np.concatenate(mpjpe_smpl_per_frame, axis=0) mpjpe_graph_per_frame = np.concatenate(mpjpe_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'mpjpe_per_frame.npy'), mpjpe_smpl_per_frame) np.save(os.path.join(save_path, 'mpjpe_graph_per_frame.npy'), mpjpe_graph_per_frame) if 'mpjpe_scale_corrected' in metrics: mpjpe_sc_smpl = mpjpe_scale_corrected_smpl_sum / (num_samples * num_joints3d) print('MPJPE SC SMPL: {:.5f}'.format(mpjpe_sc_smpl)) mpjpe_sc_graph = mpjpe_scale_corrected_graph_sum / (num_samples * num_joints3d) print('MPJPE SC GRAPH: {:.5f}'.format(mpjpe_sc_graph)) mpjpe_scale_corrected_smpl_per_frame = np.concatenate( mpjpe_scale_corrected_smpl_per_frame, axis=0) mpjpe_scale_corrected_graph_per_frame = np.concatenate( mpjpe_scale_corrected_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'mpjpe_scale_corrected_per_frame.npy'), mpjpe_scale_corrected_smpl_per_frame) np.save(os.path.join(save_path, 'mpjpe_scale_corrected_graph_per_frame.npy'), mpjpe_scale_corrected_graph_per_frame) if 'j3d_rec_err' in metrics: j3d_rec_err_smpl = j3d_rec_err_smpl_sum / (num_samples * num_joints3d) print('Rec Err SMPL: {:.5f}'.format(j3d_rec_err_smpl)) j3d_rec_err_graph = j3d_rec_err_graph_sum / (num_samples * num_joints3d) print('Rec Err GRAPH: {:.5f}'.format(j3d_rec_err_graph)) j3d_rec_err_smpl_per_frame = np.concatenate(j3d_rec_err_smpl_per_frame, axis=0) j3d_rec_err_graph_per_frame = np.concatenate(j3d_rec_err_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'j3d_rec_err_per_frame.npy'), j3d_rec_err_smpl_per_frame) np.save(os.path.join(save_path, 'j3d_rec_err_graph_per_frame.npy'), j3d_rec_err_graph_per_frame) if 'pve_2d' in metrics: pve_2d_smpl = pve_2d_smpl_sum / (num_samples * num_vertices) print('PVE 2D SMPL: {:.5f}'.format(pve_2d_smpl)) pve_2d_graph = pve_2d_graph_sum / (num_samples * num_vertices) print('PVE 2D GRAPH: {:.5f}'.format(pve_2d_graph)) if 'pve_2d_scale_corrected' in metrics: pve_2d_sc_smpl = pve_2d_scale_corrected_smpl_sum / (num_samples * num_vertices) print('PVE 2D SC SMPL: {:.5f}'.format(pve_2d_sc_smpl)) pve_2d_sc_graph = pve_2d_scale_corrected_graph_sum / (num_samples * num_vertices) print('PVE 2D SC GRAPH: {:.5f}'.format(pve_2d_sc_graph)) if 'pve_2d_pa' in metrics: pve_2d_pa_smpl = pve_2d_pa_smpl_sum / (num_samples * num_vertices) print('PVE 2D PA SMPL: {:.5f}'.format(pve_2d_pa_smpl)) pve_2d_pa_graph = pve_2d_pa_graph_sum / (num_samples * num_vertices) print('PVE 2D PA GRAPH: {:.5f}'.format(pve_2d_pa_graph)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--checkpoint', default=None, help='Path to network checkpoint') parser.add_argument('--gpu', default="0", type=str, help='GPU') args = parser.parse_args() # Device os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') # Load model mesh = Mesh(device=device) # Our pretrained networks have 5 residual blocks with 256 channels. # You might want to change this if you use a different architecture. model = CMR(mesh, 5, 256, pretrained_checkpoint=args.checkpoint, device=device) model.to(device) model.eval() # Setup evaluation dataset dataset_path = '/scratch2/as2562/datasets/3DPW/test' dataset = PW3DEvalDataset(dataset_path, img_wh=config.INPUT_RES) print("Eval examples found:", len(dataset)) # Metrics metrics = ['pve', 'pve-t', 'pve_pa', 'pve-t_pa', 'mpjpe', 'j3d_rec_err', 'pve_2d', 'pve_2d_pa', 'pve_2d_scale_corrected', 'pve_scale_corrected', 'pve-t_scale_corrected', 'mpjpe_scale_corrected'] save_path = '/data/cvfs/as2562/GraphCMR/evaluations/3dpw' if not os.path.exists(save_path): os.makedirs(save_path) # Run evaluation evaluate_3dpw(model=model, eval_dataset=dataset, metrics=metrics, device=device, vis_save_path=save_path, num_workers=4, pin_memory=True, vis_every_n_batches=1000)

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import os import numpy as np import matplotlib matplotlib.use('agg') import matplotlib.pyplot as plt import torch from torch.utils.data import DataLoader from tqdm import tqdm import argparse import cv2 import config from utils import Mesh from models import CMR from models.smpl_from_lib import SMPL from utils.pose_utils import compute_similarity_transform_batch, \ scale_and_translation_transform_batch from utils.cam_utils import orthographic_project_torch, undo_keypoint_normalisation from datasets.my_3dpw_eval_dataset import PW3DEvalDataset def evaluate_3dpw(model, eval_dataset, metrics, device, vis_save_path, num_workers=4, pin_memory=True, vis_every_n_batches=1000): eval_dataloader = DataLoader(eval_dataset, batch_size=1, shuffle=False, drop_last=True, num_workers=num_workers, pin_memory=pin_memory) smpl = SMPL(config.SMPL_MODEL_DIR, batch_size=1) smpl_male = SMPL(config.SMPL_MODEL_DIR, batch_size=1, gender='male') smpl_female = SMPL(config.SMPL_MODEL_DIR, batch_size=1, gender='female') smpl.to(device) smpl_male.to(device) smpl_female.to(device) J_regressor = torch.from_numpy(np.load(config.JOINT_REGRESSOR_H36M)).float() J_regressor_batch = J_regressor[None, :].to(device) if 'pve' in metrics: pve_smpl_sum = 0.0 pve_graph_sum = 0.0 pve_smpl_per_frame = [] pve_graph_per_frame = [] if 'pve_scale_corrected' in metrics: pve_scale_corrected_smpl_sum = 0.0 pve_scale_corrected_graph_sum = 0.0 pve_scale_corrected_smpl_per_frame = [] pve_scale_corrected_graph_per_frame = [] if 'pve_pa' in metrics: pve_pa_smpl_sum = 0.0 pve_pa_graph_sum = 0.0 pve_pa_smpl_per_frame = [] pve_pa_graph_per_frame = [] if 'pve-t' in metrics: pvet_sum = 0.0 pvet_per_frame = [] if 'pve-t_scale_corrected' in metrics: pvet_scale_corrected_sum = 0.0 pvet_scale_corrected_per_frame = [] if 'mpjpe' in metrics: mpjpe_smpl_sum = 0.0 mpjpe_graph_sum = 0.0 mpjpe_smpl_per_frame = [] mpjpe_graph_per_frame = [] if 'mpjpe_scale_corrected' in metrics: mpjpe_scale_corrected_smpl_sum = 0.0 mpjpe_scale_corrected_graph_sum = 0.0 mpjpe_scale_corrected_smpl_per_frame = [] mpjpe_scale_corrected_graph_per_frame = [] if 'j3d_rec_err' in metrics: j3d_rec_err_smpl_sum = 0.0 j3d_rec_err_graph_sum = 0.0 j3d_rec_err_smpl_per_frame = [] j3d_rec_err_graph_per_frame = [] if 'pve_2d' in metrics: pve_2d_smpl_sum = 0.0 pve_2d_graph_sum = 0.0 if 'pve_2d_scale_corrected' in metrics: pve_2d_scale_corrected_smpl_sum = 0.0 pve_2d_scale_corrected_graph_sum = 0.0 if 'pve_2d_pa' in metrics: pve_2d_pa_smpl_sum = 0.0 pve_2d_pa_graph_sum = 0.0 num_samples = 0 num_vertices = 6890 num_joints3d = 14 model.eval() for batch_num, samples_batch in enumerate(tqdm(eval_dataloader)): input = samples_batch['input'] input = input.to(device) target_pose = samples_batch['pose'].to(device) target_shape = samples_batch['shape'].to(device) target_gender = samples_batch['gender'][0] if target_gender == 'm': target_smpl_output = smpl_male(body_pose=target_pose[:, 3:], global_orient=target_pose[:, :3], betas=target_shape) target_vertices = target_smpl_output.vertices target_reposed_smpl_output = smpl_male(betas=target_shape) target_reposed_vertices = target_reposed_smpl_output.vertices target_joints_h36m = torch.matmul(J_regressor_batch, target_vertices) target_joints_h36mlsp = target_joints_h36m[:, config.H36M_TO_J14, :] elif target_gender == 'f': target_smpl_output = smpl_female(body_pose=target_pose[:, 3:], global_orient=target_pose[:, :3], betas=target_shape) target_vertices = target_smpl_output.vertices target_reposed_smpl_output = smpl_female(betas=target_shape) target_reposed_vertices = target_reposed_smpl_output.vertices target_joints_h36m = torch.matmul(J_regressor_batch, target_vertices) target_joints_h36mlsp = target_joints_h36m[:, config.H36M_TO_J14, :] pred_vertices, pred_vertices_smpl, pred_camera, pred_rotmat, pred_betas = model(input) pred_vertices_projected2d = orthographic_project_torch(pred_vertices, pred_camera) pred_vertices_projected2d = undo_keypoint_normalisation(pred_vertices_projected2d, input.shape[-1]) pred_vertices_smpl_projected2d = orthographic_project_torch(pred_vertices_smpl, pred_camera) pred_vertices_smpl_projected2d = undo_keypoint_normalisation(pred_vertices_smpl_projected2d, input.shape[-1]) pred_reposed_smpl_output = smpl(betas=pred_betas) pred_reposed_vertices = pred_reposed_smpl_output.vertices pred_joints_h36m = torch.matmul(J_regressor_batch, pred_vertices) pred_joints_h36mlsp = pred_joints_h36m[:, config.H36M_TO_J14, :] pred_joints_smpl_h36m = torch.matmul(J_regressor_batch, pred_vertices_smpl) pred_joints_smpl_h36mlsp = pred_joints_smpl_h36m[:, config.H36M_TO_J14, :] target_vertices = target_vertices.cpu().detach().numpy() target_reposed_vertices = target_reposed_vertices.cpu().detach().numpy() target_joints_h36mlsp = target_joints_h36mlsp.cpu().detach().numpy() pred_vertices = pred_vertices.cpu().detach().numpy() pred_vertices_smpl = pred_vertices_smpl.cpu().detach().numpy() pred_vertices_projected2d = pred_vertices_projected2d.cpu().detach().numpy() pred_vertices_smpl_projected2d = pred_vertices_smpl_projected2d.cpu().detach().numpy() pred_reposed_vertices = pred_reposed_vertices.cpu().detach().numpy() pred_joints_h36mlsp = pred_joints_h36mlsp.cpu().detach().numpy() pred_joints_smpl_h36mlsp = pred_joints_smpl_h36mlsp.cpu().detach().numpy() if 'pve' in metrics: pve_smpl_batch = np.linalg.norm(pred_vertices_smpl - target_vertices, axis=-1) pve_graph_batch = np.linalg.norm(pred_vertices - target_vertices, axis=-1) pve_smpl_sum += np.sum(pve_smpl_batch) pve_graph_sum += np.sum(pve_graph_batch) pve_smpl_per_frame.append(np.mean(pve_smpl_batch, axis=-1)) pve_graph_per_frame.append(np.mean(pve_graph_batch, axis=-1)) if 'pve_scale_corrected' in metrics: pred_vertices_smpl_sc = scale_and_translation_transform_batch(pred_vertices_smpl, target_vertices) pred_vertices_sc = scale_and_translation_transform_batch(pred_vertices, target_vertices) pve_sc_smpl_batch = np.linalg.norm(pred_vertices_smpl_sc - target_vertices, axis=-1) pve_sc_graph_batch = np.linalg.norm(pred_vertices_sc - target_vertices, axis=-1) pve_scale_corrected_smpl_sum += np.sum(pve_sc_smpl_batch) pve_scale_corrected_graph_sum += np.sum(pve_sc_graph_batch) pve_scale_corrected_smpl_per_frame.append(np.mean(pve_sc_smpl_batch, axis=-1)) pve_scale_corrected_graph_per_frame.append(np.mean(pve_sc_graph_batch, axis=-1)) if 'pve_pa' in metrics: pred_vertices_smpl_pa = compute_similarity_transform_batch(pred_vertices_smpl, target_vertices) pred_vertices_pa = compute_similarity_transform_batch(pred_vertices, target_vertices) pve_pa_smpl_batch = np.linalg.norm(pred_vertices_smpl_pa - target_vertices, axis=-1) pve_pa_graph_batch = np.linalg.norm(pred_vertices_pa - target_vertices, axis=-1) pve_pa_smpl_sum += np.sum(pve_pa_smpl_batch) pve_pa_graph_sum += np.sum(pve_pa_graph_batch) pve_pa_smpl_per_frame.append(np.mean(pve_pa_smpl_batch, axis=-1)) pve_pa_graph_per_frame.append(np.mean(pve_pa_graph_batch, axis=-1)) if 'pve-t' in metrics: pvet_batch = np.linalg.norm(pred_reposed_vertices - target_reposed_vertices, axis=-1) pvet_sum += np.sum(pvet_batch) pvet_per_frame.append(np.mean(pvet_batch, axis=-1)) if 'pve-t_scale_corrected' in metrics: pred_reposed_vertices_sc = scale_and_translation_transform_batch(pred_reposed_vertices, target_reposed_vertices) pvet_scale_corrected_batch = np.linalg.norm(pred_reposed_vertices_sc - target_reposed_vertices, axis=-1) pvet_scale_corrected_sum += np.sum(pvet_scale_corrected_batch) pvet_scale_corrected_per_frame.append(np.mean(pvet_scale_corrected_batch, axis=-1)) if 'mpjpe' in metrics: mpjpe_smpl_batch = np.linalg.norm(pred_joints_smpl_h36mlsp - target_joints_h36mlsp, axis=-1) mpjpe_graph_batch = np.linalg.norm(pred_joints_h36mlsp - target_joints_h36mlsp, axis=-1) mpjpe_smpl_sum += np.sum(mpjpe_smpl_batch) mpjpe_graph_sum += np.sum(mpjpe_graph_batch) mpjpe_smpl_per_frame.append(np.mean(mpjpe_smpl_batch, axis=-1)) mpjpe_graph_per_frame.append(np.mean(mpjpe_graph_batch, axis=-1)) if 'mpjpe_scale_corrected' in metrics: pred_joints_smpl_h36mlsp_sc = scale_and_translation_transform_batch(pred_joints_smpl_h36mlsp, target_joints_h36mlsp) pred_joints_h36mlsp_sc = scale_and_translation_transform_batch(pred_joints_h36mlsp, target_joints_h36mlsp) mpjpe_scale_corrected_smpl_batch = np.linalg.norm(pred_joints_smpl_h36mlsp_sc - target_joints_h36mlsp, axis=-1) mpjpe_scale_corrected_graph_batch = np.linalg.norm(pred_joints_h36mlsp_sc - target_joints_h36mlsp, axis=-1) mpjpe_scale_corrected_smpl_sum += np.sum(mpjpe_scale_corrected_smpl_batch) mpjpe_scale_corrected_graph_sum += np.sum(mpjpe_scale_corrected_graph_batch) mpjpe_scale_corrected_smpl_per_frame.append(np.mean(mpjpe_scale_corrected_smpl_batch, axis=-1)) mpjpe_scale_corrected_graph_per_frame.append(np.mean(mpjpe_scale_corrected_graph_batch, axis=-1)) if 'j3d_rec_err' in metrics: pred_joints_smpl_h36mlsp_pa = compute_similarity_transform_batch(pred_joints_smpl_h36mlsp, target_joints_h36mlsp) pred_joints_h36mlsp_pa = compute_similarity_transform_batch(pred_joints_h36mlsp, target_joints_h36mlsp) j3d_rec_err_smpl_batch = np.linalg.norm(pred_joints_smpl_h36mlsp_pa - target_joints_h36mlsp, axis=-1) j3d_rec_err_graph_batch = np.linalg.norm(pred_joints_h36mlsp_pa - target_joints_h36mlsp, axis=-1) j3d_rec_err_smpl_sum += np.sum(j3d_rec_err_smpl_batch) j3d_rec_err_graph_sum += np.sum(j3d_rec_err_graph_batch) j3d_rec_err_smpl_per_frame.append(np.mean(j3d_rec_err_smpl_batch, axis=-1)) j3d_rec_err_graph_per_frame.append(np.mean(j3d_rec_err_graph_batch, axis=-1)) if 'pve_2d' in metrics: pred_vertices_smpl_2d = pred_vertices_smpl[:, :, :2] pred_vertices_2d = pred_vertices[:, :, :2] target_vertices_2d = target_vertices[:, :, :2] pve_2d_smpl_batch = np.linalg.norm(pred_vertices_smpl_2d - target_vertices_2d, axis=-1) pve_2d_graph_batch = np.linalg.norm(pred_vertices_2d - target_vertices_2d, axis=-1) pve_2d_smpl_sum += np.sum(pve_2d_smpl_batch) pve_2d_graph_sum += np.sum(pve_2d_graph_batch) if 'pve_2d_scale_corrected' in metrics: pred_vertices_smpl_sc = scale_and_translation_transform_batch(pred_vertices_smpl, target_vertices) pred_vertices_sc = scale_and_translation_transform_batch(pred_vertices, target_vertices) pred_vertices_smpl_2d_sc = pred_vertices_smpl_sc[:, :, :2] pred_vertices_2d_sc = pred_vertices_sc[:, :, :2] target_vertices_2d = target_vertices[:, :, :2] pve_2d_sc_smpl_batch = np.linalg.norm(pred_vertices_smpl_2d_sc - target_vertices_2d, axis=-1) pve_2d_sc_graph_batch = np.linalg.norm(pred_vertices_2d_sc - target_vertices_2d, axis=-1) pve_2d_scale_corrected_smpl_sum += np.sum(pve_2d_sc_smpl_batch) pve_2d_scale_corrected_graph_sum += np.sum(pve_2d_sc_graph_batch) if 'pve_2d_pa' in metrics: pred_vertices_smpl_pa = compute_similarity_transform_batch(pred_vertices_smpl, target_vertices) pred_vertices_pa = compute_similarity_transform_batch(pred_vertices, target_vertices) pred_vertices_smpl_2d_pa = pred_vertices_smpl_pa[:, :, :2] pred_vertices_2d_pa = pred_vertices_pa[:, :, :2] target_vertices_2d = target_vertices[:, :, :2] pve_2d_pa_smpl_batch = np.linalg.norm(pred_vertices_smpl_2d_pa - target_vertices_2d, axis=-1) pve_2d_pa_graph_batch = np.linalg.norm(pred_vertices_2d_pa - target_vertices_2d, axis=-1) pve_2d_pa_smpl_sum += np.sum(pve_2d_pa_smpl_batch) pve_2d_pa_graph_sum += np.sum(pve_2d_pa_graph_batch) num_samples += target_pose.shape[0] if vis_every_n_batches is not None: if batch_num % vis_every_n_batches == 0: vis_imgs = samples_batch['vis_img'].numpy() vis_imgs = np.transpose(vis_imgs, [0, 2, 3, 1]) fnames = samples_batch['fname'] plt.figure(figsize=(16, 12)) plt.subplot(341) plt.imshow(vis_imgs[0]) plt.subplot(342) plt.imshow(vis_imgs[0]) plt.scatter(pred_vertices_projected2d[0, :, 0], pred_vertices_projected2d[0, :, 1], s=0.1, c='r') plt.subplot(343) plt.imshow(vis_imgs[0]) plt.scatter(pred_vertices_smpl_projected2d[0, :, 0], pred_vertices_smpl_projected2d[0, :, 1], s=0.1, c='r') plt.subplot(345) plt.scatter(target_vertices[0, :, 0], target_vertices[0, :, 1], s=0.1, c='b') plt.scatter(pred_vertices[0, :, 0], pred_vertices[0, :, 1], s=0.1, c='r') plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(346) plt.scatter(target_vertices[0, :, 0], target_vertices[0, :, 1], s=0.1, c='b') plt.scatter(pred_vertices_smpl[0, :, 0], pred_vertices_smpl[0, :, 1], s=0.1, c='r') plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(347) plt.scatter(target_vertices[0, :, 0], target_vertices[0, :, 1], s=0.1, c='b') plt.scatter(pred_vertices_pa[0, :, 0], pred_vertices_pa[0, :, 1], s=0.1, c='r') plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(348) plt.scatter(target_vertices[0, :, 0], target_vertices[0, :, 1], s=0.1, c='b') plt.scatter(pred_vertices_smpl_pa[0, :, 0], pred_vertices_smpl_pa[0, :, 1], s=0.1, c='r') plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(349) plt.scatter(target_reposed_vertices[0, :, 0], target_reposed_vertices[0, :, 1], s=0.1, c='b') plt.scatter(pred_reposed_vertices_sc[0, :, 0], pred_reposed_vertices_sc[0, :, 1], s=0.1, c='r') plt.gca().set_aspect('equal', adjustable='box') plt.subplot(3, 4, 10) for j in range(num_joints3d): plt.scatter(pred_joints_h36mlsp[0, j, 0], pred_joints_h36mlsp[0, j, 1], c='r') plt.scatter(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], c='b') plt.text(pred_joints_h36mlsp[0, j, 0], pred_joints_h36mlsp[0, j, 1], s=str(j)) plt.text(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], s=str(j)) plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(3, 4, 11) for j in range(num_joints3d): plt.scatter(pred_joints_h36mlsp_pa[0, j, 0], pred_joints_h36mlsp_pa[0, j, 1], c='r') plt.scatter(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], c='b') plt.text(pred_joints_h36mlsp_pa[0, j, 0], pred_joints_h36mlsp_pa[0, j, 1], s=str(j)) plt.text(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], s=str(j)) plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') plt.subplot(3, 4, 12) for j in range(num_joints3d): plt.scatter(pred_joints_smpl_h36mlsp_pa[0, j, 0], pred_joints_smpl_h36mlsp_pa[0, j, 1], c='r') plt.scatter(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], c='b') plt.text(pred_joints_smpl_h36mlsp_pa[0, j, 0], pred_joints_smpl_h36mlsp_pa[0, j, 1], s=str(j)) plt.text(target_joints_h36mlsp[0, j, 0], target_joints_h36mlsp[0, j, 1], s=str(j)) plt.gca().invert_yaxis() plt.gca().set_aspect('equal', adjustable='box') save_fig_path = os.path.join(vis_save_path, fnames[0]) plt.savefig(save_fig_path, bbox_inches='tight') plt.close() if 'pve' in metrics: pve_smpl = pve_smpl_sum / (num_samples * num_vertices) print('PVE SMPL: {:.5f}'.format(pve_smpl)) pve_graph = pve_graph_sum / (num_samples * num_vertices) print('PVE GRAPH: {:.5f}'.format(pve_graph)) pve_smpl_per_frame = np.concatenate(pve_smpl_per_frame, axis=0) pve_graph_per_frame = np.concatenate(pve_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'pve_per_frame.npy'), pve_smpl_per_frame) np.save(os.path.join(save_path, 'pve_graph_per_frame.npy'), pve_graph_per_frame) if 'pve_scale_corrected' in metrics: pve_sc_smpl = pve_scale_corrected_smpl_sum / (num_samples * num_vertices) print('PVE SC SMPL: {:.5f}'.format(pve_sc_smpl)) pve_sc_graph = pve_scale_corrected_graph_sum / (num_samples * num_vertices) print('PVE SC GRAPH: {:.5f}'.format(pve_sc_graph)) pve_scale_corrected_smpl_per_frame = np.concatenate(pve_scale_corrected_smpl_per_frame, axis=0) pve_scale_corrected_graph_per_frame = np.concatenate(pve_scale_corrected_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'pve_scale_corrected_per_frame.npy'), pve_scale_corrected_smpl_per_frame) np.save(os.path.join(save_path, 'pve_scale_corrected_graph_per_frame.npy'), pve_scale_corrected_graph_per_frame) if 'pve_pa' in metrics: pve_pa_smpl = pve_pa_smpl_sum / (num_samples * num_vertices) print('PVE PA SMPL: {:.5f}'.format(pve_pa_smpl)) pve_pa_graph = pve_pa_graph_sum / (num_samples * num_vertices) print('PVE PA GRAPH: {:.5f}'.format(pve_pa_graph)) pve_pa_smpl_per_frame = np.concatenate(pve_pa_smpl_per_frame, axis=0) pve_pa_graph_per_frame = np.concatenate(pve_pa_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'pve_pa_per_frame.npy'), pve_pa_smpl_per_frame) np.save(os.path.join(save_path, 'pve_pa_graph_per_frame.npy'), pve_pa_graph_per_frame) if 'pve-t' in metrics: pvet = pvet_sum / (num_samples * num_vertices) print('PVE-T: {:.5f}'.format(pvet)) pvet_per_frame = np.concatenate(pvet_per_frame, axis=0) np.save(os.path.join(save_path, 'pvet_per_frame.npy'), pvet_per_frame) if 'pve-t_scale_corrected' in metrics: pvet_sc = pvet_scale_corrected_sum / (num_samples * num_vertices) print('PVE-T SC: {:.5f}'.format(pvet_sc)) pvet_scale_corrected_per_frame = np.concatenate(pvet_scale_corrected_per_frame, axis=0) np.save(os.path.join(save_path, 'pvet_scale_corrected_per_frame.npy'), pvet_scale_corrected_per_frame) if 'mpjpe' in metrics: mpjpe_smpl = mpjpe_smpl_sum / (num_samples * num_joints3d) print('MPJPE SMPL: {:.5f}'.format(mpjpe_smpl)) mpjpe_graph = mpjpe_graph_sum / (num_samples * num_joints3d) print('MPJPE GRAPH: {:.5f}'.format(mpjpe_graph)) mpjpe_smpl_per_frame = np.concatenate(mpjpe_smpl_per_frame, axis=0) mpjpe_graph_per_frame = np.concatenate(mpjpe_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'mpjpe_per_frame.npy'), mpjpe_smpl_per_frame) np.save(os.path.join(save_path, 'mpjpe_graph_per_frame.npy'), mpjpe_graph_per_frame) if 'mpjpe_scale_corrected' in metrics: mpjpe_sc_smpl = mpjpe_scale_corrected_smpl_sum / (num_samples * num_joints3d) print('MPJPE SC SMPL: {:.5f}'.format(mpjpe_sc_smpl)) mpjpe_sc_graph = mpjpe_scale_corrected_graph_sum / (num_samples * num_joints3d) print('MPJPE SC GRAPH: {:.5f}'.format(mpjpe_sc_graph)) mpjpe_scale_corrected_smpl_per_frame = np.concatenate( mpjpe_scale_corrected_smpl_per_frame, axis=0) mpjpe_scale_corrected_graph_per_frame = np.concatenate( mpjpe_scale_corrected_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'mpjpe_scale_corrected_per_frame.npy'), mpjpe_scale_corrected_smpl_per_frame) np.save(os.path.join(save_path, 'mpjpe_scale_corrected_graph_per_frame.npy'), mpjpe_scale_corrected_graph_per_frame) if 'j3d_rec_err' in metrics: j3d_rec_err_smpl = j3d_rec_err_smpl_sum / (num_samples * num_joints3d) print('Rec Err SMPL: {:.5f}'.format(j3d_rec_err_smpl)) j3d_rec_err_graph = j3d_rec_err_graph_sum / (num_samples * num_joints3d) print('Rec Err GRAPH: {:.5f}'.format(j3d_rec_err_graph)) j3d_rec_err_smpl_per_frame = np.concatenate(j3d_rec_err_smpl_per_frame, axis=0) j3d_rec_err_graph_per_frame = np.concatenate(j3d_rec_err_graph_per_frame, axis=0) np.save(os.path.join(save_path, 'j3d_rec_err_per_frame.npy'), j3d_rec_err_smpl_per_frame) np.save(os.path.join(save_path, 'j3d_rec_err_graph_per_frame.npy'), j3d_rec_err_graph_per_frame) if 'pve_2d' in metrics: pve_2d_smpl = pve_2d_smpl_sum / (num_samples * num_vertices) print('PVE 2D SMPL: {:.5f}'.format(pve_2d_smpl)) pve_2d_graph = pve_2d_graph_sum / (num_samples * num_vertices) print('PVE 2D GRAPH: {:.5f}'.format(pve_2d_graph)) if 'pve_2d_scale_corrected' in metrics: pve_2d_sc_smpl = pve_2d_scale_corrected_smpl_sum / (num_samples * num_vertices) print('PVE 2D SC SMPL: {:.5f}'.format(pve_2d_sc_smpl)) pve_2d_sc_graph = pve_2d_scale_corrected_graph_sum / (num_samples * num_vertices) print('PVE 2D SC GRAPH: {:.5f}'.format(pve_2d_sc_graph)) if 'pve_2d_pa' in metrics: pve_2d_pa_smpl = pve_2d_pa_smpl_sum / (num_samples * num_vertices) print('PVE 2D PA SMPL: {:.5f}'.format(pve_2d_pa_smpl)) pve_2d_pa_graph = pve_2d_pa_graph_sum / (num_samples * num_vertices) print('PVE 2D PA GRAPH: {:.5f}'.format(pve_2d_pa_graph)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--checkpoint', default=None, help='Path to network checkpoint') parser.add_argument('--gpu', default="0", type=str, help='GPU') args = parser.parse_args() os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') mesh = Mesh(device=device) model = CMR(mesh, 5, 256, pretrained_checkpoint=args.checkpoint, device=device) model.to(device) model.eval() dataset_path = '/scratch2/as2562/datasets/3DPW/test' dataset = PW3DEvalDataset(dataset_path, img_wh=config.INPUT_RES) print("Eval examples found:", len(dataset)) metrics = ['pve', 'pve-t', 'pve_pa', 'pve-t_pa', 'mpjpe', 'j3d_rec_err', 'pve_2d', 'pve_2d_pa', 'pve_2d_scale_corrected', 'pve_scale_corrected', 'pve-t_scale_corrected', 'mpjpe_scale_corrected'] save_path = '/data/cvfs/as2562/GraphCMR/evaluations/3dpw' if not os.path.exists(save_path): os.makedirs(save_path) evaluate_3dpw(model=model, eval_dataset=dataset, metrics=metrics, device=device, vis_save_path=save_path, num_workers=4, pin_memory=True, vis_every_n_batches=1000)

true

790bc953207ced07f54191f143f08a64efb6f56a

1,017

py

Python

my_site/users/admin.py

pshortt/my_site

b8b22e3138642e1509e4e476c678ac09615fecd8

[ "MIT" ]

null

my_site/users/admin.py

pshortt/my_site

b8b22e3138642e1509e4e476c678ac09615fecd8

[ "MIT" ]

14

2022-01-21T05:22:47.000Z

2022-03-31T05:29:24.000Z

my_site/users/admin.py

pshortt/my_site

b8b22e3138642e1509e4e476c678ac09615fecd8

[ "MIT" ]

null

from django.contrib import admin from django.contrib.auth import admin as auth_admin from django.contrib.auth import get_user_model from django.utils.translation import gettext_lazy as _ from my_site.users.forms import UserAdminChangeForm, UserAdminCreationForm User = get_user_model() @admin.register(User) class UserAdmin(auth_admin.UserAdmin): form = UserAdminChangeForm add_form = UserAdminCreationForm fieldsets = ( (None, {"fields": ("username", "password")}), (_("Personal info"), {"fields": ("name", "email")}), ( _("Permissions"), { "fields": ( "is_active", "is_staff", "is_superuser", "groups", "user_permissions", ), }, ), (_("Important dates"), {"fields": ("last_login", "date_joined")}), ) list_display = ["username", "name", "is_superuser"] search_fields = ["name"]

29.057143

74

0.564405

from django.contrib import admin from django.contrib.auth import admin as auth_admin from django.contrib.auth import get_user_model from django.utils.translation import gettext_lazy as _ from my_site.users.forms import UserAdminChangeForm, UserAdminCreationForm User = get_user_model() @admin.register(User) class UserAdmin(auth_admin.UserAdmin): form = UserAdminChangeForm add_form = UserAdminCreationForm fieldsets = ( (None, {"fields": ("username", "password")}), (_("Personal info"), {"fields": ("name", "email")}), ( _("Permissions"), { "fields": ( "is_active", "is_staff", "is_superuser", "groups", "user_permissions", ), }, ), (_("Important dates"), {"fields": ("last_login", "date_joined")}), ) list_display = ["username", "name", "is_superuser"] search_fields = ["name"]

true

790bc9c086190b6638eddd623ad4c7185a055404

5,264

py

Python

SymbolExtractorAndRenamer/lldb/packages/Python/lldbsuite/test/lang/objc/objc-property/TestObjCProperty.py

Polidea/SiriusObfuscator

b0e590d8130e97856afe578869b83a209e2b19be

[ "Apache-2.0" ]

427

2018-05-29T14:21:02.000Z

2022-03-16T03:17:54.000Z

SymbolExtractorAndRenamer/lldb/packages/Python/lldbsuite/test/lang/objc/objc-property/TestObjCProperty.py

PolideaPlayground/SiriusObfuscator

b0e590d8130e97856afe578869b83a209e2b19be

[ "Apache-2.0" ]

25

2018-07-23T08:34:15.000Z

2021-11-05T07:13:36.000Z

SymbolExtractorAndRenamer/lldb/packages/Python/lldbsuite/test/lang/objc/objc-property/TestObjCProperty.py

PolideaPlayground/SiriusObfuscator

b0e590d8130e97856afe578869b83a209e2b19be

[ "Apache-2.0" ]

52

2018-07-19T19:57:32.000Z

2022-03-11T16:05:38.000Z

""" Use lldb Python API to verify that expression evaluation for property references uses the correct getters and setters """ from __future__ import print_function import os import time import re import lldb from lldbsuite.test.decorators import * from lldbsuite.test.lldbtest import * from lldbsuite.test import lldbutil class ObjCPropertyTestCase(TestBase): mydir = TestBase.compute_mydir(__file__) def setUp(self): # Call super's setUp(). TestBase.setUp(self) # Find the line number to break for main.c. self.source_name = 'main.m' @skipUnlessDarwin @add_test_categories(['pyapi']) def test_objc_properties(self): """Test that expr uses the correct property getters and setters""" if self.getArchitecture() == 'i386': self.skipTest("requires modern objc runtime") self.build() exe = os.path.join(os.getcwd(), "a.out") # Create a target from the debugger. target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) # Set up our breakpoints: main_bkpt = target.BreakpointCreateBySourceRegex( "Set a breakpoint here.", lldb.SBFileSpec(self.source_name)) self.assertTrue(main_bkpt and main_bkpt.GetNumLocations() == 1, VALID_BREAKPOINT) # Now launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) self.assertTrue(process.GetState() == lldb.eStateStopped, PROCESS_STOPPED) threads = lldbutil.get_threads_stopped_at_breakpoint( process, main_bkpt) self.assertTrue(len(threads) == 1) thread = threads[0] frame = thread.GetFrameAtIndex(0) mine = frame.FindVariable("mine") self.assertTrue(mine.IsValid()) access_count = mine.GetChildMemberWithName("_access_count") self.assertTrue(access_count.IsValid()) start_access_count = access_count.GetValueAsUnsigned(123456) self.assertTrue(start_access_count != 123456) # # The first set of tests test calling the getter & setter of # a property that actually only has a getter & setter and no # @property. # nonexistant_value = frame.EvaluateExpression( "mine.nonexistantInt", False) nonexistant_error = nonexistant_value.GetError() self.assertTrue(nonexistant_error.Success()) nonexistant_int = nonexistant_value.GetValueAsUnsigned(123456) self.assertTrue(nonexistant_int == 6) # Calling the getter function would up the access count, so make sure # that happened. new_access_count = access_count.GetValueAsUnsigned(123456) self.assertTrue(new_access_count - start_access_count == 1) start_access_count = new_access_count # # Now call the setter, then make sure that nonexistant_change = frame.EvaluateExpression( "mine.nonexistantInt = 10", False) nonexistant_error = nonexistant_change.GetError() self.assertTrue(nonexistant_error.Success()) # Calling the setter function would up the access count, so make sure # that happened. new_access_count = access_count.GetValueAsUnsigned(123456) self.assertTrue(new_access_count - start_access_count == 1) start_access_count = new_access_count # # Now we call the getter of a property that is backed by an ivar, # make sure it works and that we actually update the backing ivar. # backed_value = frame.EvaluateExpression("mine.backedInt", False) backed_error = backed_value.GetError() self.assertTrue(backed_error.Success()) backing_value = mine.GetChildMemberWithName("_backedInt") self.assertTrue(backing_value.IsValid()) self.assertTrue(backed_value.GetValueAsUnsigned(12345) == backing_value.GetValueAsUnsigned(23456)) unbacked_value = frame.EvaluateExpression("mine.unbackedInt", False) unbacked_error = unbacked_value.GetError() self.assertTrue(unbacked_error.Success()) idWithProtocol_value = frame.EvaluateExpression( "mine.idWithProtocol", False) idWithProtocol_error = idWithProtocol_value.GetError() self.assertTrue(idWithProtocol_error.Success()) self.assertTrue(idWithProtocol_value.GetTypeName() == "id") # Make sure that class property getter works as expected value = frame.EvaluateExpression("BaseClass.classInt", False) self.assertTrue(value.GetError().Success()) self.assertTrue(value.GetValueAsUnsigned(11111) == 123) # Make sure that class property setter works as expected value = frame.EvaluateExpression("BaseClass.classInt = 234", False) self.assertTrue(value.GetError().Success()) # Verify that setter above actually worked value = frame.EvaluateExpression("BaseClass.classInt", False) self.assertTrue(value.GetError().Success()) self.assertTrue(value.GetValueAsUnsigned(11111) == 234)

37.6

117

0.671922

from __future__ import print_function import os import time import re import lldb from lldbsuite.test.decorators import * from lldbsuite.test.lldbtest import * from lldbsuite.test import lldbutil class ObjCPropertyTestCase(TestBase): mydir = TestBase.compute_mydir(__file__) def setUp(self): TestBase.setUp(self) # Find the line number to break for main.c. self.source_name = 'main.m' @skipUnlessDarwin @add_test_categories(['pyapi']) def test_objc_properties(self): if self.getArchitecture() == 'i386': self.skipTest("requires modern objc runtime") self.build() exe = os.path.join(os.getcwd(), "a.out") # Create a target from the debugger. target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) # Set up our breakpoints: main_bkpt = target.BreakpointCreateBySourceRegex( "Set a breakpoint here.", lldb.SBFileSpec(self.source_name)) self.assertTrue(main_bkpt and main_bkpt.GetNumLocations() == 1, VALID_BREAKPOINT) # Now launch the process, and do not stop at the entry point. process = target.LaunchSimple( None, None, self.get_process_working_directory()) self.assertTrue(process.GetState() == lldb.eStateStopped, PROCESS_STOPPED) threads = lldbutil.get_threads_stopped_at_breakpoint( process, main_bkpt) self.assertTrue(len(threads) == 1) thread = threads[0] frame = thread.GetFrameAtIndex(0) mine = frame.FindVariable("mine") self.assertTrue(mine.IsValid()) access_count = mine.GetChildMemberWithName("_access_count") self.assertTrue(access_count.IsValid()) start_access_count = access_count.GetValueAsUnsigned(123456) self.assertTrue(start_access_count != 123456) # # The first set of tests test calling the getter & setter of # a property that actually only has a getter & setter and no # @property. # nonexistant_value = frame.EvaluateExpression( "mine.nonexistantInt", False) nonexistant_error = nonexistant_value.GetError() self.assertTrue(nonexistant_error.Success()) nonexistant_int = nonexistant_value.GetValueAsUnsigned(123456) self.assertTrue(nonexistant_int == 6) # Calling the getter function would up the access count, so make sure # that happened. new_access_count = access_count.GetValueAsUnsigned(123456) self.assertTrue(new_access_count - start_access_count == 1) start_access_count = new_access_count # # Now call the setter, then make sure that nonexistant_change = frame.EvaluateExpression( "mine.nonexistantInt = 10", False) nonexistant_error = nonexistant_change.GetError() self.assertTrue(nonexistant_error.Success()) # Calling the setter function would up the access count, so make sure # that happened. new_access_count = access_count.GetValueAsUnsigned(123456) self.assertTrue(new_access_count - start_access_count == 1) start_access_count = new_access_count # # Now we call the getter of a property that is backed by an ivar, # make sure it works and that we actually update the backing ivar. # backed_value = frame.EvaluateExpression("mine.backedInt", False) backed_error = backed_value.GetError() self.assertTrue(backed_error.Success()) backing_value = mine.GetChildMemberWithName("_backedInt") self.assertTrue(backing_value.IsValid()) self.assertTrue(backed_value.GetValueAsUnsigned(12345) == backing_value.GetValueAsUnsigned(23456)) unbacked_value = frame.EvaluateExpression("mine.unbackedInt", False) unbacked_error = unbacked_value.GetError() self.assertTrue(unbacked_error.Success()) idWithProtocol_value = frame.EvaluateExpression( "mine.idWithProtocol", False) idWithProtocol_error = idWithProtocol_value.GetError() self.assertTrue(idWithProtocol_error.Success()) self.assertTrue(idWithProtocol_value.GetTypeName() == "id") # Make sure that class property getter works as expected value = frame.EvaluateExpression("BaseClass.classInt", False) self.assertTrue(value.GetError().Success()) self.assertTrue(value.GetValueAsUnsigned(11111) == 123) # Make sure that class property setter works as expected value = frame.EvaluateExpression("BaseClass.classInt = 234", False) self.assertTrue(value.GetError().Success()) # Verify that setter above actually worked value = frame.EvaluateExpression("BaseClass.classInt", False) self.assertTrue(value.GetError().Success()) self.assertTrue(value.GetValueAsUnsigned(11111) == 234)

true

790bca0d1f46d46e28ef4406eb3ecb240b0e1c9e

1,581

py

Python

torchvision/extension.py

jamt9000/vision

598b61d93357139cec558af6eff38a77ac60cabc

[ "BSD-3-Clause" ]

3

2019-11-03T01:31:37.000Z

2020-01-08T10:48:31.000Z

torchvision/extension.py

JXQJ/vision

b6f28ec1a8c5fdb8d01cc61946e8f87dddcfa830

[ "BSD-3-Clause" ]

1

2019-03-02T06:43:20.000Z

torchvision/extension.py

JXQJ/vision

b6f28ec1a8c5fdb8d01cc61946e8f87dddcfa830

[ "BSD-3-Clause" ]

1

2020-09-11T20:54:56.000Z

_HAS_OPS = False def _register_extensions(): import os import imp import torch # load the custom_op_library and register the custom ops lib_dir = os.path.dirname(__file__) _, path, _ = imp.find_module("_C", [lib_dir]) torch.ops.load_library(path) try: _register_extensions() _HAS_OPS = True except (ImportError, OSError): pass def _check_cuda_version(): """ Make sure that CUDA versions match between the pytorch install and torchvision install """ if not _HAS_OPS: return -1 import torch _version = torch.ops.torchvision._cuda_version() if _version != -1 and torch.version.cuda is not None: tv_version = str(_version) if int(tv_version) < 10000: tv_major = int(tv_version[0]) tv_minor = int(tv_version[2]) else: tv_major = int(tv_version[0:2]) tv_minor = int(tv_version[3]) t_version = torch.version.cuda t_version = t_version.split('.') t_major = int(t_version[0]) t_minor = int(t_version[1]) if t_major != tv_major or t_minor != tv_minor: raise RuntimeError("Detected that PyTorch and torchvision were compiled with different CUDA versions. " "PyTorch has CUDA Version={}.{} and torchvision has CUDA Version={}.{}. " "Please reinstall the torchvision that matches your PyTorch install." .format(t_major, t_minor, tv_major, tv_minor)) return _version _check_cuda_version()

31

115

0.619861

_HAS_OPS = False def _register_extensions(): import os import imp import torch lib_dir = os.path.dirname(__file__) _, path, _ = imp.find_module("_C", [lib_dir]) torch.ops.load_library(path) try: _register_extensions() _HAS_OPS = True except (ImportError, OSError): pass def _check_cuda_version(): if not _HAS_OPS: return -1 import torch _version = torch.ops.torchvision._cuda_version() if _version != -1 and torch.version.cuda is not None: tv_version = str(_version) if int(tv_version) < 10000: tv_major = int(tv_version[0]) tv_minor = int(tv_version[2]) else: tv_major = int(tv_version[0:2]) tv_minor = int(tv_version[3]) t_version = torch.version.cuda t_version = t_version.split('.') t_major = int(t_version[0]) t_minor = int(t_version[1]) if t_major != tv_major or t_minor != tv_minor: raise RuntimeError("Detected that PyTorch and torchvision were compiled with different CUDA versions. " "PyTorch has CUDA Version={}.{} and torchvision has CUDA Version={}.{}. " "Please reinstall the torchvision that matches your PyTorch install." .format(t_major, t_minor, tv_major, tv_minor)) return _version _check_cuda_version()

true

790bcb6e4ae0780250906478faa2909be47baeec

846

py

Python

tests/events_test.py

jjinno/pygerduty

9624b7616a91ccfbaaff2a51bd19da9406c718b4

[ "MIT" ]

144

2015-01-30T08:49:52.000Z

2022-02-02T15:06:05.000Z

tests/events_test.py

cugini-dbx/pygerduty

1982aa4ccb33eb10b98b695056517f7bc5f4dff6

[ "MIT" ]

56

2015-01-02T20:50:42.000Z

2020-06-23T18:30:22.000Z

tests/events_test.py

cugini-dbx/pygerduty

1982aa4ccb33eb10b98b695056517f7bc5f4dff6

[ "MIT" ]

67

2015-01-13T02:34:42.000Z

2021-04-19T22:34:08.000Z

import httpretty import json import textwrap import pygerduty.events from pygerduty.events import INTEGRATION_API_URL from pygerduty.common import Requester @httpretty.activate def test_create_event(): body = textwrap.dedent(""" { "status": "success", "message": "Event processed", "incident_key": "srv01/HTTP" } """) httpretty.register_uri( httpretty.POST, INTEGRATION_API_URL, body=body, status=200) requester = Requester() p = pygerduty.events.Events('my_key', requester) request_json = open('tests/fixtures/event_request.json').read() request = json.loads(request_json) response = p.create_event( request['description'], request['event_type'], request['details'], request['incident_key'], ) assert response == 'srv01/HTTP'

22.263158

67

0.665485

import httpretty import json import textwrap import pygerduty.events from pygerduty.events import INTEGRATION_API_URL from pygerduty.common import Requester @httpretty.activate def test_create_event(): body = textwrap.dedent(""" { "status": "success", "message": "Event processed", "incident_key": "srv01/HTTP" } """) httpretty.register_uri( httpretty.POST, INTEGRATION_API_URL, body=body, status=200) requester = Requester() p = pygerduty.events.Events('my_key', requester) request_json = open('tests/fixtures/event_request.json').read() request = json.loads(request_json) response = p.create_event( request['description'], request['event_type'], request['details'], request['incident_key'], ) assert response == 'srv01/HTTP'

true

790bcbff1747cdc62f9caf9c4546926661711a3f

1,209

py

Python

reversing/pyast64++.rev/solver/solve.py

SECCON/SECCON2021_online_CTF

628008ae2d150723352aed2c95abff41501c51f2

[ "Apache-2.0" ]

7

2022-02-07T10:15:22.000Z

2022-02-10T07:13:07.000Z

reversing/pyast64++.rev/solver/solve.py

SECCON/SECCON2021_online_CTF

628008ae2d150723352aed2c95abff41501c51f2

[ "Apache-2.0" ]

null

reversing/pyast64++.rev/solver/solve.py

SECCON/SECCON2021_online_CTF

628008ae2d150723352aed2c95abff41501c51f2

[ "Apache-2.0" ]

null

cipher = [75, 203, 190, 126, 184, 169, 27, 74, 35, 83, 113, 65, 207, 193, 27, 137, 37, 98, 0, 68, 219, 113, 21, 180, 223, 135, 5, 129, 189, 200, 245, 100, 117, 62, 192, 101, 239, 92, 182, 136, 159, 235, 166, 90, 74, 133, 83, 78, 6, 225, 101, 103, 82, 78, 144, 205, 130, 238, 175, 245, 172, 62, 157, 176] key = b"SECCON2021" Sbox = [0xff - i for i in range(0x100)] j = 0 for i in range(0x100): j = (j + Sbox[i] + key[i % 10]) % 0x100 Sbox[i], Sbox[j] = Sbox[j], Sbox[i] def FYinv(bits): for i in range(63, -1, -1): j = (i**3 % 67) % 64 bits[i], bits[j] = bits[j], bits[i] def Pinv(data, length): for i in range(length // 8): bits = [] for j in range(8): bits += [(data[i*8+j] >> k) & 1 for k in range(8)] FYinv(bits) for j in range(8): c = 0 for k in range(8): c |= bits[j*8+k] << k data[i*8+j] = c def Sinv(Sbox, data, length): for i in range(length): data[i] = Sbox.index(data[i]) for rnd in range(10): for i in range(0x40): cipher[i] ^= key[9 - rnd] Pinv(cipher, 0x40) Sinv(Sbox, cipher, 0x40) print(cipher) print(''.join(map(chr, cipher)))

31

303

0.507858

cipher = [75, 203, 190, 126, 184, 169, 27, 74, 35, 83, 113, 65, 207, 193, 27, 137, 37, 98, 0, 68, 219, 113, 21, 180, 223, 135, 5, 129, 189, 200, 245, 100, 117, 62, 192, 101, 239, 92, 182, 136, 159, 235, 166, 90, 74, 133, 83, 78, 6, 225, 101, 103, 82, 78, 144, 205, 130, 238, 175, 245, 172, 62, 157, 176] key = b"SECCON2021" Sbox = [0xff - i for i in range(0x100)] j = 0 for i in range(0x100): j = (j + Sbox[i] + key[i % 10]) % 0x100 Sbox[i], Sbox[j] = Sbox[j], Sbox[i] def FYinv(bits): for i in range(63, -1, -1): j = (i**3 % 67) % 64 bits[i], bits[j] = bits[j], bits[i] def Pinv(data, length): for i in range(length // 8): bits = [] for j in range(8): bits += [(data[i*8+j] >> k) & 1 for k in range(8)] FYinv(bits) for j in range(8): c = 0 for k in range(8): c |= bits[j*8+k] << k data[i*8+j] = c def Sinv(Sbox, data, length): for i in range(length): data[i] = Sbox.index(data[i]) for rnd in range(10): for i in range(0x40): cipher[i] ^= key[9 - rnd] Pinv(cipher, 0x40) Sinv(Sbox, cipher, 0x40) print(cipher) print(''.join(map(chr, cipher)))

true

790bcc558648dd4cdf9c42ece65a3798865e4af5

7,943

py

Python

docs/conf.py

aryamccarthy/ANES

29c56f8c46fd4e8b6725f329cb609f4f14a8acb0

[ "MIT" ]

1

2017-04-18T22:46:02.000Z

docs/conf.py

aryamccarthy/ANES

29c56f8c46fd4e8b6725f329cb609f4f14a8acb0

[ "MIT" ]

1

2017-07-17T20:28:24.000Z

docs/conf.py

aryamccarthy/ANES

29c56f8c46fd4e8b6725f329cb609f4f14a8acb0

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Political Dynamics documentation build configuration file, created by # sphinx-quickstart. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import os import sys # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'Political Dynamics' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '0.1' # The full version, including alpha/beta/rc tags. release = '0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". # html_title = None # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # If false, no module index is generated. # html_domain_indices = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'political-dynamicsdoc' # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # 'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'political-dynamics.tex', u'Political Dynamics Documentation', u"Arya D. McCarthy", 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # latex_use_parts = False # If true, show page references after internal links. # latex_show_pagerefs = False # If true, show URL addresses after external links. # latex_show_urls = False # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. # latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'political-dynamics', u'Political Dynamics Documentation', [u"Arya D. McCarthy"], 1) ] # If true, show URL addresses after external links. # man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'political-dynamics', u'Political Dynamics Documentation', u"Arya D. McCarthy", 'Political Dynamics', 'A differential equations perspective on American National Election Studies (ANES) over time.[D[D[D', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. # texinfo_appendices = [] # If false, no module index is generated. # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # texinfo_show_urls = 'footnote'

32.420408

127

0.709178

import os import sys extensions = [] templates_path = ['_templates'] source_suffix = '.rst' master_doc = 'index' project = u'Political Dynamics' # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '0.1' # The full version, including alpha/beta/rc tags. release = '0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". # html_title = None # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # If false, no module index is generated. # html_domain_indices = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'political-dynamicsdoc' # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # 'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'political-dynamics.tex', u'Political Dynamics Documentation', u"Arya D. McCarthy", 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # latex_use_parts = False # If true, show page references after internal links. # latex_show_pagerefs = False # If true, show URL addresses after external links. # latex_show_urls = False # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. # latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'political-dynamics', u'Political Dynamics Documentation', [u"Arya D. McCarthy"], 1) ] # If true, show URL addresses after external links. # man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'political-dynamics', u'Political Dynamics Documentation', u"Arya D. McCarthy", 'Political Dynamics', 'A differential equations perspective on American National Election Studies (ANES) over time.[D[D[D', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. # texinfo_appendices = [] # If false, no module index is generated. # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # texinfo_show_urls = 'footnote'

true

790bce87491729f0ceb8329d462bd82770f80bc5

496

py

Python

renzongxian/0001/0001.py

saurabh896/python-1

f8d3aedf4c0fe6e24dfa3269ea7e642c9f7dd9b7

[ "MIT" ]

3,976

2015-01-01T15:49:39.000Z

2022-03-31T03:47:56.000Z

renzongxian/0001/0001.py

dwh65416396/python

1a7e3edd1cd3422cc0eaa55471a0b42e004a9a1a

[ "MIT" ]

97

2015-01-11T02:59:46.000Z

2022-03-16T14:01:56.000Z

renzongxian/0001/0001.py

dwh65416396/python

1a7e3edd1cd3422cc0eaa55471a0b42e004a9a1a

[ "MIT" ]

3,533

2015-01-01T06:19:30.000Z

2022-03-28T13:14:54.000Z

# Source:https://github.com/Show-Me-the-Code/show-me-the-code # Author:renzongxian # Date:2014-11-30 # Python 3.4 """ 第 0001 题：做为 Apple Store App 独立开发者，你要搞限时促销，为你的应用生成激活码（或者优惠券），使用 Python 如何生成 200 个激活码（或者优惠券）？ """ import uuid def generate_key(): key_list = [] for i in range(200): uuid_key = uuid.uuid3(uuid.NAMESPACE_DNS, str(uuid.uuid1())) key_list.append(str(uuid_key).replace('-', '')) return key_list if __name__ == '__main__': print(generate_key())

19.076923

68

0.663306

import uuid def generate_key(): key_list = [] for i in range(200): uuid_key = uuid.uuid3(uuid.NAMESPACE_DNS, str(uuid.uuid1())) key_list.append(str(uuid_key).replace('-', '')) return key_list if __name__ == '__main__': print(generate_key())

true

790bd025072163898d8e1fd15588848834a0eba2

1,626

py

Python

doc/gen_javadoc.py

ChillingVan/LocalHtmlSearchBox

722f05b4efc660a8f8a6b81767493eb7a25c4d99

[ "Apache-2.0" ]

1

2019-07-13T02:33:45.000Z

doc/gen_javadoc.py

dxjia/LocalHtmlSearchBox

722f05b4efc660a8f8a6b81767493eb7a25c4d99

[ "Apache-2.0" ]

null

doc/gen_javadoc.py

dxjia/LocalHtmlSearchBox

722f05b4efc660a8f8a6b81767493eb7a25c4d99

[ "Apache-2.0" ]

1

2019-07-13T02:33:46.000Z

import os import sys import gen_database as gendb import json from shutil import copyfile def run_cmd(cmd): cmd_pipe = os.popen(cmd) cmd_print = cmd_pipe.read() print(cmd_print) if __name__ == '__main__': print("") root_read_dir = sys.argv[1] if root_read_dir[-1] != r"/" or root_read_dir[-1] != "\\": root_read_dir = root_read_dir + "/" # Generate java doc by javadoc command run_cmd(r"javadoc -locale en -encoding UTF-8 -charset UTF-8 -sourcepath " + r"../src ../src/main/java/com/chillingvan/docsearcher/Foooo.java ../src/main/java/com/chillingvan/docsearcher/foo/SubFoo.java" + r" -subpackages com -overview ./overview.html -d ../build/doc_java") # copy js and css to target dir copyfile('search.html', root_read_dir + 'search.html') copyfile('docsearcher.css', root_read_dir + 'docsearcher.css') copyfile('searchlib.js', root_read_dir + 'searchlib.js') # Read the html documents under /com to generate json data to a .js database_dir = root_read_dir def on_read_file(path, resultArr): if 'html' in path: url = path[path.index(root_read_dir) + len(path):] url = url.replace('\\', '/') resultArr.extend(gendb.simple_read_one(path, url)) result_arr = [] gendb.read_files(root_read_dir + 'com/', on_read_file, result_arr) final_result_arr = [] gendb.remove_same(result_arr, final_result_arr) with open(database_dir + 'searchData.js', 'w') as fl: fl.write("var searchData = " + json.dumps(final_result_arr))

36.133333

141

0.642681

import os import sys import gen_database as gendb import json from shutil import copyfile def run_cmd(cmd): cmd_pipe = os.popen(cmd) cmd_print = cmd_pipe.read() print(cmd_print) if __name__ == '__main__': print("") root_read_dir = sys.argv[1] if root_read_dir[-1] != r"/" or root_read_dir[-1] != "\\": root_read_dir = root_read_dir + "/" run_cmd(r"javadoc -locale en -encoding UTF-8 -charset UTF-8 -sourcepath " + r"../src ../src/main/java/com/chillingvan/docsearcher/Foooo.java ../src/main/java/com/chillingvan/docsearcher/foo/SubFoo.java" + r" -subpackages com -overview ./overview.html -d ../build/doc_java") copyfile('search.html', root_read_dir + 'search.html') copyfile('docsearcher.css', root_read_dir + 'docsearcher.css') copyfile('searchlib.js', root_read_dir + 'searchlib.js') database_dir = root_read_dir def on_read_file(path, resultArr): if 'html' in path: url = path[path.index(root_read_dir) + len(path):] url = url.replace('\\', '/') resultArr.extend(gendb.simple_read_one(path, url)) result_arr = [] gendb.read_files(root_read_dir + 'com/', on_read_file, result_arr) final_result_arr = [] gendb.remove_same(result_arr, final_result_arr) with open(database_dir + 'searchData.js', 'w') as fl: fl.write("var searchData = " + json.dumps(final_result_arr))

true

790bd2032cd33f2ce921af7205594a5110c8e4cb

6,952

py

Python

node_modules/nuclide/pkg/nuclide-clang-rpc/python/outline.py

kevingatera/kgatewebapp

f0dbc50b7af2736e1f6c6f96f0a26fc7ff69db20

[ "Unlicense" ]

1

2017-08-19T08:13:28.000Z

node_modules/nuclide/pkg/nuclide-clang-rpc/python/outline.py

kevingatera/kgatewebapp

f0dbc50b7af2736e1f6c6f96f0a26fc7ff69db20

[ "Unlicense" ]

null

node_modules/nuclide/pkg/nuclide-clang-rpc/python/outline.py

kevingatera/kgatewebapp

f0dbc50b7af2736e1f6c6f96f0a26fc7ff69db20

[ "Unlicense" ]

null

#!/usr/bin/env python # Copyright (c) 2015-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the LICENSE file in # the root directory of this source tree. from __future__ import print_function from clang.cindex import Cursor, CursorKind, TokenKind from utils import range_dict_relative import ctypes import itertools import re # Function/method cursor kinds. FUNCTION_KINDS = set([ 'FUNCTION_DECL', 'FUNCTION_TEMPLATE', 'CXX_METHOD', 'CONSTRUCTOR', 'DESTRUCTOR', 'OBJC_INSTANCE_METHOD_DECL', 'OBJC_CLASS_METHOD_DECL', ]) # Class-like cursors. CLASS_KINDS = set([ 'STRUCT_DECL', 'UNION_DECL', 'CLASS_DECL', 'ENUM_DECL', 'OBJC_INTERFACE_DECL', 'OBJC_CATEGORY_DECL', 'OBJC_PROTOCOL_DECL', 'OBJC_IMPLEMENTATION_DECL', 'OBJC_CATEGORY_IMPL_DECL', 'CLASS_TEMPLATE', 'CLASS_TEMPLATE_PARTIAL_SPECIALIZATION', 'NAMESPACE', ]) # (Possibly external) members of CLASS_KINDS. MEMBER_KINDS = set([ 'CXX_METHOD', 'CONSTRUCTOR', 'DESTRUCTOR', 'FIELD_DECL', 'VAR_DECL', 'ENUM_CONSTANT_DECL', ]) # Variables and fields. VAR_KINDS = set([ 'OBJC_IVAR_DECL', 'FIELD_DECL', 'VAR_DECL', ]) # Capture the ubiquitous GTest-style TEST/TEST_F macros. GTEST_MACROS = set(['TEST', 'TEST_F']) MACRO_INSTANTIATION = 'MACRO_INSTANTIATION' OTHER_KINDS = set([ MACRO_INSTANTIATION, ]) # Record any of the cursor types listed above. ALL_KINDS = FUNCTION_KINDS | CLASS_KINDS | MEMBER_KINDS | VAR_KINDS | OTHER_KINDS # People like adding a '-' by convention, but strip that out. PRAGMA_MARK_REGEX = re.compile( '^[ \t]*#[ \t]*pragma[ \t]+mark[ \t]+(?:-[ \t]*)?(.+)$', re.MULTILINE) def visit_cursor(libclang, cursor): try: kind = cursor.kind.name except: # Some cursor kinds aren't supported by the Python binding. return None if kind not in ALL_KINDS: return None # Skip symbols from other files. if not libclang.clang_Location_isFromMainFile(cursor.location): return None # Names of function parameters. params = None # Names of template parameters. tparams = None children = None name = cursor.spelling # Display types for variables and typedefs. cursor_type = cursor.type.spelling if kind in VAR_KINDS else None if kind in FUNCTION_KINDS: # We can't use displayname as it also includes the arguments. params = [] tparams = [] for child in cursor.get_children(): if child.kind == CursorKind.PARM_DECL: # Use the param name, but fall back to the raw type if unnamed. params.append(child.spelling or child.type.spelling) elif child.kind == CursorKind.TEMPLATE_TYPE_PARAMETER: tparams.append(child.spelling) # TODO(hansonw): non-type and "template template" params? if kind in MEMBER_KINDS: # Name should be fully qualified if outside the parent. if cursor.semantic_parent != cursor.lexical_parent: name = cursor.semantic_parent.spelling + '::' + name elif kind in CLASS_KINDS: # Include template information. name = cursor.displayname children = [] for child in cursor.get_children(): child_outline = visit_cursor(libclang, child) if child_outline is not None: children.append(child_outline) if kind == MACRO_INSTANTIATION: params = [] if name in GTEST_MACROS: # Should look like TEST(id, id). tokens = list(itertools.islice(cursor.get_tokens(), 1, 6)) if len(tokens) == 5 and ( tokens[0].kind == TokenKind.PUNCTUATION and tokens[1].kind == TokenKind.IDENTIFIER and tokens[2].kind == TokenKind.PUNCTUATION and tokens[3].kind == TokenKind.IDENTIFIER and tokens[4].kind == TokenKind.PUNCTUATION ): params = [tokens[1].spelling, tokens[3].spelling] else: return None else: # TODO(hansonw): Handle other special macros like DEFINE_ params. return None ret = { 'name': name, 'cursor_kind': kind, 'cursor_type': cursor_type, 'extent': range_dict_relative(cursor.extent), 'params': params, 'tparams': tparams, 'children': children, } return {k: v for k, v in ret.items() if v is not None} # Scan through the outline tree and insert pragma marks as we pass by them. def insert_pragma_marks(marks, outline_tree, tree_end=None): new_result = [] for node in outline_tree: while len(marks) > 0: if marks[-1]['extent']['start']['row'] > node['extent']['start']['row']: break new_result.append(marks.pop()) children = node.get('children') if children: children[:] = insert_pragma_marks(marks, children, node['extent']['end']['row']) new_result.append(node) # Consume all remaining marks included in this subtree. while len(marks) > 0: if tree_end is not None and marks[-1]['extent']['start']['row'] > tree_end: break new_result.append(marks.pop()) return new_result def get_outline(libclang, translation_unit, contents): root_cursor = translation_unit.cursor # This is the same as Cursor.get_children minus an assert in visitor(). # This results in a ~2x speedup! callback_type = ctypes.CFUNCTYPE(ctypes.c_int, Cursor, Cursor, ctypes.py_object) def visitor(child, parent, result): child._tu = translation_unit child_outline = visit_cursor(libclang, child) if child_outline is not None: result.append(child_outline) return 1 # continue result = [] libclang.clang_visitChildren(root_cursor, callback_type(visitor), result) # Look for pragma marks. These are not detectable in the AST. line = 0 lastpos = 0 pragma_marks = [] for mark in PRAGMA_MARK_REGEX.finditer(contents): while lastpos < mark.start(): if contents[lastpos] == '\n': line += 1 lastpos += 1 pragma_marks.append({ 'name': mark.group(1), 'cursor_kind': 'PRAGMA_MARK', 'extent': { 'start': {'row': line, 'column': 0}, 'end': {'row': line + 1, 'column': 0}, }, }) # Top-level macro instantiations appear out of order. result = sorted(result, key=lambda x: ( x['extent']['start']['row'], x['extent']['start']['column'], x['extent']['end']['row'], x['extent']['end']['column'], )) # Convert into a stack for efficient removal. pragma_marks.reverse() return insert_pragma_marks(pragma_marks, result)

31.035714

92

0.621116

from __future__ import print_function from clang.cindex import Cursor, CursorKind, TokenKind from utils import range_dict_relative import ctypes import itertools import re FUNCTION_KINDS = set([ 'FUNCTION_DECL', 'FUNCTION_TEMPLATE', 'CXX_METHOD', 'CONSTRUCTOR', 'DESTRUCTOR', 'OBJC_INSTANCE_METHOD_DECL', 'OBJC_CLASS_METHOD_DECL', ]) CLASS_KINDS = set([ 'STRUCT_DECL', 'UNION_DECL', 'CLASS_DECL', 'ENUM_DECL', 'OBJC_INTERFACE_DECL', 'OBJC_CATEGORY_DECL', 'OBJC_PROTOCOL_DECL', 'OBJC_IMPLEMENTATION_DECL', 'OBJC_CATEGORY_IMPL_DECL', 'CLASS_TEMPLATE', 'CLASS_TEMPLATE_PARTIAL_SPECIALIZATION', 'NAMESPACE', ]) MEMBER_KINDS = set([ 'CXX_METHOD', 'CONSTRUCTOR', 'DESTRUCTOR', 'FIELD_DECL', 'VAR_DECL', 'ENUM_CONSTANT_DECL', ]) VAR_KINDS = set([ 'OBJC_IVAR_DECL', 'FIELD_DECL', 'VAR_DECL', ]) GTEST_MACROS = set(['TEST', 'TEST_F']) MACRO_INSTANTIATION = 'MACRO_INSTANTIATION' OTHER_KINDS = set([ MACRO_INSTANTIATION, ]) ALL_KINDS = FUNCTION_KINDS | CLASS_KINDS | MEMBER_KINDS | VAR_KINDS | OTHER_KINDS PRAGMA_MARK_REGEX = re.compile( '^[ \t]*#[ \t]*pragma[ \t]+mark[ \t]+(?:-[ \t]*)?(.+)$', re.MULTILINE) def visit_cursor(libclang, cursor): try: kind = cursor.kind.name except: return None if kind not in ALL_KINDS: return None # Skip symbols from other files. if not libclang.clang_Location_isFromMainFile(cursor.location): return None # Names of function parameters. params = None # Names of template parameters. tparams = None children = None name = cursor.spelling # Display types for variables and typedefs. cursor_type = cursor.type.spelling if kind in VAR_KINDS else None if kind in FUNCTION_KINDS: # We can't use displayname as it also includes the arguments. params = [] tparams = [] for child in cursor.get_children(): if child.kind == CursorKind.PARM_DECL: params.append(child.spelling or child.type.spelling) elif child.kind == CursorKind.TEMPLATE_TYPE_PARAMETER: tparams.append(child.spelling) if kind in MEMBER_KINDS: if cursor.semantic_parent != cursor.lexical_parent: name = cursor.semantic_parent.spelling + '::' + name elif kind in CLASS_KINDS: name = cursor.displayname children = [] for child in cursor.get_children(): child_outline = visit_cursor(libclang, child) if child_outline is not None: children.append(child_outline) if kind == MACRO_INSTANTIATION: params = [] if name in GTEST_MACROS: tokens = list(itertools.islice(cursor.get_tokens(), 1, 6)) if len(tokens) == 5 and ( tokens[0].kind == TokenKind.PUNCTUATION and tokens[1].kind == TokenKind.IDENTIFIER and tokens[2].kind == TokenKind.PUNCTUATION and tokens[3].kind == TokenKind.IDENTIFIER and tokens[4].kind == TokenKind.PUNCTUATION ): params = [tokens[1].spelling, tokens[3].spelling] else: return None else: return None ret = { 'name': name, 'cursor_kind': kind, 'cursor_type': cursor_type, 'extent': range_dict_relative(cursor.extent), 'params': params, 'tparams': tparams, 'children': children, } return {k: v for k, v in ret.items() if v is not None} def insert_pragma_marks(marks, outline_tree, tree_end=None): new_result = [] for node in outline_tree: while len(marks) > 0: if marks[-1]['extent']['start']['row'] > node['extent']['start']['row']: break new_result.append(marks.pop()) children = node.get('children') if children: children[:] = insert_pragma_marks(marks, children, node['extent']['end']['row']) new_result.append(node) while len(marks) > 0: if tree_end is not None and marks[-1]['extent']['start']['row'] > tree_end: break new_result.append(marks.pop()) return new_result def get_outline(libclang, translation_unit, contents): root_cursor = translation_unit.cursor callback_type = ctypes.CFUNCTYPE(ctypes.c_int, Cursor, Cursor, ctypes.py_object) def visitor(child, parent, result): child._tu = translation_unit child_outline = visit_cursor(libclang, child) if child_outline is not None: result.append(child_outline) return 1 result = [] libclang.clang_visitChildren(root_cursor, callback_type(visitor), result) line = 0 lastpos = 0 pragma_marks = [] for mark in PRAGMA_MARK_REGEX.finditer(contents): while lastpos < mark.start(): if contents[lastpos] == '\n': line += 1 lastpos += 1 pragma_marks.append({ 'name': mark.group(1), 'cursor_kind': 'PRAGMA_MARK', 'extent': { 'start': {'row': line, 'column': 0}, 'end': {'row': line + 1, 'column': 0}, }, }) result = sorted(result, key=lambda x: ( x['extent']['start']['row'], x['extent']['start']['column'], x['extent']['end']['row'], x['extent']['end']['column'], )) pragma_marks.reverse() return insert_pragma_marks(pragma_marks, result)

true

790bd4359eb7e0a3ca1113f4a548318acb5650c8

1,263

py

Python

tests/test_set_random_seed.py

iblamedom/kuenstliche-intelligenz

382ba611cb5f2ac108243d535be1d457023cc50c

[ "MIT" ]

3

2020-08-28T21:55:42.000Z

2022-03-28T10:29:19.000Z

tests/test_set_random_seed.py

xduan7/dl-project-template

733ecdcfcb561aa1b39854b1b3632c0fda07f841

[ "MIT" ]

1

2022-02-27T17:06:21.000Z

tests/test_set_random_seed.py

xduan7/dl-project-template

733ecdcfcb561aa1b39854b1b3632c0fda07f841

[ "MIT" ]

null

import random import unittest from typing import Tuple import torch import numpy as np from src.utilities import set_random_seed _RANDOM_SEED: int = random.randint(0, 100) _TEST_ARRAY_SIZE: Tuple[int, int] = (2, 2) _TEST_TENSOR_SIZE: Tuple[int, int] = (2, 2) def _set_random_seed(): set_random_seed( random_seed=_RANDOM_SEED, ) class TestSetRandomSeed(unittest.TestCase): """Unit test class for ``set_random_seed`` function. The test checks the random seed function for Python random, NumPy, and PyTorch by asserting the first random number, array, or tensor is always the same after seeding. """ def test_random(self): _set_random_seed() _random = random.random() _set_random_seed() assert _random == random.random() def test_numpy(self): _set_random_seed() _array = np.random.random(size=_TEST_ARRAY_SIZE) _set_random_seed() assert (_array == np.random.random(size=_TEST_ARRAY_SIZE)).all() def test_torch(self): _set_random_seed() _tensor = torch.rand(size=_TEST_TENSOR_SIZE) _set_random_seed() assert (_tensor == torch.rand(size=_TEST_TENSOR_SIZE)).all() if __name__ == '__main__': unittest.main()

24.764706

72

0.684086

import random import unittest from typing import Tuple import torch import numpy as np from src.utilities import set_random_seed _RANDOM_SEED: int = random.randint(0, 100) _TEST_ARRAY_SIZE: Tuple[int, int] = (2, 2) _TEST_TENSOR_SIZE: Tuple[int, int] = (2, 2) def _set_random_seed(): set_random_seed( random_seed=_RANDOM_SEED, ) class TestSetRandomSeed(unittest.TestCase): def test_random(self): _set_random_seed() _random = random.random() _set_random_seed() assert _random == random.random() def test_numpy(self): _set_random_seed() _array = np.random.random(size=_TEST_ARRAY_SIZE) _set_random_seed() assert (_array == np.random.random(size=_TEST_ARRAY_SIZE)).all() def test_torch(self): _set_random_seed() _tensor = torch.rand(size=_TEST_TENSOR_SIZE) _set_random_seed() assert (_tensor == torch.rand(size=_TEST_TENSOR_SIZE)).all() if __name__ == '__main__': unittest.main()

true

790bd481fc247599756784d878b515a933d57c7c

416

py

Python

Book/api/permissions.py

imran110219/Book_Review_App

48655da14420af64a2b5460f1f635cd61ae30779

[ "MIT" ]

2

2019-05-24T21:08:54.000Z

2021-12-29T11:29:45.000Z

Book/api/permissions.py

imran110219/Book_Review_App

48655da14420af64a2b5460f1f635cd61ae30779

[ "MIT" ]

8

2019-04-17T05:46:40.000Z

2022-03-11T23:17:20.000Z

Book/api/permissions.py

imran110219/Book_Review_App

48655da14420af64a2b5460f1f635cd61ae30779

[ "MIT" ]

2

2018-02-04T10:16:40.000Z

2019-06-24T19:43:01.000Z

from rest_framework.permissions import BasePermission, SAFE_METHODS class IsOwnerOrReadOnly(BasePermission): message = 'You must be the owner of this object.' def has_object_permission(self, request, view, obj): # member = Membership.objects.get(user=user.request) # member.is_active if request.method in SAFE_METHODS: return True return obj.user == request.user

37.818182

67

0.706731

from rest_framework.permissions import BasePermission, SAFE_METHODS class IsOwnerOrReadOnly(BasePermission): message = 'You must be the owner of this object.' def has_object_permission(self, request, view, obj): if request.method in SAFE_METHODS: return True return obj.user == request.user

true

790bd48c4149fb209bc53ba278f357aee07eb98d

23,669

py

Python

sdk/python/pulumi_kubernetes/storage/v1beta1/CSIStorageCapacity.py

csssuf/pulumi-kubernetes

8d007166d0e8968fcabaeecd0cee13f9c08d97f1

[ "Apache-2.0" ]

null

sdk/python/pulumi_kubernetes/storage/v1beta1/CSIStorageCapacity.py

csssuf/pulumi-kubernetes

8d007166d0e8968fcabaeecd0cee13f9c08d97f1

[ "Apache-2.0" ]

null

sdk/python/pulumi_kubernetes/storage/v1beta1/CSIStorageCapacity.py

csssuf/pulumi-kubernetes

8d007166d0e8968fcabaeecd0cee13f9c08d97f1

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by pulumigen. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from ... import meta as _meta __all__ = ['CSIStorageCapacityArgs', 'CSIStorageCapacity'] @pulumi.input_type class CSIStorageCapacityArgs: def __init__(__self__, *, storage_class_name: pulumi.Input[str], api_version: Optional[pulumi.Input[str]] = None, capacity: Optional[pulumi.Input[str]] = None, kind: Optional[pulumi.Input[str]] = None, maximum_volume_size: Optional[pulumi.Input[str]] = None, metadata: Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']] = None, node_topology: Optional[pulumi.Input['_meta.v1.LabelSelectorArgs']] = None): """ The set of arguments for constructing a CSIStorageCapacity resource. :param pulumi.Input[str] storage_class_name: The name of the StorageClass that the reported capacity applies to. It must meet the same requirements as the name of a StorageClass object (non-empty, DNS subdomain). If that object no longer exists, the CSIStorageCapacity object is obsolete and should be removed by its creator. This field is immutable. :param pulumi.Input[str] api_version: APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources :param pulumi.Input[str] capacity: Capacity is the value reported by the CSI driver in its GetCapacityResponse for a GetCapacityRequest with topology and parameters that match the previous fields. The semantic is currently (CSI spec 1.2) defined as: The available capacity, in bytes, of the storage that can be used to provision volumes. If not set, that information is currently unavailable and treated like zero capacity. :param pulumi.Input[str] kind: Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds :param pulumi.Input[str] maximum_volume_size: MaximumVolumeSize is the value reported by the CSI driver in its GetCapacityResponse for a GetCapacityRequest with topology and parameters that match the previous fields. This is defined since CSI spec 1.4.0 as the largest size that may be used in a CreateVolumeRequest.capacity_range.required_bytes field to create a volume with the same parameters as those in GetCapacityRequest. The corresponding value in the Kubernetes API is ResourceRequirements.Requests in a volume claim. :param pulumi.Input['_meta.v1.ObjectMetaArgs'] metadata: Standard object's metadata. The name has no particular meaning. It must be be a DNS subdomain (dots allowed, 253 characters). To ensure that there are no conflicts with other CSI drivers on the cluster, the recommendation is to use csisc-<uuid>, a generated name, or a reverse-domain name which ends with the unique CSI driver name. Objects are namespaced. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata :param pulumi.Input['_meta.v1.LabelSelectorArgs'] node_topology: NodeTopology defines which nodes have access to the storage for which capacity was reported. If not set, the storage is not accessible from any node in the cluster. If empty, the storage is accessible from all nodes. This field is immutable. """ pulumi.set(__self__, "storage_class_name", storage_class_name) if api_version is not None: pulumi.set(__self__, "api_version", 'storage.k8s.io/v1beta1') if capacity is not None: pulumi.set(__self__, "capacity", capacity) if kind is not None: pulumi.set(__self__, "kind", 'CSIStorageCapacity') if maximum_volume_size is not None: pulumi.set(__self__, "maximum_volume_size", maximum_volume_size) if metadata is not None: pulumi.set(__self__, "metadata", metadata) if node_topology is not None: pulumi.set(__self__, "node_topology", node_topology) @property @pulumi.getter(name="storageClassName") def storage_class_name(self) -> pulumi.Input[str]: """ The name of the StorageClass that the reported capacity applies to. It must meet the same requirements as the name of a StorageClass object (non-empty, DNS subdomain). If that object no longer exists, the CSIStorageCapacity object is obsolete and should be removed by its creator. This field is immutable. """ return pulumi.get(self, "storage_class_name") @storage_class_name.setter def storage_class_name(self, value: pulumi.Input[str]): pulumi.set(self, "storage_class_name", value) @property @pulumi.getter(name="apiVersion") def api_version(self) -> Optional[pulumi.Input[str]]: """ APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources """ return pulumi.get(self, "api_version") @api_version.setter def api_version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "api_version", value) @property @pulumi.getter def capacity(self) -> Optional[pulumi.Input[str]]: """ Capacity is the value reported by the CSI driver in its GetCapacityResponse for a GetCapacityRequest with topology and parameters that match the previous fields. The semantic is currently (CSI spec 1.2) defined as: The available capacity, in bytes, of the storage that can be used to provision volumes. If not set, that information is currently unavailable and treated like zero capacity. """ return pulumi.get(self, "capacity") @capacity.setter def capacity(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "capacity", value) @property @pulumi.getter def kind(self) -> Optional[pulumi.Input[str]]: """ Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds """ return pulumi.get(self, "kind") @kind.setter def kind(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "kind", value) @property @pulumi.getter(name="maximumVolumeSize") def maximum_volume_size(self) -> Optional[pulumi.Input[str]]: """ MaximumVolumeSize is the value reported by the CSI driver in its GetCapacityResponse for a GetCapacityRequest with topology and parameters that match the previous fields. This is defined since CSI spec 1.4.0 as the largest size that may be used in a CreateVolumeRequest.capacity_range.required_bytes field to create a volume with the same parameters as those in GetCapacityRequest. The corresponding value in the Kubernetes API is ResourceRequirements.Requests in a volume claim. """ return pulumi.get(self, "maximum_volume_size") @maximum_volume_size.setter def maximum_volume_size(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "maximum_volume_size", value) @property @pulumi.getter def metadata(self) -> Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']]: """ Standard object's metadata. The name has no particular meaning. It must be be a DNS subdomain (dots allowed, 253 characters). To ensure that there are no conflicts with other CSI drivers on the cluster, the recommendation is to use csisc-<uuid>, a generated name, or a reverse-domain name which ends with the unique CSI driver name. Objects are namespaced. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata """ return pulumi.get(self, "metadata") @metadata.setter def metadata(self, value: Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']]): pulumi.set(self, "metadata", value) @property @pulumi.getter(name="nodeTopology") def node_topology(self) -> Optional[pulumi.Input['_meta.v1.LabelSelectorArgs']]: """ NodeTopology defines which nodes have access to the storage for which capacity was reported. If not set, the storage is not accessible from any node in the cluster. If empty, the storage is accessible from all nodes. This field is immutable. """ return pulumi.get(self, "node_topology") @node_topology.setter def node_topology(self, value: Optional[pulumi.Input['_meta.v1.LabelSelectorArgs']]): pulumi.set(self, "node_topology", value) class CSIStorageCapacity(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, api_version: Optional[pulumi.Input[str]] = None, capacity: Optional[pulumi.Input[str]] = None, kind: Optional[pulumi.Input[str]] = None, maximum_volume_size: Optional[pulumi.Input[str]] = None, metadata: Optional[pulumi.Input[pulumi.InputType['_meta.v1.ObjectMetaArgs']]] = None, node_topology: Optional[pulumi.Input[pulumi.InputType['_meta.v1.LabelSelectorArgs']]] = None, storage_class_name: Optional[pulumi.Input[str]] = None, __props__=None): """ CSIStorageCapacity stores the result of one CSI GetCapacity call. For a given StorageClass, this describes the available capacity in a particular topology segment. This can be used when considering where to instantiate new PersistentVolumes. For example this can express things like: - StorageClass "standard" has "1234 GiB" available in "topology.kubernetes.io/zone=us-east1" - StorageClass "localssd" has "10 GiB" available in "kubernetes.io/hostname=knode-abc123" The following three cases all imply that no capacity is available for a certain combination: - no object exists with suitable topology and storage class name - such an object exists, but the capacity is unset - such an object exists, but the capacity is zero The producer of these objects can decide which approach is more suitable. They are consumed by the kube-scheduler if the CSIStorageCapacity beta feature gate is enabled there and a CSI driver opts into capacity-aware scheduling with CSIDriver.StorageCapacity. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] api_version: APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources :param pulumi.Input[str] capacity: Capacity is the value reported by the CSI driver in its GetCapacityResponse for a GetCapacityRequest with topology and parameters that match the previous fields. The semantic is currently (CSI spec 1.2) defined as: The available capacity, in bytes, of the storage that can be used to provision volumes. If not set, that information is currently unavailable and treated like zero capacity. :param pulumi.Input[str] kind: Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds :param pulumi.Input[str] maximum_volume_size: MaximumVolumeSize is the value reported by the CSI driver in its GetCapacityResponse for a GetCapacityRequest with topology and parameters that match the previous fields. This is defined since CSI spec 1.4.0 as the largest size that may be used in a CreateVolumeRequest.capacity_range.required_bytes field to create a volume with the same parameters as those in GetCapacityRequest. The corresponding value in the Kubernetes API is ResourceRequirements.Requests in a volume claim. :param pulumi.Input[pulumi.InputType['_meta.v1.ObjectMetaArgs']] metadata: Standard object's metadata. The name has no particular meaning. It must be be a DNS subdomain (dots allowed, 253 characters). To ensure that there are no conflicts with other CSI drivers on the cluster, the recommendation is to use csisc-<uuid>, a generated name, or a reverse-domain name which ends with the unique CSI driver name. Objects are namespaced. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata :param pulumi.Input[pulumi.InputType['_meta.v1.LabelSelectorArgs']] node_topology: NodeTopology defines which nodes have access to the storage for which capacity was reported. If not set, the storage is not accessible from any node in the cluster. If empty, the storage is accessible from all nodes. This field is immutable. :param pulumi.Input[str] storage_class_name: The name of the StorageClass that the reported capacity applies to. It must meet the same requirements as the name of a StorageClass object (non-empty, DNS subdomain). If that object no longer exists, the CSIStorageCapacity object is obsolete and should be removed by its creator. This field is immutable. """ ... @overload def __init__(__self__, resource_name: str, args: CSIStorageCapacityArgs, opts: Optional[pulumi.ResourceOptions] = None): """ CSIStorageCapacity stores the result of one CSI GetCapacity call. For a given StorageClass, this describes the available capacity in a particular topology segment. This can be used when considering where to instantiate new PersistentVolumes. For example this can express things like: - StorageClass "standard" has "1234 GiB" available in "topology.kubernetes.io/zone=us-east1" - StorageClass "localssd" has "10 GiB" available in "kubernetes.io/hostname=knode-abc123" The following three cases all imply that no capacity is available for a certain combination: - no object exists with suitable topology and storage class name - such an object exists, but the capacity is unset - such an object exists, but the capacity is zero The producer of these objects can decide which approach is more suitable. They are consumed by the kube-scheduler if the CSIStorageCapacity beta feature gate is enabled there and a CSI driver opts into capacity-aware scheduling with CSIDriver.StorageCapacity. :param str resource_name: The name of the resource. :param CSIStorageCapacityArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(CSIStorageCapacityArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, api_version: Optional[pulumi.Input[str]] = None, capacity: Optional[pulumi.Input[str]] = None, kind: Optional[pulumi.Input[str]] = None, maximum_volume_size: Optional[pulumi.Input[str]] = None, metadata: Optional[pulumi.Input[pulumi.InputType['_meta.v1.ObjectMetaArgs']]] = None, node_topology: Optional[pulumi.Input[pulumi.InputType['_meta.v1.LabelSelectorArgs']]] = None, storage_class_name: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = CSIStorageCapacityArgs.__new__(CSIStorageCapacityArgs) __props__.__dict__["api_version"] = 'storage.k8s.io/v1beta1' __props__.__dict__["capacity"] = capacity __props__.__dict__["kind"] = 'CSIStorageCapacity' __props__.__dict__["maximum_volume_size"] = maximum_volume_size __props__.__dict__["metadata"] = metadata __props__.__dict__["node_topology"] = node_topology if storage_class_name is None and not opts.urn: raise TypeError("Missing required property 'storage_class_name'") __props__.__dict__["storage_class_name"] = storage_class_name alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="kubernetes:storage.k8s.io/v1alpha1:CSIStorageCapacity")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(CSIStorageCapacity, __self__).__init__( 'kubernetes:storage.k8s.io/v1beta1:CSIStorageCapacity', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'CSIStorageCapacity': """ Get an existing CSIStorageCapacity resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = CSIStorageCapacityArgs.__new__(CSIStorageCapacityArgs) __props__.__dict__["api_version"] = None __props__.__dict__["capacity"] = None __props__.__dict__["kind"] = None __props__.__dict__["maximum_volume_size"] = None __props__.__dict__["metadata"] = None __props__.__dict__["node_topology"] = None __props__.__dict__["storage_class_name"] = None return CSIStorageCapacity(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="apiVersion") def api_version(self) -> pulumi.Output[Optional[str]]: """ APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources """ return pulumi.get(self, "api_version") @property @pulumi.getter def capacity(self) -> pulumi.Output[Optional[str]]: """ Capacity is the value reported by the CSI driver in its GetCapacityResponse for a GetCapacityRequest with topology and parameters that match the previous fields. The semantic is currently (CSI spec 1.2) defined as: The available capacity, in bytes, of the storage that can be used to provision volumes. If not set, that information is currently unavailable and treated like zero capacity. """ return pulumi.get(self, "capacity") @property @pulumi.getter def kind(self) -> pulumi.Output[Optional[str]]: """ Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds """ return pulumi.get(self, "kind") @property @pulumi.getter(name="maximumVolumeSize") def maximum_volume_size(self) -> pulumi.Output[Optional[str]]: """ MaximumVolumeSize is the value reported by the CSI driver in its GetCapacityResponse for a GetCapacityRequest with topology and parameters that match the previous fields. This is defined since CSI spec 1.4.0 as the largest size that may be used in a CreateVolumeRequest.capacity_range.required_bytes field to create a volume with the same parameters as those in GetCapacityRequest. The corresponding value in the Kubernetes API is ResourceRequirements.Requests in a volume claim. """ return pulumi.get(self, "maximum_volume_size") @property @pulumi.getter def metadata(self) -> pulumi.Output[Optional['_meta.v1.outputs.ObjectMeta']]: """ Standard object's metadata. The name has no particular meaning. It must be be a DNS subdomain (dots allowed, 253 characters). To ensure that there are no conflicts with other CSI drivers on the cluster, the recommendation is to use csisc-<uuid>, a generated name, or a reverse-domain name which ends with the unique CSI driver name. Objects are namespaced. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata """ return pulumi.get(self, "metadata") @property @pulumi.getter(name="nodeTopology") def node_topology(self) -> pulumi.Output[Optional['_meta.v1.outputs.LabelSelector']]: """ NodeTopology defines which nodes have access to the storage for which capacity was reported. If not set, the storage is not accessible from any node in the cluster. If empty, the storage is accessible from all nodes. This field is immutable. """ return pulumi.get(self, "node_topology") @property @pulumi.getter(name="storageClassName") def storage_class_name(self) -> pulumi.Output[str]: """ The name of the StorageClass that the reported capacity applies to. It must meet the same requirements as the name of a StorageClass object (non-empty, DNS subdomain). If that object no longer exists, the CSIStorageCapacity object is obsolete and should be removed by its creator. This field is immutable. """ return pulumi.get(self, "storage_class_name")

68.014368

415

0.713634

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from ... import meta as _meta __all__ = ['CSIStorageCapacityArgs', 'CSIStorageCapacity'] @pulumi.input_type class CSIStorageCapacityArgs: def __init__(__self__, *, storage_class_name: pulumi.Input[str], api_version: Optional[pulumi.Input[str]] = None, capacity: Optional[pulumi.Input[str]] = None, kind: Optional[pulumi.Input[str]] = None, maximum_volume_size: Optional[pulumi.Input[str]] = None, metadata: Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']] = None, node_topology: Optional[pulumi.Input['_meta.v1.LabelSelectorArgs']] = None): pulumi.set(__self__, "storage_class_name", storage_class_name) if api_version is not None: pulumi.set(__self__, "api_version", 'storage.k8s.io/v1beta1') if capacity is not None: pulumi.set(__self__, "capacity", capacity) if kind is not None: pulumi.set(__self__, "kind", 'CSIStorageCapacity') if maximum_volume_size is not None: pulumi.set(__self__, "maximum_volume_size", maximum_volume_size) if metadata is not None: pulumi.set(__self__, "metadata", metadata) if node_topology is not None: pulumi.set(__self__, "node_topology", node_topology) @property @pulumi.getter(name="storageClassName") def storage_class_name(self) -> pulumi.Input[str]: return pulumi.get(self, "storage_class_name") @storage_class_name.setter def storage_class_name(self, value: pulumi.Input[str]): pulumi.set(self, "storage_class_name", value) @property @pulumi.getter(name="apiVersion") def api_version(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "api_version") @api_version.setter def api_version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "api_version", value) @property @pulumi.getter def capacity(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "capacity") @capacity.setter def capacity(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "capacity", value) @property @pulumi.getter def kind(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "kind") @kind.setter def kind(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "kind", value) @property @pulumi.getter(name="maximumVolumeSize") def maximum_volume_size(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "maximum_volume_size") @maximum_volume_size.setter def maximum_volume_size(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "maximum_volume_size", value) @property @pulumi.getter def metadata(self) -> Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']]: return pulumi.get(self, "metadata") @metadata.setter def metadata(self, value: Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']]): pulumi.set(self, "metadata", value) @property @pulumi.getter(name="nodeTopology") def node_topology(self) -> Optional[pulumi.Input['_meta.v1.LabelSelectorArgs']]: return pulumi.get(self, "node_topology") @node_topology.setter def node_topology(self, value: Optional[pulumi.Input['_meta.v1.LabelSelectorArgs']]): pulumi.set(self, "node_topology", value) class CSIStorageCapacity(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, api_version: Optional[pulumi.Input[str]] = None, capacity: Optional[pulumi.Input[str]] = None, kind: Optional[pulumi.Input[str]] = None, maximum_volume_size: Optional[pulumi.Input[str]] = None, metadata: Optional[pulumi.Input[pulumi.InputType['_meta.v1.ObjectMetaArgs']]] = None, node_topology: Optional[pulumi.Input[pulumi.InputType['_meta.v1.LabelSelectorArgs']]] = None, storage_class_name: Optional[pulumi.Input[str]] = None, __props__=None): ... @overload def __init__(__self__, resource_name: str, args: CSIStorageCapacityArgs, opts: Optional[pulumi.ResourceOptions] = None): ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(CSIStorageCapacityArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, api_version: Optional[pulumi.Input[str]] = None, capacity: Optional[pulumi.Input[str]] = None, kind: Optional[pulumi.Input[str]] = None, maximum_volume_size: Optional[pulumi.Input[str]] = None, metadata: Optional[pulumi.Input[pulumi.InputType['_meta.v1.ObjectMetaArgs']]] = None, node_topology: Optional[pulumi.Input[pulumi.InputType['_meta.v1.LabelSelectorArgs']]] = None, storage_class_name: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = CSIStorageCapacityArgs.__new__(CSIStorageCapacityArgs) __props__.__dict__["api_version"] = 'storage.k8s.io/v1beta1' __props__.__dict__["capacity"] = capacity __props__.__dict__["kind"] = 'CSIStorageCapacity' __props__.__dict__["maximum_volume_size"] = maximum_volume_size __props__.__dict__["metadata"] = metadata __props__.__dict__["node_topology"] = node_topology if storage_class_name is None and not opts.urn: raise TypeError("Missing required property 'storage_class_name'") __props__.__dict__["storage_class_name"] = storage_class_name alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="kubernetes:storage.k8s.io/v1alpha1:CSIStorageCapacity")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(CSIStorageCapacity, __self__).__init__( 'kubernetes:storage.k8s.io/v1beta1:CSIStorageCapacity', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'CSIStorageCapacity': opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = CSIStorageCapacityArgs.__new__(CSIStorageCapacityArgs) __props__.__dict__["api_version"] = None __props__.__dict__["capacity"] = None __props__.__dict__["kind"] = None __props__.__dict__["maximum_volume_size"] = None __props__.__dict__["metadata"] = None __props__.__dict__["node_topology"] = None __props__.__dict__["storage_class_name"] = None return CSIStorageCapacity(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="apiVersion") def api_version(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "api_version") @property @pulumi.getter def capacity(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "capacity") @property @pulumi.getter def kind(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "kind") @property @pulumi.getter(name="maximumVolumeSize") def maximum_volume_size(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "maximum_volume_size") @property @pulumi.getter def metadata(self) -> pulumi.Output[Optional['_meta.v1.outputs.ObjectMeta']]: return pulumi.get(self, "metadata") @property @pulumi.getter(name="nodeTopology") def node_topology(self) -> pulumi.Output[Optional['_meta.v1.outputs.LabelSelector']]: return pulumi.get(self, "node_topology") @property @pulumi.getter(name="storageClassName") def storage_class_name(self) -> pulumi.Output[str]: return pulumi.get(self, "storage_class_name")

true

790bd5447db6bb621163c59dcbf310f1773cbef7

9,605

py

Python

sdk/python/pulumi_azure_native/datafactory/private_endpoint_connection.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/datafactory/private_endpoint_connection.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/datafactory/private_endpoint_connection.py

sebtelko/pulumi-azure-native

711ec021b5c73da05611c56c8a35adb0ce3244e4

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs from ._inputs import * __all__ = ['PrivateEndpointConnectionArgs', 'PrivateEndpointConnection'] @pulumi.input_type class PrivateEndpointConnectionArgs: def __init__(__self__, *, factory_name: pulumi.Input[str], resource_group_name: pulumi.Input[str], private_endpoint_connection_name: Optional[pulumi.Input[str]] = None, properties: Optional[pulumi.Input['PrivateLinkConnectionApprovalRequestArgs']] = None): """ The set of arguments for constructing a PrivateEndpointConnection resource. :param pulumi.Input[str] factory_name: The factory name. :param pulumi.Input[str] resource_group_name: The resource group name. :param pulumi.Input[str] private_endpoint_connection_name: The private endpoint connection name. :param pulumi.Input['PrivateLinkConnectionApprovalRequestArgs'] properties: Core resource properties """ pulumi.set(__self__, "factory_name", factory_name) pulumi.set(__self__, "resource_group_name", resource_group_name) if private_endpoint_connection_name is not None: pulumi.set(__self__, "private_endpoint_connection_name", private_endpoint_connection_name) if properties is not None: pulumi.set(__self__, "properties", properties) @property @pulumi.getter(name="factoryName") def factory_name(self) -> pulumi.Input[str]: """ The factory name. """ return pulumi.get(self, "factory_name") @factory_name.setter def factory_name(self, value: pulumi.Input[str]): pulumi.set(self, "factory_name", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ The resource group name. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter(name="privateEndpointConnectionName") def private_endpoint_connection_name(self) -> Optional[pulumi.Input[str]]: """ The private endpoint connection name. """ return pulumi.get(self, "private_endpoint_connection_name") @private_endpoint_connection_name.setter def private_endpoint_connection_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "private_endpoint_connection_name", value) @property @pulumi.getter def properties(self) -> Optional[pulumi.Input['PrivateLinkConnectionApprovalRequestArgs']]: """ Core resource properties """ return pulumi.get(self, "properties") @properties.setter def properties(self, value: Optional[pulumi.Input['PrivateLinkConnectionApprovalRequestArgs']]): pulumi.set(self, "properties", value) class PrivateEndpointConnection(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, factory_name: Optional[pulumi.Input[str]] = None, private_endpoint_connection_name: Optional[pulumi.Input[str]] = None, properties: Optional[pulumi.Input[pulumi.InputType['PrivateLinkConnectionApprovalRequestArgs']]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, __props__=None): """ Private Endpoint Connection ARM resource. API Version: 2018-06-01. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] factory_name: The factory name. :param pulumi.Input[str] private_endpoint_connection_name: The private endpoint connection name. :param pulumi.Input[pulumi.InputType['PrivateLinkConnectionApprovalRequestArgs']] properties: Core resource properties :param pulumi.Input[str] resource_group_name: The resource group name. """ ... @overload def __init__(__self__, resource_name: str, args: PrivateEndpointConnectionArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Private Endpoint Connection ARM resource. API Version: 2018-06-01. :param str resource_name: The name of the resource. :param PrivateEndpointConnectionArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(PrivateEndpointConnectionArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, factory_name: Optional[pulumi.Input[str]] = None, private_endpoint_connection_name: Optional[pulumi.Input[str]] = None, properties: Optional[pulumi.Input[pulumi.InputType['PrivateLinkConnectionApprovalRequestArgs']]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = PrivateEndpointConnectionArgs.__new__(PrivateEndpointConnectionArgs) if factory_name is None and not opts.urn: raise TypeError("Missing required property 'factory_name'") __props__.__dict__["factory_name"] = factory_name __props__.__dict__["private_endpoint_connection_name"] = private_endpoint_connection_name __props__.__dict__["properties"] = properties if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__.__dict__["resource_group_name"] = resource_group_name __props__.__dict__["etag"] = None __props__.__dict__["name"] = None __props__.__dict__["type"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:datafactory:PrivateEndpointConnection"), pulumi.Alias(type_="azure-native:datafactory/v20180601:PrivateEndpointConnection"), pulumi.Alias(type_="azure-nextgen:datafactory/v20180601:PrivateEndpointConnection")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(PrivateEndpointConnection, __self__).__init__( 'azure-native:datafactory:PrivateEndpointConnection', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'PrivateEndpointConnection': """ Get an existing PrivateEndpointConnection resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = PrivateEndpointConnectionArgs.__new__(PrivateEndpointConnectionArgs) __props__.__dict__["etag"] = None __props__.__dict__["name"] = None __props__.__dict__["properties"] = None __props__.__dict__["type"] = None return PrivateEndpointConnection(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def etag(self) -> pulumi.Output[str]: """ Etag identifies change in the resource. """ return pulumi.get(self, "etag") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The resource name. """ return pulumi.get(self, "name") @property @pulumi.getter def properties(self) -> pulumi.Output['outputs.RemotePrivateEndpointConnectionResponse']: """ Core resource properties """ return pulumi.get(self, "properties") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ The resource type. """ return pulumi.get(self, "type")

43.659091

297

0.669755

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs from ._inputs import * __all__ = ['PrivateEndpointConnectionArgs', 'PrivateEndpointConnection'] @pulumi.input_type class PrivateEndpointConnectionArgs: def __init__(__self__, *, factory_name: pulumi.Input[str], resource_group_name: pulumi.Input[str], private_endpoint_connection_name: Optional[pulumi.Input[str]] = None, properties: Optional[pulumi.Input['PrivateLinkConnectionApprovalRequestArgs']] = None): pulumi.set(__self__, "factory_name", factory_name) pulumi.set(__self__, "resource_group_name", resource_group_name) if private_endpoint_connection_name is not None: pulumi.set(__self__, "private_endpoint_connection_name", private_endpoint_connection_name) if properties is not None: pulumi.set(__self__, "properties", properties) @property @pulumi.getter(name="factoryName") def factory_name(self) -> pulumi.Input[str]: return pulumi.get(self, "factory_name") @factory_name.setter def factory_name(self, value: pulumi.Input[str]): pulumi.set(self, "factory_name", value) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter(name="privateEndpointConnectionName") def private_endpoint_connection_name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "private_endpoint_connection_name") @private_endpoint_connection_name.setter def private_endpoint_connection_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "private_endpoint_connection_name", value) @property @pulumi.getter def properties(self) -> Optional[pulumi.Input['PrivateLinkConnectionApprovalRequestArgs']]: return pulumi.get(self, "properties") @properties.setter def properties(self, value: Optional[pulumi.Input['PrivateLinkConnectionApprovalRequestArgs']]): pulumi.set(self, "properties", value) class PrivateEndpointConnection(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, factory_name: Optional[pulumi.Input[str]] = None, private_endpoint_connection_name: Optional[pulumi.Input[str]] = None, properties: Optional[pulumi.Input[pulumi.InputType['PrivateLinkConnectionApprovalRequestArgs']]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, __props__=None): ... @overload def __init__(__self__, resource_name: str, args: PrivateEndpointConnectionArgs, opts: Optional[pulumi.ResourceOptions] = None): ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(PrivateEndpointConnectionArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, factory_name: Optional[pulumi.Input[str]] = None, private_endpoint_connection_name: Optional[pulumi.Input[str]] = None, properties: Optional[pulumi.Input[pulumi.InputType['PrivateLinkConnectionApprovalRequestArgs']]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = PrivateEndpointConnectionArgs.__new__(PrivateEndpointConnectionArgs) if factory_name is None and not opts.urn: raise TypeError("Missing required property 'factory_name'") __props__.__dict__["factory_name"] = factory_name __props__.__dict__["private_endpoint_connection_name"] = private_endpoint_connection_name __props__.__dict__["properties"] = properties if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__.__dict__["resource_group_name"] = resource_group_name __props__.__dict__["etag"] = None __props__.__dict__["name"] = None __props__.__dict__["type"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:datafactory:PrivateEndpointConnection"), pulumi.Alias(type_="azure-native:datafactory/v20180601:PrivateEndpointConnection"), pulumi.Alias(type_="azure-nextgen:datafactory/v20180601:PrivateEndpointConnection")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(PrivateEndpointConnection, __self__).__init__( 'azure-native:datafactory:PrivateEndpointConnection', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'PrivateEndpointConnection': opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = PrivateEndpointConnectionArgs.__new__(PrivateEndpointConnectionArgs) __props__.__dict__["etag"] = None __props__.__dict__["name"] = None __props__.__dict__["properties"] = None __props__.__dict__["type"] = None return PrivateEndpointConnection(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def etag(self) -> pulumi.Output[str]: return pulumi.get(self, "etag") @property @pulumi.getter def name(self) -> pulumi.Output[str]: return pulumi.get(self, "name") @property @pulumi.getter def properties(self) -> pulumi.Output['outputs.RemotePrivateEndpointConnectionResponse']: return pulumi.get(self, "properties") @property @pulumi.getter def type(self) -> pulumi.Output[str]: return pulumi.get(self, "type")

true

790bd583b0ced0d04970656e1a3968478d4e8aff

6,596

py

Python

federatedml/feature/feature_scale/standard_scale.py

chenj133/FATE

7065fc73ab83f83e699efec69ff8efb499159ef4

[ "Apache-2.0" ]

3

2019-10-18T02:22:05.000Z

2019-10-18T02:22:42.000Z

federatedml/feature/feature_scale/standard_scale.py

chenj133/FATE

7065fc73ab83f83e699efec69ff8efb499159ef4

[ "Apache-2.0" ]

14

2020-01-28T23:02:45.000Z

2022-02-10T00:22:08.000Z

federatedml/feature/feature_scale/standard_scale.py

chenj133/FATE

7065fc73ab83f83e699efec69ff8efb499159ef4

[ "Apache-2.0" ]

2

2019-09-05T02:32:05.000Z

2019-09-17T10:30:48.000Z

# # Copyright 2019 The FATE Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import functools import numpy as np from arch.api.proto.feature_scale_meta_pb2 import ScaleMeta from arch.api.proto.feature_scale_param_pb2 import ScaleParam from arch.api.proto.feature_scale_param_pb2 import ColumnScaleParam from arch.api.utils import log_utils from federatedml.feature.feature_scale.base_scale import BaseScale from federatedml.statistic.statics import MultivariateStatisticalSummary LOGGER = log_utils.getLogger() class StandardScale(BaseScale): """ Standardize features by removing the mean and scaling to unit variance. The standard score of a sample x is calculated as: z = (x - u) / s, where u is the mean of the training samples, and s is the standard deviation of the training samples """ def __init__(self, params): super().__init__(params) self.with_mean = params.with_mean self.with_std = params.with_std self.mean = None self.std = None def set_param(self, mean, std): self.mean = mean self.std = std @staticmethod def __scale_with_column_range(data, column_upper, column_lower, mean, std, process_cols_list): for i in process_cols_list: value = data.features[i] if value > column_upper[i]: value = column_upper[i] elif value < column_lower[i]: value = column_lower[i] data.features[i] = np.around((value - mean[i]) / std[i], 6) return data @staticmethod def __scale(data, mean, std, process_cols_list): for i in process_cols_list: data.features[i] = np.around((data.features[i] - mean[i]) / std[i], 6) return data def fit(self, data): """ Apply standard scale for input data Parameters ---------- data: data_instance, input data Returns ---------- data:data_instance, data after scale mean: list, each column mean value std: list, each column standard deviation """ self.column_min_value, self.column_max_value = self._get_min_max_value(data) self.scale_column_idx = self._get_scale_column_idx(data) self.header = self._get_header(data) self.data_shape = self._get_data_shape(data) # fit column value if larger than parameter upper or less than parameter lower data = self.fit_feature_range(data) if not self.with_mean and not self.with_std: self.mean = [0 for _ in range(self.data_shape)] self.std = [1 for _ in range(self.data_shape)] else: self.summary_obj = MultivariateStatisticalSummary(data, -1) if self.with_mean: self.mean = self.summary_obj.get_mean() self.mean = [self.mean[key] for key in self.header] else: self.mean = [0 for _ in range(self.data_shape)] if self.with_std: self.std = self.summary_obj.get_std_variance() self.std = [self.std[key] for key in self.header] for i, value in enumerate(self.std): if np.abs(value - 0) < 1e-6: self.std[i] = 1 else: self.std = [1 for _ in range(self.data_shape)] f = functools.partial(self.__scale, mean=self.mean, std=self.std, process_cols_list=self.scale_column_idx) fit_data = data.mapValues(f) return fit_data def transform(self, data): """ Transform input data using standard scale with fit results Parameters ---------- data: data_instance, input data Returns ---------- transform_data:data_instance, data after transform """ f = functools.partial(self.__scale_with_column_range, column_upper=self.column_max_value, column_lower=self.column_min_value, mean=self.mean, std=self.std, process_cols_list=self.scale_column_idx) transform_data = data.mapValues(f) return transform_data def __get_meta(self): if self.header: scale_column = [self.header[i] for i in self.scale_column_idx] else: scale_column = ["_".join(["col", str(i)]) for i in self.scale_column_idx] if not self.data_shape: self.data_shape = -1 meta_proto_obj = ScaleMeta(method="standard_scale", area=self.area, scale_column=scale_column, feat_upper=self._get_upper(self.data_shape), feat_lower=self._get_lower(self.data_shape), with_mean=self.with_mean, with_std=self.with_std ) return meta_proto_obj def __get_param(self, need_run): column_scale_param_dict = {} if self.header: for i, header in enumerate(self.header): if i in self.scale_column_idx: param_obj = ColumnScaleParam(column_upper=self.column_max_value[i], column_lower=self.column_min_value[i], mean=self.mean[i], std=self.std[i]) column_scale_param_dict[header] = param_obj param_proto_obj = ScaleParam(col_scale_param=column_scale_param_dict, header=self.header, need_run=need_run) return param_proto_obj def export_model(self, need_run): meta_obj = self.__get_meta() param_obj = self.__get_param(need_run) result = { self.model_meta_name: meta_obj, self.model_param_name: param_obj } return result

37.05618

126

0.595512

import functools import numpy as np from arch.api.proto.feature_scale_meta_pb2 import ScaleMeta from arch.api.proto.feature_scale_param_pb2 import ScaleParam from arch.api.proto.feature_scale_param_pb2 import ColumnScaleParam from arch.api.utils import log_utils from federatedml.feature.feature_scale.base_scale import BaseScale from federatedml.statistic.statics import MultivariateStatisticalSummary LOGGER = log_utils.getLogger() class StandardScale(BaseScale): def __init__(self, params): super().__init__(params) self.with_mean = params.with_mean self.with_std = params.with_std self.mean = None self.std = None def set_param(self, mean, std): self.mean = mean self.std = std @staticmethod def __scale_with_column_range(data, column_upper, column_lower, mean, std, process_cols_list): for i in process_cols_list: value = data.features[i] if value > column_upper[i]: value = column_upper[i] elif value < column_lower[i]: value = column_lower[i] data.features[i] = np.around((value - mean[i]) / std[i], 6) return data @staticmethod def __scale(data, mean, std, process_cols_list): for i in process_cols_list: data.features[i] = np.around((data.features[i] - mean[i]) / std[i], 6) return data def fit(self, data): self.column_min_value, self.column_max_value = self._get_min_max_value(data) self.scale_column_idx = self._get_scale_column_idx(data) self.header = self._get_header(data) self.data_shape = self._get_data_shape(data) data = self.fit_feature_range(data) if not self.with_mean and not self.with_std: self.mean = [0 for _ in range(self.data_shape)] self.std = [1 for _ in range(self.data_shape)] else: self.summary_obj = MultivariateStatisticalSummary(data, -1) if self.with_mean: self.mean = self.summary_obj.get_mean() self.mean = [self.mean[key] for key in self.header] else: self.mean = [0 for _ in range(self.data_shape)] if self.with_std: self.std = self.summary_obj.get_std_variance() self.std = [self.std[key] for key in self.header] for i, value in enumerate(self.std): if np.abs(value - 0) < 1e-6: self.std[i] = 1 else: self.std = [1 for _ in range(self.data_shape)] f = functools.partial(self.__scale, mean=self.mean, std=self.std, process_cols_list=self.scale_column_idx) fit_data = data.mapValues(f) return fit_data def transform(self, data): f = functools.partial(self.__scale_with_column_range, column_upper=self.column_max_value, column_lower=self.column_min_value, mean=self.mean, std=self.std, process_cols_list=self.scale_column_idx) transform_data = data.mapValues(f) return transform_data def __get_meta(self): if self.header: scale_column = [self.header[i] for i in self.scale_column_idx] else: scale_column = ["_".join(["col", str(i)]) for i in self.scale_column_idx] if not self.data_shape: self.data_shape = -1 meta_proto_obj = ScaleMeta(method="standard_scale", area=self.area, scale_column=scale_column, feat_upper=self._get_upper(self.data_shape), feat_lower=self._get_lower(self.data_shape), with_mean=self.with_mean, with_std=self.with_std ) return meta_proto_obj def __get_param(self, need_run): column_scale_param_dict = {} if self.header: for i, header in enumerate(self.header): if i in self.scale_column_idx: param_obj = ColumnScaleParam(column_upper=self.column_max_value[i], column_lower=self.column_min_value[i], mean=self.mean[i], std=self.std[i]) column_scale_param_dict[header] = param_obj param_proto_obj = ScaleParam(col_scale_param=column_scale_param_dict, header=self.header, need_run=need_run) return param_proto_obj def export_model(self, need_run): meta_obj = self.__get_meta() param_obj = self.__get_param(need_run) result = { self.model_meta_name: meta_obj, self.model_param_name: param_obj } return result

true

790bd7a07442994e0ce4b5b0c893be3b7b309cb5

2,171

py

Python

file/py/result_mashara.py

piscalpratama/KMSV2

7677d26c83236c25007f375a7157989e1322e9f9

[ "MIT" ]

null

file/py/result_mashara.py

piscalpratama/KMSV2

7677d26c83236c25007f375a7157989e1322e9f9

[ "MIT" ]

null

file/py/result_mashara.py

piscalpratama/KMSV2

7677d26c83236c25007f375a7157989e1322e9f9

[ "MIT" ]

null

import sys import json import scrapapps import scrapping from textrank import TextRankSentences import preprocessing import summ import textrankkeyword import bss4 url = sys.argv[1] # url = request.POST.get('web_link', None) #web_link = scrapapps.scrap_data(url) web_link = scrapping.scrap_data(url) #Get Title judul = scrapping.get_title(url) raw_text = str(web_link) # Preprocessing View lower = preprocessing.text_lowercase(str(web_link)) rnumber = preprocessing.remove_numbers(lower) white_space = preprocessing.remove_whitespace(rnumber) stopword_list = preprocessing.remove_stopwords(white_space) new_sentence = ' '.join(stopword_list) stagging = preprocessing.stagging_text(new_sentence) stop_plus = preprocessing.stopword_plus(new_sentence) kalimat = ' '.join(stop_plus) # Skenario 1 # n = 10; # if len(stagging) < 10: # n = 5 # if len(stagging) == 10: # n = len(stagging) - 2 # if len(stagging) > 30: # n = 15 # if len(stagging) < 5: # n = len(stagging) - 1 # if len(stagging) == 1: # n = len(stagging) # Skenario 2 n = 7 if len(stagging) < 7: n = len(stagging) - 1 if len(stagging) == 1: n = len(stagging) textrank = TextRankSentences() text = textrank.analyze(str(new_sentence)) text = textrank.get_top_sentences(n) # View Similarity Matriks sim_mat = textrank._build_similarity_matrix(stagging) #View Hasil Perhitungan Textrank top_rank = textrank._run_page_rank(sim_mat) result = textrank._run_page_rank(sim_mat) # Clean Hasil ringkasan = preprocessing.remove_punctuation(text) # Panjang Plaintext len_raw = len(str(web_link)) # Jumlah Text len_text = len(str(text)) # Jumlah Kalimat len_kalimat = len(stagging) #Presentase Reduce presentase = round(((len_text/len_raw)*100)) # keyphrases = textrankkeyword.extract_key_phrases(raw_text) data = { 'raw_text' : raw_text, 'url' : url, 'judul' : judul, 'ringkasan':ringkasan, 'text':text, 'len_raw':len_raw, 'len_text':len_text, 'len_kalimat':len_kalimat, 'stagging':stagging, 'new_sentence':new_sentence, # 'sim_mat':sim_mat, # 'result':result, 'presentase':presentase, 'keyword':'-', } print(json.dumps(data))

19.558559

60

0.722248

import sys import json import scrapapps import scrapping from textrank import TextRankSentences import preprocessing import summ import textrankkeyword import bss4 url = sys.argv[1] web_link = scrapping.scrap_data(url) judul = scrapping.get_title(url) raw_text = str(web_link) lower = preprocessing.text_lowercase(str(web_link)) rnumber = preprocessing.remove_numbers(lower) white_space = preprocessing.remove_whitespace(rnumber) stopword_list = preprocessing.remove_stopwords(white_space) new_sentence = ' '.join(stopword_list) stagging = preprocessing.stagging_text(new_sentence) stop_plus = preprocessing.stopword_plus(new_sentence) kalimat = ' '.join(stop_plus) n = 7 if len(stagging) < 7: n = len(stagging) - 1 if len(stagging) == 1: n = len(stagging) textrank = TextRankSentences() text = textrank.analyze(str(new_sentence)) text = textrank.get_top_sentences(n) sim_mat = textrank._build_similarity_matrix(stagging) top_rank = textrank._run_page_rank(sim_mat) result = textrank._run_page_rank(sim_mat) ringkasan = preprocessing.remove_punctuation(text) len_raw = len(str(web_link)) len_text = len(str(text)) len_kalimat = len(stagging) presentase = round(((len_text/len_raw)*100)) data = { 'raw_text' : raw_text, 'url' : url, 'judul' : judul, 'ringkasan':ringkasan, 'text':text, 'len_raw':len_raw, 'len_text':len_text, 'len_kalimat':len_kalimat, 'stagging':stagging, 'new_sentence':new_sentence, 'presentase':presentase, 'keyword':'-', } print(json.dumps(data))

true

790bd80c1f90e56a600eccb4cc68bcd75db2dae5

6,839

py

Python

src/datadog_api_client/v2/model/security_filter_exclusion_filter.py

rchenzheng/datadog-api-client-python

2e86ac098c6f0c7fdd90ed218224587c0f8eafef

[ "Apache-2.0" ]

null

src/datadog_api_client/v2/model/security_filter_exclusion_filter.py

rchenzheng/datadog-api-client-python

2e86ac098c6f0c7fdd90ed218224587c0f8eafef

[ "Apache-2.0" ]

null

src/datadog_api_client/v2/model/security_filter_exclusion_filter.py

rchenzheng/datadog-api-client-python

2e86ac098c6f0c7fdd90ed218224587c0f8eafef

[ "Apache-2.0" ]

null

# Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License. # This product includes software developed at Datadog (https://www.datadoghq.com/). # Copyright 2019-Present Datadog, Inc. import re # noqa: F401 import sys # noqa: F401 from datadog_api_client.v2.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) class SecurityFilterExclusionFilter(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = {} validations = {} additional_properties_type = None _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ return { "name": (str,), # noqa: E501 "query": (str,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { "name": "name", # noqa: E501 "query": "query", # noqa: E501 } _composed_schemas = {} required_properties = set( [ "_data_store", "_check_type", "_spec_property_naming", "_path_to_item", "_configuration", "_visited_composed_classes", ] ) @convert_js_args_to_python_args def __init__(self, name, query, *args, **kwargs): # noqa: E501 """SecurityFilterExclusionFilter - a model defined in OpenAPI Args: name (str): Exclusion filter name. query (str): Exclusion filter query. Logs that match this query are excluded from the security filter. Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) """ _check_type = kwargs.pop("_check_type", True) _spec_property_naming = kwargs.pop("_spec_property_naming", False) _path_to_item = kwargs.pop("_path_to_item", ()) _configuration = kwargs.pop("_configuration", None) _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.name = name self.query = query for var_name, var_value in kwargs.items(): if ( var_name not in self.attribute_map and self._configuration is not None and self._configuration.discard_unknown_keys and self.additional_properties_type is None ): # discard variable. continue setattr(self, var_name, var_value)

39.994152

114

0.583126

import re import sys from datadog_api_client.v2.model_utils import ( ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) class SecurityFilterExclusionFilter(ModelNormal): allowed_values = {} validations = {} additional_properties_type = None _nullable = False @cached_property def openapi_types(): return { "name": (str,), "query": (str,), } @cached_property def discriminator(): return None attribute_map = { "name": "name", "query": "query", } _composed_schemas = {} required_properties = set( [ "_data_store", "_check_type", "_spec_property_naming", "_path_to_item", "_configuration", "_visited_composed_classes", ] ) @convert_js_args_to_python_args def __init__(self, name, query, *args, **kwargs): _check_type = kwargs.pop("_check_type", True) _spec_property_naming = kwargs.pop("_spec_property_naming", False) _path_to_item = kwargs.pop("_path_to_item", ()) _configuration = kwargs.pop("_configuration", None) _visited_composed_classes = kwargs.pop("_visited_composed_classes", ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.name = name self.query = query for var_name, var_value in kwargs.items(): if ( var_name not in self.attribute_map and self._configuration is not None and self._configuration.discard_unknown_keys and self.additional_properties_type is None ): continue setattr(self, var_name, var_value)

true

790bd8399ffdc82443bb930b63332f494e70a964

19,287

py

Python

linkedin_api/linkedin.py

cicizh/linkedin-api

1609ec705c95d5e05c0622db154be6dbac1eecb4

[ "MIT" ]

null

linkedin_api/linkedin.py

cicizh/linkedin-api

1609ec705c95d5e05c0622db154be6dbac1eecb4

[ "MIT" ]

null

linkedin_api/linkedin.py

cicizh/linkedin-api

1609ec705c95d5e05c0622db154be6dbac1eecb4

[ "MIT" ]

1

2019-09-04T16:09:15.000Z

""" Provides linkedin api-related code """ import random import logging from time import sleep import json from linkedin_api.utils.helpers import get_id_from_urn from linkedin_api.client import Client logger = logging.getLogger(__name__) class Linkedin(object): """ Class for accessing Linkedin API. """ _MAX_UPDATE_COUNT = 100 # max seems to be 100 _MAX_SEARCH_COUNT = 49 # max seems to be 49 _MAX_REPEATED_REQUESTS = ( 200 ) # VERY conservative max requests count to avoid rate-limit def __init__(self, username, password): self.client = Client(debug=True) self.client.authenticate(username, password) self.logger = logger def search(self, params, max_results=None, results=[]): """ Do a search. """ sleep( random.randint(0, 1) ) # sleep a random duration to try and evade suspention count = ( max_results if max_results and max_results <= Linkedin._MAX_SEARCH_COUNT else Linkedin._MAX_SEARCH_COUNT ) default_params = { "count": count, "guides": "List()", "origin": "GLOBAL_SEARCH_HEADER", "q": "guided", "start": len(results), } default_params.update(params) res = self.client.session.get( f"{self.client.API_BASE_URL}/search/cluster", params=default_params ) data = res.json() total_found = data.get("paging", {}).get("total") # recursive base case if ( len(data["elements"]) == 0 or (max_results is not None and len(results) >= max_results) or total_found is None or len(results) >= total_found or (max_results is not None and len(results) / max_results >= Linkedin._MAX_REPEATED_REQUESTS) ): return results results.extend(data["elements"][0]["elements"]) self.logger.debug(f"results grew: {len(results)}") return self.search(params, results=results, max_results=max_results) def search_people( self, keywords=None, connection_of=None, network_depth=None, regions=None, industries=None, ): """ Do a people search. """ guides = ["v->PEOPLE"] if connection_of: guides.append(f"facetConnectionOf->{connection_of}") if network_depth: guides.append(f"facetNetwork->{network_depth}") if regions: guides.append(f'facetGeoRegion->{"|".join(regions)}') if industries: guides.append(f'facetIndustry->{"|".join(industries)}') params = {"guides": "List({})".format(",".join(guides))} if keywords: params["keywords"] = keywords data = self.search(params) results = [] for item in data: search_profile = item["hitInfo"][ "com.linkedin.voyager.search.SearchProfile" ] profile_id = search_profile["id"] distance = search_profile["distance"]["value"] results.append( { "urn_id": profile_id, "distance": distance, "public_id": search_profile["miniProfile"]["publicIdentifier"], } ) return results def search_companies(self, max_results=None, results=[]): """ Do a company search Note: try swap from blended search to cluster """ sleep( random.randint(2, 5) ) # sleep a random duration to try and evade suspention #Search params from main search, here for reference ''' default_params = { "count": count, "guides": "List()", "origin": "GLOBAL_SEARCH_HEADER", "q": "guided", "start": len(results), } ''' default_params = { "origin": "GLOBAL_SEARCH_HEADER", "guides": "List(resultType->companies)", "count": "10", "q": "guided", "filters": "List(resultType->companies)", "start": len(results) } res = self.client.session.get( f"{self.client.API_BASE_URL}/search/blended?keywords=s&origin=GLOBAL_SEARCH_HEADER&count=10&guides=List(resultType-%3Ecompanies)&q=all&filters=List(resultType-%3Ecompanies)&start={len(results)}" ) data = res.json() total_found = data.get("paging", {}).get("total") if ( len(data["elements"]) == 0 or len(data["elements"][0]["elements"]) == 0 or total_found is None or (max_results is not None and len(results) >= max_results) or (max_results is not None and len(results) / max_results >= Linkedin._MAX_REPEATED_REQUESTS) ): return results results.extend(data["elements"][0]["elements"]) self.logger.debug(f"results grew: {len(results)}") return self.search_companies(max_results=max_results, results=results) def get_profile_contact_info(self, public_id=None, urn_id=None): """ Return data for a single profile. [public_id] - public identifier i.e. tom-quirk-1928345 [urn_id] - id provided by the related URN """ res = self.client.session.get( f"{self.client.API_BASE_URL}/identity/profiles/{public_id or urn_id}/profileContactInfo" ) data = res.json() contact_info = { "email_address": data.get("emailAddress"), "websites": [], "phone_numbers": data.get("phoneNumbers", []), } websites = data.get("websites", []) for item in websites: if "com.linkedin.voyager.identity.profile.StandardWebsite" in item["type"]: item["label"] = item["type"][ "com.linkedin.voyager.identity.profile.StandardWebsite" ]["category"] elif "" in item["type"]: item["label"] = item["type"][ "com.linkedin.voyager.identity.profile.CustomWebsite" ]["label"] del item["type"] contact_info["websites"] = websites return contact_info def get_profile(self, public_id=None, urn_id=None): """ Return data for a single profile. [public_id] - public identifier i.e. tom-quirk-1928345 [urn_id] - id provided by the related URN """ sleep( random.randint(2, 5) ) # sleep a random duration to try and evade suspention res = self.client.session.get( f"{self.client.API_BASE_URL}/identity/profiles/{public_id or urn_id}/profileView" ) data = res.json() if data and "status" in data and data["status"] != 200: self.logger.info("request failed: {}".format(data["message"])) return {} # massage [profile] data profile = data["profile"] if "miniProfile" in profile: if "picture" in profile["miniProfile"]: profile["displayPictureUrl"] = profile["miniProfile"]["picture"][ "com.linkedin.common.VectorImage" ]["rootUrl"] profile["profile_id"] = get_id_from_urn(profile["miniProfile"]["entityUrn"]) del profile["miniProfile"] del profile["defaultLocale"] del profile["supportedLocales"] del profile["versionTag"] del profile["showEducationOnProfileTopCard"] # massage [experience] data experience = data["positionView"]["elements"] for item in experience: if "company" in item and "miniCompany" in item["company"]: if "logo" in item["company"]["miniCompany"]: logo = item["company"]["miniCompany"]["logo"].get( "com.linkedin.common.VectorImage" ) if logo: item["companyLogoUrl"] = logo["rootUrl"] del item["company"]["miniCompany"] profile["experience"] = experience # massage [skills] data skills = [item["name"] for item in data["skillView"]["elements"]] profile["skills"] = skills # massage [education] data education = data["educationView"]["elements"] for item in education: if "school" in item: if "logo" in item["school"]: item["school"]["logoUrl"] = item["school"]["logo"][ "com.linkedin.common.VectorImage" ]["rootUrl"] del item["school"]["logo"] profile["education"] = education return profile def get_profile_connections(self, urn_id): """ Return a list of profile ids connected to profile of given [urn_id] """ return self.search_people(connection_of=urn_id, network_depth="F") def get_profile_networkinfo(self, urn_id): """ Return the nework info connected to the profile of the given [urn_id] """ sleep( random.randint(2, 5) ) # sleep a random duration to try and evade suspention res = self.client.session.get( f"{self.client.API_BASE_URL}/identity/profiles/{urn_id}/networkinfo" ) return res.json() def get_company_updates(self, public_id=None, urn_id=None, max_results=None, results=[]): """" Return a list of company posts [public_id] - public identifier ie - microsoft [urn_id] - id provided by the related URN """ sleep( random.randint(2, 5) ) # sleep a random duration to try and evade suspention params = { "companyUniversalName": {public_id or urn_id}, "q": "companyFeedByUniversalName", "moduleKey": "member-share", "count": Linkedin._MAX_UPDATE_COUNT, "start": len(results), } res = self.client.session.get( f"{self.client.API_BASE_URL}/feed/updates", params=params ) data = res.json() if ( len(data["elements"]) == 0 or (max_results is not None and len(results) >= max_results) or (max_results is not None and len(results) / max_results >= Linkedin._MAX_REPEATED_REQUESTS) ): return results results.extend(data["elements"]) self.logger.debug(f"results grew: {len(results)}") return self.get_company_updates(public_id=public_id, urn_id=urn_id, results=results, max_results=max_results) def get_profile_updates(self, public_id=None, urn_id=None, max_results=None, results=[]): """" Return a list of profile posts [public_id] - public identifier i.e. tom-quirk-1928345 [urn_id] - id provided by the related URN """ sleep( random.randint(2, 5) ) # sleep a random duration to try and evade suspention params = { "profileId": {public_id or urn_id}, "q": "memberShareFeed", "moduleKey": "member-share", "count": Linkedin._MAX_UPDATE_COUNT, "start": len(results), } res = self.client.session.get( f"{self.client.API_BASE_URL}/feed/updates", params=params ) data = res.json() if ( len(data["elements"]) == 0 or (max_results is not None and len(results) >= max_results) or (max_results is not None and len(results) / max_results >= Linkedin._MAX_REPEATED_REQUESTS) ): return results results.extend(data["elements"]) self.logger.debug(f"results grew: {len(results)}") return self.get_profile_updates(public_id=public_id, urn_id=urn_id, results=results, max_results=max_results) def get_current_profile_views(self): """ Get profile view statistics, including chart data. """ res = self.client.session.get( f"{self.client.API_BASE_URL}/identity/panels" ) data = res.json() return data['elements'][0]['value']['com.linkedin.voyager.identity.me.ProfileViewsByTimePanel'] def get_school(self, public_id): """ Return data for a single school. [public_id] - public identifier i.e. uq """ sleep( random.randint(2, 5) ) # sleep a random duration to try and evade suspention params = { "decoration": ( """ ( autoGenerated,backgroundCoverImage, companyEmployeesSearchPageUrl,companyPageUrl,confirmedLocations*,coverPhoto,dataVersion,description, entityUrn,followingInfo,foundedOn,headquarter,jobSearchPageUrl,lcpTreatment,logo,name,type,overviewPhoto, paidCompany,partnerCompanyUrl,partnerLogo,partnerLogoImage,rankForTopCompanies,salesNavigatorCompanyUrl, school,showcase,staffCount,staffCountRange,staffingCompany,topCompaniesListName,universalName,url, companyIndustries*,industries,specialities, acquirerCompany~(entityUrn,logo,name,industries,followingInfo,url,paidCompany,universalName), affiliatedCompanies*~(entityUrn,logo,name,industries,followingInfo,url,paidCompany,universalName), groups*~(entityUrn,largeLogo,groupName,memberCount,websiteUrl,url), showcasePages*~(entityUrn,logo,name,industries,followingInfo,url,description,universalName) ) """ ), "q": "universalName", "universalName": public_id, } res = self.client.session.get( f"{self.client.API_BASE_URL}/organization/companies", params=params ) data = res.json() if data and "status" in data and data["status"] != 200: self.logger.info("request failed: {}".format(data["message"])) return {} school = data["elements"][0] return school def get_similar_companies(self, public_id): """ Return similar companies for a single company. [public_id] - public identifier i.e. univeristy-of-queensland """ sleep( random.randint(2, 5) ) # sleep a random duration to try and evade suspention res = self.client.session.get( f"{self.client.API_BASE_URL}/organization/companies?count={Linkedin._MAX_SEARCH_COUNT}&companyUniversalName={public_id}&q=similarCompanies&start=0&decorationId=com.linkedin.voyager.deco.organization.web.WebSimilarCompanyCardWithRelevanceReason-3" ) data = res.json() return data def get_company(self, public_id): """ Return data for a single company. [public_id] - public identifier i.e. univeristy-of-queensland """ sleep( random.randint(2, 5) ) # sleep a random duration to try and evade suspention params = { "decoration": ( """ ( affiliatedCompaniesWithEmployeesRollup,affiliatedCompaniesWithJobsRollup,articlePermalinkForTopCompanies, autoGenerated,backgroundCoverImage,companyEmployeesSearchPageUrl, companyPageUrl,confirmedLocations*,coverPhoto,dataVersion,description,entityUrn,followingInfo, foundedOn,headquarter,jobSearchPageUrl,lcpTreatment,logo,name,type,overviewPhoto,paidCompany, partnerCompanyUrl,partnerLogo,partnerLogoImage,permissions,rankForTopCompanies, salesNavigatorCompanyUrl,school,showcase,staffCount,staffCountRange,staffingCompany, topCompaniesListName,universalName,url,companyIndustries*,industries,specialities, acquirerCompany~(entityUrn,logo,name,industries,followingInfo,url,paidCompany,universalName), affiliatedCompanies*~(entityUrn,logo,name,industries,followingInfo,url,paidCompany,universalName), groups*~(entityUrn,largeLogo,groupName,memberCount,websiteUrl,url), showcasePages*~(entityUrn,logo,name,industries,followingInfo,url,description,universalName) ) """ ), "q": "universalName", "universalName": public_id, } res = self.client.session.get( f"{self.client.API_BASE_URL}/organization/companies", params=params ) data = res.json() if data and "status" in data and data["status"] != 200: self.logger.info("request failed: {}".format(data["message"])) return {} company = data["elements"][0] return company def get_conversation_details(self, profile_urn_id): """ Return the conversation (or "message thread") details for a given [public_profile_id] """ # passing `params` doesn't work properly, think it's to do with List(). # Might be a bug in `requests`? res = self.client.session.get( f"{self.client.API_BASE_URL}/messaging/conversations?\ keyVersion=LEGACY_INBOX&q=participants&recipients=List({profile_urn_id})" ) data = res.json() item = data["elements"][0] item["id"] = get_id_from_urn(item["entityUrn"]) return item def get_conversations(self): """ Return list of conversations the user is in. """ params = {"keyVersion": "LEGACY_INBOX"} res = self.client.session.get( f"{self.client.API_BASE_URL}/messaging/conversations", params=params ) return res.json() def get_conversation(self, conversation_urn_id): """ Return the full conversation at a given [conversation_urn_id] """ res = self.client.session.get( f"{self.client.API_BASE_URL}/messaging/conversations/{conversation_urn_id}/events" ) return res.json() def send_message(self, conversation_urn_id, message_body): """ Return the full conversation at a given [conversation_urn_id] """ params = {"action": "create"} payload = json.dumps( { "eventCreate": { "value": { "com.linkedin.voyager.messaging.create.MessageCreate": { "body": message_body, "attachments": [], "attributedBody": {"text": message_body, "attributes": []}, "mediaAttachments": [], } } } } ) res = self.client.session.post( f"{self.client.API_BASE_URL}/messaging/conversations/{conversation_urn_id}/events", params=params, data=payload, ) return res.status_code == 201

34.379679

258

0.575932

import random import logging from time import sleep import json from linkedin_api.utils.helpers import get_id_from_urn from linkedin_api.client import Client logger = logging.getLogger(__name__) class Linkedin(object): _MAX_UPDATE_COUNT = 100 _MAX_SEARCH_COUNT = 49 _MAX_REPEATED_REQUESTS = ( 200 ) def __init__(self, username, password): self.client = Client(debug=True) self.client.authenticate(username, password) self.logger = logger def search(self, params, max_results=None, results=[]): sleep( random.randint(0, 1) ) count = ( max_results if max_results and max_results <= Linkedin._MAX_SEARCH_COUNT else Linkedin._MAX_SEARCH_COUNT ) default_params = { "count": count, "guides": "List()", "origin": "GLOBAL_SEARCH_HEADER", "q": "guided", "start": len(results), } default_params.update(params) res = self.client.session.get( f"{self.client.API_BASE_URL}/search/cluster", params=default_params ) data = res.json() total_found = data.get("paging", {}).get("total") if ( len(data["elements"]) == 0 or (max_results is not None and len(results) >= max_results) or total_found is None or len(results) >= total_found or (max_results is not None and len(results) / max_results >= Linkedin._MAX_REPEATED_REQUESTS) ): return results results.extend(data["elements"][0]["elements"]) self.logger.debug(f"results grew: {len(results)}") return self.search(params, results=results, max_results=max_results) def search_people( self, keywords=None, connection_of=None, network_depth=None, regions=None, industries=None, ): guides = ["v->PEOPLE"] if connection_of: guides.append(f"facetConnectionOf->{connection_of}") if network_depth: guides.append(f"facetNetwork->{network_depth}") if regions: guides.append(f'facetGeoRegion->{"|".join(regions)}') if industries: guides.append(f'facetIndustry->{"|".join(industries)}') params = {"guides": "List({})".format(",".join(guides))} if keywords: params["keywords"] = keywords data = self.search(params) results = [] for item in data: search_profile = item["hitInfo"][ "com.linkedin.voyager.search.SearchProfile" ] profile_id = search_profile["id"] distance = search_profile["distance"]["value"] results.append( { "urn_id": profile_id, "distance": distance, "public_id": search_profile["miniProfile"]["publicIdentifier"], } ) return results def search_companies(self, max_results=None, results=[]): sleep( random.randint(2, 5) ) default_params = { "origin": "GLOBAL_SEARCH_HEADER", "guides": "List(resultType->companies)", "count": "10", "q": "guided", "filters": "List(resultType->companies)", "start": len(results) } res = self.client.session.get( f"{self.client.API_BASE_URL}/search/blended?keywords=s&origin=GLOBAL_SEARCH_HEADER&count=10&guides=List(resultType-%3Ecompanies)&q=all&filters=List(resultType-%3Ecompanies)&start={len(results)}" ) data = res.json() total_found = data.get("paging", {}).get("total") if ( len(data["elements"]) == 0 or len(data["elements"][0]["elements"]) == 0 or total_found is None or (max_results is not None and len(results) >= max_results) or (max_results is not None and len(results) / max_results >= Linkedin._MAX_REPEATED_REQUESTS) ): return results results.extend(data["elements"][0]["elements"]) self.logger.debug(f"results grew: {len(results)}") return self.search_companies(max_results=max_results, results=results) def get_profile_contact_info(self, public_id=None, urn_id=None): res = self.client.session.get( f"{self.client.API_BASE_URL}/identity/profiles/{public_id or urn_id}/profileContactInfo" ) data = res.json() contact_info = { "email_address": data.get("emailAddress"), "websites": [], "phone_numbers": data.get("phoneNumbers", []), } websites = data.get("websites", []) for item in websites: if "com.linkedin.voyager.identity.profile.StandardWebsite" in item["type"]: item["label"] = item["type"][ "com.linkedin.voyager.identity.profile.StandardWebsite" ]["category"] elif "" in item["type"]: item["label"] = item["type"][ "com.linkedin.voyager.identity.profile.CustomWebsite" ]["label"] del item["type"] contact_info["websites"] = websites return contact_info def get_profile(self, public_id=None, urn_id=None): sleep( random.randint(2, 5) ) res = self.client.session.get( f"{self.client.API_BASE_URL}/identity/profiles/{public_id or urn_id}/profileView" ) data = res.json() if data and "status" in data and data["status"] != 200: self.logger.info("request failed: {}".format(data["message"])) return {} profile = data["profile"] if "miniProfile" in profile: if "picture" in profile["miniProfile"]: profile["displayPictureUrl"] = profile["miniProfile"]["picture"][ "com.linkedin.common.VectorImage" ]["rootUrl"] profile["profile_id"] = get_id_from_urn(profile["miniProfile"]["entityUrn"]) del profile["miniProfile"] del profile["defaultLocale"] del profile["supportedLocales"] del profile["versionTag"] del profile["showEducationOnProfileTopCard"] experience = data["positionView"]["elements"] for item in experience: if "company" in item and "miniCompany" in item["company"]: if "logo" in item["company"]["miniCompany"]: logo = item["company"]["miniCompany"]["logo"].get( "com.linkedin.common.VectorImage" ) if logo: item["companyLogoUrl"] = logo["rootUrl"] del item["company"]["miniCompany"] profile["experience"] = experience skills = [item["name"] for item in data["skillView"]["elements"]] profile["skills"] = skills education = data["educationView"]["elements"] for item in education: if "school" in item: if "logo" in item["school"]: item["school"]["logoUrl"] = item["school"]["logo"][ "com.linkedin.common.VectorImage" ]["rootUrl"] del item["school"]["logo"] profile["education"] = education return profile def get_profile_connections(self, urn_id): return self.search_people(connection_of=urn_id, network_depth="F") def get_profile_networkinfo(self, urn_id): sleep( random.randint(2, 5) ) res = self.client.session.get( f"{self.client.API_BASE_URL}/identity/profiles/{urn_id}/networkinfo" ) return res.json() def get_company_updates(self, public_id=None, urn_id=None, max_results=None, results=[]): sleep( random.randint(2, 5) ) params = { "companyUniversalName": {public_id or urn_id}, "q": "companyFeedByUniversalName", "moduleKey": "member-share", "count": Linkedin._MAX_UPDATE_COUNT, "start": len(results), } res = self.client.session.get( f"{self.client.API_BASE_URL}/feed/updates", params=params ) data = res.json() if ( len(data["elements"]) == 0 or (max_results is not None and len(results) >= max_results) or (max_results is not None and len(results) / max_results >= Linkedin._MAX_REPEATED_REQUESTS) ): return results results.extend(data["elements"]) self.logger.debug(f"results grew: {len(results)}") return self.get_company_updates(public_id=public_id, urn_id=urn_id, results=results, max_results=max_results) def get_profile_updates(self, public_id=None, urn_id=None, max_results=None, results=[]): sleep( random.randint(2, 5) ) params = { "profileId": {public_id or urn_id}, "q": "memberShareFeed", "moduleKey": "member-share", "count": Linkedin._MAX_UPDATE_COUNT, "start": len(results), } res = self.client.session.get( f"{self.client.API_BASE_URL}/feed/updates", params=params ) data = res.json() if ( len(data["elements"]) == 0 or (max_results is not None and len(results) >= max_results) or (max_results is not None and len(results) / max_results >= Linkedin._MAX_REPEATED_REQUESTS) ): return results results.extend(data["elements"]) self.logger.debug(f"results grew: {len(results)}") return self.get_profile_updates(public_id=public_id, urn_id=urn_id, results=results, max_results=max_results) def get_current_profile_views(self): res = self.client.session.get( f"{self.client.API_BASE_URL}/identity/panels" ) data = res.json() return data['elements'][0]['value']['com.linkedin.voyager.identity.me.ProfileViewsByTimePanel'] def get_school(self, public_id): sleep( random.randint(2, 5) ) params = { "decoration": ( """ ( autoGenerated,backgroundCoverImage, companyEmployeesSearchPageUrl,companyPageUrl,confirmedLocations*,coverPhoto,dataVersion,description, entityUrn,followingInfo,foundedOn,headquarter,jobSearchPageUrl,lcpTreatment,logo,name,type,overviewPhoto, paidCompany,partnerCompanyUrl,partnerLogo,partnerLogoImage,rankForTopCompanies,salesNavigatorCompanyUrl, school,showcase,staffCount,staffCountRange,staffingCompany,topCompaniesListName,universalName,url, companyIndustries*,industries,specialities, acquirerCompany~(entityUrn,logo,name,industries,followingInfo,url,paidCompany,universalName), affiliatedCompanies*~(entityUrn,logo,name,industries,followingInfo,url,paidCompany,universalName), groups*~(entityUrn,largeLogo,groupName,memberCount,websiteUrl,url), showcasePages*~(entityUrn,logo,name,industries,followingInfo,url,description,universalName) ) """ ), "q": "universalName", "universalName": public_id, } res = self.client.session.get( f"{self.client.API_BASE_URL}/organization/companies", params=params ) data = res.json() if data and "status" in data and data["status"] != 200: self.logger.info("request failed: {}".format(data["message"])) return {} school = data["elements"][0] return school def get_similar_companies(self, public_id): sleep( random.randint(2, 5) ) res = self.client.session.get( f"{self.client.API_BASE_URL}/organization/companies?count={Linkedin._MAX_SEARCH_COUNT}&companyUniversalName={public_id}&q=similarCompanies&start=0&decorationId=com.linkedin.voyager.deco.organization.web.WebSimilarCompanyCardWithRelevanceReason-3" ) data = res.json() return data def get_company(self, public_id): sleep( random.randint(2, 5) ) params = { "decoration": ( """ ( affiliatedCompaniesWithEmployeesRollup,affiliatedCompaniesWithJobsRollup,articlePermalinkForTopCompanies, autoGenerated,backgroundCoverImage,companyEmployeesSearchPageUrl, companyPageUrl,confirmedLocations*,coverPhoto,dataVersion,description,entityUrn,followingInfo, foundedOn,headquarter,jobSearchPageUrl,lcpTreatment,logo,name,type,overviewPhoto,paidCompany, partnerCompanyUrl,partnerLogo,partnerLogoImage,permissions,rankForTopCompanies, salesNavigatorCompanyUrl,school,showcase,staffCount,staffCountRange,staffingCompany, topCompaniesListName,universalName,url,companyIndustries*,industries,specialities, acquirerCompany~(entityUrn,logo,name,industries,followingInfo,url,paidCompany,universalName), affiliatedCompanies*~(entityUrn,logo,name,industries,followingInfo,url,paidCompany,universalName), groups*~(entityUrn,largeLogo,groupName,memberCount,websiteUrl,url), showcasePages*~(entityUrn,logo,name,industries,followingInfo,url,description,universalName) ) """ ), "q": "universalName", "universalName": public_id, } res = self.client.session.get( f"{self.client.API_BASE_URL}/organization/companies", params=params ) data = res.json() if data and "status" in data and data["status"] != 200: self.logger.info("request failed: {}".format(data["message"])) return {} company = data["elements"][0] return company def get_conversation_details(self, profile_urn_id): res = self.client.session.get( f"{self.client.API_BASE_URL}/messaging/conversations?\ keyVersion=LEGACY_INBOX&q=participants&recipients=List({profile_urn_id})" ) data = res.json() item = data["elements"][0] item["id"] = get_id_from_urn(item["entityUrn"]) return item def get_conversations(self): params = {"keyVersion": "LEGACY_INBOX"} res = self.client.session.get( f"{self.client.API_BASE_URL}/messaging/conversations", params=params ) return res.json() def get_conversation(self, conversation_urn_id): res = self.client.session.get( f"{self.client.API_BASE_URL}/messaging/conversations/{conversation_urn_id}/events" ) return res.json() def send_message(self, conversation_urn_id, message_body): params = {"action": "create"} payload = json.dumps( { "eventCreate": { "value": { "com.linkedin.voyager.messaging.create.MessageCreate": { "body": message_body, "attachments": [], "attributedBody": {"text": message_body, "attributes": []}, "mediaAttachments": [], } } } } ) res = self.client.session.post( f"{self.client.API_BASE_URL}/messaging/conversations/{conversation_urn_id}/events", params=params, data=payload, ) return res.status_code == 201

true

790bd849eabb113e5268e23dadba301216d44dda

24

py

Python

views/stations/__init__.py

atzorvas/droughtmeteo

265282ea5a333dd303747df6b13155789dfc938e

[ "MIT" ]

null

views/stations/__init__.py

atzorvas/droughtmeteo

265282ea5a333dd303747df6b13155789dfc938e

[ "MIT" ]

null

views/stations/__init__.py

atzorvas/droughtmeteo

265282ea5a333dd303747df6b13155789dfc938e

[ "MIT" ]

null

__author__ = 'atzorvas'

12

23

0.75

__author__ = 'atzorvas'

true

790bd893e8c7366add01d0ac757b05d1c2bdbefa

159

py

Python

python/testData/refactoring/makeFunctionTopLevel/localFunctionSimple.after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/refactoring/makeFunctionTopLevel/localFunctionSimple.after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/refactoring/makeFunctionTopLevel/localFunctionSimple.after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

global_var = 'spam' def enclosing(p1, p2): x = 42 local(p1, x, 'foo') def local(p1, x, p): def nested(): print(p, x) print(p1, p)

