xszheng2020/the_stack_dedup_python_hits_2 · Datasets at Hugging Face

9677bbcd0dfdb24413182900511a1e43b219d536

517

py

Python

notecard_pseudo_sensor/notecard_pseudo_sensor.py

blues/notecard-pseudo-sensor-python

19429e3420d168eda622562f092b1e4b1dcb77d5

[ "MIT" ]

null

notecard_pseudo_sensor/notecard_pseudo_sensor.py

blues/notecard-pseudo-sensor-python

19429e3420d168eda622562f092b1e4b1dcb77d5

[ "MIT" ]

null

notecard_pseudo_sensor/notecard_pseudo_sensor.py

blues/notecard-pseudo-sensor-python

19429e3420d168eda622562f092b1e4b1dcb77d5

[ "MIT" ]

null

import random class NotecardPseudoSensor: def __init__(self, card): self.card = card # Read the temperature from the Notecard’s temperature # sensor. The Notecard captures a new temperature sample every # five minutes. def temp(self): temp_req = {"req": "card.temp"} temp_rsp = self.card.Transaction(temp_req) return temp_rsp["value"] # Generate a random humidity that’s close to an average # indoor humidity reading. def humidity(self): return round(random.uniform(45, 50), 4)

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null

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96843be69995d5261982a7a95d54bc0c85b10d1c

1,124

py

Python

pyclick/click_models/task_centric/SearchTask.py

gaudel/ranking_bandits

1fe4a38b17a3bb7ccab3ae0f4d0afb70fe54dbc9

[ "MIT" ]

3

2021-07-22T14:46:01.000Z

2021-07-23T08:55:01.000Z

pyclick/click_models/task_centric/SearchTask.py

gaudel/ranking_bandits

1fe4a38b17a3bb7ccab3ae0f4d0afb70fe54dbc9

[ "MIT" ]

null

pyclick/click_models/task_centric/SearchTask.py

gaudel/ranking_bandits

1fe4a38b17a3bb7ccab3ae0f4d0afb70fe54dbc9

[ "MIT" ]

null

# # Copyright (C) 2015 Ilya Markov # # Full copyright notice can be found in LICENSE. # from collections import OrderedDict __author__ = 'Ilya Markov' class SearchTask(object): """A search task consisting of multiple search sessions.""" def __init__(self, task): self._task = task self.search_sessions = [] def __str__(self): return "%s:%r" % (self._task, [search_session.query for search_session in self.search_sessions]) def __repr__(self): return str(self) @staticmethod def get_search_tasks(search_sessions): """ Groups search sessions by task and returns the list of all tasks. :param search_sessions: Task-centric search sessions. :returns: The list of tasks. """ search_tasks = OrderedDict() for search_session in search_sessions: if search_session.task not in search_tasks: search_tasks[search_session.task] = SearchTask(search_session.task) search_tasks[search_session.task].search_sessions.append(search_session) return search_tasks.values()

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1

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2

968b31d315d2794504114cd12a305550671ccee4

782

py

Python

Exercises/Databases/playlist-app/forms.py

pedwards95/Springboard_Class

9df8dbd8832223e89b89d12db3f7e0b178e2ed79

[ "MIT" ]

null

Exercises/Databases/playlist-app/forms.py

pedwards95/Springboard_Class

9df8dbd8832223e89b89d12db3f7e0b178e2ed79

[ "MIT" ]

null

Exercises/Databases/playlist-app/forms.py

pedwards95/Springboard_Class

9df8dbd8832223e89b89d12db3f7e0b178e2ed79

[ "MIT" ]

null

"""Forms for playlist app.""" from wtforms import SelectField, StringField, PasswordField, ValidationError from wtforms.validators import InputRequired, Email from flask_wtf import FlaskForm class PlaylistForm(FlaskForm): """Form for adding playlists.""" name = StringField("Name",validators=[InputRequired()]) description = StringField("Description",validators=[InputRequired()]) class SongForm(FlaskForm): """Form for adding songs.""" title = StringField("Title",validators=[InputRequired()]) artist = StringField("Artist",validators=[InputRequired()]) # DO NOT MODIFY THIS FORM - EVERYTHING YOU NEED IS HERE class NewSongForPlaylistForm(FlaskForm): """Form for adding a song to playlist.""" song = SelectField('Song To Add', coerce=int)

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96b228ae9e7649879cdeda2fdd3f35cf03b7b213

57

py

Python

day1/main.py

HallerPatrick/aoc-2021

4df56c940f5fcd0b9967e3f8d8f7a80ef251217e

[ "MIT" ]

null

day1/main.py

HallerPatrick/aoc-2021

4df56c940f5fcd0b9967e3f8d8f7a80ef251217e

[ "MIT" ]

null

day1/main.py

HallerPatrick/aoc-2021

4df56c940f5fcd0b9967e3f8d8f7a80ef251217e

[ "MIT" ]

null

x = 3 def foo(): y = "String" return y foo()

5.7

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null

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96baf8d07ea198031649ec3fdb101d5cf09a30ac

1,291

py

Python

schedpy/config.py

safiya03/schedpy

719fd08e0b5d2ed95bf70c4c0d3b1b4a8463d1ac

[ "MIT" ]

2

2019-12-04T18:58:47.000Z

2020-03-07T13:09:10.000Z

schedpy/config.py

safiya03/schedpy

719fd08e0b5d2ed95bf70c4c0d3b1b4a8463d1ac

[ "MIT" ]

4

2019-12-04T19:02:04.000Z

2019-12-04T19:10:54.000Z

schedpy/config.py

safiya03/schedpy

719fd08e0b5d2ed95bf70c4c0d3b1b4a8463d1ac

[ "MIT" ]

3

2019-12-05T07:30:37.000Z

2020-11-27T15:05:33.000Z

from datetime import date class Config(object): def __init__(self): self.start_day = 0 self.start_date = date.today() def _set_configs(self, start_day, start_date): """Method to set start day.""" self.start_day = start_day self.start_date = start_date days_list = [ "mon", "tue", "wed", "thu", "fri", "sat", "sun", "monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday", ] if isinstance(self.start_day, str): if self.start_day.lower() in days_list: self.start_day = ( days_list.index(self.start_day.lower()) if days_list.index(self.start_day.lower()) < 7 else days_list.index(self.start_day.lower()) - 7 ) # self.start_day = self.start_day print("Start day set to: " + str(self.start_day) + "/" + days_list[7 + self.start_day].capitalize()) print("Start date set to: " + str(self.start_date)) return self.start_day def get_configs(self): return self.start_day, self.start_date

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null

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null

0

2

96bfc271402cd1c2d0a61f6e1be27c1f167119be

898

py

Python

WeatherStationSensorsReader/controllers/ground_temperature_controller.py

weather-station-project/weather-station-sensors-reader

cda7902ee382248b41d14b9a2c0543817decbb4a

[ "MIT" ]

null

WeatherStationSensorsReader/controllers/ground_temperature_controller.py

weather-station-project/weather-station-sensors-reader

cda7902ee382248b41d14b9a2c0543817decbb4a

[ "MIT" ]

null

WeatherStationSensorsReader/controllers/ground_temperature_controller.py

weather-station-project/weather-station-sensors-reader

cda7902ee382248b41d14b9a2c0543817decbb4a

[ "MIT" ]

null

from controllers.controller import Controller from dao.ground_temperature_dao import GroundTemperatureDao from sensors.ground_temperature_sensor import GroundTemperatureSensor class GroundTemperatureController(Controller): """ Represents the controller with the ground temperature sensor and DAO """ def __init__(self, server, database, user, password): super(GroundTemperatureController, self).__init__(sensor=GroundTemperatureSensor(), dao=GroundTemperatureDao(server=server, database=database, user=user, password=password))

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73707d826b46ea5a349c8e03b0cafc919ea900a5

1,982

py

Python

src/sims4communitylib/events/build_buy/common_build_buy_event_dispatcher.py

velocist/TS4CheatsInfo

b59ea7e5f4bd01d3b3bd7603843d525a9c179867

[ "Apache-2.0" ]

118

2019-08-31T04:33:18.000Z

2022-03-28T21:12:14.000Z

src/sims4communitylib/events/build_buy/common_build_buy_event_dispatcher.py

velocist/TS4CheatsInfo

b59ea7e5f4bd01d3b3bd7603843d525a9c179867

[ "Apache-2.0" ]

15

2019-12-05T01:29:46.000Z

2022-02-18T17:13:46.000Z

src/sims4communitylib/events/build_buy/common_build_buy_event_dispatcher.py

velocist/TS4CheatsInfo

b59ea7e5f4bd01d3b3bd7603843d525a9c179867

[ "Apache-2.0" ]

28

2019-09-07T04:11:05.000Z

2022-02-07T18:31:40.000Z

""" The Sims 4 Community Library is licensed under the Creative Commons Attribution 4.0 International public license (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/legalcode Copyright (c) COLONOLNUTTY """ from typing import Any from sims4communitylib.events.build_buy.events.build_buy_enter import S4CLBuildBuyEnterEvent from sims4communitylib.events.build_buy.events.build_buy_exit import S4CLBuildBuyExitEvent from sims4communitylib.events.event_handling.common_event_registry import CommonEventRegistry from sims4communitylib.modinfo import ModInfo from sims4communitylib.services.common_service import CommonService from sims4communitylib.utils.common_injection_utils import CommonInjectionUtils from zone import Zone class CommonBuildBuyEventDispatcherService(CommonService): """A service that dispatches Build/Buy events. .. warning:: Do not use this service directly to listen for events!\ Use the :class:`.CommonEventRegistry` to listen for dispatched events. """ def _on_build_buy_enter(self, zone: Zone, *_, **__): return CommonEventRegistry.get().dispatch(S4CLBuildBuyEnterEvent(zone)) def _on_build_buy_exit(self, zone: Zone, *_, **__): return CommonEventRegistry.get().dispatch(S4CLBuildBuyExitEvent(zone)) @CommonInjectionUtils.inject_safely_into(ModInfo.get_identity(), Zone, Zone.on_build_buy_enter.__name__) def _common_build_buy_enter(original, self, *args, **kwargs) -> Any: result = original(self, *args, **kwargs) CommonBuildBuyEventDispatcherService.get()._on_build_buy_enter(self, *args, **kwargs) return result @CommonInjectionUtils.inject_safely_into(ModInfo.get_identity(), Zone, Zone.on_build_buy_exit.__name__) def _common_build_buy_exit(original, self, *args, **kwargs) -> Any: result = original(self, *args, **kwargs) CommonBuildBuyEventDispatcherService.get()._on_build_buy_exit(self, *args, **kwargs) return result

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null

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1

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7372d8e280bfee2d014c4e70726c289a411ac62b

1,202

py

Python

mail.py

Landers1037/simple-email-sms

d9172e389c24ceb4a9160d5108502a8ba9337d5e

[ "MIT" ]

1

2019-05-27T08:56:52.000Z

mail.py

Landers1037/simple-email-sms

d9172e389c24ceb4a9160d5108502a8ba9337d5e

[ "MIT" ]

1

2019-05-27T02:53:35.000Z

2019-05-27T02:53:56.000Z

mail.py

Landers1037/simple-email-sms

d9172e389c24ceb4a9160d5108502a8ba9337d5e

[ "MIT" ]

null

import smtplib import schedule from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText from email.mime.image import MIMEImage from email.header import Header import time import json # 设置smtplib所需的参数 # 下面的发件人，收件人是用于邮件传输的。 class pymail: with open('service.json', 'r', encoding='utf8') as file: data = json.load(file) smtpserver = data["smtp"] username = data["username"] password = data["password"] sender = data["sender"] receiver = data["receiver"] text = data["text"] subject = data["subject"] sslflag = data["ssl"] msg = MIMEMultipart('mixed') msg['Subject'] = subject msg['From'] = data["source"] msg['To'] = ";".join(receiver) text_plain = MIMEText(text, 'plain', 'utf-8') msg.attach(text_plain) def email(self): if self.sslflag == "true": smtp = smtplib.SMTP_SSL(self.data["smtp"], self.data["port"]) else: smtp = smtplib.SMTP(self.data["smtp"], self.data["port"]) smtp.login(self.username, self.password) smtp.sendmail(self.sender, self.receiver, self.msg.as_string()) smtp.quit()

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py

Python

stack/__init__.py

FireHead90544/stack.py

783f59928e7f58519864e6e7a2306f18655c9f65

[ "Apache-2.0" ]

1

2021-07-30T16:52:03.000Z

stack/__init__.py

FireHead90544/stack.py

783f59928e7f58519864e6e7a2306f18655c9f65

[ "Apache-2.0" ]

null

stack/__init__.py

FireHead90544/stack.py

783f59928e7f58519864e6e7a2306f18655c9f65

[ "Apache-2.0" ]

null

""" Stack.py - LIFO Stack & FIFO Queue Implementation in Python For Docs, Visit The GitHub Repo: https://github.com/FireHead90544/stack.py Author: Rudransh Joshi (https://github.com/FireHead90544) Issues: https://github.com/FireHead90544/stack.py/issues """ from __future__ import annotations from typing import List, Any import copy __author__ = "Rudransh Joshi" __version__ = 2.0 class Stack: """ LIFO Implementation of Stack in Python\n Last element to get in will be the first to get out Creates a Stack object """ def __init__(self, stackLength: int = 5) -> None: """ LIFO Implementation of Stack in Python\n Last element to get in will be the first to get out. Initialises a stack object with given stack length.\n Stack length defaults to 5 """ self.stackLength = stackLength self._top = -1 self._stackList = [] @property def top(self) -> int: """ Returns the top index of the stack. """ self._top = len(self._stackList) - 1 return self._top @property def list(self) -> List[Any]: """ Returns the values that the stack holds as a list """ return self._stackList @property def empty(self) -> bool: """ Returns True if the stack is empty, else returns False """ return False if not self.top == -1 else True def put(self, e: Any) -> None: """ Pushes an element to the stack.\n If the stack is already full, then shows OverflowError.\n Returns None """ if len(self._stackList) >= self.stackLength: raise OverflowError('The stack is already full. Unable to push any more elements.') self._stackList.append(e) def get(self) -> Any: """ Pops/Gets the last element from the stack.\n If the stack is already empty, then shows UnderflowError.\n Returns the value popped """ if self.top < 0: raise Exception('UnderflowError: The stack is empty. Unable to pop/get any elements.') else: return self._stackList.pop() def clear(self) -> None: """ Clears the stack, removes every element from the stack.\n Returns None """ self._stackList = [] def copy(self) -> 'Stack': """ Returns the copy of the stack as a Stack object """ return copy.deepcopy(self) def __str__(self) -> str: """ Overrides __str__ dunder method. Returns the stack as a stringified list object """ return f"{self._stackList}" def __repr__(self) -> str: """ Overrides __repr__ dunder method.\n Returns the class object representation of the stack with the values it holds """ return f"Stack(length={self.stackLength}, top={self.top}, stack={self._stackList})" class Queue: """ FIFO Implementation of Queue in Python\n First element to get in will be the first to get out Creates a Queue object """ def __init__(self, queueLength: int = 5): """ FIFO Implementation of Queue in Python\n First element to get in will be the first to get out. Initialises a queue object with given queue length.\n Queue length defaults to 5 """ self.queueLength = queueLength self._front = 0 self._rear = -1 self._queueList = [] @property def front(self) -> int: """ Returns the front index of the queue. """ return self._front @property def rear(self) -> int: """ Returns the rear index of the queue. """ self._rear = len(self._queueList) - 1 return self._rear @property def list(self) -> List[Any]: """ Returns the values that the queue holds as a list """ return self._queueList @property def empty(self) -> bool: """ Returns True if the queue is empty, else returns False """ return False if not self.rear == -1 else True def enqueue(self, e: Any) -> None: """ Enqueues an element to the queue.\n If the queue is already full, then shows OverflowError.\n Returns None """ if len(self._queueList) >= self.queueLength: raise OverflowError('The queue is already full. Unable to enqueue any more elements.') self._queueList.append(e) def dequeue(self) -> Any: """ Dequeues the first element from the queue.\n If the queue is already empty, then shows UnderflowError.\n Returns the value dequeued """ if self.rear < 0: raise Exception('UnderflowError: The queue is empty. Unable to dequeue any element.') else: return self._queueList.pop(self.front) def clear(self) -> None: """ Clears the queue, removes every element from the queue.\n Returns None """ self._queueList = [] def copy(self) -> 'Queue': """ Returns the copy of the queue as a Queue object """ return copy.deepcopy(self) def __str__(self) -> str: """ Overrides __str__ dunder method. Returns the queue as a stringified list object """ return f"{self._queueList}" def __repr__(self) -> str: """ Overrides __repr__ dunder method.\n Returns the class object representation of the queue with the values it holds """ return f"Queue(length={self.queueLength}, front={self.front}, rear={self.rear}, queue={self._queueList})"

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737c558dfd529e7f30a69f2924eca68b610b71de

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py

Python

autumn/models/covid_19/stratifications/history.py

emmamcbryde/AuTuMN-1

b1e7de15ac6ef6bed95a80efab17f0780ec9ff6f

[ "BSD-2-Clause-FreeBSD" ]

null

autumn/models/covid_19/stratifications/history.py

emmamcbryde/AuTuMN-1

b1e7de15ac6ef6bed95a80efab17f0780ec9ff6f

[ "BSD-2-Clause-FreeBSD" ]

null

autumn/models/covid_19/stratifications/history.py

emmamcbryde/AuTuMN-1

b1e7de15ac6ef6bed95a80efab17f0780ec9ff6f

[ "BSD-2-Clause-FreeBSD" ]

1

2019-10-22T04:47:34.000Z

from typing import Dict from summer import Stratification from autumn.models.covid_19.parameters import Parameters from autumn.models.covid_19.constants import COMPARTMENTS, History, HISTORY_STRATA from autumn.models.covid_19.stratifications.vaccination import apply_immunity_to_strat def get_history_strat(params: Parameters, stratified_adjusters: Dict[str, Dict[str, float]]) -> Stratification: """ Stratification to represent status regarding past infection/disease with Covid. Currently three strata, with everyone entering the experienced stratum after they have recovered from an episode. Args: params: All model parameters stratified_adjusters: VoC and severity stratification adjusters Returns: The history stratification summer object for application to the main model """ history_strat = Stratification("history", HISTORY_STRATA, COMPARTMENTS) # Everyone starts out infection-naive pop_split = {stratum: 0. for stratum in HISTORY_STRATA} pop_split[History.NAIVE] = 1. history_strat.set_population_split(pop_split) # Immunity adjustments equivalent to vaccination approach apply_immunity_to_strat(history_strat, params, stratified_adjusters, History.NAIVE) return history_strat

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737dfa7c6e4ef99be8c84a2f5c58379914d6c165

233

py

Python

API/onepanman_api/admin/rule.py

CMS0503/CodeOnBoard

2df8c9d934f6ffb05dbfbde329f84c66f2348618

[ "MIT" ]

null

API/onepanman_api/admin/rule.py

CMS0503/CodeOnBoard

2df8c9d934f6ffb05dbfbde329f84c66f2348618

[ "MIT" ]

12

2020-11-19T09:24:02.000Z

2020-12-02T11:07:22.000Z

API/onepanman_api/admin/rule.py

CMS0503/CodeOnBoard

2df8c9d934f6ffb05dbfbde329f84c66f2348618

[ "MIT" ]

null

from django.contrib import admin from .. import models @admin.register(models.Rule) class RuleAdmin(admin.ModelAdmin): """ 규칙 정보 """ list_display = ['id', 'type', 'name'] class Meta: model = models.Rule

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null

0

null

0

1

0

2

738ff9b464b875ec7ce2a25b19c5b5d1f421f123

946

py

Python

mne/viz/__init__.py

jaeilepp/eggie

a7e812f27e33f9c43ac2e36c6b45a26a01530a06

[ "BSD-2-Clause" ]

null

mne/viz/__init__.py

jaeilepp/eggie

a7e812f27e33f9c43ac2e36c6b45a26a01530a06

[ "BSD-2-Clause" ]

null

mne/viz/__init__.py

jaeilepp/eggie

a7e812f27e33f9c43ac2e36c6b45a26a01530a06

[ "BSD-2-Clause" ]

null

"""Visualization routines """ from .topomap import plot_evoked_topomap, plot_projs_topomap from .topomap import plot_ica_components, plot_ica_topomap from .topomap import plot_tfr_topomap, plot_topomap from .topo import (plot_topo, plot_topo_tfr, plot_topo_image_epochs, iter_topography) from .utils import tight_layout, mne_analyze_colormap, compare_fiff from ._3d import plot_sparse_source_estimates, plot_source_estimates from ._3d import plot_trans, plot_evoked_field from .misc import plot_cov, plot_bem, plot_events from .misc import plot_source_spectrogram from .utils import _mutable_defaults from .evoked import plot_evoked, plot_evoked_image from .circle import plot_connectivity_circle, circular_layout from .epochs import plot_image_epochs, plot_drop_log, plot_epochs from .epochs import _drop_log_stats from .raw import plot_raw, plot_raw_psds from .ica import plot_ica_scores, plot_ica_sources, plot_ica_overlay

45.047619

68

0.842495

143

946

5.13986

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0.112051

946

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0

null

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null

0

1

0

1

0

2

73968a64038186a231ad939ca0949adb285e3fe1

1,191

py

Python

nextactions/card.py

stevecshanks/trello-next-actions

d9a5086185bccf969d551a25f966e1b24ce4a299

[ "MIT" ]

null

nextactions/card.py

stevecshanks/trello-next-actions

d9a5086185bccf969d551a25f966e1b24ce4a299

[ "MIT" ]

1

2016-12-28T16:23:19.000Z

2016-12-28T16:25:18.000Z

nextactions/card.py

stevecshanks/trello-next-actions

d9a5086185bccf969d551a25f966e1b24ce4a299

[ "MIT" ]

null

from urllib.parse import urlparse class Card: AUTO_GENERATED_TEXT = 'Auto-created by TrelloNextActions' def __init__(self, trello, json): self._trello = trello self.id = json['id'] self.name = json['name'] self.board_id = json['idBoard'] self.description = json['desc'] self.url = json['url'] def isAutoGenerated(self): return Card.AUTO_GENERATED_TEXT in self.description def getProjectBoard(self): board_id = self._getProjectBoardId() return self._trello.getBoardById(board_id) def _getProjectBoardId(self): url_components = urlparse(self.description) path_segments = url_components.path.split('/') if (len(path_segments) >= 3): return path_segments[2] else: raise ValueError("Description could not be parsed as project URL") def __eq__(self, other): return self.id == other.id def linksTo(self, other): return self.description.startswith(other.url) def archive(self): self._trello.put( 'https://api.trello.com/1/cards/' + self.id + '/closed', {'value': "true"} )

28.357143

78

0.617968

137

1,191

5.189781

0.452555

0.056259

0.04782

0.059072

0

0.00344

0.267842

1,191

41

79

29.04878

0.811927

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0

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0.225806

false

0

0.032258

0.096774

0.483871

0

null

0

1

0

null

0

1

0

2

739e55523a04f2d7ccad72f1592313ae63718b29

673

py

Python

Strings/1255_2.py

jeconiassantos/uriissues

f6c32f8632b9940a4886240ea5d22300922dc79a

[ "MIT" ]

null

Strings/1255_2.py

jeconiassantos/uriissues

f6c32f8632b9940a4886240ea5d22300922dc79a

[ "MIT" ]

null

Strings/1255_2.py

jeconiassantos/uriissues

f6c32f8632b9940a4886240ea5d22300922dc79a

[ "MIT" ]

null

N = int(input()) while N > 0: texto = input().lower().replace(' ', '') alfabeto = 'abcdefghijklmnopqrstuvwxyz' contador = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] result = '' a, i, count, maior = 0, 0, 0, 0 break_ = True while count < 52: if break_ == True: contador[i] = texto.count(alfabeto[i]) if contador[i] > maior: maior = contador[i] i += 1 if i == 26: break_ = False else: if maior == texto.count(alfabeto[a]): result += alfabeto[a] a += 1 count += 1 print(result) N -= 1

28.041667

68

0.451709

94

673

3.202128

0.265957

0.186047

0.259136

0.318937

0.086379

0

0.094203

0.384844

673

23

69

29.26087

0.63285

0

0.040119

0.038633

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false

0

0.043478

0

null

0

1

0

null

0

2

739f2e72b2518d429688ce94886662287a341dd5

78

py

Python

activities/renderers/activity/pdf/colors.py

zemogle/astroEDU

8d240ff35a288c9e920f6527f1cd3957d116e6ae

[ "MIT" ]

1

2021-09-09T12:32:34.000Z

activities/renderers/activity/pdf/colors.py

zemogle/astroEDU

8d240ff35a288c9e920f6527f1cd3957d116e6ae

[ "MIT" ]

4

2021-09-09T19:53:18.000Z

2021-09-24T09:11:26.000Z

activities/renderers/activity/pdf/colors.py

zemogle/astroEDU

8d240ff35a288c9e920f6527f1cd3957d116e6ae

[ "MIT" ]

null

HEADER_COLOR = '#F78606' TEXT_COLOR = '#676867' FOOTER_LINE_COLOR = '#B0B0AE'

19.5

29

0.730769

10

78

5.3

0.8

0

0.188406

0.115385

78

3

30

26

0.57971

0

0.269231

0

1

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false

0

1

0

null

0

1

0

null

0

2

73a30f48f20340b22a4c76ca06d5d7cfa5438e73

553

py

Python

shop/migrations/0001_initial.py

aashish01/CRUD-Operation-Django-Rest-Framework

f6950e37c5eb25942c15ff733416dd90347b2a25

[ "MIT" ]

null

shop/migrations/0001_initial.py

aashish01/CRUD-Operation-Django-Rest-Framework

f6950e37c5eb25942c15ff733416dd90347b2a25

[ "MIT" ]

null

shop/migrations/0001_initial.py

aashish01/CRUD-Operation-Django-Rest-Framework

f6950e37c5eb25942c15ff733416dd90347b2a25

[ "MIT" ]

null

# Generated by Django 3.0.7 on 2020-07-02 05:41 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='item', fields=[ ('id', models.AutoField(primary_key=True, serialize=False)), ('name', models.CharField(max_length=50)), ('price', models.FloatField()), ('brand', models.CharField(max_length=40)), ], ), ]

23.041667

76

0.540687

54

553

5.481481

0.759259

0.101351

0.121622

0.162162

0

0.051213

0.329114

553

23

77

24.043478

0.746631

0.081374

0

1

0

0.039526

0

1

0

false

0

0.0625

0

0.3125

0

null

0

1

0

1

0

null

0

2

73addeab48b465aab956af75fcbbd1d44a07f92f

15,000

py

Python

scuole/stats/schemas/tapr/pre2014schema.py

texastribune/scuole

8ab316ee50ef0d8e71b94b50dc889d10c6e83412

[ "MIT" ]

1

2019-03-12T04:30:02.000Z

scuole/stats/schemas/tapr/pre2014schema.py

texastribune/scuole

8ab316ee50ef0d8e71b94b50dc889d10c6e83412

[ "MIT" ]

616

2017-08-18T21:15:39.000Z

2022-03-25T11:17:10.000Z

scuole/stats/schemas/tapr/pre2014schema.py

texastribune/scuole

8ab316ee50ef0d8e71b94b50dc889d10c6e83412

[ "MIT" ]

null

# Schema used for pre-2013-2014 TAPR data SCHEMA = { 'staff-and-student-information': { 'all_students_count': 'PETALLC', 'african_american_count': 'PETBLAC', 'african_american_percent': 'PETBLAP', 'american_indian_count': 'PETINDC', 'american_indian_percent': 'PETINDP', 'asian_count': 'PETASIC', 'asian_percent': 'PETASIP', 'hispanic_count': 'PETHISC', 'hispanic_percent': 'PETHISP', 'pacific_islander_count': 'PETPCIC', 'pacific_islander_percent': 'PETPCIP', 'two_or_more_races_count': 'PETTWOC', 'two_or_more_races_percent': 'PETTWOP', 'white_count': 'PETWHIC', 'white_percent': 'PETWHIP', 'early_childhood_education_count': 'PETGEEC', 'early_childhood_education_percent': 'PETGEEP', 'prek_count': 'PETGPKC', 'prek_percent': 'PETGPKP', 'kindergarten_count': 'PETGKNC', 'kindergarten_percent': 'PETGKNP', 'first_count': 'PETG01C', 'first_percent': 'PETG01P', 'second_count': 'PETG02C', 'second_percent': 'PETG02P', 'third_count': 'PETG03C', 'third_percent': 'PETG03P', 'fourth_count': 'PETG04C', 'fourth_percent': 'PETG04P', 'fifth_count': 'PETG05C', 'fifth_percent': 'PETG05P', 'sixth_count': 'PETG06C', 'sixth_percent': 'PETG06P', 'seventh_count': 'PETG07C', 'seventh_percent': 'PETG07P', 'eighth_count': 'PETG08C', 'eighth_percent': 'PETG08P', 'ninth_count': 'PETG09C', 'ninth_percent': 'PETG09P', 'tenth_count': 'PETG10C', 'tenth_percent': 'PETG10P', 'eleventh_count': 'PETG11C', 'eleventh_percent': 'PETG11P', 'twelfth_count': 'PETG12C', 'twelfth_percent': 'PETG12P', 'at_risk_count': 'PETRSKC', 'at_risk_percent': 'PETRSKP', 'economically_disadvantaged_count': 'PETECOC', 'economically_disadvantaged_percent': 'PETECOP', 'limited_english_proficient_count': 'PETLEPC', 'limited_english_proficient_percent': 'PETLEPP', 'bilingual_esl_count': 'PETBILC', 'bilingual_esl_percent': 'PETBILP', 'career_technical_education_count': 'PETVOCC', 'career_technical_education_percent': 'PETVOCP', 'gifted_and_talented_count': 'PETGIFC', 'gifted_and_talented_percent': 'PETGIFP', 'special_education_count': 'PETSPEC', 'special_education_percent': 'PETSPEP', 'class_size_avg_kindergarten': 'PCTGKGA', 'class_size_avg_first': 'PCTG01A', 'class_size_avg_second': 'PCTG02A', 'class_size_avg_third': 'PCTG03A', 'class_size_avg_fourth': 'PCTG04A', 'class_size_avg_fifth': 'PCTG05A', 'class_size_avg_sixth': 'PCTG06A', 'class_size_avg_mixed_elementary': 'PCTGMEA', 'class_size_avg_secondary_english': 'PCTENGA', 'class_size_avg_secondary_foreign_language': 'PCTFLAA', 'class_size_avg_secondary_math': 'PCTMATA', 'class_size_avg_secondary_science': 'PCTSCIA', 'class_size_avg_secondary_social_studies': 'PCTSOCA', 'students_per_teacher': 'PSTKIDR', # teacher_avg_tenure is Average Years Experience of Teachers with District 'teacher_avg_tenure': 'PSTTENA', # teacher_avg_experience is Average Years Experience of Teachers 'teacher_avg_experience': 'PSTEXPA', 'teacher_avg_base_salary': 'PSTTOSA', 'teacher_avg_beginning_salary': 'PST00SA', 'teacher_avg_1_to_5_year_salary': 'PST01SA', 'teacher_avg_6_to_10_year_salary': 'PST06SA', 'teacher_avg_11_to_20_year_salary': 'PST11SA', 'teacher_avg_20_plus_year_salary': 'PST20SA', 'teacher_total_fte_count': 'PSTTOFC', 'teacher_african_american_fte_count': 'PSTBLFC', 'teacher_american_indian_fte_count': 'PSTINFC', 'teacher_asian_fte_count': 'PSTASFC', 'teacher_hispanic_fte_count': 'PSTHIFC', 'teacher_pacific_islander_fte_count': 'PSTPIFC', 'teacher_two_or_more_races_fte_count': 'PSTTWFC', 'teacher_white_fte_count': 'PSTWHFC', 'teacher_total_fte_percent': 'PSTTOFC', 'teacher_african_american_fte_percent': 'PSTBLFP', 'teacher_american_indian_fte_percent': 'PSTINFP', 'teacher_asian_fte_percent': 'PSTASFP', 'teacher_hispanic_fte_percent': 'PSTHIFP', 'teacher_pacific_islander_fte_percent': 'PSTPIFP', 'teacher_two_or_more_races_fte_percent': 'PSTTWFP', 'teacher_white_fte_percent': 'PSTWHFP', # 'teacher_no_degree_count': 'PSTNOFC', # 'teacher_bachelors_count': 'PSTBAFC', # 'teacher_masters_count': 'PSTMSFC', # 'teacher_doctorate_count': 'PSTPHFC', # 'teacher_no_degree_percent': 'PSTNOFP', # 'teacher_bachelors_percent': 'PSTBAFP', # 'teacher_masters_percent': 'PSTMSFP', # 'teacher_doctorate_percent': 'PSTPHFP', }, 'postsecondary-readiness-and-non-staar-performance-indicators': { # 'college_ready_graduates_english_all_students_count': 'ACRR', 'college_ready_graduates_english_all_students_percent': 'ACRR', # 'college_ready_graduates_english_african_american_count': 'BCRR', 'college_ready_graduates_english_african_american_percent': 'BCRR', # 'college_ready_graduates_english_american_indian_count': 'ICRR', 'college_ready_graduates_english_american_indian_percent': 'ICRR', # 'college_ready_graduates_english_asian_count': '3CRR', 'college_ready_graduates_english_asian_percent': '3CRR', # 'college_ready_graduates_english_hispanic_count': 'HCRR', 'college_ready_graduates_english_hispanic_percent': 'HCRR', # 'college_ready_graduates_english_pacific_islander_count': '4CRR', 'college_ready_graduates_english_pacific_islander_percent': '4CRR', # 'college_ready_graduates_english_two_or_more_races_count': '2CRR', 'college_ready_graduates_english_two_or_more_races_percent': '2CRR', # 'college_ready_graduates_english_white_count': 'WCRR', 'college_ready_graduates_english_white_percent': 'WCRR', # 'college_ready_graduates_english_economically_disadvantaged_count': 'ECRR', 'college_ready_graduates_english_economically_disadvantaged_percent': 'ECRR', # 'college_ready_graduates_english_limited_english_proficient_count': 'LCRR', 'college_ready_graduates_english_limited_english_proficient_percent': 'LCRR', # 'college_ready_graduates_english_at_risk_count': 'RCRR', 'college_ready_graduates_english_at_risk_percent': 'RCRR', # 'college_ready_graduates_math_all_students_count': 'ACRM', 'college_ready_graduates_math_all_students_percent': 'ACRM', # 'college_ready_graduates_math_african_american_count': 'BCRM', 'college_ready_graduates_math_african_american_percent': 'BCRM', # 'college_ready_graduates_math_american_indian_count': 'ICRM', 'college_ready_graduates_math_american_indian_percent': 'ICRM', # 'college_ready_graduates_math_asian_count': '3CRM', 'college_ready_graduates_math_asian_percent': '3CRM', # 'college_ready_graduates_math_hispanic_count': 'HCRM', 'college_ready_graduates_math_hispanic_percent': 'HCRM', # 'college_ready_graduates_math_pacific_islander_count': '4CRM', 'college_ready_graduates_math_pacific_islander_percent': '4CRM', # 'college_ready_graduates_math_two_or_more_races_count': '2CRM', 'college_ready_graduates_math_two_or_more_races_percent': '2CRM', # 'college_ready_graduates_math_white_count': 'WCRM', 'college_ready_graduates_math_white_percent': 'WCRM', # 'college_ready_graduates_math_economically_disadvantaged_count': 'ECRM', 'college_ready_graduates_math_economically_disadvantaged_percent': 'ECRM', # 'college_ready_graduates_math_limited_english_proficient_count': 'LCRM', 'college_ready_graduates_math_limited_english_proficient_percent': 'LCRM', # 'college_ready_graduates_math_at_risk_count': 'RCRM', 'college_ready_graduates_math_at_risk_percent': 'RCRM', # 'college_ready_graduates_both_all_students_count': 'ACRB', 'college_ready_graduates_both_all_students_percent': 'ACRB', # 'college_ready_graduates_both_african_american_count': 'BCRB', 'college_ready_graduates_both_african_american_percent': 'BCRB', # 'college_ready_graduates_both_asian_count': '3CRB', 'college_ready_graduates_both_asian_percent': '3CRB', # 'college_ready_graduates_both_hispanic_count': 'HCRB', 'college_ready_graduates_both_hispanic_percent': 'HCRB', # 'college_ready_graduates_both_american_indian_count': 'ICRB', 'college_ready_graduates_both_american_indian_percent': 'ICRB', # 'college_ready_graduates_both_pacific_islander_count': '4CRB', 'college_ready_graduates_both_pacific_islander_percent': '4CRB', # 'college_ready_graduates_both_two_or_more_races_count': '2CRB', 'college_ready_graduates_both_two_or_more_races_percent': '2CRB', # 'college_ready_graduates_both_white_count': 'WCRB', 'college_ready_graduates_both_white_percent': 'WCRB', # 'college_ready_graduates_both_economically_disadvantaged_count': 'ECRB', 'college_ready_graduates_both_economically_disadvantaged_percent': 'ECRB', # 'college_ready_graduates_both_limited_english_proficient_count': 'LCRB', 'college_ready_graduates_both_limited_english_proficient_percent': 'LCRB', # 'college_ready_graduates_both_at_risk_count': 'RCRB', 'college_ready_graduates_both_at_risk_percent': 'RCRB', 'avg_sat_score_all_students': 'A0CSA', 'avg_sat_score_african_american': 'B0CSA', 'avg_sat_score_american_indian': 'I0CSA', 'avg_sat_score_asian': '30CSA', 'avg_sat_score_hispanic': 'H0CSA', 'avg_sat_score_pacific_islander': '40CSA', 'avg_sat_score_two_or_more_races': '20CSA', 'avg_sat_score_white': 'W0CSA', 'avg_sat_score_economically_disadvantaged': 'E0CSA', 'avg_act_score_all_students': 'A0CAA', 'avg_act_score_african_american': 'B0CAA', 'avg_act_score_american_indian': 'I0CAA', 'avg_act_score_asian': '30CAA', 'avg_act_score_hispanic': 'H0CAA', 'avg_act_score_pacific_islander': '40CAA', 'avg_act_score_two_or_more_races': '20CAA', 'avg_act_score_white': 'W0CAA', 'avg_act_score_economically_disadvantaged': 'E0CAA', # 'ap_ib_all_students_count_above_criterion': 'A0BKA', 'ap_ib_all_students_percent_above_criterion': 'A0BKA', # 'ap_ib_african_american_count_above_criterion': 'B0BKA', 'ap_ib_african_american_percent_above_criterion': 'B0BKA', # 'ap_ib_asian_count_above_criterion': '30BKA', 'ap_ib_asian_percent_above_criterion': '30BKA', # 'ap_ib_hispanic_count_above_criterion': 'H0BKA', 'ap_ib_hispanic_percent_above_criterion': 'H0BKA', # 'ap_ib_american_indian_count_above_criterion': 'I0BKA', 'ap_ib_american_indian_percent_above_criterion': 'I0BKA', # 'ap_ib_pacific_islander_count_above_criterion': '40BKA', 'ap_ib_pacific_islander_percent_above_criterion': '40BKA', # 'ap_ib_two_or_more_races_count_above_criterion': '20BKA', 'ap_ib_two_or_more_races_percent_above_criterion': '20BKA', # 'ap_ib_white_count_above_criterion': 'W0BKA', 'ap_ib_white_percent_above_criterion': 'W0BKA', # 'ap_ib_economically_disadvantaged_count_above_criterion': 'E0BKA', 'ap_ib_economically_disadvantaged_percent_above_criterion': 'E0BKA', 'ap_ib_all_students_percent_taking': 'A0BTA', 'ap_ib_african_american_percent_taking': 'B0BTA', 'ap_ib_asian_percent_taking': '30BTA', 'ap_ib_hispanic_percent_taking': 'H0BTA', 'ap_ib_american_indian_percent_taking': 'I0BTA', 'ap_ib_pacific_islander_percent_taking': '40BTA', 'ap_ib_two_or_more_races_percent_taking': '20BTA', 'ap_ib_white_percent_taking': 'W0BTA', 'ap_ib_economically_disadvantaged_percent_taking': 'E0BTA', # 'dropout_all_students_count': 'A0912DR', 'dropout_all_students_percent': 'A0912DR', # 'dropout_african_american_count': 'B0912DR', 'dropout_african_american_percent': 'B0912DR', # 'dropout_asian_count': '30912DR', 'dropout_asian_percent': '30912DR', # 'dropout_hispanic_count': 'H0912DR', 'dropout_hispanic_percent': 'H0912DR', # 'dropout_american_indian_count': 'I0912DR', 'dropout_american_indian_percent': 'I0912DR', # 'dropout_pacific_islander_count': '40912DR', 'dropout_pacific_islander_percent': '40912DR', # 'dropout_two_or_more_races_count': '20912DR', 'dropout_two_or_more_races_percent': '20912DR', # 'dropout_white_count': 'W0912DR', 'dropout_white_percent': 'W0912DR', # 'dropout_at_risk_count': 'R0912DR', 'dropout_at_risk_percent': 'R0912DR', # 'dropout_economically_disadvantaged_count': 'E0912DR', 'dropout_economically_disadvantaged_percent': 'E0912DR', # 'dropout_limited_english_proficient_count': 'E0912DR', 'dropout_limited_english_proficient_percent': 'E0912DR', # 'four_year_graduate_all_students_count': 'AGC4X', 'four_year_graduate_all_students_percent': 'AGC4X', # 'four_year_graduate_african_american_count': 'BGC4X', 'four_year_graduate_african_american_percent': 'BGC4X', # 'four_year_graduate_american_indian_count': 'IGC4X', 'four_year_graduate_american_indian_percent': 'IGC4X', # 'four_year_graduate_asian_count': '3GC4X', 'four_year_graduate_asian_percent': '3GC4X', # 'four_year_graduate_hispanic_count': 'HGC4X', 'four_year_graduate_hispanic_percent': 'HGC4X', # 'four_year_graduate_pacific_islander_count': '4GC4X', 'four_year_graduate_pacific_islander_percent': '4GC4X', # 'four_year_graduate_two_or_more_races_count': '2GC4X', 'four_year_graduate_two_or_more_races_percent': '2GC4X', # 'four_year_graduate_white_count': 'WGC4X', 'four_year_graduate_white_percent': 'WGC4X', # 'four_year_graduate_at_risk_count': 'RGC4X', 'four_year_graduate_at_risk_percent': 'RGC4X', # 'four_year_graduate_economically_disadvantaged_count': 'EGC4X', 'four_year_graduate_economically_disadvantaged_percent': 'EGC4X', # 'four_year_graduate_limited_english_proficient_count': 'L3C4X', 'four_year_graduate_limited_english_proficient_percent': 'L3C4X', 'attendance_rate': 'A0AT', }, 'reference': { 'accountability_rating': '_RATING', }, }

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null

0

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null

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73c5bf01be8b25980264bc227433957a55bd99b7

368

py

Python

analyticsclient/exceptions.py

Jawayria/edx-analytics-data-api-client

1ff83fc0ab1f56032826157502d987ecc5ac2e82

[ "Apache-2.0" ]

12

2015-07-24T17:06:18.000Z

2021-07-21T15:22:30.000Z

analyticsclient/exceptions.py

Jawayria/edx-analytics-data-api-client

1ff83fc0ab1f56032826157502d987ecc5ac2e82

[ "Apache-2.0" ]

61

2015-01-06T02:55:17.000Z

2021-11-18T20:51:19.000Z

analyticsclient/exceptions.py

Jawayria/edx-analytics-data-api-client

1ff83fc0ab1f56032826157502d987ecc5ac2e82

[ "Apache-2.0" ]

26

2015-01-26T14:39:33.000Z

2021-03-26T06:38:06.000Z

class ClientError(Exception): """Common base class for all client errors.""" class NotFoundError(ClientError): """URL was not found.""" class InvalidRequestError(ClientError): """The API request was invalid.""" class TimeoutError(ClientError): # pylint: disable=redefined-builtin """The API server did not respond before the timeout expired."""

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73d7639a690983572e46a8202fb7895db1bb1e78

1,852

py

Python

app/converters.py

UniOulu-Ubicomp-Programming-Courses/pwp-inventory-service

5631ae42780b3af693e6ba71872a9b6ffda708d8

[ "MIT" ]

null

app/converters.py

UniOulu-Ubicomp-Programming-Courses/pwp-inventory-service

5631ae42780b3af693e6ba71872a9b6ffda708d8

[ "MIT" ]

null

app/converters.py

UniOulu-Ubicomp-Programming-Courses/pwp-inventory-service

5631ae42780b3af693e6ba71872a9b6ffda708d8

[ "MIT" ]

null

""" This module defines custom converters for routing. These converters streamline resource code by performing the process of getting the model instance from the database before the view method is called. The converter converts a resource's slug to the corresponding model instance, which is then placed into the view method arguments. This eliminates the need to get all of the model instances referenced in the URI and doing a 404 check for each one, avoiding a whole lot of boilerplate code in the view methods. The same happens in reverse: when costructing a URI for a resource, a model instance is passed to *url_for* instead of the model slug, and the converter will take care of placing a convertible value into the URI. """ from werkzeug.exceptions import NotFound from werkzeug.routing import BaseConverter from app.models import Map, Observer, Obstacle class MapConverter(BaseConverter): """ A converter for the Map model. Uses map slug as the resource handle. """ def to_python(self, map_slug) -> object: """ Converts a map slug into the corresponding map model instance. """ map = Map.query.filter_by(slug=map_slug).first() if map is None: raise NotFound return map def to_url(self, map) -> str: """ Converts a map model instance to its corresponding slug for use in URI. """ return map.slug class ObserverConverter(BaseConverter): """ A converter for the Map model. Uses map slug as the resource handle. """ def to_python(self, obs_slug): observer = Observer.query.filter_by(slug=obs_slug).first() if observer is None: raise NotFound return observer def to_url(self, observer): return observer.slug

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73d7eb2238d3b8f6b37085a9c2e1479e64d16baf

31,342

py

Python

AppDashboard/test/functional/test_dashboard.py

eabyshev/appscale

1cfb5a609130f415143ec76718e839b0f73ac668

[ "Apache-2.0" ]

2

2018-10-09T17:48:12.000Z

2019-01-15T10:18:19.000Z

AppDashboard/test/functional/test_dashboard.py

christianbaun/appscale

c24ddfd987c8eed8ed8864cc839cc0556a8af3c7

[ "Apache-2.0" ]

null

AppDashboard/test/functional/test_dashboard.py

christianbaun/appscale

c24ddfd987c8eed8ed8864cc839cc0556a8af3c7

[ "Apache-2.0" ]

