ytzi/the-stack-dedup-python-scored · Datasets at Hugging Face

e1c41ef577f4a05e6ab921f39322d64330728ff4

1,688

py

Python

start.py

AndrewJanuary/PM-Monitor

43548c805d2ee11ac54f8df874cc06be458454a8

[ "MIT" ]

null

start.py

AndrewJanuary/PM-Monitor

43548c805d2ee11ac54f8df874cc06be458454a8

[ "MIT" ]

6

2021-01-28T22:04:45.000Z

2021-12-20T20:59:03.000Z

start.py

AndrewJanuary/PM-Monitor

43548c805d2ee11ac54f8df874cc06be458454a8

[ "MIT" ]

null

from app.sensor import Sensor from app.uploader import Uploader from app.offline import Offline import time, logging, argparse, sys, random, datetime logging.basicConfig(filename='airquality.log', level=logging.DEBUG, filemode='a', format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') sen = Sensor('PM Sensor 1', '/dev/ttyUSB0', b'\xaa', b'0xAB', b'\xc0') def main(): parser = argparse.ArgumentParser() parser.add_argument('-o','--offline', action="store_true") args = parser.parse_args(sys.argv[2:]) if args.offline: start_offline() else: try: start_online() except: start_offline() def start_offline(): print("Starting Air Monitor in offline mode") off = Offline('pm25', 'pm10') while True: data = sen.read_from_sensor() sen.check_message(data) pm_two_five = sen.get_pm_two_five(data) pm_ten = sen.get_pm_ten(data) off.write_pm_two_five(pm_two_five) off.write_pm_ten(pm_ten) def start_online(): print("Starting Air Monitor") file = 'config.yml' up = Uploader('AIO') username, key, feed_two_five, feed_ten = up.read_config(file) aio = up.connect_to_aio(username, key) while True: up.get_feeds(aio) data = sen.read_from_sensor() sen.check_message(data) pm_two_five = sen.get_pm_two_five(data) pm_ten = sen.get_pm_ten(data) up.send_to_aio(aio, feed_two_five, pm_two_five) up.send_to_aio(aio, feed_ten, pm_ten) print(up.retrieve_from_feed(aio, feed_two_five)) print(up.retrieve_from_feed(aio, feed_ten)) time.sleep(60)

30.690909

82

0.64455

0

237

0.140403

e1c4600d073fba00b0a31f0113ee9536694f12a6

3,364

py

Python

py_trees_ros/visitors.py

geoc1234/py_trees_ros

65a055624f9261d67f0168ef419aa650302f96d0

[ "BSD-3-Clause" ]

65

2019-05-01T08:21:42.000Z

2022-03-23T15:49:55.000Z

py_trees_ros/visitors.py

geoc1234/py_trees_ros

65a055624f9261d67f0168ef419aa650302f96d0

[ "BSD-3-Clause" ]

62

2019-02-27T14:27:42.000Z

2022-02-08T03:54:30.000Z

py_trees_ros/visitors.py

geoc1234/py_trees_ros

65a055624f9261d67f0168ef419aa650302f96d0

[ "BSD-3-Clause" ]

23

2019-03-03T17:09:59.000Z

2022-01-06T03:07:59.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # License: BSD # https://raw.githubusercontent.com/splintered-reality/py_trees_ros/devel/LICENSE # ############################################################################## # Documentation ############################################################################## """ ROS Visitors are entities that can be passed to a ROS tree implementation (e.g. :class:`~py_trees_ros.trees.BehaviourTree`) and used to either visit each and every behaviour in the tree, or visit behaviours as the tree is traversed in an executing tick. At each behaviour, the visitor runs its own method on the behaviour to do as it wishes - logging, introspecting). .. warning:: Visitors should not modify the behaviours they visit. .. seealso:: The base interface and core visitors in :mod:`py_trees.visitors` """ ############################################################################## # Imports ############################################################################## import py_trees.visitors import py_trees_ros_interfaces.msg as py_trees_msgs import rclpy import time from . import conversions ############################################################################## # Visitors ############################################################################## class SetupLogger(py_trees.visitors.VisitorBase): """ Use as a visitor to :meth:`py_trees_ros.trees.TreeManager.setup` to log the name and timings of each behaviours' setup to the ROS debug channel. Args: node: an rclpy node that will provide debug logger """ def __init__(self, node: rclpy.node.Node): super().__init__(full=True) self.node = node def initialise(self): """ Initialise the timestamping chain. """ self.start_time = time.monotonic() self.last_time = self.start_time def run(self, behaviour): current_time = time.monotonic() self.node.get_logger().debug( "'{}'.setup: {:.4f}s".format(behaviour.name, current_time - self.last_time) ) self.last_time = current_time def finalise(self): current_time = time.monotonic() self.node.get_logger().debug( "Total tree setup time: {:.4f}s".format(current_time - self.start_time) ) class TreeToMsgVisitor(py_trees.visitors.VisitorBase): """ Visits the entire tree and gathers all behaviours as messages for the tree logging publishers. Attributes: tree (:class:`py_trees_msgs.msg.BehaviourTree`): tree representation in message form """ def __init__(self): """ Well """ super(TreeToMsgVisitor, self).__init__() self.full = True # examine all nodes def initialise(self): """ Initialise and stamp a :class:`py_trees_msgs.msg.BehaviourTree` instance. """ self.tree = py_trees_msgs.BehaviourTree() # TODO: crystal api # self.tree.stamp = rclpy.clock.Clock.now().to_msg() def run(self, behaviour): """ Convert the behaviour into a message and append to the tree. Args: behaviour (:class:`~py_trees.behaviour.Behaviour`): behaviour to convert """ self.tree.behaviours.append(conversions.behaviour_to_msg(behaviour))

31.735849

92

0.568668

2,040

0.606421

0

2,148

0.638526

e1c8c4baec324f5e5f8e13e03541f29a1a32842d

11,394

py

Python

Jarvis/features/Friday_Blueprint.py

faizeraza/Jarvis-Virtual-Assistant-

da88fc0124e6020aff1030317dc3dc918f7aa017

[ "MIT" ]

1

2021-12-14T00:18:10.000Z

Jarvis/features/Friday_Blueprint.py

faizeraza/Jarvis-Virtual-Assistant-

da88fc0124e6020aff1030317dc3dc918f7aa017

[ "MIT" ]

null

Jarvis/features/Friday_Blueprint.py

faizeraza/Jarvis-Virtual-Assistant-

da88fc0124e6020aff1030317dc3dc918f7aa017

[ "MIT" ]

1

2021-12-29T05:01:02.000Z

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'Friday_Blueprint.ui' # # Created by: PyQt5 UI code generator 5.15.4 # # WARNING: Any manual changes made to this file will be lost when pyuic5 is # run again. Do not edit this file unless you know what you are doing. from PyQt5 import QtCore, QtGui, QtWidgets class Ui_MainWindow(object): def setupUi(self, MainWindow): MainWindow.setObjectName("MainWindow") MainWindow.resize(420, 650) MainWindow.setSizeIncrement(QtCore.QSize(0, 0)) self.centralwidget = QtWidgets.QWidget(MainWindow) self.centralwidget.setObjectName("centralwidget") self.label = QtWidgets.QLabel(self.centralwidget) self.label.setGeometry(QtCore.QRect(0, 0, 421, 651)) self.label.setText("") self.label.setPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/see.jpg")) self.label.setScaledContents(True) self.label.setObjectName("label") self.verticalLayoutWidget = QtWidgets.QWidget(self.centralwidget) self.verticalLayoutWidget.setGeometry(QtCore.QRect(0, 0, 71, 651)) self.verticalLayoutWidget.setObjectName("verticalLayoutWidget") self.verticalLayout_5 = QtWidgets.QVBoxLayout(self.verticalLayoutWidget) self.verticalLayout_5.setSizeConstraint(QtWidgets.QLayout.SetMaximumSize) self.verticalLayout_5.setContentsMargins(0, 0, 0, 0) self.verticalLayout_5.setObjectName("verticalLayout_5") self.pushButton_9 = QtWidgets.QPushButton(self.verticalLayoutWidget) self.pushButton_9.setText("") icon = QtGui.QIcon() icon.addPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/user.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.pushButton_9.setIcon(icon) self.pushButton_9.setIconSize(QtCore.QSize(30, 30)) self.pushButton_9.setAutoDefault(True) self.pushButton_9.setDefault(True) self.pushButton_9.setFlat(True) self.pushButton_9.setObjectName("pushButton_9") self.verticalLayout_5.addWidget(self.pushButton_9) self.pushButton_10 = QtWidgets.QPushButton(self.verticalLayoutWidget) self.pushButton_10.setText("") icon1 = QtGui.QIcon() icon1.addPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/data.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.pushButton_10.setIcon(icon1) self.pushButton_10.setIconSize(QtCore.QSize(30, 30)) self.pushButton_10.setAutoDefault(True) self.pushButton_10.setDefault(True) self.pushButton_10.setFlat(True) self.pushButton_10.setObjectName("pushButton_10") self.verticalLayout_5.addWidget(self.pushButton_10) self.pushButton_11 = QtWidgets.QPushButton(self.verticalLayoutWidget) self.pushButton_11.setText("") icon2 = QtGui.QIcon() icon2.addPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/bot.jpg"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.pushButton_11.setIcon(icon2) self.pushButton_11.setIconSize(QtCore.QSize(49, 30)) self.pushButton_11.setDefault(True) self.pushButton_11.setFlat(True) self.pushButton_11.setObjectName("pushButton_11") self.verticalLayout_5.addWidget(self.pushButton_11) self.pushButton_12 = QtWidgets.QPushButton(self.verticalLayoutWidget) self.pushButton_12.setMinimumSize(QtCore.QSize(69, 0)) self.pushButton_12.setMaximumSize(QtCore.QSize(75, 16777215)) self.pushButton_12.setText("") icon3 = QtGui.QIcon() icon3.addPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/settings.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.pushButton_12.setIcon(icon3) self.pushButton_12.setIconSize(QtCore.QSize(30, 30)) self.pushButton_12.setAutoDefault(True) self.pushButton_12.setDefault(True) self.pushButton_12.setFlat(True) self.pushButton_12.setObjectName("pushButton_12") self.verticalLayout_5.addWidget(self.pushButton_12) spacerItem = QtWidgets.QSpacerItem(20, 151, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed) self.verticalLayout_5.addItem(spacerItem) spacerItem1 = QtWidgets.QSpacerItem(20, 69, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed) self.verticalLayout_5.addItem(spacerItem1) spacerItem2 = QtWidgets.QSpacerItem(13, 253, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed) self.verticalLayout_5.addItem(spacerItem2) self.pushButton_13 = QtWidgets.QPushButton(self.verticalLayoutWidget) self.pushButton_13.setText("") icon4 = QtGui.QIcon() icon4.addPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/feedback.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.pushButton_13.setIcon(icon4) self.pushButton_13.setIconSize(QtCore.QSize(40, 40)) self.pushButton_13.setDefault(True) self.pushButton_13.setFlat(True) self.pushButton_13.setObjectName("pushButton_13") self.verticalLayout_5.addWidget(self.pushButton_13) self.horizontalLayoutWidget = QtWidgets.QWidget(self.centralwidget) self.horizontalLayoutWidget.setGeometry(QtCore.QRect(70, 600, 351, 51)) self.horizontalLayoutWidget.setObjectName("horizontalLayoutWidget") self.horizontalLayout_4 = QtWidgets.QHBoxLayout(self.horizontalLayoutWidget) self.horizontalLayout_4.setContentsMargins(0, 0, 0, 0) self.horizontalLayout_4.setObjectName("horizontalLayout_4") self.pushButton_14 = QtWidgets.QPushButton(self.horizontalLayoutWidget) self.pushButton_14.setText("") icon5 = QtGui.QIcon() icon5.addPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/lens.svg"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.pushButton_14.setIcon(icon5) self.pushButton_14.setIconSize(QtCore.QSize(40, 40)) self.pushButton_14.setAutoDefault(True) self.pushButton_14.setDefault(True) self.pushButton_14.setFlat(True) self.pushButton_14.setObjectName("pushButton_14") self.horizontalLayout_4.addWidget(self.pushButton_14) spacerItem3 = QtWidgets.QSpacerItem(65, 15, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_4.addItem(spacerItem3) self.label_2 = QtWidgets.QLabel(self.horizontalLayoutWidget) #Self.label_2.setPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/Speak.gif")) self.label_2.setText("waiting") self.label_2.setScaledContents(True) self.label_2.setObjectName("label_2") self.horizontalLayout_4.addWidget(self.label_2) spacerItem4 = QtWidgets.QSpacerItem(68, 15, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_4.addItem(spacerItem4) self.pushButton_15 = QtWidgets.QPushButton(self.horizontalLayoutWidget) self.pushButton_15.setText("") icon6 = QtGui.QIcon() icon6.addPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/mic.gif"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.pushButton_15.setIcon(icon6) self.pushButton_15.setIconSize(QtCore.QSize(40, 40)) self.pushButton_15.setAutoDefault(True) self.pushButton_15.setDefault(True) self.pushButton_15.setFlat(True) self.pushButton_15.setObjectName("pushButton_15") self.horizontalLayout_4.addWidget(self.pushButton_15) spacerItem5 = QtWidgets.QSpacerItem(10, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_4.addItem(spacerItem5) self.horizontalLayoutWidget_2 = QtWidgets.QWidget(self.centralwidget) self.horizontalLayoutWidget_2.setGeometry(QtCore.QRect(70, 560, 351, 41)) self.horizontalLayoutWidget_2.setObjectName("horizontalLayoutWidget_2") self.horizontalLayout_5 = QtWidgets.QHBoxLayout(self.horizontalLayoutWidget_2) self.horizontalLayout_5.setSizeConstraint(QtWidgets.QLayout.SetNoConstraint) self.horizontalLayout_5.setContentsMargins(0, 0, 0, 0) self.horizontalLayout_5.setObjectName("horizontalLayout_5") self.textEdit_2 = QtWidgets.QTextEdit(self.horizontalLayoutWidget_2) self.textEdit_2.setObjectName("textEdit_2") self.horizontalLayout_5.addWidget(self.textEdit_2) self.pushButton_16 = QtWidgets.QPushButton(self.horizontalLayoutWidget_2) self.pushButton_16.setText("") icon7 = QtGui.QIcon() icon7.addPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/send.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.pushButton_16.setIcon(icon7) self.pushButton_16.setIconSize(QtCore.QSize(40, 40)) self.pushButton_16.setCheckable(False) self.pushButton_16.setAutoRepeatDelay(300) self.pushButton_16.setAutoDefault(True) self.pushButton_16.setDefault(True) self.pushButton_16.setFlat(True) self.pushButton_16.setObjectName("pushButton_16") self.horizontalLayout_5.addWidget(self.pushButton_16) spacerItem6 = QtWidgets.QSpacerItem(10, 10, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_5.addItem(spacerItem6) self.verticalLayoutWidget_2 = QtWidgets.QWidget(self.centralwidget) self.verticalLayoutWidget_2.setGeometry(QtCore.QRect(70, 0, 351, 561)) self.verticalLayoutWidget_2.setObjectName("verticalLayoutWidget_2") self.verticalLayout = QtWidgets.QVBoxLayout(self.verticalLayoutWidget_2) self.verticalLayout.setContentsMargins(0, 0, 0, 0) self.verticalLayout.setObjectName("verticalLayout") self.textEdit = QtWidgets.QTextEdit(self.verticalLayoutWidget_2) self.textEdit.setObjectName("textEdit") self.verticalLayout.addWidget(self.textEdit) self.label_3 = QtWidgets.QLabel(self.centralwidget) self.label_3.setGeometry(QtCore.QRect(420, 0, 961, 741)) self.label_3.setText("") self.label_3.setScaledContents(True) self.label_3.setObjectName("label_3") self.label_5 = QtWidgets.QLabel(self.centralwidget) self.label_5.setGeometry(QtCore.QRect(0, 650, 421, 91)) self.label_5.setText("") self.label_5.setPixmap(QtGui.QPixmap("D:/jarvis/Jarvis/utils/images/Recognizer.gif")) self.label_5.setScaledContents(True) self.label_5.setObjectName("label_5") MainWindow.setCentralWidget(self.centralwidget) self.movie = QtGui.QMovie("D:/jarvis/Jarvis/utils/images/AIassistant.gif") self.label_3.setMovie(self.movie) self.movie1 = QtGui.QMovie("D:/jarvis/Jarvis/utils/images/Recognizer.gif") self.label_5.setMovie(self.movie1) self.startAnimation() self.retranslateUi(MainWindow) QtCore.QMetaObject.connectSlotsByName(MainWindow) def startAnimation(self): self.movie.start() self.movie1.start() def retranslateUi(self, MainWindow): _translate = QtCore.QCoreApplication.translate MainWindow.setWindowTitle(_translate("MainWindow", "JARVIS")) if __name__ == "__main__": import sys app = QtWidgets.QApplication(sys.argv) MainWindow = QtWidgets.QMainWindow() ui = Ui_MainWindow() ui.setupUi(MainWindow) MainWindow.show() sys.exit(app.exec_())

49.755459

121

0.722924

10,821

0.94971

0

1,307

0.114709

e1c8d5b0e59bc3cff42a51e6c70986bae9cb73c9

3,201

py

Python

pints/toy/_logistic_model.py

iamleeg/pints

bd1c11472ff3ec0990f3d55f0b2f20d92397926d

[ "BSD-3-Clause" ]

null

pints/toy/_logistic_model.py

iamleeg/pints

bd1c11472ff3ec0990f3d55f0b2f20d92397926d

[ "BSD-3-Clause" ]

null

pints/toy/_logistic_model.py

iamleeg/pints

bd1c11472ff3ec0990f3d55f0b2f20d92397926d

[ "BSD-3-Clause" ]

null

# # Logistic toy model. # # This file is part of PINTS. # Copyright (c) 2017-2019, University of Oxford. # For licensing information, see the LICENSE file distributed with the PINTS # software package. # from __future__ import absolute_import, division from __future__ import print_function, unicode_literals import numpy as np import pints from . import ToyModel class LogisticModel(pints.ForwardModelS1, ToyModel): """ Logistic model of population growth [1]. .. math:: f(t) &= \\frac{k}{1+(k/p_0 - 1)*\exp(-r t)} \\\\ \\frac{\\partial f(t)}{\\partial r} &= \\frac{k t (k / p_0 - 1) \exp(-r t)} {((k/p_0-1) \exp(-r t) + 1)^2} \\\\ \\frac{\\partial f(t)}{ \\partial k} &= \\frac{k \exp(-r t)} {p_0 ((k/p_0-1)\exp(-r t) + 1)^2} + \\frac{1}{(k/p_0 - 1)\exp(-r t) + 1} Has two parameters: A growth rate :math:`r` and a carrying capacity :math:`k`. The initial population size :math:`f(0) = p_0` can be set using the (optional) named constructor arg ``initial_population_size`` [1] https://en.wikipedia.org/wiki/Population_growth *Extends:* :class:`pints.ForwardModel`, :class:`pints.toy.ToyModel`. """ def __init__(self, initial_population_size=2): super(LogisticModel, self).__init__() self._p0 = float(initial_population_size) if self._p0 < 0: raise ValueError('Population size cannot be negative.') def n_parameters(self): """ See :meth:`pints.ForwardModel.n_parameters()`. """ return 2 def simulate(self, parameters, times): """ See :meth:`pints.ForwardModel.simulate()`. """ return self._simulate(parameters, times, False) def simulateS1(self, parameters, times): """ See :meth:`pints.ForwardModelS1.simulateS1()`. """ return self._simulate(parameters, times, True) def _simulate(self, parameters, times, sensitivities): r, k = [float(x) for x in parameters] times = np.asarray(times) if np.any(times < 0): raise ValueError('Negative times are not allowed.') if self._p0 == 0 or k < 0: if sensitivities: return np.zeros(times.shape), \ np.zeros((len(times), len(parameters))) else: return np.zeros(times.shape) exp = np.exp(-r * times) c = (k / self._p0 - 1) values = k / (1 + c * exp) if sensitivities: dvalues_dp = np.empty((len(times), len(parameters))) dvalues_dp[:, 0] = k * times * c * exp / (c * exp + 1)**2 dvalues_dp[:, 1] = -k * exp / \ (self._p0 * (c * exp + 1)**2) + 1 / (c * exp + 1) return values, dvalues_dp else: return values def suggested_parameters(self): """ See :meth:`pints.toy.ToyModel.suggested_parameters()`. """ return np.array([0.1, 50]) def suggested_times(self): """ See :meth:`pints.toy.ToyModel.suggested_times()`. """ return np.linspace(0, 100, 100)

34.419355

79

0.555764

2,830

0.884099

0

1,441

0.450172

e1ca3f7ea92eaa76db3dc052ef98666164a81b5e

414

py

Python

poo/pybank/bank.py

fredsonchaves07/python-fundamentals

4aee479c48f86319a2041e35ea985f971393c2ce

[ "MIT" ]

null

poo/pybank/bank.py

fredsonchaves07/python-fundamentals

4aee479c48f86319a2041e35ea985f971393c2ce

[ "MIT" ]

null

poo/pybank/bank.py

fredsonchaves07/python-fundamentals

4aee479c48f86319a2041e35ea985f971393c2ce

[ "MIT" ]

null

class Bank: def __init__(self): self.__agencies = [1111, 2222, 3333] self.__costumers = [] self.__accounts = [] def insert_costumers(self, costumer): self.__costumers.append(costumer) def insert_accounts(self, account): self.__accounts.append(account) def authenticate(self, costumer): if costumer not in self.__costumers: return None

25.875

44

0.63285

413

0.997585

0

e1cc98556e1e617de9737efeaed139473d56ebaf

426

py

Python

homeworks/vecutil.py

JediKoder/coursera-CodeMatrix

1ac461d22ebaf2777eabdcf31d76d709c33f472a

[ "MIT" ]

3

2018-01-11T07:48:06.000Z

2020-04-27T20:49:02.000Z

homeworks/vecutil.py

JediKoder/coursera-CodeMatrix

1ac461d22ebaf2777eabdcf31d76d709c33f472a

[ "MIT" ]

null

homeworks/vecutil.py

JediKoder/coursera-CodeMatrix

1ac461d22ebaf2777eabdcf31d76d709c33f472a

[ "MIT" ]

1

2021-01-26T07:25:48.000Z

# Copyright 2013 Philip N. Klein from vec import Vec def list2vec(L): """Given a list L of field elements, return a Vec with domain {0...len(L)-1} whose entry i is L[i] >>> list2vec([10, 20, 30]) Vec({0, 1, 2},{0: 10, 1: 20, 2: 30}) """ return Vec(set(range(len(L))), {k:L[k] for k in range(len(L))}) def zero_vec(D): """Returns a zero vector with the given domain """ return Vec(D, {})

25.058824

80

0.577465

0

269

0.631455

e1ccdf7c98befd87dd0fafb60f1dfb6a4f453f5f

59,998

py

Python

squidpy/im/_container.py

Emberwhirl/squidpy

456c49ac9149e16562617a8a4236a9faa2c0480d

[ "BSD-3-Clause" ]

1

2022-02-02T13:41:36.000Z

squidpy/im/_container.py

Emberwhirl/squidpy

456c49ac9149e16562617a8a4236a9faa2c0480d

[ "BSD-3-Clause" ]

null

squidpy/im/_container.py

Emberwhirl/squidpy

456c49ac9149e16562617a8a4236a9faa2c0480d

[ "BSD-3-Clause" ]