11.357143

23

0.503145

global_var = 'spam' def enclosing(p1, p2): x = 42 local(p1, x, 'foo') def local(p1, x, p): def nested(): print(p, x) print(p1, p)

true

790bd8d07d28713b742fcfa0ad6e9fe885cbc3fd

4,051

py

Python

data/modules/graphic/two_D/player_gui/health.py

Sheidaas/gamee

434db4648e1719a648b8784f201b03b4c8e243c3

[ "CC-BY-3.0" ]

null

data/modules/graphic/two_D/player_gui/health.py

Sheidaas/gamee

434db4648e1719a648b8784f201b03b4c8e243c3

[ "CC-BY-3.0" ]

null

data/modules/graphic/two_D/player_gui/health.py

Sheidaas/gamee

434db4648e1719a648b8784f201b03b4c8e243c3

[ "CC-BY-3.0" ]

null

import pygame from .gui_abstract_object import GuiAbstractObject class Health(GuiAbstractObject): def __init__(self, x, y, player, screen): super().__init__() self.player = player self.position = (x, y, 400, 40) self.rects_pos = { 'main': ((), ()), 'black_hp': ((), ()), 'hp': ((), ()), } self.string = { 'hp': (None, ()) } self.screen = screen def create(self): position = (self.position[0] * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], self.position[1] * self.screen.engine.settings.graphic['screen']['resolution_scale'][1], self.position[2] * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], self.position[3] * self.screen.engine.settings.graphic['screen']['resolution_scale'][1]) self.rects_pos['main'] = (position, (255, 255, 255)) pygame.draw.rect(self.screen.screen, self.rects_pos['main'][1], self.rects_pos['main'][0]) position = ((self.position[0] + 10) * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], (self.position[1] + 10) * self.screen.engine.settings.graphic['screen']['resolution_scale'][1], 380 * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], 20 * self.screen.engine.settings.graphic['screen']['resolution_scale'][1]) self.rects_pos['black_hp'] = (position, (0, 0, 0)) pygame.draw.rect(self.screen.screen, self.rects_pos['black_hp'][1], self.rects_pos['black_hp'][0]) text = str(self.player.statistics.health_points) + '/' + str(self.player.statistics.max_health_points) self.string['hp'] = (text, position) health_percent = (self.player.statistics.health_points / self.player.statistics.max_health_points) * 100 self.render_text(self.string['hp'][1][0], self.string['hp'][1][2], self.string['hp'][1][1], self.string['hp'][1][3], self.string['hp'][0]) position = ((self.position[0] + 10) * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], (self.position[1] + 30) * self.screen.engine.settings.graphic['screen']['resolution_scale'][1], ((380 / 100) * health_percent) * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], 20 * self.screen.engine.settings.graphic['screen']['resolution_scale'][1]) self.rects_pos['hp'] = (position, (200, 0, 0)) pygame.draw.rect(self.screen.screen, (200, 0, 0), position) def render_text(self, x1, x2, y1, y2, string): x = x2 - x1 y = y2 - y1 x /= 2 y /= 2 x += x1 y += y1 string = self.screen.font.render(string, self.screen.engine.settings.graphic['screen']['antialias'], (0, 0, 0)) #self.screen.screen.blit(string, (x, y)) def render(self): pygame.draw.rect(self.screen.screen, self.rects_pos['main'][1], self.rects_pos['main'][0]) pygame.draw.rect(self.screen.screen, self.rects_pos['black_hp'][1], self.rects_pos['black_hp'][0]) self.render_text(self.string['hp'][1][0], self.string['hp'][1][2], self.string['hp'][1][1], self.string['hp'][1][3], self.string['hp'][0]) health_percent = (self.player.statistics.health_points / self.player.statistics.max_health_points) * 100 position = ((self.position[0] + 10) * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], (self.position[1] + 10) * self.screen.engine.settings.graphic['screen']['resolution_scale'][1], ((380 / 100) * health_percent) * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], 20 * self.screen.engine.settings.graphic['screen']['resolution_scale'][1]) pygame.draw.rect(self.screen.screen, (200, 0, 0), position)

52.61039

122

0.59294

import pygame from .gui_abstract_object import GuiAbstractObject class Health(GuiAbstractObject): def __init__(self, x, y, player, screen): super().__init__() self.player = player self.position = (x, y, 400, 40) self.rects_pos = { 'main': ((), ()), 'black_hp': ((), ()), 'hp': ((), ()), } self.string = { 'hp': (None, ()) } self.screen = screen def create(self): position = (self.position[0] * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], self.position[1] * self.screen.engine.settings.graphic['screen']['resolution_scale'][1], self.position[2] * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], self.position[3] * self.screen.engine.settings.graphic['screen']['resolution_scale'][1]) self.rects_pos['main'] = (position, (255, 255, 255)) pygame.draw.rect(self.screen.screen, self.rects_pos['main'][1], self.rects_pos['main'][0]) position = ((self.position[0] + 10) * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], (self.position[1] + 10) * self.screen.engine.settings.graphic['screen']['resolution_scale'][1], 380 * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], 20 * self.screen.engine.settings.graphic['screen']['resolution_scale'][1]) self.rects_pos['black_hp'] = (position, (0, 0, 0)) pygame.draw.rect(self.screen.screen, self.rects_pos['black_hp'][1], self.rects_pos['black_hp'][0]) text = str(self.player.statistics.health_points) + '/' + str(self.player.statistics.max_health_points) self.string['hp'] = (text, position) health_percent = (self.player.statistics.health_points / self.player.statistics.max_health_points) * 100 self.render_text(self.string['hp'][1][0], self.string['hp'][1][2], self.string['hp'][1][1], self.string['hp'][1][3], self.string['hp'][0]) position = ((self.position[0] + 10) * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], (self.position[1] + 30) * self.screen.engine.settings.graphic['screen']['resolution_scale'][1], ((380 / 100) * health_percent) * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], 20 * self.screen.engine.settings.graphic['screen']['resolution_scale'][1]) self.rects_pos['hp'] = (position, (200, 0, 0)) pygame.draw.rect(self.screen.screen, (200, 0, 0), position) def render_text(self, x1, x2, y1, y2, string): x = x2 - x1 y = y2 - y1 x /= 2 y /= 2 x += x1 y += y1 string = self.screen.font.render(string, self.screen.engine.settings.graphic['screen']['antialias'], (0, 0, 0)) def render(self): pygame.draw.rect(self.screen.screen, self.rects_pos['main'][1], self.rects_pos['main'][0]) pygame.draw.rect(self.screen.screen, self.rects_pos['black_hp'][1], self.rects_pos['black_hp'][0]) self.render_text(self.string['hp'][1][0], self.string['hp'][1][2], self.string['hp'][1][1], self.string['hp'][1][3], self.string['hp'][0]) health_percent = (self.player.statistics.health_points / self.player.statistics.max_health_points) * 100 position = ((self.position[0] + 10) * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], (self.position[1] + 10) * self.screen.engine.settings.graphic['screen']['resolution_scale'][1], ((380 / 100) * health_percent) * self.screen.engine.settings.graphic['screen']['resolution_scale'][0], 20 * self.screen.engine.settings.graphic['screen']['resolution_scale'][1]) pygame.draw.rect(self.screen.screen, (200, 0, 0), position)

true

790bd96b30fe676462081306a479a799d322e3be

1,192

py

Python

app/api/v1/models/bucketlist.py

johnseremba/bucket-list

079b8bd0c775240aec8b417731643b27a3bb3cc7

[ "MIT" ]

1

2017-07-18T18:03:28.000Z

app/api/v1/models/bucketlist.py

SerryJohns/bucket-list

079b8bd0c775240aec8b417731643b27a3bb3cc7

[ "MIT" ]

7

2017-07-18T10:16:44.000Z

2019-10-18T17:02:56.000Z

app/api/v1/models/bucketlist.py

johnseremba/bucket-list

079b8bd0c775240aec8b417731643b27a3bb3cc7

[ "MIT" ]

1

2017-06-29T08:03:36.000Z

import datetime from app import db class BucketList(db.Model): id = db.Column(db.Integer, primary_key=True, autoincrement=True) name = db.Column(db.String(100), unique=True) description = db.Column(db.Text, nullable=True) interests = db.Column(db.String(120), nullable=True) date_created = db.Column(db.DateTime, default=datetime.datetime.utcnow()) date_modified = db.Column(db.DateTime) created_by = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False) items = db.relationship('Item', backref='bucket_list_items', lazy='dynamic') def __repr__(self): return "<Bucketlist {}>".format(self.name) class Item(db.Model): id = db.Column(db.Integer, primary_key=True, autoincrement=True) name = db.Column(db.String(100), unique=True) description = db.Column(db.Text) status = db.Column(db.Text) date_accomplished = db.Column(db.DateTime) date_created = db.Column(db.DateTime, default=datetime.datetime.utcnow()) date_modified = db.Column(db.DateTime) bucketlists = db.Column(db.Integer, db.ForeignKey('bucket_list.id'), nullable=False) def __repr__(self): return "<Items {}>".format(self.name)

38.451613

88

0.703859

import datetime from app import db class BucketList(db.Model): id = db.Column(db.Integer, primary_key=True, autoincrement=True) name = db.Column(db.String(100), unique=True) description = db.Column(db.Text, nullable=True) interests = db.Column(db.String(120), nullable=True) date_created = db.Column(db.DateTime, default=datetime.datetime.utcnow()) date_modified = db.Column(db.DateTime) created_by = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False) items = db.relationship('Item', backref='bucket_list_items', lazy='dynamic') def __repr__(self): return "<Bucketlist {}>".format(self.name) class Item(db.Model): id = db.Column(db.Integer, primary_key=True, autoincrement=True) name = db.Column(db.String(100), unique=True) description = db.Column(db.Text) status = db.Column(db.Text) date_accomplished = db.Column(db.DateTime) date_created = db.Column(db.DateTime, default=datetime.datetime.utcnow()) date_modified = db.Column(db.DateTime) bucketlists = db.Column(db.Integer, db.ForeignKey('bucket_list.id'), nullable=False) def __repr__(self): return "<Items {}>".format(self.name)

true

790bd993fa52900079da534f0eddbaf962ef1c89

16,037

py

Python

desktop/libs/metadata/src/metadata/optimizer_api.py

maulikjs/hue

59ac879b55bb6fb26ecb4e85f4c70836fc21173f

[ "Apache-2.0" ]

1

2020-05-17T06:40:33.000Z

desktop/libs/metadata/src/metadata/optimizer_api.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

4

2021-03-11T04:02:00.000Z

2022-03-27T08:31:56.000Z

desktop/libs/metadata/src/metadata/optimizer_api.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

1

2017-11-09T09:31:28.000Z

#!/usr/bin/env python # Licensed to Cloudera, Inc. under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. Cloudera, Inc. licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import base64 import json import logging import struct from django.http import Http404 from django.utils.translation import ugettext as _ from django.views.decorators.http import require_POST from desktop.lib.django_util import JsonResponse from desktop.lib.i18n import force_unicode from desktop.models import Document2 from libsentry.privilege_checker import MissingSentryPrivilegeException from notebook.api import _get_statement from notebook.models import Notebook from metadata.optimizer_client import OptimizerApi, NavOptException, _get_table_name, _clean_query from metadata.conf import OPTIMIZER from desktop.auth.backend import is_admin LOG = logging.getLogger(__name__) try: from beeswax.api import get_table_stats from beeswax.design import hql_query from metastore.views import _get_db except ImportError, e: LOG.warn("Hive lib not enabled") def error_handler(view_fn): def decorator(*args, **kwargs): try: return view_fn(*args, **kwargs) except Http404, e: raise e except NavOptException, e: LOG.exception(e) response = { 'status': -1, 'message': e.message } except MissingSentryPrivilegeException, e: LOG.exception(e) response = { 'status': -1, 'message': 'Missing privileges for %s' % force_unicode(str(e)) } except Exception, e: LOG.exception(e) response = { 'status': -1, 'message': force_unicode(str(e)) } return JsonResponse(response, status=500) return decorator @require_POST @error_handler def get_tenant(request): response = {'status': -1} cluster_id = request.POST.get('cluster_id') api = OptimizerApi(request.user) data = api.get_tenant(cluster_id=cluster_id) if data: response['status'] = 0 response['data'] = data['tenant'] else: response['message'] = 'Optimizer: %s' % data['details'] return JsonResponse(response) @require_POST @error_handler def top_tables(request): response = {'status': -1} database = request.POST.get('database', 'default') limit = request.POST.get('len', 1000) api = OptimizerApi(user=request.user) data = api.top_tables(database_name=database, page_size=limit) tables = [{ 'eid': table['eid'], 'database': _get_table_name(table['name'])['database'], 'name': _get_table_name(table['name'])['table'], 'popularity': table['workloadPercent'], 'column_count': table['columnCount'], 'patternCount': table['patternCount'], 'total': table['total'], 'is_fact': table['type'] != 'Dimension' } for table in data['results'] ] response['top_tables'] = tables response['status'] = 0 return JsonResponse(response) @require_POST @error_handler def table_details(request): response = {'status': -1} database_name = request.POST.get('databaseName') table_name = request.POST.get('tableName') api = OptimizerApi(request.user) data = api.table_details(database_name=database_name, table_name=table_name) if data: response['status'] = 0 response['details'] = data else: response['message'] = 'Optimizer: %s' % data['details'] return JsonResponse(response) @require_POST @error_handler def query_compatibility(request): response = {'status': -1} source_platform = request.POST.get('sourcePlatform') target_platform = request.POST.get('targetPlatform') query = request.POST.get('query') api = OptimizerApi(request.user) data = api.query_compatibility(source_platform=source_platform, target_platform=target_platform, query=query) if data: response['status'] = 0 response['query_compatibility'] = data else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def query_risk(request): response = {'status': -1} query = json.loads(request.POST.get('query')) source_platform = request.POST.get('sourcePlatform') db_name = request.POST.get('dbName') api = OptimizerApi(request.user) data = api.query_risk(query=query, source_platform=source_platform, db_name=db_name) if data: response['status'] = 0 response['query_risk'] = data else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def similar_queries(request): response = {'status': -1} source_platform = request.POST.get('sourcePlatform') query = json.loads(request.POST.get('query')) api = OptimizerApi(request.user) data = api.similar_queries(source_platform=source_platform, query=query) if data: response['status'] = 0 response['similar_queries'] = data else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def top_filters(request): response = {'status': -1} db_tables = json.loads(request.POST.get('dbTables'), '[]') column_name = request.POST.get('columnName') # Unused api = OptimizerApi(request.user) data = api.top_filters(db_tables=db_tables) if data: response['status'] = 0 response['values'] = data['results'] else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def top_joins(request): response = {'status': -1} db_tables = json.loads(request.POST.get('dbTables'), '[]') api = OptimizerApi(request.user) data = api.top_joins(db_tables=db_tables) if data: response['status'] = 0 response['values'] = data['results'] else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def top_aggs(request): response = {'status': -1} db_tables = json.loads(request.POST.get('dbTables'), '[]') api = OptimizerApi(request.user) data = api.top_aggs(db_tables=db_tables) if data: response['status'] = 0 response['values'] = data['results'] else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def top_databases(request): response = {'status': -1} api = OptimizerApi(request.user) data = api.top_databases() if data: response['status'] = 0 response['values'] = data['results'] else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def top_columns(request): response = {'status': -1} db_tables = json.loads(request.POST.get('dbTables'), '[]') api = OptimizerApi(request.user) data = api.top_columns(db_tables=db_tables) if data: response['status'] = 0 response['values'] = data else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) def _convert_queries(queries_data): queries = [] for query_data in queries_data: try: snippet = query_data['snippets'][0] if 'guid' in snippet['result']['handle']: # Not failed query original_query_id = '%s:%s' % struct.unpack(b"QQ", base64.decodestring(snippet['result']['handle']['guid'])) # unpack_guid uses '%016x:%016x' while optmizer api uses '%s:%s'. execution_time = snippet['result']['executionTime'] * 100 if snippet['status'] in ('available', 'expired') else -1 statement = _clean_query(_get_statement(query_data)) queries.append((original_query_id, execution_time, statement, snippet.get('database', 'default').strip())) except Exception, e: LOG.warning('Skipping upload of %s: %s' % (query_data['uuid'], e)) return queries @require_POST @error_handler def upload_history(request): response = {'status': -1} if is_admin(request.user): api = OptimizerApi(request.user) histories = [] upload_stats = {} if request.POST.get('sourcePlatform'): n = min(request.POST.get('n', OPTIMIZER.QUERY_HISTORY_UPLOAD_LIMIT.get())) source_platform = request.POST.get('sourcePlatform', 'hive') histories = [(source_platform, Document2.objects.get_history(doc_type='query-%s' % source_platform, user=request.user)[:n])] elif OPTIMIZER.QUERY_HISTORY_UPLOAD_LIMIT.get() > 0: histories = [ (source_platform, Document2.objects.filter(type='query-%s' % source_platform, is_history=True, is_managed=False, is_trashed=False).order_by('-last_modified')[:OPTIMIZER.QUERY_HISTORY_UPLOAD_LIMIT.get()]) for source_platform in ['hive', 'impala'] ] for source_platform, history in histories: queries = _convert_queries([Notebook(document=doc).get_data() for doc in history]) upload_stats[source_platform] = api.upload(data=queries, data_type='queries', source_platform=source_platform) response['upload_history'] = upload_stats response['status'] = 0 else: response['message'] = _('Query history upload requires Admin privileges or feature is disabled.') return JsonResponse(response) @require_POST @error_handler def upload_query(request): response = {'status': -1} source_platform = request.POST.get('sourcePlatform', 'default') query_id = request.POST.get('query_id') if OPTIMIZER.AUTO_UPLOAD_QUERIES.get() and source_platform in ('hive', 'impala') and query_id: try: doc = Document2.objects.document(request.user, doc_id=query_id) query_data = Notebook(document=doc).get_data() queries = _convert_queries([query_data]) source_platform = query_data['snippets'][0]['type'] api = OptimizerApi(request.user) response['query_upload'] = api.upload(data=queries, data_type='queries', source_platform=source_platform) except Document2.DoesNotExist: response['query_upload'] = _('Skipped as task query') else: response['query_upload'] = _('Skipped') response['status'] = 0 return JsonResponse(response) @require_POST @error_handler def upload_table_stats(request): response = {'status': -1} db_tables = json.loads(request.POST.get('db_tables'), '[]') source_platform = json.loads(request.POST.get('sourcePlatform', '"hive"')) with_ddl = json.loads(request.POST.get('with_ddl', 'false')) with_table_stats = json.loads(request.POST.get('with_table', 'false')) with_columns_stats = json.loads(request.POST.get('with_columns', 'false')) table_ddls = [] table_stats = [] column_stats = [] if not OPTIMIZER.AUTO_UPLOAD_DDL.get(): with_ddl = False if not OPTIMIZER.AUTO_UPLOAD_STATS.get(): with_table_stats = with_columns_stats = False for db_table in db_tables: path = _get_table_name(db_table) try: if with_ddl: db = _get_db(request.user, source_type=source_platform) query = hql_query('SHOW CREATE TABLE `%(database)s`.`%(table)s`' % path) handle = db.execute_and_wait(query, timeout_sec=5.0) if handle: result = db.fetch(handle, rows=5000) db.close(handle) table_ddls.append((0, 0, ' '.join([row[0] for row in result.rows()]), path['database'])) if with_table_stats: mock_request = MockRequest(user=request.user, source_platform=source_platform) full_table_stats = json.loads(get_table_stats(mock_request, database=path['database'], table=path['table']).content) stats = dict((stat['data_type'], stat['comment']) for stat in full_table_stats['stats']) table_stats.append({ 'table_name': '%(database)s.%(table)s' % path, # DB Prefix 'num_rows': stats.get('numRows', -1), 'last_modified_time': stats.get('transient_lastDdlTime', -1), 'total_size': stats.get('totalSize', -1), 'raw_data_size': stats.get('rawDataSize', -1), 'num_files': stats.get('numFiles', -1), 'num_partitions': stats.get('numPartitions', -1), # bytes_cached # cache_replication # format }) if with_columns_stats: if source_platform == 'impala': colum_stats = json.loads(get_table_stats(mock_request, database=path['database'], table=path['table'], column=-1).content)['stats'] else: colum_stats = [ json.loads(get_table_stats(mock_request, database=path['database'], table=path['table'], column=col).content)['stats'] for col in full_table_stats['columns'][:25] ] raw_column_stats = [dict([(key, val if val is not None else '') for col_stat in col for key, val in col_stat.iteritems()]) for col in colum_stats] for col_stats in raw_column_stats: column_stats.append({ 'table_name': '%(database)s.%(table)s' % path, # DB Prefix 'column_name': col_stats['col_name'], 'data_type': col_stats['data_type'], "num_distinct": int(col_stats.get('distinct_count')) if col_stats.get('distinct_count') != '' else -1, "num_nulls": int(col_stats['num_nulls']) if col_stats['num_nulls'] != '' else -1, "avg_col_len": int(float(col_stats['avg_col_len'])) if col_stats['avg_col_len'] != '' else -1, "max_size": int(float(col_stats['max_col_len'])) if col_stats['max_col_len'] != '' else -1, "min": col_stats['min'] if col_stats.get('min', '') != '' else -1, "max": col_stats['max'] if col_stats.get('max', '') != '' else -1, "num_trues": col_stats['num_trues'] if col_stats.get('num_trues', '') != '' else -1, "num_falses": col_stats['num_falses'] if col_stats.get('num_falses', '') != '' else -1, }) except Exception, e: LOG.exception('Skipping upload of %s: %s' % (db_table, e)) api = OptimizerApi(request.user) response['status'] = 0 if table_stats: response['upload_table_stats'] = api.upload(data=table_stats, data_type='table_stats', source_platform=source_platform) response['upload_table_stats_status'] = 0 if response['upload_table_stats']['status']['state'] in ('WAITING', 'FINISHED', 'IN_PROGRESS') else -1 response['status'] = response['upload_table_stats_status'] if column_stats: response['upload_cols_stats'] = api.upload(data=column_stats, data_type='cols_stats', source_platform=source_platform) response['upload_cols_stats_status'] = response['status'] if response['upload_cols_stats']['status']['state'] in ('WAITING', 'FINISHED', 'IN_PROGRESS') else -1 if response['upload_cols_stats_status'] != 0: response['status'] = response['upload_cols_stats_status'] if table_ddls: response['upload_table_ddl'] = api.upload(data=table_ddls, data_type='queries', source_platform=source_platform) response['upload_table_ddl_status'] = response['status'] if response['upload_table_ddl']['status']['state'] in ('WAITING', 'FINISHED', 'IN_PROGRESS') else -1 if response['upload_table_ddl_status'] != 0: response['status'] = response['upload_table_ddl_status'] return JsonResponse(response) @require_POST @error_handler def upload_status(request): response = {'status': -1} workload_id = request.POST.get('workloadId') api = OptimizerApi(request.user) response['upload_status'] = api.upload_status(workload_id=workload_id) response['status'] = 0 return JsonResponse(response) class MockRequest(): def __init__(self, user, source_platform): self.user = user self.path = '/%s/' % source_platform if source_platform != 'hive' else 'beeswax'

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import base64 import json import logging import struct from django.http import Http404 from django.utils.translation import ugettext as _ from django.views.decorators.http import require_POST from desktop.lib.django_util import JsonResponse from desktop.lib.i18n import force_unicode from desktop.models import Document2 from libsentry.privilege_checker import MissingSentryPrivilegeException from notebook.api import _get_statement from notebook.models import Notebook from metadata.optimizer_client import OptimizerApi, NavOptException, _get_table_name, _clean_query from metadata.conf import OPTIMIZER from desktop.auth.backend import is_admin LOG = logging.getLogger(__name__) try: from beeswax.api import get_table_stats from beeswax.design import hql_query from metastore.views import _get_db except ImportError, e: LOG.warn("Hive lib not enabled") def error_handler(view_fn): def decorator(*args, **kwargs): try: return view_fn(*args, **kwargs) except Http404, e: raise e except NavOptException, e: LOG.exception(e) response = { 'status': -1, 'message': e.message } except MissingSentryPrivilegeException, e: LOG.exception(e) response = { 'status': -1, 'message': 'Missing privileges for %s' % force_unicode(str(e)) } except Exception, e: LOG.exception(e) response = { 'status': -1, 'message': force_unicode(str(e)) } return JsonResponse(response, status=500) return decorator @require_POST @error_handler def get_tenant(request): response = {'status': -1} cluster_id = request.POST.get('cluster_id') api = OptimizerApi(request.user) data = api.get_tenant(cluster_id=cluster_id) if data: response['status'] = 0 response['data'] = data['tenant'] else: response['message'] = 'Optimizer: %s' % data['details'] return JsonResponse(response) @require_POST @error_handler def top_tables(request): response = {'status': -1} database = request.POST.get('database', 'default') limit = request.POST.get('len', 1000) api = OptimizerApi(user=request.user) data = api.top_tables(database_name=database, page_size=limit) tables = [{ 'eid': table['eid'], 'database': _get_table_name(table['name'])['database'], 'name': _get_table_name(table['name'])['table'], 'popularity': table['workloadPercent'], 'column_count': table['columnCount'], 'patternCount': table['patternCount'], 'total': table['total'], 'is_fact': table['type'] != 'Dimension' } for table in data['results'] ] response['top_tables'] = tables response['status'] = 0 return JsonResponse(response) @require_POST @error_handler def table_details(request): response = {'status': -1} database_name = request.POST.get('databaseName') table_name = request.POST.get('tableName') api = OptimizerApi(request.user) data = api.table_details(database_name=database_name, table_name=table_name) if data: response['status'] = 0 response['details'] = data else: response['message'] = 'Optimizer: %s' % data['details'] return JsonResponse(response) @require_POST @error_handler def query_compatibility(request): response = {'status': -1} source_platform = request.POST.get('sourcePlatform') target_platform = request.POST.get('targetPlatform') query = request.POST.get('query') api = OptimizerApi(request.user) data = api.query_compatibility(source_platform=source_platform, target_platform=target_platform, query=query) if data: response['status'] = 0 response['query_compatibility'] = data else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def query_risk(request): response = {'status': -1} query = json.loads(request.POST.get('query')) source_platform = request.POST.get('sourcePlatform') db_name = request.POST.get('dbName') api = OptimizerApi(request.user) data = api.query_risk(query=query, source_platform=source_platform, db_name=db_name) if data: response['status'] = 0 response['query_risk'] = data else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def similar_queries(request): response = {'status': -1} source_platform = request.POST.get('sourcePlatform') query = json.loads(request.POST.get('query')) api = OptimizerApi(request.user) data = api.similar_queries(source_platform=source_platform, query=query) if data: response['status'] = 0 response['similar_queries'] = data else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def top_filters(request): response = {'status': -1} db_tables = json.loads(request.POST.get('dbTables'), '[]') column_name = request.POST.get('columnName') api = OptimizerApi(request.user) data = api.top_filters(db_tables=db_tables) if data: response['status'] = 0 response['values'] = data['results'] else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def top_joins(request): response = {'status': -1} db_tables = json.loads(request.POST.get('dbTables'), '[]') api = OptimizerApi(request.user) data = api.top_joins(db_tables=db_tables) if data: response['status'] = 0 response['values'] = data['results'] else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def top_aggs(request): response = {'status': -1} db_tables = json.loads(request.POST.get('dbTables'), '[]') api = OptimizerApi(request.user) data = api.top_aggs(db_tables=db_tables) if data: response['status'] = 0 response['values'] = data['results'] else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def top_databases(request): response = {'status': -1} api = OptimizerApi(request.user) data = api.top_databases() if data: response['status'] = 0 response['values'] = data['results'] else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) @require_POST @error_handler def top_columns(request): response = {'status': -1} db_tables = json.loads(request.POST.get('dbTables'), '[]') api = OptimizerApi(request.user) data = api.top_columns(db_tables=db_tables) if data: response['status'] = 0 response['values'] = data else: response['message'] = 'Optimizer: %s' % data return JsonResponse(response) def _convert_queries(queries_data): queries = [] for query_data in queries_data: try: snippet = query_data['snippets'][0] if 'guid' in snippet['result']['handle']: original_query_id = '%s:%s' % struct.unpack(b"QQ", base64.decodestring(snippet['result']['handle']['guid'])) execution_time = snippet['result']['executionTime'] * 100 if snippet['status'] in ('available', 'expired') else -1 statement = _clean_query(_get_statement(query_data)) queries.append((original_query_id, execution_time, statement, snippet.get('database', 'default').strip())) except Exception, e: LOG.warning('Skipping upload of %s: %s' % (query_data['uuid'], e)) return queries @require_POST @error_handler def upload_history(request): response = {'status': -1} if is_admin(request.user): api = OptimizerApi(request.user) histories = [] upload_stats = {} if request.POST.get('sourcePlatform'): n = min(request.POST.get('n', OPTIMIZER.QUERY_HISTORY_UPLOAD_LIMIT.get())) source_platform = request.POST.get('sourcePlatform', 'hive') histories = [(source_platform, Document2.objects.get_history(doc_type='query-%s' % source_platform, user=request.user)[:n])] elif OPTIMIZER.QUERY_HISTORY_UPLOAD_LIMIT.get() > 0: histories = [ (source_platform, Document2.objects.filter(type='query-%s' % source_platform, is_history=True, is_managed=False, is_trashed=False).order_by('-last_modified')[:OPTIMIZER.QUERY_HISTORY_UPLOAD_LIMIT.get()]) for source_platform in ['hive', 'impala'] ] for source_platform, history in histories: queries = _convert_queries([Notebook(document=doc).get_data() for doc in history]) upload_stats[source_platform] = api.upload(data=queries, data_type='queries', source_platform=source_platform) response['upload_history'] = upload_stats response['status'] = 0 else: response['message'] = _('Query history upload requires Admin privileges or feature is disabled.') return JsonResponse(response) @require_POST @error_handler def upload_query(request): response = {'status': -1} source_platform = request.POST.get('sourcePlatform', 'default') query_id = request.POST.get('query_id') if OPTIMIZER.AUTO_UPLOAD_QUERIES.get() and source_platform in ('hive', 'impala') and query_id: try: doc = Document2.objects.document(request.user, doc_id=query_id) query_data = Notebook(document=doc).get_data() queries = _convert_queries([query_data]) source_platform = query_data['snippets'][0]['type'] api = OptimizerApi(request.user) response['query_upload'] = api.upload(data=queries, data_type='queries', source_platform=source_platform) except Document2.DoesNotExist: response['query_upload'] = _('Skipped as task query') else: response['query_upload'] = _('Skipped') response['status'] = 0 return JsonResponse(response) @require_POST @error_handler def upload_table_stats(request): response = {'status': -1} db_tables = json.loads(request.POST.get('db_tables'), '[]') source_platform = json.loads(request.POST.get('sourcePlatform', '"hive"')) with_ddl = json.loads(request.POST.get('with_ddl', 'false')) with_table_stats = json.loads(request.POST.get('with_table', 'false')) with_columns_stats = json.loads(request.POST.get('with_columns', 'false')) table_ddls = [] table_stats = [] column_stats = [] if not OPTIMIZER.AUTO_UPLOAD_DDL.get(): with_ddl = False if not OPTIMIZER.AUTO_UPLOAD_STATS.get(): with_table_stats = with_columns_stats = False for db_table in db_tables: path = _get_table_name(db_table) try: if with_ddl: db = _get_db(request.user, source_type=source_platform) query = hql_query('SHOW CREATE TABLE `%(database)s`.`%(table)s`' % path) handle = db.execute_and_wait(query, timeout_sec=5.0) if handle: result = db.fetch(handle, rows=5000) db.close(handle) table_ddls.append((0, 0, ' '.join([row[0] for row in result.rows()]), path['database'])) if with_table_stats: mock_request = MockRequest(user=request.user, source_platform=source_platform) full_table_stats = json.loads(get_table_stats(mock_request, database=path['database'], table=path['table']).content) stats = dict((stat['data_type'], stat['comment']) for stat in full_table_stats['stats']) table_stats.append({ 'table_name': '%(database)s.%(table)s' % path, 'num_rows': stats.get('numRows', -1), 'last_modified_time': stats.get('transient_lastDdlTime', -1), 'total_size': stats.get('totalSize', -1), 'raw_data_size': stats.get('rawDataSize', -1), 'num_files': stats.get('numFiles', -1), 'num_partitions': stats.get('numPartitions', -1), }) if with_columns_stats: if source_platform == 'impala': colum_stats = json.loads(get_table_stats(mock_request, database=path['database'], table=path['table'], column=-1).content)['stats'] else: colum_stats = [ json.loads(get_table_stats(mock_request, database=path['database'], table=path['table'], column=col).content)['stats'] for col in full_table_stats['columns'][:25] ] raw_column_stats = [dict([(key, val if val is not None else '') for col_stat in col for key, val in col_stat.iteritems()]) for col in colum_stats] for col_stats in raw_column_stats: column_stats.append({ 'table_name': '%(database)s.%(table)s' % path, 'column_name': col_stats['col_name'], 'data_type': col_stats['data_type'], "num_distinct": int(col_stats.get('distinct_count')) if col_stats.get('distinct_count') != '' else -1, "num_nulls": int(col_stats['num_nulls']) if col_stats['num_nulls'] != '' else -1, "avg_col_len": int(float(col_stats['avg_col_len'])) if col_stats['avg_col_len'] != '' else -1, "max_size": int(float(col_stats['max_col_len'])) if col_stats['max_col_len'] != '' else -1, "min": col_stats['min'] if col_stats.get('min', '') != '' else -1, "max": col_stats['max'] if col_stats.get('max', '') != '' else -1, "num_trues": col_stats['num_trues'] if col_stats.get('num_trues', '') != '' else -1, "num_falses": col_stats['num_falses'] if col_stats.get('num_falses', '') != '' else -1, }) except Exception, e: LOG.exception('Skipping upload of %s: %s' % (db_table, e)) api = OptimizerApi(request.user) response['status'] = 0 if table_stats: response['upload_table_stats'] = api.upload(data=table_stats, data_type='table_stats', source_platform=source_platform) response['upload_table_stats_status'] = 0 if response['upload_table_stats']['status']['state'] in ('WAITING', 'FINISHED', 'IN_PROGRESS') else -1 response['status'] = response['upload_table_stats_status'] if column_stats: response['upload_cols_stats'] = api.upload(data=column_stats, data_type='cols_stats', source_platform=source_platform) response['upload_cols_stats_status'] = response['status'] if response['upload_cols_stats']['status']['state'] in ('WAITING', 'FINISHED', 'IN_PROGRESS') else -1 if response['upload_cols_stats_status'] != 0: response['status'] = response['upload_cols_stats_status'] if table_ddls: response['upload_table_ddl'] = api.upload(data=table_ddls, data_type='queries', source_platform=source_platform) response['upload_table_ddl_status'] = response['status'] if response['upload_table_ddl']['status']['state'] in ('WAITING', 'FINISHED', 'IN_PROGRESS') else -1 if response['upload_table_ddl_status'] != 0: response['status'] = response['upload_table_ddl_status'] return JsonResponse(response) @require_POST @error_handler def upload_status(request): response = {'status': -1} workload_id = request.POST.get('workloadId') api = OptimizerApi(request.user) response['upload_status'] = api.upload_status(workload_id=workload_id) response['status'] = 0 return JsonResponse(response) class MockRequest(): def __init__(self, user, source_platform): self.user = user self.path = '/%s/' % source_platform if source_platform != 'hive' else 'beeswax'

false

true

790bd9b739afc6a41dfca6fa5848442a73392a1a

3,761

py

Python

icepyx/tests/test_visualization.py

nsidc/icepyx

7f387073a0d6c9e9f5fba90ba10dd2ad4ff04c8b

[ "BSD-3-Clause" ]

113

2019-11-19T17:17:11.000Z

2022-03-28T13:52:42.000Z

icepyx/tests/test_visualization.py

nsidc/icepyx

7f387073a0d6c9e9f5fba90ba10dd2ad4ff04c8b

[ "BSD-3-Clause" ]

211

2020-01-08T20:18:19.000Z

2022-03-31T19:53:39.000Z

icepyx/tests/test_visualization.py

nsidc/icepyx

7f387073a0d6c9e9f5fba90ba10dd2ad4ff04c8b

[ "BSD-3-Clause" ]

90

2019-12-28T01:29:25.000Z

2022-03-25T22:27:56.000Z

import pytest from icepyx.core.visualization import Visualize import icepyx.core.visualization as vis @pytest.mark.parametrize( "n, exp", [ ( 1, [ "ATL06_20200702014158_01020810_004_01.h5", "ATL06_20200703011618_01170810_004_01.h5", ], ), ( 2, [ "ATL06_20200612151119_11920712_004_01.h5", "ATL06_20200616021517_12450710_004_01.h5", "ATL06_20200702014158_01020810_004_01.h5", "ATL06_20200703011618_01170810_004_01.h5", ], ), ( 3, [ "ATL06_20200612151119_11920712_004_01.h5", "ATL06_20200616021517_12450710_004_01.h5", "ATL06_20200702014158_01020810_004_01.h5", "ATL06_20200703011618_01170810_004_01.h5", ], ), ], ) def test_files_in_latest_cycles(n, exp): files = [ "ATL06_20190710071617_01860412_004_01.h5", "ATL06_20190713182016_02390410_004_01.h5", "ATL06_20200612151119_11920712_004_01.h5", "ATL06_20200616021517_12450710_004_01.h5", "ATL06_20200702014158_01020810_004_01.h5", "ATL06_20200703011618_01170810_004_01.h5", ] cycles = [8, 7, 4] obs = vis.files_in_latest_n_cycles(files, cycles=cycles, n=n) assert obs == exp @pytest.mark.parametrize( "filename, expect", [ ('ATL06_20190525202604_08790310_004_01.h5', [879, 3, '2019-05-25']), ('ATL06_20190614194425_11840310_004_01.h5', [1184, 3, '2019-06-14']), ('ATL07-02_20190624063616_13290301_004_01.h5', [1329, 3, '2019-06-24']), ('ATL07-02_20190602190916_10010301_004_01.h5', [1001, 3, '2019-06-02']), ('ATL10-02_20190611072656_11310301_004_01.h5', [1131, 3, '2019-06-11']), ('ATL10-02_20190731045538_05060401_004_01.h5', [506, 4, '2019-07-31']), ('ATL12_20190615023544_11890301_004_01.h5', [1189, 3, '2019-06-15']), ('ATL12_20190721170332_03610401_004_01.h5', [361, 4, '2019-07-21']), ], ) def test_gran_paras(filename, expect): para_list = vis.gran_paras(filename) assert para_list == expect @pytest.mark.parametrize( "product, date_range, bbox, expect", [ ("ATL06", ["2019-6-15", "2019-7-1"], [-64.5, -66, -63.5, -65], 3240), ("ATL07", ["2019-7-1", "2019-8-1"], [-65, -66, -64.5, -65], 7160), ("ATL08", ["2019-6-15", "2019-7-1"], [-18, 63, -17, 64], 852), ("ATL10", ["2019-8-1", "2019-9-1"], [-64, -67, -60, -60], 7375), ("ATL12", ["2019-7-1", "2019-10-1"], [-65.5, -65.5, -64.5, -65], 95), ("ATL13", ["2019-6-1", "2019-12-1"], [-75, -51, -74, -50], 20), ], ) def test_visualization_date_range(product, date_range, bbox, expect): region_viz = Visualize(product=product, spatial_extent=bbox, date_range=date_range) data_size = region_viz.parallel_request_OA().size assert data_size == expect @pytest.mark.parametrize( "product, bbox, cycles, tracks, expect", [ ("ATL06", [-64.5, -66, -63.5, -65], ["03"], ["1306"], 3240), ("ATL07", [-65, -66, -64.5, -65], ["04"], ["0186"], 7130), ("ATL08", [-18, 63, -17, 64], ["03"], ["1320"], 852), ("ATL10", [-64, -67, -60, -60], ["04"], ["0681"], 6015), ("ATL12", [-65.5, -65.5, -64.5, -65], ["05"], ["0041"], 95), ("ATL13", [-75, -51, -74, -50], ["05"], ["0293"], 20), ], ) def test_visualization_orbits(product, bbox, cycles, tracks, expect): region_viz = Visualize( product=product, spatial_extent=bbox, cycles=cycles, tracks=tracks ) data_size = region_viz.parallel_request_OA().size assert data_size == expect

34.504587

87

0.576442

import pytest from icepyx.core.visualization import Visualize import icepyx.core.visualization as vis @pytest.mark.parametrize( "n, exp", [ ( 1, [ "ATL06_20200702014158_01020810_004_01.h5", "ATL06_20200703011618_01170810_004_01.h5", ], ), ( 2, [ "ATL06_20200612151119_11920712_004_01.h5", "ATL06_20200616021517_12450710_004_01.h5", "ATL06_20200702014158_01020810_004_01.h5", "ATL06_20200703011618_01170810_004_01.h5", ], ), ( 3, [ "ATL06_20200612151119_11920712_004_01.h5", "ATL06_20200616021517_12450710_004_01.h5", "ATL06_20200702014158_01020810_004_01.h5", "ATL06_20200703011618_01170810_004_01.h5", ], ), ], ) def test_files_in_latest_cycles(n, exp): files = [ "ATL06_20190710071617_01860412_004_01.h5", "ATL06_20190713182016_02390410_004_01.h5", "ATL06_20200612151119_11920712_004_01.h5", "ATL06_20200616021517_12450710_004_01.h5", "ATL06_20200702014158_01020810_004_01.h5", "ATL06_20200703011618_01170810_004_01.h5", ] cycles = [8, 7, 4] obs = vis.files_in_latest_n_cycles(files, cycles=cycles, n=n) assert obs == exp @pytest.mark.parametrize( "filename, expect", [ ('ATL06_20190525202604_08790310_004_01.h5', [879, 3, '2019-05-25']), ('ATL06_20190614194425_11840310_004_01.h5', [1184, 3, '2019-06-14']), ('ATL07-02_20190624063616_13290301_004_01.h5', [1329, 3, '2019-06-24']), ('ATL07-02_20190602190916_10010301_004_01.h5', [1001, 3, '2019-06-02']), ('ATL10-02_20190611072656_11310301_004_01.h5', [1131, 3, '2019-06-11']), ('ATL10-02_20190731045538_05060401_004_01.h5', [506, 4, '2019-07-31']), ('ATL12_20190615023544_11890301_004_01.h5', [1189, 3, '2019-06-15']), ('ATL12_20190721170332_03610401_004_01.h5', [361, 4, '2019-07-21']), ], ) def test_gran_paras(filename, expect): para_list = vis.gran_paras(filename) assert para_list == expect @pytest.mark.parametrize( "product, date_range, bbox, expect", [ ("ATL06", ["2019-6-15", "2019-7-1"], [-64.5, -66, -63.5, -65], 3240), ("ATL07", ["2019-7-1", "2019-8-1"], [-65, -66, -64.5, -65], 7160), ("ATL08", ["2019-6-15", "2019-7-1"], [-18, 63, -17, 64], 852), ("ATL10", ["2019-8-1", "2019-9-1"], [-64, -67, -60, -60], 7375), ("ATL12", ["2019-7-1", "2019-10-1"], [-65.5, -65.5, -64.5, -65], 95), ("ATL13", ["2019-6-1", "2019-12-1"], [-75, -51, -74, -50], 20), ], ) def test_visualization_date_range(product, date_range, bbox, expect): region_viz = Visualize(product=product, spatial_extent=bbox, date_range=date_range) data_size = region_viz.parallel_request_OA().size assert data_size == expect @pytest.mark.parametrize( "product, bbox, cycles, tracks, expect", [ ("ATL06", [-64.5, -66, -63.5, -65], ["03"], ["1306"], 3240), ("ATL07", [-65, -66, -64.5, -65], ["04"], ["0186"], 7130), ("ATL08", [-18, 63, -17, 64], ["03"], ["1320"], 852), ("ATL10", [-64, -67, -60, -60], ["04"], ["0681"], 6015), ("ATL12", [-65.5, -65.5, -64.5, -65], ["05"], ["0041"], 95), ("ATL13", [-75, -51, -74, -50], ["05"], ["0293"], 20), ], ) def test_visualization_orbits(product, bbox, cycles, tracks, expect): region_viz = Visualize( product=product, spatial_extent=bbox, cycles=cycles, tracks=tracks ) data_size = region_viz.parallel_request_OA().size assert data_size == expect

true

790bdc2b49eb80e85b1daeec29291b189a50693c

15,985

py

Python

lite/examples/model_personalization/converter/tfltransfer/model_correctness_test.py

non778/examples

d1eed1a6a987b0ebbb0341925a480dc3e60489ee

[ "Apache-2.0" ]

3

2020-09-15T13:00:51.000Z

2020-10-07T17:43:51.000Z

lite/examples/model_personalization/converter/tfltransfer/model_correctness_test.py

non778/examples

d1eed1a6a987b0ebbb0341925a480dc3e60489ee

[ "Apache-2.0" ]

7

2020-11-13T19:02:15.000Z

2022-03-12T00:43:42.000Z

lite/examples/model_personalization/converter/tfltransfer/model_correctness_test.py

non778/examples

d1eed1a6a987b0ebbb0341925a480dc3e60489ee

[ "Apache-2.0" ]

8

2021-05-01T04:50:58.000Z

2021-05-01T07:57:04.000Z

# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """End-to-end tests that check model correctness.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import tempfile import unittest import numpy as np import tensorflow as tf from tensorflow.compat import v1 as tfv1 # pylint: disable=g-bad-import-order from tfltransfer import bases from tfltransfer import optimizers from tfltransfer import heads from tfltransfer import tflite_transfer_converter # pylint: enable=g-bad-import-order IMAGE_SIZE = 224 BATCH_SIZE = 128 NUM_CLASSES = 5 VALIDATION_SPLIT = 0.2 LEARNING_RATE = 0.001 BOTTLENECK_SHAPE = (7, 7, 1280) DATASET_URL = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz' class TransferModel(object): """Test consumer of models generated by the converter.""" def __init__(self, dataset_dir, base_model, head_model, optimizer): """Creates a wrapper for a set of models and a data set.""" self.dataset_dir = dataset_dir datagen = tf.keras.preprocessing.image.ImageDataGenerator( rescale=1. / 255, validation_split=VALIDATION_SPLIT) self.train_img_generator = datagen.flow_from_directory( self.dataset_dir, target_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=BATCH_SIZE, subset='training') self.val_img_generator = datagen.flow_from_directory( self.dataset_dir, target_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=BATCH_SIZE, subset='validation') converter = tflite_transfer_converter.TFLiteTransferConverter( NUM_CLASSES, base_model, head_model, optimizer, BATCH_SIZE) models = converter._convert() self.initialize_model = models['initialize'] self.bottleneck_model = models['bottleneck'] self.train_head_model = models['train_head'] self.inference_model = models['inference'] self.optimizer_model = models['optimizer'] self.variables = self._generate_initial_variables() optim_state_shapes = self._optimizer_state_shapes() self.optim_state = [ np.zeros(shape, dtype=np.float32) for shape in optim_state_shapes ] def _generate_initial_variables(self): """Generates the initial model variables.""" interpreter = tf.lite.Interpreter(model_content=self.initialize_model) zero_in = interpreter.get_input_details()[0] variable_outs = interpreter.get_output_details() interpreter.allocate_tensors() interpreter.set_tensor(zero_in['index'], np.float32(0.)) interpreter.invoke() return [interpreter.get_tensor(var['index']) for var in variable_outs] def _optimizer_state_shapes(self): """Reads the shapes of the optimizer parameters (mutable state).""" interpreter = tf.lite.Interpreter(model_content=self.optimizer_model) num_variables = len(self.variables) optim_state_inputs = interpreter.get_input_details()[num_variables * 2:] return [input_['shape'] for input_ in optim_state_inputs] def prepare_bottlenecks(self): """Passes all images through the base model and save the bottlenecks. This method has to be called before any training or inference. """ self.train_bottlenecks, self.train_labels = ( self._collect_and_generate_bottlenecks(self.train_img_generator)) self.val_bottlenecks, self.val_labels = ( self._collect_and_generate_bottlenecks(self.val_img_generator)) def _collect_and_generate_bottlenecks(self, image_gen): """Consumes a generator and converts all images to bottlenecks. Args: image_gen: A Keras data generator for images to process Returns: Two NumPy arrays: (bottlenecks, labels). """ collected_bottlenecks = np.zeros( (image_gen.samples,) + BOTTLENECK_SHAPE, dtype=np.float32) collected_labels = np.zeros((image_gen.samples, NUM_CLASSES), dtype=np.float32) next_idx = 0 for bottlenecks, truth in self._generate_bottlenecks( make_finite(image_gen)): batch_size = bottlenecks.shape[0] collected_bottlenecks[next_idx:next_idx + batch_size] = bottlenecks collected_labels[next_idx:next_idx + batch_size] = truth next_idx += batch_size return collected_bottlenecks, collected_labels def _generate_bottlenecks(self, image_gen): """Generator adapter that passes images through the bottleneck model. Args: image_gen: A generator that returns images to be processed. Images are paired with ground truth labels. Yields: Bottlenecks from input images, paired with ground truth labels. """ interpreter = tf.lite.Interpreter(model_content=self.bottleneck_model) [x_in] = interpreter.get_input_details() [bottleneck_out] = interpreter.get_output_details() for (x, y) in image_gen: batch_size = x.shape[0] interpreter.resize_tensor_input(x_in['index'], (batch_size, IMAGE_SIZE, IMAGE_SIZE, 3)) interpreter.allocate_tensors() interpreter.set_tensor(x_in['index'], x) interpreter.invoke() bottleneck = interpreter.get_tensor(bottleneck_out['index']) yield bottleneck, y def train_head(self, num_epochs): """Trains the head model for a given number of epochs. SGD is used as an optimizer. Args: num_epochs: how many epochs should be trained Returns: A list of train_loss values after every epoch trained. Raises: RuntimeError: when prepare_bottlenecks() has not been called. """ if not hasattr(self, 'train_bottlenecks'): raise RuntimeError('prepare_bottlenecks has not been called') results = [] for _ in range(num_epochs): loss = self._train_one_epoch( self._generate_batches(self.train_bottlenecks, self.train_labels)) results.append(loss) return results def _generate_batches(self, x, y): """Creates a generator that iterates over the data in batches.""" num_total = x.shape[0] for begin in range(0, num_total, BATCH_SIZE): end = min(begin + BATCH_SIZE, num_total) yield x[begin:end], y[begin:end] def _train_one_epoch(self, train_gen): """Performs one training epoch.""" interpreter = tf.lite.Interpreter(model_content=self.train_head_model) interpreter.allocate_tensors() x_in, y_in = interpreter.get_input_details()[:2] variable_ins = interpreter.get_input_details()[2:] loss_out = interpreter.get_output_details()[0] gradient_outs = interpreter.get_output_details()[1:] epoch_loss = 0. num_processed = 0 for bottlenecks, truth in train_gen: batch_size = bottlenecks.shape[0] if batch_size < BATCH_SIZE: bottlenecks = pad_batch(bottlenecks, BATCH_SIZE) truth = pad_batch(truth, BATCH_SIZE) interpreter.set_tensor(x_in['index'], bottlenecks) interpreter.set_tensor(y_in['index'], truth) for variable_in, variable_value in zip(variable_ins, self.variables): interpreter.set_tensor(variable_in['index'], variable_value) interpreter.invoke() loss = interpreter.get_tensor(loss_out['index']) gradients = [ interpreter.get_tensor(gradient_out['index']) for gradient_out in gradient_outs ] self._apply_gradients(gradients) epoch_loss += loss * batch_size num_processed += batch_size epoch_loss /= num_processed return epoch_loss def _apply_gradients(self, gradients): """Applies the optimizer to the model parameters.""" interpreter = tf.lite.Interpreter(model_content=self.optimizer_model) interpreter.allocate_tensors() num_variables = len(self.variables) variable_ins = interpreter.get_input_details()[:num_variables] gradient_ins = interpreter.get_input_details()[num_variables:num_variables * 2] state_ins = interpreter.get_input_details()[num_variables * 2:] variable_outs = interpreter.get_output_details()[:num_variables] state_outs = interpreter.get_output_details()[num_variables:] for variable, gradient, variable_in, gradient_in in zip( self.variables, gradients, variable_ins, gradient_ins): interpreter.set_tensor(variable_in['index'], variable) interpreter.set_tensor(gradient_in['index'], gradient) for optim_state_elem, state_in in zip(self.optim_state, state_ins): interpreter.set_tensor(state_in['index'], optim_state_elem) interpreter.invoke() self.variables = [ interpreter.get_tensor(variable_out['index']) for variable_out in variable_outs ] self.optim_state = [ interpreter.get_tensor(state_out['index']) for state_out in state_outs ] def measure_inference_accuracy(self): """Runs the inference model and measures accuracy on the validation set.""" interpreter = tf.lite.Interpreter(model_content=self.inference_model) bottleneck_in = interpreter.get_input_details()[0] variable_ins = interpreter.get_input_details()[1:] [y_out] = interpreter.get_output_details() inference_accuracy = 0. num_processed = 0 for bottleneck, truth in self._generate_batches(self.val_bottlenecks, self.val_labels): batch_size = bottleneck.shape[0] interpreter.resize_tensor_input(bottleneck_in['index'], (batch_size,) + BOTTLENECK_SHAPE) interpreter.allocate_tensors() interpreter.set_tensor(bottleneck_in['index'], bottleneck) for variable_in, variable_value in zip(variable_ins, self.variables): interpreter.set_tensor(variable_in['index'], variable_value) interpreter.invoke() preds = interpreter.get_tensor(y_out['index']) acc = (np.argmax(preds, axis=1) == np.argmax(truth, axis=1)).sum() / batch_size inference_accuracy += acc * batch_size num_processed += batch_size inference_accuracy /= num_processed return inference_accuracy def make_finite(data_gen): """An adapter for Keras data generators that makes them finite. The default behavior in Keras is to keep looping infinitely through the data. Args: data_gen: An infinite Keras data generator. Yields: Same values as the parameter generator. """ num_samples = data_gen.samples num_processed = 0 for batch in data_gen: batch_size = batch[0].shape[0] if batch_size + num_processed > num_samples: batch_size = num_samples - num_processed should_stop = True else: should_stop = False if batch_size == 0: return batch = tuple(x[:batch_size] for x in batch) yield batch num_processed += batch_size if should_stop: return # TODO(b/135138207) investigate if we can get rid of this. def pad_batch(batch, batch_size): """Resize batch to a given size, tiling present samples over missing. Example: Suppose batch_size is 5, batch is [1, 2]. Then the return value is [1, 2, 1, 2, 1]. Args: batch: An ndarray with first dimension size <= batch_size. batch_size: Desired size for first dimension. Returns: An ndarray of the same shape, except first dimension has the desired size. """ padded = np.zeros((batch_size,) + batch.shape[1:], dtype=batch.dtype) next_idx = 0 while next_idx < batch_size: fill_len = min(batch.shape[0], batch_size - next_idx) padded[next_idx:next_idx + fill_len] = batch[:fill_len] next_idx += fill_len return padded class ModelCorrectnessTest(unittest.TestCase): @classmethod def setUpClass(cls): super(ModelCorrectnessTest, cls).setUpClass() zip_file = tf.keras.utils.get_file( origin=DATASET_URL, fname='flower_photos.tgz', extract=True) cls.dataset_dir = os.path.join(os.path.dirname(zip_file), 'flower_photos') mobilenet_dir = tempfile.mkdtemp('tflite-transfer-test') mobilenet_keras = tf.keras.applications.MobileNetV2( input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3), include_top=False, weights='imagenet') tfv1.keras.experimental.export_saved_model(mobilenet_keras, mobilenet_dir) cls.mobilenet_dir = mobilenet_dir def setUp(self): super(ModelCorrectnessTest, self).setUp() self.mobilenet_dir = ModelCorrectnessTest.mobilenet_dir self.dataset_dir = ModelCorrectnessTest.dataset_dir def test_mobilenet_v2_saved_model_and_softmax_classifier(self): base_model = bases.SavedModelBase(self.mobilenet_dir) head_model = heads.SoftmaxClassifierHead(BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES) optimizer = optimizers.SGD(LEARNING_RATE) model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def test_mobilenet_v2_saved_model_quantized_and_softmax_classifier(self): base_model = bases.SavedModelBase(self.mobilenet_dir, quantize=True) head_model = heads.SoftmaxClassifierHead(BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES) optimizer = optimizers.SGD(LEARNING_RATE) model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def test_mobilenet_v2_base_and_softmax_classifier(self): base_model = bases.MobileNetV2Base() head_model = heads.SoftmaxClassifierHead(BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES) optimizer = optimizers.SGD(LEARNING_RATE) model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def test_mobilenet_v2_base_and_softmax_classifier_l2(self): base_model = bases.MobileNetV2Base() head_model = heads.SoftmaxClassifierHead( BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES, l2_reg=0.1) optimizer = optimizers.SGD(LEARNING_RATE) model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def test_mobilenet_v2_base_quantized_and_softmax_classifier(self): base_model = bases.MobileNetV2Base(quantize=True) head_model = heads.SoftmaxClassifierHead(BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES) optimizer = optimizers.SGD(LEARNING_RATE) model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def test_mobilenet_v2_base_and_softmax_classifier_adam(self): base_model = bases.MobileNetV2Base() head_model = heads.SoftmaxClassifierHead(BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES) optimizer = optimizers.Adam() model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def assertModelAchievesAccuracy(self, model, target_accuracy, num_epochs=30): model.prepare_bottlenecks() print('Bottlenecks prepared') history = model.train_head(num_epochs) print('Training completed, history = {}'.format(history)) accuracy = model.measure_inference_accuracy() print('Final accuracy = {:.2f}'.format(accuracy)) self.assertGreater(accuracy, target_accuracy) if __name__ == '__main__': unittest.main()

38.059524

103

0.714983

from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import tempfile import unittest import numpy as np import tensorflow as tf from tensorflow.compat import v1 as tfv1 from tfltransfer import bases from tfltransfer import optimizers from tfltransfer import heads from tfltransfer import tflite_transfer_converter IMAGE_SIZE = 224 BATCH_SIZE = 128 NUM_CLASSES = 5 VALIDATION_SPLIT = 0.2 LEARNING_RATE = 0.001 BOTTLENECK_SHAPE = (7, 7, 1280) DATASET_URL = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz' class TransferModel(object): def __init__(self, dataset_dir, base_model, head_model, optimizer): self.dataset_dir = dataset_dir datagen = tf.keras.preprocessing.image.ImageDataGenerator( rescale=1. / 255, validation_split=VALIDATION_SPLIT) self.train_img_generator = datagen.flow_from_directory( self.dataset_dir, target_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=BATCH_SIZE, subset='training') self.val_img_generator = datagen.flow_from_directory( self.dataset_dir, target_size=(IMAGE_SIZE, IMAGE_SIZE), batch_size=BATCH_SIZE, subset='validation') converter = tflite_transfer_converter.TFLiteTransferConverter( NUM_CLASSES, base_model, head_model, optimizer, BATCH_SIZE) models = converter._convert() self.initialize_model = models['initialize'] self.bottleneck_model = models['bottleneck'] self.train_head_model = models['train_head'] self.inference_model = models['inference'] self.optimizer_model = models['optimizer'] self.variables = self._generate_initial_variables() optim_state_shapes = self._optimizer_state_shapes() self.optim_state = [ np.zeros(shape, dtype=np.float32) for shape in optim_state_shapes ] def _generate_initial_variables(self): interpreter = tf.lite.Interpreter(model_content=self.initialize_model) zero_in = interpreter.get_input_details()[0] variable_outs = interpreter.get_output_details() interpreter.allocate_tensors() interpreter.set_tensor(zero_in['index'], np.float32(0.)) interpreter.invoke() return [interpreter.get_tensor(var['index']) for var in variable_outs] def _optimizer_state_shapes(self): interpreter = tf.lite.Interpreter(model_content=self.optimizer_model) num_variables = len(self.variables) optim_state_inputs = interpreter.get_input_details()[num_variables * 2:] return [input_['shape'] for input_ in optim_state_inputs] def prepare_bottlenecks(self): self.train_bottlenecks, self.train_labels = ( self._collect_and_generate_bottlenecks(self.train_img_generator)) self.val_bottlenecks, self.val_labels = ( self._collect_and_generate_bottlenecks(self.val_img_generator)) def _collect_and_generate_bottlenecks(self, image_gen): collected_bottlenecks = np.zeros( (image_gen.samples,) + BOTTLENECK_SHAPE, dtype=np.float32) collected_labels = np.zeros((image_gen.samples, NUM_CLASSES), dtype=np.float32) next_idx = 0 for bottlenecks, truth in self._generate_bottlenecks( make_finite(image_gen)): batch_size = bottlenecks.shape[0] collected_bottlenecks[next_idx:next_idx + batch_size] = bottlenecks collected_labels[next_idx:next_idx + batch_size] = truth next_idx += batch_size return collected_bottlenecks, collected_labels def _generate_bottlenecks(self, image_gen): interpreter = tf.lite.Interpreter(model_content=self.bottleneck_model) [x_in] = interpreter.get_input_details() [bottleneck_out] = interpreter.get_output_details() for (x, y) in image_gen: batch_size = x.shape[0] interpreter.resize_tensor_input(x_in['index'], (batch_size, IMAGE_SIZE, IMAGE_SIZE, 3)) interpreter.allocate_tensors() interpreter.set_tensor(x_in['index'], x) interpreter.invoke() bottleneck = interpreter.get_tensor(bottleneck_out['index']) yield bottleneck, y def train_head(self, num_epochs): if not hasattr(self, 'train_bottlenecks'): raise RuntimeError('prepare_bottlenecks has not been called') results = [] for _ in range(num_epochs): loss = self._train_one_epoch( self._generate_batches(self.train_bottlenecks, self.train_labels)) results.append(loss) return results def _generate_batches(self, x, y): num_total = x.shape[0] for begin in range(0, num_total, BATCH_SIZE): end = min(begin + BATCH_SIZE, num_total) yield x[begin:end], y[begin:end] def _train_one_epoch(self, train_gen): interpreter = tf.lite.Interpreter(model_content=self.train_head_model) interpreter.allocate_tensors() x_in, y_in = interpreter.get_input_details()[:2] variable_ins = interpreter.get_input_details()[2:] loss_out = interpreter.get_output_details()[0] gradient_outs = interpreter.get_output_details()[1:] epoch_loss = 0. num_processed = 0 for bottlenecks, truth in train_gen: batch_size = bottlenecks.shape[0] if batch_size < BATCH_SIZE: bottlenecks = pad_batch(bottlenecks, BATCH_SIZE) truth = pad_batch(truth, BATCH_SIZE) interpreter.set_tensor(x_in['index'], bottlenecks) interpreter.set_tensor(y_in['index'], truth) for variable_in, variable_value in zip(variable_ins, self.variables): interpreter.set_tensor(variable_in['index'], variable_value) interpreter.invoke() loss = interpreter.get_tensor(loss_out['index']) gradients = [ interpreter.get_tensor(gradient_out['index']) for gradient_out in gradient_outs ] self._apply_gradients(gradients) epoch_loss += loss * batch_size num_processed += batch_size epoch_loss /= num_processed return epoch_loss def _apply_gradients(self, gradients): interpreter = tf.lite.Interpreter(model_content=self.optimizer_model) interpreter.allocate_tensors() num_variables = len(self.variables) variable_ins = interpreter.get_input_details()[:num_variables] gradient_ins = interpreter.get_input_details()[num_variables:num_variables * 2] state_ins = interpreter.get_input_details()[num_variables * 2:] variable_outs = interpreter.get_output_details()[:num_variables] state_outs = interpreter.get_output_details()[num_variables:] for variable, gradient, variable_in, gradient_in in zip( self.variables, gradients, variable_ins, gradient_ins): interpreter.set_tensor(variable_in['index'], variable) interpreter.set_tensor(gradient_in['index'], gradient) for optim_state_elem, state_in in zip(self.optim_state, state_ins): interpreter.set_tensor(state_in['index'], optim_state_elem) interpreter.invoke() self.variables = [ interpreter.get_tensor(variable_out['index']) for variable_out in variable_outs ] self.optim_state = [ interpreter.get_tensor(state_out['index']) for state_out in state_outs ] def measure_inference_accuracy(self): interpreter = tf.lite.Interpreter(model_content=self.inference_model) bottleneck_in = interpreter.get_input_details()[0] variable_ins = interpreter.get_input_details()[1:] [y_out] = interpreter.get_output_details() inference_accuracy = 0. num_processed = 0 for bottleneck, truth in self._generate_batches(self.val_bottlenecks, self.val_labels): batch_size = bottleneck.shape[0] interpreter.resize_tensor_input(bottleneck_in['index'], (batch_size,) + BOTTLENECK_SHAPE) interpreter.allocate_tensors() interpreter.set_tensor(bottleneck_in['index'], bottleneck) for variable_in, variable_value in zip(variable_ins, self.variables): interpreter.set_tensor(variable_in['index'], variable_value) interpreter.invoke() preds = interpreter.get_tensor(y_out['index']) acc = (np.argmax(preds, axis=1) == np.argmax(truth, axis=1)).sum() / batch_size inference_accuracy += acc * batch_size num_processed += batch_size inference_accuracy /= num_processed return inference_accuracy def make_finite(data_gen): num_samples = data_gen.samples num_processed = 0 for batch in data_gen: batch_size = batch[0].shape[0] if batch_size + num_processed > num_samples: batch_size = num_samples - num_processed should_stop = True else: should_stop = False if batch_size == 0: return batch = tuple(x[:batch_size] for x in batch) yield batch num_processed += batch_size if should_stop: return def pad_batch(batch, batch_size): padded = np.zeros((batch_size,) + batch.shape[1:], dtype=batch.dtype) next_idx = 0 while next_idx < batch_size: fill_len = min(batch.shape[0], batch_size - next_idx) padded[next_idx:next_idx + fill_len] = batch[:fill_len] next_idx += fill_len return padded class ModelCorrectnessTest(unittest.TestCase): @classmethod def setUpClass(cls): super(ModelCorrectnessTest, cls).setUpClass() zip_file = tf.keras.utils.get_file( origin=DATASET_URL, fname='flower_photos.tgz', extract=True) cls.dataset_dir = os.path.join(os.path.dirname(zip_file), 'flower_photos') mobilenet_dir = tempfile.mkdtemp('tflite-transfer-test') mobilenet_keras = tf.keras.applications.MobileNetV2( input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3), include_top=False, weights='imagenet') tfv1.keras.experimental.export_saved_model(mobilenet_keras, mobilenet_dir) cls.mobilenet_dir = mobilenet_dir def setUp(self): super(ModelCorrectnessTest, self).setUp() self.mobilenet_dir = ModelCorrectnessTest.mobilenet_dir self.dataset_dir = ModelCorrectnessTest.dataset_dir def test_mobilenet_v2_saved_model_and_softmax_classifier(self): base_model = bases.SavedModelBase(self.mobilenet_dir) head_model = heads.SoftmaxClassifierHead(BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES) optimizer = optimizers.SGD(LEARNING_RATE) model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def test_mobilenet_v2_saved_model_quantized_and_softmax_classifier(self): base_model = bases.SavedModelBase(self.mobilenet_dir, quantize=True) head_model = heads.SoftmaxClassifierHead(BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES) optimizer = optimizers.SGD(LEARNING_RATE) model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def test_mobilenet_v2_base_and_softmax_classifier(self): base_model = bases.MobileNetV2Base() head_model = heads.SoftmaxClassifierHead(BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES) optimizer = optimizers.SGD(LEARNING_RATE) model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def test_mobilenet_v2_base_and_softmax_classifier_l2(self): base_model = bases.MobileNetV2Base() head_model = heads.SoftmaxClassifierHead( BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES, l2_reg=0.1) optimizer = optimizers.SGD(LEARNING_RATE) model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def test_mobilenet_v2_base_quantized_and_softmax_classifier(self): base_model = bases.MobileNetV2Base(quantize=True) head_model = heads.SoftmaxClassifierHead(BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES) optimizer = optimizers.SGD(LEARNING_RATE) model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def test_mobilenet_v2_base_and_softmax_classifier_adam(self): base_model = bases.MobileNetV2Base() head_model = heads.SoftmaxClassifierHead(BATCH_SIZE, BOTTLENECK_SHAPE, NUM_CLASSES) optimizer = optimizers.Adam() model = TransferModel(self.dataset_dir, base_model, head_model, optimizer) self.assertModelAchievesAccuracy(model, 0.80) def assertModelAchievesAccuracy(self, model, target_accuracy, num_epochs=30): model.prepare_bottlenecks() print('Bottlenecks prepared') history = model.train_head(num_epochs) print('Training completed, history = {}'.format(history)) accuracy = model.measure_inference_accuracy() print('Final accuracy = {:.2f}'.format(accuracy)) self.assertGreater(accuracy, target_accuracy) if __name__ == '__main__': unittest.main()

true

790bdc3ea34a2bbf34251dec2df58f723df4e0a4

35,323

py

Python

flair/models/tars_model.py

marleneDebatin/flair

4d17509f358158f66d43e85db1b6990523b0b095

[ "MIT" ]

1

2022-02-06T04:04:27.000Z

flair/models/tars_model.py

marleneDebatin/flair

4d17509f358158f66d43e85db1b6990523b0b095

[ "MIT" ]

null

flair/models/tars_model.py

marleneDebatin/flair

4d17509f358158f66d43e85db1b6990523b0b095

[ "MIT" ]

null

import logging from collections import OrderedDict from pathlib import Path from typing import List, Optional, Set, Tuple, Union import numpy as np import torch from sklearn.metrics.pairwise import cosine_similarity from sklearn.preprocessing import minmax_scale from tqdm import tqdm import flair from flair.data import Dictionary, Sentence, Span, SpanLabel from flair.datasets import DataLoader, FlairDatapointDataset from flair.embeddings import ( TokenEmbeddings, TransformerDocumentEmbeddings, TransformerWordEmbeddings, ) from flair.file_utils import cached_path from flair.models.sequence_tagger_model import SequenceTagger from flair.models.text_classification_model import TextClassifier from flair.training_utils import store_embeddings log = logging.getLogger("flair") class FewshotClassifier(flair.nn.Classifier[Sentence]): def __init__(self): self._current_task = None self._task_specific_attributes = {} self.label_nearest_map = None self.tars_model: flair.nn.Classifier[Sentence] super(FewshotClassifier, self).__init__() def forward_loss( self, data_points: Union[List[Sentence], Sentence] ) -> Union[torch.Tensor, Tuple[torch.Tensor, int]]: if not isinstance(data_points, list): data_points = [data_points] # Transform input data into TARS format sentences = self._get_tars_formatted_sentences(data_points) loss = self.tars_model.forward_loss(sentences) return loss @property def tars_embeddings(self): raise NotImplementedError def _get_tars_formatted_sentence(self, label, sentence): raise NotImplementedError def _get_tars_formatted_sentences(self, sentences: List[Sentence]): label_text_pairs = [] all_labels = [label.decode("utf-8") for label in self.get_current_label_dictionary().idx2item] for sentence in sentences: label_text_pairs_for_sentence = [] if self.training and self.num_negative_labels_to_sample is not None: positive_labels = list( OrderedDict.fromkeys([label.value for label in sentence.get_labels(self.label_type)]) ) sampled_negative_labels = self._get_nearest_labels_for(positive_labels) for label in positive_labels: label_text_pairs_for_sentence.append(self._get_tars_formatted_sentence(label, sentence)) for label in sampled_negative_labels: label_text_pairs_for_sentence.append(self._get_tars_formatted_sentence(label, sentence)) else: for label in all_labels: label_text_pairs_for_sentence.append(self._get_tars_formatted_sentence(label, sentence)) label_text_pairs.extend(label_text_pairs_for_sentence) return label_text_pairs def _get_nearest_labels_for(self, labels): # if there are no labels, return a random sample as negatives if len(labels) == 0: tags = self.get_current_label_dictionary().get_items() import random sample = random.sample(tags, k=self.num_negative_labels_to_sample) return sample already_sampled_negative_labels = set() # otherwise, go through all labels for label in labels: plausible_labels = [] plausible_label_probabilities = [] for plausible_label in self.label_nearest_map[label]: if plausible_label in already_sampled_negative_labels or plausible_label in labels: continue else: plausible_labels.append(plausible_label) plausible_label_probabilities.append(self.label_nearest_map[label][plausible_label]) # make sure the probabilities always sum up to 1 plausible_label_probabilities = np.array(plausible_label_probabilities, dtype="float64") plausible_label_probabilities += 1e-08 plausible_label_probabilities /= np.sum(plausible_label_probabilities) if len(plausible_labels) > 0: num_samples = min(self.num_negative_labels_to_sample, len(plausible_labels)) sampled_negative_labels = np.random.choice( plausible_labels, num_samples, replace=False, p=plausible_label_probabilities, ) already_sampled_negative_labels.update(sampled_negative_labels) return already_sampled_negative_labels def train(self, mode=True): """Populate label similarity map based on cosine similarity before running epoch If the `num_negative_labels_to_sample` is set to an integer value then before starting each epoch the model would create a similarity measure between the label names based on cosine distances between their BERT encoded embeddings. """ if mode and self.num_negative_labels_to_sample is not None: self._compute_label_similarity_for_current_epoch() super().train(mode) super().train(mode) def _compute_label_similarity_for_current_epoch(self): """ Compute the similarity between all labels for better sampling of negatives """ # get and embed all labels by making a Sentence object that contains only the label text all_labels = [label.decode("utf-8") for label in self.get_current_label_dictionary().idx2item] label_sentences = [Sentence(label) for label in all_labels] self.tars_embeddings.eval() # TODO: check if this is necessary self.tars_embeddings.embed(label_sentences) self.tars_embeddings.train() # get each label embedding and scale between 0 and 1 if isinstance(self.tars_embeddings, TokenEmbeddings): encodings_np = [sentence[0].get_embedding().cpu().detach().numpy() for sentence in label_sentences] else: encodings_np = [sentence.get_embedding().cpu().detach().numpy() for sentence in label_sentences] normalized_encoding = minmax_scale(encodings_np) # compute similarity matrix similarity_matrix = cosine_similarity(normalized_encoding) # the higher the similarity, the greater the chance that a label is # sampled as negative example negative_label_probabilities = {} for row_index, label in enumerate(all_labels): negative_label_probabilities[label] = {} for column_index, other_label in enumerate(all_labels): if label != other_label: negative_label_probabilities[label][other_label] = similarity_matrix[row_index][column_index] self.label_nearest_map = negative_label_probabilities def get_current_label_dictionary(self): label_dictionary = self._task_specific_attributes[self._current_task]["label_dictionary"] return label_dictionary def get_current_label_type(self): return self._task_specific_attributes[self._current_task]["label_type"] def is_current_task_multi_label(self): return self._task_specific_attributes[self._current_task]["multi_label"] def add_and_switch_to_new_task( self, task_name, label_dictionary: Union[List, Set, Dictionary, str], label_type: str, multi_label: bool = True, force_switch: bool = False, ): """ Adds a new task to an existing TARS model. Sets necessary attributes and finally 'switches' to the new task. Parameters are similar to the constructor except for model choice, batch size and negative sampling. This method does not store the resultant model onto disk. :param task_name: a string depicting the name of the task :param label_dictionary: dictionary of the labels you want to predict :param label_type: string to identify the label type ('ner', 'sentiment', etc.) :param multi_label: whether this task is a multi-label prediction problem :param force_switch: if True, will overwrite existing task with same name """ if task_name in self._task_specific_attributes and not force_switch: log.warning("Task `%s` already exists in TARS model. Switching to it.", task_name) else: # make label dictionary if no Dictionary object is passed if isinstance(label_dictionary, Dictionary): label_dictionary = label_dictionary.get_items() if type(label_dictionary) == str: label_dictionary = [label_dictionary] # prepare dictionary of tags (without B- I- prefixes and without UNK) tag_dictionary = Dictionary(add_unk=False) for tag in label_dictionary: if tag == "<unk>" or tag == "O": continue if tag[1] == "-": tag = tag[2:] tag_dictionary.add_item(tag) else: tag_dictionary.add_item(tag) self._task_specific_attributes[task_name] = { "label_dictionary": tag_dictionary, "label_type": label_type, "multi_label": multi_label, } self.switch_to_task(task_name) def list_existing_tasks(self) -> Set[str]: """ Lists existing tasks in the loaded TARS model on the console. """ return set(self._task_specific_attributes.keys()) def switch_to_task(self, task_name): """ Switches to a task which was previously added. """ if task_name not in self._task_specific_attributes: log.error( "Provided `%s` does not exist in the model. Consider calling " "`add_and_switch_to_new_task` first.", task_name, ) else: self._current_task = task_name def _drop_task(self, task_name): if task_name in self._task_specific_attributes: if self._current_task == task_name: log.error( "`%s` is the current task." " Switch to some other task before dropping this.", task_name, ) else: self._task_specific_attributes.pop(task_name) else: log.warning("No task exists with the name `%s`.", task_name) @staticmethod def _filter_empty_sentences(sentences: List[Sentence]) -> List[Sentence]: filtered_sentences = [sentence for sentence in sentences if sentence.tokens] if len(sentences) != len(filtered_sentences): log.warning(f"Ignore {len(sentences) - len(filtered_sentences)} sentence(s) with no tokens.") return filtered_sentences @property def label_type(self): return self.get_current_label_type() def predict_zero_shot( self, sentences: Union[List[Sentence], Sentence], candidate_label_set: Union[List[str], Set[str], str], multi_label: bool = True, ): """ Method to make zero shot predictions from the TARS model :param sentences: input sentence objects to classify :param candidate_label_set: set of candidate labels :param multi_label: indicates whether multi-label or single class prediction. Defaults to True. """ # check if candidate_label_set is empty if candidate_label_set is None or len(candidate_label_set) == 0: log.warning("Provided candidate_label_set is empty") return # make list if only one candidate label is passed if isinstance(candidate_label_set, str): candidate_label_set = {candidate_label_set} # create label dictionary label_dictionary = Dictionary(add_unk=False) for label in candidate_label_set: label_dictionary.add_item(label) # note current task existing_current_task = self._current_task # create a temporary task self.add_and_switch_to_new_task( task_name="ZeroShot", label_dictionary=label_dictionary, label_type="-".join(label_dictionary.get_items()), multi_label=multi_label, ) try: # make zero shot predictions self.predict(sentences) finally: # switch to the pre-existing task self.switch_to_task(existing_current_task) self._drop_task("ZeroShot") return class TARSTagger(FewshotClassifier): """ TARS model for sequence tagging. In the backend, the model uses a BERT based 5-class sequence labeler which given a <label, text> pair predicts the probability for each word to belong to one of the BIOES classes. The input data is a usual Sentence object which is inflated by the model internally before pushing it through the transformer stack of BERT. """ static_label_type = "tars_label" def __init__( self, task_name: Optional[str] = None, label_dictionary: Optional[Dictionary] = None, label_type: Optional[str] = None, embeddings: Union[TransformerWordEmbeddings, str] = "bert-base-uncased", num_negative_labels_to_sample: int = 2, prefix: bool = True, **tagger_args, ): """ Initializes a TextClassifier :param task_name: a string depicting the name of the task :param label_dictionary: dictionary of labels you want to predict :param embeddings: name of the pre-trained transformer model e.g., 'bert-base-uncased' etc :param num_negative_labels_to_sample: number of negative labels to sample for each positive labels against a sentence during training. Defaults to 2 negative labels for each positive label. The model would sample all the negative labels if None is passed. That slows down the training considerably. """ super(TARSTagger, self).__init__() if isinstance(embeddings, str): embeddings = TransformerWordEmbeddings( model=embeddings, fine_tune=True, layers="-1", layer_mean=False, ) # prepare TARS dictionary tars_dictionary = Dictionary(add_unk=False) tars_dictionary.add_item("entity") tars_dictionary.span_labels = True # initialize a bare-bones sequence tagger self.tars_model: SequenceTagger = SequenceTagger( hidden_size=123, embeddings=embeddings, tag_dictionary=tars_dictionary, tag_type=self.static_label_type, use_crf=False, use_rnn=False, reproject_embeddings=False, **tagger_args, ) # transformer separator self.separator = str(self.tars_embeddings.tokenizer.sep_token) if self.tars_embeddings.tokenizer._bos_token: self.separator += str(self.tars_embeddings.tokenizer.bos_token) self.prefix = prefix self.num_negative_labels_to_sample = num_negative_labels_to_sample if task_name and label_dictionary and label_type: # Store task specific labels since TARS can handle multiple tasks self.add_and_switch_to_new_task(task_name, label_dictionary, label_type) else: log.info( "TARS initialized without a task. You need to call .add_and_switch_to_new_task() " "before training this model" ) def _get_tars_formatted_sentence(self, label, sentence): original_text = sentence.to_tokenized_string() label_text_pair = ( f"{label} {self.separator} {original_text}" if self.prefix else f"{original_text} {self.separator} {label}" ) label_length = 0 if not self.prefix else len(label.split(" ")) + len(self.separator.split(" ")) # make a tars sentence where all labels are O by default tars_sentence = Sentence(label_text_pair, use_tokenizer=False) for entity_label in sentence.get_labels(self.label_type): if entity_label.value == label: new_span = [tars_sentence.get_token(token.idx + label_length) for token in entity_label.span] tars_sentence.add_complex_label(self.static_label_type, SpanLabel(Span(new_span), value="entity")) return tars_sentence def _get_state_dict(self): model_state = { "state_dict": self.state_dict(), "current_task": self._current_task, "tag_type": self.get_current_label_type(), "tag_dictionary": self.get_current_label_dictionary(), "tars_model": self.tars_model, "num_negative_labels_to_sample": self.num_negative_labels_to_sample, "prefix": self.prefix, "task_specific_attributes": self._task_specific_attributes, } return model_state @staticmethod def _fetch_model(model_name) -> str: if model_name == "tars-ner": cache_dir = Path("models") model_name = cached_path( "https://nlp.informatik.hu-berlin.de/resources/models/tars-ner/tars-ner.pt", cache_dir=cache_dir, ) return model_name @staticmethod def _init_model_with_state_dict(state): # init new TARS classifier model = TARSTagger( task_name=state["current_task"], label_dictionary=state["tag_dictionary"], label_type=state["tag_type"], embeddings=state["tars_model"].embeddings, num_negative_labels_to_sample=state["num_negative_labels_to_sample"], prefix=state["prefix"], ) # set all task information model._task_specific_attributes = state["task_specific_attributes"] # linear layers of internal classifier model.load_state_dict(state["state_dict"]) return model @property def tars_embeddings(self): return self.tars_model.embeddings def predict( self, sentences: Union[List[Sentence], Sentence], mini_batch_size=32, return_probabilities_for_all_classes: bool = False, verbose: bool = False, label_name: Optional[str] = None, return_loss=False, embedding_storage_mode="none", most_probable_first: bool = True, ): # return """ Predict sequence tags for Named Entity Recognition task :param sentences: a Sentence or a List of Sentence :param mini_batch_size: size of the minibatch, usually bigger is more rapid but consume more memory, up to a point when it has no more effect. :param all_tag_prob: True to compute the score for each tag on each token, otherwise only the score of the best tag is returned :param verbose: set to True to display a progress bar :param return_loss: set to True to return loss :param label_name: set this to change the name of the label type that is predicted :param embedding_storage_mode: default is 'none' which is always best. Only set to 'cpu' or 'gpu' if you wish to not only predict, but also keep the generated embeddings in CPU or GPU memory respectively. 'gpu' to store embeddings in GPU memory. """ if label_name is None: label_name = self.get_current_label_type() # with torch.no_grad(): if not sentences: return sentences if not isinstance(sentences, list): sentences = [sentences] reordered_sentences = sorted(sentences, key=lambda s: len(s), reverse=True) dataloader = DataLoader( dataset=FlairDatapointDataset(reordered_sentences), batch_size=mini_batch_size, ) # progress bar for verbosity if verbose: dataloader = tqdm(dataloader) overall_loss = 0 overall_count = 0 with torch.no_grad(): for batch in dataloader: batch = self._filter_empty_sentences(batch) # stop if all sentences are empty if not batch: continue # go through each sentence in the batch for sentence in batch: # always remove tags first sentence.remove_labels(label_name) all_labels = [label.decode("utf-8") for label in self.get_current_label_dictionary().idx2item] all_detected = {} for label in all_labels: tars_sentence = self._get_tars_formatted_sentence(label, sentence) loss_and_count = self.tars_model.predict( tars_sentence, label_name=label_name, return_loss=True, ) overall_loss += loss_and_count[0].item() overall_count += loss_and_count[1] for predicted in tars_sentence.get_labels(label_name): predicted.value = label all_detected[predicted] = predicted.score if most_probable_first: import operator already_set_indices: List[int] = [] sorted_x = sorted(all_detected.items(), key=operator.itemgetter(1)) sorted_x.reverse() for tuple in sorted_x: # get the span and its label label = tuple[0] # label = span.get_labels("tars_temp_label")[0].value label_length = ( 0 if not self.prefix else len(label.value.split(" ")) + len(self.separator.split(" ")) ) # determine whether tokens in this span already have a label tag_this = True for token in label.span: corresponding_token = sentence.get_token(token.idx - label_length) if corresponding_token is None: tag_this = False continue if token.idx in already_set_indices: tag_this = False continue # only add if all tokens have no label if tag_this: already_set_indices.extend(token.idx for token in label.span) predicted_span = [sentence.get_token(token.idx - label_length) for token in label.span] sentence.add_complex_label( label_name, label=SpanLabel(Span(predicted_span), value=label.value, score=label.score), ) # clearing token embeddings to save memory store_embeddings(batch, storage_mode=embedding_storage_mode) if return_loss: return overall_loss, overall_count class TARSClassifier(FewshotClassifier): """ TARS model for text classification. In the backend, the model uses a BERT based binary text classifier which given a <label, text> pair predicts the probability of two classes "True", and "False". The input data is a usual Sentence object which is inflated by the model internally before pushing it through the transformer stack of BERT. """ static_label_type = "tars_label" LABEL_MATCH = "YES" LABEL_NO_MATCH = "NO" def __init__( self, task_name: Optional[str] = None, label_dictionary: Optional[Dictionary] = None, label_type: Optional[str] = None, embeddings: Union[TransformerDocumentEmbeddings, str] = "bert-base-uncased", num_negative_labels_to_sample: int = 2, prefix: bool = True, **tagger_args, ): """ Initializes a TextClassifier :param task_name: a string depicting the name of the task :param label_dictionary: dictionary of labels you want to predict :param embeddings: name of the pre-trained transformer model e.g., 'bert-base-uncased' etc :param num_negative_labels_to_sample: number of negative labels to sample for each positive labels against a sentence during training. Defaults to 2 negative labels for each positive label. The model would sample all the negative labels if None is passed. That slows down the training considerably. :param multi_label: auto-detected by default, but you can set this to True to force multi-label predictionor False to force single-label prediction :param multi_label_threshold: If multi-label you can set the threshold to make predictions :param beta: Parameter for F-beta score for evaluation and training annealing """ super(TARSClassifier, self).__init__() if isinstance(embeddings, str): embeddings = TransformerDocumentEmbeddings( model=embeddings, fine_tune=True, layers="-1", layer_mean=False, ) # prepare TARS dictionary tars_dictionary = Dictionary(add_unk=False) tars_dictionary.add_item(self.LABEL_NO_MATCH) tars_dictionary.add_item(self.LABEL_MATCH) # initialize a bare-bones sequence tagger self.tars_model = TextClassifier( document_embeddings=embeddings, label_dictionary=tars_dictionary, label_type=self.static_label_type, **tagger_args, ) # transformer separator self.separator = str(self.tars_embeddings.tokenizer.sep_token) if self.tars_embeddings.tokenizer._bos_token: self.separator += str(self.tars_embeddings.tokenizer.bos_token) self.prefix = prefix self.num_negative_labels_to_sample = num_negative_labels_to_sample if task_name and label_dictionary and label_type: # Store task specific labels since TARS can handle multiple tasks self.add_and_switch_to_new_task(task_name, label_dictionary, label_type) else: log.info( "TARS initialized without a task. You need to call .add_and_switch_to_new_task() " "before training this model" ) self.clean_up_labels = True def _clean(self, label_value: str) -> str: if self.clean_up_labels: return label_value.replace("_", " ") else: return label_value def _get_tars_formatted_sentence(self, label, sentence): label = self._clean(label) original_text = sentence.to_tokenized_string() label_text_pair = ( f"{label} {self.separator} {original_text}" if self.prefix else f"{original_text} {self.separator} {label}" ) sentence_labels = [self._clean(label.value) for label in sentence.get_labels(self.get_current_label_type())] tars_label = self.LABEL_MATCH if label in sentence_labels else self.LABEL_NO_MATCH tars_sentence = Sentence(label_text_pair, use_tokenizer=False).add_label(self.static_label_type, tars_label) return tars_sentence def _get_state_dict(self): model_state = { "state_dict": self.state_dict(), "current_task": self._current_task, "label_type": self.get_current_label_type(), "label_dictionary": self.get_current_label_dictionary(), "tars_model": self.tars_model, "num_negative_labels_to_sample": self.num_negative_labels_to_sample, "task_specific_attributes": self._task_specific_attributes, } return model_state @staticmethod def _init_model_with_state_dict(state): # init new TARS classifier label_dictionary = state["label_dictionary"] label_type = "default_label" if not state["label_type"] else state["label_type"] model: TARSClassifier = TARSClassifier( task_name=state["current_task"], label_dictionary=label_dictionary, label_type=label_type, embeddings=state["tars_model"].document_embeddings, num_negative_labels_to_sample=state["num_negative_labels_to_sample"], ) # set all task information model._task_specific_attributes = state["task_specific_attributes"] # linear layers of internal classifier model.load_state_dict(state["state_dict"]) return model @staticmethod def _fetch_model(model_name) -> str: model_map = {} hu_path: str = "https://nlp.informatik.hu-berlin.de/resources/models" model_map["tars-base"] = "/".join([hu_path, "tars-base", "tars-base-v8.pt"]) cache_dir = Path("models") if model_name in model_map: model_name = cached_path(model_map[model_name], cache_dir=cache_dir) return model_name @property def tars_embeddings(self): return self.tars_model.document_embeddings def predict( self, sentences: Union[List[Sentence], Sentence], mini_batch_size=32, return_probabilities_for_all_classes: bool = False, verbose: bool = False, label_name: Optional[str] = None, return_loss=False, embedding_storage_mode="none", label_threshold: float = 0.5, multi_label: Optional[bool] = None, ): """ Predict sequence tags for Named Entity Recognition task :param sentences: a Sentence or a List of Sentence :param mini_batch_size: size of the minibatch, usually bigger is more rapid but consume more memory, up to a point when it has no more effect. :param all_tag_prob: True to compute the score for each tag on each token, otherwise only the score of the best tag is returned :param verbose: set to True to display a progress bar :param return_loss: set to True to return loss :param label_name: set this to change the name of the label type that is predicted :param embedding_storage_mode: default is 'none' which is always best. Only set to 'cpu' or 'gpu' if you wish to not only predict, but also keep the generated embeddings in CPU or GPU memory respectively. 'gpu' to store embeddings in GPU memory. """ if label_name is None: label_name = self.get_current_label_type() if multi_label is None: multi_label = self.is_current_task_multi_label() # with torch.no_grad(): if not sentences: return sentences if isinstance(sentences, Sentence): sentences = [sentences] # set context if not set already previous_sentence = None for sentence in sentences: if sentence.is_context_set(): continue sentence._previous_sentence = previous_sentence sentence._next_sentence = None if previous_sentence: previous_sentence._next_sentence = sentence previous_sentence = sentence reordered_sentences = sorted(sentences, key=lambda s: len(s), reverse=True) dataloader = DataLoader( dataset=FlairDatapointDataset(reordered_sentences), batch_size=mini_batch_size, ) # progress bar for verbosity if verbose: progressbar = tqdm(dataloader) progressbar.set_description("Batch inference") dataloader = progressbar overall_loss = 0 overall_count = 0 batch_no = 0 with torch.no_grad(): for batch in dataloader: batch_no += 1 batch = self._filter_empty_sentences(batch) # stop if all sentences are empty if not batch: continue # go through each sentence in the batch for sentence in batch: # always remove tags first sentence.remove_labels(label_name) all_labels = [label.decode("utf-8") for label in self.get_current_label_dictionary().idx2item] best_label = None for label in all_labels: tars_sentence = self._get_tars_formatted_sentence(label, sentence) loss_and_count = self.tars_model.predict( tars_sentence, label_name=label_name, return_loss=True, return_probabilities_for_all_classes=True if label_threshold < 0.5 else False, ) overall_loss += loss_and_count[0].item() overall_count += loss_and_count[1] # add all labels that according to TARS match the text and are above threshold for predicted_tars_label in tars_sentence.get_labels(label_name): if ( predicted_tars_label.value == self.LABEL_MATCH and predicted_tars_label.score > label_threshold ): # do not add labels below confidence threshold sentence.add_label(label_name, label, predicted_tars_label.score) # only use label with highest confidence if enforcing single-label predictions if not multi_label: if len(sentence.get_labels(label_name)) > 0: # get all label scores and do an argmax to get the best label label_scores = torch.tensor( [label.score for label in sentence.get_labels(label_name)], dtype=torch.float, ) best_label = sentence.get_labels(label_name)[torch.argmax(label_scores)] # remove previously added labels and only add the best label sentence.remove_labels(label_name) sentence.add_label( typename=label_name, value=best_label.value, score=best_label.score, ) # clearing token embeddings to save memory store_embeddings(batch, storage_mode=embedding_storage_mode) if return_loss: return overall_loss, overall_count

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import logging from collections import OrderedDict from pathlib import Path from typing import List, Optional, Set, Tuple, Union import numpy as np import torch from sklearn.metrics.pairwise import cosine_similarity from sklearn.preprocessing import minmax_scale from tqdm import tqdm import flair from flair.data import Dictionary, Sentence, Span, SpanLabel from flair.datasets import DataLoader, FlairDatapointDataset from flair.embeddings import ( TokenEmbeddings, TransformerDocumentEmbeddings, TransformerWordEmbeddings, ) from flair.file_utils import cached_path from flair.models.sequence_tagger_model import SequenceTagger from flair.models.text_classification_model import TextClassifier from flair.training_utils import store_embeddings log = logging.getLogger("flair") class FewshotClassifier(flair.nn.Classifier[Sentence]): def __init__(self): self._current_task = None self._task_specific_attributes = {} self.label_nearest_map = None self.tars_model: flair.nn.Classifier[Sentence] super(FewshotClassifier, self).__init__() def forward_loss( self, data_points: Union[List[Sentence], Sentence] ) -> Union[torch.Tensor, Tuple[torch.Tensor, int]]: if not isinstance(data_points, list): data_points = [data_points] sentences = self._get_tars_formatted_sentences(data_points) loss = self.tars_model.forward_loss(sentences) return loss @property def tars_embeddings(self): raise NotImplementedError def _get_tars_formatted_sentence(self, label, sentence): raise NotImplementedError def _get_tars_formatted_sentences(self, sentences: List[Sentence]): label_text_pairs = [] all_labels = [label.decode("utf-8") for label in self.get_current_label_dictionary().idx2item] for sentence in sentences: label_text_pairs_for_sentence = [] if self.training and self.num_negative_labels_to_sample is not None: positive_labels = list( OrderedDict.fromkeys([label.value for label in sentence.get_labels(self.label_type)]) ) sampled_negative_labels = self._get_nearest_labels_for(positive_labels) for label in positive_labels: label_text_pairs_for_sentence.append(self._get_tars_formatted_sentence(label, sentence)) for label in sampled_negative_labels: label_text_pairs_for_sentence.append(self._get_tars_formatted_sentence(label, sentence)) else: for label in all_labels: label_text_pairs_for_sentence.append(self._get_tars_formatted_sentence(label, sentence)) label_text_pairs.extend(label_text_pairs_for_sentence) return label_text_pairs def _get_nearest_labels_for(self, labels): if len(labels) == 0: tags = self.get_current_label_dictionary().get_items() import random sample = random.sample(tags, k=self.num_negative_labels_to_sample) return sample already_sampled_negative_labels = set() for label in labels: plausible_labels = [] plausible_label_probabilities = [] for plausible_label in self.label_nearest_map[label]: if plausible_label in already_sampled_negative_labels or plausible_label in labels: continue else: plausible_labels.append(plausible_label) plausible_label_probabilities.append(self.label_nearest_map[label][plausible_label]) plausible_label_probabilities = np.array(plausible_label_probabilities, dtype="float64") plausible_label_probabilities += 1e-08 plausible_label_probabilities /= np.sum(plausible_label_probabilities) if len(plausible_labels) > 0: num_samples = min(self.num_negative_labels_to_sample, len(plausible_labels)) sampled_negative_labels = np.random.choice( plausible_labels, num_samples, replace=False, p=plausible_label_probabilities, ) already_sampled_negative_labels.update(sampled_negative_labels) return already_sampled_negative_labels def train(self, mode=True): if mode and self.num_negative_labels_to_sample is not None: self._compute_label_similarity_for_current_epoch() super().train(mode) super().train(mode) def _compute_label_similarity_for_current_epoch(self): all_labels = [label.decode("utf-8") for label in self.get_current_label_dictionary().idx2item] label_sentences = [Sentence(label) for label in all_labels] self.tars_embeddings.eval() self.tars_embeddings.embed(label_sentences) self.tars_embeddings.train() if isinstance(self.tars_embeddings, TokenEmbeddings): encodings_np = [sentence[0].get_embedding().cpu().detach().numpy() for sentence in label_sentences] else: encodings_np = [sentence.get_embedding().cpu().detach().numpy() for sentence in label_sentences] normalized_encoding = minmax_scale(encodings_np) similarity_matrix = cosine_similarity(normalized_encoding) negative_label_probabilities = {} for row_index, label in enumerate(all_labels): negative_label_probabilities[label] = {} for column_index, other_label in enumerate(all_labels): if label != other_label: negative_label_probabilities[label][other_label] = similarity_matrix[row_index][column_index] self.label_nearest_map = negative_label_probabilities def get_current_label_dictionary(self): label_dictionary = self._task_specific_attributes[self._current_task]["label_dictionary"] return label_dictionary def get_current_label_type(self): return self._task_specific_attributes[self._current_task]["label_type"] def is_current_task_multi_label(self): return self._task_specific_attributes[self._current_task]["multi_label"] def add_and_switch_to_new_task( self, task_name, label_dictionary: Union[List, Set, Dictionary, str], label_type: str, multi_label: bool = True, force_switch: bool = False, ): if task_name in self._task_specific_attributes and not force_switch: log.warning("Task `%s` already exists in TARS model. Switching to it.", task_name) else: if isinstance(label_dictionary, Dictionary): label_dictionary = label_dictionary.get_items() if type(label_dictionary) == str: label_dictionary = [label_dictionary] tag_dictionary = Dictionary(add_unk=False) for tag in label_dictionary: if tag == "<unk>" or tag == "O": continue if tag[1] == "-": tag = tag[2:] tag_dictionary.add_item(tag) else: tag_dictionary.add_item(tag) self._task_specific_attributes[task_name] = { "label_dictionary": tag_dictionary, "label_type": label_type, "multi_label": multi_label, } self.switch_to_task(task_name) def list_existing_tasks(self) -> Set[str]: return set(self._task_specific_attributes.keys()) def switch_to_task(self, task_name): if task_name not in self._task_specific_attributes: log.error( "Provided `%s` does not exist in the model. Consider calling " "`add_and_switch_to_new_task` first.", task_name, ) else: self._current_task = task_name def _drop_task(self, task_name): if task_name in self._task_specific_attributes: if self._current_task == task_name: log.error( "`%s` is the current task." " Switch to some other task before dropping this.", task_name, ) else: self._task_specific_attributes.pop(task_name) else: log.warning("No task exists with the name `%s`.", task_name) @staticmethod def _filter_empty_sentences(sentences: List[Sentence]) -> List[Sentence]: filtered_sentences = [sentence for sentence in sentences if sentence.tokens] if len(sentences) != len(filtered_sentences): log.warning(f"Ignore {len(sentences) - len(filtered_sentences)} sentence(s) with no tokens.") return filtered_sentences @property def label_type(self): return self.get_current_label_type() def predict_zero_shot( self, sentences: Union[List[Sentence], Sentence], candidate_label_set: Union[List[str], Set[str], str], multi_label: bool = True, ): if candidate_label_set is None or len(candidate_label_set) == 0: log.warning("Provided candidate_label_set is empty") return if isinstance(candidate_label_set, str): candidate_label_set = {candidate_label_set} label_dictionary = Dictionary(add_unk=False) for label in candidate_label_set: label_dictionary.add_item(label) existing_current_task = self._current_task self.add_and_switch_to_new_task( task_name="ZeroShot", label_dictionary=label_dictionary, label_type="-".join(label_dictionary.get_items()), multi_label=multi_label, ) try: self.predict(sentences) finally: self.switch_to_task(existing_current_task) self._drop_task("ZeroShot") return class TARSTagger(FewshotClassifier): static_label_type = "tars_label" def __init__( self, task_name: Optional[str] = None, label_dictionary: Optional[Dictionary] = None, label_type: Optional[str] = None, embeddings: Union[TransformerWordEmbeddings, str] = "bert-base-uncased", num_negative_labels_to_sample: int = 2, prefix: bool = True, **tagger_args, ): super(TARSTagger, self).__init__() if isinstance(embeddings, str): embeddings = TransformerWordEmbeddings( model=embeddings, fine_tune=True, layers="-1", layer_mean=False, ) tars_dictionary = Dictionary(add_unk=False) tars_dictionary.add_item("entity") tars_dictionary.span_labels = True self.tars_model: SequenceTagger = SequenceTagger( hidden_size=123, embeddings=embeddings, tag_dictionary=tars_dictionary, tag_type=self.static_label_type, use_crf=False, use_rnn=False, reproject_embeddings=False, **tagger_args, ) self.separator = str(self.tars_embeddings.tokenizer.sep_token) if self.tars_embeddings.tokenizer._bos_token: self.separator += str(self.tars_embeddings.tokenizer.bos_token) self.prefix = prefix self.num_negative_labels_to_sample = num_negative_labels_to_sample if task_name and label_dictionary and label_type: self.add_and_switch_to_new_task(task_name, label_dictionary, label_type) else: log.info( "TARS initialized without a task. You need to call .add_and_switch_to_new_task() " "before training this model" ) def _get_tars_formatted_sentence(self, label, sentence): original_text = sentence.to_tokenized_string() label_text_pair = ( f"{label} {self.separator} {original_text}" if self.prefix else f"{original_text} {self.separator} {label}" ) label_length = 0 if not self.prefix else len(label.split(" ")) + len(self.separator.split(" ")) tars_sentence = Sentence(label_text_pair, use_tokenizer=False) for entity_label in sentence.get_labels(self.label_type): if entity_label.value == label: new_span = [tars_sentence.get_token(token.idx + label_length) for token in entity_label.span] tars_sentence.add_complex_label(self.static_label_type, SpanLabel(Span(new_span), value="entity")) return tars_sentence def _get_state_dict(self): model_state = { "state_dict": self.state_dict(), "current_task": self._current_task, "tag_type": self.get_current_label_type(), "tag_dictionary": self.get_current_label_dictionary(), "tars_model": self.tars_model, "num_negative_labels_to_sample": self.num_negative_labels_to_sample, "prefix": self.prefix, "task_specific_attributes": self._task_specific_attributes, } return model_state @staticmethod def _fetch_model(model_name) -> str: if model_name == "tars-ner": cache_dir = Path("models") model_name = cached_path( "https://nlp.informatik.hu-berlin.de/resources/models/tars-ner/tars-ner.pt", cache_dir=cache_dir, ) return model_name @staticmethod def _init_model_with_state_dict(state): model = TARSTagger( task_name=state["current_task"], label_dictionary=state["tag_dictionary"], label_type=state["tag_type"], embeddings=state["tars_model"].embeddings, num_negative_labels_to_sample=state["num_negative_labels_to_sample"], prefix=state["prefix"], ) model._task_specific_attributes = state["task_specific_attributes"] model.load_state_dict(state["state_dict"]) return model @property def tars_embeddings(self): return self.tars_model.embeddings def predict( self, sentences: Union[List[Sentence], Sentence], mini_batch_size=32, return_probabilities_for_all_classes: bool = False, verbose: bool = False, label_name: Optional[str] = None, return_loss=False, embedding_storage_mode="none", most_probable_first: bool = True, ): if label_name is None: label_name = self.get_current_label_type() if not sentences: return sentences if not isinstance(sentences, list): sentences = [sentences] reordered_sentences = sorted(sentences, key=lambda s: len(s), reverse=True) dataloader = DataLoader( dataset=FlairDatapointDataset(reordered_sentences), batch_size=mini_batch_size, ) if verbose: dataloader = tqdm(dataloader) overall_loss = 0 overall_count = 0 with torch.no_grad(): for batch in dataloader: batch = self._filter_empty_sentences(batch) if not batch: continue for sentence in batch: sentence.remove_labels(label_name) all_labels = [label.decode("utf-8") for label in self.get_current_label_dictionary().idx2item] all_detected = {} for label in all_labels: tars_sentence = self._get_tars_formatted_sentence(label, sentence) loss_and_count = self.tars_model.predict( tars_sentence, label_name=label_name, return_loss=True, ) overall_loss += loss_and_count[0].item() overall_count += loss_and_count[1] for predicted in tars_sentence.get_labels(label_name): predicted.value = label all_detected[predicted] = predicted.score if most_probable_first: import operator already_set_indices: List[int] = [] sorted_x = sorted(all_detected.items(), key=operator.itemgetter(1)) sorted_x.reverse() for tuple in sorted_x: label = tuple[0] label_length = ( 0 if not self.prefix else len(label.value.split(" ")) + len(self.separator.split(" ")) ) tag_this = True for token in label.span: corresponding_token = sentence.get_token(token.idx - label_length) if corresponding_token is None: tag_this = False continue if token.idx in already_set_indices: tag_this = False continue if tag_this: already_set_indices.extend(token.idx for token in label.span) predicted_span = [sentence.get_token(token.idx - label_length) for token in label.span] sentence.add_complex_label( label_name, label=SpanLabel(Span(predicted_span), value=label.value, score=label.score), ) store_embeddings(batch, storage_mode=embedding_storage_mode) if return_loss: return overall_loss, overall_count class TARSClassifier(FewshotClassifier): static_label_type = "tars_label" LABEL_MATCH = "YES" LABEL_NO_MATCH = "NO" def __init__( self, task_name: Optional[str] = None, label_dictionary: Optional[Dictionary] = None, label_type: Optional[str] = None, embeddings: Union[TransformerDocumentEmbeddings, str] = "bert-base-uncased", num_negative_labels_to_sample: int = 2, prefix: bool = True, **tagger_args, ): super(TARSClassifier, self).__init__() if isinstance(embeddings, str): embeddings = TransformerDocumentEmbeddings( model=embeddings, fine_tune=True, layers="-1", layer_mean=False, ) tars_dictionary = Dictionary(add_unk=False) tars_dictionary.add_item(self.LABEL_NO_MATCH) tars_dictionary.add_item(self.LABEL_MATCH) self.tars_model = TextClassifier( document_embeddings=embeddings, label_dictionary=tars_dictionary, label_type=self.static_label_type, **tagger_args, ) self.separator = str(self.tars_embeddings.tokenizer.sep_token) if self.tars_embeddings.tokenizer._bos_token: self.separator += str(self.tars_embeddings.tokenizer.bos_token) self.prefix = prefix self.num_negative_labels_to_sample = num_negative_labels_to_sample if task_name and label_dictionary and label_type: self.add_and_switch_to_new_task(task_name, label_dictionary, label_type) else: log.info( "TARS initialized without a task. You need to call .add_and_switch_to_new_task() " "before training this model" ) self.clean_up_labels = True def _clean(self, label_value: str) -> str: if self.clean_up_labels: return label_value.replace("_", " ") else: return label_value def _get_tars_formatted_sentence(self, label, sentence): label = self._clean(label) original_text = sentence.to_tokenized_string() label_text_pair = ( f"{label} {self.separator} {original_text}" if self.prefix else f"{original_text} {self.separator} {label}" ) sentence_labels = [self._clean(label.value) for label in sentence.get_labels(self.get_current_label_type())] tars_label = self.LABEL_MATCH if label in sentence_labels else self.LABEL_NO_MATCH tars_sentence = Sentence(label_text_pair, use_tokenizer=False).add_label(self.static_label_type, tars_label) return tars_sentence def _get_state_dict(self): model_state = { "state_dict": self.state_dict(), "current_task": self._current_task, "label_type": self.get_current_label_type(), "label_dictionary": self.get_current_label_dictionary(), "tars_model": self.tars_model, "num_negative_labels_to_sample": self.num_negative_labels_to_sample, "task_specific_attributes": self._task_specific_attributes, } return model_state @staticmethod def _init_model_with_state_dict(state): label_dictionary = state["label_dictionary"] label_type = "default_label" if not state["label_type"] else state["label_type"] model: TARSClassifier = TARSClassifier( task_name=state["current_task"], label_dictionary=label_dictionary, label_type=label_type, embeddings=state["tars_model"].document_embeddings, num_negative_labels_to_sample=state["num_negative_labels_to_sample"], ) model._task_specific_attributes = state["task_specific_attributes"] model.load_state_dict(state["state_dict"]) return model @staticmethod def _fetch_model(model_name) -> str: model_map = {} hu_path: str = "https://nlp.informatik.hu-berlin.de/resources/models" model_map["tars-base"] = "/".join([hu_path, "tars-base", "tars-base-v8.pt"]) cache_dir = Path("models") if model_name in model_map: model_name = cached_path(model_map[model_name], cache_dir=cache_dir) return model_name @property def tars_embeddings(self): return self.tars_model.document_embeddings def predict( self, sentences: Union[List[Sentence], Sentence], mini_batch_size=32, return_probabilities_for_all_classes: bool = False, verbose: bool = False, label_name: Optional[str] = None, return_loss=False, embedding_storage_mode="none", label_threshold: float = 0.5, multi_label: Optional[bool] = None, ): if label_name is None: label_name = self.get_current_label_type() if multi_label is None: multi_label = self.is_current_task_multi_label() if not sentences: return sentences if isinstance(sentences, Sentence): sentences = [sentences] previous_sentence = None for sentence in sentences: if sentence.is_context_set(): continue sentence._previous_sentence = previous_sentence sentence._next_sentence = None if previous_sentence: previous_sentence._next_sentence = sentence previous_sentence = sentence reordered_sentences = sorted(sentences, key=lambda s: len(s), reverse=True) dataloader = DataLoader( dataset=FlairDatapointDataset(reordered_sentences), batch_size=mini_batch_size, ) if verbose: progressbar = tqdm(dataloader) progressbar.set_description("Batch inference") dataloader = progressbar overall_loss = 0 overall_count = 0 batch_no = 0 with torch.no_grad(): for batch in dataloader: batch_no += 1 batch = self._filter_empty_sentences(batch) if not batch: continue for sentence in batch: sentence.remove_labels(label_name) all_labels = [label.decode("utf-8") for label in self.get_current_label_dictionary().idx2item] best_label = None for label in all_labels: tars_sentence = self._get_tars_formatted_sentence(label, sentence) loss_and_count = self.tars_model.predict( tars_sentence, label_name=label_name, return_loss=True, return_probabilities_for_all_classes=True if label_threshold < 0.5 else False, ) overall_loss += loss_and_count[0].item() overall_count += loss_and_count[1] for predicted_tars_label in tars_sentence.get_labels(label_name): if ( predicted_tars_label.value == self.LABEL_MATCH and predicted_tars_label.score > label_threshold ): sentence.add_label(label_name, label, predicted_tars_label.score) if not multi_label: if len(sentence.get_labels(label_name)) > 0: label_scores = torch.tensor( [label.score for label in sentence.get_labels(label_name)], dtype=torch.float, ) best_label = sentence.get_labels(label_name)[torch.argmax(label_scores)] sentence.remove_labels(label_name) sentence.add_label( typename=label_name, value=best_label.value, score=best_label.score, ) store_embeddings(batch, storage_mode=embedding_storage_mode) if return_loss: return overall_loss, overall_count

true

790bddf374f1b0f87510fa0b5072d6a169218040

508

py

Python

satchmo/apps/payment/modules/cod/urls.py

pyarun/satchmo

78fc9a923aada312924c1476e4653ee6527c11ef

[ "BSD-3-Clause" ]

16

2015-03-06T14:42:27.000Z

2019-12-23T21:37:01.000Z

satchmo/apps/payment/modules/cod/urls.py

pyarun/satchmo

78fc9a923aada312924c1476e4653ee6527c11ef

[ "BSD-3-Clause" ]

null

satchmo/apps/payment/modules/cod/urls.py

pyarun/satchmo

78fc9a923aada312924c1476e4653ee6527c11ef

[ "BSD-3-Clause" ]

8

2015-01-28T16:02:37.000Z

2022-03-03T21:29:40.000Z

from django.conf.urls.defaults import patterns from satchmo_store.shop.satchmo_settings import get_satchmo_setting ssl = get_satchmo_setting('SSL', default_value=False) urlpatterns = patterns('', (r'^$', 'payment.modules.cod.views.pay_ship_info', {'SSL':ssl}, 'COD_satchmo_checkout-step2'), (r'^confirm/$', 'payment.modules.cod.views.confirm_info', {'SSL':ssl}, 'COD_satchmo_checkout-step3'), (r'^success/$', 'payment.views.checkout.success', {'SSL':ssl}, 'COD_satchmo_checkout-success'), )

46.181818

106

0.732283

from django.conf.urls.defaults import patterns from satchmo_store.shop.satchmo_settings import get_satchmo_setting ssl = get_satchmo_setting('SSL', default_value=False) urlpatterns = patterns('', (r'^$', 'payment.modules.cod.views.pay_ship_info', {'SSL':ssl}, 'COD_satchmo_checkout-step2'), (r'^confirm/$', 'payment.modules.cod.views.confirm_info', {'SSL':ssl}, 'COD_satchmo_checkout-step3'), (r'^success/$', 'payment.views.checkout.success', {'SSL':ssl}, 'COD_satchmo_checkout-success'), )

true

790bde4c8f915dec0c95f3c3f81e6ec9e79321d8

25,965

py

Python

python/Lib/test/test_compileall.py

jasam/ciclo_vida_datos_scraping

3f7cffc944a0a0752a502dc7868cf43c4144f16c

[ "MIT" ]

null

python/Lib/test/test_compileall.py

jasam/ciclo_vida_datos_scraping

3f7cffc944a0a0752a502dc7868cf43c4144f16c

[ "MIT" ]

null

python/Lib/test/test_compileall.py

jasam/ciclo_vida_datos_scraping

3f7cffc944a0a0752a502dc7868cf43c4144f16c

[ "MIT" ]

null

import sys import compileall import importlib.util import test.test_importlib.util import os import pathlib import py_compile import shutil import struct import tempfile import time import unittest import io from unittest import mock, skipUnless try: from concurrent.futures import ProcessPoolExecutor _have_multiprocessing = True except ImportError: _have_multiprocessing = False from test import support from test.support import script_helper from .test_py_compile import without_source_date_epoch from .test_py_compile import SourceDateEpochTestMeta class CompileallTestsBase: def setUp(self): self.directory = tempfile.mkdtemp() self.source_path = os.path.join(self.directory, '_test.py') self.bc_path = importlib.util.cache_from_source(self.source_path) with open(self.source_path, 'w') as file: file.write('x = 123\n') self.source_path2 = os.path.join(self.directory, '_test2.py') self.bc_path2 = importlib.util.cache_from_source(self.source_path2) shutil.copyfile(self.source_path, self.source_path2) self.subdirectory = os.path.join(self.directory, '_subdir') os.mkdir(self.subdirectory) self.source_path3 = os.path.join(self.subdirectory, '_test3.py') shutil.copyfile(self.source_path, self.source_path3) def tearDown(self): shutil.rmtree(self.directory) def add_bad_source_file(self): self.bad_source_path = os.path.join(self.directory, '_test_bad.py') with open(self.bad_source_path, 'w') as file: file.write('x (\n') def timestamp_metadata(self): with open(self.bc_path, 'rb') as file: data = file.read(12) mtime = int(os.stat(self.source_path).st_mtime) compare = struct.pack('<4sll', importlib.util.MAGIC_NUMBER, 0, mtime) return data, compare def recreation_check(self, metadata): """Check that compileall recreates bytecode when the new metadata is used.""" if os.environ.get('SOURCE_DATE_EPOCH'): raise unittest.SkipTest('SOURCE_DATE_EPOCH is set') py_compile.compile(self.source_path) self.assertEqual(*self.timestamp_metadata()) with open(self.bc_path, 'rb') as file: bc = file.read()[len(metadata):] with open(self.bc_path, 'wb') as file: file.write(metadata) file.write(bc) self.assertNotEqual(*self.timestamp_metadata()) compileall.compile_dir(self.directory, force=False, quiet=True) self.assertTrue(*self.timestamp_metadata()) def test_mtime(self): # Test a change in mtime leads to a new .pyc. self.recreation_check(struct.pack('<4sll', importlib.util.MAGIC_NUMBER, 0, 1)) def test_magic_number(self): # Test a change in mtime leads to a new .pyc. self.recreation_check(b'\0\0\0\0') def test_compile_files(self): # Test compiling a single file, and complete directory for fn in (self.bc_path, self.bc_path2): try: os.unlink(fn) except: pass self.assertTrue(compileall.compile_file(self.source_path, force=False, quiet=True)) self.assertTrue(os.path.isfile(self.bc_path) and not os.path.isfile(self.bc_path2)) os.unlink(self.bc_path) self.assertTrue(compileall.compile_dir(self.directory, force=False, quiet=True)) self.assertTrue(os.path.isfile(self.bc_path) and os.path.isfile(self.bc_path2)) os.unlink(self.bc_path) os.unlink(self.bc_path2) # Test against bad files self.add_bad_source_file() self.assertFalse(compileall.compile_file(self.bad_source_path, force=False, quiet=2)) self.assertFalse(compileall.compile_dir(self.directory, force=False, quiet=2)) def test_compile_file_pathlike(self): self.assertFalse(os.path.isfile(self.bc_path)) # we should also test the output with support.captured_stdout() as stdout: self.assertTrue(compileall.compile_file(pathlib.Path(self.source_path))) self.assertRegex(stdout.getvalue(), r'Compiling ([^WindowsPath|PosixPath].*)') self.assertTrue(os.path.isfile(self.bc_path)) def test_compile_file_pathlike_ddir(self): self.assertFalse(os.path.isfile(self.bc_path)) self.assertTrue(compileall.compile_file(pathlib.Path(self.source_path), ddir=pathlib.Path('ddir_path'), quiet=2)) self.assertTrue(os.path.isfile(self.bc_path)) def test_compile_path(self): with test.test_importlib.util.import_state(path=[self.directory]): self.assertTrue(compileall.compile_path(quiet=2)) with test.test_importlib.util.import_state(path=[self.directory]): self.add_bad_source_file() self.assertFalse(compileall.compile_path(skip_curdir=False, force=True, quiet=2)) def test_no_pycache_in_non_package(self): # Bug 8563 reported that __pycache__ directories got created by # compile_file() for non-.py files. data_dir = os.path.join(self.directory, 'data') data_file = os.path.join(data_dir, 'file') os.mkdir(data_dir) # touch data/file with open(data_file, 'w'): pass compileall.compile_file(data_file) self.assertFalse(os.path.exists(os.path.join(data_dir, '__pycache__'))) def test_optimize(self): # make sure compiling with different optimization settings than the # interpreter's creates the correct file names optimize, opt = (1, 1) if __debug__ else (0, '') compileall.compile_dir(self.directory, quiet=True, optimize=optimize) cached = importlib.util.cache_from_source(self.source_path, optimization=opt) self.assertTrue(os.path.isfile(cached)) cached2 = importlib.util.cache_from_source(self.source_path2, optimization=opt) self.assertTrue(os.path.isfile(cached2)) cached3 = importlib.util.cache_from_source(self.source_path3, optimization=opt) self.assertTrue(os.path.isfile(cached3)) def test_compile_dir_pathlike(self): self.assertFalse(os.path.isfile(self.bc_path)) with support.captured_stdout() as stdout: compileall.compile_dir(pathlib.Path(self.directory)) line = stdout.getvalue().splitlines()[0] self.assertRegex(line, r'Listing ([^WindowsPath|PosixPath].*)') self.assertTrue(os.path.isfile(self.bc_path)) @mock.patch('concurrent.futures.ProcessPoolExecutor') def test_compile_pool_called(self, pool_mock): compileall.compile_dir(self.directory, quiet=True, workers=5) self.assertTrue(pool_mock.called) def test_compile_workers_non_positive(self): with self.assertRaisesRegex(ValueError, "workers must be greater or equal to 0"): compileall.compile_dir(self.directory, workers=-1) @mock.patch('concurrent.futures.ProcessPoolExecutor') def test_compile_workers_cpu_count(self, pool_mock): compileall.compile_dir(self.directory, quiet=True, workers=0) self.assertEqual(pool_mock.call_args[1]['max_workers'], None) @mock.patch('concurrent.futures.ProcessPoolExecutor') @mock.patch('compileall.compile_file') def test_compile_one_worker(self, compile_file_mock, pool_mock): compileall.compile_dir(self.directory, quiet=True) self.assertFalse(pool_mock.called) self.assertTrue(compile_file_mock.called) @mock.patch('concurrent.futures.ProcessPoolExecutor', new=None) @mock.patch('compileall.compile_file') def test_compile_missing_multiprocessing(self, compile_file_mock): compileall.compile_dir(self.directory, quiet=True, workers=5) self.assertTrue(compile_file_mock.called) class CompileallTestsWithSourceEpoch(CompileallTestsBase, unittest.TestCase, metaclass=SourceDateEpochTestMeta, source_date_epoch=True): pass class CompileallTestsWithoutSourceEpoch(CompileallTestsBase, unittest.TestCase, metaclass=SourceDateEpochTestMeta, source_date_epoch=False): pass class EncodingTest(unittest.TestCase): """Issue 6716: compileall should escape source code when printing errors to stdout.""" def setUp(self): self.directory = tempfile.mkdtemp() self.source_path = os.path.join(self.directory, '_test.py') with open(self.source_path, 'w', encoding='utf-8') as file: file.write('# -*- coding: utf-8 -*-\n') file.write('print u"\u20ac"\n') def tearDown(self): shutil.rmtree(self.directory) def test_error(self): try: orig_stdout = sys.stdout sys.stdout = io.TextIOWrapper(io.BytesIO(),encoding='ascii') compileall.compile_dir(self.directory) finally: sys.stdout = orig_stdout class CommandLineTestsBase: """Test compileall's CLI.""" @classmethod def setUpClass(cls): for path in filter(os.path.isdir, sys.path): directory_created = False directory = pathlib.Path(path) / '__pycache__' path = directory / 'test.try' try: if not directory.is_dir(): directory.mkdir() directory_created = True with path.open('w') as file: file.write('# for test_compileall') except OSError: sys_path_writable = False break finally: support.unlink(str(path)) if directory_created: directory.rmdir() else: sys_path_writable = True cls._sys_path_writable = sys_path_writable def _skip_if_sys_path_not_writable(self): if not self._sys_path_writable: raise unittest.SkipTest('not all entries on sys.path are writable') def _get_run_args(self, args): return [*support.optim_args_from_interpreter_flags(), '-S', '-m', 'compileall', *args] def assertRunOK(self, *args, **env_vars): rc, out, err = script_helper.assert_python_ok( *self._get_run_args(args), **env_vars) self.assertEqual(b'', err) return out def assertRunNotOK(self, *args, **env_vars): rc, out, err = script_helper.assert_python_failure( *self._get_run_args(args), **env_vars) return rc, out, err def assertCompiled(self, fn): path = importlib.util.cache_from_source(fn) self.assertTrue(os.path.exists(path)) def assertNotCompiled(self, fn): path = importlib.util.cache_from_source(fn) self.assertFalse(os.path.exists(path)) def setUp(self): self.directory = tempfile.mkdtemp() self.addCleanup(support.rmtree, self.directory) self.pkgdir = os.path.join(self.directory, 'foo') os.mkdir(self.pkgdir) self.pkgdir_cachedir = os.path.join(self.pkgdir, '__pycache__') # Create the __init__.py and a package module. self.initfn = script_helper.make_script(self.pkgdir, '__init__', '') self.barfn = script_helper.make_script(self.pkgdir, 'bar', '') def test_no_args_compiles_path(self): # Note that -l is implied for the no args case. self._skip_if_sys_path_not_writable() bazfn = script_helper.make_script(self.directory, 'baz', '') self.assertRunOK(PYTHONPATH=self.directory) self.assertCompiled(bazfn) self.assertNotCompiled(self.initfn) self.assertNotCompiled(self.barfn) @without_source_date_epoch # timestamp invalidation test def test_no_args_respects_force_flag(self): self._skip_if_sys_path_not_writable() bazfn = script_helper.make_script(self.directory, 'baz', '') self.assertRunOK(PYTHONPATH=self.directory) pycpath = importlib.util.cache_from_source(bazfn) # Set atime/mtime backward to avoid file timestamp resolution issues os.utime(pycpath, (time.time()-60,)*2) mtime = os.stat(pycpath).st_mtime # Without force, no recompilation self.assertRunOK(PYTHONPATH=self.directory) mtime2 = os.stat(pycpath).st_mtime self.assertEqual(mtime, mtime2) # Now force it. self.assertRunOK('-f', PYTHONPATH=self.directory) mtime2 = os.stat(pycpath).st_mtime self.assertNotEqual(mtime, mtime2) def test_no_args_respects_quiet_flag(self): self._skip_if_sys_path_not_writable() script_helper.make_script(self.directory, 'baz', '') noisy = self.assertRunOK(PYTHONPATH=self.directory) self.assertIn(b'Listing ', noisy) quiet = self.assertRunOK('-q', PYTHONPATH=self.directory) self.assertNotIn(b'Listing ', quiet) # Ensure that the default behavior of compileall's CLI is to create # PEP 3147/PEP 488 pyc files. for name, ext, switch in [ ('normal', 'pyc', []), ('optimize', 'opt-1.pyc', ['-O']), ('doubleoptimize', 'opt-2.pyc', ['-OO']), ]: def f(self, ext=ext, switch=switch): script_helper.assert_python_ok(*(switch + ['-m', 'compileall', '-q', self.pkgdir])) # Verify the __pycache__ directory contents. self.assertTrue(os.path.exists(self.pkgdir_cachedir)) expected = sorted(base.format(sys.implementation.cache_tag, ext) for base in ('__init__.{}.{}', 'bar.{}.{}')) self.assertEqual(sorted(os.listdir(self.pkgdir_cachedir)), expected) # Make sure there are no .pyc files in the source directory. self.assertFalse([fn for fn in os.listdir(self.pkgdir) if fn.endswith(ext)]) locals()['test_pep3147_paths_' + name] = f def test_legacy_paths(self): # Ensure that with the proper switch, compileall leaves legacy # pyc files, and no __pycache__ directory. self.assertRunOK('-b', '-q', self.pkgdir) # Verify the __pycache__ directory contents. self.assertFalse(os.path.exists(self.pkgdir_cachedir)) expected = sorted(['__init__.py', '__init__.pyc', 'bar.py', 'bar.pyc']) self.assertEqual(sorted(os.listdir(self.pkgdir)), expected) def test_multiple_runs(self): # Bug 8527 reported that multiple calls produced empty # __pycache__/__pycache__ directories. self.assertRunOK('-q', self.pkgdir) # Verify the __pycache__ directory contents. self.assertTrue(os.path.exists(self.pkgdir_cachedir)) cachecachedir = os.path.join(self.pkgdir_cachedir, '__pycache__') self.assertFalse(os.path.exists(cachecachedir)) # Call compileall again. self.assertRunOK('-q', self.pkgdir) self.assertTrue(os.path.exists(self.pkgdir_cachedir)) self.assertFalse(os.path.exists(cachecachedir)) @without_source_date_epoch # timestamp invalidation test def test_force(self): self.assertRunOK('-q', self.pkgdir) pycpath = importlib.util.cache_from_source(self.barfn) # set atime/mtime backward to avoid file timestamp resolution issues os.utime(pycpath, (time.time()-60,)*2) mtime = os.stat(pycpath).st_mtime # without force, no recompilation self.assertRunOK('-q', self.pkgdir) mtime2 = os.stat(pycpath).st_mtime self.assertEqual(mtime, mtime2) # now force it. self.assertRunOK('-q', '-f', self.pkgdir) mtime2 = os.stat(pycpath).st_mtime self.assertNotEqual(mtime, mtime2) def test_recursion_control(self): subpackage = os.path.join(self.pkgdir, 'spam') os.mkdir(subpackage) subinitfn = script_helper.make_script(subpackage, '__init__', '') hamfn = script_helper.make_script(subpackage, 'ham', '') self.assertRunOK('-q', '-l', self.pkgdir) self.assertNotCompiled(subinitfn) self.assertFalse(os.path.exists(os.path.join(subpackage, '__pycache__'))) self.assertRunOK('-q', self.pkgdir) self.assertCompiled(subinitfn) self.assertCompiled(hamfn) def test_recursion_limit(self): subpackage = os.path.join(self.pkgdir, 'spam') subpackage2 = os.path.join(subpackage, 'ham') subpackage3 = os.path.join(subpackage2, 'eggs') for pkg in (subpackage, subpackage2, subpackage3): script_helper.make_pkg(pkg) subinitfn = os.path.join(subpackage, '__init__.py') hamfn = script_helper.make_script(subpackage, 'ham', '') spamfn = script_helper.make_script(subpackage2, 'spam', '') eggfn = script_helper.make_script(subpackage3, 'egg', '') self.assertRunOK('-q', '-r 0', self.pkgdir) self.assertNotCompiled(subinitfn) self.assertFalse( os.path.exists(os.path.join(subpackage, '__pycache__'))) self.assertRunOK('-q', '-r 1', self.pkgdir) self.assertCompiled(subinitfn) self.assertCompiled(hamfn) self.assertNotCompiled(spamfn) self.assertRunOK('-q', '-r 2', self.pkgdir) self.assertCompiled(subinitfn) self.assertCompiled(hamfn) self.assertCompiled(spamfn) self.assertNotCompiled(eggfn) self.assertRunOK('-q', '-r 5', self.pkgdir) self.assertCompiled(subinitfn) self.assertCompiled(hamfn) self.assertCompiled(spamfn) self.assertCompiled(eggfn) def test_quiet(self): noisy = self.assertRunOK(self.pkgdir) quiet = self.assertRunOK('-q', self.pkgdir) self.assertNotEqual(b'', noisy) self.assertEqual(b'', quiet) def test_silent(self): script_helper.make_script(self.pkgdir, 'crunchyfrog', 'bad(syntax') _, quiet, _ = self.assertRunNotOK('-q', self.pkgdir) _, silent, _ = self.assertRunNotOK('-qq', self.pkgdir) self.assertNotEqual(b'', quiet) self.assertEqual(b'', silent) def test_regexp(self): self.assertRunOK('-q', '-x', r'ba[^\\/]*$', self.pkgdir) self.assertNotCompiled(self.barfn) self.assertCompiled(self.initfn) def test_multiple_dirs(self): pkgdir2 = os.path.join(self.directory, 'foo2') os.mkdir(pkgdir2) init2fn = script_helper.make_script(pkgdir2, '__init__', '') bar2fn = script_helper.make_script(pkgdir2, 'bar2', '') self.assertRunOK('-q', self.pkgdir, pkgdir2) self.assertCompiled(self.initfn) self.assertCompiled(self.barfn) self.assertCompiled(init2fn) self.assertCompiled(bar2fn) def test_d_compile_error(self): script_helper.make_script(self.pkgdir, 'crunchyfrog', 'bad(syntax') rc, out, err = self.assertRunNotOK('-q', '-d', 'dinsdale', self.pkgdir) self.assertRegex(out, b'File "dinsdale') def test_d_runtime_error(self): bazfn = script_helper.make_script(self.pkgdir, 'baz', 'raise Exception') self.assertRunOK('-q', '-d', 'dinsdale', self.pkgdir) fn = script_helper.make_script(self.pkgdir, 'bing', 'import baz') pyc = importlib.util.cache_from_source(bazfn) os.rename(pyc, os.path.join(self.pkgdir, 'baz.pyc')) os.remove(bazfn) rc, out, err = script_helper.assert_python_failure(fn, __isolated=False) self.assertRegex(err, b'File "dinsdale') def test_include_bad_file(self): rc, out, err = self.assertRunNotOK( '-i', os.path.join(self.directory, 'nosuchfile'), self.pkgdir) self.assertRegex(out, b'rror.*nosuchfile') self.assertNotRegex(err, b'Traceback') self.assertFalse(os.path.exists(importlib.util.cache_from_source( self.pkgdir_cachedir))) def test_include_file_with_arg(self): f1 = script_helper.make_script(self.pkgdir, 'f1', '') f2 = script_helper.make_script(self.pkgdir, 'f2', '') f3 = script_helper.make_script(self.pkgdir, 'f3', '') f4 = script_helper.make_script(self.pkgdir, 'f4', '') with open(os.path.join(self.directory, 'l1'), 'w') as l1: l1.write(os.path.join(self.pkgdir, 'f1.py')+os.linesep) l1.write(os.path.join(self.pkgdir, 'f2.py')+os.linesep) self.assertRunOK('-i', os.path.join(self.directory, 'l1'), f4) self.assertCompiled(f1) self.assertCompiled(f2) self.assertNotCompiled(f3) self.assertCompiled(f4) def test_include_file_no_arg(self): f1 = script_helper.make_script(self.pkgdir, 'f1', '') f2 = script_helper.make_script(self.pkgdir, 'f2', '') f3 = script_helper.make_script(self.pkgdir, 'f3', '') f4 = script_helper.make_script(self.pkgdir, 'f4', '') with open(os.path.join(self.directory, 'l1'), 'w') as l1: l1.write(os.path.join(self.pkgdir, 'f2.py')+os.linesep) self.assertRunOK('-i', os.path.join(self.directory, 'l1')) self.assertNotCompiled(f1) self.assertCompiled(f2) self.assertNotCompiled(f3) self.assertNotCompiled(f4) def test_include_on_stdin(self): f1 = script_helper.make_script(self.pkgdir, 'f1', '') f2 = script_helper.make_script(self.pkgdir, 'f2', '') f3 = script_helper.make_script(self.pkgdir, 'f3', '') f4 = script_helper.make_script(self.pkgdir, 'f4', '') p = script_helper.spawn_python(*(self._get_run_args(()) + ['-i', '-'])) p.stdin.write((f3+os.linesep).encode('ascii')) script_helper.kill_python(p) self.assertNotCompiled(f1) self.assertNotCompiled(f2) self.assertCompiled(f3) self.assertNotCompiled(f4) def test_compiles_as_much_as_possible(self): bingfn = script_helper.make_script(self.pkgdir, 'bing', 'syntax(error') rc, out, err = self.assertRunNotOK('nosuchfile', self.initfn, bingfn, self.barfn) self.assertRegex(out, b'rror') self.assertNotCompiled(bingfn) self.assertCompiled(self.initfn) self.assertCompiled(self.barfn) def test_invalid_arg_produces_message(self): out = self.assertRunOK('badfilename') self.assertRegex(out, b"Can't list 'badfilename'") def test_pyc_invalidation_mode(self): script_helper.make_script(self.pkgdir, 'f1', '') pyc = importlib.util.cache_from_source( os.path.join(self.pkgdir, 'f1.py')) self.assertRunOK('--invalidation-mode=checked-hash', self.pkgdir) with open(pyc, 'rb') as fp: data = fp.read() self.assertEqual(int.from_bytes(data[4:8], 'little'), 0b11) self.assertRunOK('--invalidation-mode=unchecked-hash', self.pkgdir) with open(pyc, 'rb') as fp: data = fp.read() self.assertEqual(int.from_bytes(data[4:8], 'little'), 0b01) @skipUnless(_have_multiprocessing, "requires multiprocessing") def test_workers(self): bar2fn = script_helper.make_script(self.directory, 'bar2', '') files = [] for suffix in range(5): pkgdir = os.path.join(self.directory, 'foo{}'.format(suffix)) os.mkdir(pkgdir) fn = script_helper.make_script(pkgdir, '__init__', '') files.append(script_helper.make_script(pkgdir, 'bar2', '')) self.assertRunOK(self.directory, '-j', '0') self.assertCompiled(bar2fn) for file in files: self.assertCompiled(file) @mock.patch('compileall.compile_dir') def test_workers_available_cores(self, compile_dir): with mock.patch("sys.argv", new=[sys.executable, self.directory, "-j0"]): compileall.main() self.assertTrue(compile_dir.called) self.assertEqual(compile_dir.call_args[-1]['workers'], 0) class CommmandLineTestsWithSourceEpoch(CommandLineTestsBase, unittest.TestCase, metaclass=SourceDateEpochTestMeta, source_date_epoch=True): pass class CommmandLineTestsNoSourceEpoch(CommandLineTestsBase, unittest.TestCase, metaclass=SourceDateEpochTestMeta, source_date_epoch=False): pass if __name__ == "__main__": unittest.main()