1

2022-02-20T20:57:12.000Z

#!/usr/bin/env python2 from flexmock import flexmock import os import re import SOAPpy import StringIO import sys import unittest sys.path.append(os.path.join(os.path.dirname(__file__), '../../../AppDashboard')) from dashboard import AppDeletePage from dashboard import AppUploadPage from dashboard import AuthorizePage from dashboard import IndexPage from dashboard import LoginPage from dashboard import LoginVerify from dashboard import LogoutPage from dashboard import NewUserPage from dashboard import StatusPage from dashboard import StatusRefreshPage sys.path.append(os.path.join(os.path.dirname(__file__), '../../../AppServer')) from google.appengine.api.appcontroller_client import AppControllerClient from google.appengine.ext import db from google.appengine.api import taskqueue from google.appengine.api import users sys.path.append(os.path.join(os.path.dirname(__file__), '../../lib')) import app_dashboard_data from app_dashboard_data import AppDashboardData from app_dashboard_helper import AppDashboardHelper from secret_key import GLOBAL_SECRET_KEY class FunctionalTestAppDashboard(unittest.TestCase): def setUp(self): acc = flexmock(AppControllerClient) acc.should_receive('get_uaserver_host').and_return('public1') acc.should_receive('get_stats').and_return([ {'ip' : '1.1.1.1', 'cpu' : '50', 'memory' : '50', 'disk' : '50', 'cloud' : 'cloud1', 'roles' : 'roles1', 'apps':{ 'app1':True, 'app2':False } }, {'ip' : '2.2.2.2', 'cpu' : '50', 'memory' : '50', 'disk' : '50', 'cloud' : 'cloud1', 'roles' : 'roles1'} ]) acc.should_receive('get_role_info').and_return( [{'jobs':['shadow', 'login'], 'public_ip':'1.1.1.1'} ] ) acc.should_receive('get_database_information').and_return( {'table':'fake_database', 'replication':1} ) acc.should_receive('get_api_status').and_return( {'api1':'running', 'api2':'failed', 'api3':'unknown'} ) acc.should_receive('upload_tgz').and_return('true') acc.should_receive('stop_app').and_return('true') fake_soap = flexmock(name='fake_soap') soap = flexmock(SOAPpy) soap.should_receive('SOAPProxy').and_return(fake_soap) fake_soap.should_receive('get_app_data').and_return( "\n\n ports: 8080\n num_ports:1\n" ) fake_soap.should_receive('get_capabilities')\ .with_args('a@a.com', GLOBAL_SECRET_KEY)\ .and_return('upload_app') fake_soap.should_receive('get_capabilities')\ .with_args('b@a.com', GLOBAL_SECRET_KEY)\ .and_return('upload_app') fake_soap.should_receive('get_capabilities')\ .with_args('c@a.com', GLOBAL_SECRET_KEY)\ .and_return('') fake_soap.should_receive('get_user_data')\ .with_args('a@a.com', GLOBAL_SECRET_KEY)\ .and_return( "is_cloud_admin:true\napplications:app1:app2\npassword:79951d98d43c1830c5e5e4de58244a621595dfaa\n" ) fake_soap.should_receive('get_user_data')\ .with_args('b@a.com', GLOBAL_SECRET_KEY)\ .and_return( "is_cloud_admin:false\napplications:app2\npassword:79951d98d43c1830c5e5e4de58244a621595dfaa\n" ) fake_soap.should_receive('get_user_data')\ .with_args('c@a.com', GLOBAL_SECRET_KEY)\ .and_return( "is_cloud_admin:false\napplications:app2\npassword:79951d98d43c1830c5e5e4de58244a621595dfaa\n" ) fake_soap.should_receive('commit_new_user').and_return('true') fake_soap.should_receive('commit_new_token').and_return() fake_soap.should_receive('get_all_users').and_return("a@a.com:b@a.com") fake_soap.should_receive('set_capabilities').and_return('true') self.request = self.fakeRequest() self.response = self.fakeResponse() self.set_user() fake_tq = flexmock(taskqueue) fake_tq.should_receive('add').and_return() self.setup_fake_db() def setup_fake_db(self): fake_root = flexmock() fake_root.head_node_ip = '1.1.1.1' fake_root.table = 'table' fake_root.replication = 'replication' fake_root.should_receive('put').and_return() flexmock(app_dashboard_data).should_receive('DashboardDataRoot')\ .and_return(fake_root) flexmock(AppDashboardData).should_receive('get_one')\ .with_args(app_dashboard_data.DashboardDataRoot, AppDashboardData.ROOT_KEYNAME)\ .and_return(None)\ .and_return(fake_root) fake_api1 = flexmock(name='APIstatus') fake_api1.name = 'api1' fake_api1.value = 'running' fake_api1.should_receive('put').and_return() fake_api2 = flexmock(name='APIstatus') fake_api2.name = 'api2' fake_api2.value = 'failed' fake_api2.should_receive('put').and_return() fake_api3 = flexmock(name='APIstatus') fake_api3.name = 'api3' fake_api3.value = 'unknown' fake_api3.should_receive('put').and_return() fake_api_q = flexmock() fake_api_q.should_receive('ancestor').and_return() fake_api_q.should_receive('run')\ .and_yield(fake_api1, fake_api2, fake_api3) flexmock(AppDashboardData).should_receive('get_one')\ .with_args(app_dashboard_data.APIstatus, re.compile('api'))\ .and_return(fake_api1)\ .and_return(fake_api3)\ .and_return(fake_api3) flexmock(AppDashboardData).should_receive('get_all')\ .with_args(app_dashboard_data.APIstatus)\ .and_return(fake_api_q) fake_server1 = flexmock(name='ServerStatus') fake_server1.ip = '1.1.1.1' fake_server1.cpu = '25' fake_server1.memory = '50' fake_server1.disk = '100' fake_server1.cloud = 'cloud1' fake_server1.roles = 'roles2' fake_server1.should_receive('put').and_return() fake_server2 = flexmock(name='ServerStatus') fake_server2.ip = '2.2.2.2' fake_server2.cpu = '75' fake_server2.memory = '55' fake_server2.disk = '100' fake_server2.cloud = 'cloud1' fake_server2.roles = 'roles2' fake_server2.should_receive('put').and_return() flexmock(app_dashboard_data).should_receive('ServerStatus')\ .and_return(fake_server1) fake_server_q = flexmock() fake_server_q.should_receive('ancestor').and_return() fake_server_q.should_receive('run')\ .and_yield(fake_server1, fake_server2) fake_server_q.should_receive('get')\ .and_return(fake_server1)\ .and_return(fake_server2) flexmock(AppDashboardData).should_receive('get_all')\ .with_args(app_dashboard_data.ServerStatus)\ .and_return(fake_server_q) flexmock(AppDashboardData).should_receive('get_one')\ .with_args(app_dashboard_data.ServerStatus, re.compile('\d'))\ .and_return(fake_server1)\ .and_return(fake_server2) fake_app1 = flexmock(name='AppStatus') fake_app1.name = 'app1' fake_app1.url = 'http://1.1.1.1:8080' fake_app1.should_receive('put').and_return() fake_app1.should_receive('delete').and_return() fake_app2 = flexmock(name='AppStatus') fake_app2.name = 'app2' fake_app2.url = None fake_app2.should_receive('put').and_return() fake_app2.should_receive('delete').and_return() flexmock(app_dashboard_data).should_receive('AppStatus')\ .and_return(fake_app1) fake_app_q = flexmock() fake_app_q.should_receive('ancestor').and_return() fake_app_q.should_receive('run')\ .and_yield(fake_app1, fake_app2) flexmock(AppDashboardData).should_receive('get_all')\ .with_args(app_dashboard_data.AppStatus)\ .and_return(fake_app_q) flexmock(AppDashboardData).should_receive('get_all')\ .with_args(app_dashboard_data.AppStatus, keys_only=True)\ .and_return(fake_app_q) flexmock(AppDashboardData).should_receive('get_one')\ .with_args(app_dashboard_data.AppStatus, re.compile('app'))\ .and_return(fake_app1)\ .and_return(fake_app2) user_info1 = flexmock(name='UserInfo') user_info1.email = 'a@a.com' user_info1.is_user_cloud_admin = True user_info1.can_upload_apps = True user_info1.owned_apps = 'app1:app2' user_info1.should_receive('put').and_return() user_info2 = flexmock(name='UserInfo') user_info2.email = 'b@a.com' user_info2.is_user_cloud_admin = False user_info2.can_upload_apps = True user_info2.owned_apps = 'app2' user_info2.should_receive('put').and_return() user_info3 = flexmock(name='UserInfo') user_info3.email = 'c@a.com' user_info3.is_user_cloud_admin = False user_info3.can_upload_apps = False user_info3.owned_apps = 'app2' user_info3.should_receive('put').and_return() flexmock(app_dashboard_data).should_receive('UserInfo')\ .and_return(user_info1) flexmock(AppDashboardData).should_receive('get_one')\ .with_args(app_dashboard_data.UserInfo, re.compile('a@a.com'))\ .and_return(user_info1) flexmock(AppDashboardData).should_receive('get_one')\ .with_args(app_dashboard_data.UserInfo, re.compile('b@a.com'))\ .and_return(user_info2) flexmock(AppDashboardData).should_receive('get_one')\ .with_args(app_dashboard_data.UserInfo, re.compile('c@a.com'))\ .and_return(user_info3) flexmock(db).should_receive('delete').and_return() flexmock(db).should_receive('run_in_transaction').and_return() def set_user(self, email=None): self.usrs = flexmock(users) if email is not None: user_obj = flexmock(name='users') user_obj.should_receive('email').and_return(email) self.usrs.should_receive('get_current_user').and_return(user_obj) else: self.usrs.should_receive('get_current_user').and_return(None) def set_post(self, post_dict): self.request.POST = post_dict for key in post_dict.keys(): self.request.should_receive('get').with_args(key)\ .and_return(post_dict[key]) def set_fileupload(self, fieldname): self.request.POST = flexmock(name='POST') self.request.POST.multi = {} self.request.POST.multi[fieldname] = flexmock(name='file') self.request.POST.multi[fieldname].file = StringIO.StringIO("FILE CONTENTS") def set_get(self, post_dict): self.request.GET = post_dict for key in post_dict.keys(): self.request.should_receive('get').with_args(key)\ .and_return(post_dict[key]) def fakeRequest(self): req = flexmock(name='request') req.should_receive('get').and_return('') req.url = '/' return req def fakeResponse(self): res = flexmock(name='response') res.headers = {} res.cookies = {} res.deleted_cookies = {} res.redirect_location = None res.out = StringIO.StringIO() def fake_set_cookie(key, value='', max_age=None, path='/', domain=None, secure=None, httponly=False, comment=None, expires=None, overwrite=False): res.cookies[key] = value def fake_delete_cookie(key, path='/', domain=None): res.deleted_cookies[key] = 1 def fake_clear(): pass def fake_redirect(path, response): res.redirect_location = path res.set_cookie = fake_set_cookie res.delete_cookie = fake_delete_cookie res.clear = fake_clear res.redirect = fake_redirect return res def test_landing_notloggedin(self): IndexPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('<a href="/users/login">Login to this cloud.</a>', html)) self.assertFalse(re.search('<a href="/authorize">Manage users.</a>', html)) def test_landing_loggedin_notAdmin(self): self.set_user('b@a.com') IndexPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('<a href="/users/logout">Logout now.</a>', html)) self.assertFalse(re.search('<a href="/authorize">Manage users.</a>', html)) def test_landing_loggedin_isAdmin(self): self.set_user('a@a.com') IndexPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('<a href="/users/logout">Logout now.</a>', html)) self.assertTrue(re.search('<a href="/authorize">Manage users.</a>', html)) def test_status_notloggedin_refresh(self): self.set_get({ 'forcerefresh' : '1', }) StatusPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('<a href="/users/login">Login</a>', html)) def test_status_notloggedin(self): StatusPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('<a href="/users/login">Login</a>', html)) def test_status_loggedin_notAdmin(self): self.set_user('b@a.com') StatusPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('<a href="/users/logout">Logout</a>', html)) self.assertFalse(re.search('CPU / Memory Usage', html)) def test_status_loggedin_isAdmin(self): self.set_user('a@a.com') StatusPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('<a href="/users/logout">Logout</a>', html)) self.assertTrue(re.search('CPU / Memory Usage', html)) def test_newuser_page(self): NewUserPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) def test_newuser_bademail(self): self.set_post({ 'user_email' : 'c@a', 'user_password' : 'aaaaaa', 'user_password_confirmation' : 'aaaaaa', }) NewUserPage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Format must be foo@boo.goo.', html)) def test_newuser_shortpasswd(self): self.set_post({ 'user_email' : 'c@a.com', 'user_password' : 'aaa', 'user_password_confirmation' : 'aaa', }) NewUserPage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Password must be at least 6 characters long.', html)) def test_newuser_passwdnomatch(self): self.set_post({ 'user_email' : 'c@a.com', 'user_password' : 'aaaaa', 'user_password_confirmation' : 'aaabbb', }) NewUserPage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Passwords do not match.', html)) def test_newuser_success(self): self.set_post({ 'user_email' : 'c@a.com', 'user_password' : 'aaaaaa', 'user_password_confirmation' : 'aaaaaa', }) page = NewUserPage(self.request, self.response) page.redirect = self.response.redirect page.post() self.assertTrue(AppDashboardHelper.DEV_APPSERVER_LOGIN_COOKIE in self.response.cookies) self.assertEqual(self.response.redirect_location, '/') def test_loginverify_page(self): self.set_get({ 'continue' : 'http%3A//192.168.33.168%3A8080/_ah/login%3Fcontinue%3Dhttp%3A//192.168.33.168%3A8080/' }) LoginVerify(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('http://192.168.33.168:8080/', html)) def test_loginverify_submitcontinue(self): self.set_post({ 'commit' : 'Yes', 'continue' : 'http://192.168.33.168:8080/' }) page = LoginVerify(self.request, self.response) page.redirect = self.response.redirect page.post() self.assertEqual(self.response.redirect_location, 'http://192.168.33.168:8080/') def test_loginverify_submitnocontinue(self): self.set_post({ 'commit' : 'No', 'continue' : 'http://192.168.33.168:8080/' }) page = LoginVerify(self.request, self.response) page.redirect = self.response.redirect page.post() self.assertEqual(self.response.redirect_location, '/') def test_logout_page(self): self.set_user('a@a.com') page = LogoutPage(self.request, self.response) page.redirect = self.response.redirect page.get() self.assertEqual(self.response.redirect_location, '/') self.assertTrue(AppDashboardHelper.DEV_APPSERVER_LOGIN_COOKIE in self.response.deleted_cookies) def test_login_page(self): continue_url = 'http%3A//192.168.33.168%3A8080/_ah/login%3Fcontinue%3Dhttp%3A//192.168.33.168%3A8080/' self.set_get({ 'continue' : continue_url }) LoginPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search(continue_url, html)) def test_login_success(self): self.set_post({ 'user_email' : 'a@a.com', 'user_password' : 'aaaaaa' }) page = LoginPage(self.request, self.response) page.redirect = self.response.redirect page.post() html = self.response.out.getvalue() self.assertEqual(self.response.redirect_location, '/') self.assertTrue(AppDashboardHelper.DEV_APPSERVER_LOGIN_COOKIE in self.response.cookies) def test_login_success_redir(self): continue_url = 'http%3A//192.168.33.168%3A8080/_ah/login%3Fcontinue%3Dhttp%3A//192.168.33.168%3A8080/' self.set_post({ 'continue' : continue_url, 'user_email' : 'a@a.com', 'user_password' : 'aaaaaa' }) page = LoginPage(self.request, self.response) page.redirect = self.response.redirect page.post() html = self.response.out.getvalue() self.assertTrue(re.search('/users/confirm\?continue=',self.response.redirect_location)) self.assertTrue(AppDashboardHelper.DEV_APPSERVER_LOGIN_COOKIE in self.response.cookies) def test_login_fail(self): self.set_post({ 'user_email' : 'a@a.com', 'user_password' : 'bbbbbb' }) page = LoginPage(self.request, self.response) page.redirect = self.response.redirect page.post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Incorrect username / password combination. Please try again', html)) def test_authorize_page_notloggedin(self): AuthorizePage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Only the cloud administrator can change permissions.', html)) def test_authorize_page_loggedin_notadmin(self): self.set_user('b@a.com') AuthorizePage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Only the cloud administrator can change permissions.', html)) def test_authorize_page_loggedin_admin(self): self.set_user('a@a.com') AuthorizePage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('a@a.com-upload_app', html)) self.assertTrue(re.search('b@a.com-upload_app', html)) def test_authorize_submit_notloggedin(self): self.set_post({ 'user_permission_1' : 'a@a.com', 'CURRENT-a@a.com-upload_app' : 'True', 'a@a.com-upload_app' : 'a@a.com-upload_app', #this box is checked 'user_permission_1' : 'b@a.com', 'CURRENT-b@a.com-upload_app' : 'True', #this box is unchecked }) AuthorizePage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Only the cloud administrator can change permissions.', html)) def test_authorize_submit_notadmin(self): self.set_user('b@a.com') self.set_post({ 'user_permission_1' : 'a@a.com', 'CURRENT-a@a.com-upload_app' : 'True', 'a@a.com-upload_app' : 'a@a.com-upload_app', #this box is checked 'user_permission_1' : 'b@a.com', 'CURRENT-b@a.com-upload_app' : 'True', #this box is unchecked }) AuthorizePage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Only the cloud administrator can change permissions.', html)) def test_authorize_submit_remove(self): self.set_user('a@a.com') self.set_post({ 'user_permission_1' : 'a@a.com', 'CURRENT-a@a.com-upload_app' : 'True', 'a@a.com-upload_app' : 'a@a.com-upload_app', #this box is checked 'user_permission_1' : 'b@a.com', 'CURRENT-b@a.com-upload_app' : 'True', #this box is unchecked }) AuthorizePage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Disabling upload_app for b@a.com', html)) def test_authorize_submit_add(self): self.set_user('a@a.com') self.set_post({ 'user_permission_1' : 'a@a.com', 'CURRENT-a@a.com-upload_app' : 'True', 'a@a.com-upload_app' : 'a@a.com-upload_app', #this box is checked 'user_permission_1' : 'c@a.com', 'CURRENT-c@a.com-upload_app' : 'False', #this box is unchecked 'c@a.com-upload_app' : 'c@a.com-upload_app', #this box is checked }) AuthorizePage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Enabling upload_app for c@a.com', html)) def test_upload_page_notloggedin(self): AppUploadPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('You do not have permission to upload application. Please contact your cloud administrator', html)) def test_upload_page_loggedin(self): self.set_user('a@a.com') AppUploadPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('<input accept="tar.gz, tgz" id="app_file_data" name="app_file_data" size="30" type="file" />', html)) def test_upload_submit_notloggedin(self): self.set_fileupload('app_file_data') AppUploadPage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('You do not have permission to upload application. Please contact your cloud administrator', html)) def test_upload_submit_loggedin(self): self.set_user('a@a.com') self.set_fileupload('app_file_data') AppUploadPage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Application uploaded successfully. Please wait for the application to start running.', html)) def test_appdelete_page_nologgedin(self): AppDeletePage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertFalse(re.search('<option ', html)) def test_appdelete_page_loggedin_twoapps(self): self.set_user('a@a.com') AppDeletePage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('<option value="app1">app1</option>', html)) self.assertTrue(re.search('<option value="app2">app2</option>', html)) def test_appdelete_page_loggedin_oneapp(self): self.set_user('b@a.com') AppDeletePage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertFalse(re.search('<option value="app1">app1</option>', html)) self.assertTrue(re.search('<option value="app2">app2</option>', html)) def test_appdelete_submit_notloggedin(self): self.set_post({ 'appname' : 'app1' }) AppDeletePage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('There are no running applications that you have permission to delete.', html)) def test_appdelete_submit_notappadmin(self): self.set_user('b@a.com') self.set_post({ 'appname' : 'app1' }) AppDeletePage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('You do not have permission to delete the application: app1', html)) def test_appdelete_submit_success(self): self.set_user('a@a.com') self.set_post({ 'appname' : 'app1' }) AppDeletePage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('', html)) self.assertTrue(re.search('Application removed successfully. Please wait for your app to shut', html)) def test_refresh_data_get(self): StatusRefreshPage(self.request, self.response).get() html = self.response.out.getvalue() self.assertTrue(re.search('datastore updated', html)) def test_refresh_data_post(self): StatusRefreshPage(self.request, self.response).post() html = self.response.out.getvalue() self.assertTrue(re.search('datastore updated', html))

44.019663

132

0.688246

4,085

31,342

5.108935

0.080539

0.049066

0.093531

0.128606

0.770244

0.719454

0.688356

0.665213

0.649928

0.633253

0

0.016567

0.144885

31,342

711

133

44.081575

0.762136

0.006381

0

0.523292

0

0.006211

0.270076

0.1334

0

0.21118

1

0.079193

false

0.031056

0.03882

0

0.122671

0

null

0

1

0

1

0

null

0

2

73dbcbd67c906acef272848de33b325a6f949ae9

329

py

Python

Metric.py

noooway/exj

4f9adf2e340c0e215f5138a848cddba6567725a9

[ "MIT" ]

1

2020-04-15T21:42:42.000Z

Metric.py

noooway/exj

4f9adf2e340c0e215f5138a848cddba6567725a9

[ "MIT" ]

null

Metric.py

noooway/exj

4f9adf2e340c0e215f5138a848cddba6567725a9

[ "MIT" ]

null

from Exercise import * class Metric( Exercise ): """Metric is just another sort of exercise""" def __init__( self, description_dict ): super( Metric, self ).__init__( description_dict ) @classmethod def init_from_json( cls, dict_from_json ): metric = cls( dict_from_json ) return metric

27.416667

58

0.668693

40

329

5.1

0.5

0.117647

0.107843

0.147059

0

0.246201

329

11

59

29.909091

0.822581

0.118541

0

1

0.25

false

0

0.125

0

0.625

0

null

0

null

0

1

0

1

0

2

73e41fcd951dffad95d9bd5bc29e479aa153adf4

430

py

Python

myproject/poco/models.py

rg3915/geodjango

b92e26088158c1601a976b87ec2f14f33ca16722

[ "Xnet", "X11" ]

null

myproject/poco/models.py

rg3915/geodjango

b92e26088158c1601a976b87ec2f14f33ca16722

[ "Xnet", "X11" ]

4

2021-03-19T10:15:30.000Z

2022-02-10T10:27:56.000Z

myproject/poco/models.py

rg3915/geodjango

b92e26088158c1601a976b87ec2f14f33ca16722

[ "Xnet", "X11" ]

null

# This is an auto-generated Django model module created by ogrinspect. from django.contrib.gis.db import models class Poco(models.Model): proprietar = models.CharField(max_length=254) orgao = models.CharField(max_length=254) data_perfu = models.DateField() profundida = models.FloatField() q_m3h = models.FloatField() equipament = models.CharField(max_length=254) geom = models.PointField(srid=4326)

33.076923

70

0.744186

56

430

5.625

0.660714

0.142857

0.171429

0.228571

0.257143

0

0.038674

0.15814

430

12

71

35.833333

0.831492

0.15814

0

1

0

1

0

false

0

0.111111

0

1

0

null

0

1

0

1

0

null

0

2

73eed2dfddc3c295590fed1967638ba75b89bb7a

924

py

Python

Dataset/Leetcode/test/5/179.py

kkcookies99/UAST

fff81885aa07901786141a71e5600a08d7cb4868

[ "MIT" ]

null

Dataset/Leetcode/test/5/179.py

kkcookies99/UAST

fff81885aa07901786141a71e5600a08d7cb4868

[ "MIT" ]

null

Dataset/Leetcode/test/5/179.py

kkcookies99/UAST

fff81885aa07901786141a71e5600a08d7cb4868

[ "MIT" ]

null

class Solution(object): def XXX(self, s): """ :type s: str :rtype: str """ s_length = len(s) mark = [[0 for i in range(s_length)] for _ in range(s_length)] max_length = 0 max_sub_str = "" for j in range(0, s_length): for i in range(0, j + 1): if j - i <= 1: if s[i] == s[j]: mark[i][j] = 1 if max_length < j - i + 1: max_sub_str = s[i:j+1] max_length = j - i + 1 else: if s[i] == s[j] and mark[i+1][j-1]: mark[i][j] = 1 if max_length < j - i + 1: max_sub_str = s[i:j+1] max_length = j - i + 1 return max_sub_str

31.862069

70

0.331169

118

924

2.440678

0.220339

0.041667

0.052083

0.152778

0.361111

0.319444

0

0.039312

0.559524

924

28

71

33

0.668305

0

0.380952

0

null

0

null

0

null

0

1

0

null

0

1

0

2

73f0b4625b14381b39b9abee8de13a246d6aa36e

603

py

Python

apps/odoo/lib/odoo-10.0.post20170615-py2.7.egg/odoo/addons/base/__init__.py

gtfarng/Odoo_migrade

9cc28fae4c379e407645248a29d22139925eafe7

[ "Apache-2.0" ]

1

2019-12-19T01:53:13.000Z

apps/odoo/lib/odoo-10.0.post20170615-py2.7.egg/odoo/addons/base/__init__.py

gtfarng/Odoo_migrade

9cc28fae4c379e407645248a29d22139925eafe7

[ "Apache-2.0" ]

null

apps/odoo/lib/odoo-10.0.post20170615-py2.7.egg/odoo/addons/base/__init__.py

gtfarng/Odoo_migrade

9cc28fae4c379e407645248a29d22139925eafe7

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Part of Odoo. See LICENSE file for full copyright and licensing details. import ir import workflow import module import res import report import tests def post_init(cr, registry): """Rewrite ICP's to force groups""" from odoo import api, SUPERUSER_ID from odoo.addons.base.ir.ir_config_parameter import _default_parameters env = api.Environment(cr, SUPERUSER_ID, {}) ICP = env['ir.config_parameter'] for key, func in _default_parameters.iteritems(): val = ICP.get_param(key) _, groups = func() ICP.set_param(key, val, groups)

27.409091

75

0.699834

86

603

4.755814

0.616279

0.03912

0.08313

0

0.002075

0.200663

603

21

76

28.714286

0.846473

0.207297

0

0.04034

0

1

0.066667

false

0

0.533333

0

0.6

0

null

0

null

0

1

0

1

0

2

73f41924b685c051a11fa3e2dc69caad82125502

1,734

py

Python

tests/test_key.py

fortesp/PyBitcoinAddress

da9dd65e529600bc7ad0b5427c91bbff533fe773

[ "MIT" ]

85

2020-03-21T02:57:25.000Z

2022-03-25T12:13:14.000Z

tests/test_key.py

fortesp/PyBitcoinAddress

da9dd65e529600bc7ad0b5427c91bbff533fe773

[ "MIT" ]

15

2020-09-09T18:14:15.000Z

2021-12-12T13:54:36.000Z

tests/test_key.py

fortesp/PyBitcoinAddress

da9dd65e529600bc7ad0b5427c91bbff533fe773

[ "MIT" ]

37

2020-03-21T02:40:59.000Z

2022-03-25T14:33:32.000Z

import unittest from unittest import TestCase from bitcoinaddress import Key, Seed class TestKey(TestCase): def testFromRandomSeed(self): # given key = Key.of(Seed()) # then self.assertEqual(len(key.hex), 64) self.assertEqual(len(key.mainnet.wif), 51) self.assertEqual(len(key.mainnet.wifc), 52) def testFromHex_K(self): # given key = Key.of('669182eb2c3169e01cfc305034dc0b1df8328c274865e70d632c711ba62ec3d3') # then self.assertEqual(key.hex, '669182eb2c3169e01cfc305034dc0b1df8328c274865e70d632c711ba62ec3d3') self.assertEqual(key.mainnet.wif, '5JbTZ4zCTn1rwCfdkPWLddFgqzieGaG9Qjp3iRhf7R8gNroj4KM') self.assertEqual(key.mainnet.wifc, 'Kzf6CYbTbBgoQEVXCWLVef1psFkoVjor7mxeyr2TDKWto7iHfXHh') def testFromHex_L(self): # given key = Key.of('c2814c56793485f803430ef28ea93ba34e1dc74a74cead43407378350a958792') # then self.assertEqual(key.hex, 'c2814c56793485f803430ef28ea93ba34e1dc74a74cead43407378350a958792') self.assertEqual(key.mainnet.wif, '5KHwxCT8Nrb3MSiQRS5h6fqmAJWrXzi9min15xSzY1EuR3EgLHT') self.assertEqual(key.mainnet.wifc, 'L3joYdYKZTsFPEVkNqhhz2SDv4JmdoidiPPdNsjiwr4NLr31PkqK') def testFromWIF(self): # given key = Key.of('5JbTZ4zCTn1rwCfdkPWLddFgqzieGaG9Qjp3iRhf7R8gNroj4KM') # then self.assertEqual(key.hex, '669182eb2c3169e01cfc305034dc0b1df8328c274865e70d632c711ba62ec3d3') self.assertEqual(key.mainnet.wif, '5JbTZ4zCTn1rwCfdkPWLddFgqzieGaG9Qjp3iRhf7R8gNroj4KM') self.assertEqual(key.mainnet.wifc, 'Kzf6CYbTbBgoQEVXCWLVef1psFkoVjor7mxeyr2TDKWto7iHfXHh') if __name__ == "__main__": unittest.main()

36.125

101

0.739908

128

1,734

9.945313

0.289063

0.141398

0.127258

0.117832

0.553024

0.391202

0

0.193571

0.17474

1,734

47

102

36.893617

0.696017

0.024798

0

0.230769

0

0.409037

0.404281

0

0.461538

1

0.153846

false

0

0.115385

0

0.307692

0

null

0

1

null

0

1

0

2

73fa083daa51cb6eb020483a5e2a963e0266dfaf

3,509

py

Python

tpi1/tree_search.py

vascoalramos/ia

4fdfa29877822f184c668900a0253cbe0084da12

[ "MIT" ]

1

2019-11-13T14:23:05.000Z

tpi1/tree_search.py

vascoalramos/ia

4fdfa29877822f184c668900a0253cbe0084da12

[ "MIT" ]

null

tpi1/tree_search.py

vascoalramos/ia

4fdfa29877822f184c668900a0253cbe0084da12

[ "MIT" ]

1

2019-10-28T16:29:04.000Z

# Module: tree_search # # This module provides a set o classes for automated # problem solving through tree search: # SearchDomain - problem domains # SearchProblem - concrete problems to be solved # SearchNode - search tree nodes # SearchTree - search tree with the necessary methods for searhing # # (c) Luis Seabra Lopes # Introducao a Inteligencia Artificial, 2012-2019, # Inteligência Artificial, 2014-2019 from abc import ABC, abstractmethod # Dominios de pesquisa # Permitem calcular # as accoes possiveis em cada estado, etc class SearchDomain(ABC): # construtor @abstractmethod def __init__(self): pass # lista de accoes possiveis num estado @abstractmethod def actions(self, state): pass # resultado de uma accao num estado, ou seja, o estado seguinte @abstractmethod def result(self, state, action): pass # custo de uma accao num estado @abstractmethod def cost(self, state, action): pass # custo estimado de chegar de um estado a outro @abstractmethod def heuristic(self, state, goal): pass # test if the given "goal" is satisfied in "state" @abstractmethod def satisfies(self, state, goal): pass # Problemas concretos a resolver # dentro de um determinado dominio class SearchProblem: def __init__(self, domain, initial, goal): self.domain = domain self.initial = initial self.goal = goal def goal_test(self, state): return self.domain.satisfies(state,self.goal) # Nos de uma arvore de pesquisa class SearchNode: def __init__(self,state,parent): self.state = state self.parent = parent def __str__(self): return "no(" + str(self.state) + "," + str(self.parent) + ")" def __repr__(self): return str(self) # Arvores de pesquisa class SearchTree: # construtor def __init__(self,problem, strategy='breadth'): self.problem = problem self.root = SearchNode(problem.initial, None) self.open_nodes = [self.root] self.strategy = strategy # obter o caminho (sequencia de estados) da raiz ate um no def get_path(self,node): if node.parent == None: return [node.state] path = self.get_path(node.parent) path += [node.state] return(path) # procurar a solucao def search(self): while self.open_nodes != []: node = self.open_nodes.pop(0) if self.problem.goal_test(node.state): return self.get_path(node) lnewnodes = [] for a in self.problem.domain.actions(node.state): newstate = self.problem.domain.result(node.state,a) if newstate not in self.get_path(node): newnode = SearchNode(newstate,node) lnewnodes.append(newnode) self.add_to_open(lnewnodes) return None # juntar novos nos a lista de nos abertos de acordo com a estrategia def add_to_open(self,lnewnodes): if self.strategy == 'breadth': self.open_nodes.extend(lnewnodes) elif self.strategy == 'depth': self.open_nodes[:0] = lnewnodes elif self.strategy == 'astar': self.astar_add_to_open(lnewnodes) elif self.strategy == 'uniform': pass

29.991453

73

0.61043

411

3,509

5.107056

0.347932

0.03859

0.030967

0.021439

0.040972

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0.007401

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116

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30.25

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0.101449

0.014493

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0

null

0

null

0

1

0

1

0

2

73ffb35e5502576c65abc69058974b01ee34a8b9

452

py

Python

parser/parser/database.py

spider19281/parser

ca26bc1010fd82c79e110b99ef1fdb77875fe87b

[ "MIT" ]

null

parser/parser/database.py

spider19281/parser

ca26bc1010fd82c79e110b99ef1fdb77875fe87b

[ "MIT" ]

null

parser/parser/database.py

spider19281/parser

ca26bc1010fd82c79e110b99ef1fdb77875fe87b

[ "MIT" ]

null

import sqlite3 class Database: def __init__(self): self.connection = sqlite3.connect('./database.db') self.cursor = self.connection.cursor() def insert_item(self, item): cur = self.cursor.execute( "insert into posts (title, file, link) values (?, ?, ?)", (item['title'], item['file'], item['link'])) self.connection.commit() def close(self): self.connection.close()

30.133333

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0.469388

0.21875

0.140625

0

0.006042

0.267699

452

15

70

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0.767372

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0.1766

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0.416667

0

null

1

0

null

0

1

0

2

fb478ae297be5611a34c083982cab7c0be777e53

600

py

Python

alma/entities/installment.py

alma/alma-python-client

be895691772f7939dc6d0af39db4a48c9f9ae193

[ "MIT" ]

4

2020-01-27T16:44:58.000Z

2020-06-26T13:14:52.000Z

alma/entities/installment.py

alma/alma-python-client

be895691772f7939dc6d0af39db4a48c9f9ae193

[ "MIT" ]

7

2020-01-27T16:50:27.000Z

2020-10-12T16:21:25.000Z

alma/entities/installment.py

alma/alma-python-client

be895691772f7939dc6d0af39db4a48c9f9ae193

[ "MIT" ]

7

2019-05-10T19:20:13.000Z

2022-03-24T07:08:55.000Z

from enum import Enum from . import Base class InstallmentState(Enum): PENDING = "pending" PAID = "paid" INCIDENT = "incident" CLAIMED = "claimed" COVERED = "covered" class Installment(Base): def __init__(self, data): state = data.pop("state", None) if state: try: self.state = InstallmentState(state) except ValueError: # Pass on unrecognized state values # they will be accessible as-is in the Installment data pass super(Installment, self).__init__(data)

23.076923

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0.58

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0

null

0

null

0

1

0

1

0

2

fb48cfe3601c82c6fb83669d1c5cafe5eb9c4955

932

py

Python

createtransitionsnetworkweighted.py

trovdimi/wikilinks

835feb3a982d9a77afc88b6787b4b84c411442db

[ "MIT" ]

6

2016-03-11T08:31:02.000Z

2020-06-25T14:12:47.000Z

createtransitionsnetworkweighted.py

trovdimi/wikilinks

835feb3a982d9a77afc88b6787b4b84c411442db

[ "MIT" ]

null

createtransitionsnetworkweighted.py

trovdimi/wikilinks

835feb3a982d9a77afc88b6787b4b84c411442db

[ "MIT" ]

1

2018-03-24T13:06:25.000Z

from wsd.database import MySQLDatabase from graph_tool.all import * from conf import * __author__ = 'dimitrovdr' db = MySQLDatabase(DATABASE_HOST, DATABASE_USER, DATABASE_PASSWORD, DATABASE_NAME) db_work_view = db.get_work_view() wikipedia = Graph() for link in db_work_view.retrieve_all_internal_transitions_counts(): for i in range(int(link['counts'])) : wikipedia.add_edge(link['from'], link['to']) #print 'from %s, to %s', link['from'], link['to'] #wikipedia.save("output/transitionsnetwork.xml.gz") # filter all nodes that have no edges transitions_network = GraphView(wikipedia, vfilt=lambda v : v.out_degree()+v.in_degree()>0 ) transitions_network.save("output/transitionsnetworkweighted.xml.gz") print "Stats for transitions network:" print "number of nodes: %d" %transitions_network.num_vertices() print "number of edges: %d" %transitions_network.num_edges()

27.411765

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932

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0.030864

0.04321

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932

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94

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null

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null

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fb5af87917b83ad53f87907aaeff1e1a1d31c16c

203

py

Python

src/art_of_geom/geom/euclid/_rD/sphere.py

Mathverse/Art-of-Geometry

8fd89fd3526f871815c38953580a48b017d39847

[ "MIT" ]

1

2021-12-25T01:16:10.000Z

src/art_of_geom/geom/euclid/_rD/sphere.py

Mathverse/Art-of-Geometry

8fd89fd3526f871815c38953580a48b017d39847

[ "MIT" ]

null

src/art_of_geom/geom/euclid/_rD/sphere.py

Mathverse/Art-of-Geometry

8fd89fd3526f871815c38953580a48b017d39847

[ "MIT" ]

null

__all__ = 'SphereInRD', 'SphereRD', 'Sphere' from .._abc._entity import _EuclideanGeometryEntityABC class SphereInRD(_EuclideanGeometryEntityABC): pass # aliases Sphere = SphereRD = SphereInRD

15.615385

54

0.768473

17

203

8.705882

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203

12

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16.916667

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0.2

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0

1

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null

0

null

0

1

0

2

fb60f82721085c6f787997bf7afcbf2e5d555a26

910

py

Python

dmri_handler.py

clegling/deep-brain-stimulation

943b76ce45ed53d269be5a6682c91cadbb40aa0c

[ "Apache-2.0" ]

1

2017-02-23T19:42:11.000Z

dmri_handler.py

clegling/deep-brain-stimulation

943b76ce45ed53d269be5a6682c91cadbb40aa0c

[ "Apache-2.0" ]

null

dmri_handler.py

clegling/deep-brain-stimulation

943b76ce45ed53d269be5a6682c91cadbb40aa0c

[ "Apache-2.0" ]

null

"""This file represents dmri handling""" from abc import ABCMeta, abstractmethod class DMRIHandler: """This class is an abstract class for dti manipulations""" __metaclass__ = ABCMeta def __init__(self, dmri_file, fbvals, fbvecs): self.dmri_file = dmri_file self.fbvals = fbvals self.fbvecs = fbvecs @abstractmethod def get_shape(self): """Returns number of voxels for each dmri dimension""" @abstractmethod def handle(self): """Abstract method which includes all processing and calculations""" pass @abstractmethod def get_eigen_vectors(self): """Abstract method which returns eigen vectors for each voxel of the DMRI""" pass @abstractmethod def get_eigen_values(self): """Abstract method which returns eigen values for each voxel of the DMRI""" pass

30.333333

85

0.650549

105

910

5.485714

0.438095

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0.104167

0.119792

0.302083

0.208333

0.086806

0

0.275824

910

29

86

31.37931

0.874052

0.374725

0

0.388889

0

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0.277778

false

0.166667

0.055556

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0

null

0

null

0

1

0

1

0

2

fb7c153d134d1af457e68040379bbb124e2701e9

6,242

py

Python

usermgmt.py

roclops/usermgmtlib

34d1bb17c2a4d914a12ddeafa7c7820b2fc7d62f

[ "MIT" ]

null

usermgmt.py

roclops/usermgmtlib

34d1bb17c2a4d914a12ddeafa7c7820b2fc7d62f

[ "MIT" ]

null

usermgmt.py

roclops/usermgmtlib

34d1bb17c2a4d914a12ddeafa7c7820b2fc7d62f

[ "MIT" ]

null

from passlib.hash import ldap_salted_sha1 from passlib.hash import ldap_pbkdf2_sha256 from sshpubkeys import SSHKey import datetime class Usermgmt(object): def attrs(self): raise NotImplementedError def get_dict(self): return dict((key, value) for key, value in self.__dict__.items() if not callable(value) and not key.startswith('__')) def __values(self): return (getattr(self, attr) for attr in self.attrs()) def __eq__(self, other): # print('self:\t' + str(self.get_dict())) # print('other:\t' + str(other.get_dict())) return self.get_dict() == other.get_dict() def __str__(self): return "<Usermgmt {0}>".format(self.get_dict()) def refresh(): raise NotImplementedError def save(): raise NotImplementedError class Role(Usermgmt): def __init__(self, rolename=None, groups=[]): self.rolename = str(rolename) if groups: self.groups = set(sorted(groups)) else: self.groups = set() def __eq__(self, other): return self.rolename == other.rolename and \ self.groups == other.groups def __str__(self): return "<Role {}>".format(self.rolename) def attrs(self): return ['rolename', 'groups'] class Group(Usermgmt): def __init__(self, groupname=None, gid=None): self.groupname = str(groupname) self.gid = str(gid) def __cmp__(self, other): return self.gid == other.gid and self.groupname == other.groupname def __eq__(self, other): return self.__cmp__(other) def attrs(self): return ['groupname', 'gid'] class User(Usermgmt): def __init__(self, username=None, hash_ldap=None, password_mod_date=None, email=None, uidNumber=None, public_keys=[], sshkey_mod_date=None, groups=[], auth_code=None, auth_code_date=None): self.username = str(username) self.hash_ldap = str(hash_ldap) self.password_mod_date = str(password_mod_date) self.email = str(email) self.uidNumber = str(uidNumber) if public_keys: self.public_keys = set(public_keys) else: self.public_keys = set() self.sshkey_mod_date = str(sshkey_mod_date) if groups: self.groups = set(sorted(groups)) else: self.groups = set() self.auth_code= str(auth_code) self.auth_code_date = str(auth_code_date) def __eq__(self, other): return self.username == other.username and \ self.hash_ldap == other.hash_ldap and \ self.password_mod_date == other.password_mod_date and \ self.email == other.email and \ self.uidNumber == other.uidNumber and \ self.public_keys == other.public_keys and \ self.groups == other.groups and \ self.auth_code == other.auth_code and \ self.auth_code_date == other.auth_code_date def __cmp__(self, other): for a in self.attrs(): self_a = getattr(self, a) other_a = getattr(other, a) if type(self_a) == list and type(other_a) == list or type(self_a) == set and type(other_a) == set: self_a = sorted(list(self_a)) other_a = sorted(list(other_a)) c = cmp(self_a, other_a) if c: return c return 0 def attrs(self): return ['username', 'password', 'email', 'uidNumber', 'public_keys', 'groups', 'hash_ldap', 'password_mod_date', 'sshkey_mod_date', 'auth_code', 'auth_code_date'] def set(self, attribute, value): attr = setattr(self, attribute, value) self.save() return True def check_password(self, password): return ( ldap_pbkdf2_sha256.identify(self.hash_ldap) and \ ldap_pbkdf2_sha256.verify(password, self.hash_ldap) ) \ or (ldap_salted_sha1.identify(self.hash_ldap) and \ ldap_salted_sha1.verify(password, self.hash_ldap)) def set_password(self, password): try: self.hash_ldap = ldap_pbkdf2_sha256.hash(password) self.password_mod_date = datetime.datetime.now().strftime("%Y-%m-%d %H:%M") self.auth_code = None self.auth_code_date = None self.save() return True except Exception as e: print("Exception: %s" % e) return False def validate_key(self, key): try: ssh = SSHKey(key) ssh.parse() return ssh except: return False def get_ssh_key_comment(self, key): ssh = SSHKey(key) ssh.parse() return ssh.comment def get_ssh_key_hash(self, key): ssh = SSHKey(key) ssh.parse() return ssh.hash_md5().split('MD5:').pop() def check_key_exist(self, key): for test_key in self.public_keys: if self.get_ssh_key_hash(key) == self.get_ssh_key_hash(test_key): return True return False def add_ssh_key(self, key): try: ssh = self.validate_key(key) if self.check_key_exist(key): return False self.public_keys.add(ssh.keydata) self.sshkey_mod_date = datetime.datetime.now().strftime("%Y-%m-%d %H:%M") self.save() return True except Exception as e: print(e) return False def remove_ssh_key(self, key): self.public_keys.discard(key) self.save() return True def remove_ssh_key_by_hash(self, hash_md5): key = self.find_key_by_hash(hash_md5) self.public_keys.discard(key) self.save() return True def find_key_by_hash(self, hash_md5): for key in self.public_keys: test_hash = self.get_ssh_key_hash(key) if hash_md5 == test_hash: return key return None def is_admin(self): return self.is_group_member('internal.admins') or self.is_group_member('unix.admins') def is_group_member(self, group): if group in self.groups: return True else: return False

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6,242

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0.02567

0.271535

0.188534

0.129778

0.121506

0.078722

0

0.006148

0.296379

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192

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32.510417

0.792122

0.012977

0

0.348101

0

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0

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0.202532

false

0.075949

0.025316

0.088608

0.462025

0.012658

0

null

0

null

0

1

0

1

0

2

fb83648a32b4a3b292948ec8d2929d777d570337

2,240

py

Python

release/stubs.min/System/Net/__init___parts/IAuthenticationModule.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

release/stubs.min/System/Net/__init___parts/IAuthenticationModule.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

release/stubs.min/System/Net/__init___parts/IAuthenticationModule.py

YKato521/ironpython-stubs

b1f7c580de48528490b3ee5791b04898be95a9ae

[ "MIT" ]

null

class IAuthenticationModule: """ Provides the base authentication interface for Web client authentication modules. """ def Authenticate(self, challenge, request, credentials): """ Authenticate(self: IAuthenticationModule,challenge: str,request: WebRequest,credentials: ICredentials) -> Authorization Returns an instance of the System.Net.Authorization class in respose to an authentication challenge from a server. challenge: The authentication challenge sent by the server. request: The System.Net.WebRequest instance associated with the challenge. credentials: The credentials associated with the challenge. Returns: An System.Net.Authorization instance containing the authorization message for the request,or null if the challenge cannot be handled. """ pass def PreAuthenticate(self, request, credentials): """ PreAuthenticate(self: IAuthenticationModule,request: WebRequest,credentials: ICredentials) -> Authorization Returns an instance of the System.Net.Authorization class for an authentication request to a server. request: The System.Net.WebRequest instance associated with the authentication request. credentials: The credentials associated with the authentication request. Returns: An System.Net.Authorization instance containing the authorization message for the request. """ pass def __init__(self, *args): """ x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """ pass AuthenticationType = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets the authentication type provided by this authentication module. Get: AuthenticationType(self: IAuthenticationModule) -> str """ CanPreAuthenticate = property( lambda self: object(), lambda self, v: None, lambda self: None ) """Gets a value indicating whether the authentication module supports preauthentication. Get: CanPreAuthenticate(self: IAuthenticationModule) -> bool """

27.654321

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null

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null

0

1

0

1

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2

fb9d01ce0e570e97220240a2ba058cf64890a1f9

838

py

Python

src/models/order.py

NurseHack4Health/LocAid

3c2e735e9d0383934df47c7a42a9fe5f6523d9ab

[ "MIT" ]

null

src/models/order.py

NurseHack4Health/LocAid

3c2e735e9d0383934df47c7a42a9fe5f6523d9ab

[ "MIT" ]

null

src/models/order.py

NurseHack4Health/LocAid

3c2e735e9d0383934df47c7a42a9fe5f6523d9ab

[ "MIT" ]

1

2021-05-15T18:20:29.000Z

import uuid from src.data_layer.db_connector import Base from sqlalchemy import Column, Boolean, DateTime from sqlalchemy.dialects.postgresql import UUID from sqlalchemy import func class OrderModel(Base): """ Define Item database table ORM model """ __tablename__ = "order" # Register columns id = Column(UUID(as_uuid=True), default=uuid.uuid4, unique=True, primary_key=True, index=True) user_id = Column(UUID(as_uuid=True), index=True) from_hospital_id = Column(UUID(as_uuid=True), index=True) to_hospital_id = Column(UUID(as_uuid=True), index=True) item_id = Column(UUID(as_uuid=True), index=True) emergency = Column(Boolean, default=False) created_at = Column(DateTime, default=func.now()) approved = Column(Boolean, default=False) processed = Column(Boolean, default=False)

34.916667

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null

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fba4dedb5ca42bdb935e73389d8c86edb8ce30dd

14,205

py

Python

pysnmp/ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

11

2021-02-02T16:27:16.000Z

2021-08-31T06:22:49.000Z

pysnmp/ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

75

2021-02-24T17:30:31.000Z

2021-12-08T00:01:18.000Z

pysnmp/ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

10

2019-04-30T05:51:36.000Z

2022-02-16T03:33:41.000Z

# # PySNMP MIB module ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB # Produced by pysmi-0.3.4 at Mon Apr 29 18:50:07 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ConstraintsIntersection, ValueSizeConstraint, ConstraintsUnion, SingleValueConstraint, ValueRangeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "ValueSizeConstraint", "ConstraintsUnion", "SingleValueConstraint", "ValueRangeConstraint") etsysModules, = mibBuilder.importSymbols("ENTERASYS-MIB-NAMES", "etsysModules") InetAddress, InetAddressType = mibBuilder.importSymbols("INET-ADDRESS-MIB", "InetAddress", "InetAddressType") ObjectGroup, ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ObjectGroup", "ModuleCompliance", "NotificationGroup") IpAddress, Counter32, iso, MibScalar, MibTable, MibTableRow, MibTableColumn, Integer32, Bits, ModuleIdentity, TimeTicks, ObjectIdentity, MibIdentifier, Counter64, Unsigned32, NotificationType, Gauge32 = mibBuilder.importSymbols("SNMPv2-SMI", "IpAddress", "Counter32", "iso", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Integer32", "Bits", "ModuleIdentity", "TimeTicks", "ObjectIdentity", "MibIdentifier", "Counter64", "Unsigned32", "NotificationType", "Gauge32") RowStatus, TextualConvention, DisplayString, TruthValue = mibBuilder.importSymbols("SNMPv2-TC", "RowStatus", "TextualConvention", "DisplayString", "TruthValue") etsysRadiusAcctClientMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27)) etsysRadiusAcctClientMIB.setRevisions(('2009-08-07 15:48', '2004-11-12 15:23', '2004-09-09 14:37', '2004-08-30 15:55', '2004-08-25 15:03', '2002-09-13 19:30',)) if mibBuilder.loadTexts: etsysRadiusAcctClientMIB.setLastUpdated('200908071548Z') if mibBuilder.loadTexts: etsysRadiusAcctClientMIB.setOrganization('Enterasys Networks') etsysRadiusAcctClientMIBObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1)) etsysRadiusAcctClientEnable = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("enable", 1), ("disable", 2))).clone('disable')).setMaxAccess("readwrite") if mibBuilder.loadTexts: etsysRadiusAcctClientEnable.setStatus('current') etsysRadiusAcctClientUpdateInterval = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 2147483647)).clone(1800)).setUnits('seconds').setMaxAccess("readwrite") if mibBuilder.loadTexts: etsysRadiusAcctClientUpdateInterval.setStatus('current') etsysRadiusAcctClientIntervalMinimum = MibScalar((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 3), Integer32().subtype(subtypeSpec=ValueRangeConstraint(60, 2147483647)).clone(600)).setUnits('seconds').setMaxAccess("readwrite") if mibBuilder.loadTexts: etsysRadiusAcctClientIntervalMinimum.setStatus('current') etsysRadiusAcctClientServerTable = MibTable((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4), ) if mibBuilder.loadTexts: etsysRadiusAcctClientServerTable.setStatus('current') etsysRadiusAcctClientServerEntry = MibTableRow((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1), ).setIndexNames((0, "ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerIndex")) if mibBuilder.loadTexts: etsysRadiusAcctClientServerEntry.setStatus('current') etsysRadiusAcctClientServerIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 2147483647))) if mibBuilder.loadTexts: etsysRadiusAcctClientServerIndex.setStatus('current') etsysRadiusAcctClientServerAddressType = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 2), InetAddressType().clone('ipv4')).setMaxAccess("readcreate") if mibBuilder.loadTexts: etsysRadiusAcctClientServerAddressType.setStatus('current') etsysRadiusAcctClientServerAddress = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 3), InetAddress().subtype(subtypeSpec=ValueSizeConstraint(1, 64))).setMaxAccess("readcreate") if mibBuilder.loadTexts: etsysRadiusAcctClientServerAddress.setStatus('current') etsysRadiusAcctClientServerPortNumber = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 4), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 65535)).clone(1813)).setMaxAccess("readcreate") if mibBuilder.loadTexts: etsysRadiusAcctClientServerPortNumber.setStatus('current') etsysRadiusAcctClientServerSecret = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 5), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess("readcreate") if mibBuilder.loadTexts: etsysRadiusAcctClientServerSecret.setStatus('current') etsysRadiusAcctClientServerSecretEntered = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 6), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: etsysRadiusAcctClientServerSecretEntered.setStatus('current') etsysRadiusAcctClientServerRetryTimeout = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 7), Integer32().subtype(subtypeSpec=ValueRangeConstraint(2, 10)).clone(5)).setUnits('seconds').setMaxAccess("readcreate") if mibBuilder.loadTexts: etsysRadiusAcctClientServerRetryTimeout.setStatus('current') etsysRadiusAcctClientServerRetries = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 8), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 20)).clone(2)).setMaxAccess("readcreate") if mibBuilder.loadTexts: etsysRadiusAcctClientServerRetries.setStatus('current') etsysRadiusAcctClientServerClearTime = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 9), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 2147483647))).setMaxAccess("readcreate") if mibBuilder.loadTexts: etsysRadiusAcctClientServerClearTime.setStatus('deprecated') etsysRadiusAcctClientServerStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 10), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: etsysRadiusAcctClientServerStatus.setStatus('current') etsysRadiusAcctClientServerUpdateInterval = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 11), Integer32().subtype(subtypeSpec=ConstraintsUnion(ValueRangeConstraint(-1, -1), ValueRangeConstraint(0, 2147483647), )).clone(-1)).setUnits('seconds').setMaxAccess("readcreate") if mibBuilder.loadTexts: etsysRadiusAcctClientServerUpdateInterval.setStatus('current') etsysRadiusAcctClientServerIntervalMinimum = MibTableColumn((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 1, 4, 1, 12), Integer32().subtype(subtypeSpec=ConstraintsUnion(ValueRangeConstraint(-1, -1), ValueRangeConstraint(60, 2147483647), )).clone(-1)).setUnits('seconds').setMaxAccess("readcreate") if mibBuilder.loadTexts: etsysRadiusAcctClientServerIntervalMinimum.setStatus('current') etsysRadiusAcctClientMIBConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 2)) etsysRadiusAcctClientMIBCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 2, 1)) etsysRadiusAcctClientMIBGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 2, 2)) etsysRadiusAcctClientMIBGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 2, 2, 1)).setObjects(("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientEnable"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientUpdateInterval"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientIntervalMinimum"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerAddressType"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerAddress"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerPortNumber"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerSecret"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerSecretEntered"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerRetryTimeout"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerRetries"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerClearTime"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): etsysRadiusAcctClientMIBGroup = etsysRadiusAcctClientMIBGroup.setStatus('deprecated') etsysRadiusAcctClientMIBGroupV2 = ObjectGroup((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 2, 2, 2)).setObjects(("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientEnable"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientUpdateInterval"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientIntervalMinimum"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerAddressType"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerAddress"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerPortNumber"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerSecret"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerSecretEntered"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerRetryTimeout"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerRetries"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerStatus")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): etsysRadiusAcctClientMIBGroupV2 = etsysRadiusAcctClientMIBGroupV2.setStatus('deprecated') etsysRadiusAcctClientMIBGroupV3 = ObjectGroup((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 2, 2, 3)).setObjects(("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientEnable"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientUpdateInterval"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientIntervalMinimum"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerAddressType"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerAddress"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerPortNumber"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerSecret"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerSecretEntered"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerRetryTimeout"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerRetries"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerStatus"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerIntervalMinimum"), ("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientServerUpdateInterval")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): etsysRadiusAcctClientMIBGroupV3 = etsysRadiusAcctClientMIBGroupV3.setStatus('current') etsysRadiusAcctClientMIBCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 2, 1, 2)).setObjects(("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientMIBGroup")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): etsysRadiusAcctClientMIBCompliance = etsysRadiusAcctClientMIBCompliance.setStatus('deprecated') etsysRadiusAcctClientMIBComplianceV2 = ModuleCompliance((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 2, 1, 3)).setObjects(("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientMIBGroupV2")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): etsysRadiusAcctClientMIBComplianceV2 = etsysRadiusAcctClientMIBComplianceV2.setStatus('deprecated') etsysRadiusAcctClientMIBComplianceV3 = ModuleCompliance((1, 3, 6, 1, 4, 1, 5624, 1, 2, 27, 2, 1, 4)).setObjects(("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", "etsysRadiusAcctClientMIBGroupV3")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): etsysRadiusAcctClientMIBComplianceV3 = etsysRadiusAcctClientMIBComplianceV3.setStatus('current') mibBuilder.exportSymbols("ENTERASYS-RADIUS-ACCT-CLIENT-EXT-MIB", etsysRadiusAcctClientMIBComplianceV2=etsysRadiusAcctClientMIBComplianceV2, etsysRadiusAcctClientServerClearTime=etsysRadiusAcctClientServerClearTime, etsysRadiusAcctClientServerPortNumber=etsysRadiusAcctClientServerPortNumber, etsysRadiusAcctClientServerAddressType=etsysRadiusAcctClientServerAddressType, etsysRadiusAcctClientIntervalMinimum=etsysRadiusAcctClientIntervalMinimum, etsysRadiusAcctClientServerAddress=etsysRadiusAcctClientServerAddress, etsysRadiusAcctClientServerSecret=etsysRadiusAcctClientServerSecret, etsysRadiusAcctClientMIBCompliances=etsysRadiusAcctClientMIBCompliances, etsysRadiusAcctClientServerIndex=etsysRadiusAcctClientServerIndex, etsysRadiusAcctClientServerRetryTimeout=etsysRadiusAcctClientServerRetryTimeout, etsysRadiusAcctClientMIB=etsysRadiusAcctClientMIB, etsysRadiusAcctClientServerUpdateInterval=etsysRadiusAcctClientServerUpdateInterval, PYSNMP_MODULE_ID=etsysRadiusAcctClientMIB, etsysRadiusAcctClientMIBObjects=etsysRadiusAcctClientMIBObjects, etsysRadiusAcctClientMIBGroupV2=etsysRadiusAcctClientMIBGroupV2, etsysRadiusAcctClientMIBGroup=etsysRadiusAcctClientMIBGroup, etsysRadiusAcctClientServerSecretEntered=etsysRadiusAcctClientServerSecretEntered, etsysRadiusAcctClientServerStatus=etsysRadiusAcctClientServerStatus, etsysRadiusAcctClientServerTable=etsysRadiusAcctClientServerTable, etsysRadiusAcctClientMIBCompliance=etsysRadiusAcctClientMIBCompliance, etsysRadiusAcctClientMIBGroupV3=etsysRadiusAcctClientMIBGroupV3, etsysRadiusAcctClientMIBGroups=etsysRadiusAcctClientMIBGroups, etsysRadiusAcctClientEnable=etsysRadiusAcctClientEnable, etsysRadiusAcctClientServerRetries=etsysRadiusAcctClientServerRetries, etsysRadiusAcctClientUpdateInterval=etsysRadiusAcctClientUpdateInterval, etsysRadiusAcctClientServerEntry=etsysRadiusAcctClientServerEntry, etsysRadiusAcctClientServerIntervalMinimum=etsysRadiusAcctClientServerIntervalMinimum, etsysRadiusAcctClientMIBConformance=etsysRadiusAcctClientMIBConformance, etsysRadiusAcctClientMIBComplianceV3=etsysRadiusAcctClientMIBComplianceV3)