null

from __future__ import annotations from copy import copy, deepcopy from types import MappingProxyType from typing import ( Any, Union, Mapping, TypeVar, Callable, Iterable, Iterator, Sequence, TYPE_CHECKING, ) from pathlib import Path from functools import partial from itertools import chain from typing_extensions import Literal import re import validators from scanpy import logging as logg from anndata import AnnData from scanpy.plotting.palettes import default_102 as default_palette from dask import delayed import numpy as np import xarray as xr import dask.array as da from matplotlib.colors import ListedColormap import matplotlib as mpl import matplotlib.pyplot as plt from skimage.util import img_as_float from skimage.transform import rescale from squidpy._docs import d, inject_docs from squidpy._utils import NDArrayA, singledispatchmethod from squidpy.im._io import _lazy_load_image, _infer_dimensions, _assert_dims_present from squidpy.gr._utils import ( _assert_in_range, _assert_positive, _assert_non_negative, _assert_spatial_basis, _assert_non_empty_sequence, ) from squidpy.im._coords import ( CropCoords, CropPadding, _NULL_COORDS, _NULL_PADDING, TupleSerializer, _update_attrs_scale, _update_attrs_coords, ) from squidpy.im._feature_mixin import FeatureMixin from squidpy._constants._constants import InferDimensions from squidpy._constants._pkg_constants import Key FoI_t = Union[int, float] Pathlike_t = Union[str, Path] Arraylike_t = Union[NDArrayA, xr.DataArray] InferDims_t = Union[Literal["default", "prefer_channels", "prefer_z"], Sequence[str]] Input_t = Union[Pathlike_t, Arraylike_t, "ImageContainer"] Interactive = TypeVar("Interactive") # cannot import because of cyclic dependencies _ERROR_NOTIMPLEMENTED_LIBID = f"It seems there are multiple `library_id` in `adata.uns[{Key.uns.spatial!r}]`.\n \ Loading multiple images is not implemented (yet), please specify a `library_id`." __all__ = ["ImageContainer"] @d.dedent # trick to overcome not top-down order @d.dedent class ImageContainer(FeatureMixin): """ Container for in memory arrays or on-disk images. Wraps :class:`xarray.Dataset` to store several image layers with the same `x`, `y` and `z` dimensions in one object. Dimensions of stored images are ``(y, x, z, channels)``. The channel dimension may vary between image layers. This class also allows for lazy loading and processing using :mod:`dask`, and is given to all image processing functions, along with :class:`anndata.AnnData` instance, if necessary. Parameters ---------- %(add_img.parameters)s scale Scaling factor of the image with respect to the spatial coordinates saved in the accompanying :class:`anndata.AnnData`. Raises ------ %(add_img.raises)s """ def __init__( self, img: Input_t | None = None, layer: str = "image", lazy: bool = True, scale: float = 1.0, **kwargs: Any, ): self._data: xr.Dataset = xr.Dataset() self._data.attrs[Key.img.coords] = _NULL_COORDS # can't save None to NetCDF self._data.attrs[Key.img.padding] = _NULL_PADDING self._data.attrs[Key.img.scale] = scale self._data.attrs[Key.img.mask_circle] = False if img is not None: self.add_img(img, layer=layer, **kwargs) if not lazy: self.compute() @classmethod def concat( cls, imgs: Iterable[ImageContainer], library_ids: Sequence[str | None] | None = None, combine_attrs: str = "identical", **kwargs: Any, ) -> ImageContainer: """ Concatenate ``imgs`` in Z-dimension. All ``imgs`` need to have the same shape and the same name to be concatenated. Parameters ---------- imgs Images that should be concatenated in Z-dimension. library_ids Name for each image that will be associated to each Z-dimension. This should match the ``library_id`` in the corresponding :class:`anndata.AnnData` object. If `None`, the existing name of the Z-dimension is used for each image. combine_attrs How to combine attributes of ``imgs``. By default, all ``imgs`` need to have the same scale and crop attributes. Use ``combine_attrs = 'override'`` to relax this requirement. This might lead to a mismatch between :class:`ImageContainer` and :class:`anndata.AnnData` coordinates. kwargs Keyword arguments for :func:`xarray.concat`. Returns ------- Concatenated :class:`squidpy.img.ImageContainer` with ``imgs`` stacks in Z-dimension. Raises ------ ValueError If any of the ``imgs`` have more than 1 Z-dimension or if ``library_ids`` are not unique. """ # check that imgs are not already 3d imgs = list(imgs) for img in imgs: if img.data.dims["z"] > 1: raise ValueError( f"Currently, can concatenate only images with 1 Z-dimension, found `{img.data.dims['z']}`." ) # check library_ids if library_ids is None: library_ids = [None] * len(imgs) if len(library_ids) != len(imgs): raise ValueError(f"Expected library ids to be of length `{len(imgs)}`, found `{len(library_ids)}`.") _library_ids = np.concatenate( [img._get_library_ids(library_id, allow_new=True) for img, library_id in zip(imgs, library_ids)] ) if len(set(_library_ids)) != len(_library_ids): raise ValueError(f"Found non-unique library ids `{list(_library_ids)}`.") # add library_id to z dim prep_imgs = [] for lid, img in zip(_library_ids, imgs): prep_img = img.copy() prep_img._data = prep_img.data.assign_coords(z=[lid]) prep_imgs.append(prep_img) return cls._from_dataset( xr.concat([img.data for img in prep_imgs], dim="z", combine_attrs=combine_attrs, **kwargs) ) @classmethod def load(cls, path: Pathlike_t, lazy: bool = True, chunks: int | None = None) -> ImageContainer: """ Load data from a *Zarr* store. Parameters ---------- path Path to *Zarr* store. lazy Whether to use :mod:`dask` to lazily load image. chunks Chunk size for :mod:`dask`. Only used when ``lazy = True``. Returns ------- The loaded container. """ res = cls() res.add_img(path, layer="image", chunks=chunks, lazy=True) return res if lazy else res.compute() def save(self, path: Pathlike_t, **kwargs: Any) -> None: """ Save the container into a *Zarr* store. Parameters ---------- path Path to a *Zarr* store. Returns ------- Nothing, just saves the container. """ attrs = self.data.attrs try: self._data = self.data.load() # if we're loading lazily and immediately saving self.data.attrs = { k: (v.to_tuple() if isinstance(v, TupleSerializer) else v) for k, v in self.data.attrs.items() } self.data.to_zarr(str(path), mode="w", **kwargs, **kwargs) finally: self.data.attrs = attrs @d.get_sections(base="add_img", sections=["Parameters", "Raises"]) @d.dedent @inject_docs(id=InferDimensions) def add_img( self, img: Input_t, layer: str | None = None, dims: InferDims_t = InferDimensions.DEFAULT.s, library_id: str | Sequence[str] | None = None, lazy: bool = True, chunks: str | tuple[int, ...] | None = None, copy: bool = True, **kwargs: Any, ) -> None: """ Add a new image to the container. Parameters ---------- img In-memory 2, 3 or 4-dimensional array, a URL to a *Zarr* store (ending in *.zarr*), or a path to an on-disk image. %(img_layer)s dims Where to save channel dimension when reading from a file or loading an array. Valid options are: - `{id.CHANNELS_LAST.s!r}` - load the last non-spatial dimension as channels. - `{id.Z_LAST.s!r}` - load the last non-spatial dimension as Z-dimension. - `{id.DEFAULT.s!r}` - same as `{id.CHANNELS_LAST.s!r}`, but for 4-dimensional arrays, tries to also load the first dimension as channels if the last non-spatial dimension is 1. - a sequence of dimension names matching the shape of ``img``, e.g. ``('y', 'x', 'z', 'channels')``. `'y'`, `'x'` and `'z'` must always be present. library_id Name for each Z-dimension of the image. This should correspond to the ``library_id`` in :attr:`anndata.AnnData.uns`. lazy Whether to use :mod:`dask` to lazily load image. chunks Chunk size for :mod:`dask`. Only used when ``lazy = True``. copy Whether to copy the underlying data if ``img`` is an in-memory array. Returns ------- Nothing, just adds a new ``layer`` to :attr:`data`. Raises ------ ValueError If loading from a file/store with an unknown format or if a supplied channel dimension cannot be aligned. NotImplementedError If loading a specific data type has not been implemented. """ layer = self._get_next_image_id("image") if layer is None else layer dims: InferDimensions | Sequence[str] = ( # type: ignore[no-redef] InferDimensions(dims) if isinstance(dims, str) else dims ) res: xr.DataArray | None = self._load_img(img, chunks=chunks, layer=layer, copy=copy, dims=dims, **kwargs) if res is not None: library_id = self._get_library_ids(library_id, res, allow_new=not len(self)) try: res = res.assign_coords({"z": library_id}) except ValueError as e: if "conflicting sizes for dimension 'z'" not in str(e): raise # at this point, we know the container is not empty raise ValueError( f"Expected image to have `{len(self.library_ids)}` Z-dimension(s), found `{res.sizes['z']}`." ) from None if TYPE_CHECKING: assert isinstance(res, xr.DataArray) logg.info(f"{'Overwriting' if layer in self else 'Adding'} image layer `{layer}`") try: self.data[layer] = res except ValueError as e: c_dim = res.dims[-1] if f"along dimension {str(c_dim)!r} cannot be aligned" not in str(e): raise channel_dim = self._get_next_channel_id(res) logg.warning(f"Channel dimension cannot be aligned with an existing one, using `{channel_dim}`") self.data[layer] = res.rename({res.dims[-1]: channel_dim}) if not lazy: self.compute(layer) @singledispatchmethod def _load_img(self, img: Pathlike_t | Input_t | ImageContainer, layer: str, **kwargs: Any) -> xr.DataArray | None: if isinstance(img, ImageContainer): if layer not in img: raise KeyError(f"Image identifier `{layer}` not found in `{img}`.") _ = kwargs.pop("dims", None) return self._load_img(img[layer], **kwargs) raise NotImplementedError(f"Loading `{type(img).__name__}` is not yet implemented.") @_load_img.register(str) @_load_img.register(Path) def _( self, img_path: Pathlike_t, chunks: int | None = None, dims: InferDimensions | tuple[str, ...] = InferDimensions.DEFAULT, **_: Any, ) -> xr.DataArray | None: def transform_metadata(data: xr.Dataset) -> xr.Dataset: for key, img in data.items(): if len(img.dims) != 4: data[key] = img = img.expand_dims({"z": 1}, axis=-2) # assume only channel dim is present _assert_dims_present(img.dims, include_z=True) data.attrs[Key.img.coords] = CropCoords.from_tuple(data.attrs.get(Key.img.coords, _NULL_COORDS.to_tuple())) data.attrs[Key.img.padding] = CropPadding.from_tuple( data.attrs.get(Key.img.padding, _NULL_PADDING.to_tuple()) ) data.attrs.setdefault(Key.img.mask_circle, False) data.attrs.setdefault(Key.img.scale, 1) return data img_path = str(img_path) is_url, suffix = validators.url(img_path), Path(img_path).suffix.lower() logg.debug(f"Loading data from `{img_path}`") if not is_url and not Path(img_path).exists(): raise OSError(f"Path `{img_path}` does not exist.") if suffix in (".jpg", ".jpeg", ".png", ".tif", ".tiff"): return _lazy_load_image(img_path, dims=dims, chunks=chunks) if suffix == ".zarr" or Path(img_path).is_dir(): # can also be a URL if len(self._data): raise ValueError("Loading data from `Zarr` store is disallowed when the container is not empty.") self._data = transform_metadata(xr.open_zarr(img_path, chunks=chunks)) elif suffix in (".nc", ".cdf"): if len(self._data): raise ValueError("Loading data from `NetCDF` is disallowed when the container is not empty.") self._data = transform_metadata(xr.open_dataset(img_path, chunks=chunks)) else: raise ValueError(f"Unable to handle path `{img_path}`.") @_load_img.register(da.Array) @_load_img.register(np.ndarray) def _( self, img: NDArrayA, copy: bool = True, dims: InferDimensions | tuple[str, ...] = InferDimensions.DEFAULT, **_: Any, ) -> xr.DataArray: logg.debug(f"Loading `numpy.array` of shape `{img.shape}`") return self._load_img(xr.DataArray(img), copy=copy, dims=dims, warn=False) @_load_img.register(xr.DataArray) def _( self, img: xr.DataArray, copy: bool = True, warn: bool = True, dims: InferDimensions | tuple[str, ...] = InferDimensions.DEFAULT, **_: Any, ) -> xr.DataArray: logg.debug(f"Loading `xarray.DataArray` of shape `{img.shape}`") img = img.copy() if copy else img if not ("y" in img.dims and "x" in img.dims and "z" in img.dims): _, dims, _, expand_axes = _infer_dimensions(img, infer_dimensions=dims) if TYPE_CHECKING: assert isinstance(dims, Iterable) if warn: logg.warning(f"Unable to find `y`, `x` or `z` dimension in `{img.dims}`. Renaming to `{dims}`") # `axes` is always of length 0, 1 or 2 if len(expand_axes): dimnames = ("z", "channels") if len(expand_axes) == 2 else (("channels",) if "z" in dims else ("z",)) img = img.expand_dims([d for _, d in zip(expand_axes, dimnames)], axis=expand_axes) img = img.rename(dict(zip(img.dims, dims))) return img.transpose("y", "x", "z", ...) @classmethod @d.dedent def from_adata( cls, adata: AnnData, img_key: str | None = None, library_id: Sequence[str] | str | None = None, spatial_key: str = Key.uns.spatial, **kwargs: Any, ) -> ImageContainer: """ Load an image from :mod:`anndata` object. Parameters ---------- %(adata)s img_key Key in :attr:`anndata.AnnData.uns` ``['{spatial_key}']['{library_id}']['images']``. If `None`, the first key found is used. library_id Key in :attr:`anndata.AnnData.uns` ``['{spatial_key}']`` specifying which library to access. spatial_key Key in :attr:`anndata.AnnData.uns` where spatial metadata is stored. kwargs Keyword arguments for :class:`squidpy.im.ImageContainer`. Returns ------- The image container. """ library_id = Key.uns.library_id(adata, spatial_key, library_id) if not isinstance(library_id, str): raise NotImplementedError(_ERROR_NOTIMPLEMENTED_LIBID) spatial_data = adata.uns[spatial_key][library_id] if img_key is None: try: img_key = next(k for k in spatial_data.get("images", [])) except StopIteration: raise KeyError(f"No images found in `adata.uns[{spatial_key!r}][{library_id!r}]['images']`") from None img: NDArrayA | None = spatial_data.get("images", {}).get(img_key, None) if img is None: raise KeyError( f"Unable to find the image in `adata.uns[{spatial_key!r}][{library_id!r}]['images'][{img_key!r}]`." ) scale = spatial_data.get("scalefactors", {}).get(f"tissue_{img_key}_scalef", None) if scale is None and "scale" not in kwargs: logg.warning( f"Unable to determine the scale factor from " f"`adata.uns[{spatial_key!r}][{library_id!r}]['scalefactors']['tissue_{img_key}_scalef']`, " f"using `1.0`. Consider specifying it manually as `scale=...`" ) scale = 1.0 kwargs.setdefault("scale", scale) return cls(img, layer=img_key, library_id=library_id, **kwargs) @d.get_sections(base="crop_corner", sections=["Parameters", "Returns"]) @d.dedent def crop_corner( self, y: FoI_t, x: FoI_t, size: FoI_t | tuple[FoI_t, FoI_t] | None = None, library_id: str | None = None, scale: float = 1.0, cval: int | float = 0, mask_circle: bool = False, preserve_dtypes: bool = True, ) -> ImageContainer: """ Extract a crop from the upper-left corner. Parameters ---------- %(yx)s %(size)s library_id Name of the Z-dimension to be cropped. If `None`, all Z-dimensions are cropped. scale Rescale the crop using :func:`skimage.transform.rescale`. cval Fill value to use if ``mask_circle = True`` or if crop goes out of the image boundary. mask_circle Whether to mask out values that are not within a circle defined by this crop. Only available if ``size`` defines a square. preserve_dtypes Whether to preserver the data types of underlying :class:`xarray.DataArray`, even if ``cval`` is of different type. Returns ------- The cropped image of size ``size * scale``. Raises ------ ValueError If the crop would completely lie outside of the image or if ``mask_circle = True`` and ``size`` does not define a square. Notes ----- If ``preserve_dtypes = True`` but ``cval`` cannot be safely cast, ``cval`` will be set to 0. """ self._assert_not_empty() y, x = self._convert_to_pixel_space((y, x)) size = self._get_size(size) size = self._convert_to_pixel_space(size) ys, xs = size _assert_positive(ys, name="height") _assert_positive(xs, name="width") _assert_positive(scale, name="scale") orig = CropCoords(x0=x, y0=y, x1=x + xs, y1=y + ys) ymin, xmin = self.shape coords = CropCoords( x0=min(max(x, 0), xmin), y0=min(max(y, 0), ymin), x1=min(x + xs, xmin), y1=min(y + ys, ymin) ) if not coords.dy: raise ValueError("Height of the crop is empty.") if not coords.dx: raise ValueError("Width of the crop is empty.") crop = self.data.isel(x=slice(coords.x0, coords.x1), y=slice(coords.y0, coords.y1)).copy(deep=False) if len(crop.z) > 1: crop = crop.sel(z=self._get_library_ids(library_id)) crop.attrs = _update_attrs_coords(crop.attrs, coords) if orig != coords: padding = orig - coords # because padding does not change dtype by itself for key, arr in crop.items(): if preserve_dtypes: if not np.can_cast(cval, arr.dtype, casting="safe"): cval = 0 else: crop[key] = crop[key].astype(np.dtype(type(cval)), copy=False) crop = crop.pad( y=(padding.y_pre, padding.y_post), x=(padding.x_pre, padding.x_post), mode="constant", constant_values=cval, ) crop.attrs[Key.img.padding] = padding else: crop.attrs[Key.img.padding] = _NULL_PADDING return self._from_dataset( self._post_process( data=crop, scale=scale, cval=cval, mask_circle=mask_circle, preserve_dtypes=preserve_dtypes ) ) def _post_process( self, data: xr.Dataset, scale: FoI_t = 1, cval: FoI_t = 0, mask_circle: bool = False, preserve_dtypes: bool = True, **_: Any, ) -> xr.Dataset: def _rescale(arr: xr.DataArray) -> xr.DataArray: scaling_fn = partial( rescale, scale=[scale, scale, 1], preserve_range=True, order=1, channel_axis=-1, cval=cval ) dtype = arr.dtype if isinstance(arr.data, da.Array): shape = np.maximum(np.round(scale * np.asarray(arr.shape)), 1) shape[-1] = arr.shape[-1] shape[-2] = arr.shape[-2] return xr.DataArray( da.from_delayed(delayed(lambda arr: scaling_fn(arr).astype(dtype))(arr), shape=shape, dtype=dtype), dims=arr.dims, ) return xr.DataArray(scaling_fn(arr).astype(dtype), dims=arr.dims) if scale != 1: attrs = data.attrs library_ids = data.coords["z"] data = data.map(_rescale).assign_coords({"z": library_ids}) data.attrs = _update_attrs_scale(attrs, scale) if mask_circle: if data.dims["y"] != data.dims["x"]: raise ValueError( f"Masking circle is only available for square crops, " f"found crop of shape `{(data.dims['y'], data.dims['x'])}`." ) c = data.x.shape[0] // 2 # manually reassign coordinates library_ids = data.coords["z"] data = data.where((data.x - c) ** 2 + (data.y - c) ** 2 <= c**2, other=cval).assign_coords( {"z": library_ids} ) data.attrs[Key.img.mask_circle] = True if preserve_dtypes: for key, arr in self.data.items(): data[key] = data[key].astype(arr.dtype, copy=False) return data @d.dedent def crop_center( self, y: FoI_t, x: FoI_t, radius: FoI_t | tuple[FoI_t, FoI_t], **kwargs: Any, ) -> ImageContainer: """ Extract a circular crop. The extracted crop will have shape ``(radius[0] * 2 + 1, radius[1] * 2 + 1)``. Parameters ---------- %(yx)s radius Radius along the ``height`` and ``width`` dimensions, respectively. kwargs Keyword arguments for :meth:`crop_corner`. Returns ------- %(crop_corner.returns)s """ y, x = self._convert_to_pixel_space((y, x)) _assert_in_range(y, 0, self.shape[0], name="height") _assert_in_range(x, 0, self.shape[1], name="width") if not isinstance(radius, Iterable): radius = (radius, radius) (yr, xr) = self._convert_to_pixel_space(radius) _assert_non_negative(yr, name="radius height") _assert_non_negative(xr, name="radius width") return self.crop_corner( # type: ignore[no-any-return] y=y - yr, x=x - xr, size=(yr * 2 + 1, xr * 2 + 1), **kwargs ) @d.dedent def generate_equal_crops( self, size: FoI_t | tuple[FoI_t, FoI_t] | None = None, as_array: str | bool = False, squeeze: bool = True, **kwargs: Any, ) -> Iterator[ImageContainer] | Iterator[dict[str, NDArrayA]]: """ Decompose image into equally sized crops. Parameters ---------- %(size)s %(as_array)s squeeze Remove singleton dimensions from the results if ``as_array = True``. kwargs Keyword arguments for :meth:`crop_corner`. Yields ------ The crops, whose type depends on ``as_array``. Notes ----- Crops going outside out of the image boundary are padded with ``cval``. """ self._assert_not_empty() size = self._get_size(size) size = self._convert_to_pixel_space(size) y, x = self.shape ys, xs = size _assert_in_range(ys, 0, y, name="height") _assert_in_range(xs, 0, x, name="width") unique_ycoord = np.arange(start=0, stop=(y // ys + (y % ys != 0)) * ys, step=ys) unique_xcoord = np.arange(start=0, stop=(x // xs + (x % xs != 0)) * xs, step=xs) ycoords = np.repeat(unique_ycoord, len(unique_xcoord)) xcoords = np.tile(unique_xcoord, len(unique_ycoord)) for y, x in zip(ycoords, xcoords): yield self.crop_corner(y=y, x=x, size=(ys, xs), **kwargs)._maybe_as_array( as_array, squeeze=squeeze, lazy=True ) @d.dedent def generate_spot_crops( self, adata: AnnData, spatial_key: str = Key.obsm.spatial, library_id: Sequence[str] | str | None = None, spot_diameter_key: str = "spot_diameter_fullres", spot_scale: float = 1.0, obs_names: Iterable[Any] | None = None, as_array: str | bool = False, squeeze: bool = True, return_obs: bool = False, **kwargs: Any, ) -> ( Iterator[ImageContainer] | Iterator[NDArrayA] | Iterator[tuple[NDArrayA, ...]] | Iterator[dict[str, NDArrayA]] ): """ Iterate over :attr:`anndata.AnnData.obs_names` and extract crops. Implemented for 10X spatial datasets. For Z-stacks, the specified ``library_id`` or list of ``library_id`` need to match the name of the Z-dimension. Always extracts 2D crops from the specified Z-dimension. Parameters ---------- %(adata)s %(spatial_key)s %(img_library_id)s spot_diameter_key Key in :attr:`anndata.AnnData.uns` ``['{spatial_key}']['{library_id}']['scalefactors']`` where the spot diameter is stored. spot_scale Scaling factor for the spot diameter. Larger values mean more context. obs_names Observations from :attr:`anndata.AnnData.obs_names` for which to generate the crops. If `None`, all observations are used. %(as_array)s squeeze Remove singleton dimensions from the results if ``as_array = True``. return_obs Whether to also yield names from ``obs_names``. kwargs Keyword arguments for :meth:`crop_center`. Yields ------ If ``return_obs = True``, yields a :class:`tuple` ``(crop, obs_name)``. Otherwise, yields just the crops. The type of the crops depends on ``as_array`` and the number of dimensions on ``squeeze``. """ self._assert_not_empty() _assert_positive(spot_scale, name="scale") _assert_spatial_basis(adata, spatial_key) # limit to obs_names if obs_names is None: obs_names = adata.obs_names obs_names = _assert_non_empty_sequence(obs_names, name="observations") adata = adata[obs_names, :] scale = self.data.attrs.get(Key.img.scale, 1) spatial = adata.obsm[spatial_key][:, :2] if library_id is None: try: library_id = Key.uns.library_id(adata, spatial_key=spatial_key, library_id=None) if not isinstance(library_id, str): raise NotImplementedError(_ERROR_NOTIMPLEMENTED_LIBID) obs_library_ids = [library_id] * adata.n_obs except ValueError as e: if "Unable to determine which library id to use" in str(e): raise ValueError( str(e) + " Or specify a key in `adata.obs` containing a mapping from observations to library ids." ) else: raise e else: try: obs_library_ids = adata.obs[library_id] except KeyError: logg.debug( f"Unable to find library ids in `adata.obs[{library_id!r}]`. " f"Trying in `adata.uns[{spatial_key!r}]`" ) library_id = Key.uns.library_id(adata, spatial_key=spatial_key, library_id=library_id) if not isinstance(library_id, str): raise NotImplementedError(_ERROR_NOTIMPLEMENTED_LIBID) obs_library_ids = [library_id] * adata.n_obs lids = set(obs_library_ids) if len(self.data.z) > 1 and len(lids) == 1: logg.warning( f"ImageContainer has `{len(self.data.z)}` Z-dimensions, using library id `{next(iter(lids))}` for all" ) if adata.n_obs != len(obs_library_ids): raise ValueError(f"Expected library ids to be of length `{adata.n_obs}`, found `{len(obs_library_ids)}`.") for i, (obs, lid) in enumerate(zip(adata.obs_names, obs_library_ids)): # get spot diameter of current obs (might be different library ids) diameter = ( Key.uns.spot_diameter( adata, spatial_key=spatial_key, library_id=lid, spot_diameter_key=spot_diameter_key ) * scale ) radius = int(round(diameter // 2 * spot_scale)) # get coords in image pixel space from original space y = int(spatial[i][1] * scale) x = int(spatial[i][0] * scale) # if CropCoords exist, need to offset y and x if self.data.attrs.get(Key.img.coords, _NULL_COORDS) != _NULL_COORDS: y = int(y - self.data.attrs[Key.img.coords].y0) x = int(x - self.data.attrs[Key.img.coords].x0) crop = self.crop_center(y=y, x=x, radius=radius, library_id=obs_library_ids[i], **kwargs) crop.data.attrs[Key.img.obs] = obs crop = crop._maybe_as_array(as_array, squeeze=squeeze, lazy=False) yield (crop, obs) if return_obs else crop @classmethod @d.get_sections(base="uncrop", sections=["Parameters", "Returns"]) def uncrop( cls, crops: list[ImageContainer], shape: tuple[int, int] | None = None, ) -> ImageContainer: """ Re-assemble image from crops and their positions. Fills remaining positions with zeros. Parameters ---------- crops List of image crops. shape Requested image shape as ``(height, width)``. If `None`, it is automatically determined from ``crops``. Returns ------- Re-assembled image from ``crops``. Raises ------ ValueError If crop metadata was not found or if the requested ``shape`` is smaller than required by ``crops``. """ if not len(crops): raise ValueError("No crops were supplied.") keys = set(crops[0].data.keys()) scales = set() dy, dx = -1, -1 for crop in crops: if set(crop.data.keys()) != keys: raise KeyError(f"Expected to find `{sorted(keys)}` keys, found `{sorted(crop.data.keys())}`.") coord = crop.data.attrs.get(Key.img.coords, None) if coord is None: raise ValueError("Crop does not have coordinate metadata.") if coord == _NULL_COORDS: raise ValueError(f"Null coordinates detected `{coord}`.") scales.add(crop.data.attrs.get(Key.img.scale, None)) dy, dx = max(dy, coord.y0 + coord.dy), max(dx, coord.x0 + coord.dx) scales.discard(None) if len(scales) != 1: raise ValueError(f"Unable to uncrop images of different scales `{sorted((scales))}`.") scale, *_ = scales if shape is None: shape = (dy, dx) # can be float because coords can be scaled shape = tuple(map(int, shape)) # type: ignore[assignment] if len(shape) != 2: raise ValueError(f"Expected `shape` to be of length `2`, found `{len(shape)}`.") if shape < (dy, dx): raise ValueError(f"Requested final image shape `{shape}`, but minimal is `({dy}, {dx})`.") # create resulting dataset dataset = xr.Dataset() dataset.attrs[Key.img.scale] = scale for key in keys: img = crop.data[key] # get shape for this DataArray dataset[key] = xr.DataArray( np.zeros(shape + tuple(img.shape[2:]), dtype=img.dtype), dims=img.dims, coords=img.coords ) # fill data with crops for crop in crops: coord = crop.data.attrs[Key.img.coords] padding = crop.data.attrs.get(Key.img.padding, _NULL_PADDING) # maybe warn dataset[key][coord.slice] = crop[key][coord.to_image_coordinates(padding=padding).slice] return cls._from_dataset(dataset) @d.dedent def show( self, layer: str | None = None, library_id: str | Sequence[str] | None = None, channel: int | Sequence[int] | None = None, channelwise: bool = False, segmentation_layer: str | None = None, segmentation_alpha: float = 0.75, transpose: bool | None = None, ax: mpl.axes.Axes | None = None, figsize: tuple[float, float] | None = None, dpi: int | None = None, save: Pathlike_t | None = None, **kwargs: Any, ) -> None: """ Show an image within this container. Parameters ---------- %(img_layer)s library_id Name of Z-dimension to plot. In `None`, plot all Z-dimensions as separate images. channel Channels to plot. If `None`, use all channels. channelwise Whether to plot each channel separately or not. segmentation_layer Segmentation layer to plot over each ax. segmentation_alpha Alpha value for ``segmentation_layer``. transpose Whether to plot Z-dimensions in columns or in rows. If `None`, it will be set to ``not channelwise``. ax Optional :mod:`matplotlib` axes where to plot the image. If not `None`, ``save``, ``figsize`` and ``dpi`` have no effect. %(plotting)s kwargs Keyword arguments for :meth:`matplotlib.axes.Axes.imshow`. Returns ------- %(plotting_returns)s Raises ------ ValueError If number of supplied axes is different than the number of requested Z-dimensions or channels. """ from squidpy.pl._utils import save_fig layer = self._get_layer(layer) arr: xr.DataArray = self[layer] library_ids = self._get_library_ids(library_id) arr = arr.sel(z=library_ids) if channel is not None: channel = np.asarray([channel]).ravel() # type: ignore[assignment] if not len(channel): # type: ignore[arg-type] raise ValueError("No channels have been selected.") arr = arr[{arr.dims[-1]: channel}] else: channel = np.arange(arr.shape[-1]) if TYPE_CHECKING: assert isinstance(channel, Sequence) n_channels = arr.shape[-1] if n_channels not in (1, 3, 4) and not channelwise: logg.warning(f"Unable to plot image with `{n_channels}`. Setting `channelwise=True`") channelwise = True if transpose is None: transpose = not channelwise fig = None nrows, ncols = len(library_ids), (n_channels if channelwise else 1) if transpose: nrows, ncols = ncols, nrows if ax is None: fig, ax = plt.subplots( nrows=nrows, ncols=ncols, figsize=(8, 8) if figsize is None else figsize, dpi=dpi, tight_layout=True, squeeze=False, ) elif isinstance(ax, mpl.axes.Axes): ax = np.array([ax]) ax = np.asarray(ax) try: ax = ax.reshape(nrows, ncols) except ValueError: raise ValueError(f"Expected `ax` to be of shape `{(nrows, ncols)}`, found `{ax.shape}`.") from None if segmentation_layer is not None: seg_arr = self[segmentation_layer].sel(z=library_ids) if not seg_arr.attrs.get("segmentation", False): raise TypeError(f"Expected layer `{segmentation_layer!r}` to be marked as segmentation layer.") if not np.issubdtype(seg_arr.dtype, np.integer): raise TypeError( f"Expected segmentation layer `{segmentation_layer!r}` to be of integer type, " f"found `{seg_arr.dtype}`." ) seg_arr = seg_arr.values seg_cmap = np.array(default_palette, dtype=object)[np.arange(np.max(seg_arr)) % len(default_palette)] seg_cmap[0] = "#00000000" # transparent background seg_cmap = ListedColormap(seg_cmap) else: seg_arr, seg_cmap = None, None for z, row in enumerate(ax): for c, ax_ in enumerate(row): if transpose: z, c = c, z title = layer if channelwise: img = arr[..., z, c] title += f":{channel[c]}" else: img = arr[..., z, :] if len(self.data.coords["z"]) > 1: title += f", library_id:{library_ids[z]}" ax_.imshow(img_as_float(img.values, force_copy=False), **kwargs) if seg_arr is not None: ax_.imshow( seg_arr[:, :, z, ...], cmap=seg_cmap, interpolation="nearest", # avoid artifacts alpha=segmentation_alpha, **{k: v for k, v in kwargs.items() if k not in ("cmap", "interpolation")}, ) ax_.set_title(title) ax_.set_axis_off() if save and fig is not None: save_fig(fig, save) @d.get_sections(base="_interactive", sections=["Parameters"]) @d.dedent def interactive( self, adata: AnnData, spatial_key: str = Key.obsm.spatial, library_key: str | None = None, library_id: str | Sequence[str] | None = None, cmap: str = "viridis", palette: str | None = None, blending: Literal["opaque", "translucent", "additive"] = "opaque", symbol: Literal["disc", "square"] = "disc", key_added: str = "shapes", ) -> Interactive: """ Launch :mod:`napari` viewer. Parameters ---------- %(adata)s %(spatial_key)s library_key Key in :attr:`adata.AnnData.obs` specifying mapping between observations and library ids. Required if the container has more than 1 Z-dimension. library_id Subset of library ids to visualize. If `None`, visualize all library ids. cmap Colormap for continuous variables. palette Colormap for categorical variables in :attr:`anndata.AnnData.obs`. If `None`, use :mod:`scanpy`'s default. blending Method which determines how RGB and alpha values of :class:`napari.layers.Shapes` are mixed. symbol Symbol to use for the spots. Valid options are: - `'disc'` - circle. - `'square'` - square. key_added Key where to store :class:`napari.layers.Shapes`, which can be exported by pressing `SHIFT-E`: - :attr:`anndata.AnnData.obs` ``['{layer_name}_{key_added}']`` - boolean mask containing the selected cells. - :attr:`anndata.AnnData.uns` ``['{layer_name}_{key_added}']['meshes']`` - list of :class:`numpy.array`, defining a mesh in the spatial coordinates. See :mod:`napari`'s `tutorial <https://napari.org/howtos/layers/shapes.html>`_ for more information about different mesh types, such as circles, squares etc. Returns ------- Interactive view of this container. Screenshot of the canvas can be taken by :meth:`squidpy.pl.Interactive.screenshot`. """ from squidpy.pl import Interactive # type: ignore[attr-defined] return Interactive( # type: ignore[no-any-return] img=self, adata=adata, spatial_key=spatial_key, library_key=library_key, library_id=library_id, cmap=cmap, palette=palette, blending=blending, key_added=key_added, symbol=symbol, ).show() @d.dedent def apply( self, func: Callable[..., NDArrayA] | Mapping[str, Callable[..., NDArrayA]], layer: str | None = None, new_layer: str | None = None, channel: int | None = None, lazy: bool = False, chunks: str | tuple[int, int] | None = None, copy: bool = True, drop: bool = True, fn_kwargs: Mapping[str, Any] = MappingProxyType({}), **kwargs: Any, ) -> ImageContainer | None: """ Apply a function to a layer within this container. For each Z-dimension a different function can be defined, using its ``library_id`` name. For not mentioned ``library_id``'s the identity function is applied. Parameters ---------- func A function or a mapping of ``{'{library_id}': function}`` which takes a :class:`numpy.ndarray` as input and produces an image-like output. %(img_layer)s new_layer Name of the new layer. If `None` and ``copy = False``, overwrites the data in ``layer``. channel Apply ``func`` only over a specific ``channel``. If `None`, use all channels. chunks Chunk size for :mod:`dask`. If `None`, don't use :mod:`dask`. %(copy_cont)s drop Whether to drop Z-dimensions that were not selected by ``func``. Only used when ``copy = True``. fn_kwargs Keyword arguments for ``func``. kwargs Keyword arguments for :func:`dask.array.map_overlap` or :func:`dask.array.map_blocks`, depending whether ``depth`` is present in ``fn_kwargs``. Only used when ``chunks != None``. Use ``depth`` to control boundary artifacts if ``func`` requires data from neighboring chunks, by default, ``boundary = 'reflect`` is used. Returns ------- If ``copy = True``, returns a new container with ``layer``. Raises ------ ValueError If the ``func`` returns 0 or 1 dimensional array. """ def apply_func(func: Callable[..., NDArrayA], arr: xr.DataArray) -> NDArrayA | da.Array: if chunks is None: return func(arr.data, **fn_kwargs) arr = da.asarray(arr.data).rechunk(chunks) return ( da.map_overlap(func, arr, **fn_kwargs, **kwargs) if "depth" in kwargs else da.map_blocks(func, arr, **fn_kwargs, **kwargs, dtype=arr.dtype) ) if "depth" in kwargs: kwargs.setdefault("boundary", "reflect") layer = self._get_layer(layer) if new_layer is None: new_layer = layer arr = self[layer] library_ids = list(arr.coords["z"].values) dims, channel_dim = arr.dims, arr.dims[-1] if channel is not None: arr = arr[{channel_dim: channel}] if callable(func): res = apply_func(func, arr) new_library_ids = library_ids else: res = {} noop_library_ids = [] if copy and drop else list(set(library_ids) - set(func.keys())) for key, fn in func.items(): res[key] = apply_func(fn, arr.sel(z=key)) for key in noop_library_ids: res[key] = arr.sel(z=key).data new_library_ids = [lid for lid in library_ids if lid in res] try: res = da.stack([res[lid] for lid in new_library_ids], axis=2) except ValueError as e: if not len(noop_library_ids) or "must have the same shape" not in str(e): # processing functions returned wrong shape raise ValueError( "Unable to stack an array because functions returned arrays of different shapes." ) from e # funcs might have changed channel dims, replace noops with 0 logg.warning( f"Function changed the number of channels, cannot use identity " f"for library ids `{noop_library_ids}`. Replacing with 0" ) # TODO(michalk8): once (or if) Z-dim is not fixed, always drop ids tmp = next(iter(res.values())) for lid in noop_library_ids: res[lid] = (np.zeros_like if chunks is None else da.zeros_like)(tmp) res = da.stack([res[lid] for lid in new_library_ids], axis=2) if res.ndim == 2: # assume that dims are y, x res = res[..., np.newaxis] if res.ndim == 3: # assume dims are y, x, z (changing of z dim is not supported) res = res[..., np.newaxis] if res.ndim != 4: raise ValueError(f"Expected `2`, `3` or `4` dimensional array, found `{res.ndim}`.") if copy: cont = ImageContainer( res, layer=new_layer, copy=True, lazy=lazy, dims=dims, library_id=new_library_ids, ) cont.data.attrs = self.data.attrs.copy() return cont self.add_img( res, layer=new_layer, lazy=lazy, copy=new_layer != layer, dims=dims, library_id=new_library_ids, ) @d.dedent def subset(self, adata: AnnData, spatial_key: str = Key.obsm.spatial, copy: bool = False) -> AnnData: """ Subset :class:`anndata.AnnData` using this container. Useful when this container is a crop of the original image. Parameters ---------- %(adata)s %(spatial_key)s copy Whether to return a copy of ``adata``. Returns ------- Subset of :class:`anndata.AnnData`. """ c: CropCoords = self.data.attrs.get(Key.img.coords, _NULL_COORDS) if c == _NULL_COORDS: # not a crop return adata.copy() if copy else adata _assert_spatial_basis(adata, spatial_key) coordinates = adata.obsm[spatial_key] coordinates = coordinates * self.data.attrs.get(Key.img.scale, 1) mask = ( (coordinates[:, 0] >= c.x0) & (coordinates[:, 0] <= c.x1) & (coordinates[:, 1] >= c.y0) & (coordinates[:, 1] <= c.y1) ) return adata[mask, :].copy() if copy else adata[mask, :] def rename(self, old: str, new: str) -> ImageContainer: """ Rename a layer. Parameters ---------- old Name of the layer to rename. new New name. Returns ------- Modifies and returns self. """ self._data = self.data.rename_vars({old: new}) return self def compute(self, layer: str | None = None) -> ImageContainer: """ Trigger lazy computation in-place. Parameters ---------- layer Layer which to compute. If `None`, compute all layers. Returns ------- Modifies and returns self. """ if layer is None: self.data.load() else: self[layer].load() return self @property def library_ids(self) -> list[str]: """Library ids.""" try: return list(map(str, self.data.coords["z"].values)) except KeyError: return [] @library_ids.setter def library_ids(self, library_ids: str | Sequence[str] | Mapping[str, str]) -> None: """Set library ids.""" if isinstance(library_ids, Mapping): library_ids = [str(library_ids.get(lid, lid)) for lid in self.library_ids] elif isinstance(library_ids, str): library_ids = (library_ids,) library_ids = list(map(str, library_ids)) if len(set(library_ids)) != len(library_ids): raise ValueError(f"Remapped library ids must be unique, found `{library_ids}`.") self._data = self.data.assign_coords({"z": library_ids}) @property def data(self) -> xr.Dataset: """Underlying :class:`xarray.Dataset`.""" return self._data @property def shape(self) -> tuple[int, int]: """Image shape ``(y, x)``.""" if not len(self): return 0, 0 return self.data.dims["y"], self.data.dims["x"] def copy(self, deep: bool = False) -> ImageContainer: """ Return a copy of self. Parameters ---------- deep Whether to make a deep copy or not. Returns ------- Copy of self. """ return deepcopy(self) if deep else copy(self) @classmethod def _from_dataset(cls, data: xr.Dataset, deep: bool | None = None) -> ImageContainer: """ Utility function used for initialization. Parameters ---------- data The :class:`xarray.Dataset` to use. deep If `None`, don't copy the ``data``. If `True`, make a deep copy of the data, otherwise, make a shallow copy. Returns ------- The newly created container. """ # noqa: D401 res = cls() res._data = data if deep is None else data.copy(deep=deep) res._data.attrs.setdefault(Key.img.coords, _NULL_COORDS) # can't save None to NetCDF res._data.attrs.setdefault(Key.img.padding, _NULL_PADDING) res._data.attrs.setdefault(Key.img.scale, 1.0) res._data.attrs.setdefault(Key.img.mask_circle, False) return res def _maybe_as_array( self, as_array: str | Sequence[str] | bool = False, squeeze: bool = True, lazy: bool = True, ) -> ImageContainer | dict[str, NDArrayA] | NDArrayA | tuple[NDArrayA, ...]: res = self if as_array: # do not trigger dask computation res = {key: (res[key].data if lazy else res[key].values) for key in res} # type: ignore[assignment] if squeeze: axis = (2,) if len(self.data.z) == 1 else () res = { k: v.squeeze(axis=axis + ((3,) if v.shape[-1] == 1 else ())) for k, v in res.items() # type: ignore[assignment,attr-defined] } # this is just for convenience for DL iterators if isinstance(as_array, str): res = res[as_array] elif isinstance(as_array, Sequence): res = tuple(res[key] for key in as_array) # type: ignore[assignment] if lazy: return res return res.compute() if isinstance(res, ImageContainer) else res def _get_next_image_id(self, layer: str) -> str: pat = re.compile(rf"^{layer}_(\d*)$") iterator = chain.from_iterable(pat.finditer(k) for k in self.data.keys()) return f"{layer}_{(max(map(lambda m: int(m.groups()[0]), iterator), default=-1) + 1)}" def _get_next_channel_id(self, channel: str | xr.DataArray) -> str: if isinstance(channel, xr.DataArray): channel, *_ = (str(dim) for dim in channel.dims if dim not in ("y", "x", "z")) pat = re.compile(rf"^{channel}_(\d*)$") iterator = chain.from_iterable(pat.finditer(v.dims[-1]) for v in self.data.values()) return f"{channel}_{(max(map(lambda m: int(m.groups()[0]), iterator), default=-1) + 1)}" def _get_library_id(self, library_id: str | None = None) -> str: self._assert_not_empty() if library_id is None: if len(self.library_ids) > 1: raise ValueError( f"Unable to determine which library id to use. Please supply one from `{self.library_ids}`." ) library_id = self.library_ids[0] if library_id not in self.library_ids: raise KeyError(f"Library id `{library_id}` not found in `{self.library_ids}`.") return library_id def _get_library_ids( self, library_id: str | Sequence[str] | None = None, arr: xr.DataArray | None = None, allow_new: bool = False, ) -> list[str]: """ Get library ids. Parameters ---------- library_id Requested library ids. arr If the current container is empty, try getting the library ids from the ``arr``. allow_new If `True`, don't check if the returned library ids are present in the non-empty container. This is set to `True` only in :meth:`concat` to allow for remapping. Returns ------- The library ids. """ if library_id is None: if len(self): library_id = self.library_ids elif isinstance(arr, xr.DataArray): try: library_id = list(arr.coords["z"].values) except (KeyError, AttributeError) as e: logg.warning(f"Unable to retrieve library ids, reason `{e}`. Using default names") # at this point, it should have Z-dim library_id = [str(i) for i in range(arr.sizes["z"])] else: raise ValueError("Please specify the number of library ids if the container is empty.") if isinstance(library_id, str): library_id = [library_id] if not isinstance(library_id, Iterable): raise TypeError(f"Expected library ids to be `iterable`, found `{type(library_id).__name__!r}`.") res = list(map(str, library_id)) if not len(res): raise ValueError("No library ids have been selected.") if not allow_new and len(self) and not (set(res) & set(self.library_ids)): raise ValueError(f"Invalid library ids have been selected `{res}`. Valid options are `{self.library_ids}`.") return res def _get_layer(self, layer: str | None) -> str: self._assert_not_empty() if layer is None: if len(self) > 1: raise ValueError( f"Unable to determine which layer to use. Please supply one from `{sorted(self.data.keys())}`." ) layer = list(self)[0] if layer not in self: raise KeyError(f"Image layer `{layer}` not found in `{sorted(self)}`.") return layer def _assert_not_empty(self) -> None: if not len(self): raise ValueError("The container is empty.") def _get_size(self, size: FoI_t | tuple[FoI_t | None, FoI_t | None] | None) -> tuple[FoI_t, FoI_t]: if size is None: size = (None, None) if not isinstance(size, Iterable): size = (size, size) res = list(size) if size[0] is None: res[0] = self.shape[0] if size[1] is None: res[1] = self.shape[1] return tuple(res) # type: ignore[return-value] def _convert_to_pixel_space(self, size: tuple[FoI_t, FoI_t]) -> tuple[int, int]: y, x = size if isinstance(y, float): _assert_in_range(y, 0, 1, name="y") y = int(self.shape[0] * y) if isinstance(x, float): _assert_in_range(x, 0, 1, name="x") x = int(self.shape[1] * x) return y, x def __delitem__(self, key: str) -> None: del self.data[key] def __iter__(self) -> Iterator[str]: yield from self.data.keys() def __len__(self) -> int: return len(self.data) def __getitem__(self, key: str) -> xr.DataArray: return self.data[key] def __setitem__(self, key: str, value: NDArrayA | xr.DataArray | da.Array) -> None: if not isinstance(value, (np.ndarray, xr.DataArray, da.Array)): raise NotImplementedError(f"Adding `{type(value).__name__}` is not yet implemented.") self.add_img(value, layer=key, copy=True) def _ipython_key_completions_(self) -> Iterable[str]: return sorted(map(str, self.data.keys())) def __copy__(self) -> ImageContainer: return type(self)._from_dataset(self.data, deep=False) def __deepcopy__(self, memodict: Mapping[str, Any] = MappingProxyType({})) -> ImageContainer: return type(self)._from_dataset(self.data, deep=True) def _repr_html_(self) -> str: import html if not len(self): return f"{self.__class__.__name__} object with 0 layers" inflection = "" if len(self) <= 1 else "s" s = f"{self.__class__.__name__} object with {len(self.data.keys())} layer{inflection}:" style = "text-indent: 25px; margin-top: 0px; margin-bottom: 0px;" for i, layer in enumerate(self.data.keys()): s += f"{html.escape(str(layer))}: " s += ", ".join( f"{html.escape(str(dim))} ({shape})" for dim, shape in zip(self.data[layer].dims, self.data[layer].shape) ) s += "" if i == 9 and i < len(self) - 1: # show only first 10 layers s += f"and {len(self) - i - 1} more..." break return s def __repr__(self) -> str: return f"{self.__class__.__name__}[shape={self.shape}, layers={sorted(self.data.keys())}]" def __str__(self) -> str: return repr(self)

37.173482

120

0.561252

57,865

0.964449

6,881

0.114687

57,925

0.965449

0

22,706

0.378446

e1cd563f597751eb051e125f9959363e2f96050c

397

py

Python

users/forms.py

kurosh-wss/Personal-Finance-Management

9c7c467b95999974492df19a0f0286809f877c87

[ "MIT" ]

null

users/forms.py

kurosh-wss/Personal-Finance-Management

9c7c467b95999974492df19a0f0286809f877c87

[ "MIT" ]

null

users/forms.py

kurosh-wss/Personal-Finance-Management

9c7c467b95999974492df19a0f0286809f877c87

[ "MIT" ]

null

from django import forms from django.contrib.auth.forms import UserCreationForm from crispy_bootstrap5.bootstrap5 import FloatingField from crispy_forms.layout import Layout from crispy_forms.helper import FormHelper class CustomUserCreationForm(UserCreationForm): email = forms.EmailField() class Meta(UserCreationForm.Meta): fields = UserCreationForm.Meta.fields + ("email",)

30.538462

58

0.806045

177

0.445844

0

7

0.017632

e1cdbef2e0091f3d12ceeeebb7a6739477ce69ea

1,214

py

Python

tutorzzz_reminder/reminder.py

xiebei1108/tools

8a3141e5d97305f4438e828c62eb7be512767aa9

[ "Apache-2.0" ]

null

tutorzzz_reminder/reminder.py

xiebei1108/tools

8a3141e5d97305f4438e828c62eb7be512767aa9

[ "Apache-2.0" ]

null

tutorzzz_reminder/reminder.py

xiebei1108/tools

8a3141e5d97305f4438e828c62eb7be512767aa9

[ "Apache-2.0" ]

null

import json import requests import config assignedIdList = list() def __getList(): HEADERS = { 'Cookie': config.tutorzzzCookie, 'Content-Type': 'application/json' } res = requests.post(config.tutorzzzURL, headers = HEADERS, json = config.tutorzzzReqBody) if res.status_code == 200: try: body = res.json() except: print("[ERROR]: tutorzzz cookie expired") return if body['msg'] == '操作成功': return body['data']['data'] def __filter(): wanted = [] assignList = __getList() if assignList == None: return for al in assignList: if al['orderStatus'] == '招募中' and al['id'] not in assignedIdList: d = {} d['id'] = al['id'] d['title'] = al['title'] d['devPrice'] = al['devPrice'] wanted.append(d) assignedIdList.append(d['id']) return wanted def remind(): wanted = __filter() if wanted == None: return if len(wanted) == 0: return '尚无招募任务' content = '招募中任务\n' for a in wanted: content += a['id'] + '\t' + a['title'] + '\t' + a['devPrice'] + '\n' return content

25.829787

93

0.528007

0

251

0.2008

e1ce81097515faf4dfa5b55142281e9cb5ff0a2c

3,315

py

Python

invoicing/crud/invoice_crud.py

dnegreira/Invoicing

0bc8133e989f095c10151f67482e249416274947

[ "MIT" ]

null

invoicing/crud/invoice_crud.py

dnegreira/Invoicing

0bc8133e989f095c10151f67482e249416274947

[ "MIT" ]

null

invoicing/crud/invoice_crud.py

dnegreira/Invoicing

0bc8133e989f095c10151f67482e249416274947

[ "MIT" ]

null

from invoicing.crud.base_crud import BaseCrud from invoicing.latex.latex_invoice import LatexInvoice from invoicing.models.invoice_model import InvoiceModel from invoicing.repository.invoice_repository import InvoiceRepository from invoicing.repository.job_repository import JobRepository from invoicing.ui.date import Date from invoicing.ui.menu import Menu from invoicing.ui.style import Style from invoicing.value_validation.value_validation import Validation class InvoiceCrud(BaseCrud): def __init__(self): super().__init__('Invoices', InvoiceRepository, InvoiceModel) self.menu_actions.add_action('Generate', self.generate) def make_paginated_menu(self): return self.paginated_menu( find=self.repository.find_paginated_join_clients_and_companies, find_by_id=self.repository.find_by_id_join_clients_and_companies ) def generate(self): print(Style.create_title('Generate Invoice')) invoice = self.make_paginated_menu() if invoice: jobRepository = JobRepository() jobs = jobRepository.find_jobs_by_invoice_id(invoice['id']) self.enter_billable_time(jobRepository, jobs) jobs = jobRepository.find_jobs_by_invoice_id(invoice['id']) invoice_data = self.make_invoice_dictionary(invoice, jobs) LatexInvoice().generate(**invoice_data) self.mark_invoiced_jobs_as_complete(jobRepository, jobs) Menu.wait_for_input() def enter_billable_time(self, jobRepository, jobs): print(Style.create_title('Enter Billable Time')) for job in jobs: print('Title: %s' % job['title']) print('Description: %s' % job['description']) print('Estimated Time: %s' % job['estimated_time']) print('Logged Time: %s' % job['actual_time']) billable = '' while not Validation.isFloat(billable): billable = input('Billable Time: ') jobRepository.update_billable_time(job['id'], billable) jobRepository.save() jobRepository.check_rows_updated('Job Updated') def make_invoice_dictionary(self, invoice, jobs): invoice_data = { 'reference_code': invoice['reference_code'], 'company_name': invoice['company_name'], 'company_address': invoice['company_address'], 'created_at': Date().convert_date_time_for_printing(invoice['created_at']), 'total_cost': str(sum([float(job['rate']) * float(job['billable_time']) for job in jobs])), 'jobs': [{ 'title': job['title'], 'description': job['description'], 'type': 'hours', 'billable_time': str(job['billable_time']), 'staff_rate': str(job['rate']), 'cost': str(float(job['rate']) * float(job['billable_time'])) } for job in jobs] } return invoice_data def mark_invoiced_jobs_as_complete(self, jobRepository, jobs): if len(jobs): for job in jobs: jobRepository.update_mark_as_complete(job['id']) jobRepository.save() jobRepository.check_rows_updated('The selected jobs have been marked as completed')

44.2

103

0.650075

2,848

0.859125

0

555

0.167421

e1cfe37a6f7f6e565038ee9ac5851b8cdd75207b

946

py

Python

ds3225_client.py

kim-tom/dbus_server

b16d1b47dfe4d699ef0177592ba528ba988f17be

[ "MIT" ]

null

ds3225_client.py

kim-tom/dbus_server

b16d1b47dfe4d699ef0177592ba528ba988f17be

[ "MIT" ]

null

ds3225_client.py

kim-tom/dbus_server

b16d1b47dfe4d699ef0177592ba528ba988f17be

[ "MIT" ]

null

from ds3225 import DS3225 import dbus import dbus.mainloop.glib import dbus.service from gi.repository import GObject, GLib UNLOCKED_DEG = 175 dbus.mainloop.glib.DBusGMainLoop(set_as_default=True) BUS_NAME = 'jp.kimura.DS3225Service' OBJECT_PATH = '/jp/kimura/DS3225Server' INTERFACE = 'jp.kimura.DS3225' class DS3225Client(dbus.service.Object): def __init__(self): bus = dbus.SessionBus() bus_name = dbus.service.BusName(BUS_NAME, bus) super(DS3225Client, self).__init__(bus_name, OBJECT_PATH) self._proxy = bus.get_object(BUS_NAME, OBJECT_PATH) def get_pos(self): return self._proxy.get_pos() def set_pos(self, pos): self._proxy.set_pos(pos) if __name__ == '__main__': import time ds3225_client = DS3225Client() while True: ds3225_client.set_pos(UNLOCKED_DEG) time.sleep(2) ds3225_client.set_pos(UNLOCKED_DEG-90) time.sleep(2)