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import sys import compileall import importlib.util import test.test_importlib.util import os import pathlib import py_compile import shutil import struct import tempfile import time import unittest import io from unittest import mock, skipUnless try: from concurrent.futures import ProcessPoolExecutor _have_multiprocessing = True except ImportError: _have_multiprocessing = False from test import support from test.support import script_helper from .test_py_compile import without_source_date_epoch from .test_py_compile import SourceDateEpochTestMeta class CompileallTestsBase: def setUp(self): self.directory = tempfile.mkdtemp() self.source_path = os.path.join(self.directory, '_test.py') self.bc_path = importlib.util.cache_from_source(self.source_path) with open(self.source_path, 'w') as file: file.write('x = 123\n') self.source_path2 = os.path.join(self.directory, '_test2.py') self.bc_path2 = importlib.util.cache_from_source(self.source_path2) shutil.copyfile(self.source_path, self.source_path2) self.subdirectory = os.path.join(self.directory, '_subdir') os.mkdir(self.subdirectory) self.source_path3 = os.path.join(self.subdirectory, '_test3.py') shutil.copyfile(self.source_path, self.source_path3) def tearDown(self): shutil.rmtree(self.directory) def add_bad_source_file(self): self.bad_source_path = os.path.join(self.directory, '_test_bad.py') with open(self.bad_source_path, 'w') as file: file.write('x (\n') def timestamp_metadata(self): with open(self.bc_path, 'rb') as file: data = file.read(12) mtime = int(os.stat(self.source_path).st_mtime) compare = struct.pack('<4sll', importlib.util.MAGIC_NUMBER, 0, mtime) return data, compare def recreation_check(self, metadata): if os.environ.get('SOURCE_DATE_EPOCH'): raise unittest.SkipTest('SOURCE_DATE_EPOCH is set') py_compile.compile(self.source_path) self.assertEqual(*self.timestamp_metadata()) with open(self.bc_path, 'rb') as file: bc = file.read()[len(metadata):] with open(self.bc_path, 'wb') as file: file.write(metadata) file.write(bc) self.assertNotEqual(*self.timestamp_metadata()) compileall.compile_dir(self.directory, force=False, quiet=True) self.assertTrue(*self.timestamp_metadata()) def test_mtime(self): self.recreation_check(struct.pack('<4sll', importlib.util.MAGIC_NUMBER, 0, 1)) def test_magic_number(self): self.recreation_check(b'\0\0\0\0') def test_compile_files(self): for fn in (self.bc_path, self.bc_path2): try: os.unlink(fn) except: pass self.assertTrue(compileall.compile_file(self.source_path, force=False, quiet=True)) self.assertTrue(os.path.isfile(self.bc_path) and not os.path.isfile(self.bc_path2)) os.unlink(self.bc_path) self.assertTrue(compileall.compile_dir(self.directory, force=False, quiet=True)) self.assertTrue(os.path.isfile(self.bc_path) and os.path.isfile(self.bc_path2)) os.unlink(self.bc_path) os.unlink(self.bc_path2) self.add_bad_source_file() self.assertFalse(compileall.compile_file(self.bad_source_path, force=False, quiet=2)) self.assertFalse(compileall.compile_dir(self.directory, force=False, quiet=2)) def test_compile_file_pathlike(self): self.assertFalse(os.path.isfile(self.bc_path)) with support.captured_stdout() as stdout: self.assertTrue(compileall.compile_file(pathlib.Path(self.source_path))) self.assertRegex(stdout.getvalue(), r'Compiling ([^WindowsPath|PosixPath].*)') self.assertTrue(os.path.isfile(self.bc_path)) def test_compile_file_pathlike_ddir(self): self.assertFalse(os.path.isfile(self.bc_path)) self.assertTrue(compileall.compile_file(pathlib.Path(self.source_path), ddir=pathlib.Path('ddir_path'), quiet=2)) self.assertTrue(os.path.isfile(self.bc_path)) def test_compile_path(self): with test.test_importlib.util.import_state(path=[self.directory]): self.assertTrue(compileall.compile_path(quiet=2)) with test.test_importlib.util.import_state(path=[self.directory]): self.add_bad_source_file() self.assertFalse(compileall.compile_path(skip_curdir=False, force=True, quiet=2)) def test_no_pycache_in_non_package(self): data_dir = os.path.join(self.directory, 'data') data_file = os.path.join(data_dir, 'file') os.mkdir(data_dir) with open(data_file, 'w'): pass compileall.compile_file(data_file) self.assertFalse(os.path.exists(os.path.join(data_dir, '__pycache__'))) def test_optimize(self): optimize, opt = (1, 1) if __debug__ else (0, '') compileall.compile_dir(self.directory, quiet=True, optimize=optimize) cached = importlib.util.cache_from_source(self.source_path, optimization=opt) self.assertTrue(os.path.isfile(cached)) cached2 = importlib.util.cache_from_source(self.source_path2, optimization=opt) self.assertTrue(os.path.isfile(cached2)) cached3 = importlib.util.cache_from_source(self.source_path3, optimization=opt) self.assertTrue(os.path.isfile(cached3)) def test_compile_dir_pathlike(self): self.assertFalse(os.path.isfile(self.bc_path)) with support.captured_stdout() as stdout: compileall.compile_dir(pathlib.Path(self.directory)) line = stdout.getvalue().splitlines()[0] self.assertRegex(line, r'Listing ([^WindowsPath|PosixPath].*)') self.assertTrue(os.path.isfile(self.bc_path)) @mock.patch('concurrent.futures.ProcessPoolExecutor') def test_compile_pool_called(self, pool_mock): compileall.compile_dir(self.directory, quiet=True, workers=5) self.assertTrue(pool_mock.called) def test_compile_workers_non_positive(self): with self.assertRaisesRegex(ValueError, "workers must be greater or equal to 0"): compileall.compile_dir(self.directory, workers=-1) @mock.patch('concurrent.futures.ProcessPoolExecutor') def test_compile_workers_cpu_count(self, pool_mock): compileall.compile_dir(self.directory, quiet=True, workers=0) self.assertEqual(pool_mock.call_args[1]['max_workers'], None) @mock.patch('concurrent.futures.ProcessPoolExecutor') @mock.patch('compileall.compile_file') def test_compile_one_worker(self, compile_file_mock, pool_mock): compileall.compile_dir(self.directory, quiet=True) self.assertFalse(pool_mock.called) self.assertTrue(compile_file_mock.called) @mock.patch('concurrent.futures.ProcessPoolExecutor', new=None) @mock.patch('compileall.compile_file') def test_compile_missing_multiprocessing(self, compile_file_mock): compileall.compile_dir(self.directory, quiet=True, workers=5) self.assertTrue(compile_file_mock.called) class CompileallTestsWithSourceEpoch(CompileallTestsBase, unittest.TestCase, metaclass=SourceDateEpochTestMeta, source_date_epoch=True): pass class CompileallTestsWithoutSourceEpoch(CompileallTestsBase, unittest.TestCase, metaclass=SourceDateEpochTestMeta, source_date_epoch=False): pass class EncodingTest(unittest.TestCase): def setUp(self): self.directory = tempfile.mkdtemp() self.source_path = os.path.join(self.directory, '_test.py') with open(self.source_path, 'w', encoding='utf-8') as file: file.write(' file.write('print u"\u20ac"\n') def tearDown(self): shutil.rmtree(self.directory) def test_error(self): try: orig_stdout = sys.stdout sys.stdout = io.TextIOWrapper(io.BytesIO(),encoding='ascii') compileall.compile_dir(self.directory) finally: sys.stdout = orig_stdout class CommandLineTestsBase: @classmethod def setUpClass(cls): for path in filter(os.path.isdir, sys.path): directory_created = False directory = pathlib.Path(path) / '__pycache__' path = directory / 'test.try' try: if not directory.is_dir(): directory.mkdir() directory_created = True with path.open('w') as file: file.write(' except OSError: sys_path_writable = False break finally: support.unlink(str(path)) if directory_created: directory.rmdir() else: sys_path_writable = True cls._sys_path_writable = sys_path_writable def _skip_if_sys_path_not_writable(self): if not self._sys_path_writable: raise unittest.SkipTest('not all entries on sys.path are writable') def _get_run_args(self, args): return [*support.optim_args_from_interpreter_flags(), '-S', '-m', 'compileall', *args] def assertRunOK(self, *args, **env_vars): rc, out, err = script_helper.assert_python_ok( *self._get_run_args(args), **env_vars) self.assertEqual(b'', err) return out def assertRunNotOK(self, *args, **env_vars): rc, out, err = script_helper.assert_python_failure( *self._get_run_args(args), **env_vars) return rc, out, err def assertCompiled(self, fn): path = importlib.util.cache_from_source(fn) self.assertTrue(os.path.exists(path)) def assertNotCompiled(self, fn): path = importlib.util.cache_from_source(fn) self.assertFalse(os.path.exists(path)) def setUp(self): self.directory = tempfile.mkdtemp() self.addCleanup(support.rmtree, self.directory) self.pkgdir = os.path.join(self.directory, 'foo') os.mkdir(self.pkgdir) self.pkgdir_cachedir = os.path.join(self.pkgdir, '__pycache__') # Create the __init__.py and a package module. self.initfn = script_helper.make_script(self.pkgdir, '__init__', '') self.barfn = script_helper.make_script(self.pkgdir, 'bar', '') def test_no_args_compiles_path(self): # Note that -l is implied for the no args case. self._skip_if_sys_path_not_writable() bazfn = script_helper.make_script(self.directory, 'baz', '') self.assertRunOK(PYTHONPATH=self.directory) self.assertCompiled(bazfn) self.assertNotCompiled(self.initfn) self.assertNotCompiled(self.barfn) @without_source_date_epoch # timestamp invalidation test def test_no_args_respects_force_flag(self): self._skip_if_sys_path_not_writable() bazfn = script_helper.make_script(self.directory, 'baz', '') self.assertRunOK(PYTHONPATH=self.directory) pycpath = importlib.util.cache_from_source(bazfn) # Set atime/mtime backward to avoid file timestamp resolution issues os.utime(pycpath, (time.time()-60,)*2) mtime = os.stat(pycpath).st_mtime # Without force, no recompilation self.assertRunOK(PYTHONPATH=self.directory) mtime2 = os.stat(pycpath).st_mtime self.assertEqual(mtime, mtime2) # Now force it. self.assertRunOK('-f', PYTHONPATH=self.directory) mtime2 = os.stat(pycpath).st_mtime self.assertNotEqual(mtime, mtime2) def test_no_args_respects_quiet_flag(self): self._skip_if_sys_path_not_writable() script_helper.make_script(self.directory, 'baz', '') noisy = self.assertRunOK(PYTHONPATH=self.directory) self.assertIn(b'Listing ', noisy) quiet = self.assertRunOK('-q', PYTHONPATH=self.directory) self.assertNotIn(b'Listing ', quiet) # Ensure that the default behavior of compileall's CLI is to create for name, ext, switch in [ ('normal', 'pyc', []), ('optimize', 'opt-1.pyc', ['-O']), ('doubleoptimize', 'opt-2.pyc', ['-OO']), ]: def f(self, ext=ext, switch=switch): script_helper.assert_python_ok(*(switch + ['-m', 'compileall', '-q', self.pkgdir])) self.assertTrue(os.path.exists(self.pkgdir_cachedir)) expected = sorted(base.format(sys.implementation.cache_tag, ext) for base in ('__init__.{}.{}', 'bar.{}.{}')) self.assertEqual(sorted(os.listdir(self.pkgdir_cachedir)), expected) self.assertFalse([fn for fn in os.listdir(self.pkgdir) if fn.endswith(ext)]) locals()['test_pep3147_paths_' + name] = f def test_legacy_paths(self): self.assertRunOK('-b', '-q', self.pkgdir) self.assertFalse(os.path.exists(self.pkgdir_cachedir)) expected = sorted(['__init__.py', '__init__.pyc', 'bar.py', 'bar.pyc']) self.assertEqual(sorted(os.listdir(self.pkgdir)), expected) def test_multiple_runs(self): self.assertRunOK('-q', self.pkgdir) self.assertTrue(os.path.exists(self.pkgdir_cachedir)) cachecachedir = os.path.join(self.pkgdir_cachedir, '__pycache__') self.assertFalse(os.path.exists(cachecachedir)) self.assertRunOK('-q', self.pkgdir) self.assertTrue(os.path.exists(self.pkgdir_cachedir)) self.assertFalse(os.path.exists(cachecachedir)) @without_source_date_epoch def test_force(self): self.assertRunOK('-q', self.pkgdir) pycpath = importlib.util.cache_from_source(self.barfn) os.utime(pycpath, (time.time()-60,)*2) mtime = os.stat(pycpath).st_mtime self.assertRunOK('-q', self.pkgdir) mtime2 = os.stat(pycpath).st_mtime self.assertEqual(mtime, mtime2) self.assertRunOK('-q', '-f', self.pkgdir) mtime2 = os.stat(pycpath).st_mtime self.assertNotEqual(mtime, mtime2) def test_recursion_control(self): subpackage = os.path.join(self.pkgdir, 'spam') os.mkdir(subpackage) subinitfn = script_helper.make_script(subpackage, '__init__', '') hamfn = script_helper.make_script(subpackage, 'ham', '') self.assertRunOK('-q', '-l', self.pkgdir) self.assertNotCompiled(subinitfn) self.assertFalse(os.path.exists(os.path.join(subpackage, '__pycache__'))) self.assertRunOK('-q', self.pkgdir) self.assertCompiled(subinitfn) self.assertCompiled(hamfn) def test_recursion_limit(self): subpackage = os.path.join(self.pkgdir, 'spam') subpackage2 = os.path.join(subpackage, 'ham') subpackage3 = os.path.join(subpackage2, 'eggs') for pkg in (subpackage, subpackage2, subpackage3): script_helper.make_pkg(pkg) subinitfn = os.path.join(subpackage, '__init__.py') hamfn = script_helper.make_script(subpackage, 'ham', '') spamfn = script_helper.make_script(subpackage2, 'spam', '') eggfn = script_helper.make_script(subpackage3, 'egg', '') self.assertRunOK('-q', '-r 0', self.pkgdir) self.assertNotCompiled(subinitfn) self.assertFalse( os.path.exists(os.path.join(subpackage, '__pycache__'))) self.assertRunOK('-q', '-r 1', self.pkgdir) self.assertCompiled(subinitfn) self.assertCompiled(hamfn) self.assertNotCompiled(spamfn) self.assertRunOK('-q', '-r 2', self.pkgdir) self.assertCompiled(subinitfn) self.assertCompiled(hamfn) self.assertCompiled(spamfn) self.assertNotCompiled(eggfn) self.assertRunOK('-q', '-r 5', self.pkgdir) self.assertCompiled(subinitfn) self.assertCompiled(hamfn) self.assertCompiled(spamfn) self.assertCompiled(eggfn) def test_quiet(self): noisy = self.assertRunOK(self.pkgdir) quiet = self.assertRunOK('-q', self.pkgdir) self.assertNotEqual(b'', noisy) self.assertEqual(b'', quiet) def test_silent(self): script_helper.make_script(self.pkgdir, 'crunchyfrog', 'bad(syntax') _, quiet, _ = self.assertRunNotOK('-q', self.pkgdir) _, silent, _ = self.assertRunNotOK('-qq', self.pkgdir) self.assertNotEqual(b'', quiet) self.assertEqual(b'', silent) def test_regexp(self): self.assertRunOK('-q', '-x', r'ba[^\\/]*$', self.pkgdir) self.assertNotCompiled(self.barfn) self.assertCompiled(self.initfn) def test_multiple_dirs(self): pkgdir2 = os.path.join(self.directory, 'foo2') os.mkdir(pkgdir2) init2fn = script_helper.make_script(pkgdir2, '__init__', '') bar2fn = script_helper.make_script(pkgdir2, 'bar2', '') self.assertRunOK('-q', self.pkgdir, pkgdir2) self.assertCompiled(self.initfn) self.assertCompiled(self.barfn) self.assertCompiled(init2fn) self.assertCompiled(bar2fn) def test_d_compile_error(self): script_helper.make_script(self.pkgdir, 'crunchyfrog', 'bad(syntax') rc, out, err = self.assertRunNotOK('-q', '-d', 'dinsdale', self.pkgdir) self.assertRegex(out, b'File "dinsdale') def test_d_runtime_error(self): bazfn = script_helper.make_script(self.pkgdir, 'baz', 'raise Exception') self.assertRunOK('-q', '-d', 'dinsdale', self.pkgdir) fn = script_helper.make_script(self.pkgdir, 'bing', 'import baz') pyc = importlib.util.cache_from_source(bazfn) os.rename(pyc, os.path.join(self.pkgdir, 'baz.pyc')) os.remove(bazfn) rc, out, err = script_helper.assert_python_failure(fn, __isolated=False) self.assertRegex(err, b'File "dinsdale') def test_include_bad_file(self): rc, out, err = self.assertRunNotOK( '-i', os.path.join(self.directory, 'nosuchfile'), self.pkgdir) self.assertRegex(out, b'rror.*nosuchfile') self.assertNotRegex(err, b'Traceback') self.assertFalse(os.path.exists(importlib.util.cache_from_source( self.pkgdir_cachedir))) def test_include_file_with_arg(self): f1 = script_helper.make_script(self.pkgdir, 'f1', '') f2 = script_helper.make_script(self.pkgdir, 'f2', '') f3 = script_helper.make_script(self.pkgdir, 'f3', '') f4 = script_helper.make_script(self.pkgdir, 'f4', '') with open(os.path.join(self.directory, 'l1'), 'w') as l1: l1.write(os.path.join(self.pkgdir, 'f1.py')+os.linesep) l1.write(os.path.join(self.pkgdir, 'f2.py')+os.linesep) self.assertRunOK('-i', os.path.join(self.directory, 'l1'), f4) self.assertCompiled(f1) self.assertCompiled(f2) self.assertNotCompiled(f3) self.assertCompiled(f4) def test_include_file_no_arg(self): f1 = script_helper.make_script(self.pkgdir, 'f1', '') f2 = script_helper.make_script(self.pkgdir, 'f2', '') f3 = script_helper.make_script(self.pkgdir, 'f3', '') f4 = script_helper.make_script(self.pkgdir, 'f4', '') with open(os.path.join(self.directory, 'l1'), 'w') as l1: l1.write(os.path.join(self.pkgdir, 'f2.py')+os.linesep) self.assertRunOK('-i', os.path.join(self.directory, 'l1')) self.assertNotCompiled(f1) self.assertCompiled(f2) self.assertNotCompiled(f3) self.assertNotCompiled(f4) def test_include_on_stdin(self): f1 = script_helper.make_script(self.pkgdir, 'f1', '') f2 = script_helper.make_script(self.pkgdir, 'f2', '') f3 = script_helper.make_script(self.pkgdir, 'f3', '') f4 = script_helper.make_script(self.pkgdir, 'f4', '') p = script_helper.spawn_python(*(self._get_run_args(()) + ['-i', '-'])) p.stdin.write((f3+os.linesep).encode('ascii')) script_helper.kill_python(p) self.assertNotCompiled(f1) self.assertNotCompiled(f2) self.assertCompiled(f3) self.assertNotCompiled(f4) def test_compiles_as_much_as_possible(self): bingfn = script_helper.make_script(self.pkgdir, 'bing', 'syntax(error') rc, out, err = self.assertRunNotOK('nosuchfile', self.initfn, bingfn, self.barfn) self.assertRegex(out, b'rror') self.assertNotCompiled(bingfn) self.assertCompiled(self.initfn) self.assertCompiled(self.barfn) def test_invalid_arg_produces_message(self): out = self.assertRunOK('badfilename') self.assertRegex(out, b"Can't list 'badfilename'") def test_pyc_invalidation_mode(self): script_helper.make_script(self.pkgdir, 'f1', '') pyc = importlib.util.cache_from_source( os.path.join(self.pkgdir, 'f1.py')) self.assertRunOK('--invalidation-mode=checked-hash', self.pkgdir) with open(pyc, 'rb') as fp: data = fp.read() self.assertEqual(int.from_bytes(data[4:8], 'little'), 0b11) self.assertRunOK('--invalidation-mode=unchecked-hash', self.pkgdir) with open(pyc, 'rb') as fp: data = fp.read() self.assertEqual(int.from_bytes(data[4:8], 'little'), 0b01) @skipUnless(_have_multiprocessing, "requires multiprocessing") def test_workers(self): bar2fn = script_helper.make_script(self.directory, 'bar2', '') files = [] for suffix in range(5): pkgdir = os.path.join(self.directory, 'foo{}'.format(suffix)) os.mkdir(pkgdir) fn = script_helper.make_script(pkgdir, '__init__', '') files.append(script_helper.make_script(pkgdir, 'bar2', '')) self.assertRunOK(self.directory, '-j', '0') self.assertCompiled(bar2fn) for file in files: self.assertCompiled(file) @mock.patch('compileall.compile_dir') def test_workers_available_cores(self, compile_dir): with mock.patch("sys.argv", new=[sys.executable, self.directory, "-j0"]): compileall.main() self.assertTrue(compile_dir.called) self.assertEqual(compile_dir.call_args[-1]['workers'], 0) class CommmandLineTestsWithSourceEpoch(CommandLineTestsBase, unittest.TestCase, metaclass=SourceDateEpochTestMeta, source_date_epoch=True): pass class CommmandLineTestsNoSourceEpoch(CommandLineTestsBase, unittest.TestCase, metaclass=SourceDateEpochTestMeta, source_date_epoch=False): pass if __name__ == "__main__": unittest.main()

true

790bde71f32ef92a78d6f1c5ef6f9b6e506297fb

1,166

py

Python

ml/Graph/pieChart2.py

Shivams9/pythoncodecamp

e6cd27f4704a407ee360414a8c9236b254117a59

[ "MIT" ]

6

2021-08-04T08:15:22.000Z

2022-02-02T11:15:56.000Z

ML/Graph/pieChart2.py

Maurya232Abhishek/Python-repository-for-basics

3dcec5c529a0847df07c9dcc1424675754ce6376

[ "MIT" ]

14

2021-08-02T06:28:00.000Z

2022-03-25T10:44:15.000Z

ML/Graph/pieChart2.py

Maurya232Abhishek/Python-repository-for-basics

3dcec5c529a0847df07c9dcc1424675754ce6376

[ "MIT" ]

6

2021-07-16T04:56:41.000Z

2022-02-16T04:40:06.000Z

import pandas as pd import numpy as np import matplotlib.pyplot as plt def f(x): return 1-x data=pd.read_csv("test.csv") print(data) roll=data["Rollno"] t1 =data["t1"] t2 = data["t2"] print(roll,t1,t2) plt.pie(t1,labels=roll,autopct="%1.2f%%") plt.title("Marks in test1") plt.show() plt.pie(t2,labels=roll,autopct="%1.2f%%") plt.title("Marks in test2") plt.show() data["t2-t1"]=data["t2"]-data["t1"] print(data) plt.title("Marks in test1") benefit=0 notbenefit=0 for i in data['t2-t1']: if i>0: benefit +=1 else: notbenefit +=1 print(benefit,notbenefit) plt.pie([benefit,notbenefit],labels=["Benefitted","Not Benefitted"],autopct="%1.2f%%",explode=[0.1,0.1]) plt.title("Deciding") plt.show() range=["0-15","15-18","18-21","21-23","23-26"] n = [0,0,0,0,0] for i in data["t1"]: if i < 15: n[0] += 1 elif i < 18: n[1] += 1 elif i < 21: n[2] += 1 elif i < 23: n[3] += 1 elif i < 26: n[4] += 1 plt.pie(n,labels=range,autopct="%1.2f%%") plt.show() x = np.linspace(0,1,100) plt.plot(x,f(x),color="red") plt.xlim(0,1) plt.ylim(0,1) plt.title("happening Vs Not happening") plt.show()

20.821429

104

0.596913

import pandas as pd import numpy as np import matplotlib.pyplot as plt def f(x): return 1-x data=pd.read_csv("test.csv") print(data) roll=data["Rollno"] t1 =data["t1"] t2 = data["t2"] print(roll,t1,t2) plt.pie(t1,labels=roll,autopct="%1.2f%%") plt.title("Marks in test1") plt.show() plt.pie(t2,labels=roll,autopct="%1.2f%%") plt.title("Marks in test2") plt.show() data["t2-t1"]=data["t2"]-data["t1"] print(data) plt.title("Marks in test1") benefit=0 notbenefit=0 for i in data['t2-t1']: if i>0: benefit +=1 else: notbenefit +=1 print(benefit,notbenefit) plt.pie([benefit,notbenefit],labels=["Benefitted","Not Benefitted"],autopct="%1.2f%%",explode=[0.1,0.1]) plt.title("Deciding") plt.show() range=["0-15","15-18","18-21","21-23","23-26"] n = [0,0,0,0,0] for i in data["t1"]: if i < 15: n[0] += 1 elif i < 18: n[1] += 1 elif i < 21: n[2] += 1 elif i < 23: n[3] += 1 elif i < 26: n[4] += 1 plt.pie(n,labels=range,autopct="%1.2f%%") plt.show() x = np.linspace(0,1,100) plt.plot(x,f(x),color="red") plt.xlim(0,1) plt.ylim(0,1) plt.title("happening Vs Not happening") plt.show()

true

790bdeca139be6e684caea747631d810625a0bf6

25,834

py

Python

magenta/models/score2perf/score2perf.py

flyingleafe/magenta

2eb641e8f48c52e78d6b44fcbe9a7d168f787616

[ "Apache-2.0" ]

null

magenta/models/score2perf/score2perf.py

flyingleafe/magenta

2eb641e8f48c52e78d6b44fcbe9a7d168f787616

[ "Apache-2.0" ]

null

magenta/models/score2perf/score2perf.py

flyingleafe/magenta

2eb641e8f48c52e78d6b44fcbe9a7d168f787616

[ "Apache-2.0" ]

null

# Copyright 2020 The Magenta 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. """Performance generation from score in Tensor2Tensor.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import itertools from magenta.models.score2perf import datagen_beam from magenta.models.score2perf import modalities from magenta.models.score2perf import music_encoders from note_seq import chord_symbols_lib from note_seq import sequences_lib from tensor2tensor.data_generators import problem from tensor2tensor.layers import modalities as t2t_modalities from tensor2tensor.models import transformer from tensor2tensor.utils import registry import tensorflow.compat.v1 as tf # TODO(iansimon): figure out the best way not to hard-code these constants NUM_VELOCITY_BINS = 32 STEPS_PER_SECOND = 100 MIN_PITCH = 21 MAX_PITCH = 108 # pylint: disable=line-too-long MAESTRO_TFRECORD_PATHS = { 'train': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_train.tfrecord', 'dev': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_validation.tfrecord', 'test': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_test.tfrecord' } # pylint: enable=line-too-long class Score2PerfProblem(problem.Problem): """Base class for musical score-to-performance problems. Data files contain tf.Example protos with encoded performance in 'targets' and optional encoded score in 'inputs'. """ @property def splits(self): """Dictionary of split names and probabilities. Must sum to one.""" raise NotImplementedError() @property def min_hop_size_seconds(self): """Minimum hop size in seconds at which to split input performances.""" raise NotImplementedError() @property def max_hop_size_seconds(self): """Maximum hop size in seconds at which to split input performances.""" raise NotImplementedError() @property def num_replications(self): """Number of times entire input performances will be split.""" return 1 @property def add_eos_symbol(self): """Whether to append EOS to encoded performances.""" raise NotImplementedError() @property def absolute_timing(self): """Whether or not score should use absolute (vs. tempo-relative) timing.""" return False @property def stretch_factors(self): """Temporal stretch factors for data augmentation (in datagen).""" return [1.0] @property def transpose_amounts(self): """Pitch transposition amounts for data augmentation (in datagen).""" return [0] @property def random_crop_length_in_datagen(self): """Randomly crop targets to this length in datagen.""" return None @property def random_crop_in_train(self): """Whether to randomly crop each training example when preprocessing.""" return False @property def split_in_eval(self): """Whether to split each eval example when preprocessing.""" return False def performances_input_transform(self, tmp_dir): """Input performances beam transform (or dictionary thereof) for datagen.""" raise NotImplementedError() def generate_data(self, data_dir, tmp_dir, task_id=-1): del task_id def augment_note_sequence(ns, stretch_factor, transpose_amount): """Augment a NoteSequence by time stretch and pitch transposition.""" augmented_ns = sequences_lib.stretch_note_sequence( ns, stretch_factor, in_place=False) try: _, num_deleted_notes = sequences_lib.transpose_note_sequence( augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True) except chord_symbols_lib.ChordSymbolError: raise datagen_beam.DataAugmentationError( 'Transposition of chord symbol(s) failed.') if num_deleted_notes: raise datagen_beam.DataAugmentationError( 'Transposition caused out-of-range pitch(es).') return augmented_ns augment_params = itertools.product( self.stretch_factors, self.transpose_amounts) augment_fns = [ functools.partial(augment_note_sequence, stretch_factor=s, transpose_amount=t) for s, t in augment_params ] datagen_beam.generate_examples( input_transform=self.performances_input_transform(tmp_dir), output_dir=data_dir, problem_name=self.dataset_filename(), splits=self.splits, min_hop_size_seconds=self.min_hop_size_seconds, max_hop_size_seconds=self.max_hop_size_seconds, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, num_replications=self.num_replications, encode_performance_fn=self.performance_encoder().encode_note_sequence, encode_score_fns=dict((name, encoder.encode_note_sequence) for name, encoder in self.score_encoders()), augment_fns=augment_fns, absolute_timing=self.absolute_timing, random_crop_length=self.random_crop_length_in_datagen) def hparams(self, defaults, model_hparams): del model_hparams # unused perf_encoder = self.get_feature_encoders()['targets'] defaults.modality = {'targets': t2t_modalities.ModalityType.SYMBOL} defaults.vocab_size = {'targets': perf_encoder.vocab_size} if self.has_inputs: score_encoder = self.get_feature_encoders()['inputs'] if isinstance(score_encoder.vocab_size, list): # TODO(trandustin): We default to not applying any transformation; to # apply one, pass modalities.bottom to the model's hparams.bottom. In # future, refactor the tuple of the "inputs" feature to be part of the # features dict itself, i.e., have multiple inputs each with its own # modality and vocab size. modality_cls = t2t_modalities.ModalityType.IDENTITY else: modality_cls = t2t_modalities.ModalityType.SYMBOL defaults.modality['inputs'] = modality_cls defaults.vocab_size['inputs'] = score_encoder.vocab_size def performance_encoder(self): """Encoder for target performances.""" return music_encoders.MidiPerformanceEncoder( steps_per_second=STEPS_PER_SECOND, num_velocity_bins=NUM_VELOCITY_BINS, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, add_eos=self.add_eos_symbol) def score_encoders(self): """List of (name, encoder) tuples for input score components.""" return [] def feature_encoders(self, data_dir): del data_dir encoders = { 'targets': self.performance_encoder() } score_encoders = self.score_encoders() if score_encoders: if len(score_encoders) > 1: # Create a composite score encoder, only used for inference. encoders['inputs'] = music_encoders.CompositeScoreEncoder( [encoder for _, encoder in score_encoders]) else: # If only one score component, just use its encoder. _, encoders['inputs'] = score_encoders[0] return encoders def example_reading_spec(self): data_fields = { 'targets': tf.VarLenFeature(tf.int64) } for name, _ in self.score_encoders(): data_fields[name] = tf.VarLenFeature(tf.int64) # We don't actually "decode" anything here; the encodings are simply read as # tensors. data_items_to_decoders = None return data_fields, data_items_to_decoders def preprocess_example(self, example, mode, hparams): if self.has_inputs: # Stack encoded score components depthwise as inputs. inputs = [] for name, _ in self.score_encoders(): inputs.append(tf.expand_dims(example[name], axis=1)) del example[name] example['inputs'] = tf.stack(inputs, axis=2) if self.random_crop_in_train and mode == tf.estimator.ModeKeys.TRAIN: # Take a random crop of the training example. assert not self.has_inputs max_offset = tf.maximum( tf.shape(example['targets'])[0] - hparams.max_target_seq_length, 0) offset = tf.cond( max_offset > 0, lambda: tf.random_uniform([], maxval=max_offset, dtype=tf.int32), lambda: 0 ) example['targets'] = ( example['targets'][offset:offset + hparams.max_target_seq_length]) return example elif self.split_in_eval and mode == tf.estimator.ModeKeys.EVAL: # Split the example into non-overlapping segments. assert not self.has_inputs length = tf.shape(example['targets'])[0] extra_length = tf.mod(length, hparams.max_target_seq_length) examples = { 'targets': tf.reshape( example['targets'][:length - extra_length], [-1, hparams.max_target_seq_length, 1, 1]) } extra_example = { 'targets': tf.reshape( example['targets'][-extra_length:], [1, -1, 1, 1]) } dataset = tf.data.Dataset.from_tensor_slices(examples) extra_dataset = tf.data.Dataset.from_tensor_slices(extra_example) return dataset.concatenate(extra_dataset) else: # If not cropping or splitting, do standard preprocessing. return super(Score2PerfProblem, self).preprocess_example( example, mode, hparams) class ConditionalScore2PerfProblem(Score2PerfProblem): """Lightweight version of base class for musical score-to-performance problems. This version incorporates one performance conditioning signal. Data files contain tf.Example protos with encoded performance in 'targets' and optional encoded score in 'inputs'. """ def generate_data(self, data_dir, tmp_dir, task_id=-1): del task_id def augment_note_sequence(ns, stretch_factor, transpose_amount): """Augment a NoteSequence by time stretch and pitch transposition.""" augmented_ns = sequences_lib.stretch_note_sequence( ns, stretch_factor, in_place=False) try: _, num_deleted_notes = sequences_lib.transpose_note_sequence( augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True) except chord_symbols_lib.ChordSymbolError: raise datagen_beam.DataAugmentationError( 'Transposition of chord symbol(s) failed.') if num_deleted_notes: raise datagen_beam.DataAugmentationError( 'Transposition caused out-of-range pitch(es).') return augmented_ns augment_params = itertools.product( self.stretch_factors, self.transpose_amounts) augment_fns = [ functools.partial(augment_note_sequence, stretch_factor=s, transpose_amount=t) for s, t in augment_params ] datagen_beam.generate_conditional_examples( input_transform=self.performances_input_transform(tmp_dir), output_dir=data_dir, problem_name=self.dataset_filename(), splits=self.splits, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, melody=False, noisy=False, encode_performance_fn=self.performance_encoder().encode_note_sequence, encode_score_fns=dict((name, encoder.encode_note_sequence) for name, encoder in self.score_encoders()), augment_fns=augment_fns, num_replications=self.num_replications) def example_reading_spec(self): data_fields = { 'inputs': tf.VarLenFeature(tf.int64), 'targets': tf.VarLenFeature(tf.int64) } for name, _ in self.score_encoders(): data_fields[name] = tf.VarLenFeature(tf.int64) # We don't actually "decode" anything here; the encodings are simply read as # tensors. data_items_to_decoders = None return data_fields, data_items_to_decoders def preprocess_example(self, example, mode, hparams): return problem.preprocess_example_common(example, mode, hparams) class ConditionalMelodyScore2PerfProblem(Score2PerfProblem): """Lightweight version of base class for musical score-to-performance problems. This version incorporates one performance conditioning signal. Data files contain tf.Example protos with encoded performance in 'targets' and encoded score in 'melody' and 'performance'. """ def generate_data(self, data_dir, tmp_dir, task_id=-1): del task_id def augment_note_sequence(ns, stretch_factor, transpose_amount): """Augment a NoteSequence by time stretch and pitch transposition.""" augmented_ns = sequences_lib.stretch_note_sequence( ns, stretch_factor, in_place=False) try: _, num_deleted_notes = sequences_lib.transpose_note_sequence( augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True) except chord_symbols_lib.ChordSymbolError: raise datagen_beam.DataAugmentationError( 'Transposition of chord symbol(s) failed.') if num_deleted_notes: raise datagen_beam.DataAugmentationError( 'Transposition caused out-of-range pitch(es).') return augmented_ns augment_params = itertools.product( self.stretch_factors, self.transpose_amounts) augment_fns = [ functools.partial(augment_note_sequence, stretch_factor=s, transpose_amount=t) for s, t in augment_params ] datagen_beam.generate_conditional_examples( input_transform=self.performances_input_transform(tmp_dir), output_dir=data_dir, problem_name=self.dataset_filename(), splits=self.splits, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, melody=True, noisy=False, encode_performance_fn=self.performance_encoder().encode_note_sequence, encode_score_fns=dict((name, encoder.encode_note_sequence) for name, encoder in self.score_encoders()), augment_fns=augment_fns, num_replications=self.num_replications) def hparams(self, defaults, model_hparams): del model_hparams # unused perf_encoder = self.get_feature_encoders()['targets'] defaults.modality = {'targets': t2t_modalities.ModalityType.SYMBOL} defaults.vocab_size = {'targets': perf_encoder.vocab_size} if self.has_inputs: score_encoder = self.score_encoders() # iterate over each score encoder and update modality/vocab_size for name, se in score_encoder: defaults.modality[name] = t2t_modalities.ModalityType.SYMBOL defaults.vocab_size[name] = se.vocab_size def feature_encoders(self, data_dir): del data_dir encoders = { 'targets': self.performance_encoder() } score_encoders = self.score_encoders() # CompositeScoreEncoder is tricky, so using a list of encoders instead. if len(score_encoders) > 1: for name, encoder in score_encoders: encoders[name] = encoder else: # If only one score component, just use its encoder. _, encoders['inputs'] = score_encoders[0] return encoders def example_reading_spec(self): data_fields = { 'targets': tf.VarLenFeature(tf.int64), } for name, _ in self.score_encoders(): data_fields[name] = tf.VarLenFeature(tf.int64) # We don't actually "decode" anything here; the encodings are simply read as # tensors. data_items_to_decoders = None return data_fields, data_items_to_decoders def preprocess_example(self, example, mode, hparams): return problem.preprocess_example_common(example, mode, hparams) class ConditionalMelodyNoisyScore2PerfProblem( ConditionalMelodyScore2PerfProblem): """Lightweight version of base class for musical score-to-performance problems. This version incorporates one performance conditioning signal. Data files contain tf.Example protos with encoded performance in 'targets' and encoded score in 'melody' and 'performance'. """ def generate_data(self, data_dir, tmp_dir, task_id=-1): del task_id def augment_note_sequence(ns, stretch_factor, transpose_amount): """Augment a NoteSequence by time stretch and pitch transposition.""" augmented_ns = sequences_lib.stretch_note_sequence( ns, stretch_factor, in_place=False) try: _, num_deleted_notes = sequences_lib.transpose_note_sequence( augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True) except chord_symbols_lib.ChordSymbolError: raise datagen_beam.DataAugmentationError( 'Transposition of chord symbol(s) failed.') if num_deleted_notes: raise datagen_beam.DataAugmentationError( 'Transposition caused out-of-range pitch(es).') return augmented_ns augment_params = itertools.product( self.stretch_factors, self.transpose_amounts) augment_fns = [ functools.partial(augment_note_sequence, stretch_factor=s, transpose_amount=t) for s, t in augment_params ] datagen_beam.generate_conditional_examples( input_transform=self.performances_input_transform(tmp_dir), output_dir=data_dir, problem_name=self.dataset_filename(), splits=self.splits, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, melody=True, noisy=True, encode_performance_fn=self.performance_encoder().encode_note_sequence, encode_score_fns=dict((name, encoder.encode_note_sequence) for name, encoder in self.score_encoders()), augment_fns=augment_fns, num_replications=self.num_replications) class Chords2PerfProblem(Score2PerfProblem): """Base class for musical chords-to-performance problems.""" def score_encoders(self): return [('chords', music_encoders.TextChordsEncoder(steps_per_quarter=1))] class Melody2PerfProblem(Score2PerfProblem): """Base class for musical melody-to-performance problems.""" def score_encoders(self): return [ ('melody', music_encoders.TextMelodyEncoder( steps_per_quarter=4, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH)) ] class AbsoluteMelody2PerfProblem(Score2PerfProblem): """Base class for musical (absolute-timed) melody-to-performance problems.""" @property def absolute_timing(self): return True def score_encoders(self): return [ ('melody', music_encoders.TextMelodyEncoderAbsolute( steps_per_second=10, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH)) ] class LeadSheet2PerfProblem(Score2PerfProblem): """Base class for musical lead-sheet-to-performance problems.""" def score_encoders(self): return [ ('chords', music_encoders.TextChordsEncoder(steps_per_quarter=4)), ('melody', music_encoders.TextMelodyEncoder( steps_per_quarter=4, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH)) ] @registry.register_problem('score2perf_maestro_language_uncropped_aug') class Score2PerfMaestroLanguageUncroppedAug(Score2PerfProblem): """Piano performance language model on the MAESTRO dataset.""" def performances_input_transform(self, tmp_dir): del tmp_dir return dict( (split_name, datagen_beam.ReadNoteSequencesFromTFRecord(tfrecord_path)) for split_name, tfrecord_path in MAESTRO_TFRECORD_PATHS.items()) @property def splits(self): return None @property def min_hop_size_seconds(self): return 0.0 @property def max_hop_size_seconds(self): return 0.0 @property def add_eos_symbol(self): return False @property def stretch_factors(self): # Stretch by -5%, -2.5%, 0%, 2.5%, and 5%. return [0.95, 0.975, 1.0, 1.025, 1.05] @property def transpose_amounts(self): # Transpose no more than a minor third. return [-3, -2, -1, 0, 1, 2, 3] @property def random_crop_in_train(self): return True @property def split_in_eval(self): return True @registry.register_problem('score2perf_maestro_absmel2perf_5s_to_30s_aug10x') class Score2PerfMaestroAbsMel2Perf5sTo30sAug10x(AbsoluteMelody2PerfProblem): """Generate performances from an absolute-timed melody, with augmentation.""" def performances_input_transform(self, tmp_dir): del tmp_dir return dict( (split_name, datagen_beam.ReadNoteSequencesFromTFRecord(tfrecord_path)) for split_name, tfrecord_path in MAESTRO_TFRECORD_PATHS.items()) @property def splits(self): return None @property def min_hop_size_seconds(self): return 5.0 @property def max_hop_size_seconds(self): return 30.0 @property def num_replications(self): return 10 @property def add_eos_symbol(self): return True @property def stretch_factors(self): # Stretch by -5%, -2.5%, 0%, 2.5%, and 5%. return [0.95, 0.975, 1.0, 1.025, 1.05] @property def transpose_amounts(self): # Transpose no more than a minor third. return [-3, -2, -1, 0, 1, 2, 3] @registry.register_problem('score2perf_maestro_perf_conditional_aug_10x') class Score2PerfMaestroPerfConditionalAug10x(ConditionalScore2PerfProblem): """Generate performances from scratch (or from primer).""" def performances_input_transform(self, tmp_dir): del tmp_dir return dict( (split_name, datagen_beam.ReadNoteSequencesFromTFRecord(tfrecord_path)) for split_name, tfrecord_path in MAESTRO_TFRECORD_PATHS.items()) @property def splits(self): return @property def num_replications(self): return 10 @property def add_eos_symbol(self): return False @property def stretch_factors(self): # Stretch by -5%, -2.5%, 0%, 2.5%, and 5%. return [0.95, 0.975, 1.0, 1.025, 1.05] @property def transpose_amounts(self): # Transpose no more than a minor third. return [-3, -2, -1, 0, 1, 2, 3] @property def has_inputs(self): encoders = self.get_feature_encoders() return ('performance' in encoders) or ('inputs' in encoders) def score_encoders(self): return [ ('performance', music_encoders.MidiPerformanceEncoder( steps_per_second=100, num_velocity_bins=32, min_pitch=21, max_pitch=108, add_eos=self.add_eos_symbol)) ] @registry.register_problem('score2perf_maestro_mel_perf_conditional_aug_10x') class Score2PerfMaestroMelPerfConditionalAug10x( ConditionalMelodyScore2PerfProblem): """Generate performances from scratch (or from primer).""" def performances_input_transform(self, tmp_dir): del tmp_dir return dict( (split_name, datagen_beam.ReadNoteSequencesFromTFRecord(tfrecord_path)) for split_name, tfrecord_path in MAESTRO_TFRECORD_PATHS.items()) @property def splits(self): return @property def num_replications(self): return 10 @property def add_eos_symbol(self): return False @property def stretch_factors(self): # Stretch by -5%, -2.5%, 0%, 2.5%, and 5%. return [0.95, 0.975, 1.0, 1.025, 1.05] @property def transpose_amounts(self): # Transpose no more than a minor third. return [-3, -2, -1, 0, 1, 2, 3] @property def has_inputs(self): encoders = self.get_feature_encoders() return ('performance' in encoders) or ('inputs' in encoders) def score_encoders(self): return [ ('performance', music_encoders.MidiPerformanceEncoder( steps_per_second=100, num_velocity_bins=32, min_pitch=21, max_pitch=108, add_eos=self.add_eos_symbol)), ('melody', music_encoders.TextMelodyEncoderAbsolute( steps_per_second=10, min_pitch=21, max_pitch=108)) ] @registry.register_problem('score2perf_maestro_mel_perf_conditional_noisy_10x') class Score2PerfMaestroMelPerfConditionalNoisy10x( ConditionalMelodyNoisyScore2PerfProblem): """Generate performances from scratch (or from primer).""" def performances_input_transform(self, tmp_dir): del tmp_dir return dict( (split_name, datagen_beam.ReadNoteSequencesFromTFRecord(tfrecord_path)) for split_name, tfrecord_path in MAESTRO_TFRECORD_PATHS.items()) @property def splits(self): return @property def num_replications(self): return 10 @property def add_eos_symbol(self): return False @property def stretch_factors(self): # Stretch by -5%, -2.5%, 0%, 2.5%, and 5%. return [0.95, 0.975, 1.0, 1.025, 1.05] @property def transpose_amounts(self): # Transpose no more than a minor third. return [-3, -2, -1, 0, 1, 2, 3] @property def has_inputs(self): encoders = self.get_feature_encoders() return ('performance' in encoders) or ('inputs' in encoders) def score_encoders(self): return [ ('performance', music_encoders.MidiPerformanceEncoder( steps_per_second=100, num_velocity_bins=32, min_pitch=21, max_pitch=108, add_eos=self.add_eos_symbol)), ('melody', music_encoders.TextMelodyEncoderAbsolute( steps_per_second=10, min_pitch=21, max_pitch=108)) ] @registry.register_hparams def score2perf_transformer_base(): hparams = transformer.transformer_base() hparams.bottom['inputs'] = modalities.bottom return hparams

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import itertools from magenta.models.score2perf import datagen_beam from magenta.models.score2perf import modalities from magenta.models.score2perf import music_encoders from note_seq import chord_symbols_lib from note_seq import sequences_lib from tensor2tensor.data_generators import problem from tensor2tensor.layers import modalities as t2t_modalities from tensor2tensor.models import transformer from tensor2tensor.utils import registry import tensorflow.compat.v1 as tf NUM_VELOCITY_BINS = 32 STEPS_PER_SECOND = 100 MIN_PITCH = 21 MAX_PITCH = 108 MAESTRO_TFRECORD_PATHS = { 'train': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_train.tfrecord', 'dev': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_validation.tfrecord', 'test': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_test.tfrecord' } class Score2PerfProblem(problem.Problem): @property def splits(self): raise NotImplementedError() @property def min_hop_size_seconds(self): raise NotImplementedError() @property def max_hop_size_seconds(self): raise NotImplementedError() @property def num_replications(self): return 1 @property def add_eos_symbol(self): raise NotImplementedError() @property def absolute_timing(self): return False @property def stretch_factors(self): return [1.0] @property def transpose_amounts(self): return [0] @property def random_crop_length_in_datagen(self): return None @property def random_crop_in_train(self): return False @property def split_in_eval(self): return False def performances_input_transform(self, tmp_dir): raise NotImplementedError() def generate_data(self, data_dir, tmp_dir, task_id=-1): del task_id def augment_note_sequence(ns, stretch_factor, transpose_amount): augmented_ns = sequences_lib.stretch_note_sequence( ns, stretch_factor, in_place=False) try: _, num_deleted_notes = sequences_lib.transpose_note_sequence( augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True) except chord_symbols_lib.ChordSymbolError: raise datagen_beam.DataAugmentationError( 'Transposition of chord symbol(s) failed.') if num_deleted_notes: raise datagen_beam.DataAugmentationError( 'Transposition caused out-of-range pitch(es).') return augmented_ns augment_params = itertools.product( self.stretch_factors, self.transpose_amounts) augment_fns = [ functools.partial(augment_note_sequence, stretch_factor=s, transpose_amount=t) for s, t in augment_params ] datagen_beam.generate_examples( input_transform=self.performances_input_transform(tmp_dir), output_dir=data_dir, problem_name=self.dataset_filename(), splits=self.splits, min_hop_size_seconds=self.min_hop_size_seconds, max_hop_size_seconds=self.max_hop_size_seconds, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, num_replications=self.num_replications, encode_performance_fn=self.performance_encoder().encode_note_sequence, encode_score_fns=dict((name, encoder.encode_note_sequence) for name, encoder in self.score_encoders()), augment_fns=augment_fns, absolute_timing=self.absolute_timing, random_crop_length=self.random_crop_length_in_datagen) def hparams(self, defaults, model_hparams): del model_hparams perf_encoder = self.get_feature_encoders()['targets'] defaults.modality = {'targets': t2t_modalities.ModalityType.SYMBOL} defaults.vocab_size = {'targets': perf_encoder.vocab_size} if self.has_inputs: score_encoder = self.get_feature_encoders()['inputs'] if isinstance(score_encoder.vocab_size, list): # future, refactor the tuple of the "inputs" feature to be part of the # features dict itself, i.e., have multiple inputs each with its own # modality and vocab size. modality_cls = t2t_modalities.ModalityType.IDENTITY else: modality_cls = t2t_modalities.ModalityType.SYMBOL defaults.modality['inputs'] = modality_cls defaults.vocab_size['inputs'] = score_encoder.vocab_size def performance_encoder(self): return music_encoders.MidiPerformanceEncoder( steps_per_second=STEPS_PER_SECOND, num_velocity_bins=NUM_VELOCITY_BINS, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, add_eos=self.add_eos_symbol) def score_encoders(self): return [] def feature_encoders(self, data_dir): del data_dir encoders = { 'targets': self.performance_encoder() } score_encoders = self.score_encoders() if score_encoders: if len(score_encoders) > 1: # Create a composite score encoder, only used for inference. encoders['inputs'] = music_encoders.CompositeScoreEncoder( [encoder for _, encoder in score_encoders]) else: # If only one score component, just use its encoder. _, encoders['inputs'] = score_encoders[0] return encoders def example_reading_spec(self): data_fields = { 'targets': tf.VarLenFeature(tf.int64) } for name, _ in self.score_encoders(): data_fields[name] = tf.VarLenFeature(tf.int64) # We don't actually "decode" anything here; the encodings are simply read as data_items_to_decoders = None return data_fields, data_items_to_decoders def preprocess_example(self, example, mode, hparams): if self.has_inputs: inputs = [] for name, _ in self.score_encoders(): inputs.append(tf.expand_dims(example[name], axis=1)) del example[name] example['inputs'] = tf.stack(inputs, axis=2) if self.random_crop_in_train and mode == tf.estimator.ModeKeys.TRAIN: assert not self.has_inputs max_offset = tf.maximum( tf.shape(example['targets'])[0] - hparams.max_target_seq_length, 0) offset = tf.cond( max_offset > 0, lambda: tf.random_uniform([], maxval=max_offset, dtype=tf.int32), lambda: 0 ) example['targets'] = ( example['targets'][offset:offset + hparams.max_target_seq_length]) return example elif self.split_in_eval and mode == tf.estimator.ModeKeys.EVAL: assert not self.has_inputs length = tf.shape(example['targets'])[0] extra_length = tf.mod(length, hparams.max_target_seq_length) examples = { 'targets': tf.reshape( example['targets'][:length - extra_length], [-1, hparams.max_target_seq_length, 1, 1]) } extra_example = { 'targets': tf.reshape( example['targets'][-extra_length:], [1, -1, 1, 1]) } dataset = tf.data.Dataset.from_tensor_slices(examples) extra_dataset = tf.data.Dataset.from_tensor_slices(extra_example) return dataset.concatenate(extra_dataset) else: return super(Score2PerfProblem, self).preprocess_example( example, mode, hparams) class ConditionalScore2PerfProblem(Score2PerfProblem): def generate_data(self, data_dir, tmp_dir, task_id=-1): del task_id def augment_note_sequence(ns, stretch_factor, transpose_amount): augmented_ns = sequences_lib.stretch_note_sequence( ns, stretch_factor, in_place=False) try: _, num_deleted_notes = sequences_lib.transpose_note_sequence( augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True) except chord_symbols_lib.ChordSymbolError: raise datagen_beam.DataAugmentationError( 'Transposition of chord symbol(s) failed.') if num_deleted_notes: raise datagen_beam.DataAugmentationError( 'Transposition caused out-of-range pitch(es).') return augmented_ns augment_params = itertools.product( self.stretch_factors, self.transpose_amounts) augment_fns = [ functools.partial(augment_note_sequence, stretch_factor=s, transpose_amount=t) for s, t in augment_params ] datagen_beam.generate_conditional_examples( input_transform=self.performances_input_transform(tmp_dir), output_dir=data_dir, problem_name=self.dataset_filename(), splits=self.splits, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, melody=False, noisy=False, encode_performance_fn=self.performance_encoder().encode_note_sequence, encode_score_fns=dict((name, encoder.encode_note_sequence) for name, encoder in self.score_encoders()), augment_fns=augment_fns, num_replications=self.num_replications) def example_reading_spec(self): data_fields = { 'inputs': tf.VarLenFeature(tf.int64), 'targets': tf.VarLenFeature(tf.int64) } for name, _ in self.score_encoders(): data_fields[name] = tf.VarLenFeature(tf.int64) # tensors. data_items_to_decoders = None return data_fields, data_items_to_decoders def preprocess_example(self, example, mode, hparams): return problem.preprocess_example_common(example, mode, hparams) class ConditionalMelodyScore2PerfProblem(Score2PerfProblem): def generate_data(self, data_dir, tmp_dir, task_id=-1): del task_id def augment_note_sequence(ns, stretch_factor, transpose_amount): augmented_ns = sequences_lib.stretch_note_sequence( ns, stretch_factor, in_place=False) try: _, num_deleted_notes = sequences_lib.transpose_note_sequence( augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True) except chord_symbols_lib.ChordSymbolError: raise datagen_beam.DataAugmentationError( 'Transposition of chord symbol(s) failed.') if num_deleted_notes: raise datagen_beam.DataAugmentationError( 'Transposition caused out-of-range pitch(es).') return augmented_ns augment_params = itertools.product( self.stretch_factors, self.transpose_amounts) augment_fns = [ functools.partial(augment_note_sequence, stretch_factor=s, transpose_amount=t) for s, t in augment_params ] datagen_beam.generate_conditional_examples( input_transform=self.performances_input_transform(tmp_dir), output_dir=data_dir, problem_name=self.dataset_filename(), splits=self.splits, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, melody=True, noisy=False, encode_performance_fn=self.performance_encoder().encode_note_sequence, encode_score_fns=dict((name, encoder.encode_note_sequence) for name, encoder in self.score_encoders()), augment_fns=augment_fns, num_replications=self.num_replications) def hparams(self, defaults, model_hparams): del model_hparams # unused perf_encoder = self.get_feature_encoders()['targets'] defaults.modality = {'targets': t2t_modalities.ModalityType.SYMBOL} defaults.vocab_size = {'targets': perf_encoder.vocab_size} if self.has_inputs: score_encoder = self.score_encoders() # iterate over each score encoder and update modality/vocab_size for name, se in score_encoder: defaults.modality[name] = t2t_modalities.ModalityType.SYMBOL defaults.vocab_size[name] = se.vocab_size def feature_encoders(self, data_dir): del data_dir encoders = { 'targets': self.performance_encoder() } score_encoders = self.score_encoders() # CompositeScoreEncoder is tricky, so using a list of encoders instead. if len(score_encoders) > 1: for name, encoder in score_encoders: encoders[name] = encoder else: # If only one score component, just use its encoder. _, encoders['inputs'] = score_encoders[0] return encoders def example_reading_spec(self): data_fields = { 'targets': tf.VarLenFeature(tf.int64), } for name, _ in self.score_encoders(): data_fields[name] = tf.VarLenFeature(tf.int64) # We don't actually "decode" anything here; the encodings are simply read as data_items_to_decoders = None return data_fields, data_items_to_decoders def preprocess_example(self, example, mode, hparams): return problem.preprocess_example_common(example, mode, hparams) class ConditionalMelodyNoisyScore2PerfProblem( ConditionalMelodyScore2PerfProblem): def generate_data(self, data_dir, tmp_dir, task_id=-1): del task_id def augment_note_sequence(ns, stretch_factor, transpose_amount): augmented_ns = sequences_lib.stretch_note_sequence( ns, stretch_factor, in_place=False) try: _, num_deleted_notes = sequences_lib.transpose_note_sequence( augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True) except chord_symbols_lib.ChordSymbolError: raise datagen_beam.DataAugmentationError( 'Transposition of chord symbol(s) failed.') if num_deleted_notes: raise datagen_beam.DataAugmentationError( 'Transposition caused out-of-range pitch(es).') return augmented_ns augment_params = itertools.product( self.stretch_factors, self.transpose_amounts) augment_fns = [ functools.partial(augment_note_sequence, stretch_factor=s, transpose_amount=t) for s, t in augment_params ] datagen_beam.generate_conditional_examples( input_transform=self.performances_input_transform(tmp_dir), output_dir=data_dir, problem_name=self.dataset_filename(), splits=self.splits, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH, melody=True, noisy=True, encode_performance_fn=self.performance_encoder().encode_note_sequence, encode_score_fns=dict((name, encoder.encode_note_sequence) for name, encoder in self.score_encoders()), augment_fns=augment_fns, num_replications=self.num_replications) class Chords2PerfProblem(Score2PerfProblem): def score_encoders(self): return [('chords', music_encoders.TextChordsEncoder(steps_per_quarter=1))] class Melody2PerfProblem(Score2PerfProblem): def score_encoders(self): return [ ('melody', music_encoders.TextMelodyEncoder( steps_per_quarter=4, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH)) ] class AbsoluteMelody2PerfProblem(Score2PerfProblem): @property def absolute_timing(self): return True def score_encoders(self): return [ ('melody', music_encoders.TextMelodyEncoderAbsolute( steps_per_second=10, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH)) ] class LeadSheet2PerfProblem(Score2PerfProblem): def score_encoders(self): return [ ('chords', music_encoders.TextChordsEncoder(steps_per_quarter=4)), ('melody', music_encoders.TextMelodyEncoder( steps_per_quarter=4, min_pitch=MIN_PITCH, max_pitch=MAX_PITCH)) ] @registry.register_problem('score2perf_maestro_language_uncropped_aug') class Score2PerfMaestroLanguageUncroppedAug(Score2PerfProblem): def performances_input_transform(self, tmp_dir): del tmp_dir return dict( (split_name, datagen_beam.ReadNoteSequencesFromTFRecord(tfrecord_path)) for split_name, tfrecord_path in MAESTRO_TFRECORD_PATHS.items()) @property def splits(self): return None @property def min_hop_size_seconds(self): return 0.0 @property def max_hop_size_seconds(self): return 0.0 @property def add_eos_symbol(self): return False @property def stretch_factors(self): return [0.95, 0.975, 1.0, 1.025, 1.05] @property def transpose_amounts(self): return [-3, -2, -1, 0, 1, 2, 3] @property def random_crop_in_train(self): return True @property def split_in_eval(self): return True @registry.register_problem('score2perf_maestro_absmel2perf_5s_to_30s_aug10x') class Score2PerfMaestroAbsMel2Perf5sTo30sAug10x(AbsoluteMelody2PerfProblem): def performances_input_transform(self, tmp_dir): del tmp_dir return dict( (split_name, datagen_beam.ReadNoteSequencesFromTFRecord(tfrecord_path)) for split_name, tfrecord_path in MAESTRO_TFRECORD_PATHS.items()) @property def splits(self): return None @property def min_hop_size_seconds(self): return 5.0 @property def max_hop_size_seconds(self): return 30.0 @property def num_replications(self): return 10 @property def add_eos_symbol(self): return True @property def stretch_factors(self): return [0.95, 0.975, 1.0, 1.025, 1.05] @property def transpose_amounts(self): return [-3, -2, -1, 0, 1, 2, 3] @registry.register_problem('score2perf_maestro_perf_conditional_aug_10x') class Score2PerfMaestroPerfConditionalAug10x(ConditionalScore2PerfProblem): def performances_input_transform(self, tmp_dir): del tmp_dir return dict( (split_name, datagen_beam.ReadNoteSequencesFromTFRecord(tfrecord_path)) for split_name, tfrecord_path in MAESTRO_TFRECORD_PATHS.items()) @property def splits(self): return @property def num_replications(self): return 10 @property def add_eos_symbol(self): return False @property def stretch_factors(self): return [0.95, 0.975, 1.0, 1.025, 1.05] @property def transpose_amounts(self): return [-3, -2, -1, 0, 1, 2, 3] @property def has_inputs(self): encoders = self.get_feature_encoders() return ('performance' in encoders) or ('inputs' in encoders) def score_encoders(self): return [ ('performance', music_encoders.MidiPerformanceEncoder( steps_per_second=100, num_velocity_bins=32, min_pitch=21, max_pitch=108, add_eos=self.add_eos_symbol)) ] @registry.register_problem('score2perf_maestro_mel_perf_conditional_aug_10x') class Score2PerfMaestroMelPerfConditionalAug10x( ConditionalMelodyScore2PerfProblem): def performances_input_transform(self, tmp_dir): del tmp_dir return dict( (split_name, datagen_beam.ReadNoteSequencesFromTFRecord(tfrecord_path)) for split_name, tfrecord_path in MAESTRO_TFRECORD_PATHS.items()) @property def splits(self): return @property def num_replications(self): return 10 @property def add_eos_symbol(self): return False @property def stretch_factors(self): return [0.95, 0.975, 1.0, 1.025, 1.05] @property def transpose_amounts(self): return [-3, -2, -1, 0, 1, 2, 3] @property def has_inputs(self): encoders = self.get_feature_encoders() return ('performance' in encoders) or ('inputs' in encoders) def score_encoders(self): return [ ('performance', music_encoders.MidiPerformanceEncoder( steps_per_second=100, num_velocity_bins=32, min_pitch=21, max_pitch=108, add_eos=self.add_eos_symbol)), ('melody', music_encoders.TextMelodyEncoderAbsolute( steps_per_second=10, min_pitch=21, max_pitch=108)) ] @registry.register_problem('score2perf_maestro_mel_perf_conditional_noisy_10x') class Score2PerfMaestroMelPerfConditionalNoisy10x( ConditionalMelodyNoisyScore2PerfProblem): def performances_input_transform(self, tmp_dir): del tmp_dir return dict( (split_name, datagen_beam.ReadNoteSequencesFromTFRecord(tfrecord_path)) for split_name, tfrecord_path in MAESTRO_TFRECORD_PATHS.items()) @property def splits(self): return @property def num_replications(self): return 10 @property def add_eos_symbol(self): return False @property def stretch_factors(self): return [0.95, 0.975, 1.0, 1.025, 1.05] @property def transpose_amounts(self): return [-3, -2, -1, 0, 1, 2, 3] @property def has_inputs(self): encoders = self.get_feature_encoders() return ('performance' in encoders) or ('inputs' in encoders) def score_encoders(self): return [ ('performance', music_encoders.MidiPerformanceEncoder( steps_per_second=100, num_velocity_bins=32, min_pitch=21, max_pitch=108, add_eos=self.add_eos_symbol)), ('melody', music_encoders.TextMelodyEncoderAbsolute( steps_per_second=10, min_pitch=21, max_pitch=108)) ] @registry.register_hparams def score2perf_transformer_base(): hparams = transformer.transformer_base() hparams.bottom['inputs'] = modalities.bottom return hparams

true

790bdef2fee711a5826e4d0648860796c9a44151

1,302

py

Python

src/virtual-wan/azext_vwan/vendored_sdks/v2018_08_01/v2018_08_01/models/subnet_association.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

207

2017-11-29T06:59:41.000Z

2022-03-31T10:00:53.000Z

src/virtual-wan/azext_vwan/vendored_sdks/v2018_08_01/v2018_08_01/models/subnet_association.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

4,061

2017-10-27T23:19:56.000Z

2022-03-31T23:18:30.000Z

src/virtual-wan/azext_vwan/vendored_sdks/v2018_08_01/v2018_08_01/models/subnet_association.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

802

2017-10-11T17:36:26.000Z

2022-03-31T22:24:32.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class SubnetAssociation(Model): """Network interface and its custom security rules. Variables are only populated by the server, and will be ignored when sending a request. :ivar id: Subnet ID. :vartype id: str :param security_rules: Collection of custom security rules. :type security_rules: list[~azure.mgmt.network.v2018_08_01.models.SecurityRule] """ _validation = { 'id': {'readonly': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'security_rules': {'key': 'securityRules', 'type': '[SecurityRule]'}, } def __init__(self, **kwargs): super(SubnetAssociation, self).__init__(**kwargs) self.id = None self.security_rules = kwargs.get('security_rules', None)

31.756098

77

0.596774

from msrest.serialization import Model class SubnetAssociation(Model): _validation = { 'id': {'readonly': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'security_rules': {'key': 'securityRules', 'type': '[SecurityRule]'}, } def __init__(self, **kwargs): super(SubnetAssociation, self).__init__(**kwargs) self.id = None self.security_rules = kwargs.get('security_rules', None)

true

790be1d5689871179d2d10998e06a2ab2217b9a2

684

py

Python

00_DataPreprocessing/data_preprocessing_template.py

sreecodeslayer/udemy-machine-learning

11fb166358a29993ed352fb204ab79e04bd9c05e

[ "MIT" ]

null

00_DataPreprocessing/data_preprocessing_template.py

sreecodeslayer/udemy-machine-learning

11fb166358a29993ed352fb204ab79e04bd9c05e

[ "MIT" ]

null

00_DataPreprocessing/data_preprocessing_template.py

sreecodeslayer/udemy-machine-learning

11fb166358a29993ed352fb204ab79e04bd9c05e

[ "MIT" ]

null

# Data Preprocessing Template # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv('Data.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, 3].values # Splitting the dataset into the Training set and Test set from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) # Feature Scaling """from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test) sc_y = StandardScaler() y_train = sc_y.fit_transform(y_train)"""

29.73913

92

0.773392

import numpy as np import matplotlib.pyplot as plt import pandas as pd dataset = pd.read_csv('Data.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, 3].values from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

true

790be4aed0c2212da622c520d11a1ff8026b2e72

563

py

Python

ex100 sorteio e soma.py

joaoschweikart/python_projects

a30361551ec71ac3bef6d38e4b6ffc7bad21f1cc

[ "MIT" ]

null

ex100 sorteio e soma.py

joaoschweikart/python_projects

a30361551ec71ac3bef6d38e4b6ffc7bad21f1cc

[ "MIT" ]

null

ex100 sorteio e soma.py

joaoschweikart/python_projects

a30361551ec71ac3bef6d38e4b6ffc7bad21f1cc

[ "MIT" ]

null

from time import sleep from random import randint numeros = [] def sorteio(): c = 0 while True: n = randint(0, 20) numeros.append(n) c = c+1 if c == 5: break print('=-'*20) print('SORTEANDO OS 5 VALORES DA LISTA:', end=' ') for n in numeros: sleep(0.5) print(n, end=' ') print() def somapar(): soma = 0 for n in numeros: if n % 2 == 0: soma = soma + n sleep(2) print(f'Somando os valores PARES de {numeros}: {soma}') sorteio() somapar()

17.060606

59

0.50444

from time import sleep from random import randint numeros = [] def sorteio(): c = 0 while True: n = randint(0, 20) numeros.append(n) c = c+1 if c == 5: break print('=-'*20) print('SORTEANDO OS 5 VALORES DA LISTA:', end=' ') for n in numeros: sleep(0.5) print(n, end=' ') print() def somapar(): soma = 0 for n in numeros: if n % 2 == 0: soma = soma + n sleep(2) print(f'Somando os valores PARES de {numeros}: {soma}') sorteio() somapar()

true

790be57a235f32004113fc9be553d302c4d5fdd5

564

py

Python

roster/migrations/0022_auto_20181206_1148.py

ankanb240/otis-web

45eda65b419705c65c02b15872a137969d53d8e9

[ "MIT" ]

15

2021-08-28T18:18:37.000Z

2022-03-13T07:48:15.000Z

roster/migrations/0022_auto_20181206_1148.py

ankanb240/otis-web

45eda65b419705c65c02b15872a137969d53d8e9

[ "MIT" ]

65

2021-08-20T02:37:27.000Z

2022-02-07T17:19:23.000Z

roster/migrations/0022_auto_20181206_1148.py

ankanb240/otis-web

45eda65b419705c65c02b15872a137969d53d8e9

[ "MIT" ]

31

2020-01-09T02:35:29.000Z

2022-03-13T07:48:18.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.9 on 2018-12-06 16:48 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('roster', '0021_auto_20180825_1843'), ] operations = [ migrations.AlterField( model_name='student', name='track', field=models.CharField(choices=[('A', 'Weekly'), ('B', 'Biweekly'), ('C', 'Correspondence'), ('E', 'External'), ('N', 'Not applicable')], max_length=5), ), ]

26.857143

164

0.597518

from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('roster', '0021_auto_20180825_1843'), ] operations = [ migrations.AlterField( model_name='student', name='track', field=models.CharField(choices=[('A', 'Weekly'), ('B', 'Biweekly'), ('C', 'Correspondence'), ('E', 'External'), ('N', 'Not applicable')], max_length=5), ), ]

true

790be5b201bc1b61342d9239a85b912577a4564c

22,936

py

Python

traci_pedestrian_crossing/movexy_ped.py

KarlRong/Safe-RL-for-Driving

67484911ca8ad9f1476e96043c379c01cd5ced8c

[ "Apache-2.0" ]

null

traci_pedestrian_crossing/movexy_ped.py

KarlRong/Safe-RL-for-Driving

67484911ca8ad9f1476e96043c379c01cd5ced8c

[ "Apache-2.0" ]

null

traci_pedestrian_crossing/movexy_ped.py

KarlRong/Safe-RL-for-Driving

67484911ca8ad9f1476e96043c379c01cd5ced8c

[ "Apache-2.0" ]

null

# the TestEnv environment is used to simply simulate the network from flow.envs import TestEnv # the Experiment class is used for running simulations from flow.core.experiment import Experiment # the base network class from flow.networks import Network from flow.envs.base import Env # all other imports are standard from flow.core.params import VehicleParams, SumoCarFollowingParams, SumoLaneChangeParams from flow.controllers import IDMController from flow.core.params import InFlows from flow.core.params import NetParams from flow.core.params import TrafficLightParams from flow.core.params import InitialConfig from flow.core.params import EnvParams from flow.controllers import IDMController, RLController, StaticLaneChanger from gym.spaces.box import Box import numpy as np import collections # create some default parameters parameters HORIZON = 3000 env_params = EnvParams( horizon=HORIZON, sims_per_step=1, warmup_steps=0, additional_params={ "max_accel": 3, "max_decel": -2, "target_velocity": 20, "lane_change_duration": 4, "num_rl": 5, }) initial_config = InitialConfig(edges_distribution=['highway_0']) vehicles = VehicleParams() vehicles.add( veh_id="human", acceleration_controller=(IDMController, { "noise": 0.2 }), # lane_change_controller=(StaticLaneChanger, {}), car_following_params=SumoCarFollowingParams( speed_mode="obey_safe_speed", ), lane_change_params=SumoLaneChangeParams( lane_change_mode=1621, model="SL2015", lc_impatience="0.1", lc_time_to_impatience="1.0" )) vehicles.add( veh_id="rl", acceleration_controller=(RLController, {}), lane_change_controller=(StaticLaneChanger, {}), # routing_controller=(HighwayRouter, {}), car_following_params=SumoCarFollowingParams( speed_mode="obey_safe_speed", ), lane_change_params=SumoLaneChangeParams( lane_change_mode=256, model="SL2015", lc_impatience="0.1", lc_time_to_impatience="1.0" ), num_vehicles=0) from flow.core.params import SumoParams sim_params = SumoParams( sim_step=0.2, render=True, lateral_resolution=1.0, restart_instance=True, ) import os inflow = InFlows() inflow.add(veh_type="human", edge="WC", # depart_lane="best", depart_lane=1, arrivalLane=0, probability=0.1, depart_speed="random", ) inflow.add(veh_type="human", edge="WC", # depart_lane="best", depart_lane=0, arrivalLane=1, probability=0.1, depart_speed="random", ) inflow.add(veh_type="human", edge="EC", # depart_lane="best", # vehs_per_hour=2000, depart_lane=1, arrivalLane=0, probability=0.1, depart_speed="random", ) inflow.add(veh_type="human", edge="EC", # depart_lane="best", # vehs_per_hour=2000, depart_lane=0, arrivalLane=1, probability=0.1, depart_speed="random", ) inflow.add( veh_type="rl", edge="WC", vehs_per_hour=100, depart_lane="free", depart_speed=5) net_params = NetParams( template={ "net":"/home/rong/Safe-RL-for-Driving/traci_pedestrian_crossing/pedcrossing.net.xml", # features associated with the routes vehicles take "vtype": "/home/rong/Safe-RL-for-Driving/traci_pedestrian_crossing/pedcrossing.add.xml", # 和下方specify_routes一致 "rou":"/home/rong/Safe-RL-for-Driving/traci_pedestrian_crossing/data/pedcrossing.rou.xml", "trip":"/home/rong/Safe-RL-for-Driving/traci_pedestrian_crossing/pedestrians.trip.xml" }, inflows=inflow, ) # specify the edges vehicles can originate on initial_config = InitialConfig( edges_distribution=["WC"] ) tl_logic = TrafficLightParams(baseline=False) phases = [{"duration": "100000", "state": "GGGGr"}, {"duration": "4", "state": "yyyyr"}, {"duration": "10", "state": "rrrrG"}, {"duration": "10", "state": "rrrrr"}] tl_logic.add("C", phases=phases, programID="custom", offset="0") # specify the routes for vehicles in the network class PedCrossing(Network): def specify_routes(self, net_params): return {'EC': ['EC', 'CW'], 'WC': ['WC', 'CE']} class MoveXYPedEnv(Env): def __init__(self, env_params, sim_params, network, simulator='traci'): super().__init__(env_params, sim_params, network, simulator) # 环境相关 self.activeRequest = False self.greenTimeSoFar = 0 # minimum green time for the vehicles self.MIN_GREEN_TIME = 15 # the first phase in tls plan. see 'pedcrossing.tll.xml' self.VEHICLE_GREEN_PHASE = 0 self.PEDESTRIAN_GREEN_PHASE = 2 # the id of the traffic light (there is only one). This is identical to the # id of the controlled intersection (by default) self.TLSID = 'C' # pedestrian edges at the controlled intersection self.WALKINGAREAS = [':C_w0', ':C_w1'] self.CROSSINGS = [':C_c0'] # Move xy相关 self.num_lanes = max(self.k.network.num_lanes(edge) for edge in self.k.network.get_edge_list()) self.visible = [] self.stuck = False # variables used to sort vehicles by their initial position plus # distance traveled self.prev_pos = dict() self.absolute_position = dict() # maximum number of controlled vehicles self.num_rl = env_params.additional_params["num_rl"] # queue of rl vehicles waiting to be controlled self.rl_queue = collections.deque() # names of the rl vehicles controlled at any step self.rl_veh = [] # used for visualization: the vehicles behind and after RL vehicles # (ie the observed vehicles) will have a different color self.leader = [] self.follower = [] @property def action_space(self): """See class definition.""" max_decel = self.env_params.additional_params["max_decel"] max_accel = self.env_params.additional_params["max_accel"] lb = [1, -0.2] * self.num_rl ub = [2, 0.2] * self.num_rl # print("num_rl_vehicles:", self.num_rl) return Box(np.array(lb), np.array(ub), dtype=np.float32) @property def observation_space(self): """See class definition.""" # print("observation sapce shape: ", 4 * self.num_rl * # self.num_lanes + self.num_rl) return Box( low=-1000, high=3000, shape=(4 * self.num_rl * self.num_lanes + 2 * self.num_rl, ), dtype=np.float32) def compute_reward(self, rl_actions, **kwargs): """See class definition.""" reward = 0 # rl 车辆向前，并惩罚停止 rl_velocity = np.array(self.k.vehicle.get_speed(self.rl_veh)) target_vel = self.env_params.additional_params['target_velocity'] max_cost = np.array([target_vel] * self.num_rl) max_cost = np.linalg.norm(max_cost) cost = rl_velocity - target_vel cost = np.linalg.norm(cost) # epsilon term (to deal with ZeroDivisionError exceptions) eps = np.finfo(np.float32).eps reward += max(max_cost - cost, 0) / (max_cost + eps) gain = 0.5 thresh = 0.3 penalize = len(rl_velocity[rl_velocity < thresh]) reward -= gain * penalize # punish excessive lane changes by reducing the reward by a set value # every time an rl car changes lanes (10% of max reward) for veh_id in self.rl_veh: if self.k.vehicle.get_last_lc(veh_id) == self.time_counter: reward -= 10 if self.stuck: reward -= 100 # print("reward: ", reward) return reward def _apply_rl_actions(self, actions): """See class definition.""" acceleration = actions[::2] direction = actions[1::2] # represents vehicles that are allowed to change lanes # non_lane_changing_veh = [] # non_lane_changing_veh = \ # [self.time_counter <= # self.env_params.additional_params["lane_change_duration"] # + self.k.vehicle.get_last_lc(veh_id) # for veh_id in self.rl_veh] # # vehicle that are not allowed to change have their directions set to 0 # print(non_lane_changing_veh) # direction[non_lane_changing_veh] = \ # np.array([0] * sum(non_lane_changing_veh)) for i, veh_id in enumerate(self.rl_veh): if self.time_counter <= self.env_params.additional_params["lane_change_duration"]\ + self.k.vehicle.get_last_lc(veh_id): direction[i] = 0 x, y = self.k.vehicle.kernel_api.vehicle.getPosition(veh_id) print(x, y) print("edgeID", self.k.vehicle.get_edge(veh_id)) print("lane", self.k.vehicle.get_lane(veh_id)) self.k.vehicle.kernel_api.vehicle.moveToXY(vehID=veh_id, edgeID="highway_1", lane=1, x=x+acceleration[i], y=y+direction[i], keepRoute=2) for x in np.nditer(direction, op_flags=['readwrite']): if x > 0.7: x[...] = 1 elif x < -0.7: x[...] = -1 else: x[...] = 0 # print("actions:", actions) # print("veh id: ", self.rl_veh) # print("acceleration: ", acceleration) # print("direction", direction) # self.k.vehicle.apply_acceleration(self.rl_veh, acc=acceleration) # self.k.vehicle.apply_lane_change(self.rl_veh, direction=direction) def get_state(self): """See class definition.""" obs = [ 0 for _ in range(4 * self.num_rl * self.num_lanes + 2 * self.num_rl) ] # print("rl veh id: ", self.rl_veh) self.visible = [] self.update_veh_id() speeds = [] for i, rl_id in enumerate(self.rl_veh): # x, y = self.k.vehicle.kernel_api.vehicle.getPosition(rl_id) # print(x, y) # print("edgeID", self.k.vehicle.get_edge(rl_id)) # print("lane", self.k.vehicle.get_lane(rl_id)) # self.k.vehicle.kernel_api.vehicle.moveToXY(vehID=[rl_id, rl_id], edgeID="highway_1", lane=1, x=600, y=134) # add the speed for the ego rl vehicle x = self.k.vehicle.get_x_by_id(rl_id) if x == -1001: continue speed = self.k.vehicle.get_speed(rl_id) obs[-2*i - 1] = speed speeds.append(speed) obs[-2*i - 2] = x # if rl_id not in self.k.vehicle.get_ids(): # print("not in:", rl_id) # self.additional_command() # normalizers max_length = self.k.network.length() max_speed = self.k.network.max_speed() # set to 1000 since the absence of a vehicle implies a large # headway headway = [1] * self.num_lanes tailway = [1] * self.num_lanes vel_in_front = [0] * self.num_lanes vel_behind = [0] * self.num_lanes lane_leaders = self.k.vehicle.get_lane_leaders(rl_id) lane_followers = self.k.vehicle.get_lane_followers(rl_id) lane_headways = self.k.vehicle.get_lane_headways(rl_id) lane_tailways = self.k.vehicle.get_lane_tailways(rl_id) headway[0:len(lane_headways)] = lane_headways tailway[0:len(lane_tailways)] = lane_tailways for j, lane_leader in enumerate(lane_leaders): if lane_leader != '': lane_headways[j] /= max_length vel_in_front[j] = self.k.vehicle.get_speed(lane_leader) \ / max_speed self.visible.extend([lane_leader]) for j, lane_follower in enumerate(lane_followers): if lane_follower != '': lane_headways[j] /= max_length vel_behind[j] = self.k.vehicle.get_speed(lane_follower) \ / max_speed self.visible.extend([lane_follower]) # add the headways, tailways, and speed for all lane leaders # and followers obs[4*self.num_lanes*i:4*self.num_lanes*(i+1)] = \ np.concatenate((headway, tailway, vel_in_front, vel_behind)) # if len(speeds) > 3: # self.stuck = True # for speed in speeds: # if speed != 0: # self.stuck = False obs = np.array(obs) # print("observation: ", obs) # print("observation shape: ", obs.shape) np.clip(obs, -1000, 3000, out=obs) return obs def additional_command(self): # 红绿灯相关 # decide wether there is a waiting pedestrian and switch if the green # phase for the vehicles exceeds its minimum duration if not self.activeRequest: self.activeRequest = self.checkWaitingPersons() if self.k.kernel_api.trafficlight.getPhase(self.TLSID) == self.VEHICLE_GREEN_PHASE: self.greenTimeSoFar += 1 if self.greenTimeSoFar > self.MIN_GREEN_TIME: # check whether someone has pushed the button if self.activeRequest: # switch to the next phase self.k.kernel_api.trafficlight.setPhase( self.TLSID, self.VEHICLE_GREEN_PHASE + 1) # reset state self.activeRequest = False # MOVE XY相关 # specify observed vehicles for veh_id in self.leader + self.follower: self.k.vehicle.set_observed(veh_id) # update the "absolute_position" variable for veh_id in self.k.vehicle.get_ids(): this_pos = self.k.vehicle.get_x_by_id(veh_id) if this_pos == -1001: # in case the vehicle isn't in the network self.absolute_position[veh_id] = -1001 else: change = this_pos - self.prev_pos.get(veh_id, this_pos) self.absolute_position[veh_id] = \ (self.absolute_position.get(veh_id, this_pos) + change) \ % self.k.network.length() self.prev_pos[veh_id] = this_pos return def update_veh_id(self): # add rl vehicles that just entered the network into the rl queue for veh_id in self.k.vehicle.get_rl_ids(): if veh_id not in list(self.rl_queue) + self.rl_veh: self.rl_queue.append(veh_id) # remove rl vehicles that exited the network for veh_id in list(self.rl_queue): if veh_id not in self.k.vehicle.get_rl_ids() or veh_id not in self.k.vehicle.get_ids(): self.rl_queue.remove(veh_id) for veh_id in self.rl_veh: if veh_id not in self.k.vehicle.get_rl_ids() or veh_id not in self.k.vehicle.get_ids(): # print("rm veh_id", veh_id) self.rl_veh.remove(veh_id) # fil up rl_veh until they are enough controlled vehicles while len(self.rl_queue) > 0 and len(self.rl_veh) < self.num_rl: rl_id = self.rl_queue.popleft() self.rl_veh.append(rl_id) # print("add rl_veh:", rl_id) # print("update_veh_id, self.rl_veh:", self.rl_veh) def checkWaitingPersons(self): """check whether a person has requested to cross the street""" # check both sides of the crossing for edge in self.WALKINGAREAS: peds = self.k.kernel_api.edge.getLastStepPersonIDs(edge) # check who is waiting at the crossing # we assume that pedestrians push the button upon # standing still for 1s for ped in peds: if (self.k.kernel_api.person.getWaitingTime(ped) == 1 and self.k.kernel_api.person.getNextEdge(ped) in self.CROSSINGS): numWaiting = self.k.kernel_api.trafficlight.getServedPersonCount(self.TLSID, self.PEDESTRIAN_GREEN_PHASE) print("%s: pedestrian %s pushes the button (waiting: %s)" % (self.k.kernel_api.simulation.getTime(), ped, numWaiting)) return True return False def step(self, rl_actions): """Advance the environment by one step. Assigns actions to autonomous and human-driven agents (i.e. vehicles, traffic lights, etc...). Actions that are not assigned are left to the control of the simulator. The actions are then used to advance the simulator by the number of time steps requested per environment step. Results from the simulations are processed through various classes, such as the Vehicle and TrafficLight kernels, to produce standardized methods for identifying specific network state features. Finally, results from the simulator are used to generate appropriate observations. Parameters ---------- rl_actions : array_like an list of actions provided by the rl algorithm Returns ------- observation : array_like agent's observation of the current environment reward : float amount of reward associated with the previous state/action pair done : bool indicates whether the episode has ended info : dict contains other diagnostic information from the previous action """ for _ in range(self.env_params.sims_per_step): self.time_counter += 1 self.step_counter += 1 # perform acceleration actions for controlled human-driven vehicles if len(self.k.vehicle.get_controlled_ids()) > 0: accel = [] for veh_id in self.k.vehicle.get_controlled_ids(): action = self.k.vehicle.get_acc_controller( veh_id).get_action(self) accel.append(action) self.k.vehicle.apply_acceleration( self.k.vehicle.get_controlled_ids(), accel) # perform lane change actions for controlled human-driven vehicles if len(self.k.vehicle.get_controlled_lc_ids()) > 0: direction = [] for veh_id in self.k.vehicle.get_controlled_lc_ids(): target_lane = self.k.vehicle.get_lane_changing_controller( veh_id).get_action(self) direction.append(target_lane) self.k.vehicle.apply_lane_change( self.k.vehicle.get_controlled_lc_ids(), direction=direction) # perform (optionally) routing actions for all vehicles in the # network, including RL and SUMO-controlled vehicles routing_ids = [] routing_actions = [] for veh_id in self.k.vehicle.get_ids(): if self.k.vehicle.get_routing_controller(veh_id) \ is not None: routing_ids.append(veh_id) route_contr = self.k.vehicle.get_routing_controller( veh_id) routing_actions.append(route_contr.choose_route(self)) self.k.vehicle.choose_routes(routing_ids, routing_actions) self.apply_rl_actions(rl_actions) self.additional_command() # advance the simulation in the simulator by one step self.k.simulation.simulation_step() # store new observations in the vehicles and traffic lights class self.k.update(reset=False) # update the colors of vehicles if self.sim_params.render: self.k.vehicle.update_vehicle_colors() # crash encodes whether the simulator experienced a collision crash = self.k.simulation.check_collision() # stop collecting new simulation steps if there is a collision if crash: break # render a frame self.render() states = self.get_state() # collect information of the state of the network based on the # environment class used self.state = np.asarray(states).T # collect observation new state associated with action next_observation = np.copy(states) # test if the environment should terminate due to a collision or the # time horizon being met done = (self.time_counter >= self.env_params.warmup_steps + self.env_params.horizon) or self.stuck if done: print("done") if self.stuck: print("stuck") else: print("time up") # compute the info for each agent infos = {} # compute the reward if self.env_params.clip_actions: rl_clipped = self.clip_actions(rl_actions) reward = self.compute_reward(rl_clipped, fail=crash) else: reward = self.compute_reward(rl_actions, fail=crash) return next_observation, reward, done, infos def reset(self): """See parent class. This also includes updating the initial absolute position and previous position. """ self.rl_queue.clear() self.rl_veh.clear() obs = super().reset() print("reset") for veh_id in self.k.vehicle.get_ids(): self.absolute_position[veh_id] = self.k.vehicle.get_x_by_id(veh_id) self.prev_pos[veh_id] = self.k.vehicle.get_x_by_id(veh_id) self.leader = [] self.follower = [] return obs if __name__ == "__main__": flow_params = dict( exp_tag='template', env_name=MoveXYPedEnv, network=PedCrossing, simulator='traci', sim=sim_params, env=env_params, net=net_params, veh=vehicles, initial=initial_config, tls=tl_logic, ) # number of time steps flow_params['env'].horizon = 10000 exp = Experiment(flow_params) # run the sumo simulation _ = exp.run(1)

37.053312

125

0.588158

from flow.envs import TestEnv from flow.core.experiment import Experiment from flow.networks import Network from flow.envs.base import Env from flow.core.params import VehicleParams, SumoCarFollowingParams, SumoLaneChangeParams from flow.controllers import IDMController from flow.core.params import InFlows from flow.core.params import NetParams from flow.core.params import TrafficLightParams from flow.core.params import InitialConfig from flow.core.params import EnvParams from flow.controllers import IDMController, RLController, StaticLaneChanger from gym.spaces.box import Box import numpy as np import collections HORIZON = 3000 env_params = EnvParams( horizon=HORIZON, sims_per_step=1, warmup_steps=0, additional_params={ "max_accel": 3, "max_decel": -2, "target_velocity": 20, "lane_change_duration": 4, "num_rl": 5, }) initial_config = InitialConfig(edges_distribution=['highway_0']) vehicles = VehicleParams() vehicles.add( veh_id="human", acceleration_controller=(IDMController, { "noise": 0.2 }), car_following_params=SumoCarFollowingParams( speed_mode="obey_safe_speed", ), lane_change_params=SumoLaneChangeParams( lane_change_mode=1621, model="SL2015", lc_impatience="0.1", lc_time_to_impatience="1.0" )) vehicles.add( veh_id="rl", acceleration_controller=(RLController, {}), lane_change_controller=(StaticLaneChanger, {}), car_following_params=SumoCarFollowingParams( speed_mode="obey_safe_speed", ), lane_change_params=SumoLaneChangeParams( lane_change_mode=256, model="SL2015", lc_impatience="0.1", lc_time_to_impatience="1.0" ), num_vehicles=0) from flow.core.params import SumoParams sim_params = SumoParams( sim_step=0.2, render=True, lateral_resolution=1.0, restart_instance=True, ) import os inflow = InFlows() inflow.add(veh_type="human", edge="WC", depart_lane=1, arrivalLane=0, probability=0.1, depart_speed="random", ) inflow.add(veh_type="human", edge="WC", depart_lane=0, arrivalLane=1, probability=0.1, depart_speed="random", ) inflow.add(veh_type="human", edge="EC", depart_lane=1, arrivalLane=0, probability=0.1, depart_speed="random", ) inflow.add(veh_type="human", edge="EC", depart_lane=0, arrivalLane=1, probability=0.1, depart_speed="random", ) inflow.add( veh_type="rl", edge="WC", vehs_per_hour=100, depart_lane="free", depart_speed=5) net_params = NetParams( template={ "net":"/home/rong/Safe-RL-for-Driving/traci_pedestrian_crossing/pedcrossing.net.xml", "vtype": "/home/rong/Safe-RL-for-Driving/traci_pedestrian_crossing/pedcrossing.add.xml", "rou":"/home/rong/Safe-RL-for-Driving/traci_pedestrian_crossing/data/pedcrossing.rou.xml", "trip":"/home/rong/Safe-RL-for-Driving/traci_pedestrian_crossing/pedestrians.trip.xml" }, inflows=inflow, ) initial_config = InitialConfig( edges_distribution=["WC"] ) tl_logic = TrafficLightParams(baseline=False) phases = [{"duration": "100000", "state": "GGGGr"}, {"duration": "4", "state": "yyyyr"}, {"duration": "10", "state": "rrrrG"}, {"duration": "10", "state": "rrrrr"}] tl_logic.add("C", phases=phases, programID="custom", offset="0") class PedCrossing(Network): def specify_routes(self, net_params): return {'EC': ['EC', 'CW'], 'WC': ['WC', 'CE']} class MoveXYPedEnv(Env): def __init__(self, env_params, sim_params, network, simulator='traci'): super().__init__(env_params, sim_params, network, simulator) self.activeRequest = False self.greenTimeSoFar = 0 self.MIN_GREEN_TIME = 15 self.VEHICLE_GREEN_PHASE = 0 self.PEDESTRIAN_GREEN_PHASE = 2 self.TLSID = 'C' self.WALKINGAREAS = [':C_w0', ':C_w1'] self.CROSSINGS = [':C_c0'] self.num_lanes = max(self.k.network.num_lanes(edge) for edge in self.k.network.get_edge_list()) self.visible = [] self.stuck = False self.prev_pos = dict() self.absolute_position = dict() self.num_rl = env_params.additional_params["num_rl"] self.rl_queue = collections.deque() self.rl_veh = [] self.leader = [] self.follower = [] @property def action_space(self): max_decel = self.env_params.additional_params["max_decel"] max_accel = self.env_params.additional_params["max_accel"] lb = [1, -0.2] * self.num_rl ub = [2, 0.2] * self.num_rl return Box(np.array(lb), np.array(ub), dtype=np.float32) @property def observation_space(self): return Box( low=-1000, high=3000, shape=(4 * self.num_rl * self.num_lanes + 2 * self.num_rl, ), dtype=np.float32) def compute_reward(self, rl_actions, **kwargs): reward = 0 rl_velocity = np.array(self.k.vehicle.get_speed(self.rl_veh)) target_vel = self.env_params.additional_params['target_velocity'] max_cost = np.array([target_vel] * self.num_rl) max_cost = np.linalg.norm(max_cost) cost = rl_velocity - target_vel cost = np.linalg.norm(cost) eps = np.finfo(np.float32).eps reward += max(max_cost - cost, 0) / (max_cost + eps) gain = 0.5 thresh = 0.3 penalize = len(rl_velocity[rl_velocity < thresh]) reward -= gain * penalize for veh_id in self.rl_veh: if self.k.vehicle.get_last_lc(veh_id) == self.time_counter: reward -= 10 if self.stuck: reward -= 100 return reward def _apply_rl_actions(self, actions): acceleration = actions[::2] direction = actions[1::2] veh): if self.time_counter <= self.env_params.additional_params["lane_change_duration"]\ + self.k.vehicle.get_last_lc(veh_id): direction[i] = 0 x, y = self.k.vehicle.kernel_api.vehicle.getPosition(veh_id) print(x, y) print("edgeID", self.k.vehicle.get_edge(veh_id)) print("lane", self.k.vehicle.get_lane(veh_id)) self.k.vehicle.kernel_api.vehicle.moveToXY(vehID=veh_id, edgeID="highway_1", lane=1, x=x+acceleration[i], y=y+direction[i], keepRoute=2) for x in np.nditer(direction, op_flags=['readwrite']): if x > 0.7: x[...] = 1 elif x < -0.7: x[...] = -1 else: x[...] = 0 def get_state(self): obs = [ 0 for _ in range(4 * self.num_rl * self.num_lanes + 2 * self.num_rl) ] self.visible = [] self.update_veh_id() speeds = [] for i, rl_id in enumerate(self.rl_veh): x = self.k.vehicle.get_x_by_id(rl_id) if x == -1001: continue speed = self.k.vehicle.get_speed(rl_id) obs[-2*i - 1] = speed speeds.append(speed) obs[-2*i - 2] = x max_length = self.k.network.length() max_speed = self.k.network.max_speed() headway = [1] * self.num_lanes tailway = [1] * self.num_lanes vel_in_front = [0] * self.num_lanes vel_behind = [0] * self.num_lanes lane_leaders = self.k.vehicle.get_lane_leaders(rl_id) lane_followers = self.k.vehicle.get_lane_followers(rl_id) lane_headways = self.k.vehicle.get_lane_headways(rl_id) lane_tailways = self.k.vehicle.get_lane_tailways(rl_id) headway[0:len(lane_headways)] = lane_headways tailway[0:len(lane_tailways)] = lane_tailways for j, lane_leader in enumerate(lane_leaders): if lane_leader != '': lane_headways[j] /= max_length vel_in_front[j] = self.k.vehicle.get_speed(lane_leader) \ / max_speed self.visible.extend([lane_leader]) for j, lane_follower in enumerate(lane_followers): if lane_follower != '': lane_headways[j] /= max_length vel_behind[j] = self.k.vehicle.get_speed(lane_follower) \ / max_speed self.visible.extend([lane_follower]) obs[4*self.num_lanes*i:4*self.num_lanes*(i+1)] = \ np.concatenate((headway, tailway, vel_in_front, vel_behind)) obs = np.array(obs) np.clip(obs, -1000, 3000, out=obs) return obs def additional_command(self): if not self.activeRequest: self.activeRequest = self.checkWaitingPersons() if self.k.kernel_api.trafficlight.getPhase(self.TLSID) == self.VEHICLE_GREEN_PHASE: self.greenTimeSoFar += 1 if self.greenTimeSoFar > self.MIN_GREEN_TIME: if self.activeRequest: self.k.kernel_api.trafficlight.setPhase( self.TLSID, self.VEHICLE_GREEN_PHASE + 1) self.activeRequest = False for veh_id in self.leader + self.follower: self.k.vehicle.set_observed(veh_id) for veh_id in self.k.vehicle.get_ids(): this_pos = self.k.vehicle.get_x_by_id(veh_id) if this_pos == -1001: self.absolute_position[veh_id] = -1001 else: change = this_pos - self.prev_pos.get(veh_id, this_pos) self.absolute_position[veh_id] = \ (self.absolute_position.get(veh_id, this_pos) + change) \ % self.k.network.length() self.prev_pos[veh_id] = this_pos return def update_veh_id(self): # add rl vehicles that just entered the network into the rl queue for veh_id in self.k.vehicle.get_rl_ids(): if veh_id not in list(self.rl_queue) + self.rl_veh: self.rl_queue.append(veh_id) # remove rl vehicles that exited the network for veh_id in list(self.rl_queue): if veh_id not in self.k.vehicle.get_rl_ids() or veh_id not in self.k.vehicle.get_ids(): self.rl_queue.remove(veh_id) for veh_id in self.rl_veh: if veh_id not in self.k.vehicle.get_rl_ids() or veh_id not in self.k.vehicle.get_ids(): # print("rm veh_id", veh_id) self.rl_veh.remove(veh_id) # fil up rl_veh until they are enough controlled vehicles while len(self.rl_queue) > 0 and len(self.rl_veh) < self.num_rl: rl_id = self.rl_queue.popleft() self.rl_veh.append(rl_id) # print("add rl_veh:", rl_id) # print("update_veh_id, self.rl_veh:", self.rl_veh) def checkWaitingPersons(self): # check both sides of the crossing for edge in self.WALKINGAREAS: peds = self.k.kernel_api.edge.getLastStepPersonIDs(edge) # check who is waiting at the crossing # we assume that pedestrians push the button upon # standing still for 1s for ped in peds: if (self.k.kernel_api.person.getWaitingTime(ped) == 1 and self.k.kernel_api.person.getNextEdge(ped) in self.CROSSINGS): numWaiting = self.k.kernel_api.trafficlight.getServedPersonCount(self.TLSID, self.PEDESTRIAN_GREEN_PHASE) print("%s: pedestrian %s pushes the button (waiting: %s)" % (self.k.kernel_api.simulation.getTime(), ped, numWaiting)) return True return False def step(self, rl_actions): for _ in range(self.env_params.sims_per_step): self.time_counter += 1 self.step_counter += 1 # perform acceleration actions for controlled human-driven vehicles if len(self.k.vehicle.get_controlled_ids()) > 0: accel = [] for veh_id in self.k.vehicle.get_controlled_ids(): action = self.k.vehicle.get_acc_controller( veh_id).get_action(self) accel.append(action) self.k.vehicle.apply_acceleration( self.k.vehicle.get_controlled_ids(), accel) # perform lane change actions for controlled human-driven vehicles if len(self.k.vehicle.get_controlled_lc_ids()) > 0: direction = [] for veh_id in self.k.vehicle.get_controlled_lc_ids(): target_lane = self.k.vehicle.get_lane_changing_controller( veh_id).get_action(self) direction.append(target_lane) self.k.vehicle.apply_lane_change( self.k.vehicle.get_controlled_lc_ids(), direction=direction) # perform (optionally) routing actions for all vehicles in the # network, including RL and SUMO-controlled vehicles routing_ids = [] routing_actions = [] for veh_id in self.k.vehicle.get_ids(): if self.k.vehicle.get_routing_controller(veh_id) \ is not None: routing_ids.append(veh_id) route_contr = self.k.vehicle.get_routing_controller( veh_id) routing_actions.append(route_contr.choose_route(self)) self.k.vehicle.choose_routes(routing_ids, routing_actions) self.apply_rl_actions(rl_actions) self.additional_command() # advance the simulation in the simulator by one step self.k.simulation.simulation_step() # store new observations in the vehicles and traffic lights class self.k.update(reset=False) # update the colors of vehicles if self.sim_params.render: self.k.vehicle.update_vehicle_colors() # crash encodes whether the simulator experienced a collision crash = self.k.simulation.check_collision() # stop collecting new simulation steps if there is a collision if crash: break # render a frame self.render() states = self.get_state() # collect information of the state of the network based on the # environment class used self.state = np.asarray(states).T # collect observation new state associated with action next_observation = np.copy(states) # test if the environment should terminate due to a collision or the # time horizon being met done = (self.time_counter >= self.env_params.warmup_steps + self.env_params.horizon) or self.stuck if done: print("done") if self.stuck: print("stuck") else: print("time up") # compute the info for each agent infos = {} # compute the reward if self.env_params.clip_actions: rl_clipped = self.clip_actions(rl_actions) reward = self.compute_reward(rl_clipped, fail=crash) else: reward = self.compute_reward(rl_actions, fail=crash) return next_observation, reward, done, infos def reset(self): self.rl_queue.clear() self.rl_veh.clear() obs = super().reset() print("reset") for veh_id in self.k.vehicle.get_ids(): self.absolute_position[veh_id] = self.k.vehicle.get_x_by_id(veh_id) self.prev_pos[veh_id] = self.k.vehicle.get_x_by_id(veh_id) self.leader = [] self.follower = [] return obs if __name__ == "__main__": flow_params = dict( exp_tag='template', env_name=MoveXYPedEnv, network=PedCrossing, simulator='traci', sim=sim_params, env=env_params, net=net_params, veh=vehicles, initial=initial_config, tls=tl_logic, ) # number of time steps flow_params['env'].horizon = 10000 exp = Experiment(flow_params) # run the sumo simulation _ = exp.run(1)

true

790be614a340f7ba78a86e453628b9bbc3592651

2,805

py

Python

sdk/storage/azure-mgmt-storage/azure/mgmt/storage/v2018_11_01/models/update_history_property.py

pjquirk/azure-sdk-for-python

cbf02ec4f177b96eae1dbbba87c34c2c93880150

[ "MIT" ]

1

2021-09-07T18:36:04.000Z

sdk/storage/azure-mgmt-storage/azure/mgmt/storage/v2018_11_01/models/update_history_property.py

pjquirk/azure-sdk-for-python

cbf02ec4f177b96eae1dbbba87c34c2c93880150

[ "MIT" ]

2

2019-10-02T23:37:38.000Z

2020-10-02T01:17:31.000Z

azure-mgmt-storage/azure/mgmt/storage/v2018_11_01/models/update_history_property.py

xiafu-msft/azure-sdk-for-python

4d9560cfd519ee60667f3cc2f5295a58c18625db

[ "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class UpdateHistoryProperty(Model): """An update history of the ImmutabilityPolicy of a blob container. Variables are only populated by the server, and will be ignored when sending a request. :ivar update: The ImmutabilityPolicy update type of a blob container, possible values include: put, lock and extend. Possible values include: 'put', 'lock', 'extend' :vartype update: str or ~azure.mgmt.storage.v2018_11_01.models.ImmutabilityPolicyUpdateType :ivar immutability_period_since_creation_in_days: The immutability period for the blobs in the container since the policy creation, in days. :vartype immutability_period_since_creation_in_days: int :ivar timestamp: Returns the date and time the ImmutabilityPolicy was updated. :vartype timestamp: datetime :ivar object_identifier: Returns the Object ID of the user who updated the ImmutabilityPolicy. :vartype object_identifier: str :ivar tenant_id: Returns the Tenant ID that issued the token for the user who updated the ImmutabilityPolicy. :vartype tenant_id: str :ivar upn: Returns the User Principal Name of the user who updated the ImmutabilityPolicy. :vartype upn: str """ _validation = { 'update': {'readonly': True}, 'immutability_period_since_creation_in_days': {'readonly': True}, 'timestamp': {'readonly': True}, 'object_identifier': {'readonly': True}, 'tenant_id': {'readonly': True}, 'upn': {'readonly': True}, } _attribute_map = { 'update': {'key': 'update', 'type': 'str'}, 'immutability_period_since_creation_in_days': {'key': 'immutabilityPeriodSinceCreationInDays', 'type': 'int'}, 'timestamp': {'key': 'timestamp', 'type': 'iso-8601'}, 'object_identifier': {'key': 'objectIdentifier', 'type': 'str'}, 'tenant_id': {'key': 'tenantId', 'type': 'str'}, 'upn': {'key': 'upn', 'type': 'str'}, } def __init__(self, **kwargs): super(UpdateHistoryProperty, self).__init__(**kwargs) self.update = None self.immutability_period_since_creation_in_days = None self.timestamp = None self.object_identifier = None self.tenant_id = None self.upn = None

40.652174

118

0.647772

from msrest.serialization import Model class UpdateHistoryProperty(Model): _validation = { 'update': {'readonly': True}, 'immutability_period_since_creation_in_days': {'readonly': True}, 'timestamp': {'readonly': True}, 'object_identifier': {'readonly': True}, 'tenant_id': {'readonly': True}, 'upn': {'readonly': True}, } _attribute_map = { 'update': {'key': 'update', 'type': 'str'}, 'immutability_period_since_creation_in_days': {'key': 'immutabilityPeriodSinceCreationInDays', 'type': 'int'}, 'timestamp': {'key': 'timestamp', 'type': 'iso-8601'}, 'object_identifier': {'key': 'objectIdentifier', 'type': 'str'}, 'tenant_id': {'key': 'tenantId', 'type': 'str'}, 'upn': {'key': 'upn', 'type': 'str'}, } def __init__(self, **kwargs): super(UpdateHistoryProperty, self).__init__(**kwargs) self.update = None self.immutability_period_since_creation_in_days = None self.timestamp = None self.object_identifier = None self.tenant_id = None self.upn = None

true

790be61c348d5993e377955e149fd1abe0c7fda9

1,592

bzl

Python

sqldelight.bzl

ThomasCJY/sqldelight_bazel_rules

07171e39d5340861123368607e6118de9f2b140e

[ "Apache-2.0" ]

null

sqldelight.bzl

ThomasCJY/sqldelight_bazel_rules

07171e39d5340861123368607e6118de9f2b140e

[ "Apache-2.0" ]

null

sqldelight.bzl

ThomasCJY/sqldelight_bazel_rules

07171e39d5340861123368607e6118de9f2b140e

[ "Apache-2.0" ]

null

"""provides an sqldelight compiler""" def _sqldelight_codegen_impl(ctx): srcjar = ctx.outputs.srcjar args = ctx.actions.args() args.add("-o", srcjar) if not ctx.attr.module_name or not ctx.attr.package_name: fail("Non-legacy SQLDelightc requires both module_name and package_name set.") args.add("--module_name", ctx.attr.module_name) args.add("--package_name", ctx.attr.package_name) args.add_all(ctx.files.srcs) src_roots = {} for f in ctx.files.srcs: (pre, src, rel_name) = f.short_path.partition(ctx.attr.src_dir) src_roots[pre + src] = True args.add_joined("--src_dirs", src_roots.keys(), join_with = ",") ctx.actions.run( executable = ctx.executable._sqldelight_compiler, inputs = ctx.files.srcs, outputs = [srcjar], arguments = [args], ) return struct( providers = [DefaultInfo(files = depset([srcjar]))], ) sqldelight_codegen = rule( _sqldelight_codegen_impl, attrs = { "_sqldelight_compiler": attr.label( default = Label("@rules_sqldelight//:sqldelightc"), executable = True, cfg = "host", ), "srcs": attr.label_list(allow_files = [".sq"]), "src_dir": attr.string( mandatory = True, doc = "root directory of the source tree, used to derived the classnames.", ), "module_name": attr.string(), "package_name": attr.string(), }, output_to_genfiles = True, outputs = { "srcjar": "%{name}_sqldelight.srcjar", }, )

30.615385

87

0.606156

def _sqldelight_codegen_impl(ctx): srcjar = ctx.outputs.srcjar args = ctx.actions.args() args.add("-o", srcjar) if not ctx.attr.module_name or not ctx.attr.package_name: fail("Non-legacy SQLDelightc requires both module_name and package_name set.") args.add("--module_name", ctx.attr.module_name) args.add("--package_name", ctx.attr.package_name) args.add_all(ctx.files.srcs) src_roots = {} for f in ctx.files.srcs: (pre, src, rel_name) = f.short_path.partition(ctx.attr.src_dir) src_roots[pre + src] = True args.add_joined("--src_dirs", src_roots.keys(), join_with = ",") ctx.actions.run( executable = ctx.executable._sqldelight_compiler, inputs = ctx.files.srcs, outputs = [srcjar], arguments = [args], ) return struct( providers = [DefaultInfo(files = depset([srcjar]))], ) sqldelight_codegen = rule( _sqldelight_codegen_impl, attrs = { "_sqldelight_compiler": attr.label( default = Label("@rules_sqldelight//:sqldelightc"), executable = True, cfg = "host", ), "srcs": attr.label_list(allow_files = [".sq"]), "src_dir": attr.string( mandatory = True, doc = "root directory of the source tree, used to derived the classnames.", ), "module_name": attr.string(), "package_name": attr.string(), }, output_to_genfiles = True, outputs = { "srcjar": "%{name}_sqldelight.srcjar", }, )

true

790be68617a1d612e24c1bf1fb4470a66869f5bf

1,917

py

Python

dataPrepare.py

asterberova/unet

7cac389f9176a59f8f2d136be0751631361dcaf8

[ "MIT" ]

null

dataPrepare.py

asterberova/unet

7cac389f9176a59f8f2d136be0751631361dcaf8

[ "MIT" ]

null

dataPrepare.py

asterberova/unet

7cac389f9176a59f8f2d136be0751631361dcaf8

[ "MIT" ]

null

from data import * # data augmentation #In deep learning tasks, a lot of data is need to train DNN model, when the dataset is not big enough, data augmentation should be applied. #keras.preprocessing.image.ImageDataGenerator is a data generator, which can feed the DNN with data like : (data,label), it can also do data augmentation at the same time. #It is very convenient for us to use keras.preprocessing.image.ImageDataGenerator to do data augmentation by implement image rotation, shift, rescale and so on... see [keras documentation](https://keras.io/preprocessing/image/) for detail. #For image segmentation tasks, the image and mask must be transformed **together!!** ## define your data generator # If you want to visualize your data augmentation result, set save_to_dir = your path #if you don't want to do data augmentation, set data_gen_args as an empty dict. #data_gen_args = dict() data_gen_args = dict(rotation_range=0.2, width_shift_range=0.05, height_shift_range=0.05, shear_range=0.05, zoom_range=0.05, horizontal_flip=True, fill_mode='nearest') myGenerator = trainGenerator(20, '/data/s2732815/unet/data/train', 'image', 'label', data_gen_args, save_to_dir = '/data/s2732815/unet/data/train/aug') ## visualize your data augmentation result #you will see 60 transformed images and their masks in data/membrane/train/aug num_batch = 3 for i,batch in enumerate(myGenerator): if(i >= num_batch): break ## create .npy data # If your computer has enough memory, you can create npy files containing all your images and masks, and feed your DNN with them. # image_arr, mask_arr = geneTrainNpy("data/membrane/train/aug/", "data/membrane/train/aug/") # np.save("data/image_arr.npy",image_arr) # np.save("data/mask_arr.npy",mask_arr)

42.6

239

0.70579

from data import * data_gen_args = dict(rotation_range=0.2, width_shift_range=0.05, height_shift_range=0.05, shear_range=0.05, zoom_range=0.05, horizontal_flip=True, fill_mode='nearest') myGenerator = trainGenerator(20, '/data/s2732815/unet/data/train', 'image', 'label', data_gen_args, save_to_dir = '/data/s2732815/unet/data/train/aug') ## visualize your data augmentation result #you will see 60 transformed images and their masks in data/membrane/train/aug num_batch = 3 for i,batch in enumerate(myGenerator): if(i >= num_batch): break ## create .npy data # If your computer has enough memory, you can create npy files containing all your images and masks, and feed your DNN with them. # image_arr, mask_arr = geneTrainNpy("data/membrane/train/aug/", "data/membrane/train/aug/") # np.save("data/image_arr.npy",image_arr) # np.save("data/mask_arr.npy",mask_arr)

true

790be6df526f235235b1c80f97047dec72f18cc7

915

py

Python

tests/test_stormtrack/test_core/test_features/data/circle_on_globe_clat-00_rad-800_delta-1.0_pyproj.py

ruestefa/stormtrack

e9378f013c406d387ea944c97e5adc68df864dee

[ "MIT" ]

null

tests/test_stormtrack/test_core/test_features/data/circle_on_globe_clat-00_rad-800_delta-1.0_pyproj.py

ruestefa/stormtrack

e9378f013c406d387ea944c97e5adc68df864dee

[ "MIT" ]

2

2021-01-06T17:37:42.000Z

2021-02-05T18:40:52.000Z

tests/test_stormtrack/test_core/test_features/data/circle_on_globe_clat-00_rad-800_delta-1.0_pyproj.py

ruestefa/stormtrack

e9378f013c406d387ea944c97e5adc68df864dee

[ "MIT" ]

null

import numpy as np # fmt: off clon, clat = 0.0, 0.0 rad_km = 800.0 area_km2 = np.pi*rad_km**2 nlat, nlon = 17, 17 lat1d = np.linspace(-8.0, 8.0, nlat) lon1d = np.linspace(-8.0, 8.0, nlon) lat2d, lon2d = np.meshgrid(lat1d, lon1d) _, X = 0, 1 mask = np.array([ [_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_], [_,_,_,_,_,X,X,X,X,X,X,X,_,_,_,_,_], [_,_,_,X,X,X,X,X,X,X,X,X,X,X,_,_,_], [_,_,X,X,X,X,X,X,X,X,X,X,X,X,X,_,_], [_,_,X,X,X,X,X,X,X,X,X,X,X,X,X,_,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,_,X,X,X,X,X,X,X,X,X,X,X,X,X,_,_], [_,_,X,X,X,X,X,X,X,X,X,X,X,X,X,_,_], [_,_,_,X,X,X,X,X,X,X,X,X,X,X,_,_,_], [_,_,_,_,_,X,X,X,X,X,X,X,_,_,_,_,_], [_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_], ], np.bool).T[:, ::-1]

26.911765

40

0.504918

import numpy as np clon, clat = 0.0, 0.0 rad_km = 800.0 area_km2 = np.pi*rad_km**2 nlat, nlon = 17, 17 lat1d = np.linspace(-8.0, 8.0, nlat) lon1d = np.linspace(-8.0, 8.0, nlon) lat2d, lon2d = np.meshgrid(lat1d, lon1d) _, X = 0, 1 mask = np.array([ [_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_], [_,_,_,_,_,X,X,X,X,X,X,X,_,_,_,_,_], [_,_,_,X,X,X,X,X,X,X,X,X,X,X,_,_,_], [_,_,X,X,X,X,X,X,X,X,X,X,X,X,X,_,_], [_,_,X,X,X,X,X,X,X,X,X,X,X,X,X,_,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,X,X,X,X,X,X,X,X,X,X,X,X,X,X,X,_], [_,_,X,X,X,X,X,X,X,X,X,X,X,X,X,_,_], [_,_,X,X,X,X,X,X,X,X,X,X,X,X,X,_,_], [_,_,_,X,X,X,X,X,X,X,X,X,X,X,_,_,_], [_,_,_,_,_,X,X,X,X,X,X,X,_,_,_,_,_], [_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_], ], np.bool).T[:, ::-1]

true

790be748b1fd7585742d5a6611ef913bb380cf05

8,267

py

Python

wagtail/wagtailadmin/tests/test_rich_text.py

Girbons/wagtail

8a055addad739ff73f6e84ba553b28389122299f

[ "BSD-3-Clause" ]

1

2019-11-06T10:51:42.000Z

wagtail/wagtailadmin/tests/test_rich_text.py

Girbons/wagtail

8a055addad739ff73f6e84ba553b28389122299f

[ "BSD-3-Clause" ]

null

wagtail/wagtailadmin/tests/test_rich_text.py

Girbons/wagtail

8a055addad739ff73f6e84ba553b28389122299f

[ "BSD-3-Clause" ]

2

2017-08-08T01:39:02.000Z

2018-05-06T06:16:10.000Z

from __future__ import absolute_import, unicode_literals from django.conf import settings from django.core.urlresolvers import reverse from django.test import TestCase from django.test.utils import override_settings from wagtail.tests.testapp.models import SingleEventPage from wagtail.tests.testapp.rich_text import CustomRichTextArea from wagtail.tests.utils import WagtailTestUtils from wagtail.wagtailadmin.rich_text import HalloRichTextArea, get_rich_text_editor_widget from wagtail.wagtailcore.models import Page, get_page_models from wagtail.wagtailcore.rich_text import RichText class BaseRichTextEditHandlerTestCase(TestCase): def _clear_edit_handler_cache(self): """ These tests generate new EditHandlers with different settings. The cached edit handlers should be cleared before and after each test run to ensure that no changes leak through to other tests. """ from wagtail.tests.testapp.models import DefaultRichBlockFieldPage block_page_edit_handler = DefaultRichBlockFieldPage.get_edit_handler() if block_page_edit_handler._form_class: rich_text_block = block_page_edit_handler._form_class.base_fields['body'].block.child_blocks['rich_text'] if hasattr(rich_text_block, 'field'): del rich_text_block.field for page_class in get_page_models(): page_class.get_edit_handler.cache_clear() def setUp(self): super(BaseRichTextEditHandlerTestCase, self).setUp() self._clear_edit_handler_cache() def tearDown(self): self._clear_edit_handler_cache() super(BaseRichTextEditHandlerTestCase, self).tearDown() class TestGetRichTextEditorWidget(TestCase): @override_settings() def test_default(self): # Simulate the absence of a setting if hasattr(settings, 'WAGTAILADMIN_RICH_TEXT_EDITORS'): del settings.WAGTAILADMIN_RICH_TEXT_EDITORS self.assertIsInstance(get_rich_text_editor_widget(), HalloRichTextArea) @override_settings(WAGTAILADMIN_RICH_TEXT_EDITORS={ 'default': { 'WIDGET': 'wagtail.tests.testapp.rich_text.CustomRichTextArea' }, }) def test_overridden_default_editor(self): self.assertIsInstance(get_rich_text_editor_widget(), CustomRichTextArea) @override_settings(WAGTAILADMIN_RICH_TEXT_EDITORS={ 'custom': { 'WIDGET': 'wagtail.tests.testapp.rich_text.CustomRichTextArea' }, }) def test_custom_editor_without_default(self): self.assertIsInstance(get_rich_text_editor_widget('custom'), CustomRichTextArea) @override_settings(WAGTAILADMIN_RICH_TEXT_EDITORS={ 'default': { 'WIDGET': 'wagtail.wagtailadmin.rich_text.HalloRichTextArea' }, 'custom': { 'WIDGET': 'wagtail.tests.testapp.rich_text.CustomRichTextArea' }, }) def test_custom_editor_with_default(self): self.assertIsInstance(get_rich_text_editor_widget(), HalloRichTextArea) self.assertIsInstance(get_rich_text_editor_widget('custom'), CustomRichTextArea) @override_settings() class TestDefaultRichText(BaseRichTextEditHandlerTestCase, WagtailTestUtils): def setUp(self): super(TestDefaultRichText, self).setUp() # Find root page self.root_page = Page.objects.get(id=2) self.login() # Simulate the absence of a setting if hasattr(settings, 'WAGTAILADMIN_RICH_TEXT_EDITORS'): del settings.WAGTAILADMIN_RICH_TEXT_EDITORS def test_default_editor_in_rich_text_field(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'defaultrichtextfieldpage', self.root_page.id) )) # Check status code self.assertEqual(response.status_code, 200) # Check that hallo (default editor by now) self.assertContains(response, 'makeHalloRichTextEditable("id_body");') def test_default_editor_in_rich_text_block(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'defaultrichblockfieldpage', self.root_page.id) )) # Check status code self.assertEqual(response.status_code, 200) # Check that hallo (default editor by now) self.assertContains(response, 'makeHalloRichTextEditable("__PREFIX__-value");') @override_settings(WAGTAILADMIN_RICH_TEXT_EDITORS={ 'default': { 'WIDGET': 'wagtail.tests.testapp.rich_text.CustomRichTextArea' }, }) class TestOverriddenDefaultRichText(BaseRichTextEditHandlerTestCase, WagtailTestUtils): def setUp(self): super(TestOverriddenDefaultRichText, self).setUp() # Find root page self.root_page = Page.objects.get(id=2) self.login() def test_overridden_default_editor_in_rich_text_field(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'defaultrichtextfieldpage', self.root_page.id) )) # Check status code self.assertEqual(response.status_code, 200) # Check that hallo (default editor by now) was replaced with fake editor self.assertNotContains(response, 'makeHalloRichTextEditable("id_body");') self.assertContains(response, 'customEditorInitScript("id_body");') def test_overridden_default_editor_in_rich_text_block(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'defaultrichblockfieldpage', self.root_page.id) )) # Check status code self.assertEqual(response.status_code, 200) # Check that hallo (default editor by now) was replaced with fake editor self.assertNotContains(response, 'makeHalloRichTextEditable("__PREFIX__-value");') self.assertContains(response, 'customEditorInitScript("__PREFIX__-value");') @override_settings(WAGTAILADMIN_RICH_TEXT_EDITORS={ 'default': { 'WIDGET': 'wagtail.wagtailadmin.rich_text.HalloRichTextArea' }, 'custom': { 'WIDGET': 'wagtail.tests.testapp.rich_text.CustomRichTextArea' }, }) class TestCustomDefaultRichText(BaseRichTextEditHandlerTestCase, WagtailTestUtils): def setUp(self): super(TestCustomDefaultRichText, self).setUp() # Find root page self.root_page = Page.objects.get(id=2) self.login() def test_custom_editor_in_rich_text_field(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'customrichtextfieldpage', self.root_page.id) )) # Check status code self.assertEqual(response.status_code, 200) # Check that hallo (default editor by now) was replaced with fake editor self.assertNotContains(response, 'makeHalloRichTextEditable("id_body");') self.assertContains(response, 'customEditorInitScript("id_body");') def test_custom_editor_in_rich_text_block(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'customrichblockfieldpage', self.root_page.id) )) # Check status code self.assertEqual(response.status_code, 200) # Check that hallo (default editor by now) was replaced with fake editor self.assertNotContains(response, 'makeHalloRichTextEditable("__PREFIX__-value");') self.assertContains(response, 'customEditorInitScript("__PREFIX__-value");') class TestRichTextValue(TestCase): def setUp(self): self.root_page = Page.objects.get(id=2) self.single_event_page = SingleEventPage( title="foo", location='the moon', audience='public', cost='free', date_from='2001-01-01', ) self.root_page.add_child(instance=self.single_event_page) def test_render(self): text = 'To the <a linktype="page" id="{}">moon</a>!'.format( self.single_event_page.id ) value = RichText(text) result = str(value) expected = ( '<div class="rich-text">To the <a href="' '/foo/pointless-suffix/">moon</a>!</div>') self.assertEqual(result, expected)

36.90625

117

0.697835

from __future__ import absolute_import, unicode_literals from django.conf import settings from django.core.urlresolvers import reverse from django.test import TestCase from django.test.utils import override_settings from wagtail.tests.testapp.models import SingleEventPage from wagtail.tests.testapp.rich_text import CustomRichTextArea from wagtail.tests.utils import WagtailTestUtils from wagtail.wagtailadmin.rich_text import HalloRichTextArea, get_rich_text_editor_widget from wagtail.wagtailcore.models import Page, get_page_models from wagtail.wagtailcore.rich_text import RichText class BaseRichTextEditHandlerTestCase(TestCase): def _clear_edit_handler_cache(self): from wagtail.tests.testapp.models import DefaultRichBlockFieldPage block_page_edit_handler = DefaultRichBlockFieldPage.get_edit_handler() if block_page_edit_handler._form_class: rich_text_block = block_page_edit_handler._form_class.base_fields['body'].block.child_blocks['rich_text'] if hasattr(rich_text_block, 'field'): del rich_text_block.field for page_class in get_page_models(): page_class.get_edit_handler.cache_clear() def setUp(self): super(BaseRichTextEditHandlerTestCase, self).setUp() self._clear_edit_handler_cache() def tearDown(self): self._clear_edit_handler_cache() super(BaseRichTextEditHandlerTestCase, self).tearDown() class TestGetRichTextEditorWidget(TestCase): @override_settings() def test_default(self): if hasattr(settings, 'WAGTAILADMIN_RICH_TEXT_EDITORS'): del settings.WAGTAILADMIN_RICH_TEXT_EDITORS self.assertIsInstance(get_rich_text_editor_widget(), HalloRichTextArea) @override_settings(WAGTAILADMIN_RICH_TEXT_EDITORS={ 'default': { 'WIDGET': 'wagtail.tests.testapp.rich_text.CustomRichTextArea' }, }) def test_overridden_default_editor(self): self.assertIsInstance(get_rich_text_editor_widget(), CustomRichTextArea) @override_settings(WAGTAILADMIN_RICH_TEXT_EDITORS={ 'custom': { 'WIDGET': 'wagtail.tests.testapp.rich_text.CustomRichTextArea' }, }) def test_custom_editor_without_default(self): self.assertIsInstance(get_rich_text_editor_widget('custom'), CustomRichTextArea) @override_settings(WAGTAILADMIN_RICH_TEXT_EDITORS={ 'default': { 'WIDGET': 'wagtail.wagtailadmin.rich_text.HalloRichTextArea' }, 'custom': { 'WIDGET': 'wagtail.tests.testapp.rich_text.CustomRichTextArea' }, }) def test_custom_editor_with_default(self): self.assertIsInstance(get_rich_text_editor_widget(), HalloRichTextArea) self.assertIsInstance(get_rich_text_editor_widget('custom'), CustomRichTextArea) @override_settings() class TestDefaultRichText(BaseRichTextEditHandlerTestCase, WagtailTestUtils): def setUp(self): super(TestDefaultRichText, self).setUp() self.root_page = Page.objects.get(id=2) self.login() if hasattr(settings, 'WAGTAILADMIN_RICH_TEXT_EDITORS'): del settings.WAGTAILADMIN_RICH_TEXT_EDITORS def test_default_editor_in_rich_text_field(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'defaultrichtextfieldpage', self.root_page.id) )) self.assertEqual(response.status_code, 200) self.assertContains(response, 'makeHalloRichTextEditable("id_body");') def test_default_editor_in_rich_text_block(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'defaultrichblockfieldpage', self.root_page.id) )) self.assertEqual(response.status_code, 200) self.assertContains(response, 'makeHalloRichTextEditable("__PREFIX__-value");') @override_settings(WAGTAILADMIN_RICH_TEXT_EDITORS={ 'default': { 'WIDGET': 'wagtail.tests.testapp.rich_text.CustomRichTextArea' }, }) class TestOverriddenDefaultRichText(BaseRichTextEditHandlerTestCase, WagtailTestUtils): def setUp(self): super(TestOverriddenDefaultRichText, self).setUp() self.root_page = Page.objects.get(id=2) self.login() def test_overridden_default_editor_in_rich_text_field(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'defaultrichtextfieldpage', self.root_page.id) )) self.assertEqual(response.status_code, 200) self.assertNotContains(response, 'makeHalloRichTextEditable("id_body");') self.assertContains(response, 'customEditorInitScript("id_body");') def test_overridden_default_editor_in_rich_text_block(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'defaultrichblockfieldpage', self.root_page.id) )) self.assertEqual(response.status_code, 200) self.assertNotContains(response, 'makeHalloRichTextEditable("__PREFIX__-value");') self.assertContains(response, 'customEditorInitScript("__PREFIX__-value");') @override_settings(WAGTAILADMIN_RICH_TEXT_EDITORS={ 'default': { 'WIDGET': 'wagtail.wagtailadmin.rich_text.HalloRichTextArea' }, 'custom': { 'WIDGET': 'wagtail.tests.testapp.rich_text.CustomRichTextArea' }, }) class TestCustomDefaultRichText(BaseRichTextEditHandlerTestCase, WagtailTestUtils): def setUp(self): super(TestCustomDefaultRichText, self).setUp() self.root_page = Page.objects.get(id=2) self.login() def test_custom_editor_in_rich_text_field(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'customrichtextfieldpage', self.root_page.id) )) self.assertEqual(response.status_code, 200) self.assertNotContains(response, 'makeHalloRichTextEditable("id_body");') self.assertContains(response, 'customEditorInitScript("id_body");') def test_custom_editor_in_rich_text_block(self): response = self.client.get(reverse( 'wagtailadmin_pages:add', args=('tests', 'customrichblockfieldpage', self.root_page.id) )) self.assertEqual(response.status_code, 200) self.assertNotContains(response, 'makeHalloRichTextEditable("__PREFIX__-value");') self.assertContains(response, 'customEditorInitScript("__PREFIX__-value");') class TestRichTextValue(TestCase): def setUp(self): self.root_page = Page.objects.get(id=2) self.single_event_page = SingleEventPage( title="foo", location='the moon', audience='public', cost='free', date_from='2001-01-01', ) self.root_page.add_child(instance=self.single_event_page) def test_render(self): text = 'To the <a linktype="page" id="{}">moon</a>!'.format( self.single_event_page.id ) value = RichText(text) result = str(value) expected = ( '<div class="rich-text">To the <a href="' '/foo/pointless-suffix/">moon</a>!</div>') self.assertEqual(result, expected)

true

790be92e1e25a0d3e40d9053ddaae04fab3592ef

2,359

py

Python

virtual_env/lib/python3.5/site-packages/google/auth/_service_account_info.py

straydag/To_Due_Backend

ac91f5ebabe8e4f2b6db7faa5ccbd30ebdb4e3f6

[ "MIT" ]

3

2020-10-12T15:47:01.000Z

2022-01-14T19:51:26.000Z

virtual_env/lib/python3.5/site-packages/google/auth/_service_account_info.py

straydag/To_Due_Backend

ac91f5ebabe8e4f2b6db7faa5ccbd30ebdb4e3f6

[ "MIT" ]

16

2021-03-19T09:44:52.000Z

2022-03-12T00:22:14.000Z

virtual_env/lib/python3.5/site-packages/google/auth/_service_account_info.py

straydag/To_Due_Backend

ac91f5ebabe8e4f2b6db7faa5ccbd30ebdb4e3f6

[ "MIT" ]

2

2019-11-13T05:27:48.000Z

2020-01-21T06:35:19.000Z

# Copyright 2016 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Helper functions for loading data from a Google service account file.""" import io import json import six from google.auth import crypt def from_dict(data, require=None): """Validates a dictionary containing Google service account data. Creates and returns a :class:`google.auth.crypt.Signer` instance from the private key specified in the data. Args: data (Mapping[str, str]): The service account data require (Sequence[str]): List of keys required to be present in the info. Returns: google.auth.crypt.Signer: A signer created from the private key in the service account file. Raises: ValueError: if the data was in the wrong format, or if one of the required keys is missing. """ keys_needed = set(require if require is not None else []) missing = keys_needed.difference(six.iterkeys(data)) if missing: raise ValueError( "Service account info was not in the expected format, missing " "fields {}.".format(", ".join(missing)) ) # Create a signer. signer = crypt.RSASigner.from_service_account_info(data) return signer def from_filename(filename, require=None): """Reads a Google service account JSON file and returns its parsed info. Args: filename (str): The path to the service account .json file. require (Sequence[str]): List of keys required to be present in the info. Returns: Tuple[ Mapping[str, str], google.auth.crypt.Signer ]: The verified info and a signer instance. """ with io.open(filename, "r", encoding="utf-8") as json_file: data = json.load(json_file) return data, from_dict(data, require=require)

31.453333

78

0.680797

import io import json import six from google.auth import crypt def from_dict(data, require=None): keys_needed = set(require if require is not None else []) missing = keys_needed.difference(six.iterkeys(data)) if missing: raise ValueError( "Service account info was not in the expected format, missing " "fields {}.".format(", ".join(missing)) ) signer = crypt.RSASigner.from_service_account_info(data) return signer def from_filename(filename, require=None): with io.open(filename, "r", encoding="utf-8") as json_file: data = json.load(json_file) return data, from_dict(data, require=require)

true

790be99f361b330bc9299b73af97971e2e715eb6

33,313

py

Python

electrum_vestx/plugins/revealer/qt.py

anonymouszar/electrum-vestx

ca1dd0fc9ca8fc547d9def1934018b1a4acd7ecb

[ "MIT" ]

1

2021-04-30T10:50:54.000Z

electrum_vestx/plugins/revealer/qt.py

anonymouszar/electrum-vestx

ca1dd0fc9ca8fc547d9def1934018b1a4acd7ecb

[ "MIT" ]

2

2021-06-01T23:47:48.000Z

2021-11-15T17:48:49.000Z

electrum_vestx/plugins/revealer/qt.py

anonymouszar/electrum-vestx

ca1dd0fc9ca8fc547d9def1934018b1a4acd7ecb

[ "MIT" ]

2

2019-07-04T02:49:33.000Z

2021-11-24T09:27:47.000Z

''' Revealer Do you have something to hide? Secret backup plug-in for the electrum wallet. Tiago Romagnani Silveira, 2017 ''' import os import random import traceback from decimal import Decimal from functools import partial import sys import qrcode from PyQt5.QtPrintSupport import QPrinter from PyQt5.QtCore import Qt, QRectF, QRect, QSizeF, QUrl, QPoint, QSize from PyQt5.QtGui import (QPixmap, QImage, QBitmap, QPainter, QFontDatabase, QPen, QFont, QColor, QDesktopServices, qRgba, QPainterPath) from PyQt5.QtWidgets import (QGridLayout, QVBoxLayout, QHBoxLayout, QLabel, QPushButton, QLineEdit) from electrum_vestx.plugin import hook from electrum_vestx.i18n import _ from electrum_vestx.util import make_dir, InvalidPassword, UserCancelled from electrum_vestx.gui.qt.util import (read_QIcon, EnterButton, WWLabel, icon_path, WindowModalDialog, Buttons, CloseButton, OkButton) from electrum_vestx.gui.qt.qrtextedit import ScanQRTextEdit from electrum_vestx.gui.qt.main_window import StatusBarButton from .revealer import RevealerPlugin class Plugin(RevealerPlugin): MAX_PLAINTEXT_LEN = 189 # chars def __init__(self, parent, config, name): RevealerPlugin.__init__(self, parent, config, name) self.base_dir = os.path.join(config.electrum_path(), 'revealer') if self.config.get('calibration_h') is None: self.config.set_key('calibration_h', 0) if self.config.get('calibration_v') is None: self.config.set_key('calibration_v', 0) self.calibration_h = self.config.get('calibration_h') self.calibration_v = self.config.get('calibration_v') self.f_size = QSize(1014*2, 642*2) self.abstand_h = 21 self.abstand_v = 34 self.calibration_noise = int('10' * 128) self.rawnoise = False make_dir(self.base_dir) self.extension = False @hook def create_status_bar(self, parent): b = StatusBarButton(read_QIcon('revealer.png'), "Revealer "+_("secret backup utility"), partial(self.setup_dialog, parent)) parent.addPermanentWidget(b) def requires_settings(self): return True def settings_widget(self, window): return EnterButton(_('Printer Calibration'), partial(self.calibration_dialog, window)) def password_dialog(self, msg=None, parent=None): from electrum_vestx.gui.qt.password_dialog import PasswordDialog parent = parent or self d = PasswordDialog(parent, msg) return d.run() def get_seed(self): password = None if self.wallet.has_keystore_encryption(): password = self.password_dialog(parent=self.d.parent()) if not password: raise UserCancelled() keystore = self.wallet.get_keystore() if not keystore or not keystore.has_seed(): return self.extension = bool(keystore.get_passphrase(password)) return keystore.get_seed(password) def setup_dialog(self, window): self.wallet = window.parent().wallet self.update_wallet_name(self.wallet) self.user_input = False self.d = WindowModalDialog(window, "Setup Dialog") self.d.setMinimumWidth(500) self.d.setMinimumHeight(210) self.d.setMaximumHeight(320) self.d.setContentsMargins(11,11,1,1) self.hbox = QHBoxLayout(self.d) vbox = QVBoxLayout() logo = QLabel() self.hbox.addWidget(logo) logo.setPixmap(QPixmap(icon_path('revealer.png'))) logo.setAlignment(Qt.AlignLeft) self.hbox.addSpacing(16) vbox.addWidget(WWLabel(""+_("Revealer Secret Backup Plugin")+" " +_("To encrypt your backup, first we need to load some noise.")+" ")) vbox.addSpacing(7) bcreate = QPushButton(_("Create a new Revealer")) bcreate.setMaximumWidth(181) bcreate.setDefault(True) vbox.addWidget(bcreate, Qt.AlignCenter) self.load_noise = ScanQRTextEdit() self.load_noise.setTabChangesFocus(True) self.load_noise.textChanged.connect(self.on_edit) self.load_noise.setMaximumHeight(33) self.hbox.addLayout(vbox) vbox.addWidget(WWLabel(_("or type an existing revealer code below and click 'next':"))) vbox.addWidget(self.load_noise) vbox.addSpacing(3) self.next_button = QPushButton(_("Next"), self.d) self.next_button.setEnabled(False) vbox.addLayout(Buttons(self.next_button)) self.next_button.clicked.connect(self.d.close) self.next_button.clicked.connect(partial(self.cypherseed_dialog, window)) vbox.addWidget( QLabel("" + _("Warning") + ": " + _("Each revealer should be used only once.") +" "+_("more information at <a href=\"https://revealer.cc/faq\">https://revealer.cc/faq</a>"))) def mk_digital(): try: self.make_digital(self.d) except Exception: self.logger.exception('') else: self.cypherseed_dialog(window) bcreate.clicked.connect(mk_digital) return bool(self.d.exec_()) def get_noise(self): text = self.load_noise.text() return ''.join(text.split()).lower() def on_edit(self): txt = self.get_noise() versioned_seed = self.get_versioned_seed_from_user_input(txt) if versioned_seed: self.versioned_seed = versioned_seed self.user_input = bool(versioned_seed) self.next_button.setEnabled(bool(versioned_seed)) def make_digital(self, dialog): self.make_rawnoise(True) self.bdone(dialog) self.d.close() def get_path_to_revealer_file(self, ext: str= '') -> str: version = self.versioned_seed.version code_id = self.versioned_seed.checksum filename = self.filename_prefix + version + "_" + code_id + ext path = os.path.join(self.base_dir, filename) return os.path.normcase(os.path.abspath(path)) def get_path_to_calibration_file(self): path = os.path.join(self.base_dir, 'calibration.pdf') return os.path.normcase(os.path.abspath(path)) def bcrypt(self, dialog): self.rawnoise = False version = self.versioned_seed.version code_id = self.versioned_seed.checksum dialog.show_message(''.join([_("{} encrypted for Revealer {}_{} saved as PNG and PDF at: ").format(self.was, version, code_id), "", self.get_path_to_revealer_file(), "", " ", " ", "", _("Always check your backups.")]), rich_text=True) dialog.close() def ext_warning(self, dialog): dialog.show_message(''.join(["",_("Warning"), ": ", _("your seed extension will not be included in the encrypted backup.")]), rich_text=True) dialog.close() def bdone(self, dialog): version = self.versioned_seed.version code_id = self.versioned_seed.checksum dialog.show_message(''.join([_("Digital Revealer ({}_{}) saved as PNG and PDF at:").format(version, code_id), " ","", self.get_path_to_revealer_file(), '']), rich_text=True) def customtxt_limits(self): txt = self.text.text() self.max_chars.setVisible(False) self.char_count.setText(f"({len(txt)}/{self.MAX_PLAINTEXT_LEN})") if len(txt)>0: self.ctext.setEnabled(True) if len(txt) > self.MAX_PLAINTEXT_LEN: self.text.setPlainText(txt[:self.MAX_PLAINTEXT_LEN]) self.max_chars.setVisible(True) def t(self): self.txt = self.text.text() self.seed_img(is_seed=False) def warn_old_revealer(self): if self.versioned_seed.version == '0': link = "https://revealer.cc/revealer-warning-and-upgrade/" self.d.show_warning(("{warning}: {ver0} " "{url} " "{risk}") .format(warning=_("Warning"), ver0=_("Revealers starting with 0 are not secure due to a vulnerability."), url=_("More info at: {}").format(f'<a href="{link}">{link}</a>'), risk=_("Proceed at your own risk.")), rich_text=True) def cypherseed_dialog(self, window): self.warn_old_revealer() d = WindowModalDialog(window, "Encryption Dialog") d.setMinimumWidth(500) d.setMinimumHeight(210) d.setMaximumHeight(450) d.setContentsMargins(11, 11, 1, 1) self.c_dialog = d hbox = QHBoxLayout(d) self.vbox = QVBoxLayout() logo = QLabel() hbox.addWidget(logo) logo.setPixmap(QPixmap(icon_path('revealer.png'))) logo.setAlignment(Qt.AlignLeft) hbox.addSpacing(16) self.vbox.addWidget(WWLabel("" + _("Revealer Secret Backup Plugin") + " " + _("Ready to encrypt for revealer {}") .format(self.versioned_seed.version+'_'+self.versioned_seed.checksum))) self.vbox.addSpacing(11) hbox.addLayout(self.vbox) grid = QGridLayout() self.vbox.addLayout(grid) cprint = QPushButton(_("Encrypt {}'s seed").format(self.wallet_name)) cprint.setMaximumWidth(250) cprint.clicked.connect(partial(self.seed_img, True)) self.vbox.addWidget(cprint) self.vbox.addSpacing(1) self.vbox.addWidget(WWLabel(""+_("OR")+" "+_("type a custom alphanumerical secret below:"))) self.text = ScanQRTextEdit() self.text.setTabChangesFocus(True) self.text.setMaximumHeight(70) self.text.textChanged.connect(self.customtxt_limits) self.vbox.addWidget(self.text) self.char_count = WWLabel("") self.char_count.setAlignment(Qt.AlignRight) self.vbox.addWidget(self.char_count) self.max_chars = WWLabel("" + _("This version supports a maximum of {} characters.").format(self.MAX_PLAINTEXT_LEN) +"") self.vbox.addWidget(self.max_chars) self.max_chars.setVisible(False) self.ctext = QPushButton(_("Encrypt custom secret")) self.ctext.clicked.connect(self.t) self.vbox.addWidget(self.ctext) self.ctext.setEnabled(False) self.vbox.addSpacing(11) self.vbox.addLayout(Buttons(CloseButton(d))) return bool(d.exec_()) def update_wallet_name(self, name): self.wallet_name = str(name) def seed_img(self, is_seed = True): if is_seed: try: cseed = self.get_seed() except UserCancelled: return except InvalidPassword as e: self.d.show_error(str(e)) return if not cseed: self.d.show_message(_("This wallet has no seed")) return txt = cseed.upper() else: txt = self.txt.upper() img = QImage(self.SIZE[0], self.SIZE[1], QImage.Format_Mono) bitmap = QBitmap.fromImage(img, Qt.MonoOnly) bitmap.fill(Qt.white) painter = QPainter() painter.begin(bitmap) QFontDatabase.addApplicationFont(os.path.join(os.path.dirname(__file__), 'SourceSansPro-Bold.otf') ) if len(txt) < 102 : fontsize = 15 linespace = 15 max_letters = 17 max_lines = 6 max_words = 3 else: fontsize = 12 linespace = 10 max_letters = 21 max_lines = 9 max_words = int(max_letters/4) font = QFont('Source Sans Pro', fontsize, QFont.Bold) font.setLetterSpacing(QFont.PercentageSpacing, 100) font.setPixelSize(fontsize) painter.setFont(font) seed_array = txt.split(' ') for n in range(max_lines): nwords = max_words temp_seed = seed_array[:nwords] while len(' '.join(map(str, temp_seed))) > max_letters: nwords = nwords - 1 temp_seed = seed_array[:nwords] painter.drawText(QRect(0, linespace*n , self.SIZE[0], self.SIZE[1]), Qt.AlignHCenter, ' '.join(map(str, temp_seed))) del seed_array[:nwords] painter.end() img = bitmap.toImage() if (self.rawnoise == False): self.make_rawnoise() self.make_cypherseed(img, self.rawnoise, False, is_seed) return img def make_rawnoise(self, create_revealer=False): if not self.user_input: self.versioned_seed = self.gen_random_versioned_seed() assert self.versioned_seed w, h = self.SIZE rawnoise = QImage(w, h, QImage.Format_Mono) noise_map = self.get_noise_map(self.versioned_seed) for (x,y), pixel in noise_map.items(): rawnoise.setPixel(x, y, pixel) self.rawnoise = rawnoise if create_revealer: self.make_revealer() def make_calnoise(self): random.seed(self.calibration_noise) w, h = self.SIZE rawnoise = QImage(w, h, QImage.Format_Mono) for x in range(w): for y in range(h): rawnoise.setPixel(x,y,random.randint(0, 1)) self.calnoise = self.pixelcode_2x2(rawnoise) def make_revealer(self): revealer = self.pixelcode_2x2(self.rawnoise) revealer.invertPixels() revealer = QBitmap.fromImage(revealer) revealer = revealer.scaled(self.f_size, Qt.KeepAspectRatio) revealer = self.overlay_marks(revealer) self.filename_prefix = 'revealer_' revealer.save(self.get_path_to_revealer_file('.png')) self.toPdf(QImage(revealer)) QDesktopServices.openUrl(QUrl.fromLocalFile(self.get_path_to_revealer_file('.pdf'))) def make_cypherseed(self, img, rawnoise, calibration=False, is_seed = True): img = img.convertToFormat(QImage.Format_Mono) p = QPainter() p.begin(img) p.setCompositionMode(26) #xor p.drawImage(0, 0, rawnoise) p.end() cypherseed = self.pixelcode_2x2(img) cypherseed = QBitmap.fromImage(cypherseed) cypherseed = cypherseed.scaled(self.f_size, Qt.KeepAspectRatio) cypherseed = self.overlay_marks(cypherseed, True, calibration) if not is_seed: self.filename_prefix = 'custom_secret_' self.was = _('Custom secret') else: self.filename_prefix = self.wallet_name + '_seed_' self.was = self.wallet_name + ' ' + _('seed') if self.extension: self.ext_warning(self.c_dialog) if not calibration: self.toPdf(QImage(cypherseed)) QDesktopServices.openUrl(QUrl.fromLocalFile(self.get_path_to_revealer_file('.pdf'))) cypherseed.save(self.get_path_to_revealer_file('.png')) self.bcrypt(self.c_dialog) return cypherseed def calibration(self): img = QImage(self.SIZE[0], self.SIZE[1], QImage.Format_Mono) bitmap = QBitmap.fromImage(img, Qt.MonoOnly) bitmap.fill(Qt.black) self.make_calnoise() img = self.overlay_marks(self.calnoise.scaledToHeight(self.f_size.height()), False, True) self.calibration_pdf(img) QDesktopServices.openUrl(QUrl.fromLocalFile(self.get_path_to_calibration_file())) return img def toPdf(self, image): printer = QPrinter() printer.setPaperSize(QSizeF(210, 297), QPrinter.Millimeter) printer.setResolution(600) printer.setOutputFormat(QPrinter.PdfFormat) printer.setOutputFileName(self.get_path_to_revealer_file('.pdf')) printer.setPageMargins(0,0,0,0,6) painter = QPainter() painter.begin(printer) delta_h = round(image.width()/self.abstand_v) delta_v = round(image.height()/self.abstand_h) size_h = 2028+((int(self.calibration_h)*2028/(2028-(delta_h*2)+int(self.calibration_h)))/2) size_v = 1284+((int(self.calibration_v)*1284/(1284-(delta_v*2)+int(self.calibration_v)))/2) image = image.scaled(size_h, size_v) painter.drawImage(553,533, image) wpath = QPainterPath() wpath.addRoundedRect(QRectF(553,533, size_h, size_v), 19, 19) painter.setPen(QPen(Qt.black, 1)) painter.drawPath(wpath) painter.end() def calibration_pdf(self, image): printer = QPrinter() printer.setPaperSize(QSizeF(210, 297), QPrinter.Millimeter) printer.setResolution(600) printer.setOutputFormat(QPrinter.PdfFormat) printer.setOutputFileName(self.get_path_to_calibration_file()) printer.setPageMargins(0,0,0,0,6) painter = QPainter() painter.begin(printer) painter.drawImage(553,533, image) font = QFont('Source Sans Pro', 10, QFont.Bold) painter.setFont(font) painter.drawText(254,277, _("Calibration sheet")) font = QFont('Source Sans Pro', 7, QFont.Bold) painter.setFont(font) painter.drawText(600,2077, _("Instructions:")) font = QFont('Source Sans Pro', 7, QFont.Normal) painter.setFont(font) painter.drawText(700, 2177, _("1. Place this paper on a flat and well iluminated surface.")) painter.drawText(700, 2277, _("2. Align your Revealer borderlines to the dashed lines on the top and left.")) painter.drawText(700, 2377, _("3. Press slightly the Revealer against the paper and read the numbers that best " "match on the opposite sides. ")) painter.drawText(700, 2477, _("4. Type the numbers in the software")) painter.end() def pixelcode_2x2(self, img): result = QImage(img.width()*2, img.height()*2, QImage.Format_ARGB32 ) white = qRgba(255,255,255,0) black = qRgba(0,0,0,255) for x in range(img.width()): for y in range(img.height()): c = img.pixel(QPoint(x,y)) colors = QColor(c).getRgbF() if colors[0]: result.setPixel(x*2+1,y*2+1, black) result.setPixel(x*2,y*2+1, white) result.setPixel(x*2+1,y*2, white) result.setPixel(x*2, y*2, black) else: result.setPixel(x*2+1,y*2+1, white) result.setPixel(x*2,y*2+1, black) result.setPixel(x*2+1,y*2, black) result.setPixel(x*2, y*2, white) return result def overlay_marks(self, img, is_cseed=False, calibration_sheet=False): border_color = Qt.white base_img = QImage(self.f_size.width(),self.f_size.height(), QImage.Format_ARGB32) base_img.fill(border_color) img = QImage(img) painter = QPainter() painter.begin(base_img) total_distance_h = round(base_img.width() / self.abstand_v) dist_v = round(total_distance_h) / 2 dist_h = round(total_distance_h) / 2 img = img.scaledToWidth(base_img.width() - (2 * (total_distance_h))) painter.drawImage(total_distance_h, total_distance_h, img) #frame around image pen = QPen(Qt.black, 2) painter.setPen(pen) #horz painter.drawLine(0, total_distance_h, base_img.width(), total_distance_h) painter.drawLine(0, base_img.height()-(total_distance_h), base_img.width(), base_img.height()-(total_distance_h)) #vert painter.drawLine(total_distance_h, 0, total_distance_h, base_img.height()) painter.drawLine(base_img.width()-(total_distance_h), 0, base_img.width()-(total_distance_h), base_img.height()) #border around img border_thick = 6 Rpath = QPainterPath() Rpath.addRect(QRectF((total_distance_h)+(border_thick/2), (total_distance_h)+(border_thick/2), base_img.width()-((total_distance_h)*2)-((border_thick)-1), (base_img.height()-((total_distance_h))*2)-((border_thick)-1))) pen = QPen(Qt.black, border_thick) pen.setJoinStyle (Qt.MiterJoin) painter.setPen(pen) painter.drawPath(Rpath) Bpath = QPainterPath() Bpath.addRect(QRectF((total_distance_h), (total_distance_h), base_img.width()-((total_distance_h)*2), (base_img.height()-((total_distance_h))*2))) pen = QPen(Qt.black, 1) painter.setPen(pen) painter.drawPath(Bpath) pen = QPen(Qt.black, 1) painter.setPen(pen) painter.drawLine(0, base_img.height()/2, total_distance_h, base_img.height()/2) painter.drawLine(base_img.width()/2, 0, base_img.width()/2, total_distance_h) painter.drawLine(base_img.width()-total_distance_h, base_img.height()/2, base_img.width(), base_img.height()/2) painter.drawLine(base_img.width()/2, base_img.height(), base_img.width()/2, base_img.height() - total_distance_h) #print code f_size = 37 QFontDatabase.addApplicationFont(os.path.join(os.path.dirname(__file__), 'DejaVuSansMono-Bold.ttf')) font = QFont("DejaVu Sans Mono", f_size-11, QFont.Bold) font.setPixelSize(35) painter.setFont(font) if not calibration_sheet: if is_cseed: #its a secret painter.setPen(QPen(Qt.black, 1, Qt.DashDotDotLine)) painter.drawLine(0, dist_v, base_img.width(), dist_v) painter.drawLine(dist_h, 0, dist_h, base_img.height()) painter.drawLine(0, base_img.height()-dist_v, base_img.width(), base_img.height()-(dist_v)) painter.drawLine(base_img.width()-(dist_h), 0, base_img.width()-(dist_h), base_img.height()) painter.drawImage(((total_distance_h))+11, ((total_distance_h))+11, QImage(icon_path('electrumb.png')).scaledToWidth(2.1*(total_distance_h), Qt.SmoothTransformation)) painter.setPen(QPen(Qt.white, border_thick*8)) painter.drawLine(base_img.width()-((total_distance_h))-(border_thick*8)/2-(border_thick/2)-2, (base_img.height()-((total_distance_h)))-((border_thick*8)/2)-(border_thick/2)-2, base_img.width()-((total_distance_h))-(border_thick*8)/2-(border_thick/2)-2 - 77, (base_img.height()-((total_distance_h)))-((border_thick*8)/2)-(border_thick/2)-2) painter.setPen(QColor(0,0,0,255)) painter.drawText(QRect(0, base_img.height()-107, base_img.width()-total_distance_h - border_thick - 11, base_img.height()-total_distance_h - border_thick), Qt.AlignRight, self.versioned_seed.version + '_'+self.versioned_seed.checksum) painter.end() else: # revealer painter.setPen(QPen(border_color, 17)) painter.drawLine(0, dist_v, base_img.width(), dist_v) painter.drawLine(dist_h, 0, dist_h, base_img.height()) painter.drawLine(0, base_img.height()-dist_v, base_img.width(), base_img.height()-(dist_v)) painter.drawLine(base_img.width()-(dist_h), 0, base_img.width()-(dist_h), base_img.height()) painter.setPen(QPen(Qt.black, 2)) painter.drawLine(0, dist_v, base_img.width(), dist_v) painter.drawLine(dist_h, 0, dist_h, base_img.height()) painter.drawLine(0, base_img.height()-dist_v, base_img.width(), base_img.height()-(dist_v)) painter.drawLine(base_img.width()-(dist_h), 0, base_img.width()-(dist_h), base_img.height()) logo = QImage(icon_path('revealer_c.png')).scaledToWidth(1.3*(total_distance_h)) painter.drawImage((total_distance_h)+ (border_thick), ((total_distance_h))+ (border_thick), logo, Qt.SmoothTransformation) #frame around logo painter.setPen(QPen(Qt.black, border_thick)) painter.drawLine(total_distance_h+border_thick, total_distance_h+logo.height()+3*(border_thick/2), total_distance_h+logo.width()+border_thick, total_distance_h+logo.height()+3*(border_thick/2)) painter.drawLine(logo.width()+total_distance_h+3*(border_thick/2), total_distance_h+(border_thick), total_distance_h+logo.width()+3*(border_thick/2), total_distance_h+logo.height()+(border_thick)) #frame around code/qr qr_size = 179 painter.drawLine((base_img.width()-((total_distance_h))-(border_thick/2)-2)-qr_size, (base_img.height()-((total_distance_h)))-((border_thick*8))-(border_thick/2)-2, (base_img.width()/2+(total_distance_h/2)-border_thick-(border_thick*8)/2)-qr_size, (base_img.height()-((total_distance_h)))-((border_thick*8))-(border_thick/2)-2) painter.drawLine((base_img.width()/2+(total_distance_h/2)-border_thick-(border_thick*8)/2)-qr_size, (base_img.height()-((total_distance_h)))-((border_thick*8))-(border_thick/2)-2, base_img.width()/2 + (total_distance_h/2)-border_thick-(border_thick*8)/2-qr_size, ((base_img.height()-((total_distance_h)))-(border_thick/2)-2)) painter.setPen(QPen(Qt.white, border_thick * 8)) painter.drawLine( base_img.width() - ((total_distance_h)) - (border_thick * 8) / 2 - (border_thick / 2) - 2, (base_img.height() - ((total_distance_h))) - ((border_thick * 8) / 2) - (border_thick / 2) - 2, base_img.width() / 2 + (total_distance_h / 2) - border_thick - qr_size, (base_img.height() - ((total_distance_h))) - ((border_thick * 8) / 2) - (border_thick / 2) - 2) painter.setPen(QColor(0,0,0,255)) painter.drawText(QRect(((base_img.width()/2) +21)-qr_size, base_img.height()-107, base_img.width()-total_distance_h - border_thick -93, base_img.height()-total_distance_h - border_thick), Qt.AlignLeft, self.versioned_seed.get_ui_string_version_plus_seed()) painter.drawText(QRect(0, base_img.height()-107, base_img.width()-total_distance_h - border_thick -3 -qr_size, base_img.height()-total_distance_h - border_thick), Qt.AlignRight, self.versioned_seed.checksum) # draw qr code qr_qt = self.paintQR(self.versioned_seed.get_ui_string_version_plus_seed() + self.versioned_seed.checksum) target = QRectF(base_img.width()-65-qr_size, base_img.height()-65-qr_size, qr_size, qr_size ) painter.drawImage(target, qr_qt) painter.setPen(QPen(Qt.black, 4)) painter.drawLine(base_img.width()-65-qr_size, base_img.height()-65-qr_size, base_img.width() - 65 - qr_size, (base_img.height() - ((total_distance_h))) - ((border_thick * 8)) - (border_thick / 2) - 4 ) painter.drawLine(base_img.width()-65-qr_size, base_img.height()-65-qr_size, base_img.width() - 65, base_img.height()-65-qr_size ) painter.end() else: # calibration only painter.end() cal_img = QImage(self.f_size.width() + 100, self.f_size.height() + 100, QImage.Format_ARGB32) cal_img.fill(Qt.white) cal_painter = QPainter() cal_painter.begin(cal_img) cal_painter.drawImage(0,0, base_img) #black lines in the middle of border top left only cal_painter.setPen(QPen(Qt.black, 1, Qt.DashDotDotLine)) cal_painter.drawLine(0, dist_v, base_img.width(), dist_v) cal_painter.drawLine(dist_h, 0, dist_h, base_img.height()) pen = QPen(Qt.black, 2, Qt.DashDotDotLine) cal_painter.setPen(pen) n=15 cal_painter.setFont(QFont("DejaVu Sans Mono", 21, QFont.Bold)) for x in range(-n,n): #lines on bottom (vertical calibration) cal_painter.drawLine((((base_img.width())/(n*2)) *(x))+ (base_img.width()/2)-13, x+2+base_img.height()-(dist_v), (((base_img.width())/(n*2)) *(x))+ (base_img.width()/2)+13, x+2+base_img.height()-(dist_v)) num_pos = 9 if x > 9 : num_pos = 17 if x < 0 : num_pos = 20 if x < -9: num_pos = 27 cal_painter.drawText((((base_img.width())/(n*2)) *(x))+ (base_img.width()/2)-num_pos, 50+base_img.height()-(dist_v), str(x)) #lines on the right (horizontal calibrations) cal_painter.drawLine(x+2+(base_img.width()-(dist_h)), ((base_img.height()/(2*n)) *(x))+ (base_img.height()/n)+(base_img.height()/2)-13, x+2+(base_img.width()-(dist_h)), ((base_img.height()/(2*n)) *(x))+ (base_img.height()/n)+(base_img.height()/2)+13) cal_painter.drawText(30+(base_img.width()-(dist_h)), ((base_img.height()/(2*n)) *(x))+ (base_img.height()/2)+13, str(x)) cal_painter.end() base_img = cal_img return base_img def paintQR(self, data): if not data: return qr = qrcode.QRCode() qr.add_data(data) matrix = qr.get_matrix() k = len(matrix) border_color = Qt.white base_img = QImage(k * 5, k * 5, QImage.Format_ARGB32) base_img.fill(border_color) qrpainter = QPainter() qrpainter.begin(base_img) boxsize = 5 size = k * boxsize left = (base_img.width() - size)/2 top = (base_img.height() - size)/2 qrpainter.setBrush(Qt.black) qrpainter.setPen(Qt.black) for r in range(k): for c in range(k): if matrix[r][c]: qrpainter.drawRect(left+c*boxsize, top+r*boxsize, boxsize - 1, boxsize - 1) qrpainter.end() return base_img def calibration_dialog(self, window): d = WindowModalDialog(window, _("Revealer - Printer calibration settings")) d.setMinimumSize(100, 200) vbox = QVBoxLayout(d) vbox.addWidget(QLabel(''.join([" ", _("If you have an old printer, or want optimal precision")," ", _("print the calibration pdf and follow the instructions "), " "," ", ]))) self.calibration_h = self.config.get('calibration_h') self.calibration_v = self.config.get('calibration_v') cprint = QPushButton(_("Open calibration pdf")) cprint.clicked.connect(self.calibration) vbox.addWidget(cprint) vbox.addWidget(QLabel(_('Calibration values:'))) grid = QGridLayout() vbox.addLayout(grid) grid.addWidget(QLabel(_('Right side')), 0, 0) horizontal = QLineEdit() horizontal.setText(str(self.calibration_h)) grid.addWidget(horizontal, 0, 1) grid.addWidget(QLabel(_('Bottom')), 1, 0) vertical = QLineEdit() vertical.setText(str(self.calibration_v)) grid.addWidget(vertical, 1, 1) vbox.addStretch() vbox.addSpacing(13) vbox.addLayout(Buttons(CloseButton(d), OkButton(d))) if not d.exec_(): return self.calibration_h = int(Decimal(horizontal.text())) self.config.set_key('calibration_h', self.calibration_h) self.calibration_v = int(Decimal(vertical.text())) self.config.set_key('calibration_v', self.calibration_v)