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14,205

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179.810127

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0.115942

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null

0

1

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null

0

2

fbce834a735ad51e7b51a5da9375f7ec19153a43

682

py

Python

analytics/BeMoBI_PyAnalytics/BeMoBI_PyAnalytics.py

xfleckx/BeMoBI_Tools

90a6066648a3249e6ea22165609aaaa763e02f05

[ "MIT" ]

null

analytics/BeMoBI_PyAnalytics/BeMoBI_PyAnalytics.py

xfleckx/BeMoBI_Tools

90a6066648a3249e6ea22165609aaaa763e02f05

[ "MIT" ]

null

analytics/BeMoBI_PyAnalytics/BeMoBI_PyAnalytics.py

xfleckx/BeMoBI_Tools

90a6066648a3249e6ea22165609aaaa763e02f05

[ "MIT" ]

null

import os import pandas as pd import seaborn as sns dataDir = '..\\Test_Data\\' pilotMarkerDataFile = 'Pilot.csv' df = pd.read_csv( dataDir + '\\' + pilotMarkerDataFile,sep='\t', engine='python') repr(df.head()) # TODO times per position # plotting a heatmap http://stanford.edu/~mwaskom/software/seaborn/examples/many_pairwise_correlations.html ## Generate a custom diverging colormap #cmap = sns.diverging_palette(220, 10, as_cmap=True) # Draw the heatmap with the mask and correct aspect ratio #sns.heatmap(timesAtPositions, mask=mask, cmap=cmap, vmax=.3, # square=True, xticklabels=5, yticklabels=5, # linewidths=.5, cbar_kws={"shrink": .5}, ax=ax)

31

107

0.717009

94

682

5.12766

0.712766

0

0.017182

0.146628

682

22

108

31

0.810997

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null

0

null

0

1

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1

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2

fbde4302833ec88b5839af1a2bdd4789b1c8ae09

478

py

Python

main.py

fossabot/superstructure

f4ab5cac269fb3dedfbd3a54c441af23edf3840b

[ "MIT" ]

null

main.py

fossabot/superstructure

f4ab5cac269fb3dedfbd3a54c441af23edf3840b

[ "MIT" ]

null

main.py

fossabot/superstructure

f4ab5cac269fb3dedfbd3a54c441af23edf3840b

[ "MIT" ]

null

from redisworks import Root from superstructure.geist import Bewusstsein # TODO find way to pickle objects def main(): root = Root # redis.Redis('localhost') try: weltgeist = root.weltgeist except BaseException: print("Creating new weltgeist") weltgeist = Bewusstsein(name="Weltgeist") root.weltgeist = weltgeist # print(weltgeist) print(root.weltgeist) root.weltgeist.spill() if __name__ == "__main__": main()

20.782609

49

0.669456

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478

6.117647

0.529412

0.166667

0.211538

0

0.236402

478

22

50

21.727273

0.854795

0.15272

0

0.097257

0

0.045455

0

1

0.071429

false

0

0.142857

0

0.214286

0.142857

0

null

0

1

0

null

0

1

0

2

837c19eef088865d2dc6aafc140effefa8fcbc78

688

py

Python

watcher.py

Craeckie/Container-Bot

a3bc55fe9c560d2f4a92403a4d661ddde9f7132d

[ "MIT" ]

null

watcher.py

Craeckie/Container-Bot

a3bc55fe9c560d2f4a92403a4d661ddde9f7132d

[ "MIT" ]

null

watcher.py

Craeckie/Container-Bot

a3bc55fe9c560d2f4a92403a4d661ddde9f7132d

[ "MIT" ]

null

import docker class Watcher: def __init__(self, socket_path='/var/run/docker.sock'): self.client = docker.DockerClient(base_url='unix://' + socket_path) def container_list(self): return self.containers.list() def listen_events(self, event_callback, *args, **kwargs): for event in self.client.events(decode=True): try: if 'status' in event and not event['status'].startswith('exec_') and event['status'] != 'pull': msg = event['Actor']['Attributes']['name'] + ": " + event['status'] event_callback(event, msg, *args, **kwargs) except Exception as e: pass

40.470588

111

0.584302

79

688

4.936709

0.607595

0.084615

0

0.273256

688

17

112

40.470588

0.78

0

0.117562

0

1

0.214286

false

0.071429

0.428571

0

null

0

null

0

1

0

1

0

2

837e3d2d96c78455ca68897af18dd213d2e999f1

515

py

Python

ois_api_client/v3_0/deserialization/deserialize_detailed_reason.py

peterkulik/ois_api_client

51dabcc9f920f89982c4419bb058f5a88193cee0

[ "MIT" ]

7

2020-10-22T08:15:29.000Z

2022-01-27T07:59:39.000Z

ois_api_client/v3_0/deserialization/deserialize_detailed_reason.py

peterkulik/ois_api_client

51dabcc9f920f89982c4419bb058f5a88193cee0

[ "MIT" ]

null

ois_api_client/v3_0/deserialization/deserialize_detailed_reason.py

peterkulik/ois_api_client

51dabcc9f920f89982c4419bb058f5a88193cee0

[ "MIT" ]

null

from typing import Optional import xml.etree.ElementTree as ET from ...xml.XmlReader import XmlReader as XR from ..namespaces import COMMON from ..namespaces import DATA from ..dto.DetailedReason import DetailedReason def deserialize_detailed_reason(element: ET.Element) -> Optional[DetailedReason]: if element is None: return None result = DetailedReason( case=XR.get_child_text(element, 'case', DATA), reason=XR.get_child_text(element, 'reason', DATA), ) return result

27.105263

81

0.733981

65

515

5.723077

0.461538

0.075269

0.107527

0.075269

0.112903

0

0.180583

515

18

82

28.611111

0.881517

0

0.019417

0

1

0.071429

false

0

0.428571

0

0.642857

0

null

0

null

0

1

0

1

0

2

837e4bab46beccba993bb55df20966fe17f92d9a

823

py

Python

mldash/plugins/viewer/handler.py

K-A-R-T/JacMLDash

5cb5d66da32ac55002319301f82d1db9091f0c56

[ "MIT" ]

5

2019-09-07T09:35:19.000Z

2022-03-09T14:33:44.000Z

mldash/plugins/viewer/handler.py

K-A-R-T/JacMLDash

5cb5d66da32ac55002319301f82d1db9091f0c56

[ "MIT" ]

null

mldash/plugins/viewer/handler.py

K-A-R-T/JacMLDash

5cb5d66da32ac55002319301f82d1db9091f0c56

[ "MIT" ]

1

2021-03-17T04:17:06.000Z

#! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : handler.py # Author : Jiayuan Mao # Email : maojiayuan@gmail.com # Date : 09/08/2019 # # This file is part of JacMLDash. # Distributed under terms of the MIT license. import os import os.path as osp import mimetypes from tornado.web import StaticFileHandler from jacweb.web import route, JacRequestHandler @route(r'/viewer/(.*)') class FileViewerHandler(StaticFileHandler): def initialize(self, path=None, default_filename='index.html'): if path is None: path = os.getcwd() super().initialize(path, default_filename) def get_content_type(self) -> str: assert self.absolute_path is not None if self.absolute_path.endswith('.log'): return 'text/plain' return super().get_content_type()

26.548387

67

0.679222

108

823

5.101852

0.666667

0.029038

0.050817

0

0.015337

0.207776

823

30

68

27.433333

0.829755

0.256379

0

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0.125

false

0

0.3125

0

0.625

0

null

0

null

0

1

0

1

0

2

83827304f7a0429aa38c4a9f8e6141696a9b691b

94

py

Python

src/mathematical/bitwise_operations.py

Venkat0273/Python-Notes

bb3901315bd688acf61a97dc9f45353376f8ff39

[ "MIT" ]

null

src/mathematical/bitwise_operations.py

Venkat0273/Python-Notes

bb3901315bd688acf61a97dc9f45353376f8ff39

[ "MIT" ]

null

src/mathematical/bitwise_operations.py

Venkat0273/Python-Notes

bb3901315bd688acf61a97dc9f45353376f8ff39

[ "MIT" ]

null

# -*- coding: utf-8 -*- __author__ = "venkat" __author_email__ = "venkatram0273@gmail.com"

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838cf8818d2fa2350abd45b9478277fc6154989b

228

py

Python

notification/urls.py

opendream/asip

20583aca6393102d425401d55ea32ac6b78be048

[ "MIT" ]

null

notification/urls.py

opendream/asip

20583aca6393102d425401d55ea32ac6b78be048

[ "MIT" ]

8

2020-03-24T17:11:49.000Z

2022-01-13T01:18:11.000Z

notification/urls.py

opendream/asip

20583aca6393102d425401d55ea32ac6b78be048

[ "MIT" ]

null

from django.conf.urls import url, patterns urlpatterns = patterns('notification.views', url(r'^notification/$', 'notification_list', name='notification_list'), url(r'^request/$', 'request_list', name='request_list'), )

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null

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838ea63d5bff1bf8f011f7fa53191bb877f6eb41

1,706

py

Python

aix360/algorithms/protodash/PDASH.py

PurplePean/AIX360

4037d6347c40405f342b07da5d341fcd21081cfa

[ "Apache-2.0" ]

1

2019-10-21T20:07:44.000Z

aix360/algorithms/protodash/PDASH.py

PurplePean/AIX360

4037d6347c40405f342b07da5d341fcd21081cfa

[ "Apache-2.0" ]

12

2020-01-28T23:06:13.000Z

2022-02-10T00:23:14.000Z

aix360/algorithms/protodash/PDASH.py

PurplePean/AIX360

4037d6347c40405f342b07da5d341fcd21081cfa

[ "Apache-2.0" ]

1

2020-04-20T08:15:36.000Z

from __future__ import print_function from aix360.algorithms.die import DIExplainer from .PDASH_utils import HeuristicSetSelection class ProtodashExplainer(DIExplainer): """ ProtodashExplainer provides exemplar-based explanations for summarizing datasets as well as explaining predictions made by an AI model. It employs a fast gradient based algorithm to find prototypes along with their (non-negative) importance weights. The algorithm minimizes the maximum mean discrepancy metric and has constant factor approximation guarantees for this weakly submodular function. [#]_. References: .. [#] `Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo Cecchi, "ProtoDash: Fast Interpretable Prototype Selection" <https://arxiv.org/abs/1707.01212>`_ """ def __init__(self): """ Constructor method, initializes the explainer """ super(ProtodashExplainer, self).__init__() def set_params(self, *argv, **kwargs): """ Set parameters for the explainer. """ pass def explain(self, X, Y, m, kernelType='other', sigma=2): """ Return prototypes for data X, Y. Args: X (double 2d array): Dataset to select prototypical explanations from. Y (double 2d array): Dataset you want to explain. m (int): Number of prototypes kernelType (str): Type of kernel (viz. 'Gaussian', / 'other') sigma (double): width of kernel Returns: m selected prototypes from X and their (unnormalized) importance weights """ return( HeuristicSetSelection(X, Y, m, kernelType, sigma) )

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83922c5d46dd9cf938810b0116e304b37e96df4a

984

py

Python

test/rpcframework/ClientTest.py

crylearner/PythonRpcFramework

ccae8dfe82ec5b957296e288ae21b9e292cc52a0

[ "Apache-2.0" ]

1

2017-11-16T09:58:06.000Z

test/rpcframework/ClientTest.py

crylearner/PythonRpcFramework

ccae8dfe82ec5b957296e288ae21b9e292cc52a0

[ "Apache-2.0" ]

null

test/rpcframework/ClientTest.py

crylearner/PythonRpcFramework

ccae8dfe82ec5b957296e288ae21b9e292cc52a0

[ "Apache-2.0" ]

null

''' Created on 2015年12月22日 @author: sunshyran ''' import time import unittest from framework.client.Client import AbstractClient from framework.driver.Invoker import Invoker from framework.driver.InvokerHandler import InvokerHandler from test.rpcframework.FakeChannel import FakeChannel class FakeClient(AbstractClient): def __init__(self): invokerhandler = InvokerHandler(FakeChannel()) super().__init__(invokerhandler) def onResponse(self, rsp): print(rsp) class ClientTest(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testName(self): client = FakeClient() client.start() client.asyncrequest(Invoker(1, 'test message')) time.sleep(1) client.stop() if __name__ == "__main__": #import sys;sys.argv = ['', 'Test.testName'] unittest.main()

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null

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83a501cd315e08dc04e8e6ecd6ba453c02b942ba

2,925

py

Python

mvsgst/mvsgst/spiders/vcmv.py

miemiekurisu/mvsgst

a8efe763c988cae6f3298074d5d03fccef8fdbce

[ "MIT" ]

null

mvsgst/mvsgst/spiders/vcmv.py

miemiekurisu/mvsgst

a8efe763c988cae6f3298074d5d03fccef8fdbce

[ "MIT" ]

null

mvsgst/mvsgst/spiders/vcmv.py

miemiekurisu/mvsgst

a8efe763c988cae6f3298074d5d03fccef8fdbce

[ "MIT" ]

null

# -*- coding: utf-8 -*- import scrapy import sys from scrapy.contrib.spiders import CrawlSpider, Rule from scrapy.contrib.linkextractors.sgml import SgmlLinkExtractor from mvsgst.items import MvsgstItem from scrapy.selector import HtmlXPathSelector import time import re import random class DbmvSpider(scrapy.Spider): name = "vcmv" tags = [] # for i in range(1900,2016,1): # tags.append( "http://movie.douban.com/tag/"+str(i)) start_urls = ("http://www.verycd.com/base/movie/~all/",) #tuple(tags) def load_item(self,ct): staticurl = "http://www.verycd.com" item = MvsgstItem() item['url']=[] for i in ct.xpath('@href').extract(): item['url'].append(staticurl+i) return item def regprocess(self,lst): ret = [] if len(lst) ==0: return ret for i in lst: i=i.replace('"','') i=i.replace(',','') i=i.replace(u'\u201c',u'') i=i.replace('\\','') ret.append(i) return ret def load_detail(self,itemurl): dre = re.compile(ur'\u4e0a\u6620\u65e5\u671f.*\>(.*)\<\/em\>') details = itemurl.meta['item'] x = scrapy.Selector(itemurl) details['mvname']= self.regprocess(x.xpath('//*[@class="titleDiv"]/h1/text()').extract()) enn = x.xpath('//*[@class="titleDiv"]/h2/text()').extract() details['enname'] = self.regprocess(enn) details['director']=self.regprocess(x.xpath('//*[@rel="v:directedBy"]/text()').extract()) details['actors']=self.regprocess(x.xpath('//*[@rel="v:starring"]/text()').extract()) details['types']=self.regprocess(x.xpath('//*[@rel="v:genre"]/text()').extract()) details['date'] = dre.findall(itemurl.body.decode('utf8')) details['length'] = x.xpath('//*[@property="v:runtime"]/text()').extract() summ= x.xpath('//*[@property="v:summary"]/p/text()').extract() details['summary'] = self.regprocess(summ) details['imdblink'] = x.xpath('//*[@id="imdb_rate_id"]/a/text()').extract() #details['rank']= x.xpath('//*[@id="scoreDivDiv"]/text()').extract() #TODO this place should be rebuilt, but for this case, enough time.sleep(random.randint(1,3)) return details def parse(self, response): x = scrapy.Selector(response) sites = x.xpath('//*[@class="clearfix entry_cover_list"]/li/a') i=0 for ct in sites: i+=1 item = self.load_item(ct) yield scrapy.Request(item['url'][0],meta={'item':item},callback=self.load_detail) time.sleep(random.randint(3,10)) nexturl=x.xpath('//*[@rel="next"]/@href').extract() if len(nexturl)>0: yield scrapy.Request('http://www.verycd.com'+nexturl[0],callback=self.parse) #if i==10: # sys.exit(0)

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null

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83b2455793ac0102ae6c3ddd7a4e3cd3efc16519

380

py

Python

sets.py

Varanasi-Software-Junction/Python-repository-for-basics

01128ccb91866cb1abb6d8abf035213f722f5750

[ "MIT" ]

2

2021-07-14T11:01:58.000Z

2021-07-14T11:02:01.000Z

sets.py

Maurya232Abhishek/Python-repository-for-basics

3dcec5c529a0847df07c9dcc1424675754ce6376

[ "MIT" ]

4

2021-04-09T10:14:06.000Z

2021-04-13T10:25:58.000Z

sets.py

Maurya232Abhishek/Python-repository-for-basics

3dcec5c529a0847df07c9dcc1424675754ce6376

[ "MIT" ]

2

2021-07-11T08:17:30.000Z

2021-07-14T11:10:58.000Z

s1=set([1,3,7,94]) s2=set([2,3]) print(s1) print(s2) print(s1.intersection(s2)) print(s1.difference(s2)) print(s2.difference(s1)) print(s1.symmetric_difference(s2)) print(s1.union(s2)) s1.difference_update(s2) #S1 becomes equal to the difference print(s1) s1=set([1,3]) s1.discard(1) s1.remove(3) print(s1) s1.add(5) print(s1) t=([6,7]) s2.update(t) print(s2) x=s2.pop() print(x)

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null

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null

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83c212e47df5af280b2e08dd307c66c52fc7c620

746

py

Python

Bidding/migrations/0007_auto_20201125_1049.py

Ishikashah2510/nirvaas_main

5eaf92756d06261a7f555b10aad864a34c9e761b

[ "MIT" ]

null

Bidding/migrations/0007_auto_20201125_1049.py

Ishikashah2510/nirvaas_main

5eaf92756d06261a7f555b10aad864a34c9e761b

[ "MIT" ]

null

Bidding/migrations/0007_auto_20201125_1049.py

Ishikashah2510/nirvaas_main

5eaf92756d06261a7f555b10aad864a34c9e761b

[ "MIT" ]

3

2020-12-30T11:35:22.000Z

2021-01-07T13:10:26.000Z

# Generated by Django 3.1.3 on 2020-11-25 05:19 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('Bidding', '0006_auto_20201125_1007'), ] operations = [ migrations.AddField( model_name='old_items_on_bid', name='buyer_email', field=models.EmailField(default='', max_length=254), ), migrations.AddField( model_name='old_items_on_bid', name='last_bid_value', field=models.FloatField(default=0), ), migrations.AddField( model_name='old_items_on_bid', name='threshold_value', field=models.FloatField(default=0), ), ]

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0

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0

null

0

1

0

1

0

null

0

2

83c657faaeb5f77a3afd9ea7d3c64106353332cb

413

py

Python

src/tests/part2/q071_test_simplify_path.py

hychrisli/PyAlgorithms

71e537180f3b371d0d2cc47b11cb68ec13a8ac68

[ "Apache-2.0" ]

null

src/tests/part2/q071_test_simplify_path.py

hychrisli/PyAlgorithms

71e537180f3b371d0d2cc47b11cb68ec13a8ac68

[ "Apache-2.0" ]

null

src/tests/part2/q071_test_simplify_path.py

hychrisli/PyAlgorithms

71e537180f3b371d0d2cc47b11cb68ec13a8ac68

[ "Apache-2.0" ]

null

from src.base.test_cases import TestCases class SimplifiyPathTestCases(TestCases): def __init__(self): super(SimplifiyPathTestCases, self).__init__() self.__add_test_case__('Test 1', '/home/', '/home') self.__add_test_case__('Test 2', '/a/./b/../../c/', '/c') self.__add_test_case__('Test 3', '/../', '/') self.__add_test_case__('Test 4', '/home//foo/', '/home/foo')

37.545455

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0.125561

0.197309

0.269058

0.340807

0

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0.174334

413

11

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0

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0.375

0

null

0

1

0

null

0

2

83db8a59b1d72d9359ecb0571d02bfaf26f6d0fe

2,612

py

Python

survol/sources_types/Linux/modules_dependencies.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

9

2017-10-05T23:36:23.000Z

2021-08-09T15:40:03.000Z

survol/sources_types/Linux/modules_dependencies.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

21

2018-01-02T09:33:03.000Z

2018-08-27T11:09:52.000Z

survol/sources_types/Linux/modules_dependencies.py

rchateauneu/survol

ba66d3ec453b2d9dd3a8dabc6d53f71aa9ba8c78

[ "BSD-3-Clause" ]

4

2018-06-23T09:05:45.000Z

2021-01-22T15:36:50.000Z

#!/usr/bin/env python """ Linux modules dependencies """ import sys import socket import lib_common import lib_util import lib_modules from lib_properties import pc # # The modules.dep as generated by module-init-tools depmod, # lists the dependencies for every module in the directories # under /lib/modules/version, where modules.dep is. # # cat /proc/version # Linux version 2.6.24.7-desktop586-2mnb (qateam@titan.mandriva.com) (gcc version 4.2.3 (4.2.3-6mnb1)) #1 SMP Thu Oct 30 17:39:28 EDT 2008 # ls /lib/modules/$(cat /proc/version | cut -d " " -f3)/modules.dep # # /lib/modules/2.6.24.7-desktop586-2mnb/modules.dep # /lib/modules/2.6.24.7-desktop586-2mnb/dkms-binary/drivers/char/hsfmc97via.ko.gz: /lib/modules/2.6.24.7-desktop586-2mnb/dkms-binary/drivers/char/hsfserial.ko.gz /lib/modules/2.6.24.7-desktop586-2mnb/dkms-binary/drivers/char/hsfengine.ko.gz /lib/modules/2.6.24.7-desktop586-2mnb/dkms-binary/drivers/char/hsfosspec.ko.gz /lib/modules/2.6.24.7-desktop586-2mnb/kernel/drivers/usb/core/usbcore.ko.gz /lib/modules/2.6.24.7-desktop586-2mnb/dkms-binary/drivers/char/hsfsoar.ko.gz # # def Main(): cgiEnv = lib_common.ScriptEnvironment() grph = cgiEnv.GetGraph() # TODO: The dependency network is huge, so we put a limit, for the moment. max_cnt = 0 try: modudeps = lib_modules.Dependencies() except Exception as exc: lib_common.ErrorMessageHtml("Caught:"+str(exc)) for module_name in modudeps: # NOT TOO MUCH NODES: BEYOND THIS, IT IS FAR TOO SLOW, UNUSABLE. HARDCODE_LIMIT max_cnt += 1 if max_cnt > 2000: logging.error("Too many modules to display. Break.") break file_parent = lib_modules.ModuleToNode(module_name) file_child = None for module_dep in modudeps[module_name]: # print ( module_name + " => " + module_dep ) # This generates a directed acyclic graph, # but not a tree in the general case. file_child = lib_modules.ModuleToNode(module_dep) grph.add((file_parent, pc.property_module_dep, file_child)) # TODO: Ugly trick, otherwise nodes without connections are not displayed. # TODO: I think this is a BUG in the dot file generation. Or in RDF ?... if file_child is None: grph.add((file_parent, pc.property_information, lib_util.NodeLiteral(""))) # Splines are rather slow. if max_cnt > 100: layout_type = "LAYOUT_XXX" else: layout_type = "LAYOUT_SPLINE" cgiEnv.OutCgiRdf(layout_type) if __name__ == '__main__': Main()

34.368421

472

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0.42711

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2,612

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null

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null

0

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0

2

83eb15797d997733d9f93ba8fd7b40602b6fddd5

444

py

Python

kryta_server/app/utils/init_template/database_init.py

mattholy/Kryta-MC

67126e3bb61ae97efd08576d49b3b0d7a01a8822

[ "MIT" ]

1

2021-10-05T10:35:02.000Z

kryta_server/app/utils/init_template/database_init.py

mattholy/Kryta-MC

67126e3bb61ae97efd08576d49b3b0d7a01a8822

[ "MIT" ]

null

kryta_server/app/utils/init_template/database_init.py

mattholy/Kryta-MC

67126e3bb61ae97efd08576d49b3b0d7a01a8822

[ "MIT" ]

null

# -*- encoding: utf-8 -*- ''' database_init.py ---- 初始化数据库 @Time : 2021/10/03 12:25:03 @Author : Mattholy @Version : 1.0 @Contact : smile.used@hotmail.com @License : MIT License ''' from sqlalchemy import create_engine from sqlalchemy.ext.declarative import declarative_base from sqlalchemy import Column, Integer, String Base = declarative_base() engine = create_engine('sqlite:///kryta-system.db?check_same_thread=False')

19.304348

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5.288136

0.745763

0.134615

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null

0

null

0

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1

0

2

83ee3ca805a83db54b0d1425a4b1dec0918e1078

2,113

py

Python

benchmark/wrappers/rllib/wrapper.py

JenishPatel99/SMARTS

feee8fd8a1f0c10ab2aaf6f12acc8c9cc0f861af

[ "MIT" ]

null

benchmark/wrappers/rllib/wrapper.py

JenishPatel99/SMARTS

feee8fd8a1f0c10ab2aaf6f12acc8c9cc0f861af

[ "MIT" ]

null

benchmark/wrappers/rllib/wrapper.py

JenishPatel99/SMARTS

feee8fd8a1f0c10ab2aaf6f12acc8c9cc0f861af

[ "MIT" ]

null

# MIT License # # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import copy from ray.rllib.env.multi_agent_env import MultiAgentEnv class Wrapper(MultiAgentEnv): def __init__(self, config): base_env_cls = config["base_env_cls"] self.env = base_env_cls(config) self._agent_keys = list(config["agent_specs"].keys()) self._last_observations = {k: None for k in self._agent_keys} def _get_observations(self, observations): return observations def _get_rewards(self, last_observations, observations, rewards): return rewards def _get_infos(self, observations, rewards, infos): return infos def _update_last_observation(self, observations): for agent_id, obs in observations.items(): self._last_observations[agent_id] = copy.copy(obs) def step(self, agent_actions): return self.env.step(agent_actions) def reset(self, **kwargs): return self.env.reset(**kwargs) def close(self): self.env.close()

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0

null

0

null

0

1

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1

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2

83fb49c0fad272e8d8e34b432f2715b58dd4bc53

261

py

Python

Darlington/phase1/python Basic 1/day 13 solution/qtn3.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

6

2020-05-23T19:53:25.000Z

2021-05-08T20:21:30.000Z

Darlington/phase1/python Basic 1/day 13 solution/qtn3.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

8

2020-05-14T18:53:12.000Z

2020-07-03T00:06:20.000Z

Darlington/phase1/python Basic 1/day 13 solution/qtn3.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

39

2020-05-10T20:55:02.000Z

2020-09-12T17:40:59.000Z

#program to input a number, if it is not a number generate an error message. while True: try: a = int(input("Input a number: ")) break except ValueError: print("\nThis is not a number. Try again...") print() break

29

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0.156863

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261

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0

null

0

1

0

1

0

null

0

2

83fee75301f392ac61e8ac89a8f2291ac2d94169

396

py

Python

src/bxcommon/feed/filter_parsing.py

thabaptiser/bxcommon

ee8547c9fc68c71b8acf4ce0989a344681ea273c

[ "MIT" ]

null

src/bxcommon/feed/filter_parsing.py

thabaptiser/bxcommon

ee8547c9fc68c71b8acf4ce0989a344681ea273c

[ "MIT" ]

null

src/bxcommon/feed/filter_parsing.py

thabaptiser/bxcommon

ee8547c9fc68c71b8acf4ce0989a344681ea273c

[ "MIT" ]

null

from typing import Callable, Dict import pycond as pc from bxutils import logging logger = logging.get_logger(__name__) pc.ops_use_symbolic_and_txt(allow_single_eq=True) def get_validator(filter_string: str) -> Callable[[Dict], bool]: logger.trace("Getting validator for filters {}", filter_string) res = pc.qualify(filter_string.lower(), brkts="()", add_cached=True) return res

26.4

72

0.757576

57

396

4.982456

0.684211

0.126761

0

0.133838

396

14

73

28.285714

0.827988

0

0.085859

0

1

0.111111

false

0

0.333333

0

0.555556

0

null

0

null

0

1

0

1

0

2

860362618af49e26f9510a14dc6d5c9d5c45caad

2,297

py

Python

tests/test_dib.py

noda/pyMeterBus

a1bb6b6ef9b3db4583dfb2b154e4f65365dee9d9

[ "BSD-3-Clause" ]

44

2016-12-11T14:43:14.000Z

2022-03-17T18:31:14.000Z

tests/test_dib.py

noda/pyMeterBus

a1bb6b6ef9b3db4583dfb2b154e4f65365dee9d9

[ "BSD-3-Clause" ]

13

2017-11-29T14:36:34.000Z

2020-12-20T18:33:35.000Z

tests/test_dib.py

noda/pyMeterBus

a1bb6b6ef9b3db4583dfb2b154e4f65365dee9d9

[ "BSD-3-Clause" ]

32

2015-09-15T12:23:19.000Z

2022-03-22T08:32:22.000Z

# -*- coding: utf-8 -*- import os import sys myPath = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, myPath + '/../') import unittest import meterbus from meterbus.exceptions import * class TestSequenceFunctions(unittest.TestCase): def setUp(self): self.dib_empty = meterbus.DataInformationBlock() self.dib0 = meterbus.DataInformationBlock([0x0C]) self.dib7 = meterbus.DataInformationBlock([0x2F]) self.dib8 = meterbus.DataInformationBlock([0x0F]) self.dib9 = meterbus.DataInformationBlock([0x1F]) def test_empty_dib_has_extension_bit(self): self.assertEqual(self.dib_empty.has_extension_bit, False) def test_empty_dib_has_lvar_bit(self): self.assertEqual(self.dib_empty.has_lvar_bit, False) def test_empty_dib_is_eoud(self): self.assertEqual(self.dib_empty.is_eoud, False) def test_empty_dib_more_records_follow(self): self.assertEqual(self.dib_empty.more_records_follow, False) def test_empty_dib_is_variable_length(self): self.assertEqual(self.dib_empty.is_variable_length, False) def test_dib0_has_extension_bit(self): self.assertEqual(self.dib0.has_extension_bit, False) def test_dib0_has_lvar_bit(self): self.assertEqual(self.dib0.has_lvar_bit, False) def test_dib0_is_eoud(self): self.assertEqual(self.dib0.is_eoud, False) def test_dib0_is_variable_length(self): self.assertEqual(self.dib0.is_variable_length, False) def test_dib0_function_type(self): self.assertEqual(self.dib0.function_type, meterbus.FunctionType.INSTANTANEOUS_VALUE) def test_dib7_function_type(self): self.assertEqual(self.dib7.function_type, meterbus.FunctionType.SPECIAL_FUNCTION_FILL_BYTE) def test_dib8_function_type(self): self.assertEqual(self.dib8.function_type, meterbus.FunctionType.SPECIAL_FUNCTION) def test_dib9_more_records_follow(self): self.assertEqual(self.dib9.more_records_follow, True) def test_dib9_function_type(self): self.assertEqual(self.dib9.function_type, meterbus.FunctionType.MORE_RECORDS_FOLLOW) if __name__ == '__main__': unittest.main()

32.814286

74

0.715281

288

2,297

5.350694

0.208333

0.077872

0.172615

0.208955

0.602855

0.55159

0.286178

0.048021

0

0.017232

0.191554

2,297

69

75

33.289855

0.812601

0.009142

0

0.005277

0

0.007036

0

0.291667

1

0.3125

false

0

0.104167

0

0.4375

0

null

0

1

0

null

0

1

0

2

8605cb268bff0bc91478ddd74d6557acca5d57dc

893

py

Python

mercury/migrations/0012_gfconfig.py

ab7289/mercury-telemetry

db886fce9a2328bdcc85127130c6c6f42f9155eb

[ "MIT" ]

5

2020-05-05T20:05:12.000Z

2020-11-10T23:57:44.000Z

mercury/migrations/0012_gfconfig.py

ab7289/mercury-telemetry

db886fce9a2328bdcc85127130c6c6f42f9155eb

[ "MIT" ]

38

2020-05-06T23:30:13.000Z

2020-12-01T15:07:08.000Z

mercury/migrations/0012_gfconfig.py

ab7289/mercury-telemetry

db886fce9a2328bdcc85127130c6c6f42f9155eb

[ "MIT" ]

10

2020-05-04T17:08:07.000Z

2020-05-23T17:35:47.000Z

# Generated by Django 2.2.10 on 2020-03-20 16:15 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("mercury", "0011_merge_20200314_0111"), ] operations = [ migrations.CreateModel( name="GFConfig", fields=[ ( "id", models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name="ID", ), ), ("gf_name", models.CharField(max_length=64)), ("gf_host", models.CharField(max_length=128)), ("gf_token", models.CharField(max_length=256)), ("gf_current", models.BooleanField(default=False)), ], ), ]

27.90625

67

0.464726

75

893

5.36

0.68

0.11194

0.134328

0.179104

0

0.077973

0.425532

893

31

68

28.806452

0.705653

0.051512

0

0.12

1

0

0.088757

0.028402

0

1

0

false

0

0.04

0

0.16

0

null

0

1

0

1

0

null

0

2

f7adbf1a3101c5e95093139f62a7b9fd7a905e61

1,440

py

Python

tensorflow/mycode/src/tf_layer_utils.py

christinazavou/O-CNN

88cda0aea9bf07e14686fff1fe476e8080296dcf

[ "MIT" ]

null

tensorflow/mycode/src/tf_layer_utils.py

christinazavou/O-CNN

88cda0aea9bf07e14686fff1fe476e8080296dcf

[ "MIT" ]

null

tensorflow/mycode/src/tf_layer_utils.py

christinazavou/O-CNN

88cda0aea9bf07e14686fff1fe476e8080296dcf

[ "MIT" ]

null

import tensorflow as tf def make_weights(shape, name='weights'): return tf.Variable(tf.truncated_normal(shape=shape, stddev=0.05), name=name) def make_biases(shape, name='biases'): return tf.Variable(tf.constant(0.05, shape=shape), name=name) def convolution_layer(prev_layer, f_size, inp_c, out_c, stride_s): _weights = make_weights([f_size, f_size, inp_c, out_c]) _bias = make_biases([out_c]) return tf.add(tf.nn.conv2d(prev_layer, _weights, [1, stride_s, stride_s, 1], padding='SAME'), _bias) def pool_layer(prev_layer, size, stride_s): kernel = [1, size, size, 1] stride = [1, stride_s, stride_s, 1] return tf.nn.max_pool(prev_layer, kernel, stride, padding='SAME') def activation_layer(prev_layer, type): if type == 'relu': return tf.nn.relu(prev_layer) else: raise NotImplemented('unsupported activation type') def flat_layer(inp): input_size = inp.get_shape().as_list() if len(input_size) != 4: raise NotImplemented('flat layer unsupported for input with dim != 4') output_size = input_size[-1] * input_size[-2] * input_size[-3] return tf.reshape(inp, [-1, output_size]), output_size def fc_layer(prev_layer, h_in, h_out): _weights = make_weights([h_in, h_out]) _bias = make_biases([h_out]) return tf.add(tf.matmul(prev_layer, _weights), _bias) def dropout_layer(prev_layer, prob): return tf.nn.dropout(prev_layer, prob)

30.638298

104

0.696528

228

1,440

4.131579

0.285088

0.095541

0.07431

0.038217

0.061571

0

0.015847

0.167361

1,440

46

105

31.304348

0.769808

0

0.068056

0

1

0.266667

false

0

0.033333

0.1

0.566667

0

null

0

null

0

1

0

1

0

2

f7b5a8ea18b93a0e2420ee64ad68108c327e73b0

5,322

py

Python

sql_queries.py

bsulayman/Data_modeling_Postgres

5442c7d4be0a789ef6b2ab0dc15ce956bc70581c

[ "MIT" ]

null

sql_queries.py

bsulayman/Data_modeling_Postgres

5442c7d4be0a789ef6b2ab0dc15ce956bc70581c

[ "MIT" ]

null

sql_queries.py

bsulayman/Data_modeling_Postgres

5442c7d4be0a789ef6b2ab0dc15ce956bc70581c

[ "MIT" ]

null

# DROP TABLES songplay_table_drop = "DROP TABLE IF EXISTS songplays;" user_table_drop = "DROP TABLE IF EXISTS users;" song_table_drop = "DROP TABLE IF EXISTS songs;" artist_table_drop = "DROP TABLE IF EXISTS artists;" time_table_drop = "DROP TABLE IF EXISTS time;" # CREATE TABLES songplay_table_create = ("""CREATE TABLE IF NOT EXISTS songplays ( songplay_id SERIAL PRIMARY KEY, start_time TIMESTAMP NOT NULL, user_id INT NOT NULL, level VARCHAR(4), song_id VARCHAR, artist_id VARCHAR, session_id INT NOT NULL, location TEXT, user_agent TEXT ) """) user_table_create = ("""CREATE TABLE IF NOT EXISTS users ( user_id INT UNIQUE NOT NULL PRIMARY KEY, first_name TEXT, last_name TEXT, gender VARCHAR(1), level VARCHAR(4) ) """) song_table_create = ("""CREATE TABLE IF NOT EXISTS songs ( song_id VARCHAR UNIQUE NOT NULL PRIMARY KEY, title TEXT, artist_id VARCHAR, year INT, duration NUMERIC ) """) artist_table_create = ("""CREATE TABLE IF NOT EXISTS artists ( artist_id VARCHAR UNIQUE NOT NULL PRIMARY KEY, name TEXT, location TEXT, latitude NUMERIC, longitude NUMERIC ) """) time_table_create = ("""CREATE TABLE IF NOT EXISTS time ( start_time TIME UNIQUE NOT NULL, hour INT, day INT, week INT, month VARCHAR(10), year INT, weekday VARCHAR(10) ) """) # INSERT RECORDS songplay_table_insert = ("""INSERT INTO songplays ( start_time, user_id, level, song_id, artist_id, session_id, location, user_agent ) VALUES (to_timestamp(%s), %s, %s, %s, %s, %s, %s, %s) """) user_table_insert = ("""INSERT INTO users ( user_id, first_name, last_name, gender, level ) VALUES (%s, %s, %s, %s, %s) ON CONFLICT (user_id) DO UPDATE SET level = EXCLUDED.level """) song_table_insert = ("""INSERT INTO songs ( song_id, title, artist_id, year, duration ) VALUES (%s, %s, %s, %s, %s) ON CONFLICT (song_id) DO NOTHING """) artist_table_insert = ("""INSERT INTO artists ( artist_id, name, location, latitude, longitude ) VALUES (%s, %s, %s, %s, %s) ON CONFLICT (artist_id) DO NOTHING """) time_table_insert = ("""INSERT INTO time ( start_time, hour, day, week, month, year, weekday ) VALUES (%s, %s, %s, %s, %s, %s, %s) ON CONFLICT (start_time) DO NOTHING """) # FIND SONGS song_select = ("""SELECT songs.song_id, songs.artist_id FROM songs JOIN artists ON songs.artist_id = artists.artist_id WHERE songs.title = (%s) AND artists.name = (%s) AND songs.duration = (%s); """) # QUERY LISTS create_table_queries = [songplay_table_create, user_table_create, song_table_create, artist_table_create, time_table_create] drop_table_queries = [songplay_table_drop, user_table_drop, song_table_drop, artist_table_drop, time_table_drop]

38.846715

124

0.360955

408

5,322

4.485294

0.17402

0.027322

0.032787

0.260109

0.247541

0.173224

0.034426

0

0.003126

0.579294

5,322

137

125

38.846715

0.814203

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0

0.313043

0

0.026087

0.866768

0

1

0

false

0

null

0

1

0

1

0

null

0

2

f7cddff083977ef868affd95a78df624756cab8e

24,961

py

Python

genshi/core.py

litebook/litebook

3e0fc6daa62a4782a4a1103fe41b9bc56e677e00

[ "Python-2.0" ]

20

2015-01-27T04:50:16.000Z

2019-12-09T02:23:15.000Z

genshi/core.py

litebook/litebook

3e0fc6daa62a4782a4a1103fe41b9bc56e677e00

[ "Python-2.0" ]

1

2020-11-26T04:10:27.000Z

2021-01-03T22:36:12.000Z

genshi/core.py

hujun-open/litebook

3e0fc6daa62a4782a4a1103fe41b9bc56e677e00

[ "Python-2.0" ]

2

2017-05-09T06:56:00.000Z

2020-11-20T15:23:16.000Z

# -*- coding: utf-8 -*- # # Copyright (C) 2006-2009 Edgewall Software # All rights reserved. # # This software is licensed as described in the file COPYING, which # you should have received as part of this distribution. The terms # are also available at http://genshi.edgewall.org/wiki/License. # # This software consists of voluntary contributions made by many # individuals. For the exact contribution history, see the revision # history and logs, available at http://genshi.edgewall.org/log/. """Core classes for markup processing.""" try: reduce # builtin in Python < 3 except NameError: from functools import reduce from itertools import chain import operator from genshi.util import plaintext, stripentities, striptags, stringrepr __all__ = ['Stream', 'Markup', 'escape', 'unescape', 'Attrs', 'Namespace', 'QName'] __docformat__ = 'restructuredtext en' class StreamEventKind(str): """A kind of event on a markup stream.""" __slots__ = [] _instances = {} def __new__(cls, val): return cls._instances.setdefault(val, str.__new__(cls, val)) class Stream(object): """Represents a stream of markup events. This class is basically an iterator over the events. Stream events are tuples of the form:: (kind, data, position) where ``kind`` is the event kind (such as `START`, `END`, `TEXT`, etc), ``data`` depends on the kind of event, and ``position`` is a ``(filename, line, offset)`` tuple that contains the location of the original element or text in the input. If the original location is unknown, ``position`` is ``(None, -1, -1)``. Also provided are ways to serialize the stream to text. The `serialize()` method will return an iterator over generated strings, while `render()` returns the complete generated text at once. Both accept various parameters that impact the way the stream is serialized. """ __slots__ = ['events', 'serializer'] START = StreamEventKind('START') #: a start tag END = StreamEventKind('END') #: an end tag TEXT = StreamEventKind('TEXT') #: literal text XML_DECL = StreamEventKind('XML_DECL') #: XML declaration DOCTYPE = StreamEventKind('DOCTYPE') #: doctype declaration START_NS = StreamEventKind('START_NS') #: start namespace mapping END_NS = StreamEventKind('END_NS') #: end namespace mapping START_CDATA = StreamEventKind('START_CDATA') #: start CDATA section END_CDATA = StreamEventKind('END_CDATA') #: end CDATA section PI = StreamEventKind('PI') #: processing instruction COMMENT = StreamEventKind('COMMENT') #: comment def __init__(self, events, serializer=None): """Initialize the stream with a sequence of markup events. :param events: a sequence or iterable providing the events :param serializer: the default serialization method to use for this stream :note: Changed in 0.5: added the `serializer` argument """ self.events = events #: The underlying iterable producing the events self.serializer = serializer #: The default serializion method def __iter__(self): return iter(self.events) def __or__(self, function): """Override the "bitwise or" operator to apply filters or serializers to the stream, providing a syntax similar to pipes on Unix shells. Assume the following stream produced by the `HTML` function: >>> from genshi.input import HTML >>> html = HTML('''Hello, world!''') >>> print(html) Hello, world! A filter such as the HTML sanitizer can be applied to that stream using the pipe notation as follows: >>> from genshi.filters import HTMLSanitizer >>> sanitizer = HTMLSanitizer() >>> print(html | sanitizer) Hello, world! Filters can be any function that accepts and produces a stream (where a stream is anything that iterates over events): >>> def uppercase(stream): ... for kind, data, pos in stream: ... if kind is TEXT: ... data = data.upper() ... yield kind, data, pos >>> print(html | sanitizer | uppercase) HELLO, WORLD! Serializers can also be used with this notation: >>> from genshi.output import TextSerializer >>> output = TextSerializer() >>> print(html | sanitizer | uppercase | output) HELLO, WORLD! Commonly, serializers should be used at the end of the "pipeline"; using them somewhere in the middle may produce unexpected results. :param function: the callable object that should be applied as a filter :return: the filtered stream :rtype: `Stream` """ return Stream(_ensure(function(self)), serializer=self.serializer) def filter(self, *filters): """Apply filters to the stream. This method returns a new stream with the given filters applied. The filters must be callables that accept the stream object as parameter, and return the filtered stream. The call:: stream.filter(filter1, filter2) is equivalent to:: stream | filter1 | filter2 :param filters: one or more callable objects that should be applied as filters :return: the filtered stream :rtype: `Stream` """ return reduce(operator.or_, (self,) + filters) def render(self, method=None, encoding='utf-8', out=None, **kwargs): """Return a string representation of the stream. Any additional keyword arguments are passed to the serializer, and thus depend on the `method` parameter value. :param method: determines how the stream is serialized; can be either "xml", "xhtml", "html", "text", or a custom serializer class; if `None`, the default serialization method of the stream is used :param encoding: how the output string should be encoded; if set to `None`, this method returns a `unicode` object :param out: a file-like object that the output should be written to instead of being returned as one big string; note that if this is a file or socket (or similar), the `encoding` must not be `None` (that is, the output must be encoded) :return: a `str` or `unicode` object (depending on the `encoding` parameter), or `None` if the `out` parameter is provided :rtype: `basestring` :see: XMLSerializer, XHTMLSerializer, HTMLSerializer, TextSerializer :note: Changed in 0.5: added the `out` parameter """ from genshi.output import encode if method is None: method = self.serializer or 'xml' generator = self.serialize(method=method, **kwargs) return encode(generator, method=method, encoding=encoding, out=out) def select(self, path, namespaces=None, variables=None): """Return a new stream that contains the events matching the given XPath expression. >>> from genshi import HTML >>> stream = HTML('<doc><elem>foo</elem><elem>bar</elem></doc>') >>> print(stream.select('elem')) <elem>foo</elem><elem>bar</elem> >>> print(stream.select('elem/text()')) foobar Note that the outermost element of the stream becomes the *context node* for the XPath test. That means that the expression "doc" would not match anything in the example above, because it only tests against child elements of the outermost element: >>> print(stream.select('doc')) <BLANKLINE> You can use the "." expression to match the context node itself (although that usually makes little sense): >>> print(stream.select('.')) <doc><elem>foo</elem><elem>bar</elem></doc> :param path: a string containing the XPath expression :param namespaces: mapping of namespace prefixes used in the path :param variables: mapping of variable names to values :return: the selected substream :rtype: `Stream` :raises PathSyntaxError: if the given path expression is invalid or not supported """ from genshi.path import Path return Path(path).select(self, namespaces, variables) def serialize(self, method='xml', **kwargs): """Generate strings corresponding to a specific serialization of the stream. Unlike the `render()` method, this method is a generator that returns the serialized output incrementally, as opposed to returning a single string. Any additional keyword arguments are passed to the serializer, and thus depend on the `method` parameter value. :param method: determines how the stream is serialized; can be either "xml", "xhtml", "html", "text", or a custom serializer class; if `None`, the default serialization method of the stream is used :return: an iterator over the serialization results (`Markup` or `unicode` objects, depending on the serialization method) :rtype: ``iterator`` :see: XMLSerializer, XHTMLSerializer, HTMLSerializer, TextSerializer """ from genshi.output import get_serializer if method is None: method = self.serializer or 'xml' return get_serializer(method, **kwargs)(_ensure(self)) def __str__(self): return self.render() def __unicode__(self): return self.render(encoding=None) def __html__(self): return self START = Stream.START END = Stream.END TEXT = Stream.TEXT XML_DECL = Stream.XML_DECL DOCTYPE = Stream.DOCTYPE START_NS = Stream.START_NS END_NS = Stream.END_NS START_CDATA = Stream.START_CDATA END_CDATA = Stream.END_CDATA PI = Stream.PI COMMENT = Stream.COMMENT def _ensure(stream): """Ensure that every item on the stream is actually a markup event.""" stream = iter(stream) event = stream.next() # Check whether the iterable is a real markup event stream by examining the # first item it yields; if it's not we'll need to do some conversion if type(event) is not tuple or len(event) != 3: for event in chain([event], stream): if hasattr(event, 'totuple'): event = event.totuple() else: event = TEXT, unicode(event), (None, -1, -1) yield event return # This looks like a markup event stream, so we'll just pass it through # unchanged yield event for event in stream: yield event class Attrs(tuple): """Immutable sequence type that stores the attributes of an element. Ordering of the attributes is preserved, while access by name is also supported. >>> attrs = Attrs([('href', '#'), ('title', 'Foo')]) >>> attrs Attrs([('href', '#'), ('title', 'Foo')]) >>> 'href' in attrs True >>> 'tabindex' in attrs False >>> attrs.get('title') 'Foo' Instances may not be manipulated directly. Instead, the operators ``|`` and ``-`` can be used to produce new instances that have specific attributes added, replaced or removed. To remove an attribute, use the ``-`` operator. The right hand side can be either a string or a set/sequence of strings, identifying the name(s) of the attribute(s) to remove: >>> attrs - 'title' Attrs([('href', '#')]) >>> attrs - ('title', 'href') Attrs() The original instance is not modified, but the operator can of course be used with an assignment: >>> attrs Attrs([('href', '#'), ('title', 'Foo')]) >>> attrs -= 'title' >>> attrs Attrs([('href', '#')]) To add a new attribute, use the ``|`` operator, where the right hand value is a sequence of ``(name, value)`` tuples (which includes `Attrs` instances): >>> attrs | [('title', 'Bar')] Attrs([('href', '#'), ('title', 'Bar')]) If the attributes already contain an attribute with a given name, the value of that attribute is replaced: >>> attrs | [('href', 'http://example.org/')] Attrs([('href', 'http://example.org/')]) """ __slots__ = [] def __contains__(self, name): """Return whether the list includes an attribute with the specified name. :return: `True` if the list includes the attribute :rtype: `bool` """ for attr, _ in self: if attr == name: return True def __getitem__(self, i): """Return an item or slice of the attributes list. >>> attrs = Attrs([('href', '#'), ('title', 'Foo')]) >>> attrs[1] ('title', 'Foo') >>> attrs[1:] Attrs([('title', 'Foo')]) """ items = tuple.__getitem__(self, i) if type(i) is slice: return Attrs(items) return items def __getslice__(self, i, j): """Return a slice of the attributes list. >>> attrs = Attrs([('href', '#'), ('title', 'Foo')]) >>> attrs[1:] Attrs([('title', 'Foo')]) """ return Attrs(tuple.__getslice__(self, i, j)) def __or__(self, attrs): """Return a new instance that contains the attributes in `attrs` in addition to any already existing attributes. :return: a new instance with the merged attributes :rtype: `Attrs` """ repl = dict([(an, av) for an, av in attrs if an in self]) return Attrs([(sn, repl.get(sn, sv)) for sn, sv in self] + [(an, av) for an, av in attrs if an not in self]) def __repr__(self): if not self: return 'Attrs()' return 'Attrs([%s])' % ', '.join([repr(item) for item in self]) def __sub__(self, names): """Return a new instance with all attributes with a name in `names` are removed. :param names: the names of the attributes to remove :return: a new instance with the attribute removed :rtype: `Attrs` """ if isinstance(names, basestring): names = (names,) return Attrs([(name, val) for name, val in self if name not in names]) def get(self, name, default=None): """Return the value of the attribute with the specified name, or the value of the `default` parameter if no such attribute is found. :param name: the name of the attribute :param default: the value to return when the attribute does not exist :return: the attribute value, or the `default` value if that attribute does not exist :rtype: `object` """ for attr, value in self: if attr == name: return value return default def totuple(self): """Return the attributes as a markup event. The returned event is a `TEXT` event, the data is the value of all attributes joined together. >>> Attrs([('href', '#'), ('title', 'Foo')]).totuple() ('TEXT', '#Foo', (None, -1, -1)) :return: a `TEXT` event :rtype: `tuple` """ return TEXT, ''.join([x[1] for x in self]), (None, -1, -1) class Markup(unicode): """Marks a string as being safe for inclusion in HTML/XML output without needing to be escaped. """ __slots__ = [] def __add__(self, other): return Markup(unicode.__add__(self, escape(other))) def __radd__(self, other): return Markup(unicode.__add__(escape(other), self)) def __mod__(self, args): if isinstance(args, dict): args = dict(zip(args.keys(), map(escape, args.values()))) elif isinstance(args, (list, tuple)): args = tuple(map(escape, args)) else: args = escape(args) return Markup(unicode.__mod__(self, args)) def __mul__(self, num): return Markup(unicode.__mul__(self, num)) __rmul__ = __mul__ def __repr__(self): return "<%s %s>" % (type(self).__name__, unicode.__repr__(self)) def join(self, seq, escape_quotes=True): """Return a `Markup` object which is the concatenation of the strings in the given sequence, where this `Markup` object is the separator between the joined elements. Any element in the sequence that is not a `Markup` instance is automatically escaped. :param seq: the sequence of strings to join :param escape_quotes: whether double quote characters in the elements should be escaped :return: the joined `Markup` object :rtype: `Markup` :see: `escape` """ return Markup(unicode.join(self, [escape(item, quotes=escape_quotes) for item in seq])) @classmethod def escape(cls, text, quotes=True): """Create a Markup instance from a string and escape special characters it may contain (<, >, & and \"). >>> escape('"1 < 2"') <Markup u'"1 < 2"'> If the `quotes` parameter is set to `False`, the \" character is left as is. Escaping quotes is generally only required for strings that are to be used in attribute values. >>> escape('"1 < 2"', quotes=False) <Markup u'"1 < 2"'> :param text: the text to escape :param quotes: if ``True``, double quote characters are escaped in addition to the other special characters :return: the escaped `Markup` string :rtype: `Markup` """ if not text: return cls() if type(text) is cls: return text if hasattr(text, '__html__'): return Markup(text.__html__()) text = text.replace('&', '&') \ .replace('<', '<') \ .replace('>', '>') if quotes: text = text.replace('"', '"') return cls(text) def unescape(self): """Reverse-escapes &, <, >, and \" and returns a `unicode` object. >>> Markup('1 < 2').unescape() u'1 < 2' :return: the unescaped string :rtype: `unicode` :see: `genshi.core.unescape` """ if not self: return '' return unicode(self).replace('"', '"') \ .replace('>', '>') \ .replace('<', '<') \ .replace('&', '&') def stripentities(self, keepxmlentities=False): """Return a copy of the text with any character or numeric entities replaced by the equivalent UTF-8 characters. If the `keepxmlentities` parameter is provided and evaluates to `True`, the core XML entities (``&``, ``'``, ``>``, ``<`` and ``"``) are not stripped. :return: a `Markup` instance with entities removed :rtype: `Markup` :see: `genshi.util.stripentities` """ return Markup(stripentities(self, keepxmlentities=keepxmlentities)) def striptags(self): """Return a copy of the text with all XML/HTML tags removed. :return: a `Markup` instance with all tags removed :rtype: `Markup` :see: `genshi.util.striptags` """ return Markup(striptags(self)) try: from genshi._speedups import Markup except ImportError: pass # just use the Python implementation escape = Markup.escape def unescape(text): """Reverse-escapes &, <, >, and \" and returns a `unicode` object. >>> unescape(Markup('1 < 2')) u'1 < 2' If the provided `text` object is not a `Markup` instance, it is returned unchanged. >>> unescape('1 < 2') '1 < 2' :param text: the text to unescape :return: the unescsaped string :rtype: `unicode` """ if not isinstance(text, Markup): return text return text.unescape() class Namespace(object): """Utility class creating and testing elements with a namespace. Internally, namespace URIs are encoded in the `QName` of any element or attribute, the namespace URI being enclosed in curly braces. This class helps create and test these strings. A `Namespace` object is instantiated with the namespace URI. >>> html = Namespace('http://www.w3.org/1999/xhtml') >>> html Namespace('http://www.w3.org/1999/xhtml') >>> html.uri u'http://www.w3.org/1999/xhtml' The `Namespace` object can than be used to generate `QName` objects with that namespace: >>> html.body QName('http://www.w3.org/1999/xhtml}body') >>> html.body.localname u'body' >>> html.body.namespace u'http://www.w3.org/1999/xhtml' The same works using item access notation, which is useful for element or attribute names that are not valid Python identifiers: >>> html['body'] QName('http://www.w3.org/1999/xhtml}body') A `Namespace` object can also be used to test whether a specific `QName` belongs to that namespace using the ``in`` operator: >>> qname = html.body >>> qname in html True >>> qname in Namespace('http://www.w3.org/2002/06/xhtml2') False """ def __new__(cls, uri): if type(uri) is cls: return uri return object.__new__(cls) def __getnewargs__(self): return (self.uri,) def __getstate__(self): return self.uri def __setstate__(self, uri): self.uri = uri def __init__(self, uri): self.uri = unicode(uri) def __contains__(self, qname): return qname.namespace == self.uri def __ne__(self, other): return not self == other def __eq__(self, other): if isinstance(other, Namespace): return self.uri == other.uri return self.uri == other def __getitem__(self, name): return QName(self.uri + '}' + name) __getattr__ = __getitem__ def __hash__(self): return hash(self.uri) def __repr__(self): return '%s(%s)' % (type(self).__name__, stringrepr(self.uri)) def __str__(self): return self.uri.encode('utf-8') def __unicode__(self): return self.uri # The namespace used by attributes such as xml:lang and xml:space XML_NAMESPACE = Namespace('http://www.w3.org/XML/1998/namespace') class QName(unicode): """A qualified element or attribute name. The unicode value of instances of this class contains the qualified name of the element or attribute, in the form ``{namespace-uri}local-name``. The namespace URI can be obtained through the additional `namespace` attribute, while the local name can be accessed through the `localname` attribute. >>> qname = QName('foo') >>> qname QName('foo') >>> qname.localname u'foo' >>> qname.namespace >>> qname = QName('http://www.w3.org/1999/xhtml}body') >>> qname QName('http://www.w3.org/1999/xhtml}body') >>> qname.localname u'body' >>> qname.namespace u'http://www.w3.org/1999/xhtml' """ __slots__ = ['namespace', 'localname'] def __new__(cls, qname): """Create the `QName` instance. :param qname: the qualified name as a string of the form ``{namespace-uri}local-name``, where the leading curly brace is optional """ if type(qname) is cls: return qname parts = qname.lstrip('{').split('}', 1) if len(parts) > 1: self = unicode.__new__(cls, '{%s' % qname) self.namespace, self.localname = map(unicode, parts) else: self = unicode.__new__(cls, qname) self.namespace, self.localname = None, unicode(qname) return self def __getnewargs__(self): return (self.lstrip('{'),) def __repr__(self): return '%s(%s)' % (type(self).__name__, stringrepr(self.lstrip('{')))