27.823529

65

0.701903

407

0.430233

0

78

0.082452

e1d05a453d3d0e33ff80baf493eec26c3cbe59f9

437

py

Python

Extra kunskap/Kod/Farmen.py

abbindustrigymnasium/Programmering-1-Slutuppgift

679069ebb632ee59f6b4ee3035c18ae204cde145

[ "Apache-2.0" ]

null

Extra kunskap/Kod/Farmen.py

abbindustrigymnasium/Programmering-1-Slutuppgift

679069ebb632ee59f6b4ee3035c18ae204cde145

[ "Apache-2.0" ]

null

Extra kunskap/Kod/Farmen.py

abbindustrigymnasium/Programmering-1-Slutuppgift

679069ebb632ee59f6b4ee3035c18ae204cde145

[ "Apache-2.0" ]

1

2020-03-09T12:04:31.000Z

import openpyxl wb= openpyxl.load_workbook('Farmen.xlsx') # sheet= wb.active # print(wb.get_sheet_names()) # Deltagar_sheet= wb.get_sheet_by_name('Deltagare') # artists=[{"Namn":sheet.cell(row=i, column=2).value, # "Sång":sheet.cell(row=i, column=3).value, # "Poäng":sheet.cell(row=i, column=6).value, # "Röst":sheet.cell(row=i, column=5).value # } for i in range(2,sheet.max_row) ] # print(artists)

33.615385

54

0.649886

0

381

0.865909

e1d12741ffdd5338667faa2590522d4debf232f5

3,437

py

Python

archive/simple_nn.py

petebond/MarketPlaceML

347ea5eab84673b846c85c58ce6c525e3f1dd0ff

[ "CC0-1.0" ]

null

archive/simple_nn.py

petebond/MarketPlaceML

347ea5eab84673b846c85c58ce6c525e3f1dd0ff

[ "CC0-1.0" ]

null

archive/simple_nn.py

petebond/MarketPlaceML

347ea5eab84673b846c85c58ce6c525e3f1dd0ff

[ "CC0-1.0" ]

null

from os import access import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from torch.utils.data import DataLoader from torchvision import datasets, transforms import numpy as np import matplotlib.pyplot as plt # Create fully connected neural network class NN(nn.Module): def __init__(self, input_size, num_classes): super(NN, self).__init__() self.fc1 = nn.Linear(input_size, 50) self.fc2 = nn.Linear(50, num_classes) def forward(self, x): x = F.relu(self.fc1(x)) x = self.fc2(x) return x class CNN(nn.Module): def __init__(self, in_channels=1, num_classes=10): super(CNN, self).__init__() self.conv1 = nn.Conv2d(in_channels=1, out_channels=8, kernel_size=(3,3), stride=(1,1), padding=(1,1)) self.pool = nn.MaxPool2d(kernel_size = (2, 2), stride=(2,2)) self.conv2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3,3), stride=(1,1), padding=(1,1)) self.fc1 = nn.Linear(16 * 7 * 7, num_classes) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.reshape(x.shape[0], -1) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x model = CNN(784, 10) x = torch.randn(64, 784) print(model(x).shape) # Set device device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Hyperparameters batch_size = 64 learning_rate = 1e-3 num_epochs = 10 input_size = 784 num_classes = 10 # Load data train_dataset = datasets.MNIST(root='dataset/', train=True, download=True, transform=transforms.ToTensor()) train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) test_dataset = datasets.MNIST(root='dataset/', train=False, download=True, transform=transforms.ToTensor()) test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True) # Initialize model model = NN(input_size=input_size, num_classes=num_classes).to(device) # Define loss and optimizer criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) # Train model for epoch in range(num_epochs): for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) # Correct shape data = data.reshape(data.shape[0], -1) # Forward pass scores = model(data) loss = criterion(scores, target) # Backward pass optimizer.zero_grad() loss.backward() # Gradient descent step optimizer.step() # Check accuracy def check_accuracy(loader, model): if loader.dataset.train: print('Checking accuracy on training set') else: print('Checking accuracy on test set') num_correct = 0 num_samples = 0 model.eval() with torch.no_grad(): for x, y in loader: x = x.to(device) y = y.to(device) x = x.reshape(x.shape[0], -1) scores = model(x) _, predictions = scores.max(1) num_correct += (predictions == y).sum() num_samples += predictions.size(0) print('Got %d / %d correct (%.2f)' % (num_correct, num_samples, 100 * float(num_correct) / num_samples)) model.train() check_accuracy(train_loader, model) check_accuracy(test_loader, model)

30.6875

112

0.647949

1,012

0.294443

0

345

0.100378

becbc66bb4d180935eed7f6a49ea9b7ed75ae703

998

py

Python

plot_loss.py

ngachago/tabular_comp

799a1e0dbf7a51bb04454f1f14a57f883dbd2da7

[ "MIT" ]

null

plot_loss.py

ngachago/tabular_comp

799a1e0dbf7a51bb04454f1f14a57f883dbd2da7

[ "MIT" ]

null

plot_loss.py

ngachago/tabular_comp

799a1e0dbf7a51bb04454f1f14a57f883dbd2da7

[ "MIT" ]

null

import matplotlib.pyplot as plt def plot_loss_mae(history): plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Validation'], loc='best') plt.show() plt.plot(history.history['mae']) plt.plot(history.history['val_mae']) plt.title('Model MAE') plt.ylabel('MAE') plt.xlabel('Epoch') plt.legend(['Train', 'Validation'], loc='best') plt.show() def plot_loss_accuracy(history): plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Validation'], loc='best') plt.show() plt.plot(history.history['accuracy']) plt.plot(history.history['val_accuracy']) plt.title('Model Accuracy') plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Train', 'Validation'], loc='best') plt.show()

26.972973

51

0.635271

0

276

0.276553

becc2f00075040cb4b100c7b8d0736d719593862

18,695

py

Python

hotkey.py

RMPiria/painelContreole

bd07def485981456a7e7390f2b18db71740ce8da

[ "Unlicense" ]

null

hotkey.py

RMPiria/painelContreole

bd07def485981456a7e7390f2b18db71740ce8da

[ "Unlicense" ]

null

hotkey.py

RMPiria/painelContreole

bd07def485981456a7e7390f2b18db71740ce8da

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- import os from os.path import dirname, join, normpath import sys from sys import platform from config import config if platform == 'darwin': import objc from AppKit import NSApplication, NSWorkspace, NSBeep, NSSound, NSEvent, NSKeyDown, NSKeyUp, NSFlagsChanged, NSKeyDownMask, NSFlagsChangedMask, NSShiftKeyMask, NSControlKeyMask, NSAlternateKeyMask, NSCommandKeyMask, NSNumericPadKeyMask, NSDeviceIndependentModifierFlagsMask, NSF1FunctionKey, NSF35FunctionKey, NSDeleteFunctionKey, NSClearLineFunctionKey class HotkeyMgr: MODIFIERMASK = NSShiftKeyMask|NSControlKeyMask|NSAlternateKeyMask|NSCommandKeyMask|NSNumericPadKeyMask POLL = 250 # https://developer.apple.com/library/mac/documentation/Cocoa/Reference/ApplicationKit/Classes/NSEvent_Class/#//apple_ref/doc/constant_group/Function_Key_Unicodes DISPLAY = { 0x03: u'⌅', 0x09: u'⇥', 0xd: u'↩', 0x19: u'⇤', 0x1b: u'esc', 0x20: u'⏘', 0x7f: u'⌫', 0xf700: u'↑', 0xf701: u'↓', 0xf702: u'←', 0xf703: u'→', 0xf727: u'Ins', 0xf728: u'⌦', 0xf729: u'↖', 0xf72a: u'Fn', 0xf72b: u'↘', 0xf72c: u'⇞', 0xf72d: u'⇟', 0xf72e: u'PrtScr', 0xf72f: u'ScrollLock', 0xf730: u'Pause', 0xf731: u'SysReq', 0xf732: u'Break', 0xf733: u'Reset', 0xf739: u'⌧', } (ACQUIRE_INACTIVE, ACQUIRE_ACTIVE, ACQUIRE_NEW) = range(3) def __init__(self): self.root = None self.keycode = 0 self.modifiers = 0 self.activated = False self.observer = None self.acquire_key = 0 self.acquire_state = HotkeyMgr.ACQUIRE_INACTIVE self.tkProcessKeyEvent_old = None self.snd_good = NSSound.alloc().initWithContentsOfFile_byReference_(join(config.respath, 'snd_good.wav'), False) self.snd_bad = NSSound.alloc().initWithContentsOfFile_byReference_(join(config.respath, 'snd_bad.wav'), False) def register(self, root, keycode, modifiers): self.root = root self.keycode = keycode self.modifiers = modifiers self.activated = False if keycode: if not self.observer: self.root.after_idle(self._observe) self.root.after(HotkeyMgr.POLL, self._poll) # Monkey-patch tk (tkMacOSXKeyEvent.c) if not self.tkProcessKeyEvent_old: sel = 'tkProcessKeyEvent:' cls = NSApplication.sharedApplication().class__() self.tkProcessKeyEvent_old = NSApplication.sharedApplication().methodForSelector_(sel) newmethod = objc.selector(self.tkProcessKeyEvent, selector = self.tkProcessKeyEvent_old.selector, signature = self.tkProcessKeyEvent_old.signature) objc.classAddMethod(cls, sel, newmethod) # Monkey-patch tk (tkMacOSXKeyEvent.c) to: # - workaround crash on OSX 10.9 & 10.10 on seeing a composing character # - notice when modifier key state changes # - keep a copy of NSEvent.charactersIgnoringModifiers, which is what we need for the hotkey # (Would like to use a decorator but need to ensure the application is created before this is installed) def tkProcessKeyEvent(self, cls, theEvent): if self.acquire_state: if theEvent.type() == NSFlagsChanged: self.acquire_key = theEvent.modifierFlags() & NSDeviceIndependentModifierFlagsMask self.acquire_state = HotkeyMgr.ACQUIRE_NEW # suppress the event by not chaining the old function return theEvent elif theEvent.type() in (NSKeyDown, NSKeyUp): c = theEvent.charactersIgnoringModifiers() self.acquire_key = (c and ord(c[0]) or 0) | (theEvent.modifierFlags() & NSDeviceIndependentModifierFlagsMask) self.acquire_state = HotkeyMgr.ACQUIRE_NEW # suppress the event by not chaining the old function return theEvent # replace empty characters with charactersIgnoringModifiers to avoid crash elif theEvent.type() in (NSKeyDown, NSKeyUp) and not theEvent.characters(): theEvent = NSEvent.keyEventWithType_location_modifierFlags_timestamp_windowNumber_context_characters_charactersIgnoringModifiers_isARepeat_keyCode_(theEvent.type(), theEvent.locationInWindow(), theEvent.modifierFlags(), theEvent.timestamp(), theEvent.windowNumber(), theEvent.context(), theEvent.charactersIgnoringModifiers(), theEvent.charactersIgnoringModifiers(), theEvent.isARepeat(), theEvent.keyCode()) return self.tkProcessKeyEvent_old(cls, theEvent) def _observe(self): # Must be called after root.mainloop() so that the app's message loop has been created self.observer = NSEvent.addGlobalMonitorForEventsMatchingMask_handler_(NSKeyDownMask, self._handler) def _poll(self): # No way of signalling to Tkinter from within the callback handler block that doesn't # cause Python to crash, so poll. if self.activated: self.activated = False self.root.event_generate('<<Invoke>>', when="tail") if self.keycode or self.modifiers: self.root.after(HotkeyMgr.POLL, self._poll) def unregister(self): self.keycode = None self.modifiers = None @objc.callbackFor(NSEvent.addGlobalMonitorForEventsMatchingMask_handler_) def _handler(self, event): # use event.charactersIgnoringModifiers to handle composing characters like Alt-e if (event.modifierFlags() & HotkeyMgr.MODIFIERMASK) == self.modifiers and ord(event.charactersIgnoringModifiers()[0]) == self.keycode: if config.getint('hotkey_always'): self.activated = True else: # Only trigger if game client is front process front = NSWorkspace.sharedWorkspace().frontmostApplication() if front and front.bundleIdentifier() == 'uk.co.frontier.EliteDangerous': self.activated = True def acquire_start(self): self.acquire_state = HotkeyMgr.ACQUIRE_ACTIVE self.root.after_idle(self._acquire_poll) def acquire_stop(self): self.acquire_state = HotkeyMgr.ACQUIRE_INACTIVE def _acquire_poll(self): # No way of signalling to Tkinter from within the monkey-patched event handler that doesn't # cause Python to crash, so poll. if self.acquire_state: if self.acquire_state == HotkeyMgr.ACQUIRE_NEW: # Abuse tkEvent's keycode field to hold our acquired key & modifier self.root.event_generate('<KeyPress>', keycode = self.acquire_key) self.acquire_state = HotkeyMgr.ACQUIRE_ACTIVE self.root.after(50, self._acquire_poll) def fromevent(self, event): # Return configuration (keycode, modifiers) or None=clear or False=retain previous (keycode, modifiers) = (event.keycode & 0xffff, event.keycode & 0xffff0000) # Set by _acquire_poll() if keycode and not (modifiers & (NSShiftKeyMask|NSControlKeyMask|NSAlternateKeyMask|NSCommandKeyMask)): if keycode == 0x1b: # Esc = retain previous self.acquire_state = HotkeyMgr.ACQUIRE_INACTIVE return False elif keycode in [0x7f, ord(NSDeleteFunctionKey), ord(NSClearLineFunctionKey)]: # BkSp, Del, Clear = clear hotkey self.acquire_state = HotkeyMgr.ACQUIRE_INACTIVE return None elif keycode in [0x13, 0x20, 0x2d] or 0x61 <= keycode <= 0x7a: # don't allow keys needed for typing in System Map NSBeep() self.acquire_state = HotkeyMgr.ACQUIRE_INACTIVE return None return (keycode, modifiers) def display(self, keycode, modifiers): # Return displayable form text = '' if modifiers & NSControlKeyMask: text += u'⌃' if modifiers & NSAlternateKeyMask: text += u'⌥' if modifiers & NSShiftKeyMask: text += u'⇧' if modifiers & NSCommandKeyMask: text += u'⌘' if (modifiers & NSNumericPadKeyMask) and keycode <= 0x7f: text += u'№' if not keycode: pass elif ord(NSF1FunctionKey) <= keycode <= ord(NSF35FunctionKey): text += 'F%d' % (keycode + 1 - ord(NSF1FunctionKey)) elif keycode in HotkeyMgr.DISPLAY: # specials text += HotkeyMgr.DISPLAY[keycode] elif keycode < 0x20: # control keys text += unichr(keycode+0x40) elif keycode < 0xf700: # key char text += unichr(keycode).upper() else: text += u'⁈' return text def play_good(self): self.snd_good.play() def play_bad(self): self.snd_bad.play() elif platform == 'win32': import atexit import ctypes from ctypes.wintypes import * import threading import winsound RegisterHotKey = ctypes.windll.user32.RegisterHotKey UnregisterHotKey = ctypes.windll.user32.UnregisterHotKey MOD_ALT = 0x0001 MOD_CONTROL = 0x0002 MOD_SHIFT = 0x0004 MOD_WIN = 0x0008 MOD_NOREPEAT = 0x4000 GetMessage = ctypes.windll.user32.GetMessageW TranslateMessage = ctypes.windll.user32.TranslateMessage DispatchMessage = ctypes.windll.user32.DispatchMessageW PostThreadMessage = ctypes.windll.user32.PostThreadMessageW WM_QUIT = 0x0012 WM_HOTKEY = 0x0312 WM_APP = 0x8000 WM_SND_GOOD = WM_APP + 1 WM_SND_BAD = WM_APP + 2 GetKeyState = ctypes.windll.user32.GetKeyState MapVirtualKey = ctypes.windll.user32.MapVirtualKeyW VK_BACK = 0x08 VK_CLEAR = 0x0c VK_RETURN = 0x0d VK_SHIFT = 0x10 VK_CONTROL = 0x11 VK_MENU = 0x12 VK_CAPITAL = 0x14 VK_MODECHANGE= 0x1f VK_ESCAPE = 0x1b VK_SPACE = 0x20 VK_DELETE = 0x2e VK_LWIN = 0x5b VK_RWIN = 0x5c VK_NUMPAD0 = 0x60 VK_DIVIDE = 0x6f VK_F1 = 0x70 VK_F24 = 0x87 VK_OEM_MINUS = 0xbd VK_NUMLOCK = 0x90 VK_SCROLL = 0x91 VK_PROCESSKEY= 0xe5 VK_OEM_CLEAR = 0xfe GetForegroundWindow = ctypes.windll.user32.GetForegroundWindow GetWindowText = ctypes.windll.user32.GetWindowTextW GetWindowText.argtypes = [HWND, LPWSTR, ctypes.c_int] GetWindowTextLength = ctypes.windll.user32.GetWindowTextLengthW def WindowTitle(h): if h: l = GetWindowTextLength(h) + 1 buf = ctypes.create_unicode_buffer(l) if GetWindowText(h, buf, l): return buf.value return '' class MOUSEINPUT(ctypes.Structure): _fields_ = [('dx', LONG), ('dy', LONG), ('mouseData', DWORD), ('dwFlags', DWORD), ('time', DWORD), ('dwExtraInfo', ctypes.POINTER(ULONG))] class KEYBDINPUT(ctypes.Structure): _fields_ = [('wVk', WORD), ('wScan', WORD), ('dwFlags', DWORD), ('time', DWORD), ('dwExtraInfo', ctypes.POINTER(ULONG))] class HARDWAREINPUT(ctypes.Structure): _fields_ = [('uMsg', DWORD), ('wParamL', WORD), ('wParamH', WORD)] class INPUT_union(ctypes.Union): _fields_ = [('mi', MOUSEINPUT), ('ki', KEYBDINPUT), ('hi', HARDWAREINPUT)] class INPUT(ctypes.Structure): _fields_ = [('type', DWORD), ('union', INPUT_union)] SendInput = ctypes.windll.user32.SendInput SendInput.argtypes = [ctypes.c_uint, ctypes.POINTER(INPUT), ctypes.c_int] INPUT_MOUSE = 0 INPUT_KEYBOARD = 1 INPUT_HARDWARE = 2 class HotkeyMgr: # https://msdn.microsoft.com/en-us/library/windows/desktop/dd375731%28v=vs.85%29.aspx # Limit ourselves to symbols in Windows 7 Segoe UI DISPLAY = { 0x03: 'Break', 0x08: 'Bksp', 0x09: u'↹', 0x0c: 'Clear', 0x0d: u'↵', 0x13: 'Pause', 0x14: u'Ⓐ', 0x1b: 'Esc', 0x20: u'⏘', 0x21: 'PgUp', 0x22: 'PgDn', 0x23: 'End', 0x24: 'Home', 0x25: u'←', 0x26: u'↑', 0x27: u'→', 0x28: u'↓', 0x2c: 'PrtScn', 0x2d: 'Ins', 0x2e: 'Del', 0x2f: 'Help', 0x5d: u'▤', 0x5f: u'☾', 0x90: u'➀', 0x91: 'ScrLk', 0xa6: u'⇦', 0xa7: u'⇨', 0xa9: u'⊗', 0xab: u'☆', 0xac: u'⌂', 0xb4: u'✉', } def __init__(self): self.root = None self.thread = None self.snd_good = open(join(config.respath, 'snd_good.wav'), 'rb').read() self.snd_bad = open(join(config.respath, 'snd_bad.wav'), 'rb').read() atexit.register(self.unregister) def register(self, root, keycode, modifiers): self.root = root if self.thread: self.unregister() if keycode or modifiers: self.thread = threading.Thread(target = self.worker, name = 'Hotkey "%x:%x"' % (keycode,modifiers), args = (keycode,modifiers)) self.thread.daemon = True self.thread.start() def unregister(self): thread = self.thread if thread: self.thread = None PostThreadMessage(thread.ident, WM_QUIT, 0, 0) thread.join() # Wait for it to unregister hotkey and quit def worker(self, keycode, modifiers): # Hotkey must be registered by the thread that handles it if not RegisterHotKey(None, 1, modifiers|MOD_NOREPEAT, keycode): self.thread = None return fake = INPUT(INPUT_KEYBOARD, INPUT_union(ki = KEYBDINPUT(keycode, keycode, 0, 0, None))) msg = MSG() while GetMessage(ctypes.byref(msg), None, 0, 0) != 0: if msg.message == WM_HOTKEY: if config.getint('hotkey_always') or WindowTitle(GetForegroundWindow()).startswith('Elite - Dangerous'): self.root.event_generate('<<Invoke>>', when="tail") else: # Pass the key on UnregisterHotKey(None, 1) SendInput(1, fake, ctypes.sizeof(INPUT)) if not RegisterHotKey(None, 1, modifiers|MOD_NOREPEAT, keycode): break elif msg.message == WM_SND_GOOD: winsound.PlaySound(self.snd_good, winsound.SND_MEMORY) # synchronous elif msg.message == WM_SND_BAD: winsound.PlaySound(self.snd_bad, winsound.SND_MEMORY) # synchronous else: TranslateMessage(ctypes.byref(msg)) DispatchMessage(ctypes.byref(msg)) UnregisterHotKey(None, 1) self.thread = None def acquire_start(self): pass def acquire_stop(self): pass def fromevent(self, event): # event.state is a pain - it shows the state of the modifiers *before* a modifier key was pressed. # event.state *does* differentiate between left and right Ctrl and Alt and between Return and Enter # by putting KF_EXTENDED in bit 18, but RegisterHotKey doesn't differentiate. modifiers = ((GetKeyState(VK_MENU) & 0x8000) and MOD_ALT) | ((GetKeyState(VK_CONTROL) & 0x8000) and MOD_CONTROL) | ((GetKeyState(VK_SHIFT) & 0x8000) and MOD_SHIFT) | ((GetKeyState(VK_LWIN) & 0x8000) and MOD_WIN) | ((GetKeyState(VK_RWIN) & 0x8000) and MOD_WIN) keycode = event.keycode if keycode in [ VK_SHIFT, VK_CONTROL, VK_MENU, VK_LWIN, VK_RWIN ]: return (0, modifiers) if not modifiers: if keycode == VK_ESCAPE: # Esc = retain previous return False elif keycode in [ VK_BACK, VK_DELETE, VK_CLEAR, VK_OEM_CLEAR ]: # BkSp, Del, Clear = clear hotkey return None elif keycode in [ VK_RETURN, VK_SPACE, VK_OEM_MINUS] or ord('A') <= keycode <= ord('Z'): # don't allow keys needed for typing in System Map winsound.MessageBeep() return None elif keycode in [ VK_NUMLOCK, VK_SCROLL, VK_PROCESSKEY ] or VK_CAPITAL <= keycode <= VK_MODECHANGE: # ignore unmodified mode switch keys return (0, modifiers) # See if the keycode is usable and available if RegisterHotKey(None, 2, modifiers|MOD_NOREPEAT, keycode): UnregisterHotKey(None, 2) return (keycode, modifiers) else: winsound.MessageBeep() return None def display(self, keycode, modifiers): text = '' if modifiers & MOD_WIN: text += u'❖+' if modifiers & MOD_CONTROL: text += u'Ctrl+' if modifiers & MOD_ALT: text += u'Alt+' if modifiers & MOD_SHIFT: text += u'⇧+' if VK_NUMPAD0 <= keycode <= VK_DIVIDE: text += u'№' if not keycode: pass elif VK_F1 <= keycode <= VK_F24: text += 'F%d' % (keycode + 1 - VK_F1) elif keycode in HotkeyMgr.DISPLAY: # specials text += HotkeyMgr.DISPLAY[keycode] else: c = MapVirtualKey(keycode, 2) # printable ? if not c: # oops not printable text += u'⁈' elif c < 0x20: # control keys text += unichr(c+0x40) else: text += unichr(c).upper() return text def play_good(self): if self.thread: PostThreadMessage(self.thread.ident, WM_SND_GOOD, 0, 0) def play_bad(self): if self.thread: PostThreadMessage(self.thread.ident, WM_SND_BAD, 0, 0) else: # Linux class HotkeyMgr: def register(self, root, keycode, modifiers): pass def unregister(self): pass def play_good(self): pass def play_bad(self): pass # singleton hotkeymgr = HotkeyMgr()

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732

0.038976

0

3,193

0.170012

becc66adc74c550995995f2d2b08dfaa9d6845d1

3,530

py

Python

Chapter10/ch10_r1_grover_aqua.py

georgekorpas/Quantum-Computing-in-Practice-with-Qiskit-and-IBM-Quantum-Experience

938123d051c5bab72110011b3a05e515bb69ca09

[ "MIT" ]

24

2020-11-21T20:33:51.000Z

2022-03-26T06:41:27.000Z

Chapter10/ch10_r1_grover_aqua.py

videomover/Quantum-Computing-in-Practice-with-Qiskit-and-IBM-Quantum-Experience

938123d051c5bab72110011b3a05e515bb69ca09

[ "MIT" ]

2

2021-02-07T14:32:12.000Z

2022-03-25T07:23:46.000Z

Chapter10/ch10_r1_grover_aqua.py

videomover/Quantum-Computing-in-Practice-with-Qiskit-and-IBM-Quantum-Experience

938123d051c5bab72110011b3a05e515bb69ca09

[ "MIT" ]

16

2020-11-03T07:49:11.000Z

2022-03-26T06:41:29.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created Nov 2020 @author: hassi """ from qiskit import Aer, IBMQ # Do the necessary import for our program from qiskit.aqua.algorithms import Grover from qiskit.aqua.components.oracles import LogicalExpressionOracle, TruthTableOracle # Import basic plot tools from qiskit.tools.visualization import plot_histogram from IPython.core.display import display global oracle_method, oracle_type def log_length(oracle_input,oracle_method): from math import sqrt, pow, pi, log if oracle_method=="log": filtered = [c.lower() for c in oracle_input if c.isalpha()] result = len(filtered) num_iterations=int(pi/4*(sqrt(pow(2,result)))) else: num_iterations = int(pi/4*(sqrt(pow(2,log(len(oracle_input),2))))) print("Iterations: ", num_iterations) return num_iterations def create_oracle(oracle_method): oracle_text={"log":"~A & ~B & C","bit":"00001000"} # set the input global num_iterations print("Enter the oracle input string, such as:"+oracle_text[oracle_method]+"\nor enter 'def' for a default string.") oracle_input=input('\nOracle input:\n ') if oracle_input=="def": oracle_type=oracle_text[oracle_method] else: oracle_type = oracle_input num_iterations=log_length(oracle_type, oracle_method) return(oracle_type) def create_grover(oracle_type, oracle_method): # Build the circuit if oracle_method=="log": algorithm = Grover(LogicalExpressionOracle(oracle_type),num_iterations=num_iterations) oracle_circuit = Grover(LogicalExpressionOracle(oracle_type)).construct_circuit() else: algorithm = Grover(TruthTableOracle(oracle_type),num_iterations=num_iterations) oracle_circuit = Grover(TruthTableOracle(oracle_type)).construct_circuit() display(oracle_circuit.draw(output="mpl")) display(algorithm) return(algorithm) def run_grover(algorithm,oracle_type,oracle_method): # Run the algorithm on a simulator, printing the most frequently occurring result backend = Aer.get_backend('qasm_simulator') result = algorithm.run(backend) print("Oracle method:",oracle_method) print("Oracle for:", oracle_type) print("Aer Result:",result['top_measurement']) display(plot_histogram(result['measurement'])) # Run the algorithm on an IBM Q backend, printing the most frequently occurring result print("Getting provider...") if not IBMQ.active_account(): IBMQ.load_account() provider = IBMQ.get_provider() from qiskit.providers.ibmq import least_busy filtered_backend = least_busy(provider.backends(n_qubits=5, operational=True, simulator=False)) result = algorithm.run(filtered_backend) print("Oracle method:",oracle_method) print("Oracle for:", oracle_type) print("IBMQ "+filtered_backend.name()+" Result:",result['top_measurement']) display(plot_histogram(result['measurement'])) print(result) # Main loop def main(): oracle_method="log" while oracle_method!=0: print("Ch 11: Grover search with Aqua") print("------------------------------") # set the oracle method: "Log" for logical expression or "Bit" for bit string. oracle_method = input("Select oracle method (log or bit):\n") type=create_oracle(oracle_method) algorithm=create_grover(type, oracle_method) run_grover(algorithm,type, oracle_method) if __name__ == '__main__': main()

34.950495

120

0.703116

0

912

0.258357

becc7d9f66f89922de66fb46f1bec24640debbb3

1,428

py

Python

setup_pre-commit.py

fierte-product-development/Setups

d6151888ecc82a94e894b5d926c85eb193a5b97c

[ "MIT" ]

1

2021-07-06T09:26:48.000Z

setup_pre-commit.py

fierte-product-development/Setups

d6151888ecc82a94e894b5d926c85eb193a5b97c

[ "MIT" ]

3

2022-01-26T05:29:04.000Z

2022-02-16T10:16:02.000Z

setup_pre-commit.py

fierte-product-development/Setups

d6151888ecc82a94e894b5d926c85eb193a5b97c

[ "MIT" ]

1

2020-11-11T01:23:01.000Z

import sys import os import subprocess from subprocess import PIPE, STDOUT from pathlib import Path proc_arg = { 'shell': True, 'stdout': PIPE, 'text': True } def _run(*cmd): proc = subprocess.run(cmd, **proc_arg, stderr=PIPE) return proc.stdout.replace('\n', '') def _popen(*cmd): print(f'$ {" ".join(cmd)}') proc = subprocess.Popen(cmd, **proc_arg, stderr=STDOUT) while True: if line := proc.stdout.readline(): print(line.replace('\n', '')) elif poll := proc.poll() is not None: print() return poll def _conf_exit(code, rm=False): input('\n処理を終了します。メッセージを確認してEnterを押してください。') if rm: os.remove(__file__) sys.exit(code) def pip_install(*names): _popen('python', '-m', 'pip', 'install', '--upgrade', 'pip') for name in names: _popen('pip', 'install', name) def exists_in_cd(tgt): cd = Path(_run('cd')) if not (cd/tgt).exists(): print(f'{tgt} が存在しません。カレントディレクトリを確認してください。') _conf_exit(1) def install_pre_commit(): print('.git/hooks に commit-msg ファイルを作成します。') _popen('pre-commit', 'install', '-t', 'commit-msg') def main(): print('pre-commitのセットアップを行います…\n') pip_install('pre-commit') exists_in_cd('.git') install_pre_commit() _conf_exit(0, True) if __name__ == "__main__": main()

21.969231

65

0.579832

0

478

0.300629

bece2e0f8b2de4615e59523ec76f70e91828684b

889

py

Python

5-buscar.py

gustavodp22/TP_DE_-CRUD-create-read-update-delete-

8edfba57cbbad37f8fb1af2d42d1d601301e8dd6

[ "MIT" ]

null

5-buscar.py

gustavodp22/TP_DE_-CRUD-create-read-update-delete-

8edfba57cbbad37f8fb1af2d42d1d601301e8dd6

[ "MIT" ]

null

5-buscar.py

gustavodp22/TP_DE_-CRUD-create-read-update-delete-

8edfba57cbbad37f8fb1af2d42d1d601301e8dd6

[ "MIT" ]

null

""" CRUD de SQLite3 con Python 3 """ import sqlite3 try: bd = sqlite3.connect("libros.db") cursor = bd.cursor() busqueda = input("Escribe tu búsqueda: ") if not busqueda: print("Búsqueda inválida") exit() sentencia = "SELECT * FROM libros WHERE titulo LIKE ?;" cursor.execute(sentencia, [ "%{}%".format(busqueda) ]) libros = cursor.fetchall() print("+{:-<20}+{:-<20}+{:-<10}+{:-<50}+".format("", "", "", "")) print("|{:^20}|{:^20}|{:^10}|{:^50}|".format("Autor", "Género", "Precio", "Título")) print("+{:-<20}+{:-<20}+{:-<10}+{:-<50}+".format("", "", "", "")) for autor, genero, precio, titulo in libros: print("|{:^20}|{:^20}|{:^10}|{:^50}|".format(autor, genero, precio, titulo)) print("+{:-<20}+{:-<20}+{:-<10}+{:-<50}+".format("", "", "", "")) except sqlite3.OperationalError as error: print("Error al abrir:", error)

27.78125

86

0.533183

0

386

0.431767

becf153d29dc0bf9abcbf500a0d578ce48e9150a

4,686

py

Python

tests/advanced_tests/regressors.py

amlanbanerjee/auto_ml

db8e1d2cfa93f13a21e55739acfc8d99837e91b0

[ "MIT" ]

1,671

2016-08-09T04:44:48.000Z

2022-03-27T01:29:23.000Z

tests/advanced_tests/regressors.py

amlanbanerjee/auto_ml

db8e1d2cfa93f13a21e55739acfc8d99837e91b0

[ "MIT" ]

428

2016-08-08T00:13:04.000Z

2022-01-19T10:09:05.000Z

tests/advanced_tests/regressors.py

amlanbanerjee/auto_ml

db8e1d2cfa93f13a21e55739acfc8d99837e91b0

[ "MIT" ]

334

2016-08-29T12:34:18.000Z

2022-01-31T09:14:30.000Z

import datetime import os import random import sys sys.path = [os.path.abspath(os.path.dirname(__file__))] + sys.path from auto_ml import Predictor from auto_ml.utils_models import load_ml_model import dill from nose.tools import assert_equal, assert_not_equal, with_setup import numpy as np from sklearn.model_selection import train_test_split import utils_testing as utils def optimize_final_model_regression(model_name=None): np.random.seed(0) df_boston_train, df_boston_test = utils.get_boston_regression_dataset() # We just want to make sure these run, not necessarily make sure that they're super accurate (which takes more time, and is dataset dependent) df_boston_train = df_boston_train.sample(frac=0.5) column_descriptions = { 'MEDV': 'output' , 'CHAS': 'categorical' } ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions) ml_predictor.train(df_boston_train, optimize_final_model=True, model_names=model_name) test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV) print('test_score') print(test_score) # the random seed gets a score of -3.21 on python 3.5 # There's a ton of noise here, due to small sample sizes lower_bound = -3.4 if model_name == 'DeepLearningRegressor': lower_bound = -24 if model_name == 'LGBMRegressor': lower_bound = -16 if model_name == 'GradientBoostingRegressor': lower_bound = -5.1 if model_name == 'CatBoostRegressor': lower_bound = -4.5 if model_name == 'XGBRegressor': lower_bound = -4.8 assert lower_bound < test_score < -2.75 def getting_single_predictions_regression(model_name=None): np.random.seed(0) df_boston_train, df_boston_test = utils.get_boston_regression_dataset() column_descriptions = { 'MEDV': 'output' , 'CHAS': 'categorical' } ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions) ml_predictor.train(df_boston_train, model_names=model_name) file_name = ml_predictor.save(str(random.random())) saved_ml_pipeline = load_ml_model(file_name) os.remove(file_name) try: keras_file_name = file_name[:-5] + '_keras_deep_learning_model.h5' os.remove(keras_file_name) except: pass df_boston_test_dictionaries = df_boston_test.to_dict('records') # 1. make sure the accuracy is the same predictions = [] for row in df_boston_test_dictionaries: predictions.append(saved_ml_pipeline.predict(row)) print('predictions') print(predictions) print('predictions[0]') print(predictions[0]) print('type(predictions)') print(type(predictions)) first_score = utils.calculate_rmse(df_boston_test.MEDV, predictions) print('first_score') print(first_score) # Make sure our score is good, but not unreasonably good lower_bound = -2.9 if model_name == 'DeepLearningRegressor': lower_bound = -7.8 if model_name == 'LGBMRegressor': lower_bound = -4.95 if model_name == 'XGBRegressor': lower_bound = -3.4 if model_name == 'CatBoostRegressor': lower_bound = -3.7 assert lower_bound < first_score < -2.7 # 2. make sure the speed is reasonable (do it a few extra times) data_length = len(df_boston_test_dictionaries) start_time = datetime.datetime.now() for idx in range(1000): row_num = idx % data_length saved_ml_pipeline.predict(df_boston_test_dictionaries[row_num]) end_time = datetime.datetime.now() duration = end_time - start_time print('duration.total_seconds()') print(duration.total_seconds()) # It's very difficult to set a benchmark for speed that will work across all machines. # On my 2013 bottom of the line 15" MacBook Pro, this runs in about 0.8 seconds for 1000 predictions # That's about 1 millisecond per prediction # Assuming we might be running on a test box that's pretty weak, multiply by 3 # Also make sure we're not running unreasonably quickly assert 0.1 < duration.total_seconds() / 1.0 < 60 # 3. make sure we're not modifying the dictionaries (the score is the same after running a few experiments as it is the first time) predictions = [] for row in df_boston_test_dictionaries: predictions.append(saved_ml_pipeline.predict(row)) second_score = utils.calculate_rmse(df_boston_test.MEDV, predictions) print('second_score') print(second_score) # Make sure our score is good, but not unreasonably good assert lower_bound < second_score < -2.7

31.877551

146

0.712761

0

1,369

0.292147

bed0237d9ebc522d5a4384033d2b57c729cc7ede

39

py

Python

__init__.py

amueller/information-theoretic-mst

178fd4396bc9a9a499ec3d18d5047b320a5c32f2

[ "Unlicense" ]

20

2016-05-03T13:29:09.000Z

2021-10-06T20:41:36.000Z

__init__.py

amueller/information-theoretic-mst

178fd4396bc9a9a499ec3d18d5047b320a5c32f2

[ "Unlicense" ]

1

2018-04-21T15:32:07.000Z

2020-05-19T00:28:52.000Z

__init__.py

amueller/information-theoretic-mst

178fd4396bc9a9a499ec3d18d5047b320a5c32f2

[ "Unlicense" ]

5

2015-04-21T00:27:49.000Z

2019-02-23T20:46:33.000Z

from itm import ITM __all__ = ['ITM']

9.75

19

0.666667

0

5

0.128205

bed3d451e4686403bc8880395163b3b1b1569208

2,827

py

Python

scripts/left_right_hemisphere_data/hemisphere_asymmetry.py

mwinding/connectome_analysis

dbc747290891805863c9481921d8080dc2043d21

[ "MIT" ]

1

2021-06-10T05:48:16.000Z

left_right_hemisphere_data/hemisphere_asymmetry.py

mwinding/connectome_tools

0392f6b1e924194299ea7760d8386eb01f3371a3

[ "MIT" ]

2

2022-01-21T11:48:45.000Z

scripts/left_right_hemisphere_data/hemisphere_asymmetry.py

mwinding/connectome_analysis

dbc747290891805863c9481921d8080dc2043d21

[ "MIT" ]