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import os import random import traceback from decimal import Decimal from functools import partial import sys import qrcode from PyQt5.QtPrintSupport import QPrinter from PyQt5.QtCore import Qt, QRectF, QRect, QSizeF, QUrl, QPoint, QSize from PyQt5.QtGui import (QPixmap, QImage, QBitmap, QPainter, QFontDatabase, QPen, QFont, QColor, QDesktopServices, qRgba, QPainterPath) from PyQt5.QtWidgets import (QGridLayout, QVBoxLayout, QHBoxLayout, QLabel, QPushButton, QLineEdit) from electrum_vestx.plugin import hook from electrum_vestx.i18n import _ from electrum_vestx.util import make_dir, InvalidPassword, UserCancelled from electrum_vestx.gui.qt.util import (read_QIcon, EnterButton, WWLabel, icon_path, WindowModalDialog, Buttons, CloseButton, OkButton) from electrum_vestx.gui.qt.qrtextedit import ScanQRTextEdit from electrum_vestx.gui.qt.main_window import StatusBarButton from .revealer import RevealerPlugin class Plugin(RevealerPlugin): MAX_PLAINTEXT_LEN = 189 def __init__(self, parent, config, name): RevealerPlugin.__init__(self, parent, config, name) self.base_dir = os.path.join(config.electrum_path(), 'revealer') if self.config.get('calibration_h') is None: self.config.set_key('calibration_h', 0) if self.config.get('calibration_v') is None: self.config.set_key('calibration_v', 0) self.calibration_h = self.config.get('calibration_h') self.calibration_v = self.config.get('calibration_v') self.f_size = QSize(1014*2, 642*2) self.abstand_h = 21 self.abstand_v = 34 self.calibration_noise = int('10' * 128) self.rawnoise = False make_dir(self.base_dir) self.extension = False @hook def create_status_bar(self, parent): b = StatusBarButton(read_QIcon('revealer.png'), "Revealer "+_("secret backup utility"), partial(self.setup_dialog, parent)) parent.addPermanentWidget(b) def requires_settings(self): return True def settings_widget(self, window): return EnterButton(_('Printer Calibration'), partial(self.calibration_dialog, window)) def password_dialog(self, msg=None, parent=None): from electrum_vestx.gui.qt.password_dialog import PasswordDialog parent = parent or self d = PasswordDialog(parent, msg) return d.run() def get_seed(self): password = None if self.wallet.has_keystore_encryption(): password = self.password_dialog(parent=self.d.parent()) if not password: raise UserCancelled() keystore = self.wallet.get_keystore() if not keystore or not keystore.has_seed(): return self.extension = bool(keystore.get_passphrase(password)) return keystore.get_seed(password) def setup_dialog(self, window): self.wallet = window.parent().wallet self.update_wallet_name(self.wallet) self.user_input = False self.d = WindowModalDialog(window, "Setup Dialog") self.d.setMinimumWidth(500) self.d.setMinimumHeight(210) self.d.setMaximumHeight(320) self.d.setContentsMargins(11,11,1,1) self.hbox = QHBoxLayout(self.d) vbox = QVBoxLayout() logo = QLabel() self.hbox.addWidget(logo) logo.setPixmap(QPixmap(icon_path('revealer.png'))) logo.setAlignment(Qt.AlignLeft) self.hbox.addSpacing(16) vbox.addWidget(WWLabel(""+_("Revealer Secret Backup Plugin")+" " +_("To encrypt your backup, first we need to load some noise.")+" ")) vbox.addSpacing(7) bcreate = QPushButton(_("Create a new Revealer")) bcreate.setMaximumWidth(181) bcreate.setDefault(True) vbox.addWidget(bcreate, Qt.AlignCenter) self.load_noise = ScanQRTextEdit() self.load_noise.setTabChangesFocus(True) self.load_noise.textChanged.connect(self.on_edit) self.load_noise.setMaximumHeight(33) self.hbox.addLayout(vbox) vbox.addWidget(WWLabel(_("or type an existing revealer code below and click 'next':"))) vbox.addWidget(self.load_noise) vbox.addSpacing(3) self.next_button = QPushButton(_("Next"), self.d) self.next_button.setEnabled(False) vbox.addLayout(Buttons(self.next_button)) self.next_button.clicked.connect(self.d.close) self.next_button.clicked.connect(partial(self.cypherseed_dialog, window)) vbox.addWidget( QLabel("" + _("Warning") + ": " + _("Each revealer should be used only once.") +" "+_("more information at <a href=\"https://revealer.cc/faq\">https://revealer.cc/faq</a>"))) def mk_digital(): try: self.make_digital(self.d) except Exception: self.logger.exception('') else: self.cypherseed_dialog(window) bcreate.clicked.connect(mk_digital) return bool(self.d.exec_()) def get_noise(self): text = self.load_noise.text() return ''.join(text.split()).lower() def on_edit(self): txt = self.get_noise() versioned_seed = self.get_versioned_seed_from_user_input(txt) if versioned_seed: self.versioned_seed = versioned_seed self.user_input = bool(versioned_seed) self.next_button.setEnabled(bool(versioned_seed)) def make_digital(self, dialog): self.make_rawnoise(True) self.bdone(dialog) self.d.close() def get_path_to_revealer_file(self, ext: str= '') -> str: version = self.versioned_seed.version code_id = self.versioned_seed.checksum filename = self.filename_prefix + version + "_" + code_id + ext path = os.path.join(self.base_dir, filename) return os.path.normcase(os.path.abspath(path)) def get_path_to_calibration_file(self): path = os.path.join(self.base_dir, 'calibration.pdf') return os.path.normcase(os.path.abspath(path)) def bcrypt(self, dialog): self.rawnoise = False version = self.versioned_seed.version code_id = self.versioned_seed.checksum dialog.show_message(''.join([_("{} encrypted for Revealer {}_{} saved as PNG and PDF at: ").format(self.was, version, code_id), "", self.get_path_to_revealer_file(), "", " ", " ", "", _("Always check your backups.")]), rich_text=True) dialog.close() def ext_warning(self, dialog): dialog.show_message(''.join(["",_("Warning"), ": ", _("your seed extension will not be included in the encrypted backup.")]), rich_text=True) dialog.close() def bdone(self, dialog): version = self.versioned_seed.version code_id = self.versioned_seed.checksum dialog.show_message(''.join([_("Digital Revealer ({}_{}) saved as PNG and PDF at:").format(version, code_id), " ","", self.get_path_to_revealer_file(), '']), rich_text=True) def customtxt_limits(self): txt = self.text.text() self.max_chars.setVisible(False) self.char_count.setText(f"({len(txt)}/{self.MAX_PLAINTEXT_LEN})") if len(txt)>0: self.ctext.setEnabled(True) if len(txt) > self.MAX_PLAINTEXT_LEN: self.text.setPlainText(txt[:self.MAX_PLAINTEXT_LEN]) self.max_chars.setVisible(True) def t(self): self.txt = self.text.text() self.seed_img(is_seed=False) def warn_old_revealer(self): if self.versioned_seed.version == '0': link = "https://revealer.cc/revealer-warning-and-upgrade/" self.d.show_warning(("{warning}: {ver0} " "{url} " "{risk}") .format(warning=_("Warning"), ver0=_("Revealers starting with 0 are not secure due to a vulnerability."), url=_("More info at: {}").format(f'<a href="{link}">{link}</a>'), risk=_("Proceed at your own risk.")), rich_text=True) def cypherseed_dialog(self, window): self.warn_old_revealer() d = WindowModalDialog(window, "Encryption Dialog") d.setMinimumWidth(500) d.setMinimumHeight(210) d.setMaximumHeight(450) d.setContentsMargins(11, 11, 1, 1) self.c_dialog = d hbox = QHBoxLayout(d) self.vbox = QVBoxLayout() logo = QLabel() hbox.addWidget(logo) logo.setPixmap(QPixmap(icon_path('revealer.png'))) logo.setAlignment(Qt.AlignLeft) hbox.addSpacing(16) self.vbox.addWidget(WWLabel("" + _("Revealer Secret Backup Plugin") + " " + _("Ready to encrypt for revealer {}") .format(self.versioned_seed.version+'_'+self.versioned_seed.checksum))) self.vbox.addSpacing(11) hbox.addLayout(self.vbox) grid = QGridLayout() self.vbox.addLayout(grid) cprint = QPushButton(_("Encrypt {}'s seed").format(self.wallet_name)) cprint.setMaximumWidth(250) cprint.clicked.connect(partial(self.seed_img, True)) self.vbox.addWidget(cprint) self.vbox.addSpacing(1) self.vbox.addWidget(WWLabel(""+_("OR")+" "+_("type a custom alphanumerical secret below:"))) self.text = ScanQRTextEdit() self.text.setTabChangesFocus(True) self.text.setMaximumHeight(70) self.text.textChanged.connect(self.customtxt_limits) self.vbox.addWidget(self.text) self.char_count = WWLabel("") self.char_count.setAlignment(Qt.AlignRight) self.vbox.addWidget(self.char_count) self.max_chars = WWLabel("" + _("This version supports a maximum of {} characters.").format(self.MAX_PLAINTEXT_LEN) +"") self.vbox.addWidget(self.max_chars) self.max_chars.setVisible(False) self.ctext = QPushButton(_("Encrypt custom secret")) self.ctext.clicked.connect(self.t) self.vbox.addWidget(self.ctext) self.ctext.setEnabled(False) self.vbox.addSpacing(11) self.vbox.addLayout(Buttons(CloseButton(d))) return bool(d.exec_()) def update_wallet_name(self, name): self.wallet_name = str(name) def seed_img(self, is_seed = True): if is_seed: try: cseed = self.get_seed() except UserCancelled: return except InvalidPassword as e: self.d.show_error(str(e)) return if not cseed: self.d.show_message(_("This wallet has no seed")) return txt = cseed.upper() else: txt = self.txt.upper() img = QImage(self.SIZE[0], self.SIZE[1], QImage.Format_Mono) bitmap = QBitmap.fromImage(img, Qt.MonoOnly) bitmap.fill(Qt.white) painter = QPainter() painter.begin(bitmap) QFontDatabase.addApplicationFont(os.path.join(os.path.dirname(__file__), 'SourceSansPro-Bold.otf') ) if len(txt) < 102 : fontsize = 15 linespace = 15 max_letters = 17 max_lines = 6 max_words = 3 else: fontsize = 12 linespace = 10 max_letters = 21 max_lines = 9 max_words = int(max_letters/4) font = QFont('Source Sans Pro', fontsize, QFont.Bold) font.setLetterSpacing(QFont.PercentageSpacing, 100) font.setPixelSize(fontsize) painter.setFont(font) seed_array = txt.split(' ') for n in range(max_lines): nwords = max_words temp_seed = seed_array[:nwords] while len(' '.join(map(str, temp_seed))) > max_letters: nwords = nwords - 1 temp_seed = seed_array[:nwords] painter.drawText(QRect(0, linespace*n , self.SIZE[0], self.SIZE[1]), Qt.AlignHCenter, ' '.join(map(str, temp_seed))) del seed_array[:nwords] painter.end() img = bitmap.toImage() if (self.rawnoise == False): self.make_rawnoise() self.make_cypherseed(img, self.rawnoise, False, is_seed) return img def make_rawnoise(self, create_revealer=False): if not self.user_input: self.versioned_seed = self.gen_random_versioned_seed() assert self.versioned_seed w, h = self.SIZE rawnoise = QImage(w, h, QImage.Format_Mono) noise_map = self.get_noise_map(self.versioned_seed) for (x,y), pixel in noise_map.items(): rawnoise.setPixel(x, y, pixel) self.rawnoise = rawnoise if create_revealer: self.make_revealer() def make_calnoise(self): random.seed(self.calibration_noise) w, h = self.SIZE rawnoise = QImage(w, h, QImage.Format_Mono) for x in range(w): for y in range(h): rawnoise.setPixel(x,y,random.randint(0, 1)) self.calnoise = self.pixelcode_2x2(rawnoise) def make_revealer(self): revealer = self.pixelcode_2x2(self.rawnoise) revealer.invertPixels() revealer = QBitmap.fromImage(revealer) revealer = revealer.scaled(self.f_size, Qt.KeepAspectRatio) revealer = self.overlay_marks(revealer) self.filename_prefix = 'revealer_' revealer.save(self.get_path_to_revealer_file('.png')) self.toPdf(QImage(revealer)) QDesktopServices.openUrl(QUrl.fromLocalFile(self.get_path_to_revealer_file('.pdf'))) def make_cypherseed(self, img, rawnoise, calibration=False, is_seed = True): img = img.convertToFormat(QImage.Format_Mono) p = QPainter() p.begin(img) p.setCompositionMode(26) #xor p.drawImage(0, 0, rawnoise) p.end() cypherseed = self.pixelcode_2x2(img) cypherseed = QBitmap.fromImage(cypherseed) cypherseed = cypherseed.scaled(self.f_size, Qt.KeepAspectRatio) cypherseed = self.overlay_marks(cypherseed, True, calibration) if not is_seed: self.filename_prefix = 'custom_secret_' self.was = _('Custom secret') else: self.filename_prefix = self.wallet_name + '_seed_' self.was = self.wallet_name + ' ' + _('seed') if self.extension: self.ext_warning(self.c_dialog) if not calibration: self.toPdf(QImage(cypherseed)) QDesktopServices.openUrl(QUrl.fromLocalFile(self.get_path_to_revealer_file('.pdf'))) cypherseed.save(self.get_path_to_revealer_file('.png')) self.bcrypt(self.c_dialog) return cypherseed def calibration(self): img = QImage(self.SIZE[0], self.SIZE[1], QImage.Format_Mono) bitmap = QBitmap.fromImage(img, Qt.MonoOnly) bitmap.fill(Qt.black) self.make_calnoise() img = self.overlay_marks(self.calnoise.scaledToHeight(self.f_size.height()), False, True) self.calibration_pdf(img) QDesktopServices.openUrl(QUrl.fromLocalFile(self.get_path_to_calibration_file())) return img def toPdf(self, image): printer = QPrinter() printer.setPaperSize(QSizeF(210, 297), QPrinter.Millimeter) printer.setResolution(600) printer.setOutputFormat(QPrinter.PdfFormat) printer.setOutputFileName(self.get_path_to_revealer_file('.pdf')) printer.setPageMargins(0,0,0,0,6) painter = QPainter() painter.begin(printer) delta_h = round(image.width()/self.abstand_v) delta_v = round(image.height()/self.abstand_h) size_h = 2028+((int(self.calibration_h)*2028/(2028-(delta_h*2)+int(self.calibration_h)))/2) size_v = 1284+((int(self.calibration_v)*1284/(1284-(delta_v*2)+int(self.calibration_v)))/2) image = image.scaled(size_h, size_v) painter.drawImage(553,533, image) wpath = QPainterPath() wpath.addRoundedRect(QRectF(553,533, size_h, size_v), 19, 19) painter.setPen(QPen(Qt.black, 1)) painter.drawPath(wpath) painter.end() def calibration_pdf(self, image): printer = QPrinter() printer.setPaperSize(QSizeF(210, 297), QPrinter.Millimeter) printer.setResolution(600) printer.setOutputFormat(QPrinter.PdfFormat) printer.setOutputFileName(self.get_path_to_calibration_file()) printer.setPageMargins(0,0,0,0,6) painter = QPainter() painter.begin(printer) painter.drawImage(553,533, image) font = QFont('Source Sans Pro', 10, QFont.Bold) painter.setFont(font) painter.drawText(254,277, _("Calibration sheet")) font = QFont('Source Sans Pro', 7, QFont.Bold) painter.setFont(font) painter.drawText(600,2077, _("Instructions:")) font = QFont('Source Sans Pro', 7, QFont.Normal) painter.setFont(font) painter.drawText(700, 2177, _("1. Place this paper on a flat and well iluminated surface.")) painter.drawText(700, 2277, _("2. Align your Revealer borderlines to the dashed lines on the top and left.")) painter.drawText(700, 2377, _("3. Press slightly the Revealer against the paper and read the numbers that best " "match on the opposite sides. ")) painter.drawText(700, 2477, _("4. Type the numbers in the software")) painter.end() def pixelcode_2x2(self, img): result = QImage(img.width()*2, img.height()*2, QImage.Format_ARGB32 ) white = qRgba(255,255,255,0) black = qRgba(0,0,0,255) for x in range(img.width()): for y in range(img.height()): c = img.pixel(QPoint(x,y)) colors = QColor(c).getRgbF() if colors[0]: result.setPixel(x*2+1,y*2+1, black) result.setPixel(x*2,y*2+1, white) result.setPixel(x*2+1,y*2, white) result.setPixel(x*2, y*2, black) else: result.setPixel(x*2+1,y*2+1, white) result.setPixel(x*2,y*2+1, black) result.setPixel(x*2+1,y*2, black) result.setPixel(x*2, y*2, white) return result def overlay_marks(self, img, is_cseed=False, calibration_sheet=False): border_color = Qt.white base_img = QImage(self.f_size.width(),self.f_size.height(), QImage.Format_ARGB32) base_img.fill(border_color) img = QImage(img) painter = QPainter() painter.begin(base_img) total_distance_h = round(base_img.width() / self.abstand_v) dist_v = round(total_distance_h) / 2 dist_h = round(total_distance_h) / 2 img = img.scaledToWidth(base_img.width() - (2 * (total_distance_h))) painter.drawImage(total_distance_h, total_distance_h, img) #frame around image pen = QPen(Qt.black, 2) painter.setPen(pen) #horz painter.drawLine(0, total_distance_h, base_img.width(), total_distance_h) painter.drawLine(0, base_img.height()-(total_distance_h), base_img.width(), base_img.height()-(total_distance_h)) #vert painter.drawLine(total_distance_h, 0, total_distance_h, base_img.height()) painter.drawLine(base_img.width()-(total_distance_h), 0, base_img.width()-(total_distance_h), base_img.height()) #border around img border_thick = 6 Rpath = QPainterPath() Rpath.addRect(QRectF((total_distance_h)+(border_thick/2), (total_distance_h)+(border_thick/2), base_img.width()-((total_distance_h)*2)-((border_thick)-1), (base_img.height()-((total_distance_h))*2)-((border_thick)-1))) pen = QPen(Qt.black, border_thick) pen.setJoinStyle (Qt.MiterJoin) painter.setPen(pen) painter.drawPath(Rpath) Bpath = QPainterPath() Bpath.addRect(QRectF((total_distance_h), (total_distance_h), base_img.width()-((total_distance_h)*2), (base_img.height()-((total_distance_h))*2))) pen = QPen(Qt.black, 1) painter.setPen(pen) painter.drawPath(Bpath) pen = QPen(Qt.black, 1) painter.setPen(pen) painter.drawLine(0, base_img.height()/2, total_distance_h, base_img.height()/2) painter.drawLine(base_img.width()/2, 0, base_img.width()/2, total_distance_h) painter.drawLine(base_img.width()-total_distance_h, base_img.height()/2, base_img.width(), base_img.height()/2) painter.drawLine(base_img.width()/2, base_img.height(), base_img.width()/2, base_img.height() - total_distance_h) #print code f_size = 37 QFontDatabase.addApplicationFont(os.path.join(os.path.dirname(__file__), 'DejaVuSansMono-Bold.ttf')) font = QFont("DejaVu Sans Mono", f_size-11, QFont.Bold) font.setPixelSize(35) painter.setFont(font) if not calibration_sheet: if is_cseed: #its a secret painter.setPen(QPen(Qt.black, 1, Qt.DashDotDotLine)) painter.drawLine(0, dist_v, base_img.width(), dist_v) painter.drawLine(dist_h, 0, dist_h, base_img.height()) painter.drawLine(0, base_img.height()-dist_v, base_img.width(), base_img.height()-(dist_v)) painter.drawLine(base_img.width()-(dist_h), 0, base_img.width()-(dist_h), base_img.height()) painter.drawImage(((total_distance_h))+11, ((total_distance_h))+11, QImage(icon_path('electrumb.png')).scaledToWidth(2.1*(total_distance_h), Qt.SmoothTransformation)) painter.setPen(QPen(Qt.white, border_thick*8)) painter.drawLine(base_img.width()-((total_distance_h))-(border_thick*8)/2-(border_thick/2)-2, (base_img.height()-((total_distance_h)))-((border_thick*8)/2)-(border_thick/2)-2, base_img.width()-((total_distance_h))-(border_thick*8)/2-(border_thick/2)-2 - 77, (base_img.height()-((total_distance_h)))-((border_thick*8)/2)-(border_thick/2)-2) painter.setPen(QColor(0,0,0,255)) painter.drawText(QRect(0, base_img.height()-107, base_img.width()-total_distance_h - border_thick - 11, base_img.height()-total_distance_h - border_thick), Qt.AlignRight, self.versioned_seed.version + '_'+self.versioned_seed.checksum) painter.end() else: # revealer painter.setPen(QPen(border_color, 17)) painter.drawLine(0, dist_v, base_img.width(), dist_v) painter.drawLine(dist_h, 0, dist_h, base_img.height()) painter.drawLine(0, base_img.height()-dist_v, base_img.width(), base_img.height()-(dist_v)) painter.drawLine(base_img.width()-(dist_h), 0, base_img.width()-(dist_h), base_img.height()) painter.setPen(QPen(Qt.black, 2)) painter.drawLine(0, dist_v, base_img.width(), dist_v) painter.drawLine(dist_h, 0, dist_h, base_img.height()) painter.drawLine(0, base_img.height()-dist_v, base_img.width(), base_img.height()-(dist_v)) painter.drawLine(base_img.width()-(dist_h), 0, base_img.width()-(dist_h), base_img.height()) logo = QImage(icon_path('revealer_c.png')).scaledToWidth(1.3*(total_distance_h)) painter.drawImage((total_distance_h)+ (border_thick), ((total_distance_h))+ (border_thick), logo, Qt.SmoothTransformation) #frame around logo painter.setPen(QPen(Qt.black, border_thick)) painter.drawLine(total_distance_h+border_thick, total_distance_h+logo.height()+3*(border_thick/2), total_distance_h+logo.width()+border_thick, total_distance_h+logo.height()+3*(border_thick/2)) painter.drawLine(logo.width()+total_distance_h+3*(border_thick/2), total_distance_h+(border_thick), total_distance_h+logo.width()+3*(border_thick/2), total_distance_h+logo.height()+(border_thick)) #frame around code/qr qr_size = 179 painter.drawLine((base_img.width()-((total_distance_h))-(border_thick/2)-2)-qr_size, (base_img.height()-((total_distance_h)))-((border_thick*8))-(border_thick/2)-2, (base_img.width()/2+(total_distance_h/2)-border_thick-(border_thick*8)/2)-qr_size, (base_img.height()-((total_distance_h)))-((border_thick*8))-(border_thick/2)-2) painter.drawLine((base_img.width()/2+(total_distance_h/2)-border_thick-(border_thick*8)/2)-qr_size, (base_img.height()-((total_distance_h)))-((border_thick*8))-(border_thick/2)-2, base_img.width()/2 + (total_distance_h/2)-border_thick-(border_thick*8)/2-qr_size, ((base_img.height()-((total_distance_h)))-(border_thick/2)-2)) painter.setPen(QPen(Qt.white, border_thick * 8)) painter.drawLine( base_img.width() - ((total_distance_h)) - (border_thick * 8) / 2 - (border_thick / 2) - 2, (base_img.height() - ((total_distance_h))) - ((border_thick * 8) / 2) - (border_thick / 2) - 2, base_img.width() / 2 + (total_distance_h / 2) - border_thick - qr_size, (base_img.height() - ((total_distance_h))) - ((border_thick * 8) / 2) - (border_thick / 2) - 2) painter.setPen(QColor(0,0,0,255)) painter.drawText(QRect(((base_img.width()/2) +21)-qr_size, base_img.height()-107, base_img.width()-total_distance_h - border_thick -93, base_img.height()-total_distance_h - border_thick), Qt.AlignLeft, self.versioned_seed.get_ui_string_version_plus_seed()) painter.drawText(QRect(0, base_img.height()-107, base_img.width()-total_distance_h - border_thick -3 -qr_size, base_img.height()-total_distance_h - border_thick), Qt.AlignRight, self.versioned_seed.checksum) # draw qr code qr_qt = self.paintQR(self.versioned_seed.get_ui_string_version_plus_seed() + self.versioned_seed.checksum) target = QRectF(base_img.width()-65-qr_size, base_img.height()-65-qr_size, qr_size, qr_size ) painter.drawImage(target, qr_qt) painter.setPen(QPen(Qt.black, 4)) painter.drawLine(base_img.width()-65-qr_size, base_img.height()-65-qr_size, base_img.width() - 65 - qr_size, (base_img.height() - ((total_distance_h))) - ((border_thick * 8)) - (border_thick / 2) - 4 ) painter.drawLine(base_img.width()-65-qr_size, base_img.height()-65-qr_size, base_img.width() - 65, base_img.height()-65-qr_size ) painter.end() else: # calibration only painter.end() cal_img = QImage(self.f_size.width() + 100, self.f_size.height() + 100, QImage.Format_ARGB32) cal_img.fill(Qt.white) cal_painter = QPainter() cal_painter.begin(cal_img) cal_painter.drawImage(0,0, base_img) #black lines in the middle of border top left only cal_painter.setPen(QPen(Qt.black, 1, Qt.DashDotDotLine)) cal_painter.drawLine(0, dist_v, base_img.width(), dist_v) cal_painter.drawLine(dist_h, 0, dist_h, base_img.height()) pen = QPen(Qt.black, 2, Qt.DashDotDotLine) cal_painter.setPen(pen) n=15 cal_painter.setFont(QFont("DejaVu Sans Mono", 21, QFont.Bold)) for x in range(-n,n): #lines on bottom (vertical calibration) cal_painter.drawLine((((base_img.width())/(n*2)) *(x))+ (base_img.width()/2)-13, x+2+base_img.height()-(dist_v), (((base_img.width())/(n*2)) *(x))+ (base_img.width()/2)+13, x+2+base_img.height()-(dist_v)) num_pos = 9 if x > 9 : num_pos = 17 if x < 0 : num_pos = 20 if x < -9: num_pos = 27 cal_painter.drawText((((base_img.width())/(n*2)) *(x))+ (base_img.width()/2)-num_pos, 50+base_img.height()-(dist_v), str(x)) #lines on the right (horizontal calibrations) cal_painter.drawLine(x+2+(base_img.width()-(dist_h)), ((base_img.height()/(2*n)) *(x))+ (base_img.height()/n)+(base_img.height()/2)-13, x+2+(base_img.width()-(dist_h)), ((base_img.height()/(2*n)) *(x))+ (base_img.height()/n)+(base_img.height()/2)+13) cal_painter.drawText(30+(base_img.width()-(dist_h)), ((base_img.height()/(2*n)) *(x))+ (base_img.height()/2)+13, str(x)) cal_painter.end() base_img = cal_img return base_img def paintQR(self, data): if not data: return qr = qrcode.QRCode() qr.add_data(data) matrix = qr.get_matrix() k = len(matrix) border_color = Qt.white base_img = QImage(k * 5, k * 5, QImage.Format_ARGB32) base_img.fill(border_color) qrpainter = QPainter() qrpainter.begin(base_img) boxsize = 5 size = k * boxsize left = (base_img.width() - size)/2 top = (base_img.height() - size)/2 qrpainter.setBrush(Qt.black) qrpainter.setPen(Qt.black) for r in range(k): for c in range(k): if matrix[r][c]: qrpainter.drawRect(left+c*boxsize, top+r*boxsize, boxsize - 1, boxsize - 1) qrpainter.end() return base_img def calibration_dialog(self, window): d = WindowModalDialog(window, _("Revealer - Printer calibration settings")) d.setMinimumSize(100, 200) vbox = QVBoxLayout(d) vbox.addWidget(QLabel(''.join([" ", _("If you have an old printer, or want optimal precision")," ", _("print the calibration pdf and follow the instructions "), " "," ", ]))) self.calibration_h = self.config.get('calibration_h') self.calibration_v = self.config.get('calibration_v') cprint = QPushButton(_("Open calibration pdf")) cprint.clicked.connect(self.calibration) vbox.addWidget(cprint) vbox.addWidget(QLabel(_('Calibration values:'))) grid = QGridLayout() vbox.addLayout(grid) grid.addWidget(QLabel(_('Right side')), 0, 0) horizontal = QLineEdit() horizontal.setText(str(self.calibration_h)) grid.addWidget(horizontal, 0, 1) grid.addWidget(QLabel(_('Bottom')), 1, 0) vertical = QLineEdit() vertical.setText(str(self.calibration_v)) grid.addWidget(vertical, 1, 1) vbox.addStretch() vbox.addSpacing(13) vbox.addLayout(Buttons(CloseButton(d), OkButton(d))) if not d.exec_(): return self.calibration_h = int(Decimal(horizontal.text())) self.config.set_key('calibration_h', self.calibration_h) self.calibration_v = int(Decimal(vertical.text())) self.config.set_key('calibration_v', self.calibration_v)

true

790bea5cfa99570e690f18ab6edf2a2b4ec861fe

11,546

py

Python

gru.py

KingPixil/gram-rnn

b109e653d6b2657955931ee28553a61ac05271b0

[ "MIT" ]

4

2017-03-11T02:25:34.000Z

2017-08-01T17:19:08.000Z

gru.py

KingPixil/text-rnn

b109e653d6b2657955931ee28553a61ac05271b0

[ "MIT" ]

null

gru.py

KingPixil/text-rnn

b109e653d6b2657955931ee28553a61ac05271b0

[ "MIT" ]

null

import numpy as np import pickle def unique(seq): seen = set() seen_add = seen.add return [x for x in seq if not (x in seen or seen_add(x))] def sigmoid(x): return 1 / (1 + np.exp(-x)) def softmax(x, temperature=1.0): exp_x = np.exp(x / temperature) return exp_x / np.sum(exp_x) class TextRNN(object): def __init__(self, hiddenLayers=300, sequenceLength=100): # Hidden Layers self.hiddenLayers = hiddenLayers # Learning Rate self.learningRate = 2e-3 # Hidden State self.h = {} # Internal cursor self.cursor = 0 # Sequence Length self.sequenceLength = sequenceLength def train(self, text, ngrams=7, delimiter=" "): # Setup delimiter self.delimiter = delimiter # Split by delimiter grams = text.split(delimiter) if delimiter != "" else list(text) # Setup Data by Ngrams self.data = [delimiter.join(grams[i:i+ngrams]) for i in range(len(grams))[::ngrams]] # Get Unique Data self.uniqueData = unique(self.data) # Get Vocab Maps self.indexToGram = {i:gram for i, gram in enumerate(self.uniqueData)} self.gramToIndex = {gram:i for i, gram in enumerate(self.uniqueData)} # Get vocab size self.vocabSize = len(self.uniqueData) # Setup Inputs inputs = [] outputs = [] inputGrams = [self.gramToIndex[gram] for gram in self.data] outputGrams = [self.gramToIndex[gram] for gram in self.data[1:]] for i, inputGram in enumerate(inputGrams[0:-1]): X = np.zeros((self.vocabSize, 1)) X[inputGram, 0] = 1 y = np.zeros((self.vocabSize, 1)) y[outputGrams[i], 0] = 1 inputs.append(X) outputs.append(y) self.inputs = inputs self.outputs = outputs # Input Weights self.WXZ = np.random.randn(self.hiddenLayers, self.vocabSize) * 0.1 # Update Gate self.WXR = np.random.randn(self.hiddenLayers, self.vocabSize) * 0.1 # Reset Gate self.WXC = np.random.randn(self.hiddenLayers, self.vocabSize) * 0.1 # Candidate # Hidden Layer Weights self.WHZ = np.random.randn(self.hiddenLayers, self.hiddenLayers) * 0.1 # Update Gate self.WHR = np.random.randn(self.hiddenLayers, self.hiddenLayers) * 0.1 # Reset Gate self.WHC = np.random.randn(self.hiddenLayers, self.hiddenLayers) * 0.1 # Candidate Gate # Biases self.bC = np.zeros((self.hiddenLayers, 1)) # Candidate Gate self.bR = np.zeros((self.hiddenLayers, 1)) # Reset Gate self.bZ = np.zeros((self.hiddenLayers, 1)) # Update Gate self.bY = np.zeros((self.vocabSize, 1)) # Output # Output Layer Weights self.WY = np.random.randn(self.vocabSize, self.hiddenLayers) * 0.1 # Cache for Update self.dXZM = np.zeros_like(self.WXZ) self.dXRM = np.zeros_like(self.WXR) self.dXCM = np.zeros_like(self.WXC) self.dHZM = np.zeros_like(self.WHZ) self.dHRM = np.zeros_like(self.WHR) self.dHCM = np.zeros_like(self.WHC) self.dbZM = np.zeros_like(self.bZ) self.dbRM = np.zeros_like(self.bR) self.dbCM = np.zeros_like(self.bC) self.dYM = np.zeros_like(self.WY) self.dXZV = np.zeros_like(self.WXZ) self.dXRV = np.zeros_like(self.WXR) self.dXCV = np.zeros_like(self.WXC) self.dHZV = np.zeros_like(self.WHZ) self.dHRV = np.zeros_like(self.WHR) self.dHCV = np.zeros_like(self.WHC) self.dbZV = np.zeros_like(self.bZ) self.dbRV = np.zeros_like(self.bR) self.dbCV = np.zeros_like(self.bC) self.dYV = np.zeros_like(self.WY) def forward(self, X, hPrev, temperature=1.0): # Update Gate zbar = np.dot(self.WXZ, X) + np.dot(self.WHZ, hPrev) + self.bZ z = sigmoid(zbar) # Reset Gate rbar = np.dot(self.WXR, X) + np.dot(self.WHR, hPrev) + self.bR r = sigmoid(rbar) # Candidate cbar = np.dot(self.WXC, X) + np.dot(self.WHC, np.multiply(r, hPrev)) + self.bC c = np.tanh(cbar) # Hidden State h = np.multiply(c, z) + np.multiply(hPrev, 1 - z) # h = np.multiply(z, hPrev) + np.multiply((1 - z), c) # Output o = softmax(np.dot(self.WY, h) + self.bY, temperature) return z, zbar, r, rbar, c, cbar, h, o def step(self): # Hidden State self.h = {} self.h[-1] = np.zeros((self.hiddenLayers, 1)) # Update Gates z = {} zbars = {} # Reset Gates r = {} rbars = {} # Candidates c = {} cbars = {} # Inputs x = {} # Outputs o = {} # Target Indexes targets = {} # Timesteps to Unroll totalLen = len(self.inputs) if self.cursor + self.sequenceLength > totalLen: self.cursor = 0 # Total Loss loss = 0 for i in xrange(self.sequenceLength): # Get inputs and outputs X = self.inputs[self.cursor + i] y = self.outputs[self.cursor + i] # Move inputs forward through network z[i], zbars[i], r[i], rbars[i], c[i], cbars[i], self.h[i], o[i] = self.forward(X, self.h[i - 1]) # Calculate loss target = np.argmax(y) loss += -np.log(o[i][target, 0]) x[i] = X targets[i] = target # Back Propagation dXZ = np.zeros_like(self.WXZ) dXR = np.zeros_like(self.WXR) dXC = np.zeros_like(self.WXC) dHZ = np.zeros_like(self.WHZ) dHR = np.zeros_like(self.WHR) dHC = np.zeros_like(self.WHC) dbZ = np.zeros_like(self.bZ) dbR = np.zeros_like(self.bR) dbC = np.zeros_like(self.bC) dbY = np.zeros_like(self.bY) dY = np.zeros_like(self.WY) dhnext = np.zeros_like(self.h[0]) dzbarnext = np.zeros_like(zbars[0]) drbarnext = np.zeros_like(rbars[0]) dcbarnext = np.zeros_like(cbars[0]) z[self.sequenceLength] = np.zeros_like(z[0]) r[self.sequenceLength] = np.zeros_like(r[0]) for i in reversed(xrange(self.sequenceLength)): # Back Propagate Through Y dSY = np.copy(o[i]) dSY[targets[i]] -= 1 dY += np.dot(dSY, self.h[i].T) dbY += dSY # Back Propagate Through H and X dha = np.multiply(dhnext, 1 - z[i + 1]) # Through Update Gate dhb = np.dot(self.WHR.T, drbarnext) # Weights into rbar dhc = np.dot(self.WHZ.T, dzbarnext) # Weights into zbar dhd = np.multiply(r[i + 1], np.dot(self.WHC.T, dcbarnext)) # Weights into cbar dhe = np.dot(self.WY.T, dSY) # Weights at output dh = dha + dhb + dhc + dhd + dhe dcbar = np.multiply(np.multiply(dh, z[i]) , 1 - np.square(c[i])) drbar = np.multiply(np.multiply(self.h[i - 1], np.dot(self.WHC.T, dcbar)), np.multiply(r[i] , (1 - r[i]))) dzbar = np.multiply(np.multiply(dh, (c[i] - self.h[i - 1])), np.multiply(z[i], (1 - z[i]))) dXZ += np.dot(dzbar, x[i].T) dXR += np.dot(drbar, x[i].T) dXC += np.dot(dcbar, x[i].T) dHZ += np.dot(dzbar, self.h[i - 1].T) dHR += np.dot(drbar, self.h[i - 1].T) dHC += np.dot(dcbar, np.multiply(r[i], self.h[i - 1]).T) dbZ += dzbar dbR += drbar dbC += dcbar dhnext = dh drbarnext = drbar dzbarnext = dzbar dcbarnext = dcbar # Parameter Update (Adam) for param, delta, m, v in zip([self.WXZ, self.WXR, self.WXC, self.WHZ, self.WHR, self.WHC, self.WY, self.bZ, self.bR, self.bC], [dXZ, dXR, dXC, dHZ, dHR, dHC, dY, dbZ, dbR, dbC], [self.dXZM, self.dXRM, self.dXCM, self.dHZM, self.dHRM, self.dHCM, self.dYM, self.dbZM, self.dbRM, self.dbCM], [self.dXZV, self.dXRV, self.dXCV, self.dHZV, self.dHRV, self.dHCV, self.dYV, self.dbZV, self.dbRV, self.dbCV]): m = 0.9 * m + 0.1 * delta v = 0.99 * v + 0.01 * (delta ** 2) param += -self.learningRate * m / (np.sqrt(v) + 1e-8) # Update cursor self.cursor += self.sequenceLength return loss def sample(self, num=100, temperature=1.0, start=False): # Output output = "" # Sample hidden state h = {} h[-1] = np.zeros((self.hiddenLayers, 1)) # Sample Update Gate z = {} zbar = {} # Sample Reset Gate r = {} rbar = {} # Sample Candidate Gate c = {} cbar = {} # Make inputs from seed if start == False: lastCursor = self.cursor - self.sequenceLength seedIdx = lastCursor if lastCursor >= 0 else 0 seed = self.data[seedIdx] else: seedIdx = self.gramToIndex[start] seed = start X = np.zeros((self.vocabSize, 1)) X[self.gramToIndex[seed], 0] = 1 # Add seed to output output += seed # Generate sample for i in xrange(num - 1): # Move through network z[i], zbar[i], r[i], rbar[i], c[i], cbar[i], h[i], prediction = self.forward(X, h[i - 1], temperature) # Pick ngram using probabilities idx = np.random.choice(range(self.vocabSize), p=prediction.ravel()) # Add to output output += self.delimiter + self.indexToGram[idx] # Update input to feed back in X = np.zeros((self.vocabSize, 1)) X[idx, 0] = 1 return output def run(self, iterations=1000, size=100, temperatures=[1.0], sampleFile=False, printSample=5, seed=False): if sampleFile != False: sampleFile = open(sampleFile, 'w') for i in xrange(iterations): loss = bot.step() if i % printSample == 0: for temperature in temperatures: print '======= Temperature: ' + str(temperature) + ' =======' sample = bot.sample(size, temperature, seed) print sample if(sampleFile != False): sampleFile.write(sample + '\n\n\n') print '\n' print '======= Iteration ' + str(i + 1) + ' =======' print '======= Samples Seen: ' + str(self.cursor) + ' =======' print '======= Loss: ' + str(loss) + ' =======' if sampleFile != False: sampleFile.close() def save(self, small=True): savedObj = {item:value for item, value in self.__dict__.iteritems()} if small == True: for param in ["data", "uniqueData", "indexToGram", "gramToIndex", "inputs", "outputs"]: del savedObj[param] pickle.dump(savedObj, open("TEXT_RNN_DUMP3", "w+")) def load(self, dump): newSelf = pickle.load(dump) for item, value in newSelf.iteritems(): setattr(self, item, value) data = open('data.txt').read().lower() bot = TextRNN() bot.train(data, 1, '') bot.run() bot.save(True)

31.807163

150

0.529188

import numpy as np import pickle def unique(seq): seen = set() seen_add = seen.add return [x for x in seq if not (x in seen or seen_add(x))] def sigmoid(x): return 1 / (1 + np.exp(-x)) def softmax(x, temperature=1.0): exp_x = np.exp(x / temperature) return exp_x / np.sum(exp_x) class TextRNN(object): def __init__(self, hiddenLayers=300, sequenceLength=100): self.hiddenLayers = hiddenLayers self.learningRate = 2e-3 self.h = {} self.cursor = 0 self.sequenceLength = sequenceLength def train(self, text, ngrams=7, delimiter=" "): self.delimiter = delimiter grams = text.split(delimiter) if delimiter != "" else list(text) self.data = [delimiter.join(grams[i:i+ngrams]) for i in range(len(grams))[::ngrams]] self.uniqueData = unique(self.data) self.indexToGram = {i:gram for i, gram in enumerate(self.uniqueData)} self.gramToIndex = {gram:i for i, gram in enumerate(self.uniqueData)} self.vocabSize = len(self.uniqueData) inputs = [] outputs = [] inputGrams = [self.gramToIndex[gram] for gram in self.data] outputGrams = [self.gramToIndex[gram] for gram in self.data[1:]] for i, inputGram in enumerate(inputGrams[0:-1]): X = np.zeros((self.vocabSize, 1)) X[inputGram, 0] = 1 y = np.zeros((self.vocabSize, 1)) y[outputGrams[i], 0] = 1 inputs.append(X) outputs.append(y) self.inputs = inputs self.outputs = outputs self.WXZ = np.random.randn(self.hiddenLayers, self.vocabSize) * 0.1 self.WXR = np.random.randn(self.hiddenLayers, self.vocabSize) * 0.1 self.WXC = np.random.randn(self.hiddenLayers, self.vocabSize) * 0.1 self.WHZ = np.random.randn(self.hiddenLayers, self.hiddenLayers) * 0.1 self.WHR = np.random.randn(self.hiddenLayers, self.hiddenLayers) * 0.1 self.WHC = np.random.randn(self.hiddenLayers, self.hiddenLayers) * 0.1 self.bC = np.zeros((self.hiddenLayers, 1)) self.bR = np.zeros((self.hiddenLayers, 1)) self.bZ = np.zeros((self.hiddenLayers, 1)) self.bY = np.zeros((self.vocabSize, 1)) self.WY = np.random.randn(self.vocabSize, self.hiddenLayers) * 0.1 self.dXZM = np.zeros_like(self.WXZ) self.dXRM = np.zeros_like(self.WXR) self.dXCM = np.zeros_like(self.WXC) self.dHZM = np.zeros_like(self.WHZ) self.dHRM = np.zeros_like(self.WHR) self.dHCM = np.zeros_like(self.WHC) self.dbZM = np.zeros_like(self.bZ) self.dbRM = np.zeros_like(self.bR) self.dbCM = np.zeros_like(self.bC) self.dYM = np.zeros_like(self.WY) self.dXZV = np.zeros_like(self.WXZ) self.dXRV = np.zeros_like(self.WXR) self.dXCV = np.zeros_like(self.WXC) self.dHZV = np.zeros_like(self.WHZ) self.dHRV = np.zeros_like(self.WHR) self.dHCV = np.zeros_like(self.WHC) self.dbZV = np.zeros_like(self.bZ) self.dbRV = np.zeros_like(self.bR) self.dbCV = np.zeros_like(self.bC) self.dYV = np.zeros_like(self.WY) def forward(self, X, hPrev, temperature=1.0): zbar = np.dot(self.WXZ, X) + np.dot(self.WHZ, hPrev) + self.bZ z = sigmoid(zbar) rbar = np.dot(self.WXR, X) + np.dot(self.WHR, hPrev) + self.bR r = sigmoid(rbar) cbar = np.dot(self.WXC, X) + np.dot(self.WHC, np.multiply(r, hPrev)) + self.bC c = np.tanh(cbar) h = np.multiply(c, z) + np.multiply(hPrev, 1 - z) o = softmax(np.dot(self.WY, h) + self.bY, temperature) return z, zbar, r, rbar, c, cbar, h, o def step(self): self.h = {} self.h[-1] = np.zeros((self.hiddenLayers, 1)) z = {} zbars = {} r = {} rbars = {} c = {} cbars = {} x = {} o = {} targets = {} totalLen = len(self.inputs) if self.cursor + self.sequenceLength > totalLen: self.cursor = 0 loss = 0 for i in xrange(self.sequenceLength): X = self.inputs[self.cursor + i] y = self.outputs[self.cursor + i] z[i], zbars[i], r[i], rbars[i], c[i], cbars[i], self.h[i], o[i] = self.forward(X, self.h[i - 1]) target = np.argmax(y) loss += -np.log(o[i][target, 0]) x[i] = X targets[i] = target dXZ = np.zeros_like(self.WXZ) dXR = np.zeros_like(self.WXR) dXC = np.zeros_like(self.WXC) dHZ = np.zeros_like(self.WHZ) dHR = np.zeros_like(self.WHR) dHC = np.zeros_like(self.WHC) dbZ = np.zeros_like(self.bZ) dbR = np.zeros_like(self.bR) dbC = np.zeros_like(self.bC) dbY = np.zeros_like(self.bY) dY = np.zeros_like(self.WY) dhnext = np.zeros_like(self.h[0]) dzbarnext = np.zeros_like(zbars[0]) drbarnext = np.zeros_like(rbars[0]) dcbarnext = np.zeros_like(cbars[0]) z[self.sequenceLength] = np.zeros_like(z[0]) r[self.sequenceLength] = np.zeros_like(r[0]) for i in reversed(xrange(self.sequenceLength)): dSY = np.copy(o[i]) dSY[targets[i]] -= 1 dY += np.dot(dSY, self.h[i].T) dbY += dSY dha = np.multiply(dhnext, 1 - z[i + 1]) dhb = np.dot(self.WHR.T, drbarnext) dhc = np.dot(self.WHZ.T, dzbarnext) dhd = np.multiply(r[i + 1], np.dot(self.WHC.T, dcbarnext)) dhe = np.dot(self.WY.T, dSY) dh = dha + dhb + dhc + dhd + dhe dcbar = np.multiply(np.multiply(dh, z[i]) , 1 - np.square(c[i])) drbar = np.multiply(np.multiply(self.h[i - 1], np.dot(self.WHC.T, dcbar)), np.multiply(r[i] , (1 - r[i]))) dzbar = np.multiply(np.multiply(dh, (c[i] - self.h[i - 1])), np.multiply(z[i], (1 - z[i]))) dXZ += np.dot(dzbar, x[i].T) dXR += np.dot(drbar, x[i].T) dXC += np.dot(dcbar, x[i].T) dHZ += np.dot(dzbar, self.h[i - 1].T) dHR += np.dot(drbar, self.h[i - 1].T) dHC += np.dot(dcbar, np.multiply(r[i], self.h[i - 1]).T) dbZ += dzbar dbR += drbar dbC += dcbar dhnext = dh drbarnext = drbar dzbarnext = dzbar dcbarnext = dcbar for param, delta, m, v in zip([self.WXZ, self.WXR, self.WXC, self.WHZ, self.WHR, self.WHC, self.WY, self.bZ, self.bR, self.bC], [dXZ, dXR, dXC, dHZ, dHR, dHC, dY, dbZ, dbR, dbC], [self.dXZM, self.dXRM, self.dXCM, self.dHZM, self.dHRM, self.dHCM, self.dYM, self.dbZM, self.dbRM, self.dbCM], [self.dXZV, self.dXRV, self.dXCV, self.dHZV, self.dHRV, self.dHCV, self.dYV, self.dbZV, self.dbRV, self.dbCV]): m = 0.9 * m + 0.1 * delta v = 0.99 * v + 0.01 * (delta ** 2) param += -self.learningRate * m / (np.sqrt(v) + 1e-8) self.cursor += self.sequenceLength return loss def sample(self, num=100, temperature=1.0, start=False): output = "" h = {} h[-1] = np.zeros((self.hiddenLayers, 1)) z = {} zbar = {} r = {} rbar = {} c = {} cbar = {} if start == False: lastCursor = self.cursor - self.sequenceLength seedIdx = lastCursor if lastCursor >= 0 else 0 seed = self.data[seedIdx] else: seedIdx = self.gramToIndex[start] seed = start X = np.zeros((self.vocabSize, 1)) X[self.gramToIndex[seed], 0] = 1 output += seed for i in xrange(num - 1): z[i], zbar[i], r[i], rbar[i], c[i], cbar[i], h[i], prediction = self.forward(X, h[i - 1], temperature) idx = np.random.choice(range(self.vocabSize), p=prediction.ravel()) output += self.delimiter + self.indexToGram[idx] X = np.zeros((self.vocabSize, 1)) X[idx, 0] = 1 return output def run(self, iterations=1000, size=100, temperatures=[1.0], sampleFile=False, printSample=5, seed=False): if sampleFile != False: sampleFile = open(sampleFile, 'w') for i in xrange(iterations): loss = bot.step() if i % printSample == 0: for temperature in temperatures: print '======= Temperature: ' + str(temperature) + ' =======' sample = bot.sample(size, temperature, seed) print sample if(sampleFile != False): sampleFile.write(sample + '\n\n\n') print '\n' print '======= Iteration ' + str(i + 1) + ' =======' print '======= Samples Seen: ' + str(self.cursor) + ' =======' print '======= Loss: ' + str(loss) + ' =======' if sampleFile != False: sampleFile.close() def save(self, small=True): savedObj = {item:value for item, value in self.__dict__.iteritems()} if small == True: for param in ["data", "uniqueData", "indexToGram", "gramToIndex", "inputs", "outputs"]: del savedObj[param] pickle.dump(savedObj, open("TEXT_RNN_DUMP3", "w+")) def load(self, dump): newSelf = pickle.load(dump) for item, value in newSelf.iteritems(): setattr(self, item, value) data = open('data.txt').read().lower() bot = TextRNN() bot.train(data, 1, '') bot.run() bot.save(True)

false

true

790beba750f1c9ac459c484fc1795a670c0a4bda

154

py

Python

tests/urls.py

sflems/django-rest-friendship

c096372e65b1282859ccfb0db2b7d1058631ffa0

[ "ISC" ]

1

2022-01-26T05:46:21.000Z

tests/urls.py

sflems/django-rest-friendship

c096372e65b1282859ccfb0db2b7d1058631ffa0

[ "ISC" ]

null

tests/urls.py

sflems/django-rest-friendship

c096372e65b1282859ccfb0db2b7d1058631ffa0

[ "ISC" ]

null

from django.urls import path, include urlpatterns = [ path('', include(('rest_friendship.urls', 'rest_friendship'), namespace='rest_friendship')), ]

25.666667

96

0.720779

from django.urls import path, include urlpatterns = [ path('', include(('rest_friendship.urls', 'rest_friendship'), namespace='rest_friendship')), ]

true

790beca3a983ecb26cd665db044e84e01f5e1d86

4,024

py

Python

hotelrooms/hotelrooms/settings.py

atombrella/hotel-room-reservation

5dade1e95bb27e2847d03c03c4d00e707a50438e

[ "MIT" ]

null

hotelrooms/hotelrooms/settings.py

atombrella/hotel-room-reservation

5dade1e95bb27e2847d03c03c4d00e707a50438e

[ "MIT" ]

3

2021-06-04T23:18:26.000Z

2021-09-22T19:07:30.000Z

hotelrooms/hotelrooms/settings.py

atombrella/hotel-room-reservation

5dade1e95bb27e2847d03c03c4d00e707a50438e

[ "MIT" ]

null

""" Django settings for hotelrooms project. Generated by 'django-admin startproject' using Django 3.0.6. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # 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/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '^b7=e99!2(t7csio=(lospr6ebgbp-2(*n^il4vt8dotctorm*' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.postgres', 'booking', ] 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 = 'hotelrooms.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ os.path.join(BASE_DIR, "hotelrooms", "templates"), os.path.join(BASE_DIR, "booking", "templates"), ], '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', ], }, }, ] WSGI_APPLICATION = 'hotelrooms.wsgi.application' PROJECT_DIR = os.path.dirname(__file__) # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'NAME': 'hotelrooms', 'PORT': 5433, 'HOST': os.getenv("DB_HOST", "localhost"), 'USER': 'django', 'PASSWORD': 'hotelrooms', } } # Password validation # https://docs.djangoproject.com/en/3.0/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/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True DATE_INPUT_FORMATS = [ '%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06' '%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006' '%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006' '%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006' '%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006' ] # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_ROOT = os.path.join(PROJECT_DIR, 'static/') STATIC_URL = '/static/' STATICFILES_DIRS = [ os.path.join(BASE_DIR, "static"), ] MEDIA_ROOT = os.path.join(BASE_DIR, 'media') MEDIA_URL = '/media/'

27.751724

91

0.65333

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = '^b7=e99!2(t7csio=(lospr6ebgbp-2(*n^il4vt8dotctorm*' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.postgres', 'booking', ] 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 = 'hotelrooms.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ os.path.join(BASE_DIR, "hotelrooms", "templates"), os.path.join(BASE_DIR, "booking", "templates"), ], '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', ], }, }, ] WSGI_APPLICATION = 'hotelrooms.wsgi.application' PROJECT_DIR = os.path.dirname(__file__) # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'NAME': 'hotelrooms', 'PORT': 5433, 'HOST': os.getenv("DB_HOST", "localhost"), 'USER': 'django', 'PASSWORD': 'hotelrooms', } } # Password validation # https://docs.djangoproject.com/en/3.0/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/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True DATE_INPUT_FORMATS = [ '%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06' '%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006' '%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006' '%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006' '%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006' ] # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_ROOT = os.path.join(PROJECT_DIR, 'static/') STATIC_URL = '/static/' STATICFILES_DIRS = [ os.path.join(BASE_DIR, "static"), ] MEDIA_ROOT = os.path.join(BASE_DIR, 'media') MEDIA_URL = '/media/'

true

790bed39f912fbb80665860e34afb05d378a6e59

1,787

py

Python

Chapter11/webapp/blog/models.py

jayakumardhananjayan/pythonwebtut

a7547473fec5b90a91aea5395131e6eff245b495

[ "MIT" ]

135

2018-10-31T11:52:35.000Z

2022-03-23T12:23:04.000Z

Chapter11/webapp/blog/models.py

jayakumardhananjayan/pythonwebtut

a7547473fec5b90a91aea5395131e6eff245b495

[ "MIT" ]

6

2019-03-21T02:04:43.000Z

2022-03-22T11:07:25.000Z

Chapter11/webapp/blog/models.py

jayakumardhananjayan/pythonwebtut

a7547473fec5b90a91aea5395131e6eff245b495

[ "MIT" ]

109

2018-10-30T22:26:23.000Z

2022-03-24T14:53:13.000Z

import datetime from .. import db tags = db.Table( 'post_tags', db.Column('post_id', db.Integer, db.ForeignKey('post.id')), db.Column('tag_id', db.Integer, db.ForeignKey('tag.id')) ) class Post(db.Model): id = db.Column(db.Integer(), primary_key=True) title = db.Column(db.String(255), nullable=False) text = db.Column(db.Text(), nullable=False) publish_date = db.Column(db.DateTime(), default=datetime.datetime.now) user_id = db.Column(db.Integer(), db.ForeignKey('user.id')) youtube_id = db.Column(db.String(255)) comments = db.relationship('Comment', backref='post', lazy='dynamic') tags = db.relationship('Tag', secondary=tags, backref=db.backref('posts', lazy='dynamic')) def __init__(self, title=""): self.title = title def __repr__(self): return "<Post '{}'>".format(self.title) class Comment(db.Model): id = db.Column(db.Integer(), primary_key=True) name = db.Column(db.String(255), nullable=False) text = db.Column(db.Text(), nullable=False) date = db.Column(db.DateTime(), default=datetime.datetime.now) post_id = db.Column(db.Integer(), db.ForeignKey('post.id')) def __repr__(self): return "<Comment '{}'>".format(self.text[:15]) class Tag(db.Model): id = db.Column(db.Integer(), primary_key=True) title = db.Column(db.String(255), nullable=False, unique=True) def __init__(self, title=""): self.title = title def __repr__(self): return "<Tag '{}'>".format(self.title) class Reminder(db.Model): id = db.Column(db.Integer(), primary_key=True) date = db.Column(db.DateTime()) email = db.Column(db.String()) text = db.Column(db.Text()) def __repr__(self): return "<Reminder '{}'>".format(self.text[:20])

30.810345

94

0.641858

import datetime from .. import db tags = db.Table( 'post_tags', db.Column('post_id', db.Integer, db.ForeignKey('post.id')), db.Column('tag_id', db.Integer, db.ForeignKey('tag.id')) ) class Post(db.Model): id = db.Column(db.Integer(), primary_key=True) title = db.Column(db.String(255), nullable=False) text = db.Column(db.Text(), nullable=False) publish_date = db.Column(db.DateTime(), default=datetime.datetime.now) user_id = db.Column(db.Integer(), db.ForeignKey('user.id')) youtube_id = db.Column(db.String(255)) comments = db.relationship('Comment', backref='post', lazy='dynamic') tags = db.relationship('Tag', secondary=tags, backref=db.backref('posts', lazy='dynamic')) def __init__(self, title=""): self.title = title def __repr__(self): return "<Post '{}'>".format(self.title) class Comment(db.Model): id = db.Column(db.Integer(), primary_key=True) name = db.Column(db.String(255), nullable=False) text = db.Column(db.Text(), nullable=False) date = db.Column(db.DateTime(), default=datetime.datetime.now) post_id = db.Column(db.Integer(), db.ForeignKey('post.id')) def __repr__(self): return "<Comment '{}'>".format(self.text[:15]) class Tag(db.Model): id = db.Column(db.Integer(), primary_key=True) title = db.Column(db.String(255), nullable=False, unique=True) def __init__(self, title=""): self.title = title def __repr__(self): return "<Tag '{}'>".format(self.title) class Reminder(db.Model): id = db.Column(db.Integer(), primary_key=True) date = db.Column(db.DateTime()) email = db.Column(db.String()) text = db.Column(db.Text()) def __repr__(self): return "<Reminder '{}'>".format(self.text[:20])

true

790bed5fe2f8f36d830032650767d577aea42711

6,303

py

Python

pcapng/flags.py

Boolean263/python-pcapng

447c375456fc107376fd7f884f791d48a89f1f16

[ "Apache-2.0" ]

null

pcapng/flags.py

Boolean263/python-pcapng

447c375456fc107376fd7f884f791d48a89f1f16

[ "Apache-2.0" ]

null

pcapng/flags.py

Boolean263/python-pcapng

447c375456fc107376fd7f884f791d48a89f1f16

[ "Apache-2.0" ]

null

""" Module to wrap an integer in bitwise flag/field accessors. """ from collections import OrderedDict from pcapng.ngsix import namedtuple, Iterable class FlagBase(object): """\ Base class for flag types to be used in a Flags object. Handles the bitwise math so subclasses don't have to worry about it. """ __slots__ = [ 'owner', 'offset', 'size', 'extra', 'mask', ] def __init__(self, owner, offset, size, extra=None): if size < 1: raise TypeError('Flag must be at least 1 bit wide') if size > owner._nbits: raise TypeError('Flag must fit into owner size') self.owner = owner self.offset = offset self.size = size self.extra = extra self.mask = ((1 << self.size)-1) << self.offset def get_bits(self): return (self.owner._value & self.mask) >> self.offset def set_bits(self, val): val &= (1 << self.size) - 1 self.owner._value &= ~self.mask self.owner._value |= (val << self.offset) class FlagBool(FlagBase): """Object representing a single boolean flag""" def __init__(self, owner, offset, size, extra=None): if size != 1: raise TypeError('{cls} can only be 1 bit in size'.format(cls=self.__class__.__name__)) super(FlagBool, self).__init__(owner, offset, size) def get(self): return bool(self.get_bits()) def set(self, val): self.set_bits(int(bool(val))) class FlagUInt(FlagBase): """\ Object representing an unsigned integer of the given size stored in a larger bitfield """ def get(self): return self.get_bits() def set(self, val): self.set_bits(val) class FlagEnum(FlagBase): """\ Object representing a range of values stored in part of a larger bitfield """ def __init__(self, owner, offset, size, extra=None): if not isinstance(extra, Iterable): raise TypeError('{cls} needs an iterable of values'.format(cls=self.__class__.__name__)) extra = list(extra) if len(extra) > 2**size: raise TypeError('{cls} iterable has too many values (got {got}, {size} bits only address {max})'.format(cls=self.__class__.__name__, got=len(extra), size=size, max=2**size)) super(FlagEnum, self).__init__(owner, offset, size, extra) def get(self): val = self.get_bits() try: return self.extra[val] except IndexError: return '[invalid value]' def set(self, val): if val in self.extra: self.set_bits(self.extra.index(val)) elif isinstance(val, int): self.set_bits(val) else: raise TypeError('Invalid value {val} for {cls}'.format(val=val, cls=self.__class__.__name__)) # Class representing a single flag schema for FlagWord. # 'nbits' defaults to 1, and 'extra' defaults to None. FlagField = namedtuple('FlagField', ('name', 'ftype', 'nbits', 'extra'), defaults=(1, None)) class FlagWord(object): """\ Class to wrap an integer in bitwise flag/field accessors. """ __slots__ = [ '_nbits', '_value', '_schema', ] def __init__(self, schema, nbits=32, initial=0): """ :param schema: A list of FlagField objects representing the values to be packed into this object, in order from LSB to MSB of the underlying int :param nbits: An integer representing the total number of bits used for flags :param initial: The initial integer value of the flags field """ self._nbits = nbits self._value = initial self._schema = OrderedDict() tot_bits = sum([item.nbits for item in schema]) if tot_bits > nbits: raise TypeError("Too many fields for {nbits}-bit field (schema defines {tot} bits)".format(nbits=nbits, tot=tot_bits)) bitn = 0 for item in schema: if not isinstance(item, FlagField): raise TypeError('Schema must be composed of FlagField objects') if not issubclass(item.ftype, FlagBase): raise TypeError('Expected FlagBase, got {}'.format(item.ftype)) self._schema[item.name] = item.ftype(self, bitn, item.nbits, item.extra) bitn += item.nbits def __int__(self): return self._value def __repr__(self): rv = '<{0} (value={1})'.format(self.__class__.__name__, self._value) for k, v in self._schema.items(): rv += ' {0}={1}'.format(k, v.get()) return rv+'>' def __getattr__(self, name): try: v = self._schema[name] except KeyError: raise AttributeError(name) return v.get() def __setattr__(self, name, val): try: return object.__setattr__(self, name, val) except AttributeError: pass try: v = self._schema[name] except KeyError: raise AttributeError(name) return v.set(val) if __name__ == '__main__': f = FlagWord([ FlagField('inout', FlagEnum, 2, ('NA', 'inbound', 'outbound')), FlagField('casttype', FlagEnum, 3, ('NA', 'unicast', 'multicast', 'broadcast', 'promiscuous')), FlagField('fcslen', FlagUInt, 4), FlagField('reserved', FlagUInt, 7), FlagField('err_16', FlagBool), FlagField('err_17', FlagBool), FlagField('err_18', FlagBool), FlagField('err_19', FlagBool), FlagField('err_20', FlagBool), FlagField('err_21', FlagBool), FlagField('err_22', FlagBool), FlagField('err_23', FlagBool), FlagField('err_crc', FlagBool), FlagField('err_long', FlagBool), FlagField('err_short', FlagBool), FlagField('err_frame_gap', FlagBool), FlagField('err_frame_align', FlagBool), FlagField('err_frame_delim', FlagBool), FlagField('err_preamble', FlagBool), FlagField('err_symbol', FlagBool), ]) f.fcslen = 12 print(f) print(int(f))

30.746341

185

0.5802

from collections import OrderedDict from pcapng.ngsix import namedtuple, Iterable class FlagBase(object): __slots__ = [ 'owner', 'offset', 'size', 'extra', 'mask', ] def __init__(self, owner, offset, size, extra=None): if size < 1: raise TypeError('Flag must be at least 1 bit wide') if size > owner._nbits: raise TypeError('Flag must fit into owner size') self.owner = owner self.offset = offset self.size = size self.extra = extra self.mask = ((1 << self.size)-1) << self.offset def get_bits(self): return (self.owner._value & self.mask) >> self.offset def set_bits(self, val): val &= (1 << self.size) - 1 self.owner._value &= ~self.mask self.owner._value |= (val << self.offset) class FlagBool(FlagBase): def __init__(self, owner, offset, size, extra=None): if size != 1: raise TypeError('{cls} can only be 1 bit in size'.format(cls=self.__class__.__name__)) super(FlagBool, self).__init__(owner, offset, size) def get(self): return bool(self.get_bits()) def set(self, val): self.set_bits(int(bool(val))) class FlagUInt(FlagBase): def get(self): return self.get_bits() def set(self, val): self.set_bits(val) class FlagEnum(FlagBase): def __init__(self, owner, offset, size, extra=None): if not isinstance(extra, Iterable): raise TypeError('{cls} needs an iterable of values'.format(cls=self.__class__.__name__)) extra = list(extra) if len(extra) > 2**size: raise TypeError('{cls} iterable has too many values (got {got}, {size} bits only address {max})'.format(cls=self.__class__.__name__, got=len(extra), size=size, max=2**size)) super(FlagEnum, self).__init__(owner, offset, size, extra) def get(self): val = self.get_bits() try: return self.extra[val] except IndexError: return '[invalid value]' def set(self, val): if val in self.extra: self.set_bits(self.extra.index(val)) elif isinstance(val, int): self.set_bits(val) else: raise TypeError('Invalid value {val} for {cls}'.format(val=val, cls=self.__class__.__name__)) FlagField = namedtuple('FlagField', ('name', 'ftype', 'nbits', 'extra'), defaults=(1, None)) class FlagWord(object): __slots__ = [ '_nbits', '_value', '_schema', ] def __init__(self, schema, nbits=32, initial=0): self._nbits = nbits self._value = initial self._schema = OrderedDict() tot_bits = sum([item.nbits for item in schema]) if tot_bits > nbits: raise TypeError("Too many fields for {nbits}-bit field (schema defines {tot} bits)".format(nbits=nbits, tot=tot_bits)) bitn = 0 for item in schema: if not isinstance(item, FlagField): raise TypeError('Schema must be composed of FlagField objects') if not issubclass(item.ftype, FlagBase): raise TypeError('Expected FlagBase, got {}'.format(item.ftype)) self._schema[item.name] = item.ftype(self, bitn, item.nbits, item.extra) bitn += item.nbits def __int__(self): return self._value def __repr__(self): rv = '<{0} (value={1})'.format(self.__class__.__name__, self._value) for k, v in self._schema.items(): rv += ' {0}={1}'.format(k, v.get()) return rv+'>' def __getattr__(self, name): try: v = self._schema[name] except KeyError: raise AttributeError(name) return v.get() def __setattr__(self, name, val): try: return object.__setattr__(self, name, val) except AttributeError: pass try: v = self._schema[name] except KeyError: raise AttributeError(name) return v.set(val) if __name__ == '__main__': f = FlagWord([ FlagField('inout', FlagEnum, 2, ('NA', 'inbound', 'outbound')), FlagField('casttype', FlagEnum, 3, ('NA', 'unicast', 'multicast', 'broadcast', 'promiscuous')), FlagField('fcslen', FlagUInt, 4), FlagField('reserved', FlagUInt, 7), FlagField('err_16', FlagBool), FlagField('err_17', FlagBool), FlagField('err_18', FlagBool), FlagField('err_19', FlagBool), FlagField('err_20', FlagBool), FlagField('err_21', FlagBool), FlagField('err_22', FlagBool), FlagField('err_23', FlagBool), FlagField('err_crc', FlagBool), FlagField('err_long', FlagBool), FlagField('err_short', FlagBool), FlagField('err_frame_gap', FlagBool), FlagField('err_frame_align', FlagBool), FlagField('err_frame_delim', FlagBool), FlagField('err_preamble', FlagBool), FlagField('err_symbol', FlagBool), ]) f.fcslen = 12 print(f) print(int(f))

true

790bed8d7cc85d9dfc7095934007a4938b817029

16,031

py

Python

tfx/orchestration/kubeflow/kubeflow_dag_runner.py

TimoKerr/tfx

10d13d57eeac21514fed73118cb43464dada67f1

[ "Apache-2.0" ]

null

tfx/orchestration/kubeflow/kubeflow_dag_runner.py

TimoKerr/tfx

10d13d57eeac21514fed73118cb43464dada67f1

[ "Apache-2.0" ]

null

tfx/orchestration/kubeflow/kubeflow_dag_runner.py

TimoKerr/tfx

10d13d57eeac21514fed73118cb43464dada67f1

[ "Apache-2.0" ]

null

# Lint as: python2, python3 # Copyright 2019 Google LLC. 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. """TFX runner for Kubeflow.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import re from typing import Callable, Dict, List, Optional, Text, Type, cast from absl import logging from kfp import compiler from kfp import dsl from kfp import gcp from kubernetes import client as k8s_client from tfx import version from tfx.dsl.compiler import compiler as tfx_compiler from tfx.dsl.components.base import base_component as tfx_base_component from tfx.orchestration import data_types from tfx.orchestration import pipeline as tfx_pipeline from tfx.orchestration import tfx_runner from tfx.orchestration.config import pipeline_config from tfx.orchestration.kubeflow import base_component from tfx.orchestration.kubeflow import utils from tfx.orchestration.kubeflow.proto import kubeflow_pb2 from tfx.orchestration.launcher import base_component_launcher from tfx.orchestration.launcher import in_process_component_launcher from tfx.orchestration.launcher import kubernetes_component_launcher from tfx.proto.orchestration import pipeline_pb2 from tfx.utils import json_utils from tfx.utils import telemetry_utils # OpFunc represents the type of a function that takes as input a # dsl.ContainerOp and returns the same object. Common operations such as adding # k8s secrets, mounting volumes, specifying the use of TPUs and so on can be # specified as an OpFunc. # See example usage here: # https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/gcp.py OpFunc = Callable[[dsl.ContainerOp], dsl.ContainerOp] # Default secret name for GCP credentials. This secret is installed as part of # a typical Kubeflow installation when the component is GKE. _KUBEFLOW_GCP_SECRET_NAME = 'user-gcp-sa' # Default TFX container image to use in KubeflowDagRunner. DEFAULT_KUBEFLOW_TFX_IMAGE = 'tensorflow/tfx:%s' % (version.__version__,) def _mount_config_map_op(config_map_name: Text) -> OpFunc: """Mounts all key-value pairs found in the named Kubernetes ConfigMap. All key-value pairs in the ConfigMap are mounted as environment variables. Args: config_map_name: The name of the ConfigMap resource. Returns: An OpFunc for mounting the ConfigMap. """ def mount_config_map(container_op: dsl.ContainerOp): config_map_ref = k8s_client.V1ConfigMapEnvSource( name=config_map_name, optional=True) container_op.container.add_env_from( k8s_client.V1EnvFromSource(config_map_ref=config_map_ref)) return mount_config_map def _mount_secret_op(secret_name: Text) -> OpFunc: """Mounts all key-value pairs found in the named Kubernetes Secret. All key-value pairs in the Secret are mounted as environment variables. Args: secret_name: The name of the Secret resource. Returns: An OpFunc for mounting the Secret. """ def mount_secret(container_op: dsl.ContainerOp): secret_ref = k8s_client.V1ConfigMapEnvSource( name=secret_name, optional=True) container_op.container.add_env_from( k8s_client.V1EnvFromSource(secret_ref=secret_ref)) return mount_secret def get_default_pipeline_operator_funcs( use_gcp_sa: bool = False) -> List[OpFunc]: """Returns a default list of pipeline operator functions. Args: use_gcp_sa: If true, mount a GCP service account secret to each pod, with the name _KUBEFLOW_GCP_SECRET_NAME. Returns: A list of functions with type OpFunc. """ # Enables authentication for GCP services if needed. gcp_secret_op = gcp.use_gcp_secret(_KUBEFLOW_GCP_SECRET_NAME) # Mounts configmap containing Metadata gRPC server configuration. mount_config_map_op = _mount_config_map_op('metadata-grpc-configmap') if use_gcp_sa: return [gcp_secret_op, mount_config_map_op] else: return [mount_config_map_op] def get_default_kubeflow_metadata_config( ) -> kubeflow_pb2.KubeflowMetadataConfig: """Returns the default metadata connection config for Kubeflow. Returns: A config proto that will be serialized as JSON and passed to the running container so the TFX component driver is able to communicate with MLMD in a Kubeflow cluster. """ # The default metadata configuration for a Kubeflow Pipelines cluster is # codified as a Kubernetes ConfigMap # https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/base/metadata/metadata-grpc-configmap.yaml config = kubeflow_pb2.KubeflowMetadataConfig() # The environment variable to use to obtain the Metadata gRPC service host in # the cluster that is backing Kubeflow Metadata. Note that the key in the # config map and therefore environment variable used, are lower-cased. config.grpc_config.grpc_service_host.environment_variable = 'METADATA_GRPC_SERVICE_HOST' # The environment variable to use to obtain the Metadata grpc service port in # the cluster that is backing Kubeflow Metadata. config.grpc_config.grpc_service_port.environment_variable = 'METADATA_GRPC_SERVICE_PORT' return config def get_default_pod_labels() -> Dict[Text, Text]: """Returns the default pod label dict for Kubeflow.""" # KFP default transformers add pod env: # https://github.com/kubeflow/pipelines/blob/0.1.32/sdk/python/kfp/compiler/_default_transformers.py result = { 'add-pod-env': 'true', telemetry_utils.LABEL_KFP_SDK_ENV: 'tfx' } return result def get_default_output_filename(pipeline_name: str) -> str: return pipeline_name + '.tar.gz' class KubeflowDagRunnerConfig(pipeline_config.PipelineConfig): """Runtime configuration parameters specific to execution on Kubeflow.""" def __init__( self, pipeline_operator_funcs: Optional[List[OpFunc]] = None, tfx_image: Optional[Text] = None, kubeflow_metadata_config: Optional[ kubeflow_pb2.KubeflowMetadataConfig] = None, # TODO(b/143883035): Figure out the best practice to put the # SUPPORTED_LAUNCHER_CLASSES supported_launcher_classes: List[Type[ base_component_launcher.BaseComponentLauncher]] = None, **kwargs): """Creates a KubeflowDagRunnerConfig object. The user can use pipeline_operator_funcs to apply modifications to ContainerOps used in the pipeline. For example, to ensure the pipeline steps mount a GCP secret, and a Persistent Volume, one can create config object like so: from kfp import gcp, onprem mount_secret_op = gcp.use_secret('my-secret-name) mount_volume_op = onprem.mount_pvc( "my-persistent-volume-claim", "my-volume-name", "/mnt/volume-mount-path") config = KubeflowDagRunnerConfig( pipeline_operator_funcs=[mount_secret_op, mount_volume_op] ) Args: pipeline_operator_funcs: A list of ContainerOp modifying functions that will be applied to every container step in the pipeline. tfx_image: The TFX container image to use in the pipeline. kubeflow_metadata_config: Runtime configuration to use to connect to Kubeflow metadata. supported_launcher_classes: A list of component launcher classes that are supported by the current pipeline. List sequence determines the order in which launchers are chosen for each component being run. **kwargs: keyword args for PipelineConfig. """ supported_launcher_classes = supported_launcher_classes or [ in_process_component_launcher.InProcessComponentLauncher, kubernetes_component_launcher.KubernetesComponentLauncher, ] super(KubeflowDagRunnerConfig, self).__init__( supported_launcher_classes=supported_launcher_classes, **kwargs) self.pipeline_operator_funcs = ( pipeline_operator_funcs or get_default_pipeline_operator_funcs()) self.tfx_image = tfx_image or DEFAULT_KUBEFLOW_TFX_IMAGE self.kubeflow_metadata_config = ( kubeflow_metadata_config or get_default_kubeflow_metadata_config()) class KubeflowDagRunner(tfx_runner.TfxRunner): """Kubeflow Pipelines runner. Constructs a pipeline definition YAML file based on the TFX logical pipeline. """ def __init__( self, output_dir: Optional[Text] = None, output_filename: Optional[Text] = None, config: Optional[KubeflowDagRunnerConfig] = None, pod_labels_to_attach: Optional[Dict[Text, Text]] = None ): """Initializes KubeflowDagRunner for compiling a Kubeflow Pipeline. Args: output_dir: An optional output directory into which to output the pipeline definition files. Defaults to the current working directory. output_filename: An optional output file name for the pipeline definition file. Defaults to pipeline_name.tar.gz when compiling a TFX pipeline. Currently supports .tar.gz, .tgz, .zip, .yaml, .yml formats. See https://github.com/kubeflow/pipelines/blob/181de66cf9fa87bcd0fe9291926790c400140783/sdk/python/kfp/compiler/compiler.py#L851 for format restriction. config: An optional KubeflowDagRunnerConfig object to specify runtime configuration when running the pipeline under Kubeflow. pod_labels_to_attach: Optional set of pod labels to attach to GKE pod spinned up for this pipeline. Default to the 3 labels: 1. add-pod-env: true, 2. pipeline SDK type, 3. pipeline unique ID, where 2 and 3 are instrumentation of usage tracking. """ if config and not isinstance(config, KubeflowDagRunnerConfig): raise TypeError('config must be type of KubeflowDagRunnerConfig.') super(KubeflowDagRunner, self).__init__(config or KubeflowDagRunnerConfig()) self._config = cast(KubeflowDagRunnerConfig, self._config) self._output_dir = output_dir or os.getcwd() self._output_filename = output_filename self._compiler = compiler.Compiler() self._tfx_compiler = tfx_compiler.Compiler() self._params = [] # List of dsl.PipelineParam used in this pipeline. self._deduped_parameter_names = set() # Set of unique param names used. if pod_labels_to_attach is None: self._pod_labels_to_attach = get_default_pod_labels() else: self._pod_labels_to_attach = pod_labels_to_attach def _parse_parameter_from_component( self, component: base_component.BaseComponent) -> None: """Extract embedded RuntimeParameter placeholders from a component. Extract embedded RuntimeParameter placeholders from a component, then append the corresponding dsl.PipelineParam to KubeflowDagRunner. Args: component: a TFX component. """ serialized_component = json_utils.dumps(component) placeholders = re.findall(data_types.RUNTIME_PARAMETER_PATTERN, serialized_component) for placeholder in placeholders: placeholder = placeholder.replace('\\', '') # Clean escapes. placeholder = utils.fix_brackets(placeholder) # Fix brackets if needed. parameter = json_utils.loads(placeholder) # Escape pipeline root because it will be added later. if parameter.name == tfx_pipeline.ROOT_PARAMETER.name: continue if parameter.name not in self._deduped_parameter_names: self._deduped_parameter_names.add(parameter.name) # TODO(b/178436919): Create a test to cover default value rendering # and move the external code reference over there. # The default needs to be serialized then passed to dsl.PipelineParam. # See # https://github.com/kubeflow/pipelines/blob/f65391309650fdc967586529e79af178241b4c2c/sdk/python/kfp/dsl/_pipeline_param.py#L154 dsl_parameter = dsl.PipelineParam( name=parameter.name, value=str(parameter.default)) self._params.append(dsl_parameter) def _parse_parameter_from_pipeline(self, pipeline: tfx_pipeline.Pipeline) -> None: """Extract all the RuntimeParameter placeholders from the pipeline.""" for component in pipeline.components: self._parse_parameter_from_component(component) def _construct_pipeline_graph(self, pipeline: tfx_pipeline.Pipeline, pipeline_root: dsl.PipelineParam): """Constructs a Kubeflow Pipeline graph. Args: pipeline: The logical TFX pipeline to base the construction on. pipeline_root: dsl.PipelineParam representing the pipeline root. """ component_to_kfp_op = {} tfx_ir = self._generate_tfx_ir(pipeline) # Assumption: There is a partial ordering of components in the list, i.e., # if component A depends on component B and C, then A appears after B and C # in the list. for component in pipeline.components: # Keep track of the set of upstream dsl.ContainerOps for this component. depends_on = set() for upstream_component in component.upstream_nodes: depends_on.add(component_to_kfp_op[upstream_component]) kfp_component = base_component.BaseComponent( component=component, depends_on=depends_on, pipeline=pipeline, pipeline_root=pipeline_root, tfx_image=self._config.tfx_image, kubeflow_metadata_config=self._config.kubeflow_metadata_config, pod_labels_to_attach=self._pod_labels_to_attach, tfx_ir=tfx_ir) for operator in self._config.pipeline_operator_funcs: kfp_component.container_op.apply(operator) component_to_kfp_op[component] = kfp_component.container_op def _generate_tfx_ir( self, pipeline: tfx_pipeline.Pipeline) -> Optional[pipeline_pb2.Pipeline]: result = self._tfx_compiler.compile(pipeline) logging.info('Generated pipeline:\n %s', result) return result def run(self, pipeline: tfx_pipeline.Pipeline): """Compiles and outputs a Kubeflow Pipeline YAML definition file. Args: pipeline: The logical TFX pipeline to use when building the Kubeflow pipeline. """ for component in pipeline.components: # TODO(b/187122662): Pass through pip dependencies as a first-class # component flag. if isinstance(component, tfx_base_component.BaseComponent): component._resolve_pip_dependencies( # pylint: disable=protected-access pipeline.pipeline_info.pipeline_root) # KFP DSL representation of pipeline root parameter. dsl_pipeline_root = dsl.PipelineParam( name=tfx_pipeline.ROOT_PARAMETER.name, value=pipeline.pipeline_info.pipeline_root) self._params.append(dsl_pipeline_root) def _construct_pipeline(): """Constructs a Kubeflow pipeline. Creates Kubeflow ContainerOps for each TFX component encountered in the logical pipeline definition. """ self._construct_pipeline_graph(pipeline, dsl_pipeline_root) # Need to run this first to get self._params populated. Then KFP compiler # can correctly match default value with PipelineParam. self._parse_parameter_from_pipeline(pipeline) file_name = self._output_filename or get_default_output_filename( pipeline.pipeline_info.pipeline_name) # Create workflow spec and write out to package. self._compiler._create_and_write_workflow( # pylint: disable=protected-access pipeline_func=_construct_pipeline, pipeline_name=pipeline.pipeline_info.pipeline_name, params_list=self._params, package_path=os.path.join(self._output_dir, file_name))

40.895408

136

0.748487

from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import re from typing import Callable, Dict, List, Optional, Text, Type, cast from absl import logging from kfp import compiler from kfp import dsl from kfp import gcp from kubernetes import client as k8s_client from tfx import version from tfx.dsl.compiler import compiler as tfx_compiler from tfx.dsl.components.base import base_component as tfx_base_component from tfx.orchestration import data_types from tfx.orchestration import pipeline as tfx_pipeline from tfx.orchestration import tfx_runner from tfx.orchestration.config import pipeline_config from tfx.orchestration.kubeflow import base_component from tfx.orchestration.kubeflow import utils from tfx.orchestration.kubeflow.proto import kubeflow_pb2 from tfx.orchestration.launcher import base_component_launcher from tfx.orchestration.launcher import in_process_component_launcher from tfx.orchestration.launcher import kubernetes_component_launcher from tfx.proto.orchestration import pipeline_pb2 from tfx.utils import json_utils from tfx.utils import telemetry_utils OpFunc = Callable[[dsl.ContainerOp], dsl.ContainerOp] _KUBEFLOW_GCP_SECRET_NAME = 'user-gcp-sa' DEFAULT_KUBEFLOW_TFX_IMAGE = 'tensorflow/tfx:%s' % (version.__version__,) def _mount_config_map_op(config_map_name: Text) -> OpFunc: def mount_config_map(container_op: dsl.ContainerOp): config_map_ref = k8s_client.V1ConfigMapEnvSource( name=config_map_name, optional=True) container_op.container.add_env_from( k8s_client.V1EnvFromSource(config_map_ref=config_map_ref)) return mount_config_map def _mount_secret_op(secret_name: Text) -> OpFunc: def mount_secret(container_op: dsl.ContainerOp): secret_ref = k8s_client.V1ConfigMapEnvSource( name=secret_name, optional=True) container_op.container.add_env_from( k8s_client.V1EnvFromSource(secret_ref=secret_ref)) return mount_secret def get_default_pipeline_operator_funcs( use_gcp_sa: bool = False) -> List[OpFunc]: gcp_secret_op = gcp.use_gcp_secret(_KUBEFLOW_GCP_SECRET_NAME) mount_config_map_op = _mount_config_map_op('metadata-grpc-configmap') if use_gcp_sa: return [gcp_secret_op, mount_config_map_op] else: return [mount_config_map_op] def get_default_kubeflow_metadata_config( ) -> kubeflow_pb2.KubeflowMetadataConfig: config = kubeflow_pb2.KubeflowMetadataConfig() config.grpc_config.grpc_service_host.environment_variable = 'METADATA_GRPC_SERVICE_HOST' config.grpc_config.grpc_service_port.environment_variable = 'METADATA_GRPC_SERVICE_PORT' return config def get_default_pod_labels() -> Dict[Text, Text]: result = { 'add-pod-env': 'true', telemetry_utils.LABEL_KFP_SDK_ENV: 'tfx' } return result def get_default_output_filename(pipeline_name: str) -> str: return pipeline_name + '.tar.gz' class KubeflowDagRunnerConfig(pipeline_config.PipelineConfig): def __init__( self, pipeline_operator_funcs: Optional[List[OpFunc]] = None, tfx_image: Optional[Text] = None, kubeflow_metadata_config: Optional[ kubeflow_pb2.KubeflowMetadataConfig] = None, supported_launcher_classes: List[Type[ base_component_launcher.BaseComponentLauncher]] = None, **kwargs): supported_launcher_classes = supported_launcher_classes or [ in_process_component_launcher.InProcessComponentLauncher, kubernetes_component_launcher.KubernetesComponentLauncher, ] super(KubeflowDagRunnerConfig, self).__init__( supported_launcher_classes=supported_launcher_classes, **kwargs) self.pipeline_operator_funcs = ( pipeline_operator_funcs or get_default_pipeline_operator_funcs()) self.tfx_image = tfx_image or DEFAULT_KUBEFLOW_TFX_IMAGE self.kubeflow_metadata_config = ( kubeflow_metadata_config or get_default_kubeflow_metadata_config()) class KubeflowDagRunner(tfx_runner.TfxRunner): def __init__( self, output_dir: Optional[Text] = None, output_filename: Optional[Text] = None, config: Optional[KubeflowDagRunnerConfig] = None, pod_labels_to_attach: Optional[Dict[Text, Text]] = None ): if config and not isinstance(config, KubeflowDagRunnerConfig): raise TypeError('config must be type of KubeflowDagRunnerConfig.') super(KubeflowDagRunner, self).__init__(config or KubeflowDagRunnerConfig()) self._config = cast(KubeflowDagRunnerConfig, self._config) self._output_dir = output_dir or os.getcwd() self._output_filename = output_filename self._compiler = compiler.Compiler() self._tfx_compiler = tfx_compiler.Compiler() self._params = [] self._deduped_parameter_names = set() if pod_labels_to_attach is None: self._pod_labels_to_attach = get_default_pod_labels() else: self._pod_labels_to_attach = pod_labels_to_attach def _parse_parameter_from_component( self, component: base_component.BaseComponent) -> None: serialized_component = json_utils.dumps(component) placeholders = re.findall(data_types.RUNTIME_PARAMETER_PATTERN, serialized_component) for placeholder in placeholders: placeholder = placeholder.replace('\\', '') placeholder = utils.fix_brackets(placeholder) parameter = json_utils.loads(placeholder) if parameter.name == tfx_pipeline.ROOT_PARAMETER.name: continue if parameter.name not in self._deduped_parameter_names: self._deduped_parameter_names.add(parameter.name) dsl_parameter = dsl.PipelineParam( name=parameter.name, value=str(parameter.default)) self._params.append(dsl_parameter) def _parse_parameter_from_pipeline(self, pipeline: tfx_pipeline.Pipeline) -> None: for component in pipeline.components: self._parse_parameter_from_component(component) def _construct_pipeline_graph(self, pipeline: tfx_pipeline.Pipeline, pipeline_root: dsl.PipelineParam): component_to_kfp_op = {} tfx_ir = self._generate_tfx_ir(pipeline) for component in pipeline.components: depends_on = set() for upstream_component in component.upstream_nodes: depends_on.add(component_to_kfp_op[upstream_component]) kfp_component = base_component.BaseComponent( component=component, depends_on=depends_on, pipeline=pipeline, pipeline_root=pipeline_root, tfx_image=self._config.tfx_image, kubeflow_metadata_config=self._config.kubeflow_metadata_config, pod_labels_to_attach=self._pod_labels_to_attach, tfx_ir=tfx_ir) for operator in self._config.pipeline_operator_funcs: kfp_component.container_op.apply(operator) component_to_kfp_op[component] = kfp_component.container_op def _generate_tfx_ir( self, pipeline: tfx_pipeline.Pipeline) -> Optional[pipeline_pb2.Pipeline]: result = self._tfx_compiler.compile(pipeline) logging.info('Generated pipeline:\n %s', result) return result def run(self, pipeline: tfx_pipeline.Pipeline): for component in pipeline.components: if isinstance(component, tfx_base_component.BaseComponent): component._resolve_pip_dependencies( pipeline.pipeline_info.pipeline_root) dsl_pipeline_root = dsl.PipelineParam( name=tfx_pipeline.ROOT_PARAMETER.name, value=pipeline.pipeline_info.pipeline_root) self._params.append(dsl_pipeline_root) def _construct_pipeline(): self._construct_pipeline_graph(pipeline, dsl_pipeline_root) self._parse_parameter_from_pipeline(pipeline) file_name = self._output_filename or get_default_output_filename( pipeline.pipeline_info.pipeline_name) self._compiler._create_and_write_workflow( pipeline_func=_construct_pipeline, pipeline_name=pipeline.pipeline_info.pipeline_name, params_list=self._params, package_path=os.path.join(self._output_dir, file_name))

true

790bedc36125b8b607589b033654f44942369dca

3,220

py

Python

multi_camera_multi_person_tracking/utils/network_wrappers.py

565353780/open-vino

362c11ca90026c0e1c21bb1f76f9dbfd339bdc05

[ "MIT" ]

null

multi_camera_multi_person_tracking/utils/network_wrappers.py

565353780/open-vino

362c11ca90026c0e1c21bb1f76f9dbfd339bdc05

[ "MIT" ]

null

multi_camera_multi_person_tracking/utils/network_wrappers.py

565353780/open-vino

362c11ca90026c0e1c21bb1f76f9dbfd339bdc05

[ "MIT" ]

null

""" Copyright (c) 2019 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from utils.ie_tools import load_ie_model class Detector: """Wrapper class for detector""" def __init__(self, model_path, conf=.6, device='CPU', ext_path='', max_num_frames=1): self.net = load_ie_model(model_path, device, None, ext_path, num_reqs=max_num_frames) self.confidence = conf self.expand_ratio = (1., 1.) self.max_num_frames = max_num_frames def get_detections(self, frames): """Returns all detections on frames""" assert len(frames) <= self.max_num_frames all_detections = [] for i in range(len(frames)): self.net.forward_async(frames[i]) outputs = self.net.grab_all_async() for i, out in enumerate(outputs): detections = self.__decode_detections(out, frames[i].shape) all_detections.append(detections) return all_detections def __decode_detections(self, out, frame_shape): """Decodes raw SSD output""" detections = [] for detection in out[0, 0]: confidence = detection[2] if confidence > self.confidence: left = int(max(detection[3], 0) * frame_shape[1]) top = int(max(detection[4], 0) * frame_shape[0]) right = int(max(detection[5], 0) * frame_shape[1]) bottom = int(max(detection[6], 0) * frame_shape[0]) if self.expand_ratio != (1., 1.): w = (right - left) h = (bottom - top) dw = w * (self.expand_ratio[0] - 1.) / 2 dh = h * (self.expand_ratio[1] - 1.) / 2 left = max(int(left - dw), 0) right = int(right + dw) top = max(int(top - dh), 0) bottom = int(bottom + dh) detections.append(((left, top, right, bottom), confidence)) if len(detections) > 1: detections.sort(key=lambda x: x[1], reverse=True) return detections class VectorCNN: """Wrapper class for a network returning a vector""" def __init__(self, model_path, device='CPU', max_reqs=100): self.max_reqs = max_reqs self.net = load_ie_model(model_path, device, None, num_reqs=self.max_reqs) def forward(self, batch): """Performs forward of the underlying network on a given batch""" assert len(batch) <= self.max_reqs for frame in batch: self.net.forward_async(frame) outputs = self.net.grab_all_async() return outputs

38.333333

94

0.590373

from utils.ie_tools import load_ie_model class Detector: def __init__(self, model_path, conf=.6, device='CPU', ext_path='', max_num_frames=1): self.net = load_ie_model(model_path, device, None, ext_path, num_reqs=max_num_frames) self.confidence = conf self.expand_ratio = (1., 1.) self.max_num_frames = max_num_frames def get_detections(self, frames): assert len(frames) <= self.max_num_frames all_detections = [] for i in range(len(frames)): self.net.forward_async(frames[i]) outputs = self.net.grab_all_async() for i, out in enumerate(outputs): detections = self.__decode_detections(out, frames[i].shape) all_detections.append(detections) return all_detections def __decode_detections(self, out, frame_shape): detections = [] for detection in out[0, 0]: confidence = detection[2] if confidence > self.confidence: left = int(max(detection[3], 0) * frame_shape[1]) top = int(max(detection[4], 0) * frame_shape[0]) right = int(max(detection[5], 0) * frame_shape[1]) bottom = int(max(detection[6], 0) * frame_shape[0]) if self.expand_ratio != (1., 1.): w = (right - left) h = (bottom - top) dw = w * (self.expand_ratio[0] - 1.) / 2 dh = h * (self.expand_ratio[1] - 1.) / 2 left = max(int(left - dw), 0) right = int(right + dw) top = max(int(top - dh), 0) bottom = int(bottom + dh) detections.append(((left, top, right, bottom), confidence)) if len(detections) > 1: detections.sort(key=lambda x: x[1], reverse=True) return detections class VectorCNN: def __init__(self, model_path, device='CPU', max_reqs=100): self.max_reqs = max_reqs self.net = load_ie_model(model_path, device, None, num_reqs=self.max_reqs) def forward(self, batch): assert len(batch) <= self.max_reqs for frame in batch: self.net.forward_async(frame) outputs = self.net.grab_all_async() return outputs

true

790bedfbdd1631e4090932e4cdaa29302ea59268

792

py

Python

test_taster5.py

pythononwheels/opentoni

666a014a956670ff6ec55a97b9a26bd3412353ad

[ "MIT" ]

null

test_taster5.py

pythononwheels/opentoni

666a014a956670ff6ec55a97b9a26bd3412353ad

[ "MIT" ]

null

test_taster5.py

pythononwheels/opentoni

666a014a956670ff6ec55a97b9a26bd3412353ad

[ "MIT" ]

null

import RPi.GPIO as gpio import time from subprocess import Popen, PIPE, call pin =38 gpio.setmode(gpio.BOARD) gpio.setup(pin, gpio.IN, pull_up_down = gpio.PUD_UP) PRESSED = 0 prev_state = 1 pressed_time = 0.1 skip_song_mode = False try: while True: cur_state = gpio.input(pin) if cur_state == PRESSED: pressed_time += 0.1 print "pressed : " + str( pressed_time) if pressed_time > 1: call(["espeak", "-ven", "shutting down"]) elif pressed_time == 0.1: skip_song_mode = True else: skip_song_mode = False else: pressed_time = 0 if skip_song_mode == True: call(["espeak", "-ven", "skip song"]) skip_song_mode = False time.sleep(0.1) finally: gpio.cleanup()

24.75

53

0.60101

import RPi.GPIO as gpio import time from subprocess import Popen, PIPE, call pin =38 gpio.setmode(gpio.BOARD) gpio.setup(pin, gpio.IN, pull_up_down = gpio.PUD_UP) PRESSED = 0 prev_state = 1 pressed_time = 0.1 skip_song_mode = False try: while True: cur_state = gpio.input(pin) if cur_state == PRESSED: pressed_time += 0.1 print "pressed : " + str( pressed_time) if pressed_time > 1: call(["espeak", "-ven", "shutting down"]) elif pressed_time == 0.1: skip_song_mode = True else: skip_song_mode = False else: pressed_time = 0 if skip_song_mode == True: call(["espeak", "-ven", "skip song"]) skip_song_mode = False time.sleep(0.1) finally: gpio.cleanup()

false

true

790bee28635e8713588b60b8549f757d44bc9039

3,545

py

Python

mlreflect/curve_fitter/minimizer.py

schreiber-lab/mlreflect

88a80ccac48461cc8934a46041726b70e469c6b8

[ "MIT" ]

null

mlreflect/curve_fitter/minimizer.py

schreiber-lab/mlreflect

88a80ccac48461cc8934a46041726b70e469c6b8

[ "MIT" ]

null

mlreflect/curve_fitter/minimizer.py

schreiber-lab/mlreflect

88a80ccac48461cc8934a46041726b70e469c6b8

[ "MIT" ]

null

import numpy as np from scipy.optimize import curve_fit from ..data_generation import interp_reflectivity, ReflectivityGenerator def q_shift_variants(q_values_prediction, q_values_input, corrected_reflectivity, n_variants, scale=0.001): """Create ``n_variants`` interpolated reflectivity curve variants with randomly distributed q shifts.""" shift = np.random.normal(loc=0, size=n_variants, scale=scale).reshape(n_variants, 1) shifted_qs = np.tile(q_values_input, (n_variants, 1)) + shift interpolated_curves = np.zeros((n_variants, len(q_values_prediction))) for i in range(n_variants): interpolated_curves[i] = interp_reflectivity(q_values_prediction, shifted_qs[i], corrected_reflectivity) return interpolated_curves, shift def curve_scaling_variants(corrected_reflectivity, n_variants, scale=0.1): """Create ``n_variants`` reflectivity curve variants with randomly distributed scaling factors.""" scalings = np.random.normal(loc=1, size=n_variants, scale=scale).reshape(n_variants, 1) scaled_curves = np.zeros((n_variants, len(corrected_reflectivity))) for i in range(n_variants): scaled_curves[i] = corrected_reflectivity.copy() * scalings[i] return scaled_curves, scalings def curve_variant_log_mse(curve, variant_curves): """Calculate the log MSE of a curve and a :class:`ndarray` of curves""" errors = np.log10(curve) - np.log10(variant_curves) return np.mean(errors ** 2, axis=1) def least_log_mean_squares_fit(q_values, data, predicted_labels, sample, output_preprocessor, fraction_bounds=(0.5, 0.5, 0.1)): """Fits the data with a model curve with ``scipy.optimize.curve_fit`` using ``predicted_labels`` as start values.""" prep_labels = output_preprocessor.apply_preprocessing(predicted_labels)[0] start_values = np.array(prep_labels)[0] bounds = ([val - bound * abs(val) for val, bound in zip(start_values, fraction_bounds)], [val + bound * abs(val) for val, bound in zip(start_values, fraction_bounds)]) fit_result = curve_fit(fitting_model(q_values, sample, output_preprocessor), q_values, np.log10(data), p0=start_values, bounds=bounds) return output_preprocessor.restore_labels(np.atleast_2d(fit_result[0])) def fitting_model(q_values, sample, output_preprocessor): def log_refl_curve(q, *prep_labels): generator = ReflectivityGenerator(q_values, sample) restored_labels = output_preprocessor.restore_labels(np.atleast_2d(prep_labels)) model = generator.simulate_reflectivity(restored_labels, progress_bar=False)[0] return np.log10(model) return log_refl_curve def log_mse_loss(prep_labels, data, generator, output_preprocessor): """MSE loss between a reflectivity curve and a model curve generated with the given normalized labels.""" restored_labels = output_preprocessor.restore_labels(np.atleast_2d(prep_labels)) model = generator.simulate_reflectivity(restored_labels, progress_bar=False)[0] loss = mean_squared_error(np.log10(data), np.log10(model)) return loss def mean_squared_error(array1, array2): """Returns element-wise mean squared error between two arrays.""" if len(array1) != len(array2): raise ValueError(f'array1 and array2 must be of same length ({len(array1)} != {len(array2)})') else: error = np.asarray(array1) - np.asarray(array2) return np.mean(np.atleast_2d(error ** 2), axis=1)

49.929577

120

0.725811

import numpy as np from scipy.optimize import curve_fit from ..data_generation import interp_reflectivity, ReflectivityGenerator def q_shift_variants(q_values_prediction, q_values_input, corrected_reflectivity, n_variants, scale=0.001): shift = np.random.normal(loc=0, size=n_variants, scale=scale).reshape(n_variants, 1) shifted_qs = np.tile(q_values_input, (n_variants, 1)) + shift interpolated_curves = np.zeros((n_variants, len(q_values_prediction))) for i in range(n_variants): interpolated_curves[i] = interp_reflectivity(q_values_prediction, shifted_qs[i], corrected_reflectivity) return interpolated_curves, shift def curve_scaling_variants(corrected_reflectivity, n_variants, scale=0.1): scalings = np.random.normal(loc=1, size=n_variants, scale=scale).reshape(n_variants, 1) scaled_curves = np.zeros((n_variants, len(corrected_reflectivity))) for i in range(n_variants): scaled_curves[i] = corrected_reflectivity.copy() * scalings[i] return scaled_curves, scalings def curve_variant_log_mse(curve, variant_curves): errors = np.log10(curve) - np.log10(variant_curves) return np.mean(errors ** 2, axis=1) def least_log_mean_squares_fit(q_values, data, predicted_labels, sample, output_preprocessor, fraction_bounds=(0.5, 0.5, 0.1)): prep_labels = output_preprocessor.apply_preprocessing(predicted_labels)[0] start_values = np.array(prep_labels)[0] bounds = ([val - bound * abs(val) for val, bound in zip(start_values, fraction_bounds)], [val + bound * abs(val) for val, bound in zip(start_values, fraction_bounds)]) fit_result = curve_fit(fitting_model(q_values, sample, output_preprocessor), q_values, np.log10(data), p0=start_values, bounds=bounds) return output_preprocessor.restore_labels(np.atleast_2d(fit_result[0])) def fitting_model(q_values, sample, output_preprocessor): def log_refl_curve(q, *prep_labels): generator = ReflectivityGenerator(q_values, sample) restored_labels = output_preprocessor.restore_labels(np.atleast_2d(prep_labels)) model = generator.simulate_reflectivity(restored_labels, progress_bar=False)[0] return np.log10(model) return log_refl_curve def log_mse_loss(prep_labels, data, generator, output_preprocessor): restored_labels = output_preprocessor.restore_labels(np.atleast_2d(prep_labels)) model = generator.simulate_reflectivity(restored_labels, progress_bar=False)[0] loss = mean_squared_error(np.log10(data), np.log10(model)) return loss def mean_squared_error(array1, array2): if len(array1) != len(array2): raise ValueError(f'array1 and array2 must be of same length ({len(array1)} != {len(array2)})') else: error = np.asarray(array1) - np.asarray(array2) return np.mean(np.atleast_2d(error ** 2), axis=1)

true

790bef11f686d55ca3e87f1884de366413f7cf15

5,296

py

Python

vbridge/utils/entityset_helpers.py

sibyl-dev/VBridge

5c4c49dad7cc1ad4e734dfac24b934088fc75bc6

[ "MIT" ]

5

2021-10-30T02:18:31.000Z

2021-12-01T18:13:09.000Z

vbridge/utils/entityset_helpers.py

sibyl-dev/VBridge

5c4c49dad7cc1ad4e734dfac24b934088fc75bc6

[ "MIT" ]

null

vbridge/utils/entityset_helpers.py

sibyl-dev/VBridge

5c4c49dad7cc1ad4e734dfac24b934088fc75bc6

[ "MIT" ]

1

2022-03-11T12:50:33.000Z

def remove_nan_entries(df, key_columns, verbose=True): n_row = len(df) for column in key_columns: df = df[df[column] == df[column]] if verbose: print("Prune ({}/{}) rows.".format(n_row - len(df), n_row)) return df def parse_relationship_path(relationship_path): # TODO: get the relationship with a public function instead relationship = relationship_path._relationships_with_direction[0][1] return { 'parent_entity_id': relationship.parent_entity.id, 'parent_variable_id': relationship.parent_variable.id, 'child_entity_id': relationship.child_entity.id, 'child_variable_id': relationship.child_variable.id, } def get_forward_entities(entityset, entity_id): ids = [] entity_id_pipe = [entity_id] while len(entity_id_pipe): entity_id = entity_id_pipe[0] entity_id_pipe = entity_id_pipe[1:] ids.append(entity_id) for child_id, _ in entityset.get_forward_entities(entity_id): entity_id_pipe.append(child_id) return ids def get_forward_attributes(entityset, target_entity, direct_id, interesting_ids=None): info = [] entity_id_pipe = [(target_entity, direct_id)] while len(entity_id_pipe): entity_id, direct_id = entity_id_pipe.pop() if interesting_ids is not None and entity_id not in interesting_ids: continue df = entityset[entity_id].df info = [{'entityId': entity_id, 'items': df.loc[direct_id].fillna('N/A').to_dict()}] + info for child_id, relationship_path in entityset.get_forward_entities(entity_id): relation = parse_relationship_path(relationship_path) entity_id_pipe.append((child_id, df.loc[direct_id][relation['parent_variable_id']])) return info def find_path(entityset, source_entity, target_entity): """Find a path of the source entity to the target_entity.""" nodes_pipe = [target_entity] parent_dict = {target_entity: None} while len(nodes_pipe): parent_node = nodes_pipe.pop() if parent_node == source_entity: break child_nodes = [e[0] for e in entityset.get_backward_entities(parent_node)] \ + [e[0] for e in entityset.get_forward_entities(parent_node)] for child in child_nodes: if child not in parent_dict: parent_dict[child] = parent_node nodes_pipe.append(child) node = source_entity paths = [[node]] while node != target_entity: node = parent_dict[node] paths.append(paths[-1] + [node]) return paths def transfer_cutoff_times(entityset, cutoff_times, source_entity, target_entity, reduce="latest"): path = find_path(entityset, source_entity, target_entity)[-1] for i, source in enumerate(path[:-1]): target = path[i + 1] options = list(filter(lambda r: (r.child_entity.id == source and r.parent_entity.id == target) or (r.parent_entity.id == source and r.child_entity.id == target), entityset.relationships)) if len(options) == 0: raise ValueError("No Relationship between {} and {}".format(source, target)) r = options[0] if target == r.child_entity.id: # Transfer cutoff_times to "child", e.g., PATIENTS -> ADMISSIONS child_df_index = r.child_entity.df[r.child_variable.id].values cutoff_times = cutoff_times.loc[child_df_index] cutoff_times.index = r.child_entity.df.index elif source == r.child_entity.id: # Transfer cutoff_times to "parent", e.g., ADMISSIONS -> PATIENTS cutoff_times[r.child_variable.id] = r.child_entity.df[r.child_variable.id] if reduce == "latest": idx = cutoff_times.groupby(r.child_variable.id).time.idxmax().values elif reduce == 'earist': idx = cutoff_times.groupby(r.child_variable.id).time.idxmin().values else: raise ValueError("Unknown reduce option.") cutoff_times = cutoff_times.loc[idx] cutoff_times = cutoff_times.set_index(r.child_variable.id, drop=True) return cutoff_times def get_records(entityset, subject_id, entity_id, time_index=None, cutoff_time=None): entity = entityset[entity_id].df # select records by SUBJECT_ID if 'SUBJECT_ID' in entity.columns: entity_df = entity[entity['SUBJECT_ID'] == subject_id] else: entity_df = entity # select records before or at the cutoff_time if cutoff_time is not None and time_index is not None: entity_df = entity_df[entity_df[time_index] <= cutoff_time] # TODO filter records according to secondary time index return entity_df def get_item_dict(es): item_dict = {'LABEVENTS': es['D_LABITEMS'].df.loc[:, 'LABEL'].to_dict()} for entity_id in ['CHARTEVENTS', 'SURGERY_VITAL_SIGNS']: df = es['D_ITEMS'].df # TODO: Change 'LABEL' to 'LABEL_CN' for Chinese labels items = df[df['LINKSTO'] == entity_id.lower()].loc[:, 'LABEL'] item_dict[entity_id] = items.to_dict() return item_dict

41.375

99

0.643882

def remove_nan_entries(df, key_columns, verbose=True): n_row = len(df) for column in key_columns: df = df[df[column] == df[column]] if verbose: print("Prune ({}/{}) rows.".format(n_row - len(df), n_row)) return df def parse_relationship_path(relationship_path): relationship = relationship_path._relationships_with_direction[0][1] return { 'parent_entity_id': relationship.parent_entity.id, 'parent_variable_id': relationship.parent_variable.id, 'child_entity_id': relationship.child_entity.id, 'child_variable_id': relationship.child_variable.id, } def get_forward_entities(entityset, entity_id): ids = [] entity_id_pipe = [entity_id] while len(entity_id_pipe): entity_id = entity_id_pipe[0] entity_id_pipe = entity_id_pipe[1:] ids.append(entity_id) for child_id, _ in entityset.get_forward_entities(entity_id): entity_id_pipe.append(child_id) return ids def get_forward_attributes(entityset, target_entity, direct_id, interesting_ids=None): info = [] entity_id_pipe = [(target_entity, direct_id)] while len(entity_id_pipe): entity_id, direct_id = entity_id_pipe.pop() if interesting_ids is not None and entity_id not in interesting_ids: continue df = entityset[entity_id].df info = [{'entityId': entity_id, 'items': df.loc[direct_id].fillna('N/A').to_dict()}] + info for child_id, relationship_path in entityset.get_forward_entities(entity_id): relation = parse_relationship_path(relationship_path) entity_id_pipe.append((child_id, df.loc[direct_id][relation['parent_variable_id']])) return info def find_path(entityset, source_entity, target_entity): nodes_pipe = [target_entity] parent_dict = {target_entity: None} while len(nodes_pipe): parent_node = nodes_pipe.pop() if parent_node == source_entity: break child_nodes = [e[0] for e in entityset.get_backward_entities(parent_node)] \ + [e[0] for e in entityset.get_forward_entities(parent_node)] for child in child_nodes: if child not in parent_dict: parent_dict[child] = parent_node nodes_pipe.append(child) node = source_entity paths = [[node]] while node != target_entity: node = parent_dict[node] paths.append(paths[-1] + [node]) return paths def transfer_cutoff_times(entityset, cutoff_times, source_entity, target_entity, reduce="latest"): path = find_path(entityset, source_entity, target_entity)[-1] for i, source in enumerate(path[:-1]): target = path[i + 1] options = list(filter(lambda r: (r.child_entity.id == source and r.parent_entity.id == target) or (r.parent_entity.id == source and r.child_entity.id == target), entityset.relationships)) if len(options) == 0: raise ValueError("No Relationship between {} and {}".format(source, target)) r = options[0] if target == r.child_entity.id: child_df_index = r.child_entity.df[r.child_variable.id].values cutoff_times = cutoff_times.loc[child_df_index] cutoff_times.index = r.child_entity.df.index elif source == r.child_entity.id: cutoff_times[r.child_variable.id] = r.child_entity.df[r.child_variable.id] if reduce == "latest": idx = cutoff_times.groupby(r.child_variable.id).time.idxmax().values elif reduce == 'earist': idx = cutoff_times.groupby(r.child_variable.id).time.idxmin().values else: raise ValueError("Unknown reduce option.") cutoff_times = cutoff_times.loc[idx] cutoff_times = cutoff_times.set_index(r.child_variable.id, drop=True) return cutoff_times def get_records(entityset, subject_id, entity_id, time_index=None, cutoff_time=None): entity = entityset[entity_id].df if 'SUBJECT_ID' in entity.columns: entity_df = entity[entity['SUBJECT_ID'] == subject_id] else: entity_df = entity if cutoff_time is not None and time_index is not None: entity_df = entity_df[entity_df[time_index] <= cutoff_time] return entity_df def get_item_dict(es): item_dict = {'LABEVENTS': es['D_LABITEMS'].df.loc[:, 'LABEL'].to_dict()} for entity_id in ['CHARTEVENTS', 'SURGERY_VITAL_SIGNS']: df = es['D_ITEMS'].df items = df[df['LINKSTO'] == entity_id.lower()].loc[:, 'LABEL'] item_dict[entity_id] = items.to_dict() return item_dict

true

790bef5ee5ccf4aa4c38c98c4471cf856d2ee6f3

3,000

py

Python

Fusion/fillet_polygon.py

HeNeos/autodesk_scripts

b0cf77915bc48eb3b27dc3739115d8f20a5ba434

[ "MIT" ]

null

Fusion/fillet_polygon.py

HeNeos/autodesk_scripts

b0cf77915bc48eb3b27dc3739115d8f20a5ba434

[ "MIT" ]

null

Fusion/fillet_polygon.py

HeNeos/autodesk_scripts

b0cf77915bc48eb3b27dc3739115d8f20a5ba434

[ "MIT" ]

null

#Author-HeNeos #Description-Many triangles, I love triangles import adsk.core, adsk.fusion, adsk.cam, traceback import math def get_points(n, angle, r): ans = [[0.0, 0.0]]*n for i in range(0, n): ans[i] = [r*math.cos(angle + 2*i*math.pi/n), r*math.sin(angle + 2*i*math.pi/n)] return ans def run(context): try: app = adsk.core.Application.get() ui = app.userInterface ui.messageBox('Are you ready') product = app.activeProduct design = adsk.fusion.Design.cast(product) rootComp = design.rootComponent sketches = rootComp.sketches xyPlane = rootComp.xYConstructionPlane # Create a new ObjectCollection. revolves = rootComp.features.revolveFeatures r = 4 loftFeats = rootComp.features.loftFeatures loftInput = loftFeats.createInput(adsk.fusion.FeatureOperations.NewBodyFeatureOperation) loftSectionsObj = loftInput.loftSections n = 6 for i in range(0, 100): angle = (math.pi)*abs(math.sin(i/10)) ctorPlanes = rootComp.constructionPlanes plane = ctorPlanes.createInput() offset = adsk.core.ValueInput.createByString(str(i)+" cm") plane.setByOffset(xyPlane, offset) Plane = ctorPlanes.add(plane) sketch = sketches.add(Plane) lines = sketch.sketchCurves.sketchLines Points = [] Lines = [] p = get_points(n, angle, r) for j in range(0, n): point = adsk.core.Point3D.create(p[j][0], p[j][1], 0) Points.append(point) for j in range(0, n-1): line = lines.addByTwoPoints(Points[j], Points[j+1]) Lines.append(line) Lines.append(lines.addByTwoPoints(Points[n-1], Points[0])) for i in range(0, n-1): sketch.sketchCurves.sketchArcs.addFillet(Lines[i], Lines[i].endSketchPoint.geometry, Lines[i+1], Lines[i+1].startSketchPoint.geometry, 0.5) sketch.sketchCurves.sketchArcs.addFillet(Lines[n-1], Lines[n-1].endSketchPoint.geometry, Lines[0], Lines[0].startSketchPoint.geometry, 0.5) profile = sketch.profiles.item(0) sketch.isVisible = False Plane.isLightBulbOn = False loftSectionsObj.add(profile) loftInput.isSolid=True loftFeats.add(loftInput) except: if ui: ui.messageBox('Failed:\n{}'.format(traceback.format_exc())) #axis = lines.addByTwoPoints(adsk.core.Point3D.create(-1,-4,0), adsk.core.Point3D.create(1,-4,0)) #circle1 = circles.addByCenterRadius(adsk.core.Point3D.create(0,0,0), 2) def stop(context): try: app = adsk.core.Application.get() ui = app.userInterface ui.messageBox('Finished') except: if ui: ui.messageBox('Failed:\n{}'.format(traceback.format_exc()))

35.714286

155

0.595

import adsk.core, adsk.fusion, adsk.cam, traceback import math def get_points(n, angle, r): ans = [[0.0, 0.0]]*n for i in range(0, n): ans[i] = [r*math.cos(angle + 2*i*math.pi/n), r*math.sin(angle + 2*i*math.pi/n)] return ans def run(context): try: app = adsk.core.Application.get() ui = app.userInterface ui.messageBox('Are you ready') product = app.activeProduct design = adsk.fusion.Design.cast(product) rootComp = design.rootComponent sketches = rootComp.sketches xyPlane = rootComp.xYConstructionPlane revolves = rootComp.features.revolveFeatures r = 4 loftFeats = rootComp.features.loftFeatures loftInput = loftFeats.createInput(adsk.fusion.FeatureOperations.NewBodyFeatureOperation) loftSectionsObj = loftInput.loftSections n = 6 for i in range(0, 100): angle = (math.pi)*abs(math.sin(i/10)) ctorPlanes = rootComp.constructionPlanes plane = ctorPlanes.createInput() offset = adsk.core.ValueInput.createByString(str(i)+" cm") plane.setByOffset(xyPlane, offset) Plane = ctorPlanes.add(plane) sketch = sketches.add(Plane) lines = sketch.sketchCurves.sketchLines Points = [] Lines = [] p = get_points(n, angle, r) for j in range(0, n): point = adsk.core.Point3D.create(p[j][0], p[j][1], 0) Points.append(point) for j in range(0, n-1): line = lines.addByTwoPoints(Points[j], Points[j+1]) Lines.append(line) Lines.append(lines.addByTwoPoints(Points[n-1], Points[0])) for i in range(0, n-1): sketch.sketchCurves.sketchArcs.addFillet(Lines[i], Lines[i].endSketchPoint.geometry, Lines[i+1], Lines[i+1].startSketchPoint.geometry, 0.5) sketch.sketchCurves.sketchArcs.addFillet(Lines[n-1], Lines[n-1].endSketchPoint.geometry, Lines[0], Lines[0].startSketchPoint.geometry, 0.5) profile = sketch.profiles.item(0) sketch.isVisible = False Plane.isLightBulbOn = False loftSectionsObj.add(profile) loftInput.isSolid=True loftFeats.add(loftInput) except: if ui: ui.messageBox('Failed:\n{}'.format(traceback.format_exc())) def stop(context): try: app = adsk.core.Application.get() ui = app.userInterface ui.messageBox('Finished') except: if ui: ui.messageBox('Failed:\n{}'.format(traceback.format_exc()))

true

790bf08c8ddb1dfe491a43d811f87a660dedd594

5,103

py

Python

src/ggrc/rbac/permissions_provider.py

sriharshakappala/ggrc-core

7561ce27cd987d73468a44df5b6e2b7425f050ef

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

1

2019-04-21T12:21:17.000Z

src/ggrc/rbac/permissions_provider.py

sriharshakappala/ggrc-core

7561ce27cd987d73468a44df5b6e2b7425f050ef

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

null

src/ggrc/rbac/permissions_provider.py

sriharshakappala/ggrc-core

7561ce27cd987d73468a44df5b6e2b7425f050ef

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

null

# Copyright (C) 2013 Google Inc., authors, and contributors <see AUTHORS file> # Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file> # Created By: david@reciprocitylabs.com # Maintained By: david@reciprocitylabs.com from collections import namedtuple from flask import session from flask.ext.login import current_user from .user_permissions import UserPermissions from ggrc.models import get_model Permission = namedtuple('Permission', 'action resource_type context_id') _contributing_resource_types = {} # Return a list of resource types using the same context space. # This is needed because permissions may be given for, e.g., "Contract", but # the restriction on join is done knowing only "Directive". def get_contributing_resource_types(resource_type): resource_types = _contributing_resource_types.get(resource_type, None) if resource_types is None: resource_types = [resource_type] resource_model = get_model(resource_type) if resource_model: resource_manager = resource_model._sa_class_manager resource_types.extend( manager.class_.__name__ for manager in resource_manager.subclass_managers(True)) _contributing_resource_types[resource_type] = resource_types return resource_types class DefaultUserPermissionsProvider(object): def __init__(self, settings): pass def permissions_for(self, user): return DefaultUserPermissions() class DefaultUserPermissions(UserPermissions): # super user, context_id 0 indicates all contexts ADMIN_PERMISSION = Permission( '__GGRC_ADMIN__', '__GGRC_ALL__', 0, ) def _admin_permission_for_context(self, context_id): return Permission( self.ADMIN_PERMISSION.action, self.ADMIN_PERMISSION.resource_type, context_id) def _permission_match(self, permission, permissions): return permission.context_id in \ permissions\ .get(permission.action, {})\ .get(permission.resource_type, []) def _is_allowed(self, permission): if 'permissions' not in session: return True permissions = session['permissions'] if permissions is None: return True if self._permission_match(permission, permissions): return True if self._permission_match(self.ADMIN_PERMISSION, permissions): return True return self._permission_match( self._admin_permission_for_context(permission.context_id), permissions) def is_allowed_create(self, resource_type, context_id): """Whether or not the user is allowed to create a resource of the specified type in the context.""" return self._is_allowed(Permission('create', resource_type, context_id)) def is_allowed_read(self, resource_type, context_id): """Whether or not the user is allowed to read a resource of the specified type in the context.""" return self._is_allowed(Permission('read', resource_type, context_id)) def is_allowed_update(self, resource_type, context_id): """Whether or not the user is allowed to update a resource of the specified type in the context.""" return self._is_allowed(Permission('update', resource_type, context_id)) def is_allowed_delete(self, resource_type, context_id): """Whether or not the user is allowed to delete a resource of the specified type in the context.""" return self._is_allowed(Permission('delete', resource_type, context_id)) def _get_contexts_for(self, action, resource_type): # FIXME: (Security) When applicable, we should explicitly assert that no # permissions are expected (e.g. that every user has ADMIN_PERMISSION). if 'permissions' not in session: return None permissions = session['permissions'] if permissions is None: return None if self._permission_match(self.ADMIN_PERMISSION, permissions): return None # Get the list of contexts for a given resource type and any # superclasses resource_types = get_contributing_resource_types(resource_type) ret = [] for resource_type in resource_types: ret.extend(permissions.get(action, {}).get(resource_type, ())) # Extend with the list of all contexts for which the user is an ADMIN admin_list = list( permissions.get(self.ADMIN_PERMISSION.action, {})\ .get(self.ADMIN_PERMISSION.resource_type, ())) ret.extend(admin_list) return ret def create_contexts_for(self, resource_type): """All contexts in which the user has create permission.""" return self._get_contexts_for('create', resource_type) def read_contexts_for(self, resource_type): """All contexts in which the user has read permission.""" return self._get_contexts_for('read', resource_type) def update_contexts_for(self, resource_type): """All contexts in which the user has update permission.""" return self._get_contexts_for('update', resource_type) def delete_contexts_for(self, resource_type): """All contexts in which the user has delete permission.""" return self._get_contexts_for('delete', resource_type)

37.8

79

0.737997

from collections import namedtuple from flask import session from flask.ext.login import current_user from .user_permissions import UserPermissions from ggrc.models import get_model Permission = namedtuple('Permission', 'action resource_type context_id') _contributing_resource_types = {} def get_contributing_resource_types(resource_type): resource_types = _contributing_resource_types.get(resource_type, None) if resource_types is None: resource_types = [resource_type] resource_model = get_model(resource_type) if resource_model: resource_manager = resource_model._sa_class_manager resource_types.extend( manager.class_.__name__ for manager in resource_manager.subclass_managers(True)) _contributing_resource_types[resource_type] = resource_types return resource_types class DefaultUserPermissionsProvider(object): def __init__(self, settings): pass def permissions_for(self, user): return DefaultUserPermissions() class DefaultUserPermissions(UserPermissions): ADMIN_PERMISSION = Permission( '__GGRC_ADMIN__', '__GGRC_ALL__', 0, ) def _admin_permission_for_context(self, context_id): return Permission( self.ADMIN_PERMISSION.action, self.ADMIN_PERMISSION.resource_type, context_id) def _permission_match(self, permission, permissions): return permission.context_id in \ permissions\ .get(permission.action, {})\ .get(permission.resource_type, []) def _is_allowed(self, permission): if 'permissions' not in session: return True permissions = session['permissions'] if permissions is None: return True if self._permission_match(permission, permissions): return True if self._permission_match(self.ADMIN_PERMISSION, permissions): return True return self._permission_match( self._admin_permission_for_context(permission.context_id), permissions) def is_allowed_create(self, resource_type, context_id): return self._is_allowed(Permission('create', resource_type, context_id)) def is_allowed_read(self, resource_type, context_id): return self._is_allowed(Permission('read', resource_type, context_id)) def is_allowed_update(self, resource_type, context_id): return self._is_allowed(Permission('update', resource_type, context_id)) def is_allowed_delete(self, resource_type, context_id): return self._is_allowed(Permission('delete', resource_type, context_id)) def _get_contexts_for(self, action, resource_type): if 'permissions' not in session: return None permissions = session['permissions'] if permissions is None: return None if self._permission_match(self.ADMIN_PERMISSION, permissions): return None resource_types = get_contributing_resource_types(resource_type) ret = [] for resource_type in resource_types: ret.extend(permissions.get(action, {}).get(resource_type, ())) admin_list = list( permissions.get(self.ADMIN_PERMISSION.action, {})\ .get(self.ADMIN_PERMISSION.resource_type, ())) ret.extend(admin_list) return ret def create_contexts_for(self, resource_type): return self._get_contexts_for('create', resource_type) def read_contexts_for(self, resource_type): return self._get_contexts_for('read', resource_type) def update_contexts_for(self, resource_type): return self._get_contexts_for('update', resource_type) def delete_contexts_for(self, resource_type): return self._get_contexts_for('delete', resource_type)

true

790bf17b6cb4944d81b66c46ddad1c11e994815f

2,411

py

Python

Python/Development/T-Bot_Tracking/getHSVThresh.py

garethnisbet/T-BOTS

70e211191cc6c713084836bff89241e811667378

[ "Apache-2.0" ]

20

2018-07-16T21:34:35.000Z

2022-01-07T02:33:10.000Z

Python/Development/T-Bot_Tracking/getHSVThresh.py

garethnisbet/T-BOTS

70e211191cc6c713084836bff89241e811667378

[ "Apache-2.0" ]

5

2018-07-02T23:00:36.000Z

2020-01-23T17:38:32.000Z

Python/Development/T-Bot_Tracking/getHSVThresh.py

garethnisbet/T-BOTS

70e211191cc6c713084836bff89241e811667378

[ "Apache-2.0" ]

10

2018-05-15T10:38:40.000Z

2021-06-03T07:07:21.000Z

#!/usr/bin/env python import cv2 import numpy as np # from scipy import ndimage maskgridL = np.meshgrid(np.r_[0:359],np.r_[0:130]) maskgridR = np.meshgrid(np.r_[0:359],np.r_[639-130:639]) # key value # cam.set(3 , 640) # width # cam.set(4 , 480) # height # cam.set(10, 120) # brightness min: 0 , max: 255 , increment:1 # cam.set(11, 50) # contrast min: 0 , max: 255 , increment:1 # cam.set(12, 70) # saturation min: 0 , max: 255 , increment:1 # cam.set(13, 13) # hue # cam.set(14, 50) # gain min: 0 , max: 127 , increment:1 # cam.set(15, -3) # exposure min: -7 , max: -1 , increment:1 # cam.set(17, 5000) # white_balance min: 4000, max: 7000, increment:1 # cam.set(28, 0) # focus min: 0 , max: 255 , increment:5 def callback(value): pass def setup_trackbars(range_filter): cv2.namedWindow("Thresholds",cv2.WINDOW_NORMAL) cv2.resizeWindow("Thresholds", 720, 720) for i in ["MIN", "MAX"]: v = 0 if i == "MIN" else 255 for j in range_filter: cv2.createTrackbar("%s_%s" % (j, i), "Thresholds", v, 255, callback) def get_trackbar_values(range_filter): values = [] for i in ["MIN", "MAX"]: for j in range_filter: v = cv2.getTrackbarPos("%s_%s" % (j, i), "Thresholds") values.append(v) return values got_lowpass = 0 # range_filter = 'RGB' range_filter = 'HSV' cam = cv2.VideoCapture(0,cv2.CAP_V4L2) cam.set(cv2.CAP_PROP_AUTOFOCUS, 0) cam.set(28, 0) cam.set(cv2.CAP_PROP_GAIN,0) cam.set(cv2.CAP_PROP_BRIGHTNESS,0) cam.set(cv2.CAP_PROP_FRAME_WIDTH, 640) cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 360) cam.set(cv2.CAP_PROP_BRIGHTNESS, 100) setup_trackbars(range_filter) while True: success, image = cam.read() # image[maskgridL] = 0 # image[maskgridR] = 0 if range_filter == 'RGB': frame_to_thresh = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) else: frame_to_thresh = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) v1_min, v2_min, v3_min, v1_max, v2_max, v3_max = get_trackbar_values(range_filter) thresh = cv2.inRange(frame_to_thresh, (v1_min, v2_min, v3_min), (v1_max, v2_max, v3_max)) preview = cv2.bitwise_and(image, image, mask=thresh) cv2.imshow("Thresholds", preview) if cv2.waitKey(1) & 0xFF is ord('q'): cam.release() cv2.destroyAllWindows() break

33.027397

93

0.62754

import cv2 import numpy as np maskgridL = np.meshgrid(np.r_[0:359],np.r_[0:130]) maskgridR = np.meshgrid(np.r_[0:359],np.r_[639-130:639]) s = [] for i in ["MIN", "MAX"]: for j in range_filter: v = cv2.getTrackbarPos("%s_%s" % (j, i), "Thresholds") values.append(v) return values got_lowpass = 0 range_filter = 'HSV' cam = cv2.VideoCapture(0,cv2.CAP_V4L2) cam.set(cv2.CAP_PROP_AUTOFOCUS, 0) cam.set(28, 0) cam.set(cv2.CAP_PROP_GAIN,0) cam.set(cv2.CAP_PROP_BRIGHTNESS,0) cam.set(cv2.CAP_PROP_FRAME_WIDTH, 640) cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 360) cam.set(cv2.CAP_PROP_BRIGHTNESS, 100) setup_trackbars(range_filter) while True: success, image = cam.read() if range_filter == 'RGB': frame_to_thresh = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) else: frame_to_thresh = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) v1_min, v2_min, v3_min, v1_max, v2_max, v3_max = get_trackbar_values(range_filter) thresh = cv2.inRange(frame_to_thresh, (v1_min, v2_min, v3_min), (v1_max, v2_max, v3_max)) preview = cv2.bitwise_and(image, image, mask=thresh) cv2.imshow("Thresholds", preview) if cv2.waitKey(1) & 0xFF is ord('q'): cam.release() cv2.destroyAllWindows() break

true

790bf25816a42104ab54d4f5d28003777a230e67

2,271

py

Python

hathor/p2p/states/base.py

mbnunes/hathor-core

e5e0d4a627341e2a37ee46db5c9354ddb7f8dfb8

[ "Apache-2.0" ]

51

2019-12-28T03:33:27.000Z

2022-03-10T14:03:03.000Z

hathor/p2p/states/base.py

mbnunes/hathor-core

e5e0d4a627341e2a37ee46db5c9354ddb7f8dfb8

[ "Apache-2.0" ]

316

2019-09-10T09:20:05.000Z

2022-03-31T20:18:56.000Z

hathor/p2p/states/base.py

jansegre/hathor-core

22b3de6be2518e7a0797edbf0e4f6eb1cf28d6fd

[ "Apache-2.0" ]