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null

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1

0

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f7d26b92c29c05eca33d08c9a884bd1721344a5c

324

py

Python

spec/construct/test_position_in_seq.py

DarkShadow44/kaitai_struct_tests

4bb13cef82965cca66dda2eb2b77cd64e9f70a12

[ "MIT" ]

11

2018-04-01T03:58:15.000Z

2021-08-14T09:04:55.000Z

spec/construct/test_position_in_seq.py

DarkShadow44/kaitai_struct_tests

4bb13cef82965cca66dda2eb2b77cd64e9f70a12

[ "MIT" ]

73

2016-07-20T10:27:15.000Z

2020-12-17T18:56:46.000Z

spec/construct/test_position_in_seq.py

DarkShadow44/kaitai_struct_tests

4bb13cef82965cca66dda2eb2b77cd64e9f70a12

[ "MIT" ]

37

2016-08-15T08:25:56.000Z

2021-08-28T14:48:46.000Z

# Autogenerated from KST: please remove this line if doing any edits by hand! import unittest from position_in_seq import _schema class TestPositionInSeq(unittest.TestCase): def test_position_in_seq(self): r = _schema.parse_file('src/position_in_seq.bin') self.assertEqual(r.numbers, [(0 + 1), 2, 3])

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null

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0

2

f7ed3c244a1994c9f619cb4b81bb90d7d2abe5a9

1,155

py

Python

naiveSpider/spiders/quotes-spider.py

shusheng007/NaiveSpider

402a60db594fcf19006f9e237c9c58d9fd922ed5

[ "Apache-2.0" ]

null

naiveSpider/spiders/quotes-spider.py

shusheng007/NaiveSpider

402a60db594fcf19006f9e237c9c58d9fd922ed5

[ "Apache-2.0" ]

null

naiveSpider/spiders/quotes-spider.py

shusheng007/NaiveSpider

402a60db594fcf19006f9e237c9c58d9fd922ed5

[ "Apache-2.0" ]

null

import scrapy import random class QuotesSpider(scrapy.Spider): name = "quotes" start_urls = [ 'https://jinqiangua.911cha.com/', ] my_headers=["Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.153 Safari/537.36", "Mozilla/5.0 (Windows NT 6.1; WOW64; rv:30.0) Gecko/20100101 Firefox/30.0" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.75.14 (KHTML, like Gecko) Version/7.0.3 Safari/537.75.14", "Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.2; Win64; x64; Trident/6.0)" 'Mozilla/5.0 (X11; Linux x86_64; rv:48.0) Gecko/20100101 Firefox/48.0' ] def start_requests(self): headers= {'User-Agent': random.choice(headers) } for url in self.start_urls: yield scrapy.Request(url,headers=headers) def parse(self, response): page = response.url.split("/")[-1] filename = 'gua-%s' % page with open(filename, 'wb') as f: f.write(response.body)

33.970588

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0.638095

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null

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null

0

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2

f7eda255525fa58a15b1301ce759f10405f1deda

558

py

Python

app/migrations/0019_auto_20210206_1309.py

LP-Dev-Web/LeBonRecoin

7c52c797d14c7043dfc85c0a5cc5221793c752a8

[ "MIT" ]

null

app/migrations/0019_auto_20210206_1309.py

LP-Dev-Web/LeBonRecoin

7c52c797d14c7043dfc85c0a5cc5221793c752a8

[ "MIT" ]

null

app/migrations/0019_auto_20210206_1309.py

LP-Dev-Web/LeBonRecoin

7c52c797d14c7043dfc85c0a5cc5221793c752a8

[ "MIT" ]

null

# Generated by Django 3.1.4 on 2021-02-06 12:09 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("app", "0018_auto_20210206_0402"), ] operations = [ migrations.AlterField( model_name="product", name="created_at", field=models.DateTimeField(auto_now_add=True), ), migrations.AlterField( model_name="user", name="created_at", field=models.DateTimeField(auto_now_add=True), ), ]

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0.157729

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null

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1

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1

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null

0

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f7eff5d2e53dae84d1b3fce760d7f85ffc335c1f

84

py

Python

Back-End/Python/Basics/Part -1 - Functional/08 - Modules, Packages/04 - structuring_imports_package/common/models/users/user.py

ASHISHKUMAR2411/Programming-CookBook

9c60655d64d21985ccb4196360858d98344701f9

[ "MIT" ]

25

2021-04-28T02:51:26.000Z

2022-03-24T13:58:04.000Z

Back-End/Python/Basics/Part -1 - Functional/08 - Modules, Packages/04 - structuring_imports_package/common/models/users/user.py

ASHISHKUMAR2411/Programming-CookBook

9c60655d64d21985ccb4196360858d98344701f9

[ "MIT" ]

1

2022-03-03T23:33:41.000Z

2022-03-03T23:35:41.000Z

Back-End/Python/Basics/Part -1 - Functional/08 - Modules, Packages/04 - structuring_imports_package/common/models/users/user.py

ASHISHKUMAR2411/Programming-CookBook

9c60655d64d21985ccb4196360858d98344701f9

[ "MIT" ]

15

2021-05-30T01:35:20.000Z

2022-03-25T12:38:25.000Z

# user.py __all__ = ['User'] class User: pass def user_helper_1(): pass

7.636364

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false

0.4

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1

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null

0

null

0

1

0

2

f7fc3dc791c89d5fddee570aa17ebd97f05374de

6,990

py

Python

pySVG/src/pysvg/text.py

Estevo-Aleixo/pysvg

88d2f98eea43fdb16a6d7679048a24985709c850

[ "Unlicense" ]

null

pySVG/src/pysvg/text.py

Estevo-Aleixo/pysvg

88d2f98eea43fdb16a6d7679048a24985709c850

[ "Unlicense" ]

null

pySVG/src/pysvg/text.py

Estevo-Aleixo/pysvg

88d2f98eea43fdb16a6d7679048a24985709c850

[ "Unlicense" ]

null

#!/usr/bin/python # -*- coding: iso-8859-1 -*- ''' (C) 2008, 2009 Kerim Mansour For licensing information please refer to license.txt ''' from attributes import * from core import BaseElement, PointAttrib, DeltaPointAttrib, RotateAttrib class AltGlyphDef(BaseElement, CoreAttrib): """ Class representing the altGlyphDef element of an svg doc. """ def __init__(self, **kwargs): BaseElement.__init__(self, 'altGlypfDef') self.setKWARGS(**kwargs) class AltGlyphItem(BaseElement, CoreAttrib): """ Class representing the altGlyphItem element of an svg doc. """ def __init__(self, **kwargs): BaseElement.__init__(self, 'altGlypfItem') self.setKWARGS(**kwargs) class GlyphRef(BaseElement, CoreAttrib, ExternalAttrib, StyleAttrib, FontAttrib, XLinkAttrib, PaintAttrib, PointAttrib, DeltaPointAttrib): """ Class representing the glyphRef element of an svg doc. """ def __init__(self, **kwargs): BaseElement.__init__(self, 'glyphRef') self.setKWARGS(**kwargs) def set_glyphRef(self, glyphRef): self._attributes['glyphRef'] = glyphRef def get_glyphRef(self): return self._attributes.get('glyphRef') def set_format(self, format): self._attributes['format'] = format def get_format(self): return self._attributes.get('format') def set_lengthAdjust(self, lengthAdjust): self._attributes['lengthAdjust'] = lengthAdjust def get_lengthAdjust(self): return self._attributes.get('lengthAdjust') class AltGlyph(GlyphRef, ConditionalAttrib, GraphicalEventsAttrib, OpacityAttrib, GraphicsAttrib, CursorAttrib, FilterAttrib, MaskAttrib, ClipAttrib, TextContentAttrib, RotateAttrib): """ Class representing the altGlyph element of an svg doc. """ def __init__(self, **kwargs): BaseElement.__init__(self, 'altGlyph') self.setKWARGS(**kwargs) def set_textLength(self, textLength): self._attributes['textLength'] = textLength def get_textLength(self): return self._attributes.get('textLength') class TextPath(BaseElement, CoreAttrib, ConditionalAttrib, ExternalAttrib, StyleAttrib, XLinkAttrib, FontAttrib, PaintAttrib, GraphicalEventsAttrib, OpacityAttrib, GraphicsAttrib, CursorAttrib, FilterAttrib, MaskAttrib, ClipAttrib, TextContentAttrib): """ Class representing the textPath element of an svg doc. """ def __init__(self, **kwargs): BaseElement.__init__(self, 'textPath') self.setKWARGS(**kwargs) def set_startOffset(self, startOffset): self._attributes['startOffset'] = startOffset def get_startOffset(self): return self._attributes.get('startOffset') def set_textLength(self, textLength): self._attributes['textLength'] = textLength def get_textLength(self): return self._attributes.get('textLength') def set_lengthAdjust(self, lengthAdjust): self._attributes['lengthAdjust'] = lengthAdjust def get_lengthAdjust(self): return self._attributes.get('lengthAdjust') def set_method(self, method): self._attributes['method'] = method def get_method(self): return self._attributes.get('method') def set_spacing(self, spacing): self._attributes['spacing'] = spacing def get_spacing(self): return self._attributes.get('spacing') class Tref(BaseElement, CoreAttrib, ConditionalAttrib, ExternalAttrib, StyleAttrib, XLinkAttrib, PointAttrib, DeltaPointAttrib, RotateAttrib, GraphicalEventsAttrib, PaintAttrib, FontAttrib, OpacityAttrib, GraphicsAttrib, CursorAttrib, FilterAttrib, MaskAttrib, ClipAttrib, TextContentAttrib): """ Class representing the tref element of an svg doc. """ def __init__(self, **kwargs): BaseElement.__init__(self, 'tref') self.setKWARGS(**kwargs) def set_textLength(self, textLength): self._attributes['textLength'] = textLength def get_textLength(self): return self._attributes.get('textLength') def set_lengthAdjust(self, lengthAdjust): self._attributes['lengthAdjust'] = lengthAdjust def get_lengthAdjust(self): return self._attributes.get('lengthAdjust') class Tspan(BaseElement, CoreAttrib, ConditionalAttrib, ExternalAttrib, StyleAttrib, PointAttrib, DeltaPointAttrib, RotateAttrib, GraphicalEventsAttrib, PaintAttrib, FontAttrib, OpacityAttrib, GraphicsAttrib, CursorAttrib, FilterAttrib, MaskAttrib, ClipAttrib, TextContentAttrib): """ Class representing the tspan element of an svg doc. """ def __init__(self, x=None, y=None, dx=None, dy=None, rotate=None, textLength=None, lengthAdjust=None, **kwargs): BaseElement.__init__(self, 'tspan') self.set_x(x) self.set_y(y) self.set_dx(dx) self.set_dy(dy) self.set_rotate(rotate) self.set_textLength(textLength) self.set_lengthAdjust(lengthAdjust) self.setKWARGS(**kwargs) def set_textLength(self, textLength): self._attributes['textLength'] = textLength def get_textLength(self): return self._attributes.get('textLength') def set_lengthAdjust(self, lengthAdjust): self._attributes['lengthAdjust'] = lengthAdjust def get_lengthAdjust(self): return self._attributes.get('lengthAdjust') class Text(BaseElement, CoreAttrib, ConditionalAttrib, ExternalAttrib, StyleAttrib, PointAttrib, DeltaPointAttrib, RotateAttrib, GraphicalEventsAttrib, PaintAttrib, FontAttrib, OpacityAttrib, GraphicsAttrib, CursorAttrib, FilterAttrib, MaskAttrib, ClipAttrib, TextContentAttrib, TextAttrib): """ Class representing the text element of an svg doc. """ def __init__(self, content=None, x=None, y=None, dx=None, dy=None, rotate=None, textLength=None, lengthAdjust=None, **kwargs): BaseElement.__init__(self, 'text') if content <> None: self.appendTextContent(content) self.set_x(x) self.set_y(y) self.set_dx(dx) self.set_dy(dy) self.set_rotate(rotate) self.set_textLength(textLength) self.set_lengthAdjust(lengthAdjust) self.setKWARGS(**kwargs) def set_transform(self, transform): self._attributes['transform'] = transform def get_transform(self): return self._attributes.get('transform') def set_textLength(self, textLength): self._attributes['textLength'] = textLength def get_textLength(self): return self._attributes.get('textLength') def set_lengthAdjust(self, lengthAdjust): self._attributes['lengthAdjust'] = lengthAdjust def get_lengthAdjust(self): return self._attributes.get('lengthAdjust')

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0.679971

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6,990

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0.083279

0.750813

0.687053

0.654956

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null

0

1

0

2

f7fcd003bc9e3f57ec944b1eb041edb86ca93e84

6,381

py

Python

rafiki/model/model.py

dcslin/rafiki

b617ac2536ac13095c4930d6d3f1f9b3c231b5e7

[ "Apache-2.0" ]

null

rafiki/model/model.py

dcslin/rafiki

b617ac2536ac13095c4930d6d3f1f9b3c231b5e7

[ "Apache-2.0" ]

null

rafiki/model/model.py

dcslin/rafiki

b617ac2536ac13095c4930d6d3f1f9b3c231b5e7

[ "Apache-2.0" ]

null

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # import abc import numpy as np from typing import Union, Dict, Optional, Any, List from .knob import BaseKnob KnobConfig = Dict[str, BaseKnob] Knobs = Dict[str, Any] Params = Dict[str, Union[str, int, float, np.ndarray]] class BaseModel(abc.ABC): ''' Rafiki's base model class that Rafiki models must extend. Rafiki models must implement all abstract methods below, according to the specification of its associated task (see :ref:`tasks`). They configure how this model template will be trained, evaluated, tuned, serialized and served on Rafiki. In the model's ``__init__`` method, call ``super().__init__(**knobs)`` as the first line, followed by the model's initialization logic. The model should be initialize itself with ``knobs``, a set of generated knob values for the created model instance. These knob values are chosen by Rafiki based on the model's knob configuration (defined in :meth:`rafiki.model.BaseModel.get_knob_config`). For example: :: def __init__(self, **knobs): super().__init__(**knobs) self.__dict__.update(knobs) ... self._build_model(self.knob1, self.knob2) :param knobs: Dictionary mapping knob names to knob values :type knobs: :obj:`rafiki.model.Knobs` ''' def __init__(self, **knobs: Knobs): pass @abc.abstractstaticmethod def get_knob_config() -> KnobConfig: ''' Return a dictionary that defines the search space for this model template's knobs (i.e. knobs' names, their types & their ranges). Over the course of training, your model will be initialized with different values of knobs within this search space to maximize this model’s performance. Refer to :ref:`model-tuning` to understand more about how this works. :returns: Dictionary mapping knob names to knob specifications ''' raise NotImplementedError() @abc.abstractmethod def train(self, dataset_path: str, shared_params: Optional[Params] = None, **train_args): ''' Train this model instance with the given traing dataset and initialized knob values. Additional keyword arguments could be passed depending on the task's specification. Additionally, trained parameters shared from previous trials could be passed, as part of the ``SHARE_PARAMS`` policy (see :ref:`model-policies`). Subsequently, the model is considered *trained*. :param dataset_path: File path of the train dataset file in the *local filesystem*, in a format specified by the task :param shared_params: Dictionary mapping parameter names to values, as produced by your model's :meth:`rafiki.model.BaseModel.dump_parameters`. ''' raise NotImplementedError() @abc.abstractmethod def evaluate(self, dataset_path: str) -> float: ''' Evaluate this model instance with the given validation dataset after training. This will be called only when model is *trained*. :param dataset_path: File path of the validation dataset file in the *local filesystem*, in a format specified by the task :returns: A score associated with the validation performance for the trained model instance, the higher the better e.g. classification accuracy. ''' raise NotImplementedError() @abc.abstractmethod def predict(self, queries: List[Any]) -> List[Any]: ''' Make predictions on a batch of queries after training. This will be called only when model is *trained*. :param queries: List of queries, where a query is in the format specified by the task :returns: List of predictions, in an order corresponding to the queries, where a prediction is in the format specified by the task ''' raise NotImplementedError() @abc.abstractmethod def dump_parameters(self) -> Params: ''' Returns a dictionary of model parameters that *fully define the trained state of the model*. This dictionary must conform to the format :obj:`rafiki.model.Params`. This will be used to save the trained model in Rafiki. Additionally, trained parameters produced by this method could be shared with future trials, as part of the ``SHARE_PARAMS`` policy (see :ref:`model-policies`). This will be called only when model is *trained*. :returns: Dictionary mapping parameter names to values ''' raise NotImplementedError() @abc.abstractmethod def load_parameters(self, params: Params): ''' Loads this model instance with previously trained model parameters produced by your model's :meth:`rafiki.model.BaseModel.dump_parameters`. *This model instance's initialized knob values will match those during training*. Subsequently, the model is considered *trained*. ''' raise NotImplementedError() def destroy(self): ''' Destroy this model instance, freeing any resources held by this model instance. No other instance methods will be called subsequently. ''' pass @staticmethod def teardown(): ''' Runs class-wide teardown logic (e.g. close a training session shared across trials). ''' pass class PandaModel(BaseModel): def __init__(self, **knobs: Knobs): super().__init__(**knobs) @abc.abstractmethod def local_explain(self, queries, params: Params): raise NotImplementedError()

39.147239

152

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6,381

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0.027473

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6,381

162

153

39.388889

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0

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false

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0

null

0

null

0

1

0

1

0

2

7901435b0febe6d85163a17baac50a97baf9102a

8,086

py

Python

Dev/Cpp/CreateHeader.py

Shockblast/Effekseer

bac86c0fc965f04a0f57c5863d37a9c2d5c3be97

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

1

2021-12-21T07:03:42.000Z

Dev/Cpp/CreateHeader.py

Shockblast/Effekseer

bac86c0fc965f04a0f57c5863d37a9c2d5c3be97

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

null

Dev/Cpp/CreateHeader.py

Shockblast/Effekseer

bac86c0fc965f04a0f57c5863d37a9c2d5c3be97

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

null

import os import re import codecs def isValidLine(line): if re.search('include \"', line) == None or line.find('.PSVita') != -1 or line.find('.PS4') != -1 or line.find('.Switch') != -1 or line.find('.XBoxOne') != -1: return True return False class CreateHeader: def __init__(self): self.lines = [] def addLine(self,line): self.lines.append(line) def readLines(self,path): f = codecs.open(path, 'r','utf-8_sig') line = f.readline() while line: if isValidLine(line): self.lines.append(line.strip(os.linesep)) line = f.readline() f.close() def output(self,path): f = codecs.open(path, 'w','utf-8_sig') for line in self.lines: f.write(line + os.linesep) f.close() effekseerHeader = CreateHeader() effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Base.Pre.h') effekseerHeader.readLines('Effekseer/Effekseer/Utils/Effekseer.CustomAllocator.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Vector2D.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Vector3D.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Color.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.RectF.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Matrix43.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Matrix44.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.File.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.DefaultFile.h') effekseerHeader.readLines('Effekseer/Effekseer/Backend/GraphicsDevice.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Resource.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Effect.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Manager.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Setting.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Server.h') effekseerHeader.readLines('Effekseer/Effekseer/Effekseer.Client.h') effekseerHeader.addLine('') effekseerHeader.addLine('#include "Effekseer.Modules.h"') effekseerHeader.addLine('') effekseerHeader.output('Effekseer/Effekseer.h') effekseerSimdHeader = CreateHeader() effekseerSimdHeader.addLine('#pragma once') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Base.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Float4_Gen.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Float4_NEON.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Float4_SSE.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Int4_Gen.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Int4_NEON.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Int4_SSE.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Bridge_Gen.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Bridge_NEON.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Bridge_SSE.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Vec2f.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Vec3f.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Vec4f.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Mat43f.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Mat44f.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Quaternionf.h') effekseerSimdHeader.readLines('Effekseer/Effekseer/SIMD/Utils.h') effekseerSimdHeader.output('Effekseer/Effekseer.SIMD.h') effekseerModulesHeader = CreateHeader() effekseerModulesHeader.addLine('#pragma once') effekseerModulesHeader.addLine('') effekseerModulesHeader.addLine('#include "Effekseer.h"') effekseerModulesHeader.addLine('#include "Effekseer.SIMD.h"') effekseerModulesHeader.addLine('') effekseerModulesHeader.addLine('// A header to access internal data of effekseer') effekseerModulesHeader.readLines('Effekseer/Effekseer/Parameter/Effekseer.Parameters.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Renderer/Effekseer.SpriteRenderer.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Renderer/Effekseer.RibbonRenderer.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Renderer/Effekseer.RingRenderer.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Renderer/Effekseer.ModelRenderer.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Renderer/Effekseer.TrackRenderer.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Effekseer.EffectLoader.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Effekseer.TextureLoader.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Model/Model.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Model/ModelLoader.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Effekseer.MaterialLoader.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Model/Model.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Effekseer.Curve.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Effekseer.CurveLoader.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Sound/Effekseer.SoundPlayer.h') effekseerModulesHeader.readLines('Effekseer/Effekseer/Effekseer.SoundLoader.h') effekseerModulesHeader.output('Effekseer/Effekseer.Modules.h') effekseerRendererDX9Header = CreateHeader() effekseerRendererDX9Header.readLines('EffekseerRendererDX9/EffekseerRenderer/EffekseerRendererDX9.Base.Pre.h') effekseerRendererDX9Header.readLines('EffekseerRendererCommon/EffekseerRenderer.Renderer.h') effekseerRendererDX9Header.readLines('EffekseerRendererDX9/EffekseerRenderer/EffekseerRendererDX9.Renderer.h') effekseerRendererDX9Header.output('EffekseerRendererDX9/EffekseerRendererDX9.h') effekseerRendererDX11Header = CreateHeader() effekseerRendererDX11Header.readLines('EffekseerRendererDX11/EffekseerRenderer/EffekseerRendererDX11.Base.Pre.h') effekseerRendererDX11Header.readLines('EffekseerRendererCommon/EffekseerRenderer.Renderer.h') effekseerRendererDX11Header.readLines('EffekseerRendererDX11/EffekseerRenderer/EffekseerRendererDX11.Renderer.h') effekseerRendererDX11Header.output('EffekseerRendererDX11/EffekseerRendererDX11.h') effekseerRendererDX12Header = CreateHeader() effekseerRendererDX12Header.readLines('EffekseerRendererDX12/EffekseerRenderer/EffekseerRendererDX12.Base.Pre.h') effekseerRendererDX12Header.readLines('EffekseerRendererCommon/EffekseerRenderer.Renderer.h') effekseerRendererDX12Header.readLines('EffekseerRendererDX12/EffekseerRenderer/EffekseerRendererDX12.Renderer.h') effekseerRendererDX12Header.readLines('EffekseerRendererLLGI/Common.h') effekseerRendererDX12Header.output('EffekseerRendererDX12/EffekseerRendererDX12.h') effekseerRendererVulkanHeader = CreateHeader() effekseerRendererVulkanHeader.readLines('EffekseerRendererVulkan/EffekseerRenderer/EffekseerRendererVulkan.Base.Pre.h') effekseerRendererVulkanHeader.readLines('EffekseerRendererCommon/EffekseerRenderer.Renderer.h') effekseerRendererVulkanHeader.readLines('EffekseerRendererVulkan/EffekseerRenderer/EffekseerRendererVulkan.Renderer.h') effekseerRendererVulkanHeader.readLines('EffekseerRendererLLGI/Common.h') effekseerRendererVulkanHeader.output('EffekseerRendererVulkan/EffekseerRendererVulkan.h') effekseerRendererGLHeader = CreateHeader() effekseerRendererGLHeader.readLines('EffekseerRendererGL/EffekseerRenderer/EffekseerRendererGL.Base.Pre.h') effekseerRendererGLHeader.readLines('EffekseerRendererCommon/EffekseerRenderer.Renderer.h') effekseerRendererGLHeader.readLines('EffekseerRendererGL/EffekseerRenderer/EffekseerRendererGL.Renderer.h') effekseerRendererGLHeader.output('EffekseerRendererGL/EffekseerRendererGL.h') effekseerRendererMetalHeader = CreateHeader() effekseerRendererMetalHeader.readLines('EffekseerRendererMetal/EffekseerRenderer/EffekseerRendererMetal.Base.Pre.h') effekseerRendererMetalHeader.readLines('EffekseerRendererCommon/EffekseerRenderer.Renderer.h') effekseerRendererMetalHeader.readLines('EffekseerRendererMetal/EffekseerRenderer/EffekseerRendererMetal.Renderer.h') effekseerRendererMetalHeader.readLines('EffekseerRendererLLGI/Common.h') effekseerRendererMetalHeader.output('EffekseerRendererMetal/EffekseerRendererMetal.h')

57.757143

160

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8,086

9.460798

0.176066

0.193661

0.196278

0.109916

0.625618

0.36813

0.166182

0.031986

0

0.010608

0.032402

8,086

139

161

58.172662

0.868482

0

0.080645

0

0.473535

0.450532

0

1

0.040323

false

0

0.024194

0

0.08871

0

null

0

1

0

1

null

0

2

7903025f1f9a9404dc70aaab4d2f4d39ef35b4fc

3,729

py

Python

CircuitPython_101/basic_data_structures/song_book/code.py

billagee/Adafruit_Learning_System_Guides

6e90bd839161573780ab9937c3deaa115deca055

[ "MIT" ]

1

2018-10-17T19:37:08.000Z

CircuitPython_101/basic_data_structures/song_book/code.py

billagee/Adafruit_Learning_System_Guides

6e90bd839161573780ab9937c3deaa115deca055

[ "MIT" ]

null

CircuitPython_101/basic_data_structures/song_book/code.py

billagee/Adafruit_Learning_System_Guides

6e90bd839161573780ab9937c3deaa115deca055

[ "MIT" ]

1

2018-07-16T15:47:52.000Z

import time import board import debouncer import busio as io import digitalio import pulseio import adafruit_ssd1306 i2c = io.I2C(board.SCL, board.SDA) reset_pin = digitalio.DigitalInOut(board.D11) oled = adafruit_ssd1306.SSD1306_I2C(128, 32, i2c, reset=reset_pin) button_select = debouncer.Debouncer(board.D7, mode=digitalio.Pull.UP) button_play = debouncer.Debouncer(board.D9, mode=digitalio.Pull.UP) C4 = 261 C_SH_4 = 277 D4 = 293 D_SH_4 = 311 E4 = 329 F4 = 349 F_SH_4 = 369 G4 = 392 G_SH_4 = 415 A4 = 440 A_SH_4 = 466 B4 = 493 # pylint: disable=line-too-long songbook = {'Twinkle Twinkle': [(C4, 0.5), (C4, 0.5), (G4, 0.5), (G4, 0.5), (A4, 0.5), (A4, 0.5), (G4, 1.0), (0, 0.5), (F4, 0.5), (F4, 0.5), (E4, 0.5), (E4, 0.5), (D4, 0.5), (D4, 0.5), (C4, 0.5), (0, 0.5), (G4, 0.5), (G4, 0.5), (F4, 0.5), (F4, 0.5), (E4, 0.5), (E4, 0.5), (D4, 0.5), (0, 0.5), (G4, 0.5), (G4, 0.5), (F4, 0.5), (F4, 0.5), (E4, 0.5), (E4, 0.5), (D4, 0.5), (0, 0.5), (C4, 0.5), (C4, 0.5), (G4, 0.5), (G4, 0.5), (A4, 0.5), (A4, 0.5), (G4, 1.0), (0, 0.5), (F4, 0.5), (F4, 0.5), (E4, 0.5), (E4, 0.5), (D4, 0.5), (D4, 0.5), (C4, 0.5), (0, 0.5)], 'ItsyBitsy Spider': [(G4, 0.5), (C4, 0.5), (C4, 0.5), (C4, 0.5), (D4, 0.5), (E4, 0.5), (E4, 0.5), (E4, 0.5), (D4, 0.5), (C4, 0.5), (D4, 0.5), (E4, 0.5), (C4, 0.5), (0, 0.5), (E4, 0.5), (E4, 0.5), (F4, 0.5), (G4, 0.5), (G4, 0.5), (F4, 0.5), (E4, 0.5), (F4, 0.5), (G4, 0.5), (E4, 0.5), (0, 0.5)], 'Old MacDonald': [(G4, 0.5), (G4, 0.5), (G4, 0.5), (D4, 0.5), (E4, 0.5), (E4, 0.5), (D4, 0.5), (0, 0.5), (B4, 0.5), (B4, 0.5), (A4, 0.5), (A4, 0.5), (G4, 0.5), (0, 0.5), (D4, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (D4, 0.5), (E4, 0.5), (E4, 0.5), (D4, 0.5), (0, 0.5), (B4, 0.5), (B4, 0.5), (A4, 0.5), (A4, 0.5), (G4, 0.5), (0, 0.5), (D4, 0.5), (D4, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (D4, 0.5), (D4, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (0, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (0, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (0, 0.5), (G4, 0.5), (G4, 0.5), (G4, 0.5), (D4, 0.5), (E4, 0.5), (E4, 0.5), (D4, 0.5), (0, 0.5), (B4, 0.5), (B4, 0.5), (A4, 0.5), (A4, 0.5), (G4, 0.5), (0, 0.5)] } # pylint: enable=line-too-long def play_note(note): if note[0] != 0: pwm = pulseio.PWMOut(board.D12, duty_cycle = 0, frequency=note[0]) # Hex 7FFF (binary 0111111111111111) is half of the largest value for a 16-bit int, # i.e. 50% pwm.duty_cycle = 0x7FFF time.sleep(note[1]) if note[0] != 0: pwm.deinit() def play_song(songname): for note in songbook[songname]: play_note(note) def update(songnames, selected): oled.fill(0) line = 0 for songname in songnames: if line == selected: oled.text(">", 0, line * 8) oled.text(songname, 10, line * 8) line += 1 oled.show() selected_song = 0 song_names = sorted(list(songbook.keys())) while True: button_select.update() button_play.update() update(song_names, selected_song) if button_select.fell: print("select") selected_song = (selected_song + 1) % len(songbook) elif button_play.fell: print("play") play_song(song_names[selected_song])

41.433333

185

0.448914

676

3,729

2.424556

0.177515

0.169616

0.102502

0.122026

0.345943

0.328249

0.317877

0

0.208446

0.307857

3,729

89

186

41.898876

0.426579

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0.109589

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0.015385

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0

0.09589

0

0.136986

0.027397

0

null

0

1

0

1

0

null

0

2

7903881baf28fb04948dceaf26f6f1e7b726da74

417

py

Python

polyaxon/api/repos/serializers.py

elyase/polyaxon

1c19f059a010a6889e2b7ea340715b2bcfa382a0

[ "MIT" ]

null

polyaxon/api/repos/serializers.py

elyase/polyaxon

1c19f059a010a6889e2b7ea340715b2bcfa382a0

[ "MIT" ]

null

polyaxon/api/repos/serializers.py

elyase/polyaxon

1c19f059a010a6889e2b7ea340715b2bcfa382a0

[ "MIT" ]

null

from rest_framework import fields, serializers from db.models.repos import Repo class RepoSerializer(serializers.ModelSerializer): project = fields.SerializerMethodField() class Meta: model = Repo fields = ('project', 'created_at', 'updated_at', 'is_public', ) def get_user(self, obj): return obj.user.username def get_project(self, obj): return obj.project.name

23.166667

71

0.688249

49

417

5.734694

0.612245

0.042705

0.092527

0.113879

0

0.215827

417

17

72

24.529412

0.859327

0

0.086331

0

1

0.181818

false

0

0.181818

0.818182

0

null

0

null

0

1

0

2

790ab10c114ed809a1b80f3e101c2509b9257268

332

py

Python

modules/timeblock.py

5225225/bar

cc72eb45f21ac2b2e070c6d9f66b306ed51aef35

[ "MIT" ]

1

2015-09-05T17:07:59.000Z

modules/timeblock.py

5225225/bar

cc72eb45f21ac2b2e070c6d9f66b306ed51aef35

[ "MIT" ]

null

modules/timeblock.py

5225225/bar

cc72eb45f21ac2b2e070c6d9f66b306ed51aef35

[ "MIT" ]

2

2015-09-05T17:08:02.000Z

2019-02-22T21:14:08.000Z

import linelib import datetime import signal def handler(x, y): pass signal.signal(signal.SIGUSR1, handler) signal.signal(signal.SIGALRM, handler) while True: linelib.sendblock("date", {"full_text": datetime.datetime.now().strftime( "%Y-%m-%e %H:%M:%S" )}) linelib.sendPID("date") linelib.waitsig(1)

18.444444

77

0.671687

44

332

5.045455

0.590909

0.216216

0.162162

0

0.007246

0.168675

332

17

78

19.529412

0.797101

0

0.10241

0

1

0.076923

false

0.076923

0.230769

0

0.307692

0

null

1

0

null

0

1

0

2

790b99b7c8510d3b99bd51ef86e99adaa01fb768

183

py

Python

modules/isrunning.py

ShaderLight/autochampselect

b7d346cc99011b5f84867f3a01dc2e8d815c05d7

[ "MIT" ]

null

modules/isrunning.py

ShaderLight/autochampselect

b7d346cc99011b5f84867f3a01dc2e8d815c05d7

[ "MIT" ]

null

modules/isrunning.py

ShaderLight/autochampselect

b7d346cc99011b5f84867f3a01dc2e8d815c05d7

[ "MIT" ]

null

from subprocess import check_output def isrunning(processName): tasklist = check_output('tasklist', shell=False) tasklist = str(tasklist) return(processName in tasklist)

26.142857

52

0.759563

21

183

6.52381

0.666667

0.160584

0

0.15847

183

7

53

26.142857

0.88961

0

0.043478

0

1

0.2

false

0

0.2

0

0.4

0

1

0

null

0

1

0

null

0

2

7912dbc97deac732656616d2bb8fce94ba34891e

1,829

py

Python

tests/test_simple supported_beam.py

bteodoru/ebbef2p-python

449e6414e4ce3ef3deccf9fd892410b2d15578ef

[ "MIT" ]

2

2020-05-03T19:14:39.000Z

2020-05-03T19:20:19.000Z

tests/test_simple supported_beam.py

bteodoru/ebbef2p-python

449e6414e4ce3ef3deccf9fd892410b2d15578ef

[ "MIT" ]

null

tests/test_simple supported_beam.py

bteodoru/ebbef2p-python

449e6414e4ce3ef3deccf9fd892410b2d15578ef

[ "MIT" ]

null

import pytest import numpy as np from ebbef2p.structure import Structure L = 2 E = 1 I = 1 def test_center_load(): P = 100 M_max = P * L / 4 # maximum moment S_max = P/2 # max shearing force w_max = -P * L ** 3 / (48 * E * I) # max displacement tolerance = 1e-6 #set a tolerance of 0.0001% s = Structure('test') s.add_beam(coord=[0, L], E=E, I=I) s.add_nodal_load(P, L/2, 'fz') s.add_nodal_support({'uz': 0, 'ur': "NaN"}, 0) s.add_nodal_support({'uz': 0, 'ur': "NaN"}, L) s.add_nodes(25) s.add_elements(s.nodes) s.solve(s.build_global_matrix(), s.build_load_vector(), s.get_boudary_conditions()) assert min(s.get_displacements()['vertical_displacements']) == pytest.approx(w_max, rel=tolerance) assert max(s.get_bending_moments()['values']) == pytest.approx(M_max, rel=tolerance) assert max(s.get_shear_forces()['values']) == pytest.approx(S_max, rel=tolerance) def test_uniformly_distributed_load(): q = 10 M_max = q * L ** 2 / 8 # maximum moment S_max = q * L/2 # max shearing force w_max = -5 * q * L ** 4 / (384 * E * I) # max displacement tolerance = 1e-4 #set a tolerance of 0.01% s = Structure('test') s.add_beam(coord=[0, L], E=E, I=I) s.add_distributed_load((q, q), (0, L)) s.add_nodal_support({'uz': 0, 'ur': "NaN"}, 0) s.add_nodal_support({'uz': 0, 'ur': "NaN"}, L) s.add_nodes(200) s.add_elements(s.nodes) s.solve(s.build_global_matrix(), s.build_load_vector(), s.get_boudary_conditions()) assert min(s.get_displacements()['vertical_displacements']) == pytest.approx(w_max, rel=tolerance) assert max(s.get_bending_moments()['values']) == pytest.approx(M_max, rel=tolerance) assert max(s.get_shear_forces()['values']) == pytest.approx(S_max, rel=1e-2)

34.509434

102

0.630946

302

1,829

3.625828

0.261589

0.043836

0.041096

0.058447

0.776256

0.747032

0.657534

0

0.034812

0.199016

1,829

53

103

34.509434

0.712628

0.083652

0

0.4

0

0.063549

0.026379

0

0.15

1

0.05

false

0

0.075

0

0.125

0

null

0

1

0

1

0

null

0

2

79210aefb1eb42f15c46a5decf553a199213c5f4

155,776

py

Python

src/ansys/mapdl/core/_commands/solution/analysis_options.py

Miiicah/pymapdl

ce85393ca82db7556a5d05883ca3fd9296444cba

[ "MIT" ]

194

2016-10-21T08:46:41.000Z

2021-01-06T20:39:23.000Z

ansys/mapdl/core/_commands/solution/analysis_options.py

NewsamNiu/pymapdl

482c960142a612997eb33216731aaa88f1371168

[ "MIT" ]

463

2021-01-12T14:07:38.000Z

2022-03-31T22:42:25.000Z

ansys/mapdl/core/_commands/solution/analysis_options.py

NewsamNiu/pymapdl

482c960142a612997eb33216731aaa88f1371168

[ "MIT" ]