1

2022-02-02T15:39:52.000Z

import pandas as pd import numpy as np import csv # import synapses divided across hemispheres hemisphere_data = pd.read_csv('left_right_hemisphere_data/brain_hemisphere_membership.csv', header = 0) #print(hemisphere_data) # import pair list CSV, manually generated #pairs = pd.read_csv('data/bp-pairs-2020-01-28.csv', header = 0) # import skids of neurons that cross commissure commissure_neurons = pd.read_json('left_right_hemisphere_data/cross_commissure-2020-3-2.json')['skeleton_id'].values #print(type(commissure_neurons[0])) #print(type(hemisphere_data['skeleton'][0])) ipsi_neurons = np.setdiff1d(hemisphere_data['skeleton'], commissure_neurons) ipsi_neurons_bool = pd.Series(hemisphere_data['skeleton'].values).isin(ipsi_neurons) contra_neurons_bool = ~pd.Series(hemisphere_data['skeleton'].values).isin(ipsi_neurons) print("IPSI") print("Postsynaptic Sites") print(sum(hemisphere_data[ipsi_neurons_bool]['n_inputs_left'].values)) print(sum(hemisphere_data[ipsi_neurons_bool]['n_inputs_right'].values)) print(sum(hemisphere_data[ipsi_neurons_bool]['n_inputs_left'].values)/sum(hemisphere_data[ipsi_neurons_bool]['n_inputs_right'].values)) print("") print("Presynaptic Sites") print(sum(hemisphere_data[ipsi_neurons_bool]['n_outputs_left'].values)) print(sum(hemisphere_data[ipsi_neurons_bool]['n_outputs_right'].values)) print(sum(hemisphere_data[ipsi_neurons_bool]['n_outputs_left'].values)/sum(hemisphere_data[ipsi_neurons_bool]['n_outputs_right'].values)) print("") print("Treenodes") print(sum(hemisphere_data[ipsi_neurons_bool]['n_treenodes_left'].values)) print(sum(hemisphere_data[ipsi_neurons_bool]['n_treenodes_right'].values)) print(sum(hemisphere_data[ipsi_neurons_bool]['n_treenodes_left'].values)/sum(hemisphere_data[ipsi_neurons_bool]['n_treenodes_right'].values)) print("") print("") print("") print("CONTRA") print("Postsynaptic Sites") print(sum(hemisphere_data[contra_neurons_bool]['n_inputs_left'].values)) print(sum(hemisphere_data[contra_neurons_bool]['n_inputs_right'].values)) print(sum(hemisphere_data[contra_neurons_bool]['n_inputs_left'].values)/sum(hemisphere_data[contra_neurons_bool]['n_inputs_right'].values)) print("") print("Presynaptic Sites") print(sum(hemisphere_data[contra_neurons_bool]['n_outputs_left'].values)) print(sum(hemisphere_data[contra_neurons_bool]['n_outputs_right'].values)) print(sum(hemisphere_data[contra_neurons_bool]['n_outputs_left'].values)/sum(hemisphere_data[contra_neurons_bool]['n_outputs_right'].values)) print("") print("Treenodes") print(sum(hemisphere_data[contra_neurons_bool]['n_treenodes_left'].values)) print(sum(hemisphere_data[contra_neurons_bool]['n_treenodes_right'].values)) print(sum(hemisphere_data[contra_neurons_bool]['n_treenodes_left'].values)/sum(hemisphere_data[contra_neurons_bool]['n_treenodes_right'].values)) print("")

45.596774

145

0.812168

0

995

0.351963

bed4073b9e79a28ea38d1cc06f6e14cb5d4efcb7

777

py

Python

__determineTripplesSumToZeroFromList.py

simdevex/01.Basics

cf4f372384e66f4b26e4887d2f5d815a1f8e929c

[ "MIT" ]

null

__determineTripplesSumToZeroFromList.py

simdevex/01.Basics

cf4f372384e66f4b26e4887d2f5d815a1f8e929c

[ "MIT" ]

null

__determineTripplesSumToZeroFromList.py

simdevex/01.Basics

cf4f372384e66f4b26e4887d2f5d815a1f8e929c

[ "MIT" ]

null

''' Python program to determine which triples sum to zero from a given list of lists. Input: [[1343532, -2920635, 332], [-27, 18, 9], [4, 0, -4], [2, 2, 2], [-20, 16, 4]] Output: [False, True, True, False, True] Input: [[1, 2, -3], [-4, 0, 4], [0, 1, -5], [1, 1, 1], [-2, 4, -1]] Output: [True, True, False, False, False] ''' #License: https://bit.ly/3oLErEI def test(nums): return [sum(t)==0 for t in nums] nums = [[1343532, -2920635, 332], [-27, 18, 9], [4, 0, -4], [2, 2, 2], [-20, 16, 4]] print("Original list of lists:",nums) print("Determine which triples sum to zero:") print(test(nums)) nums = [[1, 2, -3], [-4, 0, 4], [0, 1, -5], [1, 1, 1], [-2, 4, -1]] print("\nOriginal list of lists:",nums) print("Determine which triples sum to zero:") print(test(nums))

32.375

84

0.574003

0

485

0.624196

bed6c276e3757d89c0d4a20b188e77bced930a94

701

py

Python

fastNLP/modules/encoder/lstm.py

h00Jiang/fastNLP

79ddb469d81946c87a3d066122a8a3aba6e40f3a

[ "Apache-2.0" ]

null

fastNLP/modules/encoder/lstm.py

h00Jiang/fastNLP

79ddb469d81946c87a3d066122a8a3aba6e40f3a

[ "Apache-2.0" ]

null

fastNLP/modules/encoder/lstm.py

h00Jiang/fastNLP

79ddb469d81946c87a3d066122a8a3aba6e40f3a

[ "Apache-2.0" ]

null

import torch.nn as nn class Lstm(nn.Module): """ LSTM module Args: input_size : input size hidden_size : hidden size num_layers : number of hidden layers. Default: 1 dropout : dropout rate. Default: 0.5 bidirectional : If True, becomes a bidirectional RNN. Default: False. """ def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0, bidirectional=False): super(Lstm, self).__init__() self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=True, batch_first=True, dropout=dropout, bidirectional=bidirectional) def forward(self, x): x, _ = self.lstm(x) return x

29.208333

98

0.636234

676

0.964337

0

264

0.376605

bed723c002fdd1ab37526c62f785025bbbd1fbd1

838

py

Python

geoLocApp/signals.py

KKWaxy/geoLoc

23e33b9fd7cb3b1031bd11475612dcc324680975

[ "Apache-2.0" ]

null

geoLocApp/signals.py

KKWaxy/geoLoc

23e33b9fd7cb3b1031bd11475612dcc324680975

[ "Apache-2.0" ]

null

geoLocApp/signals.py

KKWaxy/geoLoc

23e33b9fd7cb3b1031bd11475612dcc324680975

[ "Apache-2.0" ]

null

from django.db.models.signals import pre_save,post_save from django.dispatch import receiver import geoLocApp.models import geoLocApp.distance # @receiver(post_save,sender=geoLocApp.models.Position,dispatch_uid="only_before_registered") # def setDistance(sender, **kwargs): # position = kwargs["instance"] # coordonnees = position.coordonnees.all() # print(coordonnees) # for coordonnee in coordonnees: # coordonnee.distance = geoLocApp.distance.distance(coordonnee.latitude,position.latitude,coordonnee.longitude,position.longitude) # print(coordonnee.distance) # @receiver(post_save,sender=geoLocApp.models.Position,dispatch_uid="new_position_added") # def new_position(sender,**kwargs): # if kwargs['created']==True: # return ['intance'] # else: # return 0

39.904762

139

0.72673

0

672

0.801909

bed7a7f211ac4ca2170057d5dae27d3248efc33a

2,198

py

Python

src/main/python/shabda/data/iterators/internal/data_iterator_base.py

dhiraa/sabdha

f428418962dcc76f49e0a451ffc0545fda9b6b59

[ "Apache-2.0" ]

4

2018-10-26T07:00:34.000Z

2020-10-07T01:03:08.000Z

src/main/python/shabda/data/iterators/internal/data_iterator_base.py

dhiraa/sabdha

f428418962dcc76f49e0a451ffc0545fda9b6b59

[ "Apache-2.0" ]

null

src/main/python/shabda/data/iterators/internal/data_iterator_base.py

dhiraa/sabdha

f428418962dcc76f49e0a451ffc0545fda9b6b59

[ "Apache-2.0" ]

1

2018-10-26T07:00:38.000Z

# Copyright 2018 The Shabda Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Iterator that creates features for LSTM based models """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from shabda.hyperparams.hyperparams import HParams class DataIteratorBase(): """ """ def __init__(self, hparams, dataset): self._hparams = HParams(hparams, default_hparams=self.get_default_params()) self._dataset = dataset @staticmethod def get_default_params(): return {"key": "value"} def _get_train_input_fn(self): """ Inheriting class must implement this :return: callable """ raise NotImplementedError def _get_val_input_fn(self): """ Inheriting class must implement this :return: callable """ raise NotImplementedError def _get_test_input_function(self): """ Inheriting class must implement this :return: callable """ raise NotImplementedError def get_train_input_fn(self): """ Returns an data set iterator function that can be used in estimator :return: """ return self._get_train_input_fn() def get_val_input_fn(self): """ Returns an data set iterator function that can be used in estimator :return: """ return self._get_val_input_fn() def get_test_input_function(self): """ Returns an data set iterator function that can be used in estimator :return: """ return self._get_test_input_function()

28.179487

83

0.670155

1,368

0.622384

0

75

0.034122

0

1,258

0.572338

bed7c4898d58c738f63aa212ff888514735a6694

441

py

Python

app/filters.py

dakhnovskaya/organization

72fb6f0a2daf03e7f7ce4dfb2cb3c3eaf1c40851

[ "Apache-2.0" ]

null

app/filters.py

dakhnovskaya/organization

72fb6f0a2daf03e7f7ce4dfb2cb3c3eaf1c40851

[ "Apache-2.0" ]

null

app/filters.py

dakhnovskaya/organization

72fb6f0a2daf03e7f7ce4dfb2cb3c3eaf1c40851

[ "Apache-2.0" ]

null

import django_filters from app.models import Company class CompanyFilter(django_filters.FilterSet): min_cost = django_filters.NumberFilter(field_name='companyproduct__cost', lookup_expr='gte') max_cost = django_filters.NumberFilter(field_name='companyproduct__cost', lookup_expr='lte') class Meta: model = Company fields = ('districts', 'products__category', 'name', 'products__name', 'min_cost', 'max_cost')

33.923077

102

0.750567

384

0.870748

0

127

0.287982

bed83cf2a356ae3011eb19bade5063e46f89b28c

6,598

py

Python

reaction/rpc/rabbitmq.py

Inkln/reaction

7a57bd642ac3db15012717130a5f2655c3b7b177

[ "Apache-2.0" ]

73

2019-10-01T15:59:57.000Z

2021-06-29T11:59:16.000Z

reaction/rpc/rabbitmq.py

Inkln/reaction

7a57bd642ac3db15012717130a5f2655c3b7b177

[ "Apache-2.0" ]

2

2020-06-25T10:26:38.000Z

2022-02-21T06:20:47.000Z

reaction/rpc/rabbitmq.py

Inkln/reaction

7a57bd642ac3db15012717130a5f2655c3b7b177

[ "Apache-2.0" ]

7

2019-10-08T05:46:22.000Z

2020-07-27T12:58:18.000Z

from typing import List import asyncio import inspect import logging import uuid import aio_pika import aio_pika.exceptions from .base import BaseRPC from .common import RPCError, RPCHandler, RPCRequest, RPCResponse class RPC(BaseRPC): HEARTBEAT_INTERVAL = 300 def __init__( self, url: str = None, name: str = None, handler: RPCHandler = None, timeout: float = None, pool_size: int = 0, batch_size: int = 0, wait_for_batch: bool = False, max_jobs: int = 0, loop: asyncio.AbstractEventLoop = None, ): self._loop = loop self._url = url or self.URL self._name = name self._handler = handler self._timeout = timeout self._pool_size = pool_size self._batch_size = batch_size self._wait_for_batch = wait_for_batch self._max_jobs = max_jobs self._mconn: aio_pika.RobustConnection = None self._mch: aio_pika.RobustChannel = None self._mq: aio_pika.RobustQueue = None self._queue = asyncio.Queue(loop=loop) self._pool = [] self._consuming = False async def _run_pool(self): self._pool = [self._run_worker() for _ in range(self._pool_size)] self._consuming = True await asyncio.gather(*self._pool, loop=self._loop) self._pool = [] async def _run_worker(self): bs = self._batch_size q = self._queue while self._consuming: batch = [await q.get()] if self._wait_for_batch and bs > 0: while len(batch) < bs: batch.append(await q.get()) else: while (bs <= 0 or len(batch) < bs) and not q.empty(): batch.append(q.get_nowait()) await asyncio.wait_for( asyncio.ensure_future( self._process_batch(batch), loop=self._loop, ), self._timeout, loop=self._loop, ) async def _process_single(self, message: aio_pika.IncomingMessage): return await asyncio.wait_for( asyncio.ensure_future( self._process_batch([message]), loop=self._loop, ), self._timeout, loop=self._loop, ) async def _process_batch(self, messages: List[aio_pika.IncomingMessage]): try: reqs = [] for m in messages: # logging.debug(f"message: correlation_id={m.correlation_id}") req: RPCRequest = self.decode_request(m.body) reqs.append(req) # logging.debug(f"handler: {self._handler}") results = self._handler(*reqs) if inspect.isawaitable(results): results = await results except KeyboardInterrupt: self._consuming = False for m in messages: await m.reject(requeue=True) return except Exception as e: if len(messages) == 1: results = [RPCError()] logging.exception(e) await messages[0].reject() else: for m in messages: await asyncio.wait_for( asyncio.ensure_future( self._process_batch([m]), loop=self._loop, ), self._timeout, loop=self._loop, ) return for message, result in zip(messages, results): result = aio_pika.Message( self.encode_response(result), correlation_id=message.correlation_id, delivery_mode=message.delivery_mode, ) await self._mch.default_exchange.publish( result, routing_key=message.reply_to, mandatory=False, ) if not message.processed: await message.ack() async def consume(self): while True: try: self._mconn = await aio_pika.connect_robust( self._url, loop=self._loop, heartbeat_interval=self.HEARTBEAT_INTERVAL, ) break except ConnectionError: # This case is not handled by aio-pika by some reasons logging.warning("wait for queue...") await asyncio.sleep(1, loop=self._loop) self._mch = await self._mconn.channel() await self._mch.set_qos(prefetch_count=self._max_jobs) self._mq = await self._mch.declare_queue(self._name) if self._pool_size > 0: await asyncio.gather( self._run_pool(), self._mq.consume(self._queue.put), loop=self._loop, ) else: await self._mq.consume(self._process_single) return self._mconn async def call(self, msg: RPCRequest) -> RPCResponse: return await asyncio.wait_for( asyncio.ensure_future(self._call(msg), loop=self._loop,), self._timeout, loop=self._loop, ) async def _call(self, msg: RPCRequest) -> RPCResponse: if not self._mconn: self._mconn = await aio_pika.connect_robust( self._url, loop=self._loop, heartbeat_interval=self.HEARTBEAT_INTERVAL, ) if not self._mch: self._mch: aio_pika.RobustChannel = await self._mconn.channel() mq: aio_pika.RobustQueue = await self._mch.declare_queue() try: correlation_id = str(uuid.uuid4()) message = aio_pika.Message( self.encode_request(msg), correlation_id=correlation_id, reply_to=mq.name, ) await self._mch.default_exchange.publish( message, routing_key=self._name, ) async with mq.iterator(no_ack=True) as it: async for message in it: break if message.correlation_id != correlation_id: raise ValueError("wrong correlation_id") response: RPCResponse = self.decode_response(message.body) # logging.debug(f"response: {response}") if isinstance(response, RPCError): response.reraise() return response finally: await mq.delete(if_empty=False, if_unused=False)

34.910053

78

0.543346

6,377

0.966505

0

5,401

0.818581

241

0.036526

bed8ffa1e73ffa405bfc1005a04f4f722ab41812

2,069

py

Python

api/migrations/0005_auto_20200906_1951.py

sh2MAN/yamdb_final

17f84bacd832237d88d3389605cf2acdf2a590f5

[ "BSD-3-Clause" ]

null

api/migrations/0005_auto_20200906_1951.py

sh2MAN/yamdb_final

17f84bacd832237d88d3389605cf2acdf2a590f5

[ "BSD-3-Clause" ]

null

api/migrations/0005_auto_20200906_1951.py

sh2MAN/yamdb_final

17f84bacd832237d88d3389605cf2acdf2a590f5

[ "BSD-3-Clause" ]

12

2021-02-11T16:39:00.000Z

2022-03-30T19:18:24.000Z

# Generated by Django 3.0.5 on 2020-09-06 19:51 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('api', '0004_auto_20200906_1752'), ] operations = [ migrations.AlterModelOptions( name='category', options={'verbose_name': 'Категория', 'verbose_name_plural': 'Категории'}, ), migrations.AlterModelOptions( name='genre', options={'verbose_name': 'Жанр', 'verbose_name_plural': 'Жанры'}, ), migrations.AlterModelOptions( name='title', options={'ordering': ('-id',), 'verbose_name': 'Произведение', 'verbose_name_plural': 'Произведения'}, ), migrations.RemoveConstraint( model_name='review', name='unique_review', ), migrations.AlterField( model_name='category', name='name', field=models.CharField(max_length=20, verbose_name='Наименование'), ), migrations.AlterField( model_name='genre', name='name', field=models.CharField(max_length=20, verbose_name='Наименование'), ), migrations.AlterField( model_name='title', name='category', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='categories', to='api.Category', verbose_name='Категория'), ), migrations.AlterField( model_name='title', name='description', field=models.TextField(blank=True, null=True, verbose_name='Описание'), ), migrations.AlterField( model_name='title', name='name', field=models.CharField(max_length=100, verbose_name='Название'), ), migrations.AddConstraint( model_name='review', constraint=models.UniqueConstraint(fields=('title', 'author'), name='unique_review'), ), ]

34.483333

177

0.585307

2,043

0.941909

0

609

0.280775

bed9a7c33d1cf837bf05eedf9e2389f71612ac64

1,104

py

Python

user_activity/models.py

adithya-bhat-b/user-activity

d2577bbb295ac381e08a31e296e3d681da7ab036

[ "MIT" ]

null

user_activity/models.py

adithya-bhat-b/user-activity

d2577bbb295ac381e08a31e296e3d681da7ab036

[ "MIT" ]

3

2021-04-08T22:04:18.000Z

2021-06-09T19:14:16.000Z

user_activity/models.py

adithya-bhat-b/user-activity

d2577bbb295ac381e08a31e296e3d681da7ab036

[ "MIT" ]

null

import pytz from django.db import models # Create your models here. def _get_time_zones(): """ Function to get all the timezones """ timezone_choices = [(tz, tz) for tz in pytz.all_timezones] return timezone_choices # Model for user class User(models.Model): """ User model: attributes: id - unique id of the user real_name - user name time_zone - user timezone """ id = models.CharField(primary_key=True, max_length=50) real_name = models.CharField(max_length=100) time_zone = models.CharField(max_length=50, choices=_get_time_zones()) class Meta: # Db table name db_table = "user" # Model for user class UserActivity(models.Model): """ UserActivity model: start_time: start time of an user activity end_time: end time of an user activity """ user_id = models.ForeignKey(User, on_delete=models.CASCADE) start_time = models.DateTimeField() end_time = models.DateTimeField() class Meta: # Db table name db_table = "user_activity"

25.674419

74

0.646739

827

0.749094

0

451

0.408514

bedad5c3db81102c82212833e871a369686befc7

1,992

py

Python

Learning/python_data_analysis1.py

VictoriaGuXY/MCO-Menu-Checker-Online

706e2e1bf7395cc344f382ea2ac53d964d459f86

[ "MIT" ]

null

Learning/python_data_analysis1.py

VictoriaGuXY/MCO-Menu-Checker-Online

706e2e1bf7395cc344f382ea2ac53d964d459f86

[ "MIT" ]

null

Learning/python_data_analysis1.py

VictoriaGuXY/MCO-Menu-Checker-Online

706e2e1bf7395cc344f382ea2ac53d964d459f86

[ "MIT" ]

null

import pandas as pd # pandas provides us lots of data frame and functions that we can quickly use # to analyze data. """ output """ # This file contains notes of basic data analyzing strategies using Python. # I will introduce two ways to read a csv file: pathway and URL. # Also, I will introduce how to output data and save them into a csv file. # ------------------------------------------------------------------------------ # read a csv file using the pathway # This is based on the pathway that we saved the file. # In python, to represent a pathway, we should either use / or //. df = pd.read_csv('E:\\tips.csv') # ------------------------------------------------------------------------------ # read data online using a URL data_url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv" df = pd.read_csv(data_url) # same output for the above two methods # output is shown below """ total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 5 25.29 4.71 Male No Sun Dinner 4 .. ... ... ... ... ... ... ... 240 27.18 2.00 Female Yes Sat Dinner 2 241 22.67 2.00 Male Yes Sat Dinner 2 242 17.82 1.75 Male No Sat Dinner 2 243 18.78 3.00 Female No Thur Dinner 2 [244 rows x 7 columns] """ # ------------------------------------------------------------------------------ # output data and save them into a csv file df.to_csv('E:\\demo.csv', encoding='utf-8', index=False) # When index = False, when output as a csv file, the name of each line will be # removed. # If we contain some special characters in data, encoding will treat it as # utf-8.

36.888889

84

0.533133

0

1,844

0.925703

bedb078b9701b035a6b7ffcad6706445ac8dade2

768

py

Python

InvoiceItemModel.py

kevinyjiang/cpa-generator

c21cd1f898cf068daff8a6937d6cefc591b16ab1

[ "MIT" ]

2

2018-09-26T19:18:45.000Z

2018-11-14T00:38:28.000Z

InvoiceItemModel.py

kevinyjiang/cpa-generator

c21cd1f898cf068daff8a6937d6cefc591b16ab1

[ "MIT" ]

null

InvoiceItemModel.py

kevinyjiang/cpa-generator

c21cd1f898cf068daff8a6937d6cefc591b16ab1

[ "MIT" ]

null

import config class InvoiceItemModel(object): def __init__(self, itemType, quantity): self.itemType = itemType self.quantity = int(quantity) self.unitPrice = 0 if itemType == 'Portraiture': self.unitPrice = config.PORTRAIT_RATE else: self.unitPrice = config.EVENT_RATE def __str__(self): return """<tr> <td>{} (1hr)</td> <td>{}</td> <td>${}</td> <td>${}</td> </tr>""".format(str(self.itemType), str(self.quantity), str(self.unitPrice), str(self.unitPrice * self.quantity))

30.72

68

0.453125

753

0.980469

0

198

0.257813

bedb1dc2f3fdaeceb37c80ae1a87e69944c3c668

1,725

py

Python

lambda/populateDB/lambda_function.py

aws-samples/amazon-connect-dynamic-ivr-menus

911f5d04cf78d3097cfe7e169bd0062459d61ec4

[ "MIT-0" ]

4

2021-06-24T14:42:42.000Z

2021-12-13T07:08:48.000Z

lambda/populateDB/lambda_function.py

aws-samples/amazon-connect-dynamic-ivr-menus

911f5d04cf78d3097cfe7e169bd0062459d61ec4

[ "MIT-0" ]

1

2021-12-13T06:53:39.000Z

lambda/populateDB/lambda_function.py

aws-samples/amazon-connect-dynamic-ivr-menus

911f5d04cf78d3097cfe7e169bd0062459d61ec4

[ "MIT-0" ]

2

2021-06-10T18:54:03.000Z

2021-12-13T08:07:05.000Z

import json import boto3 import os def lambda_handler(event, context): # TODO implement dynamodb = boto3.resource('dynamodb') customerTable = os.environ['customerTable'] table1 = dynamodb.Table(customerTable) policiesTable = os.environ['policiesTable'] table2 = dynamodb.Table(policiesTable) # Phone numbers should follow international format E.164 table1.put_item( Item={ 'clientID': '+3526919xxxxxx', 'clientName': 'Marius', 'clientPolicies': ['car','house'] } ) table1.put_item( Item={ 'clientID': '+3526919xxxxxx', 'clientName': 'John', 'clientPolicies': ['boat','pet'] } ) table2.put_item( Item={ 'policyID': 'car', 'description': 'Your car insurance covers third party damage and theft. Authorized service points are this and that.' } ) table2.put_item( Item={ 'policyID': 'house', 'description': 'Your house insurance covers damage caused by natural disasters, fires and earthquakes. To fill a claim, please visit our website.' } ) table2.put_item( Item={ 'policyID': 'boat', 'description': 'Your boat insurance covers damage caused by natural distasters and fires. To fill a claim, please visit our website.' } ) table2.put_item( Item={ 'policyID': 'pet', 'description': 'Your pet insurance covers any medical interventions required to keep your pet healty. For a list of approved vet centers, please visit our website.' } ) return 'ok'

28.278689

176

0.584348

0

876

0.507826

bedcd44ac29b275e927dc09d0e22f32d04f7138a

59

py

Python

pyds/heap/__init__.py

nitinkatyal1314/data-structures

2e7f5b99a6b09cea48f729682d9431b72afbfd7a

[ "MIT" ]

6

2021-04-06T18:14:59.000Z

2021-07-18T03:26:03.000Z

pyds/heap/__init__.py

nitinkatyal1314/data-structures

2e7f5b99a6b09cea48f729682d9431b72afbfd7a

[ "MIT" ]

null

pyds/heap/__init__.py

nitinkatyal1314/data-structures

2e7f5b99a6b09cea48f729682d9431b72afbfd7a

[ "MIT" ]

null

from .api import HeapAPI as Heap from .api import HeapType

19.666667

32

0.79661

0

bedeaa04e3aa523fae916c1f3ad83805bf94106f

2,849

py

Python

examples/s5b_transfer/s5b_receiver.py

isabella232/slixmpp

e15e6735f1dbfc66a5d43efe9fa9e7f5c9d1610a

[ "BSD-3-Clause" ]

null

examples/s5b_transfer/s5b_receiver.py

isabella232/slixmpp

e15e6735f1dbfc66a5d43efe9fa9e7f5c9d1610a

[ "BSD-3-Clause" ]

1

2021-02-24T07:58:40.000Z

examples/s5b_transfer/s5b_receiver.py

isabella232/slixmpp

e15e6735f1dbfc66a5d43efe9fa9e7f5c9d1610a

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Slixmpp: The Slick XMPP Library Copyright (C) 2015 Emmanuel Gil Peyrot This file is part of Slixmpp. See the file LICENSE for copying permission. """ import asyncio import logging from getpass import getpass from argparse import ArgumentParser import slixmpp class S5BReceiver(slixmpp.ClientXMPP): """ A basic example of creating and using a SOCKS5 bytestream. """ def __init__(self, jid, password, filename): slixmpp.ClientXMPP.__init__(self, jid, password) self.file = open(filename, 'wb') self.add_event_handler("socks5_connected", self.stream_opened) self.add_event_handler("socks5_data", self.stream_data) self.add_event_handler("socks5_closed", self.stream_closed) def stream_opened(self, sid): logging.info('Stream opened. %s', sid) def stream_data(self, data): self.file.write(data) def stream_closed(self, exception): logging.info('Stream closed. %s', exception) self.file.close() self.disconnect() if __name__ == '__main__': # Setup the command line arguments. parser = ArgumentParser() # Output verbosity options. parser.add_argument("-q", "--quiet", help="set logging to ERROR", action="store_const", dest="loglevel", const=logging.ERROR, default=logging.INFO) parser.add_argument("-d", "--debug", help="set logging to DEBUG", action="store_const", dest="loglevel", const=logging.DEBUG, default=logging.INFO) # JID and password options. parser.add_argument("-j", "--jid", dest="jid", help="JID to use") parser.add_argument("-p", "--password", dest="password", help="password to use") parser.add_argument("-o", "--out", dest="filename", help="file to save to") args = parser.parse_args() # Setup logging. logging.basicConfig(level=args.loglevel, format='%(levelname)-8s %(message)s') if args.jid is None: args.jid = input("Username: ") if args.password is None: args.password = getpass("Password: ") if args.filename is None: args.filename = input("File path: ") # Setup the S5BReceiver and register plugins. Note that while plugins may # have interdependencies, the order in which you register them does # not matter. xmpp = S5BReceiver(args.jid, args.password, args.filename) xmpp.register_plugin('xep_0030') # Service Discovery xmpp.register_plugin('xep_0065', { 'auto_accept': True }) # SOCKS5 Bytestreams # Connect to the XMPP server and start processing XMPP stanzas. xmpp.connect() xmpp.process(forever=False)

31.307692

77

0.630046

763

0.267813

0

1,072

0.376272

bedfb697a1311d179da9b0d371384f0a26973131

2,170

py

Python

api/app/routers/weather_models.py

bcgov/wps

71df0de72de9cd656dc9ebf8461ffe47cfb155f6

[ "Apache-2.0" ]

19

2020-01-31T21:51:31.000Z

2022-01-07T14:40:03.000Z

api/app/routers/weather_models.py

bcgov/wps

71df0de72de9cd656dc9ebf8461ffe47cfb155f6

[ "Apache-2.0" ]

1,680

2020-01-24T23:25:08.000Z

2022-03-31T23:50:27.000Z

api/app/routers/weather_models.py

bcgov/wps

71df0de72de9cd656dc9ebf8461ffe47cfb155f6

[ "Apache-2.0" ]

6

2020-04-28T22:41:08.000Z

2021-05-05T18:16:06.000Z

""" Routers for weather_models. """ import logging from fastapi import APIRouter, Depends from app.auth import authentication_required, audit from app.weather_models import ModelEnum from app.schemas.weather_models import ( WeatherModelPredictionSummaryResponse, WeatherStationsModelRunsPredictionsResponse) from app.schemas.shared import WeatherDataRequest from app.weather_models.fetch.summaries import fetch_model_prediction_summaries from app.weather_models.fetch.predictions import ( fetch_model_run_predictions_by_station_code) logger = logging.getLogger(__name__) router = APIRouter( prefix="/weather_models", dependencies=[Depends(audit), Depends(authentication_required)], ) @router.post('/{model}/predictions/summaries/', response_model=WeatherModelPredictionSummaryResponse) async def get_model_prediction_summaries( model: ModelEnum, request: WeatherDataRequest): """ Returns a summary of predictions for a given model. """ try: logger.info('/weather_models/%s/predictions/summaries/', model.name) summaries = await fetch_model_prediction_summaries(model, request.stations, request.time_of_interest) return WeatherModelPredictionSummaryResponse(summaries=summaries) except Exception as exception: logger.critical(exception, exc_info=True) raise @router.post('/{model}/predictions/most_recent/', response_model=WeatherStationsModelRunsPredictionsResponse) async def get_most_recent_model_values( model: ModelEnum, request: WeatherDataRequest): """ Returns the weather values for the last model prediction that was issued for the station before actual weather readings became available. """ try: logger.info('/weather_models/%s/predictions/most_recent/', model.name) station_predictions = await fetch_model_run_predictions_by_station_code( model, request.stations, request.time_of_interest) return WeatherStationsModelRunsPredictionsResponse( stations=station_predictions) except Exception as exception: logger.critical(exception, exc_info=True) raise

38.070175

109

0.765438

0

1,458

0.671889

1,220

0.562212

420

0.193548

bee066a8fc595636f1ed42106327e650d743c5d7

1,529

py

Python

155.min-stack.py

elfgzp/leetCode

964c6574d310a9a6c486bf638487fd2f72b83b3f

[ "MIT" ]

3

2019-04-12T06:22:56.000Z

2019-05-04T04:25:01.000Z

155.min-stack.py

elfgzp/Leetcode

964c6574d310a9a6c486bf638487fd2f72b83b3f

[ "MIT" ]

null

155.min-stack.py

elfgzp/Leetcode

964c6574d310a9a6c486bf638487fd2f72b83b3f

[ "MIT" ]

null

# # @lc app=leetcode.cn id=155 lang=python3 # # [155] 最小栈 # # https://leetcode-cn.com/problems/min-stack/description/ # # algorithms # Easy (47.45%) # Total Accepted: 19.4K # Total Submissions: 40.3K # Testcase Example: '["MinStack","push","push","push","getMin","pop","top","getMin"]\n[[],[-2],[0],[-3],[],[],[],[]]' # # 设计一个支持 push，pop，top 操作，并能在常数时间内检索到最小元素的栈。 # # # push(x) -- 将元素 x 推入栈中。 # pop() -- 删除栈顶的元素。 # top() -- 获取栈顶元素。 # getMin() -- 检索栈中的最小元素。 # # # 示例: # # MinStack minStack = new MinStack(); # minStack.push(-2); # minStack.push(0); # minStack.push(-3); # minStack.getMin(); --> 返回 -3. # minStack.pop(); # minStack.top(); --> 返回 0. # minStack.getMin(); --> 返回 -2. # # # class MinStack: def __init__(self): """ initialize your data structure here. """ self._min = None self._stack = [] def push(self, x: int) -> None: if self._min is None: self._min = x else: self._min = min(self._min, x) self._stack.append(x) def pop(self) -> None: self._stack.pop(-1) if self._stack: self._min = min(self._stack) else: self._min = None def top(self) -> int: return self._stack[-1] def getMin(self) -> int: return self._min # Your MinStack object will be instantiated and called as such: # obj = MinStack() # obj.push(x) # obj.pop() # param_3 = obj.top() # param_4 = obj.getMin()

19.602564

118

0.530412

655

0.390346

0

1,029

0.61323

bee082fd43d018efe615e1efde05a3a482204b84

48,845

py

Python

demisto_client/demisto_api/models/investigation_playbook_task.py

guytest/demisto-py

8ca4f56a6177668151b5656cbe675a377003c0e9

[ "Apache-2.0" ]