19

2020-01-04T00:13:18.000Z

2022-02-08T21:18:46.000Z

# Copyright 2021 Hathor Labs # # 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 typing import TYPE_CHECKING, Callable, Dict, Optional from structlog import get_logger from hathor.p2p.messages import ProtocolMessages if TYPE_CHECKING: from hathor.p2p.protocol import HathorProtocol # noqa: F401 logger = get_logger() class BaseState: protocol: 'HathorProtocol' cmd_map: Dict[ProtocolMessages, Callable[[str], None]] def __init__(self, protocol: 'HathorProtocol'): self.log = logger.new(**protocol.get_logger_context()) self.protocol = protocol self.cmd_map = { ProtocolMessages.ERROR: self.handle_error, ProtocolMessages.THROTTLE: self.handle_throttle, } # This variable is set by HathorProtocol after instantiating the state self.state_name = None def handle_error(self, payload: str) -> None: self.protocol.handle_error(payload) def handle_throttle(self, payload: str) -> None: self.log.info('throttled', payload=payload) def send_message(self, cmd: ProtocolMessages, payload: Optional[str] = None) -> None: self.protocol.send_message(cmd, payload) def send_throttle(self, key: str) -> None: limit = self.protocol.ratelimit.get_limit(key) if limit is None: return max_hits, window_seconds = limit payload = '{} At most {} hits every {} seconds'.format(key, max_hits, window_seconds) self.protocol.send_message(ProtocolMessages.THROTTLE, payload) def on_enter(self) -> None: raise NotImplementedError def on_exit(self) -> None: pass def prepare_to_disconnect(self) -> None: """Called when we will disconnect with the peer.""" pass

33.397059

93

0.69749

from typing import TYPE_CHECKING, Callable, Dict, Optional from structlog import get_logger from hathor.p2p.messages import ProtocolMessages if TYPE_CHECKING: from hathor.p2p.protocol import HathorProtocol logger = get_logger() class BaseState: protocol: 'HathorProtocol' cmd_map: Dict[ProtocolMessages, Callable[[str], None]] def __init__(self, protocol: 'HathorProtocol'): self.log = logger.new(**protocol.get_logger_context()) self.protocol = protocol self.cmd_map = { ProtocolMessages.ERROR: self.handle_error, ProtocolMessages.THROTTLE: self.handle_throttle, } self.state_name = None def handle_error(self, payload: str) -> None: self.protocol.handle_error(payload) def handle_throttle(self, payload: str) -> None: self.log.info('throttled', payload=payload) def send_message(self, cmd: ProtocolMessages, payload: Optional[str] = None) -> None: self.protocol.send_message(cmd, payload) def send_throttle(self, key: str) -> None: limit = self.protocol.ratelimit.get_limit(key) if limit is None: return max_hits, window_seconds = limit payload = '{} At most {} hits every {} seconds'.format(key, max_hits, window_seconds) self.protocol.send_message(ProtocolMessages.THROTTLE, payload) def on_enter(self) -> None: raise NotImplementedError def on_exit(self) -> None: pass def prepare_to_disconnect(self) -> None: pass

true

790bf273115a1868208ab15d6177efe0aaf9a02d

239

py

Python

frontend-failure-model/frontend_failure.py

prl-tokyo/MAPE-validators

fd553c81fe45bd122ab339c286067c876d928d16

[ "MIT" ]

null

frontend-failure-model/frontend_failure.py

prl-tokyo/MAPE-validators

fd553c81fe45bd122ab339c286067c876d928d16

[ "MIT" ]

null

frontend-failure-model/frontend_failure.py

prl-tokyo/MAPE-validators

fd553c81fe45bd122ab339c286067c876d928d16

[ "MIT" ]

null

""" A validator for a frontend failure model. The model contains all the failing web frontends and their status, as well as the virtual machines they run on. """ from vuluptuous import Schema schema = Schema({ 'web_frontends_failures' })

21.727273

66

0.769874

from vuluptuous import Schema schema = Schema({ 'web_frontends_failures' })

true

790bf2c2ea1c72fa7902d6f10d78459299fecd85

146

py

Python

1080.py

gabriel1lima/Questoes---URI---Python

4e88d76cf7ea68baf0464071bc4f72ced7d746cd

[ "MIT" ]

1

2020-10-01T13:10:41.000Z

1080.py

gabriel1lima/Questoes---URI---Python

4e88d76cf7ea68baf0464071bc4f72ced7d746cd

[ "MIT" ]

null

1080.py

gabriel1lima/Questoes---URI---Python

4e88d76cf7ea68baf0464071bc4f72ced7d746cd

[ "MIT" ]

7

2020-10-01T13:03:22.000Z

2020-10-02T16:10:25.000Z

vet = [] i = 1 while(i <= 100): valor = int(input()) vet.append(valor) i = i + 1 print(max(vet)) print(vet.index(max(vet)) + 1)

9.733333

30

0.513699

vet = [] i = 1 while(i <= 100): valor = int(input()) vet.append(valor) i = i + 1 print(max(vet)) print(vet.index(max(vet)) + 1)

true

790bf2cca1fa97e62bd819107a1e197a21f1aa97

4,428

py

Python

test/functional/rpc_signrawtransaction.py

anandsinha095/JDCOIN

f77d87e7ba2b3d34d8b7425d33cdd1cf8a09f821

[ "MIT" ]

null

test/functional/rpc_signrawtransaction.py

anandsinha095/JDCOIN

f77d87e7ba2b3d34d8b7425d33cdd1cf8a09f821

[ "MIT" ]

null

test/functional/rpc_signrawtransaction.py

anandsinha095/JDCOIN

f77d87e7ba2b3d34d8b7425d33cdd1cf8a09f821

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2015-2017 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test transaction signing using the signrawtransaction RPC.""" from test_framework.test_framework import JdcoinTestFramework from test_framework.util import * class SignRawTransactionsTest(JdcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 def successful_signing_test(self): """Create and sign a valid raw transaction with one input. Expected results: 1) The transaction has a complete set of signatures 2) No script verification error occurred""" privKeys = ['cUeKHd5orzT3mz8P9pxyREHfsWtVfgsfDjiZZBcjUBAaGk1BTj7N'] inputs = [ # Valid pay-to-pubkey script {'txid': '9b907ef1e3c26fc71fe4a4b3580bc75264112f95050014157059c736f0202e71', 'vout': 0, 'scriptPubKey': '76a91460baa0f494b38ce3c940dea67f3804dc52d1fb9488ac'} ] outputs = {'xwMWGTnBNUmGxMm8vfAdbL45bWXyVTYctd': 0.1} rawTx = self.nodes[0].createrawtransaction(inputs, outputs) rawTxSigned = self.nodes[0].signrawtransaction(rawTx, inputs, privKeys) # 1) The transaction has a complete set of signatures assert 'complete' in rawTxSigned assert_equal(rawTxSigned['complete'], True) # 2) No script verification error occurred assert 'errors' not in rawTxSigned def script_verification_error_test(self): """Create and sign a raw transaction with valid (vin 0), invalid (vin 1) and one missing (vin 2) input script. Expected results: 3) The transaction has no complete set of signatures 4) Two script verification errors occurred 5) Script verification errors have certain properties ("txid", "vout", "scriptSig", "sequence", "error") 6) The verification errors refer to the invalid (vin 1) and missing input (vin 2)""" privKeys = ['cUeKHd5orzT3mz8P9pxyREHfsWtVfgsfDjiZZBcjUBAaGk1BTj7N'] inputs = [ # Valid pay-to-pubkey script {'txid': '9b907ef1e3c26fc71fe4a4b3580bc75264112f95050014157059c736f0202e71', 'vout': 0}, # Invalid script {'txid': '5b8673686910442c644b1f4993d8f7753c7c8fcb5c87ee40d56eaeef25204547', 'vout': 7}, # Missing scriptPubKey {'txid': '9b907ef1e3c26fc71fe4a4b3580bc75264112f95050014157059c736f0202e71', 'vout': 1}, ] scripts = [ # Valid pay-to-pubkey script {'txid': '9b907ef1e3c26fc71fe4a4b3580bc75264112f95050014157059c736f0202e71', 'vout': 0, 'scriptPubKey': '76a91460baa0f494b38ce3c940dea67f3804dc52d1fb9488ac'}, # Invalid script {'txid': '5b8673686910442c644b1f4993d8f7753c7c8fcb5c87ee40d56eaeef25204547', 'vout': 7, 'scriptPubKey': 'badbadbadbad'} ] outputs = {'xwMWGTnBNUmGxMm8vfAdbL45bWXyVTYctd': 0.1} rawTx = self.nodes[0].createrawtransaction(inputs, outputs) rawTxSigned = self.nodes[0].signrawtransaction(rawTx, scripts, privKeys) # 3) The transaction has no complete set of signatures assert 'complete' in rawTxSigned assert_equal(rawTxSigned['complete'], False) # 4) Two script verification errors occurred assert 'errors' in rawTxSigned assert_equal(len(rawTxSigned['errors']), 2) # 5) Script verification errors have certain properties assert 'txid' in rawTxSigned['errors'][0] assert 'vout' in rawTxSigned['errors'][0] assert 'scriptSig' in rawTxSigned['errors'][0] assert 'sequence' in rawTxSigned['errors'][0] assert 'error' in rawTxSigned['errors'][0] # 6) The verification errors refer to the invalid (vin 1) and missing input (vin 2) assert_equal(rawTxSigned['errors'][0]['txid'], inputs[1]['txid']) assert_equal(rawTxSigned['errors'][0]['vout'], inputs[1]['vout']) assert_equal(rawTxSigned['errors'][1]['txid'], inputs[2]['txid']) assert_equal(rawTxSigned['errors'][1]['vout'], inputs[2]['vout']) def run_test(self): self.successful_signing_test() self.script_verification_error_test() if __name__ == '__main__': SignRawTransactionsTest().main()

42.171429

118

0.676603

from test_framework.test_framework import JdcoinTestFramework from test_framework.util import * class SignRawTransactionsTest(JdcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 def successful_signing_test(self): privKeys = ['cUeKHd5orzT3mz8P9pxyREHfsWtVfgsfDjiZZBcjUBAaGk1BTj7N'] inputs = [ {'txid': '9b907ef1e3c26fc71fe4a4b3580bc75264112f95050014157059c736f0202e71', 'vout': 0, 'scriptPubKey': '76a91460baa0f494b38ce3c940dea67f3804dc52d1fb9488ac'} ] outputs = {'xwMWGTnBNUmGxMm8vfAdbL45bWXyVTYctd': 0.1} rawTx = self.nodes[0].createrawtransaction(inputs, outputs) rawTxSigned = self.nodes[0].signrawtransaction(rawTx, inputs, privKeys) assert 'complete' in rawTxSigned assert_equal(rawTxSigned['complete'], True) assert 'errors' not in rawTxSigned def script_verification_error_test(self): privKeys = ['cUeKHd5orzT3mz8P9pxyREHfsWtVfgsfDjiZZBcjUBAaGk1BTj7N'] inputs = [ {'txid': '9b907ef1e3c26fc71fe4a4b3580bc75264112f95050014157059c736f0202e71', 'vout': 0}, {'txid': '5b8673686910442c644b1f4993d8f7753c7c8fcb5c87ee40d56eaeef25204547', 'vout': 7}, {'txid': '9b907ef1e3c26fc71fe4a4b3580bc75264112f95050014157059c736f0202e71', 'vout': 1}, ] scripts = [ {'txid': '9b907ef1e3c26fc71fe4a4b3580bc75264112f95050014157059c736f0202e71', 'vout': 0, 'scriptPubKey': '76a91460baa0f494b38ce3c940dea67f3804dc52d1fb9488ac'}, {'txid': '5b8673686910442c644b1f4993d8f7753c7c8fcb5c87ee40d56eaeef25204547', 'vout': 7, 'scriptPubKey': 'badbadbadbad'} ] outputs = {'xwMWGTnBNUmGxMm8vfAdbL45bWXyVTYctd': 0.1} rawTx = self.nodes[0].createrawtransaction(inputs, outputs) rawTxSigned = self.nodes[0].signrawtransaction(rawTx, scripts, privKeys) assert 'complete' in rawTxSigned assert_equal(rawTxSigned['complete'], False) assert 'errors' in rawTxSigned assert_equal(len(rawTxSigned['errors']), 2) assert 'txid' in rawTxSigned['errors'][0] assert 'vout' in rawTxSigned['errors'][0] assert 'scriptSig' in rawTxSigned['errors'][0] assert 'sequence' in rawTxSigned['errors'][0] assert 'error' in rawTxSigned['errors'][0] assert_equal(rawTxSigned['errors'][0]['txid'], inputs[1]['txid']) assert_equal(rawTxSigned['errors'][0]['vout'], inputs[1]['vout']) assert_equal(rawTxSigned['errors'][1]['txid'], inputs[2]['txid']) assert_equal(rawTxSigned['errors'][1]['vout'], inputs[2]['vout']) def run_test(self): self.successful_signing_test() self.script_verification_error_test() if __name__ == '__main__': SignRawTransactionsTest().main()

true

790bf35d0bfdda69d1fb1a8ffe7ed80edc56e1c5

735

py

Python

class3/exercise2/exercise2.py

papri-entropy/nornir-course

122c5ecce19cca6c17a1eec0066be7c6b58e6eb5

[ "MIT" ]

1

2020-06-23T06:36:43.000Z

class3/exercise2/exercise2.py

papri-entropy/nornir-course

122c5ecce19cca6c17a1eec0066be7c6b58e6eb5

[ "MIT" ]

null

class3/exercise2/exercise2.py

papri-entropy/nornir-course

122c5ecce19cca6c17a1eec0066be7c6b58e6eb5

[ "MIT" ]

null

#!/usr/bin/env python # import general use modules import os from pprint import pprint as pp # import nornir specifics from nornir import InitNornir from nornir.plugins.functions.text import print_result from nornir.core.filter import F nr = InitNornir() hosts = nr.inventory.hosts arista1_filter = nr.filter(name="arista1") arista1 = arista1_filter.inventory.hosts #print(hosts) print(arista1) wan_filter = nr.filter(role="WAN") wan_filter = wan_filter.inventory.hosts print(wan_filter) wan_port_filter = nr.filter(role="WAN").filter(port=22) wan_port_filter = wan_port_filter.inventory.hosts print(wan_port_filter) sfo_filter = nr.filter(F(groups__contains="sfo")) sfo_filter = sfo_filter.inventory.hosts print(sfo_filter)

21.617647

55

0.794558

import os from pprint import pprint as pp from nornir import InitNornir from nornir.plugins.functions.text import print_result from nornir.core.filter import F nr = InitNornir() hosts = nr.inventory.hosts arista1_filter = nr.filter(name="arista1") arista1 = arista1_filter.inventory.hosts print(arista1) wan_filter = nr.filter(role="WAN") wan_filter = wan_filter.inventory.hosts print(wan_filter) wan_port_filter = nr.filter(role="WAN").filter(port=22) wan_port_filter = wan_port_filter.inventory.hosts print(wan_port_filter) sfo_filter = nr.filter(F(groups__contains="sfo")) sfo_filter = sfo_filter.inventory.hosts print(sfo_filter)

true

790bf39686d8013c41bd21c7540eeadc3bc8e96b

6,718

py

Python

socketio/server.py

jykim16/gevent-socketio

429424c5e738d442e509031e998c091c8b20a766

[ "BSD-3-Clause" ]

1

2018-12-11T23:06:06.000Z

socketio/server.py

jykim16/gevent-socketio

429424c5e738d442e509031e998c091c8b20a766

[ "BSD-3-Clause" ]

null

socketio/server.py

jykim16/gevent-socketio

429424c5e738d442e509031e998c091c8b20a766

[ "BSD-3-Clause" ]

2

2018-09-06T20:57:45.000Z

2018-09-06T21:18:31.000Z

import sys import traceback from socket import error from gevent.pywsgi import WSGIServer from socketio.handler import SocketIOHandler from socketio.policyserver import FlashPolicyServer from socketio.virtsocket import Socket from geventwebsocket.handler import WebSocketHandler __all__ = ['SocketIOServer'] class SocketIOServer(WSGIServer): """A WSGI Server with a resource that acts like an SocketIO.""" def __init__(self, *args, **kwargs): """This is just like the standard WSGIServer __init__, except with a few additional ``kwargs``: :param resource: The URL which has to be identified as a socket.io request. Defaults to the /socket.io/ URL. :param transports: Optional list of transports to allow. List of strings, each string should be one of handler.SocketIOHandler.handler_types. :param policy_server: Boolean describing whether or not to use the Flash policy server. Default True. :param policy_listener: A tuple containing (host, port) for the policy server. This is optional and used only if policy server is set to true. The default value is 0.0.0.0:843 :param heartbeat_interval: int The timeout for the server, we should receive a heartbeat from the client within this interval. This should be less than the ``heartbeat_timeout``. :param heartbeat_timeout: int The timeout for the client when it should send a new heartbeat to the server. This value is sent to the client after a successful handshake. :param close_timeout: int The timeout for the client, when it closes the connection it still X amounts of seconds to do re open of the connection. This value is sent to the client after a successful handshake. :param log_file: str The file in which you want the PyWSGI server to write its access log. If not specified, it is sent to `stderr` (with gevent 0.13). """ self.sockets = {} if 'namespace' in kwargs: print("DEPRECATION WARNING: use resource instead of namespace") self.resource = kwargs.pop('namespace', 'socket.io') else: self.resource = kwargs.pop('resource', 'socket.io') self.transports = kwargs.pop('transports', None) if kwargs.pop('policy_server', True): wsock = args[0] try: address, port = wsock.getsockname() except AttributeError: try: address = wsock[0] except TypeError: try: address = wsock.address[0] except AttributeError: address = wsock.cfg_addr[0] policylistener = kwargs.pop('policy_listener', (address, 10843)) self.policy_server = FlashPolicyServer(policylistener) else: self.policy_server = None # Extract other config options self.config = { 'heartbeat_timeout': 60, 'close_timeout': 60, 'heartbeat_interval': 25, } for f in ('heartbeat_timeout', 'heartbeat_interval', 'close_timeout'): if f in kwargs: self.config[f] = int(kwargs.pop(f)) if not 'handler_class' in kwargs: kwargs['handler_class'] = SocketIOHandler if not 'ws_handler_class' in kwargs: self.ws_handler_class = WebSocketHandler else: self.ws_handler_class = kwargs.pop('ws_handler_class') log_file = kwargs.pop('log_file', None) if log_file: kwargs['log'] = open(log_file, 'a') super(SocketIOServer, self).__init__(*args, **kwargs) def start_accepting(self): if self.policy_server is not None: try: if not self.policy_server.started: self.policy_server.start() except error as ex: sys.stderr.write( 'FAILED to start flash policy server: %s\n' % (ex, )) except Exception: traceback.print_exc() sys.stderr.write('FAILED to start flash policy server.\n\n') super(SocketIOServer, self).start_accepting() def stop(self, timeout=None): if self.policy_server is not None: self.policy_server.stop() super(SocketIOServer, self).stop(timeout=timeout) def handle(self, socket, address): # Pass in the config about timeouts, heartbeats, also... handler = self.handler_class(self.config, socket, address, self) handler.handle() def get_socket(self, sessid=''): """Return an existing or new client Socket.""" socket = self.sockets.get(sessid) if sessid and not socket: return None # you ask for a session that doesn't exist! if socket is None: socket = Socket(self, self.config) self.sockets[socket.sessid] = socket else: socket.incr_hits() return socket def serve(app, **kw): _quiet = kw.pop('_quiet', False) _resource = kw.pop('resource', 'socket.io') if not _quiet: # pragma: no cover # idempotent if logging has already been set up import logging logging.basicConfig() host = kw.pop('host', '127.0.0.1') port = int(kw.pop('port', 6543)) transports = kw.pop('transports', None) if transports: transports = [x.strip() for x in transports.split(',')] policy_server = kw.pop('policy_server', False) if policy_server in (True, 'True', 'true', 'enable', 'yes', 'on', '1'): policy_server = True policy_listener_host = kw.pop('policy_listener_host', host) policy_listener_port = int(kw.pop('policy_listener_port', 10843)) kw['policy_listener'] = (policy_listener_host, policy_listener_port) else: policy_server = False server = SocketIOServer((host, port), app, resource=_resource, transports=transports, policy_server=policy_server, **kw) if not _quiet: print(('serving on http://%s:%s' % (host, port))) server.serve_forever() def serve_paste(app, global_conf, **kw): """pserve / paster serve / waitress replacement / integration You can pass as parameters: transports = websockets, xhr-multipart, xhr-longpolling, etc... policy_server = True """ serve(app, **kw) return 0

35.172775

78

0.601221

import sys import traceback from socket import error from gevent.pywsgi import WSGIServer from socketio.handler import SocketIOHandler from socketio.policyserver import FlashPolicyServer from socketio.virtsocket import Socket from geventwebsocket.handler import WebSocketHandler __all__ = ['SocketIOServer'] class SocketIOServer(WSGIServer): def __init__(self, *args, **kwargs): self.sockets = {} if 'namespace' in kwargs: print("DEPRECATION WARNING: use resource instead of namespace") self.resource = kwargs.pop('namespace', 'socket.io') else: self.resource = kwargs.pop('resource', 'socket.io') self.transports = kwargs.pop('transports', None) if kwargs.pop('policy_server', True): wsock = args[0] try: address, port = wsock.getsockname() except AttributeError: try: address = wsock[0] except TypeError: try: address = wsock.address[0] except AttributeError: address = wsock.cfg_addr[0] policylistener = kwargs.pop('policy_listener', (address, 10843)) self.policy_server = FlashPolicyServer(policylistener) else: self.policy_server = None self.config = { 'heartbeat_timeout': 60, 'close_timeout': 60, 'heartbeat_interval': 25, } for f in ('heartbeat_timeout', 'heartbeat_interval', 'close_timeout'): if f in kwargs: self.config[f] = int(kwargs.pop(f)) if not 'handler_class' in kwargs: kwargs['handler_class'] = SocketIOHandler if not 'ws_handler_class' in kwargs: self.ws_handler_class = WebSocketHandler else: self.ws_handler_class = kwargs.pop('ws_handler_class') log_file = kwargs.pop('log_file', None) if log_file: kwargs['log'] = open(log_file, 'a') super(SocketIOServer, self).__init__(*args, **kwargs) def start_accepting(self): if self.policy_server is not None: try: if not self.policy_server.started: self.policy_server.start() except error as ex: sys.stderr.write( 'FAILED to start flash policy server: %s\n' % (ex, )) except Exception: traceback.print_exc() sys.stderr.write('FAILED to start flash policy server.\n\n') super(SocketIOServer, self).start_accepting() def stop(self, timeout=None): if self.policy_server is not None: self.policy_server.stop() super(SocketIOServer, self).stop(timeout=timeout) def handle(self, socket, address): handler = self.handler_class(self.config, socket, address, self) handler.handle() def get_socket(self, sessid=''): socket = self.sockets.get(sessid) if sessid and not socket: return None if socket is None: socket = Socket(self, self.config) self.sockets[socket.sessid] = socket else: socket.incr_hits() return socket def serve(app, **kw): _quiet = kw.pop('_quiet', False) _resource = kw.pop('resource', 'socket.io') if not _quiet: # pragma: no cover # idempotent if logging has already been set up import logging logging.basicConfig() host = kw.pop('host', '127.0.0.1') port = int(kw.pop('port', 6543)) transports = kw.pop('transports', None) if transports: transports = [x.strip() for x in transports.split(',')] policy_server = kw.pop('policy_server', False) if policy_server in (True, 'True', 'true', 'enable', 'yes', 'on', '1'): policy_server = True policy_listener_host = kw.pop('policy_listener_host', host) policy_listener_port = int(kw.pop('policy_listener_port', 10843)) kw['policy_listener'] = (policy_listener_host, policy_listener_port) else: policy_server = False server = SocketIOServer((host, port), app, resource=_resource, transports=transports, policy_server=policy_server, **kw) if not _quiet: print(('serving on http://%s:%s' % (host, port))) server.serve_forever() def serve_paste(app, global_conf, **kw): serve(app, **kw) return 0

true

790bf42b4d5ef992d9d2d26dacefc8fde9a6b75d

2,194

py

Python

eahub/base/admin.py

rtcharity/eahub.org

0abb235e9b99f3d35cf69c3d630aeea9496d9220

[ "MIT" ]

36

2019-02-22T23:07:14.000Z

2022-02-10T13:24:27.000Z

eahub/base/admin.py

rtcharity/eahub.org

0abb235e9b99f3d35cf69c3d630aeea9496d9220

[ "MIT" ]

717

2019-02-21T22:07:55.000Z

2022-02-26T15:17:49.000Z

eahub/base/admin.py

rtcharity/eahub.org

0abb235e9b99f3d35cf69c3d630aeea9496d9220

[ "MIT" ]

19

2019-04-14T14:37:56.000Z

2022-02-14T22:05:16.000Z

from typing import Optional import django_admin_relation_links from adminutils import options from authtools import admin as authtools_admin from django.contrib import admin from enumfields.admin import EnumFieldListFilter from rangefilter.filter import DateRangeFilter from solo.admin import SingletonModelAdmin from eahub.base import models from eahub.base.models import User from eahub.profiles.models import Profile @admin.register(models.User) class UserAdmin( django_admin_relation_links.AdminChangeLinksMixin, authtools_admin.UserAdmin ): list_select_related = ["profile"] list_display = [ "is_active", "email", "profile_link", "is_profile_approved", "date_joined", "last_login", "is_superuser", "is_staff", "get_visibility", ] change_links = ["profile"] list_filter = [ "is_superuser", "is_staff", "is_active", "profile__is_approved", ("profile__visibility", EnumFieldListFilter), ("date_joined", DateRangeFilter), ("last_login", DateRangeFilter), ] search_fields = ["email", "profile__first_name", "profile__last_name"] @options(desc="Approved", boolean=True) def is_profile_approved(self, user) -> Optional[bool]: profile = get_profile(user) if profile is None: return None return profile.is_approved @options(desc="Visibility") def get_visibility(self, user) -> str: profile = get_profile(user) if profile is None: return "" return profile.visibility.value def get_profile(user: User) -> Optional[Profile]: try: return user.profile except Profile.DoesNotExist: return None @admin.register(models.MessagingLog) class MessagingLogAdmin(admin.ModelAdmin): list_display = [ "sender_email", "recipient_email", "recipient_type", "send_action_uuid", "time", ] list_filter = [ "recipient_type", ("time", DateRangeFilter), ] search_fields = ["sender", "recipient"] admin.site.register(models.FeedbackURLConfig, SingletonModelAdmin)

26.433735

80

0.667274

from typing import Optional import django_admin_relation_links from adminutils import options from authtools import admin as authtools_admin from django.contrib import admin from enumfields.admin import EnumFieldListFilter from rangefilter.filter import DateRangeFilter from solo.admin import SingletonModelAdmin from eahub.base import models from eahub.base.models import User from eahub.profiles.models import Profile @admin.register(models.User) class UserAdmin( django_admin_relation_links.AdminChangeLinksMixin, authtools_admin.UserAdmin ): list_select_related = ["profile"] list_display = [ "is_active", "email", "profile_link", "is_profile_approved", "date_joined", "last_login", "is_superuser", "is_staff", "get_visibility", ] change_links = ["profile"] list_filter = [ "is_superuser", "is_staff", "is_active", "profile__is_approved", ("profile__visibility", EnumFieldListFilter), ("date_joined", DateRangeFilter), ("last_login", DateRangeFilter), ] search_fields = ["email", "profile__first_name", "profile__last_name"] @options(desc="Approved", boolean=True) def is_profile_approved(self, user) -> Optional[bool]: profile = get_profile(user) if profile is None: return None return profile.is_approved @options(desc="Visibility") def get_visibility(self, user) -> str: profile = get_profile(user) if profile is None: return "" return profile.visibility.value def get_profile(user: User) -> Optional[Profile]: try: return user.profile except Profile.DoesNotExist: return None @admin.register(models.MessagingLog) class MessagingLogAdmin(admin.ModelAdmin): list_display = [ "sender_email", "recipient_email", "recipient_type", "send_action_uuid", "time", ] list_filter = [ "recipient_type", ("time", DateRangeFilter), ] search_fields = ["sender", "recipient"] admin.site.register(models.FeedbackURLConfig, SingletonModelAdmin)

true

790bf477aba7e1ba5dca6e8b97e71ea572c0bdbf

2,880

py

Python

tests/test_adders.py

fgarci03/pylectronics

bfcbb60e2aa64bc0a97d43abe69c5a5c0dfa43f2

[ "MIT" ]

45

2021-08-30T03:21:58.000Z

2021-10-31T01:18:00.000Z

tests/test_adders.py

thequux/pylectronics

7d806afb59e5172ae710a13eb370ac64afa77a6d

[ "MIT" ]

4

2021-08-30T02:23:41.000Z

2021-10-07T02:35:44.000Z

tests/test_adders.py

fgarci03/pylectronics

bfcbb60e2aa64bc0a97d43abe69c5a5c0dfa43f2

[ "MIT" ]

2

2021-08-30T14:22:55.000Z

2021-09-01T17:48:10.000Z

from unittest import TestCase from src.adders import HalfAdder, FullAdder, FourBitFullAdder from tests.utils import decimal_to_boolean_list class HalfAdderTests(TestCase): TRUTH_TABLE = ( # A B S Cout ((False, False), (False, False)), ((False, True), (True, False)), ((True, False), (True, False)), ((True, True), (False, True)), ) def setUp(self): self.half_adder = HalfAdder() def test_truth_table(self): for test_case in self.TRUTH_TABLE: assert self.half_adder.set_inputs(*test_case[0]) == test_case[1] class FullAdderTests(TestCase): TRUTH_TABLE = ( # A B Cin S Cout ((False, False, False), (False, False)), ((False, False, True), (True, False)), ((False, True, False), (True, False)), ((False, True, True), (False, True)), ((True, False, False), (True, False)), ((True, False, True), (False, True)), ((True, True, False), (False, True)), ((True, True, True), (True, True)), ) def setUp(self): self.full_adder = FullAdder() def test_truth_table(self): for test_case in self.TRUTH_TABLE: assert self.full_adder.set_inputs(*test_case[0]) == test_case[1] class FourBitFullAdderTests(TestCase): def setUp(self): self.full_adder = FourBitFullAdder() self.TRUTH_TABLE = [] # Generate the truth table, since it is HUGE for a 4 bit adder # Note: it will generate items like: # (((False, True, False, False), (False, False, True, True)), (False, False, True, True, True)) # and # (((False, True, True, False), (False, True, True, True)), (False, True, True, False, True)) # for 4 + 3 = 7 and 6 + 7 = 13, respectively for addend_1 in range(0, 16): for addend_2 in range(0, 16): self.TRUTH_TABLE.append( ( (decimal_to_boolean_list(addend_1, padding=4), decimal_to_boolean_list(addend_2, padding=4)), decimal_to_boolean_list(addend_1 + addend_2, padding=5), ) ) def test_truth_table(self): for test_case in self.TRUTH_TABLE: # Note, generate the inputs arguments by setting both addends and the carry in (which is always 0 *false*) inputs = (test_case[0][0], test_case[0][1], False) assert self.full_adder.set_inputs(*inputs) == test_case[1] # Test adding 15+15 with a carry in, which will result in 31 assert ( self.full_adder.set_inputs( value_1=(True, True, True, True), value_2=(True, True, True, True), carry_in=True, ) == (True, True, True, True, True) )

35.121951

118

0.557292

from unittest import TestCase from src.adders import HalfAdder, FullAdder, FourBitFullAdder from tests.utils import decimal_to_boolean_list class HalfAdderTests(TestCase): TRUTH_TABLE = ( ((False, False), (False, False)), ((False, True), (True, False)), ((True, False), (True, False)), ((True, True), (False, True)), ) def setUp(self): self.half_adder = HalfAdder() def test_truth_table(self): for test_case in self.TRUTH_TABLE: assert self.half_adder.set_inputs(*test_case[0]) == test_case[1] class FullAdderTests(TestCase): TRUTH_TABLE = ( ((False, False, False), (False, False)), ((False, False, True), (True, False)), ((False, True, False), (True, False)), ((False, True, True), (False, True)), ((True, False, False), (True, False)), ((True, False, True), (False, True)), ((True, True, False), (False, True)), ((True, True, True), (True, True)), ) def setUp(self): self.full_adder = FullAdder() def test_truth_table(self): for test_case in self.TRUTH_TABLE: assert self.full_adder.set_inputs(*test_case[0]) == test_case[1] class FourBitFullAdderTests(TestCase): def setUp(self): self.full_adder = FourBitFullAdder() self.TRUTH_TABLE = [] for addend_1 in range(0, 16): for addend_2 in range(0, 16): self.TRUTH_TABLE.append( ( (decimal_to_boolean_list(addend_1, padding=4), decimal_to_boolean_list(addend_2, padding=4)), decimal_to_boolean_list(addend_1 + addend_2, padding=5), ) ) def test_truth_table(self): for test_case in self.TRUTH_TABLE: inputs = (test_case[0][0], test_case[0][1], False) assert self.full_adder.set_inputs(*inputs) == test_case[1] assert ( self.full_adder.set_inputs( value_1=(True, True, True, True), value_2=(True, True, True, True), carry_in=True, ) == (True, True, True, True, True) )

true

790bf5338d4310ce89ee5d358060e44a01884d20

142,260

py

Python

release/stubs/Autodesk/Civil/Settings.py

paoloemilioserra/ironpython-stubs

49d92db7f28f25ccd3654c5f6ae83daa0c401fa1

[ "MIT" ]

null

release/stubs/Autodesk/Civil/Settings.py

paoloemilioserra/ironpython-stubs

49d92db7f28f25ccd3654c5f6ae83daa0c401fa1

[ "MIT" ]

null

release/stubs/Autodesk/Civil/Settings.py

paoloemilioserra/ironpython-stubs

49d92db7f28f25ccd3654c5f6ae83daa0c401fa1

[ "MIT" ]

null

# encoding: utf-8 # module Autodesk.Civil.Settings calls itself Settings # from AeccDbMgd, Version=13.3.854.0, Culture=neutral, PublicKeyToken=null, AeccPressurePipesMgd, Version=13.3.854.0, Culture=neutral, PublicKeyToken=null # by generator 1.145 # no doc # no imports # no functions # classes class AbbreviationAlignmentEnhancedType(Enum): """ enum AbbreviationAlignmentEnhancedType, values: AlignmentBeginningPoint (402706556), AlignmentEndPoint (402706557), CompoundSpiralLargeRadiusAtBeginning (402706566), CompoundSpiralLargeRadiusAtEnd (402706567), CompoundSpiralSmallRadiusAtBeginning (402706568), CompoundSpiralSmallRadiusAtEnd (402706569), CurveBeginning (402706560), CurveEnd (402706561), LineBeginning (402706558), LineEnd (402706559), SimpleSpiralLargeRadiusAtBeginning (402706562), SimpleSpiralLargeRadiusAtEnd (402706563), SimpleSpiralSmallRadiusAtBeginning (402706564), SimpleSpiralSmallRadiusAtEnd (402706565) """ AlignmentBeginningPoint = None AlignmentEndPoint = None CompoundSpiralLargeRadiusAtBeginning = None CompoundSpiralLargeRadiusAtEnd = None CompoundSpiralSmallRadiusAtBeginning = None CompoundSpiralSmallRadiusAtEnd = None CurveBeginning = None CurveEnd = None LineBeginning = None LineEnd = None SimpleSpiralLargeRadiusAtBeginning = None SimpleSpiralLargeRadiusAtEnd = None SimpleSpiralSmallRadiusAtBeginning = None SimpleSpiralSmallRadiusAtEnd = None value__ = None class AbbreviationAlignmentType(Enum): """ enum AbbreviationAlignmentType, values: AlignmentBeginning (67162235), AlignmentEnd (67162234), CompoundCurveCurveIntersect (67162197), CurveSpiralIntersect (67162201), CurveTangentIntersect (67162196), MidCurvePoint (67162254), ReverseCurveCurveIntersect (67162198), ReverseSpiralIntersect (67162204), SpiralCurveIntersect (67162202), SpiralSpiralIntersect (67162203), SpiralTangentIntersect (67162200), StationEquationDecreasing (67162253), StationEquationIncreasing (67162252), TangentCurveIntersect (67162195), TangentSpiralIntersect (67162199), TangentTangentIntersect (67162194) """ AlignmentBeginning = None AlignmentEnd = None CompoundCurveCurveIntersect = None CurveSpiralIntersect = None CurveTangentIntersect = None MidCurvePoint = None ReverseCurveCurveIntersect = None ReverseSpiralIntersect = None SpiralCurveIntersect = None SpiralSpiralIntersect = None SpiralTangentIntersect = None StationEquationDecreasing = None StationEquationIncreasing = None TangentCurveIntersect = None TangentSpiralIntersect = None TangentTangentIntersect = None value__ = None class AbbreviationCantType(Enum): """ enum AbbreviationCantType, values: BeginAlignment (67163513), BeginFullCant (67163510), BeginLevelRail (67163509), EndAlignment (67163514), EndFullCant (67163511), EndLevelRail (67163508), Manual (67163512) """ BeginAlignment = None BeginFullCant = None BeginLevelRail = None EndAlignment = None EndFullCant = None EndLevelRail = None Manual = None value__ = None class AbbreviationProfileType(Enum): """ enum AbbreviationProfileType, values: BeginVerticalCurve (67173890), BeginVerticalCurveElevation (67173892), BeginVerticalCurveStation (67173891), CurveCoefficient (67173898), EndVerticalCurve (67173893), EndVerticalCurveElevation (67173895), EndVerticalCurveStation (67173894), GradeBreak (67173889), GradeChange (67173899), HighPoint (67173896), LowPoint (67173897), OverallHighPoint (67173909), OverallLowPoint (67173910), PointOfVerticalIntersection (67173888), ProfileEnd (67173902), ProfileStart (67173901), VerticalCompoundCurveIntersect (67173903), VerticalCompoundCurveIntersectElevation (67173906), VerticalCompoundCurveIntersectStation (67173905), VerticalReverseCurveIntersect (67173904), VerticalReverseCurveIntersectElevation (67173908), VerticalReverseCurveIntersectStation (67173907) """ BeginVerticalCurve = None BeginVerticalCurveElevation = None BeginVerticalCurveStation = None CurveCoefficient = None EndVerticalCurve = None EndVerticalCurveElevation = None EndVerticalCurveStation = None GradeBreak = None GradeChange = None HighPoint = None LowPoint = None OverallHighPoint = None OverallLowPoint = None PointOfVerticalIntersection = None ProfileEnd = None ProfileStart = None value__ = None VerticalCompoundCurveIntersect = None VerticalCompoundCurveIntersectElevation = None VerticalCompoundCurveIntersectStation = None VerticalReverseCurveIntersect = None VerticalReverseCurveIntersectElevation = None VerticalReverseCurveIntersectStation = None class AbbreviationSuperelevationType(Enum): """ enum AbbreviationSuperelevationType, values: BeginFullSuper (67163478), BeginNormalCrown (67163476), BeginNormalShoulder (67163480), BeginOfAlignment (67163474), BeginShoulderRollover (67163506), EndFullSuper (67163479), EndNormalCrown (67163477), EndNormalShoulder (67163481), EndOfAlignment (67163475), EndShoulderRollover (67163507), LevelCrown (67163482), LowShoulderMatch (67163483), Manual (67163486), ReverseCrown (67163484), ShoulderBreakover (67163485) """ BeginFullSuper = None BeginNormalCrown = None BeginNormalShoulder = None BeginOfAlignment = None BeginShoulderRollover = None EndFullSuper = None EndNormalCrown = None EndNormalShoulder = None EndOfAlignment = None EndShoulderRollover = None LevelCrown = None LowShoulderMatch = None Manual = None ReverseCrown = None ShoulderBreakover = None value__ = None class AutomaticManual(Enum): """ enum AutomaticManual, values: Automatic (0), AutomaticObject (1), Manual (2), None (3) """ Automatic = None AutomaticObject = None Manual = None None = None value__ = None class DrawingUnitType(Enum): """ enum DrawingUnitType, values: Feet (30), Meters (2) """ Feet = None Meters = None value__ = None class GeographicCoordinateType(Enum): """ enum GeographicCoordinateType, values: LatLong (0), LongLat (1) """ LatLong = None LongLat = None value__ = None class GridCoordinateType(Enum): """ enum GridCoordinateType, values: EastingNorthing (0), NorthingEasting (1) """ EastingNorthing = None NorthingEasting = None value__ = None class GridScaleFactorType(Enum): """ enum GridScaleFactorType, values: PrismodialFormula (3), ReferencePoint (2), Unity (0), UserDefined (1) """ PrismodialFormula = None ReferencePoint = None Unity = None UserDefined = None value__ = None class ImperialToMetricConversionType(Enum): """ enum ImperialToMetricConversionType, values: InternationalFoot (536870912), UsSurveyFoot (1073741824) """ InternationalFoot = None UsSurveyFoot = None value__ = None class LandXMLAngularUnits(Enum): """ enum LandXMLAngularUnits, values: DegreesDecimal (0), DegreesDms (1), Grads (2), Radians (3) """ DegreesDecimal = None DegreesDms = None Grads = None Radians = None value__ = None class LandXMLAttributeExportType(Enum): """ enum LandXMLAttributeExportType, values: Disabled (0), FullDescription (2), RawDescription (1) """ Disabled = None FullDescription = None RawDescription = None value__ = None class LandXMLConflictResolutionType(Enum): """ enum LandXMLConflictResolutionType, values: Append (2), Skip (0), Update (1) """ Append = None Skip = None Update = None value__ = None class LandXMLImperialUnitType(Enum): """ enum LandXMLImperialUnitType, values: Foot (30), Inch (31), Mile (44), Yard (33) """ Foot = None Inch = None Mile = None value__ = None Yard = None class LandXMLLinearUnits(Enum): """ enum LandXMLLinearUnits, values: InternationalFoot (30), SurveyFoot (54) """ InternationalFoot = None SurveyFoot = None value__ = None class LandXMLMetricUnitType(Enum): """ enum LandXMLMetricUnitType, values: CentiMeter (24), DeciMeter (23), KiloMeter (20), Meter (2), MilliMeter (25) """ CentiMeter = None DeciMeter = None KiloMeter = None Meter = None MilliMeter = None value__ = None class LandXMLPointDescriptionType(Enum): """ enum LandXMLPointDescriptionType, values: UseCodeThenDesc (2), UseCodeValue (0), UseDescThenCode (3), UseDescValue (1) """ UseCodeThenDesc = None UseCodeValue = None UseDescThenCode = None UseDescValue = None value__ = None class LandXMLSurfaceDataExportType(Enum): """ enum LandXMLSurfaceDataExportType, values: PointsAndFaces (1), PointsOnly (0) """ PointsAndFaces = None PointsOnly = None value__ = None class LandXMLSurfaceDataImportType(Enum): """ enum LandXMLSurfaceDataImportType, values: FullImport (1), QuickImport (0) """ FullImport = None QuickImport = None value__ = None class LocalCoordinateType(Enum): """ enum LocalCoordinateType, values: EastingNorthing (0), NorthingEasting (1), XY (2), YX (3) """ EastingNorthing = None NorthingEasting = None value__ = None XY = None YX = None class MapcheckAngleType(Enum): """ enum MapcheckAngleType, values: Angle (1), DeflectionAngle (2), Direction (0) """ Angle = None DeflectionAngle = None Direction = None value__ = None class MapcheckCurveDirectionType(Enum): """ enum MapcheckCurveDirectionType, values: Clockwise (0), CounterClockwise (1) """ Clockwise = None CounterClockwise = None value__ = None class MapcheckSideType(Enum): """ enum MapcheckSideType, values: Curve (1), Line (0) """ Curve = None Line = None value__ = None class MapcheckTraverseMethodType(Enum): """ enum MapcheckTraverseMethodType, values: AcrossChord (0), ThroughRadius (1) """ AcrossChord = None ThroughRadius = None value__ = None class ObjectLayerModifierType(Enum): """ enum ObjectLayerModifierType, values: None (0), Prefix (1), Suffix (2) """ None = None Prefix = None Suffix = None value__ = None class SectionViewAnchorType(Enum): """ enum SectionViewAnchorType, values: BottomCenter (7), BottomLeft (6), BottomRight (8), MiddleCenter (4), MiddleLeft (3), MiddleRight (5), TopCenter (1), TopLeft (0), TopRight (2) """ BottomCenter = None BottomLeft = None BottomRight = None MiddleCenter = None MiddleLeft = None MiddleRight = None TopCenter = None TopLeft = None TopRight = None value__ = None class SettingsAbbreviation(CivilWrapper<AcDbDatabase>): # no doc def Dispose(self): """ Dispose(self: CivilWrapper<AcDbDatabase>, A_0: bool) """ pass AlignmentGeoPointEntityData = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AlignmentGeoPointEntityData(self: SettingsAbbreviation) -> SettingsAbbreviationAlignmentEnhanced """ AlignmentGeoPointText = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AlignmentGeoPointText(self: SettingsAbbreviation) -> SettingsAbbreviationAlignment """ Cant = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Cant(self: SettingsAbbreviation) -> SettingsAbbreviationCant """ GeneralText = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GeneralText(self: SettingsAbbreviation) -> SettingsAbbreviationGeneral """ Profile = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Profile(self: SettingsAbbreviation) -> SettingsAbbreviationProfile """ Superelevation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Superelevation(self: SettingsAbbreviation) -> SettingsAbbreviationSuperelevation """ class SettingsAbbreviationAlignment(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetAlignmentAbbreviation(self, type): """ GetAlignmentAbbreviation(self: SettingsAbbreviationAlignment, type: AbbreviationAlignmentType) -> str """ pass def SetAlignmentAbbreviation(self, type, value): """ SetAlignmentAbbreviation(self: SettingsAbbreviationAlignment, type: AbbreviationAlignmentType, value: str) """ pass class SettingsAbbreviationAlignmentEnhanced(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetAlignmentEnhancedAbbreviation(self, type): """ GetAlignmentEnhancedAbbreviation(self: SettingsAbbreviationAlignmentEnhanced, type: AbbreviationAlignmentEnhancedType) -> str """ pass def SetAlignmentEnhancedAbbreviation(self, type, newValue): """ SetAlignmentEnhancedAbbreviation(self: SettingsAbbreviationAlignmentEnhanced, type: AbbreviationAlignmentEnhancedType, newValue: str) """ pass class SettingsAbbreviationCant(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetCantAbbreviation(self, type): """ GetCantAbbreviation(self: SettingsAbbreviationCant, type: AbbreviationCantType) -> str """ pass def SetCantAbbreviation(self, type, newValue): """ SetCantAbbreviation(self: SettingsAbbreviationCant, type: AbbreviationCantType, newValue: str) """ pass class SettingsAbbreviationGeneral(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Infinity = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Infinity(self: SettingsAbbreviationGeneral) -> str Set: Infinity(self: SettingsAbbreviationGeneral) = value """ Left = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Left(self: SettingsAbbreviationGeneral) -> str Set: Left(self: SettingsAbbreviationGeneral) = value """ Right = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Right(self: SettingsAbbreviationGeneral) -> str Set: Right(self: SettingsAbbreviationGeneral) = value """ class SettingsAbbreviationProfile(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetProfileAbbreviation(self, type): """ GetProfileAbbreviation(self: SettingsAbbreviationProfile, type: AbbreviationProfileType) -> str """ pass def SetProfileAbbreviation(self, type, newValue): """ SetProfileAbbreviation(self: SettingsAbbreviationProfile, type: AbbreviationProfileType, newValue: str) """ pass class SettingsAbbreviationSuperelevation(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetSuperelevationAbbreviation(self, type): """ GetSuperelevationAbbreviation(self: SettingsAbbreviationSuperelevation, type: AbbreviationSuperelevationType) -> str """ pass def SetSuperelevationAbbreviation(self, type, newValue): """ SetSuperelevationAbbreviation(self: SettingsAbbreviationSuperelevation, type: AbbreviationSuperelevationType, newValue: str) """ pass class SettingsAmbient(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass @staticmethod # known case of __new__ def __new__(self, *args): #cannot find CLR constructor """ __new__(cls: type, root: SettingsRoot, path: str) """ pass Acceleration = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Acceleration(self: SettingsAmbient) -> SettingsAcceleration """ Angle = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Angle(self: SettingsAmbient) -> SettingsAngle """ Area = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Area(self: SettingsAmbient) -> SettingsArea """ Coordinate = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Coordinate(self: SettingsAmbient) -> SettingsCoordinate """ DegreeOfCurvature = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DegreeOfCurvature(self: SettingsAmbient) -> SettingsDegreeOfCurvature """ Dimension = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Dimension(self: SettingsAmbient) -> SettingsDimension """ Direction = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Direction(self: SettingsAmbient) -> SettingsDirection """ Distance = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Distance(self: SettingsAmbient) -> SettingsDistance """ Elevation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Elevation(self: SettingsAmbient) -> SettingsElevation """ General = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: General(self: SettingsAmbient) -> SettingsGeneral """ Grade = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Grade(self: SettingsAmbient) -> SettingsGrade """ GradeSlope = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GradeSlope(self: SettingsAmbient) -> SettingsGradeSlope """ GridCoordinate = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GridCoordinate(self: SettingsAmbient) -> SettingsGridCoordinate """ Labeling = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Labeling(self: SettingsAmbient) -> SettingsLabeling """ LatLong = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: LatLong(self: SettingsAmbient) -> SettingsLatLong """ Pressure = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Pressure(self: SettingsAmbient) -> SettingsPressure """ Slope = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Slope(self: SettingsAmbient) -> SettingsSlope """ Speed = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Speed(self: SettingsAmbient) -> SettingsSpeed """ Station = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Station(self: SettingsAmbient) -> SettingsStation """ Time = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Time(self: SettingsAmbient) -> SettingsTime """ TransparentCommands = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TransparentCommands(self: SettingsAmbient) -> SettingsTransparentCommands """ Unitless = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Unitless(self: SettingsAmbient) -> SettingsUnitless """ Volume = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Volume(self: SettingsAmbient) -> SettingsVolume """ SettingsAcceleration = None SettingsAngle = None SettingsArea = None SettingsCoordinate = None SettingsDegreeOfCurvature = None SettingsDimension = None SettingsDirection = None SettingsDistance = None SettingsElevation = None SettingsFormatNumber`1 = None SettingsGeneral = None SettingsGrade = None SettingsGradeSlope = None SettingsGridCoordinate = None SettingsLabeling = None SettingsLatLong = None SettingsPressure = None SettingsSlope = None SettingsSpeed = None SettingsStation = None SettingsTime = None SettingsTransparentCommands = None SettingsUnitFormatNumber`2 = None SettingsUnitless = None SettingsUnitlessNumber = None SettingsUnitNumber`1 = None SettingsVolume = None class SettingsAlignment(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AutomaticWideningAroundCurves = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AutomaticWideningAroundCurves(self: SettingsAlignment) -> SettingsAutomaticWideningAroundCurves """ CantOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CantOptions(self: SettingsAlignment) -> SettingsCantOptions """ ConstraintEditing = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ConstraintEditing(self: SettingsAlignment) -> SettingsConstraintEditing """ CriteriaBasedDesignOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CriteriaBasedDesignOptions(self: SettingsAlignment) -> SettingsCriteriaBasedDesignOptions """ Data = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Data(self: SettingsAlignment) -> SettingsData """ DefaultNameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DefaultNameFormat(self: SettingsAlignment) -> SettingsDefaultNameFormat """ DynamicAlignmentHighlight = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DynamicAlignmentHighlight(self: SettingsAlignment) -> SettingsDynamicAlignmentHighlight """ ImpliedPointOfIntersection = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ImpliedPointOfIntersection(self: SettingsAlignment) -> SettingsImpliedPointOfIntersection """ RailOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RailOptions(self: SettingsAlignment) -> SettingsRailAlignmentOptions """ StationIndexing = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: StationIndexing(self: SettingsAlignment) -> SettingsStationIndexing """ StyleSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: StyleSettings(self: SettingsAlignment) -> SettingsStyles """ SuperelevationOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SuperelevationOptions(self: SettingsAlignment) -> SettingsSuperelevationOptions """ SettingsAutomaticWideningAroundCurves = None SettingsCantOptions = None SettingsConstraintEditing = None SettingsCriteriaBasedDesignOptions = None SettingsData = None SettingsDefaultNameFormat = None SettingsDynamicAlignmentHighlight = None SettingsImpliedPointOfIntersection = None SettingsRailAlignmentOptions = None SettingsStationIndexing = None SettingsStyles = None SettingsSuperelevationOptions = None class SettingsAssembly(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsAssembly) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsAssembly) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsBuildingSite(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsBuildingSite) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsBuildingSite) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCantView(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsCantView) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsCantView) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCatchment(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameTemplate = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameTemplate(self: SettingsCatchment) -> PropertyString """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsCatchment) -> SettingsStyles """ SettingsStyles = None class SettingsCmdAddAlignmentCurveTable(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddAlignmentCurveTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddAlignmentLineTable(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddAlignmentLineTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddAlignmentOffLbl(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignmentOffXYLbl(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignmentSegmentTable(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddAlignmentSegmentTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddAlignmentSpiralTable(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddAlignmentSpiralTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddAlignPointOfIntLbl(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignPointOfIntLbls(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignSegLbl(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignSegLbls(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignTagentLbl(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignTagentLbls(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsPressureNetwork(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Cover = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Cover(self: SettingsPressureNetwork) -> SettingsDepthOfCover """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsPressureNetwork) -> SettingsNameFormat """ ProfileLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ProfileLabelPlacement(self: SettingsPressureNetwork) -> SettingsProfileLabelPlacement """ SectionLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SectionLabelPlacement(self: SettingsPressureNetwork) -> SettingsSectionLabelPlacement """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsPressureNetwork) -> SettingsStyles """ SettingsDepthOfCover = None SettingsNameFormat = None SettingsProfileLabelPlacement = None SettingsSectionLabelPlacement = None SettingsStyles = None class SettingsCmdAddAppurtTable(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddAppurtTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsSurface(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ContourLabeling = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ContourLabeling(self: SettingsSurface) -> SettingsContourLabeling """ Defaults = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Defaults(self: SettingsSurface) -> SettingsDefaults """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsSurface) -> SettingsStyles """ SettingsContourLabeling = None SettingsDefaults = None SettingsStyles = None class SettingsCmdAddContourLabeling(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddContourLabelingGroup(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AddContourLabeling = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AddContourLabeling(self: SettingsCmdAddContourLabelingGroup) -> SettingsCmdAddContourLabeling """ SettingsCmdAddContourLabeling = None class SettingsCmdAddContourLabelingSingle(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddFittingTable(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddFittingTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsIntersection(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsIntersection) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsIntersection) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCmdAddIntersectionLabel(SettingsIntersection): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsGeneral(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsGeneral) -> SettingsStyles """ SettingsStyles = None class SettingsCmdAddLineBetweenPoints(SettingsGeneral): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsQuantityTakeoff(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsQuantityTakeoff) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsQuantityTakeoff) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCmdAddMaterialVolumeTable(SettingsQuantityTakeoff): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddMaterialVolumeTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsPipeNetwork(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Default = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Default(self: SettingsPipeNetwork) -> SettingsDefault """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsPipeNetwork) -> SettingsNameFormat """ ProfileLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ProfileLabelPlacement(self: SettingsPipeNetwork) -> SettingsProfileLabelPlacement """ Rules = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Rules(self: SettingsPipeNetwork) -> SettingsRules """ SectionLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SectionLabelPlacement(self: SettingsPipeNetwork) -> SettingsSectionLabelPlacement """ StormSewersMigration = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: StormSewersMigration(self: SettingsPipeNetwork) -> SettingsStormSewersMigration """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsPipeNetwork) -> SettingsStyles """ SettingsDefault = None SettingsNameFormat = None SettingsProfileLabelPlacement = None SettingsRules = None SettingsSectionLabelPlacement = None SettingsStormSewersMigration = None SettingsStyles = None class SettingsCmdAddNetworkPartPlanLabel(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkPartProfLabel(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkPartSectLabel(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkPartsToProf(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkPipeTable(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddNetworkPipeTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddNetworkPlanLabels(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkProfLabels(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkSectLabels(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkStructTable(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddNetworkStructTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddNoteLabel(SettingsGeneral): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsParcel(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsParcel) -> SettingsStyles """ SettingsStyles = None class SettingsCmdAddParcelAreaLabel(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddParcelCurveTable(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddParcelCurveTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddParcelLineLabel(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddParcelLineTable(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddParcelLineTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddParcelSegmentLabels(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Options = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Options(self: SettingsCmdAddParcelSegmentLabels) -> SettingsCmdOptions """ SettingsCmdOptions = None class SettingsCmdAddParcelSegmentTable(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddParcelSegmentTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddParcelTable(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddParcelTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsPointCloud(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefaultNameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DefaultNameFormat(self: SettingsPointCloud) -> SettingsDefaultNameFormat """ StyleSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: StyleSettings(self: SettingsPointCloud) -> SettingsStyles """ SettingsDefaultNameFormat = None SettingsStyles = None class SettingsCmdAddPointCloudPoints(SettingsPointCloud): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefaultFileFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DefaultFileFormat(self: SettingsCmdAddPointCloudPoints) -> PropertyEnum[PointCloudDefaultFileExtensionType] """ class SettingsCmdAddPointsToSurface(SettingsPointCloud): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MidOrdinateDistance = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: MidOrdinateDistance(self: SettingsCmdAddPointsToSurface) -> PropertyDouble """ RegionOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RegionOption(self: SettingsCmdAddPointsToSurface) -> PropertyEnum[PointCloudRegionType] """ SurfaceOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SurfaceOption(self: SettingsCmdAddPointsToSurface) -> PropertyEnum[PointCloudSurfaceType] """ class SettingsPoint(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsPoint) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsPoint) -> SettingsStyles """ UpdatePoints = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: UpdatePoints(self: SettingsPoint) -> SettingsUpdatePoints """ SettingsNameFormat = None SettingsStyles = None SettingsUpdatePoints = None class SettingsCmdAddPointTable(SettingsPoint): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddPointTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddPressurePartPlanLabel(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddPressurePartProfLabel(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddPressurePartsToProf(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddPressurePipeTable(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddPressurePipeTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddPressurePlanLabels(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddPressureProfLabels(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsProfileView(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Creation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Creation(self: SettingsProfileView) -> SettingsCreation """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsProfileView) -> SettingsNameFormat """ ProjectionLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ProjectionLabelPlacement(self: SettingsProfileView) -> SettingsProjectionLabelPlacement """ SplitOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SplitOptions(self: SettingsProfileView) -> SettingsSplitOptions """ StackedOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: StackedOptions(self: SettingsProfileView) -> SettingsStackedOptions """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsProfileView) -> SettingsStyles """ SettingsCreation = None SettingsNameFormat = None SettingsProjectionLabelPlacement = None SettingsSplitOptions = None SettingsStackedOptions = None SettingsStyles = None class SettingsCmdAddProfileViewDepthLbl(SettingsProfileView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddProfileViewStaElevLbl(SettingsProfileView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsSectionView(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsSectionView) -> SettingsNameFormat """ ProjectionLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ProjectionLabelPlacement(self: SettingsSectionView) -> SettingsProjectionLabelPlacement """ SectionViewCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SectionViewCreation(self: SettingsSectionView) -> SettingsSectionViewCreation """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsSectionView) -> SettingsStyles """ SettingsNameFormat = None SettingsProjectionLabelPlacement = None SettingsSectionViewCreation = None SettingsStyles = None class SettingsCmdAddSectionViewGradeLbl(SettingsSectionView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSectionViewOffElevLbl(SettingsSectionView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSegmentLabel(SettingsGeneral): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSegmentLabels(SettingsGeneral): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSpanningPipePlanLabel(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSpanningPipeProfLabel(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSpotElevLabelsOnGrid(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSurfaceBoundaries(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DataOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DataOptions(self: SettingsCmdAddSurfaceBoundaries) -> SettingsCmdAddDataOptions """ SettingsCmdAddDataOptions = None class SettingsCmdAddSurfaceBreaklines(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DataOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DataOptions(self: SettingsCmdAddSurfaceBreaklines) -> SettingsCmdAddDataOptions """ SettingsCmdAddDataOptions = None class SettingsCmdAddSurfaceContours(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AddDataOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AddDataOptions(self: SettingsCmdAddSurfaceContours) -> SettingsCmdAddDataOptions """ SettingsCmdAddDataOptions = None class SettingsCmdAddSurfaceDemFile(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ImportOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ImportOptions(self: SettingsCmdAddSurfaceDemFile) -> SettingsCmdImportOptions """ SettingsCmdImportOptions = None class SettingsCmdAddSurfaceDrawingObjects(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DataOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DataOptions(self: SettingsCmdAddSurfaceDrawingObjects) -> SettingsCmdAddDataOptions """ SettingsCmdAddDataOptions = None class SettingsCmdAddSurfaceFigSurveyQuery(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DataOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DataOptions(self: SettingsCmdAddSurfaceFigSurveyQuery) -> SettingsCmdAddDataOptions """ SettingsCmdAddDataOptions = None class SettingsCmdAddSurfacePointSurveyQuery(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSurfaceSlopeLabel(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSurfaceSpotElevLabel(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsSurvey(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsSurvey) -> SettingsStyles """ SettingsStyles = None class SettingsCmdAddSvFigureLabel(SettingsSurvey): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSvFigureSegmentLabel(SettingsSurvey): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSvFigureSegmentLabels(SettingsSurvey): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddTotalVolumeTable(SettingsQuantityTakeoff): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdAddTotalVolumeTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddWidening(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass LinearTransitionAroundCurves = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: LinearTransitionAroundCurves(self: SettingsCmdAddWidening) -> SettingsCmdLinearTransitionAroundCurves """ WideningOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: WideningOptions(self: SettingsCmdAddWidening) -> SettingsCmdWideningOptions """ SettingsCmdLinearTransitionAroundCurves = None SettingsCmdWideningOptions = None class SettingsCmdAssignPayItemToArea(SettingsQuantityTakeoff): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AssignPayItemToAreaOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AssignPayItemToAreaOption(self: SettingsCmdAssignPayItemToArea) -> SettingsCmdAssignPayItemToAreaOptions """ SettingsCmdAssignPayItemToAreaOptions = None class SettingsCmdCatchmentArea(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DischargePointStyle = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DischargePointStyle(self: SettingsCmdCatchmentArea) -> PropertyString """ DischargePointStyleId = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DischargePointStyleId(self: SettingsCmdCatchmentArea) -> PropertyObjectId """ DisplayDisChargePoint = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DisplayDisChargePoint(self: SettingsCmdCatchmentArea) -> PropertyBoolean """ Layer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Layer(self: SettingsCmdCatchmentArea) -> PropertyLayer """ ObjectType = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ObjectType(self: SettingsCmdCatchmentArea) -> PropertyEnum[CatchmentObjectType] """ class SettingsCmdComputeMaterials(SettingsQuantityTakeoff): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefineMaterialOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DefineMaterialOption(self: SettingsCmdComputeMaterials) -> SettingsCmdDefineMaterial """ SettingsCmdDefineMaterial = None class SettingsCmdConvertPointstoSdskPoints(SettingsPoint): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Layer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Layer(self: SettingsCmdConvertPointstoSdskPoints) -> SettingsCmdLayer """ SettingsCmdLayer = None class SettingsCorridor(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsCorridor) -> SettingsNameFormat """ RegionHighlightGraphics = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RegionHighlightGraphics(self: SettingsCorridor) -> SettingsRegionHighlightGraphics """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsCorridor) -> SettingsStyles """ SettingsNameFormat = None SettingsRegionHighlightGraphics = None SettingsStyles = None class SettingsCmdCorridorExtractSurfaces(SettingsCorridor): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateAlignFromCorridor(SettingsCorridor): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentTypeOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AlignmentTypeOption(self: SettingsCmdCreateAlignFromCorridor) -> SettingsCmdAlignmentTypeOption """ CriteriaBasedDesignOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CriteriaBasedDesignOptions(self: SettingsCmdCreateAlignFromCorridor) -> SettingsCmdCriteriaBasedDesignOptions """ ProfileCreationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ProfileCreationOption(self: SettingsCmdCreateAlignFromCorridor) -> SettingsCmdProfileCreationOption """ SettingsCmdAlignmentTypeOption = None SettingsCmdCriteriaBasedDesignOptions = None SettingsCmdProfileCreationOption = None class SettingsCmdCreateAlignFromNetwork(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentTypeOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AlignmentTypeOption(self: SettingsCmdCreateAlignFromNetwork) -> SettingsCmdAlignmentTypeOption """ SettingsCmdAlignmentTypeOption = None class SettingsCmdCreateAlignFromPressureNW(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentTypeOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AlignmentTypeOption(self: SettingsCmdCreateAlignFromPressureNW) -> SettingsCmdAlignmentTypeOption """ SettingsCmdAlignmentTypeOption = None class SettingsCmdCreateAlignmentEntities(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentTypeOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AlignmentTypeOption(self: SettingsCmdCreateAlignmentEntities) -> SettingsCmdAlignmentTypeOption """ CreateFromEntities = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CreateFromEntities(self: SettingsCmdCreateAlignmentEntities) -> SettingsCmdCreateFromEntities """ SettingsCmdAlignmentTypeOption = None SettingsCmdCreateFromEntities = None class SettingsCmdCreateAlignmentLayout(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentTypeOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AlignmentTypeOption(self: SettingsCmdCreateAlignmentLayout) -> SettingsCmdAlignmentTypeOption """ CurveAndSpiralSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CurveAndSpiralSettings(self: SettingsCmdCreateAlignmentLayout) -> SettingsCmdCurveAndSpiralSettings """ CurveTessellationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CurveTessellationOption(self: SettingsCmdCreateAlignmentLayout) -> SettingsCmdCurveTessellationOption """ RegressionGraphOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RegressionGraphOption(self: SettingsCmdCreateAlignmentLayout) -> SettingsCmdRegressionGraphOption """ SettingsCmdAlignmentTypeOption = None SettingsCmdCurveAndSpiralSettings = None SettingsCmdCurveTessellationOption = None SettingsCmdRegressionGraphOption = None class SettingsCmdCreateAlignmentReference(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateArcByBestFit(SettingsGeneral): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CurveTessellationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CurveTessellationOption(self: SettingsCmdCreateArcByBestFit) -> SettingsCmdCurveTessellationOption """ RegressionGraphOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RegressionGraphOption(self: SettingsCmdCreateArcByBestFit) -> SettingsCmdRegressionGraphOption """ SettingsCmdCurveTessellationOption = None SettingsCmdRegressionGraphOption = None class SettingsCmdCreateAssembly(SettingsAssembly): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateAssemblyTool(SettingsAssembly): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateCantView(SettingsCantView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateCatchmentFromObject(SettingsCatchment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Catchment = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Catchment(self: SettingsCmdCreateCatchmentFromObject) -> SettingsCmdCatchment """ ChannelFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ChannelFlow(self: SettingsCmdCreateCatchmentFromObject) -> SettingsCmdChannelFlow """ HydrologicalProperties = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: HydrologicalProperties(self: SettingsCmdCreateCatchmentFromObject) -> SettingsCmdHydrologicalProperties """ ShallowConcentratedFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ShallowConcentratedFlow(self: SettingsCmdCreateCatchmentFromObject) -> SettingsCmdShallowConcentratedFlow """ SheetFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SheetFlow(self: SettingsCmdCreateCatchmentFromObject) -> SettingsCmdSheetFlow """ TimeOfConcentrationMethod = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TimeOfConcentrationMethod(self: SettingsCmdCreateCatchmentFromObject) -> PropertyEnum[CatchmentTimeOfConcentrationMethodType] """ SettingsCmdCatchment = None SettingsCmdChannelFlow = None SettingsCmdHydrologicalProperties = None SettingsCmdShallowConcentratedFlow = None SettingsCmdSheetFlow = None class SettingsCmdCreateCatchmentFromSurface(SettingsCatchment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Catchment = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Catchment(self: SettingsCmdCreateCatchmentFromSurface) -> SettingsCmdCatchment """ ChannelFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ChannelFlow(self: SettingsCmdCreateCatchmentFromSurface) -> SettingsCmdChannelFlow """ HydrologicalProperties = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: HydrologicalProperties(self: SettingsCmdCreateCatchmentFromSurface) -> SettingsCmdHydrologicalProperties """ ShallowConcentratedFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ShallowConcentratedFlow(self: SettingsCmdCreateCatchmentFromSurface) -> SettingsCmdShallowConcentratedFlow """ SheetFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SheetFlow(self: SettingsCmdCreateCatchmentFromSurface) -> SettingsCmdSheetFlow """ TimeOfConcentrationMethod = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TimeOfConcentrationMethod(self: SettingsCmdCreateCatchmentFromSurface) -> PropertyEnum[CatchmentTimeOfConcentrationMethodType] """ SettingsCmdCatchment = None SettingsCmdChannelFlow = None SettingsCmdHydrologicalProperties = None SettingsCmdShallowConcentratedFlow = None SettingsCmdSheetFlow = None class SettingsCmdCreateCatchmentGroup(SettingsCatchment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateCorridor(SettingsCorridor): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AssemblyInsertion = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AssemblyInsertion(self: SettingsCmdCreateCorridor) -> SettingsCmdAssemblyInsertion """ SettingsCmdAssemblyInsertion = None class SettingsGrading(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsGrading) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsGrading) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCmdCreateFeatureLineFromAlign(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FeatureLineCreation(self: SettingsCmdCreateFeatureLineFromAlign) -> SettingsCmdFeatureLineCreation """ SettingsCmdFeatureLineCreation = None class SettingsCmdCreateFeatureLines(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FeatureLineCreation(self: SettingsCmdCreateFeatureLines) -> SettingsCmdFeatureLineCreation """ SettingsCmdFeatureLineCreation = None class SettingsCmdCreateFlowSegment(SettingsCatchment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ChannelFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ChannelFlow(self: SettingsCmdCreateFlowSegment) -> SettingsCmdChannelFlow """ ShallowConcentratedFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ShallowConcentratedFlow(self: SettingsCmdCreateFlowSegment) -> SettingsCmdShallowConcentratedFlow """ SheetFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SheetFlow(self: SettingsCmdCreateFlowSegment) -> SettingsCmdSheetFlow """ SettingsCmdChannelFlow = None SettingsCmdShallowConcentratedFlow = None SettingsCmdSheetFlow = None class SettingsCmdCreateGrading(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass GradingCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GradingCreation(self: SettingsCmdCreateGrading) -> SettingsCmdGradingCreation """ SettingsCmdGradingCreation = None class SettingsCmdCreateGradingGroup(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass GradingGroupCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GradingGroupCreation(self: SettingsCmdCreateGradingGroup) -> SettingsCmdGradingGroupCreation """ SettingsCmdGradingGroupCreation = None class SettingsCmdCreateInterferenceCheck(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass InterferenceCriteria = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: InterferenceCriteria(self: SettingsCmdCreateInterferenceCheck) -> SettingsCmdInterferenceCriteria """ SettingsCmdInterferenceCriteria = None class SettingsCmdCreateIntersection(SettingsIntersection): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AssemblyInsertion = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AssemblyInsertion(self: SettingsCmdCreateIntersection) -> SettingsCmdAssemblyInsertion """ CrossSlopes = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CrossSlopes(self: SettingsCmdCreateIntersection) -> SettingsCmdCrossSlopes """ CurbReturnParameters = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CurbReturnParameters(self: SettingsCmdCreateIntersection) -> SettingsCmdCurbReturnParameters """ CurbReturnProfileRules = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CurbReturnProfileRules(self: SettingsCmdCreateIntersection) -> SettingsCmdCurbReturnProfileRules """ IntersectionOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: IntersectionOptions(self: SettingsCmdCreateIntersection) -> SettingsCmdIntersectionOptions """ Offsets = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Offsets(self: SettingsCmdCreateIntersection) -> SettingsCmdOffsets """ SecondaryRoadProfileRules = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SecondaryRoadProfileRules(self: SettingsCmdCreateIntersection) -> SettingsCmdSecondaryRoadProfileRules """ WideningParameters = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: WideningParameters(self: SettingsCmdCreateIntersection) -> SettingsCmdWideningParameters """ SettingsCmdAssemblyInsertion = None SettingsCmdCrossSlopes = None SettingsCmdCurbReturnParameters = None SettingsCmdCurbReturnProfileRules = None SettingsCmdIntersectionOptions = None SettingsCmdOffsets = None SettingsCmdSecondaryRoadProfileRules = None SettingsCmdWideningParameters = None class SettingsCmdCreateLineByBestFit(SettingsGeneral): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CurveTessellationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CurveTessellationOption(self: SettingsCmdCreateLineByBestFit) -> SettingsCmdCurveTessellationOption """ RegressionGraphOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RegressionGraphOption(self: SettingsCmdCreateLineByBestFit) -> SettingsCmdRegressionGraphOption """ SettingsCmdCurveTessellationOption = None SettingsCmdRegressionGraphOption = None class SettingsMassHaulView(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MassHaulCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: MassHaulCreation(self: SettingsMassHaulView) -> SettingsMassHaulCreation """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsMassHaulView) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsMassHaulView) -> SettingsStyles """ SettingsMassHaulCreation = None SettingsNameFormat = None SettingsStyles = None class SettingsCmdCreateMassHaulDiagram(SettingsMassHaulView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MassHaulCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: MassHaulCreation(self: SettingsCmdCreateMassHaulDiagram) -> SettingsCmdMassHaulCreation """ SettingsCmdMassHaulCreation = None class SettingsCmdCreateMultipleProfileView(SettingsProfileView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MultipleProfileViewCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: MultipleProfileViewCreation(self: SettingsCmdCreateMultipleProfileView) -> SettingsCmdMultipleProfileViewCreation """ SettingsCmdMultipleProfileViewCreation = None class SettingsCmdCreateMultipleSectionView(SettingsSectionView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MultipleSectionViewCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: MultipleSectionViewCreation(self: SettingsCmdCreateMultipleSectionView) -> SettingsCmdMultipleSectionViewCreation """ TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdCreateMultipleSectionView) -> SettingsCmdTableCreation """ SettingsCmdMultipleSectionViewCreation = None SettingsCmdTableCreation = None class SettingsCmdCreateNetwork(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefaultLayoutCommand = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DefaultLayoutCommand(self: SettingsCmdCreateNetwork) -> PropertyEnum[NetworkDefaultLayoutCommandType] """ LabelNewParts = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: LabelNewParts(self: SettingsCmdCreateNetwork) -> SettingsCmdLabelNewParts """ SettingsCmdLabelNewParts = None class SettingsCmdCreateNetworkFromObject(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateNetworkPartsList(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateNetworkPartsListFull(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateNetworkReference(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateOffsetAlignment(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass OffsetAlignmentOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: OffsetAlignmentOptions(self: SettingsCmdCreateOffsetAlignment) -> SettingsCmdOffsetAlignmentOptions """ SettingsCmdOffsetAlignmentOptions = None class SettingsCmdCreateParabolaByBestFit(SettingsGeneral): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CurveTessellationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CurveTessellationOption(self: SettingsCmdCreateParabolaByBestFit) -> SettingsCmdCurveTessellationOption """ RegressionGraphOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RegressionGraphOption(self: SettingsCmdCreateParabolaByBestFit) -> SettingsCmdRegressionGraphOption """ SettingsCmdCurveTessellationOption = None SettingsCmdRegressionGraphOption = None class SettingsCmdCreateParcelByLayout(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AutomaticLayout = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AutomaticLayout(self: SettingsCmdCreateParcelByLayout) -> SettingsCmdAutomaticLayout """ ConvertFromEntities = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ConvertFromEntities(self: SettingsCmdCreateParcelByLayout) -> SettingsCmdConvertFromEntities """ ParcelSizing = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ParcelSizing(self: SettingsCmdCreateParcelByLayout) -> SettingsCmdParcelSizing """ PreviewGraphics = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: PreviewGraphics(self: SettingsCmdCreateParcelByLayout) -> SettingsCmdPreviewGraphics """ SettingsCmdAutomaticLayout = None SettingsCmdConvertFromEntities = None SettingsCmdParcelSizing = None SettingsCmdPreviewGraphics = None class SettingsCmdCreateParcelFromObjects(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ConvertFromEntities = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ConvertFromEntities(self: SettingsCmdCreateParcelFromObjects) -> SettingsCmdConvertFromEntities """ SettingsCmdConvertFromEntities = None class SettingsCmdCreateParcelROW(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CleanupAtAlignmentIntersections = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CleanupAtAlignmentIntersections(self: SettingsCmdCreateParcelROW) -> SettingsCmdCleanupAtAlignmentIntersections """ CleanupAtParcelBoundaries = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CleanupAtParcelBoundaries(self: SettingsCmdCreateParcelROW) -> SettingsCmdCleanupAtParcelBoundaries """ CreateParcelRightOfWay = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CreateParcelRightOfWay(self: SettingsCmdCreateParcelROW) -> SettingsCmdCreateParcelRightOfWay """ SettingsCmdCleanupAtAlignmentIntersections = None SettingsCmdCleanupAtParcelBoundaries = None SettingsCmdCreateParcelRightOfWay = None class SettingsCmdCreatePointCloud(SettingsPointCloud): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefaultLayer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DefaultLayer(self: SettingsCmdCreatePointCloud) -> SettingsCmdDefaultLayer """ FileFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FileFormat(self: SettingsCmdCreatePointCloud) -> PropertyEnum[PointCloudDefaultFileExtensionType] """ SettingsCmdDefaultLayer = None class SettingsCmdCreatePointGroup(SettingsPoint): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreatePoints(SettingsPoint): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Layer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Layer(self: SettingsCmdCreatePoints) -> SettingsCmdLayer """ PointIdentity = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: PointIdentity(self: SettingsCmdCreatePoints) -> SettingsCmdPointIdentity """ PointsCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: PointsCreation(self: SettingsCmdCreatePoints) -> SettingsCmdPointsCreation """ SettingsCmdLayer = None SettingsCmdPointIdentity = None SettingsCmdPointsCreation = None class SettingsCmdCreatePointsFromCorridor(SettingsCorridor): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreatePolylineFromCorridor(SettingsCorridor): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsSuperelevationView(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsSuperelevationView) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsSuperelevationView) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCmdCreatePolylineFromSuper(SettingsSuperelevationView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreatePressureFromIndModel(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreatePressureNetwork(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DepthOfCover = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DepthOfCover(self: SettingsCmdCreatePressureNetwork) -> SettingsCmdDepthOfCover """ LabelNewParts = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: LabelNewParts(self: SettingsCmdCreatePressureNetwork) -> SettingsCmdLabelNewParts """ SettingsCmdDepthOfCover = None SettingsCmdLabelNewParts = None class SettingsCmdCreatePressurePartList(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreatePressurePartListFull(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateProfileFromCorridor(SettingsCorridor): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CriteriaBasedDesignOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CriteriaBasedDesignOptions(self: SettingsCmdCreateProfileFromCorridor) -> SettingsCmdCriteriaBasedDesignOptions """ SettingsCmdCriteriaBasedDesignOptions = None class SettingsProfile(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CriteriaBasedDesignOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CriteriaBasedDesignOptions(self: SettingsProfile) -> SettingsCriteriaBasedDesignOptions """ DefaultNameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DefaultNameFormat(self: SettingsProfile) -> SettingsDefaultNameFormat """ ProfilesCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ProfilesCreation(self: SettingsProfile) -> SettingsProfileCreation """ StyleSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: StyleSettings(self: SettingsProfile) -> SettingsStyles """ SettingsCriteriaBasedDesignOptions = None SettingsDefaultNameFormat = None SettingsProfileCreation = None SettingsStyles = None class SettingsCmdCreateProfileFromFile(SettingsProfile): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateProfileFromSurface(SettingsProfile): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Geometry = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Geometry(self: SettingsCmdCreateProfileFromSurface) -> SettingsCmdGeometry """ SettingsCmdGeometry = None class SettingsCmdCreateProfileLayout(SettingsProfile): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CurveTessellationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CurveTessellationOption(self: SettingsCmdCreateProfileLayout) -> SettingsCmdCurveTessellationOption """ RegressionGraphOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RegressionGraphOption(self: SettingsCmdCreateProfileLayout) -> SettingsCmdRegressionGraphOption """ SettingsCmdCurveTessellationOption = None SettingsCmdRegressionGraphOption = None class SettingsCmdCreateProfileReference(SettingsProfile): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateProfileView(SettingsProfileView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateQuickProfile(SettingsProfile): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass QuickProfile = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: QuickProfile(self: SettingsCmdCreateQuickProfile) -> SettingsCmdQuickProfile """ SettingsCmdQuickProfile = None class SettingsSampleLine(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsSampleLine) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsSampleLine) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCmdCreateSampleLines(SettingsSampleLine): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AdditionalSampleControls = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AdditionalSampleControls(self: SettingsCmdCreateSampleLines) -> SettingsCmdAdditionalSampleControls """ Miscellaneous = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Miscellaneous(self: SettingsCmdCreateSampleLines) -> SettingsCmdMiscellaneous """ SamplingIncrements = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SamplingIncrements(self: SettingsCmdCreateSampleLines) -> SettingsCmdSamplingIncrements """ SwathWidths = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SwathWidths(self: SettingsCmdCreateSampleLines) -> SettingsCmdSwathWidths """ SettingsCmdAdditionalSampleControls = None SettingsCmdMiscellaneous = None SettingsCmdSamplingIncrements = None SettingsCmdSwathWidths = None class SettingsCmdCreateSectionSheets(SettingsSectionView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass SheetCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SheetCreation(self: SettingsCmdCreateSectionSheets) -> SettingsCmdSheetCreation """ SettingsCmdSheetCreation = None class SettingsCmdCreateSectionView(SettingsSectionView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TableCreation(self: SettingsCmdCreateSectionView) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsViewFrameGroup(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Information = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Information(self: SettingsViewFrameGroup) -> SettingsInformation """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsViewFrameGroup) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsViewFrameGroup) -> SettingsStyles """ SettingsInformation = None SettingsNameFormat = None SettingsStyles = None class SettingsCmdCreateSheets(SettingsViewFrameGroup): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass SheetCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SheetCreation(self: SettingsCmdCreateSheets) -> SettingsCmdSheetCreation """ SettingsCmdSheetCreation = None class SettingsCmdCreateSimpleCorridor(SettingsCorridor): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AssemblyInsertion = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AssemblyInsertion(self: SettingsCmdCreateSimpleCorridor) -> SettingsCmdAssemblyInsertion """ SettingsCmdAssemblyInsertion = None class SettingsCmdCreateSite(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Alignment = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Alignment(self: SettingsCmdCreateSite) -> SettingsCmdAlignment """ FeatureLine = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FeatureLine(self: SettingsCmdCreateSite) -> SettingsCmdFeatureLine """ Parcel = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Parcel(self: SettingsCmdCreateSite) -> SettingsCmdParcel """ SettingsCmdAlignment = None SettingsCmdFeatureLine = None SettingsCmdParcel = None class SettingsSubassembly(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefaultStyles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DefaultStyles(self: SettingsSubassembly) -> SettingsDefaultStyles """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsSubassembly) -> SettingsNameFormat """ SettingsDefaultStyles = None SettingsNameFormat = None class SettingsCmdCreateSubassemblyTool(SettingsSubassembly): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass SubassemblyOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SubassemblyOptions(self: SettingsCmdCreateSubassemblyTool) -> SettingsCmdSubassemblyOptions """ SettingsCmdSubassemblyOptions = None class SettingsCmdCreateSubFromPline(SettingsSubassembly): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CreateFromEntities = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CreateFromEntities(self: SettingsCmdCreateSubFromPline) -> SettingsCmdCreateFromEntities """ SettingsCmdCreateFromEntities = None class SettingsCmdCreateSuperelevationView(SettingsSuperelevationView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateSurface(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass BuildOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: BuildOptions(self: SettingsCmdCreateSurface) -> SettingsCmdBuildOptions """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsCmdCreateSurface) -> SettingsNameFormat """ SurfaceCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SurfaceCreation(self: SettingsCmdCreateSurface) -> SettingsCmdSurfaceCreation """ SettingsCmdBuildOptions = None SettingsCmdSurfaceCreation = None SettingsNameFormat = None class SettingsCmdCreateSurfaceFromTIN(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateSurfaceGridFromDEM(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass BuildOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: BuildOptions(self: SettingsCmdCreateSurfaceGridFromDEM) -> SettingsCmdBuildOptions """ ImportOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ImportOptions(self: SettingsCmdCreateSurfaceGridFromDEM) -> SettingsCmdImportOptions """ SettingsCmdBuildOptions = None SettingsCmdImportOptions = None class SettingsCmdCreateSurfaceReference(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsCmdCreateSurfaceReference) -> SettingsNameFormat """ SettingsNameFormat = None class SettingsCmdCreateSurfaceWaterdrop(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass WaterdropMarker = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: WaterdropMarker(self: SettingsCmdCreateSurfaceWaterdrop) -> SettingsCmdWaterdropMarker """ WaterdropPath = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: WaterdropPath(self: SettingsCmdCreateSurfaceWaterdrop) -> SettingsCmdWaterdropPath """ SettingsCmdWaterdropMarker = None SettingsCmdWaterdropPath = None class SettingsCmdCreateViewFrames(SettingsViewFrameGroup): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ViewFrameCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ViewFrameCreation(self: SettingsCmdCreateViewFrames) -> SettingsCmdViewFrameCreation """ SettingsCmdViewFrameCreation = None class SettingsCmdDrawFeatureLine(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FeatureLineCreation(self: SettingsCmdDrawFeatureLine) -> SettingsCmdFeatureLineCreation """ SettingsCmdFeatureLineCreation = None class SettingsCmdEditFlowSegments(SettingsCatchment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ChannelFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ChannelFlow(self: SettingsCmdEditFlowSegments) -> SettingsCmdChannelFlow """ ShallowConcentratedFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ShallowConcentratedFlow(self: SettingsCmdEditFlowSegments) -> SettingsCmdShallowConcentratedFlow """ SheetFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SheetFlow(self: SettingsCmdEditFlowSegments) -> SettingsCmdSheetFlow """ SettingsCmdChannelFlow = None SettingsCmdShallowConcentratedFlow = None SettingsCmdSheetFlow = None class SettingsCmdEditInStormSewers(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdEditSVGroupStyle(SettingsSectionView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdExportParcelAnalysis(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ParcelAnalysis = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ParcelAnalysis(self: SettingsCmdExportParcelAnalysis) -> SettingsCmdParcelAnalysis """ SettingsCmdParcelAnalysis = None class SettingsCmdExportStormSewerData(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdFeatureLinesFromCorridor(SettingsCorridor): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FeatureLineCreation(self: SettingsCmdFeatureLinesFromCorridor) -> SettingsCmdFeatureLineCreation """ SettingsCmdFeatureLineCreation = None class SettingsCmdFitCurveFeature(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineFitCurve = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FeatureLineFitCurve(self: SettingsCmdFitCurveFeature) -> SettingsCmdFeatureLineFitCurve """ SettingsCmdFeatureLineFitCurve = None class SettingsCmdGenerateQuantitiesReport(SettingsQuantityTakeoff): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DisplayXmlReport = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DisplayXmlReport(self: SettingsCmdGenerateQuantitiesReport) -> PropertyBoolean """ class SettingsCmdGradingElevEditor(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass GradingElevationEditor = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GradingElevationEditor(self: SettingsCmdGradingElevEditor) -> SettingsCmdGradingElevationEditor """ SettingsCmdGradingElevationEditor = None class SettingsCmdGradingTools(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass GradingLayoutTools = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GradingLayoutTools(self: SettingsCmdGradingTools) -> SettingsCmdGradingLayoutTools """ SettingsCmdGradingLayoutTools = None class SettingsCmdGradingVolumeTools(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass LimitFeatureSelectionToCurrentGroup = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: LimitFeatureSelectionToCurrentGroup(self: SettingsCmdGradingVolumeTools) -> PropertyBoolean """ RaiseLowerElevationIncrement = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RaiseLowerElevationIncrement(self: SettingsCmdGradingVolumeTools) -> PropertyDouble """ class SettingsCmdImportBuildingSite(SettingsBuildingSite): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdImportGISData(SettingsGeneral): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass PipeNetwork = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: PipeNetwork(self: SettingsCmdImportGISData) -> SettingsCmdPipeNetwork """ SettingsCmdPipeNetwork = None class SettingsCmdImportStormSewerData(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdJoinFeatures(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineJoin = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FeatureLineJoin(self: SettingsCmdJoinFeatures) -> SettingsCmdFeatureLineJoin """ SettingsCmdFeatureLineJoin = None class SettingsCmdLayoutSectionViewGroup(SettingsSectionView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdMapCheck(SettingsGeneral): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Mapcheck = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Mapcheck(self: SettingsCmdMapCheck) -> SettingsCmdMapcheck """ SettingsCmdMapcheck = None class SettingsCmdMinimizeSurfaceFlatAreas(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AddPointsToFlatEdges = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AddPointsToFlatEdges(self: SettingsCmdMinimizeSurfaceFlatAreas) -> PropertyBoolean """ AddPointsToFlatTriangles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AddPointsToFlatTriangles(self: SettingsCmdMinimizeSurfaceFlatAreas) -> PropertyBoolean """ FillGapsInContour = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FillGapsInContour(self: SettingsCmdMinimizeSurfaceFlatAreas) -> PropertyBoolean """ SwapEdges = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SwapEdges(self: SettingsCmdMinimizeSurfaceFlatAreas) -> PropertyBoolean """ class SettingsCmdMoveBlockstoAttribElev(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdMoveBlocksToSurface(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdMoveTextToElevation(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdProjectObjectsToMultiSect(SettingsSectionView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ObjectSelectionOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ObjectSelectionOptions(self: SettingsCmdProjectObjectsToMultiSect) -> SettingsCmdObjectSelectionOptions """ SettingsCmdObjectSelectionOptions = None class SettingsCmdProjectObjectsToProf(SettingsProfileView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdProjectObjectsToSect(SettingsSectionView): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdReAddParcelAreaLabel(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdReAddParcelSegmentLabels(SettingsParcel): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdRenamePipeNetworkParts(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdResetAnchorPipe(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdReverseAlignmentDirection(SettingsAlignment): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdRunDepthCheck(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DepthCheckOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DepthCheckOption(self: SettingsCmdRunDepthCheck) -> SettingsCmdDepthCheckOption """ SettingsCmdDepthCheckOption = None class SettingsCmdRunDesignCheck(SettingsPressureNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DesignCheckOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DesignCheckOption(self: SettingsCmdRunDesignCheck) -> SettingsCmdDesignCheckOption """ SettingsCmdDesignCheckOption = None class SettingsCmdShowGeodeticCalculator(SettingsPoint): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdShowPointGroupProperties(SettingsPoint): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdShowSpanningPipes(SettingsPipeNetwork): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdSimplifySurface(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MaximumChangeInElevation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: MaximumChangeInElevation(self: SettingsCmdSimplifySurface) -> PropertyDouble """ PercentageOfPointsToRemove = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: PercentageOfPointsToRemove(self: SettingsCmdSimplifySurface) -> PropertyDouble """ RegionOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RegionOptions(self: SettingsCmdSimplifySurface) -> PropertyEnum[SurfaceRegionOptionsType] """ SimplifyMethod = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SimplifyMethod(self: SettingsCmdSimplifySurface) -> PropertyEnum[SurfaceSimplifyType] """ UseMaximumChangeInElevation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: UseMaximumChangeInElevation(self: SettingsCmdSimplifySurface) -> PropertyBoolean """ UsePercentageOfPointsToRemove = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: UsePercentageOfPointsToRemove(self: SettingsCmdSimplifySurface) -> PropertyBoolean """ class SettingsCmdSuperimposeProfile(SettingsProfile): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass SuperimposeProfile = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SuperimposeProfile(self: SettingsCmdSuperimposeProfile) -> SettingsCmdSuperimposeProfileOption """ SettingsCmdSuperimposeProfileOption = None class SettingsCmdSurfaceExportToDem(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ExportOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ExportOptions(self: SettingsCmdSurfaceExportToDem) -> SettingsCmdExportOptions """ SettingsCmdExportOptions = None class SettingsCmdSurfaceExtractObjects(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdTakeOff(SettingsQuantityTakeoff): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ComputeTakeOffOption = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ComputeTakeOffOption(self: SettingsCmdTakeOff) -> SettingsCmdComputeTakeOff """ SettingsCmdComputeTakeOff = None class SettingsCmdViewEditCorridorSection(SettingsCorridor): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass GridSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GridSettings(self: SettingsCmdViewEditCorridorSection) -> SettingsCmdGridSettings """ GridTextSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GridTextSettings(self: SettingsCmdViewEditCorridorSection) -> SettingsCmdGridTextSettings """ SectionSliderInMultipleViewports = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SectionSliderInMultipleViewports(self: SettingsCmdViewEditCorridorSection) -> SettingsCmdSectionSliderInMultipleViewports """ ViewEditOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ViewEditOptions(self: SettingsCmdViewEditCorridorSection) -> SettingsCmdViewEditOptions """ SettingsCmdGridSettings = None SettingsCmdGridTextSettings = None SettingsCmdSectionSliderInMultipleViewports = None SettingsCmdViewEditOptions = None class SettingsCmdVolumesDashboard(SettingsSurface): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass BoundedVolumeCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: BoundedVolumeCreation(self: SettingsCmdVolumesDashboard) -> SettingsCmdBoundedVolumeCreation """ BuildOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: BuildOptions(self: SettingsCmdVolumesDashboard) -> SettingsCmdBuildOptions """ DynamicHighlightOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DynamicHighlightOptions(self: SettingsCmdVolumesDashboard) -> SettingsCmdDynamicHighlightOptions """ VolumeSurfaceCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: VolumeSurfaceCreation(self: SettingsCmdVolumesDashboard) -> SettingsCmdVolumeSurfaceCreation """ SettingsCmdBoundedVolumeCreation = None SettingsCmdBuildOptions = None SettingsCmdDynamicHighlightOptions = None SettingsCmdVolumeSurfaceCreation = None class SettingsCmdWeedFeatures(SettingsGrading): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineWeed = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FeatureLineWeed(self: SettingsCmdWeedFeatures) -> SettingsCmdFeatureLineWeed """ SettingsCmdFeatureLineWeed = None class SettingsCoordinateSystem(object): # no doc Category = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Category(self: SettingsCoordinateSystem) -> str """ Code = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Code(self: SettingsCoordinateSystem) -> str """ Datum = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Datum(self: SettingsCoordinateSystem) -> str """ Description = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Description(self: SettingsCoordinateSystem) -> str """ Projection = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Projection(self: SettingsCoordinateSystem) -> str """ Unit = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Unit(self: SettingsCoordinateSystem) -> str """ class SettingsDrawing(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AbbreviationsSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AbbreviationsSettings(self: SettingsDrawing) -> SettingsAbbreviation """ AmbientSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AmbientSettings(self: SettingsDrawing) -> SettingsAmbient """ ApplyTransformSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ApplyTransformSettings(self: SettingsDrawing) -> bool Set: ApplyTransformSettings(self: SettingsDrawing) = value """ ObjectLayerSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ObjectLayerSettings(self: SettingsDrawing) -> SettingsObjectLayers """ TransformationSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TransformationSettings(self: SettingsDrawing) -> SettingsTransformation """ UnitZoneSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: UnitZoneSettings(self: SettingsDrawing) -> SettingsUnitZone """ class SettingsLandXML(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Export = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Export(self: SettingsLandXML) -> SettingsLandXMLExport """ Import = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Import(self: SettingsLandXML) -> SettingsLandXMLImport """ class SettingsLandXMLExport(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentExport = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AlignmentExport(self: SettingsLandXMLExport) -> SettingsAlignmentExport """ Data = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Data(self: SettingsLandXMLExport) -> SettingsData """ FeatureLineExport = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FeatureLineExport(self: SettingsLandXMLExport) -> SettingsFeatureLineExport """ Identification = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Identification(self: SettingsLandXMLExport) -> SettingsIdentification """ ParcelExport = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ParcelExport(self: SettingsLandXMLExport) -> SettingsParcelExport """ PointExport = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: PointExport(self: SettingsLandXMLExport) -> SettingsPointExport """ SurfaceExport = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SurfaceExport(self: SettingsLandXMLExport) -> SettingsSurfaceExport """ SettingsAlignmentExport = None SettingsData = None SettingsFeatureLineExport = None SettingsIdentification = None SettingsParcelExport = None SettingsPointExport = None SettingsSurfaceExport = None class SettingsLandXMLImport(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentImport = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AlignmentImport(self: SettingsLandXMLImport) -> SettingsAlignmentImport """ ConflictResolution = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ConflictResolution(self: SettingsLandXMLImport) -> SettingsConflictResolution """ DiameterUnits = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DiameterUnits(self: SettingsLandXMLImport) -> SettingsDiameterUnits """ FeatureLineImport = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: FeatureLineImport(self: SettingsLandXMLImport) -> SettingsFeatureLineImport """ PipeNetwork = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: PipeNetwork(self: SettingsLandXMLImport) -> SettingsPipeNetwork """ PointImport = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: PointImport(self: SettingsLandXMLImport) -> SettingsPointImport """ PropertySetData = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: PropertySetData(self: SettingsLandXMLImport) -> SettingsPropertySetData """ Rotation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Rotation(self: SettingsLandXMLImport) -> SettingsRotation """ SurfaceImport = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SurfaceImport(self: SettingsLandXMLImport) -> SettingsSurfaceImport """ Translation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Translation(self: SettingsLandXMLImport) -> SettingsTranslation """ SettingsAlignmentImport = None SettingsConflictResolution = None SettingsDiameterUnits = None SettingsFeatureLineImport = None SettingsPipeNetwork = None SettingsPointImport = None SettingsPropertySetData = None SettingsRotation = None SettingsSurfaceImport = None SettingsTranslation = None class SettingsMassHaulLine(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsMatchLine(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsObjectLayer(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass LayerId = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: LayerId(self: SettingsObjectLayer) -> ObjectId Set: LayerId(self: SettingsObjectLayer) = value """ LayerName = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: LayerName(self: SettingsObjectLayer) -> str Set: LayerName(self: SettingsObjectLayer) = value """ Locked = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Locked(self: SettingsObjectLayer) -> bool Set: Locked(self: SettingsObjectLayer) = value """ Modifier = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Modifier(self: SettingsObjectLayer) -> ObjectLayerModifierType Set: Modifier(self: SettingsObjectLayer) = value """ ModifierValue = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ModifierValue(self: SettingsObjectLayer) -> str Set: ModifierValue(self: SettingsObjectLayer) = value """ ObjectType = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ObjectType(self: SettingsObjectLayer) -> SettingsObjectLayerType """ class SettingsObjectLayers(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetObjectLayerSetting(self, settingsType): """ GetObjectLayerSetting(self: SettingsObjectLayers, settingsType: SettingsObjectLayerType) -> SettingsObjectLayer """ pass ObjectControlledByLayer = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ObjectControlledByLayer(self: SettingsObjectLayers) -> bool Set: ObjectControlledByLayer(self: SettingsObjectLayers) = value """ class SettingsObjectLayerType(Enum): """ enum SettingsObjectLayerType, values: Alignment (0), AlignmentLabeling (1), AlignmentTable (2), Appurtenance (56), AppurtenanceLabeling (57), Assembly (3), BuildingSite (53), CantView (58), Catchment (59), CatchmentLabeling (60), Corridor (4), CorridorSection (5), FeatureLine (6), Fitting (61), FittingLabeling (62), GeneralNoteLabel (7), GeneralSegmentLabel (8), Grading (9), GradingLabeling (10), GridSurface (11), GridSurfaceLabeling (12), Interference (13), Intersection (54), IntersectionLabeling (55), MassHaulLine (14), MassHaulView (15), MatchLine (16), MatchLineLabeling (17), MaterialSection (18), MaterialTable (19), Parcel (20), ParcelLabeling (21), ParcelSegment (22), ParcelSegmentLabeling (23), ParcelTable (24), Pipe (25), PipeAndStructureTable (27), PipeLabeling (26), PipeNetworkSection (28), PipeOrStructureProfile (29), PointTable (30), PressureNetworkSection (63), PressurePartProfile (64), PressurePartTable (65), PressurePipe (66), PressurePipeLabeling (67), Profile (31), ProfileLabeling (32), ProfileView (33), ProfileViewLabeling (34), SampleLine (35), SampleLineLabeling (36), Section (37), SectionLabeling (38), SectionView (39), SectionViewLabeling (40), SectionViewQuantityTakeoffTable (41), Sheet (42), Structure (43), StructureLabeling (44), Subassembly (45), SuperelevationView (68), SurfaceLegendTable (46), SurveyFigure (47), SurveyFigureLabeling (69), SurveyFigureSegmentLable (70), SurveyNetwork (48), TinSurface (49), TinSurfaceLabeling (50), ViewFrame (51), ViewFrameLabeling (52) """ Alignment = None AlignmentLabeling = None AlignmentTable = None Appurtenance = None AppurtenanceLabeling = None Assembly = None BuildingSite = None CantView = None Catchment = None CatchmentLabeling = None Corridor = None CorridorSection = None FeatureLine = None Fitting = None FittingLabeling = None GeneralNoteLabel = None GeneralSegmentLabel = None Grading = None GradingLabeling = None GridSurface = None GridSurfaceLabeling = None Interference = None Intersection = None IntersectionLabeling = None MassHaulLine = None MassHaulView = None MatchLine = None MatchLineLabeling = None MaterialSection = None MaterialTable = None Parcel = None ParcelLabeling = None ParcelSegment = None ParcelSegmentLabeling = None ParcelTable = None Pipe = None PipeAndStructureTable = None PipeLabeling = None PipeNetworkSection = None PipeOrStructureProfile = None PointTable = None PressureNetworkSection = None PressurePartProfile = None PressurePartTable = None PressurePipe = None PressurePipeLabeling = None Profile = None ProfileLabeling = None ProfileView = None ProfileViewLabeling = None SampleLine = None SampleLineLabeling = None Section = None SectionLabeling = None SectionView = None SectionViewLabeling = None SectionViewQuantityTakeoffTable = None Sheet = None Structure = None StructureLabeling = None Subassembly = None SuperelevationView = None SurfaceLegendTable = None SurveyFigure = None SurveyFigureLabeling = None SurveyFigureSegmentLable = None SurveyNetwork = None TinSurface = None TinSurfaceLabeling = None value__ = None ViewFrame = None ViewFrameLabeling = None class SettingsPipe(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsPressureAppurtenance(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsPressureFitting(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsPressurePipe(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsRoot(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetSettings(self): # Error generating skeleton for function GetSettings: Method must be called on a Type for which Type.IsGenericParameter is false. AssociateShortcutProjectId = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AssociateShortcutProjectId(self: SettingsRoot) -> str Set: AssociateShortcutProjectId(self: SettingsRoot) = value """ DrawingSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DrawingSettings(self: SettingsRoot) -> SettingsDrawing """ LandXMLSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: LandXMLSettings(self: SettingsRoot) -> SettingsLandXML """ TagSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: TagSettings(self: SettingsRoot) -> SettingsTag """ class SettingsSection(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: NameFormat(self: SettingsSection) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Styles(self: SettingsSection) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsStructure(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsTag(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Creation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Creation(self: SettingsTag) -> SettingsCreation """ Renumbering = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: Renumbering(self: SettingsTag) -> SettingsRenumbering """ SettingsCreation = None SettingsRenumbering = None class SettingsTransformation(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ApplySeaLevelScaleFactor = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ApplySeaLevelScaleFactor(self: SettingsTransformation) -> bool Set: ApplySeaLevelScaleFactor(self: SettingsTransformation) = value """ GridReferencePoint = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GridReferencePoint(self: SettingsTransformation) -> Point2d Set: GridReferencePoint(self: SettingsTransformation) = value """ GridRotationPoint = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GridRotationPoint(self: SettingsTransformation) -> Point2d Set: GridRotationPoint(self: SettingsTransformation) = value """ GridScaleFactor = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GridScaleFactor(self: SettingsTransformation) -> float Set: GridScaleFactor(self: SettingsTransformation) = value """ GridScaleFactorComputation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: GridScaleFactorComputation(self: SettingsTransformation) -> GridScaleFactorType Set: GridScaleFactorComputation(self: SettingsTransformation) = value """ LocalReferencePoint = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: LocalReferencePoint(self: SettingsTransformation) -> Point2d Set: LocalReferencePoint(self: SettingsTransformation) = value """ LocalRotationPoint = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: LocalRotationPoint(self: SettingsTransformation) -> Point2d Set: LocalRotationPoint(self: SettingsTransformation) = value """ RotationToGridAzimuth = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RotationToGridAzimuth(self: SettingsTransformation) -> float Set: RotationToGridAzimuth(self: SettingsTransformation) = value """ RotationToGridNorth = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: RotationToGridNorth(self: SettingsTransformation) -> float Set: RotationToGridNorth(self: SettingsTransformation) = value """ SeaLevelScaleElevation = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SeaLevelScaleElevation(self: SettingsTransformation) -> float Set: SeaLevelScaleElevation(self: SettingsTransformation) = value """ SpecifyRotationType = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SpecifyRotationType(self: SettingsTransformation) -> SpecifyRotationType Set: SpecifyRotationType(self: SettingsTransformation) = value """ SpheroidRadius = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: SpheroidRadius(self: SettingsTransformation) -> float """ class SettingsUnitZone(TreeOidWrapper): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass @staticmethod def GetAllCodes(): """ GetAllCodes() -> Array[str] """ pass @staticmethod def GetCoordinateSystemByCode(code): """ GetCoordinateSystemByCode(code: str) -> SettingsCoordinateSystem """ pass AngularUnits = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: AngularUnits(self: SettingsUnitZone) -> AngleUnitType Set: AngularUnits(self: SettingsUnitZone) = value """ CoordinateSystemCode = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: CoordinateSystemCode(self: SettingsUnitZone) -> str Set: CoordinateSystemCode(self: SettingsUnitZone) = value """ DrawingScale = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DrawingScale(self: SettingsUnitZone) -> float Set: DrawingScale(self: SettingsUnitZone) = value """ DrawingUnits = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: DrawingUnits(self: SettingsUnitZone) -> DrawingUnitType Set: DrawingUnits(self: SettingsUnitZone) = value """ ImperialToMetricConversion = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ImperialToMetricConversion(self: SettingsUnitZone) -> ImperialToMetricConversionType Set: ImperialToMetricConversion(self: SettingsUnitZone) = value """ MatchAutoCADVariables = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: MatchAutoCADVariables(self: SettingsUnitZone) -> bool Set: MatchAutoCADVariables(self: SettingsUnitZone) = value """ ScaleObjectsFromOtherDrawings = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Get: ScaleObjectsFromOtherDrawings(self: SettingsUnitZone) -> bool Set: ScaleObjectsFromOtherDrawings(self: SettingsUnitZone) = value """ class SettingsViewFrame(SettingsAmbient): # no doc def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SpecifyRotationType(Enum): """ enum SpecifyRotationType, values: GridRotationAngle (1), RotationPoint (0) """ GridRotationAngle = None RotationPoint = None value__ = None class TableAnchorType(Enum): """ enum TableAnchorType, values: BottomCenter (7), BottomLeft (6), BottomRight (8), MiddleCenter (4), MiddleLeft (3), MiddleRight (5), TopCenter (1), TopLeft (0), TopRight (2) """ BottomCenter = None BottomLeft = None BottomRight = None MiddleCenter = None MiddleLeft = None MiddleRight = None TopCenter = None TopLeft = None TopRight = None value__ = None class TableLayoutType(Enum): """ enum TableLayoutType, values: Horizontal (0), Vertical (1) """ Horizontal = None value__ = None Vertical = None class TileDirectionType(Enum): """ enum TileDirectionType, values: Across (0), Down (1) """ Across = None Down = None value__ = None

32.170963

1,533

0.679776

class AbbreviationAlignmentEnhancedType(Enum): """ enum AbbreviationAlignmentEnhancedType, values: AlignmentBeginningPoint (402706556), AlignmentEndPoint (402706557), CompoundSpiralLargeRadiusAtBeginning (402706566), CompoundSpiralLargeRadiusAtEnd (402706567), CompoundSpiralSmallRadiusAtBeginning (402706568), CompoundSpiralSmallRadiusAtEnd (402706569), CurveBeginning (402706560), CurveEnd (402706561), LineBeginning (402706558), LineEnd (402706559), SimpleSpiralLargeRadiusAtBeginning (402706562), SimpleSpiralLargeRadiusAtEnd (402706563), SimpleSpiralSmallRadiusAtBeginning (402706564), SimpleSpiralSmallRadiusAtEnd (402706565) """ AlignmentBeginningPoint = None AlignmentEndPoint = None CompoundSpiralLargeRadiusAtBeginning = None CompoundSpiralLargeRadiusAtEnd = None CompoundSpiralSmallRadiusAtBeginning = None CompoundSpiralSmallRadiusAtEnd = None CurveBeginning = None CurveEnd = None LineBeginning = None LineEnd = None SimpleSpiralLargeRadiusAtBeginning = None SimpleSpiralLargeRadiusAtEnd = None SimpleSpiralSmallRadiusAtBeginning = None SimpleSpiralSmallRadiusAtEnd = None value__ = None class AbbreviationAlignmentType(Enum): """ enum AbbreviationAlignmentType, values: AlignmentBeginning (67162235), AlignmentEnd (67162234), CompoundCurveCurveIntersect (67162197), CurveSpiralIntersect (67162201), CurveTangentIntersect (67162196), MidCurvePoint (67162254), ReverseCurveCurveIntersect (67162198), ReverseSpiralIntersect (67162204), SpiralCurveIntersect (67162202), SpiralSpiralIntersect (67162203), SpiralTangentIntersect (67162200), StationEquationDecreasing (67162253), StationEquationIncreasing (67162252), TangentCurveIntersect (67162195), TangentSpiralIntersect (67162199), TangentTangentIntersect (67162194) """ AlignmentBeginning = None AlignmentEnd = None CompoundCurveCurveIntersect = None CurveSpiralIntersect = None CurveTangentIntersect = None MidCurvePoint = None ReverseCurveCurveIntersect = None ReverseSpiralIntersect = None SpiralCurveIntersect = None SpiralSpiralIntersect = None SpiralTangentIntersect = None StationEquationDecreasing = None StationEquationIncreasing = None TangentCurveIntersect = None TangentSpiralIntersect = None TangentTangentIntersect = None value__ = None class AbbreviationCantType(Enum): """ enum AbbreviationCantType, values: BeginAlignment (67163513), BeginFullCant (67163510), BeginLevelRail (67163509), EndAlignment (67163514), EndFullCant (67163511), EndLevelRail (67163508), Manual (67163512) """ BeginAlignment = None BeginFullCant = None BeginLevelRail = None EndAlignment = None EndFullCant = None EndLevelRail = None Manual = None value__ = None class AbbreviationProfileType(Enum): """ enum AbbreviationProfileType, values: BeginVerticalCurve (67173890), BeginVerticalCurveElevation (67173892), BeginVerticalCurveStation (67173891), CurveCoefficient (67173898), EndVerticalCurve (67173893), EndVerticalCurveElevation (67173895), EndVerticalCurveStation (67173894), GradeBreak (67173889), GradeChange (67173899), HighPoint (67173896), LowPoint (67173897), OverallHighPoint (67173909), OverallLowPoint (67173910), PointOfVerticalIntersection (67173888), ProfileEnd (67173902), ProfileStart (67173901), VerticalCompoundCurveIntersect (67173903), VerticalCompoundCurveIntersectElevation (67173906), VerticalCompoundCurveIntersectStation (67173905), VerticalReverseCurveIntersect (67173904), VerticalReverseCurveIntersectElevation (67173908), VerticalReverseCurveIntersectStation (67173907) """ BeginVerticalCurve = None BeginVerticalCurveElevation = None BeginVerticalCurveStation = None CurveCoefficient = None EndVerticalCurve = None EndVerticalCurveElevation = None EndVerticalCurveStation = None GradeBreak = None GradeChange = None HighPoint = None LowPoint = None OverallHighPoint = None OverallLowPoint = None PointOfVerticalIntersection = None ProfileEnd = None ProfileStart = None value__ = None VerticalCompoundCurveIntersect = None VerticalCompoundCurveIntersectElevation = None VerticalCompoundCurveIntersectStation = None VerticalReverseCurveIntersect = None VerticalReverseCurveIntersectElevation = None VerticalReverseCurveIntersectStation = None class AbbreviationSuperelevationType(Enum): """ enum AbbreviationSuperelevationType, values: BeginFullSuper (67163478), BeginNormalCrown (67163476), BeginNormalShoulder (67163480), BeginOfAlignment (67163474), BeginShoulderRollover (67163506), EndFullSuper (67163479), EndNormalCrown (67163477), EndNormalShoulder (67163481), EndOfAlignment (67163475), EndShoulderRollover (67163507), LevelCrown (67163482), LowShoulderMatch (67163483), Manual (67163486), ReverseCrown (67163484), ShoulderBreakover (67163485) """ BeginFullSuper = None BeginNormalCrown = None BeginNormalShoulder = None BeginOfAlignment = None BeginShoulderRollover = None EndFullSuper = None EndNormalCrown = None EndNormalShoulder = None EndOfAlignment = None EndShoulderRollover = None LevelCrown = None LowShoulderMatch = None Manual = None ReverseCrown = None ShoulderBreakover = None value__ = None class AutomaticManual(Enum): """ enum AutomaticManual, values: Automatic (0), AutomaticObject (1), Manual (2), None (3) """ Automatic = None AutomaticObject = None Manual = None None = None value__ = None class DrawingUnitType(Enum): """ enum DrawingUnitType, values: Feet (30), Meters (2) """ Feet = None Meters = None value__ = None class GeographicCoordinateType(Enum): """ enum GeographicCoordinateType, values: LatLong (0), LongLat (1) """ LatLong = None LongLat = None value__ = None class GridCoordinateType(Enum): """ enum GridCoordinateType, values: EastingNorthing (0), NorthingEasting (1) """ EastingNorthing = None NorthingEasting = None value__ = None class GridScaleFactorType(Enum): """ enum GridScaleFactorType, values: PrismodialFormula (3), ReferencePoint (2), Unity (0), UserDefined (1) """ PrismodialFormula = None ReferencePoint = None Unity = None UserDefined = None value__ = None class ImperialToMetricConversionType(Enum): """ enum ImperialToMetricConversionType, values: InternationalFoot (536870912), UsSurveyFoot (1073741824) """ InternationalFoot = None UsSurveyFoot = None value__ = None class LandXMLAngularUnits(Enum): """ enum LandXMLAngularUnits, values: DegreesDecimal (0), DegreesDms (1), Grads (2), Radians (3) """ DegreesDecimal = None DegreesDms = None Grads = None Radians = None value__ = None class LandXMLAttributeExportType(Enum): """ enum LandXMLAttributeExportType, values: Disabled (0), FullDescription (2), RawDescription (1) """ Disabled = None FullDescription = None RawDescription = None value__ = None class LandXMLConflictResolutionType(Enum): """ enum LandXMLConflictResolutionType, values: Append (2), Skip (0), Update (1) """ Append = None Skip = None Update = None value__ = None class LandXMLImperialUnitType(Enum): """ enum LandXMLImperialUnitType, values: Foot (30), Inch (31), Mile (44), Yard (33) """ Foot = None Inch = None Mile = None value__ = None Yard = None class LandXMLLinearUnits(Enum): """ enum LandXMLLinearUnits, values: InternationalFoot (30), SurveyFoot (54) """ InternationalFoot = None SurveyFoot = None value__ = None class LandXMLMetricUnitType(Enum): """ enum LandXMLMetricUnitType, values: CentiMeter (24), DeciMeter (23), KiloMeter (20), Meter (2), MilliMeter (25) """ CentiMeter = None DeciMeter = None KiloMeter = None Meter = None MilliMeter = None value__ = None class LandXMLPointDescriptionType(Enum): """ enum LandXMLPointDescriptionType, values: UseCodeThenDesc (2), UseCodeValue (0), UseDescThenCode (3), UseDescValue (1) """ UseCodeThenDesc = None UseCodeValue = None UseDescThenCode = None UseDescValue = None value__ = None class LandXMLSurfaceDataExportType(Enum): """ enum LandXMLSurfaceDataExportType, values: PointsAndFaces (1), PointsOnly (0) """ PointsAndFaces = None PointsOnly = None value__ = None class LandXMLSurfaceDataImportType(Enum): """ enum LandXMLSurfaceDataImportType, values: FullImport (1), QuickImport (0) """ FullImport = None QuickImport = None value__ = None class LocalCoordinateType(Enum): """ enum LocalCoordinateType, values: EastingNorthing (0), NorthingEasting (1), XY (2), YX (3) """ EastingNorthing = None NorthingEasting = None value__ = None XY = None YX = None class MapcheckAngleType(Enum): """ enum MapcheckAngleType, values: Angle (1), DeflectionAngle (2), Direction (0) """ Angle = None DeflectionAngle = None Direction = None value__ = None class MapcheckCurveDirectionType(Enum): """ enum MapcheckCurveDirectionType, values: Clockwise (0), CounterClockwise (1) """ Clockwise = None CounterClockwise = None value__ = None class MapcheckSideType(Enum): """ enum MapcheckSideType, values: Curve (1), Line (0) """ Curve = None Line = None value__ = None class MapcheckTraverseMethodType(Enum): """ enum MapcheckTraverseMethodType, values: AcrossChord (0), ThroughRadius (1) """ AcrossChord = None ThroughRadius = None value__ = None class ObjectLayerModifierType(Enum): """ enum ObjectLayerModifierType, values: None (0), Prefix (1), Suffix (2) """ None = None Prefix = None Suffix = None value__ = None class SectionViewAnchorType(Enum): """ enum SectionViewAnchorType, values: BottomCenter (7), BottomLeft (6), BottomRight (8), MiddleCenter (4), MiddleLeft (3), MiddleRight (5), TopCenter (1), TopLeft (0), TopRight (2) """ BottomCenter = None BottomLeft = None BottomRight = None MiddleCenter = None MiddleLeft = None MiddleRight = None TopCenter = None TopLeft = None TopRight = None value__ = None class SettingsAbbreviation(CivilWrapper<AcDbDatabase>): def Dispose(self): """ Dispose(self: CivilWrapper<AcDbDatabase>, A_0: bool) """ pass AlignmentGeoPointEntityData = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AlignmentGeoPointEntityData(self: SettingsAbbreviation) -> SettingsAbbreviationAlignmentEnhanced """ AlignmentGeoPointText = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AlignmentGeoPointText(self: SettingsAbbreviation) -> SettingsAbbreviationAlignment """ Cant = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Cant(self: SettingsAbbreviation) -> SettingsAbbreviationCant """ GeneralText = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GeneralText(self: SettingsAbbreviation) -> SettingsAbbreviationGeneral """ Profile = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Profile(self: SettingsAbbreviation) -> SettingsAbbreviationProfile """ Superelevation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Superelevation(self: SettingsAbbreviation) -> SettingsAbbreviationSuperelevation """ class SettingsAbbreviationAlignment(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetAlignmentAbbreviation(self, type): """ GetAlignmentAbbreviation(self: SettingsAbbreviationAlignment, type: AbbreviationAlignmentType) -> str """ pass def SetAlignmentAbbreviation(self, type, value): """ SetAlignmentAbbreviation(self: SettingsAbbreviationAlignment, type: AbbreviationAlignmentType, value: str) """ pass class SettingsAbbreviationAlignmentEnhanced(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetAlignmentEnhancedAbbreviation(self, type): """ GetAlignmentEnhancedAbbreviation(self: SettingsAbbreviationAlignmentEnhanced, type: AbbreviationAlignmentEnhancedType) -> str """ pass def SetAlignmentEnhancedAbbreviation(self, type, newValue): """ SetAlignmentEnhancedAbbreviation(self: SettingsAbbreviationAlignmentEnhanced, type: AbbreviationAlignmentEnhancedType, newValue: str) """ pass class SettingsAbbreviationCant(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetCantAbbreviation(self, type): """ GetCantAbbreviation(self: SettingsAbbreviationCant, type: AbbreviationCantType) -> str """ pass def SetCantAbbreviation(self, type, newValue): """ SetCantAbbreviation(self: SettingsAbbreviationCant, type: AbbreviationCantType, newValue: str) """ pass class SettingsAbbreviationGeneral(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Infinity = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Infinity(self: SettingsAbbreviationGeneral) -> str Set: Infinity(self: SettingsAbbreviationGeneral) = value """ Left = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Left(self: SettingsAbbreviationGeneral) -> str Set: Left(self: SettingsAbbreviationGeneral) = value """ Right = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Right(self: SettingsAbbreviationGeneral) -> str Set: Right(self: SettingsAbbreviationGeneral) = value """ class SettingsAbbreviationProfile(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetProfileAbbreviation(self, type): """ GetProfileAbbreviation(self: SettingsAbbreviationProfile, type: AbbreviationProfileType) -> str """ pass def SetProfileAbbreviation(self, type, newValue): """ SetProfileAbbreviation(self: SettingsAbbreviationProfile, type: AbbreviationProfileType, newValue: str) """ pass class SettingsAbbreviationSuperelevation(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetSuperelevationAbbreviation(self, type): """ GetSuperelevationAbbreviation(self: SettingsAbbreviationSuperelevation, type: AbbreviationSuperelevationType) -> str """ pass def SetSuperelevationAbbreviation(self, type, newValue): """ SetSuperelevationAbbreviation(self: SettingsAbbreviationSuperelevation, type: AbbreviationSuperelevationType, newValue: str) """ pass class SettingsAmbient(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass @staticmethod def __new__(self, *args): """ __new__(cls: type, root: SettingsRoot, path: str) """ pass Acceleration = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Acceleration(self: SettingsAmbient) -> SettingsAcceleration """ Angle = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Angle(self: SettingsAmbient) -> SettingsAngle """ Area = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Area(self: SettingsAmbient) -> SettingsArea """ Coordinate = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Coordinate(self: SettingsAmbient) -> SettingsCoordinate """ DegreeOfCurvature = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DegreeOfCurvature(self: SettingsAmbient) -> SettingsDegreeOfCurvature """ Dimension = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Dimension(self: SettingsAmbient) -> SettingsDimension """ Direction = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Direction(self: SettingsAmbient) -> SettingsDirection """ Distance = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Distance(self: SettingsAmbient) -> SettingsDistance """ Elevation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Elevation(self: SettingsAmbient) -> SettingsElevation """ General = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: General(self: SettingsAmbient) -> SettingsGeneral """ Grade = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Grade(self: SettingsAmbient) -> SettingsGrade """ GradeSlope = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GradeSlope(self: SettingsAmbient) -> SettingsGradeSlope """ GridCoordinate = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GridCoordinate(self: SettingsAmbient) -> SettingsGridCoordinate """ Labeling = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Labeling(self: SettingsAmbient) -> SettingsLabeling """ LatLong = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: LatLong(self: SettingsAmbient) -> SettingsLatLong """ Pressure = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Pressure(self: SettingsAmbient) -> SettingsPressure """ Slope = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Slope(self: SettingsAmbient) -> SettingsSlope """ Speed = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Speed(self: SettingsAmbient) -> SettingsSpeed """ Station = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Station(self: SettingsAmbient) -> SettingsStation """ Time = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Time(self: SettingsAmbient) -> SettingsTime """ TransparentCommands = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TransparentCommands(self: SettingsAmbient) -> SettingsTransparentCommands """ Unitless = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Unitless(self: SettingsAmbient) -> SettingsUnitless """ Volume = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Volume(self: SettingsAmbient) -> SettingsVolume """ SettingsAcceleration = None SettingsAngle = None SettingsArea = None SettingsCoordinate = None SettingsDegreeOfCurvature = None SettingsDimension = None SettingsDirection = None SettingsDistance = None SettingsElevation = None SettingsFormatNumber`1 = None SettingsGeneral = None SettingsGrade = None SettingsGradeSlope = None SettingsGridCoordinate = None SettingsLabeling = None SettingsLatLong = None SettingsPressure = None SettingsSlope = None SettingsSpeed = None SettingsStation = None SettingsTime = None SettingsTransparentCommands = None SettingsUnitFormatNumber`2 = None SettingsUnitless = None SettingsUnitlessNumber = None SettingsUnitNumber`1 = None SettingsVolume = None class SettingsAlignment(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AutomaticWideningAroundCurves = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AutomaticWideningAroundCurves(self: SettingsAlignment) -> SettingsAutomaticWideningAroundCurves """ CantOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CantOptions(self: SettingsAlignment) -> SettingsCantOptions """ ConstraintEditing = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ConstraintEditing(self: SettingsAlignment) -> SettingsConstraintEditing """ CriteriaBasedDesignOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CriteriaBasedDesignOptions(self: SettingsAlignment) -> SettingsCriteriaBasedDesignOptions """ Data = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Data(self: SettingsAlignment) -> SettingsData """ DefaultNameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DefaultNameFormat(self: SettingsAlignment) -> SettingsDefaultNameFormat """ DynamicAlignmentHighlight = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DynamicAlignmentHighlight(self: SettingsAlignment) -> SettingsDynamicAlignmentHighlight """ ImpliedPointOfIntersection = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ImpliedPointOfIntersection(self: SettingsAlignment) -> SettingsImpliedPointOfIntersection """ RailOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RailOptions(self: SettingsAlignment) -> SettingsRailAlignmentOptions """ StationIndexing = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: StationIndexing(self: SettingsAlignment) -> SettingsStationIndexing """ StyleSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: StyleSettings(self: SettingsAlignment) -> SettingsStyles """ SuperelevationOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SuperelevationOptions(self: SettingsAlignment) -> SettingsSuperelevationOptions """ SettingsAutomaticWideningAroundCurves = None SettingsCantOptions = None SettingsConstraintEditing = None SettingsCriteriaBasedDesignOptions = None SettingsData = None SettingsDefaultNameFormat = None SettingsDynamicAlignmentHighlight = None SettingsImpliedPointOfIntersection = None SettingsRailAlignmentOptions = None SettingsStationIndexing = None SettingsStyles = None SettingsSuperelevationOptions = None class SettingsAssembly(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsAssembly) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsAssembly) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsBuildingSite(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsBuildingSite) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsBuildingSite) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCantView(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsCantView) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsCantView) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCatchment(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameTemplate = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameTemplate(self: SettingsCatchment) -> PropertyString """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsCatchment) -> SettingsStyles """ SettingsStyles = None class SettingsCmdAddAlignmentCurveTable(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddAlignmentCurveTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddAlignmentLineTable(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddAlignmentLineTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddAlignmentOffLbl(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignmentOffXYLbl(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignmentSegmentTable(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddAlignmentSegmentTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddAlignmentSpiralTable(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddAlignmentSpiralTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddAlignPointOfIntLbl(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignPointOfIntLbls(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignSegLbl(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignSegLbls(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignTagentLbl(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddAlignTagentLbls(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsPressureNetwork(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Cover = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Cover(self: SettingsPressureNetwork) -> SettingsDepthOfCover """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsPressureNetwork) -> SettingsNameFormat """ ProfileLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ProfileLabelPlacement(self: SettingsPressureNetwork) -> SettingsProfileLabelPlacement """ SectionLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SectionLabelPlacement(self: SettingsPressureNetwork) -> SettingsSectionLabelPlacement """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsPressureNetwork) -> SettingsStyles """ SettingsDepthOfCover = None SettingsNameFormat = None SettingsProfileLabelPlacement = None SettingsSectionLabelPlacement = None SettingsStyles = None class SettingsCmdAddAppurtTable(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddAppurtTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsSurface(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ContourLabeling = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ContourLabeling(self: SettingsSurface) -> SettingsContourLabeling """ Defaults = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Defaults(self: SettingsSurface) -> SettingsDefaults """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsSurface) -> SettingsStyles """ SettingsContourLabeling = None SettingsDefaults = None SettingsStyles = None class SettingsCmdAddContourLabeling(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddContourLabelingGroup(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AddContourLabeling = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AddContourLabeling(self: SettingsCmdAddContourLabelingGroup) -> SettingsCmdAddContourLabeling """ SettingsCmdAddContourLabeling = None class SettingsCmdAddContourLabelingSingle(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddFittingTable(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddFittingTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsIntersection(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsIntersection) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsIntersection) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCmdAddIntersectionLabel(SettingsIntersection): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsGeneral(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsGeneral) -> SettingsStyles """ SettingsStyles = None class SettingsCmdAddLineBetweenPoints(SettingsGeneral): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsQuantityTakeoff(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsQuantityTakeoff) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsQuantityTakeoff) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCmdAddMaterialVolumeTable(SettingsQuantityTakeoff): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddMaterialVolumeTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsPipeNetwork(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Default = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Default(self: SettingsPipeNetwork) -> SettingsDefault """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsPipeNetwork) -> SettingsNameFormat """ ProfileLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ProfileLabelPlacement(self: SettingsPipeNetwork) -> SettingsProfileLabelPlacement """ Rules = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Rules(self: SettingsPipeNetwork) -> SettingsRules """ SectionLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SectionLabelPlacement(self: SettingsPipeNetwork) -> SettingsSectionLabelPlacement """ StormSewersMigration = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: StormSewersMigration(self: SettingsPipeNetwork) -> SettingsStormSewersMigration """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsPipeNetwork) -> SettingsStyles """ SettingsDefault = None SettingsNameFormat = None SettingsProfileLabelPlacement = None SettingsRules = None SettingsSectionLabelPlacement = None SettingsStormSewersMigration = None SettingsStyles = None class SettingsCmdAddNetworkPartPlanLabel(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkPartProfLabel(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkPartSectLabel(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkPartsToProf(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkPipeTable(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddNetworkPipeTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddNetworkPlanLabels(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkProfLabels(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkSectLabels(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddNetworkStructTable(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddNetworkStructTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddNoteLabel(SettingsGeneral): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsParcel(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsParcel) -> SettingsStyles """ SettingsStyles = None class SettingsCmdAddParcelAreaLabel(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddParcelCurveTable(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddParcelCurveTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddParcelLineLabel(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddParcelLineTable(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddParcelLineTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddParcelSegmentLabels(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Options = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Options(self: SettingsCmdAddParcelSegmentLabels) -> SettingsCmdOptions """ SettingsCmdOptions = None class SettingsCmdAddParcelSegmentTable(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddParcelSegmentTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddParcelTable(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddParcelTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsPointCloud(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefaultNameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DefaultNameFormat(self: SettingsPointCloud) -> SettingsDefaultNameFormat """ StyleSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: StyleSettings(self: SettingsPointCloud) -> SettingsStyles """ SettingsDefaultNameFormat = None SettingsStyles = None class SettingsCmdAddPointCloudPoints(SettingsPointCloud): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefaultFileFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DefaultFileFormat(self: SettingsCmdAddPointCloudPoints) -> PropertyEnum[PointCloudDefaultFileExtensionType] """ class SettingsCmdAddPointsToSurface(SettingsPointCloud): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MidOrdinateDistance = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: MidOrdinateDistance(self: SettingsCmdAddPointsToSurface) -> PropertyDouble """ RegionOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RegionOption(self: SettingsCmdAddPointsToSurface) -> PropertyEnum[PointCloudRegionType] """ SurfaceOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SurfaceOption(self: SettingsCmdAddPointsToSurface) -> PropertyEnum[PointCloudSurfaceType] """ class SettingsPoint(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsPoint) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsPoint) -> SettingsStyles """ UpdatePoints = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: UpdatePoints(self: SettingsPoint) -> SettingsUpdatePoints """ SettingsNameFormat = None SettingsStyles = None SettingsUpdatePoints = None class SettingsCmdAddPointTable(SettingsPoint): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddPointTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddPressurePartPlanLabel(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddPressurePartProfLabel(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddPressurePartsToProf(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddPressurePipeTable(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddPressurePipeTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddPressurePlanLabels(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddPressureProfLabels(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsProfileView(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Creation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Creation(self: SettingsProfileView) -> SettingsCreation """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsProfileView) -> SettingsNameFormat """ ProjectionLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ProjectionLabelPlacement(self: SettingsProfileView) -> SettingsProjectionLabelPlacement """ SplitOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SplitOptions(self: SettingsProfileView) -> SettingsSplitOptions """ StackedOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: StackedOptions(self: SettingsProfileView) -> SettingsStackedOptions """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsProfileView) -> SettingsStyles """ SettingsCreation = None SettingsNameFormat = None SettingsProjectionLabelPlacement = None SettingsSplitOptions = None SettingsStackedOptions = None SettingsStyles = None class SettingsCmdAddProfileViewDepthLbl(SettingsProfileView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddProfileViewStaElevLbl(SettingsProfileView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsSectionView(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsSectionView) -> SettingsNameFormat """ ProjectionLabelPlacement = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ProjectionLabelPlacement(self: SettingsSectionView) -> SettingsProjectionLabelPlacement """ SectionViewCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SectionViewCreation(self: SettingsSectionView) -> SettingsSectionViewCreation """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsSectionView) -> SettingsStyles """ SettingsNameFormat = None SettingsProjectionLabelPlacement = None SettingsSectionViewCreation = None SettingsStyles = None class SettingsCmdAddSectionViewGradeLbl(SettingsSectionView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSectionViewOffElevLbl(SettingsSectionView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSegmentLabel(SettingsGeneral): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSegmentLabels(SettingsGeneral): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSpanningPipePlanLabel(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSpanningPipeProfLabel(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSpotElevLabelsOnGrid(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSurfaceBoundaries(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DataOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DataOptions(self: SettingsCmdAddSurfaceBoundaries) -> SettingsCmdAddDataOptions """ SettingsCmdAddDataOptions = None class SettingsCmdAddSurfaceBreaklines(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DataOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DataOptions(self: SettingsCmdAddSurfaceBreaklines) -> SettingsCmdAddDataOptions """ SettingsCmdAddDataOptions = None class SettingsCmdAddSurfaceContours(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AddDataOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AddDataOptions(self: SettingsCmdAddSurfaceContours) -> SettingsCmdAddDataOptions """ SettingsCmdAddDataOptions = None class SettingsCmdAddSurfaceDemFile(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ImportOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ImportOptions(self: SettingsCmdAddSurfaceDemFile) -> SettingsCmdImportOptions """ SettingsCmdImportOptions = None class SettingsCmdAddSurfaceDrawingObjects(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DataOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DataOptions(self: SettingsCmdAddSurfaceDrawingObjects) -> SettingsCmdAddDataOptions """ SettingsCmdAddDataOptions = None class SettingsCmdAddSurfaceFigSurveyQuery(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DataOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DataOptions(self: SettingsCmdAddSurfaceFigSurveyQuery) -> SettingsCmdAddDataOptions """ SettingsCmdAddDataOptions = None class SettingsCmdAddSurfacePointSurveyQuery(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSurfaceSlopeLabel(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSurfaceSpotElevLabel(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsSurvey(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsSurvey) -> SettingsStyles """ SettingsStyles = None class SettingsCmdAddSvFigureLabel(SettingsSurvey): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSvFigureSegmentLabel(SettingsSurvey): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddSvFigureSegmentLabels(SettingsSurvey): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdAddTotalVolumeTable(SettingsQuantityTakeoff): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdAddTotalVolumeTable) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsCmdAddWidening(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass LinearTransitionAroundCurves = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: LinearTransitionAroundCurves(self: SettingsCmdAddWidening) -> SettingsCmdLinearTransitionAroundCurves """ WideningOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: WideningOptions(self: SettingsCmdAddWidening) -> SettingsCmdWideningOptions """ SettingsCmdLinearTransitionAroundCurves = None SettingsCmdWideningOptions = None class SettingsCmdAssignPayItemToArea(SettingsQuantityTakeoff): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AssignPayItemToAreaOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AssignPayItemToAreaOption(self: SettingsCmdAssignPayItemToArea) -> SettingsCmdAssignPayItemToAreaOptions """ SettingsCmdAssignPayItemToAreaOptions = None class SettingsCmdCatchmentArea(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DischargePointStyle = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DischargePointStyle(self: SettingsCmdCatchmentArea) -> PropertyString """ DischargePointStyleId = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DischargePointStyleId(self: SettingsCmdCatchmentArea) -> PropertyObjectId """ DisplayDisChargePoint = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DisplayDisChargePoint(self: SettingsCmdCatchmentArea) -> PropertyBoolean """ Layer = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Layer(self: SettingsCmdCatchmentArea) -> PropertyLayer """ ObjectType = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ObjectType(self: SettingsCmdCatchmentArea) -> PropertyEnum[CatchmentObjectType] """ class SettingsCmdComputeMaterials(SettingsQuantityTakeoff): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefineMaterialOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DefineMaterialOption(self: SettingsCmdComputeMaterials) -> SettingsCmdDefineMaterial """ SettingsCmdDefineMaterial = None class SettingsCmdConvertPointstoSdskPoints(SettingsPoint): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Layer = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Layer(self: SettingsCmdConvertPointstoSdskPoints) -> SettingsCmdLayer """ SettingsCmdLayer = None class SettingsCorridor(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsCorridor) -> SettingsNameFormat """ RegionHighlightGraphics = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RegionHighlightGraphics(self: SettingsCorridor) -> SettingsRegionHighlightGraphics """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsCorridor) -> SettingsStyles """ SettingsNameFormat = None SettingsRegionHighlightGraphics = None SettingsStyles = None class SettingsCmdCorridorExtractSurfaces(SettingsCorridor): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateAlignFromCorridor(SettingsCorridor): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentTypeOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AlignmentTypeOption(self: SettingsCmdCreateAlignFromCorridor) -> SettingsCmdAlignmentTypeOption """ CriteriaBasedDesignOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CriteriaBasedDesignOptions(self: SettingsCmdCreateAlignFromCorridor) -> SettingsCmdCriteriaBasedDesignOptions """ ProfileCreationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ProfileCreationOption(self: SettingsCmdCreateAlignFromCorridor) -> SettingsCmdProfileCreationOption """ SettingsCmdAlignmentTypeOption = None SettingsCmdCriteriaBasedDesignOptions = None SettingsCmdProfileCreationOption = None class SettingsCmdCreateAlignFromNetwork(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentTypeOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AlignmentTypeOption(self: SettingsCmdCreateAlignFromNetwork) -> SettingsCmdAlignmentTypeOption """ SettingsCmdAlignmentTypeOption = None class SettingsCmdCreateAlignFromPressureNW(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentTypeOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AlignmentTypeOption(self: SettingsCmdCreateAlignFromPressureNW) -> SettingsCmdAlignmentTypeOption """ SettingsCmdAlignmentTypeOption = None class SettingsCmdCreateAlignmentEntities(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentTypeOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AlignmentTypeOption(self: SettingsCmdCreateAlignmentEntities) -> SettingsCmdAlignmentTypeOption """ CreateFromEntities = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CreateFromEntities(self: SettingsCmdCreateAlignmentEntities) -> SettingsCmdCreateFromEntities """ SettingsCmdAlignmentTypeOption = None SettingsCmdCreateFromEntities = None class SettingsCmdCreateAlignmentLayout(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentTypeOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AlignmentTypeOption(self: SettingsCmdCreateAlignmentLayout) -> SettingsCmdAlignmentTypeOption """ CurveAndSpiralSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CurveAndSpiralSettings(self: SettingsCmdCreateAlignmentLayout) -> SettingsCmdCurveAndSpiralSettings """ CurveTessellationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CurveTessellationOption(self: SettingsCmdCreateAlignmentLayout) -> SettingsCmdCurveTessellationOption """ RegressionGraphOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RegressionGraphOption(self: SettingsCmdCreateAlignmentLayout) -> SettingsCmdRegressionGraphOption """ SettingsCmdAlignmentTypeOption = None SettingsCmdCurveAndSpiralSettings = None SettingsCmdCurveTessellationOption = None SettingsCmdRegressionGraphOption = None class SettingsCmdCreateAlignmentReference(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateArcByBestFit(SettingsGeneral): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CurveTessellationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CurveTessellationOption(self: SettingsCmdCreateArcByBestFit) -> SettingsCmdCurveTessellationOption """ RegressionGraphOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RegressionGraphOption(self: SettingsCmdCreateArcByBestFit) -> SettingsCmdRegressionGraphOption """ SettingsCmdCurveTessellationOption = None SettingsCmdRegressionGraphOption = None class SettingsCmdCreateAssembly(SettingsAssembly): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateAssemblyTool(SettingsAssembly): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateCantView(SettingsCantView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateCatchmentFromObject(SettingsCatchment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Catchment = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Catchment(self: SettingsCmdCreateCatchmentFromObject) -> SettingsCmdCatchment """ ChannelFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ChannelFlow(self: SettingsCmdCreateCatchmentFromObject) -> SettingsCmdChannelFlow """ HydrologicalProperties = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: HydrologicalProperties(self: SettingsCmdCreateCatchmentFromObject) -> SettingsCmdHydrologicalProperties """ ShallowConcentratedFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ShallowConcentratedFlow(self: SettingsCmdCreateCatchmentFromObject) -> SettingsCmdShallowConcentratedFlow """ SheetFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SheetFlow(self: SettingsCmdCreateCatchmentFromObject) -> SettingsCmdSheetFlow """ TimeOfConcentrationMethod = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TimeOfConcentrationMethod(self: SettingsCmdCreateCatchmentFromObject) -> PropertyEnum[CatchmentTimeOfConcentrationMethodType] """ SettingsCmdCatchment = None SettingsCmdChannelFlow = None SettingsCmdHydrologicalProperties = None SettingsCmdShallowConcentratedFlow = None SettingsCmdSheetFlow = None class SettingsCmdCreateCatchmentFromSurface(SettingsCatchment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Catchment = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Catchment(self: SettingsCmdCreateCatchmentFromSurface) -> SettingsCmdCatchment """ ChannelFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ChannelFlow(self: SettingsCmdCreateCatchmentFromSurface) -> SettingsCmdChannelFlow """ HydrologicalProperties = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: HydrologicalProperties(self: SettingsCmdCreateCatchmentFromSurface) -> SettingsCmdHydrologicalProperties """ ShallowConcentratedFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ShallowConcentratedFlow(self: SettingsCmdCreateCatchmentFromSurface) -> SettingsCmdShallowConcentratedFlow """ SheetFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SheetFlow(self: SettingsCmdCreateCatchmentFromSurface) -> SettingsCmdSheetFlow """ TimeOfConcentrationMethod = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TimeOfConcentrationMethod(self: SettingsCmdCreateCatchmentFromSurface) -> PropertyEnum[CatchmentTimeOfConcentrationMethodType] """ SettingsCmdCatchment = None SettingsCmdChannelFlow = None SettingsCmdHydrologicalProperties = None SettingsCmdShallowConcentratedFlow = None SettingsCmdSheetFlow = None class SettingsCmdCreateCatchmentGroup(SettingsCatchment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateCorridor(SettingsCorridor): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AssemblyInsertion = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AssemblyInsertion(self: SettingsCmdCreateCorridor) -> SettingsCmdAssemblyInsertion """ SettingsCmdAssemblyInsertion = None class SettingsGrading(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsGrading) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsGrading) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCmdCreateFeatureLineFromAlign(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FeatureLineCreation(self: SettingsCmdCreateFeatureLineFromAlign) -> SettingsCmdFeatureLineCreation """ SettingsCmdFeatureLineCreation = None class SettingsCmdCreateFeatureLines(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FeatureLineCreation(self: SettingsCmdCreateFeatureLines) -> SettingsCmdFeatureLineCreation """ SettingsCmdFeatureLineCreation = None class SettingsCmdCreateFlowSegment(SettingsCatchment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ChannelFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ChannelFlow(self: SettingsCmdCreateFlowSegment) -> SettingsCmdChannelFlow """ ShallowConcentratedFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ShallowConcentratedFlow(self: SettingsCmdCreateFlowSegment) -> SettingsCmdShallowConcentratedFlow """ SheetFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SheetFlow(self: SettingsCmdCreateFlowSegment) -> SettingsCmdSheetFlow """ SettingsCmdChannelFlow = None SettingsCmdShallowConcentratedFlow = None SettingsCmdSheetFlow = None class SettingsCmdCreateGrading(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass GradingCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GradingCreation(self: SettingsCmdCreateGrading) -> SettingsCmdGradingCreation """ SettingsCmdGradingCreation = None class SettingsCmdCreateGradingGroup(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass GradingGroupCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GradingGroupCreation(self: SettingsCmdCreateGradingGroup) -> SettingsCmdGradingGroupCreation """ SettingsCmdGradingGroupCreation = None class SettingsCmdCreateInterferenceCheck(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass InterferenceCriteria = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: InterferenceCriteria(self: SettingsCmdCreateInterferenceCheck) -> SettingsCmdInterferenceCriteria """ SettingsCmdInterferenceCriteria = None class SettingsCmdCreateIntersection(SettingsIntersection): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AssemblyInsertion = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AssemblyInsertion(self: SettingsCmdCreateIntersection) -> SettingsCmdAssemblyInsertion """ CrossSlopes = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CrossSlopes(self: SettingsCmdCreateIntersection) -> SettingsCmdCrossSlopes """ CurbReturnParameters = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CurbReturnParameters(self: SettingsCmdCreateIntersection) -> SettingsCmdCurbReturnParameters """ CurbReturnProfileRules = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CurbReturnProfileRules(self: SettingsCmdCreateIntersection) -> SettingsCmdCurbReturnProfileRules """ IntersectionOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: IntersectionOptions(self: SettingsCmdCreateIntersection) -> SettingsCmdIntersectionOptions """ Offsets = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Offsets(self: SettingsCmdCreateIntersection) -> SettingsCmdOffsets """ SecondaryRoadProfileRules = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SecondaryRoadProfileRules(self: SettingsCmdCreateIntersection) -> SettingsCmdSecondaryRoadProfileRules """ WideningParameters = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: WideningParameters(self: SettingsCmdCreateIntersection) -> SettingsCmdWideningParameters """ SettingsCmdAssemblyInsertion = None SettingsCmdCrossSlopes = None SettingsCmdCurbReturnParameters = None SettingsCmdCurbReturnProfileRules = None SettingsCmdIntersectionOptions = None SettingsCmdOffsets = None SettingsCmdSecondaryRoadProfileRules = None SettingsCmdWideningParameters = None class SettingsCmdCreateLineByBestFit(SettingsGeneral): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CurveTessellationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CurveTessellationOption(self: SettingsCmdCreateLineByBestFit) -> SettingsCmdCurveTessellationOption """ RegressionGraphOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RegressionGraphOption(self: SettingsCmdCreateLineByBestFit) -> SettingsCmdRegressionGraphOption """ SettingsCmdCurveTessellationOption = None SettingsCmdRegressionGraphOption = None class SettingsMassHaulView(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MassHaulCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: MassHaulCreation(self: SettingsMassHaulView) -> SettingsMassHaulCreation """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsMassHaulView) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsMassHaulView) -> SettingsStyles """ SettingsMassHaulCreation = None SettingsNameFormat = None SettingsStyles = None class SettingsCmdCreateMassHaulDiagram(SettingsMassHaulView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MassHaulCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: MassHaulCreation(self: SettingsCmdCreateMassHaulDiagram) -> SettingsCmdMassHaulCreation """ SettingsCmdMassHaulCreation = None class SettingsCmdCreateMultipleProfileView(SettingsProfileView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MultipleProfileViewCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: MultipleProfileViewCreation(self: SettingsCmdCreateMultipleProfileView) -> SettingsCmdMultipleProfileViewCreation """ SettingsCmdMultipleProfileViewCreation = None class SettingsCmdCreateMultipleSectionView(SettingsSectionView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MultipleSectionViewCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: MultipleSectionViewCreation(self: SettingsCmdCreateMultipleSectionView) -> SettingsCmdMultipleSectionViewCreation """ TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdCreateMultipleSectionView) -> SettingsCmdTableCreation """ SettingsCmdMultipleSectionViewCreation = None SettingsCmdTableCreation = None class SettingsCmdCreateNetwork(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefaultLayoutCommand = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DefaultLayoutCommand(self: SettingsCmdCreateNetwork) -> PropertyEnum[NetworkDefaultLayoutCommandType] """ LabelNewParts = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: LabelNewParts(self: SettingsCmdCreateNetwork) -> SettingsCmdLabelNewParts """ SettingsCmdLabelNewParts = None class SettingsCmdCreateNetworkFromObject(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateNetworkPartsList(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateNetworkPartsListFull(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateNetworkReference(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateOffsetAlignment(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass OffsetAlignmentOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: OffsetAlignmentOptions(self: SettingsCmdCreateOffsetAlignment) -> SettingsCmdOffsetAlignmentOptions """ SettingsCmdOffsetAlignmentOptions = None class SettingsCmdCreateParabolaByBestFit(SettingsGeneral): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CurveTessellationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CurveTessellationOption(self: SettingsCmdCreateParabolaByBestFit) -> SettingsCmdCurveTessellationOption """ RegressionGraphOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RegressionGraphOption(self: SettingsCmdCreateParabolaByBestFit) -> SettingsCmdRegressionGraphOption """ SettingsCmdCurveTessellationOption = None SettingsCmdRegressionGraphOption = None class SettingsCmdCreateParcelByLayout(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AutomaticLayout = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AutomaticLayout(self: SettingsCmdCreateParcelByLayout) -> SettingsCmdAutomaticLayout """ ConvertFromEntities = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ConvertFromEntities(self: SettingsCmdCreateParcelByLayout) -> SettingsCmdConvertFromEntities """ ParcelSizing = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ParcelSizing(self: SettingsCmdCreateParcelByLayout) -> SettingsCmdParcelSizing """ PreviewGraphics = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: PreviewGraphics(self: SettingsCmdCreateParcelByLayout) -> SettingsCmdPreviewGraphics """ SettingsCmdAutomaticLayout = None SettingsCmdConvertFromEntities = None SettingsCmdParcelSizing = None SettingsCmdPreviewGraphics = None class SettingsCmdCreateParcelFromObjects(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ConvertFromEntities = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ConvertFromEntities(self: SettingsCmdCreateParcelFromObjects) -> SettingsCmdConvertFromEntities """ SettingsCmdConvertFromEntities = None class SettingsCmdCreateParcelROW(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CleanupAtAlignmentIntersections = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CleanupAtAlignmentIntersections(self: SettingsCmdCreateParcelROW) -> SettingsCmdCleanupAtAlignmentIntersections """ CleanupAtParcelBoundaries = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CleanupAtParcelBoundaries(self: SettingsCmdCreateParcelROW) -> SettingsCmdCleanupAtParcelBoundaries """ CreateParcelRightOfWay = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CreateParcelRightOfWay(self: SettingsCmdCreateParcelROW) -> SettingsCmdCreateParcelRightOfWay """ SettingsCmdCleanupAtAlignmentIntersections = None SettingsCmdCleanupAtParcelBoundaries = None SettingsCmdCreateParcelRightOfWay = None class SettingsCmdCreatePointCloud(SettingsPointCloud): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefaultLayer = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DefaultLayer(self: SettingsCmdCreatePointCloud) -> SettingsCmdDefaultLayer """ FileFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FileFormat(self: SettingsCmdCreatePointCloud) -> PropertyEnum[PointCloudDefaultFileExtensionType] """ SettingsCmdDefaultLayer = None class SettingsCmdCreatePointGroup(SettingsPoint): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreatePoints(SettingsPoint): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Layer = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Layer(self: SettingsCmdCreatePoints) -> SettingsCmdLayer """ PointIdentity = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: PointIdentity(self: SettingsCmdCreatePoints) -> SettingsCmdPointIdentity """ PointsCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: PointsCreation(self: SettingsCmdCreatePoints) -> SettingsCmdPointsCreation """ SettingsCmdLayer = None SettingsCmdPointIdentity = None SettingsCmdPointsCreation = None class SettingsCmdCreatePointsFromCorridor(SettingsCorridor): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreatePolylineFromCorridor(SettingsCorridor): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsSuperelevationView(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsSuperelevationView) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsSuperelevationView) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCmdCreatePolylineFromSuper(SettingsSuperelevationView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreatePressureFromIndModel(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreatePressureNetwork(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DepthOfCover = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DepthOfCover(self: SettingsCmdCreatePressureNetwork) -> SettingsCmdDepthOfCover """ LabelNewParts = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: LabelNewParts(self: SettingsCmdCreatePressureNetwork) -> SettingsCmdLabelNewParts """ SettingsCmdDepthOfCover = None SettingsCmdLabelNewParts = None class SettingsCmdCreatePressurePartList(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreatePressurePartListFull(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateProfileFromCorridor(SettingsCorridor): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CriteriaBasedDesignOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CriteriaBasedDesignOptions(self: SettingsCmdCreateProfileFromCorridor) -> SettingsCmdCriteriaBasedDesignOptions """ SettingsCmdCriteriaBasedDesignOptions = None class SettingsProfile(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CriteriaBasedDesignOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CriteriaBasedDesignOptions(self: SettingsProfile) -> SettingsCriteriaBasedDesignOptions """ DefaultNameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DefaultNameFormat(self: SettingsProfile) -> SettingsDefaultNameFormat """ ProfilesCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ProfilesCreation(self: SettingsProfile) -> SettingsProfileCreation """ StyleSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: StyleSettings(self: SettingsProfile) -> SettingsStyles """ SettingsCriteriaBasedDesignOptions = None SettingsDefaultNameFormat = None SettingsProfileCreation = None SettingsStyles = None class SettingsCmdCreateProfileFromFile(SettingsProfile): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateProfileFromSurface(SettingsProfile): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Geometry = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Geometry(self: SettingsCmdCreateProfileFromSurface) -> SettingsCmdGeometry """ SettingsCmdGeometry = None class SettingsCmdCreateProfileLayout(SettingsProfile): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CurveTessellationOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CurveTessellationOption(self: SettingsCmdCreateProfileLayout) -> SettingsCmdCurveTessellationOption """ RegressionGraphOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RegressionGraphOption(self: SettingsCmdCreateProfileLayout) -> SettingsCmdRegressionGraphOption """ SettingsCmdCurveTessellationOption = None SettingsCmdRegressionGraphOption = None class SettingsCmdCreateProfileReference(SettingsProfile): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateProfileView(SettingsProfileView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateQuickProfile(SettingsProfile): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass QuickProfile = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: QuickProfile(self: SettingsCmdCreateQuickProfile) -> SettingsCmdQuickProfile """ SettingsCmdQuickProfile = None class SettingsSampleLine(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsSampleLine) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsSampleLine) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsCmdCreateSampleLines(SettingsSampleLine): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AdditionalSampleControls = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AdditionalSampleControls(self: SettingsCmdCreateSampleLines) -> SettingsCmdAdditionalSampleControls """ Miscellaneous = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Miscellaneous(self: SettingsCmdCreateSampleLines) -> SettingsCmdMiscellaneous """ SamplingIncrements = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SamplingIncrements(self: SettingsCmdCreateSampleLines) -> SettingsCmdSamplingIncrements """ SwathWidths = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SwathWidths(self: SettingsCmdCreateSampleLines) -> SettingsCmdSwathWidths """ SettingsCmdAdditionalSampleControls = None SettingsCmdMiscellaneous = None SettingsCmdSamplingIncrements = None SettingsCmdSwathWidths = None class SettingsCmdCreateSectionSheets(SettingsSectionView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass SheetCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SheetCreation(self: SettingsCmdCreateSectionSheets) -> SettingsCmdSheetCreation """ SettingsCmdSheetCreation = None class SettingsCmdCreateSectionView(SettingsSectionView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass TableCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TableCreation(self: SettingsCmdCreateSectionView) -> SettingsCmdTableCreation """ SettingsCmdTableCreation = None class SettingsViewFrameGroup(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Information = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Information(self: SettingsViewFrameGroup) -> SettingsInformation """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsViewFrameGroup) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsViewFrameGroup) -> SettingsStyles """ SettingsInformation = None SettingsNameFormat = None SettingsStyles = None class SettingsCmdCreateSheets(SettingsViewFrameGroup): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass SheetCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SheetCreation(self: SettingsCmdCreateSheets) -> SettingsCmdSheetCreation """ SettingsCmdSheetCreation = None class SettingsCmdCreateSimpleCorridor(SettingsCorridor): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AssemblyInsertion = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AssemblyInsertion(self: SettingsCmdCreateSimpleCorridor) -> SettingsCmdAssemblyInsertion """ SettingsCmdAssemblyInsertion = None class SettingsCmdCreateSite(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Alignment = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Alignment(self: SettingsCmdCreateSite) -> SettingsCmdAlignment """ FeatureLine = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FeatureLine(self: SettingsCmdCreateSite) -> SettingsCmdFeatureLine """ Parcel = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Parcel(self: SettingsCmdCreateSite) -> SettingsCmdParcel """ SettingsCmdAlignment = None SettingsCmdFeatureLine = None SettingsCmdParcel = None class SettingsSubassembly(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DefaultStyles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DefaultStyles(self: SettingsSubassembly) -> SettingsDefaultStyles """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsSubassembly) -> SettingsNameFormat """ SettingsDefaultStyles = None SettingsNameFormat = None class SettingsCmdCreateSubassemblyTool(SettingsSubassembly): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass SubassemblyOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SubassemblyOptions(self: SettingsCmdCreateSubassemblyTool) -> SettingsCmdSubassemblyOptions """ SettingsCmdSubassemblyOptions = None class SettingsCmdCreateSubFromPline(SettingsSubassembly): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass CreateFromEntities = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CreateFromEntities(self: SettingsCmdCreateSubFromPline) -> SettingsCmdCreateFromEntities """ SettingsCmdCreateFromEntities = None class SettingsCmdCreateSuperelevationView(SettingsSuperelevationView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateSurface(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass BuildOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: BuildOptions(self: SettingsCmdCreateSurface) -> SettingsCmdBuildOptions """ NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsCmdCreateSurface) -> SettingsNameFormat """ SurfaceCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SurfaceCreation(self: SettingsCmdCreateSurface) -> SettingsCmdSurfaceCreation """ SettingsCmdBuildOptions = None SettingsCmdSurfaceCreation = None SettingsNameFormat = None class SettingsCmdCreateSurfaceFromTIN(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdCreateSurfaceGridFromDEM(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass BuildOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: BuildOptions(self: SettingsCmdCreateSurfaceGridFromDEM) -> SettingsCmdBuildOptions """ ImportOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ImportOptions(self: SettingsCmdCreateSurfaceGridFromDEM) -> SettingsCmdImportOptions """ SettingsCmdBuildOptions = None SettingsCmdImportOptions = None class SettingsCmdCreateSurfaceReference(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsCmdCreateSurfaceReference) -> SettingsNameFormat """ SettingsNameFormat = None class SettingsCmdCreateSurfaceWaterdrop(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass WaterdropMarker = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: WaterdropMarker(self: SettingsCmdCreateSurfaceWaterdrop) -> SettingsCmdWaterdropMarker """ WaterdropPath = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: WaterdropPath(self: SettingsCmdCreateSurfaceWaterdrop) -> SettingsCmdWaterdropPath """ SettingsCmdWaterdropMarker = None SettingsCmdWaterdropPath = None class SettingsCmdCreateViewFrames(SettingsViewFrameGroup): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ViewFrameCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ViewFrameCreation(self: SettingsCmdCreateViewFrames) -> SettingsCmdViewFrameCreation """ SettingsCmdViewFrameCreation = None class SettingsCmdDrawFeatureLine(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FeatureLineCreation(self: SettingsCmdDrawFeatureLine) -> SettingsCmdFeatureLineCreation """ SettingsCmdFeatureLineCreation = None class SettingsCmdEditFlowSegments(SettingsCatchment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ChannelFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ChannelFlow(self: SettingsCmdEditFlowSegments) -> SettingsCmdChannelFlow """ ShallowConcentratedFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ShallowConcentratedFlow(self: SettingsCmdEditFlowSegments) -> SettingsCmdShallowConcentratedFlow """ SheetFlow = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SheetFlow(self: SettingsCmdEditFlowSegments) -> SettingsCmdSheetFlow """ SettingsCmdChannelFlow = None SettingsCmdShallowConcentratedFlow = None SettingsCmdSheetFlow = None class SettingsCmdEditInStormSewers(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdEditSVGroupStyle(SettingsSectionView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdExportParcelAnalysis(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ParcelAnalysis = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ParcelAnalysis(self: SettingsCmdExportParcelAnalysis) -> SettingsCmdParcelAnalysis """ SettingsCmdParcelAnalysis = None class SettingsCmdExportStormSewerData(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdFeatureLinesFromCorridor(SettingsCorridor): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FeatureLineCreation(self: SettingsCmdFeatureLinesFromCorridor) -> SettingsCmdFeatureLineCreation """ SettingsCmdFeatureLineCreation = None class SettingsCmdFitCurveFeature(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineFitCurve = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FeatureLineFitCurve(self: SettingsCmdFitCurveFeature) -> SettingsCmdFeatureLineFitCurve """ SettingsCmdFeatureLineFitCurve = None class SettingsCmdGenerateQuantitiesReport(SettingsQuantityTakeoff): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DisplayXmlReport = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DisplayXmlReport(self: SettingsCmdGenerateQuantitiesReport) -> PropertyBoolean """ class SettingsCmdGradingElevEditor(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass GradingElevationEditor = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GradingElevationEditor(self: SettingsCmdGradingElevEditor) -> SettingsCmdGradingElevationEditor """ SettingsCmdGradingElevationEditor = None class SettingsCmdGradingTools(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass GradingLayoutTools = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GradingLayoutTools(self: SettingsCmdGradingTools) -> SettingsCmdGradingLayoutTools """ SettingsCmdGradingLayoutTools = None class SettingsCmdGradingVolumeTools(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass LimitFeatureSelectionToCurrentGroup = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: LimitFeatureSelectionToCurrentGroup(self: SettingsCmdGradingVolumeTools) -> PropertyBoolean """ RaiseLowerElevationIncrement = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RaiseLowerElevationIncrement(self: SettingsCmdGradingVolumeTools) -> PropertyDouble """ class SettingsCmdImportBuildingSite(SettingsBuildingSite): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdImportGISData(SettingsGeneral): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass PipeNetwork = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: PipeNetwork(self: SettingsCmdImportGISData) -> SettingsCmdPipeNetwork """ SettingsCmdPipeNetwork = None class SettingsCmdImportStormSewerData(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdJoinFeatures(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineJoin = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FeatureLineJoin(self: SettingsCmdJoinFeatures) -> SettingsCmdFeatureLineJoin """ SettingsCmdFeatureLineJoin = None class SettingsCmdLayoutSectionViewGroup(SettingsSectionView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdMapCheck(SettingsGeneral): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Mapcheck = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Mapcheck(self: SettingsCmdMapCheck) -> SettingsCmdMapcheck """ SettingsCmdMapcheck = None class SettingsCmdMinimizeSurfaceFlatAreas(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AddPointsToFlatEdges = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AddPointsToFlatEdges(self: SettingsCmdMinimizeSurfaceFlatAreas) -> PropertyBoolean """ AddPointsToFlatTriangles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AddPointsToFlatTriangles(self: SettingsCmdMinimizeSurfaceFlatAreas) -> PropertyBoolean """ FillGapsInContour = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FillGapsInContour(self: SettingsCmdMinimizeSurfaceFlatAreas) -> PropertyBoolean """ SwapEdges = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SwapEdges(self: SettingsCmdMinimizeSurfaceFlatAreas) -> PropertyBoolean """ class SettingsCmdMoveBlockstoAttribElev(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdMoveBlocksToSurface(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdMoveTextToElevation(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdProjectObjectsToMultiSect(SettingsSectionView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ObjectSelectionOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ObjectSelectionOptions(self: SettingsCmdProjectObjectsToMultiSect) -> SettingsCmdObjectSelectionOptions """ SettingsCmdObjectSelectionOptions = None class SettingsCmdProjectObjectsToProf(SettingsProfileView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdProjectObjectsToSect(SettingsSectionView): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdReAddParcelAreaLabel(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdReAddParcelSegmentLabels(SettingsParcel): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdRenamePipeNetworkParts(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdResetAnchorPipe(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdReverseAlignmentDirection(SettingsAlignment): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdRunDepthCheck(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DepthCheckOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DepthCheckOption(self: SettingsCmdRunDepthCheck) -> SettingsCmdDepthCheckOption """ SettingsCmdDepthCheckOption = None class SettingsCmdRunDesignCheck(SettingsPressureNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass DesignCheckOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DesignCheckOption(self: SettingsCmdRunDesignCheck) -> SettingsCmdDesignCheckOption """ SettingsCmdDesignCheckOption = None class SettingsCmdShowGeodeticCalculator(SettingsPoint): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdShowPointGroupProperties(SettingsPoint): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdShowSpanningPipes(SettingsPipeNetwork): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdSimplifySurface(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass MaximumChangeInElevation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: MaximumChangeInElevation(self: SettingsCmdSimplifySurface) -> PropertyDouble """ PercentageOfPointsToRemove = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: PercentageOfPointsToRemove(self: SettingsCmdSimplifySurface) -> PropertyDouble """ RegionOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RegionOptions(self: SettingsCmdSimplifySurface) -> PropertyEnum[SurfaceRegionOptionsType] """ SimplifyMethod = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SimplifyMethod(self: SettingsCmdSimplifySurface) -> PropertyEnum[SurfaceSimplifyType] """ UseMaximumChangeInElevation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: UseMaximumChangeInElevation(self: SettingsCmdSimplifySurface) -> PropertyBoolean """ UsePercentageOfPointsToRemove = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: UsePercentageOfPointsToRemove(self: SettingsCmdSimplifySurface) -> PropertyBoolean """ class SettingsCmdSuperimposeProfile(SettingsProfile): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass SuperimposeProfile = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SuperimposeProfile(self: SettingsCmdSuperimposeProfile) -> SettingsCmdSuperimposeProfileOption """ SettingsCmdSuperimposeProfileOption = None class SettingsCmdSurfaceExportToDem(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ExportOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ExportOptions(self: SettingsCmdSurfaceExportToDem) -> SettingsCmdExportOptions """ SettingsCmdExportOptions = None class SettingsCmdSurfaceExtractObjects(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsCmdTakeOff(SettingsQuantityTakeoff): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ComputeTakeOffOption = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ComputeTakeOffOption(self: SettingsCmdTakeOff) -> SettingsCmdComputeTakeOff """ SettingsCmdComputeTakeOff = None class SettingsCmdViewEditCorridorSection(SettingsCorridor): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass GridSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GridSettings(self: SettingsCmdViewEditCorridorSection) -> SettingsCmdGridSettings """ GridTextSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GridTextSettings(self: SettingsCmdViewEditCorridorSection) -> SettingsCmdGridTextSettings """ SectionSliderInMultipleViewports = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SectionSliderInMultipleViewports(self: SettingsCmdViewEditCorridorSection) -> SettingsCmdSectionSliderInMultipleViewports """ ViewEditOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ViewEditOptions(self: SettingsCmdViewEditCorridorSection) -> SettingsCmdViewEditOptions """ SettingsCmdGridSettings = None SettingsCmdGridTextSettings = None SettingsCmdSectionSliderInMultipleViewports = None SettingsCmdViewEditOptions = None class SettingsCmdVolumesDashboard(SettingsSurface): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass BoundedVolumeCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: BoundedVolumeCreation(self: SettingsCmdVolumesDashboard) -> SettingsCmdBoundedVolumeCreation """ BuildOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: BuildOptions(self: SettingsCmdVolumesDashboard) -> SettingsCmdBuildOptions """ DynamicHighlightOptions = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DynamicHighlightOptions(self: SettingsCmdVolumesDashboard) -> SettingsCmdDynamicHighlightOptions """ VolumeSurfaceCreation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: VolumeSurfaceCreation(self: SettingsCmdVolumesDashboard) -> SettingsCmdVolumeSurfaceCreation """ SettingsCmdBoundedVolumeCreation = None SettingsCmdBuildOptions = None SettingsCmdDynamicHighlightOptions = None SettingsCmdVolumeSurfaceCreation = None class SettingsCmdWeedFeatures(SettingsGrading): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass FeatureLineWeed = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FeatureLineWeed(self: SettingsCmdWeedFeatures) -> SettingsCmdFeatureLineWeed """ SettingsCmdFeatureLineWeed = None class SettingsCoordinateSystem(object): Category = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Category(self: SettingsCoordinateSystem) -> str """ Code = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Code(self: SettingsCoordinateSystem) -> str """ Datum = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Datum(self: SettingsCoordinateSystem) -> str """ Description = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Description(self: SettingsCoordinateSystem) -> str """ Projection = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Projection(self: SettingsCoordinateSystem) -> str """ Unit = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Unit(self: SettingsCoordinateSystem) -> str """ class SettingsDrawing(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AbbreviationsSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AbbreviationsSettings(self: SettingsDrawing) -> SettingsAbbreviation """ AmbientSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AmbientSettings(self: SettingsDrawing) -> SettingsAmbient """ ApplyTransformSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ApplyTransformSettings(self: SettingsDrawing) -> bool Set: ApplyTransformSettings(self: SettingsDrawing) = value """ ObjectLayerSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ObjectLayerSettings(self: SettingsDrawing) -> SettingsObjectLayers """ TransformationSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TransformationSettings(self: SettingsDrawing) -> SettingsTransformation """ UnitZoneSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: UnitZoneSettings(self: SettingsDrawing) -> SettingsUnitZone """ class SettingsLandXML(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Export = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Export(self: SettingsLandXML) -> SettingsLandXMLExport """ Import = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Import(self: SettingsLandXML) -> SettingsLandXMLImport """ class SettingsLandXMLExport(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentExport = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AlignmentExport(self: SettingsLandXMLExport) -> SettingsAlignmentExport """ Data = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Data(self: SettingsLandXMLExport) -> SettingsData """ FeatureLineExport = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FeatureLineExport(self: SettingsLandXMLExport) -> SettingsFeatureLineExport """ Identification = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Identification(self: SettingsLandXMLExport) -> SettingsIdentification """ ParcelExport = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ParcelExport(self: SettingsLandXMLExport) -> SettingsParcelExport """ PointExport = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: PointExport(self: SettingsLandXMLExport) -> SettingsPointExport """ SurfaceExport = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SurfaceExport(self: SettingsLandXMLExport) -> SettingsSurfaceExport """ SettingsAlignmentExport = None SettingsData = None SettingsFeatureLineExport = None SettingsIdentification = None SettingsParcelExport = None SettingsPointExport = None SettingsSurfaceExport = None class SettingsLandXMLImport(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass AlignmentImport = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AlignmentImport(self: SettingsLandXMLImport) -> SettingsAlignmentImport """ ConflictResolution = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ConflictResolution(self: SettingsLandXMLImport) -> SettingsConflictResolution """ DiameterUnits = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DiameterUnits(self: SettingsLandXMLImport) -> SettingsDiameterUnits """ FeatureLineImport = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: FeatureLineImport(self: SettingsLandXMLImport) -> SettingsFeatureLineImport """ PipeNetwork = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: PipeNetwork(self: SettingsLandXMLImport) -> SettingsPipeNetwork """ PointImport = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: PointImport(self: SettingsLandXMLImport) -> SettingsPointImport """ PropertySetData = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: PropertySetData(self: SettingsLandXMLImport) -> SettingsPropertySetData """ Rotation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Rotation(self: SettingsLandXMLImport) -> SettingsRotation """ SurfaceImport = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SurfaceImport(self: SettingsLandXMLImport) -> SettingsSurfaceImport """ Translation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Translation(self: SettingsLandXMLImport) -> SettingsTranslation """ SettingsAlignmentImport = None SettingsConflictResolution = None SettingsDiameterUnits = None SettingsFeatureLineImport = None SettingsPipeNetwork = None SettingsPointImport = None SettingsPropertySetData = None SettingsRotation = None SettingsSurfaceImport = None SettingsTranslation = None class SettingsMassHaulLine(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsMatchLine(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsObjectLayer(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass LayerId = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: LayerId(self: SettingsObjectLayer) -> ObjectId Set: LayerId(self: SettingsObjectLayer) = value """ LayerName = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: LayerName(self: SettingsObjectLayer) -> str Set: LayerName(self: SettingsObjectLayer) = value """ Locked = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Locked(self: SettingsObjectLayer) -> bool Set: Locked(self: SettingsObjectLayer) = value """ Modifier = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Modifier(self: SettingsObjectLayer) -> ObjectLayerModifierType Set: Modifier(self: SettingsObjectLayer) = value """ ModifierValue = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ModifierValue(self: SettingsObjectLayer) -> str Set: ModifierValue(self: SettingsObjectLayer) = value """ ObjectType = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ObjectType(self: SettingsObjectLayer) -> SettingsObjectLayerType """ class SettingsObjectLayers(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetObjectLayerSetting(self, settingsType): """ GetObjectLayerSetting(self: SettingsObjectLayers, settingsType: SettingsObjectLayerType) -> SettingsObjectLayer """ pass ObjectControlledByLayer = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ObjectControlledByLayer(self: SettingsObjectLayers) -> bool Set: ObjectControlledByLayer(self: SettingsObjectLayers) = value """ class SettingsObjectLayerType(Enum): """ enum SettingsObjectLayerType, values: Alignment (0), AlignmentLabeling (1), AlignmentTable (2), Appurtenance (56), AppurtenanceLabeling (57), Assembly (3), BuildingSite (53), CantView (58), Catchment (59), CatchmentLabeling (60), Corridor (4), CorridorSection (5), FeatureLine (6), Fitting (61), FittingLabeling (62), GeneralNoteLabel (7), GeneralSegmentLabel (8), Grading (9), GradingLabeling (10), GridSurface (11), GridSurfaceLabeling (12), Interference (13), Intersection (54), IntersectionLabeling (55), MassHaulLine (14), MassHaulView (15), MatchLine (16), MatchLineLabeling (17), MaterialSection (18), MaterialTable (19), Parcel (20), ParcelLabeling (21), ParcelSegment (22), ParcelSegmentLabeling (23), ParcelTable (24), Pipe (25), PipeAndStructureTable (27), PipeLabeling (26), PipeNetworkSection (28), PipeOrStructureProfile (29), PointTable (30), PressureNetworkSection (63), PressurePartProfile (64), PressurePartTable (65), PressurePipe (66), PressurePipeLabeling (67), Profile (31), ProfileLabeling (32), ProfileView (33), ProfileViewLabeling (34), SampleLine (35), SampleLineLabeling (36), Section (37), SectionLabeling (38), SectionView (39), SectionViewLabeling (40), SectionViewQuantityTakeoffTable (41), Sheet (42), Structure (43), StructureLabeling (44), Subassembly (45), SuperelevationView (68), SurfaceLegendTable (46), SurveyFigure (47), SurveyFigureLabeling (69), SurveyFigureSegmentLable (70), SurveyNetwork (48), TinSurface (49), TinSurfaceLabeling (50), ViewFrame (51), ViewFrameLabeling (52) """ Alignment = None AlignmentLabeling = None AlignmentTable = None Appurtenance = None AppurtenanceLabeling = None Assembly = None BuildingSite = None CantView = None Catchment = None CatchmentLabeling = None Corridor = None CorridorSection = None FeatureLine = None Fitting = None FittingLabeling = None GeneralNoteLabel = None GeneralSegmentLabel = None Grading = None GradingLabeling = None GridSurface = None GridSurfaceLabeling = None Interference = None Intersection = None IntersectionLabeling = None MassHaulLine = None MassHaulView = None MatchLine = None MatchLineLabeling = None MaterialSection = None MaterialTable = None Parcel = None ParcelLabeling = None ParcelSegment = None ParcelSegmentLabeling = None ParcelTable = None Pipe = None PipeAndStructureTable = None PipeLabeling = None PipeNetworkSection = None PipeOrStructureProfile = None PointTable = None PressureNetworkSection = None PressurePartProfile = None PressurePartTable = None PressurePipe = None PressurePipeLabeling = None Profile = None ProfileLabeling = None ProfileView = None ProfileViewLabeling = None SampleLine = None SampleLineLabeling = None Section = None SectionLabeling = None SectionView = None SectionViewLabeling = None SectionViewQuantityTakeoffTable = None Sheet = None Structure = None StructureLabeling = None Subassembly = None SuperelevationView = None SurfaceLegendTable = None SurveyFigure = None SurveyFigureLabeling = None SurveyFigureSegmentLable = None SurveyNetwork = None TinSurface = None TinSurfaceLabeling = None value__ = None ViewFrame = None ViewFrameLabeling = None class SettingsPipe(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsPressureAppurtenance(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsPressureFitting(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsPressurePipe(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsRoot(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass def GetSettings(self): AssociateShortcutProjectId = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AssociateShortcutProjectId(self: SettingsRoot) -> str Set: AssociateShortcutProjectId(self: SettingsRoot) = value """ DrawingSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DrawingSettings(self: SettingsRoot) -> SettingsDrawing """ LandXMLSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: LandXMLSettings(self: SettingsRoot) -> SettingsLandXML """ TagSettings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: TagSettings(self: SettingsRoot) -> SettingsTag """ class SettingsSection(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass NameFormat = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: NameFormat(self: SettingsSection) -> SettingsNameFormat """ Styles = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Styles(self: SettingsSection) -> SettingsStyles """ SettingsNameFormat = None SettingsStyles = None class SettingsStructure(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SettingsTag(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass Creation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Creation(self: SettingsTag) -> SettingsCreation """ Renumbering = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: Renumbering(self: SettingsTag) -> SettingsRenumbering """ SettingsCreation = None SettingsRenumbering = None class SettingsTransformation(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass ApplySeaLevelScaleFactor = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ApplySeaLevelScaleFactor(self: SettingsTransformation) -> bool Set: ApplySeaLevelScaleFactor(self: SettingsTransformation) = value """ GridReferencePoint = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GridReferencePoint(self: SettingsTransformation) -> Point2d Set: GridReferencePoint(self: SettingsTransformation) = value """ GridRotationPoint = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GridRotationPoint(self: SettingsTransformation) -> Point2d Set: GridRotationPoint(self: SettingsTransformation) = value """ GridScaleFactor = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GridScaleFactor(self: SettingsTransformation) -> float Set: GridScaleFactor(self: SettingsTransformation) = value """ GridScaleFactorComputation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: GridScaleFactorComputation(self: SettingsTransformation) -> GridScaleFactorType Set: GridScaleFactorComputation(self: SettingsTransformation) = value """ LocalReferencePoint = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: LocalReferencePoint(self: SettingsTransformation) -> Point2d Set: LocalReferencePoint(self: SettingsTransformation) = value """ LocalRotationPoint = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: LocalRotationPoint(self: SettingsTransformation) -> Point2d Set: LocalRotationPoint(self: SettingsTransformation) = value """ RotationToGridAzimuth = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RotationToGridAzimuth(self: SettingsTransformation) -> float Set: RotationToGridAzimuth(self: SettingsTransformation) = value """ RotationToGridNorth = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: RotationToGridNorth(self: SettingsTransformation) -> float Set: RotationToGridNorth(self: SettingsTransformation) = value """ SeaLevelScaleElevation = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SeaLevelScaleElevation(self: SettingsTransformation) -> float Set: SeaLevelScaleElevation(self: SettingsTransformation) = value """ SpecifyRotationType = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SpecifyRotationType(self: SettingsTransformation) -> SpecifyRotationType Set: SpecifyRotationType(self: SettingsTransformation) = value """ SpheroidRadius = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: SpheroidRadius(self: SettingsTransformation) -> float """ class SettingsUnitZone(TreeOidWrapper): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass @staticmethod def GetAllCodes(): """ GetAllCodes() -> Array[str] """ pass @staticmethod def GetCoordinateSystemByCode(code): """ GetCoordinateSystemByCode(code: str) -> SettingsCoordinateSystem """ pass AngularUnits = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: AngularUnits(self: SettingsUnitZone) -> AngleUnitType Set: AngularUnits(self: SettingsUnitZone) = value """ CoordinateSystemCode = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: CoordinateSystemCode(self: SettingsUnitZone) -> str Set: CoordinateSystemCode(self: SettingsUnitZone) = value """ DrawingScale = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DrawingScale(self: SettingsUnitZone) -> float Set: DrawingScale(self: SettingsUnitZone) = value """ DrawingUnits = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: DrawingUnits(self: SettingsUnitZone) -> DrawingUnitType Set: DrawingUnits(self: SettingsUnitZone) = value """ ImperialToMetricConversion = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ImperialToMetricConversion(self: SettingsUnitZone) -> ImperialToMetricConversionType Set: ImperialToMetricConversion(self: SettingsUnitZone) = value """ MatchAutoCADVariables = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: MatchAutoCADVariables(self: SettingsUnitZone) -> bool Set: MatchAutoCADVariables(self: SettingsUnitZone) = value """ ScaleObjectsFromOtherDrawings = property(lambda self: object(), lambda self, v: None, lambda self: None) """Get: ScaleObjectsFromOtherDrawings(self: SettingsUnitZone) -> bool Set: ScaleObjectsFromOtherDrawings(self: SettingsUnitZone) = value """ class SettingsViewFrame(SettingsAmbient): def Dispose(self): """ Dispose(self: DisposableWrapper, A_0: bool) """ pass class SpecifyRotationType(Enum): """ enum SpecifyRotationType, values: GridRotationAngle (1), RotationPoint (0) """ GridRotationAngle = None RotationPoint = None value__ = None class TableAnchorType(Enum): """ enum TableAnchorType, values: BottomCenter (7), BottomLeft (6), BottomRight (8), MiddleCenter (4), MiddleLeft (3), MiddleRight (5), TopCenter (1), TopLeft (0), TopRight (2) """ BottomCenter = None BottomLeft = None BottomRight = None MiddleCenter = None MiddleLeft = None MiddleRight = None TopCenter = None TopLeft = None TopRight = None value__ = None class TableLayoutType(Enum): """ enum TableLayoutType, values: Horizontal (0), Vertical (1) """ Horizontal = None value__ = None Vertical = None class TileDirectionType(Enum): """ enum TileDirectionType, values: Across (0), Down (1) """ Across = None Down = None value__ = None