66

2016-11-21T04:26:08.000Z

2020-12-28T09:27:27.000Z

from typing import Optional from ansys.mapdl.core.mapdl_types import MapdlInt class AnalysisOptions: def abextract(self, mode1="", mode2="", **kwargs): """Extracts the alpha-beta damping multipliers for Rayleigh damping. APDL Command: ABEXTRACT Parameters ---------- mode1 First mode number. mode2 Second mode number. Notes ----- ABEXTRACT calls the command macro DMPEXT to extract the damping ratio of MODE1 and MODE2 and then computes the Alpha and Beta damping multipliers for use in a subsequent structural harmonic or transient analysis. See Damping in the Structural Analysis Guide for more information on the alpha and beta damping multipliers. The damping multipliers are stored in parameters ALPHADMP and BETADMP and can be applied using the ALPHAD and BETAD commands. Before calling ABEXTRACT, you must issue RMFLVEC to extract the modal displacements. In addition, a node component FLUN must exist from all FLUID136 nodes. See Introduction for more information on thin film analyses. This command is also valid in PREP7. Distributed ANSYS Restriction: This command is not supported in Distributed ANSYS. """ command = f"ABEXTRACT,{mode1},{mode2}" return self.run(command, **kwargs) def accoption(self, activate="", **kwargs): """Specifies GPU accelerator capability options. APDL Command: ACCOPTION Parameters ---------- activate Activates the GPU accelerator capability within the equation solvers. Do not use GPU accelerator. - Use GPU accelerator. Notes ----- The GPU accelerator capability requires specific hardware to be installed on the machine. See the appropriate ANSYS, Inc. Installation Guide (Windows or Linux) for a list of supported GPU hardware. Use of this capability also requires HPC licensing. For more information, see GPU Accelerator Capability in the Parallel Processing Guide. The GPU accelerator capability is available for the sparse direct solver and the PCG and JCG iterative solvers. Static, buckling, modal, full harmonic, and full transient analyses are supported. For buckling analyses, the Block Lanczos and Subspace eigensolvers are supported. For modal analyses, only the Block Lanczos, PCG Lanczos, Subspace, Unsymmetric, and Damped eigensolvers are supported. Activating this capability when using other equation solvers or other analysis types has no effect. The GPU accelerator capability is supported only on the Windows 64-bit and Linux 64-bit platforms. """ command = f"ACCOPTION,{activate}" return self.run(command, **kwargs) def adams(self, nmodes="", kstress="", kshell="", **kwargs): """Performs solutions and writes flexible body information to a modal APDL Command: ADAMS neutral file (Jobname.MNF) for use in an ADAMS analysis. Parameters ---------- nmodes Number of normal modes to be written to Jobname.MNF file (no default). kstress Specifies whether to write stress or strain results: 0 - Do not write stress or strain results (default). 1 - Write stress results. 2 - Write strain results. 3 - Write both stress and strain results. kshell Shell element output location. This option is valid only for shell elements. 0, 1 - Shell top surface (default). 2 - Shell middle surface. 3 - Shell bottom surface. Notes ----- ADAMS invokes a predefined ANSYS macro that solves a series of analyses and then writes the modal neutral file, Jobname.MNF. This file can be imported into the ADAMS program in order to perform a rigid body dynamics simulation. For detailed information on how to use the ADAMS command macro to create a modal neutral file, see Rigid Body Dynamics and the ANSYS-ADAMS Interface in the Substructuring Analysis Guide. Before running the ADAMS command macro, you must specify the units with the /UNITS command. The interface points should be the only selected nodes when the command macro is initiated. (Interface points are nodes where constraints may be applied in ADAMS.) Only selected elements will be considered in the calculations. By default, stress and strain data is transferred to the ADAMS program for all nodes, as specified by the KSTRESS value. If you want to transfer stress/strain data for only a subset of nodes, select the desired subset and create a node component named "STRESS" before running the ADAMS command macro. For example, you may want to select exterior nodes for the purpose of visualization in the ADAMS program. The default filename for the modal neutral file is Jobname.MNF. In interactive (GUI) mode, you can specify a filename other than Jobname.MNF. In batch mode, there is no option to change the filename, and the modal neutral file is always written to Jobname.MNF. """ command = f"ADAMS,{nmodes},{kstress},{kshell}" return self.run(command, **kwargs) def antype(self, antype="", status="", ldstep="", substep="", action="", **kwargs): """Specifies the analysis type and restart status. APDL Command: ANTYPE Parameters ---------- antype Analysis type (defaults to the previously specified analysis type, or to STATIC if none specified): STATIC or 0 - Perform a static analysis. Valid for all degrees of freedom. BUCKLE or 1 - Perform a buckling analysis. Implies that a previous static solution was performed with prestress effects calculated (PSTRES,ON). Valid for structural degrees of freedom only. MODAL or 2 - Perform a modal analysis. Valid for structural and fluid degrees of freedom. HARMIC or 3 - Perform a harmonic analysis. Valid for structural, fluid, magnetic, and electrical degrees of freedom. TRANS or 4 - Perform a transient analysis. Valid for all degrees of freedom. SUBSTR or 7 - Perform a substructure analysis. Valid for all degrees of freedom. SPECTR or 8 - Perform a spectrum analysis. Implies that a previous modal analysis was performed. Valid for structural degrees of freedom only. status Specifies the status of the analysis (new or restart): NEW - Specifies a new analysis (default). If NEW, the remaining fields on this command are ignored. RESTART - Specifies a restart of a previous analysis. Valid for static, modal, and transient (full or mode-superposition method) analyses. For more information about restarting static and transient analyses, see Multiframe Restart in the Basic Analysis Guide. For more information on restarting a modal analysis, see Modal Analysis Restart in the Basic Analysis Guide. Multiframe restart is also valid for harmonic analysis, but is limited to 2-D magnetic analysis only. - A substructure analysis (backsubstitution method only) can be restarted for the purpose of generating additional load vectors. For more information, see the SEOPT command and Applying Loads and Creating the Superelement Matrices in the Substructuring Analysis Guide. VTREST - Specifies the restart of a previous VT Accelerator analysis. Valid only with Antype = STATIC, HARMIC, or TRANS. For more information, see VT Accelerator Re-run in the Basic Analysis Guide. ldstep Specifies the load step at which a multiframe restart begins. substep Specifies the substep at which a multiframe restart begins. action Specifies the manner of a multiframe restart. CONTINUE - The program continues the analysis based on the specified LDSTEP and SUBSTEP (default). The current load step is continued. If the end of the load step is encountered in the .Rnnn file, a new load step is started. The program deletes all .Rnnn files, or .Mnnn files for mode-superposition transient analyses, beyond the point of restart and updates the .LDHI file if a new load step is encountered. ENDSTEP - At restart, force the specified load step (LDSTEP) to end at the specified substep (SUBSTEP), even though the end of the current load step has not been reached. At the end of the specified substep, all loadings are scaled to the level of the current ending and stored in the .LDHI file. A run following this ENDSTEP starts a new load step. This capability allows you to change the load level in the middle of a load step. The program updates the .LDHI file and deletes all .Rnnn files, or .Mnnn files for mode- superposition transient analyses, beyond the point of ENDSTEP. The .Rnnn or .Mnnn file at the point of ENDSTEP are rewritten to record the rescaled load level. RSTCREATE - At restart, retrieve information to be written to the results file for the specified load step (LDSTEP) and substep (SUBSTEP). Be sure to use OUTRES to write the results to the results file. This action does not affect the .LDHI or .Rnnn files. Previous items stored in the results file at and beyond the point of RSTCREATE are deleted. This option cannot be used to restart a mode-superposition transient analysis. PERTURB - At restart, a linear perturbation analysis (static, modal, buckling, or full harmonic) is performed for the specified load step (LDSTEP) and substep (SUBSTEP). This action does not affect the .LDHI, .Rnnn, or .RST files. Notes ----- If using the ANTYPE command to change the analysis type in the same SOLVE session, the program issues the following message: "Some analysis options have been reset to their defaults. Please verify current settings or respecify as required." Typically, the program resets commands such as NLGEOM and EQSLV to their default values. The analysis type (Antype) cannot be changed if a restart is specified. Always save parameters before doing a restart. For more information on the different types of restart, see Restarting an Analysis in the Basic Analysis Guide. This command is also valid in PREP7. The ANSYS Professional - Nonlinear Structural (PRN) product supports the Antype = TRANS option for mode-superposition (TRNOPT,MSUP) analyses only. """ command = f"ANTYPE,{antype},{status},{ldstep},{substep},{action}" return self.run(command, **kwargs) def ascres(self, opt="", **kwargs): """Specifies the output type for an acoustic scattering analysis. APDL Command: ASCRES Parameters ---------- opt Output option: TOTAL - Output the total pressure field (default). SCAT - Output the scattered pressure field. Notes ----- Use the ASCRES command to specify the output type for an acoustic scattering analysis. The scattered option (Opt = SCAT) provides a scattered pressure output, psc, required for calculating target strength (TS). The default behavior (Opt = TOTAL) provides a sum of the incident and scattering fields, ptotal = pinc + psc. Issue the AWAVE command to define the incident pressure pinc. If the AWAVE command is defined with Opt2 = INT, only the total pressure field is output regardless of the ASCRES,Opt command. """ command = f"ASCRES,{opt}" return self.run(command, **kwargs) def asol(self, lab="", opt="", **kwargs): """Specifies the output type of an acoustic scattering analysis. APDL Command: ASOL Parameters ---------- lab Acoustic solver specification (no default): SCAT - Set acoustic solver to the scattered field formulation. opt Option identifying an acoustic solver status: OFF - Deactivate the specified acoustic solver (default). ON - Activate the specified acoustic solver. Notes ----- Use the ASOL command to activate the specified acoustic solution process. The scattered option (Lab = SCAT) sets the acoustic solver to the scattered-pressure field formulation. Issue the AWAVE command to define the incident pressure pinc. If the AWAVE command is defined with Opt2 = INT, the acoustic solver is set to the scattered field formulation regardless of the ASOL command issued. """ command = f"ASOL,{lab},{opt}" return self.run(command, **kwargs) def bcsoption(self, memory_option="", memory_size="", solve_info="", **kwargs): """Sets memory option for the sparse solver. APDL Command: BCSOPTION Parameters ---------- memory_option Memory allocation option: DEFAULT - Use the default memory allocation strategy for the sparse solver. The default strategy attempts to run in the INCORE memory mode. If there is not enough available physical memory when the solver starts to run in the INCORE memory mode, the solver will then attempt to run in the OUTOFCORE memory mode. INCORE - Use a memory allocation strategy in the sparse solver that will attempt to obtain enough memory to run with the entire factorized matrix in memory. This option uses the most amount of memory and should avoid doing any I/O. By avoiding I/O, this option achieves optimal solver performance. However, a significant amount of memory is required to run in this mode, and it is only recommended on machines with a large amount of memory. If the allocation for in-core memory fails, the solver will automatically revert to out-of-core memory mode. OUTOFCORE - Use a memory allocation strategy in the sparse solver that will attempt to allocate only enough work space to factor each individual frontal matrix in memory, but will store the entire factorized matrix on disk. Typically, this memory mode results in poor performance due to the potential bottleneck caused by the I/O to the various files written by the solver. FORCE - This option, when used in conjunction with the Memory_Size option, allows you to force the sparse solver to run with a specific amount of memory. This option is only recommended for the advanced user who understands sparse solver memory requirements for the problem being solved, understands the physical memory on the system, and wants to control the sparse solver memory usage. memory_size Initial memory size allocation for the sparse solver in MB. This argument allows you to tune the sparse solver memory and is not generally required. Although there is no upper limit for Memory_Size, the Memory_Size setting should always be well within the physical memory available, but not so small as to cause the sparse solver to run out of memory. Warnings and/or errors from the sparse solver will appear if this value is set too low. If the FORCE memory option is used, this value is the amount of memory allocated for the entire duration of the sparse solver solution. solve_info Solver output option: OFF - Turns off additional output printing from the sparse solver (default). PERFORMANCE - Turns on additional output printing from the sparse solver, including a performance summary and a summary of file I/O for the sparse solver. Information on memory usage during assembly of the global matrix (that is, creation of the Jobname.FULL file) is also printed with this option. Notes ----- This command controls options related to the sparse solver in all analysis types where the sparse solver can be used. It also controls the Block Lanczos eigensolver in a modal or buckling analysis. The sparse solver runs from one large work space (that is, one large memory allocation). The amount of memory required for the sparse solver is unknown until the matrix structure is preprocessed, including equation reordering. The amount of memory allocated for the sparse solver is then dynamically adjusted to supply the solver what it needs to compute the solution. If you have a very large memory system, you may want to try selecting the INCORE memory mode for larger jobs to improve performance. When running the sparse solver on a machine with very slow I/O performance (for example, slow hard drive speed), you may want to try using the INCORE memory mode to achieve better performance. However, doing so may require much more memory compared to running in the OUTOFCORE memory mode. Running with the INCORE memory mode is best for jobs which comfortably fit within the limits of the physical memory on a given system. If the sparse solver work space exceeds physical memory size, the system will be forced to use virtual memory (or the system page/swap file). In this case, it is typically more efficient to run with the OUTOFCORE memory mode. Assuming the job fits comfortably within the limits of the machine, running with the INCORE memory mode is often ideal for jobs where repeated solves are performed for a single matrix factorization. This occurs in a modal or buckling analysis or when doing multiple load steps in a linear, static analysis. For repeated runs with the sparse solver, you may set the initial sparse solver memory allocation to the amount required for factorization. This strategy reduces the frequency of allocation and reallocation in the run to make the INCORE option fully effective. If you have a very large memory system, you may use the Memory_Size argument to increase the maximum size attempted for in-core runs. """ command = f"BCSOPTION,,{memory_option},{memory_size},,,{solve_info}" return self.run(command, **kwargs) def cgrow(self, action="", par1="", par2="", **kwargs): """Defines crack-growth information APDL Command: CGROW Parameters ---------- action Specifies the action for defining or manipulating crack-growth data: NEW - Initiate a new set of crack-growth simulation data (default). CID - Specify the crack-calculation (CINT) ID for energy-release rates to be used in the fracture criterion calculation. FCOPTION - Specify the fracture criterion for crack-growth/delamination. CPATH - Specify the element component for crack growth. DTIME - Specify the initial time step for crack growth. DTMIN - Specify the minimum time step for crack growth. DTMAX - Specify the maximum time step for crack growth. FCRAT - Fracture criterion ratio (fc). STOP - Stops the analysis when the specified maximum crack extension is reached. METHOD - Define the method of crack propagation. Notes ----- When Action = NEW, the CGROW command initializes a crack-growth simulation set. Subsequent CGROW commands define the parameters necessary for the simulation. For multiple cracks, issue multiple CGROW,NEW commands (and any subsequent CGROW commands necessary to define the parameters) for each crack. If the analysis is restarted (ANTYPE,,RESTART), the CGROW command must be re-issued. For additional details on this command, see https://www.mm.bme.hu/~gyebro/files/ans_help_v182/ans_cmd/Hlp_C_CGROW.html """ command = f"CGROW,{action},{par1},{par2}" return self.run(command, **kwargs) def cmatrix( self, symfac="", condname="", numcond="", grndkey="", capname="", **kwargs ): """Performs electrostatic field solutions and calculates the self and mutual capacitances between multiple conductors.x APDL Command: CMATRIX Parameters ---------- symfac Geometric symmetry factor. Capacitance values are scaled by this factor which represents the fraction of the total device modeled. Defaults to 1. condname Alphanumeric prefix identifier used in defining named conductor components. numcond Total Number of Components. If a ground is modeled, it is to be included as a component. If a ground is not modeled, but infinite elements are used to model the far-field ground, a named component for the far-field ground is not required. grndkey Ground key: 0 - Ground is one of the components, which is not at infinity. 1 - Ground is at infinity (modeled by infinite elements). capname Array name for computed capacitance matrix. Defaults to CMATRIX. Notes ----- To invoke the CMATRIX macro, the exterior nodes of each conductor must be grouped into individual components using the CM command. Each set of independent components is assigned a component name with a common prefix followed by the conductor number. A conductor system with a ground must also include the ground nodes as a component. The ground component is numbered last in the component name sequence. A ground capacitance matrix relates charge to a voltage vector. A ground matrix cannot be applied to a circuit modeler. The lumped capacitance matrix is a combination of lumped "arrangements" of voltage differences between conductors. Use the lumped capacitance terms in a circuit modeler to represent capacitances between conductors. Enclose all name-strings in single quotes in the CMATRIX command line. See the Mechanical APDL Theory Reference and HMAGSOLV in the Low- Frequency Electromagnetic Analysis Guide for details. This command does not support multiframe restarts. """ command = f"CMATRIX,{symfac},'{condname}',{numcond},{grndkey},'{capname}'" return self.run(command, **kwargs) def cmsopt( self, cmsmeth="", nmode="", freqb="", freqe="", fbddef="", fbdval="", iokey="", **kwargs, ): """Specifies component mode synthesis (CMS) analysis options. APDL Command: CMSOPT Parameters ---------- cmsmeth The component mode synthesis method to use. This value is required. FIX - Fixed-interface method. FREE - Free-interface method. RFFB - Residual-flexible free-interface method. nmode The number of normal modes extracted and used in the superelement generation. This value is required; the minimum is 1. freqb Beginning, or lower end, of frequency range of interest. This value is optional. freqe Ending, or upper end, of frequency range of interest. This value is optional. fbddef In a free-interface (CMSMETH = FREE) or residual-flexible free- interface (CMSMETH = RFFB) CMS analysis, the method to use for defining free body modes: FNUM - The number (FDBVAL) of rigid body modes in the calculation. FTOL - Employ a specified tolerance (FDBVAL) to determine rigid body modes in the calculation. FAUTO - Automatically determine rigid body modes in the calculation. This method is the default. RIGID - If no rigid body modes exist, define your own via the RIGID command. fbdval In a free-interface CMS analysis (CMSMETH = FREE), the number of rigid body modes if FBDDEF = fnum (where the value is an integer from 0 through 6), or the tolerance to employ if FBDDEF = ftol (where the value is a positive real number representing rad/sec). This value is required only when FBDDEF = fnum or FBDDEF = ftol; otherwise, any specified value is ignored. iokey Output key to control writing of the transformation matrix to the .TCMS file (FIX or FREE methods) or body properties to the .EXB file (FIX method). TCMS - Write the transformation matrix of the nodal component defined by the OUTPR command to a .TCMS file. Refer to TCMS File Format in the Programmer's Reference for more information on the this file. EXB - Write a body property input file (.EXB file) containing the condensed substructure matrices and other body properties for use with AVL EXCITE. Refer to ANSYS Interface to AVL EXCITE in the Substructuring Analysis Guide for more information. Notes ----- CMS employs the Block Lanczos eigensolution method in the generation pass. CMS supports damping matrix reduction when a damping matrix exists. Set the matrix generation key to 3 (SEOPT,Sename,SEMATR) to generate and then reduce stiffness, mass, and damping matrices. CMS does not support the SEOPT,,,,,RESOLVE command. Instead, ANSYS sets the expansion method for the expansion pass (EXPMTH) to BACKSUB. For more information about performing a CMS analysis, see Component Mode Synthesis in the Substructuring Analysis Guide. If IOKEY = TCMS is used to output the transformation matrix, then only ITEM = NSOL is valid in the OUTPR command. In the interactive sessions, the transformation matrix will not be output if the model has more than 10 elements. This command is also valid in /PREP7. """ command = f"CMSOPT,{cmsmeth},{nmode},{freqb},{freqe},{fbddef},{fbdval},{iokey}" return self.run(command, **kwargs) def cncheck( self, option="", rid1="", rid2="", rinc="", intertype="", trlevel="", cgap="", cpen="", ioff="", **kwargs, ): """Provides and/or adjusts the initial status of contact pairs. APDL Command: CNCHECK Parameters ---------- option Option to be performed: * ``"DETAIL"`` : List all contact pair properties (default). * ``"SUMMARY"`` : List only the open/closed status for each contact pair. * ``"POST"`` : Execute a partial solution to write the initial contact configuration to the Jobname.RCN file. * ``"ADJUST"`` : Physically move contact nodes to the target in order to close a gap or reduce penetration. The initial adjustment is converted to structural displacement values (UX, UY, UZ) and stored in the Jobname.RCN file. * ``"MORPH"`` : Physically move contact nodes to the target in order to close a gap or reduce penetration, and also morph the underlying solid mesh. The initial adjustment of contact nodes and repositioning of solid element nodes due to mesh morphing are converted to structural displacement values (UX, UY, UZ) and stored in the Jobname.RCN file. * ``"RESET"`` : Reset target element and contact element key options and real constants to their default values. This option is not valid for general contact. * ``"AUTO"`` : Automatically sets certain real constants and key options to recommended values or settings in order to achieve better convergence based on overall contact pair behaviors. This option is not valid for general contact. * ``"TRIM"`` : Trim contact pair (remove certain contact and target elements). * ``"UNSE"`` : Unselect certain contact and target elements. rid1, rid2, rinc For pair-based contact, the range of real constant pair IDs for which Option will be performed. If RID2 is not specified, it defaults to RID1. If no value is specified, all contact pairs in the selected set of elements are considered. For general contact (InterType = GCN), RID1 and RID2 are section IDs associated with general contact surfaces instead of real constant IDs. If RINC = 0, the Option is performed between the two sections, RID1 and RID2. If RINC > 0, the Option is performed among all specified sections (RID1 to RID2 with increment of RINC). intertype The type of contact interface (pair-based versus general contact) to be considered; or the type of contact pair to be trimmed/unselected/auto-set. The following labels specify the type of contact interface: * ``""`` : (blank) Include all contact definitions (pair-based and general contact). * ``"GCN"`` : Include general contact definitions only (not valid when Option = RESET or AUTO). The following labels specify the type of contact pairs to be trimmed/unselected/auto-set (used only when Option = TRIM, UNSE, or AUTO, and only for pair-based contact definitions): * ``"ANY"`` : All types (default). * ``"MPC"`` : MPC-based contact pairs (KEYOPT(2) = 2). * ``"BOND"`` : Bonded contact pairs (KEYOPT(12) = 3, 5, 6). * ``"NOSP"`` : No separation contact pairs (KEYOPT(12) = 2, 4). * ``"INAC"`` : Inactive contact pairs (symmetric contact pairs for MPC contact or KEYOPT(8) = 2). * ``"TRlevel"`` : mming level (used only when Option = TRIM, UNSE, or MORPH): * ``"(blank)"`` : Normal trimming (default): remove/unselect contact and target elements which are in far-field. * ``"AGGRE"`` : Aggressive trimming: remove/unselect contact and target elements which are in far-field, and certain elements in near-field. cgap They are only valid when Option = ADJUST or MORPH. Control parameter for opening gap. Close the opening gap if the absolute value of the gap is smaller than the CGAP value. CGAP defaults to ``0.25*PINB`` (where PINB is the pinball radius) for bonded and no-separation contact; otherwise it defaults to the value of real constant ICONT. CPEN They are only valid when Option = ADJUST or MORPH. Control parameter for initial penetration. Close the initial penetration if the absolute value of the penetration is smaller than the CPEN value. CPEN defaults to ``0.25*PINB`` (where PINB is the pinball radius) for any type of interface behavior (either bonded or standard contact). IOFF They are only valid when Option = ADJUST or MORPH. Control parameter for initial adjustment. Input a positive value to adjust the contact nodes towards the target surface with a constant interference distance equal to IOFF. Input a negative value to adjust the contact node towards the target surface with a uniform gap distance equal to the absolute value of IOFF. Notes ----- The CNCHECK command provides information for surface-to-surface, node-to-surface, and line-to-line contact pairs (element types TARGE169, TARGE170, CONTA171, CONTA172, CONTA173, CONTA174, CONTA175, CONTA176, CONTA177). All contact and target elements of interest, along with the solid elements and nodes attached to them, must be selected for the command to function properly. For performance reasons, the program uses a subset of nodes and elements based on the specified contact regions (RID1, RID2, RINC) when executing the CNCHECK command. For additional details, see the notes section at: https://www.mm.bme.hu/~gyebro/files/ans_help_v182/ans_cmd/Hlp_C_CNCHECK.html """ command = f"CNCHECK,{option},{rid1},{rid2},{rinc},{intertype},{trlevel},{cgap},{cpen},{ioff}" return self.run(command, **kwargs) def cnkmod(self, itype="", knum="", value="", **kwargs): """Modifies contact element key options. APDL Command: CNKMOD Parameters ---------- itype Contact element type number as defined on the ET command. knum Number of the KEYOPT to be modified (KEYOPT(KNUM)). value Value to be assigned to the KEYOPT. Notes ----- The CNKMOD command has the same syntax as the KEYOPT command. However, it is valid only in the SOLUTION processor. This command is intended only for use in a linear perturbation analysis, and can only be used to modify certain contact element KEYOPT values as described below. Modifying KEYOPT(12) One use for this command is to modify contact interface behavior between load steps in a linear perturbation analysis; it allows the user to control the contact status locally per contact pair. For this application, this command is limited to changing the contact interface behavior key option: KEYOPT(12) of CONTA171, CONTA172, CONTA173, CONTA174, CONTA175, CONTA176, and CONTA177; and KEYOPT(10) of CONTA178. When used for this purpose, the command adjusts the contact status from the linear perturbation base analysis (at the point of restart) as described in the table below. Note that CNKMOD allows you to take points in the base analysis that are near contact (within the pinball region) and modify them to be treated as "in contact" in the perturbation analysis; see the "1 - near-field" row with KEYOPT(12) values set to 4 or 5. CNKMOD also allows you to take points that are sliding in the base analysis and treat them as sticking in the perturbation analysis, irrespective of the MU value; see the "2 - sliding" row with KEYOPT(12) values set to 1,3, 5, or 6. Table: 128:: : Adjusted Contact Status with CNKMOD is Issued (if outside of the adjusted pinball region) (if inside of the adjusted pinball region) (if outside of the adjusted pinball region) (if inside of the adjusted pinball region) If an open gap exists at the end of the previous load step and the contact status is adjusted as sliding or sticking due to a "bonded" or "no separation" contact behavior definition, then the program will treat it as near-field contact when executing CNKMOD in the subsequent load steps. In the linear perturbation analysis procedure, contact status can also be controlled or modified by the PERTURB command. The contact status always follows local controls defined by the CNKMOD command first, and is then adjusted by the global sticking or bonded setting (ContKey = STICKING or BONDED) on the PERTURB command (see the PERTURB command for details). Modifying KEYOPT(3) Another use for this command is to change the units of normal contact stiffness (contact element real constant FKN) in a linear perturbation modal analysis that is used to model brake squeal. For contact elements CONTA171, CONTA172, CONTA173, and CONTA174, KEYOPT(3) controls the units of normal contact stiffness. You can issue the command CNKMOD,ITYPE,3,1 during the first phase of the linear perturbation analysis in order to change the units of normal contact stiffness from FORCE/LENGTH3 (in the base analysis) to FORCE/LENGTH. Note that KEYOPT(3) = 1 is valid only when a penalty-based algorithm is used (KEYOPT(2) = 0 or 1) and the absolute normal contact stiffness value is explicitly specified (that is, a negative value input for real constant FKN). """ command = f"CNKMOD,{itype},{knum},{value}" return self.run(command, **kwargs) def cntr(self, option="", key="", **kwargs): """Redirects contact pair output quantities to a text file. APDL Command: CNTR Parameters ---------- option Output option: OUT - Contact output control. key Control key: NO - Write contact information to the output file or to the screen (default). YES - Write contact information to the Jobname.CNM file. Notes ----- Issue the command CNTR,OUT,YES to redirect contact pair output quantities to the Jobname.CNM file. To ensure that the contact information is written to Jobname.CNM, reissue CNTR,OUT,YES each time you reenter the solution processor (/SOLU). """ command = f"CNTR,{option},{key}" return self.run(command, **kwargs) def cutcontrol(self, lab="", value="", option="", **kwargs): """Controls time-step cutback during a nonlinear solution. APDL Command: CUTCONTROL Parameters ---------- lab Specifies the criteria for causing a cutback. Valid labels are: PLSLIMIT - Maximum equivalent plastic strain allowed within a time-step (substep). If the calculated value exceeds the VALUE, the program performs a cutback (bisection). VALUE defaults to 0.15 (15%). CRPLIMIT - Set values for calculating the maximum equivalent creep ratio allowed within a time step. If the calculated maximum creep ratio exceeds the defined creep ratio limit, the program performs a cutback. DSPLIMIT - Maximum incremental displacement within the solution field in a time step (substep). If the maximum calculated value exceeds VALUE, the program performs a cutback (bisection). VALUE defaults to 1.0 x 107. NPOINT - Number of points in a cycle for a second order dynamic equation, used to control automatic time stepping. If the number of solution points per cycle is less than VALUE, the program performs a cutback in time step size. VALUE defaults to 13 for linear analysis, 5 for nonlinear analysis. A larger number of points yields a more accurate solution but also increases the solution run time. This option works well for linear problems. For nonlinear analyses, other factors such as contact status changes and solution convergence rate can overwrite NPOINT. See Automatic Time Stepping in the Mechanical APDL Theory Reference for more information on automatic time stepping. - NOITERPREDICT If VALUE is 0 (default), an internal auto time step scheme will predict the number of iterations for nonlinear convergence and perform a cutback earlier than the number of iterations specified by the NEQIT command. This is the recommended option. If VALUE is 1, the solution will iterate (if nonconvergent) to NEQIT number of iterations before a cutback is invoked. It is sometimes useful for poorly-convergent problems, but rarely needed in general. - Bisection is also controlled by contact status change, plasticity or creep strain limit, and other factors. If any of these factors occur, bisection will still take place, regardless of the NOITERPREDICT setting. CUTBACKFACTOR - Changes the cutback value for bisection. Default is 0.5. VALUE must be greater than 0.0 and less than 1.0. This option is active only if AUTOTS,ON is set. value Numeric value for the specified cutback criterion. For Lab = CRPLIMIT, VALUE is the creep criteria for the creep ratio limit. option Type of creep analysis. Valid for Lab = CRPLIMIT only. IMPRATIO - Set the maximum creep ratio value for implicit creep. The default is 0.0 (i.e., no creep limit control) and any positive value is valid. (See Implicit Creep Procedure in the Structural Analysis Guide for information on how to define implicit creep.) EXPRATIO - Set the maximum creep ratio value for explicit creep. The default value is 0.1 and any positive value up to 0.25 is allowed. (See Explicit Creep Procedure in the Structural Analysis Guide for information on how to define explicit creep.) STSLIMIT - Stress threshold for calculating the creep ratio. For integration points with effective stress below this threshold, the creep ratio does not cause cutback. The default value is 0.0 and any positive value is valid. STNLIMIT - Elastic strain threshold for calculating the creep ratio. For integration points with effective elastic strain below this threshold, the creep ratio does not cause cutback. The default value is 0.0 and any positive value is valid. Notes ----- A cutback is a method for automatically reducing the step size when either the solution error is too large or the solution encounters convergence difficulties during a nonlinear analysis. Should a convergence failure occur, the program reduces the time step interval to a fraction of its previous size and automatically continues the solution from the last successfully converged time step. If the reduced time step again fails to converge, the program again reduces the time step size and proceeds with the solution. This process continues until convergence is achieved or the minimum specified time step value is reached. For creep analysis, the cutback procedure is similar; the process continues until the minimum specified time step size is reached. However, if the creep ratio limit is exceeded, the program issues a warning but continues the substep until the analysis is complete. In this case, convergence is achieved but the creep ratio criteria is not satisfied. The CRPLIM command is functionally equivalent to Lab = CRPLIMIT with options IMPRATIO and EXPRATIO """ command = f"CUTCONTROL,{lab},{value},{option}" return self.run(command, **kwargs) def ddoption(self, decomp="", **kwargs): """Sets domain decomposer option for Distributed ANSYS. APDL Command: DDOPTION Parameters ---------- decomp Controls which domain decomposition algorithm to use. AUTO - Use the default domain decomposition algorithm when splitting the model into domains for Distributed ANSYS (default). GREEDY - Use the "greedy" domain decomposition algorithm. METIS - Use the METIS graph partitioning domain decomposition algorithm. Notes ----- This command controls options relating to the domain decomposition algorithm used by Distributed ANSYS to split the model into pieces (or domains), with each piece being solved on a different processor. The greedy domain decomposition algorithm starts from a single element at a corner of the model. The domain grows by taking the properly connected neighboring elements and stops after reaching the optimal size. The METIS domain decomposition algorithm starts by creating a graph from the finite element mesh. It then uses a multilevel graph partitioning scheme which reduces the size of the original graph, creates domains using the reduced graph, and then creates the final CPU domains by expanding the smaller domains from the reduced graph back to the original mesh. """ command = f"DDOPTION,{decomp}" return self.run(command, **kwargs) def dmpext( self, smode="", tmode="", dmpname="", freqb="", freqe="", nsteps="", **kwargs ): """Extracts modal damping coefficients in a specified frequency range. APDL Command: DMPEXT Parameters ---------- smode Source mode number. There is no default for this field; you must enter an integer greater than zero. tmode Target mode. Defaults to SMODE. dmpname Array parameter name containing the damping results. Defaults to d_damp. freqb Beginning frequency range (real number greater than zero) or 'EIG' at eigenfrequency of source mode. 'EIG' is valid only if SMODE = TMODE. Note that EIG must be enclosed in single quotes when this command is used on the command line or in an input file. There is no default for this field; you must enter a value. freqe End of frequency range. Must be blank for Freqb = EIG. Default is Freqb. nsteps Number of substeps. Defaults to 1. Notes ----- DMPEXT invokes an ANSYS macro that uses modal projection techniques to compute the damping force by the modal velocity of the source mode onto the target mode. From the damping force, damping parameters are extracted. DMPEXT creates an array parameter Dmpname, with the following entries in each row: response frequency modal damping coefficient modal squeeze stiffness coefficient damping ratio squeeze-to-structural stiffness ratio The macro requires the modal displacements from the file Jobname.EFL obtained from the RMFLVEC command. In addition, a node component FLUN must exist from all FLUID136 nodes. The computed damping ratio may be used to specify constant or modal damping by means of the DMPRAT or MDAMP commands. For Rayleigh damping, use the ABEXTRACT command to compute ALPHAD and BETAD damping parameters. See Thin Film Analysis for more information on thin film analyses. The macro uses the LSSOLVE command to perform two load steps for each frequency. The first load case contains the solution of the source mode excitation and can be used for further postprocessing. Solid model boundary conditions are deleted from the model. In addition, prescribed nodal boundary conditions are applied to the model. You should carefully check the boundary conditions of your model prior to executing a subsequent analysis. This command is also valid in PREP7. Distributed ANSYS Restriction: This command is not supported in Distributed ANSYS. """ command = f"DMPEXT,{smode},{tmode},{dmpname},{freqb},{freqe},{nsteps}" return self.run(command, **kwargs) def dmpoption(self, filetype="", combine="", **kwargs): """Specifies distributed memory parallel (Distributed ANSYS) file APDL Command: DMPOPTION combination options. Parameters ---------- filetype Type of solution file to combine after a distributed memory parallel solution. There is no default; if (blank), the command is ignored. RST - Results files (.RST, .RTH, .RMG, .RSTP) EMAT - Element matrix files (.EMAT). ESAV - Element saved data files (.ESAVE) MODE - Modal results files (.MODE) MLV - Modal load vector file (.MLV) IST - Initial state file (.IST) FULL - Full matrix file (.FULL) RFRQ - Reduced complex displacement file (.RFRQ) RDSP - Reduced displacement file (.RDSP) combine Option to combine solution files. Yes - Combine solution files (default). No - Do not combine solution files. Notes ----- The DMPOPTION command controls how solution files are written during a distributed memory parallel (Distributed ANSYS) solution. This command is most useful for controlling how results files (.RST,.RTH, etc.) are written. In a distributed memory parallel solution, a local results file is written by each process (JobnameN.ext, where N is the process number). By default, the program automatically combines the local results files (for example, JobnameN.RST) upon leaving the SOLUTION processor (for example, upon the FINISH command) into a single global results file (Jobname.RST) which can be used in ANSYS postprocessing. To reduce the amount of communication and I/O performed by this operation, you can issue the command DMPOPTION,RST,NO to bypass this step of combining the local results files; the local files will remain on the local disks in the current working directory. You can then use the RESCOMBINE command macro in the POST1 general postprocessor (/POST1) to read all results into the database for postprocessing. The RESCOMBINE command macro is intended for use with POST1. If you want to postprocess distributed parallel solution results using the POST26 time-history postprocessor (/POST26), it is recommended that you combine your local results files into one global results file (DMPOPTION,RST,YES or COMBINE). Local .EMAT, .ESAV, .MODE, .MLV, .IST, .RFRQ, .RDSP, and .FULL files are also written (when applicable) by each process in a distributed memory parallel solution. If these files are not needed for a downstream solution or operation, you can issue the command DMPOPTION,FileType,NO for each file type to bypass the file combination step and thereby improve performance. You should not bypass the file combination step if a downstream PSD analysis or modal expansion pass will be performed. If DMPOPTION,MODE,NO or DMPOPTION,RST,NO is specified in a modal analysis, element results cannot be written to the combined mode file (Jobname.MODE). In this case, if Distributed ANSYS is used in a downstream harmonic or transient analysis that uses the mode- superposition method, the MSUPkey on the MXPAND command can retain its value. However, if shared memory parallel processing is used in the downstream harmonic or transient analysis, the MSUPkey is effectively set to NO. The DMPOPTION command can be changed between load steps; however, doing so will not affect which set of solution files are combined. Only the last values of FileType and Combine upon leaving the solution processor will be used to determine whether the solution files are combined. For example, given a two load step solution and FileType = RST, setting Combine = NO for the first load step and YES for the second load step will cause all sets on the local results files to be combined. If the opposite is true (Combine = YES for the first load step and NO for the second load step), no results will be combined. After using DMPOPTION to suppress file combination, you may find it necessary to combine the local files for a specific FileType for use in a subsequent analysis. In this case, use the COMBINE command to combine local solution files into a single, global file. """ command = f"DMPOPTION,{filetype},{combine}" return self.run(command, **kwargs) def dspoption( self, reord_option="", memory_option="", memory_size="", solve_info="", **kwargs ): """Sets memory option for the distributed sparse solver. APDL Command: DSPOPTION Parameters ---------- reord_option Reordering option: DEFAULT - Use the default reordering scheme. SEQORDER - Use a sequential equation reordering scheme within the distributed sparse solver. Relative to PARORDER, this option typically results in longer equation ordering times and therefore longer overall solver times. Occasionally, however, this option will produce better quality orderings which decrease the matrix factorization times and improve overall solver performance. PARORDER - Use a parallel equation reordering scheme within the distributed sparse solver. Relative to SEQORDER, this option typically results in shorter equation ordering times and therefore shorter overall solver times. Occasionally, however, this option will produce lower quality orderings which increase the matrix factorization times and degrade overall solver performance. memory_option Memory allocation option: DEFAULT - Use the default memory allocation strategy for the distributed sparse solver. The default strategy attempts to run in the INCORE memory mode. If there is not enough physical memory available when the solver starts to run in the INCORE memory mode, the solver will then attempt to run in the OUTOFCORE memory mode. INCORE - Use a memory allocation strategy in the distributed sparse solver that will attempt to obtain enough memory to run with the entire factorized matrix in memory. This option uses the most amount of memory and should avoid doing any I/O. By avoiding I/O, this option achieves optimal solver performance. However, a significant amount of memory is required to run in this mode, and it is only recommended on machines with a large amount of memory. If the allocation for in-core memory fails, the solver will automatically revert to out-of-core memory mode. OUTOFCORE - Use a memory allocation strategy in the distributed sparse solver that will attempt to allocate only enough work space to factor each individual frontal matrix in memory, but will share the entire factorized matrix on disk. Typically, this memory mode results in poor performance due to the potential bottleneck caused by the I/O to the various files written by the solver. FORCE - This option, when used in conjunction with the Memory_Size option, allows you to force the distributed sparse solver to run with a specific amount of memory. This option is only recommended for the advanced user who understands distributed sparse solver memory requirements for the problem being solved, understands the physical memory on the system, and wants to control the distributed sparse solver memory usage. memory_size Initial memory size allocation for the sparse solver in MB. The Memory_Size setting should always be well within the physical memory available, but not so small as to cause the distributed sparse solver to run out of memory. Warnings and/or errors from the distributed sparse solver will appear if this value is set too low. If the FORCE memory option is used, this value is the amount of memory allocated for the entire duration of the distributed sparse solver solution. solve_info Solver output option: OFF - Turns off additional output printing from the distributed sparse solver (default). PERFORMANCE - Turns on additional output printing from the distributed sparse solver, including a performance summary and a summary of file I/O for the distributed sparse solver. Information on memory usage during assembly of the global matrix (that is, creation of the Jobname.FULL file) is also printed with this option. Notes ----- This command controls options related to the distributed sparse solver in all analysis types where the distributed sparse solver can be used. The amount of memory required for the distributed sparse solver is unknown until the matrix structure is preprocessed, including equation reordering. The amount of memory allocated for the distributed sparse solver is then dynamically adjusted to supply the solver what it needs to compute the solution. If you have a large memory system, you may want to try selecting the INCORE memory mode for larger jobs to improve performance. Also, when running the distributed sparse solver with many processors on the same machine or on a machine with very slow I/O performance (e.g., slow hard drive speed), you may want to try using the INCORE memory mode to achieve better performance. However, doing so may require much more memory compared to running in the OUTOFCORE memory mode. Running with the INCORE memory mode is best for jobs which comfortably fit within the limits of the physical memory on a given system. If the distributed sparse solver workspace exceeds physical memory size, the system will be forced to use virtual memory (or the system page/swap file). In this case, it is typically more efficient to run with the OUTOFCORE memory mode. """ command = ( f"DSPOPTION,{reord_option},{memory_option},{memory_size},,,{solve_info}" ) return self.run(command, **kwargs) def exbopt( self, outinv2="", outtcms="", outsub="", outcms="", outcomp="", outrm="", noinv="", outele="", **kwargs, ): """Specifies .EXB file output options in a CMS generation pass. APDL Command: EXBOPT Parameters ---------- outinv2 Output control for 2nd order invariant: * ``"0"`` : Do not output (default). * ``"1"`` : Output the second order invariant. outtcms Output control for .TCMS file: * ``"0"`` : Do not output (default). * ``"1"`` : Output the .TCMS file. outsub Output control for .SUB file: * ``"0"`` : Do not output (default). * ``"1"`` : Output the .SUB file. OUTCMS Output control for .CMS file: * ``"0"`` : Do not output (default). * ``"1"`` : Output the .CMS file. outcomp Output control for node and element component information: * ``"0"`` : Do not output any component information. * ``"1"`` : Output node component information only. * ``"2"`` : Output element component information only. * ``"3"`` : Output both node and element component information (default). outrm Output control for the recovery matrix: * ``"0"`` : Do not output (default). * ``"1"`` : Output the recovery matrix to file.EXB. * ``"2"`` : Output the recovery matrix to a separate file, file_RECOVER.EXB. noinv Invariant calculation: * ``"0"`` : Calculate all invariants (default). * ``"1"`` : Suppress calculation of the 1st and 2nd order invariants. NOINV = 1 suppresses OUTINV2 = 1. OUTELE Output control for the element data: * ``"0"`` : Do not output (default). * ``"1"`` : Output the element data. Notes ----- When the body property file (file.EXB) is requested in a CMS generation pass (CMSOPT,,,,,,,EXB command), the .TCMS, .SUB, and .CMS files are not output by default. Use the EXBOPT command to request these files, as needed. EXBOPT can also be used to manage some content in the .EXB file for improving performance and storage (see the OUTINV2, OUTCOMP, OUTRM, NOINV, and OUTELE arguments described above). If both recovery matrix output (OUTRM = 1 or 2) and the .TCMS file (OUTTCMS = 1) are requested, the .TCMS file writing is turned off due to potentially large in-core memory use. For more information on how to generate file.EXB, see ANSYS Interface to AVL EXCITE in the Mechanical APDL Substructuring Analysis Guide """ command = f"EXBOPT,{outinv2},{outtcms},{outsub},{outcms},{outcomp},{outrm},{noinv},{outele}" return self.run(command, **kwargs) def ematwrite(self, key: str = "", **kwargs) -> Optional[str]: """Forces the writing of all the element matrices to File.EMAT. APDL Command: EMATWRITE Parameters ---------- key Write key: YES - Forces the writing of the element matrices to File.EMAT even if not normally done. NO - Element matrices are written only if required. This value is the default. Notes ----- The EMATWRITE command forces ANSYS to write the File.EMAT file. The file is necessary if you intend to follow the initial load step with a subsequent inertia relief calculation (IRLF). If used in the solution processor (/SOLU), this command is only valid within the first load step. This command is also valid in PREP7. """ command = f"EMATWRITE,{key}" return self.run(command, **kwargs) def eqslv(self, lab="", toler="", mult="", keepfile="", **kwargs): """Specifies the type of equation solver. APDL Command: EQSLV Parameters ---------- lab Equation solver type: SPARSE - Sparse direct equation solver. Applicable to real-value or complex-value symmetric and unsymmetric matrices. Available only for STATIC, HARMIC (full method only), TRANS (full method only), SUBSTR, and PSD spectrum analysis types [ANTYPE]. Can be used for nonlinear and linear analyses, especially nonlinear analysis where indefinite matrices are frequently encountered. Well suited for contact analysis where contact status alters the mesh topology. Other typical well-suited applications are: (a) models consisting of shell/beam or shell/beam and solid elements (b) models with a multi-branch structure, such as an automobile exhaust or a turbine fan. This is an alternative to iterative solvers since it combines both speed and robustness. Generally, it requires considerably more memory (~10x) than the PCG solver to obtain optimal performance (running totally in-core). When memory is limited, the solver works partly in-core and out-of-core, which can noticeably slow down the performance of the solver. See the BCSOPTION command for more details on the various modes of operation for this solver. This solver can be run in shared memory parallel or distributed memory parallel (Distributed ANSYS) mode. When used in Distributed ANSYS, this solver preserves all of the merits of the classic or shared memory sparse solver. The total sum of memory (summed for all processes) is usually higher than the shared memory sparse solver. System configuration also affects the performance of the distributed memory parallel solver. If enough physical memory is available, running this solver in the in-core memory mode achieves optimal performance. The ideal configuration when using the out-of-core memory mode is to use one processor per machine on multiple machines (a cluster), spreading the I/O across the hard drives of each machine, assuming that you are using a high-speed network such as Infiniband to efficiently support all communication across the multiple machines. - This solver supports use of the GPU accelerator capability. JCG - Jacobi Conjugate Gradient iterative equation solver. Available only for STATIC, HARMIC (full method only), and TRANS (full method only) analysis types [ANTYPE]. Can be used for structural, thermal, and multiphysics applications. Applicable for symmetric, unsymmetric, complex, definite, and indefinite matrices. Recommended for 3-D harmonic analyses in structural and multiphysics applications. Efficient for heat transfer, electromagnetics, piezoelectrics, and acoustic field problems. This solver can be run in shared memory parallel or distributed memory parallel (Distributed ANSYS) mode. When used in Distributed ANSYS, in addition to the limitations listed above, this solver only runs in a distributed parallel fashion for STATIC and TRANS (full method) analyses in which the stiffness is symmetric and only when not using the fast thermal option (THOPT). Otherwise, this solver runs in shared memory parallel mode inside Distributed ANSYS. - This solver supports use of the GPU accelerator capability. When using the GPU accelerator capability, in addition to the limitations listed above, this solver is available only for STATIC and TRANS (full method) analyses where the stiffness is symmetric and does not support the fast thermal option (THOPT). ICCG - Incomplete Cholesky Conjugate Gradient iterative equation solver. Available for STATIC, HARMIC (full method only), and TRANS (full method only) analysis types [ANTYPE]. Can be used for structural, thermal, and multiphysics applications, and for symmetric, unsymmetric, complex, definite, and indefinite matrices. The ICCG solver requires more memory than the JCG solver, but is more robust than the JCG solver for ill-conditioned matrices. This solver can only be run in shared memory parallel mode. This is also true when the solver is used inside Distributed ANSYS. - This solver does not support use of the GPU accelerator capability. QMR - Quasi-Minimal Residual iterative equation solver. Available for the HARMIC (full method only) analysis type [ANTYPE]. Can be used for high-frequency electromagnetic applications, and for symmetric, complex, definite, and indefinite matrices. The QMR solver is more stable than the ICCG solver. This solver can only be run in shared memory parallel mode. This is also true when the solver is used inside Distributed ANSYS. - This solver does not support use of the GPU accelerator capability. PCG - Preconditioned Conjugate Gradient iterative equation solver (licensed from Computational Applications and Systems Integration, Inc.). Requires less disk file space than SPARSE and is faster for large models. Useful for plates, shells, 3-D models, large 2-D models, and other problems having symmetric, sparse, definite or indefinite matrices for nonlinear analysis. Requires twice as much memory as JCG. Available only for analysis types [ANTYPE] STATIC, TRANS (full method only), or MODAL (with PCG Lanczos option only). Also available for the use pass of substructure analyses (MATRIX50). The PCG solver can robustly solve equations with constraint equations (CE, CEINTF, CPINTF, and CERIG). With this solver, you can use the MSAVE command to obtain a considerable memory savings. The PCG solver can handle ill-conditioned problems by using a higher level of difficulty (see PCGOPT). Ill-conditioning arises from elements with high aspect ratios, contact, and plasticity. - This solver can be run in shared memory parallel or distributed memory parallel (Distributed ANSYS) mode. When used in Distributed ANSYS, this solver preserves all of the merits of the classic or shared memory PCG solver. The total sum of memory (summed for all processes) is about 30% more than the shared memory PCG solver. toler Iterative solver tolerance value. Used only with the Jacobi Conjugate Gradient, Incomplete Cholesky Conjugate Gradient, Pre- conditioned Conjugate Gradient, and Quasi-Minimal Residual equation solvers. For the PCG solver, the default is 1.0E-8. The value 1.0E-5 may be acceptable in many situations. When using the PCG Lanczos mode extraction method, the default solver tolerance value is 1.0E-4. For the JCG and ICCG solvers with symmetric matrices, the default is 1.0E-8. For the JCG and ICCG solvers with unsymmetric matrices, and for the QMR solver, the default is 1.0E-6. Iterations continue until the SRSS norm of the residual is less than TOLER times the norm of the applied load vector. For the PCG solver in the linear static analysis case, 3 error norms are used. If one of the error norms is smaller than TOLER, and the SRSS norm of the residual is smaller than 1.0E-2, convergence is assumed to have been reached. See Iterative Solver in the Mechanical APDL Theory Reference for details. mult Multiplier (defaults to 2.5 for nonlinear analyses; 1.0 for linear analyses) used to control the maximum number of iterations performed during convergence calculations. Used only with the Pre- conditioned Conjugate Gradient equation solver (PCG). The maximum number of iterations is equal to the multiplier (MULT) times the number of degrees of freedom (DOF). If MULT is input as a negative value, then the maximum number of iterations is equal to abs(MULT). Iterations continue until either the maximum number of iterations or solution convergence has been reached. In general, the default value for MULT is adequate for reaching convergence. However, for ill-conditioned matrices (that is, models containing elements with high aspect ratios or material type discontinuities) the multiplier may be used to increase the maximum number of iterations used to achieve convergence. The recommended range for the multiplier is 1.0 MULT 3.0. Normally, a value greater than 3.0 adds no further benefit toward convergence, and merely increases time requirements. If the solution does not converge with 1.0 MULT 3.0, or in less than 10,000 iterations, then convergence is highly unlikely and further examination of the model is recommended. Rather than increasing the default value of MULT, consider increasing the level of difficulty (Lev_Diff) on the PCGOPT command. keepfile Determines whether files from a SPARSE solver run should be deleted or retained. Applies only to Lab = SPARSE for static and full transient analyses. """ return self.run(f"EQSLV,{lab},{toler},{mult},,{keepfile}", **kwargs) def eresx(self, key="", **kwargs): """Specifies extrapolation of integration point results. APDL Command: ERESX Parameters ---------- key Extrapolation key: DEFA - If element is fully elastic (no active plasticity, creep, or swelling nonlinearities), extrapolate the integration point results to the nodes. If any portion of the element is plastic (or other active material nonlinearity), copy the integration point results to the nodes (default). YES - Extrapolate the linear portion of the integration point results to the nodes and copy the nonlinear portion (for example, plastic strains). NO - Copy the integration point results to the nodes. Notes ----- Specifies whether the solution results at the element integration points are extrapolated or copied to the nodes for element and nodal postprocessing. The structural stresses, elastic and thermal strains, field gradients, and fluxes are affected. Nonlinear data (plastic, creep, and swelling strains) are always copied to the nodes, never extrapolated. For shell elements, ERESX applies only to integration point results in the in-plane directions. This command is also valid in PREP7. """ command = f"ERESX,{key}" return self.run(command, **kwargs) def escheck( self, sele: str = "", levl: str = "", defkey: MapdlInt = "", **kwargs ) -> Optional[str]: """Perform element shape checking for a selected element set. APDL Command: ESCHECK Parameters ---------- sele Specifies whether to select elements for checking: (blank) - List all warnings/errors from element shape checking. ESEL - Select the elements based on the .Levl criteria specified below. levl WARN - Select elements producing warning and error messages. ERR - Select only elements producing error messages ( default). defkey Specifies whether check should be performed on deformed element shapes. . 0 - Do not update node coordinates before performing shape checks (default). 1 - Update node coordinates using the current set of deformations in the database. Notes ----- Shape checking will occur according to the current SHPP settings. Although ESCHECK is valid in all processors, Defkey uses the current results in the database. If no results are available a warning will be issued. This command is also valid in PREP7, SOLUTION and POST1. """ command = f"ESCHECK,{sele},{levl},{defkey}" return self.run(command, **kwargs) def essolv( self, electit="", strutit="", dimn="", morphopt="", mcomp="", xcomp="", electol="", strutol="", mxloop="", ruseky="", restky="", eiscomp="", **kwargs, ): """Performs a coupled electrostatic-structural analysis. APDL Command: ESSOLV Parameters ---------- electit Title of the electrostatics physics file as assigned by the PHYSICS command. strutit Title of the structural physics file as assigned by the PHYSICS command. dimn Model dimensionality (a default is not allowed): 2 - 2-D model. 3 - 3-D model. morphopt Morphing option: <0 - Do not perform any mesh morphing or remeshing. 0 - Remesh the non-structural regions for each recursive loop only if mesh morphing fails (default). 1 - Remesh the non-structural regions each recursive loop and bypass mesh morphing. 2 - Perform mesh morphing only, do not remesh any non-structural regions. mcomp Component name of the region to be morphed. For 2-D models, the component may be elements or areas. For 3-D models, the component may be elements or volumes. A component must be specified. You must enclose name-strings in single quotes in the ESSOLV command line. xcomp Component name of entities excluded from morphing. In the 2-D case, it is the component name for the lines excluded from morphing. In the 3-D case, it is component name for the areas excluded from morphing. Defaults to exterior non-shared entities (see the DAMORPH, DVMORPH, and DEMORPH commands). You must enclose name-strings in single quotes in the ESSOLV command line. electol Electrostatic energy convergence tolerance. Defaults to .005 (.5%) of the value computed from the previous iteration. If less than zero, the convergence criteria based on electrostatics results is turned off. strutol Structural maximum displacement convergence tolerance. Defaults to .005 (.5%) of the value computed from the previous iteration. If less than zero, the convergence criteria base on structural results is turned off. mxloop Maximum number of allowable solution recursive loops. A single pass through both an electrostatics and structural analysis constitutes one loop. Defaults to 100. ruseky Reuse flag option: 1 - Assumes initial run of ESSOLV using base geometry for the first electrostatics solution. >1 - Assumes ESSOLV run is a continuation of a previous ESSOLV run, whereby the morphed geometry is used for the initial electrostatic simulation. restky Structural restart key. 0 - Use static solution option for structural solution. 1 - Use static restart solution option for structural solution. eiscomp Element component name for elements containing initial stress data residing in file jobname.ist. The initial stress data must be defined prior to issuing ESSOLV (see INISTATE command). Notes ----- ESSOLV invokes an ANSYS macro which automatically performs a coupled electrostatic-structural analysis. The macro displays periodic updates of the convergence. If non-structural regions are remeshed during the analysis, boundary conditions and loads applied to nodes and elements will be lost. Accordingly, it is better to assign boundary conditions and loads to the solid model. Use RUSEKY > 1 for solving multiple ESSOLV simulations for different excitation levels (i.e., for running a voltage sweep). Do not issue the SAVE command to save the database between ESSOLV calls. For nonlinear structural solutions, the structural restart option (RESTKY = 1) may improve solution time by starting from the previous converged structural solution. For solid elements, ESSOLV automatically detects the air-structure interface and applies a Maxwell surface flag on the electrostatic elements. This flag is used to initiate the transfer for forces from the electrostatic region to the structure. When using the ESSOLV command with structural shell elements (for example, SHELL181), you must manually apply the Maxwell surface flag on all air elements surrounding the shells before writing the final electrostatic physics file. Use the SFA command to apply the Maxwell surface flag to the areas representing the shell elements; doing so ensures that the air elements next to both sides of the shells receive the Maxwell surface flag. If lower-order structural solids or shells are used, set KEYOPT(7) = 1 for the electrostatic element types to ensure the correct transfer of forces. Information on creating the initial stress file is documented in the Loading chapter in the Basic Analysis Guide. Distributed ANSYS Restriction: This command is not supported in Distributed ANSYS. """ command = f"ESSOLV,{electit},{strutit},{dimn},{morphopt},{mcomp},{xcomp},{electol},{strutol},{mxloop},,{ruseky},{restky},{eiscomp}" return self.run(command, **kwargs) def expass(self, key="", **kwargs): """Specifies an expansion pass of an analysis. APDL Command: EXPASS Parameters ---------- key Expansion pass key: OFF - No expansion pass will be performed (default). ON - An expansion pass will be performed. Notes ----- Specifies that an expansion pass of a modal, substructure, buckling, transient, or harmonic analysis is to be performed. Note:: : This separate solution pass requires an explicit FINISH to preceding analysis and reentry into SOLUTION. This command is also valid in PREP7. """ command = f"EXPASS,{key}" return self.run(command, **kwargs) def gauge(self, opt="", freq="", **kwargs): """Gauges the problem domain for a magnetic edge-element formulation. APDL Command: GAUGE Parameters ---------- opt Type of gauging to be performed: ON - Perform tree gauging of the edge values (default). OFF - Gauging is off. (You must specify custom gauging via APDL specifications.) STAT - Gauging status (returns the current Opt and FREQ values) freq The following options are valid when Opt = ON: 0 - Generate tree-gauging information once, at the first load step. Gauging data is retained for subsequent load steps. (This behavior is the default.) 1 - Repeat gauging for each load step. Rewrites the gauging information at each load step to accommodate changing boundary conditions on the AZ degree of freedom (for example, adding or deleting AZ constraints via the D or CE commands). Notes ----- The GAUGE command controls the tree-gauging procedure required for electromagnetic analyses using an edge-based magnetic formulation (elements SOLID236 and SOLID237). Gauging occurs at the solver level for each solution (SOLVE). It sets additional zero constraints on the edge-flux degrees of freedom AZ to produce a unique solution; the additional constraints are removed after solution. Use the FREQ option to specify how the command generates gauging information for multiple load steps. Access the gauging information via the _TGAUGE component of gauged nodes. The program creates and uses this component internally to remove and reapply the AZ constraints required by gauging. If FREQ = 0, the _TGAUGE component is created at the first load step and is used to reapply the tree gauge constraints at subsequent load steps. If FREQ = 1, the tree-gauging information and the _TGAUGE component are generated at every load step If gauging is turned off (GAUGE,OFF), you must specify your own gauging at the APDL level. This command is also valid in PREP7. """ command = f"GAUGE,{opt},{freq}" return self.run(command, **kwargs) def gmatrix(self, symfac="", condname="", numcond="", matrixname="", **kwargs): """Performs electric field solutions and calculates the self and mutual APDL Command: GMATRIX conductance between multiple conductors. Parameters ---------- symfac Geometric symmetry factor. Conductance values are scaled by this factor which represents the fraction of the total device modeled. Defaults to 1. condname Alphanumeric prefix identifier used in defining named conductor components. numcond Total number of components. If a ground is modeled, it is to be included as a component. matrixname Array name for computed conductance matrix. Defaults to GMATRIX. Notes ----- To invoke the GMATRIX macro, the exterior nodes of each conductor must be grouped into individual components using the CM command. Each set of independent components is assigned a component name with a common prefix followed by the conductor number. A conductor system with a ground must also include the ground nodes as a component. The ground component is numbered last in the component name sequence. A ground conductance matrix relates current to a voltage vector. A ground matrix cannot be applied to a circuit modeler. The lumped conductance matrix is a combination of lumped "arrangements" of voltage differences between conductors. Use the lumped conductance terms in a circuit modeler to represent conductances between conductors. Enclose all name-strings in single quotes in the GMATRIX command line. GMATRIX works with the following elements: SOLID5 (KEYOPT(1) = 9) SOLID98 (KEYOPT(1) = 9) LINK68 PLANE230 SOLID231 SOLID232 This command is available from the menu path shown below only if existing results are available. This command does not support multiframe restarts Distributed ANSYS Restriction: This command is not supported in Distributed ANSYS. """ command = f"GMATRIX,{symfac},{condname},{numcond},,{matrixname}" return self.run(command, **kwargs) def lanboption(self, strmck="", **kwargs): """Specifies Block Lanczos eigensolver options. APDL Command: LANBOPTION strmck Controls whether the Block Lanczos eigensolver will perform a Sturm sequence check: * ``"OFF"`` : Do not perform the Sturm sequence check (default). * ``"ON"`` : Perform a Sturm sequence check. This requires additional matrix factorization (which can be expensive), but does help ensure that no modes are missed in the specified range. Notes ----- LANBOPTION specifies options to be used with the Block Lanczos eigensolver during an eigenvalue buckling analysis (BUCOPT,LANB) or a modal analysis (MODOPT,LANB). By default the sturm sequence check is off for the Block Lanczos eigensolver when it is used in a modal analysis, and on when it is used in a buckling analysis. """ return self.run(f"LANBOPTION,{strmck}", **kwargs) def lumpm(self, key="", **kwargs): """Specifies a lumped mass matrix formulation. APDL Command: LUMPM Parameters ---------- key Formulation key: OFF - Use the element-dependent default mass matrix formulation (default). ON - Use a lumped mass approximation. Notes ----- This command is also valid in PREP7. If used in SOLUTION, this command is valid only within the first load step. """ command = f"LUMPM,{key}" return self.run(command, **kwargs) def moddir(self, key="", directory="", fname="", **kwargs): """Activates the remote read-only modal files usage. APDL Command: MODDIR Parameters ---------- key Key to activate the remote modal files usage * ``"1 (ON or YES)"`` : The program performs the analysis using remote modal files. The files are read-only. * ``"0 (OFF or NO)"`` : The program performs the analysis using modal files located in the working directory (default). directory Directory path (248 characters maximum). The directory contains the modal analysis files. The directory path defaults to the current working directory. fname File name (no extension or directory path) for the modal analysis files. The file name defaults to the current Jobname. Notes ----- Only applies to spectrum analyses (ANTYPE,SPECTR). Using the default for both the directory path (Directory) and the file name (Fname) is not valid. At least one of these values must be specified. The MODDIR command must be issued during the first solution and at the beginning of the solution phase (before LVSCALE in particular). Remote modal files usage is not supported when mode file reuse is activated (modeReuseKey = YES on SPOPT). """ return self.run(f"MODDIR,{key},{directory},{fname}", **kwargs) def monitor(self, var="", node="", lab="", **kwargs): """Controls contents of three variable fields in nonlinear solution APDL Command: MONITOR monitor file. Parameters ---------- var One of three variable field numbers in the monitor file whose contents can be specified by the Lab field. Valid arguments are integers 1, 2, or 3. See Notes section for default values. node The node number for which information is monitored in the specified VAR field. In the GUI, if Node = P, graphical picking is enabled. If blank, the monitor file lists the maximum value of the specified quantity (Lab field) for the entire structure. lab The solution quantity to be monitored in the specified VAR field. Valid labels for solution quantities are UX, UY, and UZ (displacements); ROTX, ROTY, and ROTZ (rotations); and TEMP (temperature). Valid labels for reaction force are FX, FY, and FZ (structural force) and MX, MY, and MZ (structural moment). Valid label for heat flow rate is HEAT. For defaults see the Notes section. Notes ----- The monitor file always has an extension of .mntr, and takes its file name from the specified Jobname. If no Jobname is specified, the file name defaults to file. You must issue this command once for each solution quantity you want to monitor at a specified node at each load step. You cannot monitor a reaction force during a linear analysis. The variable field contents can be redefined at each load step by reissuing the command. The monitored quantities are appended to the file for each load step. Reaction forces reported in the monitor file may be incorrect if the degree of freedom of the specified node is involved in externally defined coupling (CP command) or constraint equations (CE command), or if the program has applied constraint equations internally to the node. The following example shows the format of a monitor file. Note that the file only records the solution substep history when a substep is convergent. The following details the contents of the various fields in the monitor file: The current load step number. The current substep (time step) number. The number of attempts made in solving the current substep. This number is equal to the number of failed attempts (bisections) plus one (the successful attempt). The number of iterations used by the last successful attempt. Total cumulative number of iterations (including each iteration used by a bisection). : Time or load factor increments for the current substep. Total time (or load factor) for the last successful attempt in the current substep. Variable field 1. In this example, the field is reporting the UZ value. By default, this field lists the CPU time used up to (but not including) the current substep. Variable field 2. In this example, the field is reporting the MZ value. By default, this field lists the maximum displacement in the entire structure. Variable field 3. By default (and in the example), this field reports the maximum equivalent plastic strain increment in the entire structure. """ command = f"MONITOR,{var},{node},{lab}" return self.run(command, **kwargs) def msave(self, key="", **kwargs): """Sets the solver memory saving option. This option only applies to the APDL Command: MSAVE PCG solver (including PCG Lanczos). Parameters ---------- key Activation key: 0 or OFF - Use global assembly for the stiffness matrix (and mass matrix, when using PCG Lanczos) of the entire model. 1 or ON - Use an element-by-element approach when possible to save memory during the solution. In this case, the global stiffness (and mass) matrix is not assembled; element stiffness (and mass) is regenerated during PCG or PCG Lanczos iterations. Notes ----- MSAVE,ON only applies to and is the default for parts of the model using the following element types with linear material properties that meet the conditions listed below. SOLID186 (Structural Solid only) SOLID187 The following conditions must also be true: The PCG solver has been specified. Small strains are assumed (NLGEOM,OFF). No prestress effects (PSTRES) are included. All nodes on the supported element types must be defined (i.e., the midside nodes cannot be removed using the EMID command). For elements with thermally dependent material properties, MSAVE,ON applies only to elements with uniform temperatures prescribed. The default element coordinate system must be used. If you manually force MSAVE,ON by including it in the input file, the model can include the following additional conditions: The analysis can be a modal analysis using the PCG Lanczos method (MODOPT,LANPCG). Large deflection effects (NLGEOM,ON) are included. SOLID185 (brick shapes and KEYOPT(2) = 3 only) elements can be included. All other element types or other parts of the model that don't meet the above criteria will be solved using global assembly (MSAVE,OFF). This command can result in memory savings of up to 70 percent over the global assembly approach for the part of the model that meets the criteria. Depending on the hardware (e.g., processor speed, memory bandwidth, etc.), the solution time may increase or decrease when this feature is used. This memory-saving feature runs in parallel when multiple processors are used with the /CONFIG command or with Distributed ANSYS. The gain in performance with using multiple processors with this feature turned on should be similar to the default case when this feature is turned off. Performance also improves when using the uniform reduced integration option for SOLID186 elements. This command does not support the layered option of the SOLID185 and SOLID186 elements. When using MSAVE,ON with the PCGOPT command, note the following restrictions: For static and modal analyses, MSAVE,ON is not valid when using a Lev_Diff value of 5 on the PCGOPT command; Lev_Diff will automatically be reset to 2. For modal analyses, MSAVE,ON is not valid with the StrmCk option of the PCGOPT command; Strmck will be set to OFF. For all analysis types, MSAVE,ON is not valid when the Lagrange multiplier option (LM_Key) of the PCGOPT command is set to ON; the MSAVE activation key will be set to OFF. For linear perturbation static and modal analyses, MSAVE,ON is not valid; the MSAVE activation key will be set to OFF. When using MSAVE,ON for modal analyses, no .FULL file will be created. The .FULL file may be necessary for subsequent analyses (e.g., harmonic, transient mode-superposition, or spectrum analyses). To generate the .FULL file, rerun the modal analysis using the WRFULL command. """ command = f"MSAVE,{key}" return self.run(command, **kwargs) def msolve(self, numslv="", nrmtol="", nrmchkinc="", **kwargs): """Starts multiple solutions for random acoustics analysis with diffuse APDL Command: MSOLVE sound field. Parameters ---------- numslv Number of multiple solutions (load steps) corresponding to the number of samplings. Default = 1. Notes ----- The MSOLVE command starts multiple solutions (load steps) for random acoustics analysis with multiple samplings. The process is controlled by the norm convergence tolerance NRMTOL or the number of multiple solutions NUMSLV (if the solution steps reach the defined number). The program checks the norm convergence by comparing two averaged sets of radiated sound powers with the interval NRMCHKINC over the frequency range. For example, if NRMCHKINC = 5, the averaged values from 5 solutions are compared with the averaged values from 10 solutions, then the averaged values from 10 solutions are compared with the averaged values from 15 solutions, and so on. The incident diffuse sound field is defined via the DFSWAVE command. The average result of multiple solutions with different samplings is calculated via the PLST command. """ command = f"MSOLVE,{numslv},{nrmtol},{nrmchkinc}" return self.run(command, **kwargs) def opncontrol(self, lab="", value="", numstep="", **kwargs): """Sets decision parameter for automatically increasing the time step APDL Command: OPNCONTROL interval. Parameters ---------- lab DOF DOF - Degree-of-freedom label used to base a decision for increasing the time step (substep) interval in a nonlinear or transient analysis. The only DOF label currently supported is TEMP. OPENUPFACTOR - Factor for increasing the time step interval. Specify when AUTOTS,ON is issued and specify a VALUE > 1.0 (up to 10.0). The default VALUE = 1.5 (except for thermal analysis, where it is 3.0). Generally, VALUE > 3.0 is not recommended. value, numstep Two values used in the algorithm for determining if the time step interval can be increased. Valid only when Lab = DOF. Notes ----- This command is available only for nonlinear or full transient analysis. """ command = f"OPNCONTROL,{lab},{value},{numstep}" return self.run(command, **kwargs) def outaero(self, sename="", timeb="", dtime="", **kwargs): """Outputs the superelement matrices and load vectors to formatted files APDL Command: OUTAERO for aeroelastic analysis. Parameters ---------- sename Name of the superelement that models the wind turbine supporting structure. Defaults to the current Jobname. timeb First time at which the load vector is formed (defaults to be read from SENAME.sub). dtime Time step size of the load vectors (defaults to be read from SENAME.sub). Notes ----- Both TIMEB and DTIME must be blank if the time data is to be read from the SENAME.sub file. The matrix file (SENAME.SUB) must be available from the substructure generation run before issuing this command. This superelement that models the wind turbine supporting structure must contain only one master node with six freedoms per node: UX, UY, UZ, ROTX, ROTY, ROTZ. The master node represents the connection point between the turbine and the supporting structure. This command will generate four files that are exported to the aeroelastic code for integrated wind turbine analysis. The four files are Jobname.GNK for the generalized stiffness matrix, Jobname.GNC for the generalized damping matrix, Jobname.GNM for the generalized mass matrix and Jobname.GNF for the generalized load vectors. For detailed information on how to perform a wind coupling analysis, see Coupling to External Aeroelastic Analysis of Wind Turbines in the Mechanical APDL Advanced Analysis Guide. """ command = f"OUTAERO,{sename},{timeb},{dtime}" return self.run(command, **kwargs) def ovcheck(self, method="", frequency="", set_="", **kwargs): """Checks for overconstraint among constraint equations and Lagrange APDL Command: OVCHECK multipliers. Parameters ---------- method Method used to determine which slave DOFs will be eliminated: TOPO - Topological approach (default). This method only works with constraint equations; it does not work with Lagrange multipliers. ALGE - Algebraic approach. NONE - Do not use overconstraint detection logic. frequency Frequency of overconstraint detection for static or full transient analyses: ITERATION - For all equilibrium iterations (default). SUBSTEP - At the beginning of each substep. LOADSTEP - At the beginning of each load step. set\_ Set of equations: All - Check for overconstraint between all constraint equations (default). LAG - Check for overconstraint only on the set of equations that involves Lagrange multipliers. This is faster than checking all sets, especially when the model contains large MPC bonded contact pairs. Notes ----- The OVCHECK command checks for overconstraint among the constraint equations (CE/CP) and the Lagrange multipliers for the globally assembled stiffness matrix. If overconstrained constraint equations or Lagrange multipliers are detected, they are automatically removed from the system of equations. The constraint equations that are identified as redundant are removed from the system and printed to the output file. It is very important that you check the removed equations—they may lead to convergence issues, especially for nonlinear analyses. The Frequency and Set arguments are active only for the topological method (Method = TOPO). If you do not issue the OVCHECK command, overconstraint detection is performed topologically, and the slave DOFs are also determined topologically. Overconstraint detection slows down the run. We recommend using it to validate that your model does not contain any overconstraints. Then, you can switch back to the default method (no OVCHECK command is needed). As an example, consider the redundant set of constraint equations defined below: Equation number 2 will be removed by the overconstraint detection logic. However, this is an arbitrary decision since equation number 1 could be removed instead. This is an important choice as the constant term is not the same in these two constraint equations. Therefore, you must check the removed constraint equations carefully. For detailed information on the topological and algebraic methods of overconstraint detection, see Constraints: Automatic Selection of Slave DOFs in the Mechanical APDL Theory Reference """ command = f"OVCHECK,{method},{frequency},{set_}" return self.run(command, **kwargs) def pcgopt( self, lev_diff="", reduceio="", strmck="", wrtfull="", memory="", lm_key="", **kwargs, ): """Controls PCG solver options. APDL Command: PCGOPT Parameters ---------- lev_diff Indicates the level of difficulty of the analysis. Valid settings are AUTO or 0 (default), 1, 2, 3, 4, or 5. This option applies to both the PCG solver when used in static and full transient analyses and to the PCG Lanczos method in modal analyses. Use AUTO to let ANSYS automatically choose the proper level of difficulty for the model. Lower values (1 or 2) generally provide the best performance for well-conditioned problems. Values of 3 or 4 generally provide the best performance for ill-conditioned problems; however, higher values may increase the solution time for well-conditioned problems. Higher level-of-difficulty values typically require more memory. Using the highest value of 5 essentially performs a factorization of the global matrix (similar to the sparse solver) and may require a very large amount of memory. If necessary, use Memory to reduce the memory usage when using Lev_Diff = 5. Lev_Diff = 5 is generally recommended for small- to medium-sized problems when using the PCG Lanczos mode extraction method. reduceio Controls whether the PCG solver will attempt to reduce I/O performed during equation solution: AUTO - Automatically chooses whether to reduce I/O or not (default). YES - Reduces I/O performed during equation solution in order to reduce total solver time. NO - Does NOT reduce I/O performed during equation solution. strmck Controls whether or not a Sturm sequence check is performed: OFF - Does NOT perform Sturm sequence check (default). ON - Performs Sturm sequence check wrtfull Controls whether or not the .FULL file is written. ON - Write .FULL file (default) OFF - Do not write .FULL file. memory Controls whether to run using in-core or out-of-core mode when using Lev_Diff = 5. AUTO - Automatically chooses which mode to use (default). INCORE - Run using in-core mode. OOC - Run using out-of-core mode. lm_key Controls use of the PCG solver for MPC184 Lagrange multiplier method elements. This option applies only to the PCG solver when used in static and full transient analyses. OFF - Do not use the PCG solver for the MPC184 Lagrange multiplier method (default). ON - Allow use of the PCG solver for the MPC184 Lagrange multiplier method. Notes ----- ReduceIO works independently of the MSAVE command in the PCG solver. Setting ReduceIO to YES can significantly increase the memory usage in the PCG solver. To minimize the memory used by the PCG solver with respect to the Lev_Diff option only, set Lev_Diff = 1 if you do not have sufficient memory to run the PCG solver with Lev_Diff = AUTO. The MSAVE,ON command is not valid when using Lev_Diff = 5. In this case, the Lev_Diff value will automatically be reset to 2. The MSAVE,ON command is also not valid with the StrmCk option. In this case, StrmCk will be set to OFF. Distributed ANSYS Restriction: The Memory option and the LM_Key option are not supported in Distributed ANSYS. """ command = f"PCGOPT,{lev_diff},,{reduceio},{strmck},{wrtfull},{memory},{lm_key}" return self.run(command, **kwargs) def perturb(self, type_="", matkey="", contkey="", loadcontrol="", **kwargs): """Sets linear perturbation analysis options. APDL Command: PERTURB Parameters ---------- type\_ Type of linear perturbation analysis to be performed: STATIC - Perform a linear perturbation static analysis. MODAL - Perform a linear perturbation modal analysis. BUCKLE - Perform a linear perturbation eigenvalue buckling analysis. HARMONIC - Perform a linear perturbation full harmonic analysis. SUBSTR - Perform a linear perturbation substructure generation pass. OFF - Do not perform a linear perturbation analysis (default). matkey Key for specifying how the linear perturbation analysis uses material properties, valid for all structural elements except contact elements. For more information, see Linear Perturbation Analysis in the Mechanical APDL Theory Reference. AUTO - The program selects the material properties for the linear perturbation analysis automatically (default). The materials are handled in the following way: For pure linear elastic materials used in the base analysis, the same properties are used in the linear perturbation analysis. - For hyperelastic materials used in the base analysis, the material properties are assumed to be linear elastic in the linear perturbation analysis. The material property data (or material Jacobian) is obtained based on the tangent of the hyperelastic material's constitutive law at the point where restart occurs. For any nonlinear materials other than hyperelastic materials used in the base analysis, the material properties are assumed to be linear elastic in the linear perturbation analysis. The material data is the same as the linear portion of the nonlinear materials (that is, the parts defined by MP commands). - For COMBIN39, the stiffness is that of the first segment of the force- deflection curve. TANGENT - Use the tangent (material Jacobian) on the material constitutive curve as the material property. The material property remains linear in the linear perturbation analysis and is obtained at the point of the base analysis where restart occurs. The materials are handled in the following way: For pure linear elastic materials used in the base analysis, the same properties are used in the linear perturbation analysis. Because the material constitutive curve is linear, the tangent is the same as the base analysis. - For hyperelastic materials used in the base analysis, the program uses the same tangent as that used for MatKey = AUTO, and the results are therefore identical. For any nonlinear materials other than hyperelastic materials used in the base analysis, the material properties are obtained via the material tangent on the material constitutive curve at the restart point of the base analysis. - The materials and properties typically differ from Matkey = AUTO, but it is possible the results could be identical or very similar if a.) the material is elasto-plastic rate-independent and is unloading (or has neutral loading) at the restart point, or b.) the material is rate-dependent, depending on the material properties and loading conditions. For COMBIN39, the stiffness is equal to the tangent of the current segment of the force-deflection curve. - In a modal restart solution that follows a linear perturbation modal analysis, the TANGENT option is overridden by the AUTO option and linear material properties are used for stress calculations in the modal restart. See the discussion in the Notes for more information. contkey Key that controls contact status for the linear perturbation analysis. This key controls all contact elements (TARGE169, TARGE170, and CONTA171 through CONTA178) globally for all contact pairs. Alternatively, contact status can be controlled locally per contact pair by using the CNKMOD command. Note that the contact status from the base analysis solution is always adjusted by the local contact controls specified by CNKMOD first and then modified by the global sticking or bonded control (ContKey = STICKING or BONDED). The tables in the Notes section show how the contact status is adjusted by CNKMOD and/or the ContKey setting. CURRENT - Use the current contact status from the restart snapshot (default). If the previous run is nonlinear, then the nonlinear contact status at the point of restart is frozen and used throughout the linear perturbation analysis. STICKING - For frictional contact pairs (MU > 0), use sticking contact (e.g., ``MU*KN`` for tangential contact stiffness) everywhere the contact state is closed (i.e., status is sticking or sliding). This option only applies to contact pairs that are in contact and have a frictional coefficient MU greater than zero. Contact pairs without friction (MU = 0) and in a sliding state remain free to slide in the linear perturbation analysis. BONDED - Any contact pairs that are in the closed (sticking or sliding) state are moved to bonded (for example, KN for both normal and tangential contact stiffness). Contact pairs that have a status of far-field or near-field remain open. loadcontrol Key that controls how the load vector of {Fperturbed} is calculated. This control is provided for convenience of load generation for linear perturbation analysis. In general, a new set of loads is required for a linear perturbation analysis. This key controls all mechanical loads; it does not affect non-mechanical loads. Non-mechanical loads (including thermal loads) are always kept (i.e., not deleted). ALLKEEP - Keep all the boundary conditions (loads and constraints) from the end of the load step of the current restart point. This option is convenient for further load application and is useful for a linear perturbation analysis restarted from a previous linear analysis. For this option, {Fend} is the total load vector at the end of the load step at the restart point. INERKEEP - Delete all loads and constraints from the restart step, except for displacement constraints and inertia loads (default). All displacement constraints and inertia loads are kept for convenience when performing the linear perturbation analysis. Note that nonzero and tabular displacement constraints can be considered as external loads; however, they are not deleted when using this option. PARKEEP - Delete all loads and constraints from the restart step, except for displacement constraints. All displacement constraints are kept for convenience when performing the linear perturbation analysis. Note that nonzero and tabular displacement constraints can be considered as external loads; however, they are not deleted when using this option. DZEROKEEP - Behaves the same as the PARKEEP option, except that all nonzero displacement constraints are set to zero upon the onset of linear perturbation. NOKEEP - Delete all the loads and constraints, including all displacement constraints. For this option, {Fend} is zero unless non-mechanical loads (e.g., thermal loads) are present. Notes ----- This command controls options relating to linear perturbation analyses. It must be issued in the first phase of a linear perturbation analysis. This command is also valid in PREP7. """ command = f"PERTURB,{type_},{matkey},{contkey},{loadcontrol}" return self.run(command, **kwargs) def prscontrol(self, key="", **kwargs): """Specifies whether to include pressure load stiffness in the element APDL Command: PRSCONTROL stiffness formation. Parameters ---------- key Pressure load stiffness key. In general, use the default setting. Use a non-default setting only if you encounter convergence difficulties. Pressure load stiffness is automatically included when using eigenvalue buckling analyses (ANTYPE,BUCKLE), equivalent to Key = INCP. For all other types of analyses, valid arguments for Key are: NOPL - Pressure load stiffness not included for any elements. (blank) (default) - Include pressure load stiffness for elements SURF153, SURF154, SURF156, SURF159, SHELL181, PLANE182, PLANE183, SOLID185, SOLID186, SOLID187, SOLSH190, BEAM188, BEAM189, FOLLW201, SHELL208, SHELL209, SOLID272, SOLID273, SHELL281, SOLID285, PIPE288, PIPE289, and ELBOW290. Do not include pressure load stiffness for elements SOLID65. INCP - Pressure load stiffness included for all of the default elements listed above and SOLID65. Notes ----- This command is rarely needed. The default settings are recommended for most analyses. """ command = f"PRSCONTROL,{key}" return self.run(command, **kwargs) def pscontrol(self, option="", key="", **kwargs): """Enables or disables shared-memory parallel operations. APDL Command: PSCONTROL Parameters ---------- option Specify the operations for which you intend to enable/disable parallel behavior: ALL - Enable/disable parallel for all areas (default). PREP - Enable/disable parallel during preprocessing (/PREP7). SOLU - Enable/disable parallel during solution (/SOLU). FORM - Enable/disable parallel during element matrix generation. SOLV - Enable/disable parallel during equation solver. RESU - Enable/disable parallel during element results calculation. POST - Enable/disable parallel during postprocessing (/POST1 and /POST26). STAT - List parallel operations that are enabled/disabled. key Option control key. Used for all Option values except STAT. ON - Enable parallel operation. OFF - Disable parallel operation. Notes ----- Use this command in shared-memory parallel operations. This command is useful when you encounter minor discrepancies in a nonlinear solution when using different numbers of processors. A parallel operation applied to the element matrix generation can produce a different nonlinear solution with a different number of processors. Although the nonlinear solution converges to the same nonlinear tolerance, the minor discrepancy created may not be desirable for consistency. Enabling/disabling parallel behavior for the solution (Option = SOLU) supersedes the activation/deactivation of parallel behavior for element matrix generation (FORM), equation solver (SOLV), and element results calculation (RESU). The SOLV option supports only the sparse direct and PCG solvers (EQSLV,SPARSE or PCG). No other solvers are supported. This command applies only to shared-memory architecture. It does not apply to the Distributed ANSYS product. """ command = f"PSCONTROL,{option},{key}" return self.run(command, **kwargs) def rate(self, option="", **kwargs): """Specifies whether the effect of creep strain rate will be used in the APDL Command: RATE solution of a load step. Parameters ---------- option Activates implicit creep analysis. 0 or OFF - No implicit creep analysis. This option is the default. 1 or ON - Perform implicit creep analysis. Notes ----- Set Option = 1 (or ON) to perform an implicit creep analysis (TB,CREEP with TBOPT : 1). For viscoplasticity/creep analysis, Option specifies whether or not to include the creep calculation in the solution of a load step. If Option = 1 (or ON), ANSYS performs the creep calculation. Set an appropriate time for solving the load step via a TIME,TIME command. """ command = f"RATE,{option}" return self.run(command, **kwargs) def resvec(self, key="", **kwargs): """Calculates or includes residual vectors. APDL Command: RESVEC Parameters ---------- key Residual vector key: OFF - Do not calculate or include residual vectors. This option is the default. ON - Calculate or include residual vectors. Notes ----- In a modal analysis, the RESVEC command calculates residual vectors. In a mode-superposition transient dynamic, mode-superposition harmonic, PSD or spectrum analysis, the command includes residual vectors. In a component mode synthesis (CMS) generation pass, the RESVEC command calculates one residual vector which is included in the normal modes basis used in the transformation matrix. It is supported for the three available CMS methods. RESVEC,ON can only be specified in the first load step of a generation pass and is ignored if issued at another load step. If rigid-body modes exist, pseudo-constraints are required for the calculation. Issue the D,,,SUPPORT command to specify only the minimum number of pseudo-constraints necessary to prevent rigid-body motion. For more information about residual vector formulation, see Residual Vector Method in the Mechanical APDL Theory Reference. """ command = f"RESVEC,{key}" return self.run(command, **kwargs) def rstoff(self, lab="", offset="", **kwargs): """Offsets node or element IDs in the FE geometry record. APDL Command: RSTOFF Parameters ---------- lab The offset type: NODE - Offset the node IDs. ELEM - Offset the element IDs. offset A positive integer value specifying the offset value to apply. The value must be greater than the number of nodes or elements in the existing superelement results file. Notes ----- The RSTOFF command offsets node or element IDs in the FE geometry record saved in the .rst results file. Use the command when expanding superelements in a bottom-up substructuring analysis (where each superelement is generated individually in a generation pass, and all superelements are assembled together in the use pass). With appropriate offsets, you can write results files with unique node or element IDs and thus display the entire model even if the original superelements have overlapping element or node ID sets. (Such results files are incompatible with the .db database file saved at the generation pass.) The offset that you specify is based on the original superelement node or element numbering, rather than on any offset specified via a SESYMM or SETRAN command. When issuing an RSTOFF command, avoid specifying an offset that creates conflicting node or element numbers for a superelement generated via a SESYMM or SETRAN command. If you issue the command to set non-zero offsets for node or element IDs, you must bring the geometry into the database via the SET command so that ANSYS can display the results. You must specify appropriate offsets to avoid overlapping node or element IDs with other superelement results files. The command is valid only in the first load step of a superelement expansion pass. """ command = f"RSTOFF,{lab},{offset}" return self.run(command, **kwargs) def scopt(self, tempdepkey="", **kwargs): """Specifies System Coupling options. APDL Command: SCOPT Parameters ---------- tempdepkey Temperature-dependent behavior key based on the convection coefficient: * ``"YES"`` : A negative convection coefficient, -N, is assumed to be a function of temperature and is determined from the HF property table for material N (MP command). This is the default. * ``"NO"`` : A negative convection coefficient, -N, is used as is in the convection calculation. Notes ----- By default in the Mechanical APDL program, a negative convection coefficient value triggers temperature-dependent behavior. In System Coupling, and in some one-way CFD to Mechanical APDL thermal simulations, it is desirable to allow convection coefficients to be used as negative values. To do so, issue the command ``scopt("NO")``. """ return self.run(f"SCOPT,{tempdepkey}", **kwargs) def seexp(self, sename="", usefil="", imagky="", expopt="", **kwargs): """Specifies options for the substructure expansion pass. APDL Command: SEEXP Parameters ---------- sename The name (case-sensitive) of the superelement matrix file created by the substructure generation pass (Sename.SUB). Defaults to the initial jobname File. If a number, it is the element number of the superelement as used in the use pass. usefil The name of the file containing the superelement degree-of-freedom (DOF) solution created by the substructure use pass (Usefil.DSUB). imagky Key to specify use of the imaginary component of the DOF solution. Applicable only if the use pass is a harmonic (ANTYPE,HARMIC) analysis: OFF - Use real component of DOF solution (default). ON - Use imaginary component of DOF solution. expopt Key to specify whether the superelement (ANTYPE,SUBSTR) expansion pass (EXPASS,ON) should transform the geometry: OFF - Do not transform node or element locations (default). ON - Transform node or element locations in the FE geometry record of the .rst results file. Notes ----- Specifies options for the expansion pass of the substructure analysis (ANTYPE,SUBSTR). If used in SOLUTION, this command is valid only within the first load step. If you specify geometry transformation (Expopt = ON), you must retrieve the transformation matrix (if it exists) from the specified .SUB file. The command updates the nodal X, Y, and Z coordinates to represent the transformed node locations. The Expopt option is useful when you want to expand superelements created from other superelements (via SETRAN or SESYMM commands). For more information, see Superelement Expansion in Transformed Locations and Plotting or Printing Mode Shapes. This command is also valid in /PREP7. """ command = f"SEEXP,{sename},{usefil},{imagky},{expopt}" return self.run(command, **kwargs) def seopt( self, sename="", sematr="", sepr="", sesst="", expmth="", seoclvl="", **kwargs ): """Specifies substructure analysis options. APDL Command: SEOPT Parameters ---------- sename The name (case-sensitive, thirty-two character maximum) assigned to the superelement matrix file. The matrix file will be named Sename.SUB. This field defaults to Fname on the /FILNAME command. sematr Matrix generation key: 1 - Generate stiffness (or conductivity) matrix (default). 2 - Generate stiffness and mass (or conductivity and specific heat) matrices. 3 - Generate stiffness, mass and damping matrices. sepr Print key: 0 - Do not print superelement matrices or load vectors. 1 - Print both load vectors and superelement matrices. 2 - Print load vectors but not matrices. sesst Stress stiffening key: 0 - Do not save space for stress stiffening in a later run. 1 - Save space for the stress stiffening matrix (calculated in a subsequent generation run after the expansion pass). expmth Expansion method for expansion pass: BACKSUB - Save necessary factorized matrix files for backsubstitution during subsequent expansion passes (default). This normally results in a large usage of disk space RESOLVE - Do not save factorized matrix files. Global stiffness matrix will be reformed during expansion pass. This option provides an effective way to save disk space usage. This option cannot be used if the use pass uses large deflections (NLGEOM,ON). seoclvl For the added-mass calculation, the ocean level to use when ocean waves (OCTYPE,,WAVE) are present: ATP - The ocean level at this point in time (default). MSL - The mean ocean level. Notes ----- The SEOPT command specifies substructure analysis options (ANTYPE,SUBSTR). If used during solution, the command is valid only within the first load step. When ocean waves (OCTYPE,,WAVE) are present, the SeOcLvL argument specifies the ocean height or level to use for the added-mass calculation, as the use-run analysis type is unknown during the generation run. The expansion pass method RESOLVE is not supported with component mode synthesis analysis (CMSOPT). ExpMth is automatically set to BACKSUB for CMS analysis. The RESOLVE method invalidates the use of the NUMEXP command. The RESOLVE method does not allow the computation of results based on nodal velocity and nodal acceleration (damping force, inertial force, kinetic energy, etc.) in the substructure expansion pass. This command is also valid in PREP7. """ command = f"SEOPT,{sename},{sematr},{sepr},{sesst},{expmth},{seoclvl}" return self.run(command, **kwargs) def snoption( self, rangefact="", blocksize="", robustlev="", compute="", solve_info="", **kwargs, ): """Specifies Supernode (SNODE) eigensolver options. APDL Command: SNOPTION Parameters ---------- rangefact Factor used to control the range of eigenvalues computed for each supernode. The value of RangeFact must be a number between 1.0 and 5.0. By default the RangeFact value is set to 2.0, which means that all eigenvalues between 0 and ``2*FREQE`` are computed for each supernode (where FREQE is the upper end of the frequency range of interest as specified on the MODOPT command). As the RangeFact value increases, the eigensolution for the SNODE solver becomes more accurate and the computational time increases. blocksize BlockSize to be used when computing the final eigenvectors. The value of Blocksize must be either MAX or a number between 1 and NMODE, where NMODE is the number of modes to be computed as set on the MODOPT command. Input a value of MAX to force the algorithm to allocate enough memory to hold all of the final eigenvectors in memory and, therefore, only read through the file containing the supernode eigenvectors once. Note that this setting is ONLY recommended when there is sufficient physical memory on the machine to safely hold all of the final eigenvectors in memory. robustlev Parameter used to control the robustness of the SNODE eigensolver. The value of RobustLev must be a number between 0 and 10. Lower values of RobustLev allow the eigensolver to run in the most efficient manner for optimal performance. Higher values of RobustLev often slow down the performance of the eigensolver, but can increase the robustness; this may be desirable if a problem is detected with the eigensolver or its eigensolution. compute Key to control which computations are performed by the Supernode eigensolver: EVALUE - The eigensolver computes only the eigenvalues. EVECTOR - The eigensolver computes only the eigenvectors (must be preceded by a modal analysis where the eigenvalues were computed using the Supernode eigensolver). BOTH - The eigensolver computes both the eigenvalues and eigenvectors in the same pass (default). solve_info Solver output option: OFF - Turns off additional output printing from the Supernode eigensolver (default). PERFORMANCE - Turns on additional output printing from the Supernode eigensolver, including a performance summary and a summary of file I/O for the Supernode eigensolver. Information on memory usage during assembly of the global matrices (that is, creation of the Jobname.FULL file) is also printed with this option. Notes ----- This command specifies options for the Supernode (SNODE) eigensolver. Setting RangeFact to a value greater than 2.0 will improve the accuracy of the computed eigenvalues and eigenvectors, but will often increase the computing time of the SNODE eigensolver. Conversely, setting RangeFact to a value less than 2.0 will deteriorate the accuracy of the computed eigenvalues and eigenvectors, but will often speedup the computing time of the SNODE eigensolver. The default value of 2.0 has been set as a good blend of accuracy and performance. The SNODE eigensolver reads the eigenvectors and related information for each supernode from a file and uses that information to compute the final eigenvectors. For each eigenvalue/eigenvector requested by the user, the program must do one pass through the entire file that contains the supernode eigenvectors. By choosing a BlockSize value greater than 1, the program can compute BlockSize number of final eigenvectors for each pass through the file. Therefore, smaller values of BlockSize result in more I/O, and larger values of BlockSize result in less I/O. Larger values of BlockSize also result in significant additional memory usage, as BlockSize number of final eigenvectors must be stored in memory. The default Blocksize of min(NMODE,40) is normally a good choice to balance memory and I/O usage. The RobustLev field should only be used when a problem is detected with the accuracy of the final solution or if the Supernode eigensolver fails while computing the eigenvalues/eigenvectors. Setting RobustLev to a value greater than 0 will cause the performance of the eigensolver to deteriorate. If the performance deteriorates too much or if the eigensolver continues to fail when setting the RobustLev field to higher values, then switching to another eigensolver such as Block Lanczos or PCG Lanczos is recommended. Setting Compute = EVALUE causes the Supernode eigensolver to compute only the requested eigenvalues. During this process a Jobname.SNODE file is written; however, a Jobname.MODE file is not written. Thus, errors will likely occur in any downstream computations that require the Jobname.MODE file (for example, participation factor computations, mode superpostion transient/harmonic analysis, PSD analysis). Setting Compute = EVECTOR causes the Supernode eigensolver to compute only the corresponding eigenvectors. The Jobname.SNODE file and the associated Jobname.FULL file are required when requesting these eigenvectors. In other words, the eigenvalues must have already been computed for this model before computing the eigenvectors. This field can be useful in order to separate the two steps (computing eigenvalues and computing eigenvectors). """ command = ( f"SNOPTION,{rangefact},{blocksize},{robustlev},{compute},,{solve_info}" ) return self.run(command, **kwargs) def solve(self, action="", **kwargs): """Starts a solution. APDL Command: SOLVE Parameters ---------- action Action to be performed on solve (used only for linear perturbation analyses). ELFORM - Reform all appropriate element matrices in the first phase of a linear perturbation analysis. Notes ----- Starts the solution of one load step of a solution sequence based on the current analysis type and option settings. Use Action = ELFORM only in the first phase of a linear perturbation analysis. """ command = f"SOLVE,{action}" return self.run(command, **kwargs) def stabilize( self, key="", method="", value="", substpopt="", forcelimit="", **kwargs ): """Activates stabilization for all elements that support nonlinear APDL Command: STABILIZE stabilization. Parameters ---------- key Key for controlling nonlinear stabilization: OFF - Deactivate stabilization. This value is the default. CONSTANT - Activate stabilization. The energy-dissipation ratio or damping factor remains constant during the load step. REDUCE - Activate stabilization. The energy-dissipation ratio or damping factor is reduced linearly to zero at the end of the load step from the specified or calculated value. method The stabilization-control method: ENERGY - Use the energy-dissipation ratio as the control. This value is the default when Key ≠ OFF. DAMPING - Use the damping factor as the control. value The energy-dissipation ratio (Method = ENERGY) or damping factor (Method = DAMPING). This value must be greater than 0 when Method = ENERGY or Method = DAMPING. When Method = ENERGY, this value is usually a number between 0 and 1. substpopt Option for the first substep of the load step: NO - Stabilization is not activated for the first substep even when it does not converge after the minimal allowed time increment is reached. This value is the default when Key ≠ OFF. MINTIME - Stabilization is activated for the first substep if it still does not converge after the minimal allowed time increment is reached. ANYTIME - Stabilization is activated for the first substep. Use this option if stabilization was active for the previous load step via Key = CONSTANT. forcelimit The stabilization force limit coefficient, such that 0 < FORCELIMIT < 1. The default value is 0.2. To omit a stabilization force check, set this value to 0. Notes ----- Once issued, a STABILIZE command remains in effect until you reissue the command. For the energy dissipation ratio, specify VALUE = 1.0e-4 if you have no prior experience with the current model; if convergence problems are still an issue, increase the value gradually. The damping factor is mesh-, material-, and time-step-dependent; an initial reference value from the previous run (such as a run with the energy-dissipation ratio as input) should suggest itself. Exercise caution when specifying SubStpOpt = MINTIME or ANYTIME for the first load step; ANSYS, Inc. recommends this option only for experienced users. If stabilization was active for the previous load step via Key = CONSTANT and convergence is an issue for the first substep, specify SubStpOpt = ANYTIME. When the L2-norm of the stabilization force (CSRSS value) exceeds the L2-norm of the internal force multiplied by the stabilization force coefficient, ANSYS issues a message displaying both the stabilization force norm and the internal force norm. The FORCELIMIT argument allows you to change the default stabilization force coefficient (normally 20 percent). This command stabilizes the degrees of freedom for current-technology elements only. Other elements can be included in the FE model, but their degrees of freedom are not stabilized. For more information about nonlinear stabilization, see Unstable Structures in the Structural Analysis Guide. For additional tips that can help you to achieve a stable final model, see Simplify Your Model in the Structural Analysis Guide. """ command = f"STABILIZE,{key},{method},{value},{substpopt},{forcelimit}" return self.run(command, **kwargs) def thexpand(self, key="", **kwargs): """Enables or disables thermal loading APDL Command: THEXPAND Parameters ---------- key Activation key: ON - Thermal loading is included in the load vector (default). OFF - Thermal loading is not included in the load vector. Notes ----- Temperatures applied in the analysis are used by default to evaluate material properties and contribute to the load vector if the temperature does not equal the reference temperature and a coefficient of thermal expansion is specified. Use THEXPAND,OFF to evaluate the material properties but not contribute to the load vector. This capability is particularly useful when performing a harmonic analysis where you do not want to include harmonically varying thermal loads. It is also useful in a modal analysis when computing a modal load vector but excluding the thermal load. This command is valid for all analysis types except linear perturbation modal and linear perturbation harmonic analyses. For these two linear perturbation analysis types, the program internally sets THEXPAND,OFF, and it cannot be set to ON by using this command (THEXPAND,ON is ignored). """ command = f"THEXPAND,{key}" return self.run(command, **kwargs) def thopt( self, refopt="", reformtol="", ntabpoints="", tempmin="", tempmax="", algo="", **kwargs, ): """Specifies nonlinear transient thermal solution options. APDL Command: THOPT Parameters ---------- refopt Matrix reform option. FULL - Use the full Newton-Raphson solution option (default). All subsequent input values are ignored. QUASI - Use a selective reform solution option based on REFORMTOL. reformtol Property change tolerance for Matrix Reformation (.05 default). The thermal matrices are reformed if the maximum material property change in an element (from the previous reform time) is greater than the reform tolerance. Valid only when Refopt = QUASI. ntabpoints Number of points in Fast Material Table (64 default). Valid only when Refopt = QUASI. tempmin Minimum temperature for Fast Material Table. Defaults to the minimum temperature defined by the MPTEMP command for any material property defined. Valid only when Refopt = QUASI. tempmax Maximum temperature for Fast Material Table. Defaults to the maximum temperature defined by the MPTEMP command for any material property defined. Valid only when Refopt = QUASI. -- Reserved field. algo Specifies which solution algorithm to apply: 0 - Multipass (default). 1 - Iterative. Notes ----- The QUASI matrix reform option is supported by the ICCG, JCG, and sparse solvers only (EQSLV). For Refopt = QUASI: Results from a restart may be different than results from a single run because the stiffness matrices are always recreated in a restart run, but may or may not be in a single run (depending on the behavior resulting from the REFORMTOL setting). Additionally, results may differ between two single runs as well, if the matrices are reformed as a result of the REFORMTOL setting. Midside node temperatures are not calculated if 20-node thermal solid elements (SOLID90 or SOLID279) are used. For more information, see Solution Algorithms Used in Transient Thermal Analysis in the Thermal Analysis Guide. """ command = f"THOPT,{refopt},{reformtol},{ntabpoints},{tempmin},{tempmax},{algo}" return self.run(command, **kwargs)