1

2020-04-08T14:36:06.000Z

demisto_client/demisto_api/models/investigation_playbook_task.py

guytest/demisto-py

8ca4f56a6177668151b5656cbe675a377003c0e9

[ "Apache-2.0" ]

null

demisto_client/demisto_api/models/investigation_playbook_task.py

guytest/demisto-py

8ca4f56a6177668151b5656cbe675a377003c0e9

[ "Apache-2.0" ]

null

# coding: utf-8 """ Demisto API This is the public REST API to integrate with the demisto server. HTTP request can be sent using any HTTP-client. For an example dedicated client take a look at: https://github.com/demisto/demisto-py. Requests must include API-key that can be generated in the Demisto web client under 'Settings' -> 'Integrations' -> 'API keys' Optimistic Locking and Versioning\\: When using Demisto REST API, you will need to make sure to work on the latest version of the item (incident, entry, etc.), otherwise, you will get a DB version error (which not allow you to override a newer item). In addition, you can pass 'version\\: -1' to force data override (make sure that other users data might be lost). Assume that Alice and Bob both read the same data from Demisto server, then they both changed the data, and then both tried to write the new versions back to the server. Whose changes should be saved? Alice’s? Bob’s? To solve this, each data item in Demisto has a numeric incremental version. If Alice saved an item with version 4 and Bob trying to save the same item with version 3, Demisto will rollback Bob request and returns a DB version conflict error. Bob will need to get the latest item and work on it so Alice work will not get lost. Example request using 'curl'\\: ``` curl 'https://hostname:443/incidents/search' -H 'content-type: application/json' -H 'accept: application/json' -H 'Authorization: <API Key goes here>' --data-binary '{\"filter\":{\"query\":\"-status:closed -category:job\",\"period\":{\"by\":\"day\",\"fromValue\":7}}}' --compressed ``` # noqa: E501 OpenAPI spec version: 2.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six from demisto_client.demisto_api.models.advance_arg import AdvanceArg # noqa: F401,E501 from demisto_client.demisto_api.models.data_collection_form import DataCollectionForm # noqa: F401,E501 from demisto_client.demisto_api.models.evidence_data import EvidenceData # noqa: F401,E501 from demisto_client.demisto_api.models.field_mapping import FieldMapping # noqa: F401,E501 from demisto_client.demisto_api.models.inv_playbook_task_complete_data import InvPlaybookTaskCompleteData # noqa: F401,E501 # from demisto_client.demisto_api.models.investigation_playbook import InvestigationPlaybook # noqa: F401,E501 from demisto_client.demisto_api.models.notifiable_item import NotifiableItem # noqa: F401,E501 from demisto_client.demisto_api.models.reputation_calc_alg import ReputationCalcAlg # noqa: F401,E501 from demisto_client.demisto_api.models.sla import SLA # noqa: F401,E501 from demisto_client.demisto_api.models.task import Task # noqa: F401,E501 from demisto_client.demisto_api.models.task_condition import TaskCondition # noqa: F401,E501 from demisto_client.demisto_api.models.task_loop import TaskLoop # noqa: F401,E501 from demisto_client.demisto_api.models.task_state import TaskState # noqa: F401,E501 from demisto_client.demisto_api.models.task_type import TaskType # noqa: F401,E501 from demisto_client.demisto_api.models.task_view import TaskView # noqa: F401,E501 from demisto_client.demisto_api.models.timer_trigger import TimerTrigger # noqa: F401,E501 class InvestigationPlaybookTask(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'arguments': 'dict(str, object)', 'assignee': 'str', 'assignee_set': 'bool', 'blocking_tasks': 'list[str]', 'comments': 'bool', 'completed_by': 'str', 'completed_count': 'int', 'completed_date': 'datetime', 'conditions': 'list[TaskCondition]', 'continue_on_error': 'bool', 'default_assignee': 'str', 'default_assignee_complex': 'AdvanceArg', 'default_reminder': 'int', 'due_date': 'datetime', 'due_date_set': 'bool', 'entries': 'list[str]', 'evidence_data': 'EvidenceData', 'execution_count': 'int', 'field_mapping': 'list[FieldMapping]', 'for_each_index': 'int', 'for_each_inputs': 'dict(str, list[object])', 'form': 'DataCollectionForm', 'id': 'str', 'ignore_worker': 'bool', 'indent': 'int', 'input': 'str', 'loop': 'TaskLoop', 'message': 'NotifiableItem', 'next_tasks': 'dict(str, list[str])', 'note': 'bool', 'outputs': 'dict(str, object)', 'parent_block_count': 'int', 'parent_playbook_id': 'str', 'patched': 'bool', 'playbook_inputs': 'dict(str, object)', 'previous_tasks': 'dict(str, list[str])', 'reminder': 'int', 'reputation_calc': 'ReputationCalcAlg', 'restricted_completion': 'bool', 'script_arguments': 'dict(str, AdvanceArg)', 'separate_context': 'bool', 'sla': 'SLA', 'sla_reminder': 'SLA', 'start_date': 'datetime', 'state': 'TaskState', 'sub_playbook': 'InvestigationPlaybook', 'task': 'Task', 'task_complete_data': 'list[InvPlaybookTaskCompleteData]', 'task_id': 'str', 'timer_triggers': 'list[TimerTrigger]', 'type': 'TaskType', 'view': 'TaskView', 'will_not_execute_count': 'int' } attribute_map = { 'arguments': 'arguments', 'assignee': 'assignee', 'assignee_set': 'assigneeSet', 'blocking_tasks': 'blockingTasks', 'comments': 'comments', 'completed_by': 'completedBy', 'completed_count': 'completedCount', 'completed_date': 'completedDate', 'conditions': 'conditions', 'continue_on_error': 'continueOnError', 'default_assignee': 'defaultAssignee', 'default_assignee_complex': 'defaultAssigneeComplex', 'default_reminder': 'defaultReminder', 'due_date': 'dueDate', 'due_date_set': 'dueDateSet', 'entries': 'entries', 'evidence_data': 'evidenceData', 'execution_count': 'executionCount', 'field_mapping': 'fieldMapping', 'for_each_index': 'forEachIndex', 'for_each_inputs': 'forEachInputs', 'form': 'form', 'id': 'id', 'ignore_worker': 'ignoreWorker', 'indent': 'indent', 'input': 'input', 'loop': 'loop', 'message': 'message', 'next_tasks': 'nextTasks', 'note': 'note', 'outputs': 'outputs', 'parent_block_count': 'parentBlockCount', 'parent_playbook_id': 'parentPlaybookID', 'patched': 'patched', 'playbook_inputs': 'playbookInputs', 'previous_tasks': 'previousTasks', 'reminder': 'reminder', 'reputation_calc': 'reputationCalc', 'restricted_completion': 'restrictedCompletion', 'script_arguments': 'scriptArguments', 'separate_context': 'separateContext', 'sla': 'sla', 'sla_reminder': 'slaReminder', 'start_date': 'startDate', 'state': 'state', 'sub_playbook': 'subPlaybook', 'task': 'task', 'task_complete_data': 'taskCompleteData', 'task_id': 'taskId', 'timer_triggers': 'timerTriggers', 'type': 'type', 'view': 'view', 'will_not_execute_count': 'willNotExecuteCount' } def __init__(self, arguments=None, assignee=None, assignee_set=None, blocking_tasks=None, comments=None, completed_by=None, completed_count=None, completed_date=None, conditions=None, continue_on_error=None, default_assignee=None, default_assignee_complex=None, default_reminder=None, due_date=None, due_date_set=None, entries=None, evidence_data=None, execution_count=None, field_mapping=None, for_each_index=None, for_each_inputs=None, form=None, id=None, ignore_worker=None, indent=None, input=None, loop=None, message=None, next_tasks=None, note=None, outputs=None, parent_block_count=None, parent_playbook_id=None, patched=None, playbook_inputs=None, previous_tasks=None, reminder=None, reputation_calc=None, restricted_completion=None, script_arguments=None, separate_context=None, sla=None, sla_reminder=None, start_date=None, state=None, sub_playbook=None, task=None, task_complete_data=None, task_id=None, timer_triggers=None, type=None, view=None, will_not_execute_count=None): # noqa: E501 """InvestigationPlaybookTask - a model defined in Swagger""" # noqa: E501 self._arguments = None self._assignee = None self._assignee_set = None self._blocking_tasks = None self._comments = None self._completed_by = None self._completed_count = None self._completed_date = None self._conditions = None self._continue_on_error = None self._default_assignee = None self._default_assignee_complex = None self._default_reminder = None self._due_date = None self._due_date_set = None self._entries = None self._evidence_data = None self._execution_count = None self._field_mapping = None self._for_each_index = None self._for_each_inputs = None self._form = None self._id = None self._ignore_worker = None self._indent = None self._input = None self._loop = None self._message = None self._next_tasks = None self._note = None self._outputs = None self._parent_block_count = None self._parent_playbook_id = None self._patched = None self._playbook_inputs = None self._previous_tasks = None self._reminder = None self._reputation_calc = None self._restricted_completion = None self._script_arguments = None self._separate_context = None self._sla = None self._sla_reminder = None self._start_date = None self._state = None self._sub_playbook = None self._task = None self._task_complete_data = None self._task_id = None self._timer_triggers = None self._type = None self._view = None self._will_not_execute_count = None self.discriminator = None if arguments is not None: self.arguments = arguments if assignee is not None: self.assignee = assignee if assignee_set is not None: self.assignee_set = assignee_set if blocking_tasks is not None: self.blocking_tasks = blocking_tasks if comments is not None: self.comments = comments if completed_by is not None: self.completed_by = completed_by if completed_count is not None: self.completed_count = completed_count if completed_date is not None: self.completed_date = completed_date if conditions is not None: self.conditions = conditions if continue_on_error is not None: self.continue_on_error = continue_on_error if default_assignee is not None: self.default_assignee = default_assignee if default_assignee_complex is not None: self.default_assignee_complex = default_assignee_complex if default_reminder is not None: self.default_reminder = default_reminder if due_date is not None: self.due_date = due_date if due_date_set is not None: self.due_date_set = due_date_set if entries is not None: self.entries = entries if evidence_data is not None: self.evidence_data = evidence_data if execution_count is not None: self.execution_count = execution_count if field_mapping is not None: self.field_mapping = field_mapping if for_each_index is not None: self.for_each_index = for_each_index if for_each_inputs is not None: self.for_each_inputs = for_each_inputs if form is not None: self.form = form if id is not None: self.id = id if ignore_worker is not None: self.ignore_worker = ignore_worker if indent is not None: self.indent = indent if input is not None: self.input = input if loop is not None: self.loop = loop if message is not None: self.message = message if next_tasks is not None: self.next_tasks = next_tasks if note is not None: self.note = note if outputs is not None: self.outputs = outputs if parent_block_count is not None: self.parent_block_count = parent_block_count if parent_playbook_id is not None: self.parent_playbook_id = parent_playbook_id if patched is not None: self.patched = patched if playbook_inputs is not None: self.playbook_inputs = playbook_inputs if previous_tasks is not None: self.previous_tasks = previous_tasks if reminder is not None: self.reminder = reminder if reputation_calc is not None: self.reputation_calc = reputation_calc if restricted_completion is not None: self.restricted_completion = restricted_completion if script_arguments is not None: self.script_arguments = script_arguments if separate_context is not None: self.separate_context = separate_context if sla is not None: self.sla = sla if sla_reminder is not None: self.sla_reminder = sla_reminder if start_date is not None: self.start_date = start_date if state is not None: self.state = state if sub_playbook is not None: self.sub_playbook = sub_playbook if task is not None: self.task = task if task_complete_data is not None: self.task_complete_data = task_complete_data if task_id is not None: self.task_id = task_id if timer_triggers is not None: self.timer_triggers = timer_triggers if type is not None: self.type = type if view is not None: self.view = view if will_not_execute_count is not None: self.will_not_execute_count = will_not_execute_count @property def arguments(self): """Gets the arguments of this InvestigationPlaybookTask. # noqa: E501 :return: The arguments of this InvestigationPlaybookTask. # noqa: E501 :rtype: dict(str, object) """ return self._arguments @arguments.setter def arguments(self, arguments): """Sets the arguments of this InvestigationPlaybookTask. :param arguments: The arguments of this InvestigationPlaybookTask. # noqa: E501 :type: dict(str, object) """ self._arguments = arguments @property def assignee(self): """Gets the assignee of this InvestigationPlaybookTask. # noqa: E501 :return: The assignee of this InvestigationPlaybookTask. # noqa: E501 :rtype: str """ return self._assignee @assignee.setter def assignee(self, assignee): """Sets the assignee of this InvestigationPlaybookTask. :param assignee: The assignee of this InvestigationPlaybookTask. # noqa: E501 :type: str """ self._assignee = assignee @property def assignee_set(self): """Gets the assignee_set of this InvestigationPlaybookTask. # noqa: E501 :return: The assignee_set of this InvestigationPlaybookTask. # noqa: E501 :rtype: bool """ return self._assignee_set @assignee_set.setter def assignee_set(self, assignee_set): """Sets the assignee_set of this InvestigationPlaybookTask. :param assignee_set: The assignee_set of this InvestigationPlaybookTask. # noqa: E501 :type: bool """ self._assignee_set = assignee_set @property def blocking_tasks(self): """Gets the blocking_tasks of this InvestigationPlaybookTask. # noqa: E501 :return: The blocking_tasks of this InvestigationPlaybookTask. # noqa: E501 :rtype: list[str] """ return self._blocking_tasks @blocking_tasks.setter def blocking_tasks(self, blocking_tasks): """Sets the blocking_tasks of this InvestigationPlaybookTask. :param blocking_tasks: The blocking_tasks of this InvestigationPlaybookTask. # noqa: E501 :type: list[str] """ self._blocking_tasks = blocking_tasks @property def comments(self): """Gets the comments of this InvestigationPlaybookTask. # noqa: E501 Whether this task had any comments or not # noqa: E501 :return: The comments of this InvestigationPlaybookTask. # noqa: E501 :rtype: bool """ return self._comments @comments.setter def comments(self, comments): """Sets the comments of this InvestigationPlaybookTask. Whether this task had any comments or not # noqa: E501 :param comments: The comments of this InvestigationPlaybookTask. # noqa: E501 :type: bool """ self._comments = comments @property def completed_by(self): """Gets the completed_by of this InvestigationPlaybookTask. # noqa: E501 :return: The completed_by of this InvestigationPlaybookTask. # noqa: E501 :rtype: str """ return self._completed_by @completed_by.setter def completed_by(self, completed_by): """Sets the completed_by of this InvestigationPlaybookTask. :param completed_by: The completed_by of this InvestigationPlaybookTask. # noqa: E501 :type: str """ self._completed_by = completed_by @property def completed_count(self): """Gets the completed_count of this InvestigationPlaybookTask. # noqa: E501 :return: The completed_count of this InvestigationPlaybookTask. # noqa: E501 :rtype: int """ return self._completed_count @completed_count.setter def completed_count(self, completed_count): """Sets the completed_count of this InvestigationPlaybookTask. :param completed_count: The completed_count of this InvestigationPlaybookTask. # noqa: E501 :type: int """ self._completed_count = completed_count @property def completed_date(self): """Gets the completed_date of this InvestigationPlaybookTask. # noqa: E501 :return: The completed_date of this InvestigationPlaybookTask. # noqa: E501 :rtype: datetime """ return self._completed_date @completed_date.setter def completed_date(self, completed_date): """Sets the completed_date of this InvestigationPlaybookTask. :param completed_date: The completed_date of this InvestigationPlaybookTask. # noqa: E501 :type: datetime """ self._completed_date = completed_date @property def conditions(self): """Gets the conditions of this InvestigationPlaybookTask. # noqa: E501 Conditions - optional list of conditions to run when task is conditional. we check conditions by their order (e.i. - considering the first one that satisfied) # noqa: E501 :return: The conditions of this InvestigationPlaybookTask. # noqa: E501 :rtype: list[TaskCondition] """ return self._conditions @conditions.setter def conditions(self, conditions): """Sets the conditions of this InvestigationPlaybookTask. Conditions - optional list of conditions to run when task is conditional. we check conditions by their order (e.i. - considering the first one that satisfied) # noqa: E501 :param conditions: The conditions of this InvestigationPlaybookTask. # noqa: E501 :type: list[TaskCondition] """ self._conditions = conditions @property def continue_on_error(self): """Gets the continue_on_error of this InvestigationPlaybookTask. # noqa: E501 :return: The continue_on_error of this InvestigationPlaybookTask. # noqa: E501 :rtype: bool """ return self._continue_on_error @continue_on_error.setter def continue_on_error(self, continue_on_error): """Sets the continue_on_error of this InvestigationPlaybookTask. :param continue_on_error: The continue_on_error of this InvestigationPlaybookTask. # noqa: E501 :type: bool """ self._continue_on_error = continue_on_error @property def default_assignee(self): """Gets the default_assignee of this InvestigationPlaybookTask. # noqa: E501 :return: The default_assignee of this InvestigationPlaybookTask. # noqa: E501 :rtype: str """ return self._default_assignee @default_assignee.setter def default_assignee(self, default_assignee): """Sets the default_assignee of this InvestigationPlaybookTask. :param default_assignee: The default_assignee of this InvestigationPlaybookTask. # noqa: E501 :type: str """ self._default_assignee = default_assignee @property def default_assignee_complex(self): """Gets the default_assignee_complex of this InvestigationPlaybookTask. # noqa: E501 :return: The default_assignee_complex of this InvestigationPlaybookTask. # noqa: E501 :rtype: AdvanceArg """ return self._default_assignee_complex @default_assignee_complex.setter def default_assignee_complex(self, default_assignee_complex): """Sets the default_assignee_complex of this InvestigationPlaybookTask. :param default_assignee_complex: The default_assignee_complex of this InvestigationPlaybookTask. # noqa: E501 :type: AdvanceArg """ self._default_assignee_complex = default_assignee_complex @property def default_reminder(self): """Gets the default_reminder of this InvestigationPlaybookTask. # noqa: E501 :return: The default_reminder of this InvestigationPlaybookTask. # noqa: E501 :rtype: int """ return self._default_reminder @default_reminder.setter def default_reminder(self, default_reminder): """Sets the default_reminder of this InvestigationPlaybookTask. :param default_reminder: The default_reminder of this InvestigationPlaybookTask. # noqa: E501 :type: int """ self._default_reminder = default_reminder @property def due_date(self): """Gets the due_date of this InvestigationPlaybookTask. # noqa: E501 :return: The due_date of this InvestigationPlaybookTask. # noqa: E501 :rtype: datetime """ return self._due_date @due_date.setter def due_date(self, due_date): """Sets the due_date of this InvestigationPlaybookTask. :param due_date: The due_date of this InvestigationPlaybookTask. # noqa: E501 :type: datetime """ self._due_date = due_date @property def due_date_set(self): """Gets the due_date_set of this InvestigationPlaybookTask. # noqa: E501 :return: The due_date_set of this InvestigationPlaybookTask. # noqa: E501 :rtype: bool """ return self._due_date_set @due_date_set.setter def due_date_set(self, due_date_set): """Sets the due_date_set of this InvestigationPlaybookTask. :param due_date_set: The due_date_set of this InvestigationPlaybookTask. # noqa: E501 :type: bool """ self._due_date_set = due_date_set @property def entries(self): """Gets the entries of this InvestigationPlaybookTask. # noqa: E501 :return: The entries of this InvestigationPlaybookTask. # noqa: E501 :rtype: list[str] """ return self._entries @entries.setter def entries(self, entries): """Sets the entries of this InvestigationPlaybookTask. :param entries: The entries of this InvestigationPlaybookTask. # noqa: E501 :type: list[str] """ self._entries = entries @property def evidence_data(self): """Gets the evidence_data of this InvestigationPlaybookTask. # noqa: E501 :return: The evidence_data of this InvestigationPlaybookTask. # noqa: E501 :rtype: EvidenceData """ return self._evidence_data @evidence_data.setter def evidence_data(self, evidence_data): """Sets the evidence_data of this InvestigationPlaybookTask. :param evidence_data: The evidence_data of this InvestigationPlaybookTask. # noqa: E501 :type: EvidenceData """ self._evidence_data = evidence_data @property def execution_count(self): """Gets the execution_count of this InvestigationPlaybookTask. # noqa: E501 :return: The execution_count of this InvestigationPlaybookTask. # noqa: E501 :rtype: int """ return self._execution_count @execution_count.setter def execution_count(self, execution_count): """Sets the execution_count of this InvestigationPlaybookTask. :param execution_count: The execution_count of this InvestigationPlaybookTask. # noqa: E501 :type: int """ self._execution_count = execution_count @property def field_mapping(self): """Gets the field_mapping of this InvestigationPlaybookTask. # noqa: E501 :return: The field_mapping of this InvestigationPlaybookTask. # noqa: E501 :rtype: list[FieldMapping] """ return self._field_mapping @field_mapping.setter def field_mapping(self, field_mapping): """Sets the field_mapping of this InvestigationPlaybookTask. :param field_mapping: The field_mapping of this InvestigationPlaybookTask. # noqa: E501 :type: list[FieldMapping] """ self._field_mapping = field_mapping @property def for_each_index(self): """Gets the for_each_index of this InvestigationPlaybookTask. # noqa: E501 Parameters needed for loops # noqa: E501 :return: The for_each_index of this InvestigationPlaybookTask. # noqa: E501 :rtype: int """ return self._for_each_index @for_each_index.setter def for_each_index(self, for_each_index): """Sets the for_each_index of this InvestigationPlaybookTask. Parameters needed for loops # noqa: E501 :param for_each_index: The for_each_index of this InvestigationPlaybookTask. # noqa: E501 :type: int """ self._for_each_index = for_each_index @property def for_each_inputs(self): """Gets the for_each_inputs of this InvestigationPlaybookTask. # noqa: E501 :return: The for_each_inputs of this InvestigationPlaybookTask. # noqa: E501 :rtype: dict(str, list[object]) """ return self._for_each_inputs @for_each_inputs.setter def for_each_inputs(self, for_each_inputs): """Sets the for_each_inputs of this InvestigationPlaybookTask. :param for_each_inputs: The for_each_inputs of this InvestigationPlaybookTask. # noqa: E501 :type: dict(str, list[object]) """ self._for_each_inputs = for_each_inputs @property def form(self): """Gets the form of this InvestigationPlaybookTask. # noqa: E501 :return: The form of this InvestigationPlaybookTask. # noqa: E501 :rtype: DataCollectionForm """ return self._form @form.setter def form(self, form): """Sets the form of this InvestigationPlaybookTask. :param form: The form of this InvestigationPlaybookTask. # noqa: E501 :type: DataCollectionForm """ self._form = form @property def id(self): """Gets the id of this InvestigationPlaybookTask. # noqa: E501 :return: The id of this InvestigationPlaybookTask. # noqa: E501 :rtype: str """ return self._id @id.setter def id(self, id): """Sets the id of this InvestigationPlaybookTask. :param id: The id of this InvestigationPlaybookTask. # noqa: E501 :type: str """ self._id = id @property def ignore_worker(self): """Gets the ignore_worker of this InvestigationPlaybookTask. # noqa: E501 Do not run this task in a worker # noqa: E501 :return: The ignore_worker of this InvestigationPlaybookTask. # noqa: E501 :rtype: bool """ return self._ignore_worker @ignore_worker.setter def ignore_worker(self, ignore_worker): """Sets the ignore_worker of this InvestigationPlaybookTask. Do not run this task in a worker # noqa: E501 :param ignore_worker: The ignore_worker of this InvestigationPlaybookTask. # noqa: E501 :type: bool """ self._ignore_worker = ignore_worker @property def indent(self): """Gets the indent of this InvestigationPlaybookTask. # noqa: E501 :return: The indent of this InvestigationPlaybookTask. # noqa: E501 :rtype: int """ return self._indent @indent.setter def indent(self, indent): """Sets the indent of this InvestigationPlaybookTask. :param indent: The indent of this InvestigationPlaybookTask. # noqa: E501 :type: int """ self._indent = indent @property def input(self): """Gets the input of this InvestigationPlaybookTask. # noqa: E501 :return: The input of this InvestigationPlaybookTask. # noqa: E501 :rtype: str """ return self._input @input.setter def input(self, input): """Sets the input of this InvestigationPlaybookTask. :param input: The input of this InvestigationPlaybookTask. # noqa: E501 :type: str """ self._input = input @property def loop(self): """Gets the loop of this InvestigationPlaybookTask. # noqa: E501 :return: The loop of this InvestigationPlaybookTask. # noqa: E501 :rtype: TaskLoop """ return self._loop @loop.setter def loop(self, loop): """Sets the loop of this InvestigationPlaybookTask. :param loop: The loop of this InvestigationPlaybookTask. # noqa: E501 :type: TaskLoop """ self._loop = loop @property def message(self): """Gets the message of this InvestigationPlaybookTask. # noqa: E501 :return: The message of this InvestigationPlaybookTask. # noqa: E501 :rtype: NotifiableItem """ return self._message @message.setter def message(self, message): """Sets the message of this InvestigationPlaybookTask. :param message: The message of this InvestigationPlaybookTask. # noqa: E501 :type: NotifiableItem """ self._message = message @property def next_tasks(self): """Gets the next_tasks of this InvestigationPlaybookTask. # noqa: E501 :return: The next_tasks of this InvestigationPlaybookTask. # noqa: E501 :rtype: dict(str, list[str]) """ return self._next_tasks @next_tasks.setter def next_tasks(self, next_tasks): """Sets the next_tasks of this InvestigationPlaybookTask. :param next_tasks: The next_tasks of this InvestigationPlaybookTask. # noqa: E501 :type: dict(str, list[str]) """ self._next_tasks = next_tasks @property def note(self): """Gets the note of this InvestigationPlaybookTask. # noqa: E501 :return: The note of this InvestigationPlaybookTask. # noqa: E501 :rtype: bool """ return self._note @note.setter def note(self, note): """Sets the note of this InvestigationPlaybookTask. :param note: The note of this InvestigationPlaybookTask. # noqa: E501 :type: bool """ self._note = note @property def outputs(self): """Gets the outputs of this InvestigationPlaybookTask. # noqa: E501 :return: The outputs of this InvestigationPlaybookTask. # noqa: E501 :rtype: dict(str, object) """ return self._outputs @outputs.setter def outputs(self, outputs): """Sets the outputs of this InvestigationPlaybookTask. :param outputs: The outputs of this InvestigationPlaybookTask. # noqa: E501 :type: dict(str, object) """ self._outputs = outputs @property def parent_block_count(self): """Gets the parent_block_count of this InvestigationPlaybookTask. # noqa: E501 the number of tasks that are waiting on blocked in subplaybooks of this task # noqa: E501 :return: The parent_block_count of this InvestigationPlaybookTask. # noqa: E501 :rtype: int """ return self._parent_block_count @parent_block_count.setter def parent_block_count(self, parent_block_count): """Sets the parent_block_count of this InvestigationPlaybookTask. the number of tasks that are waiting on blocked in subplaybooks of this task # noqa: E501 :param parent_block_count: The parent_block_count of this InvestigationPlaybookTask. # noqa: E501 :type: int """ self._parent_block_count = parent_block_count @property def parent_playbook_id(self): """Gets the parent_playbook_id of this InvestigationPlaybookTask. # noqa: E501 :return: The parent_playbook_id of this InvestigationPlaybookTask. # noqa: E501 :rtype: str """ return self._parent_playbook_id @parent_playbook_id.setter def parent_playbook_id(self, parent_playbook_id): """Sets the parent_playbook_id of this InvestigationPlaybookTask. :param parent_playbook_id: The parent_playbook_id of this InvestigationPlaybookTask. # noqa: E501 :type: str """ self._parent_playbook_id = parent_playbook_id @property def patched(self): """Gets the patched of this InvestigationPlaybookTask. # noqa: E501 Indicates whether this task was patched to InvPB and did not originally belong to the playbook # noqa: E501 :return: The patched of this InvestigationPlaybookTask. # noqa: E501 :rtype: bool """ return self._patched @patched.setter def patched(self, patched): """Sets the patched of this InvestigationPlaybookTask. Indicates whether this task was patched to InvPB and did not originally belong to the playbook # noqa: E501 :param patched: The patched of this InvestigationPlaybookTask. # noqa: E501 :type: bool """ self._patched = patched @property def playbook_inputs(self): """Gets the playbook_inputs of this InvestigationPlaybookTask. # noqa: E501 :return: The playbook_inputs of this InvestigationPlaybookTask. # noqa: E501 :rtype: dict(str, object) """ return self._playbook_inputs @playbook_inputs.setter def playbook_inputs(self, playbook_inputs): """Sets the playbook_inputs of this InvestigationPlaybookTask. :param playbook_inputs: The playbook_inputs of this InvestigationPlaybookTask. # noqa: E501 :type: dict(str, object) """ self._playbook_inputs = playbook_inputs @property def previous_tasks(self): """Gets the previous_tasks of this InvestigationPlaybookTask. # noqa: E501 :return: The previous_tasks of this InvestigationPlaybookTask. # noqa: E501 :rtype: dict(str, list[str]) """ return self._previous_tasks @previous_tasks.setter def previous_tasks(self, previous_tasks): """Sets the previous_tasks of this InvestigationPlaybookTask. :param previous_tasks: The previous_tasks of this InvestigationPlaybookTask. # noqa: E501 :type: dict(str, list[str]) """ self._previous_tasks = previous_tasks @property def reminder(self): """Gets the reminder of this InvestigationPlaybookTask. # noqa: E501 Duration in minutes, this field is not persisted here # noqa: E501 :return: The reminder of this InvestigationPlaybookTask. # noqa: E501 :rtype: int """ return self._reminder @reminder.setter def reminder(self, reminder): """Sets the reminder of this InvestigationPlaybookTask. Duration in minutes, this field is not persisted here # noqa: E501 :param reminder: The reminder of this InvestigationPlaybookTask. # noqa: E501 :type: int """ self._reminder = reminder @property def reputation_calc(self): """Gets the reputation_calc of this InvestigationPlaybookTask. # noqa: E501 :return: The reputation_calc of this InvestigationPlaybookTask. # noqa: E501 :rtype: ReputationCalcAlg """ return self._reputation_calc @reputation_calc.setter def reputation_calc(self, reputation_calc): """Sets the reputation_calc of this InvestigationPlaybookTask. :param reputation_calc: The reputation_calc of this InvestigationPlaybookTask. # noqa: E501 :type: ReputationCalcAlg """ self._reputation_calc = reputation_calc @property def restricted_completion(self): """Gets the restricted_completion of this InvestigationPlaybookTask. # noqa: E501 :return: The restricted_completion of this InvestigationPlaybookTask. # noqa: E501 :rtype: bool """ return self._restricted_completion @restricted_completion.setter def restricted_completion(self, restricted_completion): """Sets the restricted_completion of this InvestigationPlaybookTask. :param restricted_completion: The restricted_completion of this InvestigationPlaybookTask. # noqa: E501 :type: bool """ self._restricted_completion = restricted_completion @property def script_arguments(self): """Gets the script_arguments of this InvestigationPlaybookTask. # noqa: E501 :return: The script_arguments of this InvestigationPlaybookTask. # noqa: E501 :rtype: dict(str, AdvanceArg) """ return self._script_arguments @script_arguments.setter def script_arguments(self, script_arguments): """Sets the script_arguments of this InvestigationPlaybookTask. :param script_arguments: The script_arguments of this InvestigationPlaybookTask. # noqa: E501 :type: dict(str, AdvanceArg) """ self._script_arguments = script_arguments @property def separate_context(self): """Gets the separate_context of this InvestigationPlaybookTask. # noqa: E501 :return: The separate_context of this InvestigationPlaybookTask. # noqa: E501 :rtype: bool """ return self._separate_context @separate_context.setter def separate_context(self, separate_context): """Sets the separate_context of this InvestigationPlaybookTask. :param separate_context: The separate_context of this InvestigationPlaybookTask. # noqa: E501 :type: bool """ self._separate_context = separate_context @property def sla(self): """Gets the sla of this InvestigationPlaybookTask. # noqa: E501 :return: The sla of this InvestigationPlaybookTask. # noqa: E501 :rtype: SLA """ return self._sla @sla.setter def sla(self, sla): """Sets the sla of this InvestigationPlaybookTask. :param sla: The sla of this InvestigationPlaybookTask. # noqa: E501 :type: SLA """ self._sla = sla @property def sla_reminder(self): """Gets the sla_reminder of this InvestigationPlaybookTask. # noqa: E501 :return: The sla_reminder of this InvestigationPlaybookTask. # noqa: E501 :rtype: SLA """ return self._sla_reminder @sla_reminder.setter def sla_reminder(self, sla_reminder): """Sets the sla_reminder of this InvestigationPlaybookTask. :param sla_reminder: The sla_reminder of this InvestigationPlaybookTask. # noqa: E501 :type: SLA """ self._sla_reminder = sla_reminder @property def start_date(self): """Gets the start_date of this InvestigationPlaybookTask. # noqa: E501 :return: The start_date of this InvestigationPlaybookTask. # noqa: E501 :rtype: datetime """ return self._start_date @start_date.setter def start_date(self, start_date): """Sets the start_date of this InvestigationPlaybookTask. :param start_date: The start_date of this InvestigationPlaybookTask. # noqa: E501 :type: datetime """ self._start_date = start_date @property def state(self): """Gets the state of this InvestigationPlaybookTask. # noqa: E501 :return: The state of this InvestigationPlaybookTask. # noqa: E501 :rtype: TaskState """ return self._state @state.setter def state(self, state): """Sets the state of this InvestigationPlaybookTask. :param state: The state of this InvestigationPlaybookTask. # noqa: E501 :type: TaskState """ self._state = state @property def sub_playbook(self): """Gets the sub_playbook of this InvestigationPlaybookTask. # noqa: E501 :return: The sub_playbook of this InvestigationPlaybookTask. # noqa: E501 :rtype: InvestigationPlaybook """ return self._sub_playbook @sub_playbook.setter def sub_playbook(self, sub_playbook): """Sets the sub_playbook of this InvestigationPlaybookTask. :param sub_playbook: The sub_playbook of this InvestigationPlaybookTask. # noqa: E501 :type: InvestigationPlaybook """ self._sub_playbook = sub_playbook @property def task(self): """Gets the task of this InvestigationPlaybookTask. # noqa: E501 :return: The task of this InvestigationPlaybookTask. # noqa: E501 :rtype: Task """ return self._task @task.setter def task(self, task): """Sets the task of this InvestigationPlaybookTask. :param task: The task of this InvestigationPlaybookTask. # noqa: E501 :type: Task """ self._task = task @property def task_complete_data(self): """Gets the task_complete_data of this InvestigationPlaybookTask. # noqa: E501 History complete data # noqa: E501 :return: The task_complete_data of this InvestigationPlaybookTask. # noqa: E501 :rtype: list[InvPlaybookTaskCompleteData] """ return self._task_complete_data @task_complete_data.setter def task_complete_data(self, task_complete_data): """Sets the task_complete_data of this InvestigationPlaybookTask. History complete data # noqa: E501 :param task_complete_data: The task_complete_data of this InvestigationPlaybookTask. # noqa: E501 :type: list[InvPlaybookTaskCompleteData] """ self._task_complete_data = task_complete_data @property def task_id(self): """Gets the task_id of this InvestigationPlaybookTask. # noqa: E501 :return: The task_id of this InvestigationPlaybookTask. # noqa: E501 :rtype: str """ return self._task_id @task_id.setter def task_id(self, task_id): """Sets the task_id of this InvestigationPlaybookTask. :param task_id: The task_id of this InvestigationPlaybookTask. # noqa: E501 :type: str """ self._task_id = task_id @property def timer_triggers(self): """Gets the timer_triggers of this InvestigationPlaybookTask. # noqa: E501 SLA fields # noqa: E501 :return: The timer_triggers of this InvestigationPlaybookTask. # noqa: E501 :rtype: list[TimerTrigger] """ return self._timer_triggers @timer_triggers.setter def timer_triggers(self, timer_triggers): """Sets the timer_triggers of this InvestigationPlaybookTask. SLA fields # noqa: E501 :param timer_triggers: The timer_triggers of this InvestigationPlaybookTask. # noqa: E501 :type: list[TimerTrigger] """ self._timer_triggers = timer_triggers @property def type(self): """Gets the type of this InvestigationPlaybookTask. # noqa: E501 :return: The type of this InvestigationPlaybookTask. # noqa: E501 :rtype: TaskType """ return self._type @type.setter def type(self, type): """Sets the type of this InvestigationPlaybookTask. :param type: The type of this InvestigationPlaybookTask. # noqa: E501 :type: TaskType """ self._type = type @property def view(self): """Gets the view of this InvestigationPlaybookTask. # noqa: E501 :return: The view of this InvestigationPlaybookTask. # noqa: E501 :rtype: TaskView """ return self._view @view.setter def view(self, view): """Sets the view of this InvestigationPlaybookTask. :param view: The view of this InvestigationPlaybookTask. # noqa: E501 :type: TaskView """ self._view = view @property def will_not_execute_count(self): """Gets the will_not_execute_count of this InvestigationPlaybookTask. # noqa: E501 :return: The will_not_execute_count of this InvestigationPlaybookTask. # noqa: E501 :rtype: int """ return self._will_not_execute_count @will_not_execute_count.setter def will_not_execute_count(self, will_not_execute_count): """Sets the will_not_execute_count of this InvestigationPlaybookTask. :param will_not_execute_count: The will_not_execute_count of this InvestigationPlaybookTask. # noqa: E501 :type: int """ self._will_not_execute_count = will_not_execute_count def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(InvestigationPlaybookTask, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, InvestigationPlaybookTask): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

32.498337

1,584

0.649381

45,570

0.932875

0

31,877

0.652562

0

27,349

0.559868

bee216da141df68c9f7b41ae5761fcf6bc3e34f4

36,274

py

Python

make.py

beefoo/subway-inequality

a93bdbf81ea3753b0d2ec85d028f816adcc2f6f9

[ "MIT" ]

1

2021-03-08T01:44:10.000Z

make.py

beefoo/subway-inequality

a93bdbf81ea3753b0d2ec85d028f816adcc2f6f9

[ "MIT" ]

null

make.py

beefoo/subway-inequality

a93bdbf81ea3753b0d2ec85d028f816adcc2f6f9

[ "MIT" ]