false

true

790bf59a165f1925a9da1f25fa5a2e9a0d530556

25,330

py

Python

coupled_channel/cutils.py

AleksiNummelin/coupled_channel

0e96e54400bb853b8c42cfc55b968a476114dcef

[ "MIT" ]

2

2020-11-16T10:46:33.000Z

2020-11-16T10:46:35.000Z

coupled_channel/cutils.py

AleksiNummelin/coupled_channel

0e96e54400bb853b8c42cfc55b968a476114dcef

[ "MIT" ]

null

coupled_channel/cutils.py

AleksiNummelin/coupled_channel

0e96e54400bb853b8c42cfc55b968a476114dcef

[ "MIT" ]

null

#from numba import jit import numpy as np #from joblib import Parallel, delayed, parallel_backend #from joblib import load, dump #import tempfile #import shutil #import os # #import sys #sys.path.append('pyunicorn_timeseries') #from pyunicorn_timeseries.surrogates import Surrogates def set_model_constants(xx=50.E3,nx=100,va=10.,tmax=60*360*24*3600.,avep=24*3600.,dt=3600.,period=3600*24*360*1,B=2.,T0=273.15+6,dT=2.,Cs=1.E-3,Cp=1030.,ra=1.5,ro=1030.,ri=900.,Cpo=4.E3,Cpi=2.9E3,H=200.,vo=0.2,Hb=1.E3,Li=3.3E6,Tf=273.15-1.8,SW0=50.,SW_anom=100.,emissivity=0.99,Da=1.E6,Do=5.E2,tau_entrainment=30*24*3600.,**args): '''Setup model constants. All of the constants have fixed values, but one can pass in own values or even some arbitrary values via **args.''' # C={} C['xx'] = xx #grid size in [m] C['nx'] = nx #number of grid cell - the total width of the domain is xx*nx long C['va'] = va #wind in m/s # C['tmax'] = tmax #tmax seconds C['dt'] = dt #timestep # C['avep'] = avep #averaging period in seconds # C['period'] = period #period of boundary restoring C['Cs'] = Cs #exchange coefficient for bulk formula C['Cp'] = Cp #air heat capacity C['ra'] = ra #density of air [kg/m3] C['ro'] = ro #density of sea water [kg/m3] C['ri'] = ri #density of sea ice [kg/m3] C['Cpo'] = Cpo #sea water heat capacity C['T0'] = T0 #initial temp in degC C['dT'] = dT #initial temp perturbationHb=2E3 C['H'] = H #mixed layer depth in ocean [m] C['vo'] = vo #ocean current speed [m/s] C['Hb'] = Hb #boundary layer height in the atmosphere [m] C['Cpi'] = Cpi #sea ice heat capacity [J/ Kg K] C['Li'] = Li #Latent heat of fusion of sea water [J / kg K] C['Tf'] = Tf #Freezing point of sea water [C] C['B'] = B # long-wave radiation constant [W/m2] C['emissivity'] = emissivity #surface emissivity C['SW0'] = SW0 # background net downwelling SW radiation C['SW_anom']= SW_anom # amplitude of annual cycle in SW radiation C['Da'] = Da # atmospheric diffusion [m2/s] C['Do'] = Do # ocean diffusion [m2/s] C['tau_entrainment'] = tau_entrainment # ocean entrainment/damping timescale for var in args.keys(): C[var]=args[var] # return C def CoupledChannel(C,forcing, T_boundary=None, dt_f=30*24*3600, restoring=False,ice_model=True,atm_adv=True,spatial_pattern=None,atm_DA_tendencies=None,ocn_DA_tendencies=None, return_coupled_fluxes=False,random_amp=0.1): ''' This is the main function for the coupled ocean--atm channel model. ## INPUT VARIABLES ## tmax: running time in seconds avep: averaging period for the ouput T0: initial temperature forcing: dimensionless scaling for the heat flux forcing - default strength is 5 W/m2 dt_f: timestep of the forcing atm_adv: boolean, advective atmosphere atm_ocn: boolean, advective ocean ''' # # number of simulation timesteps and output timesteps nt = int(C['tmax']/C['dt']) #simulation nt1 = int(C['tmax']/C['avep']) #output # rtas = np.random.rand(C['nx']) # intitialize the model variables, first dimension is due to 2 timesteps deep scheme sst = C['T0']*np.ones((2,C['nx'])) tas = C['T0']*np.ones((2,C['nx'])) #+rtas hice = np.zeros((2,C['nx'])) # INCOMING SHORTWAVE RADIATION SW0 = np.tile(C['SW0'][:,np.newaxis],(1,nt)) naxis = np.tile(np.arange(nt)[np.newaxis,],(C['nx'],1)) SW_warming = np.max(np.concatenate([(SW0-C['SW_anom']*np.cos(2*np.pi*(naxis*C['dt'])/(360*24*3600)))[np.newaxis,],np.zeros((C['nx'],nt))[np.newaxis,]],axis=0),0) # If boundary conditions are not defined, then set initially to T0 if np.all(T_boundary==None): T_boundary=C['T0']*np.ones(nt) # sst_boundary=T_boundary[0]*np.ones((2)) #nt+1 # evolve_boundary=True #else: # sst_boundary=np.concatenate((sst_boundary[np.newaxis,],sst_boundary[np.newaxis,]),axis=0) # evolve_boundary=False # # interpolate forcing to the new timescale if np.all(forcing!=None): forcing = np.interp(np.arange(0,len(forcing)*dt_f,C['dt']),np.arange(0,len(forcing)*dt_f,dt_f),forcing) else: forcing = np.zeros(nt+1) # # initialize outputs sst_out = np.zeros((nt1,C['nx'])) tas_out = np.zeros((nt1,C['nx'])) hice_out = np.zeros((nt1,C['nx'])) sflx_f_out = np.zeros((nt1,C['nx'])) #forcing sflx_out = np.zeros((nt1,C['nx'])) # spatial pattern of the forcing - assume a sine wave if np.all(spatial_pattern==None): spatial_pattern=np.ones(C['nx']) # if np.all(atm_DA_tendencies!=None): use_atm_tendencies=True else: use_atm_tendencies=False if np.all(ocn_DA_tendencies!=None): use_ocn_tendencies=True else: use_ocn_tendencies=False # if return_coupled_fluxes: atm_DA_tendencies = np.zeros((nt,C['nx'])) ocn_DA_tendencies = np.zeros((nt,C['nx'])) # initialize counters c=0; c2=0; c3=0; n=1 ##################### # --- TIME LOOP --- ##################### for nn in range(nt): # # FORCING - WILL BE ZERO IF NOT SPECIFIED, no spatial pattern if not specified sflx=forcing[nn]*spatial_pattern #+ forcing[nn]*random_amp*np.random.rand(C['nx']) # # save the forcing component # sflx_f_out[c,:]=sflx_f_out[c,:]+sflx # # SURFACE HEAT FLUXES # Add sensible heat flux to the total surface flux in W/m**-2 sflx=sflx+C['ra']*C['Cp']*C['va']*C['Cs']*(sst[n-1,:]-tas[n-1,:]) # RADIATIVE FLUXES - LW will cool the atmosphere, SW will warm the ocean LW_cooling = C['emissivity']*5.67E-8*(tas[n-1,:]**4) # # OCEAN BOUNDARY CONDITION #if evolve_boundary: sst_boundary_tendency=SW_warming[0,nn]*C['dt']/(C['H']*C['Cpo']*C['ro'])-C['emissivity']*5.67E-8*(sst_boundary[n-1]**4)*C['dt']/(C['H']*C['Cpo']*C['ro'])+(T_boundary[nn]-sst_boundary[n-1])*C['dt']/C['period'] ############################################ # # ATMOSPHERE # ############################################ # # ADVECTION # # set atm_adv=False is no atmospheric advection - note that we still need to know the wind speed to resolve heat fluxes if atm_adv: a_adv = np.concatenate([sst_boundary[n-1]-tas[n-1,:1],tas[n-1,:-1]-tas[n-1,1:]],axis=0)*(C['va']*C['dt']/C['xx']) else: a_adv = 0 # # DIFFUSION # a_diff = (tas[n-1,2:]+tas[n-1,:-2]-2*tas[n-1,1:-1])*(C['Da']*C['dt']/(C['xx']**2)) a_diff0 = (tas[n-1,1]+sst_boundary[n-1]-2*tas[n-1,0])*(C['Da']*C['dt']/(C['xx']**2)) a_diff = np.concatenate([np.array([a_diff0]),a_diff,a_diff[-1:]],axis=0) # # SURFACE FLUXES # a_netsflx = (sflx*C['dt'])/(C['Hb']*C['Cp']*C['ra']) - LW_cooling*C['dt']/(C['Hb']*C['Cp']*C['ra']) # # if return_coupled_fluxes: atm_DA_tendencies[nn,:] = a_adv + a_diff # # ATM UPDATE # if use_atm_tendencies: tas[n,:] = tas[n-1,:] + a_netsflx + atm_DA_tendencies[c3,:] else: tas[n,:] = tas[n-1,:] + a_netsflx + a_adv + a_diff # ################################################ # # OCEAN # ################################################ # AND DIFFUSION + ENTRAINMENT # ocean advection # # ADVECTION set vo=0 for stagnant ocean (slab) # o_adv = np.concatenate([sst_boundary[n-1]-sst[n-1,:1],sst[n-1,:-1]-sst[n-1,1:]],axis=0)*(C['vo']*C['dt']/C['xx']) # # DIFFUSION # o_diff = (sst[n-1,2:]+sst[n-1,:-2]-2*sst[n-1,1:-1])*(C['Do']*C['dt']/(C['xx']**2)) o_diff0 = (sst[n-1,1]+sst_boundary[n-1]-2*sst[n-1,0])*(C['Do']*C['dt']/(C['xx']**2)) o_diff = np.concatenate([np.array([o_diff0]),o_diff,o_diff[-1:]],axis=0) # # ENTRAINMENT - RESTORING TO AN AMBIENT WATER MASS (CAN BE SEEN AS LATERAL OR VERTICAL MIXING) # set tau_entrainment=0 for no entrainment if C['tau_entrainment']>0: o_entrain = (C['T0']-sst[n-1,:])*C['dt']/C['tau_entrainment'] else: o_entrain = 0 # # SURFACE FLUXES # o_netsflx = -sflx*C['dt']/(C['H']*C['Cpo']*C['ro'])+SW_warming[:,nn]*C['dt']/(C['H']*C['Cpo']*C['ro']) # if return_coupled_fluxes: ocn_DA_tendencies[nn,:] = o_adv + o_diff + o_entrain # # OCN update if use_ocn_tendencies: sst[n,:] = sst[n-1,:] + o_netsflx + ocn_DA_tendencies[c3,:] else: sst[n,:] = sst[n-1,:] + o_netsflx + o_adv + o_diff + o_entrain # if ice_model: # THIS IS A DIAGNOSTIC SEA ICE MODEL # # SST is first allowed to cool below freezing and then we form sea ice from the excess_freeze # i.e the amount that heat that is used to cool SST below freezing is converted to ice instead. # Similarly, SST is allowed to warm above Tf even if there is ice, and then excess_melt, # i.e. the amount of heat that is used to warm the water is first used to melt ice, # and then the rest can warm the water. # # This scheme conserves energy - it simply switches it between ocean and ice storages # # advection #hice[n-1,1:]=hice[n-1,1:]-(hice[n-1,:-1]-hice[n-1,1:])*(C['vo']*C['dt']/C['xx']) #dhice = (hice[n-1,:-1]-hice[n-1,1:])*(C['vo']*C['dt']/C['xx']) #hice[n-1,:-1] = hice[n-1,:-1] -dhice #hice[n-1,-1] = hice[n-1,-1] + dhice[-1] # ice_mask = (hice[n-1,:]>0).astype(np.float) #cells where there is ice to melt freezing_mask = (sst[n,:]<C['Tf']).astype(np.float) #cells where freezing will happen # change in energy dEdt = C['H']*C['ro']*C['Cpo']*(sst[n,:]-sst[n-1,:])/C['dt'] # negative change in energy will produce ice whenver the water would otherwise cool below freezing excess_freeze = freezing_mask*np.max([-dEdt,np.zeros(C['nx'])],axis=0) # positive change will melt ice where there is ice excess_melt = ice_mask*np.max([dEdt,np.zeros(C['nx'])],axis=0) # note that freezing and melting will never happen at the same time in the same cell # freezing dhice_freeze = C['dt']*excess_freeze/(C['Li']*C['ri']) # melting dhice_melt= C['dt']*excess_melt/(C['Li']*C['ri']) # update hice[n,:] = hice[n-1,:] + dhice_freeze - dhice_melt # check how much energy was used for melting sea ice - remove this energy from ocean hice_melt = (dhice_melt>0).astype(np.float)*np.min([dhice_melt,hice[n-1,:]],axis=0) # Do not allow ice to be negative - that energy is kept in the ocean all the time. # The line above ensures that not more energy than is needed to melt the whole ice cover # is removed from the ocean at any given time hice[n,:] = np.max([hice[n,:],np.zeros(C['nx'])],axis=0) # # Update SST # Give back the energy that was used for freezing (will keep the water temperature above freezing) sst[n,:] = sst[n,:] + C['dt']*excess_freeze/(C['H']*C['Cpo']*C['ro']) # take out the heat that was used to melt ice # (need to cap to hice, the extra heat is never used and will stay in the ocean) sst[n,:] = sst[n,:] - hice_melt*(C['Li']*C['ri'])/(C['ro']*C['Cpo']*C['H']) # ############################# # --- PREPARE OUTPUT ---- ############################# # accumulate output tas_out[c,:] = tas_out[c,:]+tas[n,:] sst_out[c,:] = sst_out[c,:]+sst[n,:] hice_out[c,:] = hice_out[c,:]+hice[n,:] sflx_out[c,:] = sflx_out[c,:]+sflx # accumulate averaging counter c2=c2+1 c3=c3+1 if ((nn+1)*C['dt'])%(360*24*3600)==0: #print(nn) c3=0 #calculate the average for the output if (((nn+1)*C['dt'])%C['avep']==0 and nn>0): tas_out[c,:] = tas_out[c,:]/c2 sst_out[c,:] = sst_out[c,:]/c2 sflx_out[c,:] = sflx_out[c,:]/c2 sflx_f_out[c,:] = sflx_f_out[c,:]/c2 hice_out[c,:] = hice_out[c,:]/c2 # update counters c = c+1 c2 = 0 if ((nn+1)*C['dt'])%(360*24*3600)==0: print('Year ', (nn+1)*C['dt']/(360*24*3600), sst[1,int(C['nx']/4)], sst[1,int(3*C['nx']/4)]) #update the variables tas[0,:] = tas[1,:].copy() sst[0,:] = sst[1,:].copy() hice[0,:] = hice[1,:].copy() # SST at the boundary sst_boundary[n-1]=sst_boundary[n-1]+sst_boundary_tendency # # # if there is no ice, set to nan hice_out[np.where(hice_out==0)]=np.nan # if return_coupled_fluxes: return tas_out, sst_out, hice_out, sflx_out, sflx_f_out, nt1, nt, atm_DA_tendencies, ocn_DA_tendencies else: return tas_out, sst_out, hice_out, sflx_out, sflx_f_out, nt1, nt #@jit(nopython=True) def CoupledChannel_time(nt,nx,xx,dt,avep,sst,tas,hice,sst_boundary,sst_out,tas_out,hice_out,sflx_f_out,sflx_out,forcing,spatial_pattern,ra,Cp,va,vo,Da,Do,Cs,T0,Tf,emissivity,SW0,SW_anom,H,Hb,Cpo,ro,tau_entrainment,Li,ri,use_ocn_tendencies,use_atm_tendencies, atm_DA_tendencies, ocn_DA_tendencies,ice_model,atm_adv,return_coupled_fluxes): ''' Separate time loop to enable numba ''' #initialize counters c=0; c2=0; c3=0; n=1 ##################### # --- TIME LOOP --- ##################### for nn in range(nt): # # FORCING - WILL BE ZERO IF NOT SPECIFIED, no spatial pattern if not specified sflx=forcing[nn]*spatial_pattern #+ forcing[nn]*random_amp*np.random.rand(C['nx']) # # save the forcing component # sflx_f_out[c,:]=sflx_f_out[c,:]+sflx # # SURFACE HEAT FLUXES # Add sensible heat flux to the total surface flux in W/m**-2 sflx=sflx+ra*Cp*va*Cs*(sst[n-1,:]-tas[n-1,:]) # RADIATIVE FLUXES - LW will cool the atmosphere, SW will warm the ocean LW_cooling = emissivity*5.67E-8*(tas[n-1,:]**4) SW_warming = SW0+max(SW_anom*np.sin(2*float(nn)*dt*np.pi/(360*24*3600)),0.0) #net_radiation = SW_warming-LW_cooling net_radiation = -LW_cooling # # OCEAN BOUNDARY CONDITION - SET dT to zero to suppress the sin sst_boundary[n]=sst_boundary[n-1]+SW_warming[0]*dt/(H*Cpo*ro)-emissivity*5.67E-8*(sst_boundary[n-1]**4)*dt/(H*Cpo*ro)+(T0-sst_boundary[n-1])*dt/(360*24*3600) #C['T0']+C['dT']*np.sin(nn*C['dt']*np.pi/C['period']) + # # ATMOSPHERE - ADVECTION AND DIFFUSION # set atm_adv=False is no atmospheric advection - note that we need to know the wind speed to resolve heat fluxes if atm_adv: a_adv = np.concatenate((sst_boundary[n-1]-tas[n-1,:1],tas[n-1,:-1]-tas[n-1,1:]),axis=0)*(va*dt/xx) #tas[n,0]=tas[n-1,0]+(C['T0']-tas[n-1,0])*(C['va']*C['dt']/C['xx']) #always constant temperature blowing over the ocean from land #tas[n,0]=tas[n-1,0]+(sst[n,0]-tas[n-1,0])*(C['va']*C['dt']/C['xx']) #atmospheric temperature at the boundary is in equilibrium with the ocean #tas[n,1:]=tas[n-1,1:]+(tas[n-1,:-1]-tas[n-1,1:])*(C['va']*C['dt']/C['xx']) else: #tas[n,:] = tas[n-1,0] a_adv = np.zeros(nx) # # DIFFUSION # #tas[n,1:-1] = tas[n,1:-1] + (tas[n-1,2:]+tas[n-1,:-2]-2*tas[n-1,1:-1])*(C['Da']*C['dt']/(C['xx']**2)) a_diff = (tas[n-1,2:]+tas[n-1,:-2]-2*tas[n-1,1:-1])*(Da*dt/(xx**2)) a_diff0 = (tas[n-1,1]+sst_boundary[n-1]-2*tas[n-1,0])*(Da*dt/(xx**2)) a_diff = np.concatenate((np.array([a_diff0]),a_diff,a_diff[-1:]),axis=0) # # ATMOSPHERE - SURFACE FLUXES # a_netsflx = (sflx*dt)/(Hb*Cp*ra) + net_radiation*dt/(Hb*Cp*ra) # # full update # # if return_coupled_fluxes: atm_DA_tendencies[nn,:]=np.sum((a_adv,a_diff),axis=0) # if use_atm_tendencies: tas[n,:] = tas[n-1,:] + a_netsflx + atm_DA_tendencies[c3,:] else: tas[n,:] = tas[n-1,:] + a_netsflx + a_adv + a_diff # # OCEAN - ADVECTION AND DIFFUSION + ENTRAINMENT # ocean advection # set vo=0 for stagnant ocean (slab) # #sst[n,1:] = sst[n-1,1:]+(sst[n-1,:-1]-sst[n-1,1:])*(1-ocn_mixing_ratio)*(C['vo']*C['dt']/C['xx'])+(C['T0']-sst[n-1,1:])*ocn_mixing_ratio*(C['vo']*C['dt']/C['xx']) o_adv = np.concatenate((sst_boundary[n-1]-sst[n-1,:1],sst[n-1,:-1]-sst[n-1,1:]),axis=0)*(vo*dt/xx) # DIFFUSION #sst[n,1:-1] = sst[n,1:-1] + (sst[n-1,2:]+sst[n-1,:-2]-2*sst[n-1,1:-1])*(C['Do']*C['dt']/(C['xx']**2)) o_diff = (sst[n-1,2:]+sst[n-1,:-2]-2*sst[n-1,1:-1])*(Do*dt/(xx**2)) o_diff0 = (sst[n-1,1]+sst_boundary[n-1]-2*sst[n-1,0])*(Do*dt/(xx**2)) o_diff = np.concatenate((np.array([o_diff0]),o_diff,o_diff[-1:]),axis=0) # ENTRAINMENT (damping by a lower layer) o_entrain = (T0-sst[n-1,:])*dt/tau_entrainment #sst[n,1:]=sst[n,1:]+(C['T0']-sst[n-1,1:])*C['dt']/C['tau_entrainment'] # # OCEAN - SURFACE FLUXES # o_netsflx = -sflx*dt/(H*Cpo*ro)+SW_warming*dt/(H*Cpo*ro) #sst[n,:]=sst[n,:]-(sflx*C['dt'])/(C['H']*C['Cpo']*C['ro']) if return_coupled_fluxes: ocn_DA_tendencies[nn,:] = o_adv + o_diff + o_entrain # OCN update if use_ocn_tendencies: sst[n,:] = sst[n-1,:] + o_netsflx + ocn_DA_tendencies[c3,:] else: sst[n,:] = sst[n-1,:] + o_netsflx + o_adv + o_diff + o_entrain # if ice_model: # THIS IS A DIAGNOSTIC SEA ICE MODEL # # sst is first allowed to cool below freezing and then we forM sea ice from the excess_freeze # i.e the amount that heat that is used to cool sst below freezing is converted to ice instead # similarly sst is allowed to warm above Tf even if there is ice, and then excess_melt, # i.e. the amount of heat that is used to warm the water is first used to melt ice, # and then the rest can warm water. This scheme conserves energy - it simply switches it between ocean and ice # ice_mask = (hice[n-1,:]>0).astype(np.float) #cells where there is ice to melt freezing_mask = (sst[n,:]<Tf).astype(np.float) #cells where freezing will happen # change in energy dEdt = H*ro*Cpo*(sst[n,:]-sst[n-1,:])/dt # negative change in energy will produce ice whenver the water would otherwise cool below freezing excess_freeze = freezing_mask*np.max([-dEdt,np.zeros(nx)],axis=0) # positive change will melt ice where there is ice excess_melt = ice_mask*np.max([dEdt,np.zeros(nx)],axis=0) # note that freezing and melting will never happen at the same time in the same cell # freezing dhice_freeze = dt*excess_freeze/(Li*ri) # melting dhice_melt= dt*excess_melt/(Li*ri) # update hice[n,:] = hice[n-1,:] + dhice_freeze - dhice_melt # check how much energy was used for melting sea ice - remove this energy from ocean hice_melt = (dhice_melt>0).astype(np.float)*np.min([dhice_melt,hice[n-1,:]],axis=0) # Do not allow ice to be negative - that energy is kept in the ocean all the time. # The line above ensures that not more energy than is needed to melt the whole ice cover # is removed from the ocean at any given time hice[n,:] = np.max([hice[n,:],np.zeros(nx)],axis=0) # # Update SST # Give back the energy that was used for freezing (will keep the water temperature above freezing) sst[n,:] = sst[n,:] + dt*excess_freeze/(H*Cpo*ro) # take out the heat that was used to melt ice # (need to cap to hice, the extra heat is never used and will stay in the ocean) sst[n,:] = sst[n,:] - hice_melt*(Li*ri)/(ro*Cpo*H) # ############################# # --- PREPARE OUTPUT ---- ############################# #accumulate tas_out[c,:] = tas_out[c,:]+tas[n,:] sst_out[c,:] = sst_out[c,:]+sst[n,:] hice_out[c,:] = hice_out[c,:]+hice[n,:] sflx_out[c,:] = sflx_out[c,:]+sflx # accumulate averaging counter c2=c2+1 c3=c3+1 if ((nn+1)*dt)%(360*24*3600)==0: #print(nn) c3=0 #calculate the average for the output if (((nn+1)*dt)%avep==0 and nn>0): tas_out[c,:] = tas_out[c,:]/c2 sst_out[c,:] = sst_out[c,:]/c2 sflx_out[c,:] = sflx_out[c,:]/c2 sflx_f_out[c,:] = sflx_f_out[c,:]/c2 hice_out[c,:] = hice_out[c,:]/c2 # update counters c = c+1 c2 = 0 #if ((nn+1)*C['dt'])%(360*24*3600)==0: # print('Year ', (nn+1)*C['dt']/(360*24*3600), sst[1,int(C['nx']/4)], sst[1,int(3*C['nx']/4)]) #update the variables tas[0,:] = tas[1,:].copy() sst[0,:] = sst[1,:].copy() hice[0,:] = hice[1,:].copy() sst_boundary[0]=sst_boundary[1].copy() # hice_out[np.where(hice_out==0)]=np.nan # return tas_out, sst_out, hice_out, sflx_out, sflx_f_out, atm_DA_tendencies, ocn_DA_tendencies def CoupledChannel2(C,forcing, dt_f=30*24*3600, ocn_mixing_ratio=0, restoring=False,ice_model=True,atm_adv=True,spatial_pattern=None,atm_DA_tendencies=None,ocn_DA_tendencies=None, return_coupled_fluxes=False,random_amp=0.1): ''' This is the main function for the coupled ocean--atm channel model. ## INPUT VARIABLES ## tmax: running time in seconds avep: averaging period for the ouput T0: initial temperature forcing: dimensionless scaling for the heat flux forcing - default strength is 5 W/m2 dt_f: timestep of the forcing atm_adv: boolean, advective atmosphere atm_ocn: boolean, advective ocean ocn_mixing: add non-local mixing to ocean ocn_mixing_ratio: 0-1 ratio between advection and mixing (0 only advection; 1 only mixing) ''' # #print(C) #print(C['T0'],C['SW0'],C['Da'],C['xx']) # nt=int(C['tmax']/C['dt']) #steps nt1=int(C['tmax']/C['avep']) tau=float(C['period'])/float(C['dt']) #this is period/dt, previously nt/8 rtas=np.random.rand(C['nx']) #print(rtas.max()) #intitialize the model variables, only 2 timesteps deep scheme sst=C['T0']*np.ones((2,C['nx'])) tas=C['T0']*np.ones((2,C['nx']))+rtas hice=np.zeros((2,C['nx'])) sst_boundary=C['T0']*np.ones((2)) # #print(sst.max(),tas.max()) #interpolate forcing to the new timescale if np.all(forcing!=None): forcing = np.interp(np.arange(0,len(forcing)*dt_f,C['dt']),np.arange(0,len(forcing)*dt_f,dt_f),forcing) else: forcing = np.zeros(nt+1) # #initialize outputs sst_out = np.zeros((nt1,C['nx'])) tas_out = np.zeros((nt1,C['nx'])) hice_out = np.zeros((nt1,C['nx'])) sflx_f_out = np.zeros((nt1,C['nx'])) #forcing sflx_out = np.zeros((nt1,C['nx'])) #spatial pattern of the forcing - assume a sine wave if np.all(spatial_pattern==None): spatial_pattern=np.ones(C['nx']) # if np.all(atm_DA_tendencies!=None): use_atm_tendencies=True else: use_atm_tendencies=False if np.all(ocn_DA_tendencies!=None): use_ocn_tendencies=True else: use_ocn_tendencies=False # atm_DA_tendencies = np.zeros((nt,C['nx'])) ocn_DA_tendencies = np.zeros((nt,C['nx'])) # tas_out, sst_out, hice_out, sflx_out, sflx_f_out, atm_DA_tendencies, ocn_DA_tendencies=CoupledChannel_time(nt,C['nx'],C['xx'],C['dt'],C['avep'],sst,tas,hice,sst_boundary,sst_out,tas_out,hice_out,sflx_f_out,sflx_out,forcing,spatial_pattern,C['ra'],C['Cp'],C['va'],C['vo'],C['Da'],C['Do'],C['Cs'],C['T0'],C['Tf'],C['emissivity'],C['SW0'],C['SW_anom'],C['H'],C['Hb'],C['Cpo'],C['ro'],C['tau_entrainment'],C['Li'],C['ri'],use_ocn_tendencies,use_atm_tendencies, atm_DA_tendencies, ocn_DA_tendencies,ice_model,atm_adv,return_coupled_fluxes) # if return_coupled_fluxes: return tas_out, sst_out, hice_out, sflx_out, sflx_f_out, nt1, nt, atm_DA_tendencies, ocn_DA_tendencies else: return tas_out, sst_out, hice_out, sflx_out, sflx_f_out, nt1, nt

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0.557521

import numpy as np def set_model_constants(xx=50.E3,nx=100,va=10.,tmax=60*360*24*3600.,avep=24*3600.,dt=3600.,period=3600*24*360*1,B=2.,T0=273.15+6,dT=2.,Cs=1.E-3,Cp=1030.,ra=1.5,ro=1030.,ri=900.,Cpo=4.E3,Cpi=2.9E3,H=200.,vo=0.2,Hb=1.E3,Li=3.3E6,Tf=273.15-1.8,SW0=50.,SW_anom=100.,emissivity=0.99,Da=1.E6,Do=5.E2,tau_entrainment=30*24*3600.,**args): C={} C['xx'] = xx C['nx'] = nx C['va'] = va C['tmax'] = tmax C['dt'] = dt C['avep'] = avep C['period'] = period C['Cs'] = Cs C['Cp'] = Cp C['ra'] = ra C['ro'] = ro C['ri'] = ri C['Cpo'] = Cpo C['T0'] = T0 C['dT'] = dT C['H'] = H C['vo'] = vo C['Hb'] = Hb C['Cpi'] = Cpi C['Li'] = Li C['Tf'] = Tf C['B'] = B C['emissivity'] = emissivity C['SW0'] = SW0 C['SW_anom']= SW_anom C['Da'] = Da C['Do'] = Do C['tau_entrainment'] = tau_entrainment for var in args.keys(): C[var]=args[var] return C def CoupledChannel(C,forcing, T_boundary=None, dt_f=30*24*3600, restoring=False,ice_model=True,atm_adv=True,spatial_pattern=None,atm_DA_tendencies=None,ocn_DA_tendencies=None, return_coupled_fluxes=False,random_amp=0.1): nt = int(C['tmax']/C['dt']) nt1 = int(C['tmax']/C['avep']) sst = C['T0']*np.ones((2,C['nx'])) tas = C['T0']*np.ones((2,C['nx'])) hice = np.zeros((2,C['nx'])) SW0 = np.tile(C['SW0'][:,np.newaxis],(1,nt)) naxis = np.tile(np.arange(nt)[np.newaxis,],(C['nx'],1)) SW_warming = np.max(np.concatenate([(SW0-C['SW_anom']*np.cos(2*np.pi*(naxis*C['dt'])/(360*24*3600)))[np.newaxis,],np.zeros((C['nx'],nt))[np.newaxis,]],axis=0),0) if np.all(T_boundary==None): T_boundary=C['T0']*np.ones(nt) sst_boundary=T_boundary[0]*np.ones((2)) if np.all(forcing!=None): forcing = np.interp(np.arange(0,len(forcing)*dt_f,C['dt']),np.arange(0,len(forcing)*dt_f,dt_f),forcing) else: forcing = np.zeros(nt+1) sst_out = np.zeros((nt1,C['nx'])) tas_out = np.zeros((nt1,C['nx'])) hice_out = np.zeros((nt1,C['nx'])) sflx_f_out = np.zeros((nt1,C['nx'])) sflx_out = np.zeros((nt1,C['nx'])) if np.all(spatial_pattern==None): spatial_pattern=np.ones(C['nx']) if np.all(atm_DA_tendencies!=None): use_atm_tendencies=True else: use_atm_tendencies=False if np.all(ocn_DA_tendencies!=None): use_ocn_tendencies=True else: use_ocn_tendencies=False if return_coupled_fluxes: atm_DA_tendencies = np.zeros((nt,C['nx'])) ocn_DA_tendencies = np.zeros((nt,C['nx'])) c=0; c2=0; c3=0; n=1 /(C['H']*C['Cpo']*C['ro'])-C['emissivity']*5.67E-8*(sst_boundary[n-1]**4)*C['dt']/(C['H']*C['Cpo']*C['ro'])+(T_boundary[nn]-sst_boundary[n-1])*C['dt']/C['period'] a,Cp,va,vo,Da,Do,Cs,T0,Tf,emissivity,SW0,SW_anom,H,Hb,Cpo,ro,tau_entrainment,Li,ri,use_ocn_tendencies,use_atm_tendencies, atm_DA_tendencies, ocn_DA_tendencies,ice_model,atm_adv,return_coupled_fluxes): c=0; c2=0; c3=0; n=1 radiation = -LW_cooling sst_boundary[n]=sst_boundary[n-1]+SW_warming[0]*dt/(H*Cpo*ro)-emissivity*5.67E-8*(sst_boundary[n-1]**4)*dt/(H*Cpo*ro)+(T0-sst_boundary[n-1])*dt/(360*24*3600) if atm_adv: a_adv = np.concatenate((sst_boundary[n-1]-tas[n-1,:1],tas[n-1,:-1]-tas[n-1,1:]),axis=0)*(va*dt/xx) iff = (tas[n-1,2:]+tas[n-1,:-2]-2*tas[n-1,1:-1])*(Da*dt/(xx**2)) a_diff0 = (tas[n-1,1]+sst_boundary[n-1]-2*tas[n-1,0])*(Da*dt/(xx**2)) a_diff = np.concatenate((np.array([a_diff0]),a_diff,a_diff[-1:]),axis=0) a_netsflx = (sflx*dt)/(Hb*Cp*ra) + net_radiation*dt/(Hb*Cp*ra) if return_coupled_fluxes: atm_DA_tendencies[nn,:]=np.sum((a_adv,a_diff),axis=0) if use_atm_tendencies: tas[n,:] = tas[n-1,:] + a_netsflx + atm_DA_tendencies[c3,:] else: tas[n,:] = tas[n-1,:] + a_netsflx + a_adv + a_diff o_adv = np.concatenate((sst_boundary[n-1]-sst[n-1,:1],sst[n-1,:-1]-sst[n-1,1:]),axis=0)*(vo*dt/xx) o_diff = (sst[n-1,2:]+sst[n-1,:-2]-2*sst[n-1,1:-1])*(Do*dt/(xx**2)) o_diff0 = (sst[n-1,1]+sst_boundary[n-1]-2*sst[n-1,0])*(Do*dt/(xx**2)) o_diff = np.concatenate((np.array([o_diff0]),o_diff,o_diff[-1:]),axis=0) o_entrain = (T0-sst[n-1,:])*dt/tau_entrainment o_netsflx = -sflx*dt/(H*Cpo*ro)+SW_warming*dt/(H*Cpo*ro) if return_coupled_fluxes: ocn_DA_tendencies[nn,:] = o_adv + o_diff + o_entrain if use_ocn_tendencies: sst[n,:] = sst[n-1,:] + o_netsflx + ocn_DA_tendencies[c3,:] else: sst[n,:] = sst[n-1,:] + o_netsflx + o_adv + o_diff + o_entrain if ice_model: ice_mask = (hice[n-1,:]>0).astype(np.float) freezing_mask = (sst[n,:]<Tf).astype(np.float) dEdt = H*ro*Cpo*(sst[n,:]-sst[n-1,:])/dt excess_freeze = freezing_mask*np.max([-dEdt,np.zeros(nx)],axis=0) excess_melt = ice_mask*np.max([dEdt,np.zeros(nx)],axis=0) dhice_freeze = dt*excess_freeze/(Li*ri) dhice_melt= dt*excess_melt/(Li*ri) hice[n,:] = hice[n-1,:] + dhice_freeze - dhice_melt hice_melt = (dhice_melt>0).astype(np.float)*np.min([dhice_melt,hice[n-1,:]],axis=0) hice[n,:] = np.max([hice[n,:],np.zeros(nx)],axis=0) sst[n,:] = sst[n,:] + dt*excess_freeze/(H*Cpo*ro) sst[n,:] = sst[n,:] - hice_melt*(Li*ri)/(ro*Cpo*H) sst_boundary[0]=sst_boundary[1].copy() hice_out[np.where(hice_out==0)]=np.nan return tas_out, sst_out, hice_out, sflx_out, sflx_f_out, atm_DA_tendencies, ocn_DA_tendencies def CoupledChannel2(C,forcing, dt_f=30*24*3600, ocn_mixing_ratio=0, restoring=False,ice_model=True,atm_adv=True,spatial_pattern=None,atm_DA_tendencies=None,ocn_DA_tendencies=None, return_coupled_fluxes=False,random_amp=0.1): nt=int(C['tmax']/C['dt']) nt1=int(C['tmax']/C['avep']) tau=float(C['period'])/float(C['dt']) rtas=np.random.rand(C['nx']) sst=C['T0']*np.ones((2,C['nx'])) tas=C['T0']*np.ones((2,C['nx']))+rtas hice=np.zeros((2,C['nx'])) sst_boundary=C['T0']*np.ones((2)) if np.all(forcing!=None): forcing = np.interp(np.arange(0,len(forcing)*dt_f,C['dt']),np.arange(0,len(forcing)*dt_f,dt_f),forcing) else: forcing = np.zeros(nt+1) sst_out = np.zeros((nt1,C['nx'])) tas_out = np.zeros((nt1,C['nx'])) hice_out = np.zeros((nt1,C['nx'])) sflx_f_out = np.zeros((nt1,C['nx'])) sflx_out = np.zeros((nt1,C['nx'])) if np.all(spatial_pattern==None): spatial_pattern=np.ones(C['nx']) if np.all(atm_DA_tendencies!=None): use_atm_tendencies=True else: use_atm_tendencies=False if np.all(ocn_DA_tendencies!=None): use_ocn_tendencies=True else: use_ocn_tendencies=False atm_DA_tendencies = np.zeros((nt,C['nx'])) ocn_DA_tendencies = np.zeros((nt,C['nx'])) tas_out, sst_out, hice_out, sflx_out, sflx_f_out, atm_DA_tendencies, ocn_DA_tendencies=CoupledChannel_time(nt,C['nx'],C['xx'],C['dt'],C['avep'],sst,tas,hice,sst_boundary,sst_out,tas_out,hice_out,sflx_f_out,sflx_out,forcing,spatial_pattern,C['ra'],C['Cp'],C['va'],C['vo'],C['Da'],C['Do'],C['Cs'],C['T0'],C['Tf'],C['emissivity'],C['SW0'],C['SW_anom'],C['H'],C['Hb'],C['Cpo'],C['ro'],C['tau_entrainment'],C['Li'],C['ri'],use_ocn_tendencies,use_atm_tendencies, atm_DA_tendencies, ocn_DA_tendencies,ice_model,atm_adv,return_coupled_fluxes) if return_coupled_fluxes: return tas_out, sst_out, hice_out, sflx_out, sflx_f_out, nt1, nt, atm_DA_tendencies, ocn_DA_tendencies else: return tas_out, sst_out, hice_out, sflx_out, sflx_f_out, nt1, nt

true

790bf5f84234ec065a5d0d52c9aaa59b1d07ab77

2,728

py

Python

Module_02_Building_Your_Own_Custom_Object_Detector/2.10_Re-Training_and_Running_your_Classifier/hard_negative_mine.py

CactusJackFX/PyImageSearch_Guru

01f5bce644b58848db029f72656002e21545bb10

[ "Apache-2.0" ]

2

2020-02-12T12:17:01.000Z

2021-01-07T02:31:18.000Z

Module_02_Building_Your_Own_Custom_Object_Detector/2.10_Re-Training_and_Running_your_Classifier/hard_negative_mine.py

CactusJackFX/PyImageSearch_Guru

01f5bce644b58848db029f72656002e21545bb10

[ "Apache-2.0" ]

1

2020-03-22T06:33:10.000Z

Module_02_Building_Your_Own_Custom_Object_Detector/2.9_Hard-Negative_Mining/hard_negative_mine.py

CactusJackFX/PyImageSearch_Guru

01f5bce644b58848db029f72656002e21545bb10

[ "Apache-2.0" ]

3

2020-02-18T05:24:13.000Z

2020-09-21T06:58:58.000Z

# USAGE # python hard_negative_mine.py --conf conf/cars.json # import the necessary packages from __future__ import print_function from pyimagesearch.object_detection import ObjectDetector from pyimagesearch.descriptors import HOG from pyimagesearch.utils import dataset from pyimagesearch.utils import Conf from imutils import paths import numpy as np import progressbar import argparse import pickle import random import cv2 # construct the argument parser and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-c", "--conf", required=True, help="path to the configuration file") args = vars(ap.parse_args()) # load the configuration file and initialize the data list conf = Conf(args["conf"]) data = [] # load the classifier, then initialize the Histogram of Oriented Gradients descriptor # and the object detector model = pickle.loads(open(conf["classifier_path"], "rb").read()) hog = HOG(orientations=conf["orientations"], pixelsPerCell=tuple(conf["pixels_per_cell"]), cellsPerBlock=tuple(conf["cells_per_block"]), normalize=conf["normalize"], block_norm="L1") od = ObjectDetector(model, hog) # grab the set of distraction paths and randomly sample them dstPaths = list(paths.list_images(conf["image_distractions"])) dstPaths = random.sample(dstPaths, conf["hn_num_distraction_images"]) # setup the progress bar widgets = ["Mining: ", progressbar.Percentage(), " ", progressbar.Bar(), " ", progressbar.ETA()] pbar = progressbar.ProgressBar(maxval=len(dstPaths), widgets=widgets).start() # loop over the distraction paths for (i, imagePath) in enumerate(dstPaths): # load the image and convert it to grayscale image = cv2.imread(imagePath) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # detect objects in the image (boxes, probs) = od.detect(gray, conf["window_dim"], winStep=conf["hn_window_step"], pyramidScale=conf["hn_pyramid_scale"], minProb=conf["hn_min_probability"]) # loop over the bounding boxes for (prob, (startX, startY, endX, endY)) in zip(probs, boxes): # extract the ROI from the image, resize it to a known, canonical size, extract # HOG features from teh ROI, and finally update the data roi = cv2.resize(gray[startY:endY, startX:endX], tuple(conf["window_dim"]), interpolation=cv2.INTER_AREA) features = hog.describe(roi) data.append(np.hstack([[prob], features])) # update the progress bar pbar.update(i) # sort the data points by confidence pbar.finish() print("[INFO] sorting by probability...") data = np.array(data) data = data[data[:, 0].argsort()[::-1]] # dump the dataset to file print("[INFO] dumping hard negatives to file...") dataset.dump_dataset(data[:, 1:], [-1] * len(data), conf["features_path"], "hard_negatives", writeMethod="a")

37.369863

96

0.752933

from __future__ import print_function from pyimagesearch.object_detection import ObjectDetector from pyimagesearch.descriptors import HOG from pyimagesearch.utils import dataset from pyimagesearch.utils import Conf from imutils import paths import numpy as np import progressbar import argparse import pickle import random import cv2 ap = argparse.ArgumentParser() ap.add_argument("-c", "--conf", required=True, help="path to the configuration file") args = vars(ap.parse_args()) conf = Conf(args["conf"]) data = [] model = pickle.loads(open(conf["classifier_path"], "rb").read()) hog = HOG(orientations=conf["orientations"], pixelsPerCell=tuple(conf["pixels_per_cell"]), cellsPerBlock=tuple(conf["cells_per_block"]), normalize=conf["normalize"], block_norm="L1") od = ObjectDetector(model, hog) dstPaths = list(paths.list_images(conf["image_distractions"])) dstPaths = random.sample(dstPaths, conf["hn_num_distraction_images"]) widgets = ["Mining: ", progressbar.Percentage(), " ", progressbar.Bar(), " ", progressbar.ETA()] pbar = progressbar.ProgressBar(maxval=len(dstPaths), widgets=widgets).start() for (i, imagePath) in enumerate(dstPaths): image = cv2.imread(imagePath) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) (boxes, probs) = od.detect(gray, conf["window_dim"], winStep=conf["hn_window_step"], pyramidScale=conf["hn_pyramid_scale"], minProb=conf["hn_min_probability"]) for (prob, (startX, startY, endX, endY)) in zip(probs, boxes): roi = cv2.resize(gray[startY:endY, startX:endX], tuple(conf["window_dim"]), interpolation=cv2.INTER_AREA) features = hog.describe(roi) data.append(np.hstack([[prob], features])) pbar.update(i) pbar.finish() print("[INFO] sorting by probability...") data = np.array(data) data = data[data[:, 0].argsort()[::-1]] print("[INFO] dumping hard negatives to file...") dataset.dump_dataset(data[:, 1:], [-1] * len(data), conf["features_path"], "hard_negatives", writeMethod="a")

true

790bf6088d55d4f16f4c829e953b087d0a1c8eb4

1,422

py

Python

macaw/macaw.py

dcchambers/macaw

d16ea2d8021323d3be65d5449e06e61e7f527355

[ "MIT" ]

null

macaw/macaw.py

dcchambers/macaw

d16ea2d8021323d3be65d5449e06e61e7f527355

[ "MIT" ]

1

2021-04-16T18:37:46.000Z

macaw/macaw.py

dcchambers/macaw

d16ea2d8021323d3be65d5449e06e61e7f527355

[ "MIT" ]

null

#!/usr/bin/env python3 # Macaw # # Testing file open and string concatenation. import random import pkgutil def main(): # This dictionary of words is for testing only and should *not* be considered secure. # Courtesy of https://gist.github.com/deekayen/4148741 #f = open('dictionary.txt') f = pkgutil.get_data("macaw","dictionary.txt").decode("utf8") wordList = f.split() password = generatePassword(wordList) speakPassword(password) def speakPassword(str): print(r""" ,,,___ ,' _ \__ ___________________________________________ / { O / `\ / \ ,\ } /---./ .-' """+str+""" /\ `-.__- `--' `-. | / `._ : | \___________________________________________/ /\_; -' : ; / \_; / / /| \ \_/..-' ________|_\___/_\\\_\\\________ ----------------;;-;;-------- \/ `-'/ |\_|_/| \/ \/ \_/ """) def generatePassword(wordList): tempPass = '' for i in range(0, 5): word = wordList[random.randint(0,999)] # grab a random word from the dictionary file. tempPass = tempPass + word #concat that word to the end of the password. return tempPass

30.913043

93

0.466245

import random import pkgutil def main(): f = pkgutil.get_data("macaw","dictionary.txt").decode("utf8") wordList = f.split() password = generatePassword(wordList) speakPassword(password) def speakPassword(str): print(r""" ,,,___ ,' _ \__ ___________________________________________ / { O / `\ / \ ,\ } /---./ .-' """+str+""" /\ `-.__- `--' `-. | / `._ : | \___________________________________________/ /\_; -' : ; / \_; / / /| \ \_/..-' ________|_\___/_\\\_\\\________ ----------------;;-;;-------- \/ `-'/ |\_|_/| \/ \/ \_/ """) def generatePassword(wordList): tempPass = '' for i in range(0, 5): word = wordList[random.randint(0,999)] tempPass = tempPass + word return tempPass

true

790bf61367d85b79bae4b153328b229b10721b38

1,495

py

Python

tensorflow/contrib/losses/__init__.py

xincao79/tensorflow

7fa0cf39f854d5fdaaa19ad6425dfed02f5fea64

[ "Apache-2.0" ]

384

2017-02-21T18:38:04.000Z

2022-02-22T07:30:25.000Z

tensorflow/contrib/losses/__init__.py

xincao79/tensorflow

7fa0cf39f854d5fdaaa19ad6425dfed02f5fea64

[ "Apache-2.0" ]

15

2017-03-01T20:18:43.000Z

2020-05-07T10:33:51.000Z

tensorflow/contrib/losses/__init__.py

xincao79/tensorflow

7fa0cf39f854d5fdaaa19ad6425dfed02f5fea64

[ "Apache-2.0" ]

81

2017-02-21T19:31:19.000Z

2022-02-22T07:30:24.000Z

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Ops for building neural network losses. See @{$python/contrib.losses}. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function # pylint: disable=wildcard-import from tensorflow.contrib.losses.python.losses import * # pylint: enable=wildcard-import from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ 'absolute_difference', 'add_loss', 'hinge_loss', 'compute_weighted_loss', 'cosine_distance', 'get_losses', 'get_regularization_losses', 'get_total_loss', 'log_loss', 'mean_pairwise_squared_error', 'mean_squared_error', 'sigmoid_cross_entropy', 'softmax_cross_entropy', 'sparse_softmax_cross_entropy', ] remove_undocumented(__name__, _allowed_symbols)

31.145833

80

0.720401

from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.losses.python.losses import * from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ 'absolute_difference', 'add_loss', 'hinge_loss', 'compute_weighted_loss', 'cosine_distance', 'get_losses', 'get_regularization_losses', 'get_total_loss', 'log_loss', 'mean_pairwise_squared_error', 'mean_squared_error', 'sigmoid_cross_entropy', 'softmax_cross_entropy', 'sparse_softmax_cross_entropy', ] remove_undocumented(__name__, _allowed_symbols)

true

790bf63ebd6b665702445bddb88c8a1278a17112

172

py

Python

bookr/reviews/utils.py

rodrigobmedeiros/Bookr

bc8313226b020755c16f5ea2574f8716bd3774fd

[ "MIT" ]

null

bookr/reviews/utils.py

rodrigobmedeiros/Bookr

bc8313226b020755c16f5ea2574f8716bd3774fd

[ "MIT" ]

null

bookr/reviews/utils.py

rodrigobmedeiros/Bookr

bc8313226b020755c16f5ea2574f8716bd3774fd

[ "MIT" ]

null

def average_rating(rating_list): if not rating_list: # if rating_list is empty return 0 return 0 return round(sum(rating_list) / len(rating_list))

24.571429

53

0.680233

def average_rating(rating_list): if not rating_list: return 0 return round(sum(rating_list) / len(rating_list))

true

790bf65c4b6713bbef82dca5b557e23041d8c9ce

3,408

py

Python

python/benchmark/function/test_cumprod.py

isabella232/nnabla

82a3c6fed382f889d1a4a429c696bb8cedf6ce79

[ "Apache-2.0" ]

1

2019-05-31T14:00:58.000Z

python/benchmark/function/test_cumprod.py

Pandinosaurus/nnabla

62a21db4afc15c52ce43f3f5b87e5fa4181b2deb

[ "Apache-2.0" ]

null

python/benchmark/function/test_cumprod.py

Pandinosaurus/nnabla

62a21db4afc15c52ce43f3f5b87e5fa4181b2deb

[ "Apache-2.0" ]

null

# Copyright 2021 Sony Group Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest import numpy as np import nnabla.functions as F from function_benchmark import FunctionBenchmark, Inspec class Case: def __init__(self, shape, axis, rtol=1e-6): # rtol (relative tolerance) 1e-6 is default for assert_allclose self.shape = shape self.axis = axis self.rtol = rtol # Print this message by pytest when a test fails. def __repr__(self): return 'Case(shape=' + str(self.shape) + \ ' axes=' + str(self.axis) + \ ', rtol=' + str(self.rtol) + ')' test_cases = [ # -------------------------------- # Common use case # -------------------------------- # Axis 0 Case((512, 512), 0), Case((512, 1024), 0), Case((512, 2048), 0), Case((1024, 512), 0), Case((1024, 1024), 0), Case((1024, 2048), 0), Case((2048, 512), 0), Case((2048, 1024), 0), Case((2048, 2048), 0), # Axis 1 Case((512, 512), 1), Case((512, 1024), 1), Case((512, 2048), 1), Case((1024, 512), 1), Case((1024, 1024), 1), Case((1024, 2048), 1), Case((2048, 512), 1), Case((2048, 1024), 1), Case((2048, 2048), 1), # -------------------------------- # Large cases # -------------------------------- Case((1024*1024, 32), 1), Case((32, 1024*1024), 0), Case((2048, 2048), 1), Case((2048, 2048), 0), Case((2024*2024, 2), 0), Case((2, 2024*2024), 1), # Weak cases # PyTorch uses Cub library in these cases. Case((2024*2024, 1), 0), Case((1, 2024*2024), 1), ] def create_cumprod_input(rng, shape, axis, with_mask): x = (rng.randn(*shape)).astype(np.float32) if with_mask: # Make zero elements with the probability of `1 / x_shape[axis]`. # It is the probability of existence of one zero element in each scan axis. mask = rng.rand(*shape) > (1.0 / shape[axis]) x = x * mask return x @pytest.mark.parametrize("seed", [123]) @pytest.mark.parametrize("test_case", test_cases) @pytest.mark.parametrize('exclusive', [False, True]) @pytest.mark.parametrize('reverse', [False, True]) @pytest.mark.parametrize("with_mask", [True, False]) def test_cumprod(seed, test_case, exclusive, reverse, with_mask, nnabla_opts): x_shape = test_case.shape axis = test_case.axis def init(shape): rng = np.random.RandomState(seed) return create_cumprod_input(rng, shape, axis, with_mask) need_grad = True inputs = [Inspec(x_shape, init, need_grad)] func_kwargs = dict( axis=axis, exclusive=exclusive, reverse=reverse, ) fb = FunctionBenchmark( F.cumprod, inputs, [], func_kwargs, nnabla_opts.ext, nnabla_opts.ext_kwargs) fb.benchmark() fb.write(writer=nnabla_opts.function_benchmark_writer)

29.634783

83

0.601232

import pytest import numpy as np import nnabla.functions as F from function_benchmark import FunctionBenchmark, Inspec class Case: def __init__(self, shape, axis, rtol=1e-6): self.shape = shape self.axis = axis self.rtol = rtol def __repr__(self): return 'Case(shape=' + str(self.shape) + \ ' axes=' + str(self.axis) + \ ', rtol=' + str(self.rtol) + ')' test_cases = [ Case((512, 512), 0), Case((512, 1024), 0), Case((512, 2048), 0), Case((1024, 512), 0), Case((1024, 1024), 0), Case((1024, 2048), 0), Case((2048, 512), 0), Case((2048, 1024), 0), Case((2048, 2048), 0), Case((512, 512), 1), Case((512, 1024), 1), Case((512, 2048), 1), Case((1024, 512), 1), Case((1024, 1024), 1), Case((1024, 2048), 1), Case((2048, 512), 1), Case((2048, 1024), 1), Case((2048, 2048), 1), Case((1024*1024, 32), 1), Case((32, 1024*1024), 0), Case((2048, 2048), 1), Case((2048, 2048), 0), Case((2024*2024, 2), 0), Case((2, 2024*2024), 1), Case((2024*2024, 1), 0), Case((1, 2024*2024), 1), ] def create_cumprod_input(rng, shape, axis, with_mask): x = (rng.randn(*shape)).astype(np.float32) if with_mask: mask = rng.rand(*shape) > (1.0 / shape[axis]) x = x * mask return x @pytest.mark.parametrize("seed", [123]) @pytest.mark.parametrize("test_case", test_cases) @pytest.mark.parametrize('exclusive', [False, True]) @pytest.mark.parametrize('reverse', [False, True]) @pytest.mark.parametrize("with_mask", [True, False]) def test_cumprod(seed, test_case, exclusive, reverse, with_mask, nnabla_opts): x_shape = test_case.shape axis = test_case.axis def init(shape): rng = np.random.RandomState(seed) return create_cumprod_input(rng, shape, axis, with_mask) need_grad = True inputs = [Inspec(x_shape, init, need_grad)] func_kwargs = dict( axis=axis, exclusive=exclusive, reverse=reverse, ) fb = FunctionBenchmark( F.cumprod, inputs, [], func_kwargs, nnabla_opts.ext, nnabla_opts.ext_kwargs) fb.benchmark() fb.write(writer=nnabla_opts.function_benchmark_writer)

true

790bf68e6bb4b60e25c139472fb6878dd263f693

5,938

py

Python

test_autofit/mapper/test_take_attributes.py

rhayes777/PyAutoF

87f56419348833b285b00da1a524e329588e0b01

[ "MIT" ]

null

test_autofit/mapper/test_take_attributes.py

rhayes777/PyAutoF

87f56419348833b285b00da1a524e329588e0b01

[ "MIT" ]

null

test_autofit/mapper/test_take_attributes.py

rhayes777/PyAutoF

87f56419348833b285b00da1a524e329588e0b01

[ "MIT" ]

null

import pytest import autofit as af from autofit.mock import mock as m @pytest.fixture( name="target_gaussian" ) def make_target_gaussian(): return af.PriorModel( m.Gaussian ) @pytest.fixture( name="prior" ) def make_prior(): return af.UniformPrior() @pytest.fixture( name="source_gaussian" ) def make_source_gaussian(prior): return af.PriorModel( m.Gaussian, centre=prior ) def test_simple( source_gaussian, target_gaussian, prior ): target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre == prior def test_assertions( source_gaussian, target_gaussian ): target_gaussian.add_assertion( target_gaussian.centre <= target_gaussian.intensity ) with pytest.raises(AssertionError): target_gaussian.take_attributes( source_gaussian ) def test_assertions_collection( source_gaussian, target_gaussian ): target_gaussian.add_assertion( target_gaussian.centre <= target_gaussian.intensity ) target_collection = af.Collection( gaussian=target_gaussian ) source_collection = af.Collection( gaussian=source_gaussian ) with pytest.raises(AssertionError): target_collection.take_attributes( source_collection ) def test_in_collection( source_gaussian, target_gaussian, prior ): target = af.CollectionPriorModel( gaussian=target_gaussian ) source = af.CollectionPriorModel( gaussian=source_gaussian ) target.take_attributes( source ) assert target.gaussian.centre == prior def test_tuple( source_gaussian, target_gaussian, prior ): source_gaussian.centre = (prior, 1.0) target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre == (prior, 1.0) def test_tuple_prior( source_gaussian, target_gaussian, prior ): source_gaussian.centre = (prior, 1.0) target_gaussian.centre = af.TuplePrior() target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre == (prior, 1.0) def test_tuple_in_instance( target_gaussian, prior ): # noinspection PyTypeChecker source_gaussian = m.Gaussian( centre=(prior, 1.0) ) target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre == (prior, 1.0) def test_tuple_in_collection( source_gaussian, target_gaussian, prior ): source_gaussian.centre = (prior, 1.0) source = af.CollectionPriorModel( gaussian=source_gaussian ) target = af.CollectionPriorModel( gaussian=target_gaussian ) target.take_attributes(source) assert target.gaussian.centre == (prior, 1.0) def test_tuple_in_instance_in_collection( target_gaussian, prior ): # noinspection PyTypeChecker source_gaussian = m.Gaussian( centre=(prior, 1.0) ) source = af.CollectionPriorModel( gaussian=source_gaussian ) target = af.CollectionPriorModel( gaussian=target_gaussian ) target.take_attributes(source) assert target.gaussian.centre == (prior, 1.0) def test_source_is_dict( source_gaussian, target_gaussian, prior ): source = dict( gaussian=source_gaussian ) target = af.CollectionPriorModel( gaussian=target_gaussian ) target.take_attributes(source) assert target.gaussian.centre == prior def test_target_is_dict( source_gaussian, target_gaussian, prior ): source = af.CollectionPriorModel( collection=af.CollectionPriorModel( gaussian=source_gaussian ) ) target = af.CollectionPriorModel( collection=dict( gaussian=target_gaussian ) ) target.take_attributes(source) assert target.collection.gaussian.centre == prior def test_missing_from_source( target_gaussian, prior ): target_gaussian.centre = prior target_gaussian.take_attributes( af.CollectionPriorModel() ) assert target_gaussian.centre == prior def test_unlabelled_in_collection( source_gaussian, target_gaussian, prior ): target = af.CollectionPriorModel( [target_gaussian] ) source = af.CollectionPriorModel( [source_gaussian] ) target.take_attributes( source ) assert target[0].centre == prior def test_passing_float( source_gaussian, target_gaussian ): source_gaussian.centre = 2.0 target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre == 2.0 def test_missing_from_origin( target_gaussian ): target_gaussian.take_attributes( af.CollectionPriorModel() ) def test_limits( source_gaussian, target_gaussian ): source_gaussian.centre = af.GaussianPrior( mean=0, sigma=1, lower_limit=-1, upper_limit=1 ) target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre.lower_limit == -1 assert target_gaussian.centre.upper_limit == 1 def test_tuples(): centre = (0.0, 1.0) source = af.Model( m.Gaussian, centre=centre ) target = af.Model( m.Gaussian ) target.take_attributes(source) assert target.centre == centre

20.335616

60

0.616201

import pytest import autofit as af from autofit.mock import mock as m @pytest.fixture( name="target_gaussian" ) def make_target_gaussian(): return af.PriorModel( m.Gaussian ) @pytest.fixture( name="prior" ) def make_prior(): return af.UniformPrior() @pytest.fixture( name="source_gaussian" ) def make_source_gaussian(prior): return af.PriorModel( m.Gaussian, centre=prior ) def test_simple( source_gaussian, target_gaussian, prior ): target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre == prior def test_assertions( source_gaussian, target_gaussian ): target_gaussian.add_assertion( target_gaussian.centre <= target_gaussian.intensity ) with pytest.raises(AssertionError): target_gaussian.take_attributes( source_gaussian ) def test_assertions_collection( source_gaussian, target_gaussian ): target_gaussian.add_assertion( target_gaussian.centre <= target_gaussian.intensity ) target_collection = af.Collection( gaussian=target_gaussian ) source_collection = af.Collection( gaussian=source_gaussian ) with pytest.raises(AssertionError): target_collection.take_attributes( source_collection ) def test_in_collection( source_gaussian, target_gaussian, prior ): target = af.CollectionPriorModel( gaussian=target_gaussian ) source = af.CollectionPriorModel( gaussian=source_gaussian ) target.take_attributes( source ) assert target.gaussian.centre == prior def test_tuple( source_gaussian, target_gaussian, prior ): source_gaussian.centre = (prior, 1.0) target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre == (prior, 1.0) def test_tuple_prior( source_gaussian, target_gaussian, prior ): source_gaussian.centre = (prior, 1.0) target_gaussian.centre = af.TuplePrior() target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre == (prior, 1.0) def test_tuple_in_instance( target_gaussian, prior ): source_gaussian = m.Gaussian( centre=(prior, 1.0) ) target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre == (prior, 1.0) def test_tuple_in_collection( source_gaussian, target_gaussian, prior ): source_gaussian.centre = (prior, 1.0) source = af.CollectionPriorModel( gaussian=source_gaussian ) target = af.CollectionPriorModel( gaussian=target_gaussian ) target.take_attributes(source) assert target.gaussian.centre == (prior, 1.0) def test_tuple_in_instance_in_collection( target_gaussian, prior ): source_gaussian = m.Gaussian( centre=(prior, 1.0) ) source = af.CollectionPriorModel( gaussian=source_gaussian ) target = af.CollectionPriorModel( gaussian=target_gaussian ) target.take_attributes(source) assert target.gaussian.centre == (prior, 1.0) def test_source_is_dict( source_gaussian, target_gaussian, prior ): source = dict( gaussian=source_gaussian ) target = af.CollectionPriorModel( gaussian=target_gaussian ) target.take_attributes(source) assert target.gaussian.centre == prior def test_target_is_dict( source_gaussian, target_gaussian, prior ): source = af.CollectionPriorModel( collection=af.CollectionPriorModel( gaussian=source_gaussian ) ) target = af.CollectionPriorModel( collection=dict( gaussian=target_gaussian ) ) target.take_attributes(source) assert target.collection.gaussian.centre == prior def test_missing_from_source( target_gaussian, prior ): target_gaussian.centre = prior target_gaussian.take_attributes( af.CollectionPriorModel() ) assert target_gaussian.centre == prior def test_unlabelled_in_collection( source_gaussian, target_gaussian, prior ): target = af.CollectionPriorModel( [target_gaussian] ) source = af.CollectionPriorModel( [source_gaussian] ) target.take_attributes( source ) assert target[0].centre == prior def test_passing_float( source_gaussian, target_gaussian ): source_gaussian.centre = 2.0 target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre == 2.0 def test_missing_from_origin( target_gaussian ): target_gaussian.take_attributes( af.CollectionPriorModel() ) def test_limits( source_gaussian, target_gaussian ): source_gaussian.centre = af.GaussianPrior( mean=0, sigma=1, lower_limit=-1, upper_limit=1 ) target_gaussian.take_attributes( source_gaussian ) assert target_gaussian.centre.lower_limit == -1 assert target_gaussian.centre.upper_limit == 1 def test_tuples(): centre = (0.0, 1.0) source = af.Model( m.Gaussian, centre=centre ) target = af.Model( m.Gaussian ) target.take_attributes(source) assert target.centre == centre

true

790bf6bb1e0f9e05694d9b6a3d0724b15d046a61

890

py

Python

sfm2latex/dictionary/Image.py

redmer/sfm2latex

6d57eadf7af10058ade3999efec6c590295027b2

[ "MIT" ]

null

sfm2latex/dictionary/Image.py

redmer/sfm2latex

6d57eadf7af10058ade3999efec6c590295027b2

[ "MIT" ]

null

sfm2latex/dictionary/Image.py

redmer/sfm2latex

6d57eadf7af10058ade3999efec6c590295027b2

[ "MIT" ]

null

from ..utils import sortkey, capitalize_first FIGURE_TEX_TEMPLATE = r'\hwgraphic{{{path}}}{{{headword}}}{{{attribution}}}' # change to {filename} if you want to specify full paths. FIGURE_PATH_TEMPLATE = r'figures/ill-{filename}' class Image(object): type = 'img' def sk(self): return sortkey(self.hw) def __init__(self, hw='', img_src='', img_attrib=''): super().__init__() self.hw = hw self.img_src = img_src self.img_attrib = img_attrib def __repr__(self): return "(Image of '{headword}')".format( headword=self.hw ) def render(self, settings={}): figure_path = FIGURE_PATH_TEMPLATE.format(filename=self.img_src) return FIGURE_TEX_TEMPLATE.format( headword=capitalize_first(self.hw), path=figure_path, attribution=self.img_attrib )

27.8125

76

0.622472

from ..utils import sortkey, capitalize_first FIGURE_TEX_TEMPLATE = r'\hwgraphic{{{path}}}{{{headword}}}{{{attribution}}}' FIGURE_PATH_TEMPLATE = r'figures/ill-{filename}' class Image(object): type = 'img' def sk(self): return sortkey(self.hw) def __init__(self, hw='', img_src='', img_attrib=''): super().__init__() self.hw = hw self.img_src = img_src self.img_attrib = img_attrib def __repr__(self): return "(Image of '{headword}')".format( headword=self.hw ) def render(self, settings={}): figure_path = FIGURE_PATH_TEMPLATE.format(filename=self.img_src) return FIGURE_TEX_TEMPLATE.format( headword=capitalize_first(self.hw), path=figure_path, attribution=self.img_attrib )

true

790bf764810a2b414f0c97895b25464e4c42d581

12,706

py

Python

sympy/vector/tests/test_coordsysrect.py

ovolve/sympy

0a15782f20505673466b940454b33b8014a25c13

[ "BSD-3-Clause" ]

4

2018-07-04T17:20:12.000Z

2019-07-14T18:07:25.000Z

sympy/vector/tests/test_coordsysrect.py

ovolve/sympy

0a15782f20505673466b940454b33b8014a25c13

[ "BSD-3-Clause" ]

7

2017-05-01T14:15:32.000Z

2017-09-06T20:44:24.000Z

sympy/vector/tests/test_coordsysrect.py

ovolve/sympy

0a15782f20505673466b940454b33b8014a25c13

[ "BSD-3-Clause" ]

1

2020-10-02T04:21:11.000Z

from sympy.vector.coordsysrect import CoordSysCartesian from sympy.vector.scalar import BaseScalar from sympy import sin, cos, pi, ImmutableMatrix as Matrix, \ symbols, simplify, zeros, expand from sympy.vector.functions import express from sympy.vector.point import Point from sympy.vector.vector import Vector from sympy.vector.orienters import (AxisOrienter, BodyOrienter, SpaceOrienter, QuaternionOrienter) a, b, c, q = symbols('a b c q') q1, q2, q3, q4 = symbols('q1 q2 q3 q4') def test_func_args(): A = CoordSysCartesian('A') assert A.x.func(*A.x.args) == A.x expr = 3*A.x + 4*A.y assert expr.func(*expr.args) == expr assert A.i.func(*A.i.args) == A.i v = A.x*A.i + A.y*A.j + A.z*A.k assert v.func(*v.args) == v assert A.origin.func(*A.origin.args) == A.origin def test_coordsyscartesian_equivalence(): A = CoordSysCartesian('A') A1 = CoordSysCartesian('A') assert A1 == A B = CoordSysCartesian('B') assert A != B def test_orienters(): A = CoordSysCartesian('A') axis_orienter = AxisOrienter(a, A.k) body_orienter = BodyOrienter(a, b, c, '123') space_orienter = SpaceOrienter(a, b, c, '123') q_orienter = QuaternionOrienter(q1, q2, q3, q4) assert axis_orienter.rotation_matrix(A) == Matrix([ [ cos(a), sin(a), 0], [-sin(a), cos(a), 0], [ 0, 0, 1]]) assert body_orienter.rotation_matrix() == Matrix([ [ cos(b)*cos(c), sin(a)*sin(b)*cos(c) + sin(c)*cos(a), sin(a)*sin(c) - sin(b)*cos(a)*cos(c)], [-sin(c)*cos(b), -sin(a)*sin(b)*sin(c) + cos(a)*cos(c), sin(a)*cos(c) + sin(b)*sin(c)*cos(a)], [ sin(b), -sin(a)*cos(b), cos(a)*cos(b)]]) assert space_orienter.rotation_matrix() == Matrix([ [cos(b)*cos(c), sin(c)*cos(b), -sin(b)], [sin(a)*sin(b)*cos(c) - sin(c)*cos(a), sin(a)*sin(b)*sin(c) + cos(a)*cos(c), sin(a)*cos(b)], [sin(a)*sin(c) + sin(b)*cos(a)*cos(c), -sin(a)*cos(c) + sin(b)*sin(c)*cos(a), cos(a)*cos(b)]]) assert q_orienter.rotation_matrix() == Matrix([ [q1**2 + q2**2 - q3**2 - q4**2, 2*q1*q4 + 2*q2*q3, -2*q1*q3 + 2*q2*q4], [-2*q1*q4 + 2*q2*q3, q1**2 - q2**2 + q3**2 - q4**2, 2*q1*q2 + 2*q3*q4], [2*q1*q3 + 2*q2*q4, -2*q1*q2 + 2*q3*q4, q1**2 - q2**2 - q3**2 + q4**2]]) def test_coordinate_vars(): """ Tests the coordinate variables functionality with respect to reorientation of coordinate systems. """ A = CoordSysCartesian('A') # Note that the name given on the lhs is different from A.x._name assert BaseScalar('A.x', 0, A, 'A_x', r'\mathbf{{x}_{A}}') == A.x assert BaseScalar('A.y', 1, A, 'A_y', r'\mathbf{{y}_{A}}') == A.y assert BaseScalar('A.z', 2, A, 'A_z', r'\mathbf{{z}_{A}}') == A.z assert BaseScalar('A.x', 0, A, 'A_x', r'\mathbf{{x}_{A}}').__hash__() == A.x.__hash__() assert isinstance(A.x, BaseScalar) and \ isinstance(A.y, BaseScalar) and \ isinstance(A.z, BaseScalar) assert A.x*A.y == A.y*A.x assert A.scalar_map(A) == {A.x: A.x, A.y: A.y, A.z: A.z} assert A.x.system == A assert A.x.diff(A.x) == 1 B = A.orient_new_axis('B', q, A.k) assert B.scalar_map(A) == {B.z: A.z, B.y: -A.x*sin(q) + A.y*cos(q), B.x: A.x*cos(q) + A.y*sin(q)} assert A.scalar_map(B) == {A.x: B.x*cos(q) - B.y*sin(q), A.y: B.x*sin(q) + B.y*cos(q), A.z: B.z} assert express(B.x, A, variables=True) == A.x*cos(q) + A.y*sin(q) assert express(B.y, A, variables=True) == -A.x*sin(q) + A.y*cos(q) assert express(B.z, A, variables=True) == A.z assert expand(express(B.x*B.y*B.z, A, variables=True)) == \ expand(A.z*(-A.x*sin(q) + A.y*cos(q))*(A.x*cos(q) + A.y*sin(q))) assert express(B.x*B.i + B.y*B.j + B.z*B.k, A) == \ (B.x*cos(q) - B.y*sin(q))*A.i + (B.x*sin(q) + \ B.y*cos(q))*A.j + B.z*A.k assert simplify(express(B.x*B.i + B.y*B.j + B.z*B.k, A, \ variables=True)) == \ A.x*A.i + A.y*A.j + A.z*A.k assert express(A.x*A.i + A.y*A.j + A.z*A.k, B) == \ (A.x*cos(q) + A.y*sin(q))*B.i + \ (-A.x*sin(q) + A.y*cos(q))*B.j + A.z*B.k assert simplify(express(A.x*A.i + A.y*A.j + A.z*A.k, B, \ variables=True)) == \ B.x*B.i + B.y*B.j + B.z*B.k N = B.orient_new_axis('N', -q, B.k) assert N.scalar_map(A) == \ {N.x: A.x, N.z: A.z, N.y: A.y} C = A.orient_new_axis('C', q, A.i + A.j + A.k) mapping = A.scalar_map(C) assert mapping[A.x] == (C.x*(2*cos(q) + 1)/3 + C.y*(-2*sin(q + pi/6) + 1)/3 + C.z*(-2*cos(q + pi/3) + 1)/3) assert mapping[A.y] == (C.x*(-2*cos(q + pi/3) + 1)/3 + C.y*(2*cos(q) + 1)/3 + C.z*(-2*sin(q + pi/6) + 1)/3) assert mapping[A.z] == (C.x*(-2*sin(q + pi/6) + 1)/3 + C.y*(-2*cos(q + pi/3) + 1)/3 + C.z*(2*cos(q) + 1)/3) D = A.locate_new('D', a*A.i + b*A.j + c*A.k) assert D.scalar_map(A) == {D.z: A.z - c, D.x: A.x - a, D.y: A.y - b} E = A.orient_new_axis('E', a, A.k, a*A.i + b*A.j + c*A.k) assert A.scalar_map(E) == {A.z: E.z + c, A.x: E.x*cos(a) - E.y*sin(a) + a, A.y: E.x*sin(a) + E.y*cos(a) + b} assert E.scalar_map(A) == {E.x: (A.x - a)*cos(a) + (A.y - b)*sin(a), E.y: (-A.x + a)*sin(a) + (A.y - b)*cos(a), E.z: A.z - c} F = A.locate_new('F', Vector.zero) assert A.scalar_map(F) == {A.z: F.z, A.x: F.x, A.y: F.y} def test_rotation_matrix(): N = CoordSysCartesian('N') A = N.orient_new_axis('A', q1, N.k) B = A.orient_new_axis('B', q2, A.i) C = B.orient_new_axis('C', q3, B.j) D = N.orient_new_axis('D', q4, N.j) E = N.orient_new_space('E', q1, q2, q3, '123') F = N.orient_new_quaternion('F', q1, q2, q3, q4) G = N.orient_new_body('G', q1, q2, q3, '123') assert N.rotation_matrix(C) == Matrix([ [- sin(q1) * sin(q2) * sin(q3) + cos(q1) * cos(q3), - sin(q1) * cos(q2), sin(q1) * sin(q2) * cos(q3) + sin(q3) * cos(q1)], \ [sin(q1) * cos(q3) + sin(q2) * sin(q3) * cos(q1), \ cos(q1) * cos(q2), sin(q1) * sin(q3) - sin(q2) * cos(q1) * \ cos(q3)], [- sin(q3) * cos(q2), sin(q2), cos(q2) * cos(q3)]]) test_mat = D.rotation_matrix(C) - Matrix( [[cos(q1) * cos(q3) * cos(q4) - sin(q3) * (- sin(q4) * cos(q2) + sin(q1) * sin(q2) * cos(q4)), - sin(q2) * sin(q4) - sin(q1) * cos(q2) * cos(q4), sin(q3) * cos(q1) * cos(q4) + cos(q3) * \ (- sin(q4) * cos(q2) + sin(q1) * sin(q2) * cos(q4))], \ [sin(q1) * cos(q3) + sin(q2) * sin(q3) * cos(q1), cos(q1) * \ cos(q2), sin(q1) * sin(q3) - sin(q2) * cos(q1) * cos(q3)], \ [sin(q4) * cos(q1) * cos(q3) - sin(q3) * (cos(q2) * cos(q4) + \ sin(q1) * sin(q2) * \ sin(q4)), sin(q2) * cos(q4) - sin(q1) * sin(q4) * cos(q2), sin(q3) * \ sin(q4) * cos(q1) + cos(q3) * (cos(q2) * cos(q4) + \ sin(q1) * sin(q2) * sin(q4))]]) assert test_mat.expand() == zeros(3, 3) assert E.rotation_matrix(N) == Matrix( [[cos(q2)*cos(q3), sin(q3)*cos(q2), -sin(q2)], [sin(q1)*sin(q2)*cos(q3) - sin(q3)*cos(q1), \ sin(q1)*sin(q2)*sin(q3) + cos(q1)*cos(q3), sin(q1)*cos(q2)], \ [sin(q1)*sin(q3) + sin(q2)*cos(q1)*cos(q3), - \ sin(q1)*cos(q3) + sin(q2)*sin(q3)*cos(q1), cos(q1)*cos(q2)]]) assert F.rotation_matrix(N) == Matrix([[ q1**2 + q2**2 - q3**2 - q4**2, 2*q1*q4 + 2*q2*q3, -2*q1*q3 + 2*q2*q4],[ -2*q1*q4 + 2*q2*q3, q1**2 - q2**2 + q3**2 - q4**2, 2*q1*q2 + 2*q3*q4], [2*q1*q3 + 2*q2*q4, -2*q1*q2 + 2*q3*q4, q1**2 - q2**2 - q3**2 + q4**2]]) assert G.rotation_matrix(N) == Matrix([[ cos(q2)*cos(q3), sin(q1)*sin(q2)*cos(q3) + sin(q3)*cos(q1), sin(q1)*sin(q3) - sin(q2)*cos(q1)*cos(q3)], [ -sin(q3)*cos(q2), -sin(q1)*sin(q2)*sin(q3) + cos(q1)*cos(q3), sin(q1)*cos(q3) + sin(q2)*sin(q3)*cos(q1)],[ sin(q2), -sin(q1)*cos(q2), cos(q1)*cos(q2)]]) def test_vector(): """ Tests the effects of orientation of coordinate systems on basic vector operations. """ N = CoordSysCartesian('N') A = N.orient_new_axis('A', q1, N.k) B = A.orient_new_axis('B', q2, A.i) C = B.orient_new_axis('C', q3, B.j) #Test to_matrix v1 = a*N.i + b*N.j + c*N.k assert v1.to_matrix(A) == Matrix([[ a*cos(q1) + b*sin(q1)], [-a*sin(q1) + b*cos(q1)], [ c]]) #Test dot assert N.i.dot(A.i) == cos(q1) assert N.i.dot(A.j) == -sin(q1) assert N.i.dot(A.k) == 0 assert N.j.dot(A.i) == sin(q1) assert N.j.dot(A.j) == cos(q1) assert N.j.dot(A.k) == 0 assert N.k.dot(A.i) == 0 assert N.k.dot(A.j) == 0 assert N.k.dot(A.k) == 1 assert N.i.dot(A.i + A.j) == -sin(q1) + cos(q1) == \ (A.i + A.j).dot(N.i) assert A.i.dot(C.i) == cos(q3) assert A.i.dot(C.j) == 0 assert A.i.dot(C.k) == sin(q3) assert A.j.dot(C.i) == sin(q2)*sin(q3) assert A.j.dot(C.j) == cos(q2) assert A.j.dot(C.k) == -sin(q2)*cos(q3) assert A.k.dot(C.i) == -cos(q2)*sin(q3) assert A.k.dot(C.j) == sin(q2) assert A.k.dot(C.k) == cos(q2)*cos(q3) #Test cross assert N.i.cross(A.i) == sin(q1)*A.k assert N.i.cross(A.j) == cos(q1)*A.k assert N.i.cross(A.k) == -sin(q1)*A.i - cos(q1)*A.j assert N.j.cross(A.i) == -cos(q1)*A.k assert N.j.cross(A.j) == sin(q1)*A.k assert N.j.cross(A.k) == cos(q1)*A.i - sin(q1)*A.j assert N.k.cross(A.i) == A.j assert N.k.cross(A.j) == -A.i assert N.k.cross(A.k) == Vector.zero assert N.i.cross(A.i) == sin(q1)*A.k assert N.i.cross(A.j) == cos(q1)*A.k assert N.i.cross(A.i + A.j) == sin(q1)*A.k + cos(q1)*A.k assert (A.i + A.j).cross(N.i) == (-sin(q1) - cos(q1))*N.k assert A.i.cross(C.i) == sin(q3)*C.j assert A.i.cross(C.j) == -sin(q3)*C.i + cos(q3)*C.k assert A.i.cross(C.k) == -cos(q3)*C.j assert C.i.cross(A.i) == (-sin(q3)*cos(q2))*A.j + \ (-sin(q2)*sin(q3))*A.k assert C.j.cross(A.i) == (sin(q2))*A.j + (-cos(q2))*A.k assert express(C.k.cross(A.i), C).trigsimp() == cos(q3)*C.j def test_orient_new_methods(): N = CoordSysCartesian('N') orienter1 = AxisOrienter(q4, N.j) orienter2 = SpaceOrienter(q1, q2, q3, '123') orienter3 = QuaternionOrienter(q1, q2, q3, q4) orienter4 = BodyOrienter(q1, q2, q3, '123') D = N.orient_new('D', (orienter1, )) E = N.orient_new('E', (orienter2, )) F = N.orient_new('F', (orienter3, )) G = N.orient_new('G', (orienter4, )) assert D == N.orient_new_axis('D', q4, N.j) assert E == N.orient_new_space('E', q1, q2, q3, '123') assert F == N.orient_new_quaternion('F', q1, q2, q3, q4) assert G == N.orient_new_body('G', q1, q2, q3, '123') def test_locatenew_point(): """ Tests Point class, and locate_new method in CoordSysCartesian. """ A = CoordSysCartesian('A') assert isinstance(A.origin, Point) v = a*A.i + b*A.j + c*A.k C = A.locate_new('C', v) assert C.origin.position_wrt(A) == \ C.position_wrt(A) == \ C.origin.position_wrt(A.origin) == v assert A.origin.position_wrt(C) == \ A.position_wrt(C) == \ A.origin.position_wrt(C.origin) == -v assert A.origin.express_coordinates(C) == (-a, -b, -c) p = A.origin.locate_new('p', -v) assert p.express_coordinates(A) == (-a, -b, -c) assert p.position_wrt(C.origin) == p.position_wrt(C) == \ -2 * v p1 = p.locate_new('p1', 2*v) assert p1.position_wrt(C.origin) == Vector.zero assert p1.express_coordinates(C) == (0, 0, 0) p2 = p.locate_new('p2', A.i) assert p1.position_wrt(p2) == 2*v - A.i assert p2.express_coordinates(C) == (-2*a + 1, -2*b, -2*c) def test_evalf(): A = CoordSysCartesian('A') v = 3*A.i + 4*A.j + a*A.k assert v.n() == v.evalf() assert v.evalf(subs={a:1}) == v.subs(a, 1).evalf()

43.071186

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0.488745

from sympy.vector.coordsysrect import CoordSysCartesian from sympy.vector.scalar import BaseScalar from sympy import sin, cos, pi, ImmutableMatrix as Matrix, \ symbols, simplify, zeros, expand from sympy.vector.functions import express from sympy.vector.point import Point from sympy.vector.vector import Vector from sympy.vector.orienters import (AxisOrienter, BodyOrienter, SpaceOrienter, QuaternionOrienter) a, b, c, q = symbols('a b c q') q1, q2, q3, q4 = symbols('q1 q2 q3 q4') def test_func_args(): A = CoordSysCartesian('A') assert A.x.func(*A.x.args) == A.x expr = 3*A.x + 4*A.y assert expr.func(*expr.args) == expr assert A.i.func(*A.i.args) == A.i v = A.x*A.i + A.y*A.j + A.z*A.k assert v.func(*v.args) == v assert A.origin.func(*A.origin.args) == A.origin def test_coordsyscartesian_equivalence(): A = CoordSysCartesian('A') A1 = CoordSysCartesian('A') assert A1 == A B = CoordSysCartesian('B') assert A != B def test_orienters(): A = CoordSysCartesian('A') axis_orienter = AxisOrienter(a, A.k) body_orienter = BodyOrienter(a, b, c, '123') space_orienter = SpaceOrienter(a, b, c, '123') q_orienter = QuaternionOrienter(q1, q2, q3, q4) assert axis_orienter.rotation_matrix(A) == Matrix([ [ cos(a), sin(a), 0], [-sin(a), cos(a), 0], [ 0, 0, 1]]) assert body_orienter.rotation_matrix() == Matrix([ [ cos(b)*cos(c), sin(a)*sin(b)*cos(c) + sin(c)*cos(a), sin(a)*sin(c) - sin(b)*cos(a)*cos(c)], [-sin(c)*cos(b), -sin(a)*sin(b)*sin(c) + cos(a)*cos(c), sin(a)*cos(c) + sin(b)*sin(c)*cos(a)], [ sin(b), -sin(a)*cos(b), cos(a)*cos(b)]]) assert space_orienter.rotation_matrix() == Matrix([ [cos(b)*cos(c), sin(c)*cos(b), -sin(b)], [sin(a)*sin(b)*cos(c) - sin(c)*cos(a), sin(a)*sin(b)*sin(c) + cos(a)*cos(c), sin(a)*cos(b)], [sin(a)*sin(c) + sin(b)*cos(a)*cos(c), -sin(a)*cos(c) + sin(b)*sin(c)*cos(a), cos(a)*cos(b)]]) assert q_orienter.rotation_matrix() == Matrix([ [q1**2 + q2**2 - q3**2 - q4**2, 2*q1*q4 + 2*q2*q3, -2*q1*q3 + 2*q2*q4], [-2*q1*q4 + 2*q2*q3, q1**2 - q2**2 + q3**2 - q4**2, 2*q1*q2 + 2*q3*q4], [2*q1*q3 + 2*q2*q4, -2*q1*q2 + 2*q3*q4, q1**2 - q2**2 - q3**2 + q4**2]]) def test_coordinate_vars(): A = CoordSysCartesian('A') assert BaseScalar('A.x', 0, A, 'A_x', r'\mathbf{{x}_{A}}') == A.x assert BaseScalar('A.y', 1, A, 'A_y', r'\mathbf{{y}_{A}}') == A.y assert BaseScalar('A.z', 2, A, 'A_z', r'\mathbf{{z}_{A}}') == A.z assert BaseScalar('A.x', 0, A, 'A_x', r'\mathbf{{x}_{A}}').__hash__() == A.x.__hash__() assert isinstance(A.x, BaseScalar) and \ isinstance(A.y, BaseScalar) and \ isinstance(A.z, BaseScalar) assert A.x*A.y == A.y*A.x assert A.scalar_map(A) == {A.x: A.x, A.y: A.y, A.z: A.z} assert A.x.system == A assert A.x.diff(A.x) == 1 B = A.orient_new_axis('B', q, A.k) assert B.scalar_map(A) == {B.z: A.z, B.y: -A.x*sin(q) + A.y*cos(q), B.x: A.x*cos(q) + A.y*sin(q)} assert A.scalar_map(B) == {A.x: B.x*cos(q) - B.y*sin(q), A.y: B.x*sin(q) + B.y*cos(q), A.z: B.z} assert express(B.x, A, variables=True) == A.x*cos(q) + A.y*sin(q) assert express(B.y, A, variables=True) == -A.x*sin(q) + A.y*cos(q) assert express(B.z, A, variables=True) == A.z assert expand(express(B.x*B.y*B.z, A, variables=True)) == \ expand(A.z*(-A.x*sin(q) + A.y*cos(q))*(A.x*cos(q) + A.y*sin(q))) assert express(B.x*B.i + B.y*B.j + B.z*B.k, A) == \ (B.x*cos(q) - B.y*sin(q))*A.i + (B.x*sin(q) + \ B.y*cos(q))*A.j + B.z*A.k assert simplify(express(B.x*B.i + B.y*B.j + B.z*B.k, A, \ variables=True)) == \ A.x*A.i + A.y*A.j + A.z*A.k assert express(A.x*A.i + A.y*A.j + A.z*A.k, B) == \ (A.x*cos(q) + A.y*sin(q))*B.i + \ (-A.x*sin(q) + A.y*cos(q))*B.j + A.z*B.k assert simplify(express(A.x*A.i + A.y*A.j + A.z*A.k, B, \ variables=True)) == \ B.x*B.i + B.y*B.j + B.z*B.k N = B.orient_new_axis('N', -q, B.k) assert N.scalar_map(A) == \ {N.x: A.x, N.z: A.z, N.y: A.y} C = A.orient_new_axis('C', q, A.i + A.j + A.k) mapping = A.scalar_map(C) assert mapping[A.x] == (C.x*(2*cos(q) + 1)/3 + C.y*(-2*sin(q + pi/6) + 1)/3 + C.z*(-2*cos(q + pi/3) + 1)/3) assert mapping[A.y] == (C.x*(-2*cos(q + pi/3) + 1)/3 + C.y*(2*cos(q) + 1)/3 + C.z*(-2*sin(q + pi/6) + 1)/3) assert mapping[A.z] == (C.x*(-2*sin(q + pi/6) + 1)/3 + C.y*(-2*cos(q + pi/3) + 1)/3 + C.z*(2*cos(q) + 1)/3) D = A.locate_new('D', a*A.i + b*A.j + c*A.k) assert D.scalar_map(A) == {D.z: A.z - c, D.x: A.x - a, D.y: A.y - b} E = A.orient_new_axis('E', a, A.k, a*A.i + b*A.j + c*A.k) assert A.scalar_map(E) == {A.z: E.z + c, A.x: E.x*cos(a) - E.y*sin(a) + a, A.y: E.x*sin(a) + E.y*cos(a) + b} assert E.scalar_map(A) == {E.x: (A.x - a)*cos(a) + (A.y - b)*sin(a), E.y: (-A.x + a)*sin(a) + (A.y - b)*cos(a), E.z: A.z - c} F = A.locate_new('F', Vector.zero) assert A.scalar_map(F) == {A.z: F.z, A.x: F.x, A.y: F.y} def test_rotation_matrix(): N = CoordSysCartesian('N') A = N.orient_new_axis('A', q1, N.k) B = A.orient_new_axis('B', q2, A.i) C = B.orient_new_axis('C', q3, B.j) D = N.orient_new_axis('D', q4, N.j) E = N.orient_new_space('E', q1, q2, q3, '123') F = N.orient_new_quaternion('F', q1, q2, q3, q4) G = N.orient_new_body('G', q1, q2, q3, '123') assert N.rotation_matrix(C) == Matrix([ [- sin(q1) * sin(q2) * sin(q3) + cos(q1) * cos(q3), - sin(q1) * cos(q2), sin(q1) * sin(q2) * cos(q3) + sin(q3) * cos(q1)], \ [sin(q1) * cos(q3) + sin(q2) * sin(q3) * cos(q1), \ cos(q1) * cos(q2), sin(q1) * sin(q3) - sin(q2) * cos(q1) * \ cos(q3)], [- sin(q3) * cos(q2), sin(q2), cos(q2) * cos(q3)]]) test_mat = D.rotation_matrix(C) - Matrix( [[cos(q1) * cos(q3) * cos(q4) - sin(q3) * (- sin(q4) * cos(q2) + sin(q1) * sin(q2) * cos(q4)), - sin(q2) * sin(q4) - sin(q1) * cos(q2) * cos(q4), sin(q3) * cos(q1) * cos(q4) + cos(q3) * \ (- sin(q4) * cos(q2) + sin(q1) * sin(q2) * cos(q4))], \ [sin(q1) * cos(q3) + sin(q2) * sin(q3) * cos(q1), cos(q1) * \ cos(q2), sin(q1) * sin(q3) - sin(q2) * cos(q1) * cos(q3)], \ [sin(q4) * cos(q1) * cos(q3) - sin(q3) * (cos(q2) * cos(q4) + \ sin(q1) * sin(q2) * \ sin(q4)), sin(q2) * cos(q4) - sin(q1) * sin(q4) * cos(q2), sin(q3) * \ sin(q4) * cos(q1) + cos(q3) * (cos(q2) * cos(q4) + \ sin(q1) * sin(q2) * sin(q4))]]) assert test_mat.expand() == zeros(3, 3) assert E.rotation_matrix(N) == Matrix( [[cos(q2)*cos(q3), sin(q3)*cos(q2), -sin(q2)], [sin(q1)*sin(q2)*cos(q3) - sin(q3)*cos(q1), \ sin(q1)*sin(q2)*sin(q3) + cos(q1)*cos(q3), sin(q1)*cos(q2)], \ [sin(q1)*sin(q3) + sin(q2)*cos(q1)*cos(q3), - \ sin(q1)*cos(q3) + sin(q2)*sin(q3)*cos(q1), cos(q1)*cos(q2)]]) assert F.rotation_matrix(N) == Matrix([[ q1**2 + q2**2 - q3**2 - q4**2, 2*q1*q4 + 2*q2*q3, -2*q1*q3 + 2*q2*q4],[ -2*q1*q4 + 2*q2*q3, q1**2 - q2**2 + q3**2 - q4**2, 2*q1*q2 + 2*q3*q4], [2*q1*q3 + 2*q2*q4, -2*q1*q2 + 2*q3*q4, q1**2 - q2**2 - q3**2 + q4**2]]) assert G.rotation_matrix(N) == Matrix([[ cos(q2)*cos(q3), sin(q1)*sin(q2)*cos(q3) + sin(q3)*cos(q1), sin(q1)*sin(q3) - sin(q2)*cos(q1)*cos(q3)], [ -sin(q3)*cos(q2), -sin(q1)*sin(q2)*sin(q3) + cos(q1)*cos(q3), sin(q1)*cos(q3) + sin(q2)*sin(q3)*cos(q1)],[ sin(q2), -sin(q1)*cos(q2), cos(q1)*cos(q2)]]) def test_vector(): N = CoordSysCartesian('N') A = N.orient_new_axis('A', q1, N.k) B = A.orient_new_axis('B', q2, A.i) C = B.orient_new_axis('C', q3, B.j) v1 = a*N.i + b*N.j + c*N.k assert v1.to_matrix(A) == Matrix([[ a*cos(q1) + b*sin(q1)], [-a*sin(q1) + b*cos(q1)], [ c]]) assert N.i.dot(A.i) == cos(q1) assert N.i.dot(A.j) == -sin(q1) assert N.i.dot(A.k) == 0 assert N.j.dot(A.i) == sin(q1) assert N.j.dot(A.j) == cos(q1) assert N.j.dot(A.k) == 0 assert N.k.dot(A.i) == 0 assert N.k.dot(A.j) == 0 assert N.k.dot(A.k) == 1 assert N.i.dot(A.i + A.j) == -sin(q1) + cos(q1) == \ (A.i + A.j).dot(N.i) assert A.i.dot(C.i) == cos(q3) assert A.i.dot(C.j) == 0 assert A.i.dot(C.k) == sin(q3) assert A.j.dot(C.i) == sin(q2)*sin(q3) assert A.j.dot(C.j) == cos(q2) assert A.j.dot(C.k) == -sin(q2)*cos(q3) assert A.k.dot(C.i) == -cos(q2)*sin(q3) assert A.k.dot(C.j) == sin(q2) assert A.k.dot(C.k) == cos(q2)*cos(q3) assert N.i.cross(A.i) == sin(q1)*A.k assert N.i.cross(A.j) == cos(q1)*A.k assert N.i.cross(A.k) == -sin(q1)*A.i - cos(q1)*A.j assert N.j.cross(A.i) == -cos(q1)*A.k assert N.j.cross(A.j) == sin(q1)*A.k assert N.j.cross(A.k) == cos(q1)*A.i - sin(q1)*A.j assert N.k.cross(A.i) == A.j assert N.k.cross(A.j) == -A.i assert N.k.cross(A.k) == Vector.zero assert N.i.cross(A.i) == sin(q1)*A.k assert N.i.cross(A.j) == cos(q1)*A.k assert N.i.cross(A.i + A.j) == sin(q1)*A.k + cos(q1)*A.k assert (A.i + A.j).cross(N.i) == (-sin(q1) - cos(q1))*N.k assert A.i.cross(C.i) == sin(q3)*C.j assert A.i.cross(C.j) == -sin(q3)*C.i + cos(q3)*C.k assert A.i.cross(C.k) == -cos(q3)*C.j assert C.i.cross(A.i) == (-sin(q3)*cos(q2))*A.j + \ (-sin(q2)*sin(q3))*A.k assert C.j.cross(A.i) == (sin(q2))*A.j + (-cos(q2))*A.k assert express(C.k.cross(A.i), C).trigsimp() == cos(q3)*C.j def test_orient_new_methods(): N = CoordSysCartesian('N') orienter1 = AxisOrienter(q4, N.j) orienter2 = SpaceOrienter(q1, q2, q3, '123') orienter3 = QuaternionOrienter(q1, q2, q3, q4) orienter4 = BodyOrienter(q1, q2, q3, '123') D = N.orient_new('D', (orienter1, )) E = N.orient_new('E', (orienter2, )) F = N.orient_new('F', (orienter3, )) G = N.orient_new('G', (orienter4, )) assert D == N.orient_new_axis('D', q4, N.j) assert E == N.orient_new_space('E', q1, q2, q3, '123') assert F == N.orient_new_quaternion('F', q1, q2, q3, q4) assert G == N.orient_new_body('G', q1, q2, q3, '123') def test_locatenew_point(): A = CoordSysCartesian('A') assert isinstance(A.origin, Point) v = a*A.i + b*A.j + c*A.k C = A.locate_new('C', v) assert C.origin.position_wrt(A) == \ C.position_wrt(A) == \ C.origin.position_wrt(A.origin) == v assert A.origin.position_wrt(C) == \ A.position_wrt(C) == \ A.origin.position_wrt(C.origin) == -v assert A.origin.express_coordinates(C) == (-a, -b, -c) p = A.origin.locate_new('p', -v) assert p.express_coordinates(A) == (-a, -b, -c) assert p.position_wrt(C.origin) == p.position_wrt(C) == \ -2 * v p1 = p.locate_new('p1', 2*v) assert p1.position_wrt(C.origin) == Vector.zero assert p1.express_coordinates(C) == (0, 0, 0) p2 = p.locate_new('p2', A.i) assert p1.position_wrt(p2) == 2*v - A.i assert p2.express_coordinates(C) == (-2*a + 1, -2*b, -2*c) def test_evalf(): A = CoordSysCartesian('A') v = 3*A.i + 4*A.j + a*A.k assert v.n() == v.evalf() assert v.evalf(subs={a:1}) == v.subs(a, 1).evalf()

true

790bf88266f11d454e343bd766de7cb931a386ae

132,204

py

Python

tb_rest_client/api/api_pe/entity_group_controller_api.py

samson0v/python_tb_rest_client

08ff7898740f7cec2170e85d5c3c89e222e967f7

[ "Apache-2.0" ]

30

2020-06-19T06:42:50.000Z

2021-08-23T21:16:36.000Z

tb_rest_client/api/api_pe/entity_group_controller_api.py

samson0v/python_tb_rest_client

08ff7898740f7cec2170e85d5c3c89e222e967f7

[ "Apache-2.0" ]

25

2021-08-30T01:17:27.000Z

2022-03-16T14:10:14.000Z

tb_rest_client/api/api_pe/entity_group_controller_api.py

samson0v/python_tb_rest_client

08ff7898740f7cec2170e85d5c3c89e222e967f7

[ "Apache-2.0" ]