43.868206

539

0.630643

19,056

155,776

5.151711

0.106528

0.007334

0.006754

0.009575

0.30999

0.246743

0.209349

0.180909

0.159721

0.142751

0

0.005954

0.323927

155,776

3,550

540

43.880563

0.926175

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0

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0

1

0.210744

false

0.008264

0

0.433884

0

null

0

1

0

null

0

1

0

2

7922953fb7bf617a7ae109bd584d9b8e22523424

1,998

py

Python

shovel.py

folklabs/waste-service-standards

18025fb02056a690f672cfcd965c52c6ee85cee6

[ "MIT" ]

null

shovel.py

folklabs/waste-service-standards

18025fb02056a690f672cfcd965c52c6ee85cee6

[ "MIT" ]

null

shovel.py

folklabs/waste-service-standards

18025fb02056a690f672cfcd965c52c6ee85cee6

[ "MIT" ]

null

import csv import json import jinja2 import os import ramlfications import time from livereload import Server, shell from shovel import task RAML_FILE = 'raml/waste_services.raml' def parse_raml(template_path): api = ramlfications.parse(RAML_FILE) data = {} data['collection_event_types'] = [] with open('taxonomies/collection_event_types.csv', 'rb') as csvfile: spamreader = csv.DictReader(csvfile) # header = spamreader.read() for row in spamreader: data['collection_event_types'].append(row) env = jinja2.Environment(loader=jinja2.FileSystemLoader('./templates')) env.filters['jsonify'] = json.dumps template = env.get_template(template_path) f = open(os.path.join('docs', template_path), 'w') f.write(template.render(api=api, data=data)) f.close() def scan_files(): print 'Scanning files...' for root, subdirs, files in os.walk('templates'): for f in files: templates_sub_path = root.replace('templates/', '') template = os.path.join(templates_sub_path, f) if f == '.DS_Store': continue parse_raml(template) @task def raml(): '''Converts RAML to Markdown''' scan_files() @task def raml_watch(): '''Converts RAML to Markdown''' # TODO: move to var # template_file = open('api-templates/api.md') props = os.stat(RAML_FILE) this = last = props.st_mtime print 'Watching for changes...' while 1: if this > last: print 'Updating output.' last = this parse_raml() props = os.stat(RAML_FILE) this = props.st_mtime time.sleep(0.2) @task def watch(): server = Server() server.watch('templates/**/*', scan_files) server.watch('raml/*', scan_files) server.watch('examples/*.json', scan_files) server.serve() @task def hello(name): '''Prints hello and the provided name''' print 'Hello, %s' % name

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75

0.626627

250

1,998

4.88

0.404

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0.04918

0.039344

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0.245245

1,998

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0.044545

0

0.103448

0

0.143174

0.058269

0

0.011765

0

null

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0.137931

null

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0

null

0

null

0

1

0

1

0

2

7936895cd429fb7690cd6215759396da2451d9f6

2,184

py

Python

classNotes/object_orientation9/Polymorphism.py

minefarmer/Think_like_a_programmer

d6b1363f96600445ea47f91c637c5d0bede2e8f6

[ "Unlicense" ]

null

classNotes/object_orientation9/Polymorphism.py

minefarmer/Think_like_a_programmer

d6b1363f96600445ea47f91c637c5d0bede2e8f6

[ "Unlicense" ]

null

classNotes/object_orientation9/Polymorphism.py

minefarmer/Think_like_a_programmer

d6b1363f96600445ea47f91c637c5d0bede2e8f6

[ "Unlicense" ]

null

''' Polymorphism Polymorphism means that differient objects can behave in differient ways for the same message (FunctionCall) Consequently, sender of a message does not need to know exact class of reciever Example - Drawing Application Drawing Pane Sender Object /|\ / | \ / | \ / | \ / | \ / | \ / | \ draw / draw draw / | \ Triangle circle Rectangle |____________|___________| Reciever Objects Polymorphism (Wikipedia) In programming langues, polymorphism is the provision of a single interface to entities of differient types. A polymorphic type is one whose operations can also be applied to values of some other type, or types. ''' from abc import ABC, abstractmethod from unicodedata import name class Animal (ABC): def __init__(self): self.__name = name @abstractmethod def makeNoise(self):pass @abstractmethod def eat(self):pass def move(self): print("I can move anywhere") def getName(self): return self.__name class Lion(Animal): def __init__(self, name): super().__init__() def makeNoise(self): print("Meow meow...") def eat(self): print("I can eat buffaloes, zebras, young elephants") class Cat(Animal): def __init__(self, name): super().__init__() def makeNoise(self): print("I can roar...") def eat(self): print("I can eat mouses...") animals = [Lion("Woofie")], Cat("Max") for animal in animals: print(animal.getName()) print(animal.makeNoise()) print(animal.eat()) # Traceback (most recent call last): # File "/home/carl/Desktop/MatsHub/Think_like_a_programmer/classNotes/object_orientation9/Polymorphism.py", line 73, in <module> # print(animal.getName()) # AttributeError: 'list' object has no attribute 'getName'

26.962963

136

0.560897

225

2,184

5.217778

0.497778

0.03833

0.034072

0.044293

0.124361

0.086882

0

0.002113

0.349817

2,184

80

137

27.3

0.824648

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0

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false

0.0625

0.03125

0.53125

0.25

0

null

0

null

0

1

0

1

0

2

7937d5873594856871631b2d7befe33ec15630e0

282

py

Python

musicmore/cart/processors.py

IPenuelas/musicmore_r01

94726c84aa61201940916feb4d5e535bcd4dc230

[ "MIT" ]

null

musicmore/cart/processors.py

IPenuelas/musicmore_r01

94726c84aa61201940916feb4d5e535bcd4dc230

[ "MIT" ]

null

musicmore/cart/processors.py

IPenuelas/musicmore_r01

94726c84aa61201940916feb4d5e535bcd4dc230

[ "MIT" ]

null

from .models import Cart, CartItem from .views import _cart_id def ctx_dict_cart(request): cart = Cart.objects.filter(cart_id=_cart_id(request)) cart_items = CartItem.objects.all().filter(cart=cart[:1]) ctx_cart = {'CTX_CART_ITEMS':cart_items} return ctx_cart

28.2

65

0.730496

43

282

4.488372

0.395349

0.093264

0

0.004202

0.156028

282

9

66

31.333333

0.806723

0

0.049822

0

1

0.142857

false

0

0.285714

0

0.571429

0

null

0

1

0

null

0

1

0

2

f735d114acfbbc7d006b9ce0ce107c90ffa2fae2

324

py

Python

simulator/kruxsim/mocks/flash.py

odudex/krux

db421a3f107c0263221e5f1e877e9c38925bb17c

[ "MIT" ]

null

simulator/kruxsim/mocks/flash.py

odudex/krux

db421a3f107c0263221e5f1e877e9c38925bb17c

[ "MIT" ]

13

2022-03-21T05:35:03.000Z

2022-03-31T14:31:46.000Z

simulator/kruxsim/mocks/flash.py

odudex/krux

db421a3f107c0263221e5f1e877e9c38925bb17c

[ "MIT" ]

null

import sys from unittest import mock flash = bytearray(8 * 1024 * 1024) def read_data(addr, amount): return flash[addr : addr + amount] def write_data(addr, data): flash[addr : addr + len(data)] = data if "flash" not in sys.modules: sys.modules["flash"] = mock.MagicMock(read=read_data, write=write_data)

19.058824

75

0.691358

49

324

4.489796

0.44898

0.072727

0.118182

0

0.034091

0.185185

324

16

76

20.25

0.799242

0

0.030864

0

1

0.222222

false

0

0.222222

0.111111

0.555556

0

null

0

null

0

1

0

1

0

2

f7366410d3402dde7686f8ca4b30c6c1c2234403

52,914

py

Python

models/new/sencebgan.py

yigitozgumus/Polimi_Thesis

711c1edcf1fdb92fc6c15bf5ab1be141c13995c3

[ "MIT" ]

3

2019-07-27T14:00:42.000Z

2020-01-17T17:07:51.000Z

models/new/sencebgan.py

yigitozgumus/Polimi_Thesis

711c1edcf1fdb92fc6c15bf5ab1be141c13995c3

[ "MIT" ]

null

models/new/sencebgan.py

yigitozgumus/Polimi_Thesis

711c1edcf1fdb92fc6c15bf5ab1be141c13995c3

[ "MIT" ]