1

2021-11-02T21:55:39.000Z

# -*- coding: utf-8 -*- # python3 make.py -loc "data/lines/1.csv" -width 3840 -height 2160 -overwrite # python3 make.py -loc "data/lines/1.csv" -width 3840 -height 2160 -rtl -overwrite # python3 combine.py # python3 make.py -data "data/lines/A_LEF.csv" -width 3840 -height 2160 -loc "data/lines/C.csv" -img "img/A.png" -sw 0.1405 -tw 0.145 -overwrite # python3 make.py -data "data/lines/A_LEF.csv" -width 3840 -height 2160 -loc "data/lines/C.csv" -img "img/A.png" -sw 0.1405 -tw 0.145 -rtl -overwrite # python3 combine.py -in "output/subway_line_A.mp4,output/subway_line_A_rtl.mp4" -out "output/subway_line_A_loop.mp4" # python3 make.py -data "data/lines/7.csv" -width 3840 -height 2160 -img "img/7.png" -sw 0.11725 -tw 0.135625 -reverse -overwrite # python3 make.py -data "data/lines/7.csv" -width 3840 -height 2160 -img "img/7.png" -sw 0.11725 -tw 0.135625 -reverse -rtl -overwrite # python3 combine.py -in "output/subway_line_7.mp4,output/subway_line_7_rtl.mp4" -out "output/subway_line_7_loop.mp4" import argparse import numpy as np import os from pprint import pprint import sys from lib import * # input parser = argparse.ArgumentParser() parser.add_argument('-data', dest="DATA_FILE", default="data/lines/2.csv", help="Input csv file with preprocessed data") parser.add_argument('-loc', dest="DATA_LOCAL_FILE", default="", help="Input csv file with preprocessed data of a local train that should 'fill in' stations in-between express trains") parser.add_argument('-img', dest="IMAGE_FILE", default="img/2.png", help="Subway bullet image") parser.add_argument('-instruments', dest="INSTRUMENTS_FILE", default="data/instruments.csv", help="Input csv file with instruments config") parser.add_argument('-dir', dest="MEDIA_DIRECTORY", default="audio/", help="Input media directory") parser.add_argument('-width', dest="WIDTH", default=1920, type=int, help="Output video width") parser.add_argument('-height', dest="HEIGHT", default=1080, type=int, help="Output video height") parser.add_argument('-pad0', dest="PAD_START", default=2000, type=int, help="Pad start in ms") parser.add_argument('-pad1', dest="PAD_END", default=2000, type=int, help="Pad end in ms") parser.add_argument('-fps', dest="FPS", default=30, type=int, help="Output video frames per second") parser.add_argument('-outframe', dest="OUTPUT_FRAME", default="tmp/line_%s/frame.%s.png", help="Output frames pattern") parser.add_argument('-aout', dest="AUDIO_OUTPUT_FILE", default="output/subway_line_%s.mp3", help="Output audio file") parser.add_argument('-dout', dest="DATA_OUTPUT_FILE", default="output/subway_line_%s.csv", help="Output data file") parser.add_argument('-out', dest="OUTPUT_FILE", default="output/subway_line_%s.mp4", help="Output media file") parser.add_argument('-overwrite', dest="OVERWRITE", action="store_true", help="Overwrite existing files?") parser.add_argument('-probe', dest="PROBE", action="store_true", help="Just view statistics?") parser.add_argument('-reverse', dest="REVERSE", action="store_true", help="Reverse the line?") parser.add_argument('-rtl', dest="RIGHT_TO_LEFT", action="store_true", help="Play from right to left?") parser.add_argument('-ao', dest="AUDIO_ONLY", action="store_true", help="Only output audio?") parser.add_argument('-vo', dest="VIDEO_ONLY", action="store_true", help="Only output video?") parser.add_argument('-do', dest="DATA_ONLY", action="store_true", help="Only output data?") parser.add_argument('-viz', dest="VISUALIZE_SEQUENCE", action="store_true", help="Output a visualization of the sequence") parser.add_argument('-plot', dest="PLOT_SEQUENCE", action="store_true", help="Display a plot chart of the sequence") parser.add_argument('-frame', dest="SINGLE_FRAME", default=-1, type=int, help="Output just a single frame") # Music config parser.add_argument('-db', dest="MASTER_DB", type=float, default=-2.4, help="Master +/- decibels to be applied to final audio") parser.add_argument('-bpm', dest="BPM", type=int, default=120, help="Beats per minute, e.g. 60, 75, 100, 120, 150") parser.add_argument('-mpb', dest="METERS_PER_BEAT", type=int, default=75, help="Higher numbers creates shorter songs") parser.add_argument('-dpb', dest="DIVISIONS_PER_BEAT", type=int, default=4, help="e.g. 4 = quarter notes, 8 = eighth notes") parser.add_argument('-pm', dest="PRICE_MULTIPLIER", type=float, default=1.3, help="Makes instruments more expensive; higher numbers = less instruments playing") parser.add_argument('-vdur', dest="VARIANCE_MS", type=int, default=20, help="+/- milliseconds an instrument note should be off by to give it a little more 'natural' feel") # Visual design config parser.add_argument('-sw', dest="STATION_WIDTH", type=float, default=0.125, help="Minimum station width as a percent of the screen width; adjust this to change the overall visual speed") parser.add_argument('-tw', dest="TEXT_WIDTH", type=float, default=0.15, help="Station text width as a percent of the screen width") parser.add_argument('-cy', dest="CENTER_Y", type=float, default=0.475, help="Center y as a percent of screen height") parser.add_argument('-bty', dest="BOROUGH_TEXT_Y", type=float, default=0.55, help="Borough text center y as a percent of screen height") parser.add_argument('-sty', dest="STATION_TEXT_Y", type=float, default=0.375, help="Station text center y as a percent of screen height") parser.add_argument('-cw', dest="CIRCLE_WIDTH", type=int, default=60, help="Circle radius in pixels assuming 1920x1080") parser.add_argument('-lh', dest="LINE_HEIGHT", type=int, default=24, help="Height of horizontal line in pixels assuming 1920x1080") parser.add_argument('-bh', dest="BOUNDARY_HEIGHT", type=int, default=166, help="Height of boundary line in pixels assuming 1920x1080") parser.add_argument('-bw', dest="BOUNDARY_WIDTH", type=int, default=3, help="Width of boundary line in pixels assuming 1920x1080") parser.add_argument('-bm', dest="BOUNDARY_MARGIN", type=int, default=48, help="Horizontal margin of boundary line in pixels assuming 1920x1080") parser.add_argument('-mw', dest="MARKER_WIDTH", type=int, default=8, help="Height of horizontal line in pixels assuming 1920x1080") parser.add_argument('-sts', dest="STATION_TEXT_SIZE", type=int, default=30, help="Station text size in pixels assuming 1920x1080") parser.add_argument('-stm', dest="STATION_TEXT_MARGIN", type=int, default=20, help="Station text bottom margin in pixels assuming 1920x1080") parser.add_argument('-slm', dest="STATION_LETTER_MARGIN", type=int, default=1, help="Space after each station text letter in pixels assuming 1920x1080") parser.add_argument('-bts', dest="BOROUGH_TEXT_SIZE", type=int, default=24, help="Borough text size in pixels assuming 1920x1080") parser.add_argument('-blm', dest="BOROUGH_LETTER_MARGIN", type=int, default=1, help="Space after each borough text letter in pixels assuming 1920x1080") parser.add_argument('-bthresh', dest="BOROUGH_THRESHOLD", type=float, default=0.375, help="Minimum width available for displaying borough dividers") parser.add_argument('-dw', dest="DIVIDER_WIDTH", type=int, default=28, help="Line divider in pixels assuming 1920x1080") parser.add_argument('-dd', dest="DIVIDER_DISTANCE", type=float, default=0.333, help="Distance between dividers as a percent of screen width") parser.add_argument('-dc', dest="DIVIDER_COLOR", default="#666666", help="Distance between dividers as a percent of screen width") parser.add_argument('-bg', dest="BG_COLOR", default="#000000", help="Background color") parser.add_argument('-tc', dest="TEXT_COLOR", default="#eeeeee", help="Text color") parser.add_argument('-atc', dest="ALT_TEXT_COLOR", default="#aaaaaa", help="Secondary text color") parser.add_argument('-mc', dest="MARKER_COLOR", default="#dddddd", help="Marker color") parser.add_argument('-sfont', dest="STATION_FONT", default="fonts/OpenSans-Bold.ttf", help="Station font") parser.add_argument('-bfont', dest="BOROUGH_FONT", default="fonts/OpenSans-SemiBold.ttf", help="Borough font") parser.add_argument('-map', dest="MAP_IMAGE", default="img/nyc.png", help="Station font") parser.add_argument('-mcoord', dest="MAP_COORDS", default=" -74.1261,40.9087,-73.7066,40.5743", help="Top left, bottom right point") parser.add_argument('-mapm', dest="MAP_MARGIN", type=int, default=30, help="Margin of map in pixels assuming 1920x1080") parser.add_argument('-mapw', dest="MAP_W", type=int, default=260, help="Map width in pixels assuming 1920x1080") parser.add_argument('-mlw', dest="MAP_LINE_WIDTH", type=int, default=4, help="Map line in pixels assuming 1920x1080") parser.add_argument('-mlc', dest="MAP_LINE_COLOR", default="#eeeeee", help="Secondary text color") a = parser.parse_args() if not a.AUDIO_ONLY: import gizeh from PIL import Image, ImageDraw, ImageFont startTime = logTime() # Calculations BEAT_MS = roundInt(60.0 / a.BPM * 1000) ROUND_TO_NEAREST = roundInt(1.0 * BEAT_MS / a.DIVISIONS_PER_BEAT) basename = getBasename(a.DATA_FILE) if "_" in basename: basename, _ = tuple(basename.split("_")) lineName = basename if a.RIGHT_TO_LEFT: basename += "_rtl" # Read data _, stations = readCsv(a.DATA_FILE) _, instruments = readCsv(a.INSTRUMENTS_FILE) lstations = [] if len(a.DATA_LOCAL_FILE): _, lstations = readCsv(a.DATA_LOCAL_FILE) # Parse instruments instruments = prependAll(instruments, ("file", a.MEDIA_DIRECTORY)) instruments = [i for i in instruments if i["active"] > 0] instruments = addIndices(instruments, "index") for i, instrument in enumerate(instruments): instruments[i]["from_beat_ms"] = roundInt(1.0 * BEAT_MS / instrument["from_tempo"]) instruments[i]["to_beat_ms"] = roundInt(1.0 * BEAT_MS / instrument["to_tempo"]) instruments[i]["interval_ms"] = roundInt(instrument["interval_phase"] * BEAT_MS) instruments[i]["price"] = instrument["price"] * a.PRICE_MULTIPLIER # Buy instruments based on a specified budget def buyInstruments(station, instrumentsShelf): budget = station['income'] / 12.0 percentile = station['percentile'] instrumentsCart = [] for i in instrumentsShelf: # skip if not in bracket if percentile < i['bracket_min'] or percentile >= i['bracket_max']: continue # add to cart if in budget elif i['price'] < budget: budget -= i['price'] instrumentsCart.append(i.copy()) # out of budget, finished else: break return instrumentsCart # Add local stations in-between express ones if len(lstations) > 0: lbasename = getBasename(a.DATA_LOCAL_FILE) estations = {} addStations = [] for i, s in enumerate(stations): lines = str(s["Daytime Routes"]).split(" ") if lbasename in lines: estations[s["Station ID"]] = s.copy() sortByStart = None currentLStations = [] for i, s in enumerate(lstations): if s["Station ID"] in estations: if sortByStart is not None and len(currentLStations) > 0: step = 1.0 / (len(currentLStations) + 1) for j, ls in enumerate(currentLStations): currentLStations[j]["sortBy"] = sortByStart + (j+1) * step currentLStations[j]["isLocal"] = 1 addStations += currentLStations currentLStations = [] sortByStart = estations[s["Station ID"]]["sortBy"] elif sortByStart is not None: currentLStations.append(s) stations += addStations # stations = sorted(stations, key=lambda d: d["sortBy"]) # for s in stations: # if "isLocal" in s: # print(" --"+s["Stop Name"]) # else: # print(s["Stop Name"]) # sys.exit() # Parse stations stations = sorted(stations, key=lambda d: d["income"]) stations = addNormalizedValues(stations, "income", "nIncome") stations = addIndices(stations, "incomeIndex") isReverse = a.REVERSE if a.RIGHT_TO_LEFT: isReverse = (not isReverse) stations = sorted(stations, key=lambda d: d["sortBy"], reverse=isReverse) stations = addIndices(stations, "index") stationCount = len(stations) ms = a.PAD_START for i, station in enumerate(stations): stations[i]["percentile"] = 1.0 * station["incomeIndex"] / stationCount * 100 # stations[i]["percentile"] = min(99.999, 1.0 * station["nIncome"] * 100) stations[i]["instruments"] = buyInstruments(stations[i], instruments) # print(len(stations[i]["instruments"])) distance = beats = duration = 0 if i < stationCount-1: distance = earthDistance(stations[i+1]['GTFS Latitude'], stations[i+1]['GTFS Longitude'], station['GTFS Latitude'], station['GTFS Longitude']) beats = roundInt(1.0 * distance / a.METERS_PER_BEAT) duration = beats * BEAT_MS boroughNext = stations[i+1]["Borough"] stations[i]["distance"] = distance stations[i]["beats"] = beats stations[i]["duration"] = duration stations[i]["vduration"] = duration stations[i]["BoroughNext"] = boroughNext stations[i]["ms"] = ms stations[i]["lineName"] = lineName ms += duration if a.PROBE: print("===========================") for s in stations: if "isLocal" in s: print(formatSeconds(roundInt(s["ms"]/1000.0)) + " --- " + s["Stop Name"] + " (LOCAL) - $" + formatNumber(s["income"])) else: print(formatSeconds(roundInt(s["ms"]/1000.0)) + " - " + s["Stop Name"] + " - $" + formatNumber(s["income"])) print("===========================") else: dataFilename = a.DATA_OUTPUT_FILE % basename makeDirectories([dataFilename]) writeCsv(dataFilename, stations, headings=["ms", "Stop Name", "isLocal", "income", "Borough", "lineName"]) textFilename = replaceFileExtension(dataFilename, ".txt") text = f'Subway Inequality: {basename} train ({stations[-1]["Stop Name"]} Bound)\n\n' text += f'This song above mimics a ride along a subway line (the {basename} train), where the quantity and power of the instruments at any given moment in the song corresponds to the median household income of the neighborhood that you are passing through. The goal is to have the dramatic contrasts of the song echo the dramatic contrast of income in the city.\n\n' for s in stations: if "isLocal" not in s: text += f'{formatSeconds(roundInt(s["ms"]/1000.0))} - {s["Stop Name"]} - ${formatNumber(s["income"])} household income\n' writeTextFile(textFilename, text) if a.DATA_ONLY: sys.exit() # Calculate ranges distances = [s["distance"] for s in stations if s["distance"] > 0] totalDistance = sum(distances) minDistance, maxDistance = (min(distances), max(distances)) durations = [s["duration"] for s in stations if s["duration"] > 0] totalMs = sum(durations) minDuration, maxDuration = (min(durations), max(durations)) totalBeats = sum([s["beats"] for s in stations]) totalSeconds = roundInt(totalMs / 1000.0) secondsPerStation = roundInt(1.0*totalSeconds/stationCount) print('Total distance in meters: %s' % roundInt(totalDistance)) print('Distance range in meters: [%s, %s]' % (roundInt(minDistance), roundInt(maxDistance))) print('Average beats per station: %s' % roundInt(1.0*totalBeats/stationCount)) print('Average time per station: %s' % formatSeconds(secondsPerStation)) print('Main sequence beats: %s' % totalBeats) # Retrieve gain based on current beat def getVolume(instrument, beat): beats_per_phase = instrument['gain_phase'] percent_complete = float(beat % beats_per_phase) / beats_per_phase percent = easeSin(percent_complete) from_volume = instrument['from_volume'] to_volume = instrument['to_volume'] volume = lerp((from_volume, to_volume), percent) return volume # Get beat duration in ms based on current point in time def getBeatMs(instrument, beat, round_to): from_beat_ms = instrument['from_beat_ms'] to_beat_ms = instrument['to_beat_ms'] beats_per_phase = instrument['tempo_phase'] percent_complete = float(beat % beats_per_phase) / beats_per_phase percent = easeSin(percent_complete) ms = lerp((from_beat_ms, to_beat_ms), percent) ms = roundInt(roundToNearest(ms, round_to)) return ms # Return if the instrument should be played in the given interval def isValidInterval(instrument, elapsed_ms, start_ms, end_ms, minIntervalDuration=3000): interval_ms = instrument['interval_ms'] interval = instrument['interval'] interval_offset = instrument['interval_offset'] isValid = (int(math.floor(1.0*elapsed_ms/interval_ms)) % interval == interval_offset) # return isValid if end_ms - start_ms <= minIntervalDuration * 3: return isValid # check to see if we're at the start and not long enough if isValid and elapsed_ms < (start_ms+minIntervalDuration) and not isValidInterval(instrument, start_ms+minIntervalDuration, start_ms, end_ms, minIntervalDuration): isValid = False # make start interval earlier if necessary elif not isValid and elapsed_ms < (start_ms+minIntervalDuration) and isValidInterval(instrument, start_ms+minIntervalDuration, start_ms, end_ms, minIntervalDuration): isValid = True # check to see if we're at the end and not long enough elif isValid and elapsed_ms > (end_ms-minIntervalDuration) and not isValidInterval(instrument, end_ms-minIntervalDuration, start_ms, end_ms, minIntervalDuration): isValid = False # make start interval earlier if necessary elif not isValid and elapsed_ms > (end_ms-minIntervalDuration) and isValidInterval(instrument, end_ms-minIntervalDuration, start_ms, end_ms, minIntervalDuration): isValid = True return isValid # Add beats to sequence def addBeatsToSequence(sequence, instrument, duration, ms, beat_ms, round_to, pad_start): msStart = ms msEnd = ms + duration offset_ms = int(instrument['tempo_offset'] * beat_ms) ms += offset_ms previous_ms = int(ms) from_beat_ms = instrument['from_beat_ms'] to_beat_ms = instrument['to_beat_ms'] min_ms = min(from_beat_ms, to_beat_ms) remaining_duration = int(duration) elapsed_duration = offset_ms continue_from_prev = (instrument['bracket_min'] > 0 or instrument['bracket_max'] < 100) rn = pseudoRandom(instrument["index"]+1) while remaining_duration >= min_ms: elapsed_ms = int(ms) elapsed_beat = int((elapsed_ms-previous_ms) / beat_ms) # continue beat from previous if continue_from_prev: elapsed_beat = int(elapsed_ms / beat_ms) this_beat_ms = getBeatMs(instrument, elapsed_beat, round_to) # add to sequence if in valid interval if isValidInterval(instrument, elapsed_ms, msStart, msEnd): variance = roundInt(rn * a.VARIANCE_MS * 2 - a.VARIANCE_MS) sequence.append({ 'instrumentIndex': instrument["index"], 'filename': instrument["file"], 'volume': getVolume(instrument, elapsed_beat), 'ms': max([pad_start + elapsed_ms + variance, 0]) }) remaining_duration -= this_beat_ms elapsed_duration += this_beat_ms ms += this_beat_ms return sequence # Build main sequence sequence = [] for i, instrument in enumerate(instruments): ms = 0 stationQueueDur = 0 # Each station in stations for station in stations: # Check if instrument is in this station instrumentIndex = findInList(station['instruments'], 'index', instrument['index']) # Instrument not here, just add the station duration and continue if instrumentIndex < 0 and stationQueueDur > 0: sequence = addBeatsToSequence(sequence, instrument, stationQueueDur, ms, BEAT_MS, ROUND_TO_NEAREST, a.PAD_START) ms += stationQueueDur + station['duration'] stationQueueDur = 0 elif instrumentIndex < 0: ms += station['duration'] else: stationQueueDur += station['duration'] if stationQueueDur > 0: sequence = addBeatsToSequence(sequence, instrument, stationQueueDur, ms, BEAT_MS, ROUND_TO_NEAREST, a.PAD_START) sequenceDuration = max([s["ms"] for s in sequence]) + a.PAD_END # Now start the video frame logic # Calculations aa = vars(a) aa["STATION_WIDTH"] = roundInt(1.0 * a.WIDTH * a.STATION_WIDTH) aa["TEXT_WIDTH"] = roundInt(1.0 * a.WIDTH * a.TEXT_WIDTH) aa["CENTER_Y"] = roundInt(1.0 * a.HEIGHT * a.CENTER_Y) aa["BOROUGH_TEXT_Y"] = roundInt(1.0 * a.HEIGHT * a.BOROUGH_TEXT_Y) aa["STATION_TEXT_Y"] = roundInt(1.0 * a.HEIGHT * a.STATION_TEXT_Y) RESOLUTION = a.WIDTH / 1920.0 aa["CIRCLE_WIDTH"] = roundInt(a.CIRCLE_WIDTH * RESOLUTION) aa["LINE_HEIGHT"] = roundInt(a.LINE_HEIGHT * RESOLUTION) aa["BOUNDARY_MARGIN"] = roundInt(a.BOUNDARY_MARGIN * RESOLUTION) aa["BOUNDARY_HEIGHT"] = roundInt(a.BOUNDARY_HEIGHT * RESOLUTION) aa["BOUNDARY_WIDTH"] = roundInt(a.BOUNDARY_WIDTH * RESOLUTION) aa["BOROUGH_THRESHOLD"] = roundInt(1.0 * a.WIDTH * a.BOROUGH_THRESHOLD) aa["MARKER_WIDTH"] = roundInt(a.MARKER_WIDTH * RESOLUTION) aa["STATION_TEXT_SIZE"] = roundInt(a.STATION_TEXT_SIZE * RESOLUTION) aa["STATION_TEXT_MARGIN"] = roundInt(a.STATION_TEXT_MARGIN * RESOLUTION) aa["STATION_LETTER_MARGIN"] = roundInt(a.STATION_LETTER_MARGIN * RESOLUTION) aa["BOROUGH_TEXT_SIZE"] = roundInt(a.BOROUGH_TEXT_SIZE * RESOLUTION) aa["BOROUGH_LETTER_MARGIN"] = roundInt(a.BOROUGH_LETTER_MARGIN * RESOLUTION) aa["MAP_COORDS"] = tuple([float(c) for c in a.MAP_COORDS.strip().split(",")]) aa["MAP_MARGIN"] = roundInt(a.MAP_MARGIN * RESOLUTION) aa["MAP_W"] = roundInt(a.MAP_W * RESOLUTION) aa["MAP_LINE_WIDTH"] = roundInt(a.MAP_LINE_WIDTH * RESOLUTION) aa["DIVIDER_WIDTH"] = roundInt(a.DIVIDER_WIDTH * RESOLUTION) aa["DIVIDER_DISTANCE"] = roundInt(1.0 * a.WIDTH * a.DIVIDER_DISTANCE) # Add borough names boroughNames = { "Q": "Queens", "M": "Manhattan", "Bk": "Brooklyn", "Bx": "Bronx", "SI": "Staten Island" } for i, station in enumerate(stations): stations[i]["borough"] = boroughNames[station["Borough"]] x = 0 mlon0, mlat0, mlon1, mlat1 = a.MAP_COORDS vstations = stations[:] # If going right to left, reverse the stations visually if a.RIGHT_TO_LEFT: vstations = list(reversed(vstations)) for i, station in enumerate(vstations): if i < stationCount-1: vstations[i]["vduration"] = vstations[i+1]["duration"] else: vstations[i]["vduration"] = 0 for i, station in enumerate(vstations): boroughNext = station["borough"] if i < stationCount-1: boroughNext = vstations[i+1]["borough"] vstations[i]["boroughNext"] = boroughNext vstations[i]["width"] = roundInt(1.0 * station["vduration"] / minDuration * a.STATION_WIDTH) vstations[i]["x"] = x vstations[i]["x0"] = x - a.TEXT_WIDTH / 2 vstations[i]["x1"] = x + a.TEXT_WIDTH / 2 vstations[i]["mapNx"] = norm(station["GTFS Longitude"], (mlon0, mlon1)) vstations[i]["mapNy"] = norm(station["GTFS Latitude"], (mlat0, mlat1)) x += vstations[i]["width"] totalW = x pxPerMs = 1.0 * totalW / totalMs pxPerS = pxPerMs * 1000.0 pxPerFrame = pxPerS / a.FPS print("Total width: %s px" % totalW) print("Pixels per second: %s" % pxPerS) print("Pixels per frame: %s" % pxPerFrame) totalFrames = msToFrame(sequenceDuration, a.FPS) totalFrames = int(ceilToNearest(totalFrames, a.FPS)) print("Total frames: %s" % totalFrames) sequenceDuration = frameToMs(totalFrames, a.FPS) def drawFrame(filename, ms, xOffset, stations, totalW, bulletImg, mapImg, fontStation, fontBorough, a): if not a.OVERWRITE and os.path.isfile(filename): return im = Image.new('RGB', (a.WIDTH, a.HEIGHT), a.BG_COLOR) draw = ImageDraw.Draw(im, 'RGBA') cx = roundInt(a.WIDTH * 0.5) cy = a.CENTER_Y stationCount = len(stations) leftX = xOffset rightX = leftX + totalW # draw the center line x0 = 0 if leftX < 0 else leftX x1 = a.WIDTH if rightX > a.WIDTH else rightX y0 = cy - a.LINE_HEIGHT/2 y1 = y0 + a.LINE_HEIGHT draw.rectangle([(x0, y0), (x1, y1)], fill=a.ALT_TEXT_COLOR) for i, s in enumerate(stations): # check to see if we should draw borough divider if s["borough"] != s["boroughNext"]: deltaBx = abs(stations[i+1]["x"]-s["x"]) # don't draw boundary in tight space if deltaBx > a.BOROUGH_THRESHOLD: bdx = roundInt(xOffset + (s["x"] + stations[i+1]["x"]) * 0.5) bdx0 = bdx - a.WIDTH/2 bdx1 = bdx + a.WIDTH/2 if 0 <= bdx0 <= a.WIDTH or 0 <= bdx1 <= a.WIDTH: dx0 = bdx - a.BOUNDARY_WIDTH/2 dx1 = dx0 + a.BOUNDARY_WIDTH dy0 = cy dy1 = dy0 + a.BOUNDARY_HEIGHT draw.rectangle([(dx0, dy0), (dx1, dy1)], fill=a.ALT_TEXT_COLOR) blw, blh = getLineSize(fontBorough, s["borough"], a.BOROUGH_LETTER_MARGIN) bx = dx0 - a.BOUNDARY_MARGIN - blw/2 drawTextToImage(draw, s["borough"], fontBorough, a.BOROUGH_LETTER_MARGIN, bx, a.BOROUGH_TEXT_Y, a.ALT_TEXT_COLOR) blw, blh = getLineSize(fontBorough, s["boroughNext"], a.BOROUGH_LETTER_MARGIN) bx = dx1 + a.BOUNDARY_MARGIN + blw/2 drawTextToImage(draw, s["boroughNext"], fontBorough, a.BOROUGH_LETTER_MARGIN, bx, a.BOROUGH_TEXT_Y, a.ALT_TEXT_COLOR) sx = xOffset + s["x"] sy = a.CENTER_Y # draw dividers if i < stationCount-1: dividers = 0 dividerDistance = 0 nextSx = xOffset + stations[i+1]["x"] deltaSx = abs(nextSx - sx) if deltaSx >= a.DIVIDER_DISTANCE * 2: dividers = int(1.0 * deltaSx / a.DIVIDER_DISTANCE) - 1 if dividers > 0: dividerDistance = roundInt(1.0 * deltaSx / (dividers+1)) for di in range(dividers): divX = sx + (di+1) * dividerDistance divX0 = divX - a.DIVIDER_WIDTH/2 divX1 = divX0 + a.DIVIDER_WIDTH divY0 = y0 divY1 = y1 if divX1 > 0: draw.rectangle([(divX0, divY0), (divX1, divY1)], fill=a.DIVIDER_COLOR) # check if station is visible sx0 = xOffset + s["x0"] sx1 = xOffset + s["x1"] if not (0 <= sx0 <= a.WIDTH or 0 <= sx1 <= a.WIDTH): continue # just draw empty bullet for local stops if "isLocal" in s: brad = roundInt(a.CIRCLE_WIDTH/3) bx = sx by = sy # Draw line using gizeh so it will be smooth bsurface = gizeh.Surface(width=a.WIDTH, height=a.HEIGHT) circle = gizeh.circle(r=brad, xy=[bx, by], fill=hexToRGB(a.DIVIDER_COLOR, toFloat=True)) circle.draw(bsurface) bpixels = bsurface.get_npimage(transparent=True) # should be shape: h, w, rgba circleImg = Image.fromarray(bpixels, mode="RGBA") im.paste(circleImg, (0, 0), circleImg) continue # draw borough text bx = sx by = a.BOROUGH_TEXT_Y drawTextToImage(draw, s["borough"], fontBorough, a.BOROUGH_LETTER_MARGIN, bx, by, a.ALT_TEXT_COLOR) # draw bullet bx = roundInt(sx - a.CIRCLE_WIDTH/2) by = roundInt(sy - a.CIRCLE_WIDTH/2) im.paste(bulletImg, (bx, by), bulletImg) # draw station text stx = sx sty = a.STATION_TEXT_Y slines = getMultilines(s["Stop Name"], fontStation, a.TEXT_WIDTH, a.STATION_LETTER_MARGIN) drawTextLinesToImage(draw, slines, fontStation, a.STATION_TEXT_MARGIN, a.STATION_LETTER_MARGIN, stx, sty, a.TEXT_COLOR) # draw the map mw, mh = mapImg.size mx = a.MAP_MARGIN my = a.HEIGHT - mh - a.MAP_MARGIN im.paste(mapImg, (mx, my)) lineColor = "#"+str(stations[0]["color"]) points = [] allPoints = [] mstations = stations[:] if a.RIGHT_TO_LEFT: mstations = list(reversed(mstations)) for i, s in enumerate(mstations): sms0 = s["ms"] sms1 = sms0 + s["duration"] # print("%s, %s" % (sms0, sms1)) mprogress = norm(ms, (sms0, sms1), limit=True) if s["duration"] > 0 else 1.0 lx = lerp((mx, mx+mw), s["mapNx"]) ly = lerp((my, my+mh), s["mapNy"]) if ms >= sms0: points.append((lx, ly)) if 0.0 < mprogress < 1.0 and i < stationCount-1 and s["duration"] > 0: lx1 = lerp((mx, mx+mw), mstations[i+1]["mapNx"]) ly1 = lerp((my, my+mh), mstations[i+1]["mapNy"]) lx2 = lerp((lx, lx1), mprogress) ly2 = lerp((ly, ly1), mprogress) points.append((lx2, ly2)) allPoints.append((lx, ly)) # Draw line using gizeh so it will be smooth surface = gizeh.Surface(width=a.WIDTH, height=a.HEIGHT) line = gizeh.polyline(points=allPoints, stroke_width=max(1, a.MAP_LINE_WIDTH-1), stroke=hexToRGB(a.MAP_LINE_COLOR, toFloat=True)) line.draw(surface) if len(points) > 1: sline = gizeh.polyline(points=points, stroke_width=a.MAP_LINE_WIDTH, stroke=hexToRGB(lineColor, toFloat=True)) sline.draw(surface) spixels = surface.get_npimage(transparent=True) # should be shape: h, w, rgba lineImage = Image.fromarray(spixels, mode="RGBA") im.paste(lineImage, (0, 0), lineImage) # draw the marker x0 = cx - a.MARKER_WIDTH/2 x1 = x0 + a.MARKER_WIDTH y0 = 0 y1 = a.HEIGHT draw.rectangle([(x0, y0), (x1, y1)], fill=(255,255,255,100)) del draw im.save(filename) # print("Saved %s" % filename) def getEasedFrames(easeFrameCount, stationFrameCount, pxPerFrame): fromFrameCount = int(min(easeFrameCount, stationFrameCount) / 2) fromPx = fromFrameCount * pxPerFrame toFrameCount = easeFrameCount + fromFrameCount # 'fromPx' will be stretched into 'toFrameCount' frames # easedPoints = [easeIn(n) * pxPerFrame for n in np.linspace(0, 1.0, num=toFrameCount)] easedPoints = [n * pxPerFrame for n in np.linspace(0, 1.0, num=toFrameCount)] buckets = [0 for n in range(toFrameCount)] pxPool = fromPx for i in range(toFrameCount): index = toFrameCount-1-i bucketPx = buckets[index] addPx = easedPoints[index] if addPx > pxPool: addPx = pxPool buckets[index] = addPx pxPool -= addPx if pxPool <= 0: break if pxPool > 0: incr = 0.01 while pxPool > 0: for j in range(toFrameCount): index = toFrameCount-1-j bucketPx = buckets[index] if (bucketPx+incr) <= pxPerFrame: buckets[index] += incr pxPool -= incr # import matplotlib.pyplot as plt # plt.plot(buckets) # plt.show() # sys.exit() # print("%s ~ %s" % (fromPx, sum(buckets))) return buckets audioFilename = a.AUDIO_OUTPUT_FILE % basename print("%s steps in sequence" % len(sequence)) print('Total sequence time: %s' % formatSeconds(sequenceDuration/1000.0)) if a.VISUALIZE_SEQUENCE: instrumentsCount = len(instruments) labelW = 200 unitH = 10 unitW = 10 marginH = 2 imgH = (unitH+marginH) * instrumentsCount imgW = totalSeconds * unitW + labelW dfont = ImageFont.truetype(font="fonts/OpenSans-Regular.ttf", size=10) print("Making viz %s x %s" % (imgW, imgH)) im = Image.new('RGB', (imgW, imgH), "#000000") draw = ImageDraw.Draw(im, 'RGB') for i, ins in enumerate(instruments): y = i * (unitH + marginH) draw.text((2, y), ins["name"], fill="#FFFFFF", font=dfont) steps = [step for step in sequence if step["instrumentIndex"]==ins["index"]] for step in steps: sx = roundInt((step["ms"] - a.PAD_START) / 1000.0 / totalSeconds * (imgW-labelW) + labelW) draw.rectangle([(sx, y), (sx+3, y+unitH)], fill=(roundInt(255*step["volume"]),0,0)) if i > 0: draw.line([(0, y-1), (imgW, y-1)], fill="#cccccc", width=1) printProgress(i+1, instrumentsCount) im.save("output/viz.png") sys.exit() if a.PLOT_SEQUENCE: import matplotlib.pyplot as plt xs = [s['ms']/1000.0 for s in stations] ys = [s['income'] for s in stations] plt.plot(xs, ys) plt.show() sys.exit() if a.PROBE: sys.exit() makeDirectories([a.AUDIO_OUTPUT_FILE, a.OUTPUT_FILE]) if not a.AUDIO_ONLY: bulletImg = Image.open(a.IMAGE_FILE) bulletImg = bulletImg.resize((a.CIRCLE_WIDTH, a.CIRCLE_WIDTH), resample=Image.LANCZOS) mapImg = Image.open(a.MAP_IMAGE) mapH = roundInt((1.0 * mapImg.size[1] / mapImg.size[0]) * a.MAP_W) mapImg = mapImg.resize((a.MAP_W, mapH), resample=Image.LANCZOS) fontStation = ImageFont.truetype(font=a.STATION_FONT, size=a.STATION_TEXT_SIZE, layout_engine=ImageFont.LAYOUT_RAQM) fontBorough = ImageFont.truetype(font=a.BOROUGH_FONT, size=a.BOROUGH_TEXT_SIZE, layout_engine=ImageFont.LAYOUT_RAQM) makeDirectories([a.OUTPUT_FRAME % (basename, "*")]) if a.OVERWRITE and a.SINGLE_FRAME < 1: removeFiles(a.OUTPUT_FRAME % (basename, "*")) # calculations for easing in/out padFrameInCount = msToFrame(a.PAD_START, a.FPS) station0FrameCount = msToFrame(stations[0]["duration"], a.FPS) easeInFrames = getEasedFrames(padFrameInCount, station0FrameCount, pxPerFrame) easeInFrameCount = len(easeInFrames) padFrameOutCount = msToFrame(a.PAD_END, a.FPS) station1FrameCount = msToFrame(stations[-2]["duration"], a.FPS) easeOutFrames = getEasedFrames(padFrameOutCount, station1FrameCount, pxPerFrame) # easeOutFrames = list(reversed(easeOutFrames)) easeOutFrameCount = len(easeOutFrames) easeOutPixels = roundInt(sum(easeOutFrames)) print("Making video frame sequence...") videoFrames = [] centerX = roundInt(a.WIDTH * 0.5) xOffset = centerX direction = -1 if a.RIGHT_TO_LEFT: direction = 1 xOffset -= totalW xOffsetF = 1.0 * xOffset target = centerX-totalW if direction < 0 else centerX for f in range(totalFrames): frame = f + 1 ms = frameToMs(frame, a.FPS) frameFilename = a.OUTPUT_FRAME % (basename, zeroPad(frame, totalFrames)) if a.SINGLE_FRAME < 1 or a.SINGLE_FRAME == frame: if a.SINGLE_FRAME > 0: frameFilename = "output/frame.png" drawFrame(frameFilename, ms, xOffset, vstations, totalW, bulletImg, mapImg, fontStation, fontBorough, a) if a.SINGLE_FRAME > 0: sys.exit() pixelsLeft = abs(target - xOffset) # ease in start if frame < easeInFrameCount: xOffsetF += (direction * easeInFrames[frame-1]) xOffset = roundInt(xOffsetF) # print(abs(xOffset-centerX)) # # correct any discrepancy after ease in # elif frame <= easeInFrameCount: # xOffset = (frame - padFrameInCount) * pxPerFrame # xOffsetF = 1.0 * xOffset # ease out end elif pixelsLeft <= easeOutPixels: pxStep = easeOutFrames.pop() if len(easeOutFrames) > 0 else 1 xOffsetF += (direction * pxStep) xOffset = roundInt(xOffsetF) # print("%s > %s" % (xOffset, centerX-totalW)) else: xOffset += (direction * pxPerFrame) xOffsetF = 1.0 * xOffset xOffset = lim(xOffset, (centerX-totalW, centerX)) printProgress(frame, totalFrames) # break stepTime = logTime(startTime, "Finished frames") padZeros = len(str(totalFrames)) outfile = a.OUTPUT_FILE % basename frameInfile = a.OUTPUT_FRAME % (basename, '%s') if a.VIDEO_ONLY: compileFrames(frameInfile, a.FPS, outfile, padZeros) sys.exit() if a.OVERWRITE or not os.path.isfile(audioFilename): mixAudio(sequence, sequenceDuration, audioFilename, masterDb=a.MASTER_DB) else: print("%s already exists" % audioFilename) stepTime = logTime(stepTime, "Finished Audio") if not a.AUDIO_ONLY: if a.VIDEO_ONLY: audioFilename = None if a.OVERWRITE or not os.path.isfile(outfile): compileFrames(frameInfile, a.FPS, outfile, padZeros, audioFile=audioFilename) else: print("%s already exists" % outfile) logTime(startTime, "Total execution time")

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371

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0

10,581

0.291697

bee4367acb7e986f0d0dbc48437bdbc33f87cdab

15,487

py

Python

release/scripts/startup/bl_ui/space_text.py

vic3t3chn0/Bforartists

7c54a60dd7aa568e20ae7e3778dfef993b61b7b5

[ "Naumen", "Condor-1.1", "MS-PL" ]