23

2020-07-06T13:41:54.000Z

2021-08-23T21:04:50.000Z

# coding: utf-8 """ ThingsBoard REST API ThingsBoard Professional Edition IoT platform REST API documentation. # noqa: E501 OpenAPI spec version: 3.3.3PAAS-RC1 Contact: info@thingsboard.io 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 tb_rest_client.api_client import ApiClient class EntityGroupControllerApi(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 add_entities_to_entity_group_using_post(self, entity_group_id, **kwargs): # noqa: E501 """Add entities to the group (addEntitiesToEntityGroup) # noqa: E501 Add entities to the specified entity group. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'ADD_TO_GROUP' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.add_entities_to_entity_group_using_post(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param list[str] body: :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.add_entities_to_entity_group_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501 else: (data) = self.add_entities_to_entity_group_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501 return data def add_entities_to_entity_group_using_post_with_http_info(self, entity_group_id, **kwargs): # noqa: E501 """Add entities to the group (addEntitiesToEntityGroup) # noqa: E501 Add entities to the specified entity group. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'ADD_TO_GROUP' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.add_entities_to_entity_group_using_post_with_http_info(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param list[str] body: :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id', 'body'] # 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 add_entities_to_entity_group_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `add_entities_to_entity_group_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # 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 = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/addEntities', '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 assign_entity_group_to_edge_using_post(self, edge_id, group_type, entity_group_id, **kwargs): # noqa: E501 """Assign entity group to edge (assignEntityGroupToEdge) # noqa: E501 Creates assignment of an existing entity group to an instance of The Edge. Assignment works in async way - first, notification event pushed to edge service queue on platform. Second, remote edge service will receive a copy of assignment entity group (Edge will receive this instantly, if it's currently connected, or once it's going to be connected to platform). Third, once entity group will be delivered to edge service, edge will request entities of this group to be send to edge. Once entities will be delivered to edge service, they are going to be available for usage on remote edge instance. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for the entity (entities). # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.assign_entity_group_to_edge_using_post(edge_id, group_type, entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: A string value representing the edge id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str group_type: EntityGroup type (required) :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: EntityGroup If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.assign_entity_group_to_edge_using_post_with_http_info(edge_id, group_type, entity_group_id, **kwargs) # noqa: E501 else: (data) = self.assign_entity_group_to_edge_using_post_with_http_info(edge_id, group_type, entity_group_id, **kwargs) # noqa: E501 return data def assign_entity_group_to_edge_using_post_with_http_info(self, edge_id, group_type, entity_group_id, **kwargs): # noqa: E501 """Assign entity group to edge (assignEntityGroupToEdge) # noqa: E501 Creates assignment of an existing entity group to an instance of The Edge. Assignment works in async way - first, notification event pushed to edge service queue on platform. Second, remote edge service will receive a copy of assignment entity group (Edge will receive this instantly, if it's currently connected, or once it's going to be connected to platform). Third, once entity group will be delivered to edge service, edge will request entities of this group to be send to edge. Once entities will be delivered to edge service, they are going to be available for usage on remote edge instance. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for the entity (entities). # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.assign_entity_group_to_edge_using_post_with_http_info(edge_id, group_type, entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: A string value representing the edge id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str group_type: EntityGroup type (required) :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: EntityGroup If the method is called asynchronously, returns the request thread. """ all_params = ['edge_id', 'group_type', 'entity_group_id'] # 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 assign_entity_group_to_edge_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'edge_id' is set if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `assign_entity_group_to_edge_using_post`") # noqa: E501 # verify the required parameter 'group_type' is set if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `assign_entity_group_to_edge_using_post`") # noqa: E501 # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `assign_entity_group_to_edge_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] # noqa: E501 if 'group_type' in params: path_params['groupType'] = params['group_type'] # noqa: E501 if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/edge/{edgeId}/entityGroup/{entityGroupId}/{groupType}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroup', # 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 delete_entity_group_using_delete(self, entity_group_id, **kwargs): # noqa: E501 """Delete Entity Group (deleteEntityGroup) # noqa: E501 Deletes the entity group but does not delete the entities in the group, since they are also present in reserved group 'All'. Referencing non-existing Entity Group Id will cause an error. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'DELETE' permission for specified group. # 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_entity_group_using_delete(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (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.delete_entity_group_using_delete_with_http_info(entity_group_id, **kwargs) # noqa: E501 else: (data) = self.delete_entity_group_using_delete_with_http_info(entity_group_id, **kwargs) # noqa: E501 return data def delete_entity_group_using_delete_with_http_info(self, entity_group_id, **kwargs): # noqa: E501 """Delete Entity Group (deleteEntityGroup) # noqa: E501 Deletes the entity group but does not delete the entities in the group, since they are also present in reserved group 'All'. Referencing non-existing Entity Group Id will cause an error. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'DELETE' permission for specified group. # 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_entity_group_using_delete_with_http_info(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id'] # 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_entity_group_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `delete_entity_group_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}', '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 get_all_edge_entity_groups_using_get(self, edge_id, group_type, **kwargs): # noqa: E501 """Get All Edge Entity Groups by entity type (getAllEdgeEntityGroups) # noqa: E501 Fetch the list of Entity Group Info objects based on the provided Entity Type and assigned to the provided Edge entity. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_all_edge_entity_groups_using_get(edge_id, group_type, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: A string value representing the edge id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str group_type: EntityGroup type (required) :return: list[EntityGroupInfo] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_all_edge_entity_groups_using_get_with_http_info(edge_id, group_type, **kwargs) # noqa: E501 else: (data) = self.get_all_edge_entity_groups_using_get_with_http_info(edge_id, group_type, **kwargs) # noqa: E501 return data def get_all_edge_entity_groups_using_get_with_http_info(self, edge_id, group_type, **kwargs): # noqa: E501 """Get All Edge Entity Groups by entity type (getAllEdgeEntityGroups) # noqa: E501 Fetch the list of Entity Group Info objects based on the provided Entity Type and assigned to the provided Edge entity. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_all_edge_entity_groups_using_get_with_http_info(edge_id, group_type, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: A string value representing the edge id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str group_type: EntityGroup type (required) :return: list[EntityGroupInfo] If the method is called asynchronously, returns the request thread. """ all_params = ['edge_id', 'group_type'] # 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_all_edge_entity_groups_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'edge_id' is set if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `get_all_edge_entity_groups_using_get`") # noqa: E501 # verify the required parameter 'group_type' is set if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_all_edge_entity_groups_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] # noqa: E501 if 'group_type' in params: path_params['groupType'] = params['group_type'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/allEntityGroups/edge/{edgeId}/{groupType}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityGroupInfo]', # 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_edge_entity_groups_using_get(self, edge_id, group_type, page_size, page, **kwargs): # noqa: E501 """Get Edge Entity Groups by entity type (getEdgeEntityGroups) # noqa: E501 Returns a page of Entity Group Info objects based on the provided Entity Type and assigned to the provided Edge entity. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids.You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_edge_entity_groups_using_get(edge_id, group_type, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: A string value representing the edge id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str group_type: EntityGroup type (required) :param int page_size: Maximum amount of entities in a one page (required) :param int page: Sequence number of page starting from 0 (required) :param str sort_property: Property of entity to sort by :param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING) :return: PageDataEntityGroupInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_edge_entity_groups_using_get_with_http_info(edge_id, group_type, page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_edge_entity_groups_using_get_with_http_info(edge_id, group_type, page_size, page, **kwargs) # noqa: E501 return data def get_edge_entity_groups_using_get_with_http_info(self, edge_id, group_type, page_size, page, **kwargs): # noqa: E501 """Get Edge Entity Groups by entity type (getEdgeEntityGroups) # noqa: E501 Returns a page of Entity Group Info objects based on the provided Entity Type and assigned to the provided Edge entity. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids.You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_edge_entity_groups_using_get_with_http_info(edge_id, group_type, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: A string value representing the edge id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str group_type: EntityGroup type (required) :param int page_size: Maximum amount of entities in a one page (required) :param int page: Sequence number of page starting from 0 (required) :param str sort_property: Property of entity to sort by :param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING) :return: PageDataEntityGroupInfo If the method is called asynchronously, returns the request thread. """ all_params = ['edge_id', 'group_type', 'page_size', 'page', 'sort_property', 'sort_order'] # 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_edge_entity_groups_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'edge_id' is set if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `get_edge_entity_groups_using_get`") # noqa: E501 # verify the required parameter 'group_type' is set if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_edge_entity_groups_using_get`") # noqa: E501 # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_edge_entity_groups_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_edge_entity_groups_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] # noqa: E501 if 'group_type' in params: path_params['groupType'] = params['group_type'] # noqa: E501 query_params = [] if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroups/edge/{edgeId}/{groupType}{?page,pageSize,sortOrder,sortProperty}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataEntityGroupInfo', # 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_entities_using_get(self, entity_group_id, page_size, page, **kwargs): # noqa: E501 """Get Group Entities (getEntities) # noqa: E501 Returns a page of Short Entity View objects that belongs to specified Entity Group Id. Short Entity View object contains the entity id and number of fields (attributes, telemetry, etc). List of those fields is configurable and defined in the group configuration.You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entities_using_get(entity_group_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param int page_size: Maximum amount of entities in a one page (required) :param int page: Sequence number of page starting from 0 (required) :param str text_search: The case insensitive 'startsWith' filter based on the entity group name. :param str sort_property: Property of entity to sort by :param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING) :return: PageDataShortEntityView If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entities_using_get_with_http_info(entity_group_id, page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_entities_using_get_with_http_info(entity_group_id, page_size, page, **kwargs) # noqa: E501 return data def get_entities_using_get_with_http_info(self, entity_group_id, page_size, page, **kwargs): # noqa: E501 """Get Group Entities (getEntities) # noqa: E501 Returns a page of Short Entity View objects that belongs to specified Entity Group Id. Short Entity View object contains the entity id and number of fields (attributes, telemetry, etc). List of those fields is configurable and defined in the group configuration.You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entities_using_get_with_http_info(entity_group_id, page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param int page_size: Maximum amount of entities in a one page (required) :param int page: Sequence number of page starting from 0 (required) :param str text_search: The case insensitive 'startsWith' filter based on the entity group name. :param str sort_property: Property of entity to sort by :param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING) :return: PageDataShortEntityView If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id', 'page_size', 'page', 'text_search', 'sort_property', 'sort_order'] # 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_entities_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `get_entities_using_get`") # noqa: E501 # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_entities_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_entities_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/entities{?page,pageSize,sortOrder,sortProperty,textSearch}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataShortEntityView', # 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_entity_group_all_by_owner_and_type_using_get(self, owner_type, owner_id, group_type, **kwargs): # noqa: E501 """Get special group All by owner and entity type (getEntityGroupsByOwnerAndType) # noqa: E501 Fetch reserved group 'All' based on the provided Owner Id and Entity Type. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entity_group_all_by_owner_and_type_using_get(owner_type, owner_id, group_type, async_req=True) >>> result = thread.get() :param async_req bool :param str owner_type: Tenant or Customer (required) :param str owner_id: A string value representing the Tenant or Customer id (required) :param str group_type: Entity Group type (required) :return: EntityGroupInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_group_all_by_owner_and_type_using_get_with_http_info(owner_type, owner_id, group_type, **kwargs) # noqa: E501 else: (data) = self.get_entity_group_all_by_owner_and_type_using_get_with_http_info(owner_type, owner_id, group_type, **kwargs) # noqa: E501 return data def get_entity_group_all_by_owner_and_type_using_get_with_http_info(self, owner_type, owner_id, group_type, **kwargs): # noqa: E501 """Get special group All by owner and entity type (getEntityGroupsByOwnerAndType) # noqa: E501 Fetch reserved group 'All' based on the provided Owner Id and Entity Type. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entity_group_all_by_owner_and_type_using_get_with_http_info(owner_type, owner_id, group_type, async_req=True) >>> result = thread.get() :param async_req bool :param str owner_type: Tenant or Customer (required) :param str owner_id: A string value representing the Tenant or Customer id (required) :param str group_type: Entity Group type (required) :return: EntityGroupInfo If the method is called asynchronously, returns the request thread. """ all_params = ['owner_type', 'owner_id', 'group_type'] # 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_entity_group_all_by_owner_and_type_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'owner_type' is set if ('owner_type' not in params or params['owner_type'] is None): raise ValueError("Missing the required parameter `owner_type` when calling `get_entity_group_all_by_owner_and_type_using_get`") # noqa: E501 # verify the required parameter 'owner_id' is set if ('owner_id' not in params or params['owner_id'] is None): raise ValueError("Missing the required parameter `owner_id` when calling `get_entity_group_all_by_owner_and_type_using_get`") # noqa: E501 # verify the required parameter 'group_type' is set if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_entity_group_all_by_owner_and_type_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'owner_type' in params: path_params['ownerType'] = params['owner_type'] # noqa: E501 if 'owner_id' in params: path_params['ownerId'] = params['owner_id'] # noqa: E501 if 'group_type' in params: path_params['groupType'] = params['group_type'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/all/{ownerType}/{ownerId}/{groupType}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroupInfo', # 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_entity_group_by_id_using_get(self, entity_group_id, **kwargs): # noqa: E501 """Get Entity Group Info (getEntityGroupById) # noqa: E501 Fetch the Entity Group object based on the provided Entity Group Id. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Entity group name is unique in the scope of owner and entity type. For example, you can't create two tenant device groups called 'Water meters'. However, you may create device and asset group with the same name. And also you may create groups with the same name for two different customers of the same tenant. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entity_group_by_id_using_get(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: EntityGroupInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_group_by_id_using_get_with_http_info(entity_group_id, **kwargs) # noqa: E501 else: (data) = self.get_entity_group_by_id_using_get_with_http_info(entity_group_id, **kwargs) # noqa: E501 return data def get_entity_group_by_id_using_get_with_http_info(self, entity_group_id, **kwargs): # noqa: E501 """Get Entity Group Info (getEntityGroupById) # noqa: E501 Fetch the Entity Group object based on the provided Entity Group Id. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Entity group name is unique in the scope of owner and entity type. For example, you can't create two tenant device groups called 'Water meters'. However, you may create device and asset group with the same name. And also you may create groups with the same name for two different customers of the same tenant. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entity_group_by_id_using_get_with_http_info(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: EntityGroupInfo If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id'] # 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_entity_group_by_id_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `get_entity_group_by_id_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroupInfo', # 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_entity_group_by_owner_and_name_and_type_using_get(self, owner_type, owner_id, group_type, group_name, **kwargs): # noqa: E501 """Get Entity Group by owner, type and name (getEntityGroupByOwnerAndNameAndType) # noqa: E501 Fetch the Entity Group object based on the provided Entity Group Id. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Entity group name is unique in the scope of owner and entity type. For example, you can't create two tenant device groups called 'Water meters'. However, you may create device and asset group with the same name. And also you may create groups with the same name for two different customers of the same tenant. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entity_group_by_owner_and_name_and_type_using_get(owner_type, owner_id, group_type, group_name, async_req=True) >>> result = thread.get() :param async_req bool :param str owner_type: Tenant or Customer (required) :param str owner_id: A string value representing the Tenant or Customer id (required) :param str group_type: Entity Group type (required) :param str group_name: Entity Group name (required) :return: EntityGroupInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_group_by_owner_and_name_and_type_using_get_with_http_info(owner_type, owner_id, group_type, group_name, **kwargs) # noqa: E501 else: (data) = self.get_entity_group_by_owner_and_name_and_type_using_get_with_http_info(owner_type, owner_id, group_type, group_name, **kwargs) # noqa: E501 return data def get_entity_group_by_owner_and_name_and_type_using_get_with_http_info(self, owner_type, owner_id, group_type, group_name, **kwargs): # noqa: E501 """Get Entity Group by owner, type and name (getEntityGroupByOwnerAndNameAndType) # noqa: E501 Fetch the Entity Group object based on the provided Entity Group Id. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Entity group name is unique in the scope of owner and entity type. For example, you can't create two tenant device groups called 'Water meters'. However, you may create device and asset group with the same name. And also you may create groups with the same name for two different customers of the same tenant. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entity_group_by_owner_and_name_and_type_using_get_with_http_info(owner_type, owner_id, group_type, group_name, async_req=True) >>> result = thread.get() :param async_req bool :param str owner_type: Tenant or Customer (required) :param str owner_id: A string value representing the Tenant or Customer id (required) :param str group_type: Entity Group type (required) :param str group_name: Entity Group name (required) :return: EntityGroupInfo If the method is called asynchronously, returns the request thread. """ all_params = ['owner_type', 'owner_id', 'group_type', 'group_name'] # 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_entity_group_by_owner_and_name_and_type_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'owner_type' is set if ('owner_type' not in params or params['owner_type'] is None): raise ValueError("Missing the required parameter `owner_type` when calling `get_entity_group_by_owner_and_name_and_type_using_get`") # noqa: E501 # verify the required parameter 'owner_id' is set if ('owner_id' not in params or params['owner_id'] is None): raise ValueError("Missing the required parameter `owner_id` when calling `get_entity_group_by_owner_and_name_and_type_using_get`") # noqa: E501 # verify the required parameter 'group_type' is set if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_entity_group_by_owner_and_name_and_type_using_get`") # noqa: E501 # verify the required parameter 'group_name' is set if ('group_name' not in params or params['group_name'] is None): raise ValueError("Missing the required parameter `group_name` when calling `get_entity_group_by_owner_and_name_and_type_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'owner_type' in params: path_params['ownerType'] = params['owner_type'] # noqa: E501 if 'owner_id' in params: path_params['ownerId'] = params['owner_id'] # noqa: E501 if 'group_type' in params: path_params['groupType'] = params['group_type'] # noqa: E501 if 'group_name' in params: path_params['groupName'] = params['group_name'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{ownerType}/{ownerId}/{groupType}/{groupName}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroupInfo', # 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_entity_groups_by_ids_using_get(self, entity_group_ids, **kwargs): # noqa: E501 """Get Entity Groups by Ids (getDevicesByIds) # noqa: E501 Requested devices must be owned by tenant or assigned to customer which user is performing the request. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_entity_groups_by_ids_using_get(entity_group_ids, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_ids: A list of group ids, separated by comma ',' (required) :return: list[EntityGroup] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_groups_by_ids_using_get_with_http_info(entity_group_ids, **kwargs) # noqa: E501 else: (data) = self.get_entity_groups_by_ids_using_get_with_http_info(entity_group_ids, **kwargs) # noqa: E501 return data def get_entity_groups_by_ids_using_get_with_http_info(self, entity_group_ids, **kwargs): # noqa: E501 """Get Entity Groups by Ids (getDevicesByIds) # noqa: E501 Requested devices must be owned by tenant or assigned to customer which user is performing the request. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_entity_groups_by_ids_using_get_with_http_info(entity_group_ids, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_ids: A list of group ids, separated by comma ',' (required) :return: list[EntityGroup] If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_ids'] # 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_entity_groups_by_ids_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_ids' is set if ('entity_group_ids' not in params or params['entity_group_ids'] is None): raise ValueError("Missing the required parameter `entity_group_ids` when calling `get_entity_groups_by_ids_using_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'entity_group_ids' in params: query_params.append(('entityGroupIds', params['entity_group_ids'])) # 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroups{?entityGroupIds}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityGroup]', # 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_entity_groups_by_owner_and_type_using_get(self, owner_type, owner_id, group_type, **kwargs): # noqa: E501 """Get Entity Groups by owner and entity type (getEntityGroupsByOwnerAndType) # noqa: E501 Fetch the list of Entity Group Info objects based on the provided Owner Id and Entity Type. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entity_groups_by_owner_and_type_using_get(owner_type, owner_id, group_type, async_req=True) >>> result = thread.get() :param async_req bool :param str owner_type: Tenant or Customer (required) :param str owner_id: A string value representing the Tenant or Customer id (required) :param str group_type: Entity Group type (required) :return: list[EntityGroupInfo] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_groups_by_owner_and_type_using_get_with_http_info(owner_type, owner_id, group_type, **kwargs) # noqa: E501 else: (data) = self.get_entity_groups_by_owner_and_type_using_get_with_http_info(owner_type, owner_id, group_type, **kwargs) # noqa: E501 return data def get_entity_groups_by_owner_and_type_using_get_with_http_info(self, owner_type, owner_id, group_type, **kwargs): # noqa: E501 """Get Entity Groups by owner and entity type (getEntityGroupsByOwnerAndType) # noqa: E501 Fetch the list of Entity Group Info objects based on the provided Owner Id and Entity Type. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entity_groups_by_owner_and_type_using_get_with_http_info(owner_type, owner_id, group_type, async_req=True) >>> result = thread.get() :param async_req bool :param str owner_type: Tenant or Customer (required) :param str owner_id: A string value representing the Tenant or Customer id (required) :param str group_type: Entity Group type (required) :return: list[EntityGroupInfo] If the method is called asynchronously, returns the request thread. """ all_params = ['owner_type', 'owner_id', 'group_type'] # 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_entity_groups_by_owner_and_type_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'owner_type' is set if ('owner_type' not in params or params['owner_type'] is None): raise ValueError("Missing the required parameter `owner_type` when calling `get_entity_groups_by_owner_and_type_using_get`") # noqa: E501 # verify the required parameter 'owner_id' is set if ('owner_id' not in params or params['owner_id'] is None): raise ValueError("Missing the required parameter `owner_id` when calling `get_entity_groups_by_owner_and_type_using_get`") # noqa: E501 # verify the required parameter 'group_type' is set if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_entity_groups_by_owner_and_type_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'owner_type' in params: path_params['ownerType'] = params['owner_type'] # noqa: E501 if 'owner_id' in params: path_params['ownerId'] = params['owner_id'] # noqa: E501 if 'group_type' in params: path_params['groupType'] = params['group_type'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroups/{ownerType}/{ownerId}/{groupType}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityGroupInfo]', # 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_entity_groups_by_type_using_get(self, group_type, **kwargs): # noqa: E501 """Get Entity Groups by entity type (getEntityGroupsByType) # noqa: E501 Fetch the list of Entity Group Info objects based on the provided Entity Type. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entity_groups_by_type_using_get(group_type, async_req=True) >>> result = thread.get() :param async_req bool :param str group_type: Entity Group type (required) :return: list[EntityGroupInfo] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_groups_by_type_using_get_with_http_info(group_type, **kwargs) # noqa: E501 else: (data) = self.get_entity_groups_by_type_using_get_with_http_info(group_type, **kwargs) # noqa: E501 return data def get_entity_groups_by_type_using_get_with_http_info(self, group_type, **kwargs): # noqa: E501 """Get Entity Groups by entity type (getEntityGroupsByType) # noqa: E501 Fetch the list of Entity Group Info objects based on the provided Entity Type. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously.Entity Group Info extends Entity Group object and adds 'ownerIds' - a list of owner ids. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_entity_groups_by_type_using_get_with_http_info(group_type, async_req=True) >>> result = thread.get() :param async_req bool :param str group_type: Entity Group type (required) :return: list[EntityGroupInfo] If the method is called asynchronously, returns the request thread. """ all_params = ['group_type'] # 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_entity_groups_by_type_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'group_type' is set if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_entity_groups_by_type_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'group_type' in params: path_params['groupType'] = params['group_type'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroups/{groupType}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityGroupInfo]', # 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_entity_groups_for_entity_using_get(self, entity_type, entity_id, **kwargs): # noqa: E501 """Get Entity Groups by Entity Id (getEntityGroupsForEntity) # noqa: E501 Returns a list of groups that contain the specified Entity Id. For example, all device groups that contain specific device. The list always contain at least one element - special group 'All'.You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_entity_groups_for_entity_using_get(entity_type, entity_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_type: Entity Group type (required) :param str entity_id: A string value representing the entity id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: list[EntityGroupId] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_groups_for_entity_using_get_with_http_info(entity_type, entity_id, **kwargs) # noqa: E501 else: (data) = self.get_entity_groups_for_entity_using_get_with_http_info(entity_type, entity_id, **kwargs) # noqa: E501 return data def get_entity_groups_for_entity_using_get_with_http_info(self, entity_type, entity_id, **kwargs): # noqa: E501 """Get Entity Groups by Entity Id (getEntityGroupsForEntity) # noqa: E501 Returns a list of groups that contain the specified Entity Id. For example, all device groups that contain specific device. The list always contain at least one element - special group 'All'.You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_entity_groups_for_entity_using_get_with_http_info(entity_type, entity_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_type: Entity Group type (required) :param str entity_id: A string value representing the entity id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: list[EntityGroupId] If the method is called asynchronously, returns the request thread. """ all_params = ['entity_type', 'entity_id'] # 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_entity_groups_for_entity_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_type' is set if ('entity_type' not in params or params['entity_type'] is None): raise ValueError("Missing the required parameter `entity_type` when calling `get_entity_groups_for_entity_using_get`") # noqa: E501 # verify the required parameter 'entity_id' is set if ('entity_id' not in params or params['entity_id'] is None): raise ValueError("Missing the required parameter `entity_id` when calling `get_entity_groups_for_entity_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_type' in params: path_params['entityType'] = params['entity_type'] # noqa: E501 if 'entity_id' in params: path_params['entityId'] = params['entity_id'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroups/{entityType}/{entityId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityGroupId]', # 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_group_entity_using_get(self, entity_group_id, entity_id, **kwargs): # noqa: E501 """Get Group Entity (getGroupEntity) # noqa: E501 Fetch the Short Entity View object based on the group and entity id. Short Entity View object contains the entity id and number of fields (attributes, telemetry, etc). List of those fields is configurable and defined in the group configuration. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_group_entity_using_get(entity_group_id, entity_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str entity_id: A string value representing the entity id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: ShortEntityView If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_group_entity_using_get_with_http_info(entity_group_id, entity_id, **kwargs) # noqa: E501 else: (data) = self.get_group_entity_using_get_with_http_info(entity_group_id, entity_id, **kwargs) # noqa: E501 return data def get_group_entity_using_get_with_http_info(self, entity_group_id, entity_id, **kwargs): # noqa: E501 """Get Group Entity (getGroupEntity) # noqa: E501 Fetch the Short Entity View object based on the group and entity id. Short Entity View object contains the entity id and number of fields (attributes, telemetry, etc). List of those fields is configurable and defined in the group configuration. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_group_entity_using_get_with_http_info(entity_group_id, entity_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str entity_id: A string value representing the entity id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: ShortEntityView If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id', 'entity_id'] # 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_group_entity_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `get_group_entity_using_get`") # noqa: E501 # verify the required parameter 'entity_id' is set if ('entity_id' not in params or params['entity_id'] is None): raise ValueError("Missing the required parameter `entity_id` when calling `get_group_entity_using_get`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 if 'entity_id' in params: path_params['entityId'] = params['entity_id'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/{entityId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ShortEntityView', # 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_owners_using_get(self, page_size, page, **kwargs): # noqa: E501 """Get Owners (getOwners) # noqa: E501 Provides a rage view of Customers that the current user has READ access to. If the current user is Tenant administrator, the result set also contains the tenant. The call is designed for the UI auto-complete component to show tenant and all possible Customers that the user may select to change the owner of the particular entity or entity group. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_owners_using_get(page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param int page_size: Maximum amount of entities in a one page (required) :param int page: Sequence number of page starting from 0 (required) :param str text_search: The case insensitive 'startsWith' filter based on the entity group name. :param str sort_property: Property of entity to sort by :param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING) :return: PageDataContactBasedobject If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_owners_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501 else: (data) = self.get_owners_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501 return data def get_owners_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501 """Get Owners (getOwners) # noqa: E501 Provides a rage view of Customers that the current user has READ access to. If the current user is Tenant administrator, the result set also contains the tenant. The call is designed for the UI auto-complete component to show tenant and all possible Customers that the user may select to change the owner of the particular entity or entity group. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_owners_using_get_with_http_info(page_size, page, async_req=True) >>> result = thread.get() :param async_req bool :param int page_size: Maximum amount of entities in a one page (required) :param int page: Sequence number of page starting from 0 (required) :param str text_search: The case insensitive 'startsWith' filter based on the entity group name. :param str sort_property: Property of entity to sort by :param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING) :return: PageDataContactBasedobject If the method is called asynchronously, returns the request thread. """ all_params = ['page_size', 'page', 'text_search', 'sort_property', 'sort_order'] # 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_owners_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'page_size' is set if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_owners_using_get`") # noqa: E501 # verify the required parameter 'page' is set if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_owners_using_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) # noqa: E501 if 'page' in params: query_params.append(('page', params['page'])) # noqa: E501 if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) # noqa: E501 if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) # noqa: E501 if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) # 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/owners{?page,pageSize,sortOrder,sortProperty,textSearch}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataContactBasedobject', # 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 make_entity_group_private_using_post(self, entity_group_id, **kwargs): # noqa: E501 """Make Entity Group Private (makeEntityGroupPrivate) # noqa: E501 Make the entity group not available for non authorized users. Every group is private by default. This call is useful to hide the group that was previously made public. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.make_entity_group_private_using_post(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (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.make_entity_group_private_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501 else: (data) = self.make_entity_group_private_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501 return data def make_entity_group_private_using_post_with_http_info(self, entity_group_id, **kwargs): # noqa: E501 """Make Entity Group Private (makeEntityGroupPrivate) # noqa: E501 Make the entity group not available for non authorized users. Every group is private by default. This call is useful to hide the group that was previously made public. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.make_entity_group_private_using_post_with_http_info(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id'] # 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 make_entity_group_private_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `make_entity_group_private_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/makePrivate', '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 make_entity_group_public_using_post(self, entity_group_id, **kwargs): # noqa: E501 """Make Entity Group Publicly available (makeEntityGroupPublic) # noqa: E501 Make the entity group available for non authorized users. Useful for public dashboards that will be embedded into the public websites. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.make_entity_group_public_using_post(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (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.make_entity_group_public_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501 else: (data) = self.make_entity_group_public_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501 return data def make_entity_group_public_using_post_with_http_info(self, entity_group_id, **kwargs): # noqa: E501 """Make Entity Group Publicly available (makeEntityGroupPublic) # noqa: E501 Make the entity group available for non authorized users. Useful for public dashboards that will be embedded into the public websites. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.make_entity_group_public_using_post_with_http_info(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id'] # 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 make_entity_group_public_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `make_entity_group_public_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/makePublic', '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 remove_entities_from_entity_group_using_post(self, entity_group_id, **kwargs): # noqa: E501 """Remove entities from the group (removeEntitiesFromEntityGroup) # noqa: E501 Removes entities from the specified entity group. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'REMOVE_FROM_GROUP' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.remove_entities_from_entity_group_using_post(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param list[str] body: :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.remove_entities_from_entity_group_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501 else: (data) = self.remove_entities_from_entity_group_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501 return data def remove_entities_from_entity_group_using_post_with_http_info(self, entity_group_id, **kwargs): # noqa: E501 """Remove entities from the group (removeEntitiesFromEntityGroup) # noqa: E501 Removes entities from the specified entity group. Entity group allows you to group multiple entities of the same entity type (Device, Asset, Customer, User, Dashboard, etc). Entity Group always have an owner - particular Tenant or Customer. Each entity may belong to multiple groups simultaneously. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'REMOVE_FROM_GROUP' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.remove_entities_from_entity_group_using_post_with_http_info(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param list[str] body: :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id', 'body'] # 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 remove_entities_from_entity_group_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `remove_entities_from_entity_group_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # 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 = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/deleteEntities', '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 save_entity_group_using_post(self, **kwargs): # noqa: E501 """Create Or Update Entity Group (saveEntityGroup) # noqa: E501 Create or update the Entity Group. When creating Entity Group, platform generates Entity Group Id as [time-based UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier#Version_1_(date-time_and_MAC_address)). The newly created Entity Group Id will be present in the response. Specify existing Entity Group Id to update the group. Referencing non-existing Entity Group Id will cause 'Not Found' error. Entity group name is unique in the scope of owner and entity type. For example, you can't create two tenant device groups called 'Water meters'. However, you may create device and asset group with the same name. And also you may create groups with the same name for two different customers of the same tenant. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_entity_group_using_post(async_req=True) >>> result = thread.get() :param async_req bool :param EntityGroup body: :return: EntityGroupInfo If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.save_entity_group_using_post_with_http_info(**kwargs) # noqa: E501 else: (data) = self.save_entity_group_using_post_with_http_info(**kwargs) # noqa: E501 return data def save_entity_group_using_post_with_http_info(self, **kwargs): # noqa: E501 """Create Or Update Entity Group (saveEntityGroup) # noqa: E501 Create or update the Entity Group. When creating Entity Group, platform generates Entity Group Id as [time-based UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier#Version_1_(date-time_and_MAC_address)). The newly created Entity Group Id will be present in the response. Specify existing Entity Group Id to update the group. Referencing non-existing Entity Group Id will cause 'Not Found' error. Entity group name is unique in the scope of owner and entity type. For example, you can't create two tenant device groups called 'Water meters'. However, you may create device and asset group with the same name. And also you may create groups with the same name for two different customers of the same tenant. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.save_entity_group_using_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param EntityGroup body: :return: EntityGroupInfo If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # 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 save_entity_group_using_post" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # 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 = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroupInfo', # 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 share_entity_group_to_child_owner_user_group_using_post(self, entity_group_id, user_group_id, role_id, **kwargs): # noqa: E501 """Share the Entity Group with User group (shareEntityGroupToChildOwnerUserGroup) # noqa: E501 Share the entity group with specified user group using specified role. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.share_entity_group_to_child_owner_user_group_using_post(entity_group_id, user_group_id, role_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id that you would like to share. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str user_group_id: A string value representing the Entity(User) Group Id that you would like to share with. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str role_id: A string value representing the Role Id that describes set of permissions you would like to share (read, write, etc). For example, '784f394c-42b6-435a-983c-b7beff2784f9' (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.share_entity_group_to_child_owner_user_group_using_post_with_http_info(entity_group_id, user_group_id, role_id, **kwargs) # noqa: E501 else: (data) = self.share_entity_group_to_child_owner_user_group_using_post_with_http_info(entity_group_id, user_group_id, role_id, **kwargs) # noqa: E501 return data def share_entity_group_to_child_owner_user_group_using_post_with_http_info(self, entity_group_id, user_group_id, role_id, **kwargs): # noqa: E501 """Share the Entity Group with User group (shareEntityGroupToChildOwnerUserGroup) # noqa: E501 Share the entity group with specified user group using specified role. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.share_entity_group_to_child_owner_user_group_using_post_with_http_info(entity_group_id, user_group_id, role_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id that you would like to share. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str user_group_id: A string value representing the Entity(User) Group Id that you would like to share with. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str role_id: A string value representing the Role Id that describes set of permissions you would like to share (read, write, etc). For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id', 'user_group_id', 'role_id'] # 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 share_entity_group_to_child_owner_user_group_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `share_entity_group_to_child_owner_user_group_using_post`") # noqa: E501 # verify the required parameter 'user_group_id' is set if ('user_group_id' not in params or params['user_group_id'] is None): raise ValueError("Missing the required parameter `user_group_id` when calling `share_entity_group_to_child_owner_user_group_using_post`") # noqa: E501 # verify the required parameter 'role_id' is set if ('role_id' not in params or params['role_id'] is None): raise ValueError("Missing the required parameter `role_id` when calling `share_entity_group_to_child_owner_user_group_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 if 'user_group_id' in params: path_params['userGroupId'] = params['user_group_id'] # noqa: E501 if 'role_id' in params: path_params['roleId'] = params['role_id'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/{userGroupId}/{roleId}/share', '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 share_entity_group_using_post(self, entity_group_id, **kwargs): # noqa: E501 """Share the Entity Group (shareEntityGroup) # noqa: E501 Share the entity group with certain user group based on the provided Share Group Request. The request is quite flexible and processing of the request involves multiple security checks using platform RBAC feature. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.share_entity_group_using_post(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param ShareGroupRequest body: :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.share_entity_group_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501 else: (data) = self.share_entity_group_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501 return data def share_entity_group_using_post_with_http_info(self, entity_group_id, **kwargs): # noqa: E501 """Share the Entity Group (shareEntityGroup) # noqa: E501 Share the entity group with certain user group based on the provided Share Group Request. The request is quite flexible and processing of the request involves multiple security checks using platform RBAC feature. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.share_entity_group_using_post_with_http_info(entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param ShareGroupRequest body: :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['entity_group_id', 'body'] # 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 share_entity_group_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `share_entity_group_using_post`") # noqa: E501 collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # 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 = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/share', '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 unassign_entity_group_from_edge_using_delete(self, edge_id, group_type, entity_group_id, **kwargs): # noqa: E501 """Unassign entity group from edge (unassignEntityGroupFromEdge) # noqa: E501 Clears assignment of the entity group to the edge. Unassignment works in async way - first, 'unassign' notification event pushed to edge queue on platform. Second, remote edge service will receive an 'unassign' command to remove entity group (Edge will receive this instantly, if it's currently connected, or once it's going to be connected to platform). Third, once 'unassign' command will be delivered to edge service, it's going to remove entity group and entities inside this group locally. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for the entity (entities). # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unassign_entity_group_from_edge_using_delete(edge_id, group_type, entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: A string value representing the edge id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str group_type: EntityGroup type (required) :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: EntityGroup If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.unassign_entity_group_from_edge_using_delete_with_http_info(edge_id, group_type, entity_group_id, **kwargs) # noqa: E501 else: (data) = self.unassign_entity_group_from_edge_using_delete_with_http_info(edge_id, group_type, entity_group_id, **kwargs) # noqa: E501 return data def unassign_entity_group_from_edge_using_delete_with_http_info(self, edge_id, group_type, entity_group_id, **kwargs): # noqa: E501 """Unassign entity group from edge (unassignEntityGroupFromEdge) # noqa: E501 Clears assignment of the entity group to the edge. Unassignment works in async way - first, 'unassign' notification event pushed to edge queue on platform. Second, remote edge service will receive an 'unassign' command to remove entity group (Edge will receive this instantly, if it's currently connected, or once it's going to be connected to platform). Third, once 'unassign' command will be delivered to edge service, it's going to remove entity group and entities inside this group locally. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for the entity (entities). # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.unassign_entity_group_from_edge_using_delete_with_http_info(edge_id, group_type, entity_group_id, async_req=True) >>> result = thread.get() :param async_req bool :param str edge_id: A string value representing the edge id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :param str group_type: EntityGroup type (required) :param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required) :return: EntityGroup If the method is called asynchronously, returns the request thread. """ all_params = ['edge_id', 'group_type', 'entity_group_id'] # 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 unassign_entity_group_from_edge_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'edge_id' is set if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `unassign_entity_group_from_edge_using_delete`") # noqa: E501 # verify the required parameter 'group_type' is set if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `unassign_entity_group_from_edge_using_delete`") # noqa: E501 # verify the required parameter 'entity_group_id' is set if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `unassign_entity_group_from_edge_using_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] # noqa: E501 if 'group_type' in params: path_params['groupType'] = params['group_type'] # noqa: E501 if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501 query_params = [] 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 # Authentication setting auth_settings = ['X-Authorization'] # noqa: E501 return self.api_client.call_api( '/api/edge/{edgeId}/entityGroup/{entityGroupId}/{groupType}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroup', # 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)

56.113752

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0.670978

from __future__ import absolute_import import re import six from tb_rest_client.api_client import ApiClient class EntityGroupControllerApi(object): def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def add_entities_to_entity_group_using_post(self, entity_group_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.add_entities_to_entity_group_using_post_with_http_info(entity_group_id, **kwargs) else: (data) = self.add_entities_to_entity_group_using_post_with_http_info(entity_group_id, **kwargs) return data def add_entities_to_entity_group_using_post_with_http_info(self, entity_group_id, **kwargs): all_params = ['entity_group_id', 'body'] 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 add_entities_to_entity_group_using_post" % key ) params[key] = val del params['kwargs'] if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `add_entities_to_entity_group_using_post`") collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/addEntities', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, 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 assign_entity_group_to_edge_using_post(self, edge_id, group_type, entity_group_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.assign_entity_group_to_edge_using_post_with_http_info(edge_id, group_type, entity_group_id, **kwargs) else: (data) = self.assign_entity_group_to_edge_using_post_with_http_info(edge_id, group_type, entity_group_id, **kwargs) return data def assign_entity_group_to_edge_using_post_with_http_info(self, edge_id, group_type, entity_group_id, **kwargs): all_params = ['edge_id', 'group_type', 'entity_group_id'] 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 assign_entity_group_to_edge_using_post" % key ) params[key] = val del params['kwargs'] if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `assign_entity_group_to_edge_using_post`") if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `assign_entity_group_to_edge_using_post`") if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `assign_entity_group_to_edge_using_post`") collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] if 'group_type' in params: path_params['groupType'] = params['group_type'] if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/edge/{edgeId}/entityGroup/{entityGroupId}/{groupType}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroup', 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 delete_entity_group_using_delete(self, entity_group_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_entity_group_using_delete_with_http_info(entity_group_id, **kwargs) else: (data) = self.delete_entity_group_using_delete_with_http_info(entity_group_id, **kwargs) return data def delete_entity_group_using_delete_with_http_info(self, entity_group_id, **kwargs): all_params = ['entity_group_id'] 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_entity_group_using_delete" % key ) params[key] = val del params['kwargs'] if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `delete_entity_group_using_delete`") collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{entityGroupId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, 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_all_edge_entity_groups_using_get(self, edge_id, group_type, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_all_edge_entity_groups_using_get_with_http_info(edge_id, group_type, **kwargs) else: (data) = self.get_all_edge_entity_groups_using_get_with_http_info(edge_id, group_type, **kwargs) return data def get_all_edge_entity_groups_using_get_with_http_info(self, edge_id, group_type, **kwargs): all_params = ['edge_id', 'group_type'] 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_all_edge_entity_groups_using_get" % key ) params[key] = val del params['kwargs'] if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `get_all_edge_entity_groups_using_get`") if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_all_edge_entity_groups_using_get`") collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] if 'group_type' in params: path_params['groupType'] = params['group_type'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/allEntityGroups/edge/{edgeId}/{groupType}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityGroupInfo]', 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_edge_entity_groups_using_get(self, edge_id, group_type, page_size, page, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_edge_entity_groups_using_get_with_http_info(edge_id, group_type, page_size, page, **kwargs) else: (data) = self.get_edge_entity_groups_using_get_with_http_info(edge_id, group_type, page_size, page, **kwargs) return data def get_edge_entity_groups_using_get_with_http_info(self, edge_id, group_type, page_size, page, **kwargs): all_params = ['edge_id', 'group_type', 'page_size', 'page', 'sort_property', 'sort_order'] 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_edge_entity_groups_using_get" % key ) params[key] = val del params['kwargs'] if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `get_edge_entity_groups_using_get`") if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_edge_entity_groups_using_get`") if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_edge_entity_groups_using_get`") if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_edge_entity_groups_using_get`") collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] if 'group_type' in params: path_params['groupType'] = params['group_type'] query_params = [] if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) if 'page' in params: query_params.append(('page', params['page'])) if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroups/edge/{edgeId}/{groupType}{?page,pageSize,sortOrder,sortProperty}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataEntityGroupInfo', 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_entities_using_get(self, entity_group_id, page_size, page, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entities_using_get_with_http_info(entity_group_id, page_size, page, **kwargs) else: (data) = self.get_entities_using_get_with_http_info(entity_group_id, page_size, page, **kwargs) return data def get_entities_using_get_with_http_info(self, entity_group_id, page_size, page, **kwargs): all_params = ['entity_group_id', 'page_size', 'page', 'text_search', 'sort_property', 'sort_order'] 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_entities_using_get" % key ) params[key] = val del params['kwargs'] if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `get_entities_using_get`") if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_entities_using_get`") if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_entities_using_get`") collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] query_params = [] if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) if 'page' in params: query_params.append(('page', params['page'])) if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/entities{?page,pageSize,sortOrder,sortProperty,textSearch}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataShortEntityView', 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_entity_group_all_by_owner_and_type_using_get(self, owner_type, owner_id, group_type, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_group_all_by_owner_and_type_using_get_with_http_info(owner_type, owner_id, group_type, **kwargs) else: (data) = self.get_entity_group_all_by_owner_and_type_using_get_with_http_info(owner_type, owner_id, group_type, **kwargs) return data def get_entity_group_all_by_owner_and_type_using_get_with_http_info(self, owner_type, owner_id, group_type, **kwargs): all_params = ['owner_type', 'owner_id', 'group_type'] 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_entity_group_all_by_owner_and_type_using_get" % key ) params[key] = val del params['kwargs'] if ('owner_type' not in params or params['owner_type'] is None): raise ValueError("Missing the required parameter `owner_type` when calling `get_entity_group_all_by_owner_and_type_using_get`") if ('owner_id' not in params or params['owner_id'] is None): raise ValueError("Missing the required parameter `owner_id` when calling `get_entity_group_all_by_owner_and_type_using_get`") if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_entity_group_all_by_owner_and_type_using_get`") collection_formats = {} path_params = {} if 'owner_type' in params: path_params['ownerType'] = params['owner_type'] if 'owner_id' in params: path_params['ownerId'] = params['owner_id'] if 'group_type' in params: path_params['groupType'] = params['group_type'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/all/{ownerType}/{ownerId}/{groupType}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroupInfo', 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_entity_group_by_id_using_get(self, entity_group_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_group_by_id_using_get_with_http_info(entity_group_id, **kwargs) else: (data) = self.get_entity_group_by_id_using_get_with_http_info(entity_group_id, **kwargs) return data def get_entity_group_by_id_using_get_with_http_info(self, entity_group_id, **kwargs): all_params = ['entity_group_id'] 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_entity_group_by_id_using_get" % key ) params[key] = val del params['kwargs'] if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `get_entity_group_by_id_using_get`") collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{entityGroupId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroupInfo', 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_entity_group_by_owner_and_name_and_type_using_get(self, owner_type, owner_id, group_type, group_name, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_group_by_owner_and_name_and_type_using_get_with_http_info(owner_type, owner_id, group_type, group_name, **kwargs) else: (data) = self.get_entity_group_by_owner_and_name_and_type_using_get_with_http_info(owner_type, owner_id, group_type, group_name, **kwargs) return data def get_entity_group_by_owner_and_name_and_type_using_get_with_http_info(self, owner_type, owner_id, group_type, group_name, **kwargs): all_params = ['owner_type', 'owner_id', 'group_type', 'group_name'] 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_entity_group_by_owner_and_name_and_type_using_get" % key ) params[key] = val del params['kwargs'] if ('owner_type' not in params or params['owner_type'] is None): raise ValueError("Missing the required parameter `owner_type` when calling `get_entity_group_by_owner_and_name_and_type_using_get`") if ('owner_id' not in params or params['owner_id'] is None): raise ValueError("Missing the required parameter `owner_id` when calling `get_entity_group_by_owner_and_name_and_type_using_get`") if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_entity_group_by_owner_and_name_and_type_using_get`") if ('group_name' not in params or params['group_name'] is None): raise ValueError("Missing the required parameter `group_name` when calling `get_entity_group_by_owner_and_name_and_type_using_get`") collection_formats = {} path_params = {} if 'owner_type' in params: path_params['ownerType'] = params['owner_type'] if 'owner_id' in params: path_params['ownerId'] = params['owner_id'] if 'group_type' in params: path_params['groupType'] = params['group_type'] if 'group_name' in params: path_params['groupName'] = params['group_name'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{ownerType}/{ownerId}/{groupType}/{groupName}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroupInfo', 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_entity_groups_by_ids_using_get(self, entity_group_ids, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_groups_by_ids_using_get_with_http_info(entity_group_ids, **kwargs) else: (data) = self.get_entity_groups_by_ids_using_get_with_http_info(entity_group_ids, **kwargs) return data def get_entity_groups_by_ids_using_get_with_http_info(self, entity_group_ids, **kwargs): all_params = ['entity_group_ids'] 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_entity_groups_by_ids_using_get" % key ) params[key] = val del params['kwargs'] if ('entity_group_ids' not in params or params['entity_group_ids'] is None): raise ValueError("Missing the required parameter `entity_group_ids` when calling `get_entity_groups_by_ids_using_get`") collection_formats = {} path_params = {} query_params = [] if 'entity_group_ids' in params: query_params.append(('entityGroupIds', params['entity_group_ids'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroups{?entityGroupIds}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityGroup]', 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_entity_groups_by_owner_and_type_using_get(self, owner_type, owner_id, group_type, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_groups_by_owner_and_type_using_get_with_http_info(owner_type, owner_id, group_type, **kwargs) else: (data) = self.get_entity_groups_by_owner_and_type_using_get_with_http_info(owner_type, owner_id, group_type, **kwargs) return data def get_entity_groups_by_owner_and_type_using_get_with_http_info(self, owner_type, owner_id, group_type, **kwargs): all_params = ['owner_type', 'owner_id', 'group_type'] 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_entity_groups_by_owner_and_type_using_get" % key ) params[key] = val del params['kwargs'] if ('owner_type' not in params or params['owner_type'] is None): raise ValueError("Missing the required parameter `owner_type` when calling `get_entity_groups_by_owner_and_type_using_get`") if ('owner_id' not in params or params['owner_id'] is None): raise ValueError("Missing the required parameter `owner_id` when calling `get_entity_groups_by_owner_and_type_using_get`") if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_entity_groups_by_owner_and_type_using_get`") collection_formats = {} path_params = {} if 'owner_type' in params: path_params['ownerType'] = params['owner_type'] if 'owner_id' in params: path_params['ownerId'] = params['owner_id'] if 'group_type' in params: path_params['groupType'] = params['group_type'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroups/{ownerType}/{ownerId}/{groupType}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityGroupInfo]', 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_entity_groups_by_type_using_get(self, group_type, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_groups_by_type_using_get_with_http_info(group_type, **kwargs) else: (data) = self.get_entity_groups_by_type_using_get_with_http_info(group_type, **kwargs) return data def get_entity_groups_by_type_using_get_with_http_info(self, group_type, **kwargs): all_params = ['group_type'] 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_entity_groups_by_type_using_get" % key ) params[key] = val del params['kwargs'] if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `get_entity_groups_by_type_using_get`") collection_formats = {} path_params = {} if 'group_type' in params: path_params['groupType'] = params['group_type'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroups/{groupType}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityGroupInfo]', 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_entity_groups_for_entity_using_get(self, entity_type, entity_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_entity_groups_for_entity_using_get_with_http_info(entity_type, entity_id, **kwargs) else: (data) = self.get_entity_groups_for_entity_using_get_with_http_info(entity_type, entity_id, **kwargs) return data def get_entity_groups_for_entity_using_get_with_http_info(self, entity_type, entity_id, **kwargs): all_params = ['entity_type', 'entity_id'] 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_entity_groups_for_entity_using_get" % key ) params[key] = val del params['kwargs'] if ('entity_type' not in params or params['entity_type'] is None): raise ValueError("Missing the required parameter `entity_type` when calling `get_entity_groups_for_entity_using_get`") if ('entity_id' not in params or params['entity_id'] is None): raise ValueError("Missing the required parameter `entity_id` when calling `get_entity_groups_for_entity_using_get`") collection_formats = {} path_params = {} if 'entity_type' in params: path_params['entityType'] = params['entity_type'] if 'entity_id' in params: path_params['entityId'] = params['entity_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroups/{entityType}/{entityId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[EntityGroupId]', 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_group_entity_using_get(self, entity_group_id, entity_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_group_entity_using_get_with_http_info(entity_group_id, entity_id, **kwargs) else: (data) = self.get_group_entity_using_get_with_http_info(entity_group_id, entity_id, **kwargs) return data def get_group_entity_using_get_with_http_info(self, entity_group_id, entity_id, **kwargs): all_params = ['entity_group_id', 'entity_id'] 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_group_entity_using_get" % key ) params[key] = val del params['kwargs'] if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `get_group_entity_using_get`") if ('entity_id' not in params or params['entity_id'] is None): raise ValueError("Missing the required parameter `entity_id` when calling `get_group_entity_using_get`") collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] if 'entity_id' in params: path_params['entityId'] = params['entity_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/{entityId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ShortEntityView', 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_owners_using_get(self, page_size, page, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_owners_using_get_with_http_info(page_size, page, **kwargs) else: (data) = self.get_owners_using_get_with_http_info(page_size, page, **kwargs) return data def get_owners_using_get_with_http_info(self, page_size, page, **kwargs): all_params = ['page_size', 'page', 'text_search', 'sort_property', 'sort_order'] 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_owners_using_get" % key ) params[key] = val del params['kwargs'] if ('page_size' not in params or params['page_size'] is None): raise ValueError("Missing the required parameter `page_size` when calling `get_owners_using_get`") if ('page' not in params or params['page'] is None): raise ValueError("Missing the required parameter `page` when calling `get_owners_using_get`") collection_formats = {} path_params = {} query_params = [] if 'page_size' in params: query_params.append(('pageSize', params['page_size'])) if 'page' in params: query_params.append(('page', params['page'])) if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) if 'sort_property' in params: query_params.append(('sortProperty', params['sort_property'])) if 'sort_order' in params: query_params.append(('sortOrder', params['sort_order'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/owners{?page,pageSize,sortOrder,sortProperty,textSearch}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageDataContactBasedobject', 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 make_entity_group_private_using_post(self, entity_group_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.make_entity_group_private_using_post_with_http_info(entity_group_id, **kwargs) else: (data) = self.make_entity_group_private_using_post_with_http_info(entity_group_id, **kwargs) return data def make_entity_group_private_using_post_with_http_info(self, entity_group_id, **kwargs): all_params = ['entity_group_id'] 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 make_entity_group_private_using_post" % key ) params[key] = val del params['kwargs'] if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `make_entity_group_private_using_post`") collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/makePrivate', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, 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 make_entity_group_public_using_post(self, entity_group_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.make_entity_group_public_using_post_with_http_info(entity_group_id, **kwargs) else: (data) = self.make_entity_group_public_using_post_with_http_info(entity_group_id, **kwargs) return data def make_entity_group_public_using_post_with_http_info(self, entity_group_id, **kwargs): all_params = ['entity_group_id'] 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 make_entity_group_public_using_post" % key ) params[key] = val del params['kwargs'] if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `make_entity_group_public_using_post`") collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/makePublic', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, 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 remove_entities_from_entity_group_using_post(self, entity_group_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.remove_entities_from_entity_group_using_post_with_http_info(entity_group_id, **kwargs) else: (data) = self.remove_entities_from_entity_group_using_post_with_http_info(entity_group_id, **kwargs) return data def remove_entities_from_entity_group_using_post_with_http_info(self, entity_group_id, **kwargs): all_params = ['entity_group_id', 'body'] 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 remove_entities_from_entity_group_using_post" % key ) params[key] = val del params['kwargs'] if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `remove_entities_from_entity_group_using_post`") collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/deleteEntities', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, 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 save_entity_group_using_post(self, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.save_entity_group_using_post_with_http_info(**kwargs) else: (data) = self.save_entity_group_using_post_with_http_info(**kwargs) return data def save_entity_group_using_post_with_http_info(self, **kwargs): all_params = ['body'] 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 save_entity_group_using_post" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroupInfo', 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 share_entity_group_to_child_owner_user_group_using_post(self, entity_group_id, user_group_id, role_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.share_entity_group_to_child_owner_user_group_using_post_with_http_info(entity_group_id, user_group_id, role_id, **kwargs) else: (data) = self.share_entity_group_to_child_owner_user_group_using_post_with_http_info(entity_group_id, user_group_id, role_id, **kwargs) return data def share_entity_group_to_child_owner_user_group_using_post_with_http_info(self, entity_group_id, user_group_id, role_id, **kwargs): all_params = ['entity_group_id', 'user_group_id', 'role_id'] 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 share_entity_group_to_child_owner_user_group_using_post" % key ) params[key] = val del params['kwargs'] if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `share_entity_group_to_child_owner_user_group_using_post`") if ('user_group_id' not in params or params['user_group_id'] is None): raise ValueError("Missing the required parameter `user_group_id` when calling `share_entity_group_to_child_owner_user_group_using_post`") if ('role_id' not in params or params['role_id'] is None): raise ValueError("Missing the required parameter `role_id` when calling `share_entity_group_to_child_owner_user_group_using_post`") collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] if 'user_group_id' in params: path_params['userGroupId'] = params['user_group_id'] if 'role_id' in params: path_params['roleId'] = params['role_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/{userGroupId}/{roleId}/share', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, 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 share_entity_group_using_post(self, entity_group_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.share_entity_group_using_post_with_http_info(entity_group_id, **kwargs) else: (data) = self.share_entity_group_using_post_with_http_info(entity_group_id, **kwargs) return data def share_entity_group_using_post_with_http_info(self, entity_group_id, **kwargs): all_params = ['entity_group_id', 'body'] 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 share_entity_group_using_post" % key ) params[key] = val del params['kwargs'] if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `share_entity_group_using_post`") collection_formats = {} path_params = {} if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/entityGroup/{entityGroupId}/share', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, 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 unassign_entity_group_from_edge_using_delete(self, edge_id, group_type, entity_group_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.unassign_entity_group_from_edge_using_delete_with_http_info(edge_id, group_type, entity_group_id, **kwargs) else: (data) = self.unassign_entity_group_from_edge_using_delete_with_http_info(edge_id, group_type, entity_group_id, **kwargs) return data def unassign_entity_group_from_edge_using_delete_with_http_info(self, edge_id, group_type, entity_group_id, **kwargs): all_params = ['edge_id', 'group_type', 'entity_group_id'] 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 unassign_entity_group_from_edge_using_delete" % key ) params[key] = val del params['kwargs'] if ('edge_id' not in params or params['edge_id'] is None): raise ValueError("Missing the required parameter `edge_id` when calling `unassign_entity_group_from_edge_using_delete`") if ('group_type' not in params or params['group_type'] is None): raise ValueError("Missing the required parameter `group_type` when calling `unassign_entity_group_from_edge_using_delete`") if ('entity_group_id' not in params or params['entity_group_id'] is None): raise ValueError("Missing the required parameter `entity_group_id` when calling `unassign_entity_group_from_edge_using_delete`") collection_formats = {} path_params = {} if 'edge_id' in params: path_params['edgeId'] = params['edge_id'] if 'group_type' in params: path_params['groupType'] = params['group_type'] if 'entity_group_id' in params: path_params['entityGroupId'] = params['entity_group_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) auth_settings = ['X-Authorization'] return self.api_client.call_api( '/api/edge/{edgeId}/entityGroup/{entityGroupId}/{groupType}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EntityGroup', 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)

true

790bf8ceade6039bbd651fce1960c04f9c51c63e

28,394

py

Python

tensorflow/python/keras/_impl/keras/applications/mobilenet.py

qinchangping/tensorflow

f7f7036d1cdc5716aff976fae0ea4d1b9a931b56

[ "Apache-2.0" ]

24

2018-02-01T15:49:22.000Z

2021-01-11T16:31:18.000Z

tensorflow/python/keras/_impl/keras/applications/mobilenet.py

qinchangping/tensorflow

f7f7036d1cdc5716aff976fae0ea4d1b9a931b56

[ "Apache-2.0" ]

2

2018-09-09T07:29:07.000Z

2019-03-11T07:14:45.000Z

tensorflow/python/keras/_impl/keras/applications/mobilenet.py

qinchangping/tensorflow

f7f7036d1cdc5716aff976fae0ea4d1b9a931b56

[ "Apache-2.0" ]

4

2018-10-29T18:43:22.000Z

2020-09-28T07:19:52.000Z

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=invalid-name # pylint: disable=unused-import """MobileNet v1 models for Keras. MobileNet is a general architecture and can be used for multiple use cases. Depending on the use case, it can use different input layer size and different width factors. This allows different width models to reduce the number of multiply-adds and thereby reduce inference cost on mobile devices. MobileNets support any input size greater than 32 x 32, with larger image sizes offering better performance. The number of parameters and number of multiply-adds can be modified by using the `alpha` parameter, which increases/decreases the number of filters in each layer. By altering the image size and `alpha` parameter, all 16 models from the paper can be built, with ImageNet weights provided. The paper demonstrates the performance of MobileNets using `alpha` values of 1.0 (also called 100 % MobileNet), 0.75, 0.5 and 0.25. For each of these `alpha` values, weights for 4 different input image sizes are provided (224, 192, 160, 128). The following table describes the size and accuracy of the 100% MobileNet on size 224 x 224: ---------------------------------------------------------------------------- Width Multiplier (alpha) | ImageNet Acc | Multiply-Adds (M) | Params (M) ---------------------------------------------------------------------------- | 1.0 MobileNet-224 | 70.6 % | 529 | 4.2 | | 0.75 MobileNet-224 | 68.4 % | 325 | 2.6 | | 0.50 MobileNet-224 | 63.7 % | 149 | 1.3 | | 0.25 MobileNet-224 | 50.6 % | 41 | 0.5 | ---------------------------------------------------------------------------- The following table describes the performance of the 100 % MobileNet on various input sizes: ------------------------------------------------------------------------ Resolution | ImageNet Acc | Multiply-Adds (M) | Params (M) ------------------------------------------------------------------------ | 1.0 MobileNet-224 | 70.6 % | 529 | 4.2 | | 1.0 MobileNet-192 | 69.1 % | 529 | 4.2 | | 1.0 MobileNet-160 | 67.2 % | 529 | 4.2 | | 1.0 MobileNet-128 | 64.4 % | 529 | 4.2 | ------------------------------------------------------------------------ The weights for all 16 models are obtained and translated from TensorFlow checkpoints found at https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md # Reference - [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/pdf/1704.04861.pdf)) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import BatchNormalization from tensorflow.python.keras._impl.keras.layers import Conv2D from tensorflow.python.keras._impl.keras.layers import Dropout from tensorflow.python.keras._impl.keras.layers import GlobalAveragePooling2D from tensorflow.python.keras._impl.keras.layers import GlobalMaxPooling2D from tensorflow.python.keras._impl.keras.layers import Input from tensorflow.python.keras._impl.keras.layers import Reshape from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils import conv_utils from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging BASE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.6/' def relu6(x): return K.relu(x, max_value=6) def preprocess_input(x): """Preprocesses a numpy array encoding a batch of images. Arguments: x: a 4D numpy array consists of RGB values within [0, 255]. Returns: Preprocessed array. """ return imagenet_utils.preprocess_input(x, mode='tf') class DepthwiseConv2D(Conv2D): """Depthwise separable 2D convolution. Depthwise Separable convolutions consists in performing just the first step in a depthwise spatial convolution (which acts on each input channel separately). The `depth_multiplier` argument controls how many output channels are generated per input channel in the depthwise step. Arguments: kernel_size: An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. padding: one of `'valid'` or `'same'` (case-insensitive). depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `filters_in * depth_multiplier`. data_format: A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be 'channels_last'. activation: Activation function to use. If you don't specify anything, no activation is applied (ie. 'linear' activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. depthwise_initializer: Initializer for the depthwise kernel matrix. bias_initializer: Initializer for the bias vector. depthwise_regularizer: Regularizer function applied to the depthwise kernel matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its 'activation').. depthwise_constraint: Constraint function applied to the depthwise kernel matrix. bias_constraint: Constraint function applied to the bias vector. Input shape: 4D tensor with shape: `[batch, channels, rows, cols]` if data_format='channels_first' or 4D tensor with shape: `[batch, rows, cols, channels]` if data_format='channels_last'. Output shape: 4D tensor with shape: `[batch, filters, new_rows, new_cols]` if data_format='channels_first' or 4D tensor with shape: `[batch, new_rows, new_cols, filters]` if data_format='channels_last'. `rows` and `cols` values might have changed due to padding. """ def __init__(self, kernel_size, strides=(1, 1), padding='valid', depth_multiplier=1, data_format=None, activation=None, use_bias=True, depthwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, bias_constraint=None, **kwargs): super(DepthwiseConv2D, self).__init__( filters=None, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, bias_constraint=bias_constraint, **kwargs) self.depth_multiplier = depth_multiplier self.depthwise_initializer = initializers.get(depthwise_initializer) self.depthwise_regularizer = regularizers.get(depthwise_regularizer) self.depthwise_constraint = constraints.get(depthwise_constraint) self.bias_initializer = initializers.get(bias_initializer) @shape_type_conversion def build(self, input_shape): if len(input_shape) < 4: raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. ' 'Received input shape:', str(input_shape)) if self.data_format == 'channels_first': channel_axis = 1 else: channel_axis = 3 if input_shape[channel_axis] is None: raise ValueError('The channel dimension of the inputs to ' '`DepthwiseConv2D` ' 'should be defined. Found `None`.') input_dim = int(input_shape[channel_axis]) depthwise_kernel_shape = (self.kernel_size[0], self.kernel_size[1], input_dim, self.depth_multiplier) self.depthwise_kernel = self.add_weight( shape=depthwise_kernel_shape, initializer=self.depthwise_initializer, name='depthwise_kernel', regularizer=self.depthwise_regularizer, constraint=self.depthwise_constraint) if self.use_bias: self.bias = self.add_weight( shape=(input_dim * self.depth_multiplier,), initializer=self.bias_initializer, name='bias', regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None # Set input spec. self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim}) self.built = True def call(self, inputs, training=None): outputs = K.depthwise_conv2d( inputs, self.depthwise_kernel, strides=self.strides, padding=self.padding, dilation_rate=self.dilation_rate, data_format=self.data_format) if self.bias: outputs = K.bias_add(outputs, self.bias, data_format=self.data_format) if self.activation is not None: return self.activation(outputs) return outputs @shape_type_conversion def compute_output_shape(self, input_shape): if self.data_format == 'channels_first': rows = input_shape[2] cols = input_shape[3] out_filters = input_shape[1] * self.depth_multiplier elif self.data_format == 'channels_last': rows = input_shape[1] cols = input_shape[2] out_filters = input_shape[3] * self.depth_multiplier rows = conv_utils.conv_output_length(rows, self.kernel_size[0], self.padding, self.strides[0]) cols = conv_utils.conv_output_length(cols, self.kernel_size[1], self.padding, self.strides[1]) if self.data_format == 'channels_first': return (input_shape[0], out_filters, rows, cols) elif self.data_format == 'channels_last': return (input_shape[0], rows, cols, out_filters) def get_config(self): config = super(DepthwiseConv2D, self).get_config() config.pop('filters') config.pop('kernel_initializer') config.pop('kernel_regularizer') config.pop('kernel_constraint') config['depth_multiplier'] = self.depth_multiplier config['depthwise_initializer'] = initializers.serialize( self.depthwise_initializer) config['depthwise_regularizer'] = regularizers.serialize( self.depthwise_regularizer) config['depthwise_constraint'] = constraints.serialize( self.depthwise_constraint) return config def MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000): """Instantiates the MobileNet architecture. Note that only TensorFlow is supported for now, therefore it only works with the data format `image_data_format='channels_last'` in your Keras config at `~/.keras/keras.json`. To load a MobileNet model via `load_model`, import the custom objects `relu6` and `DepthwiseConv2D` and pass them to the `custom_objects` parameter. E.g. model = load_model('mobilenet.h5', custom_objects={ 'relu6': mobilenet.relu6, 'DepthwiseConv2D': mobilenet.DepthwiseConv2D}) Arguments: input_shape: optional shape tuple, only to be specified if `include_top` is False (otherwise the input shape has to be `(224, 224, 3)` (with `channels_last` data format) or (3, 224, 224) (with `channels_first` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value. alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. depth_multiplier: depth multiplier for depthwise convolution (also called the resolution multiplier) dropout: dropout rate include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. pooling: Optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional layer. - `avg` means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. - `max` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. Returns: A Keras model instance. Raises: ValueError: in case of invalid argument for `weights`, or invalid input shape. RuntimeError: If attempting to run this model with a backend that does not support separable convolutions. """ if K.backend() != 'tensorflow': raise RuntimeError('Only TensorFlow backend is currently supported, ' 'as other backends do not support ' 'depthwise convolution.') if not (weights in {'imagenet', None} or os.path.exists(weights)): raise ValueError('The `weights` argument should be either ' '`None` (random initialization), `imagenet` ' '(pre-training on ImageNet), ' 'or the path to the weights file to be loaded.') if weights == 'imagenet' and include_top and classes != 1000: raise ValueError('If using `weights` as ImageNet with `include_top` ' 'as true, `classes` should be 1000') # Determine proper input shape and default size. if input_shape is None: default_size = 224 else: if K.image_data_format() == 'channels_first': rows = input_shape[1] cols = input_shape[2] else: rows = input_shape[0] cols = input_shape[1] if rows == cols and rows in [128, 160, 192, 224]: default_size = rows else: default_size = 224 input_shape = _obtain_input_shape( input_shape, default_size=default_size, min_size=32, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) if K.image_data_format() == 'channels_last': row_axis, col_axis = (0, 1) else: row_axis, col_axis = (1, 2) rows = input_shape[row_axis] cols = input_shape[col_axis] if weights == 'imagenet': if depth_multiplier != 1: raise ValueError('If imagenet weights are being loaded, ' 'depth multiplier must be 1') if alpha not in [0.25, 0.50, 0.75, 1.0]: raise ValueError('If imagenet weights are being loaded, ' 'alpha can be one of' '`0.25`, `0.50`, `0.75` or `1.0` only.') if rows != cols or rows not in [128, 160, 192, 224]: raise ValueError('If imagenet weights are being loaded, ' 'input must have a static square shape (one of ' '(128,128), (160,160), (192,192), or (224, 224)).' ' Input shape provided = %s' % (input_shape,)) if K.image_data_format() != 'channels_last': logging.warning('The MobileNet family of models is only available ' 'for the input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height).' ' You should set `image_data_format="channels_last"` ' 'in your Keras config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') K.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None if input_tensor is None: img_input = Input(shape=input_shape) else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor x = _conv_block(img_input, 32, alpha, strides=(2, 2)) x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1) x = _depthwise_conv_block( x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2) x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3) x = _depthwise_conv_block( x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4) x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5) x = _depthwise_conv_block( x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11) x = _depthwise_conv_block( x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12) x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13) if include_top: if K.image_data_format() == 'channels_first': shape = (int(1024 * alpha), 1, 1) else: shape = (1, 1, int(1024 * alpha)) x = GlobalAveragePooling2D()(x) x = Reshape(shape, name='reshape_1')(x) x = Dropout(dropout, name='dropout')(x) x = Conv2D(classes, (1, 1), padding='same', name='conv_preds')(x) x = Activation('softmax', name='act_softmax')(x) x = Reshape((classes,), name='reshape_2')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = Model(inputs, x, name='mobilenet_%0.2f_%s' % (alpha, rows)) # load weights if weights == 'imagenet': if K.image_data_format() == 'channels_first': raise ValueError('Weights for "channels_last" format ' 'are not available.') if alpha == 1.0: alpha_text = '1_0' elif alpha == 0.75: alpha_text = '7_5' elif alpha == 0.50: alpha_text = '5_0' else: alpha_text = '2_5' if include_top: model_name = 'mobilenet_%s_%d_tf.h5' % (alpha_text, rows) weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file(model_name, weigh_path, cache_subdir='models') else: model_name = 'mobilenet_%s_%d_tf_no_top.h5' % (alpha_text, rows) weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file(model_name, weigh_path, cache_subdir='models') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) if old_data_format: K.set_image_data_format(old_data_format) return model def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)): """Adds an initial convolution layer (with batch normalization and relu6). Arguments: inputs: Input tensor of shape `(rows, cols, 3)` (with `channels_last` data format) or (3, rows, cols) (with `channels_first` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(224, 224, 3)` would be one valid value. filters: Integer, the dimensionality of the output space (i.e. the number output of filters in the convolution). alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. kernel: An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. Input shape: 4D tensor with shape: `(samples, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(samples, rows, cols, channels)` if data_format='channels_last'. Output shape: 4D tensor with shape: `(samples, filters, new_rows, new_cols)` if data_format='channels_first' or 4D tensor with shape: `(samples, new_rows, new_cols, filters)` if data_format='channels_last'. `rows` and `cols` values might have changed due to stride. Returns: Output tensor of block. """ channel_axis = 1 if K.image_data_format() == 'channels_first' else -1 filters = int(filters * alpha) x = Conv2D( filters, kernel, padding='same', use_bias=False, strides=strides, name='conv1')( inputs) x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x) return Activation(relu6, name='conv1_relu')(x) def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha, depth_multiplier=1, strides=(1, 1), block_id=1): """Adds a depthwise convolution block. A depthwise convolution block consists of a depthwise conv, batch normalization, relu6, pointwise convolution, batch normalization and relu6 activation. Arguments: inputs: Input tensor of shape `(rows, cols, channels)` (with `channels_last` data format) or (channels, rows, cols) (with `channels_first` data format). pointwise_conv_filters: Integer, the dimensionality of the output space (i.e. the number output of filters in the pointwise convolution). alpha: controls the width of the network. - If `alpha` < 1.0, proportionally decreases the number of filters in each layer. - If `alpha` > 1.0, proportionally increases the number of filters in each layer. - If `alpha` = 1, default number of filters from the paper are used at each layer. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `filters_in * depth_multiplier`. strides: An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1. block_id: Integer, a unique identification designating the block number. Input shape: 4D tensor with shape: `(batch, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(batch, rows, cols, channels)` if data_format='channels_last'. Output shape: 4D tensor with shape: `(batch, filters, new_rows, new_cols)` if data_format='channels_first' or 4D tensor with shape: `(batch, new_rows, new_cols, filters)` if data_format='channels_last'. `rows` and `cols` values might have changed due to stride. Returns: Output tensor of block. """ channel_axis = 1 if K.image_data_format() == 'channels_first' else -1 pointwise_conv_filters = int(pointwise_conv_filters * alpha) x = DepthwiseConv2D( # pylint: disable=not-callable (3, 3), padding='same', depth_multiplier=depth_multiplier, strides=strides, use_bias=False, name='conv_dw_%d' % block_id)( inputs) x = BatchNormalization(axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x) x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x) x = Conv2D( pointwise_conv_filters, (1, 1), padding='same', use_bias=False, strides=(1, 1), name='conv_pw_%d' % block_id)( x) x = BatchNormalization(axis=channel_axis, name='conv_pw_%d_bn' % block_id)(x) return Activation(relu6, name='conv_pw_%d_relu' % block_id)(x)

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import constraints from tensorflow.python.keras._impl.keras import initializers from tensorflow.python.keras._impl.keras import regularizers from tensorflow.python.keras._impl.keras.applications import imagenet_utils from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions from tensorflow.python.keras._impl.keras.engine import InputSpec from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs from tensorflow.python.keras._impl.keras.engine.topology import shape_type_conversion from tensorflow.python.keras._impl.keras.layers import Activation from tensorflow.python.keras._impl.keras.layers import BatchNormalization from tensorflow.python.keras._impl.keras.layers import Conv2D from tensorflow.python.keras._impl.keras.layers import Dropout from tensorflow.python.keras._impl.keras.layers import GlobalAveragePooling2D from tensorflow.python.keras._impl.keras.layers import GlobalMaxPooling2D from tensorflow.python.keras._impl.keras.layers import Input from tensorflow.python.keras._impl.keras.layers import Reshape from tensorflow.python.keras._impl.keras.models import Model from tensorflow.python.keras._impl.keras.utils import conv_utils from tensorflow.python.keras._impl.keras.utils.data_utils import get_file from tensorflow.python.platform import tf_logging as logging BASE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.6/' def relu6(x): return K.relu(x, max_value=6) def preprocess_input(x): return imagenet_utils.preprocess_input(x, mode='tf') class DepthwiseConv2D(Conv2D): def __init__(self, kernel_size, strides=(1, 1), padding='valid', depth_multiplier=1, data_format=None, activation=None, use_bias=True, depthwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, bias_constraint=None, **kwargs): super(DepthwiseConv2D, self).__init__( filters=None, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, activation=activation, use_bias=use_bias, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, bias_constraint=bias_constraint, **kwargs) self.depth_multiplier = depth_multiplier self.depthwise_initializer = initializers.get(depthwise_initializer) self.depthwise_regularizer = regularizers.get(depthwise_regularizer) self.depthwise_constraint = constraints.get(depthwise_constraint) self.bias_initializer = initializers.get(bias_initializer) @shape_type_conversion def build(self, input_shape): if len(input_shape) < 4: raise ValueError('Inputs to `DepthwiseConv2D` should have rank 4. ' 'Received input shape:', str(input_shape)) if self.data_format == 'channels_first': channel_axis = 1 else: channel_axis = 3 if input_shape[channel_axis] is None: raise ValueError('The channel dimension of the inputs to ' '`DepthwiseConv2D` ' 'should be defined. Found `None`.') input_dim = int(input_shape[channel_axis]) depthwise_kernel_shape = (self.kernel_size[0], self.kernel_size[1], input_dim, self.depth_multiplier) self.depthwise_kernel = self.add_weight( shape=depthwise_kernel_shape, initializer=self.depthwise_initializer, name='depthwise_kernel', regularizer=self.depthwise_regularizer, constraint=self.depthwise_constraint) if self.use_bias: self.bias = self.add_weight( shape=(input_dim * self.depth_multiplier,), initializer=self.bias_initializer, name='bias', regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim}) self.built = True def call(self, inputs, training=None): outputs = K.depthwise_conv2d( inputs, self.depthwise_kernel, strides=self.strides, padding=self.padding, dilation_rate=self.dilation_rate, data_format=self.data_format) if self.bias: outputs = K.bias_add(outputs, self.bias, data_format=self.data_format) if self.activation is not None: return self.activation(outputs) return outputs @shape_type_conversion def compute_output_shape(self, input_shape): if self.data_format == 'channels_first': rows = input_shape[2] cols = input_shape[3] out_filters = input_shape[1] * self.depth_multiplier elif self.data_format == 'channels_last': rows = input_shape[1] cols = input_shape[2] out_filters = input_shape[3] * self.depth_multiplier rows = conv_utils.conv_output_length(rows, self.kernel_size[0], self.padding, self.strides[0]) cols = conv_utils.conv_output_length(cols, self.kernel_size[1], self.padding, self.strides[1]) if self.data_format == 'channels_first': return (input_shape[0], out_filters, rows, cols) elif self.data_format == 'channels_last': return (input_shape[0], rows, cols, out_filters) def get_config(self): config = super(DepthwiseConv2D, self).get_config() config.pop('filters') config.pop('kernel_initializer') config.pop('kernel_regularizer') config.pop('kernel_constraint') config['depth_multiplier'] = self.depth_multiplier config['depthwise_initializer'] = initializers.serialize( self.depthwise_initializer) config['depthwise_regularizer'] = regularizers.serialize( self.depthwise_regularizer) config['depthwise_constraint'] = constraints.serialize( self.depthwise_constraint) return config def MobileNet(input_shape=None, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True, weights='imagenet', input_tensor=None, pooling=None, classes=1000): if K.backend() != 'tensorflow': raise RuntimeError('Only TensorFlow backend is currently supported, ' 'as other backends do not support ' 'depthwise convolution.') if not (weights in {'imagenet', None} or os.path.exists(weights)): raise ValueError('The `weights` argument should be either ' '`None` (random initialization), `imagenet` ' '(pre-training on ImageNet), ' 'or the path to the weights file to be loaded.') if weights == 'imagenet' and include_top and classes != 1000: raise ValueError('If using `weights` as ImageNet with `include_top` ' 'as true, `classes` should be 1000') if input_shape is None: default_size = 224 else: if K.image_data_format() == 'channels_first': rows = input_shape[1] cols = input_shape[2] else: rows = input_shape[0] cols = input_shape[1] if rows == cols and rows in [128, 160, 192, 224]: default_size = rows else: default_size = 224 input_shape = _obtain_input_shape( input_shape, default_size=default_size, min_size=32, data_format=K.image_data_format(), require_flatten=include_top, weights=weights) if K.image_data_format() == 'channels_last': row_axis, col_axis = (0, 1) else: row_axis, col_axis = (1, 2) rows = input_shape[row_axis] cols = input_shape[col_axis] if weights == 'imagenet': if depth_multiplier != 1: raise ValueError('If imagenet weights are being loaded, ' 'depth multiplier must be 1') if alpha not in [0.25, 0.50, 0.75, 1.0]: raise ValueError('If imagenet weights are being loaded, ' 'alpha can be one of' '`0.25`, `0.50`, `0.75` or `1.0` only.') if rows != cols or rows not in [128, 160, 192, 224]: raise ValueError('If imagenet weights are being loaded, ' 'input must have a static square shape (one of ' '(128,128), (160,160), (192,192), or (224, 224)).' ' Input shape provided = %s' % (input_shape,)) if K.image_data_format() != 'channels_last': logging.warning('The MobileNet family of models is only available ' 'for the input data format "channels_last" ' '(width, height, channels). ' 'However your settings specify the default ' 'data format "channels_first" (channels, width, height).' ' You should set `image_data_format="channels_last"` ' 'in your Keras config located at ~/.keras/keras.json. ' 'The model being returned right now will expect inputs ' 'to follow the "channels_last" data format.') K.set_image_data_format('channels_last') old_data_format = 'channels_first' else: old_data_format = None if input_tensor is None: img_input = Input(shape=input_shape) else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor x = _conv_block(img_input, 32, alpha, strides=(2, 2)) x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1) x = _depthwise_conv_block( x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2) x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3) x = _depthwise_conv_block( x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4) x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5) x = _depthwise_conv_block( x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10) x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11) x = _depthwise_conv_block( x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12) x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13) if include_top: if K.image_data_format() == 'channels_first': shape = (int(1024 * alpha), 1, 1) else: shape = (1, 1, int(1024 * alpha)) x = GlobalAveragePooling2D()(x) x = Reshape(shape, name='reshape_1')(x) x = Dropout(dropout, name='dropout')(x) x = Conv2D(classes, (1, 1), padding='same', name='conv_preds')(x) x = Activation('softmax', name='act_softmax')(x) x = Reshape((classes,), name='reshape_2')(x) else: if pooling == 'avg': x = GlobalAveragePooling2D()(x) elif pooling == 'max': x = GlobalMaxPooling2D()(x) if input_tensor is not None: inputs = get_source_inputs(input_tensor) else: inputs = img_input model = Model(inputs, x, name='mobilenet_%0.2f_%s' % (alpha, rows)) if weights == 'imagenet': if K.image_data_format() == 'channels_first': raise ValueError('Weights for "channels_last" format ' 'are not available.') if alpha == 1.0: alpha_text = '1_0' elif alpha == 0.75: alpha_text = '7_5' elif alpha == 0.50: alpha_text = '5_0' else: alpha_text = '2_5' if include_top: model_name = 'mobilenet_%s_%d_tf.h5' % (alpha_text, rows) weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file(model_name, weigh_path, cache_subdir='models') else: model_name = 'mobilenet_%s_%d_tf_no_top.h5' % (alpha_text, rows) weigh_path = BASE_WEIGHT_PATH + model_name weights_path = get_file(model_name, weigh_path, cache_subdir='models') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) if old_data_format: K.set_image_data_format(old_data_format) return model def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)): channel_axis = 1 if K.image_data_format() == 'channels_first' else -1 filters = int(filters * alpha) x = Conv2D( filters, kernel, padding='same', use_bias=False, strides=strides, name='conv1')( inputs) x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x) return Activation(relu6, name='conv1_relu')(x) def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha, depth_multiplier=1, strides=(1, 1), block_id=1): channel_axis = 1 if K.image_data_format() == 'channels_first' else -1 pointwise_conv_filters = int(pointwise_conv_filters * alpha) x = DepthwiseConv2D( (3, 3), padding='same', depth_multiplier=depth_multiplier, strides=strides, use_bias=False, name='conv_dw_%d' % block_id)( inputs) x = BatchNormalization(axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x) x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x) x = Conv2D( pointwise_conv_filters, (1, 1), padding='same', use_bias=False, strides=(1, 1), name='conv_pw_%d' % block_id)( x) x = BatchNormalization(axis=channel_axis, name='conv_pw_%d_bn' % block_id)(x) return Activation(relu6, name='conv_pw_%d_relu' % block_id)(x)

true

790bf9fe8a42b67994f7345b2484de5970a9982c

615

py

Python

exercise_brokencounts_solution.py

annezola/gdi-python

a806f0eca2eb17e5a975cce8d0b1d90490dd455e

[ "MIT" ]

null

exercise_brokencounts_solution.py

annezola/gdi-python

a806f0eca2eb17e5a975cce8d0b1d90490dd455e

[ "MIT" ]

null

exercise_brokencounts_solution.py

annezola/gdi-python

a806f0eca2eb17e5a975cce8d0b1d90490dd455e

[ "MIT" ]

1

2022-01-04T15:26:40.000Z

# Fix the code so that there's no error! def count_evens(start, end): """Returns the number of even numbers between start and end.""" counter = start num_evens = 0 while counter <= end: if counter % 2 == 0: num_evens += 1 counter += 1 return num_evens def count_multiples(start, end, divisor): """Returns the number of multiples of divisor between start and end.""" counter = start num_multiples = 0 while counter <= end: if counter % divisor == 0: num_multiples += 1 counter += 1 return num_multiples count_both = count_evens(10, 20) + count_multiples(10, 20, 3)

25.625

73

0.666667

def count_evens(start, end): counter = start num_evens = 0 while counter <= end: if counter % 2 == 0: num_evens += 1 counter += 1 return num_evens def count_multiples(start, end, divisor): counter = start num_multiples = 0 while counter <= end: if counter % divisor == 0: num_multiples += 1 counter += 1 return num_multiples count_both = count_evens(10, 20) + count_multiples(10, 20, 3)

true

790bf9ff34ff4483a6201d656573973b10b16f63

56,109

py

Python

seqio/dataset_providers_test.py

shism2/seqio

a2de55ee4fc17b02324d0bdae18295cd4d0df4be

[ "Apache-2.0" ]

1

2022-03-11T20:05:56.000Z

seqio/dataset_providers_test.py

00mjk/seqio

63f96f1d29f7721af67d79c0265d7f937170ee20

[ "Apache-2.0" ]

null

seqio/dataset_providers_test.py

00mjk/seqio

63f96f1d29f7721af67d79c0265d7f937170ee20

[ "Apache-2.0" ]

null

# Copyright 2022 The SeqIO Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """Tests for seqio.dataset_providers.""" import copy import functools import os import shutil from typing import Any, Callable, Mapping, Optional, Sequence from absl.testing import absltest from absl.testing import parameterized from seqio import dataset_providers from seqio import feature_converters from seqio import metrics as metrics_lib from seqio import preprocessors from seqio import test_utils from seqio import utils from seqio import vocabularies import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds tf.compat.v1.enable_eager_execution() TaskRegistry = dataset_providers.TaskRegistry MixtureRegistry = dataset_providers.MixtureRegistry mock = absltest.mock assert_dataset = test_utils.assert_dataset create_default_dataset = test_utils.create_default_dataset class TasksTest(test_utils.FakeTaskTest): def test_invalid_name(self): with self.assertRaisesRegex( ValueError, "Task name 'invalid/name' contains invalid characters. " "Must match regex: .*"): self.add_task("invalid/name", self.function_source) def test_repeat_name(self): with self.assertRaisesWithLiteralMatch( ValueError, "Attempting to register duplicate provider: text_line_task"): self.add_task("text_line_task", self.text_line_source) def test_function_source_signature(self): # Good signatures. def good_fn(split, shuffle_files): del split del shuffle_files dataset_providers.FunctionDataSource(good_fn, splits=("train",)) def default_good_fn(split, shuffle_files=False): del split del shuffle_files dataset_providers.FunctionDataSource(default_good_fn, splits=("train",)) def seed_fn(split, shuffle_files=True, seed=0): del split del shuffle_files del seed dataset_providers.FunctionDataSource(seed_fn, splits=("train",)) def extra_kwarg_good_fn(split, shuffle_files, unused_kwarg=True): del split del shuffle_files dataset_providers.FunctionDataSource(extra_kwarg_good_fn, splits=("train",)) # Bad signatures. with self.assertRaisesWithLiteralMatch( ValueError, "'missing_shuff' must have positional args ('split', 'shuffle_files'), " "got: ('split',)"): def missing_shuff(split): del split dataset_providers.FunctionDataSource(missing_shuff, splits=("train",)) with self.assertRaisesWithLiteralMatch( ValueError, "'missing_split' must have positional args ('split', 'shuffle_files'), " "got: ('shuffle_files',)"): def missing_split(shuffle_files): del shuffle_files dataset_providers.FunctionDataSource(missing_split, splits=("train",)) with self.assertRaisesWithLiteralMatch( ValueError, "'extra_pos_arg' may only have positional args ('split', " "'shuffle_files'), got: ('split', 'shuffle_files', 'unused_arg')"): def extra_pos_arg(split, shuffle_files, unused_arg): del split del shuffle_files dataset_providers.FunctionDataSource(extra_pos_arg, splits=("train",)) def test_metric_fn_signature(self): # pylint:disable=unused-argument add_task = functools.partial(self.add_task, source=self.function_source) def score_metric_fn(targets, scores): return {} def predict_metric_fn(targets, predictions): return {} valid_task = add_task( "valid_metrics", metric_fns=[score_metric_fn, predict_metric_fn]) self.assertSameElements( [score_metric_fn, predict_metric_fn], valid_task.metric_fns) self.assertSameElements( [score_metric_fn], valid_task.score_metric_fns) self.assertSameElements( [predict_metric_fn], valid_task.predict_metric_fns) def extra_arg_metric_fn(targets, predictions, extra_param): return {} expected_error_message_prefix = ( "Metric functions must have positional arguments matching either " "('targets', 'predictions') or ('targets', 'scores'). Got: ") with self.assertRaisesWithLiteralMatch( ValueError, expected_error_message_prefix + "('targets', 'predictions', 'extra_param')"): valid_task = add_task( "extra_arg_metric", metric_fns=[extra_arg_metric_fn]) def bad_order_metric_fn(predictions, targets): return {} with self.assertRaisesWithLiteralMatch( ValueError, expected_error_message_prefix + "('predictions', 'targets')"): valid_task = add_task( "bad_order_metric", metric_fns=[bad_order_metric_fn]) def bad_default_metric_fn(targets, predictions=(0)): return {} with self.assertRaisesWithLiteralMatch( ValueError, expected_error_message_prefix + "('targets',)"): valid_task = add_task( "bad_default_metric", metric_fns=[bad_default_metric_fn]) def ok_default_metric_fn(targets, predictions, extra_param=3): return {} valid_task_2 = add_task( "valid_metrics_2", metric_fns=[ok_default_metric_fn]) self.assertSameElements([ok_default_metric_fn], valid_task_2.metric_fns) self.assertEmpty(valid_task_2.score_metric_fns) self.assertSameElements( [ok_default_metric_fn], valid_task_2.predict_metric_fns) def predict_metric_fn_with_types( targets: Sequence[Mapping[str, Any]], predictions: Sequence[Mapping[str, Any]] ) -> Mapping[str, metrics_lib.MetricValue]: return {} valid_task_with_types = TaskRegistry.add( "valid_metrics_with_types", source=self.function_source, output_features={ "inputs": dataset_providers.Feature(test_utils.sentencepiece_vocab()), "targets": dataset_providers.Feature(test_utils.sentencepiece_vocab()) }, metric_fns=[predict_metric_fn_with_types]) self.assertSameElements([predict_metric_fn_with_types], valid_task_with_types.metric_fns) # pylint:enable=unused-argument def test_no_tfds_version(self): with self.assertRaisesWithLiteralMatch( ValueError, "TFDS name must contain a version number, got: fake"): dataset_providers.TfdsDataSource(tfds_name="fake") def test_tfds_splits(self): self.assertSameElements( ["train", "validation"], dataset_providers.TfdsDataSource(tfds_name="fake:0.0.0").splits) self.assertSameElements( ["validation"], dataset_providers.TfdsDataSource( tfds_name="fake:0.0.0", splits=["validation"]).splits) self.assertSameElements( ["validation"], dataset_providers.TfdsDataSource( tfds_name="fake:0.0.0", splits={"validation": "train"}).splits) def test_tfds_task(self): self.verify_task_matches_fake_datasets( "tfds_task", use_cached=False) def test_function_task(self): self.verify_task_matches_fake_datasets( "function_task", use_cached=False) def test_text_line_task(self): self.verify_task_matches_fake_datasets( "text_line_task", use_cached=False, splits=["train"]) def test_tf_example_task(self): self.verify_task_matches_fake_datasets( "tf_example_task", use_cached=False, splits=["train"]) @mock.patch.object(tf.io.gfile, "glob") def test_file_data_source_shuffle_buffer_low(self, mock_glob): mock_glob.return_value = [f"{i}" for i in range(20)] fds = dataset_providers.FileDataSource( read_file_fn=lambda x: tf.data.Dataset.from_tensor_slices([x]), split_to_filepattern={"train": "filepattern"}, file_shuffle_buffer_size=2) for _ in range(10): ds = [ d.decode() for d in tfds.as_numpy( fds.get_dataset("train", shuffle=True, seed=23)) ] self.assertListEqual( ds, [ # Not a great shuffle. "0", "2", "1", "4", "5", "3", "7", "6", "9", "10", "11", "8", "13", "14", "12", "16", "15", "18", "17", "19" ]) @mock.patch.object(tf.io.gfile, "glob") def test_file_data_source_shuffle_buffer_full(self, mock_glob): mock_glob.return_value = [f"{i}" for i in range(20)] fds = dataset_providers.FileDataSource( read_file_fn=lambda x: tf.data.Dataset.from_tensor_slices([x]), split_to_filepattern={"train": "filepattern"}, file_shuffle_buffer_size=None) for _ in range(10): ds = [ d.decode() for d in tfds.as_numpy( fds.get_dataset("train", shuffle=True, seed=23)) ] self.assertListEqual( ds, [ # Good shuffle. "2", "13", "12", "19", "15", "5", "9", "1", "6", "8", "3", "0", "10", "4", "14", "7", "16", "17", "18", "11" ]) def _get_preps_with_cache_placeholder_buffer_size(self, buffer_size): preps = list(self.DEFAULT_PREPROCESSORS) for i, p in enumerate(preps): if isinstance(p, dataset_providers.CacheDatasetPlaceholder): preps[i] = dataset_providers.CacheDatasetPlaceholder( file_shuffle_buffer_size=buffer_size) return preps def _mock_and_assert_cached_source(self, task_name, buffer_size): cached_task = dataset_providers.get_mixture_or_task(task_name) cached_task._get_cached_source = mock.MagicMock( side_effect=cached_task._get_cached_source) _ = cached_task.get_dataset(None, "train", use_cached=True) cached_task._get_cached_source.assert_called_once_with( "train", buffer_size) def test_cached_data_source_shuffle_buffer_default(self): self._mock_and_assert_cached_source("cached_task", None) def test_cached_data_source_shuffle_buffer_set(self): self.add_task("cached_task_buf_2", self.tfds_source, self._get_preps_with_cache_placeholder_buffer_size(2)) shutil.copytree(self.cached_task_dir, os.path.join(self.test_data_dir, "cached_task_buf_2")) self._mock_and_assert_cached_source("cached_task_buf_2", 2) def test_cached_data_source_shuffle_buffer_None(self): self.add_task("cached_task_buf_None", self.tfds_source, self._get_preps_with_cache_placeholder_buffer_size(None)) shutil.copytree(self.cached_task_dir, os.path.join(self.test_data_dir, "cached_task_buf_None")) self._mock_and_assert_cached_source("cached_task_buf_None", None) def test_proto_task(self): self.verify_task_matches_fake_datasets( "proto_task", use_cached=False, splits=["train"]) def test_num_input_examples(self): self.assertEqual(30, self.cached_task.num_input_examples("train")) self.assertEqual(10, self.cached_task.num_input_examples("validation")) def test_disallow_shuffle(self): task = dataset_providers.Task( "no_shuffle", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=self.DEFAULT_PREPROCESSORS, shuffle_buffer_size=None) with self.assertRaisesWithLiteralMatch( ValueError, "Shuffling is disallowed for Task 'no_shuffle' since its " "`shuffle_buffer_size` was set to `None` on construction."): task.get_dataset(None, shuffle=True) with self.assertRaisesWithLiteralMatch( ValueError, "Shuffling is disallowed for Task 'no_shuffle' since its " "`shuffle_buffer_size` was set to `None` on construction."): task.get_dataset(None, shuffle=True, shuffle_buffer_size=100) task.get_dataset(None, shuffle=False) def test_supports_caching(self): self.assertFalse( dataset_providers.Task( "nosupports_cache", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=[]).supports_caching) self.assertFalse( dataset_providers.Task( "nosupports_cache", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=[preprocessors.tokenize]).supports_caching) self.assertTrue( dataset_providers.Task( "supports_cache", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=[ preprocessors.tokenize, dataset_providers.CacheDatasetPlaceholder() ]).supports_caching) self.assertTrue( dataset_providers.Task( "supports_cache", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=[ dataset_providers.CacheDatasetPlaceholder(required=True), preprocessors.tokenize, ]).supports_caching) self.assertTrue( dataset_providers.Task( "supports_cache", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=[ dataset_providers.CacheDatasetPlaceholder(), ]).supports_caching) def test_requires_caching(self): self.assertFalse( dataset_providers.Task( "nosupports_cache", output_features=self.DEFAULT_OUTPUT_FEATURES, source=self.function_source, preprocessors=[preprocessors.tokenize]).requires_caching) self.assertFalse( dataset_providers.Task( "supports_cache", output_features=self.DEFAULT_OUTPUT_FEATURES, source=self.function_source, preprocessors=[ preprocessors.tokenize, dataset_providers.CacheDatasetPlaceholder() ]).requires_caching) task = dataset_providers.Task( "requires_cache", output_features=self.DEFAULT_OUTPUT_FEATURES, source=self.function_source, preprocessors=[ dataset_providers.CacheDatasetPlaceholder(required=True), preprocessors.tokenize, ]) self.assertTrue(task.requires_caching) with self.assertRaisesWithLiteralMatch( ValueError, "Task 'requires_cache' requires caching, but was called with " "`use_cached=False`."): task.get_dataset({"inputs": 512, "targets": 512}, use_cached=False) # We haven't actually cached the task, so it still fails but with a # different error. with self.assertRaisesWithLiteralMatch( AssertionError, "'requires_cache' does not exist in any of the task cache " "directories."): task.get_dataset({"inputs": 512, "targets": 512}, use_cached=True) def test_datasource_prohibits_caching(self): function_source_no_cache = dataset_providers.FunctionDataSource( dataset_fn=test_utils.get_fake_dataset, splits=["train", "validation"], caching_permitted=False) with self.assertRaisesWithLiteralMatch( ValueError, "Caching was requested for 'prohibits_cache', but the underlying data " "source prohibits caching. Please remove `CacheDatasetPlaceholder` and " "try again." ): dataset_providers.Task( "prohibits_cache", output_features=self.DEFAULT_OUTPUT_FEATURES, source=function_source_no_cache, preprocessors=[ dataset_providers.CacheDatasetPlaceholder(required=True), preprocessors.tokenize, ]) def test_cache_exists(self): self.assertTrue(self.cached_task.cache_dir) self.cached_task.assert_cached() self.assertEqual( os.path.join(self.test_data_dir, "cached_task"), self.cached_task.cache_dir) self.assertFalse(self.uncached_task.cache_dir) with self.assertRaisesWithLiteralMatch( AssertionError, "'tfds_task' does not exist in any of the task cache directories."): TaskRegistry.get("tfds_task").assert_cached() def test_get_cached_stats(self): expected_train_stats = { "examples": 3, "inputs_tokens": 36, "inputs_max_tokens": 13, "targets_tokens": 18, "targets_max_tokens": 6} self.assertEqual( expected_train_stats, self.cached_task.get_cached_stats("train")) # Check repeated call. self.assertEqual( expected_train_stats, self.cached_task.get_cached_stats("train")) expected_validation_stats = { "examples": 2, "inputs_tokens": 23, "inputs_max_tokens": 12, "targets_tokens": 36, "targets_max_tokens": 21} self.assertEqual( expected_validation_stats, self.cached_task.get_cached_stats("validation")) with self.assertRaisesWithLiteralMatch( ValueError, "Stats do not exist for 'cached_task' split: fake"): self.cached_task.get_cached_stats("fake") with self.assertRaisesWithLiteralMatch( AssertionError, "'uncached_task' does not exist in any of the task cache directories."): self.uncached_task.get_cached_stats("train") def test_set_global_cache_dirs(self): utils.set_global_cache_dirs([]) self.assertFalse(self.cached_task.cache_dir) utils.set_global_cache_dirs([self.test_data_dir]) self.assertTrue(self.cached_task.cache_dir) def test_get_dataset_cached(self): self.verify_task_matches_fake_datasets( "cached_task", use_cached=True, token_preprocessed=False) # Test with token preprocessor. self.cached_task._preprocessors = self.DEFAULT_PREPROCESSORS + ( test_utils.test_token_preprocessor,) self.verify_task_matches_fake_datasets( "cached_task", use_cached=True, token_preprocessed=True) def test_get_dataset_onthefly(self): self.verify_task_matches_fake_datasets( "uncached_task", use_cached=False) # Test with token preprocessor. self.cached_task._preprocessors = self.DEFAULT_PREPROCESSORS + ( test_utils.test_token_preprocessor,) self.verify_task_matches_fake_datasets( "cached_task", use_cached=False, token_preprocessed=True) def test_get_dataset_no_truncation(self): self.verify_task_matches_fake_datasets( "uncached_task", use_cached=False, sequence_length=None) def test_sharding(self): for i in range(3): self.verify_task_matches_fake_datasets( "cached_task", use_cached=False, num_shards=i, token_preprocessed=False) self.verify_task_matches_fake_datasets( "cached_task", use_cached=True, num_shards=i, token_preprocessed=False) def test_feature_validation(self): default_vocab = test_utils.sentencepiece_vocab() features = { "inputs": dataset_providers.Feature(vocabulary=default_vocab, required=False), "targets": dataset_providers.Feature(vocabulary=default_vocab, required=True), "inputs_rank2": dataset_providers.Feature( vocabulary=vocabularies.PassThroughVocabulary(5), required=False, rank=2), "continuous_features": dataset_providers.ContinuousFeature( required=False, rank=2) } def _materialize(output): task = dataset_providers.Task( "feature_validation_task", self.function_source, output_features=features, preprocessors=(lambda _: tf.data.Dataset.from_tensors(output),), metric_fns=[], ) list( task.get_dataset( {"inputs": 13, "targets": 13, "inputs_rank2": 13}, "train", use_cached=False ).as_numpy_iterator() ) # Missing optional feature: OK _materialize({"targets": [0]}) # Missing required feature. with self.assertRaisesWithLiteralMatch( ValueError, "Task dataset is missing expected output feature after preprocessing: " "targets"): _materialize({"inputs": [0]}) # Wrong type. with self.assertRaisesWithLiteralMatch( ValueError, "Task dataset has incorrect type for feature 'targets' after " "preprocessing: Got string, expected int32"): _materialize({"targets": ["wrong type"]}) # Wrong rank. with self.assertRaisesWithLiteralMatch( ValueError, "Task dataset has incorrect rank for feature 'targets' after " "preprocessing: Got 0, expected 1"): _materialize({"targets": 0}) # Verify rank > 1 works. _materialize({"targets": [0], "inputs_rank2": [[0, 0, 0], [0, 0, 0]]}) # Wrong rank (1 when 2 is expected). with self.assertRaisesWithLiteralMatch( ValueError, "Task dataset has incorrect rank for feature 'inputs_rank2' after " "preprocessing: Got 1, expected 2"): _materialize({"targets": [0], "inputs_rank2": [0]}) # Test ContinuousFeature _materialize({ "targets": [0], "continuous_features": [[1, 1], [0, 1]] }) def test_value_errors(self): dataset_fn = ( lambda split, shuffle_files: tf.data.Dataset.from_tensors(["test"])) output_features = { "inputs": dataset_providers.Feature(test_utils.sentencepiece_vocab()) } with self.assertRaisesWithLiteralMatch( ValueError, "`CacheDatasetPlaceholder` can appear at most once in the " "preprocessing pipeline. Found 2 in 'multiple_cache_placeholders'."): dataset_providers.Task( "multiple_cache_placeholders", source=dataset_providers.FunctionDataSource( dataset_fn=dataset_fn, splits=["train", "validation"] ), preprocessors=[ test_utils.test_text_preprocessor, preprocessors.tokenize, dataset_providers.CacheDatasetPlaceholder(), test_utils.test_token_preprocessor, dataset_providers.CacheDatasetPlaceholder() ], output_features=output_features, metric_fns=[]) with self.assertRaisesWithLiteralMatch( ValueError, "'test_token_preprocessor' has a `sequence_length` argument but occurs " "before `CacheDatasetPlaceholder` in 'sequence_length_pre_cache'. This " "is not allowed since the sequence length is specified at run time."): dataset_providers.Task( "sequence_length_pre_cache", dataset_providers.FunctionDataSource( dataset_fn=dataset_fn, splits=["train"], ), preprocessors=[ test_utils.test_text_preprocessor, preprocessors.tokenize, test_utils.test_token_preprocessor, dataset_providers.CacheDatasetPlaceholder() ], output_features=output_features, metric_fns=[]) def test_tfds_source_splits(self): default_splits_src = dataset_providers.TfdsDataSource("fake:0.0.0") self.assertSameElements(["train", "validation"], default_splits_src.splits) validation_split_src = dataset_providers.TfdsDataSource( "fake:0.0.0", splits=["validation"]) self.assertSameElements(["validation"], validation_split_src.splits) sliced_split_src = dataset_providers.TfdsDataSource( "fake:0.0.0", splits={"validation": "train[0:1%]"}) self.assertSameElements(["validation"], sliced_split_src.splits) def test_no_eos(self): default_vocab = test_utils.sentencepiece_vocab() features = { "inputs": dataset_providers.Feature(add_eos=True, vocabulary=default_vocab), "targets": dataset_providers.Feature(add_eos=False, vocabulary=default_vocab), } self.add_task("task_no_eos", self.function_source, output_features=features) self.verify_task_matches_fake_datasets("task_no_eos", use_cached=False) def test_dtype(self): default_vocab = test_utils.sentencepiece_vocab() features = { "inputs": # defaults to int32 dataset_providers.Feature(vocabulary=default_vocab), "targets": dataset_providers.Feature(dtype=tf.int64, vocabulary=default_vocab), } self.add_task( "task_dtypes", self.function_source, preprocessors=self.DEFAULT_PREPROCESSORS + ( utils.map_over_dataset( lambda x: {k: tf.cast(v, tf.int64) if k == "targets" else v # pylint:disable=g-long-lambda for k, v in x.items()} ), ), output_features=features ) self.verify_task_matches_fake_datasets("task_dtypes", use_cached=False) def test_num_epochs(self): # Try repeating after preprocessing the dataset to verify the outputs are # the same. epoch1_ds = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) # `random_task` has 3 examples per epoch. epoch2_ds = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0 ).repeat(2).skip(3) test_utils.assert_datasets_eq(epoch1_ds, epoch2_ds) # Try repeating before preprocessing the dataset to verify the outputs are # different. epoch1_ds = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) # `random_task` has 3 examples per epoch. epoch2_ds = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0, num_epochs=2 ).skip(3) test_utils.assert_datasets_neq(epoch1_ds, epoch2_ds) def test_same_seeds_cached_match(self): dataset1 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=True, seed=0) dataset2 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=True, seed=0) test_utils.assert_datasets_eq(dataset1, dataset2) def test_different_seeds_cached_mismatch(self): dataset1 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=True, seed=0) dataset2 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=True, seed=42) test_utils.assert_datasets_neq(dataset1, dataset2) def test_same_seeds_uncached_match(self): dataset1 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) dataset2 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) test_utils.assert_datasets_eq(dataset1, dataset2) def test_different_seeds_uncached_mismatch(self): dataset1 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) dataset2 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=42) test_utils.assert_datasets_neq(dataset1, dataset2) def test_same_seeds_random_tp_uncached_match(self): dataset1 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0).repeat(4) dataset2 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0).repeat(4) test_utils.assert_datasets_eq(dataset1, dataset2) def test_different_seeds_random_tp_uncached_mismatch(self): dataset1 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) dataset2 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=42) test_utils.assert_datasets_neq(dataset1, dataset2) def test_no_shuffle_with_seed_cached_match(self): dataset1 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=False, seed=0) dataset2 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=False, seed=42) test_utils.assert_datasets_eq(dataset1, dataset2) def test_no_shuffle_with_seed_uncached_match(self): dataset1 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=False, seed=0) dataset2 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=False, seed=42) test_utils.assert_datasets_eq(dataset1, dataset2) def test_no_shuffle_different_seeds_random_tp_uncached_mismatch(self): dataset1 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=False, seed=0) dataset2 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=False, seed=42) test_utils.assert_datasets_neq(dataset1, dataset2) def test_plaintext_to_pretokenized_rename(self): ds = self.cached_plaintext_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=False) keys = next(ds.as_numpy_iterator()).keys() self.assertSetEqual( set(keys), set(["inputs", "inputs_pretokenized", "targets", "targets_pretokenized"])) def test_list_shards(self): def _get_formatted_shards_list(task_name, split): shards = dataset_providers.get_mixture_or_task( task_name).source.list_shards(split) shards = [s.split("/")[-1] for s in shards] return sorted(shards) self.assertListEqual( _get_formatted_shards_list("tfds_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("text_line_task", "train"), ["train.tsv-00000-of-00002", "train.tsv-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("tf_example_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("proto_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("function_task", "train"), ["train"]) self.assertListEqual( _get_formatted_shards_list("fully_processed_precache", "train"), ["train"]) self.assertListEqual( _get_formatted_shards_list("tokenized_postcache", "train"), ["train"]) self.assertListEqual( _get_formatted_shards_list("random_task", "train"), ["train"]) self.assertListEqual( _get_formatted_shards_list("uncached_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("cached_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("cached_plaintext_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) class MixturesTest(test_utils.FakeTaskTest): def test_tasks(self): self.add_task("task1", self.function_source) self.add_task("task2", self.function_source) MixtureRegistry.add("test_mix1", [("task1", 1), ("task2", 1)]) mix = MixtureRegistry.get("test_mix1") self.assertEqual(len(mix.tasks), 2) for task in mix.tasks: self.verify_task_matches_fake_datasets(task.name, use_cached=False) self.assertEqual(mix.get_rate(task), 1) def test_num_examples(self): MixtureRegistry.add("test_mix2", [(self.cached_task.name, 1)]) mix = MixtureRegistry.get("test_mix2") self.assertEqual(mix.num_input_examples(split="train"), 30) def test_splits(self): MixtureRegistry.add( "test_mix", [(self.cached_task.name, 1), (self.uncached_task.name, 1)] ) mix = MixtureRegistry.get("test_mix") self.assertSameElements(["train", "validation"], mix.splits, 30) def test_get_dataset(self): MixtureRegistry.add("test_mix3", [(self.cached_task.name, 1)]) task_ds = TaskRegistry.get_dataset( self.cached_task.name, { "inputs": 13, "targets": 13 }, "validation", use_cached=False, shuffle=False) mix_ds = MixtureRegistry.get("test_mix3").get_dataset( { "inputs": 13, "targets": 13 }, "validation", use_cached=False, shuffle=False) # mix.get_dataset strips non-output features task_ds = task_ds.map(lambda x: {k: x[k] for k in ["inputs", "targets"]}) # limit size since get_dataset repeats the dataset test_utils.assert_datasets_eq(task_ds.repeat(2), mix_ds.take(4)) def test_get_dataset_mix(self): @utils.map_over_dataset def _constant_preprocessor(unused_x, val): return { "targets": tf.constant([val], tf.int32), "inputs": tf.constant([val], tf.int32), } self.add_task( "two_task", self.function_source, preprocessors=(functools.partial(_constant_preprocessor, val=2),) ) self.add_task( "three_task", self.function_source, preprocessors=(functools.partial(_constant_preprocessor, val=3),) ) MixtureRegistry.add("test_mix", [("two_task", 1), ("three_task", 1)]) sequence_length = {"inputs": 2, "targets": 2} mix_ds = MixtureRegistry.get("test_mix").get_dataset( sequence_length, "train", seed=13).take(1000) res = sum(int(item["inputs"][0]) for item in mix_ds.as_numpy_iterator()) self.assertEqual(res, 2481) def test_get_dataset_passthrough_features(self): @utils.map_over_dataset def _constant_feature_preprocessor(unused_x, val): return { "targets": tf.constant([val], tf.int32), "inputs": tf.constant([val], tf.int32), "feature": tf.constant([val], tf.int32), } self.add_task( "two_task", self.function_source, preprocessors=(functools.partial(_constant_feature_preprocessor, val=2),)) self.add_task( "three_task", self.function_source, preprocessors=(functools.partial(_constant_feature_preprocessor, val=3),)) MixtureRegistry.add("test_mix", [("two_task", 1), ("three_task", 1)]) sequence_length = {"inputs": 2, "targets": 2} passthrough_features = ["feature"] mix_ds = MixtureRegistry.get("test_mix").get_dataset( sequence_length, "train", seed=13, passthrough_features=passthrough_features).take(1000) # output features are defined as "inputs" and "targets" by default. res = sum(int(item["feature"][0]) for item in mix_ds.as_numpy_iterator()) self.assertEqual(res, 2481) def test_copy_pretokenized(self): @utils.map_over_dataset def _constant_preprocessor(unused_x, val): return { "targets": tf.constant([val], tf.int32), "targets_pretokenized": tf.constant(f"targets_{val}"), "inputs": tf.constant([val], tf.int32), "inputs_pretokenized": tf.constant(f"inputs_{val}") } self.add_task( "two_task", self.function_source, preprocessors=(functools.partial(_constant_preprocessor, val=2),) ) self.add_task( "three_task", self.function_source, preprocessors=(functools.partial(_constant_preprocessor, val=3),) ) MixtureRegistry.add("test_mix", [("two_task", 1), ("three_task", 1)]) sequence_length = {"inputs": 2, "targets": 2} mix_ds = MixtureRegistry.get("test_mix").get_dataset( sequence_length, "train", seed=13, copy_pretokenized=True).take(1000) inputs_pretokenized = set( ex["inputs_pretokenized"] for ex in mix_ds.as_numpy_iterator()) targets_pretokenized = set( ex["targets_pretokenized"] for ex in mix_ds.as_numpy_iterator()) self.assertCountEqual([b"inputs_2", b"inputs_3"], inputs_pretokenized) self.assertCountEqual([b"targets_2", b"targets_3"], targets_pretokenized) mix_ds = MixtureRegistry.get("test_mix").get_dataset( sequence_length, "train", seed=13, copy_pretokenized=False).take(1000) for ex in mix_ds.as_numpy_iterator(): self.assertNoCommonElements( ["inputs_pretokenized", "targets_pretokenized"], ex.keys()) def test_get_rate_with_callable(self): def fn(t): self.assertEqual(t.name, "task4") return 42 self.add_task("task4", self.function_source) task = TaskRegistry.get("task4") MixtureRegistry.add("test_mix5", [("task4", fn)]) mix = MixtureRegistry.get("test_mix5") self.assertEqual(mix.get_rate(task), 42) def test_mixture_of_mixtures(self): self.add_task("task_a", self.function_source) self.add_task("task_b", self.function_source) self.add_task("task_c", self.function_source) MixtureRegistry.add("another_mix", [("task_a", 1), ("task_b", 1)]) MixtureRegistry.add("supermix", [("another_mix", 1), ("task_c", 1)]) supermix = MixtureRegistry.get("supermix") names = [task.name for task in supermix.tasks] self.assertEqual(names, ["task_a", "task_b", "task_c"]) self.assertEqual([supermix.get_rate(t) for t in supermix.tasks], [0.5, 0.5, 1]) def test_mixture_of_mixtures_dupe(self): self.add_task("task2_a", self.function_source) self.add_task("task2_b", self.function_source) self.add_task("task2_c", self.function_source) MixtureRegistry.add("yet_another_mix", [("task2_a", 1), ("task2_b", 1)]) MixtureRegistry.add("supermix_with_dupe", [("yet_another_mix", 1), ("task2_a", 1), ("task2_c", 1)]) supermix = MixtureRegistry.get("supermix_with_dupe") names = [task.name for task in supermix.tasks] self.assertEqual(names, ["task2_a", "task2_b", "task2_c"]) self.assertEqual([supermix.get_rate(t) for t in supermix.tasks], [1.5, 0.5, 1]) def test_mixture_with_sample_fn(self): def sequential_intereave(datasets: Sequence[tf.data.Dataset], rates: Sequence[float], sample_seed: Optional[int]) -> tf.data.Dataset: """Sample function that simply concatenates two datasets.""" del rates, sample_seed return datasets[0].concatenate(datasets[1]) def gen_dataset(split, shuffle_files=False, seed=None, val: str = "") -> tf.data.Dataset: del split, shuffle_files, seed # Need this to pass arg validation. return tf.data.Dataset.from_tensor_slices({ "inputs": [[val]] * 3, }) # Register two very simple tasks, each with 3 repeated string values. vocab = vocabularies.PassThroughVocabulary(0) tasks = [] for task_name in ["first", "second"]: tasks.append(self.add_task( task_name, dataset_providers.FunctionDataSource( dataset_fn=functools.partial(gen_dataset, val=task_name), splits=["train"]), preprocessors=[], output_features={ "inputs": dataset_providers.Feature(vocab, dtype=tf.string) })) # Verify that by default, interleaving of datasets is random. MixtureRegistry.add("default_mix", [("first", 1), ("second", 1)]) default_ds = MixtureRegistry.get("default_mix").get_dataset( None, "train", shuffle=False, seed=2, num_epochs=1) expected = [b"second", b"first", b"second", b"first", b"second", b"first"] actual = [x["inputs"] for x in default_ds.as_numpy_iterator()] self.assertEqual(expected, actual) # Verify that we can modify sampling function correctly. MixtureRegistry.add( "sequential_mix", [("first", 1), ("second", 1)], sample_fn=sequential_intereave) sequential_ds = MixtureRegistry.get("sequential_mix").get_dataset( None, "train", shuffle=False, seed=2, num_epochs=1) expected = [b"first"] * 3 + [b"second"] * 3 actual = [x["inputs"] for x in sequential_ds.as_numpy_iterator()] self.assertEqual(expected, actual) class GetDatasetTest(parameterized.TestCase, tf.test.TestCase): def test_get_dataset_enc_dec_unpacked(self): mixture_or_task_name = "enc_dec_unpacked" x = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}, {"inputs": [8, 4], "targets": [4]}, {"inputs": [5, 6, 7], "targets": [6, 5]}] ds = create_default_dataset(x) dataset_fn = lambda split, shuffle_files: ds register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn) task_feature_lengths = {"inputs": 7, "targets": 5} converter = feature_converters.EncDecFeatureConverter(pack=False) output_ds = dataset_providers.get_dataset( mixture_or_task_name=mixture_or_task_name, task_feature_lengths=task_feature_lengths, dataset_split="train", shuffle=False, feature_converter=converter) expected = [{ "encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1], "decoder_target_tokens": [3, 9, 1, 0, 0], "decoder_input_tokens": [0, 3, 9, 1, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], }, { "encoder_input_tokens": [8, 4, 1, 0, 0, 0, 0], "decoder_target_tokens": [4, 1, 0, 0, 0], "decoder_input_tokens": [0, 4, 1, 0, 0], "decoder_loss_weights": [1, 1, 0, 0, 0], }, { "encoder_input_tokens": [5, 6, 7, 1, 0, 0, 0], "decoder_target_tokens": [6, 5, 1, 0, 0], "decoder_input_tokens": [0, 6, 5, 1, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], }] expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) @parameterized.parameters( dict( task_name="enc_dec_partial_trim_both", task_feature_lengths={ "inputs": 7, "targets": 2 }, expect_trim_inputs=True, expect_trim_targets=True), dict( task_name="enc_dec_partial_trim_targets", task_feature_lengths={ "inputs": None, "targets": 2 }, expect_trim_inputs=False, expect_trim_targets=True), dict( task_name="enc_dec_partial_trim_inputs", task_feature_lengths={ "inputs": 7, "targets": None }, expect_trim_inputs=True, expect_trim_targets=False), dict( task_name="enc_dec_partial_trim_neither", task_feature_lengths={ "inputs": None, "targets": None }, expect_trim_inputs=False, expect_trim_targets=False), dict( task_name="enc_dec_partial_trim_nothing", task_feature_lengths=None, expect_trim_inputs=False, expect_trim_targets=False)) def test_partial_sequence_length(self, task_name, task_feature_lengths, expect_trim_inputs, expect_trim_targets): x = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}, {"inputs": [8, 4], "targets": [4]}, {"inputs": [5, 6, 7], "targets": [6, 5]}] ds = create_default_dataset(x) dataset_fn = lambda split, shuffle_files: ds register_dummy_task(task_name, dataset_fn=dataset_fn) # Unlike the other tests, don't use a feature converter. Instead, test the # task.get_dataset method directly, which is similar to how evaluation.py # infers feature lengths w/trimming. task = dataset_providers.get_mixture_or_task(task_name) output_ds = task.get_dataset( sequence_length=task_feature_lengths, shuffle=False) expected = [{ "inputs": [7, 8, 5, 6, 9, 4, 3, 1], "targets": [3, 9, 1], }, { "inputs": [8, 4, 1], "targets": [4, 1], }, { "inputs": [5, 6, 7, 1], "targets": [6, 5, 1], }] if expect_trim_inputs: expected[0]["inputs"] = [7, 8, 5, 6, 9, 4, 1] if expect_trim_targets: expected[0]["targets"] = [3, 1] expected[2]["targets"] = [6, 1] expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) @parameterized.parameters( dict( task_name="enc_dec_multidim_trim_both", task_feature_lengths={ "inputs": (2, 5), "targets": 2 }, expect_trim_inputs=True, expect_trim_targets=True, ), dict( task_name="enc_dec_multidim_trim_inputs", task_feature_lengths={ "inputs": (2, 5), "targets": None }, expect_trim_inputs=True, expect_trim_targets=False, ), dict( task_name="enc_dec_multidim_trim_targets", task_feature_lengths={ "inputs": None, "targets": 2 }, expect_trim_inputs=False, expect_trim_targets=True, ), dict( task_name="enc_dec_no_multidim_trim", task_feature_lengths={ "inputs": None, "targets": None }, expect_trim_inputs=False, expect_trim_targets=False ) ) def test_multidimension_sequence_length(self, task_name, task_feature_lengths, expect_trim_inputs, expect_trim_targets): x = [{"inputs": [[7, 8, 5, 6, 9, 4, 3], [2, 3, 4, 5, 0, 0, 0], [6, 7, 1, 0, 0, 0, 0]], "targets": [3, 9]}, {"inputs": [[8, 4], [1, 0], [2, 3]], "targets": [4]}, {"inputs": [[5, 6, 7]], "targets": [6, 5, 1]}, {"inputs": [[7, 8, 9, 1, 2, 3, 4, 5, 6]], "targets": [10, 11, 1]}] ds = tf.data.Dataset.from_generator( lambda: x, output_types={"inputs": tf.int32, "targets": tf.int32}, output_shapes={"inputs": (None, None), "targets": (None,)}) dataset_fn = lambda split, shuffle_files: ds dataset_providers.TaskRegistry.add( task_name, source=dataset_providers.FunctionDataSource( dataset_fn=dataset_fn, splits=["train", "validation"]), preprocessors=[ dataset_providers.CacheDatasetPlaceholder(), ], output_features={ "inputs": dataset_providers.Feature( test_utils.sentencepiece_vocab(), rank=2), "targets": dataset_providers.Feature( test_utils.sentencepiece_vocab()) }, metric_fns=[]) # Unlike the other tests, don't use a feature converter. Instead, test the # task.get_dataset method directly, which is similar to how evaluation.py # infers feature lengths w/trimming. task = dataset_providers.get_mixture_or_task(task_name) output_ds = task.get_dataset( sequence_length=task_feature_lengths, shuffle=False) expected = copy.deepcopy(x) if expect_trim_inputs: expected[0]["inputs"] = [[7, 8, 5, 6, 9], [2, 3, 4, 5, 0]] expected[1]["inputs"] = [[8, 4], [1, 0]] expected[3]["inputs"] = [[7, 8, 9, 1, 2]] if expect_trim_targets: expected[2]["targets"] = [6, 5] expected[3]["targets"] = [10, 11] expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) def test_get_dataset_enc_dec_packed(self): mixture_or_task_name = "enc_dec_packed" x = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}, {"inputs": [8, 4], "targets": [4]}, {"inputs": [5, 6, 7], "targets": [6, 5]}] ds = create_default_dataset(x) dataset_fn = lambda split, shuffle_files: ds register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn) task_feature_lengths = {"inputs": 7, "targets": 5} converter = feature_converters.EncDecFeatureConverter(pack=True) output_ds = dataset_providers.get_dataset( mixture_or_task_name=mixture_or_task_name, task_feature_lengths=task_feature_lengths, dataset_split="train", shuffle=False, feature_converter=converter) expected = [{ # Example 1 is trimmed "encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1], "encoder_segment_ids": [1, 1, 1, 1, 1, 1, 1], "encoder_positions": [0, 1, 2, 3, 4, 5, 6], "decoder_target_tokens": [3, 9, 1, 0, 0], "decoder_input_tokens": [0, 3, 9, 0, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], "decoder_segment_ids": [1, 1, 1, 0, 0], "decoder_positions": [0, 1, 2, 0, 0], }, { # Example 2 and 3 are packed together "encoder_input_tokens": [8, 4, 1, 5, 6, 7, 1], "encoder_segment_ids": [1, 1, 1, 2, 2, 2, 2], "encoder_positions": [0, 1, 2, 0, 1, 2, 3], "decoder_target_tokens": [4, 1, 6, 5, 1], "decoder_input_tokens": [0, 4, 0, 6, 5], "decoder_loss_weights": [1, 1, 1, 1, 1], "decoder_segment_ids": [1, 1, 2, 2, 2], "decoder_positions": [0, 1, 0, 1, 2], }] expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) def test_get_dataset_both_train_and_validation_splits(self): mixture_or_task_name = "both_train_and_validation_splits" x_train = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}] x_val = [{"inputs": [8, 4], "targets": [4]}] datasets = { "train": create_default_dataset(x_train), "validation": create_default_dataset(x_val) } dataset_fn = lambda split, shuffle_files: datasets[split] register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn) task_feature_lengths = {"inputs": 7, "targets": 5} output_ds = {} for split in ["train", "validation"]: converter = feature_converters.EncDecFeatureConverter(pack=False) output_ds[split] = dataset_providers.get_dataset( mixture_or_task_name=mixture_or_task_name, task_feature_lengths=task_feature_lengths, dataset_split=split, shuffle=False, feature_converter=converter) expected_train = { "encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1], "decoder_target_tokens": [3, 9, 1, 0, 0], "decoder_input_tokens": [0, 3, 9, 1, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], } expected_val = { "encoder_input_tokens": [8, 4, 1, 0, 0, 0, 0], "decoder_target_tokens": [4, 1, 0, 0, 0], "decoder_input_tokens": [0, 4, 1, 0, 0], "decoder_loss_weights": [1, 1, 0, 0, 0], } expected_dtypes = {feat: tf.int32 for feat in expected_train.keys()} assert_dataset( output_ds["train"], expected_train, expected_dtypes=expected_dtypes) assert_dataset( output_ds["validation"], expected_val, expected_dtypes=expected_dtypes) def test_get_dataset_enc_dec_sharded(self): mixture_or_task_name = "enc_dec_sharded" x = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}, {"inputs": [8, 4], "targets": [4]}, {"inputs": [5, 6, 7], "targets": [6, 5]}] ds = create_default_dataset(x) dataset_fn = lambda split, shuffle_files: ds register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn) task_feature_lengths = {"inputs": 7, "targets": 5} converter = feature_converters.EncDecFeatureConverter(pack=False) shard_info = dataset_providers.ShardInfo(index=0, num_shards=2) output_ds = dataset_providers.get_dataset( mixture_or_task_name=mixture_or_task_name, task_feature_lengths=task_feature_lengths, dataset_split="train", shuffle=False, feature_converter=converter, shard_info=shard_info) # Example index 1 should not be present in the sharded dataset. expected = [{ "encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1], "decoder_target_tokens": [3, 9, 1, 0, 0], "decoder_input_tokens": [0, 3, 9, 1, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], }, { "encoder_input_tokens": [5, 6, 7, 1, 0, 0, 0], "decoder_target_tokens": [6, 5, 1, 0, 0], "decoder_input_tokens": [0, 6, 5, 1, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], }] expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) def test_get_dataset_enc_dec_sharded_and_packed(self): mixture_or_task_name = "enc_dec_sharded_and_packed" x = [{"inputs": [7, 8], "targets": [3, 9]}, {"inputs": [8, 4], "targets": [4]}, {"inputs": [5, 6, 7], "targets": [6]}] ds = create_default_dataset(x) dataset_fn = lambda split, shuffle_files: ds register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn) task_feature_lengths = {"inputs": 7, "targets": 5} converter = feature_converters.EncDecFeatureConverter(pack=True) shard_info = dataset_providers.ShardInfo(index=0, num_shards=2) output_ds = dataset_providers.get_dataset( mixture_or_task_name=mixture_or_task_name, task_feature_lengths=task_feature_lengths, dataset_split="train", shuffle=False, feature_converter=converter, shard_info=shard_info) # Packing should be done after the sharding. expected = { "encoder_input_tokens": [7, 8, 1, 5, 6, 7, 1], "encoder_segment_ids": [1, 1, 1, 2, 2, 2, 2], "encoder_positions": [0, 1, 2, 0, 1, 2, 3], "decoder_target_tokens": [3, 9, 1, 6, 1], "decoder_input_tokens": [0, 3, 9, 0, 6], "decoder_loss_weights": [1, 1, 1, 1, 1], "decoder_segment_ids": [1, 1, 1, 2, 2], "decoder_positions": [0, 1, 2, 0, 1], } expected_dtypes = {feat: tf.int32 for feat in expected.keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) def register_dummy_task( task_name: str, dataset_fn: Callable[[str, str], tf.data.Dataset], output_feature_names: Sequence[str] = ("inputs", "targets")) -> None: """Register a dummy task for GetDatasetTest.""" dataset_providers.TaskRegistry.add( task_name, source=dataset_providers.FunctionDataSource( dataset_fn=dataset_fn, splits=["train", "validation"]), preprocessors=[ dataset_providers.CacheDatasetPlaceholder(), preprocessors.append_eos_after_trim, ], output_features={ feat: dataset_providers.Feature(test_utils.sentencepiece_vocab()) for feat in output_feature_names }, metric_fns=[]) if __name__ == "__main__": absltest.main()

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import copy import functools import os import shutil from typing import Any, Callable, Mapping, Optional, Sequence from absl.testing import absltest from absl.testing import parameterized from seqio import dataset_providers from seqio import feature_converters from seqio import metrics as metrics_lib from seqio import preprocessors from seqio import test_utils from seqio import utils from seqio import vocabularies import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds tf.compat.v1.enable_eager_execution() TaskRegistry = dataset_providers.TaskRegistry MixtureRegistry = dataset_providers.MixtureRegistry mock = absltest.mock assert_dataset = test_utils.assert_dataset create_default_dataset = test_utils.create_default_dataset class TasksTest(test_utils.FakeTaskTest): def test_invalid_name(self): with self.assertRaisesRegex( ValueError, "Task name 'invalid/name' contains invalid characters. " "Must match regex: .*"): self.add_task("invalid/name", self.function_source) def test_repeat_name(self): with self.assertRaisesWithLiteralMatch( ValueError, "Attempting to register duplicate provider: text_line_task"): self.add_task("text_line_task", self.text_line_source) def test_function_source_signature(self): def good_fn(split, shuffle_files): del split del shuffle_files dataset_providers.FunctionDataSource(good_fn, splits=("train",)) def default_good_fn(split, shuffle_files=False): del split del shuffle_files dataset_providers.FunctionDataSource(default_good_fn, splits=("train",)) def seed_fn(split, shuffle_files=True, seed=0): del split del shuffle_files del seed dataset_providers.FunctionDataSource(seed_fn, splits=("train",)) def extra_kwarg_good_fn(split, shuffle_files, unused_kwarg=True): del split del shuffle_files dataset_providers.FunctionDataSource(extra_kwarg_good_fn, splits=("train",)) with self.assertRaisesWithLiteralMatch( ValueError, "'missing_shuff' must have positional args ('split', 'shuffle_files'), " "got: ('split',)"): def missing_shuff(split): del split dataset_providers.FunctionDataSource(missing_shuff, splits=("train",)) with self.assertRaisesWithLiteralMatch( ValueError, "'missing_split' must have positional args ('split', 'shuffle_files'), " "got: ('shuffle_files',)"): def missing_split(shuffle_files): del shuffle_files dataset_providers.FunctionDataSource(missing_split, splits=("train",)) with self.assertRaisesWithLiteralMatch( ValueError, "'extra_pos_arg' may only have positional args ('split', " "'shuffle_files'), got: ('split', 'shuffle_files', 'unused_arg')"): def extra_pos_arg(split, shuffle_files, unused_arg): del split del shuffle_files dataset_providers.FunctionDataSource(extra_pos_arg, splits=("train",)) def test_metric_fn_signature(self): add_task = functools.partial(self.add_task, source=self.function_source) def score_metric_fn(targets, scores): return {} def predict_metric_fn(targets, predictions): return {} valid_task = add_task( "valid_metrics", metric_fns=[score_metric_fn, predict_metric_fn]) self.assertSameElements( [score_metric_fn, predict_metric_fn], valid_task.metric_fns) self.assertSameElements( [score_metric_fn], valid_task.score_metric_fns) self.assertSameElements( [predict_metric_fn], valid_task.predict_metric_fns) def extra_arg_metric_fn(targets, predictions, extra_param): return {} expected_error_message_prefix = ( "Metric functions must have positional arguments matching either " "('targets', 'predictions') or ('targets', 'scores'). Got: ") with self.assertRaisesWithLiteralMatch( ValueError, expected_error_message_prefix + "('targets', 'predictions', 'extra_param')"): valid_task = add_task( "extra_arg_metric", metric_fns=[extra_arg_metric_fn]) def bad_order_metric_fn(predictions, targets): return {} with self.assertRaisesWithLiteralMatch( ValueError, expected_error_message_prefix + "('predictions', 'targets')"): valid_task = add_task( "bad_order_metric", metric_fns=[bad_order_metric_fn]) def bad_default_metric_fn(targets, predictions=(0)): return {} with self.assertRaisesWithLiteralMatch( ValueError, expected_error_message_prefix + "('targets',)"): valid_task = add_task( "bad_default_metric", metric_fns=[bad_default_metric_fn]) def ok_default_metric_fn(targets, predictions, extra_param=3): return {} valid_task_2 = add_task( "valid_metrics_2", metric_fns=[ok_default_metric_fn]) self.assertSameElements([ok_default_metric_fn], valid_task_2.metric_fns) self.assertEmpty(valid_task_2.score_metric_fns) self.assertSameElements( [ok_default_metric_fn], valid_task_2.predict_metric_fns) def predict_metric_fn_with_types( targets: Sequence[Mapping[str, Any]], predictions: Sequence[Mapping[str, Any]] ) -> Mapping[str, metrics_lib.MetricValue]: return {} valid_task_with_types = TaskRegistry.add( "valid_metrics_with_types", source=self.function_source, output_features={ "inputs": dataset_providers.Feature(test_utils.sentencepiece_vocab()), "targets": dataset_providers.Feature(test_utils.sentencepiece_vocab()) }, metric_fns=[predict_metric_fn_with_types]) self.assertSameElements([predict_metric_fn_with_types], valid_task_with_types.metric_fns) def test_no_tfds_version(self): with self.assertRaisesWithLiteralMatch( ValueError, "TFDS name must contain a version number, got: fake"): dataset_providers.TfdsDataSource(tfds_name="fake") def test_tfds_splits(self): self.assertSameElements( ["train", "validation"], dataset_providers.TfdsDataSource(tfds_name="fake:0.0.0").splits) self.assertSameElements( ["validation"], dataset_providers.TfdsDataSource( tfds_name="fake:0.0.0", splits=["validation"]).splits) self.assertSameElements( ["validation"], dataset_providers.TfdsDataSource( tfds_name="fake:0.0.0", splits={"validation": "train"}).splits) def test_tfds_task(self): self.verify_task_matches_fake_datasets( "tfds_task", use_cached=False) def test_function_task(self): self.verify_task_matches_fake_datasets( "function_task", use_cached=False) def test_text_line_task(self): self.verify_task_matches_fake_datasets( "text_line_task", use_cached=False, splits=["train"]) def test_tf_example_task(self): self.verify_task_matches_fake_datasets( "tf_example_task", use_cached=False, splits=["train"]) @mock.patch.object(tf.io.gfile, "glob") def test_file_data_source_shuffle_buffer_low(self, mock_glob): mock_glob.return_value = [f"{i}" for i in range(20)] fds = dataset_providers.FileDataSource( read_file_fn=lambda x: tf.data.Dataset.from_tensor_slices([x]), split_to_filepattern={"train": "filepattern"}, file_shuffle_buffer_size=2) for _ in range(10): ds = [ d.decode() for d in tfds.as_numpy( fds.get_dataset("train", shuffle=True, seed=23)) ] self.assertListEqual( ds, [ "0", "2", "1", "4", "5", "3", "7", "6", "9", "10", "11", "8", "13", "14", "12", "16", "15", "18", "17", "19" ]) @mock.patch.object(tf.io.gfile, "glob") def test_file_data_source_shuffle_buffer_full(self, mock_glob): mock_glob.return_value = [f"{i}" for i in range(20)] fds = dataset_providers.FileDataSource( read_file_fn=lambda x: tf.data.Dataset.from_tensor_slices([x]), split_to_filepattern={"train": "filepattern"}, file_shuffle_buffer_size=None) for _ in range(10): ds = [ d.decode() for d in tfds.as_numpy( fds.get_dataset("train", shuffle=True, seed=23)) ] self.assertListEqual( ds, [ "2", "13", "12", "19", "15", "5", "9", "1", "6", "8", "3", "0", "10", "4", "14", "7", "16", "17", "18", "11" ]) def _get_preps_with_cache_placeholder_buffer_size(self, buffer_size): preps = list(self.DEFAULT_PREPROCESSORS) for i, p in enumerate(preps): if isinstance(p, dataset_providers.CacheDatasetPlaceholder): preps[i] = dataset_providers.CacheDatasetPlaceholder( file_shuffle_buffer_size=buffer_size) return preps def _mock_and_assert_cached_source(self, task_name, buffer_size): cached_task = dataset_providers.get_mixture_or_task(task_name) cached_task._get_cached_source = mock.MagicMock( side_effect=cached_task._get_cached_source) _ = cached_task.get_dataset(None, "train", use_cached=True) cached_task._get_cached_source.assert_called_once_with( "train", buffer_size) def test_cached_data_source_shuffle_buffer_default(self): self._mock_and_assert_cached_source("cached_task", None) def test_cached_data_source_shuffle_buffer_set(self): self.add_task("cached_task_buf_2", self.tfds_source, self._get_preps_with_cache_placeholder_buffer_size(2)) shutil.copytree(self.cached_task_dir, os.path.join(self.test_data_dir, "cached_task_buf_2")) self._mock_and_assert_cached_source("cached_task_buf_2", 2) def test_cached_data_source_shuffle_buffer_None(self): self.add_task("cached_task_buf_None", self.tfds_source, self._get_preps_with_cache_placeholder_buffer_size(None)) shutil.copytree(self.cached_task_dir, os.path.join(self.test_data_dir, "cached_task_buf_None")) self._mock_and_assert_cached_source("cached_task_buf_None", None) def test_proto_task(self): self.verify_task_matches_fake_datasets( "proto_task", use_cached=False, splits=["train"]) def test_num_input_examples(self): self.assertEqual(30, self.cached_task.num_input_examples("train")) self.assertEqual(10, self.cached_task.num_input_examples("validation")) def test_disallow_shuffle(self): task = dataset_providers.Task( "no_shuffle", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=self.DEFAULT_PREPROCESSORS, shuffle_buffer_size=None) with self.assertRaisesWithLiteralMatch( ValueError, "Shuffling is disallowed for Task 'no_shuffle' since its " "`shuffle_buffer_size` was set to `None` on construction."): task.get_dataset(None, shuffle=True) with self.assertRaisesWithLiteralMatch( ValueError, "Shuffling is disallowed for Task 'no_shuffle' since its " "`shuffle_buffer_size` was set to `None` on construction."): task.get_dataset(None, shuffle=True, shuffle_buffer_size=100) task.get_dataset(None, shuffle=False) def test_supports_caching(self): self.assertFalse( dataset_providers.Task( "nosupports_cache", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=[]).supports_caching) self.assertFalse( dataset_providers.Task( "nosupports_cache", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=[preprocessors.tokenize]).supports_caching) self.assertTrue( dataset_providers.Task( "supports_cache", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=[ preprocessors.tokenize, dataset_providers.CacheDatasetPlaceholder() ]).supports_caching) self.assertTrue( dataset_providers.Task( "supports_cache", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=[ dataset_providers.CacheDatasetPlaceholder(required=True), preprocessors.tokenize, ]).supports_caching) self.assertTrue( dataset_providers.Task( "supports_cache", source=self.function_source, output_features=self.DEFAULT_OUTPUT_FEATURES, preprocessors=[ dataset_providers.CacheDatasetPlaceholder(), ]).supports_caching) def test_requires_caching(self): self.assertFalse( dataset_providers.Task( "nosupports_cache", output_features=self.DEFAULT_OUTPUT_FEATURES, source=self.function_source, preprocessors=[preprocessors.tokenize]).requires_caching) self.assertFalse( dataset_providers.Task( "supports_cache", output_features=self.DEFAULT_OUTPUT_FEATURES, source=self.function_source, preprocessors=[ preprocessors.tokenize, dataset_providers.CacheDatasetPlaceholder() ]).requires_caching) task = dataset_providers.Task( "requires_cache", output_features=self.DEFAULT_OUTPUT_FEATURES, source=self.function_source, preprocessors=[ dataset_providers.CacheDatasetPlaceholder(required=True), preprocessors.tokenize, ]) self.assertTrue(task.requires_caching) with self.assertRaisesWithLiteralMatch( ValueError, "Task 'requires_cache' requires caching, but was called with " "`use_cached=False`."): task.get_dataset({"inputs": 512, "targets": 512}, use_cached=False) # different error. with self.assertRaisesWithLiteralMatch( AssertionError, "'requires_cache' does not exist in any of the task cache " "directories."): task.get_dataset({"inputs": 512, "targets": 512}, use_cached=True) def test_datasource_prohibits_caching(self): function_source_no_cache = dataset_providers.FunctionDataSource( dataset_fn=test_utils.get_fake_dataset, splits=["train", "validation"], caching_permitted=False) with self.assertRaisesWithLiteralMatch( ValueError, "Caching was requested for 'prohibits_cache', but the underlying data " "source prohibits caching. Please remove `CacheDatasetPlaceholder` and " "try again." ): dataset_providers.Task( "prohibits_cache", output_features=self.DEFAULT_OUTPUT_FEATURES, source=function_source_no_cache, preprocessors=[ dataset_providers.CacheDatasetPlaceholder(required=True), preprocessors.tokenize, ]) def test_cache_exists(self): self.assertTrue(self.cached_task.cache_dir) self.cached_task.assert_cached() self.assertEqual( os.path.join(self.test_data_dir, "cached_task"), self.cached_task.cache_dir) self.assertFalse(self.uncached_task.cache_dir) with self.assertRaisesWithLiteralMatch( AssertionError, "'tfds_task' does not exist in any of the task cache directories."): TaskRegistry.get("tfds_task").assert_cached() def test_get_cached_stats(self): expected_train_stats = { "examples": 3, "inputs_tokens": 36, "inputs_max_tokens": 13, "targets_tokens": 18, "targets_max_tokens": 6} self.assertEqual( expected_train_stats, self.cached_task.get_cached_stats("train")) # Check repeated call. self.assertEqual( expected_train_stats, self.cached_task.get_cached_stats("train")) expected_validation_stats = { "examples": 2, "inputs_tokens": 23, "inputs_max_tokens": 12, "targets_tokens": 36, "targets_max_tokens": 21} self.assertEqual( expected_validation_stats, self.cached_task.get_cached_stats("validation")) with self.assertRaisesWithLiteralMatch( ValueError, "Stats do not exist for 'cached_task' split: fake"): self.cached_task.get_cached_stats("fake") with self.assertRaisesWithLiteralMatch( AssertionError, "'uncached_task' does not exist in any of the task cache directories."): self.uncached_task.get_cached_stats("train") def test_set_global_cache_dirs(self): utils.set_global_cache_dirs([]) self.assertFalse(self.cached_task.cache_dir) utils.set_global_cache_dirs([self.test_data_dir]) self.assertTrue(self.cached_task.cache_dir) def test_get_dataset_cached(self): self.verify_task_matches_fake_datasets( "cached_task", use_cached=True, token_preprocessed=False) # Test with token preprocessor. self.cached_task._preprocessors = self.DEFAULT_PREPROCESSORS + ( test_utils.test_token_preprocessor,) self.verify_task_matches_fake_datasets( "cached_task", use_cached=True, token_preprocessed=True) def test_get_dataset_onthefly(self): self.verify_task_matches_fake_datasets( "uncached_task", use_cached=False) # Test with token preprocessor. self.cached_task._preprocessors = self.DEFAULT_PREPROCESSORS + ( test_utils.test_token_preprocessor,) self.verify_task_matches_fake_datasets( "cached_task", use_cached=False, token_preprocessed=True) def test_get_dataset_no_truncation(self): self.verify_task_matches_fake_datasets( "uncached_task", use_cached=False, sequence_length=None) def test_sharding(self): for i in range(3): self.verify_task_matches_fake_datasets( "cached_task", use_cached=False, num_shards=i, token_preprocessed=False) self.verify_task_matches_fake_datasets( "cached_task", use_cached=True, num_shards=i, token_preprocessed=False) def test_feature_validation(self): default_vocab = test_utils.sentencepiece_vocab() features = { "inputs": dataset_providers.Feature(vocabulary=default_vocab, required=False), "targets": dataset_providers.Feature(vocabulary=default_vocab, required=True), "inputs_rank2": dataset_providers.Feature( vocabulary=vocabularies.PassThroughVocabulary(5), required=False, rank=2), "continuous_features": dataset_providers.ContinuousFeature( required=False, rank=2) } def _materialize(output): task = dataset_providers.Task( "feature_validation_task", self.function_source, output_features=features, preprocessors=(lambda _: tf.data.Dataset.from_tensors(output),), metric_fns=[], ) list( task.get_dataset( {"inputs": 13, "targets": 13, "inputs_rank2": 13}, "train", use_cached=False ).as_numpy_iterator() ) # Missing optional feature: OK _materialize({"targets": [0]}) # Missing required feature. with self.assertRaisesWithLiteralMatch( ValueError, "Task dataset is missing expected output feature after preprocessing: " "targets"): _materialize({"inputs": [0]}) # Wrong type. with self.assertRaisesWithLiteralMatch( ValueError, "Task dataset has incorrect type for feature 'targets' after " "preprocessing: Got string, expected int32"): _materialize({"targets": ["wrong type"]}) # Wrong rank. with self.assertRaisesWithLiteralMatch( ValueError, "Task dataset has incorrect rank for feature 'targets' after " "preprocessing: Got 0, expected 1"): _materialize({"targets": 0}) # Verify rank > 1 works. _materialize({"targets": [0], "inputs_rank2": [[0, 0, 0], [0, 0, 0]]}) # Wrong rank (1 when 2 is expected). with self.assertRaisesWithLiteralMatch( ValueError, "Task dataset has incorrect rank for feature 'inputs_rank2' after " "preprocessing: Got 1, expected 2"): _materialize({"targets": [0], "inputs_rank2": [0]}) # Test ContinuousFeature _materialize({ "targets": [0], "continuous_features": [[1, 1], [0, 1]] }) def test_value_errors(self): dataset_fn = ( lambda split, shuffle_files: tf.data.Dataset.from_tensors(["test"])) output_features = { "inputs": dataset_providers.Feature(test_utils.sentencepiece_vocab()) } with self.assertRaisesWithLiteralMatch( ValueError, "`CacheDatasetPlaceholder` can appear at most once in the " "preprocessing pipeline. Found 2 in 'multiple_cache_placeholders'."): dataset_providers.Task( "multiple_cache_placeholders", source=dataset_providers.FunctionDataSource( dataset_fn=dataset_fn, splits=["train", "validation"] ), preprocessors=[ test_utils.test_text_preprocessor, preprocessors.tokenize, dataset_providers.CacheDatasetPlaceholder(), test_utils.test_token_preprocessor, dataset_providers.CacheDatasetPlaceholder() ], output_features=output_features, metric_fns=[]) with self.assertRaisesWithLiteralMatch( ValueError, "'test_token_preprocessor' has a `sequence_length` argument but occurs " "before `CacheDatasetPlaceholder` in 'sequence_length_pre_cache'. This " "is not allowed since the sequence length is specified at run time."): dataset_providers.Task( "sequence_length_pre_cache", dataset_providers.FunctionDataSource( dataset_fn=dataset_fn, splits=["train"], ), preprocessors=[ test_utils.test_text_preprocessor, preprocessors.tokenize, test_utils.test_token_preprocessor, dataset_providers.CacheDatasetPlaceholder() ], output_features=output_features, metric_fns=[]) def test_tfds_source_splits(self): default_splits_src = dataset_providers.TfdsDataSource("fake:0.0.0") self.assertSameElements(["train", "validation"], default_splits_src.splits) validation_split_src = dataset_providers.TfdsDataSource( "fake:0.0.0", splits=["validation"]) self.assertSameElements(["validation"], validation_split_src.splits) sliced_split_src = dataset_providers.TfdsDataSource( "fake:0.0.0", splits={"validation": "train[0:1%]"}) self.assertSameElements(["validation"], sliced_split_src.splits) def test_no_eos(self): default_vocab = test_utils.sentencepiece_vocab() features = { "inputs": dataset_providers.Feature(add_eos=True, vocabulary=default_vocab), "targets": dataset_providers.Feature(add_eos=False, vocabulary=default_vocab), } self.add_task("task_no_eos", self.function_source, output_features=features) self.verify_task_matches_fake_datasets("task_no_eos", use_cached=False) def test_dtype(self): default_vocab = test_utils.sentencepiece_vocab() features = { "inputs": # defaults to int32 dataset_providers.Feature(vocabulary=default_vocab), "targets": dataset_providers.Feature(dtype=tf.int64, vocabulary=default_vocab), } self.add_task( "task_dtypes", self.function_source, preprocessors=self.DEFAULT_PREPROCESSORS + ( utils.map_over_dataset( lambda x: {k: tf.cast(v, tf.int64) if k == "targets" else v # pylint:disable=g-long-lambda for k, v in x.items()} ), ), output_features=features ) self.verify_task_matches_fake_datasets("task_dtypes", use_cached=False) def test_num_epochs(self): # Try repeating after preprocessing the dataset to verify the outputs are # the same. epoch1_ds = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) # `random_task` has 3 examples per epoch. epoch2_ds = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0 ).repeat(2).skip(3) test_utils.assert_datasets_eq(epoch1_ds, epoch2_ds) # Try repeating before preprocessing the dataset to verify the outputs are # different. epoch1_ds = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) # `random_task` has 3 examples per epoch. epoch2_ds = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0, num_epochs=2 ).skip(3) test_utils.assert_datasets_neq(epoch1_ds, epoch2_ds) def test_same_seeds_cached_match(self): dataset1 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=True, seed=0) dataset2 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=True, seed=0) test_utils.assert_datasets_eq(dataset1, dataset2) def test_different_seeds_cached_mismatch(self): dataset1 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=True, seed=0) dataset2 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=True, seed=42) test_utils.assert_datasets_neq(dataset1, dataset2) def test_same_seeds_uncached_match(self): dataset1 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) dataset2 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) test_utils.assert_datasets_eq(dataset1, dataset2) def test_different_seeds_uncached_mismatch(self): dataset1 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) dataset2 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=42) test_utils.assert_datasets_neq(dataset1, dataset2) def test_same_seeds_random_tp_uncached_match(self): dataset1 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0).repeat(4) dataset2 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0).repeat(4) test_utils.assert_datasets_eq(dataset1, dataset2) def test_different_seeds_random_tp_uncached_mismatch(self): dataset1 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=0) dataset2 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=True, seed=42) test_utils.assert_datasets_neq(dataset1, dataset2) def test_no_shuffle_with_seed_cached_match(self): dataset1 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=False, seed=0) dataset2 = self.cached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=False, seed=42) test_utils.assert_datasets_eq(dataset1, dataset2) def test_no_shuffle_with_seed_uncached_match(self): dataset1 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=False, seed=0) dataset2 = self.uncached_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=False, seed=42) test_utils.assert_datasets_eq(dataset1, dataset2) def test_no_shuffle_different_seeds_random_tp_uncached_mismatch(self): dataset1 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=False, seed=0) dataset2 = self.random_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=False, shuffle=False, seed=42) test_utils.assert_datasets_neq(dataset1, dataset2) def test_plaintext_to_pretokenized_rename(self): ds = self.cached_plaintext_task.get_dataset( {"inputs": 13, "targets": 13}, split="train", use_cached=True, shuffle=False) keys = next(ds.as_numpy_iterator()).keys() self.assertSetEqual( set(keys), set(["inputs", "inputs_pretokenized", "targets", "targets_pretokenized"])) def test_list_shards(self): def _get_formatted_shards_list(task_name, split): shards = dataset_providers.get_mixture_or_task( task_name).source.list_shards(split) shards = [s.split("/")[-1] for s in shards] return sorted(shards) self.assertListEqual( _get_formatted_shards_list("tfds_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("text_line_task", "train"), ["train.tsv-00000-of-00002", "train.tsv-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("tf_example_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("proto_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("function_task", "train"), ["train"]) self.assertListEqual( _get_formatted_shards_list("fully_processed_precache", "train"), ["train"]) self.assertListEqual( _get_formatted_shards_list("tokenized_postcache", "train"), ["train"]) self.assertListEqual( _get_formatted_shards_list("random_task", "train"), ["train"]) self.assertListEqual( _get_formatted_shards_list("uncached_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("cached_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) self.assertListEqual( _get_formatted_shards_list("cached_plaintext_task", "train"), ["train.tfrecord-00000-of-00002", "train.tfrecord-00001-of-00002"]) class MixturesTest(test_utils.FakeTaskTest): def test_tasks(self): self.add_task("task1", self.function_source) self.add_task("task2", self.function_source) MixtureRegistry.add("test_mix1", [("task1", 1), ("task2", 1)]) mix = MixtureRegistry.get("test_mix1") self.assertEqual(len(mix.tasks), 2) for task in mix.tasks: self.verify_task_matches_fake_datasets(task.name, use_cached=False) self.assertEqual(mix.get_rate(task), 1) def test_num_examples(self): MixtureRegistry.add("test_mix2", [(self.cached_task.name, 1)]) mix = MixtureRegistry.get("test_mix2") self.assertEqual(mix.num_input_examples(split="train"), 30) def test_splits(self): MixtureRegistry.add( "test_mix", [(self.cached_task.name, 1), (self.uncached_task.name, 1)] ) mix = MixtureRegistry.get("test_mix") self.assertSameElements(["train", "validation"], mix.splits, 30) def test_get_dataset(self): MixtureRegistry.add("test_mix3", [(self.cached_task.name, 1)]) task_ds = TaskRegistry.get_dataset( self.cached_task.name, { "inputs": 13, "targets": 13 }, "validation", use_cached=False, shuffle=False) mix_ds = MixtureRegistry.get("test_mix3").get_dataset( { "inputs": 13, "targets": 13 }, "validation", use_cached=False, shuffle=False) # mix.get_dataset strips non-output features task_ds = task_ds.map(lambda x: {k: x[k] for k in ["inputs", "targets"]}) # limit size since get_dataset repeats the dataset test_utils.assert_datasets_eq(task_ds.repeat(2), mix_ds.take(4)) def test_get_dataset_mix(self): @utils.map_over_dataset def _constant_preprocessor(unused_x, val): return { "targets": tf.constant([val], tf.int32), "inputs": tf.constant([val], tf.int32), } self.add_task( "two_task", self.function_source, preprocessors=(functools.partial(_constant_preprocessor, val=2),) ) self.add_task( "three_task", self.function_source, preprocessors=(functools.partial(_constant_preprocessor, val=3),) ) MixtureRegistry.add("test_mix", [("two_task", 1), ("three_task", 1)]) sequence_length = {"inputs": 2, "targets": 2} mix_ds = MixtureRegistry.get("test_mix").get_dataset( sequence_length, "train", seed=13).take(1000) res = sum(int(item["inputs"][0]) for item in mix_ds.as_numpy_iterator()) self.assertEqual(res, 2481) def test_get_dataset_passthrough_features(self): @utils.map_over_dataset def _constant_feature_preprocessor(unused_x, val): return { "targets": tf.constant([val], tf.int32), "inputs": tf.constant([val], tf.int32), "feature": tf.constant([val], tf.int32), } self.add_task( "two_task", self.function_source, preprocessors=(functools.partial(_constant_feature_preprocessor, val=2),)) self.add_task( "three_task", self.function_source, preprocessors=(functools.partial(_constant_feature_preprocessor, val=3),)) MixtureRegistry.add("test_mix", [("two_task", 1), ("three_task", 1)]) sequence_length = {"inputs": 2, "targets": 2} passthrough_features = ["feature"] mix_ds = MixtureRegistry.get("test_mix").get_dataset( sequence_length, "train", seed=13, passthrough_features=passthrough_features).take(1000) # output features are defined as "inputs" and "targets" by default. res = sum(int(item["feature"][0]) for item in mix_ds.as_numpy_iterator()) self.assertEqual(res, 2481) def test_copy_pretokenized(self): @utils.map_over_dataset def _constant_preprocessor(unused_x, val): return { "targets": tf.constant([val], tf.int32), "targets_pretokenized": tf.constant(f"targets_{val}"), "inputs": tf.constant([val], tf.int32), "inputs_pretokenized": tf.constant(f"inputs_{val}") } self.add_task( "two_task", self.function_source, preprocessors=(functools.partial(_constant_preprocessor, val=2),) ) self.add_task( "three_task", self.function_source, preprocessors=(functools.partial(_constant_preprocessor, val=3),) ) MixtureRegistry.add("test_mix", [("two_task", 1), ("three_task", 1)]) sequence_length = {"inputs": 2, "targets": 2} mix_ds = MixtureRegistry.get("test_mix").get_dataset( sequence_length, "train", seed=13, copy_pretokenized=True).take(1000) inputs_pretokenized = set( ex["inputs_pretokenized"] for ex in mix_ds.as_numpy_iterator()) targets_pretokenized = set( ex["targets_pretokenized"] for ex in mix_ds.as_numpy_iterator()) self.assertCountEqual([b"inputs_2", b"inputs_3"], inputs_pretokenized) self.assertCountEqual([b"targets_2", b"targets_3"], targets_pretokenized) mix_ds = MixtureRegistry.get("test_mix").get_dataset( sequence_length, "train", seed=13, copy_pretokenized=False).take(1000) for ex in mix_ds.as_numpy_iterator(): self.assertNoCommonElements( ["inputs_pretokenized", "targets_pretokenized"], ex.keys()) def test_get_rate_with_callable(self): def fn(t): self.assertEqual(t.name, "task4") return 42 self.add_task("task4", self.function_source) task = TaskRegistry.get("task4") MixtureRegistry.add("test_mix5", [("task4", fn)]) mix = MixtureRegistry.get("test_mix5") self.assertEqual(mix.get_rate(task), 42) def test_mixture_of_mixtures(self): self.add_task("task_a", self.function_source) self.add_task("task_b", self.function_source) self.add_task("task_c", self.function_source) MixtureRegistry.add("another_mix", [("task_a", 1), ("task_b", 1)]) MixtureRegistry.add("supermix", [("another_mix", 1), ("task_c", 1)]) supermix = MixtureRegistry.get("supermix") names = [task.name for task in supermix.tasks] self.assertEqual(names, ["task_a", "task_b", "task_c"]) self.assertEqual([supermix.get_rate(t) for t in supermix.tasks], [0.5, 0.5, 1]) def test_mixture_of_mixtures_dupe(self): self.add_task("task2_a", self.function_source) self.add_task("task2_b", self.function_source) self.add_task("task2_c", self.function_source) MixtureRegistry.add("yet_another_mix", [("task2_a", 1), ("task2_b", 1)]) MixtureRegistry.add("supermix_with_dupe", [("yet_another_mix", 1), ("task2_a", 1), ("task2_c", 1)]) supermix = MixtureRegistry.get("supermix_with_dupe") names = [task.name for task in supermix.tasks] self.assertEqual(names, ["task2_a", "task2_b", "task2_c"]) self.assertEqual([supermix.get_rate(t) for t in supermix.tasks], [1.5, 0.5, 1]) def test_mixture_with_sample_fn(self): def sequential_intereave(datasets: Sequence[tf.data.Dataset], rates: Sequence[float], sample_seed: Optional[int]) -> tf.data.Dataset: del rates, sample_seed return datasets[0].concatenate(datasets[1]) def gen_dataset(split, shuffle_files=False, seed=None, val: str = "") -> tf.data.Dataset: del split, shuffle_files, seed # Need this to pass arg validation. return tf.data.Dataset.from_tensor_slices({ "inputs": [[val]] * 3, }) # Register two very simple tasks, each with 3 repeated string values. vocab = vocabularies.PassThroughVocabulary(0) tasks = [] for task_name in ["first", "second"]: tasks.append(self.add_task( task_name, dataset_providers.FunctionDataSource( dataset_fn=functools.partial(gen_dataset, val=task_name), splits=["train"]), preprocessors=[], output_features={ "inputs": dataset_providers.Feature(vocab, dtype=tf.string) })) # Verify that by default, interleaving of datasets is random. MixtureRegistry.add("default_mix", [("first", 1), ("second", 1)]) default_ds = MixtureRegistry.get("default_mix").get_dataset( None, "train", shuffle=False, seed=2, num_epochs=1) expected = [b"second", b"first", b"second", b"first", b"second", b"first"] actual = [x["inputs"] for x in default_ds.as_numpy_iterator()] self.assertEqual(expected, actual) # Verify that we can modify sampling function correctly. MixtureRegistry.add( "sequential_mix", [("first", 1), ("second", 1)], sample_fn=sequential_intereave) sequential_ds = MixtureRegistry.get("sequential_mix").get_dataset( None, "train", shuffle=False, seed=2, num_epochs=1) expected = [b"first"] * 3 + [b"second"] * 3 actual = [x["inputs"] for x in sequential_ds.as_numpy_iterator()] self.assertEqual(expected, actual) class GetDatasetTest(parameterized.TestCase, tf.test.TestCase): def test_get_dataset_enc_dec_unpacked(self): mixture_or_task_name = "enc_dec_unpacked" x = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}, {"inputs": [8, 4], "targets": [4]}, {"inputs": [5, 6, 7], "targets": [6, 5]}] ds = create_default_dataset(x) dataset_fn = lambda split, shuffle_files: ds register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn) task_feature_lengths = {"inputs": 7, "targets": 5} converter = feature_converters.EncDecFeatureConverter(pack=False) output_ds = dataset_providers.get_dataset( mixture_or_task_name=mixture_or_task_name, task_feature_lengths=task_feature_lengths, dataset_split="train", shuffle=False, feature_converter=converter) expected = [{ "encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1], "decoder_target_tokens": [3, 9, 1, 0, 0], "decoder_input_tokens": [0, 3, 9, 1, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], }, { "encoder_input_tokens": [8, 4, 1, 0, 0, 0, 0], "decoder_target_tokens": [4, 1, 0, 0, 0], "decoder_input_tokens": [0, 4, 1, 0, 0], "decoder_loss_weights": [1, 1, 0, 0, 0], }, { "encoder_input_tokens": [5, 6, 7, 1, 0, 0, 0], "decoder_target_tokens": [6, 5, 1, 0, 0], "decoder_input_tokens": [0, 6, 5, 1, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], }] expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) @parameterized.parameters( dict( task_name="enc_dec_partial_trim_both", task_feature_lengths={ "inputs": 7, "targets": 2 }, expect_trim_inputs=True, expect_trim_targets=True), dict( task_name="enc_dec_partial_trim_targets", task_feature_lengths={ "inputs": None, "targets": 2 }, expect_trim_inputs=False, expect_trim_targets=True), dict( task_name="enc_dec_partial_trim_inputs", task_feature_lengths={ "inputs": 7, "targets": None }, expect_trim_inputs=True, expect_trim_targets=False), dict( task_name="enc_dec_partial_trim_neither", task_feature_lengths={ "inputs": None, "targets": None }, expect_trim_inputs=False, expect_trim_targets=False), dict( task_name="enc_dec_partial_trim_nothing", task_feature_lengths=None, expect_trim_inputs=False, expect_trim_targets=False)) def test_partial_sequence_length(self, task_name, task_feature_lengths, expect_trim_inputs, expect_trim_targets): x = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}, {"inputs": [8, 4], "targets": [4]}, {"inputs": [5, 6, 7], "targets": [6, 5]}] ds = create_default_dataset(x) dataset_fn = lambda split, shuffle_files: ds register_dummy_task(task_name, dataset_fn=dataset_fn) # Unlike the other tests, don't use a feature converter. Instead, test the task = dataset_providers.get_mixture_or_task(task_name) output_ds = task.get_dataset( sequence_length=task_feature_lengths, shuffle=False) expected = [{ "inputs": [7, 8, 5, 6, 9, 4, 3, 1], "targets": [3, 9, 1], }, { "inputs": [8, 4, 1], "targets": [4, 1], }, { "inputs": [5, 6, 7, 1], "targets": [6, 5, 1], }] if expect_trim_inputs: expected[0]["inputs"] = [7, 8, 5, 6, 9, 4, 1] if expect_trim_targets: expected[0]["targets"] = [3, 1] expected[2]["targets"] = [6, 1] expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) @parameterized.parameters( dict( task_name="enc_dec_multidim_trim_both", task_feature_lengths={ "inputs": (2, 5), "targets": 2 }, expect_trim_inputs=True, expect_trim_targets=True, ), dict( task_name="enc_dec_multidim_trim_inputs", task_feature_lengths={ "inputs": (2, 5), "targets": None }, expect_trim_inputs=True, expect_trim_targets=False, ), dict( task_name="enc_dec_multidim_trim_targets", task_feature_lengths={ "inputs": None, "targets": 2 }, expect_trim_inputs=False, expect_trim_targets=True, ), dict( task_name="enc_dec_no_multidim_trim", task_feature_lengths={ "inputs": None, "targets": None }, expect_trim_inputs=False, expect_trim_targets=False ) ) def test_multidimension_sequence_length(self, task_name, task_feature_lengths, expect_trim_inputs, expect_trim_targets): x = [{"inputs": [[7, 8, 5, 6, 9, 4, 3], [2, 3, 4, 5, 0, 0, 0], [6, 7, 1, 0, 0, 0, 0]], "targets": [3, 9]}, {"inputs": [[8, 4], [1, 0], [2, 3]], "targets": [4]}, {"inputs": [[5, 6, 7]], "targets": [6, 5, 1]}, {"inputs": [[7, 8, 9, 1, 2, 3, 4, 5, 6]], "targets": [10, 11, 1]}] ds = tf.data.Dataset.from_generator( lambda: x, output_types={"inputs": tf.int32, "targets": tf.int32}, output_shapes={"inputs": (None, None), "targets": (None,)}) dataset_fn = lambda split, shuffle_files: ds dataset_providers.TaskRegistry.add( task_name, source=dataset_providers.FunctionDataSource( dataset_fn=dataset_fn, splits=["train", "validation"]), preprocessors=[ dataset_providers.CacheDatasetPlaceholder(), ], output_features={ "inputs": dataset_providers.Feature( test_utils.sentencepiece_vocab(), rank=2), "targets": dataset_providers.Feature( test_utils.sentencepiece_vocab()) }, metric_fns=[]) # task.get_dataset method directly, which is similar to how evaluation.py # infers feature lengths w/trimming. task = dataset_providers.get_mixture_or_task(task_name) output_ds = task.get_dataset( sequence_length=task_feature_lengths, shuffle=False) expected = copy.deepcopy(x) if expect_trim_inputs: expected[0]["inputs"] = [[7, 8, 5, 6, 9], [2, 3, 4, 5, 0]] expected[1]["inputs"] = [[8, 4], [1, 0]] expected[3]["inputs"] = [[7, 8, 9, 1, 2]] if expect_trim_targets: expected[2]["targets"] = [6, 5] expected[3]["targets"] = [10, 11] expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) def test_get_dataset_enc_dec_packed(self): mixture_or_task_name = "enc_dec_packed" x = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}, {"inputs": [8, 4], "targets": [4]}, {"inputs": [5, 6, 7], "targets": [6, 5]}] ds = create_default_dataset(x) dataset_fn = lambda split, shuffle_files: ds register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn) task_feature_lengths = {"inputs": 7, "targets": 5} converter = feature_converters.EncDecFeatureConverter(pack=True) output_ds = dataset_providers.get_dataset( mixture_or_task_name=mixture_or_task_name, task_feature_lengths=task_feature_lengths, dataset_split="train", shuffle=False, feature_converter=converter) expected = [{ # Example 1 is trimmed "encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1], "encoder_segment_ids": [1, 1, 1, 1, 1, 1, 1], "encoder_positions": [0, 1, 2, 3, 4, 5, 6], "decoder_target_tokens": [3, 9, 1, 0, 0], "decoder_input_tokens": [0, 3, 9, 0, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], "decoder_segment_ids": [1, 1, 1, 0, 0], "decoder_positions": [0, 1, 2, 0, 0], }, { # Example 2 and 3 are packed together "encoder_input_tokens": [8, 4, 1, 5, 6, 7, 1], "encoder_segment_ids": [1, 1, 1, 2, 2, 2, 2], "encoder_positions": [0, 1, 2, 0, 1, 2, 3], "decoder_target_tokens": [4, 1, 6, 5, 1], "decoder_input_tokens": [0, 4, 0, 6, 5], "decoder_loss_weights": [1, 1, 1, 1, 1], "decoder_segment_ids": [1, 1, 2, 2, 2], "decoder_positions": [0, 1, 0, 1, 2], }] expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) def test_get_dataset_both_train_and_validation_splits(self): mixture_or_task_name = "both_train_and_validation_splits" x_train = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}] x_val = [{"inputs": [8, 4], "targets": [4]}] datasets = { "train": create_default_dataset(x_train), "validation": create_default_dataset(x_val) } dataset_fn = lambda split, shuffle_files: datasets[split] register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn) task_feature_lengths = {"inputs": 7, "targets": 5} output_ds = {} for split in ["train", "validation"]: converter = feature_converters.EncDecFeatureConverter(pack=False) output_ds[split] = dataset_providers.get_dataset( mixture_or_task_name=mixture_or_task_name, task_feature_lengths=task_feature_lengths, dataset_split=split, shuffle=False, feature_converter=converter) expected_train = { "encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1], "decoder_target_tokens": [3, 9, 1, 0, 0], "decoder_input_tokens": [0, 3, 9, 1, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], } expected_val = { "encoder_input_tokens": [8, 4, 1, 0, 0, 0, 0], "decoder_target_tokens": [4, 1, 0, 0, 0], "decoder_input_tokens": [0, 4, 1, 0, 0], "decoder_loss_weights": [1, 1, 0, 0, 0], } expected_dtypes = {feat: tf.int32 for feat in expected_train.keys()} assert_dataset( output_ds["train"], expected_train, expected_dtypes=expected_dtypes) assert_dataset( output_ds["validation"], expected_val, expected_dtypes=expected_dtypes) def test_get_dataset_enc_dec_sharded(self): mixture_or_task_name = "enc_dec_sharded" x = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}, {"inputs": [8, 4], "targets": [4]}, {"inputs": [5, 6, 7], "targets": [6, 5]}] ds = create_default_dataset(x) dataset_fn = lambda split, shuffle_files: ds register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn) task_feature_lengths = {"inputs": 7, "targets": 5} converter = feature_converters.EncDecFeatureConverter(pack=False) shard_info = dataset_providers.ShardInfo(index=0, num_shards=2) output_ds = dataset_providers.get_dataset( mixture_or_task_name=mixture_or_task_name, task_feature_lengths=task_feature_lengths, dataset_split="train", shuffle=False, feature_converter=converter, shard_info=shard_info) # Example index 1 should not be present in the sharded dataset. expected = [{ "encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1], "decoder_target_tokens": [3, 9, 1, 0, 0], "decoder_input_tokens": [0, 3, 9, 1, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], }, { "encoder_input_tokens": [5, 6, 7, 1, 0, 0, 0], "decoder_target_tokens": [6, 5, 1, 0, 0], "decoder_input_tokens": [0, 6, 5, 1, 0], "decoder_loss_weights": [1, 1, 1, 0, 0], }] expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) def test_get_dataset_enc_dec_sharded_and_packed(self): mixture_or_task_name = "enc_dec_sharded_and_packed" x = [{"inputs": [7, 8], "targets": [3, 9]}, {"inputs": [8, 4], "targets": [4]}, {"inputs": [5, 6, 7], "targets": [6]}] ds = create_default_dataset(x) dataset_fn = lambda split, shuffle_files: ds register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn) task_feature_lengths = {"inputs": 7, "targets": 5} converter = feature_converters.EncDecFeatureConverter(pack=True) shard_info = dataset_providers.ShardInfo(index=0, num_shards=2) output_ds = dataset_providers.get_dataset( mixture_or_task_name=mixture_or_task_name, task_feature_lengths=task_feature_lengths, dataset_split="train", shuffle=False, feature_converter=converter, shard_info=shard_info) # Packing should be done after the sharding. expected = { "encoder_input_tokens": [7, 8, 1, 5, 6, 7, 1], "encoder_segment_ids": [1, 1, 1, 2, 2, 2, 2], "encoder_positions": [0, 1, 2, 0, 1, 2, 3], "decoder_target_tokens": [3, 9, 1, 6, 1], "decoder_input_tokens": [0, 3, 9, 0, 6], "decoder_loss_weights": [1, 1, 1, 1, 1], "decoder_segment_ids": [1, 1, 1, 2, 2], "decoder_positions": [0, 1, 2, 0, 1], } expected_dtypes = {feat: tf.int32 for feat in expected.keys()} assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes) def register_dummy_task( task_name: str, dataset_fn: Callable[[str, str], tf.data.Dataset], output_feature_names: Sequence[str] = ("inputs", "targets")) -> None: dataset_providers.TaskRegistry.add( task_name, source=dataset_providers.FunctionDataSource( dataset_fn=dataset_fn, splits=["train", "validation"]), preprocessors=[ dataset_providers.CacheDatasetPlaceholder(), preprocessors.append_eos_after_trim, ], output_features={ feat: dataset_providers.Feature(test_utils.sentencepiece_vocab()) for feat in output_feature_names }, metric_fns=[]) if __name__ == "__main__": absltest.main()

true

790bfa5dcb72761c01a9ba47699f9d2ad6b32755

5,875

py

Python

2018/10_TheStarsAlign/aoc_10.py

deanearlwright/AdventOfCode

ca4cf6315c0efa38bd7748fb6f4bc99e7934871d

[ "MIT" ]