4

2019-10-22T02:58:26.000Z

2020-10-06T09:59:26.000Z

import tensorflow as tf from base.base_model import BaseModel from utils.alad_utils import get_getter import utils.alad_utils as sn class SENCEBGAN(BaseModel): def __init__(self, config): super(SENCEBGAN, self).__init__(config) self.build_model() self.init_saver() def build_model(self): ############################################################################################ # INIT ############################################################################################ # Kernel initialization for the convolutions if self.config.trainer.init_type == "normal": self.init_kernel = tf.random_normal_initializer(mean=0.0, stddev=0.02) elif self.config.trainer.init_type == "xavier": self.init_kernel = tf.contrib.layers.xavier_initializer( uniform=False, seed=None, dtype=tf.float32 ) # Placeholders self.is_training_gen = tf.placeholder(tf.bool) self.is_training_dis = tf.placeholder(tf.bool) self.is_training_enc_g = tf.placeholder(tf.bool) self.is_training_enc_r = tf.placeholder(tf.bool) self.feature_match1 = tf.placeholder(tf.float32) self.feature_match2 = tf.placeholder(tf.float32) self.image_input = tf.placeholder( tf.float32, shape=[None] + self.config.trainer.image_dims, name="x" ) self.noise_tensor = tf.placeholder( tf.float32, shape=[None, self.config.trainer.noise_dim], name="noise" ) ############################################################################################ # MODEL ############################################################################################ self.logger.info("Building training graph...") with tf.variable_scope("SENCEBGAN"): # First training part # G(z) ==> x' with tf.variable_scope("Generator_Model"): self.image_gen = self.generator(self.noise_tensor) # Discriminator outputs with tf.variable_scope("Discriminator_Model"): self.embedding_real, self.decoded_real = self.discriminator( self.image_input, do_spectral_norm=self.config.trainer.do_spectral_norm ) self.embedding_fake, self.decoded_fake = self.discriminator( self.image_gen, do_spectral_norm=self.config.trainer.do_spectral_norm ) # Second training part # E(x) ==> z' with tf.variable_scope("Encoder_G_Model"): self.image_encoded = self.encoder_g(self.image_input) # G(z') ==> G(E(x)) ==> x'' with tf.variable_scope("Generator_Model"): self.image_gen_enc = self.generator(self.image_encoded) # Discriminator outputs with tf.variable_scope("Discriminator_Model"): self.embedding_enc_fake, self.decoded_enc_fake = self.discriminator( self.image_gen_enc, do_spectral_norm=self.config.trainer.do_spectral_norm ) self.embedding_enc_real, self.decoded_enc_real = self.discriminator( self.image_input, do_spectral_norm=self.config.trainer.do_spectral_norm ) with tf.variable_scope("Discriminator_Model_XX"): self.im_logit_real, self.im_f_real = self.discriminator_xx( self.image_input, self.image_input, do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.im_logit_fake, self.im_f_fake = self.discriminator_xx( self.image_input, self.image_gen_enc, do_spectral_norm=self.config.trainer.do_spectral_norm, ) # Third training part with tf.variable_scope("Encoder_G_Model"): self.image_encoded_r = self.encoder_g(self.image_input) with tf.variable_scope("Generator_Model"): self.image_gen_enc_r = self.generator(self.image_encoded_r) with tf.variable_scope("Encoder_R_Model"): self.image_ege = self.encoder_r(self.image_gen_enc_r) with tf.variable_scope("Discriminator_Model_ZZ"): self.z_logit_real, self.z_f_real = self.discriminator_zz( self.image_encoded_r, self.image_encoded_r, do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.z_logit_fake, self.z_f_fake = self.discriminator_zz( self.image_encoded_r, self.image_ege, do_spectral_norm=self.config.trainer.do_spectral_norm, ) ############################################################################################ # LOSS FUNCTIONS ############################################################################################ with tf.name_scope("Loss_Functions"): with tf.name_scope("Generator_Discriminator"): # Discriminator Loss if self.config.trainer.mse_mode == "norm": self.disc_loss_real = tf.reduce_mean( self.mse_loss( self.decoded_real, self.image_input, mode="norm", order=self.config.trainer.order, ) ) self.disc_loss_fake = tf.reduce_mean( self.mse_loss( self.decoded_fake, self.image_gen, mode="norm", order=self.config.trainer.order, ) ) elif self.config.trainer.mse_mode == "mse": self.disc_loss_real = self.mse_loss( self.decoded_real, self.image_input, mode="mse", order=self.config.trainer.order, ) self.disc_loss_fake = self.mse_loss( self.decoded_fake, self.image_gen, mode="mse", order=self.config.trainer.order, ) self.loss_discriminator = ( tf.math.maximum(self.config.trainer.disc_margin - self.disc_loss_fake, 0) + self.disc_loss_real ) # Generator Loss pt_loss = 0 if self.config.trainer.pullaway: pt_loss = self.pullaway_loss(self.embedding_fake) self.loss_generator = self.disc_loss_fake + self.config.trainer.pt_weight * pt_loss # New addition to enforce visual similarity delta_noise = self.embedding_real - self.embedding_fake delta_flat = tf.layers.Flatten()(delta_noise) loss_noise_gen = tf.reduce_mean(tf.norm(delta_flat, ord=2, axis=1, keepdims=False)) self.loss_generator += 0.1 * loss_noise_gen with tf.name_scope("Encoder_G"): if self.config.trainer.mse_mode == "norm": self.loss_enc_rec = tf.reduce_mean( self.mse_loss( self.image_gen_enc, self.image_input, mode="norm", order=self.config.trainer.order, ) ) self.loss_enc_f = tf.reduce_mean( self.mse_loss( self.decoded_enc_real, self.decoded_enc_fake, mode="norm", order=self.config.trainer.order, ) ) elif self.config.trainer.mse_mode == "mse": self.loss_enc_rec = tf.reduce_mean( self.mse_loss( self.image_gen_enc, self.image_input, mode="mse", order=self.config.trainer.order, ) ) self.loss_enc_f = tf.reduce_mean( self.mse_loss( self.embedding_enc_real, self.embedding_enc_fake, mode="mse", order=self.config.trainer.order, ) ) self.loss_encoder_g = ( self.loss_enc_rec + self.config.trainer.encoder_f_factor * self.loss_enc_f ) if self.config.trainer.enable_disc_xx: self.enc_xx_real = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.im_logit_real, labels=tf.zeros_like(self.im_logit_real) ) self.enc_xx_fake = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.im_logit_fake, labels=tf.ones_like(self.im_logit_fake) ) self.enc_loss_xx = tf.reduce_mean(self.enc_xx_real + self.enc_xx_fake) self.loss_encoder_g += self.enc_loss_xx with tf.name_scope("Encoder_R"): if self.config.trainer.mse_mode == "norm": self.loss_encoder_r = tf.reduce_mean( self.mse_loss( self.image_ege, self.image_encoded_r, mode="norm", order=self.config.trainer.order, ) ) elif self.config.trainer.mse_mode == "mse": self.loss_encoder_r = tf.reduce_mean( self.mse_loss( self.image_ege, self.image_encoded_r, mode="mse", order=self.config.trainer.order, ) ) if self.config.trainer.enable_disc_zz: self.enc_zz_real = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.z_logit_real, labels=tf.zeros_like(self.z_logit_real) ) self.enc_zz_fake = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.z_logit_fake, labels=tf.ones_like(self.z_logit_fake) ) self.enc_loss_zz = tf.reduce_mean(self.enc_zz_real + self.enc_zz_fake) self.loss_encoder_r += self.enc_loss_zz if self.config.trainer.enable_disc_xx: with tf.name_scope("Discriminator_XX"): self.loss_xx_real = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.im_logit_real, labels=tf.ones_like(self.im_logit_real) ) self.loss_xx_fake = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.im_logit_fake, labels=tf.zeros_like(self.im_logit_fake) ) self.dis_loss_xx = tf.reduce_mean(self.loss_xx_real + self.loss_xx_fake) if self.config.trainer.enable_disc_zz: with tf.name_scope("Discriminator_ZZ"): self.loss_zz_real = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.z_logit_real, labels=tf.ones_like(self.z_logit_real) ) self.loss_zz_fake = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.z_logit_fake, labels=tf.zeros_like(self.z_logit_fake) ) self.dis_loss_zz = tf.reduce_mean(self.loss_zz_real + self.loss_zz_fake) ############################################################################################ # OPTIMIZERS ############################################################################################ with tf.name_scope("Optimizers"): self.generator_optimizer = tf.train.AdamOptimizer( self.config.trainer.standard_lr_gen, beta1=self.config.trainer.optimizer_adam_beta1, beta2=self.config.trainer.optimizer_adam_beta2, ) self.encoder_g_optimizer = tf.train.AdamOptimizer( self.config.trainer.standard_lr_enc, beta1=self.config.trainer.optimizer_adam_beta1, beta2=self.config.trainer.optimizer_adam_beta2, ) self.encoder_r_optimizer = tf.train.AdamOptimizer( self.config.trainer.standard_lr_enc, beta1=self.config.trainer.optimizer_adam_beta1, beta2=self.config.trainer.optimizer_adam_beta2, ) self.discriminator_optimizer = tf.train.AdamOptimizer( self.config.trainer.standard_lr_dis, beta1=self.config.trainer.optimizer_adam_beta1, beta2=self.config.trainer.optimizer_adam_beta2, ) # Collect all the variables all_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) # Generator Network Variables self.generator_vars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Generator_Model") ] # Discriminator Network Variables self.discriminator_vars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Discriminator_Model") ] # Discriminator Network Variables self.encoder_g_vars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Encoder_G_Model") ] self.encoder_r_vars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Encoder_R_Model") ] self.dxxvars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Discriminator_Model_XX") ] self.dzzvars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Discriminator_Model_ZZ") ] # Generator Network Operations self.gen_update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Generator_Model" ) # Discriminator Network Operations self.disc_update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Discriminator_Model" ) self.encg_update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Encoder_G_Model" ) self.encr_update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Encoder_R_Model" ) self.update_ops_dis_xx = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Discriminator_Model_XX" ) self.update_ops_dis_zz = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Discriminator_Model_ZZ" ) with tf.control_dependencies(self.gen_update_ops): self.gen_op = self.generator_optimizer.minimize( self.loss_generator, var_list=self.generator_vars, global_step=self.global_step_tensor, ) with tf.control_dependencies(self.disc_update_ops): self.disc_op = self.discriminator_optimizer.minimize( self.loss_discriminator, var_list=self.discriminator_vars ) with tf.control_dependencies(self.encg_update_ops): self.encg_op = self.encoder_g_optimizer.minimize( self.loss_encoder_g, var_list=self.encoder_g_vars, global_step=self.global_step_tensor, ) with tf.control_dependencies(self.encr_update_ops): self.encr_op = self.encoder_r_optimizer.minimize( self.loss_encoder_r, var_list=self.encoder_r_vars, global_step=self.global_step_tensor, ) if self.config.trainer.enable_disc_xx: with tf.control_dependencies(self.update_ops_dis_xx): self.disc_op_xx = self.discriminator_optimizer.minimize( self.dis_loss_xx, var_list=self.dxxvars ) if self.config.trainer.enable_disc_zz: with tf.control_dependencies(self.update_ops_dis_zz): self.disc_op_zz = self.discriminator_optimizer.minimize( self.dis_loss_zz, var_list=self.dzzvars ) # Exponential Moving Average for Estimation self.dis_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_dis = self.dis_ema.apply(self.discriminator_vars) self.gen_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_gen = self.gen_ema.apply(self.generator_vars) self.encg_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_encg = self.encg_ema.apply(self.encoder_g_vars) self.encr_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_encr = self.encr_ema.apply(self.encoder_r_vars) if self.config.trainer.enable_disc_xx: self.dis_xx_ema = tf.train.ExponentialMovingAverage( decay=self.config.trainer.ema_decay ) maintain_averages_op_dis_xx = self.dis_xx_ema.apply(self.dxxvars) if self.config.trainer.enable_disc_zz: self.dis_zz_ema = tf.train.ExponentialMovingAverage( decay=self.config.trainer.ema_decay ) maintain_averages_op_dis_zz = self.dis_zz_ema.apply(self.dzzvars) with tf.control_dependencies([self.disc_op]): self.train_dis_op = tf.group(maintain_averages_op_dis) with tf.control_dependencies([self.gen_op]): self.train_gen_op = tf.group(maintain_averages_op_gen) with tf.control_dependencies([self.encg_op]): self.train_enc_g_op = tf.group(maintain_averages_op_encg) with tf.control_dependencies([self.encr_op]): self.train_enc_r_op = tf.group(maintain_averages_op_encr) if self.config.trainer.enable_disc_xx: with tf.control_dependencies([self.disc_op_xx]): self.train_dis_op_xx = tf.group(maintain_averages_op_dis_xx) if self.config.trainer.enable_disc_zz: with tf.control_dependencies([self.disc_op_zz]): self.train_dis_op_zz = tf.group(maintain_averages_op_dis_zz) ############################################################################################ # TESTING ############################################################################################ self.logger.info("Building Testing Graph...") with tf.variable_scope("SENCEBGAN"): with tf.variable_scope("Discriminator_Model"): self.embedding_q_ema, self.decoded_q_ema = self.discriminator( self.image_input, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) with tf.variable_scope("Generator_Model"): self.image_gen_ema = self.generator( self.embedding_q_ema, getter=get_getter(self.gen_ema) ) with tf.variable_scope("Discriminator_Model"): self.embedding_rec_ema, self.decoded_rec_ema = self.discriminator( self.image_gen_ema, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) # Second Training Part with tf.variable_scope("Encoder_G_Model"): self.image_encoded_ema = self.encoder_g( self.image_input, getter=get_getter(self.encg_ema) ) with tf.variable_scope("Generator_Model"): self.image_gen_enc_ema = self.generator( self.image_encoded_ema, getter=get_getter(self.gen_ema) ) with tf.variable_scope("Discriminator_Model"): self.embedding_enc_fake_ema, self.decoded_enc_fake_ema = self.discriminator( self.image_gen_enc_ema, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.embedding_enc_real_ema, self.decoded_enc_real_ema = self.discriminator( self.image_input, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) if self.config.trainer.enable_disc_xx: with tf.variable_scope("Discriminator_Model_XX"): self.im_logit_real_ema, self.im_f_real_ema = self.discriminator_xx( self.image_input, self.image_input, getter=get_getter(self.dis_xx_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.im_logit_fake_ema, self.im_f_fake_ema = self.discriminator_xx( self.image_input, self.image_gen_enc_ema, getter=get_getter(self.dis_xx_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) # Third training part with tf.variable_scope("Encoder_G_Model"): self.image_encoded_r_ema = self.encoder_g(self.image_input) with tf.variable_scope("Generator_Model"): self.image_gen_enc_r_ema = self.generator(self.image_encoded_r_ema) with tf.variable_scope("Encoder_R_Model"): self.image_ege_ema = self.encoder_r(self.image_gen_enc_r_ema) with tf.variable_scope("Discriminator_Model"): self.embedding_encr_fake_ema, self.decoded_encr_fake_ema = self.discriminator( self.image_gen_enc_r_ema, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.embedding_encr_real_ema, self.decoded_encr_real_ema = self.discriminator( self.image_input, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) if self.config.trainer.enable_disc_zz: with tf.variable_scope("Discriminator_Model_ZZ"): self.z_logit_real_ema, self.z_f_real_ema = self.discriminator_zz( self.image_encoded_r_ema, self.image_encoded_r_ema, getter=get_getter(self.dis_zz_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.z_logit_fake_ema, self.z_f_fake_ema = self.discriminator_zz( self.image_encoded_r_ema, self.image_ege_ema, getter=get_getter(self.dis_zz_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) with tf.name_scope("Testing"): with tf.name_scope("Image_Based"): delta = self.image_input - self.image_gen_enc_ema self.rec_residual = -delta delta_flat = tf.layers.Flatten()(delta) img_score_l1 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="img_loss__1" ) self.img_score_l1 = tf.squeeze(img_score_l1) delta = self.decoded_enc_fake_ema - self.decoded_enc_real_ema delta_flat = tf.layers.Flatten()(delta) img_score_l2 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="img_loss__2" ) self.img_score_l2 = tf.squeeze(img_score_l2) with tf.name_scope("Noise_Based"): delta = self.image_encoded_r_ema - self.image_ege_ema delta_flat = tf.layers.Flatten()(delta) final_score_1 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="final_score_1" ) self.final_score_1 = tf.squeeze(final_score_1) self.score_comb_im = ( 1 * self.img_score_l1 + self.feature_match1 * self.final_score_1 ) delta = self.image_encoded_r_ema - self.embedding_enc_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_2 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="final_score_2" ) self.final_score_2 = tf.squeeze(final_score_2) delta = self.embedding_encr_real_ema - self.embedding_encr_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_3 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="final_score_3" ) self.final_score_3 = tf.squeeze(final_score_3) # Combo 1 self.score_comb_z = ( (1 - self.feature_match2) * self.final_score_2 + self.feature_match2 * self.final_score_3 ) # Combo 2 if self.config.trainer.enable_disc_xx: delta = self.im_f_real_ema - self.im_f_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_4 = tf.norm( delta_flat, ord=1, axis=1, keepdims=False, name="final_score_4" ) self.final_score_4 = tf.squeeze(final_score_4) delta = self.z_f_real_ema - self.z_f_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_6 = tf.norm( delta_flat, ord=1, axis=1, keepdims=False, name="final_score_6" ) self.final_score_6 = tf.squeeze(final_score_6) ############################################################################################ # TENSORBOARD ############################################################################################ if self.config.log.enable_summary: with tf.name_scope("train_summary"): with tf.name_scope("dis_summary"): tf.summary.scalar("loss_disc", self.loss_discriminator, ["dis"]) tf.summary.scalar("loss_disc_real", self.disc_loss_real, ["dis"]) tf.summary.scalar("loss_disc_fake", self.disc_loss_fake, ["dis"]) if self.config.trainer.enable_disc_xx: tf.summary.scalar("loss_dis_xx", self.dis_loss_xx, ["enc_g"]) if self.config.trainer.enable_disc_zz: tf.summary.scalar("loss_dis_zz", self.dis_loss_zz, ["enc_r"]) with tf.name_scope("gen_summary"): tf.summary.scalar("loss_generator", self.loss_generator, ["gen"]) with tf.name_scope("enc_summary"): tf.summary.scalar("loss_encoder_g", self.loss_encoder_g, ["enc_g"]) tf.summary.scalar("loss_encoder_r", self.loss_encoder_r, ["enc_r"]) with tf.name_scope("img_summary"): tf.summary.image("input_image", self.image_input, 1, ["img_1"]) tf.summary.image("reconstructed", self.image_gen, 1, ["img_1"]) # From discriminator in part 1 tf.summary.image("decoded_real", self.decoded_real, 1, ["img_1"]) tf.summary.image("decoded_fake", self.decoded_fake, 1, ["img_1"]) # Second Stage of Training tf.summary.image("input_enc", self.image_input, 1, ["img_2"]) tf.summary.image("reconstructed", self.image_gen_enc, 1, ["img_2"]) # From discriminator in part 2 tf.summary.image("decoded_enc_real", self.decoded_enc_real, 1, ["img_2"]) tf.summary.image("decoded_enc_fake", self.decoded_enc_fake, 1, ["img_2"]) # Testing tf.summary.image("input_image", self.image_input, 1, ["test"]) tf.summary.image("reconstructed", self.image_gen_enc_r_ema, 1, ["test"]) tf.summary.image("residual", self.rec_residual, 1, ["test"]) self.sum_op_dis = tf.summary.merge_all("dis") self.sum_op_gen = tf.summary.merge_all("gen") self.sum_op_enc_g = tf.summary.merge_all("enc_g") self.sum_op_enc_r = tf.summary.merge_all("enc_r") self.sum_op_im_1 = tf.summary.merge_all("img_1") self.sum_op_im_2 = tf.summary.merge_all("img_2") self.sum_op_im_test = tf.summary.merge_all("test") self.sum_op = tf.summary.merge([self.sum_op_dis, self.sum_op_gen]) ############################################################################################### # MODULES ############################################################################################### def generator(self, noise_input, getter=None): with tf.variable_scope("Generator", custom_getter=getter, reuse=tf.AUTO_REUSE): net_name = "Layer_1" with tf.variable_scope(net_name): x_g = tf.layers.Dense( units=2 * 2 * 256, kernel_initializer=self.init_kernel, name="fc" )(noise_input) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) x_g = tf.reshape(x_g, [-1, 2, 2, 256]) net_name = "Layer_2" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=128, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) net_name = "Layer_3" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=64, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) net_name = "Layer_4" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=32, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) net_name = "Layer_5" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=1, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.tanh(x_g, name="tanh") return x_g def discriminator(self, image_input, getter=None, do_spectral_norm=False): layers = sn if do_spectral_norm else tf.layers with tf.variable_scope("Discriminator", custom_getter=getter, reuse=tf.AUTO_REUSE): with tf.variable_scope("Encoder"): x_e = tf.reshape( image_input, [-1, self.config.data_loader.image_size, self.config.data_loader.image_size, 1], ) net_name = "Layer_1" with tf.variable_scope(net_name): x_e = layers.conv2d( x_e, filters=32, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv", ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) # 14 x 14 x 64 net_name = "Layer_2" with tf.variable_scope(net_name): x_e = layers.conv2d( x_e, filters=64, kernel_size=5, padding="same", strides=2, kernel_initializer=self.init_kernel, name="conv", ) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) # 7 x 7 x 128 net_name = "Layer_3" with tf.variable_scope(net_name): x_e = layers.conv2d( x_e, filters=128, kernel_size=5, padding="same", strides=2, kernel_initializer=self.init_kernel, name="conv", ) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) # 4 x 4 x 256 x_e = tf.layers.Flatten()(x_e) net_name = "Layer_4" with tf.variable_scope(net_name): x_e = layers.dense( x_e, units=self.config.trainer.noise_dim, kernel_initializer=self.init_kernel, name="fc", ) embedding = x_e with tf.variable_scope("Decoder"): net = tf.reshape(embedding, [-1, 1, 1, self.config.trainer.noise_dim]) net_name = "layer_1" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=256, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv1", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv1/bn", ) net = tf.nn.relu(features=net, name="tconv1/relu") net_name = "layer_2" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=128, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv2", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv2/bn", ) net = tf.nn.relu(features=net, name="tconv2/relu") net_name = "layer_3" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=64, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv3", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv3/bn", ) net = tf.nn.relu(features=net, name="tconv3/relu") net_name = "layer_4" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=32, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv4", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv4/bn", ) net = tf.nn.relu(features=net, name="tconv4/relu") net_name = "layer_5" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=1, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv5", )(net) decoded = tf.nn.tanh(net, name="tconv5/tanh") return embedding, decoded def encoder_g(self, image_input, getter=None): with tf.variable_scope("Encoder_G", custom_getter=getter, reuse=tf.AUTO_REUSE): x_e = tf.reshape( image_input, [-1, self.config.data_loader.image_size, self.config.data_loader.image_size, 1], ) net_name = "Layer_1" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=64, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_g, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_2" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=128, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_g, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_3" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=256, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_g, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) x_e = tf.layers.Flatten()(x_e) net_name = "Layer_4" with tf.variable_scope(net_name): x_e = tf.layers.Dense( units=self.config.trainer.noise_dim, kernel_initializer=self.init_kernel, name="fc", )(x_e) return x_e def encoder_r(self, image_input, getter=None): with tf.variable_scope("Encoder_R", custom_getter=getter, reuse=tf.AUTO_REUSE): x_e = tf.reshape( image_input, [-1, self.config.data_loader.image_size, self.config.data_loader.image_size, 1], ) net_name = "Layer_1" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=64, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_r, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_2" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=128, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_r, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_3" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=256, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_r, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) x_e = tf.layers.Flatten()(x_e) net_name = "Layer_4" with tf.variable_scope(net_name): x_e = tf.layers.Dense( units=self.config.trainer.noise_dim, kernel_initializer=self.init_kernel, name="fc", )(x_e) return x_e # Regularizer discriminator for the Generator Encoder def discriminator_xx(self, img_tensor, recreated_img, getter=None, do_spectral_norm=False): """ Discriminator architecture in tensorflow Discriminates between (x, x) and (x, rec_x) Args: img_tensor: recreated_img: getter: for exponential moving average during inference reuse: sharing variables or not do_spectral_norm: """ layers = sn if do_spectral_norm else tf.layers with tf.variable_scope("Discriminator_xx", reuse=tf.AUTO_REUSE, custom_getter=getter): net = tf.concat([img_tensor, recreated_img], axis=1) net_name = "layer_1" with tf.variable_scope(net_name): net = layers.conv2d( net, filters=64, kernel_size=4, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv1", ) net = tf.nn.leaky_relu( features=net, alpha=self.config.trainer.leakyReLU_alpha, name="conv2/leaky_relu" ) net = tf.layers.dropout( net, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_g, name="dropout", ) with tf.variable_scope(net_name, reuse=True): weights = tf.get_variable("conv1/kernel") net_name = "layer_2" with tf.variable_scope(net_name): net = layers.conv2d( net, filters=128, kernel_size=4, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2", ) net = tf.nn.leaky_relu( features=net, alpha=self.config.trainer.leakyReLU_alpha, name="conv2/leaky_relu" ) net = tf.layers.dropout( net, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_g, name="dropout", ) net = tf.layers.Flatten()(net) intermediate_layer = net net_name = "layer_3" with tf.variable_scope(net_name): net = tf.layers.dense(net, units=1, kernel_initializer=self.init_kernel, name="fc") logits = tf.squeeze(net) return logits, intermediate_layer # Regularizer discriminator for the Reconstruction Encoder def discriminator_zz(self, noise_tensor, recreated_noise, getter=None, do_spectral_norm=False): """ Discriminator architecture in tensorflow Discriminates between (z, z) and (z, rec_z) Args: noise_tensor: recreated_noise: getter: for exponential moving average during inference reuse: sharing variables or not do_spectral_norm: """ layers = sn if do_spectral_norm else tf.layers with tf.variable_scope("Discriminator_zz", reuse=tf.AUTO_REUSE, custom_getter=getter): y = tf.concat([noise_tensor, recreated_noise], axis=-1) net_name = "y_layer_1" with tf.variable_scope(net_name): y = layers.dense(y, units=64, kernel_initializer=self.init_kernel, name="fc") y = tf.nn.leaky_relu(features=y, alpha=self.config.trainer.leakyReLU_alpha) y = tf.layers.dropout( y, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_r, name="dropout", ) net_name = "y_layer_2" with tf.variable_scope(net_name): y = layers.dense(y, units=32, kernel_initializer=self.init_kernel, name="fc") y = tf.nn.leaky_relu(features=y, alpha=self.config.trainer.leakyReLU_alpha) y = tf.layers.dropout( y, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_r, name="dropout", ) intermediate_layer = y net_name = "y_layer_3" with tf.variable_scope(net_name): y = layers.dense(y, units=1, kernel_initializer=self.init_kernel, name="fc") logits = tf.squeeze(y) return logits, intermediate_layer ############################################################################################### # CUSTOM LOSSES ############################################################################################### def mse_loss(self, pred, data, mode="norm", order=2): if mode == "norm": delta = pred - data delta = tf.layers.Flatten()(delta) loss_val = tf.norm(delta, ord=order, axis=1, keepdims=False) elif mode == "mse": loss_val = tf.reduce_mean(tf.squared_difference(pred, data)) return loss_val def pullaway_loss(self, embeddings): norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True)) normalized_embeddings = embeddings / norm similarity = tf.matmul(normalized_embeddings, normalized_embeddings, transpose_b=True) batch_size = tf.cast(tf.shape(embeddings)[0], tf.float32) pt_loss = (tf.reduce_sum(similarity) - batch_size) / (batch_size * (batch_size - 1)) return pt_loss def init_saver(self): self.saver = tf.train.Saver(max_to_keep=self.config.log.max_to_keep)

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0.053552

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0.079455

0.046698

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0.661509

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null

0

1

0

null

0

2

f73897464eec5dfd237e31a4d8f787d41ed1bef8

1,619

py

Python

procgen_adventure/utils/torch_utils.py

Laurans/procgen_adventure

5f88f3f647f7854c8fb2ae516f3490d89845eefa

[ "MIT" ]

2

2020-04-02T11:51:43.000Z

2020-04-20T20:07:03.000Z

procgen_adventure/utils/torch_utils.py

Laurans/procgen_adventure

5f88f3f647f7854c8fb2ae516f3490d89845eefa

[ "MIT" ]

1

2020-04-08T10:34:29.000Z

2020-04-29T21:08:48.000Z

procgen_adventure/utils/torch_utils.py

Laurans/procgen_adventure

5f88f3f647f7854c8fb2ae516f3490d89845eefa

[ "MIT" ]

null

import numpy as np import torch import torch.distributed as dist def tensor(x, device): if isinstance(x, torch.Tensor): return x.to(device) x = np.asarray(x, dtype=np.float) x = torch.tensor(x, device=device, dtype=torch.float32) return x def input_preprocessing(x, device): x = tensor(x, device) x = x.float() x /= 255.0 return x def to_np(t): return t.cpu().detach().numpy() def random_seed(seed=None): np.random.seed(seed) torch.manual_seed(np.random.randint(int(1e6))) def restore_model(model, save_path): checkpoint = torch.load(save_path) model.network.load_state_dict(checkpoint["model_state_dict"]) model.optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) update = checkpoint["update"] return update def sync_initial_weights(model): for param in model.parameters(): dist.broadcast(param.data, src=0) def sync_gradients(model): for param in model.parameters(): dist.all_reduce(param.grad.data, op=dist.ReduceOp.SUM) def cleanup(): dist.destroy_process_group() def sync_values(tensor_sum_values, tensor_nb_values): dist.reduce(tensor_sum_values, dst=0) dist.reduce(tensor_nb_values, dst=0) return tensor_sum_values / tensor_nb_values def range_tensor(t, device): return torch.arange(t).long().to(device) def zeros(shape, dtype): """Attempt to return torch tensor of zeros, or if numpy dtype provided, return numpy array or zeros.""" try: return torch.zeros(shape, dtype=dtype) except TypeError: return np.zeros(shape, dtype=dtype)

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1,619

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null

0

null

0

1

0

1

0

2

f74317f78bef66c71ecf8b843045e4f5b2c8153b

5,580

py

Python

otri/utils/key_handler.py

OTRI-Unipd/OTRI

5d1fce470eeb31f5cc75cadfc06d9d2908736052

[ "FSFAP" ]

null

otri/utils/key_handler.py

OTRI-Unipd/OTRI

5d1fce470eeb31f5cc75cadfc06d9d2908736052

[ "FSFAP" ]

34

2020-04-18T13:57:05.000Z

2021-10-05T16:21:56.000Z

otri/utils/key_handler.py

OTRI-Unipd/OTRI

5d1fce470eeb31f5cc75cadfc06d9d2908736052

[ "FSFAP" ]

null

from typing import * import re LOWER_ERROR = "Only dictionaries and lists can be modified by this method." def apply_deep(data: Union[Mapping, List], fun: Callable) -> Union[dict, list]: ''' Applies fun to all keys in data. The method is recursive and applies as deep as possible in the dictionary nest. Parameters: data : Mapping or List Data to modify, must be either a dictionary or a list of dictionaries. fun : function | lambda Function to apply to each key, must take the key as its single parameter. Returns: A copy of the dict or list with the modified keys, with all nested dicts and list receiving the same treatment. It will return the original object (not a copy) if no operation could be applied, for example when: - data is not a list or dict - data is a list of non dict items - data is not a list that contains dicts at any nesting level ... ''' if isinstance(data, Mapping): return __apply_deep_dict(data, fun) if isinstance(data, List): return __apply_deep_list(data, fun) return data def __apply_deep_dict(data: Mapping, fun: Callable) -> dict: ''' Applies fun to all keys in a dictionary and all nested items. Parameters: data : dict Data to modify, must be a dictionary. fun : function | lambda Function to apply to each key, must take the key as its single parameter. Returns: A copy of the dict with the renamed keys, where all values have been replaced by copies of their original if apply_deep(value, fun) was appliable. ''' new_data = dict() for key, value in data.items(): new_key = fun(key) new_data[new_key] = apply_deep(value, fun) return new_data def __apply_deep_list(data: List, fun : Callable) -> list: ''' Applies fun to all keys in each item of the list, if appliable. Parameters: data : List Data to modify, should be a list, but can be a tuple. fun : function | lambda Function to apply to each key, must take the key as its single parameter. Returns: A copy of the list, where each item got its keys modified through apply_deep(item, fun) if appliable. ''' return [apply_deep(item, fun) for item in data] def lower_all_keys_deep(data : Union[Mapping, List]) -> Union[dict, list]: ''' Renames all the keys in a dict object to be lower case. The method is recursive and applies as deep as possible in the dict nest. Parameters: data : dict | list Data to modify, must be either a dictionary or a list of dictionaries. Should work with any dictionary. In any case, only string keys will be modified. Returns: A copy of the dict or list with the renamed keys, with all nested dicts and list receiving the same treatment. It will return the original object (not a copy) if no operation could be applied. See apply_deep(data, fun) for details. ... ''' return apply_deep(data, lambda s: s.lower() if isinstance(s, str) else s) def rename_deep(data : Union[Mapping, List], aliases: Mapping) -> Union[dict, list]: ''' Renames the keys in the dict object based on the aliases in dict. The method is recursive and applies as deep as possible in the dict nest. es. data = {"key" : "value"}, aliases {"key", "one"} data becomes {"one" : "value"} Parameters: data : dict | list Data to modify, must be either a dictionary or a list of dictionaries. Should work with any dictionary. aliases : dict Dictionary containing the aliases for the keys. For each item the key must be the original key and the value the new key. Keys of any type will be modified as long as they are a key in aliases. Returns: A copy of the dict or list with the renamed keys, with all nested dicts and list receiving the same treatment. It will return the original object (not a copy) if no operation could be applied. See apply_deep(data, fun) for details. ''' return apply_deep(data, lambda x: aliases[x] if x in aliases.keys() else x) def replace_deep(data : Union[Mapping, List], regexes: Mapping) -> Union[dict, list]: ''' Renames the keys in a dictionary replacing each given regex with the given alias. The method is recursive and applies as deep as possible in the dict nest. es. data = {"key_ciao" : "value"}, aliases {"ciao", "hi"} data becomes {"key_hi" : "value"} Parameters: data : dict | list Data to modify, must be either a dictionary or a list of dictionaries. Should work with any dictionary. aliases : dict Dictionary containing the aliases for the keys. For each item the key must be the regex to replace and the value what to replace it with. Only string keys are modified. Returns: A copy of the dict or list with the renamed keys, with all nested dicts and lists receiving the same treatment. It will return the original object (not a copy) if no operation could be applied. See apply_deep(data, fun) for details. ''' def replace_regex(string, regexes=regexes): for r, s in regexes.items(): string = re.sub(r, s, string) return string return apply_deep(data, lambda x: replace_regex(x) if isinstance(x, str) else x)

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5,580

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0.023249

0.591752

0.552449

0.539164

0.525325

0.505397

0

0.280287

5,580

133

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0

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null

0

null

0

1

0

1

0

2

f77262f78648f5065671fda0b803fd2061d327e1

481

py

Python

model_test.py

noatgnu/colossi

c936bc25b5990f8dfbf4db3ed11ce8a893553668

[ "MIT" ]

null

model_test.py

noatgnu/colossi

c936bc25b5990f8dfbf4db3ed11ce8a893553668

[ "MIT" ]

null

model_test.py

noatgnu/colossi

c936bc25b5990f8dfbf4db3ed11ce8a893553668

[ "MIT" ]

null

import unittest from model import prediction_with_model import pandas as pd import numpy as np class PredictionWithModel(unittest.TestCase): def test_prediction(self): d = pd.read_csv(r"C:\Users\Toan\Documents\GitHub\colossi\static\temp\cc7deed8140745d89f2f42f716f6fd1b\out_imac_atlas_expression_v7.1.tsv", " ") result = np.array([d['Freq'].to_list() + [0, 1800]]) print(prediction_with_model(result)) if __name__ == '__main__': unittest.main()

30.0625

151

0.733888

64

481

5.21875

0.75

0.065868

0.113772

0

0.060976

0.147609

481

15

152

32.066667

0.753659

0

0.090909

0.272349

0.245322

0

1

0.090909

false

0

0.363636

0

0.545455

0.090909

0

null

0

null

0

1

0

1

0

2

f77dd5fcff559a23973eb58cb349868330dee91f

2,159

py

Python

crawlerfeeder/sources.py

ddiazpinto/python-crawlerfeeder

ec498a11b3de2f9d68ab9245406b258b07930724

[ "MIT" ]

null

crawlerfeeder/sources.py

ddiazpinto/python-crawlerfeeder

ec498a11b3de2f9d68ab9245406b258b07930724

[ "MIT" ]

null

crawlerfeeder/sources.py

ddiazpinto/python-crawlerfeeder

ec498a11b3de2f9d68ab9245406b258b07930724

[ "MIT" ]

null

""" Data sources All the data sources must extend DataSource abstract class and define `crawl` and `feed` methods. This methods are automatically called during the crawl and feed processes. """ import httplib2 from abc import ABCMeta, abstractmethod from apiclient.discovery import build from oauth2client.service_account import ServiceAccountCredentials import pymysql.cursors from crawlerfeeder import logging class DataSource(object): __metaclass__ = ABCMeta _service = None _data = {} @abstractmethod def crawl(self, **kwargs): pass @abstractmethod def feed(self, **kwargs): pass class GoogleAnalyticsDataSource(DataSource): """ Google Analytics V4 data source """ _view_id = None def __init__(self, service_account_email, key_file_location, scopes, discovery_uri, view_id, **kwargs): credentials = ServiceAccountCredentials.from_p12_keyfile( service_account_email, key_file_location, scopes=scopes) http = credentials.authorize(httplib2.Http()) self._service = build('analytics', 'v4', http=http, discoveryServiceUrl=discovery_uri) self._view_id = view_id def crawl(self, **kwargs): return self._service.reports().batchGet(body=kwargs['request']).execute() def feed(self, **kwargs): raise NotImplementedError("This method is not implemented yet.") class MysqlDataSource(DataSource): """ MySQL data source """ def __init__(self, host, user, password, db, **kwargs): self._service = pymysql.connect(host, user, password, db, cursorclass=pymysql.cursors.DictCursor) def crawl(self, **kwargs): with self._service.cursor() as cursor: cursor.execute(**kwargs) logging.info("Affected rows: %s" % cursor.rowcount) return cursor.fetchall() def feed(self, **kwargs): with self._service.cursor() as cursor: if 'args' in kwargs: cursor.executemany(**kwargs) else: cursor.execute(**kwargs) logging.info("Affected rows: %s" % cursor.rowcount) return cursor

30.408451

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236

2,159

5.995763

0.423729

0.042403

0.025442

0.038163

0.209187

0.15265

0.097527

0

0.004202

0.228346

2,159

70

108

30.842857

0.845138

0.108847

0

0.363636

0

0.04825

0

1

0.181818

false

0.090909

0.136364

0.022727

0.545455

0

null

0

null

0

1

0

1

0

2

f77dff8f9c8ab83db0eb1e03ee889a6d8ab05f1a

364

py

Python

tests/test_doi.py

garethcmurphy/brightness

e18cc42636439d521cc904371f7c7643ea907a57

[ "BSD-2-Clause" ]

null

tests/test_doi.py

garethcmurphy/brightness

e18cc42636439d521cc904371f7c7643ea907a57

[ "BSD-2-Clause" ]

null

tests/test_doi.py

garethcmurphy/brightness

e18cc42636439d521cc904371f7c7643ea907a57

[ "BSD-2-Clause" ]

null

from ..bright import doimaker __author__ = "Gareth Murphy" __credits__ = ["Gareth Murphy"] __license__ = "GPL" __version__ = "1.0.1" __maintainer__ = "Gareth Murphy" __email__ = "garethcmurphy@gmail.com" __status__ = "Development" def test_doi(): bright = doimaker.DOIMaker() assert isinstance(bright.passw, str) assert isinstance(bright.user, str)

22.75

40

0.730769

41

364

5.780488

0.682927

0.151899

0.185654

0

0.009677

0.148352

364

15

41

24.266667

0.754839

0

0.222527

0.063187

0

0.166667

1

0.083333

false

0.083333

0

0.166667

0

null

0

1

0

null

0

1

0

2

e38af176cfef83d4e22cf82786aa09cbbdfcb4c0

1,291

py

Python

python/fizzbuzz/fizzbuzz.py

frostickflakes/isat252s20_03

03d7ee9e0ccd164be43715af57211ceff7625e6b

[ "MIT" ]

null

python/fizzbuzz/fizzbuzz.py

frostickflakes/isat252s20_03

03d7ee9e0ccd164be43715af57211ceff7625e6b

[ "MIT" ]

1

2021-05-11T04:59:35.000Z

python/fizzbuzz/fizzbuzz.py

frostickflakes/isat252s20_03

03d7ee9e0ccd164be43715af57211ceff7625e6b

[ "MIT" ]

2

2020-03-02T14:50:02.000Z

2020-03-06T18:44:14.000Z

"""A FizzBuzz program""" # import necessary supporting libraries or packages from numbers import Number def fizz(x): """ Takes an input `x` and checks to see if x is a number, and if so, also a multiple of 3. If it is both, return 'Fizz'. Otherwise, return the input. """ return 'Fizz' if isinstance(x, Number) and x % 3 == 0 else x def buzz(x): """ Takes an input `x` and checks to see if x is a number, and if so, also a multiple of 5. If it is both, return 'Buzz'. Otherwise, return the input. """ return 'Buzz' if isinstance(x, Number) and x % 5 == 0 else x def fibu(x): """ Takes an input `x` and checks to see if x is a number, and if so, also a multiple of 15. If it is both, return 'FizzBuzz'. Otherwise, return the input. """ return 'FizzBuzz' if isinstance(x, Number) and x % 15 == 0 else x def play(start, end): """ Given a start number and an end number, produce all of the output expected for a game of FizzBuzz as an array. """ # initialize an empty list (array) to hold our output output = [] # loop from the start number to the end number for x in range(start, end + 1): # append the tranformed input to the output array output.append(buzz(fizz(fibu(x)))) return output

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null

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null

0

1

0

1

0

2

e391f8b8a0bc01973d751df5fae9b530804460c4

705

py

Python

pedlarweb_old/forms.py

ThomasWongMingHei/pedlar

ba79f9e4e13ae8009a1a7a9d1d04a7fcd2535fe7

[ "Apache-2.0" ]

61

2018-09-26T06:11:53.000Z

2022-02-15T18:30:10.000Z

pedlarweb_old/forms.py

ThomasWongMingHei/pedlar

ba79f9e4e13ae8009a1a7a9d1d04a7fcd2535fe7

[ "Apache-2.0" ]

6

2019-01-26T22:48:46.000Z

2019-12-24T00:08:15.000Z

pedlarweb_old/forms.py

ThomasWongMingHei/pedlar

ba79f9e4e13ae8009a1a7a9d1d04a7fcd2535fe7

[ "Apache-2.0" ]

36

2018-10-06T09:17:57.000Z

2022-02-21T22:17:53.000Z

"""Web forms for pedlarweb.""" from flask_wtf import FlaskForm from wtforms import StringField, PasswordField from wtforms.validators import DataRequired, Regexp, Length class UserPasswordForm(FlaskForm): """Username password form used for login.""" # \w is [0-9a-zA-Z_] username = StringField('User or Team name', validators=[DataRequired(), Regexp(r"^\w(\w| )*\w$", message="At least 2 alphanumeric characters with only spaces in between.") ] ) password = PasswordField('Password', validators=[DataRequired(), Length(min=4)])

41.470588

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0.564539

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705

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0.691176

0.055556

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0.331915

705

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false

0.272727

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0.545455

0

null

0

null

0

1

0

1

0

2

e39af21b3a6e28f491c0b53a0727560abc0f9f34

1,049

py

Python

skeleton/models.py

therden/skeletal_flask

ad8af37376af888c999ab5de5b69ba0039b557c0

[ "Unlicense" ]

3

2020-01-02T07:58:52.000Z

2020-11-25T20:31:37.000Z

skeleton/models.py

therden/skeletal_flask

ad8af37376af888c999ab5de5b69ba0039b557c0

[ "Unlicense" ]

null

skeleton/models.py

therden/skeletal_flask

ad8af37376af888c999ab5de5b69ba0039b557c0

[ "Unlicense" ]

1

2020-06-19T03:00:28.000Z

from datetime import datetime from skeleton.config_db import db class User(db.Model): id = db.Column(db.Integer, primary_key=True) username = db.Column(db.String(80), unique=True, nullable=False) email = db.Column(db.String(120), unique=True, nullable=False) def __repr__(self): return '<User %r>' % self.username class Post(db.Model): id = db.Column(db.Integer, primary_key=True) title = db.Column(db.String(80), nullable=False) body = db.Column(db.Text, nullable=False) pub_date = db.Column(db.DateTime, nullable=False, default=datetime.utcnow) category_id = db.Column(db.Integer, db.ForeignKey('category.id'), nullable=False) category = db.relationship('Category', backref=db.backref('posts', lazy=True)) def __repr__(self): return '<Post %r>' % self.title class Category(db.Model): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(50), nullable=False) def __repr__(self): return '<Category %r>' % self.name

28.351351

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0.667302

146

1,049

4.671233

0.30137

0.117302

0.146628

0.070381

0.344575

0.26393

0.175953

0

0.010588

0.189704

1,049

36

70

29.138889

0.791765

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0.24

0

0.052431

0

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false

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0.08

0.12

0.88

0

null

0

null

0

1

0

2

e3a2b8f71d6de1df7388cd09949ecbfb79724959

103

py

Python

output/models/saxon_data/complex/complex012_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

1

2021-08-14T17:59:21.000Z

output/models/saxon_data/complex/complex012_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

4

2020-02-12T21:30:44.000Z

2020-04-15T20:06:46.000Z

output/models/saxon_data/complex/complex012_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

null

from output.models.saxon_data.complex.complex012_xsd.complex012 import Root __all__ = [ "Root", ]

17.166667

75

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13

103

5.538462

0.846154

0

0.067416

0.135922

103

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0

0.038835

0

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false

0

0.25

0

0.25

0

1

0

null

0

1

0

null

0

2

e3aa7a6b7ca6686219f3d46850a8ea55f4a8bb3d

4,667

py

Python

Lib/site-packages/unidecode/x07b.py

hirorin-demon/hirorin-streamlit

03fbb6f03ec94f909d451e708a3b30b177607695

[ "0BSD" ]

82

2020-03-28T02:24:38.000Z

2022-03-30T04:18:42.000Z

Lib/site-packages/unidecode/x07b.py

hirorin-demon/hirorin-streamlit

03fbb6f03ec94f909d451e708a3b30b177607695

[ "0BSD" ]

118

2020-03-14T17:34:11.000Z

2022-03-30T07:07:45.000Z

Lib/site-packages/unidecode/x07b.py

hirorin-demon/hirorin-streamlit

03fbb6f03ec94f909d451e708a3b30b177607695

[ "0BSD" ]

30

2020-06-20T15:31:53.000Z

2022-03-06T06:23:55.000Z

data = ( 'Mang ', # 0x00 'Zhu ', # 0x01 'Utsubo ', # 0x02 'Du ', # 0x03 'Ji ', # 0x04 'Xiao ', # 0x05 'Ba ', # 0x06 'Suan ', # 0x07 'Ji ', # 0x08 'Zhen ', # 0x09 'Zhao ', # 0x0a 'Sun ', # 0x0b 'Ya ', # 0x0c 'Zhui ', # 0x0d 'Yuan ', # 0x0e 'Hu ', # 0x0f 'Gang ', # 0x10 'Xiao ', # 0x11 'Cen ', # 0x12 'Pi ', # 0x13 'Bi ', # 0x14 'Jian ', # 0x15 'Yi ', # 0x16 'Dong ', # 0x17 'Shan ', # 0x18 'Sheng ', # 0x19 'Xia ', # 0x1a 'Di ', # 0x1b 'Zhu ', # 0x1c 'Na ', # 0x1d 'Chi ', # 0x1e 'Gu ', # 0x1f 'Li ', # 0x20 'Qie ', # 0x21 'Min ', # 0x22 'Bao ', # 0x23 'Tiao ', # 0x24 'Si ', # 0x25 'Fu ', # 0x26 'Ce ', # 0x27 'Ben ', # 0x28 'Pei ', # 0x29 'Da ', # 0x2a 'Zi ', # 0x2b 'Di ', # 0x2c 'Ling ', # 0x2d 'Ze ', # 0x2e 'Nu ', # 0x2f 'Fu ', # 0x30 'Gou ', # 0x31 'Fan ', # 0x32 'Jia ', # 0x33 'Ge ', # 0x34 'Fan ', # 0x35 'Shi ', # 0x36 'Mao ', # 0x37 'Po ', # 0x38 'Sey ', # 0x39 'Jian ', # 0x3a 'Qiong ', # 0x3b 'Long ', # 0x3c 'Souke ', # 0x3d 'Bian ', # 0x3e 'Luo ', # 0x3f 'Gui ', # 0x40 'Qu ', # 0x41 'Chi ', # 0x42 'Yin ', # 0x43 'Yao ', # 0x44 'Xian ', # 0x45 'Bi ', # 0x46 'Qiong ', # 0x47 'Gua ', # 0x48 'Deng ', # 0x49 'Jiao ', # 0x4a 'Jin ', # 0x4b 'Quan ', # 0x4c 'Sun ', # 0x4d 'Ru ', # 0x4e 'Fa ', # 0x4f 'Kuang ', # 0x50 'Zhu ', # 0x51 'Tong ', # 0x52 'Ji ', # 0x53 'Da ', # 0x54 'Xing ', # 0x55 'Ce ', # 0x56 'Zhong ', # 0x57 'Kou ', # 0x58 'Lai ', # 0x59 'Bi ', # 0x5a 'Shai ', # 0x5b 'Dang ', # 0x5c 'Zheng ', # 0x5d 'Ce ', # 0x5e 'Fu ', # 0x5f 'Yun ', # 0x60 'Tu ', # 0x61 'Pa ', # 0x62 'Li ', # 0x63 'Lang ', # 0x64 'Ju ', # 0x65 'Guan ', # 0x66 'Jian ', # 0x67 'Han ', # 0x68 'Tong ', # 0x69 'Xia ', # 0x6a 'Zhi ', # 0x6b 'Cheng ', # 0x6c 'Suan ', # 0x6d 'Shi ', # 0x6e 'Zhu ', # 0x6f 'Zuo ', # 0x70 'Xiao ', # 0x71 'Shao ', # 0x72 'Ting ', # 0x73 'Ce ', # 0x74 'Yan ', # 0x75 'Gao ', # 0x76 'Kuai ', # 0x77 'Gan ', # 0x78 'Chou ', # 0x79 'Kago ', # 0x7a 'Gang ', # 0x7b 'Yun ', # 0x7c 'O ', # 0x7d 'Qian ', # 0x7e 'Xiao ', # 0x7f 'Jian ', # 0x80 'Pu ', # 0x81 'Lai ', # 0x82 'Zou ', # 0x83 'Bi ', # 0x84 'Bi ', # 0x85 'Bi ', # 0x86 'Ge ', # 0x87 'Chi ', # 0x88 'Guai ', # 0x89 'Yu ', # 0x8a 'Jian ', # 0x8b 'Zhao ', # 0x8c 'Gu ', # 0x8d 'Chi ', # 0x8e 'Zheng ', # 0x8f 'Jing ', # 0x90 'Sha ', # 0x91 'Zhou ', # 0x92 'Lu ', # 0x93 'Bo ', # 0x94 'Ji ', # 0x95 'Lin ', # 0x96 'Suan ', # 0x97 'Jun ', # 0x98 'Fu ', # 0x99 'Zha ', # 0x9a 'Gu ', # 0x9b 'Kong ', # 0x9c 'Qian ', # 0x9d 'Quan ', # 0x9e 'Jun ', # 0x9f 'Chui ', # 0xa0 'Guan ', # 0xa1 'Yuan ', # 0xa2 'Ce ', # 0xa3 'Ju ', # 0xa4 'Bo ', # 0xa5 'Ze ', # 0xa6 'Qie ', # 0xa7 'Tuo ', # 0xa8 'Luo ', # 0xa9 'Dan ', # 0xaa 'Xiao ', # 0xab 'Ruo ', # 0xac 'Jian ', # 0xad 'Xuan ', # 0xae 'Bian ', # 0xaf 'Sun ', # 0xb0 'Xiang ', # 0xb1 'Xian ', # 0xb2 'Ping ', # 0xb3 'Zhen ', # 0xb4 'Sheng ', # 0xb5 'Hu ', # 0xb6 'Shi ', # 0xb7 'Zhu ', # 0xb8 'Yue ', # 0xb9 'Chun ', # 0xba 'Lu ', # 0xbb 'Wu ', # 0xbc 'Dong ', # 0xbd 'Xiao ', # 0xbe 'Ji ', # 0xbf 'Jie ', # 0xc0 'Huang ', # 0xc1 'Xing ', # 0xc2 'Mei ', # 0xc3 'Fan ', # 0xc4 'Chui ', # 0xc5 'Zhuan ', # 0xc6 'Pian ', # 0xc7 'Feng ', # 0xc8 'Zhu ', # 0xc9 'Hong ', # 0xca 'Qie ', # 0xcb 'Hou ', # 0xcc 'Qiu ', # 0xcd 'Miao ', # 0xce 'Qian ', # 0xcf None, # 0xd0 'Kui ', # 0xd1 'Sik ', # 0xd2 'Lou ', # 0xd3 'Yun ', # 0xd4 'He ', # 0xd5 'Tang ', # 0xd6 'Yue ', # 0xd7 'Chou ', # 0xd8 'Gao ', # 0xd9 'Fei ', # 0xda 'Ruo ', # 0xdb 'Zheng ', # 0xdc 'Gou ', # 0xdd 'Nie ', # 0xde 'Qian ', # 0xdf 'Xiao ', # 0xe0 'Cuan ', # 0xe1 'Gong ', # 0xe2 'Pang ', # 0xe3 'Du ', # 0xe4 'Li ', # 0xe5 'Bi ', # 0xe6 'Zhuo ', # 0xe7 'Chu ', # 0xe8 'Shai ', # 0xe9 'Chi ', # 0xea 'Zhu ', # 0xeb 'Qiang ', # 0xec 'Long ', # 0xed 'Lan ', # 0xee 'Jian ', # 0xef 'Bu ', # 0xf0 'Li ', # 0xf1 'Hui ', # 0xf2 'Bi ', # 0xf3 'Di ', # 0xf4 'Cong ', # 0xf5 'Yan ', # 0xf6 'Peng ', # 0xf7 'Sen ', # 0xf8 'Zhuan ', # 0xf9 'Pai ', # 0xfa 'Piao ', # 0xfb 'Dou ', # 0xfc 'Yu ', # 0xfd 'Mie ', # 0xfe 'Zhuan ', # 0xff )

18.019305

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0.395757

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4,667

3.60039

0.803119

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0.200557

0.384615

4,667

258

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0.442549

0.274052

0

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0.341744

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false

0

null

0

1

0

1

0

null

0

2

e3ae477cbacf87c30e4f9e467679b841119fc2ec

356

py

Python

pyleecan/Methods/Machine/LamSquirrelCage/comp_length_ring.py

harshasunder-1/pyleecan

32ae60f98b314848eb9b385e3652d7fc50a77420

[ "Apache-2.0" ]

2

2019-06-08T15:04:39.000Z

2020-09-07T13:32:22.000Z

pyleecan/Methods/Machine/LamSquirrelCage/comp_length_ring.py

harshasunder-1/pyleecan

32ae60f98b314848eb9b385e3652d7fc50a77420

[ "Apache-2.0" ]

null

pyleecan/Methods/Machine/LamSquirrelCage/comp_length_ring.py

harshasunder-1/pyleecan

32ae60f98b314848eb9b385e3652d7fc50a77420

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from numpy import pi def comp_length_ring(self): """Computation of the ring length Parameters ---------- self : LamSquirrelCage A LamSquirrelCage object Returns ------- Lring: float Length of the ring [m] """ Rmw = self.slot.comp_radius_mid_wind() return 2 * pi * Rmw