1

2020-01-18T22:13:24.000Z

release/scripts/startup/bl_ui/space_text.py

vic3t3chn0/Bforartists

7c54a60dd7aa568e20ae7e3778dfef993b61b7b5

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

release/scripts/startup/bl_ui/space_text.py

vic3t3chn0/Bforartists

7c54a60dd7aa568e20ae7e3778dfef993b61b7b5

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

# ##### BEGIN GPL LICENSE BLOCK ##### # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, # Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # # ##### END GPL LICENSE BLOCK ##### # <pep8-80 compliant> import bpy from bpy.types import Header, Menu, Panel from bpy.app.translations import pgettext_iface as iface_ class TEXT_HT_header(Header): bl_space_type = 'TEXT_EDITOR' def draw(self, context): layout = self.layout st = context.space_data text = st.text ALL_MT_editormenu.draw_hidden(context, layout) # bfa - show hide the editormenu TEXT_MT_editor_menus.draw_collapsible(context, layout) row = layout.row(align=True) if text and text.is_modified: row = layout.row(align=True) row.alert = True row.operator("text.resolve_conflict", text="", icon='HELP') #layout.separator_spacer() row = layout.row(align=True) row.template_ID(st, "text", new="text.new", unlink="text.unlink", open="text.open") layout.separator_spacer() row = layout.row(align=True) row.prop(st, "show_line_numbers", text="") row.prop(st, "show_word_wrap", text="") is_syntax_highlight_supported = st.is_syntax_highlight_supported() syntax = row.row(align=True) syntax.active = is_syntax_highlight_supported syntax.prop(st, "show_syntax_highlight", text="") if text: text_name = text.name is_osl = text_name.endswith((".osl", ".oso")) row = layout.row() if is_osl: row = layout.row() row.operator("node.shader_script_update") else: row = layout.row() row.active = text_name.endswith(".py") row.prop(text, "use_module") row = layout.row() row.active = is_syntax_highlight_supported row.operator("text.run_script") class TEXT_HT_footer(Header): bl_space_type = 'TEXT_EDITOR' bl_region_type = 'FOOTER' def draw(self, context): layout = self.layout st = context.space_data text = st.text if text: row = layout.row() if text.filepath: if text.is_dirty: row.label( text=iface_("File: *%s (unsaved)" % text.filepath), translate=False, ) else: row.label( text=iface_("File: %s" % text.filepath), translate=False, ) else: row.label( text=iface_("Text: External") if text.library else iface_("Text: Internal"), ) # bfa - show hide the editormenu class ALL_MT_editormenu(Menu): bl_label = "" def draw(self, context): self.draw_menus(self.layout, context) @staticmethod def draw_menus(layout, context): row = layout.row(align=True) row.template_header() # editor type menus class TEXT_MT_editor_menus(Menu): bl_idname = "TEXT_MT_editor_menus" bl_label = "" def draw(self, context): self.draw_menus(self.layout, context) @staticmethod def draw_menus(layout, context): st = context.space_data text = st.text layout.menu("TEXT_MT_text") layout.menu("TEXT_MT_view") if text: layout.menu("TEXT_MT_edit") layout.menu("TEXT_MT_format") class TEXT_PT_properties(Panel): bl_space_type = 'TEXT_EDITOR' bl_region_type = 'UI' bl_category = "Text" bl_label = "Properties" def draw(self, context): layout = self.layout layout.use_property_split = True layout.use_property_decorate = False st = context.space_data flow = layout.column_flow() flow.use_property_split = False flow.prop(st, "show_line_highlight") flow.prop(st, "use_live_edit") layout.use_property_split = True flow = layout.column_flow() flow.prop(st, "font_size") flow.prop(st, "tab_width") text = st.text if text: layout.prop(text, "indentation") flow.use_property_split = False flow.prop(st, "show_margin") flow.use_property_split = True if st.show_margin: col = flow.column() col.active = st.show_margin col.prop(st, "margin_column") class TEXT_PT_find(Panel): bl_space_type = 'TEXT_EDITOR' bl_region_type = 'UI' bl_category = "Text" bl_label = "Find & Replace" def draw(self, context): layout = self.layout st = context.space_data # find col = layout.column(align=True) row = col.row(align=True) row.prop(st, "find_text", text="", icon='VIEWZOOM') row.operator("text.find_set_selected", text="", icon='EYEDROPPER') col.operator("text.find") # replace col = layout.column(align=True) row = col.row(align=True) row.prop(st, "replace_text", text="", icon='DECORATE_OVERRIDE') row.operator("text.replace_set_selected", text="", icon='EYEDROPPER') col.operator("text.replace") # settings row = layout.row(align=True) if not st.text: row.active = False row.prop(st, "use_match_case", text="Case", toggle=True) row.prop(st, "use_find_wrap", text="Wrap", toggle=True) row.prop(st, "use_find_all", text="All", toggle=True) class TEXT_MT_view(Menu): bl_label = "View" def draw(self, context): layout = self.layout st = context.space_data layout.prop(st, "show_region_ui") layout.separator() layout.operator("text.move", text="Top of File", icon = "MOVE_UP").type = 'FILE_TOP' layout.operator("text.move", text="Bottom of File",icon = "MOVE_DOWN").type = 'FILE_BOTTOM' layout.separator() layout.menu("INFO_MT_area") #Redraw timer sub menu - Debug stuff class TEXT_MT_redraw_timer(Menu): bl_label = "Redraw Timer" def draw(self, context): layout = self.layout layout.operator("wm.redraw_timer", text = 'Draw Region').type ='DRAW' layout.operator("wm.redraw_timer", text = 'Draw Region Swap').type ='DRAW_SWAP' layout.operator("wm.redraw_timer", text = 'Draw Window').type ='DRAW_WIN' layout.operator("wm.redraw_timer", text = 'Draw Window Swap').type ='DRAW_WIN_SWAP' layout.operator("wm.redraw_timer", text = 'Anim Step').type ='ANIM_STEP' layout.operator("wm.redraw_timer", text = 'Anim Play').type ='ANIM_PLAY' layout.operator("wm.redraw_timer", text = 'Undo/Redo').type ='UNDO' class TEXT_MT_text(Menu): bl_label = "File" def draw(self, context): layout = self.layout st = context.space_data text = st.text layout.operator("text.new", text = "New Text", icon='NEW') layout.operator("text.open", text = "Open Text", icon='FILE_FOLDER') if text: layout.operator("text.reload", icon = "FILE_REFRESH") layout.column() layout.operator("text.save", icon='FILE_TICK') layout.operator("text.save_as", icon='SAVE_AS') if text.filepath: layout.separator() layout.operator("text.make_internal", icon = "MAKE_INTERNAL") layout.separator() row = layout.row() row.active = text.name.endswith(".py") row.prop(text, "use_module") row = layout.row() layout.prop(st, "use_live_edit") layout.separator() layout.operator("text.run_script", icon = "PLAY") layout.separator() layout.menu("TEXT_MT_templates") layout.separator() layout.menu("TEXT_MT_redraw_timer", icon='TIME') #Redraw timer sub menu - Debug stuff layout.operator("wm.debug_menu", icon='DEBUG') # debug menu layout.operator("script.reload", icon='FILE_REFRESH') # Reload all python scripts. Mainly meant for the UI scripts. class TEXT_MT_templates_py(Menu): bl_label = "Python" def draw(self, _context): self.path_menu( bpy.utils.script_paths("templates_py"), "text.open", props_default={"internal": True}, filter_ext=lambda ext: (ext.lower() == ".py") ) class TEXT_MT_templates_osl(Menu): bl_label = "Open Shading Language" def draw(self, _context): self.path_menu( bpy.utils.script_paths("templates_osl"), "text.open", props_default={"internal": True}, filter_ext=lambda ext: (ext.lower() == ".osl") ) class TEXT_MT_templates(Menu): bl_label = "Templates" def draw(self, _context): layout = self.layout layout.menu("TEXT_MT_templates_py") layout.menu("TEXT_MT_templates_osl") class TEXT_MT_format(Menu): bl_label = "Format" def draw(self, _context): layout = self.layout layout.operator("text.indent", icon = "INDENT") layout.operator("text.unindent", icon = "UNINDENT") layout.separator() layout.operator("text.comment_toggle", text = "Comment", icon = "COMMENT").type = 'COMMENT' layout.operator("text.comment_toggle", text = "Un-Comment", icon = "COMMENT").type = 'UNCOMMENT' layout.operator("text.comment_toggle", icon = "COMMENT") layout.separator() layout.operator("text.convert_whitespace", text = "Whitespace to Spaces", icon = "WHITESPACE_SPACES").type = 'SPACES' layout.operator("text.convert_whitespace", text = "Whitespace to Tabs", icon = "WHITESPACE_TABS").type = 'TABS' class TEXT_MT_edit_to3d(Menu): bl_label = "Text To 3D Object" def draw(self, _context): layout = self.layout layout.operator("text.to_3d_object", text="One Object", icon = "OUTLINER_OB_FONT").split_lines = False layout.operator("text.to_3d_object",text="One Object Per Line", icon = "OUTLINER_OB_FONT").split_lines = True class TEXT_MT_edit(Menu): bl_label = "Edit" @classmethod def poll(cls, _context): return context.space_data.text is not None def draw(self, context): layout = self.layout layout.operator("text.cut", icon = "CUT") layout.operator("text.copy", icon = "COPYDOWN") layout.operator("text.paste", icon = "PASTEDOWN") layout.operator("text.duplicate_line", icon = "DUPLICATE") layout.separator() layout.operator("text.move_lines", text="Move Line(s) Up", icon = "MOVE_UP").direction = 'UP' layout.operator("text.move_lines", text="Move Line(s) Down", icon = "MOVE_DOWN").direction = 'DOWN' layout.separator() layout.menu("TEXT_MT_edit_move_select") layout.separator() layout.menu("TEXT_MT_edit_delete") layout.separator() layout.operator("text.select_all", icon = "SELECT_ALL") layout.operator("text.select_line", icon = "SELECT_LINE") layout.separator() layout.operator("text.jump", text = "Go to line", icon = "GOTO") layout.operator("text.start_find", text="Find", icon = "ZOOM_SET") layout.operator("text.autocomplete", icon = "AUTOCOMPLETE") layout.separator() layout.menu("TEXT_MT_edit_to3d") # move_select submenu class TEXT_MT_edit_move_select(Menu): bl_label = "Select Text" def draw(self, context): layout = self.layout layout.operator("text.move_select", text = "Line End", icon = "HAND").type = 'LINE_END' layout.operator("text.move_select", text = "Line Begin", icon = "HAND").type = 'LINE_BEGIN' layout.operator("text.move_select", text = "Previous Character", icon = "HAND").type = 'PREVIOUS_CHARACTER' layout.operator("text.move_select", text = "Next Character", icon = "HAND").type = 'NEXT_CHARACTER' layout.operator("text.move_select", text = "Previous Word", icon = "HAND").type = 'PREVIOUS_WORD' layout.operator("text.move_select", text = "Next Word", icon = "HAND").type = 'NEXT_WORD' layout.operator("text.move_select", text = "Previous Line", icon = "HAND").type = 'PREVIOUS_LINE' layout.operator("text.move_select", text = "Next Line", icon = "HAND").type = 'NEXT_LINE' layout.operator("text.move_select", text = "Previous Character", icon = "HAND").type = 'PREVIOUS_CHARACTER' layout.operator("text.move_select", text = "Next Character", icon = "HAND").type = 'NEXT_CHARACTER' class TEXT_MT_context_menu(Menu): bl_label = "" def draw(self, _context): layout = self.layout layout.operator_context = 'INVOKE_DEFAULT' layout.operator("text.cut", icon = "CUT") layout.operator("text.copy", icon = "COPYDOWN") layout.operator("text.paste", icon = "PASTEDOWN") layout.separator() layout.operator("text.move_lines", text="Move Line(s) Up", icon = "MOVE_UP").direction = 'UP' layout.operator("text.move_lines", text="Move Line(s) Down", icon = "MOVE_DOWN").direction = 'DOWN' layout.separator() layout.operator("text.indent", icon = "INDENT") layout.operator("text.unindent", icon = "UNINDENT") layout.separator() layout.operator("text.comment_toggle", icon = "COMMENT") layout.separator() layout.operator("text.autocomplete", icon = "AUTOCOMPLETE") class TEXT_MT_edit_delete(Menu): bl_label = "Delete" def draw(self, context): layout = self.layout layout.operator("text.delete", text = "Next Character", icon = "DELETE").type = 'NEXT_CHARACTER' layout.operator("text.delete", text = "Previous Character", icon = "DELETE").type = 'PREVIOUS_CHARACTER' layout.operator("text.delete", text = "Next Word", icon = "DELETE").type = 'NEXT_WORD' layout.operator("text.delete", text = "Previous Word", icon = "DELETE").type = 'PREVIOUS_WORD' classes = ( ALL_MT_editormenu, TEXT_HT_header, TEXT_HT_footer, TEXT_MT_editor_menus, TEXT_PT_properties, TEXT_PT_find, TEXT_MT_view, TEXT_MT_redraw_timer, TEXT_MT_text, TEXT_MT_templates, TEXT_MT_templates_py, TEXT_MT_templates_osl, TEXT_MT_format, TEXT_MT_edit_to3d, TEXT_MT_context_menu, TEXT_MT_edit, TEXT_MT_edit_move_select, TEXT_MT_edit_delete, ) if __name__ == "__main__": # only for live edit. from bpy.utils import register_class for cls in classes: register_class(cls)

31.735656

125

0.612966

13,826

0.892749

0

516

0.033318

0

4,758

0.307225

bee4d2aa2b67b36999556e3fe3dbdddbb08d368e

6,151

py

Python

wb/main/jobs/create_setup_bundle/create_setup_bundle_job.py

apaniukov/workbench

2f2653ecfd0143d2d53e33ad84379f13443fdfaa

[ "Apache-2.0" ]

23

2022-03-17T12:24:09.000Z

2022-03-31T09:13:30.000Z

wb/main/jobs/create_setup_bundle/create_setup_bundle_job.py

apaniukov/workbench

2f2653ecfd0143d2d53e33ad84379f13443fdfaa

[ "Apache-2.0" ]

18

2022-03-21T08:17:44.000Z

2022-03-30T12:42:30.000Z

wb/main/jobs/create_setup_bundle/create_setup_bundle_job.py

apaniukov/workbench

2f2653ecfd0143d2d53e33ad84379f13443fdfaa

[ "Apache-2.0" ]

16

2022-03-17T12:24:14.000Z

2022-03-31T12:15:12.000Z

""" OpenVINO DL Workbench Class for create setup bundle job Copyright (c) 2020 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import os import shutil import tempfile from contextlib import closing from wb.extensions_factories.database import get_db_session_for_celery from wb.main.enumerates import JobTypesEnum, StatusEnum from wb.main.jobs.interfaces.ijob import IJob from wb.main.jobs.utils.database_functions import set_status_in_db from wb.main.models import CreateSetupBundleJobModel, SharedArtifactModel from wb.main.scripts.job_scripts_generators.setup_script_generator import SetupScriptGenerator from wb.main.utils.bundle_creator.setup_bundle_creator import SetupBundleCreator, SetupComponentsParams from wb.main.utils.utils import find_by_ext class CreateSetupBundleJob(IJob): job_type = JobTypesEnum.create_setup_bundle_type _job_model_class = CreateSetupBundleJobModel def __init__(self, job_id: int, **unused_kwargs): super().__init__(job_id=job_id) self._attach_default_db_and_socket_observers() with closing(get_db_session_for_celery()) as session: create_bundle_job_model: CreateSetupBundleJobModel = self.get_job_model(session) deployment_bundle_config = create_bundle_job_model.deployment_bundle_config self.deployment_bundle_id = deployment_bundle_config.deployment_bundle_id self.additional_components = [name for name, value in deployment_bundle_config.json().items() if value] self.targets = deployment_bundle_config.targets_to_json self.operating_system = deployment_bundle_config.operating_system self.include_model = deployment_bundle_config.include_model self.topology_name = create_bundle_job_model.project.topology.name if self.include_model else None self.topology_path = create_bundle_job_model.project.topology.path if self.include_model else None bundle: SharedArtifactModel = create_bundle_job_model.deployment_bundle_config.deployment_bundle self.bundle_path = bundle.build_full_artifact_path() self.is_archive = bundle.is_archive def run(self): self._job_state_subject.update_state(status=StatusEnum.running, log='Preparing setup bundle.') with tempfile.TemporaryDirectory('rw') as tmp_scripts_folder: setup_path = self.generate_script_from_template(tmp_scripts_folder, 'setup.sh') get_devices_path = self.generate_script_from_template(tmp_scripts_folder, 'get_inference_engine_devices.sh') get_resources_path = self.generate_script_from_template(tmp_scripts_folder, 'get_system_resources.sh') has_internet_connection_path = self.generate_script_from_template(tmp_scripts_folder, 'has_internet_connection.sh') topology_temporary_path = None if self.include_model: topology_temporary_path = os.path.join(tmp_scripts_folder, self.topology_name) os.makedirs(topology_temporary_path) xml_file = find_by_ext(self.topology_path, 'xml') tmp_xml_file = os.path.join(topology_temporary_path, f'{self.topology_name}.xml') shutil.copy(xml_file, tmp_xml_file) bin_file = find_by_ext(self.topology_path, 'bin') tmp_bin_file = os.path.join(topology_temporary_path, f'{self.topology_name}.bin') shutil.copy(bin_file, tmp_bin_file) setup_bundle_creator = SetupBundleCreator( log_callback=lambda message, progress: self._job_state_subject.update_state(log=message, progress=progress) ) setup_components = SetupComponentsParams(setup_path, get_devices_path, get_resources_path, has_internet_connection_path, self.operating_system, self.targets, self.additional_components, topology_temporary_path) setup_bundle_creator.create(components=setup_components, destination_bundle=self.bundle_path, is_archive=self.is_archive) self.on_success() @staticmethod def generate_script_from_template(result_scripts_path: str, script_name: str) -> str: result_script_path = os.path.join(result_scripts_path, script_name) job_script_generator = SetupScriptGenerator(script_name) job_script_generator.create(result_file_path=result_script_path) return result_script_path def on_success(self): with closing(get_db_session_for_celery()) as session: deployment_job = self.get_job_model(session) bundle = deployment_job.deployment_bundle_config.deployment_bundle bundle.update(self.bundle_path) bundle.write_record(session) self._job_state_subject.update_state(status=StatusEnum.ready, log='Setup bundle created successfully.') set_status_in_db(SharedArtifactModel, bundle.id, StatusEnum.ready, session, force=True) self._job_state_subject.detach_all_observers()

55.918182

115

0.676313

4,881

0.79353

0

351

0.057064

0

862

0.14014

bee53cb77bca7c1ce3a8035bc7f1d877d50fc52d

2,365

py

Python

ros_awsiot_agent/src/ros_awsiot_agent/mqtt2ros.py

whill-labs/ros_awsiot

4c15be53c0643fb81fd5a261a1af5be2652c4166

[ "MIT" ]

4

2021-10-06T10:19:07.000Z

2022-03-02T02:13:09.000Z

ros_awsiot_agent/src/ros_awsiot_agent/mqtt2ros.py

whill-labs/ros_awsiot

4c15be53c0643fb81fd5a261a1af5be2652c4166

[ "MIT" ]

1

2021-10-02T15:13:48.000Z

ros_awsiot_agent/src/ros_awsiot_agent/mqtt2ros.py

whill-labs/ros_awsiot

4c15be53c0643fb81fd5a261a1af5be2652c4166

[ "MIT" ]

1

2021-10-07T02:11:27.000Z

#!/usr/bin/env python3 import logging from os.path import expanduser from typing import Any, Dict from uuid import uuid4 import rospy from awsiotclient import mqtt, pubsub from ros_awsiot_agent import set_module_logger from rosbridge_library.internal.message_conversion import populate_instance from rosbridge_library.internal.ros_loader import get_message_class set_module_logger(modname="awsiotclient", level=logging.WARN) class Mqtt2Ros: def __init__( self, topic_from: str, topic_to: str, topic_type: str, conn_params: mqtt.ConnectionParams, ) -> None: topic_class = get_message_class(topic_type) self.inst = topic_class() self.mqtt_connection = mqtt.init(conn_params) connect_future = self.mqtt_connection.connect() connect_future.result() rospy.loginfo("Connected!") self.pub = rospy.Publisher(topic_to, topic_class, queue_size=10) self.mqtt_sub = pubsub.Subscriber( self.mqtt_connection, topic_from, callback=self.callback ) def callback(self, topic: str, msg_dict: Dict[str, Any]) -> None: msg = populate_instance(msg_dict, self.inst) self.pub.publish(msg) def main() -> None: rospy.init_node("mqtt2ros", anonymous=True) topic_to = rospy.get_param("~topic_to", default="~output") topic_from = rospy.get_param("~topic_from", default="/mqtt2ros") topic_type = rospy.get_param("~topic_type", default="std_msgs/String") conn_params = mqtt.ConnectionParams() conn_params.cert = expanduser( rospy.get_param("~cert", default="~/.aws/cert/certificate.pem.crt") ) conn_params.key = expanduser( rospy.get_param("~key", default="~/.aws/cert/private.pem.key") ) conn_params.root_ca = expanduser( rospy.get_param("~root_ca", default="~/.aws/cert/AmazonRootCA1.pem") ) conn_params.endpoint = rospy.get_param("~endpoint") conn_params.client_id = rospy.get_param( "~client_id", default="mqtt-" + str(uuid4()) ) conn_params.signing_region = rospy.get_param( "~signing_region", default="ap-northeast-1" ) conn_params.use_websocket = rospy.get_param("~use_websocket", default=False) Mqtt2Ros(topic_from, topic_to, topic_type, conn_params) rospy.spin() if __name__ == "__main__": main()

30.320513

80

0.689641

794

0.335729

0

337

0.142495

bee627678ed010aec77f469faec38fc6e41f1465

5,031

py

Python

poly/app.py

thdb-theo/Polynomial

9943ee5eb175ef01720954c6a95c685bd7fd5f6c

[ "MIT" ]

null

poly/app.py

thdb-theo/Polynomial

9943ee5eb175ef01720954c6a95c685bd7fd5f6c

[ "MIT" ]

null

poly/app.py

thdb-theo/Polynomial

9943ee5eb175ef01720954c6a95c685bd7fd5f6c

[ "MIT" ]

null

import sys import re from PyQt4 import QtGui, QtCore from polynomial import Polynomial from rational import Rational class Window(QtGui.QMainWindow): width, height = 420, 130 def __init__(self): super().__init__() self.setFixedSize(Window.width, Window.height) self.setWindowTitle('Find Roots') self.setWindowIcon(QtGui.QIcon('Images/roots.png')) self.poly = None self.setFont(QtGui.QFont('Times New Roman')) self.home() def home(self): self.is_imag = True self.imag_b = QtGui.QCheckBox('Return imaginary numbers?') self.imag_b.adjustSize() self.imag_b.setParent(self) self.imag_b.toggle() self.imag_b.move(10, 5) self.imag_b.stateChanged.connect(self.toggle_imag) self.instruction = QtGui.QLabel(self) self.instruction.setText('Enter coefficients of a polynomial seperated by commas.') self.instruction.move(10, 35) self.instruction.adjustSize() self.text = QtGui.QLabel(self) self.entry = QtGui.QLineEdit(self) self.entry.returnPressed.connect(self.find_roots) self.entry.move(10, 60) self.entry.resize(400, 30) self.confirm = QtGui.QPushButton('Find Roots!', self) self.confirm.move(10, 100) self.confirm.clicked.connect(self.find_roots) QtGui.QShortcut(QtGui.QKeySequence(QtCore.Qt.Key_Return), self, self.find_roots) self.plot_b = QtGui.QPushButton('Plot', self) self.plot_b.clicked.connect(self.plot) self.plot_b.move(120, 100) self.factor_b = QtGui.QPushButton('Factorise', self) self.factor_b.clicked.connect(self.factor) self.factor_b.move(230, 100) self.derivate_b = QtGui.QPushButton('Derivate', self) self.derivate_b.clicked.connect(self.derivate) self.derivate_b.move(340, 100) self.eq = QtGui.QLabel(self) self.eq.move(10, Window.height) self.show() def toggle_imag(self): self.is_imag = not self.is_imag def find_roots(self): self.entry_text = self.entry.text() try: self.poly = self.get_poly(self.entry_text) except ValueError: QtGui.QMessageBox.warning(self, 'warning', 'Invalid arguments') return roots = self.poly.roots(imag=self.is_imag) self.eq.setFont(QtGui.QFont('Consolas', 8)) s = '%s = 0' % self.poly.short_str() self.eq.setText(re.sub("(.{44})", "\\1\n", s, 0, re.DOTALL)) self.eq.adjustSize() t = [] for i, r in enumerate(roots): t.append('x%s = %s' % (i, r)) s = ' '.join(t) self.text.setText(s) self.text.adjustSize() self.text.move(10, Window.height + self.eq.height()) new_height = Window.height + self.eq.height() + self.text.height() + 10 self.setFixedSize(Window.width, new_height) def plot(self) -> None: self.entry_text = self.entry.text() try: self.poly = self.get_poly(self.entry_text) except ValueError: QtGui.QMessageBox.warning(self, 'warning', 'Invalid arguments') return self.poly.plot() def factor(self): self.entry_text = self.entry.text() try: self.poly = self.get_poly(self.entry_text) except ValueError: QtGui.QMessageBox.warning(self, 'warning', 'Invalid arguments') return self.eq.setText('') self.text.setText(self.poly.factor()) self.text.move(10, Window.height) self.text.adjustSize() self.text.setWordWrap(True) self.setFixedSize(Window.width, Window.height + self.text.height()) def derivate(self): self.entry_text = self.entry.text() try: self.poly = self.get_poly(self.entry_text) except ValueError: QtGui.QMessageBox.warning(self, 'warning', 'Invalid arguments') return self.eq.setText('') self.text.setText(str(self.poly.derivate())) self.text.setFont(QtGui.QFont('Courier')) self.text.move(10, Window.height) self.text.adjustSize() self.text.setWordWrap(True) self.setFixedSize(Window.width, Window.height + self.text.height()) @staticmethod def get_poly(text): if 'x' in text: return Polynomial.from_string(text) terms = re.findall(r'-?\d+\.?\d*|/', text) if '/' in terms: numerator, denominator = terms[:terms.index('/')], terms[terms.index('/') + 1:] num_coefs, den_coefs = list(map(float, numerator)), list(map(float, denominator)) return Rational(num_coefs, den_coefs) else: coefs = map(float, terms) return Polynomial(*coefs) def main(): app = QtGui.QApplication(sys.argv) GUI = Window() sys.exit(app.exec_()) if __name__ == '__main__': main()

33.098684

93

0.603459

4,773

0.948718

0

511

0.10157

0

395

0.078513

bee68e7de68c03f76e1ccae51e5aa678663d50fa

493

py

Python

ariadne_server/tests/fixtures/fake_context.py

seanaye/FeatherLight-API

4d42a424762311ee35b3fd4f689883aa4197eb2e

[ "MIT" ]

3

2020-06-28T17:30:57.000Z

2022-01-25T18:03:38.000Z

ariadne_server/tests/fixtures/fake_context.py

seanaye/FeatherLight-API

4d42a424762311ee35b3fd4f689883aa4197eb2e

[ "MIT" ]

null

ariadne_server/tests/fixtures/fake_context.py

seanaye/FeatherLight-API

4d42a424762311ee35b3fd4f689883aa4197eb2e

[ "MIT" ]

1

2021-02-04T07:14:08.000Z

from secrets import token_hex import pytest class Object: pass class FakeContext(dict): def __init__(self): req_obj = Object() req_obj.cookies = {} req_obj.client = Object() req_obj.client.host = token_hex(5) req_obj.headers = { 'origin': 'some_origin', 'x-real-ip': 'fake_ip' } self['request'] = req_obj @pytest.fixture(autouse=True, scope='function') def context(): return FakeContext()

18.259259

47

0.584178

353

0.716024

0

87

0.176471

0

60

0.121704

bee850ee36621b995a6de029e878f2bcfff1b23e

4,552

py

Python

libs/evaluation/training_benchmark_database.py

eeshakumar/hythe

52ca795c8370ddfb2aa6fb87ff3f63a85c55f913

[ "MIT" ]

null

libs/evaluation/training_benchmark_database.py

eeshakumar/hythe

52ca795c8370ddfb2aa6fb87ff3f63a85c55f913

[ "MIT" ]

null

libs/evaluation/training_benchmark_database.py

eeshakumar/hythe

52ca795c8370ddfb2aa6fb87ff3f63a85c55f913

[ "MIT" ]

null

# Copyright (c) 2020 Julian Bernhard, # Klemens Esterle, Patrick Hart, Tobias Kessler # # This software is released under the MIT License. # https://opensource.org/licenses/MIT from bark.benchmark.benchmark_result import BenchmarkConfig from bark_ml.library_wrappers.lib_fqf_iqn_qrdqn.agent import TrainingBenchmark from bark.benchmark.benchmark_runner import BenchmarkRunner, BehaviorConfig def default_training_evaluators(): default_config = {"success" : "EvaluatorGoalReached", "collision_other" : "EvaluatorCollisionEgoAgent", "out_of_drivable" : "EvaluatorDrivableArea", "max_steps": "EvaluatorStepCount"} return default_config def default_terminal_criteria(max_episode_steps): terminal_when = {"collision_other" : lambda x: x, "out_of_drivable" : lambda x: x, \ "max_steps": lambda x : x>max_episode_steps, "success" : lambda x: x} return terminal_when class TrainingBenchmarkDatabase(TrainingBenchmark): def __init__(self, benchmark_database=None, evaluators=None, terminal_when=None): self.database = benchmark_database self.evaluators = evaluators self.terminal_when = terminal_when def create_benchmark_configs(self, num_scenarios): benchmark_configs = [] if self.database: for scenario_generator, scenario_set_name, scenario_set_param_desc in self.database: benchmark_configs.extend(self.benchmark_configs_from_scen_gen( \ scenario_generator, scenario_set_name, \ scenario_set_param_desc, num_scenarios)) else: scenario_generator = self.training_env._scenario_generator benchmark_configs.extend(self.benchmark_configs_from_scen_gen( scenario_generator, "training_env", \ {}, num_scenarios)) return benchmark_configs def benchmark_configs_from_scen_gen(self, scenario_generator, scenario_set_name, \ scenario_set_param_desc, num_scenarios): benchmark_configs = [] for scenario, scenario_idx in scenario_generator: if num_scenarios and scenario_idx >= num_scenarios: break behavior_config = BehaviorConfig("agent", self.agent, None) benchmark_config = \ BenchmarkConfig( len(benchmark_configs), behavior_config, scenario, scenario_idx, scenario_set_name, scenario_set_param_desc ) benchmark_configs.append(benchmark_config) return benchmark_configs def reset(self, training_env, num_episodes, max_episode_steps, agent): super(TrainingBenchmarkDatabase, self).reset(training_env, num_episodes, \ max_episode_steps, agent) benchmark_configs = self.create_benchmark_configs(num_episodes) evaluators = default_training_evaluators() if self.evaluators: evaluators = {**self.evaluators, **evaluators} terminal_when = default_terminal_criteria(max_episode_steps) if self.terminal_when: terminal_when = {**self.terminal_when, **terminal_when} self.benchmark_runner = BenchmarkRunner( benchmark_configs = benchmark_configs, evaluators=evaluators, terminal_when = terminal_when, num_scenarios=num_episodes, log_eval_avg_every = 100000000000, checkpoint_dir = "checkpoints", merge_existing = False, deepcopy=False) def run(self): mean_return, formatting = super(TrainingBenchmarkDatabase, self).run() eval_result = self.benchmark_runner.run() data_frame = eval_result.get_data_frame() data_frame["max_steps"] = data_frame.Terminal.apply(lambda x: "max_steps" in x and (not "collision" in x)) data_frame["success"] = data_frame.Terminal.apply(lambda x: "success" in x and (not "collision" in x) and (not "max_steps" in x)) data_frame = data_frame.drop(columns=["scen_set", "scen_idx", "behavior", "Terminal", "step", "config_idx"]) mean = data_frame.mean(axis=0) eval_result = {**mean.to_dict(), **mean_return} return eval_result, f"Benchmark Result: {eval_result}" def is_better(self, eval_result1, than_eval_result2): pass

47.915789

133

0.65312

3,665

0.805141

0

572

0.125659

bee859bef7a37ff661836407bce80f2d3470ddd9

27,023

py

Python

goldstone/tenants/tests_cloud.py

Solinea/goldstone-server

91b078ca9fed1b33f48dc79f4af5c9d1817a1bc5

[ "Apache-2.0" ]

14

2015-05-18T22:11:11.000Z

2020-08-14T06:50:09.000Z

goldstone/tenants/tests_cloud.py

lexjacobs/goldstone-server

91b078ca9fed1b33f48dc79f4af5c9d1817a1bc5

[ "Apache-2.0" ]

568

2015-05-17T01:26:36.000Z

2021-06-10T20:36:47.000Z

goldstone/tenants/tests_cloud.py

lexjacobs/goldstone-server

91b078ca9fed1b33f48dc79f4af5c9d1817a1bc5

[ "Apache-2.0" ]

22

2015-05-25T20:16:06.000Z

2021-08-08T20:25:24.000Z

"""Unit tests for /tenants/<id>/cloud endpoints.""" # Copyright 2015 Solinea, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json from django.contrib.auth import get_user_model from rest_framework.status import HTTP_200_OK, HTTP_401_UNAUTHORIZED, \ HTTP_400_BAD_REQUEST, HTTP_201_CREATED, HTTP_403_FORBIDDEN, \ HTTP_204_NO_CONTENT from goldstone.test_utils import Setup, create_and_login, \ AUTHORIZATION_PAYLOAD, CONTENT_BAD_TOKEN, CONTENT_NO_CREDENTIALS, \ check_response_without_uuid, TEST_USER_1, CONTENT_PERMISSION_DENIED, \ BAD_TOKEN, BAD_UUID from .models import Tenant, Cloud from .tests_tenants import TENANTS_ID_URL # HTTP response content. CONTENT_MISSING_OS_USERNAME = '"username":["This field is required."]' CONTENT_MISSING_OS_NAME = '"tenant_name":["This field is required."]' CONTENT_MISSING_OS_PASSWORD = '"password":["This field is required."]' CONTENT_MISSING_OS_URL = '"auth_url":["This field is required."]' # URLs used by this module. TENANTS_ID_CLOUD_URL = TENANTS_ID_URL + "cloud/" TENANTS_ID_CLOUD_ID_URL = TENANTS_ID_CLOUD_URL + "%s/" class TenantsIdCloud(Setup): """Listing the OpenStack clouds of a tenant, and creating a new OpenStack cloud in a tenant.""" def test_not_logged_in(self): """Getting the tenant clouds, or creating a tenant cloud, without being logged in.""" # Make a tenant. tenant = Tenant.objects.create(name='tenant 1', owner='John', owner_contact='206.867.5309') # Try the GET and POST without an authorization token. responses = \ [self.client.get(TENANTS_ID_CLOUD_URL % tenant.uuid), self.client.post(TENANTS_ID_CLOUD_URL % tenant.uuid, json.dumps({"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"}), content_type="application/json")] for response in responses: self.assertContains(response, CONTENT_NO_CREDENTIALS, status_code=HTTP_401_UNAUTHORIZED) # Try the GET and POST with a bad authorization token. responses = [ self.client.get( TENANTS_ID_CLOUD_URL % tenant.uuid, HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % BAD_TOKEN), self.client.post( TENANTS_ID_CLOUD_URL % tenant.uuid, json.dumps({"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"}), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % BAD_TOKEN)] for response in responses: self.assertContains(response, CONTENT_BAD_TOKEN, status_code=HTTP_401_UNAUTHORIZED) def test_no_access(self): """Getting the tenant clouds, or creating a tenant cloud, without being a tenant admin.""" # Make a tenant. tenant = Tenant.objects.create(name='tenant 1', owner='John', owner_contact='206.867.5309') # Create a normal user who's a member of the tenant, but *not* a # tenant_admin token = create_and_login() user = get_user_model().objects.get(username=TEST_USER_1[0]) user.tenant = tenant user.save() # Try the GET and POST. responses = [ self.client.get( TENANTS_ID_CLOUD_URL % tenant.uuid, HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token), self.client.post( TENANTS_ID_CLOUD_URL % tenant.uuid, json.dumps({"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"}), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token)] for response in responses: self.assertContains(response, CONTENT_PERMISSION_DENIED, status_code=HTTP_403_FORBIDDEN) def test_no_tenant(self): """Getting a tenant, or creating a cloud in a tenant, when the tenant doesn't exist.""" # Create a Django admin user. token = create_and_login(is_superuser=True) # Make a tenant, then delete it. tenant = Tenant.objects.create(name='tenant', owner='John', owner_contact='206.867.5309') tenant.delete() # Try the GET and POST to a tenant that doesn't exist. responses = [ self.client.get( TENANTS_ID_CLOUD_URL % tenant.uuid, HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token), self.client.post( TENANTS_ID_CLOUD_URL % tenant.uuid, json.dumps({"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"}), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token)] for response in responses: self.assertContains(response, CONTENT_PERMISSION_DENIED, status_code=HTTP_403_FORBIDDEN) def test_get(self): """List a tenant's clouds.""" # The clouds in this test. TENANT_CLOUD = [{"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"}, {"tenant_name": "ee", "username": "ffffffffuuuuu", "password": "gah", "auth_url": "http://route66.com"}, {"tenant_name": "YUNO", "username": "YOLO", "password": "ZOMG", "auth_url": "http://lol.com"}, ] OTHER_CLOUD = [{"tenant_name": "lisa", "username": "sad lisa lisa", "password": "on the road", "auth_url": "http://tofindout.com"}, {"tenant_name": "left", "username": "right", "password": "center", "auth_url": "http://down.com"}, ] EXPECTED_RESULT = TENANT_CLOUD # Make a tenant tenant = Tenant.objects.create(name='tenant', owner='John', owner_contact='206.867.5309') # Create clouds in this tenant. for entry in TENANT_CLOUD: Cloud.objects.create(tenant=tenant, **entry) # Create clouds that don't belong to the tenant. tenant_2 = Tenant.objects.create(name='boris', owner='John', owner_contact='206.867.5309') for entry in OTHER_CLOUD: entry["tenant"] = tenant_2 Cloud.objects.create(**entry) # Log in as the tenant_admin. token = create_and_login(tenant=tenant) # Get the tenant's cloud list and check the response. We do a partial # check of the uuid key. It must exist, and its value must be a string # that's >= 32 characters. response = self.client.get( TENANTS_ID_CLOUD_URL % tenant.uuid, HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) # pylint: disable=E1101 self.assertEqual(response.status_code, HTTP_200_OK) response_content = json.loads(response.content) for entry in response_content["results"]: self.assertIsInstance(entry["uuid"], basestring) self.assertGreaterEqual(len(entry["uuid"]), 32) del entry["uuid"] self.assertItemsEqual(response_content["results"], EXPECTED_RESULT) def test_post(self): """Create an OpenStack cloud in a tenant.""" # The clouds in this test. TENANT_CLOUD = [{"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"}, {"tenant_name": "ee", "username": "ffffffffuuuuu", "password": "gah", "auth_url": "http://route66.com"}, ] # Make a tenant tenant = Tenant.objects.create(name='tenant', owner='John', owner_contact='206.867.5309') # Create a user who's the tenant_admin of this tenant, and log him in. token = create_and_login(tenant=tenant) # Create OpenStack clouds in this tenant, and check the results. for entry in TENANT_CLOUD: response = self.client.post( TENANTS_ID_CLOUD_URL % tenant.uuid, json.dumps(entry), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) check_response_without_uuid(response, HTTP_201_CREATED, entry) class TenantsIdCloudId(Setup): """Retrieve a particular OpenStack cloud from a tenant, update an OpenStack cloud in a tenant, and delete an OpenStack cloud from a tenant.""" def test_not_logged_in(self): """The client is not logged in.""" # Make a tenant, and put one OpenStack cloud in it. tenant = Tenant.objects.create(name='tenant 1', owner='John', owner_contact='206.867.5309') cloud = Cloud.objects.create(tenant_name="ee", username="ffffffffuuuuu", password="gah", auth_url="http://route66.com", tenant=tenant) # Try GET, PUT, and DELETE without an authorization token. responses = [self.client.get(TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid)), self.client.put(TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), json.dumps({"username": "fool"}), content_type="application/json"), self.client.delete(TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid)), ] for response in responses: self.assertContains(response, CONTENT_NO_CREDENTIALS, status_code=HTTP_401_UNAUTHORIZED) # Try again with a bad authorization token. responses = [ self.client.get( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % BAD_TOKEN), self.client.put( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), json.dumps({"username": "fool"}), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % BAD_TOKEN), self.client.delete( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % BAD_TOKEN), ] for response in responses: self.assertContains(response, CONTENT_BAD_TOKEN, status_code=HTTP_401_UNAUTHORIZED) def test_no_access(self): """The client isn't an authorized user.""" # Make a tenant, put an OpenStack cloud in it. tenant = Tenant.objects.create(name='tenant 1', owner='John', owner_contact='206.867.5309') cloud = Cloud.objects.create(tenant_name="ee", username="ffffffffuuuuu", password="gah", auth_url="http://route66.com", tenant=tenant) # Create a normal user who's a member of the tenant, but *not* a # tenant_admin token = create_and_login() user = get_user_model().objects.get(username=TEST_USER_1[0]) user.tenant = tenant user.save() # Try GET, PUT, and DELETE. responses = [ self.client.get( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token), self.client.put( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), json.dumps({"username": "fool"}), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token), self.client.delete( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token), ] for response in responses: self.assertContains(response, CONTENT_PERMISSION_DENIED, status_code=HTTP_403_FORBIDDEN) # Ensure the cloud wasn't deleted. self.assertEqual(Cloud.objects.count(), 1) def test_no_tenant(self): """Getting a cloud, updating a cloud, or deleting a cloud, when the tenant doesn't exist.""" # Make a tenant, put an OpenStack cloud in it. tenant = Tenant.objects.create(name='tenant 1', owner='John', owner_contact='206.867.5309') cloud = Cloud.objects.create(tenant_name="ee", username="ffffffffuuuuu", password="gah", auth_url="http://route66.com", tenant=tenant) # Create a tenant_admin of the tenant. token = create_and_login(tenant=tenant) # Try GET, PUT, and DELETE to a nonexistent tenant. responses = [ self.client.get( TENANTS_ID_CLOUD_ID_URL % (BAD_UUID, cloud.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token), self.client.put( TENANTS_ID_CLOUD_ID_URL % (BAD_UUID, cloud.uuid), json.dumps({"password": "fool"}), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token), self.client.delete( TENANTS_ID_CLOUD_ID_URL % (BAD_UUID, cloud.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token), ] for response in responses: self.assertContains(response, CONTENT_PERMISSION_DENIED, status_code=HTTP_403_FORBIDDEN) def test_get_no_cloud(self): """Get an OpenStack cloud that does not exist from a tenant.""" # Make a tenant. tenant = Tenant.objects.create(name='tenant', owner='John', owner_contact='206.867.5309') # Create a tenant_admin of the tenant. token = create_and_login(tenant=tenant) # Try GETing a nonexisten cloud from this tenant. response = self.client.get( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, BAD_UUID), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) self.assertContains(response, CONTENT_PERMISSION_DENIED, status_code=HTTP_403_FORBIDDEN) def test_get(self): """Get a specific OpenStack cloud from a tenant.""" # The clouds in this test. TENANT_CLOUD = [{"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"}, {"tenant_name": "ee", "username": "ffffffffuuuuu", "password": "gah", "auth_url": "http://route66.com"}, ] # Make a tenant. tenant = Tenant.objects.create(name='tenant 1', owner='John', owner_contact='206.867.5309') # Create a tenant_admin of the tenant. token = create_and_login(tenant=tenant) # For every test cloud... for entry in TENANT_CLOUD: # Make it. cloud = Cloud.objects.create(tenant=tenant, **entry) # Try GETting it. response = self.client.get( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) check_response_without_uuid(response, HTTP_200_OK, entry) def test_put_no_cloud(self): """Update a non-existent OpenStack cloud of a tenant.""" # Make a tenant. tenant = Tenant.objects.create(name='tenant', owner='John', owner_contact='206.867.5309') # Create a tenant_admin of the tenant. token = create_and_login(tenant=tenant) # Try PUTing to a nonexistent OpenStack cloud in this tenant. response = self.client.put( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, BAD_UUID), json.dumps({"tenant_name": "fool"}), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) self.assertContains(response, CONTENT_PERMISSION_DENIED, status_code=HTTP_403_FORBIDDEN) def test_put_bad_fields(self): """Update an OpenStack cloud with missing fields, unrecognized fields, or a field that's not allowed to be changed by the tenant_admin.""" # The cloud in this test. TENANT_CLOUD = {"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"} # Make a tenant, put an OpenStack cloud in it. tenant = Tenant.objects.create(name='tenant 1', owner='John', owner_contact='206.867.5309') cloud = Cloud.objects.create(tenant=tenant, **TENANT_CLOUD) # Create a tenant_admin of the tenant. token = create_and_login(tenant=tenant) # Try PUTing to the cloud with no fields. response = self.client.put( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) for content in [CONTENT_MISSING_OS_USERNAME, CONTENT_MISSING_OS_NAME, CONTENT_MISSING_OS_PASSWORD, CONTENT_MISSING_OS_URL]: self.assertContains(response, content, status_code=HTTP_400_BAD_REQUEST) # Try PUTing to the cloud with no change, and with a change to an # unrecognized field. response = self.client.put( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), json.dumps(TENANT_CLOUD), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) check_response_without_uuid(response, HTTP_200_OK, TENANT_CLOUD) bad_field = TENANT_CLOUD.copy() bad_field["forkintheroad"] = "Traci" response = self.client.put( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), json.dumps(bad_field), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) check_response_without_uuid(response, HTTP_200_OK, TENANT_CLOUD) # Try PUTing to a cloud on a field that's not allowed to be changed. # The response should be the same as the "unrecognized field" case. bad_field = TENANT_CLOUD.copy() bad_field["uuid"] = BAD_UUID response = self.client.put( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), json.dumps(bad_field), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) check_response_without_uuid(response, HTTP_200_OK, TENANT_CLOUD) def test_put(self): """Update an Openstack cloud in a tenant.""" # The cloud in this test. TENANT_CLOUD = {"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"} EXPECTED_RESPONSE = TENANT_CLOUD.copy() EXPECTED_RESPONSE["password"] = "fffffffffuuuuuuu" # Make a tenant, put an OpenStack cloud in it. tenant = Tenant.objects.create(name='tenant 1', owner='John', owner_contact='206.867.5309') cloud = Cloud.objects.create(tenant=tenant, **TENANT_CLOUD) # Create a tenant_admin of the tenant. token = create_and_login(tenant=tenant) # Try PUTing to the cloud. response = self.client.put( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), json.dumps(EXPECTED_RESPONSE), content_type="application/json", HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) check_response_without_uuid(response, HTTP_200_OK, EXPECTED_RESPONSE) # Double-check that the Cloud row was updated. self.assertEqual(Cloud.objects.count(), 1) self.assertEqual(Cloud.objects.all()[0].password, EXPECTED_RESPONSE["password"]) def test_delete_not_member(self): """Try deleting a cloud of another tenant.""" # The clouds in this test. TENANT_CLOUD = [{"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"}, {"tenant_name": "ee", "username": "ffffffffuuuuu", "password": "gah", "auth_url": "http://route66.com"}, ] # Make two tenant+cloud pairs tenant = Tenant.objects.create(name='tenant', owner='John', owner_contact='206.867.5309') tenant_2 = Tenant.objects.create(name='tenant_2', owner='John', owner_contact='206.867.5309') Cloud.objects.create(tenant=tenant, **TENANT_CLOUD[0]) cloud_2 = Cloud.objects.create(tenant=tenant_2, **TENANT_CLOUD[1]) # Create a tenant_admin of the first tenant. token = create_and_login(tenant=tenant) # Try DELETE on the second (other) tenant's cloud. response = self.client.delete( TENANTS_ID_CLOUD_ID_URL % (tenant_2.uuid, cloud_2.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) self.assertContains(response, CONTENT_PERMISSION_DENIED, status_code=HTTP_403_FORBIDDEN) # Ensure we have the right number of OpenStack clouds. self.assertEqual(Cloud.objects.count(), 2) def test_delete(self): """Delete an OpenStack cloud from a tenant.""" # The clouds in this test. TENANT_CLOUD = [{"tenant_name": 'a', "username": 'b', "password": 'c', "auth_url": "http://d.com"}, {"tenant_name": "ee", "username": "ffffffffuuuuu", "password": "gah", "auth_url": "http://route66.com"}, ] # Make a tenant with two clouds. tenant = Tenant.objects.create(name='tenant', owner='John', owner_contact='206.867.5309') cloud = Cloud.objects.create(tenant=tenant, **TENANT_CLOUD[0]) cloud_2 = Cloud.objects.create(tenant=tenant, **TENANT_CLOUD[1]) # Create a tenant_admin. token = create_and_login(tenant=tenant) # DELETE one cloud, check, DELETE the other cloud, check. response = self.client.delete( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud_2.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) self.assertContains(response, '', status_code=HTTP_204_NO_CONTENT) # Ensure we have the right number of Clouds. self.assertEqual(Cloud.objects.count(), 1) self.assertEqual(Cloud.objects.all()[0].tenant_name, TENANT_CLOUD[0]["tenant_name"]) response = self.client.delete( TENANTS_ID_CLOUD_ID_URL % (tenant.uuid, cloud.uuid), HTTP_AUTHORIZATION=AUTHORIZATION_PAYLOAD % token) self.assertContains(response, '', status_code=HTTP_204_NO_CONTENT) # Ensure we have the right number of Clouds. self.assertEqual(Cloud.objects.count(), 0)