1

2021-01-03T23:09:28.000Z

2018/10_TheStarsAlign/aoc_10.py

deanearlwright/AdventOfCode

ca4cf6315c0efa38bd7748fb6f4bc99e7934871d

[ "MIT" ]

6

2020-12-26T21:02:42.000Z

2020-12-26T21:02:52.000Z

2018/10_TheStarsAlign/aoc_10.py

deanearlwright/AdventOfCode

ca4cf6315c0efa38bd7748fb6f4bc99e7934871d

[ "MIT" ]

null

# ====================================================================== # The Stars Align # Advent of Code 2018 Day 10 -- Eric Wastl -- https://adventofcode.com # # Python implementation by Dr. Dean Earl Wright III # ====================================================================== # ====================================================================== # a o c _ 1 0 . p y # ====================================================================== "Solve the puzzles for Advent of Code 2018 day 10" # ---------------------------------------------------------------------- # import # ---------------------------------------------------------------------- import argparse import sys import lights # ---------------------------------------------------------------------- # constants # ---------------------------------------------------------------------- # ---------------------------------------------------------------------- # parse_commnd_line # ---------------------------------------------------------------------- def parse_command_line(): "Parse the command line options" # 1. Create the command line parser desc = 'The Stars Align - Day 10 of Advent of Code 2018' sample = 'sample: python aoc_10.py input.txt' parser = argparse.ArgumentParser(description=desc, epilog=sample) parser.add_argument('-v', '--verbose', action='store_true', default=False, dest='verbose', help='Print status messages to stdout') parser.add_argument('-p', '--part', action='store', default=1, type=int, dest='part', help='Puzzle Part (1 or 2)') parser.add_argument('-l', '--limit', action='store', default=0, type=int, dest='limit', help='Maximum limit (e.g., time, size, recursion) before stopping') parser.add_argument('filepath', metavar='FILENAME', action='store', type=str, help="Location of puzzle input") # 2. Get the options and arguments return parser.parse_args() # ---------------------------------------------------------------------- # part_one # ---------------------------------------------------------------------- def part_one(args, input_lines): "Process part one of the puzzle" # 1. Create the puzzle solver solver = lights.Lights(part2=False, text=input_lines) # 2. Determine the solution for part one solution = solver.part_one(verbose=args.verbose, limit=args.limit) if solution is None: print("There is no solution") else: print("The solution for part one is %s" % (solution)) # 3. Return result return solution is not None # ---------------------------------------------------------------------- # part_two # ---------------------------------------------------------------------- def part_two(args, input_lines): "Process part two of the puzzle" # 1. Create the puzzle solver solver = lights.Lights(part2=True, text=input_lines) # 2. Determine the solution for part two solution = solver.part_two(verbose=args.verbose, limit=args.limit) if solution is None: print("There is no solution") else: print("The solution for part two is %s" % (solution)) # 3. Return result return solution is not None # ---------------------------------------------------------------------- # from_file # ---------------------------------------------------------------------- def from_file(filepath): "Read the file" return from_text(open(filepath).read()) # ---------------------------------------------------------------------- # from_text # ---------------------------------------------------------------------- def from_text(text): "Break the text into trimed, non-comment lines" # 1. We start with no lines lines = [] # 2. Loop for lines in the text for line in text.split('\n'): # 3. But ignore blank and non-claim lines line = line.rstrip(' \r') if not line: continue if line.startswith('!'): continue # 4. Add the line lines.append(line) # 5. Return a list of clean lines return lines # ---------------------------------------------------------------------- # main # ---------------------------------------------------------------------- def main(): "Read the Advent of Code problem and solve it" # 1. Get the command line options args = parse_command_line() # 2. Read the puzzle file input_text = from_file(args.filepath) # 3. Process the appropiate part of the puzzle if args.part == 1: result = part_one(args, input_text) else: result = part_two(args, input_text) # 5. Set return code (0 if solution found, 2 if not) if result: sys.exit(0) sys.exit(2) # ---------------------------------------------------------------------- # module initialization # ---------------------------------------------------------------------- if __name__ == '__main__': main() # ====================================================================== # end a o c _ 1 0 . p y end # ======================================================================

35.179641

91

0.372936

import argparse import sys import lights def parse_command_line(): desc = 'The Stars Align - Day 10 of Advent of Code 2018' sample = 'sample: python aoc_10.py input.txt' parser = argparse.ArgumentParser(description=desc, epilog=sample) parser.add_argument('-v', '--verbose', action='store_true', default=False, dest='verbose', help='Print status messages to stdout') parser.add_argument('-p', '--part', action='store', default=1, type=int, dest='part', help='Puzzle Part (1 or 2)') parser.add_argument('-l', '--limit', action='store', default=0, type=int, dest='limit', help='Maximum limit (e.g., time, size, recursion) before stopping') parser.add_argument('filepath', metavar='FILENAME', action='store', type=str, help="Location of puzzle input") return parser.parse_args() def part_one(args, input_lines): solver = lights.Lights(part2=False, text=input_lines) solution = solver.part_one(verbose=args.verbose, limit=args.limit) if solution is None: print("There is no solution") else: print("The solution for part one is %s" % (solution)) return solution is not None def part_two(args, input_lines): solver = lights.Lights(part2=True, text=input_lines) solution = solver.part_two(verbose=args.verbose, limit=args.limit) if solution is None: print("There is no solution") else: print("The solution for part two is %s" % (solution)) return solution is not None def from_file(filepath): return from_text(open(filepath).read()) def from_text(text): lines = [] for line in text.split('\n'): line = line.rstrip(' \r') if not line: continue if line.startswith('!'): continue lines.append(line) return lines def main(): args = parse_command_line() input_text = from_file(args.filepath) if args.part == 1: result = part_one(args, input_text) else: result = part_two(args, input_text) if result: sys.exit(0) sys.exit(2) if __name__ == '__main__': main()

true

790bfb3c72491d430ec86eaa31bd44bfeec55858

12,525

py

Python

homeassistant/components/media_player/kodi.py

sbidoul/home-assistant

75adb7ff46e64e510c206d2b1f141253bbc4997a

[ "MIT" ]

null

homeassistant/components/media_player/kodi.py

sbidoul/home-assistant

75adb7ff46e64e510c206d2b1f141253bbc4997a

[ "MIT" ]

null

homeassistant/components/media_player/kodi.py

sbidoul/home-assistant

75adb7ff46e64e510c206d2b1f141253bbc4997a

[ "MIT" ]

2

2018-10-22T17:05:47.000Z

2021-09-22T10:52:31.000Z

""" Support for interfacing with the XBMC/Kodi JSON-RPC API. For more details about this platform, please refer to the documentation at https://home-assistant.io/components/media_player.kodi/ """ import asyncio import logging import urllib import aiohttp import voluptuous as vol from homeassistant.components.media_player import ( SUPPORT_NEXT_TRACK, SUPPORT_PAUSE, SUPPORT_PREVIOUS_TRACK, SUPPORT_SEEK, SUPPORT_PLAY_MEDIA, SUPPORT_VOLUME_MUTE, SUPPORT_VOLUME_SET, SUPPORT_STOP, SUPPORT_TURN_OFF, SUPPORT_PLAY, SUPPORT_VOLUME_STEP, MediaPlayerDevice, PLATFORM_SCHEMA) from homeassistant.const import ( STATE_IDLE, STATE_OFF, STATE_PAUSED, STATE_PLAYING, CONF_HOST, CONF_NAME, CONF_PORT, CONF_USERNAME, CONF_PASSWORD) from homeassistant.helpers.aiohttp_client import async_get_clientsession import homeassistant.helpers.config_validation as cv REQUIREMENTS = ['jsonrpc-async==0.2'] _LOGGER = logging.getLogger(__name__) CONF_TURN_OFF_ACTION = 'turn_off_action' DEFAULT_NAME = 'Kodi' DEFAULT_PORT = 8080 DEFAULT_TIMEOUT = 5 TURN_OFF_ACTION = [None, 'quit', 'hibernate', 'suspend', 'reboot', 'shutdown'] SUPPORT_KODI = SUPPORT_PAUSE | SUPPORT_VOLUME_SET | SUPPORT_VOLUME_MUTE | \ SUPPORT_PREVIOUS_TRACK | SUPPORT_NEXT_TRACK | SUPPORT_SEEK | \ SUPPORT_PLAY_MEDIA | SUPPORT_STOP | SUPPORT_PLAY | SUPPORT_VOLUME_STEP PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({ vol.Required(CONF_HOST): cv.string, vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string, vol.Optional(CONF_PORT, default=DEFAULT_PORT): cv.port, vol.Optional(CONF_TURN_OFF_ACTION, default=None): vol.In(TURN_OFF_ACTION), vol.Inclusive(CONF_USERNAME, 'auth'): cv.string, vol.Inclusive(CONF_PASSWORD, 'auth'): cv.string, }) @asyncio.coroutine def async_setup_platform(hass, config, async_add_entities, discovery_info=None): """Setup the Kodi platform.""" host = config.get(CONF_HOST) port = config.get(CONF_PORT) if host.startswith('http://') or host.startswith('https://'): host = host.lstrip('http://').lstrip('https://') _LOGGER.warning( "Kodi host name should no longer conatin http:// See updated " "definitions here: " "https://home-assistant.io/components/media_player.kodi/") entity = KodiDevice( hass, name=config.get(CONF_NAME), host=host, port=port, username=config.get(CONF_USERNAME), password=config.get(CONF_PASSWORD), turn_off_action=config.get(CONF_TURN_OFF_ACTION)) yield from async_add_entities([entity], update_before_add=True) class KodiDevice(MediaPlayerDevice): """Representation of a XBMC/Kodi device.""" def __init__(self, hass, name, host, port, username=None, password=None, turn_off_action=None): """Initialize the Kodi device.""" import jsonrpc_async self.hass = hass self._name = name kwargs = { 'timeout': DEFAULT_TIMEOUT, 'session': async_get_clientsession(hass), } if username is not None: kwargs['auth'] = aiohttp.BasicAuth(username, password) image_auth_string = "{}:{}@".format(username, password) else: image_auth_string = "" self._http_url = 'http://{}:{}/jsonrpc'.format(host, port) self._image_url = 'http://{}{}:{}/image'.format( image_auth_string, host, port) self._server = jsonrpc_async.Server(self._http_url, **kwargs) self._turn_off_action = turn_off_action self._players = list() self._properties = None self._item = None self._app_properties = None @property def name(self): """Return the name of the device.""" return self._name @asyncio.coroutine def _get_players(self): """Return the active player objects or None.""" import jsonrpc_async try: return (yield from self._server.Player.GetActivePlayers()) except jsonrpc_async.jsonrpc.TransportError: if self._players is not None: _LOGGER.info('Unable to fetch kodi data') _LOGGER.debug('Unable to fetch kodi data', exc_info=True) return None @property def state(self): """Return the state of the device.""" if self._players is None: return STATE_OFF if len(self._players) == 0: return STATE_IDLE if self._properties['speed'] == 0 and not self._properties['live']: return STATE_PAUSED else: return STATE_PLAYING @asyncio.coroutine def async_update(self): """Retrieve latest state.""" self._players = yield from self._get_players() if self._players is not None and len(self._players) > 0: player_id = self._players[0]['playerid'] assert isinstance(player_id, int) self._properties = yield from self._server.Player.GetProperties( player_id, ['time', 'totaltime', 'speed', 'live'] ) self._item = (yield from self._server.Player.GetItem( player_id, ['title', 'file', 'uniqueid', 'thumbnail', 'artist'] ))['item'] self._app_properties = \ yield from self._server.Application.GetProperties( ['volume', 'muted'] ) else: self._properties = None self._item = None self._app_properties = None @property def volume_level(self): """Volume level of the media player (0..1).""" if self._app_properties is not None: return self._app_properties['volume'] / 100.0 @property def is_volume_muted(self): """Boolean if volume is currently muted.""" if self._app_properties is not None: return self._app_properties['muted'] @property def media_content_id(self): """Content ID of current playing media.""" if self._item is not None: return self._item.get('uniqueid', None) @property def media_content_type(self): """Content type of current playing media.""" if self._players is not None and len(self._players) > 0: return self._players[0]['type'] @property def media_duration(self): """Duration of current playing media in seconds.""" if self._properties is not None and not self._properties['live']: total_time = self._properties['totaltime'] return ( total_time['hours'] * 3600 + total_time['minutes'] * 60 + total_time['seconds']) @property def media_image_url(self): """Image url of current playing media.""" if self._item is None: return None url_components = urllib.parse.urlparse(self._item['thumbnail']) if url_components.scheme == 'image': return '{}/{}'.format( self._image_url, urllib.parse.quote_plus(self._item['thumbnail'])) @property def media_title(self): """Title of current playing media.""" # find a string we can use as a title if self._item is not None: return self._item.get( 'title', self._item.get('label', self._item.get('file', 'unknown'))) @property def supported_media_commands(self): """Flag of media commands that are supported.""" supported_media_commands = SUPPORT_KODI if self._turn_off_action in TURN_OFF_ACTION: supported_media_commands |= SUPPORT_TURN_OFF return supported_media_commands @asyncio.coroutine def async_turn_off(self): """Execute turn_off_action to turn off media player.""" if self._turn_off_action == 'quit': yield from self._server.Application.Quit() elif self._turn_off_action == 'hibernate': yield from self._server.System.Hibernate() elif self._turn_off_action == 'suspend': yield from self._server.System.Suspend() elif self._turn_off_action == 'reboot': yield from self._server.System.Reboot() elif self._turn_off_action == 'shutdown': yield from self._server.System.Shutdown() else: _LOGGER.warning('turn_off requested but turn_off_action is none') @asyncio.coroutine def async_volume_up(self): """Volume up the media player.""" assert ( yield from self._server.Input.ExecuteAction('volumeup')) == 'OK' @asyncio.coroutine def async_volume_down(self): """Volume down the media player.""" assert ( yield from self._server.Input.ExecuteAction('volumedown')) == 'OK' def async_set_volume_level(self, volume): """Set volume level, range 0..1. This method must be run in the event loop and returns a coroutine. """ return self._server.Application.SetVolume(int(volume * 100)) def async_mute_volume(self, mute): """Mute (true) or unmute (false) media player. This method must be run in the event loop and returns a coroutine. """ return self._server.Application.SetMute(mute) @asyncio.coroutine def async_set_play_state(self, state): """Helper method for play/pause/toggle.""" players = yield from self._get_players() if len(players) != 0: yield from self._server.Player.PlayPause( players[0]['playerid'], state) def async_media_play_pause(self): """Pause media on media player. This method must be run in the event loop and returns a coroutine. """ return self.async_set_play_state('toggle') def async_media_play(self): """Play media. This method must be run in the event loop and returns a coroutine. """ return self.async_set_play_state(True) def async_media_pause(self): """Pause the media player. This method must be run in the event loop and returns a coroutine. """ return self.async_set_play_state(False) @asyncio.coroutine def async_media_stop(self): """Stop the media player.""" players = yield from self._get_players() if len(players) != 0: yield from self._server.Player.Stop(players[0]['playerid']) @asyncio.coroutine def _goto(self, direction): """Helper method used for previous/next track.""" players = yield from self._get_players() if len(players) != 0: if direction == 'previous': # first seek to position 0. Kodi goes to the beginning of the # current track if the current track is not at the beginning. yield from self._server.Player.Seek(players[0]['playerid'], 0) yield from self._server.Player.GoTo( players[0]['playerid'], direction) def async_media_next_track(self): """Send next track command. This method must be run in the event loop and returns a coroutine. """ return self._goto('next') def async_media_previous_track(self): """Send next track command. This method must be run in the event loop and returns a coroutine. """ return self._goto('previous') @asyncio.coroutine def async_media_seek(self, position): """Send seek command.""" players = yield from self._get_players() time = {} time['milliseconds'] = int((position % 1) * 1000) position = int(position) time['seconds'] = int(position % 60) position /= 60 time['minutes'] = int(position % 60) position /= 60 time['hours'] = int(position) if len(players) != 0: yield from self._server.Player.Seek(players[0]['playerid'], time) def async_play_media(self, media_type, media_id, **kwargs): """Send the play_media command to the media player. This method must be run in the event loop and returns a coroutine. """ if media_type == "CHANNEL": return self._server.Player.Open( {"item": {"channelid": int(media_id)}}) else: return self._server.Player.Open( {"item": {"file": str(media_id)}})

33.4

78

0.621876

import asyncio import logging import urllib import aiohttp import voluptuous as vol from homeassistant.components.media_player import ( SUPPORT_NEXT_TRACK, SUPPORT_PAUSE, SUPPORT_PREVIOUS_TRACK, SUPPORT_SEEK, SUPPORT_PLAY_MEDIA, SUPPORT_VOLUME_MUTE, SUPPORT_VOLUME_SET, SUPPORT_STOP, SUPPORT_TURN_OFF, SUPPORT_PLAY, SUPPORT_VOLUME_STEP, MediaPlayerDevice, PLATFORM_SCHEMA) from homeassistant.const import ( STATE_IDLE, STATE_OFF, STATE_PAUSED, STATE_PLAYING, CONF_HOST, CONF_NAME, CONF_PORT, CONF_USERNAME, CONF_PASSWORD) from homeassistant.helpers.aiohttp_client import async_get_clientsession import homeassistant.helpers.config_validation as cv REQUIREMENTS = ['jsonrpc-async==0.2'] _LOGGER = logging.getLogger(__name__) CONF_TURN_OFF_ACTION = 'turn_off_action' DEFAULT_NAME = 'Kodi' DEFAULT_PORT = 8080 DEFAULT_TIMEOUT = 5 TURN_OFF_ACTION = [None, 'quit', 'hibernate', 'suspend', 'reboot', 'shutdown'] SUPPORT_KODI = SUPPORT_PAUSE | SUPPORT_VOLUME_SET | SUPPORT_VOLUME_MUTE | \ SUPPORT_PREVIOUS_TRACK | SUPPORT_NEXT_TRACK | SUPPORT_SEEK | \ SUPPORT_PLAY_MEDIA | SUPPORT_STOP | SUPPORT_PLAY | SUPPORT_VOLUME_STEP PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({ vol.Required(CONF_HOST): cv.string, vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string, vol.Optional(CONF_PORT, default=DEFAULT_PORT): cv.port, vol.Optional(CONF_TURN_OFF_ACTION, default=None): vol.In(TURN_OFF_ACTION), vol.Inclusive(CONF_USERNAME, 'auth'): cv.string, vol.Inclusive(CONF_PASSWORD, 'auth'): cv.string, }) @asyncio.coroutine def async_setup_platform(hass, config, async_add_entities, discovery_info=None): host = config.get(CONF_HOST) port = config.get(CONF_PORT) if host.startswith('http://') or host.startswith('https://'): host = host.lstrip('http://').lstrip('https://') _LOGGER.warning( "Kodi host name should no longer conatin http:// See updated " "definitions here: " "https://home-assistant.io/components/media_player.kodi/") entity = KodiDevice( hass, name=config.get(CONF_NAME), host=host, port=port, username=config.get(CONF_USERNAME), password=config.get(CONF_PASSWORD), turn_off_action=config.get(CONF_TURN_OFF_ACTION)) yield from async_add_entities([entity], update_before_add=True) class KodiDevice(MediaPlayerDevice): def __init__(self, hass, name, host, port, username=None, password=None, turn_off_action=None): import jsonrpc_async self.hass = hass self._name = name kwargs = { 'timeout': DEFAULT_TIMEOUT, 'session': async_get_clientsession(hass), } if username is not None: kwargs['auth'] = aiohttp.BasicAuth(username, password) image_auth_string = "{}:{}@".format(username, password) else: image_auth_string = "" self._http_url = 'http://{}:{}/jsonrpc'.format(host, port) self._image_url = 'http://{}{}:{}/image'.format( image_auth_string, host, port) self._server = jsonrpc_async.Server(self._http_url, **kwargs) self._turn_off_action = turn_off_action self._players = list() self._properties = None self._item = None self._app_properties = None @property def name(self): return self._name @asyncio.coroutine def _get_players(self): import jsonrpc_async try: return (yield from self._server.Player.GetActivePlayers()) except jsonrpc_async.jsonrpc.TransportError: if self._players is not None: _LOGGER.info('Unable to fetch kodi data') _LOGGER.debug('Unable to fetch kodi data', exc_info=True) return None @property def state(self): if self._players is None: return STATE_OFF if len(self._players) == 0: return STATE_IDLE if self._properties['speed'] == 0 and not self._properties['live']: return STATE_PAUSED else: return STATE_PLAYING @asyncio.coroutine def async_update(self): self._players = yield from self._get_players() if self._players is not None and len(self._players) > 0: player_id = self._players[0]['playerid'] assert isinstance(player_id, int) self._properties = yield from self._server.Player.GetProperties( player_id, ['time', 'totaltime', 'speed', 'live'] ) self._item = (yield from self._server.Player.GetItem( player_id, ['title', 'file', 'uniqueid', 'thumbnail', 'artist'] ))['item'] self._app_properties = \ yield from self._server.Application.GetProperties( ['volume', 'muted'] ) else: self._properties = None self._item = None self._app_properties = None @property def volume_level(self): if self._app_properties is not None: return self._app_properties['volume'] / 100.0 @property def is_volume_muted(self): if self._app_properties is not None: return self._app_properties['muted'] @property def media_content_id(self): if self._item is not None: return self._item.get('uniqueid', None) @property def media_content_type(self): if self._players is not None and len(self._players) > 0: return self._players[0]['type'] @property def media_duration(self): if self._properties is not None and not self._properties['live']: total_time = self._properties['totaltime'] return ( total_time['hours'] * 3600 + total_time['minutes'] * 60 + total_time['seconds']) @property def media_image_url(self): if self._item is None: return None url_components = urllib.parse.urlparse(self._item['thumbnail']) if url_components.scheme == 'image': return '{}/{}'.format( self._image_url, urllib.parse.quote_plus(self._item['thumbnail'])) @property def media_title(self): if self._item is not None: return self._item.get( 'title', self._item.get('label', self._item.get('file', 'unknown'))) @property def supported_media_commands(self): supported_media_commands = SUPPORT_KODI if self._turn_off_action in TURN_OFF_ACTION: supported_media_commands |= SUPPORT_TURN_OFF return supported_media_commands @asyncio.coroutine def async_turn_off(self): if self._turn_off_action == 'quit': yield from self._server.Application.Quit() elif self._turn_off_action == 'hibernate': yield from self._server.System.Hibernate() elif self._turn_off_action == 'suspend': yield from self._server.System.Suspend() elif self._turn_off_action == 'reboot': yield from self._server.System.Reboot() elif self._turn_off_action == 'shutdown': yield from self._server.System.Shutdown() else: _LOGGER.warning('turn_off requested but turn_off_action is none') @asyncio.coroutine def async_volume_up(self): assert ( yield from self._server.Input.ExecuteAction('volumeup')) == 'OK' @asyncio.coroutine def async_volume_down(self): assert ( yield from self._server.Input.ExecuteAction('volumedown')) == 'OK' def async_set_volume_level(self, volume): return self._server.Application.SetVolume(int(volume * 100)) def async_mute_volume(self, mute): return self._server.Application.SetMute(mute) @asyncio.coroutine def async_set_play_state(self, state): players = yield from self._get_players() if len(players) != 0: yield from self._server.Player.PlayPause( players[0]['playerid'], state) def async_media_play_pause(self): return self.async_set_play_state('toggle') def async_media_play(self): return self.async_set_play_state(True) def async_media_pause(self): return self.async_set_play_state(False) @asyncio.coroutine def async_media_stop(self): players = yield from self._get_players() if len(players) != 0: yield from self._server.Player.Stop(players[0]['playerid']) @asyncio.coroutine def _goto(self, direction): players = yield from self._get_players() if len(players) != 0: if direction == 'previous': yield from self._server.Player.Seek(players[0]['playerid'], 0) yield from self._server.Player.GoTo( players[0]['playerid'], direction) def async_media_next_track(self): return self._goto('next') def async_media_previous_track(self): return self._goto('previous') @asyncio.coroutine def async_media_seek(self, position): players = yield from self._get_players() time = {} time['milliseconds'] = int((position % 1) * 1000) position = int(position) time['seconds'] = int(position % 60) position /= 60 time['minutes'] = int(position % 60) position /= 60 time['hours'] = int(position) if len(players) != 0: yield from self._server.Player.Seek(players[0]['playerid'], time) def async_play_media(self, media_type, media_id, **kwargs): if media_type == "CHANNEL": return self._server.Player.Open( {"item": {"channelid": int(media_id)}}) else: return self._server.Player.Open( {"item": {"file": str(media_id)}})

true

790bfb8d190d1cf2f4627a5bc380f5f90b282636

4,526

py

Python

sepc/exp/freeanchor/sepc_freeanchor.py

jshilong/SEPC

26624fdb66968f87500313fd99b7a1aa8ed61a8f

[ "Apache-2.0" ]

337

2020-04-23T16:13:56.000Z

2022-03-29T02:20:27.000Z

sepc/exp/freeanchor/sepc_freeanchor.py

jshilong/SEPC

26624fdb66968f87500313fd99b7a1aa8ed61a8f

[ "Apache-2.0" ]

24

2020-04-25T13:29:47.000Z

2021-04-23T08:04:19.000Z

sepc/exp/freeanchor/sepc_freeanchor.py

jshilong/SEPC

26624fdb66968f87500313fd99b7a1aa8ed61a8f

[ "Apache-2.0" ]

58

2020-04-25T11:52:09.000Z

2021-09-01T15:30:48.000Z

# model settings model = dict( type='RetinaNet', pretrained='torchvision://resnet50', backbone=dict(type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), style='pytorch'), neck=[ dict(type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, add_extra_convs=True, num_outs=5), dict( type='SEPC', out_channels=256, Pconv_num=4, pconv_deform=True, lcconv_deform=True, iBN=True, # when open, please set imgs/gpu >= 4 ) ], bbox_head=dict(type='SepcFreeAnchorRetinaHead', num_classes=81, in_channels=256, stacked_convs=0, feat_channels=256, octave_base_scale=4, scales_per_octave=3, anchor_ratios=[0.5, 1.0, 2.0], anchor_strides=[8, 16, 32, 64, 128], target_means=[.0, .0, .0, .0], target_stds=[0.1, 0.1, 0.2, 0.2], loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.75))) # training and testing settings train_cfg = dict(assigner=dict(type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1), allowed_border=-1, pos_weight=-1, debug=False) test_cfg = dict(nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100) # dataset settings dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( imgs_per_gpu=4, workers_per_gpu=2, train=dict(type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', img_prefix=data_root + 'train2017/', pipeline=train_pipeline), val=dict(type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict(type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline)) evaluation = dict(interval=1, metric='bbox') # optimizer optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) # learning policy lr_config = dict(policy='step', warmup='linear', warmup_iters=500, warmup_ratio=1.0 / 3, step=[8, 11]) checkpoint_config = dict(interval=1) # yapf:disable log_config = dict( interval=1, hooks=[ dict(type='TextLoggerHook'), # dict(type='TensorboardLoggerHook') ]) # yapf:enable # runtime settings total_epochs = 12 dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = './work_dirs/retinanet_free_anchor_r50_fpn_1x' load_from = None resume_from = None workflow = [('train', 1)]

35.359375

75

0.53977

model = dict( type='RetinaNet', pretrained='torchvision://resnet50', backbone=dict(type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), style='pytorch'), neck=[ dict(type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, start_level=1, add_extra_convs=True, num_outs=5), dict( type='SEPC', out_channels=256, Pconv_num=4, pconv_deform=True, lcconv_deform=True, iBN=True, ) ], bbox_head=dict(type='SepcFreeAnchorRetinaHead', num_classes=81, in_channels=256, stacked_convs=0, feat_channels=256, octave_base_scale=4, scales_per_octave=3, anchor_ratios=[0.5, 1.0, 2.0], anchor_strides=[8, 16, 32, 64, 128], target_means=[.0, .0, .0, .0], target_stds=[0.1, 0.1, 0.2, 0.2], loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.75))) train_cfg = dict(assigner=dict(type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1), allowed_border=-1, pos_weight=-1, debug=False) test_cfg = dict(nms_pre=1000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100) dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( imgs_per_gpu=4, workers_per_gpu=2, train=dict(type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', img_prefix=data_root + 'train2017/', pipeline=train_pipeline), val=dict(type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict(type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline)) evaluation = dict(interval=1, metric='bbox') optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) lr_config = dict(policy='step', warmup='linear', warmup_iters=500, warmup_ratio=1.0 / 3, step=[8, 11]) checkpoint_config = dict(interval=1) log_config = dict( interval=1, hooks=[ dict(type='TextLoggerHook'), ]) total_epochs = 12 dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = './work_dirs/retinanet_free_anchor_r50_fpn_1x' load_from = None resume_from = None workflow = [('train', 1)]

true

790bfb9d722715703a84bf5209777fd1bbae58ba

1,301

py

Python

Apis/App/api.py

andrew962/Python

4398da94e0f465c92bd560989ea61c324423782b

[ "MIT" ]

1

2019-04-27T19:11:11.000Z

Apis/App/api.py

andrew962/Python

4398da94e0f465c92bd560989ea61c324423782b

[ "MIT" ]

null

Apis/App/api.py

andrew962/Python

4398da94e0f465c92bd560989ea61c324423782b

[ "MIT" ]

null

import pyowm owm = pyowm.OWM('ce688b67bbf90c2a0236d4eb23d8c7bd') # You MUST provide a valid API key # Will it be sunny tomorrow at this time in Milan (Italy) ? #forecast = owm.daily_forecast('panama') tomorrow = pyowm.timeutils.tomorrow() #forecast.will_be_sunny_at(tomorrow) # Always True in Italy, right? ;-) # Search for current weather in London (UK) observation = owm.weather_at_place('ayangue') w = observation.get_weather() rete=w.get_reference_time(timeformat='date') refe=w.get_reference_time('iso') estatus=w.get_status() time=w.get_sunset_time('iso') wind=(w.get_wind()['speed']) wind1=w.get_wind() tempe=w.get_temperature('celsius') tempe1=w.get_temperature('celsius')['temp_max'] l = observation.get_location() lugar = l.get_country() # status=Clouds> # Weather details #print(forecast) print(lugar) print(w) # <Weather - reference time=2013-12-18 09:20, #print(rete) #print(time) print(estatus)# status=Clouds> print(refe) #print(tomorrow)#Dia de mañana print(wind)#velocidad de viento print(wind1) print(w.get_humidity())#humedad print(tempe)#temperatura print(tempe1) #w.get_wind() # {'speed': 4.6, 'deg': 330} #w.get_humidity() # 87 #w.get_temperature('celsius') # {'temp_max': 10.5, 'temp': 9.7, 'temp_min': 9.0}

29.568182

87

0.703305

import pyowm owm = pyowm.OWM('ce688b67bbf90c2a0236d4eb23d8c7bd') tomorrow = pyowm.timeutils.tomorrow() ace('ayangue') w = observation.get_weather() rete=w.get_reference_time(timeformat='date') refe=w.get_reference_time('iso') estatus=w.get_status() time=w.get_sunset_time('iso') wind=(w.get_wind()['speed']) wind1=w.get_wind() tempe=w.get_temperature('celsius') tempe1=w.get_temperature('celsius')['temp_max'] l = observation.get_location() lugar = l.get_country() print(lugar) print(w) print(estatus) print(refe) rint(wind1) print(w.get_humidity()) print(tempe) print(tempe1)

true

790bfbd44f16d901a0167e10b3b6d1ce6c96d9dd

239

py

Python

behaviors/youbot_behavior_simple_test/setup.py

FlexBE/youbot_behaviors

6f7a0330d6a9e883fc0a3dff22f44422e2379274

[ "BSD-3-Clause" ]

6

2015-11-17T15:59:38.000Z

2019-12-04T02:24:30.000Z

behaviors/youbot_behavior_simple_test/setup.py

FlexBE/youbot_behaviors

6f7a0330d6a9e883fc0a3dff22f44422e2379274

[ "BSD-3-Clause" ]

null

behaviors/youbot_behavior_simple_test/setup.py

FlexBE/youbot_behaviors

6f7a0330d6a9e883fc0a3dff22f44422e2379274

[ "BSD-3-Clause" ]

2

2018-05-09T13:01:30.000Z

2022-03-30T10:16:15.000Z

#!/usr/bin/env python from distutils.core import setup from catkin_pkg.python_setup import generate_distutils_setup d = generate_distutils_setup( packages = ['youbot_behavior_simple_test'], package_dir = {'': 'src'} ) setup(**d)

21.727273

60

0.748954

from distutils.core import setup from catkin_pkg.python_setup import generate_distutils_setup d = generate_distutils_setup( packages = ['youbot_behavior_simple_test'], package_dir = {'': 'src'} ) setup(**d)

true

790bfbf2fb99fe349874ed1aa55887a5ca95e7c6

357

py

Python

uw_sdbmyuw/dao.py

uw-it-aca/uw-restclients-sdbmyuw

70932a09b47530100e104b1921b72bff822c03c1

[ "Apache-2.0" ]

null

uw_sdbmyuw/dao.py

uw-it-aca/uw-restclients-sdbmyuw

70932a09b47530100e104b1921b72bff822c03c1

[ "Apache-2.0" ]

8

2017-11-08T00:18:44.000Z

2021-06-01T17:14:30.000Z

uw_sdbmyuw/dao.py

uw-it-aca/uw-restclients-sdbmyuw

70932a09b47530100e104b1921b72bff822c03c1

[ "Apache-2.0" ]

null

# Copyright 2021 UW-IT, University of Washington # SPDX-License-Identifier: Apache-2.0 import os from os.path import abspath, dirname from restclients_core.dao import DAO class Sdbmyuw_DAO(DAO): def service_name(self): return 'sdbmyuw' def service_mock_paths(self): return [abspath(os.path.join(dirname(__file__), "resources"))]

23.8

70

0.731092

import os from os.path import abspath, dirname from restclients_core.dao import DAO class Sdbmyuw_DAO(DAO): def service_name(self): return 'sdbmyuw' def service_mock_paths(self): return [abspath(os.path.join(dirname(__file__), "resources"))]

true

790bfcf51a7b404e975bf5b7d7f7e1f8ac744c16

2,104

py

Python

mewarpx/examples/thermionic_diode.py

ModernElectron/WarpX

563813bc125a01a1a54267a3d4bb3ba77bcc68a3

[ "BSD-3-Clause-LBNL" ]

1

2021-06-23T23:38:50.000Z

mewarpx/examples/thermionic_diode.py

ModernElectron/WarpX

563813bc125a01a1a54267a3d4bb3ba77bcc68a3

[ "BSD-3-Clause-LBNL" ]

106

2021-06-08T23:57:54.000Z

2022-03-08T00:36:46.000Z

mewarpx/examples/thermionic_diode.py

ModernElectron/WarpX

563813bc125a01a1a54267a3d4bb3ba77bcc68a3

[ "BSD-3-Clause-LBNL" ]

1

2021-06-21T18:50:43.000Z

import argparse import sys import numpy as np from mewarpx.utils_store import util as mwxutil mwxutil.init_libwarpx(ndim=2, rz=False) from mewarpx.mwxrun import mwxrun from mewarpx.setups_store import diode_setup def run_simulation(V_bias, steps, save_current): #################################### # Diode setup #################################### run = diode_setup.DiodeRun_V1( CATHODE_TEMP = 1100.0 + 273.15, # K CATHODE_A = 6e5, # A/m^2/K^2 CATHODE_PHI = 2.11, # eV USE_SCHOTTKY = True, ANODE_TEMP = 200, # K ANODE_PHI = 1.4, # eV V_ANODE_CATHODE = V_bias, # V D_CA = 50e-6, # m DT = 0.5e-12, # s NX = 8, NZ = 128, DIRECT_SOLVER = True, NPPC = 100, TOTAL_TIMESTEPS = steps, DIAG_STEPS = ((steps // 5) if steps > 10 else steps), ) # Only the functions we change from defaults are listed here run.setup_run( init_runinfo=True, init_fluxdiag=True, init_simcontrol=True, init_warpx=True ) ################################# # Simulation run ################################# mwxrun.simulation.step(steps) ################################# # Save IV results ################################# if save_current and mwxrun.me == 0: key = ('scrape', 'anode', 'electrons') J_diode = run.fluxdiag.ts_dict[key].get_averagevalue_by_key('J') print(f'{V_bias} {J_diode}') with open(f'results_d_{int(run.D_CA*1e6)}.dat', 'a') as f: f.write(f'{V_bias} {J_diode}\n') parser = argparse.ArgumentParser() parser.add_argument('--V', help='bias voltage in Volt', type=float, default=0) parser.add_argument('--steps', help='set the number of simulation steps', type=int, default=3000) parser.add_argument('--save', help='save voltage and current pairs', default=False, action='store_true') args, left = parser.parse_known_args() sys.argv = sys.argv[:1]+left run_simulation(args.V, args.steps, args.save)

28.821918

78

0.551806

import argparse import sys import numpy as np from mewarpx.utils_store import util as mwxutil mwxutil.init_libwarpx(ndim=2, rz=False) from mewarpx.mwxrun import mwxrun from mewarpx.setups_store import diode_setup def run_simulation(V_bias, steps, save_current):

true

790bfda2727dc109030b122bbd79807846d3f166

7,817

py

Python

tests/validation/tests.py

pavanv/django-tastypie

b4ffc642aa56d25d3c577ccae0a03c820b71c4bc

[ "BSD-3-Clause" ]

1,570

2015-02-03T10:19:33.000Z

2022-03-29T10:34:18.000Z

tests/validation/tests.py

pavanv/django-tastypie

b4ffc642aa56d25d3c577ccae0a03c820b71c4bc

[ "BSD-3-Clause" ]

587

2015-02-06T13:59:23.000Z

2022-03-09T22:56:30.000Z

tests/validation/tests.py

pavanv/django-tastypie

b4ffc642aa56d25d3c577ccae0a03c820b71c4bc

[ "BSD-3-Clause" ]

492

2015-02-07T06:18:36.000Z

2022-03-29T19:06:44.000Z

import json from django.test.utils import override_settings from tastypie.exceptions import NotFound from basic.models import Note from testcases import TestCaseWithFixture from django.test.testcases import SimpleTestCase @override_settings(ROOT_URLCONF='validation.api.urls') class FilteringErrorsTestCase(TestCaseWithFixture): def test_valid_date(self): resp = self.client.get('/api/v1/notes/', data={ 'format': 'json', 'created__gte': '2010-03-31 00:00:00Z' }) self.assertEqual(resp.status_code, 200) deserialized = json.loads(resp.content.decode('utf-8')) self.assertEqual(len(deserialized['objects']), Note.objects.filter(created__gte='2010-03-31 00:00:00Z').count()) def test_invalid_date(self): resp = self.client.get('/api/v1/notes/', data={ 'format': 'json', 'created__gte': 'foo-baz-bar' }) self.assertEqual(resp.status_code, 400) @override_settings(ROOT_URLCONF='validation.api.urls') class PostRelatedUrlValidationTestCase(TestCaseWithFixture): def test_valid_url(self): data_with_pk = json.dumps({ 'title': 'Test title related pk', 'slug': 'test-slug-related-pk', 'content': 'This is the content', 'user': {'pk': 1}, }) data_with_url = json.dumps({ 'title': 'Test title related url', 'slug': 'test-slug-related-url', 'content': 'This is the content', 'user': '/api/v1/users/1/', }) resp_with_pk = self.client.post('/api/v1/notes/', data=data_with_pk, content_type='application/json') self.assertEqual(resp_with_pk.status_code, 201) note_posted_with_pk = json.loads(self.client.get(resp_with_pk['location']).content.decode('utf-8')) resp_with_url = self.client.post('/api/v1/notes/', data=data_with_url, content_type='application/json') self.assertEqual(resp_with_url.status_code, 201) note_posted_with_url = json.loads(self.client.get(resp_with_url['location']).content.decode('utf-8')) self.assertEqual(note_posted_with_pk['user'], note_posted_with_url['user']) def test_invalid_url(self): data = json.dumps({ 'title': 'Test title related url', 'slug': 'test-slug-related-url', 'content': 'This is the content', 'user': 'invalid-url', }) with self.assertRaises(NotFound): self.client.post('/api/v1/notes/', data=data, content_type='application/json') @override_settings(ROOT_URLCONF='validation.api.urls') class PostNestResouceValidationTestCase(TestCaseWithFixture): def test_valid_data(self): data = json.dumps({ 'title': 'Test Title', 'slug': 'test-title', 'content': 'This is the content', 'user': {'pk': 1}, # loaded from fixtures 'annotated': {'annotations': 'This is an annotations'}, }) resp = self.client.post('/api/v1/notes/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 201) note = json.loads(self.client.get(resp['location']).content.decode('utf-8')) self.assertTrue(note['annotated']) def test_invalid_data(self): data = json.dumps({ 'title': '', 'slug': 'test-title', 'content': 'This is the content', 'user': {'pk': 1}, # loaded from fixtures 'annotated': {'annotations': ''}, }) resp = self.client.post('/api/v1/notes/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 400) self.assertEqual(json.loads(resp.content.decode('utf-8')), { 'notes': { 'title': ['This field is required.'] }, 'annotated': { 'annotations': ['This field is required.'] } }) @override_settings(ROOT_URLCONF='validation.api.urls') class PutDetailNestResouceValidationTestCase(TestCaseWithFixture): def test_valid_data(self): data = json.dumps({ 'title': 'Test Title', 'slug': 'test-title', 'content': 'This is the content', 'annotated': {'annotations': 'This is another annotations'}, }) resp = self.client.put('/api/v1/notes/1/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 204) note = json.loads(self.client.get('/api/v1/notes/1/', content_type='application/json').content.decode('utf-8')) self.assertTrue(note['annotated']) self.assertEqual('test-title', note['slug']) def test_invalid_data(self): data = json.dumps({ 'title': '', 'slug': '', 'content': 'This is the content', 'annotated': {'annotations': None}, }) resp = self.client.put('/api/v1/notes/1/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 400) self.assertEqual(json.loads(resp.content.decode('utf-8')), { 'notes': { 'slug': ['This field is required.'], 'title': ['This field is required.'] }, 'annotated': { 'annotations': ['This field is required.'] } }) @override_settings(ROOT_URLCONF='validation.api.urls') class PutListNestResouceValidationTestCase(TestCaseWithFixture): def test_valid_data(self): data = json.dumps({'objects': [ { 'id': 1, 'title': 'Test Title', 'slug': 'test-title', 'content': 'This is the content', 'annotated': {'annotations': 'This is another annotations'}, 'user': {'id': 1} }, { 'id': 2, 'title': 'Test Title', 'slug': 'test-title', 'content': 'This is the content', 'annotated': {'annotations': 'This is the third annotations'}, 'user': {'id': 1} } ]}) resp = self.client.put('/api/v1/notes/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 204) note = json.loads(self.client.get('/api/v1/notes/1/', content_type='application/json').content.decode('utf-8')) self.assertTrue(note['annotated']) note = json.loads(self.client.get('/api/v1/notes/2/', content_type='application/json').content.decode('utf-8')) self.assertTrue(note['annotated']) def test_invalid_data(self): data = json.dumps({'objects': [ { 'id': 1, 'title': 'Test Title', 'slug': 'test-title', 'annotated': {'annotations': None}, 'user': {'id': 1} }, { 'id': 2, 'title': 'Test Title', 'annotated': {'annotations': None}, 'user': {'id': 1} } ]}) resp = self.client.put('/api/v1/notes/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 400) self.assertEqual(json.loads(resp.content.decode('utf-8')), { 'notes': { 'content': ['This field is required.'] }, 'annotated': { 'annotations': ['This field is required.'] } }) class TestJSONPValidation(SimpleTestCase): """ Explicitly run the doctests for tastypie.utils.validate_jsonp """ def test_jsonp(self): import tastypie.utils.validate_jsonp import doctest doctest.testmod(tastypie.utils.validate_jsonp)

38.131707

120

0.567609

import json from django.test.utils import override_settings from tastypie.exceptions import NotFound from basic.models import Note from testcases import TestCaseWithFixture from django.test.testcases import SimpleTestCase @override_settings(ROOT_URLCONF='validation.api.urls') class FilteringErrorsTestCase(TestCaseWithFixture): def test_valid_date(self): resp = self.client.get('/api/v1/notes/', data={ 'format': 'json', 'created__gte': '2010-03-31 00:00:00Z' }) self.assertEqual(resp.status_code, 200) deserialized = json.loads(resp.content.decode('utf-8')) self.assertEqual(len(deserialized['objects']), Note.objects.filter(created__gte='2010-03-31 00:00:00Z').count()) def test_invalid_date(self): resp = self.client.get('/api/v1/notes/', data={ 'format': 'json', 'created__gte': 'foo-baz-bar' }) self.assertEqual(resp.status_code, 400) @override_settings(ROOT_URLCONF='validation.api.urls') class PostRelatedUrlValidationTestCase(TestCaseWithFixture): def test_valid_url(self): data_with_pk = json.dumps({ 'title': 'Test title related pk', 'slug': 'test-slug-related-pk', 'content': 'This is the content', 'user': {'pk': 1}, }) data_with_url = json.dumps({ 'title': 'Test title related url', 'slug': 'test-slug-related-url', 'content': 'This is the content', 'user': '/api/v1/users/1/', }) resp_with_pk = self.client.post('/api/v1/notes/', data=data_with_pk, content_type='application/json') self.assertEqual(resp_with_pk.status_code, 201) note_posted_with_pk = json.loads(self.client.get(resp_with_pk['location']).content.decode('utf-8')) resp_with_url = self.client.post('/api/v1/notes/', data=data_with_url, content_type='application/json') self.assertEqual(resp_with_url.status_code, 201) note_posted_with_url = json.loads(self.client.get(resp_with_url['location']).content.decode('utf-8')) self.assertEqual(note_posted_with_pk['user'], note_posted_with_url['user']) def test_invalid_url(self): data = json.dumps({ 'title': 'Test title related url', 'slug': 'test-slug-related-url', 'content': 'This is the content', 'user': 'invalid-url', }) with self.assertRaises(NotFound): self.client.post('/api/v1/notes/', data=data, content_type='application/json') @override_settings(ROOT_URLCONF='validation.api.urls') class PostNestResouceValidationTestCase(TestCaseWithFixture): def test_valid_data(self): data = json.dumps({ 'title': 'Test Title', 'slug': 'test-title', 'content': 'This is the content', 'user': {'pk': 1}, 'annotated': {'annotations': 'This is an annotations'}, }) resp = self.client.post('/api/v1/notes/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 201) note = json.loads(self.client.get(resp['location']).content.decode('utf-8')) self.assertTrue(note['annotated']) def test_invalid_data(self): data = json.dumps({ 'title': '', 'slug': 'test-title', 'content': 'This is the content', 'user': {'pk': 1}, 'annotated': {'annotations': ''}, }) resp = self.client.post('/api/v1/notes/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 400) self.assertEqual(json.loads(resp.content.decode('utf-8')), { 'notes': { 'title': ['This field is required.'] }, 'annotated': { 'annotations': ['This field is required.'] } }) @override_settings(ROOT_URLCONF='validation.api.urls') class PutDetailNestResouceValidationTestCase(TestCaseWithFixture): def test_valid_data(self): data = json.dumps({ 'title': 'Test Title', 'slug': 'test-title', 'content': 'This is the content', 'annotated': {'annotations': 'This is another annotations'}, }) resp = self.client.put('/api/v1/notes/1/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 204) note = json.loads(self.client.get('/api/v1/notes/1/', content_type='application/json').content.decode('utf-8')) self.assertTrue(note['annotated']) self.assertEqual('test-title', note['slug']) def test_invalid_data(self): data = json.dumps({ 'title': '', 'slug': '', 'content': 'This is the content', 'annotated': {'annotations': None}, }) resp = self.client.put('/api/v1/notes/1/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 400) self.assertEqual(json.loads(resp.content.decode('utf-8')), { 'notes': { 'slug': ['This field is required.'], 'title': ['This field is required.'] }, 'annotated': { 'annotations': ['This field is required.'] } }) @override_settings(ROOT_URLCONF='validation.api.urls') class PutListNestResouceValidationTestCase(TestCaseWithFixture): def test_valid_data(self): data = json.dumps({'objects': [ { 'id': 1, 'title': 'Test Title', 'slug': 'test-title', 'content': 'This is the content', 'annotated': {'annotations': 'This is another annotations'}, 'user': {'id': 1} }, { 'id': 2, 'title': 'Test Title', 'slug': 'test-title', 'content': 'This is the content', 'annotated': {'annotations': 'This is the third annotations'}, 'user': {'id': 1} } ]}) resp = self.client.put('/api/v1/notes/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 204) note = json.loads(self.client.get('/api/v1/notes/1/', content_type='application/json').content.decode('utf-8')) self.assertTrue(note['annotated']) note = json.loads(self.client.get('/api/v1/notes/2/', content_type='application/json').content.decode('utf-8')) self.assertTrue(note['annotated']) def test_invalid_data(self): data = json.dumps({'objects': [ { 'id': 1, 'title': 'Test Title', 'slug': 'test-title', 'annotated': {'annotations': None}, 'user': {'id': 1} }, { 'id': 2, 'title': 'Test Title', 'annotated': {'annotations': None}, 'user': {'id': 1} } ]}) resp = self.client.put('/api/v1/notes/', data=data, content_type='application/json') self.assertEqual(resp.status_code, 400) self.assertEqual(json.loads(resp.content.decode('utf-8')), { 'notes': { 'content': ['This field is required.'] }, 'annotated': { 'annotations': ['This field is required.'] } }) class TestJSONPValidation(SimpleTestCase): def test_jsonp(self): import tastypie.utils.validate_jsonp import doctest doctest.testmod(tastypie.utils.validate_jsonp)

true

790bfdd125a54aeb226d078c352496de6419c71c

9,413

py

Python

managesf/tests/test_resources_storyboard.py

enovance/managesf

5f6bc6857ebbffb929a063ccc3ab94317fa3784a

[ "Apache-2.0" ]

null

managesf/tests/test_resources_storyboard.py

enovance/managesf

5f6bc6857ebbffb929a063ccc3ab94317fa3784a

[ "Apache-2.0" ]

null

managesf/tests/test_resources_storyboard.py

enovance/managesf

5f6bc6857ebbffb929a063ccc3ab94317fa3784a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Copyright (c) 2017 Red Hat, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from unittest import TestCase from mock import patch, call, Mock from contextlib import nested from managesf.tests import dummy_conf from managesf.model.yamlbkd.resources.storyboard import StoryboardOps class StoryboardOpsTest(TestCase): def test_is_activated(self): conf = dummy_conf() s = StoryboardOps(conf, None) project = {'issue-tracker': 'SFStoryboard'} self.assertTrue(s.is_activated(**project)) project = {'issue-tracker': ''} self.assertFalse(s.is_activated(**project)) conf.services.remove('SFStoryboard') project = {'issue-tracker': 'SFStoryboard'} self.assertFalse(s.is_activated(**project)) def test_extra_validation(self): conf = dummy_conf() s = StoryboardOps(conf, None) project = { 'name': 'project1', 'source-repositories': ['repo1', 'repo2'] } logs = s.extra_validations(**project) self.assertTrue(len(logs) == 0) project = { 'name': 'project2', 'source-repositories': ['repo', '-hjook'] } logs = s.extra_validations(**project) self.assertTrue('Minimal len is 5' in logs[0]) self.assertTrue('should match the RE' in logs[1]) def test_update_project(self): class FakeItem(object): def __init__(self, name, id): self.name = name self.id = id conf = dummy_conf() s = StoryboardOps(conf, None) patches = [ patch('storyboardclient.v1.projects.ProjectsManager.get_all'), patch('storyboardclient.v1.projects.ProjectsManager.update'), patch('storyboardclient.v1.projects.ProjectsManager.create')] with nested(*patches) as (get_all, update, create): get_all.return_value = [FakeItem('project1', 1)] s.update_project('project1', 'A desc') self.assertTrue(get_all.called) self.assertTrue(update.called) self.assertFalse(create.called) with nested(*patches) as (get_all, update, create): get_all.return_value = [FakeItem('project1', 1)] s.update_project('project2', 'A desc') self.assertTrue(get_all.called) self.assertFalse(update.called) self.assertTrue(create.called) def test_update_project_group(self): class FakeItem(object): def __init__(self, name, id): self.name = name self.id = id conf = dummy_conf() patches = [ patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.get_all'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.create'), patch.object(StoryboardOps, 'update_project'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.get'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.update'), patch('storyboardclient.v1.projects.' 'ProjectsManager.get_all')] with nested(*patches) as (get_all, create, update_project, get, update, p_get_all): new = { 'resources': { 'repos': { 'project1': {'description': 'A desc'}, 'project2': {'description': 'A desc'} } } } s = StoryboardOps(conf, new) get_all.return_value = [FakeItem('pg1', 1)] fake_subprojects = [ FakeItem('project1', 1), FakeItem('project2', 2)] mput = Mock() mdelete = Mock() class fprojects(): def get_all(self): return fake_subprojects def put(self, id): mput(id) def delete(self, id): mdelete(id) class NestedProjects(): def __init__(self): self.projects = fprojects() get.return_value = NestedProjects() update.return_value = NestedProjects() p_get_all.return_value = fake_subprojects # Here projects are already included in the project # group so nothing will be added/removed in the project # group. Just projects will be updated. s.update_project_groups( **{'name': 'pg1', 'source-repositories': ['project1', 'project2']}) self.assertFalse(mput.called) self.assertFalse(mdelete.called) self.assertTrue(len(update_project.mock_calls), 2) # Here project1 and project2 are already included but # the resources project decription only defines the # project2 to be included. So we make sure the delete # is called with id 1. mput.reset_mock() mdelete.reset_mock() update_project.reset_mock() s.update_project_groups( **{'name': 'pg1', 'source-repositories': ['project2']}) self.assertFalse(mput.called) self.assertTrue(mdelete.called) self.assertListEqual(mdelete.call_args_list, [call(1)]) self.assertTrue(len(update_project.mock_calls), 1) # Here only project1 is already included but # the resources project decription defines the # project1 and project2 to be included. So we make sure # the put is called with id 2. mput.reset_mock() mdelete.reset_mock() update_project.reset_mock() fake_subprojects = [ FakeItem('project1', 1)] s.update_project_groups( **{'name': 'pg1', 'source-repositories': ['project1', 'project2']}) self.assertTrue(mput.called) self.assertListEqual(mput.call_args_list, [call(2)]) self.assertFalse(mdelete.called) self.assertTrue(len(update_project.mock_calls), 1) # Here the project group does not exist. So we verify # it is created and provisionned with two projects # included. get_all.return_value = [] p_get_all.return_value = [ FakeItem('project1', 1), FakeItem('project2', 2)] fake_subprojects = [] get.return_value = NestedProjects() update.return_value = NestedProjects() mput.reset_mock() mdelete.reset_mock() update_project.reset_mock() s.update_project_groups( **{'name': 'pg1', 'source-repositories': ['project1', 'project2']}) self.assertTrue(create.called) self.assertTrue(len(update_project.mock_calls), 2) self.assertTrue(len(mput.mock_calls), 2) self.assertFalse(mdelete.called) def test_delete_project_group(self): class FakeItem(object): def __init__(self, name, id): self.name = name self.id = id conf = dummy_conf() patches = [ patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.get_all'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.get'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.update'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.delete')] with nested(*patches) as (get_all, get, update, delete): s = StoryboardOps(conf, None) get_all.return_value = [FakeItem('pg1', 3)] mdelete = Mock() fake_subprojects = [ FakeItem('project1', 1), FakeItem('project2', 2)] class fprojects(): def get_all(self): return fake_subprojects def delete(self, id): mdelete(id) class NestedProjects(): def __init__(self): self.projects = fprojects() get.return_value = NestedProjects() update.return_value = NestedProjects() s.delete_project_groups(**{'name': 'pg1'}) self.assertEqual(len(mdelete.call_args_list), 2) self.assertIn(call(1), mdelete.call_args_list) self.assertIn(call(2), mdelete.call_args_list) self.assertListEqual(delete.call_args_list, [call(id=3)])

39.220833

75

0.564432

from unittest import TestCase from mock import patch, call, Mock from contextlib import nested from managesf.tests import dummy_conf from managesf.model.yamlbkd.resources.storyboard import StoryboardOps class StoryboardOpsTest(TestCase): def test_is_activated(self): conf = dummy_conf() s = StoryboardOps(conf, None) project = {'issue-tracker': 'SFStoryboard'} self.assertTrue(s.is_activated(**project)) project = {'issue-tracker': ''} self.assertFalse(s.is_activated(**project)) conf.services.remove('SFStoryboard') project = {'issue-tracker': 'SFStoryboard'} self.assertFalse(s.is_activated(**project)) def test_extra_validation(self): conf = dummy_conf() s = StoryboardOps(conf, None) project = { 'name': 'project1', 'source-repositories': ['repo1', 'repo2'] } logs = s.extra_validations(**project) self.assertTrue(len(logs) == 0) project = { 'name': 'project2', 'source-repositories': ['repo', '-hjook'] } logs = s.extra_validations(**project) self.assertTrue('Minimal len is 5' in logs[0]) self.assertTrue('should match the RE' in logs[1]) def test_update_project(self): class FakeItem(object): def __init__(self, name, id): self.name = name self.id = id conf = dummy_conf() s = StoryboardOps(conf, None) patches = [ patch('storyboardclient.v1.projects.ProjectsManager.get_all'), patch('storyboardclient.v1.projects.ProjectsManager.update'), patch('storyboardclient.v1.projects.ProjectsManager.create')] with nested(*patches) as (get_all, update, create): get_all.return_value = [FakeItem('project1', 1)] s.update_project('project1', 'A desc') self.assertTrue(get_all.called) self.assertTrue(update.called) self.assertFalse(create.called) with nested(*patches) as (get_all, update, create): get_all.return_value = [FakeItem('project1', 1)] s.update_project('project2', 'A desc') self.assertTrue(get_all.called) self.assertFalse(update.called) self.assertTrue(create.called) def test_update_project_group(self): class FakeItem(object): def __init__(self, name, id): self.name = name self.id = id conf = dummy_conf() patches = [ patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.get_all'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.create'), patch.object(StoryboardOps, 'update_project'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.get'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.update'), patch('storyboardclient.v1.projects.' 'ProjectsManager.get_all')] with nested(*patches) as (get_all, create, update_project, get, update, p_get_all): new = { 'resources': { 'repos': { 'project1': {'description': 'A desc'}, 'project2': {'description': 'A desc'} } } } s = StoryboardOps(conf, new) get_all.return_value = [FakeItem('pg1', 1)] fake_subprojects = [ FakeItem('project1', 1), FakeItem('project2', 2)] mput = Mock() mdelete = Mock() class fprojects(): def get_all(self): return fake_subprojects def put(self, id): mput(id) def delete(self, id): mdelete(id) class NestedProjects(): def __init__(self): self.projects = fprojects() get.return_value = NestedProjects() update.return_value = NestedProjects() p_get_all.return_value = fake_subprojects s.update_project_groups( **{'name': 'pg1', 'source-repositories': ['project1', 'project2']}) self.assertFalse(mput.called) self.assertFalse(mdelete.called) self.assertTrue(len(update_project.mock_calls), 2) mput.reset_mock() mdelete.reset_mock() update_project.reset_mock() s.update_project_groups( **{'name': 'pg1', 'source-repositories': ['project2']}) self.assertFalse(mput.called) self.assertTrue(mdelete.called) self.assertListEqual(mdelete.call_args_list, [call(1)]) self.assertTrue(len(update_project.mock_calls), 1) mput.reset_mock() mdelete.reset_mock() update_project.reset_mock() fake_subprojects = [ FakeItem('project1', 1)] s.update_project_groups( **{'name': 'pg1', 'source-repositories': ['project1', 'project2']}) self.assertTrue(mput.called) self.assertListEqual(mput.call_args_list, [call(2)]) self.assertFalse(mdelete.called) self.assertTrue(len(update_project.mock_calls), 1) get_all.return_value = [] p_get_all.return_value = [ FakeItem('project1', 1), FakeItem('project2', 2)] fake_subprojects = [] get.return_value = NestedProjects() update.return_value = NestedProjects() mput.reset_mock() mdelete.reset_mock() update_project.reset_mock() s.update_project_groups( **{'name': 'pg1', 'source-repositories': ['project1', 'project2']}) self.assertTrue(create.called) self.assertTrue(len(update_project.mock_calls), 2) self.assertTrue(len(mput.mock_calls), 2) self.assertFalse(mdelete.called) def test_delete_project_group(self): class FakeItem(object): def __init__(self, name, id): self.name = name self.id = id conf = dummy_conf() patches = [ patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.get_all'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.get'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.update'), patch('storyboardclient.v1.project_groups.' 'ProjectGroupsManager.delete')] with nested(*patches) as (get_all, get, update, delete): s = StoryboardOps(conf, None) get_all.return_value = [FakeItem('pg1', 3)] mdelete = Mock() fake_subprojects = [ FakeItem('project1', 1), FakeItem('project2', 2)] class fprojects(): def get_all(self): return fake_subprojects def delete(self, id): mdelete(id) class NestedProjects(): def __init__(self): self.projects = fprojects() get.return_value = NestedProjects() update.return_value = NestedProjects() s.delete_project_groups(**{'name': 'pg1'}) self.assertEqual(len(mdelete.call_args_list), 2) self.assertIn(call(1), mdelete.call_args_list) self.assertIn(call(2), mdelete.call_args_list) self.assertListEqual(delete.call_args_list, [call(id=3)])

true

790bfde1ab6b6b3d18bbab24e581e7b4bc432cbb

2,652

py

Python

plugins/QuoteGrabs/__init__.py

jlu5/Limnoria

0e1e37a5a2bd5b717e11320b20773644b44502dd

[ "BSD-3-Clause" ]

1

2021-11-11T04:48:33.000Z

plugins/QuoteGrabs/__init__.py

jlu5/Limnoria

0e1e37a5a2bd5b717e11320b20773644b44502dd

[ "BSD-3-Clause" ]

4

2017-10-23T15:16:40.000Z

2018-05-27T10:19:52.000Z

plugins/QuoteGrabs/__init__.py

jlu5/Limnoria

0e1e37a5a2bd5b717e11320b20773644b44502dd

[ "BSD-3-Clause" ]

1

2021-11-11T04:48:23.000Z

### # Copyright (c) 2004, Daniel DiPaolo # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, # this list of conditions, and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions, and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the author of this software nor the name of # contributors to this software may be used to endorse or promote products # derived from this software without specific prior written consent. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. ### """ Quotegrabs are like IRC sound bites. When someone says something funny, incriminating, stupid, outrageous, ... anything that might be worth remembering, you can grab that quote for that person. With this plugin, you can store many quotes per person and display their most recent quote, as well as see who "grabbed" the quote in the first place. """ import supybot import supybot.world as world # Use this for the version of this plugin. You may wish to put a CVS keyword # in here if you're keeping the plugin in CVS or some similar system. __version__ = "%%VERSION%%" # XXX Replace this with an appropriate author or supybot.Author instance. __author__ = supybot.authors.strike # This is a dictionary mapping supybot.Author instances to lists of # contributions. __contributors__ = {} from . import config from . import plugin from imp import reload reload(plugin) # In case we're being reloaded. if world.testing: from . import test Class = plugin.Class configure = config.configure # vim:set shiftwidth=4 softtabstop=4 expandtab textwidth=79:

40.8

79

0.768854

import supybot import supybot.world as world __version__ = "%%VERSION%%" # XXX Replace this with an appropriate author or supybot.Author instance. __author__ = supybot.authors.strike # This is a dictionary mapping supybot.Author instances to lists of # contributions. __contributors__ = {} from . import config from . import plugin from imp import reload reload(plugin) # In case we're being reloaded. if world.testing: from . import test Class = plugin.Class configure = config.configure

true

790bfe23c955a12e0756d09e044c0180b61ef9ba

20,785

py

Python

ckan/lib/dictization/model_save.py

NP-compete/ckan

3bbbe028e43a25ea2179c35e50ed4b67c404b135

[ "Apache-2.0" ]

1

2019-11-03T11:35:38.000Z

ckan/lib/dictization/model_save.py

NP-compete/ckan

3bbbe028e43a25ea2179c35e50ed4b67c404b135

[ "Apache-2.0" ]

null

ckan/lib/dictization/model_save.py

NP-compete/ckan

3bbbe028e43a25ea2179c35e50ed4b67c404b135

[ "Apache-2.0" ]

null

# encoding: utf-8 import datetime import uuid import logging from sqlalchemy.orm import class_mapper from six import string_types import ckan.lib.dictization as d import ckan.lib.helpers as h import ckan.authz as authz log = logging.getLogger(__name__) def resource_dict_save(res_dict, context): model = context["model"] session = context["session"] id = res_dict.get("id") obj = None if id: obj = session.query(model.Resource).get(id) if not obj: new = True obj = model.Resource() else: new = False table = class_mapper(model.Resource).mapped_table fields = [field.name for field in table.c] # Resource extras not submitted will be removed from the existing extras # dict new_extras = {} for key, value in res_dict.iteritems(): if isinstance(value, list): continue if key in ('extras', 'revision_timestamp', 'tracking_summary'): continue if key in fields: if isinstance(getattr(obj, key), datetime.datetime): if getattr(obj, key).isoformat() == value: continue if key == 'last_modified' and not new: obj.url_changed = True if key == 'url' and not new and obj.url != value: obj.url_changed = True setattr(obj, key, value) else: # resources save extras directly onto the object, instead # of in a separate extras field like packages and groups new_extras[key] = value obj.state = u'active' obj.extras = new_extras session.add(obj) return obj def package_resource_list_save(res_dicts, package, context): allow_partial_update = context.get("allow_partial_update", False) if res_dicts is None and allow_partial_update: return resource_list = package.resources_all old_list = package.resources_all[:] obj_list = [] for res_dict in res_dicts or []: if not u'package_id' in res_dict or not res_dict[u'package_id']: res_dict[u'package_id'] = package.id obj = resource_dict_save(res_dict, context) obj_list.append(obj) # Set the package's resources. resource_list is an ORM relation - the # package's resources. If we didn't have the slice operator "[:]" then it # would reassign the variable "resource_list" to be the obj_list. But with # the slice operator it changes the contents of the relation, setting the # package's resources. # At the table level, for each resource in the obj_list, its # resource.package_id is changed to this package (which is needed for new # resources), and every resource.position is set to ascending integers, # according to their ordering in the obj_list. resource_list[:] = obj_list # Mark any left-over resources as deleted for resource in set(old_list) - set(obj_list): resource.state = 'deleted' resource_list.append(resource) def package_extras_save(extra_dicts, obj, context): allow_partial_update = context.get("allow_partial_update", False) if extra_dicts is None and allow_partial_update: return model = context["model"] session = context["session"] extras_list = obj.extras_list old_extras = dict((extra.key, extra) for extra in extras_list) new_extras = {} for extra_dict in extra_dicts or []: if extra_dict.get("deleted"): continue if extra_dict['value'] is None: pass else: new_extras[extra_dict["key"]] = extra_dict["value"] #new for key in set(new_extras.keys()) - set(old_extras.keys()): state = 'active' extra = model.PackageExtra(state=state, key=key, value=new_extras[key]) session.add(extra) extras_list.append(extra) #changed for key in set(new_extras.keys()) & set(old_extras.keys()): extra = old_extras[key] if new_extras[key] == extra.value and extra.state != 'deleted': continue state = 'active' extra.value = new_extras[key] extra.state = state session.add(extra) #deleted for key in set(old_extras.keys()) - set(new_extras.keys()): extra = old_extras[key] if extra.state == 'deleted': continue state = 'deleted' extra.state = state def group_extras_save(extras_dicts, context): model = context["model"] session = context["session"] result_dict = {} for extra_dict in extras_dicts: if extra_dict.get("deleted"): continue result_dict[extra_dict["key"]] = extra_dict["value"] return result_dict def package_tag_list_save(tag_dicts, package, context): allow_partial_update = context.get("allow_partial_update", False) if tag_dicts is None and allow_partial_update: return model = context["model"] session = context["session"] tag_package_tag = dict((package_tag.tag, package_tag) for package_tag in package.package_tag_all) tag_package_tag_inactive = {tag: pt for tag,pt in tag_package_tag.items() if pt.state in ['deleted']} tag_name_vocab = set() tags = set() for tag_dict in tag_dicts or []: if (tag_dict.get('name'), tag_dict.get('vocabulary_id')) not in tag_name_vocab: tag_obj = d.table_dict_save(tag_dict, model.Tag, context) tags.add(tag_obj) tag_name_vocab.add((tag_obj.name, tag_obj.vocabulary_id)) # 3 cases # case 1: currently active but not in new list for tag in set(tag_package_tag.keys()) - tags: package_tag = tag_package_tag[tag] package_tag.state = 'deleted' # case 2: in new list but never used before for tag in tags - set(tag_package_tag.keys()): state = 'active' package_tag_obj = model.PackageTag(package, tag, state) session.add(package_tag_obj) tag_package_tag[tag] = package_tag_obj # case 3: in new list and already used but in deleted state for tag in tags.intersection(set(tag_package_tag_inactive.keys())): state = 'active' package_tag = tag_package_tag[tag] package_tag.state = state package.package_tag_all[:] = tag_package_tag.values() def package_membership_list_save(group_dicts, package, context): allow_partial_update = context.get("allow_partial_update", False) if group_dicts is None and allow_partial_update: return capacity = 'public' model = context["model"] session = context["session"] user = context.get('user') members = session.query(model.Member) \ .filter(model.Member.table_id == package.id) \ .filter(model.Member.capacity != 'organization') group_member = dict((member.group, member) for member in members) groups = set() for group_dict in group_dicts or []: id = group_dict.get("id") name = group_dict.get("name") capacity = group_dict.get("capacity", "public") if capacity == 'organization': continue if id: group = session.query(model.Group).get(id) else: group = session.query(model.Group).filter_by(name=name).first() if group: groups.add(group) ## need to flush so we can get out the package id model.Session.flush() # Remove any groups we are no longer in for group in set(group_member.keys()) - groups: member_obj = group_member[group] if member_obj and member_obj.state == 'deleted': continue if authz.has_user_permission_for_group_or_org( member_obj.group_id, user, 'read'): member_obj.capacity = capacity member_obj.state = 'deleted' session.add(member_obj) # Add any new groups for group in groups: member_obj = group_member.get(group) if member_obj and member_obj.state == 'active': continue if authz.has_user_permission_for_group_or_org( group.id, user, 'read'): member_obj = group_member.get(group) if member_obj: member_obj.capacity = capacity member_obj.state = 'active' else: member_obj = model.Member(table_id=package.id, table_name='package', group=group, capacity=capacity, group_id=group.id, state = 'active') session.add(member_obj) def relationship_list_save(relationship_dicts, package, attr, context): allow_partial_update = context.get("allow_partial_update", False) if relationship_dicts is None and allow_partial_update: return model = context["model"] session = context["session"] relationship_list = getattr(package, attr) old_list = relationship_list[:] relationships = [] for relationship_dict in relationship_dicts or []: obj = d.table_dict_save(relationship_dict, model.PackageRelationship, context) relationships.append(obj) relationship_list[:] = relationships for relationship in set(old_list) - set(relationship_list): relationship.state = 'deleted' relationship_list.append(relationship) def package_dict_save(pkg_dict, context): model = context["model"] package = context.get("package") allow_partial_update = context.get("allow_partial_update", False) if package: pkg_dict["id"] = package.id Package = model.Package if 'metadata_created' in pkg_dict: del pkg_dict['metadata_created'] if 'metadata_modified' in pkg_dict: del pkg_dict['metadata_modified'] pkg = d.table_dict_save(pkg_dict, Package, context) if not pkg.id: pkg.id = str(uuid.uuid4()) package_resource_list_save(pkg_dict.get("resources"), pkg, context) package_tag_list_save(pkg_dict.get("tags"), pkg, context) package_membership_list_save(pkg_dict.get("groups"), pkg, context) # relationships are not considered 'part' of the package, so only # process this if the key is provided if 'relationships_as_subject' in pkg_dict: subjects = pkg_dict.get('relationships_as_subject') relationship_list_save(subjects, pkg, 'relationships_as_subject', context) if 'relationships_as_object' in pkg_dict: objects = pkg_dict.get('relationships_as_object') relationship_list_save(objects, pkg, 'relationships_as_object', context) extras = package_extras_save(pkg_dict.get("extras"), pkg, context) return pkg def group_member_save(context, group_dict, member_table_name): model = context["model"] session = context["session"] group = context['group'] entity_list = group_dict.get(member_table_name, None) if entity_list is None: if context.get('allow_partial_update', False): return {'added': [], 'removed': []} else: entity_list = [] entities = {} Member = model.Member classname = member_table_name[:-1].capitalize() if classname == 'Organization': # Organizations use the model.Group class classname = 'Group' ModelClass = getattr(model, classname) for entity_dict in entity_list: name_or_id = entity_dict.get('id') or entity_dict.get('name') obj = ModelClass.get(name_or_id) if obj and obj not in entities.values(): entities[(obj.id, entity_dict.get('capacity', 'public'))] = obj members = session.query(Member).filter_by( table_name=member_table_name[:-1], group_id=group.id, ).all() processed = { 'added': [], 'removed': [] } entity_member = dict(((member.table_id, member.capacity), member) for member in members) for entity_id in set(entity_member.keys()) - set(entities.keys()): if entity_member[entity_id].state != 'deleted': processed['removed'].append(entity_id[0]) entity_member[entity_id].state = 'deleted' session.add(entity_member[entity_id]) for entity_id in set(entity_member.keys()) & set(entities.keys()): if entity_member[entity_id].state != 'active': processed['added'].append(entity_id[0]) entity_member[entity_id].state = 'active' session.add(entity_member[entity_id]) for entity_id in set(entities.keys()) - set(entity_member.keys()): member = Member(group=group, group_id=group.id, table_id=entity_id[0], table_name=member_table_name[:-1], capacity=entity_id[1]) processed['added'].append(entity_id[0]) session.add(member) return processed def group_dict_save(group_dict, context, prevent_packages_update=False): from ckan.lib.search import rebuild model = context["model"] session = context["session"] group = context.get("group") allow_partial_update = context.get("allow_partial_update", False) Group = model.Group if group: group_dict["id"] = group.id group = d.table_dict_save(group_dict, Group, context) if not group.id: group.id = str(uuid.uuid4()) context['group'] = group # Under the new org rules we do not want to be able to update datasets # via group edit so we need a way to prevent this. It may be more # sensible in future to send a list of allowed/disallowed updates for # groups, users, tabs etc. if not prevent_packages_update: pkgs_edited = group_member_save(context, group_dict, 'packages') else: pkgs_edited = { 'added': [], 'removed': [] } group_users_changed = group_member_save(context, group_dict, 'users') group_groups_changed = group_member_save(context, group_dict, 'groups') group_tags_changed = group_member_save(context, group_dict, 'tags') log.debug('Group save membership changes - Packages: %r Users: %r ' 'Groups: %r Tags: %r', pkgs_edited, group_users_changed, group_groups_changed, group_tags_changed) extras = group_extras_save(group_dict.get("extras", {}), context) if extras or not allow_partial_update: old_extras = set(group.extras.keys()) new_extras = set(extras.keys()) for key in old_extras - new_extras: del group.extras[key] for key in new_extras: group.extras[key] = extras[key] # We will get a list of packages that we have either added or # removed from the group, and trigger a re-index. package_ids = pkgs_edited['removed'] package_ids.extend( pkgs_edited['added'] ) if package_ids: session.commit() map( rebuild, package_ids ) return group def user_dict_save(user_dict, context): model = context['model'] session = context['session'] user = context.get('user_obj') User = model.User if user: user_dict['id'] = user.id if 'password' in user_dict and not len(user_dict['password']): del user_dict['password'] user = d.table_dict_save(user_dict, User, context) return user def package_api_to_dict(api1_dict, context): package = context.get("package") api_version = context.get('api_version') assert api_version, 'No api_version supplied in context' dictized = {} for key, value in api1_dict.iteritems(): new_value = value if key == 'tags': if isinstance(value, string_types): new_value = [{"name": item} for item in value.split()] else: new_value = [{"name": item} for item in value] if key == 'extras': updated_extras = {} if package: updated_extras.update(package.extras) updated_extras.update(value) new_value = [] for extras_key, extras_value in updated_extras.iteritems(): new_value.append({"key": extras_key, "value": extras_value}) if key == 'groups' and len(value): if api_version == 1: new_value = [{'name': item} for item in value] else: new_value = [{'id': item} for item in value] dictized[key] = new_value download_url = dictized.pop('download_url', None) if download_url and not dictized.get('resources'): dictized["resources"] = [{'url': download_url}] download_url = dictized.pop('download_url', None) return dictized def group_api_to_dict(api1_dict, context): dictized = {} for key, value in api1_dict.iteritems(): new_value = value if key == 'packages': new_value = [{"id": item} for item in value] if key == 'extras': new_value = [{"key": extra_key, "value": value[extra_key]} for extra_key in value] dictized[key] = new_value return dictized def task_status_dict_save(task_status_dict, context): model = context["model"] task_status = context.get("task_status") allow_partial_update = context.get("allow_partial_update", False) if task_status: task_status_dict["id"] = task_status.id task_status = d.table_dict_save(task_status_dict, model.TaskStatus, context) return task_status def activity_dict_save(activity_dict, context): model = context['model'] session = context['session'] user_id = activity_dict['user_id'] object_id = activity_dict['object_id'] revision_id = activity_dict['revision_id'] activity_type = activity_dict['activity_type'] if activity_dict.has_key('data'): data = activity_dict['data'] else: data = None activity_obj = model.Activity(user_id, object_id, revision_id, activity_type, data) session.add(activity_obj) # TODO: Handle activity details. return activity_obj def vocabulary_tag_list_save(new_tag_dicts, vocabulary_obj, context): model = context['model'] session = context['session'] # First delete any tags not in new_tag_dicts. for tag in vocabulary_obj.tags: if tag.name not in [t['name'] for t in new_tag_dicts]: tag.delete() # Now add any new tags. for tag_dict in new_tag_dicts: current_tag_names = [tag.name for tag in vocabulary_obj.tags] if tag_dict['name'] not in current_tag_names: # Make sure the tag belongs to this vocab.. tag_dict['vocabulary_id'] = vocabulary_obj.id # then add it. tag_dict_save(tag_dict, {'model': model, 'session': session}) def vocabulary_dict_save(vocabulary_dict, context): model = context['model'] session = context['session'] vocabulary_name = vocabulary_dict['name'] vocabulary_obj = model.Vocabulary(vocabulary_name) session.add(vocabulary_obj) if vocabulary_dict.has_key('tags'): vocabulary_tag_list_save(vocabulary_dict['tags'], vocabulary_obj, context) return vocabulary_obj def vocabulary_dict_update(vocabulary_dict, context): model = context['model'] session = context['session'] vocabulary_obj = model.vocabulary.Vocabulary.get(vocabulary_dict['id']) if vocabulary_dict.has_key('name'): vocabulary_obj.name = vocabulary_dict['name'] if vocabulary_dict.has_key('tags'): vocabulary_tag_list_save(vocabulary_dict['tags'], vocabulary_obj, context) return vocabulary_obj def tag_dict_save(tag_dict, context): model = context['model'] tag = context.get('tag') if tag: tag_dict['id'] = tag.id tag = d.table_dict_save(tag_dict, model.Tag, context) return tag def follower_dict_save(data_dict, context, FollowerClass): model = context['model'] session = context['session'] follower_obj = FollowerClass( follower_id=model.User.get(context['user']).id, object_id=data_dict['id']) session.add(follower_obj) return follower_obj def resource_view_dict_save(data_dict, context): model = context['model'] resource_view = context.get('resource_view') if resource_view: data_dict['id'] = resource_view.id config = {} for key, value in data_dict.iteritems(): if key not in model.ResourceView.get_columns(): config[key] = value data_dict['config'] = config return d.table_dict_save(data_dict, model.ResourceView, context)

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import datetime import uuid import logging from sqlalchemy.orm import class_mapper from six import string_types import ckan.lib.dictization as d import ckan.lib.helpers as h import ckan.authz as authz log = logging.getLogger(__name__) def resource_dict_save(res_dict, context): model = context["model"] session = context["session"] id = res_dict.get("id") obj = None if id: obj = session.query(model.Resource).get(id) if not obj: new = True obj = model.Resource() else: new = False table = class_mapper(model.Resource).mapped_table fields = [field.name for field in table.c] new_extras = {} for key, value in res_dict.iteritems(): if isinstance(value, list): continue if key in ('extras', 'revision_timestamp', 'tracking_summary'): continue if key in fields: if isinstance(getattr(obj, key), datetime.datetime): if getattr(obj, key).isoformat() == value: continue if key == 'last_modified' and not new: obj.url_changed = True if key == 'url' and not new and obj.url != value: obj.url_changed = True setattr(obj, key, value) else: new_extras[key] = value obj.state = u'active' obj.extras = new_extras session.add(obj) return obj def package_resource_list_save(res_dicts, package, context): allow_partial_update = context.get("allow_partial_update", False) if res_dicts is None and allow_partial_update: return resource_list = package.resources_all old_list = package.resources_all[:] obj_list = [] for res_dict in res_dicts or []: if not u'package_id' in res_dict or not res_dict[u'package_id']: res_dict[u'package_id'] = package.id obj = resource_dict_save(res_dict, context) obj_list.append(obj) # package's resources. If we didn't have the slice operator "[:]" then it # would reassign the variable "resource_list" to be the obj_list. But with # the slice operator it changes the contents of the relation, setting the # package's resources. resource_list[:] = obj_list for resource in set(old_list) - set(obj_list): resource.state = 'deleted' resource_list.append(resource) def package_extras_save(extra_dicts, obj, context): allow_partial_update = context.get("allow_partial_update", False) if extra_dicts is None and allow_partial_update: return model = context["model"] session = context["session"] extras_list = obj.extras_list old_extras = dict((extra.key, extra) for extra in extras_list) new_extras = {} for extra_dict in extra_dicts or []: if extra_dict.get("deleted"): continue if extra_dict['value'] is None: pass else: new_extras[extra_dict["key"]] = extra_dict["value"] for key in set(new_extras.keys()) - set(old_extras.keys()): state = 'active' extra = model.PackageExtra(state=state, key=key, value=new_extras[key]) session.add(extra) extras_list.append(extra) for key in set(new_extras.keys()) & set(old_extras.keys()): extra = old_extras[key] if new_extras[key] == extra.value and extra.state != 'deleted': continue state = 'active' extra.value = new_extras[key] extra.state = state session.add(extra) for key in set(old_extras.keys()) - set(new_extras.keys()): extra = old_extras[key] if extra.state == 'deleted': continue state = 'deleted' extra.state = state def group_extras_save(extras_dicts, context): model = context["model"] session = context["session"] result_dict = {} for extra_dict in extras_dicts: if extra_dict.get("deleted"): continue result_dict[extra_dict["key"]] = extra_dict["value"] return result_dict def package_tag_list_save(tag_dicts, package, context): allow_partial_update = context.get("allow_partial_update", False) if tag_dicts is None and allow_partial_update: return model = context["model"] session = context["session"] tag_package_tag = dict((package_tag.tag, package_tag) for package_tag in package.package_tag_all) tag_package_tag_inactive = {tag: pt for tag,pt in tag_package_tag.items() if pt.state in ['deleted']} tag_name_vocab = set() tags = set() for tag_dict in tag_dicts or []: if (tag_dict.get('name'), tag_dict.get('vocabulary_id')) not in tag_name_vocab: tag_obj = d.table_dict_save(tag_dict, model.Tag, context) tags.add(tag_obj) tag_name_vocab.add((tag_obj.name, tag_obj.vocabulary_id)) for tag in set(tag_package_tag.keys()) - tags: package_tag = tag_package_tag[tag] package_tag.state = 'deleted' for tag in tags - set(tag_package_tag.keys()): state = 'active' package_tag_obj = model.PackageTag(package, tag, state) session.add(package_tag_obj) tag_package_tag[tag] = package_tag_obj for tag in tags.intersection(set(tag_package_tag_inactive.keys())): state = 'active' package_tag = tag_package_tag[tag] package_tag.state = state package.package_tag_all[:] = tag_package_tag.values() def package_membership_list_save(group_dicts, package, context): allow_partial_update = context.get("allow_partial_update", False) if group_dicts is None and allow_partial_update: return capacity = 'public' model = context["model"] session = context["session"] user = context.get('user') members = session.query(model.Member) \ .filter(model.Member.table_id == package.id) \ .filter(model.Member.capacity != 'organization') group_member = dict((member.group, member) for member in members) groups = set() for group_dict in group_dicts or []: id = group_dict.get("id") name = group_dict.get("name") capacity = group_dict.get("capacity", "public") if capacity == 'organization': continue if id: group = session.query(model.Group).get(id) else: group = session.query(model.Group).filter_by(name=name).first() if group: groups.add(group) n set(group_member.keys()) - groups: member_obj = group_member[group] if member_obj and member_obj.state == 'deleted': continue if authz.has_user_permission_for_group_or_org( member_obj.group_id, user, 'read'): member_obj.capacity = capacity member_obj.state = 'deleted' session.add(member_obj) for group in groups: member_obj = group_member.get(group) if member_obj and member_obj.state == 'active': continue if authz.has_user_permission_for_group_or_org( group.id, user, 'read'): member_obj = group_member.get(group) if member_obj: member_obj.capacity = capacity member_obj.state = 'active' else: member_obj = model.Member(table_id=package.id, table_name='package', group=group, capacity=capacity, group_id=group.id, state = 'active') session.add(member_obj) def relationship_list_save(relationship_dicts, package, attr, context): allow_partial_update = context.get("allow_partial_update", False) if relationship_dicts is None and allow_partial_update: return model = context["model"] session = context["session"] relationship_list = getattr(package, attr) old_list = relationship_list[:] relationships = [] for relationship_dict in relationship_dicts or []: obj = d.table_dict_save(relationship_dict, model.PackageRelationship, context) relationships.append(obj) relationship_list[:] = relationships for relationship in set(old_list) - set(relationship_list): relationship.state = 'deleted' relationship_list.append(relationship) def package_dict_save(pkg_dict, context): model = context["model"] package = context.get("package") allow_partial_update = context.get("allow_partial_update", False) if package: pkg_dict["id"] = package.id Package = model.Package if 'metadata_created' in pkg_dict: del pkg_dict['metadata_created'] if 'metadata_modified' in pkg_dict: del pkg_dict['metadata_modified'] pkg = d.table_dict_save(pkg_dict, Package, context) if not pkg.id: pkg.id = str(uuid.uuid4()) package_resource_list_save(pkg_dict.get("resources"), pkg, context) package_tag_list_save(pkg_dict.get("tags"), pkg, context) package_membership_list_save(pkg_dict.get("groups"), pkg, context) if 'relationships_as_subject' in pkg_dict: subjects = pkg_dict.get('relationships_as_subject') relationship_list_save(subjects, pkg, 'relationships_as_subject', context) if 'relationships_as_object' in pkg_dict: objects = pkg_dict.get('relationships_as_object') relationship_list_save(objects, pkg, 'relationships_as_object', context) extras = package_extras_save(pkg_dict.get("extras"), pkg, context) return pkg def group_member_save(context, group_dict, member_table_name): model = context["model"] session = context["session"] group = context['group'] entity_list = group_dict.get(member_table_name, None) if entity_list is None: if context.get('allow_partial_update', False): return {'added': [], 'removed': []} else: entity_list = [] entities = {} Member = model.Member classname = member_table_name[:-1].capitalize() if classname == 'Organization': classname = 'Group' ModelClass = getattr(model, classname) for entity_dict in entity_list: name_or_id = entity_dict.get('id') or entity_dict.get('name') obj = ModelClass.get(name_or_id) if obj and obj not in entities.values(): entities[(obj.id, entity_dict.get('capacity', 'public'))] = obj members = session.query(Member).filter_by( table_name=member_table_name[:-1], group_id=group.id, ).all() processed = { 'added': [], 'removed': [] } entity_member = dict(((member.table_id, member.capacity), member) for member in members) for entity_id in set(entity_member.keys()) - set(entities.keys()): if entity_member[entity_id].state != 'deleted': processed['removed'].append(entity_id[0]) entity_member[entity_id].state = 'deleted' session.add(entity_member[entity_id]) for entity_id in set(entity_member.keys()) & set(entities.keys()): if entity_member[entity_id].state != 'active': processed['added'].append(entity_id[0]) entity_member[entity_id].state = 'active' session.add(entity_member[entity_id]) for entity_id in set(entities.keys()) - set(entity_member.keys()): member = Member(group=group, group_id=group.id, table_id=entity_id[0], table_name=member_table_name[:-1], capacity=entity_id[1]) processed['added'].append(entity_id[0]) session.add(member) return processed def group_dict_save(group_dict, context, prevent_packages_update=False): from ckan.lib.search import rebuild model = context["model"] session = context["session"] group = context.get("group") allow_partial_update = context.get("allow_partial_update", False) Group = model.Group if group: group_dict["id"] = group.id group = d.table_dict_save(group_dict, Group, context) if not group.id: group.id = str(uuid.uuid4()) context['group'] = group if not prevent_packages_update: pkgs_edited = group_member_save(context, group_dict, 'packages') else: pkgs_edited = { 'added': [], 'removed': [] } group_users_changed = group_member_save(context, group_dict, 'users') group_groups_changed = group_member_save(context, group_dict, 'groups') group_tags_changed = group_member_save(context, group_dict, 'tags') log.debug('Group save membership changes - Packages: %r Users: %r ' 'Groups: %r Tags: %r', pkgs_edited, group_users_changed, group_groups_changed, group_tags_changed) extras = group_extras_save(group_dict.get("extras", {}), context) if extras or not allow_partial_update: old_extras = set(group.extras.keys()) new_extras = set(extras.keys()) for key in old_extras - new_extras: del group.extras[key] for key in new_extras: group.extras[key] = extras[key] package_ids = pkgs_edited['removed'] package_ids.extend( pkgs_edited['added'] ) if package_ids: session.commit() map( rebuild, package_ids ) return group def user_dict_save(user_dict, context): model = context['model'] session = context['session'] user = context.get('user_obj') User = model.User if user: user_dict['id'] = user.id if 'password' in user_dict and not len(user_dict['password']): del user_dict['password'] user = d.table_dict_save(user_dict, User, context) return user def package_api_to_dict(api1_dict, context): package = context.get("package") api_version = context.get('api_version') assert api_version, 'No api_version supplied in context' dictized = {} for key, value in api1_dict.iteritems(): new_value = value if key == 'tags': if isinstance(value, string_types): new_value = [{"name": item} for item in value.split()] else: new_value = [{"name": item} for item in value] if key == 'extras': updated_extras = {} if package: updated_extras.update(package.extras) updated_extras.update(value) new_value = [] for extras_key, extras_value in updated_extras.iteritems(): new_value.append({"key": extras_key, "value": extras_value}) if key == 'groups' and len(value): if api_version == 1: new_value = [{'name': item} for item in value] else: new_value = [{'id': item} for item in value] dictized[key] = new_value download_url = dictized.pop('download_url', None) if download_url and not dictized.get('resources'): dictized["resources"] = [{'url': download_url}] download_url = dictized.pop('download_url', None) return dictized def group_api_to_dict(api1_dict, context): dictized = {} for key, value in api1_dict.iteritems(): new_value = value if key == 'packages': new_value = [{"id": item} for item in value] if key == 'extras': new_value = [{"key": extra_key, "value": value[extra_key]} for extra_key in value] dictized[key] = new_value return dictized def task_status_dict_save(task_status_dict, context): model = context["model"] task_status = context.get("task_status") allow_partial_update = context.get("allow_partial_update", False) if task_status: task_status_dict["id"] = task_status.id task_status = d.table_dict_save(task_status_dict, model.TaskStatus, context) return task_status def activity_dict_save(activity_dict, context): model = context['model'] session = context['session'] user_id = activity_dict['user_id'] object_id = activity_dict['object_id'] revision_id = activity_dict['revision_id'] activity_type = activity_dict['activity_type'] if activity_dict.has_key('data'): data = activity_dict['data'] else: data = None activity_obj = model.Activity(user_id, object_id, revision_id, activity_type, data) session.add(activity_obj) return activity_obj def vocabulary_tag_list_save(new_tag_dicts, vocabulary_obj, context): model = context['model'] session = context['session'] for tag in vocabulary_obj.tags: if tag.name not in [t['name'] for t in new_tag_dicts]: tag.delete() for tag_dict in new_tag_dicts: current_tag_names = [tag.name for tag in vocabulary_obj.tags] if tag_dict['name'] not in current_tag_names: tag_dict['vocabulary_id'] = vocabulary_obj.id tag_dict_save(tag_dict, {'model': model, 'session': session}) def vocabulary_dict_save(vocabulary_dict, context): model = context['model'] session = context['session'] vocabulary_name = vocabulary_dict['name'] vocabulary_obj = model.Vocabulary(vocabulary_name) session.add(vocabulary_obj) if vocabulary_dict.has_key('tags'): vocabulary_tag_list_save(vocabulary_dict['tags'], vocabulary_obj, context) return vocabulary_obj def vocabulary_dict_update(vocabulary_dict, context): model = context['model'] session = context['session'] vocabulary_obj = model.vocabulary.Vocabulary.get(vocabulary_dict['id']) if vocabulary_dict.has_key('name'): vocabulary_obj.name = vocabulary_dict['name'] if vocabulary_dict.has_key('tags'): vocabulary_tag_list_save(vocabulary_dict['tags'], vocabulary_obj, context) return vocabulary_obj def tag_dict_save(tag_dict, context): model = context['model'] tag = context.get('tag') if tag: tag_dict['id'] = tag.id tag = d.table_dict_save(tag_dict, model.Tag, context) return tag def follower_dict_save(data_dict, context, FollowerClass): model = context['model'] session = context['session'] follower_obj = FollowerClass( follower_id=model.User.get(context['user']).id, object_id=data_dict['id']) session.add(follower_obj) return follower_obj def resource_view_dict_save(data_dict, context): model = context['model'] resource_view = context.get('resource_view') if resource_view: data_dict['id'] = resource_view.id config = {} for key, value in data_dict.iteritems(): if key not in model.ResourceView.get_columns(): config[key] = value data_dict['config'] = config return d.table_dict_save(data_dict, model.ResourceView, context)

true

790bfe7b6701239a7a166a21c31872ee524d7bd3

910

py

Python

api/admin.py

emeth-/the-foot-globalhack5

c2d999a75e53aaf7a20c0b34d7057bf2ea64f69e

[ "MIT" ]

null

api/admin.py

emeth-/the-foot-globalhack5

c2d999a75e53aaf7a20c0b34d7057bf2ea64f69e

[ "MIT" ]

null

api/admin.py

emeth-/the-foot-globalhack5

c2d999a75e53aaf7a20c0b34d7057bf2ea64f69e

[ "MIT" ]

null

from django.contrib import admin from api.models import Citation class CitationAdmin(admin.ModelAdmin): list_display = ('id', 'citation_number', 'citation_date', 'first_name', 'last_name', 'date_of_birth', 'defendant_address', 'defendant_city', 'defendant_state', 'drivers_license_number', 'court_date', 'court_location', 'court_address') search_fields = ('id', 'first_name', 'last_name', 'court_location', 'drivers_license_number') admin.site.register(Citation, CitationAdmin) from api.models import Violation class ViolationAdmin(admin.ModelAdmin): list_display = ('id', 'citation_number', 'violation_number', 'violation_description', 'warrant_status', 'warrant_number', 'status', 'status_date', 'fine_amount', 'court_cost') list_filter = ('warrant_status',) search_fields = ('id', 'citation_number', 'violation_number', 'warrant_number') admin.site.register(Violation, ViolationAdmin)

60.666667

238

0.762637

from django.contrib import admin from api.models import Citation class CitationAdmin(admin.ModelAdmin): list_display = ('id', 'citation_number', 'citation_date', 'first_name', 'last_name', 'date_of_birth', 'defendant_address', 'defendant_city', 'defendant_state', 'drivers_license_number', 'court_date', 'court_location', 'court_address') search_fields = ('id', 'first_name', 'last_name', 'court_location', 'drivers_license_number') admin.site.register(Citation, CitationAdmin) from api.models import Violation class ViolationAdmin(admin.ModelAdmin): list_display = ('id', 'citation_number', 'violation_number', 'violation_description', 'warrant_status', 'warrant_number', 'status', 'status_date', 'fine_amount', 'court_cost') list_filter = ('warrant_status',) search_fields = ('id', 'citation_number', 'violation_number', 'warrant_number') admin.site.register(Violation, ViolationAdmin)

true

790bfe9e637a97c25700c02b7d5832d6e5fb589f

131

py

Python

src/pythonFEA/templates/__init__.py

honzatomek/pythonFEA

c851c20800a06cc2084ef53dfd2ab67e7dfbc3b7

[ "MIT" ]

null

src/pythonFEA/templates/__init__.py

honzatomek/pythonFEA

c851c20800a06cc2084ef53dfd2ab67e7dfbc3b7

[ "MIT" ]

null

src/pythonFEA/templates/__init__.py

honzatomek/pythonFEA

c851c20800a06cc2084ef53dfd2ab67e7dfbc3b7

[ "MIT" ]

null

# print('Reading templates/__init__.py') from .errors import * import logging logging.debug('Reading src/templates/__init__.py')

18.714286

50

0.770992

from .errors import * import logging logging.debug('Reading src/templates/__init__.py')

true

790bfebe2eb785aaa8b60e4994304b971a0f8eaa

2,846

py

Python

train.py

blufzzz/MeshCNN

54221ddaef20c2886dca17d4edaf76f8cf040af0

[ "MIT" ]

null

train.py

blufzzz/MeshCNN

54221ddaef20c2886dca17d4edaf76f8cf040af0

[ "MIT" ]

null

train.py

blufzzz/MeshCNN

54221ddaef20c2886dca17d4edaf76f8cf040af0

[ "MIT" ]

null

import time from options.train_options import TrainOptions from data import DataLoader from models import create_model from util.writer import Writer from test import run_test if __name__ == '__main__': opt = TrainOptions().parse() # opt.serial_batches = True # no shuffle print('Creating DataLoader...') dataset = DataLoader(opt) print('DataLoader created!') print('#training meshes = %d' % dataset_size) model = create_model(opt) writer = Writer(opt) total_steps = 0 for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1): epoch_start_time = time.time() iter_data_time = time.time() epoch_iter = 0 o_ncorrect = 0 o_nexamples = 0 o_pr = 0 o_re = 0 model.save_network(0) for i, data in enumerate(dataset): print(i) iter_start_time = time.time() if total_steps % opt.print_freq == 0: t_data = iter_start_time - iter_data_time total_steps += opt.batch_size epoch_iter += opt.batch_size model.set_input(data) ncorrect, nexamples, pr, re = model.optimize_parameters() o_ncorrect += ncorrect o_nexamples += nexamples o_pr += pr o_re += re if total_steps % opt.print_freq == 0: loss = model.loss t = (time.time() - iter_start_time) / opt.batch_size writer.print_current_losses(epoch, epoch_iter, loss, t, t_data) writer.plot_loss(loss, epoch, epoch_iter, dataset_size) if i % opt.save_latest_freq == 0: print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps)) model.save_network('latest') iter_data_time = time.time() if epoch % opt.save_epoch_freq == 0: print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps)) model.save_network('latest') model.save_network(epoch) print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time)) model.update_learning_rate() if opt.verbose_plot: writer.plot_model_wts(model, epoch) if epoch % opt.run_test_freq == 0: acc, pr, re = run_test(epoch) writer.plot_acc(acc, epoch) writer.plot_pr(pr, epoch) writer.plot_re(re, epoch) writer.plot_train_acc(float(o_ncorrect)/o_nexamples, epoch) writer.plot_train_pr(float(o_pr)/o_nexamples, epoch) writer.plot_train_re(float(o_re)/o_nexamples, epoch) writer.close()

34.289157

83

0.576599

import time from options.train_options import TrainOptions from data import DataLoader from models import create_model from util.writer import Writer from test import run_test if __name__ == '__main__': opt = TrainOptions().parse() Creating DataLoader...') dataset = DataLoader(opt) print('DataLoader created!') print('#training meshes = %d' % dataset_size) model = create_model(opt) writer = Writer(opt) total_steps = 0 for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1): epoch_start_time = time.time() iter_data_time = time.time() epoch_iter = 0 o_ncorrect = 0 o_nexamples = 0 o_pr = 0 o_re = 0 model.save_network(0) for i, data in enumerate(dataset): print(i) iter_start_time = time.time() if total_steps % opt.print_freq == 0: t_data = iter_start_time - iter_data_time total_steps += opt.batch_size epoch_iter += opt.batch_size model.set_input(data) ncorrect, nexamples, pr, re = model.optimize_parameters() o_ncorrect += ncorrect o_nexamples += nexamples o_pr += pr o_re += re if total_steps % opt.print_freq == 0: loss = model.loss t = (time.time() - iter_start_time) / opt.batch_size writer.print_current_losses(epoch, epoch_iter, loss, t, t_data) writer.plot_loss(loss, epoch, epoch_iter, dataset_size) if i % opt.save_latest_freq == 0: print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps)) model.save_network('latest') iter_data_time = time.time() if epoch % opt.save_epoch_freq == 0: print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps)) model.save_network('latest') model.save_network(epoch) print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time)) model.update_learning_rate() if opt.verbose_plot: writer.plot_model_wts(model, epoch) if epoch % opt.run_test_freq == 0: acc, pr, re = run_test(epoch) writer.plot_acc(acc, epoch) writer.plot_pr(pr, epoch) writer.plot_re(re, epoch) writer.plot_train_acc(float(o_ncorrect)/o_nexamples, epoch) writer.plot_train_pr(float(o_pr)/o_nexamples, epoch) writer.plot_train_re(float(o_re)/o_nexamples, epoch) writer.close()

true

790bff7a824582a5bd81e5ba50fd57cd8d353b74

803

py

Python

app/__init__.py

hazzillrodriguez/flask-user-management

1c9e9707f9302a908b8cc5cb324abe89f4db7bc9

[ "MIT" ]

1

2021-11-30T05:33:19.000Z

app/__init__.py

hazzillrodriguez/flask-user-management

1c9e9707f9302a908b8cc5cb324abe89f4db7bc9

[ "MIT" ]

null

app/__init__.py

hazzillrodriguez/flask-user-management

1c9e9707f9302a908b8cc5cb324abe89f4db7bc9

[ "MIT" ]

1

2021-09-27T11:24:52.000Z

from flask import Flask, redirect, url_for from flask_sqlalchemy import SQLAlchemy from flask_login import LoginManager from flask_migrate import Migrate from flask_bcrypt import Bcrypt from flask_mail import Mail app = Flask(__name__) # Configuration app.config.from_object('config.DevelopmentConfig') db = SQLAlchemy(app) login_manager = LoginManager(app) migrate = Migrate(app, db) bcrypt = Bcrypt(app) mail = Mail(app) from app.auth.views import auth_blueprint from app.admin.views import admin_blueprint from app.user.views import user_blueprint app.register_blueprint(auth_blueprint, url_prefix='/auth') app.register_blueprint(admin_blueprint, url_prefix='/admin') app.register_blueprint(user_blueprint, url_prefix='/user') @app.route('/') def root(): return(redirect(url_for('auth.login')))

28.678571

60

0.809465

from flask import Flask, redirect, url_for from flask_sqlalchemy import SQLAlchemy from flask_login import LoginManager from flask_migrate import Migrate from flask_bcrypt import Bcrypt from flask_mail import Mail app = Flask(__name__) app.config.from_object('config.DevelopmentConfig') db = SQLAlchemy(app) login_manager = LoginManager(app) migrate = Migrate(app, db) bcrypt = Bcrypt(app) mail = Mail(app) from app.auth.views import auth_blueprint from app.admin.views import admin_blueprint from app.user.views import user_blueprint app.register_blueprint(auth_blueprint, url_prefix='/auth') app.register_blueprint(admin_blueprint, url_prefix='/admin') app.register_blueprint(user_blueprint, url_prefix='/user') @app.route('/') def root(): return(redirect(url_for('auth.login')))

true

790bfff162b54b4b284aab426678a810628d87f2

14,194

py

Python

solaris/tile/vector_tile.py

motokimura/solaris

e3a18522ee41aad3da37ae88ba159efabf76a180

[ "Apache-2.0" ]

60

2020-07-29T23:31:18.000Z

2022-03-20T02:02:47.000Z

3-SatShipAI/solaris/tile/vector_tile.py

Z-Zheng/SpaceNet_SAR_Buildings_Solutions

6a9c3962d987d985384d0d41a187f5fbfadac82c

[ "Apache-2.0" ]

9

2021-01-15T08:57:15.000Z

2021-11-04T04:27:41.000Z

3-SatShipAI/solaris/tile/vector_tile.py

Z-Zheng/SpaceNet_SAR_Buildings_Solutions

6a9c3962d987d985384d0d41a187f5fbfadac82c

[ "Apache-2.0" ]

16

2020-07-30T12:56:03.000Z

2021-08-13T16:55:05.000Z

import os import numpy as np from shapely.geometry import box, Polygon import geopandas as gpd from ..utils.core import _check_gdf_load, _check_crs from ..utils.tile import save_empty_geojson from ..utils.geo import gdf_get_projection_unit, split_multi_geometries from ..utils.geo import reproject_geometry from tqdm import tqdm class VectorTiler(object): """An object to tile geospatial vector data into smaller pieces. Arguments --------- Attributes ---------- """ def __init__(self, dest_dir=None, dest_crs=None, output_format='GeoJSON', verbose=False, super_verbose=False): if verbose or super_verbose: print('Preparing the tiler...') self.dest_dir = dest_dir if not os.path.isdir(self.dest_dir): os.makedirs(self.dest_dir) if dest_crs is not None: self.dest_crs = _check_crs(dest_crs) self.output_format = output_format self.verbose = verbose self.super_verbose = super_verbose self.tile_paths = [] # retains the paths of the last call to .tile() if self.verbose or self.super_verbose: print('Initialization done.') def tile(self, src, tile_bounds, tile_bounds_crs=None, geom_type='Polygon', split_multi_geoms=True, min_partial_perc=0.0, dest_fname_base='geoms', obj_id_col=None, output_ext='.geojson'): """Tile `src` into vector data tiles bounded by `tile_bounds`. Arguments --------- src : `str` or :class:`geopandas.GeoDataFrame` The source vector data to tile. Must either be a path to a GeoJSON or a :class:`geopandas.GeoDataFrame`. tile_bounds : list A :class:`list` made up of ``[left, top, right, bottom] `` sublists (this can be extracted from :class:`solaris.tile.raster_tile.RasterTiler` after tiling imagery) tile_bounds_crs : int, optional The EPSG code or rasterio.crs.CRS object for the CRS that the tile bounds are in. RasterTiler.tile returns the CRS of the raster tiles and can be used here. If not provided, it's assumed that the CRS is the same as in `src`. This argument must be provided if the bound coordinates and `src` are not in the same CRS, otherwise tiling will not occur correctly. geom_type : str, optional (default: "Polygon") The type of geometries contained within `src`. Defaults to ``"Polygon"``, can also be ``"LineString"``. split_multi_geoms : bool, optional (default: True) Should multi-polygons or multi-linestrings generated by clipping a geometry into discontinuous pieces be separated? Defaults to yes (``True``). min_partial_perc : float, optional (default: 0.0) The minimum percentage of a :class:`shapely.geometry.Polygon` 's area or :class:`shapely.geometry.LineString` 's length that must be retained within a tile's bounds to be included in the output. Defaults to ``0.0``, meaning that the contained portion of a clipped geometry will be included, no matter how small. dest_fname_base : str, optional (default: 'geoms') The base filename to use when creating outputs. The lower left corner coordinates of the tile's bounding box will be appended when saving. obj_id_col : str, optional (default: None) If ``split_multi_geoms=True``, the name of a column that specifies a unique identifier for each geometry (e.g. the ``"BuildingId"`` column in many SpaceNet datasets.) See :func:`solaris.utils.geo.split_multi_geometries` for more. output_ext : str, optional, (default: geojson) Extension of output files, can be 'geojson' or 'json'. """ tile_gen = self.tile_generator(src, tile_bounds, tile_bounds_crs, geom_type, split_multi_geoms, min_partial_perc, obj_id_col=obj_id_col) self.tile_paths = [] for tile_gdf, tb in tqdm(tile_gen): if self.proj_unit not in ['meter', 'metre']: dest_path = os.path.join( self.dest_dir, '{}_{}_{}{}'.format(dest_fname_base, np.round(tb[0], 3), np.round(tb[3], 3), output_ext)) else: dest_path = os.path.join( self.dest_dir, '{}_{}_{}{}'.format(dest_fname_base, int(tb[0]), int(tb[3]), output_ext)) self.tile_paths.append(dest_path) if len(tile_gdf) > 0: tile_gdf.to_file(dest_path, driver='GeoJSON') else: save_empty_geojson(dest_path, self.dest_crs) def tile_generator(self, src, tile_bounds, tile_bounds_crs=None, geom_type='Polygon', split_multi_geoms=True, min_partial_perc=0.0, obj_id_col=None): """Generate `src` vector data tiles bounded by `tile_bounds`. Arguments --------- src : `str` or :class:`geopandas.GeoDataFrame` The source vector data to tile. Must either be a path to a GeoJSON or a :class:`geopandas.GeoDataFrame`. tile_bounds : list A :class:`list` made up of ``[left, top, right, bottom] `` sublists (this can be extracted from :class:`solaris.tile.raster_tile.RasterTiler` after tiling imagery) tile_bounds_crs : int, optional The EPSG code for the CRS that the tile bounds are in. If not provided, it's assumed that the CRS is the same as in `src`. This argument must be provided if the bound coordinates and `src` are not in the same CRS, otherwise tiling will not occur correctly. geom_type : str, optional (default: "Polygon") The type of geometries contained within `src`. Defaults to ``"Polygon"``, can also be ``"LineString"``. split_multi_geoms : bool, optional (default: True) Should multi-polygons or multi-linestrings generated by clipping a geometry into discontinuous pieces be separated? Defaults to yes (``True``). min_partial_perc : float, optional (default: 0.0) The minimum percentage of a :class:`shapely.geometry.Polygon` 's area or :class:`shapely.geometry.LineString` 's length that must be retained within a tile's bounds to be included in the output. Defaults to ``0.0``, meaning that the contained portion of a clipped geometry will be included, no matter how small. obj_id_col : str, optional (default: None) If ``split_multi_geoms=True``, the name of a column that specifies a unique identifier for each geometry (e.g. the ``"BuildingId"`` column in many SpaceNet datasets.) See :func:`solaris.utils.geo.split_multi_geometries` for more. Yields ------ tile_gdf : :class:`geopandas.GeoDataFrame` A tile geodataframe. tb : list A list with ``[left, top, right, bottom] `` coordinates for the boundaries contained by `tile_gdf`. """ self.src = _check_gdf_load(src) if self.verbose: print("Num tiles:", len(tile_bounds)) self.src_crs = _check_crs(self.src.crs) # check if the tile bounds and vector are in the same crs if tile_bounds_crs is not None: tile_bounds_crs = _check_crs(tile_bounds_crs) else: tile_bounds_crs = self.src_crs if self.src_crs != tile_bounds_crs: reproject_bounds = True # used to transform tb for clip_gdf() else: reproject_bounds = False self.proj_unit = self.src_crs.linear_units if getattr(self, 'dest_crs', None) is None: self.dest_crs = self.src_crs for i, tb in enumerate(tile_bounds): if self.super_verbose: print("\n", i, "/", len(tile_bounds)) if reproject_bounds: tile_gdf = clip_gdf(self.src, reproject_geometry(box(*tb), tile_bounds_crs, self.src_crs), min_partial_perc, geom_type, verbose=self.super_verbose) else: tile_gdf = clip_gdf(self.src, tb, min_partial_perc, geom_type, verbose=self.super_verbose) if self.src_crs != self.dest_crs: tile_gdf = tile_gdf.to_crs(crs=self.dest_crs.to_wkt()) if split_multi_geoms: split_multi_geometries(tile_gdf, obj_id_col=obj_id_col) yield tile_gdf, tb def search_gdf_polygon(gdf, tile_polygon): """Find polygons in a GeoDataFrame that overlap with `tile_polygon` . Arguments --------- gdf : :py:class:`geopandas.GeoDataFrame` A :py:class:`geopandas.GeoDataFrame` of polygons to search. tile_polygon : :py:class:`shapely.geometry.Polygon` A :py:class:`shapely.geometry.Polygon` denoting a tile's bounds. Returns ------- precise_matches : :py:class:`geopandas.GeoDataFrame` The subset of `gdf` that overlaps with `tile_polygon` . If there are no overlaps, this will return an empty :py:class:`geopandas.GeoDataFrame`. """ sindex = gdf.sindex possible_matches_index = list(sindex.intersection(tile_polygon.bounds)) possible_matches = gdf.iloc[possible_matches_index] precise_matches = possible_matches[ possible_matches.intersects(tile_polygon) ] if precise_matches.empty: precise_matches = gpd.GeoDataFrame(geometry=[]) return precise_matches def clip_gdf(gdf, tile_bounds, min_partial_perc=0.0, geom_type="Polygon", use_sindex=True, verbose=False): """Clip GDF to a provided polygon. Clips objects within `gdf` to the region defined by `poly_to_cut`. Also adds several columns to the output:: `origarea` The original area of the polygons (only used if `geom_type` == ``"Polygon"``). `origlen` The original length of the objects (only used if `geom_type` == ``"LineString"``). `partialDec` The fraction of the object that remains after clipping (fraction of area for Polygons, fraction of length for LineStrings.) Can filter based on this by using `min_partial_perc`. `truncated` Boolean indicator of whether or not an object was clipped. Arguments --------- gdf : :py:class:`geopandas.GeoDataFrame` A :py:class:`geopandas.GeoDataFrame` of polygons to clip. tile_bounds : `list` or :class:`shapely.geometry.Polygon` The geometry to clip objects in `gdf` to. This can either be a ``[left, top, right, bottom] `` bounds list or a :class:`shapely.geometry.Polygon` object defining the area to keep. min_partial_perc : float, optional The minimum fraction of an object in `gdf` that must be preserved. Defaults to 0.0 (include any object if any part remains following clipping). geom_type : str, optional Type of objects in `gdf`. Can be one of ``["Polygon", "LineString"]`` . Defaults to ``"Polygon"`` . use_sindex : bool, optional Use the `gdf` sindex be used for searching. Improves efficiency but requires `libspatialindex <http://libspatialindex.github.io/>`__ . verbose : bool, optional Switch to print relevant values. Returns ------- cut_gdf : :py:class:`geopandas.GeoDataFrame` `gdf` with all contained objects clipped to `poly_to_cut` . See notes above for details on additional clipping columns added. """ if isinstance(tile_bounds, tuple): tb = box(*tile_bounds) elif isinstance(tile_bounds, list): tb = box(*tile_bounds) elif isinstance(tile_bounds, Polygon): tb = tile_bounds if use_sindex and (geom_type == "Polygon"): gdf = search_gdf_polygon(gdf, tb) # if geom_type == "LineString": if 'origarea' in gdf.columns: pass else: if "geom_type" == "LineString": gdf['origarea'] = 0 else: gdf['origarea'] = gdf.area if 'origlen' in gdf.columns: pass else: if "geom_type" == "LineString": gdf['origlen'] = gdf.length else: gdf['origlen'] = 0 # TODO must implement different case for lines and for spatialIndex # (Assume RTree is already performed) cut_gdf = gdf.copy() cut_gdf.geometry = gdf.intersection(tb) if geom_type == 'Polygon': cut_gdf['partialDec'] = cut_gdf.area / cut_gdf['origarea'] cut_gdf = cut_gdf.loc[cut_gdf['partialDec'] > min_partial_perc, :] cut_gdf['truncated'] = (cut_gdf['partialDec'] != 1.0).astype(int) else: # assume linestrings # remove null cut_gdf = cut_gdf[cut_gdf['geometry'].notnull()] cut_gdf['partialDec'] = 1 cut_gdf['truncated'] = 0 # cut_gdf = cut_gdf[cut_gdf.geom_type != "GeometryCollection"] if len(cut_gdf) > 0 and verbose: print("clip_gdf() - gdf.iloc[0]:", gdf.iloc[0]) print("clip_gdf() - tb:", tb) print("clip_gdf() - gdf_cut:", cut_gdf) # TODO: IMPLEMENT TRUNCATION MEASUREMENT FOR LINESTRINGS return cut_gdf

44.080745

83

0.594688

import os import numpy as np from shapely.geometry import box, Polygon import geopandas as gpd from ..utils.core import _check_gdf_load, _check_crs from ..utils.tile import save_empty_geojson from ..utils.geo import gdf_get_projection_unit, split_multi_geometries from ..utils.geo import reproject_geometry from tqdm import tqdm class VectorTiler(object): def __init__(self, dest_dir=None, dest_crs=None, output_format='GeoJSON', verbose=False, super_verbose=False): if verbose or super_verbose: print('Preparing the tiler...') self.dest_dir = dest_dir if not os.path.isdir(self.dest_dir): os.makedirs(self.dest_dir) if dest_crs is not None: self.dest_crs = _check_crs(dest_crs) self.output_format = output_format self.verbose = verbose self.super_verbose = super_verbose self.tile_paths = [] if self.verbose or self.super_verbose: print('Initialization done.') def tile(self, src, tile_bounds, tile_bounds_crs=None, geom_type='Polygon', split_multi_geoms=True, min_partial_perc=0.0, dest_fname_base='geoms', obj_id_col=None, output_ext='.geojson'): tile_gen = self.tile_generator(src, tile_bounds, tile_bounds_crs, geom_type, split_multi_geoms, min_partial_perc, obj_id_col=obj_id_col) self.tile_paths = [] for tile_gdf, tb in tqdm(tile_gen): if self.proj_unit not in ['meter', 'metre']: dest_path = os.path.join( self.dest_dir, '{}_{}_{}{}'.format(dest_fname_base, np.round(tb[0], 3), np.round(tb[3], 3), output_ext)) else: dest_path = os.path.join( self.dest_dir, '{}_{}_{}{}'.format(dest_fname_base, int(tb[0]), int(tb[3]), output_ext)) self.tile_paths.append(dest_path) if len(tile_gdf) > 0: tile_gdf.to_file(dest_path, driver='GeoJSON') else: save_empty_geojson(dest_path, self.dest_crs) def tile_generator(self, src, tile_bounds, tile_bounds_crs=None, geom_type='Polygon', split_multi_geoms=True, min_partial_perc=0.0, obj_id_col=None): self.src = _check_gdf_load(src) if self.verbose: print("Num tiles:", len(tile_bounds)) self.src_crs = _check_crs(self.src.crs) if tile_bounds_crs is not None: tile_bounds_crs = _check_crs(tile_bounds_crs) else: tile_bounds_crs = self.src_crs if self.src_crs != tile_bounds_crs: reproject_bounds = True else: reproject_bounds = False self.proj_unit = self.src_crs.linear_units if getattr(self, 'dest_crs', None) is None: self.dest_crs = self.src_crs for i, tb in enumerate(tile_bounds): if self.super_verbose: print("\n", i, "/", len(tile_bounds)) if reproject_bounds: tile_gdf = clip_gdf(self.src, reproject_geometry(box(*tb), tile_bounds_crs, self.src_crs), min_partial_perc, geom_type, verbose=self.super_verbose) else: tile_gdf = clip_gdf(self.src, tb, min_partial_perc, geom_type, verbose=self.super_verbose) if self.src_crs != self.dest_crs: tile_gdf = tile_gdf.to_crs(crs=self.dest_crs.to_wkt()) if split_multi_geoms: split_multi_geometries(tile_gdf, obj_id_col=obj_id_col) yield tile_gdf, tb def search_gdf_polygon(gdf, tile_polygon): sindex = gdf.sindex possible_matches_index = list(sindex.intersection(tile_polygon.bounds)) possible_matches = gdf.iloc[possible_matches_index] precise_matches = possible_matches[ possible_matches.intersects(tile_polygon) ] if precise_matches.empty: precise_matches = gpd.GeoDataFrame(geometry=[]) return precise_matches def clip_gdf(gdf, tile_bounds, min_partial_perc=0.0, geom_type="Polygon", use_sindex=True, verbose=False): if isinstance(tile_bounds, tuple): tb = box(*tile_bounds) elif isinstance(tile_bounds, list): tb = box(*tile_bounds) elif isinstance(tile_bounds, Polygon): tb = tile_bounds if use_sindex and (geom_type == "Polygon"): gdf = search_gdf_polygon(gdf, tb) if 'origarea' in gdf.columns: pass else: if "geom_type" == "LineString": gdf['origarea'] = 0 else: gdf['origarea'] = gdf.area if 'origlen' in gdf.columns: pass else: if "geom_type" == "LineString": gdf['origlen'] = gdf.length else: gdf['origlen'] = 0 cut_gdf = gdf.copy() cut_gdf.geometry = gdf.intersection(tb) if geom_type == 'Polygon': cut_gdf['partialDec'] = cut_gdf.area / cut_gdf['origarea'] cut_gdf = cut_gdf.loc[cut_gdf['partialDec'] > min_partial_perc, :] cut_gdf['truncated'] = (cut_gdf['partialDec'] != 1.0).astype(int) else: cut_gdf = cut_gdf[cut_gdf['geometry'].notnull()] cut_gdf['partialDec'] = 1 cut_gdf['truncated'] = 0 if len(cut_gdf) > 0 and verbose: print("clip_gdf() - gdf.iloc[0]:", gdf.iloc[0]) print("clip_gdf() - tb:", tb) print("clip_gdf() - gdf_cut:", cut_gdf) return cut_gdf

true

790c00350f25bef8955e5ddf884e6c7a046a9a32

1,171

py

Python

display/plugins/Shutdown.py

rGunti/Yuki-Chan-Music-Player

d83ecc6d7fe2250725797386c670797847363f6e

[ "MIT" ]

null

display/plugins/Shutdown.py

rGunti/Yuki-Chan-Music-Player

d83ecc6d7fe2250725797386c670797847363f6e

[ "MIT" ]

null

display/plugins/Shutdown.py

rGunti/Yuki-Chan-Music-Player

d83ecc6d7fe2250725797386c670797847363f6e

[ "MIT" ]

null

#!/usr/bin/env python """ SHUTDOWN.PY Shutdown Plugin (C) 2015, rGunti """ import dot3k.lcd as lcd import dot3k.backlight as backlight import time, datetime, copy, math, psutil import sys import os from dot3k.menu import Menu, MenuOption class Shutdown(MenuOption): def __init__(self): self.last = self.millis() MenuOption.__init__(self) def redraw(self, menu): lcd.clear() lcd.set_cursor_position(3,1) lcd.write("Bye (^_^)/") for x in reversed(range(127)): backlight.rgb(0, x * 2, 0) lcd.clear() os.system("halt") sys.exit(0) class Reboot(MenuOption): def __init__(self): self.last = self.millis() MenuOption.__init__(self) def redraw(self, menu): lcd.clear() lcd.set_cursor_position(3,1) lcd.write("Bye (^_^)/") for x in reversed(range(127)): backlight.rgb(0, x * 2, 0) lcd.clear() os.system("reboot") sys.exit(0) class QuitScript(MenuOption): def __init__(self): self.last = self.millis() MenuOption.__init__(self) def redraw(self, menu): lcd.clear() lcd.set_cursor_position(3,1) lcd.write("Bye (^_^)/") for x in reversed(range(127)): backlight.rgb(0, x * 2, 0) lcd.clear() sys.exit(0)

20.189655

41

0.672075

import dot3k.lcd as lcd import dot3k.backlight as backlight import time, datetime, copy, math, psutil import sys import os from dot3k.menu import Menu, MenuOption class Shutdown(MenuOption): def __init__(self): self.last = self.millis() MenuOption.__init__(self) def redraw(self, menu): lcd.clear() lcd.set_cursor_position(3,1) lcd.write("Bye (^_^)/") for x in reversed(range(127)): backlight.rgb(0, x * 2, 0) lcd.clear() os.system("halt") sys.exit(0) class Reboot(MenuOption): def __init__(self): self.last = self.millis() MenuOption.__init__(self) def redraw(self, menu): lcd.clear() lcd.set_cursor_position(3,1) lcd.write("Bye (^_^)/") for x in reversed(range(127)): backlight.rgb(0, x * 2, 0) lcd.clear() os.system("reboot") sys.exit(0) class QuitScript(MenuOption): def __init__(self): self.last = self.millis() MenuOption.__init__(self) def redraw(self, menu): lcd.clear() lcd.set_cursor_position(3,1) lcd.write("Bye (^_^)/") for x in reversed(range(127)): backlight.rgb(0, x * 2, 0) lcd.clear() sys.exit(0)

true