14.833333

42

0.570225

42

356

4.714286

0.714286

0.050505

0.090909

0

0.008032

0.300562

356

23

43

15.478261

0.787149

0.519663

0

1

0.25

false

0

0.25

0

0.75

0

null

0

null

0

1

0

1

0

2

e3b7e2ccc401a24ce7cdda41ee6b67326027248b

1,257

py

Python

module_info.py

Ikaguia/LWBR-WarForge

0099fe20188b2dbfff237e8690ae54c33671656f

[ "Unlicense" ]

null

module_info.py

Ikaguia/LWBR-WarForge

0099fe20188b2dbfff237e8690ae54c33671656f

[ "Unlicense" ]

null

module_info.py

Ikaguia/LWBR-WarForge

0099fe20188b2dbfff237e8690ae54c33671656f

[ "Unlicense" ]

null

# Point export_dir to the folder you will be keeping your module # Make sure you use forward slashes (/) and NOT backward slashes (\) # Several possible variants for export_dir variable: # Warband being installed to C:/Games export_dir = "mod/" ################################### # W.R.E.C.K. Compiler Options # ################################### # Change this line to select where compiler will generate ID_* files. Use None instead of the string to completely suppress generation of ID_* files. # ONLY DO THIS WHEN YOU HAVE COMPLETELY REMOVED ID_* FILE DEPENDENCIES IN MODULE SYSTEM! # Default value: "ID_%s.py" #write_id_files = "ID_%s.py" # default vanilla-compatible option #write_id_files = "ID/ID_%s.py" # will put ID_* files in ID/ subfolder of module system's folder write_id_files = None # will suppress generation of ID_*.py files # Set to True to display compiler performance information at the end of compilation. Set to False to suppress. # Default value: False show_performance_data = False ########################## # W.R.E.C.K. Plugins # ########################## import plugin_ms_extension import plugin_multiplayer_troops import plugin_make_presentations import plugin_lwbr_main import plugin_end

29.232558

149

0.682578

178

1,257

4.662921

0.516854

0.050602

0.018072

0.009639

0.012048

0

0.161496

1,257

43

150

29.232558

0.787476

0.68576

0

0.015873

0

1

0

false

0

0.625

0

0.625

0

null

0

null

0

1

0

1

0

2

e3c248c64912ecddfb2709461a774a633f90e748

1,045

py

Python

myuw/views/api/current_schedule.py

uw-it-aca/myuw

3fa1fabeb3c09d81a049f7c1a8c94092d612438a

[ "Apache-2.0" ]

18

2015-02-04T01:09:11.000Z

2021-11-25T03:10:39.000Z

myuw/views/api/current_schedule.py

uw-it-aca/myuw

3fa1fabeb3c09d81a049f7c1a8c94092d612438a

[ "Apache-2.0" ]

2,323

2015-01-15T19:45:10.000Z

2022-03-21T19:57:06.000Z

myuw/views/api/current_schedule.py

uw-it-aca/myuw

3fa1fabeb3c09d81a049f7c1a8c94092d612438a

[ "Apache-2.0" ]

9

2015-01-15T19:29:26.000Z

2022-02-11T04:51:23.000Z

# Copyright 2021 UW-IT, University of Washington # SPDX-License-Identifier: Apache-2.0 import logging import traceback from myuw.dao.term import get_current_quarter from myuw.logger.timer import Timer from myuw.views.error import handle_exception from myuw.views.api.base_schedule import StudClasSche logger = logging.getLogger(__name__) class StudClasScheCurQuar(StudClasSche): """ Performs actions on resource at /api/v1/schedule/current/. """ def get(self, request, *args, **kwargs): """ GET returns 200 with the current quarter course section schedule @return class schedule data in json format status 404: no schedule found (not registered) status 543: data error """ timer = Timer() try: return self.make_http_resp(timer, get_current_quarter(request), request) except Exception: return handle_exception(logger, timer, traceback)

32.65625

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0.641148

118

1,045

5.567797

0.601695

0.048706

0.05175

0

0.021534

0.288995

1,045

31

73

33.709677

0.862719

0.321531

0

1

0.0625

false

0

0.375

0

0.625

0

null

0

null

0

1

0

1

0

2

e3c4a0f4c819f8cd9ffe203ccc389bb5ab23bc36

280

py

Python

unit1/spiders/spider_2_quotes.py

nulearn3296/scrapy-training

8981dbc33b68bd7246839eee34ca8266d5a0066f

[ "BSD-3-Clause" ]

182

2017-04-05T23:39:22.000Z

2022-02-22T19:49:52.000Z

unit1/spiders/spider_2_quotes.py

nulearn3296/scrapy-training

8981dbc33b68bd7246839eee34ca8266d5a0066f

[ "BSD-3-Clause" ]

3

2017-04-18T07:16:39.000Z

2019-05-04T22:54:53.000Z

unit1/spiders/spider_2_quotes.py

nulearn3296/scrapy-training

8981dbc33b68bd7246839eee34ca8266d5a0066f

[ "BSD-3-Clause" ]

53

2017-04-07T03:25:54.000Z

2022-02-21T21:51:01.000Z

import scrapy class QuotesSpider(scrapy.Spider): name = "quotes2" start_urls = [ 'http://quotes.toscrape.com/page/1/', 'http://quotes.toscrape.com/page/2/', ] def parse(self, response): self.log('I just visited {}'.format(response.url))

21.538462

58

0.607143

34

280

4.970588

0.764706

0.118343

0.213018

0.248521

0.295858

0

0.013825

0.225

280

12

59

23.333333

0.764977

0

0.328571

0

1

0.111111

false

0

0.111111

0

0.555556

0

null

0

1

0

null

0

2

e3cf9ceee92cef8bf273106333c95cd00354d2fd

376

py

Python

2017/01/p1.py

foxscotch/advent-of-code

20688fad4eef09ef5670ad91f8051c044dfc7baf

[ "MIT" ]

null

2017/01/p1.py

foxscotch/advent-of-code

20688fad4eef09ef5670ad91f8051c044dfc7baf

[ "MIT" ]

null

2017/01/p1.py

foxscotch/advent-of-code

20688fad4eef09ef5670ad91f8051c044dfc7baf

[ "MIT" ]

null

# Python 3.6.1 with open("input.txt", "r") as f: puzzle_input = [int(i) for i in f.read()[0:-1]] total = 0 for cur_index in range(len(puzzle_input)): next_index = cur_index + 1 if not cur_index == len(puzzle_input) - 1 else 0 puz_cur = puzzle_input[cur_index] pnext = puzzle_input[next_index] if puz_cur == pnext: total += puz_cur print(total)

23.5

79

0.648936

67

376

3.432836

0.447761

0.23913

0.121739

0.173913

0

0.030612

0.218085

376

15

80

25.066667

0.751701

0.031915

0

0.027624

0

1

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false

0

0.1

0

null

1

0

1

0

null

0

2

e3dbcbdb3ec7d20bf8430c926dca62a8ba70bef4

4,710

py

Python

tests/all_tests.py

My-Novel-Management/storybuilderunite

c003d3451e237f574c54a87ea7d4fd8da8e833be

[ "MIT" ]

1

2020-06-18T01:38:55.000Z

tests/all_tests.py

My-Novel-Management/storybuilder

1f36e56a74dbb55a25d60fce3ce81f3c650f521a

[ "MIT" ]

143

2019-11-13T00:21:11.000Z

2020-08-15T05:47:41.000Z

tests/all_tests.py

My-Novel-Management/storybuilderunite

c003d3451e237f574c54a87ea7d4fd8da8e833be

[ "MIT" ]

null

# -*- coding: utf-8 -*- ''' The test suite for all test cases ================================= ''' import unittest from tests import test_world from tests.commands import test_command from tests.commands import test_optioncmd from tests.commands import test_scode from tests.commands import test_storycmd from tests.commands import test_tagcmd from tests.containers import test_container from tests.core import test_compiler from tests.core import test_filter from tests.core import test_formatter from tests.core import test_headerupdater from tests.core import test_outputter from tests.core import test_reducer from tests.core import test_runner from tests.core import test_serializer from tests.core import test_tagreplacer from tests.core import test_validater from tests.datatypes import test_codelist from tests.datatypes import test_compilemode from tests.datatypes import test_database from tests.datatypes import test_formatmode from tests.datatypes import test_formattag from tests.datatypes import test_headerinfo from tests.datatypes import test_outputmode from tests.datatypes import test_rawdata from tests.datatypes import test_resultdata from tests.datatypes import test_storyconfig from tests.objects import test_day from tests.objects import test_item from tests.objects import test_person from tests.objects import test_rubi from tests.objects import test_sobject from tests.objects import test_stage from tests.objects import test_time from tests.objects import test_word from tests.objects import test_writer from tests.tools import test_checker from tests.tools import test_converter from tests.tools import test_counter from tests.utils import test_assertion from tests.utils import test_dict from tests.utils import test_list from tests.utils import test_logger from tests.utils import test_math from tests.utils import test_name from tests.utils import test_str def suite() -> unittest.TestSuite: ''' Packing all tests. ''' suite = unittest.TestSuite() suite.addTests(( # commands unittest.makeSuite(test_command.SCmdEnumTest), unittest.makeSuite(test_optioncmd.OptionParserTest), unittest.makeSuite(test_scode.SCodeTest), unittest.makeSuite(test_storycmd.StoryCmdTest), unittest.makeSuite(test_tagcmd.TagCmdTest), # containers unittest.makeSuite(test_container.ContainerTest), # datatypes unittest.makeSuite(test_codelist.CodeListTest), unittest.makeSuite(test_compilemode.CompileModeTest), unittest.makeSuite(test_database.DatabaseTest), unittest.makeSuite(test_formatmode.FormatModeTest), unittest.makeSuite(test_formattag.FormatTagTest), unittest.makeSuite(test_headerinfo.HeaderInfoTest), unittest.makeSuite(test_outputmode.OutputModeTest), unittest.makeSuite(test_rawdata.RawDataTest), unittest.makeSuite(test_resultdata.ResultDataTest), unittest.makeSuite(test_storyconfig.StoryConfigTest), # objects unittest.makeSuite(test_day.DayTest), unittest.makeSuite(test_item.ItemTest), unittest.makeSuite(test_person.PersonTest), unittest.makeSuite(test_rubi.RubiTest), unittest.makeSuite(test_sobject.SObjectTest), unittest.makeSuite(test_stage.StageTest), unittest.makeSuite(test_time.TimeTest), unittest.makeSuite(test_word.WordTest), unittest.makeSuite(test_writer.WriterTest), # tools unittest.makeSuite(test_checker.CheckerTest), unittest.makeSuite(test_converter.ConverterTest), unittest.makeSuite(test_counter.CounterTest), # utility unittest.makeSuite(test_assertion.MethodsTest), unittest.makeSuite(test_dict.MethodsTest), unittest.makeSuite(test_list.MethodsTest), unittest.makeSuite(test_logger.MyLoggerTest), unittest.makeSuite(test_math.MethodsTest), unittest.makeSuite(test_name.MethodsTest), unittest.makeSuite(test_str.MethodsTest), # core unittest.makeSuite(test_compiler.CompilerTest), unittest.makeSuite(test_filter.FilterTest), unittest.makeSuite(test_formatter.FormatterTest), unittest.makeSuite(test_headerupdater.HeaderUpdaterTest), unittest.makeSuite(test_outputter.OutputterTest), unittest.makeSuite(test_reducer.ReducerTest), unittest.makeSuite(test_runner.RunnerTest), unittest.makeSuite(test_serializer.SerializerTest), unittest.makeSuite(test_tagreplacer.TagReplacerTest), unittest.makeSuite(test_validater.ValidaterTest), # main unittest.makeSuite(test_world.WorldTest), )) return suite

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e3e28b729577829ef3b226cb7c4e4eb15e894a82

2,137

py

Python

pkgs/clean-pkg/src/genie/libs/clean/stages/iosxe/cat9k/tests/test_tftp_boot.py

patrickboertje/genielibs

61c37aacf3dd0f499944555e4ff940f92f53dacb

[ "Apache-2.0" ]

1

2022-01-16T10:00:24.000Z

pkgs/clean-pkg/src/genie/libs/clean/stages/iosxe/cat9k/tests/test_tftp_boot.py

patrickboertje/genielibs

61c37aacf3dd0f499944555e4ff940f92f53dacb

[ "Apache-2.0" ]

null

pkgs/clean-pkg/src/genie/libs/clean/stages/iosxe/cat9k/tests/test_tftp_boot.py

patrickboertje/genielibs

61c37aacf3dd0f499944555e4ff940f92f53dacb

[ "Apache-2.0" ]

null

import logging import unittest from unittest.mock import Mock, MagicMock from genie.libs.clean.stages.iosxe.cat9k.stages import TftpBoot from genie.libs.clean.stages.tests.utils import CommonStageTests, create_test_device from pyats.aetest.steps import Steps from pyats.results import Passed, Failed from pyats.aetest.signals import TerminateStepSignal from unicon.eal.dialogs import Statement, Dialog # Disable logging. It may be useful to comment this out when developing tests. logging.disable(logging.CRITICAL) class Tftpboot(unittest.TestCase): def setUp(self): # Instantiate class object self.cls = TftpBoot() # Instantiate device object. This also sets up commonly needed # attributes and Mock objects associated with the device. self.device = create_test_device('PE1', os='iosxe', platform='cat9k') def test_pass(self): # Make sure we have a unique Steps() object for result verification steps = Steps() # And we want the execute_no_boot_variable api to be mocked. # This simulates the pass case. self.device.api.execute_no_boot_variable = Mock() # Call the method to be tested (clean step inside class) self.cls.delete_boot_variables( steps=steps, device=self.device, timeout=0 ) # Check that the result is expected self.assertEqual(Passed, steps.details[0].result) def test_fail_tftp_boot(self): # Make sure we have a unique Steps() object for result verification steps = Steps() # And we want the execute_no_boot_variable api to be mocked to raise an # exception when called. This simulates the fail case. self.device.api.execute_no_boot_variable = Mock(side_effect=Exception) # We expect this step to fail so make sure it raises the signal with self.assertRaises(TerminateStepSignal): self.cls.delete_boot_variables( steps=steps, device=self.device, timeout=0 ) # Check the overall result is as expected self.assertEqual(Failed, steps.details[0].result)

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null

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e3e9ddb3f6a154281b95ad1d2aa584f2ce6e1a6c

21,345

py

Python

examples/axro/AXROalignment.py

bddonovan/PyXFocus

2d6722f0db28c045df35075487f9d4fdfed8b284

[ "MIT" ]

1

2018-04-20T15:32:24.000Z

examples/axro/AXROalignment.py

bddonovan/PyXFocus

2d6722f0db28c045df35075487f9d4fdfed8b284

[ "MIT" ]

6

2017-11-03T16:13:46.000Z

2019-04-26T11:13:03.000Z

examples/axro/AXROalignment.py

bddonovan/PyXFocus

2d6722f0db28c045df35075487f9d4fdfed8b284

[ "MIT" ]

4

2017-04-13T17:24:54.000Z

2019-08-08T15:27:29.000Z

from numpy import * from matplotlib.pyplot import * import traces.conicsolve as conicsolve import traces.PyTrace as PT import pdb from mpl_toolkits.mplot3d import Axes3D import time import scipy.optimize #Load in flat mirror deformations foldfig = genfromtxt("/home/rallured/Dropbox/AXRO/Alignment/Simulation/" "NIST/141202FoldFigCoeffs.txt") foldsag = genfromtxt("/home/rallured/Dropbox/AXRO/Alignment/Simulation/" "141202FoldSagCoeffs.txt")*1000 foldcoeffs = foldfig + foldsag retrofig = genfromtxt("/home/rallured/Dropbox/AXRO/Alignment/Simulation/" "NIST/141202RetroFigCoeffs.txt") retrosag = genfromtxt("/home/rallured/Dropbox/AXRO/Alignment/Simulation/" "141202RetroSagCoeffs.txt")*1000 retrocoeffs = retrosag + retrosag #Load in primary deformations pcoeff,pax,paz = genfromtxt('/home/rallured/Dropbox/AXRO/' 'Alignment/CoarseAlignment/150615_OP1S09Coeffs.txt') pcoeff = pcoeff/1000. #Set up Hartmann mask holewidth = arcsin(.3/220) holetheta = linspace(-arcsin(45./220),arcsin(45./220),15) numholes = size(holetheta) #Set up diverging beam with angular offset #Give it divergence angle, pitch, and roll def CDAbeam(num,div,pitch,roll,cda): ## PT.transform(*cda) PT.pointsource(div,num) PT.transform(0,0,0,pitch,0,0) PT.transform(0,0,0,0,0,roll) PT.itransform(*cda) return #Trace from primary focus to fold to primary def primMaskTrace(fold,primary,woltVignette=True,foldrot=0.): #Get Wolter parameters alpha,p,d,e = conicsolve.woltparam(220.,8400.) primfoc = conicsolve.primfocus(220.,8400.) #Trace to fold mirror #translate to center of fold mirror PT.transform(0.,85.12,primfoc-651.57+85.12,0,0,0) #rotate so surface normal points in correct direction PT.transform(0,0,0,-3*pi/4,0,0) PT.transform(0,0,0,0,0,pi) #trace to fold flat PT.flat() #Introduce fold misalignment PT.transform(*fold) PT.zernsurfrot(foldsag,foldfig,406./2,-174.659*pi/180+foldrot) PT.itransform(*fold) PT.reflect() PT.transform(0,0,0,0,0,-pi) PT.transform(0,0,0,pi/4,0,0) #Translate to optical axis mid-plane, then down to image of #primary focus, place primary mirror and trace PT.transform(0,85.12,651.57-85.12,0,0,0) PT.flat() ## pdb.set_trace() rt = conicsolve.primrad(8475.,220.,8400.) PT.transform(0,-rt,75.,0,0,0) PT.transform(*primary) PT.transform(0,rt,-8475.,0,0,0) ## PT.wolterprimary(220.,8400.) PT.primaryLL(220.,8400.,8525.,8425.,30.*np.pi/180.,pcoeff,pax,paz) if woltVignette is True: ind = logical_and(PT.z<8525.,PT.z>8425.) PT.vignette(ind=ind) PT.reflect() PT.transform(0,-rt,8475.,0,0,0) PT.itransform(*primary) PT.transform(0,rt,-8475.,0,0,0) #Move back up to mask plane and trace flat PT.transform(0,0,8400.+134.18,0,0,0) PT.flat() ## pdb.set_trace() #Rays should now be at Hartmann mask plane return def traceFromMask(N,numholes,cda,fold,retro,primary,foldrot=0.,retrorot=0.): #Vignette at proper hole h = hartmannMask() ind = h==N PT.vignette(ind=ind) #Continue trace up to retro and back to CDA PT.transform(0,-123.41,1156.48-651.57-134.18,0,0,0) PT.flat() PT.transform(0,0,0,pi,0,0) PT.transform(*retro) PT.zernsurfrot(retrosag,retrofig,378./2,-8.993*pi/180+retrorot) PT.itransform(*retro) PT.reflect() PT.transform(0,0,0,-pi,0,0) PT.transform(0,123.41,-1156.48+651.57+134.18,0,0,0) PT.flat() h = hartmannMask() ind = h==N PT.vignette(ind=ind) PT.transform(0,0,-134.18,0,0,0) rt = conicsolve.primrad(8475.,220.,8400.) PT.transform(0,-rt,75.,0,0,0) PT.transform(*primary) PT.transform(0,rt,-8475.,0,0,0) PT.wolterprimary(220.,8400.) ind = logical_and(PT.z<8525.,PT.z>8425.) PT.vignette(ind=ind) PT.reflect() PT.transform(0,-rt,8475.,0,0,0) PT.itransform(*primary) PT.transform(0,rt,-8475.,0,0,0) PT.transform(0,-85.12,8400.-651.57+85.12\ ,0,0,0) PT.transform(0,0,0,-pi/4,0,0) PT.transform(0,0,0,0,0,pi) PT.flat() PT.transform(*fold) PT.zernsurfrot(foldsag,foldfig,406./2,-174.659*pi/180+foldrot) PT.itransform(*fold) PT.reflect() PT.transform(0,0,0,0,0,-pi) PT.transform(0,0,0,3*pi/4,0,0) PT.transform(0,-85.12,-85.12-(conicsolve.primfocus(220.,8400.)-651.57)\ ,0,0,0) PT.transform(*cda) PT.flat() return #### DOUBLE MIRROR TRACES #### #Trace from focus to fold to Hartmann mask def fullMaskTrace(fold,prim,sec,woltVignette=True,foldrot=0.): #Get Wolter parameters alpha,p,d,e = conicsolve.woltparam(220.,8400.) foc = 8400. #Trace to fold mirror #translate to center of fold mirror PT.transform(0.,85.12,foc-651.57+85.12,0,0,0) #rotate so surface normal points in correct direction PT.transform(0,0,0,-3*pi/4,0,0) PT.transform(0,0,0,0,0,pi) #trace to fold flat PT.flat() #Introduce fold misalignment PT.transform(*fold) PT.zernsurfrot(foldsag,foldfig,406./2,-174.659*pi/180+foldrot) PT.itransform(*fold) PT.reflect() PT.transform(0,0,0,0,0,-pi) PT.transform(0,0,0,pi/4,0,0) #Translate to optical axis mid-plane, then down to image of #primary focus, place primary mirror and trace PT.transform(0,85.12,651.57-85.12,0,0,0) PT.flat() PT.transform(0,0,-8400.,0,0,0) #Place secondary #Go to tangent point, apply misalignment, place mirror, and reverse PT.transform(0,-conicsolve.secrad(8325.,220.,8400.),8325.,0,0,0) PT.transform(*sec) PT.itransform(0,-conicsolve.secrad(8325.,220.,8400.),8325.,0,0,0) PT.woltersecondary(220.,8400.) if woltVignette is True: ind = logical_and(PT.z<8375.,PT.z>8275.) PT.vignette(ind=ind) PT.reflect() PT.transform(0,-conicsolve.secrad(8325.,220.,8400.),8325.,0,0,0) PT.itransform(*sec) #Back at nominal secondary tangent point PT.itransform(0,-conicsolve.secrad(8325.,220.,8400.),8325.,0,0,0) #Place primary #Go to tangent point, apply misalignment, place mirror, and reverse PT.transform(0,-conicsolve.primrad(8425.,220.,8400.),8425.,0,0,0) PT.transform(*prim) PT.itransform(0,-conicsolve.primrad(8425.,220.,8400.),8425.,0,0,0) ## PT.transform(0,0,8475.,0,0,0) ## PT.flat() ## PT.itransform(0,0,8475.,0,0,0) ## PT.wolterprimary(220.,8400.) PT.primaryLL(220.,8400.,8525.,8425.,30.*np.pi/180.,pcoeff,pax,paz) ## pdb.set_trace() if woltVignette is True: ind = logical_and(PT.z<8525.,PT.z>8425.) PT.vignette(ind=ind) PT.reflect() PT.transform(0,-conicsolve.primrad(8425.,220.,8400.),8425.,0,0,0) PT.itransform(*prim) PT.itransform(0,-conicsolve.primrad(8425.,220.,8400.),8425.,0,0,0) #Move back up to mask plane and trace flat PT.transform(0,0,8400.+134.18,0,0,0) PT.flat() ## pdb.set_trace() #Rays should now be at Hartmann mask plane return def fullFromMask(N,cda,fold,retro,prim,sec,foldrot=0.,retrorot=0.): ## pdb.set_trace() #Vignette at proper hole h = hartmannMask() ind = h==N PT.vignette(ind=ind) #Continue trace up to retro and back to CDA PT.transform(0,-123.41,1156.48-651.57-134.18,0,0,0) PT.flat() PT.transform(0,0,0,pi,0,0) PT.transform(*retro) PT.zernsurfrot(retrosag,retrofig,378./2,-8.993*pi/180+retrorot) PT.itransform(*retro) PT.reflect() PT.transform(0,0,0,-pi,0,0) #Back to mask PT.transform(0,123.41,-1156.48+651.57+134.18,0,0,0) PT.flat() h = hartmannMask() ind = h==N PT.vignette(ind=ind) #Place Wolter surfaces PT.transform(0,0,-134.18-8400.,0,0,0) PT.transform(0,-conicsolve.primrad(8425.,220.,8400.),8425.,0,0,0) PT.transform(*prim) PT.itransform(0,-conicsolve.primrad(8425.,220.,8400.),8425.,0,0,0) ## PT.wolterprimary(220.,8400.) PT.primaryLL(220.,8400.,8525.,8425.,30.*np.pi/180.,pcoeff,pax,paz) pdb.set_trace() ind = logical_and(PT.z<8525.,PT.z>8425.) PT.vignette(ind=ind) PT.transform(0,-conicsolve.primrad(8425.,220.,8400.),8425.,0,0,0) PT.itransform(*prim) PT.itransform(0,-conicsolve.primrad(8425.,220.,8400.),8425.,0,0,0) PT.reflect() #Wolter secondary PT.transform(0,-conicsolve.secrad(8325.,220.,8400.),8325.,0,0,0) PT.transform(*sec) PT.itransform(0,-conicsolve.secrad(8325.,220.,8400.),8325.,0,0,0) PT.woltersecondary(220.,8400.) ind = logical_and(PT.z<8375.,PT.z>8275.) PT.vignette(ind=ind) PT.reflect() PT.transform(0,-conicsolve.secrad(8325.,220.,8400.),8325.,0,0,0) PT.itransform(*sec) PT.itransform(0,-conicsolve.secrad(8325.,220.,8400.),8325.,0,0,0) ## PT.woltersecondary(220.,8400.) ## ind = logical_and(PT.z<8375.,PT.z>8275.) ## PT.vignette(ind=ind) ## PT.reflect() #Back to fold PT.transform(0,-85.12,8400.-651.57+85.12\ ,0,0,0) PT.transform(0,0,0,-pi/4,0,0) PT.transform(0,0,0,0,0,pi) PT.flat() PT.transform(*fold) PT.zernsurfrot(foldsag,foldfig,406./2,-174.659*pi/180+foldrot) PT.itransform(*fold) PT.reflect() PT.transform(0,0,0,0,0,-pi) #Back to CDA PT.transform(0,0,0,3*pi/4,0,0) PT.transform(0,-85.12,-85.12-8400.+651.57\ ,0,0,0) PT.transform(*cda) PT.flat() return #Return nominal pitch and roll for Hartmann hole #Used as starting point for optimization def hartmannStartFull(N): global holetheta a,p,d,e = conicsolve.woltparam(220.,8400.) #Pitch is 4*a return 4*a,holetheta[N-1] #Return mean x and y positions as a function of pitch and roll def traceHoleFull(num,N,div,p,r,cda,fold,prim,sec,foldrot=0.): CDAbeam(num,div,p,r,cda) fullMaskTrace(fold,prim,sec,woltVignette=False,foldrot=foldrot) realx,realy = hartmannPosition(N) res = sqrt(mean((PT.x-realx)**2+(PT.y-realy)**2)) print str(N) + ': ' + str(res) return res #Use minimization routine to aim ray bundle at proper hole def fullAim(num,N,div,cda,fold,prim,sec,foldrot=0.): #Create function fun = lambda p: traceHoleFull(num,N,div,p[0],p[1],\ cda,fold,prim,sec,foldrot=foldrot) #Optimize function start = array(hartmannStartFull(N)) if abs(start[1]) < .001: start[1] = .01 print 'Begin ' + str(N) res = scipy.optimize.minimize(fun,start,method='nelder-mead',\ options={'ftol':1.e-2,'disp':True}) print 'End ' +str(N) ## traceHole2(num,N,numholes,div,res['x'][0],res['x'][1],cda,fold) return res['x'] ##Return vector of pitch and roll for a Hartmann mask def alignHartmannFull(cda,fold,prim,sec,foldrot=0.): global numholes pitch = zeros(numholes) roll = zeros(numholes) for i in range(numholes): pitch[i],roll[i] = fullAim(10**2,i+1,.00001*pi/180,\ cda,fold,prim,sec,foldrot=0.)#findHole(i+1,numholes,cda,fold) return pitch,roll ##Do full double mirror alignment trace, making use of ray aiming results ##for efficiency def fullAlign(num,cda,fold,retro,prim,sec,p=None,r=None,\ foldrot=0.,retrorot=0.): global numholes if p is None: #Grab pitch and roll vectors p,r = alignHartmannFull(cda,fold,prim,sec,foldrot=foldrot) #Trace out holes, one by one xm = [] ym = [] xstd = [] ystd = [] for i in range(numholes): #Trace up to Hartmann mask CDAbeam(num,.001*pi/180,p[i],r[i],cda) fullMaskTrace(fold,prim,sec,woltVignette=True) fullFromMask(i+1,cda,fold,retro,prim,sec) #Print out vignetting factor print size(PT.x)/num #Evaluate mean spot position xm.append(mean(PT.x)) ym.append(mean(PT.y)) xstd.append(std(PT.x)) ystd.append(std(PT.y)) return array(xm),array(ym),array(xstd),array(ystd) #Evaluate sensitivity of misalignment degree of freedom def dofSensitivityFull(num,obj,dof,step,criteria): #Initial misalignment vectors cda = zeros(6) fold = zeros(6) retro = zeros(6) prim = zeros(6) sec = zeros(6) misalign = [cda,fold,retro,prim,sec] #Get nominal spot position x0,y0 = fullAlign(num,*misalign) #Increase proper dof until spot shifts breach 10 micron requirement merit = 0. figure() while merit < criteria: misalign[obj][dof] = misalign[obj][dof] + step try: x1,y1 = fullAlign(num,*misalign) except: sys.stdout.write('Hartmann Throughput Cutoff at %7.4e' %\ misalign[obj][dof]) break dx = x1-x0 dx = dx - mean(dx) dy = y1-y0 dy = dy - mean(dy) merit = max(sqrt(dx**2+dy**2)) sys.stdout.write('DoF: %7.4e Merit : %0.4f\r' %\ (misalign[obj][dof],merit)) plot(dx,dy,'.') draw() sys.stdout.flush() return #### DOUBLE MIRROR TRACES #### #Define Hartmann mask and vignette rays #Keep track of which hole rays hit with "hole" vector #Need origin to be at Hartmann mask plane on optical axis def hartmannMask(): ## #Create hole array to handle hole positions ## hole = zeros(size(PT.x)) ## holerad = (conicsolve.primrad(8500.,220.,8400.)-220.)/2. #Hole halfwidth ## holecent = conicsolve.primrad(8475.,220.,8400.) #Radius of center of holes ## halfang = arcsin(50./220.)-.009 #Half angle of Hartmann mask ## holetheta = linspace(-halfang,halfang,numholes) #Vector of Hartmann angles ## holewidth = arcsin(holerad/220.) #Set holewidth and holetheta to be global variables global holewidth, holetheta #Loop through hole numbers rayang = arctan2(PT.y,PT.x) #Center of mirror is -pi/2 i = 1 hole = zeros(size(PT.x)) for theta in holetheta: ind = logical_and(rayang < -pi/2 + theta + holewidth,\ rayang > -pi/2 + theta - holewidth) hole[ind] = i i = i+1 return hole #Set up CDA beam to trace to a given Hartmann hole #Will use indicated beam divergence and apply #appropriate roll to beam to find hole N def traceHole(num,N,numholes,div,cda): a,p,d,e = conicsolve.woltparam(220.,8400.) #Pitch is 2*a halfang = arcsin(50./220.)-.009 holetheta = linspace(-halfang,halfang,numholes) CDAbeam(num,div,2*a,holetheta[N-1],cda) return 2*a,holetheta[N-1] #Return nominal pitch and roll for Hartmann hole #Used as starting point for optimization def hartmannStart(N,numholes): global holetheta a,p,d,e = conicsolve.woltparam(220.,8400.) #Pitch is 2*a return 2*a,holetheta[N-1] #Return mean x and y positions as a function of pitch and roll def traceHole2(num,N,numholes,div,p,r,cda,fold,primary,foldrot=0.): CDAbeam(num,div,p,r,cda) primMaskTrace(fold,primary,woltVignette=False,foldrot=foldrot) realx,realy = hartmannPosition(N) return mean(sqrt((PT.x-realx)**2+(PT.y-realy)**2)) #Use minimization routine to aim ray bundle at proper hole def aimHole(num,N,numholes,div,cda,fold,primary,foldrot=0.): #Create function fun = lambda p: traceHole2(num,N,numholes,div,p[0],p[1],\ cda,fold,primary,foldrot=foldrot) #Optimize function start = array(hartmannStart(N,numholes)) if abs(start[1]) < .001: start[1] = .01 res = scipy.optimize.minimize(fun,start,method='nelder-mead',\ options={'ftol':1.e-2,'disp':False}) ## traceHole2(num,N,numholes,div,res['x'][0],res['x'][1],cda,fold) return res['x'] #Returns nominal position of Hartmann hole in Cartesian coordinates def hartmannPosition(N): global holetheta holecent = conicsolve.primrad(8475.,220.,8400.) #Radius of center of holes thistheta = holetheta[N-1]-pi/2 return holecent*cos(thistheta),holecent*sin(thistheta) #Returns nominal position of Hartmann hole in polar coordinates def hartmannAngles(N,numholes): holecent = conicsolve.primrad(8475.,220.,8400.) #Radius of center of holes halfang = arcsin(50./220.)-.009 #Half angle of Hartmann mask holetheta = linspace(-halfang,halfang,numholes) #Vector of Hartmann angles thistheta = holetheta[N-1]-pi/2 return holecent,thistheta #Determine chief ray to a given Hartmann hole #Start with fairly wide divergence at nominal location #Take mean of rays that hit the hole, then repeat with much #smaller divergence #Probably three iterations to converge #Need to do this for each Hartmann hole and save ray directions def findHole(N,numholes,cda,fold,primary): #Trace out first iteration pitch,roll = traceHole(10**3,N,numholes,.0001*pi/180,cda) pdb.set_trace() primMaskTrace(fold,woltVignette=False) pdb.set_trace() #Mean position of rays should be equal to nominal Hartmann position rad,roll = hartmannAngles(N,numholes) actrad = mean(sqrt(PT.x**2+PT.y**2)) actroll = mean(arctan2(PT.y,PT.x)) raddiff = rad - actrad rolldiff = roll - actroll newpitch = pitch - raddiff/(conicsolve.primfocus(220.,8400.)+134.18) newroll = roll - rolldiff + pi/2 pdb.set_trace() while (abs(raddiff) > .01) or (abs(rolldiff) > .01/220.): #Fix pitch and roll CDAbeam(10**3,.0001*pi/180,newpitch,newroll,cda) pitch = newpitch roll = newroll-pi/2 primMaskTrace(fold,woltVignette=False) raddiff = rad - mean(sqrt(PT.x**2+PT.y**2)) rolldiff = roll - mean(arctan2(PT.y,PT.x)) ## print 'Rolldiff: ' + str(rolldiff) ## print 'Raddiff: ' + str(raddiff) newpitch = pitch - raddiff/(conicsolve.primfocus(220.,8400.)+134.18) newroll = roll - rolldiff + pi/2 return newpitch,newroll ##Return vector of pitch and roll for a Hartmann mask def alignHartmann(numholes,cda,fold,primary,foldrot=0.): pitch = zeros(numholes) roll = zeros(numholes) for i in range(numholes): pitch[i],roll[i] = aimHole(10**2,i+1,numholes,.00001*pi/180,\ cda,fold,primary,foldrot=0.)#findHole(i+1,numholes,cda,fold) return pitch,roll ##Do full primary alignment trace, making use of ray aiming results ##for efficiency def fullPrimary(num,numholes,cda,fold,retro,primary,p=None,r=None,\ foldrot=0.,retrorot=0.): if p is None: #Grab pitch and roll vectors p,r = alignHartmann(numholes,cda,fold,primary,foldrot=foldrot) #Trace out holes, one by one xm = [] ym = [] for i in range(numholes): #Trace up to Hartmann mask CDAbeam(num,.001*pi/180,p[i],r[i],cda) primMaskTrace(fold,primary,woltVignette=True) traceFromMask(i+1,numholes,cda,fold,retro,primary) #Evaluate mean spot position xm.append(mean(PT.x)) ym.append(mean(PT.y)) return array(xm),array(ym) #Evaluate sensitivity of misalignment degree of freedom def dofSensitivity(num,numholes,obj,dof,step): #Initial misalignment vectors cda = zeros(6) fold = zeros(6) retro = zeros(6) primary = zeros(6) misalign = [cda,fold,retro,primary] #Get nominal spot position x0,y0 = fullPrimary(num,numholes,*misalign) #Increase proper dof until spot shifts breach 10 micron requirement merit = 0. figure() while merit < .01: misalign[obj][dof] = misalign[obj][dof] + step try: x1,y1 = fullPrimary(num,numholes,*misalign) except: sys.stdout.write('Hartmann Throughput Cutoff at %7.4f' %\ misalign[obj][dof]) break dx = x1-x0 dx = dx - mean(dx) dy = y1-y0 dy = dy - mean(dy) merit = max(sqrt(dx**2+dy**2)) sys.stdout.write('DoF: %7.4f Merit : %0.4f\r' %\ (misalign[obj][dof],merit)) plot(dx,dy,'.') draw() sys.stdout.flush() return #Evaluate sensitivity of misalignment degree of freedom def rotSensitivity(num,numholes,step,foldrot=False,retrorot=False): #Initial misalignment vectors cda = zeros(6) fold = zeros(6) retro = zeros(6) primary = zeros(6) misalign = [cda,fold,retro,primary] #Get nominal spot position x0,y0 = fullPrimary(num,numholes,*misalign) #Increase proper dof until spot shifts breach 10 micron requirement merit = 0. figure() while merit < .01: if foldrot is not False: foldrot = foldrot + step retrorot = 0. else: retrorot = retrorot + step foldrot = 0. try: x1,y1 = fullPrimary(num,numholes,*misalign,\ foldrot=foldrot,retrorot=retrorot) except: sys.stdout.write('Hartmann Throughput Cutoff at %7.4f' %\ max([foldrot,retrorot])*180/pi) break dx = x1-x0 dx = dx - mean(dx) dy = y1-y0 dy = dy - mean(dy) merit = max(sqrt(dx**2+dy**2)) sys.stdout.write('DoF: %7.4f Merit : %0.4f\r' %\ (max([foldrot,retrorot])*180/pi,merit)) plot(dx,dy,'.') draw() sys.stdout.flush() return

33.14441

95

0.633544

3,203

21,345

4.216047

0.124883

0.024882

0.018439

0.013329

0.762219

0.7129

0.669505

0.638033

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21,345

643

96

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null

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null

0

1

0

2

e3eebb504a36c5e07a64cfe77e023ebab3183b29

3,131

py

Python

dependencies/src/4Suite-XML-1.0.2/test/Xml/Xslt/Borrowed/dc_20000110.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

null

dependencies/src/4Suite-XML-1.0.2/test/Xml/Xslt/Borrowed/dc_20000110.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

null

dependencies/src/4Suite-XML-1.0.2/test/Xml/Xslt/Borrowed/dc_20000110.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

1

2020-07-26T03:57:45.000Z

#Example from David Carlisle <davidc@nag.co.uk> to John Robert Gardner <jrgardn@emory.edu> on 10 Jan 2000 from Xml.Xslt import test_harness sheet_1 = """<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0" > <xsl:output method="xml" indent="yes"/> <xsl:template match="sample"> <verse> <xsl:apply-templates select="verse[@id='rv1.84.10']/mantra"/> </verse> </xsl:template> <xsl:template match="verse[@id='rv1.84.10']/mantra"> <xsl:copy-of select="."/> <xsl:variable name="x" select="position()"/> <xsl:copy-of select="../../verse[@id='rv1.16.1']/mantra[position()=$x]"/> </xsl:template> </xsl:stylesheet>""" sheet_2 = """<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0" > <xsl:output method="xml" indent="yes"/> <xsl:template match="sample"> <verse> <xsl:for-each select="verse/mantra"> <xsl:sort select="substring-after(@id,../@id)"/> <xsl:sort select="../@id" order="descending"/> <xsl:copy-of select="."/> </xsl:for-each> </verse> </xsl:template> </xsl:stylesheet>""" xml_source="""<sample> <verse meter="gaayatrii" id="rv1.16.1"> <mantra id="rv1.16.1a"> aa tvaa vahantu harayo </mantra> <mantra id="rv1.16.1b"> vRSaNaM somapiitaye </mantra> <mantra id="rv1.16.1c"> indra tvaa suuracakSasaH </mantra> </verse> <verse meter="gaayatrii" id="rv1.84.10"> <mantra id="rv1.84.10a"> svaador itthaa viSuuvato </mantra> <mantra id="rv1.84.10b"> madhvaH pibanti gauryaH </mantra> <mantra id="rv1.84.10c"> yaa indreNa sayaavariir </mantra> </verse> </sample>""" expected_1 = """<?xml version='1.0' encoding='UTF-8'?> <verse> <mantra id='rv1.84.10a'> svaador itthaa viSuuvato </mantra> <mantra id='rv1.16.1a'> aa tvaa vahantu harayo </mantra> <mantra id='rv1.84.10b'> madhvaH pibanti gauryaH </mantra> <mantra id='rv1.16.1b'> vRSaNaM somapiitaye </mantra> <mantra id='rv1.84.10c'> yaa indreNa sayaavariir </mantra> <mantra id='rv1.16.1c'> indra tvaa suuracakSasaH </mantra> </verse>""" expected_2 = """<?xml version='1.0' encoding='UTF-8'?> <verse> <mantra id='rv1.84.10a'> svaador itthaa viSuuvato </mantra> <mantra id='rv1.16.1a'> aa tvaa vahantu harayo </mantra> <mantra id='rv1.84.10b'> madhvaH pibanti gauryaH </mantra> <mantra id='rv1.16.1b'> vRSaNaM somapiitaye </mantra> <mantra id='rv1.84.10c'> yaa indreNa sayaavariir </mantra> <mantra id='rv1.16.1c'> indra tvaa suuracakSasaH </mantra> </verse>""" def Test(tester): source = test_harness.FileInfo(string=xml_source) sheet = test_harness.FileInfo(string=sheet_1) test_harness.XsltTest(tester, source, [sheet], expected_1, title='Using position()') source = test_harness.FileInfo(string=xml_source) sheet = test_harness.FileInfo(string=sheet_2) test_harness.XsltTest(tester, source, [sheet], expected_2, title='Using xsl:for-each and xsl:sort') return

24.653543

105

0.622804

421

3,131

4.589074

0.251781

0.059524

0.102484

0.123188

0.746377

0.692029

0.671325

0.625776

0

0.05461

0.19291

3,131

126

106

24.849206

0.709933

0.033216

0

0.694444

0

0.009259

0.811302

0.072373

0

1

0.009259

false

0

0.009259

0

0.027778

0

null

0

1

0

1

0

null

0

2

e3eff8a807310f1658bf9add7b3fd64cd9a65c43

435

py

Python

moyu_engine/config/system/save_system.py

MoYuStudio/MYSG01

2bb33f6258893d881466689cd2f0de50e866915d

[ "Apache-2.0" ]

null

moyu_engine/config/system/save_system.py

MoYuStudio/MYSG01

2bb33f6258893d881466689cd2f0de50e866915d

[ "Apache-2.0" ]

null

moyu_engine/config/system/save_system.py

MoYuStudio/MYSG01

2bb33f6258893d881466689cd2f0de50e866915d

[ "Apache-2.0" ]

null

import pickle import moyu_engine.config.data.constants as C class SavaSystem: def save_tilemap(): f=open('moyu_engine/config/data/game_save','wb') save_data = {'window':C.window} pickle.dump(save_data, f) f.close() def read_tilemap(): f=open('moyu_engine/config/data/game_save', 'rb') read_data = pickle.load(f) C.window = read_data['window'] f.close()

21.75

57

0.611494

60

435

4.25

0.4

0.117647

0.188235

0.235294

0.313725

0

0.252874

435

20

58

21.75

0.784615

0

0.153846

0

0.188506

0.151724

0

1

0.153846

false

0

0.153846

0

0.384615

0

null

0

1

0

null

0

2

e3f572b26ae5c5d16dc9bcfe6e977a44a4119b67

5,815

py

Python

nvflare/apis/server_engine_spec.py

ZiyueXu77/NVFlare

ec855326b91b47d54074017a12f89ec971a8139b

[ "Apache-2.0" ]

null

nvflare/apis/server_engine_spec.py

ZiyueXu77/NVFlare

ec855326b91b47d54074017a12f89ec971a8139b

[ "Apache-2.0" ]

null

nvflare/apis/server_engine_spec.py

ZiyueXu77/NVFlare

ec855326b91b47d54074017a12f89ec971a8139b

[ "Apache-2.0" ]

null

# Copyright (c) 2021-2022, NVIDIA CORPORATION. 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. from abc import ABC, abstractmethod from typing import Dict, List, Optional, Tuple from nvflare.apis.shareable import Shareable from nvflare.widgets.widget import Widget from .client import Client from .fl_context import FLContext from .fl_snapshot import RunSnapshot from .workspace import Workspace class ServerEngineSpec(ABC): @abstractmethod def fire_event(self, event_type: str, fl_ctx: FLContext): pass @abstractmethod def get_clients(self) -> List[Client]: pass @abstractmethod def sync_clients_from_main_process(self): """To fetch the participating clients from the main parent process Returns: clients """ pass @abstractmethod def validate_clients(self, client_names: List[str]) -> Tuple[List[Client], List[str]]: """Validate specified client names. Args: client_names: list of names to be validated Returns: a list of validate clients and a list of invalid client names """ pass @abstractmethod def new_context(self) -> FLContext: # the engine must use FLContextManager to create a new context! pass @abstractmethod def get_workspace(self) -> Workspace: pass @abstractmethod def get_component(self, component_id: str) -> object: pass @abstractmethod def register_aux_message_handler(self, topic: str, message_handle_func): """Register aux message handling function with specified topics. Exception is raised when: a handler is already registered for the topic; bad topic - must be a non-empty string bad message_handle_func - must be callable Implementation Note: This method should simply call the ServerAuxRunner's register_aux_message_handler method. Args: topic: the topic to be handled by the func message_handle_func: the func to handle the message. Must follow aux_message_handle_func_signature. """ pass @abstractmethod def send_aux_request(self, targets: [], topic: str, request: Shareable, timeout: float, fl_ctx: FLContext) -> dict: """Send a request to specified clients via the aux channel. Implementation: simply calls the ServerAuxRunner's send_aux_request method. Args: targets: target clients. None or empty list means all clients topic: topic of the request request: request to be sent timeout: number of secs to wait for replies. 0 means fire-and-forget. fl_ctx: FL context Returns: a dict of replies (client name => reply Shareable) """ pass def fire_and_forget_aux_request(self, targets: [], topic: str, request: Shareable, fl_ctx: FLContext) -> dict: return self.send_aux_request(targets, topic, request, 0.0, fl_ctx) @abstractmethod def get_widget(self, widget_id: str) -> Widget: """Get the widget with the specified ID. Args: widget_id: ID of the widget Returns: the widget or None if not found """ pass @abstractmethod def persist_components(self, fl_ctx: FLContext, completed: bool): """To persist the FL running components Args: fl_ctx: FLContext completed: flag to indicate where the run is complete Returns: """ pass @abstractmethod def restore_components(self, snapshot: RunSnapshot, fl_ctx: FLContext): """To restore the FL components from the saved snapshot Args: snapshot: RunSnapshot fl_ctx: FLContext Returns: """ pass @abstractmethod def start_client_job(self, run_number, client_sites): """To send the start client run commands to the clients Args: client_sites: client sites run_number: run_number Returns: """ pass @abstractmethod def check_client_resources(self, resource_reqs: Dict[str, dict]) -> Dict[str, Tuple[bool, Optional[str]]]: """Sends the check_client_resources requests to the clients. Args: resource_reqs: A dict of {client_name: resource requirements dict} Returns: A dict of {client_name: client_check_result} where client_check_result is a tuple of {client check OK, resource reserve token if any} """ pass @abstractmethod def cancel_client_resources( self, resource_check_results: Dict[str, Tuple[bool, str]], resource_reqs: Dict[str, dict] ): """Cancels the request resources for the job. Args: resource_check_results: A dict of {client_name: client_check_result} where client_check_result is a tuple of {client check OK, resource reserve token if any} resource_reqs: A dict of {client_name: resource requirements dict} """ pass @abstractmethod def get_client_name_from_token(self, token: str) -> str: """Gets client name from a client login token.""" pass

30.445026

119

0.657782

727

5,815

5.133425

0.286107

0.072883

0.078778

0.025723

0.138264

0.108253

0.084137

0

0.003561

0.275666

5,815

190

120

30.605263

0.882479

0.507825

0

0.52459

0

1

0.278689

false

0.262295

0.131148

0.016393

0.442623

0

null

0

null

0

1

0

1

0

2

5400f18a3b0bb21f6e1240bc7dcf7a7817e230ca

551

py

Python

facial recognition using mtcnn/image_count.py

yoga-suhas-km/facial-recognition

2fab92ec977430ae5d887fe7d3cf6df1b988bef2

[ "MIT" ]

null

facial recognition using mtcnn/image_count.py

yoga-suhas-km/facial-recognition

2fab92ec977430ae5d887fe7d3cf6df1b988bef2

[ "MIT" ]

null

facial recognition using mtcnn/image_count.py

yoga-suhas-km/facial-recognition

2fab92ec977430ae5d887fe7d3cf6df1b988bef2

[ "MIT" ]

null

import os def count_images(count_images_in_folder): number_of_images = [] path, dirs, files = next(os.walk(count_images_in_folder)) num_classes = len(dirs) for i in files: if i.endswith('.jpg'): number_of_images.append(1) for i in dirs: path, dirs, files = next(os.walk(os.path.join(count_images_in_folder, i))) for j in files: if j.endswith('.jpg'): number_of_images.append(1) file_count = len(number_of_images) return file_count

23.956522

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0

0.005168

0.297641

551

23

83

23.956522

0.795866

0

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0

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0

1

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false

0

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0

0.2

0

null

0

1

0

null

0

2

5409241d1256ff9679a064a143c42e39381d869a

436

py

Python

pineboolib/kugar/mreportdetail.py

Miguel-J/pineboo-buscar

41a2f3ee0425d163619b78f32544c4b4661d5fa7

[ "MIT" ]

null

pineboolib/kugar/mreportdetail.py

Miguel-J/pineboo-buscar

41a2f3ee0425d163619b78f32544c4b4661d5fa7

[ "MIT" ]

null

pineboolib/kugar/mreportdetail.py

Miguel-J/pineboo-buscar

41a2f3ee0425d163619b78f32544c4b4661d5fa7

[ "MIT" ]

null

from pineboolib import decorators from pineboolib.flcontrols import ProjectClass from pineboolib.kugar.mreportsection import MReportSection class MReportDetail(ProjectClass, MReportSection): @decorators.BetaImplementation def __init__(self, *args): super(MReportDetail, self).__init__(*args) @decorators.NotImplementedWarn # def operator=(self, mrd): #FIXME def operator(self, mrd): return self

25.647059

58

0.754587

43

436

7.465116

0.488372

0.130841

0.093458

0.11215

0

0.167431

436

16

59

27.25

0.884298

0.071101

0

0.0625

0

1

0.2

false

0

0.3

0.1

0.7

0

null

0

null

0

1

0

1

0

2

540ba0e1c7e366dfe7a263f9df7b64a920507acb

1,574

py

Python

setup.py

anshulp2912/cheapBuy

ea8df2035cf089465313e0609f0eb73272a92554

[ "MIT" ]

1

2021-11-26T18:20:34.000Z

setup.py

anshulp2912/cheapBuy

ea8df2035cf089465313e0609f0eb73272a92554

[ "MIT" ]

19

2021-11-04T03:41:46.000Z

2021-11-04T18:48:39.000Z

setup.py

anshulp2912/cheapBuy

ea8df2035cf089465313e0609f0eb73272a92554

[ "MIT" ]

4

2021-11-05T01:45:26.000Z

2021-11-29T22:04:20.000Z

from setuptools import setup setup(name='cheapBuy', version='2.0', description='cheapBuy Extension provides you ease to buy any product through your favourite website like Amazon, Walmart, Ebay, Bjs, Costco, etc, by providing prices of the same product from all different websites to extension.', author='Anshul, Bhavya, Darshan, Pragna, Rohan', author_email='anshulp2912@gmail.com', url='https://github.com/anshulp2912/cheapBuy.git', packages=['cheapBuy'], long_description="""\ Hands on for the standard github repo files. .gitignore .travis.yml CITATION.md : fill on once you've got your ZENODO DOI going CODE-OF-CONDUCT.md CONTRIBUTING.md LICENSE.md README.md setup.py requirements.txt data/ README.md test/ README.md code/ __init__.py """, classifiers=[ "License :: MIT License", "Programming Language :: Python", "Development Status :: Initial", "Intended Audience :: Developers", "Topic :: Software Engineering", ], keywords='python requirements license gitignore', license='MIT', install_requires=[ 'BeautifulSoup', 'pytest', 'Flask', 'selenium', 'streamlit', 'webdriver_manager', 'pyshorteners', 'link-button' ], )

32.791667

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0.543837

147

1,574

5.768707

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0.028302

0

0.00996

0.362135

1,574

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236

33.489362

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0

0.108696

0

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0.67662

0.013342

0

1

0

true

0

0.021739

0

0.021739

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null

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