41.130898

79

0.531029

25,423

0.940791

0

7,250

0.26829

bee920effbd17d10746b345bbf080e1ea4ae7a4f

4,750

py

Python

onnxruntime/test/server/integration_tests/model_zoo_data_prep.py

PhaniShekhar/onnxruntime

2663b9c44381b30525ae6234e13ed25c69206d07

[ "MIT" ]

null

onnxruntime/test/server/integration_tests/model_zoo_data_prep.py

PhaniShekhar/onnxruntime

2663b9c44381b30525ae6234e13ed25c69206d07

[ "MIT" ]

null

onnxruntime/test/server/integration_tests/model_zoo_data_prep.py

PhaniShekhar/onnxruntime

2663b9c44381b30525ae6234e13ed25c69206d07

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. import os import sys import shutil import onnx import onnxruntime import json from google.protobuf.json_format import MessageToJson import predict_pb2 import onnx_ml_pb2 # Current models only have one input and one output def get_io_name(model_file_name): sess = onnxruntime.InferenceSession(model_file_name) return sess.get_inputs()[0].name, sess.get_outputs()[0].name def gen_input_pb(pb_full_path, input_name, output_name, request_file_path): t = onnx_ml_pb2.TensorProto() with open(pb_full_path, 'rb') as fin: t.ParseFromString(fin.read()) predict_request = predict_pb2.PredictRequest() predict_request.inputs[input_name].CopyFrom(t) predict_request.output_filter.append(output_name) with open(request_file_path, "wb") as fout: fout.write(predict_request.SerializeToString()) def gen_output_pb(pb_full_path, output_name, response_file_path): t = onnx_ml_pb2.TensorProto() with open(pb_full_path, 'rb') as fin: t.ParseFromString(fin.read()) predict_response = predict_pb2.PredictResponse() predict_response.outputs[output_name].CopyFrom(t) with open(response_file_path, "wb") as fout: fout.write(predict_response.SerializeToString()) def tensor2dict(full_path): t = onnx.TensorProto() with open(full_path, 'rb') as f: t.ParseFromString(f.read()) jsonStr = MessageToJson(t, use_integers_for_enums=True) data = json.loads(jsonStr) return data def gen_input_json(pb_full_path, input_name, output_name, json_file_path): data = tensor2dict(pb_full_path) inputs = {} inputs[input_name] = data output_filters = [ output_name ] req = {} req["inputs"] = inputs req["outputFilter"] = output_filters with open(json_file_path, 'w') as outfile: json.dump(req, outfile) def gen_output_json(pb_full_path, output_name, json_file_path): data = tensor2dict(pb_full_path) output = {} output[output_name] = data resp = {} resp["outputs"] = output with open(json_file_path, 'w') as outfile: json.dump(resp, outfile) def gen_req_resp(model_zoo, test_data, copy_model=False): skip_list = [ ('opset8', 'mxnet_arcface') # REASON: Known issue ] opsets = [name for name in os.listdir(model_zoo) if os.path.isdir(os.path.join(model_zoo, name))] for opset in opsets: os.makedirs(os.path.join(test_data, opset), exist_ok=True) current_model_folder = os.path.join(model_zoo, opset) current_data_folder = os.path.join(test_data, opset) models = [name for name in os.listdir(current_model_folder) if os.path.isdir(os.path.join(current_model_folder, name))] for model in models: print("Working on Opset: {0}, Model: {1}".format(opset, model)) if (opset, model) in skip_list: print(" SKIP!!") continue os.makedirs(os.path.join(current_data_folder, model), exist_ok=True) src_folder = os.path.join(current_model_folder, model) dst_folder = os.path.join(current_data_folder, model) onnx_file_path = '' for fname in os.listdir(src_folder): if not fname.startswith(".") and fname.endswith(".onnx") and os.path.isfile(os.path.join(src_folder, fname)): onnx_file_path = os.path.join(src_folder, fname) break if onnx_file_path == '': raise FileNotFoundError('Could not find any *.onnx file in {0}'.format(src_folder)) if copy_model: # Copy model file target_file_path = os.path.join(dst_folder, "model.onnx") shutil.copy2(onnx_file_path, target_file_path) for fname in os.listdir(src_folder): if not fname.endswith(".onnx") and os.path.isfile(os.path.join(src_folder, fname)): shutil.copy2(os.path.join(src_folder, fname), dst_folder) iname, oname = get_io_name(onnx_file_path) model_test_data = [name for name in os.listdir(src_folder) if os.path.isdir(os.path.join(src_folder, name))] for test in model_test_data: src = os.path.join(src_folder, test) dst = os.path.join(dst_folder, test) os.makedirs(dst, exist_ok=True) gen_input_json(os.path.join(src, 'input_0.pb'), iname, oname, os.path.join(dst, 'request.json')) gen_output_json(os.path.join(src, 'output_0.pb'), oname, os.path.join(dst, 'response.json')) gen_input_pb(os.path.join(src, 'input_0.pb'), iname, oname, os.path.join(dst, 'request.pb')) gen_output_pb(os.path.join(src, 'output_0.pb'), oname, os.path.join(dst, 'response.pb')) if __name__ == '__main__': model_zoo = os.path.realpath(sys.argv[1]) test_data = os.path.realpath(sys.argv[2]) os.makedirs(test_data, exist_ok=True) gen_req_resp(model_zoo, test_data)

32.758621

123

0.708842

0

492

0.103579

beea57272100654c7600d64caab6b4c5cdc2179e

2,484

py

Python

articlequality/feature_lists/tests/test_enwiki.py

mariushoch/articlequality

57edf786636548bed466aa4e9d9e213fe8d1093b

[ "MIT" ]

null

articlequality/feature_lists/tests/test_enwiki.py

mariushoch/articlequality

57edf786636548bed466aa4e9d9e213fe8d1093b

[ "MIT" ]

null

articlequality/feature_lists/tests/test_enwiki.py

mariushoch/articlequality

57edf786636548bed466aa4e9d9e213fe8d1093b

[ "MIT" ]

null

from revscoring.datasources.revision_oriented import revision from revscoring.dependencies import solve from .. import enwiki revision_text = revision.text def test_cite_templates(): text = """ This is some text with a citation.<ref>{{cite lol|title=Made up}}</ref> This is some more text. {{foo}} {{{cite}}} I am a new paragraph.<ref>{{cite book|title=The stuff}}</ref> {{Cite hat|ascii=_n_}} """ assert solve(enwiki.cite_templates, cache={revision_text: text}) == 3 def test_infobox_templates(): text = """ {{Infobox pants|hats=2|pajams=23}} This is some text with a citation.<ref>{{cite lol|title=Made up}}</ref> This is some more text. I am a new paragraph.<ref>{{cite book|title=The stuff}}</ref> {{Cite hat|ascii=_n_}} """ assert solve(enwiki.infobox_templates, cache={revision_text: text}) == 1 def test_cn_templates(): text = """ {{Infobox pants|hats=2|pajams=23}} This is some text with a citation.{{cn}} This is some more text. {{foo}} I am a new paragraph.{{fact|date=never}} I am a new paragraph.{{Citation_needed|date=never}} """ assert solve(enwiki.cn_templates, cache={revision_text: text}) == 3 def test_who_templates(): text = """ This is some text with a citation.{{cn}} This is some more text. {{foo}} I am a new paragraph.{{who}} I am a new paragraph.{{who|date=today}} """ assert solve(enwiki.who_templates, cache={revision_text: text}) == 2 def test_main_article_templates(): text = """ This is some text with a citation.{{cn}} This is some more text. {{foo}} == Some section == {{Main|section}} I am a new paragraph.{{who|date=today}} """ assert solve(enwiki.main_article_templates, cache={revision_text: text}) == 1 def test_paragraphs_without_refs_total_length(): text = """ Here is the first paragraph. It contains some references <ref>first reference</ref>. Here is second paragraph. One line with reference <ref>reference</ref>. Here is third paragraph. It has two lines, but no references. Here is fourth paragraph. It has two lines <ref>reference</ref>. One of which has a reference. Here is fifth paragraph. One line, no references. Short line.<ref>last</ref><ref>One more reference</ref> """ assert solve(enwiki.paragraphs_without_refs_total_length, cache={revision_text: text}) == 114

27

76

0.654589

0

1,540

0.619968

beead51f0acfbaced2396459fa3fc222015aa766

6,984

py

Python

GPU_compare_CPU.py

kvmu/SFU-workterm

91c976b094097912e71dd7e0d6207ad8ce7a7e93

[ "MIT" ]

null

GPU_compare_CPU.py

kvmu/SFU-workterm

91c976b094097912e71dd7e0d6207ad8ce7a7e93

[ "MIT" ]

null

GPU_compare_CPU.py

kvmu/SFU-workterm

91c976b094097912e71dd7e0d6207ad8ce7a7e93

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Aug 10 14:31:17 2015 @author: Kevin M. Description: This script does CPU and GPU matrix element time complexity profiling. It has a function which applies the matrix element analysis for a given set of parameters, profiles the code and plots the time complexity results (with fit) and plots the matrix elements from each case. """ import numpy as np import scipy as sp import matplotlib matplotlib.use('Agg') from matplotlib import pyplot as plt from my_timer import timer from math import log from scipy.optimize import curve_fit def f_MEplaceholder(neval, mode): # Placeholder integration instead of ME calc result, error = (sp.integrate.quad(lambda x: sp.special.jv(2.5, x), 0, neval) if mode == 'gpu' else sp.integrate.quadrature(lambda x: sp.special.jv(2.5, x), 0, neval)) return result, error def flinear(N, mode): """ O(n) function """ y = np.asarray([i for i in range(N)]) np.asarray([i for i in range(N)]) np.asarray([i for i in range(N)]) return y ,1 def fsquare(N, mode): """ O(n^2) function """ for i in range(N): for j in range(N): y = i*j return y,1 def algoAnalysis(fn, nMin, nMax, mode): """ Run timer and plot time complexity """ n = [] time_result = [] y_result = [] y_err = [] for i in [j*32 for j in range(nMin,nMax+1)]: with timer() as t: temp_result, temp_err = fn(i, mode) time_result.append(t.msecs) y_result.append(temp_result) y_err.append(temp_err) n.append(i) return n, time_result, y_result, y_err def plotAll(n, time_data, y_data, err_data): n = np.asarray(n) time_data = np.asarray(time_data) y_data = np.asarray(y_data) err_data = np.asarray(err_data) err_data[0] = err_data[1]*0.5 # plotting helpers nTime = n[2] n = map(lambda x: log(x,2), n[0]) colors = ['lightblue', 'lightgreen'] edgeColors = ['#1B2ACC','#3F7F4C'] faceColors = ['#089FFF', '#7EFF99'] label_entries_for_results = ['GPU Matrix Elements', 'CPU Matrix Elements'] label_entries_for_time = ['GPU Runtime', 'CPU Runtime'] plt.figure(figsize=(15,6)) ########################################################################### # The following plots the runtime information for GPU and CPU runs. def sqFunc(x, a, b, c): return a*x**2 + b*x +c def linFunc(x, a, b): return a*x + b funcList = [linFunc, sqFunc] ax = plt.subplot(1,2,1) # draw plots for timing data for dat_mode in xrange(0,2): params = curve_fit(funcList[dat_mode], nTime, time_data[dat_mode]) x = np.linspace(nTime[0], nTime[-1], 1000) if dat_mode == 0: [a,b] = params[0] y = funcList[dat_mode](x, a, b) s = "Fit for GPU: $%.5fx$ + $%.5f$"%(a,b) if dat_mode == 1: [a,b,c] = params[0] y = funcList[dat_mode](x, a, b, c) s = "Fit for CPU: $%.5fx^2$ + $%.5fx$ + $%.2f$"%(a,b,c) ax.text(0.035, 0.75-dat_mode*0.1, s, transform = ax.transAxes, fontsize = 16) ax.plot(x,y, color='k', linestyle="--", linewidth = 4) ax.plot(nTime, time_data[dat_mode], color=colors[dat_mode], marker = 'o', label=label_entries_for_time[dat_mode], linestyle = 'None') # setting axis limits plt.xlim([min(nTime)-50, max(nTime)+50]) plt.ylim([min(min(time_data[0]), min(time_data[1]))*1.3, max(max(time_data[0]), max(time_data[1]))*1.3]) # hiding axis ticks plt.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="off", right="off", labelleft="on") # adding horizontal grid lines ax.yaxis.grid(True) # remove axis spines ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) ax.spines["bottom"].set_visible(False) ax.spines["left"].set_visible(False) # labels plt.xlabel('Maximum number of phase space points') plt.ylabel('Runtime (msec)') leg = plt.legend(loc='upper left', fancybox=True, numpoints=1) leg.get_frame().set_alpha(0.5) ########################################################################### # The following plots the Matrix Elements for the GPU and CPU respectively # on a subplot, on top of each other with their corresponding errors. ax = plt.subplot(1,2,2) # draw plots for results for dat_mode in xrange(0,2): ax.errorbar(x=n, y=y_data[dat_mode], yerr=err_data[dat_mode], fmt='o', color=colors[dat_mode], ecolor='black', alpha = 0.3) ax.plot(n, y_data[dat_mode,:], marker='o', linestyle = 'None', color=colors[dat_mode], label=label_entries_for_results[dat_mode]) ax.fill_between(n, y_data[dat_mode]-err_data[dat_mode], y_data[dat_mode]+err_data[dat_mode], alpha=0.2, edgecolor=edgeColors[dat_mode], facecolor=faceColors[dat_mode], linewidth=4, linestyle='-.', antialiased=True) # setting axis limits plt.xlim([min(n)-1*0.2, max(n)+1*0.2]) plt.ylim([min(min(y_data[0]), min(y_data[1]))*1.3, max(max(y_data[0]), max(y_data[1]))*1.3]) # hiding axis ticks plt.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="off", right="off", labelleft="on") # adding horizontal grid lines ax.yaxis.grid(True) # remove axis spines ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) ax.spines["bottom"].set_visible(False) ax.spines["left"].set_visible(False) # labels plt.xlabel('$\log_2$(Maximum number of phase space points)') plt.ylabel('Matrix Element') leg = plt.legend(loc='upper left', fancybox=True, numpoints=1) leg.get_frame().set_alpha(0.5) plt.tight_layout() plt.savefig('plots.pdf') plt.show() # main() function def main(): print('\nAnalyzing Algorithms...') n_GPU, timeGPU, yResult_GPU, yErr_GPU = algoAnalysis(f_MEplaceholder, 8, 20, 'gpu') n_CPU, time_CPU, yResult_CPU, yErr_CPU = algoAnalysis(f_MEplaceholder, 8, 20, 'cpu') nLin, timeLin, y1, y2 = algoAnalysis(flinear, 10, 50, 'cpu') nSq, timeSq, y1, y2 = algoAnalysis(fsquare, 10, 50, 'cpu') nList = [n_GPU, n_CPU, nLin, nSq] ### DELETE NLIN NSQ AFTER timeList = [timeLin, timeSq] yResultList = [yResult_GPU, yResult_CPU] yErrList = [yErr_GPU, yErr_CPU] plotAll(nList, timeList, yResultList, yErrList) # call main if __name__ == '__main__': # matplotlib.rcParams.update({'font.family': 'Zapf Chancery'}) main()

29.719149

88

0.583477

0

1,928

0.27606

beeae2374b9c6c4f75bcbbf6e4da5b6372460220

7,714

py

Python

applications/plugins/Flexible/Flexible_test/scenes/python/reInitMapping.py

sofa-framework/issofa

94855f488465bc3ed41223cbde987581dfca5389

[ "OML" ]

null

applications/plugins/Flexible/Flexible_test/scenes/python/reInitMapping.py

sofa-framework/issofa

94855f488465bc3ed41223cbde987581dfca5389

[ "OML" ]

null

applications/plugins/Flexible/Flexible_test/scenes/python/reInitMapping.py

sofa-framework/issofa

94855f488465bc3ed41223cbde987581dfca5389

[ "OML" ]

null

import Sofa import SofaTest import SofaPython.Tools OBJ = SofaPython.Tools.localPath( __file__, "beam.obj" ) RAW = SofaPython.Tools.localPath( __file__, "beam.raw" ) ##Check if calling Mapping::init() change anything # #The trick is to know that if the option evaluateShapeFunction is activated #in the ImageGaussPointSampler then a sampler::bwdInit() must be called #to update weights using gauss points. class Controller(SofaTest.Controller): def initGraph(self,node): self.success = 1 self.count = 0 return 0 def createGraph(self,node): self.node = node return 0 def initAndCheckMapping(self, node): mapping = node oldWeights = mapping.findData("weights").value oldWeightGradients = mapping.findData("weightGradients").value oldWeightHessians = mapping.findData("weightHessians").value mapping.init() newWeights = mapping.findData("weights").value newWeightGradients = mapping.findData("weightGradients").value newWeightHessians = mapping.findData("weightHessians").value if ( (oldWeights != newWeights) and (oldWeightGradients != newWeightGradients) and (oldWeightHessians != newWeightHessians) ): self.success = 0 else: self.success = 1 return 0 def onEndAnimationStep(self,dt): return 0 def onBeginAnimationStep(self,dt): self.count+=1 if(self.count == 2): barycentricMapping = self.root.getChild("barycentricFrame").getChild("behavior").getObject("mapping") self.initAndCheckMapping(barycentricMapping) if(self.success == 0): self.sendFailure("(Barycentric Shape Function) calling init once again changed linearMapping weights for no reason") voronoiMapping = self.root.getChild("voronoiFrame").getChild("behavior").getObject("mapping") self.initAndCheckMapping(voronoiMapping) if(self.success == 0): self.sendFailure("(Voronoi Shape Function) calling init once again changed linearMapping weights for no reason") self.sendSuccess(); return 0 def createBarycentricFrame( parentNode, name ): node = parentNode.createChild(name) #Solver node.createObject('EulerImplicit', name='integrator') node.createObject('CGLinearSolver', name='linearSolver', iterations='200', tolerance="1e-15", threshold='1.0e-15') #Frame dofPosition="0 1.0 -0.999 1 0 0 0 1 0 0 0 1 " + "0 1.0 0.999 1 0 0 0 1 0 0 0 1 " node.createObject('MechanicalObject', template='Affine', name='dofs', position=dofPosition, showObject='true,', showObjectScale='0.5') node.createObject('UniformMass', template='Affine',totalMass='0.01') #Constraint node.createObject('BoxROI', name='roi', template='Vec3d', box="-1 -2 -1.2 1 2 -0.8", drawBoxes='true', drawSize=1) node.createObject('FixedConstraint', indices="@[-1].indices") #Shape function node.createObject('MeshTopology', edges="0 0 0 1 1 1") node.createObject('BarycentricShapeFunction', name="shapeFunc") #Integration point sampling behaviorNode = node.createChild('behavior') behaviorNode.createObject("TopologyGaussPointSampler", name="sampler", inPosition="@../dofs.rest_position", showSamplesScale="0.1", drawMode="0") behaviorNode.createObject('MechanicalObject', name="intePts", template='F332', showObject="true", showObjectScale="0.05") behaviorNode.createObject('LinearMapping', name="mapping", template='Affine,F332', showDeformationGradientScale='0.2', showSampleScale="0", printLog="false") #Behavior eNode = behaviorNode.createChild('E') eNode.createObject( 'MechanicalObject', name='E', template='E332' ) eNode.createObject( 'CorotationalStrainMapping', template='F332,E332', printLog='false' ) eNode.createObject( 'HookeForceField', template='E332', youngModulus='100', poissonRatio='0', viscosity='0' ) #Visu child node visuNode = node.createChild('Visu') visuNode.createObject('OglModel', template="ExtVec3f", name='Visual',filename=OBJ, translation="0 1 0") visuNode.createObject('LinearMapping', template='Affine,ExtVec3f') def createVoronoiFrame( parentNode, name ): node = parentNode.createChild(name) #Solver node.createObject('EulerImplicit', name='integrator') node.createObject('CGLinearSolver', name='linearSolver', iterations='200', tolerance="1e-15", threshold='1.0e-15') #Frame node.createObject("MeshObjLoader", name="mesh", filename=OBJ, triangulate="1") node.createObject("ImageContainer", name="image", template="ImageUC", filename=RAW, drawBB="false") node.createObject("ImageSampler", name="sampler", template="ImageUC", src="@image", method="1", param="0", fixedPosition="0 0 -0.999 0 0 0.999", printLog="false") node.createObject("MergeMeshes", name="merged", nbMeshes="2", position1="@sampler.fixedPosition", position2="@sampler.position") #node.createObject("ImageViewer", template="ImageB", name="viewer", src="@image") node.createObject('MechanicalObject', template='Affine', name='dofs', src="@merged", showObject='true,', showObjectScale='0.5') #Shape function node.createObject('VoronoiShapeFunction', name="shapeFunc", position='@dofs.rest_position', src='@image', useDijkstra="true", method="0", nbRef="4") #Uniform Mass node.createObject('UniformMass', template='Affine',totalMass='0.01') #Constraint node.createObject('BoxROI', name='roi', template='Vec3d', box="-1 -2.0 -1.2 1 2.0 -0.8", drawBoxes='true', drawSize=1) node.createObject('FixedConstraint', indices="@[-1].indices") #Gauss point sampling behaviorNode = node.createChild('behavior') behaviorNode.createObject('ImageGaussPointSampler', name='sampler', indices='@../shapeFunc.indices', weights='@../shapeFunc.weights', transform='@../shapeFunc.transform', method='2', order='4', targetNumber='1', printLog='false', showSamplesScale=0.1, drawMode=0, evaluateShapeFunction="false") behaviorNode.createObject('MechanicalObject', name="intePts", template='F332', showObject="false", showObjectScale="0.05") behaviorNode.createObject('LinearMapping', name="mapping", template='Affine,F332', assembleJ='true', showDeformationGradientScale='0.2', printLog="false") #Behavior eNode = behaviorNode.createChild('E') eNode.createObject( 'MechanicalObject', name='E', template='E332' ) eNode.createObject( 'CorotationalStrainMapping', template='F332,E332', printLog='false' ) eNode.createObject( 'HookeForceField', template='E332', youngModulus='100', poissonRatio='0', viscosity='0' ) #Visu child node visuNode = node.createChild('Visu') visuNode.createObject('OglModel', template="ExtVec3f", name='Visual',filename=OBJ) visuNode.createObject('LinearMapping', template='Affine,ExtVec3f') return node def createScene( root ) : #Root node data root.findData('dt').value=0.001 root.findData('gravity').value='0 -10 0' #Required setting root.createObject('RequiredPlugin', name="flexible", pluginName='Flexible', printLog="false") root.createObject('RequiredPlugin', name="image", pluginName='image', printLog="false") #VisuStyle root.createObject('VisualStyle', name='visuStyle', displayFlags='showWireframe showBehaviorModels') #Animation Loop root.createObject('DefaultAnimationLoop'); root.createObject('DefaultVisualManagerLoop'); #Python Script Controller root.createObject('PythonScriptController', filename = __file__, classname='Controller') createVoronoiFrame(root, 'voronoiFrame'); createBarycentricFrame(root, 'barycentricFrame');

47.913043

298

0.704693

1,775

0.230101

0

2,876

0.372829

beeda21a5090a064572591c96a86d43fd6daf247

1,688

py

Python

mindspore/ops/_register_for_op.py

Vincent34/mindspore

a39a60878a46e7e9cb02db788c0bca478f2fa6e5

[ "Apache-2.0" ]

1

2021-07-16T12:05:53.000Z

mindspore/ops/_register_for_op.py

Vincent34/mindspore

a39a60878a46e7e9cb02db788c0bca478f2fa6e5

[ "Apache-2.0" ]

null

mindspore/ops/_register_for_op.py

Vincent34/mindspore

a39a60878a46e7e9cb02db788c0bca478f2fa6e5

[ "Apache-2.0" ]

null

# Copyright 2020 Huawei Technologies Co., Ltd # # 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. # ============================================================================ """Registry the relation.""" from collections import UserDict from .primitive import Primitive class Registry(UserDict): """Registry class for registry functions for grad and vm_impl on Primitive.""" def register(self, prim): """register the function.""" def deco(fn): """Decorate the function.""" if isinstance(prim, str): self[prim] = fn elif issubclass(prim, Primitive): self[id(prim)] = fn return fn return deco def get(self, prim_obj, default): """Get the value by primitive.""" fn = default if isinstance(prim_obj, str) and prim_obj in self: fn = self[prim_obj] elif isinstance(prim_obj, Primitive): key = id(prim_obj.__class__) if key in self: fn = self[key] else: key = prim_obj.name if key in self: fn = self[prim_obj.name] return fn

33.76

82

0.593009

921

0.545616

0

848

0.50237

beee49868a956aa3196803cdf539676b921996ae

11,496

py

Python

senlin-7.0.0/senlin/tests/unit/api/middleware/test_version_negotiation.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

null

senlin-7.0.0/senlin/tests/unit/api/middleware/test_version_negotiation.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

5

2019-08-14T06:46:03.000Z

2021-12-13T20:01:25.000Z

senlin-7.0.0/senlin/tests/unit/api/middleware/test_version_negotiation.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

[ "Apache-2.0" ]

2

2020-03-15T01:24:15.000Z

2020-07-22T20:34:26.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import mock import six import webob from senlin.api.common import version_request as vr from senlin.api.common import wsgi from senlin.api.middleware import version_negotiation as vn from senlin.common import exception from senlin.tests.unit.common import base @mock.patch("senlin.api.openstack.versions.Controller") class VersionNegotiationTest(base.SenlinTestCase): def test_get_version_controller(self, mock_vc): gvc = mock_vc.return_value xvc = mock.Mock() gvc.get_controller = mock.Mock(return_value=xvc) vnf = vn.VersionNegotiationFilter(None, None) request = webob.Request({}) res = vnf._get_controller('v1.0', request) self.assertEqual(xvc, res) self.assertEqual(1, request.environ['api.major']) self.assertEqual(0, request.environ['api.minor']) gvc.get_controller.assert_called_once_with('1.0') def test_get_version_controller_shorter_version(self, mock_vc): gvc = mock_vc.return_value xvc = mock.Mock() gvc.get_controller = mock.Mock(return_value=xvc) vnf = vn.VersionNegotiationFilter(None, None) request = webob.Request({}) res = vnf._get_controller('v1', request) self.assertEqual(xvc, res) self.assertEqual(1, request.environ['api.major']) self.assertEqual(0, request.environ['api.minor']) gvc.get_controller.assert_called_once_with('1.0') def test_get_controller_not_match_version(self, mock_vc): gvc = mock_vc.return_value gvc.get_controller = mock.Mock(return_value=None) vnf = vn.VersionNegotiationFilter(None, None) request = webob.Request({}) res = vnf._get_controller("invalid", request) self.assertIsNone(res) self.assertEqual(0, gvc.get_controller.call_count) def test_request_path_is_version(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) request = webob.Request({'PATH_INFO': 'versions'}) response = vnf.process_request(request) self.assertIs(mock_vc.return_value, response) def test_request_path_is_empty(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) request = webob.Request({'PATH_INFO': '/'}) response = vnf.process_request(request) self.assertIs(mock_vc.return_value, response) def test_request_path_contains_valid_version(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) gvc = mock_vc.return_value x_controller = mock.Mock() gvc.get_controller = mock.Mock(return_value=x_controller) mock_check = self.patchobject(vnf, '_check_version_request') major = 1 minor = 0 request = webob.Request({'PATH_INFO': 'v1.0/resource'}) response = vnf.process_request(request) self.assertIsNone(response) self.assertEqual(major, request.environ['api.major']) self.assertEqual(minor, request.environ['api.minor']) gvc.get_controller.assert_called_once_with('1.0') mock_check.assert_called_once_with(request, x_controller) def test_removes_version_from_request_path(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) self.patchobject(vnf, '_check_version_request') expected_path = 'resource' request = webob.Request({'PATH_INFO': 'v1.0/%s' % expected_path}) response = vnf.process_request(request) self.assertIsNone(response) self.assertEqual(expected_path, request.path_info_peek()) def test_simple_version_on_request_path(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) self.patchobject(vnf, '_check_version_request') fake_vc = mock.Mock(return_value={'foo': 'bar'}) self.patchobject(vnf.versions_app, 'get_controller', return_value=fake_vc) request = webob.Request({'PATH_INFO': 'v1'}) response = vnf.process_request(request) self.assertEqual({'foo': 'bar'}, response) def test_full_version_on_request_path(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) self.patchobject(vnf, '_check_version_request') fake_vc = mock.Mock(return_value={'foo': 'bar'}) self.patchobject(vnf.versions_app, 'get_controller', return_value=fake_vc) request = webob.Request({'PATH_INFO': 'v1.0'}) response = vnf.process_request(request) self.assertEqual({'foo': 'bar'}, response) def test_request_path_contains_unknown_version(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) gvc = mock_vc.return_value gvc.get_controller = mock.Mock(return_value=None) self.patchobject(vnf, '_check_version_request') request = webob.Request({'PATH_INFO': 'v2.0/resource'}) request.headers['Accept'] = '*/*' response = vnf.process_request(request) self.assertIs(mock_vc.return_value, response) def test_accept_header_contains_valid_version(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) self.patchobject(vnf, '_check_version_request') major = 1 minor = 0 request = webob.Request({'PATH_INFO': 'resource'}) request.headers['Accept'] = 'application/vnd.openstack.clustering-v1.0' response = vnf.process_request(request) self.assertIsNone(response) self.assertEqual(major, request.environ['api.major']) self.assertEqual(minor, request.environ['api.minor']) def test_accept_header_contains_simple_version(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) self.patchobject(vnf, '_check_version_request') fake_vc = mock.Mock(return_value={'foo': 'bar'}) self.patchobject(vnf.versions_app, 'get_controller', return_value=fake_vc) major = 1 minor = 0 request = webob.Request({'PATH_INFO': ''}) request.headers['Accept'] = 'application/vnd.openstack.clustering-v1.0' response = vnf.process_request(request) self.assertEqual(major, request.environ['api.major']) self.assertEqual(minor, request.environ['api.minor']) self.assertEqual({'foo': 'bar'}, response) def test_accept_header_contains_unknown_version(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) self.patchobject(vnf, '_check_version_request') request = webob.Request({'PATH_INFO': 'resource'}) request.headers['Accept'] = 'application/vnd.openstack.clustering-v2.0' response = vnf.process_request(request) self.assertIsNone(response) request.headers['Accept'] = 'application/vnd.openstack.clustering-vab' response = vnf.process_request(request) self.assertIsInstance(response, webob.exc.HTTPNotFound) def test_no_URI_version_accept_with_invalid_MIME_type(self, mock_vc): vnf = vn.VersionNegotiationFilter(None, None) gvc = mock_vc.return_value gvc.get_controller = mock.Mock(side_effect=[None, None]) self.patchobject(vnf, '_check_version_request') request = webob.Request({'PATH_INFO': 'resource'}) request.headers['Accept'] = 'application/invalidMIMEType' response = vnf.process_request(request) self.assertIsInstance(response, webob.exc.HTTPNotFound) request.headers['Accept'] = '' response = vnf.process_request(request) self.assertEqual(gvc, response) def test_check_version_request(self, mock_vc): controller = mock.Mock() minv = vr.APIVersionRequest('1.0') maxv = vr.APIVersionRequest('1.3') controller.min_api_version = mock.Mock(return_value=minv) controller.max_api_version = mock.Mock(return_value=maxv) request = webob.Request({'PATH_INFO': 'resource'}) request.headers[wsgi.API_VERSION_KEY] = 'clustering 1.0,compute 2.0' vnf = vn.VersionNegotiationFilter(None, None) vnf._check_version_request(request, controller) self.assertIsNotNone(request.version_request) expected = vr.APIVersionRequest('1.0') self.assertEqual(expected, request.version_request) def test_check_version_request_default(self, mock_vc): controller = mock.Mock() controller.DEFAULT_API_VERSION = "1.0" request = webob.Request({'PATH_INFO': 'resource'}) request.headers[wsgi.API_VERSION_KEY] = 'compute 2.0' vnf = vn.VersionNegotiationFilter(None, None) vnf._check_version_request(request, controller) self.assertIsNotNone(request.version_request) expected = vr.APIVersionRequest(controller.DEFAULT_API_VERSION) self.assertEqual(expected, request.version_request) def test_check_version_request_invalid_format(self, mock_vc): controller = mock.Mock() request = webob.Request({'PATH_INFO': 'resource'}) request.headers[wsgi.API_VERSION_KEY] = 'clustering 2.03' vnf = vn.VersionNegotiationFilter(None, None) ex = self.assertRaises(webob.exc.HTTPBadRequest, vnf._check_version_request, request, controller) self.assertEqual("API Version String '2.03' is of invalid format. It " "must be of format 'major.minor'.", six.text_type(ex)) def test_check_version_request_invalid_version(self, mock_vc): controller = mock.Mock() minv = vr.APIVersionRequest('1.0') maxv = vr.APIVersionRequest('1.100') controller.min_api_version = mock.Mock(return_value=minv) controller.max_api_version = mock.Mock(return_value=maxv) request = webob.Request({'PATH_INFO': 'resource'}) request.headers[wsgi.API_VERSION_KEY] = 'clustering 2.3' vnf = vn.VersionNegotiationFilter(None, None) ex = self.assertRaises(exception.InvalidGlobalAPIVersion, vnf._check_version_request, request, controller) expected = ("Version '2.3' is not supported by the API. Minimum is " "'%(min_ver)s' and maximum is '%(max_ver)s'." % {'min_ver': str(minv), 'max_ver': str(maxv)}) self.assertEqual(expected, six.text_type(ex)) def test_check_version_request_latest(self, mock_vc): controller = mock.Mock() controller.max_api_version = mock.Mock(return_value='12.34') request = webob.Request({'PATH_INFO': 'resource'}) request.headers[wsgi.API_VERSION_KEY] = 'clustering Latest' vnf = vn.VersionNegotiationFilter(None, None) vnf._check_version_request(request, controller) self.assertIsNotNone(request.version_request) expected = '12.34' self.assertEqual(expected, request.version_request)

40.336842

79

0.673626

10,626

0.924322

0

10,682

0.929193

0

1,985

0.172669

bef071b99c5638f1355cc9be272ba0f93a6cb31f

493

py

Python

iot/models.py

kkishans/IOT_DJANGO

12a19858f002a8c684e4dbb93868a8859d57615f

[ "MIT" ]

null

iot/models.py

kkishans/IOT_DJANGO

12a19858f002a8c684e4dbb93868a8859d57615f

[ "MIT" ]

null

iot/models.py

kkishans/IOT_DJANGO

12a19858f002a8c684e4dbb93868a8859d57615f

[ "MIT" ]

null

from django.db import models from django import forms # Create your models here. class User(models.Model): username = models.CharField(max_length=30) password = models.TextField() email = models.EmailField(unique=True) objects = models.Manager() def __str__(self): return self.username class Room(models.Model): room = models.CharField(max_length=30) email = models.ForeignKey(User,on_delete=models.CASCADE) class Meta: db_table = "room"

25.947368

60

0.699797

408

0.827586

0

32

0.064909

bef0b40fe98f05288d080226293e4d439b57a362

2,970

py

Python

Homework files/Think_Python_Book_Homework_1.py

SillyHatsOnly/Python-Education-Experiments

22244defc47b4e3ba41af07957a782013afe12b0

[ "MIT" ]

null

Homework files/Think_Python_Book_Homework_1.py

SillyHatsOnly/Python-Education-Experiments

22244defc47b4e3ba41af07957a782013afe12b0

[ "MIT" ]

null

Homework files/Think_Python_Book_Homework_1.py

SillyHatsOnly/Python-Education-Experiments

22244defc47b4e3ba41af07957a782013afe12b0

[ "MIT" ]

null

def do_twice(f): f() f() def print_spam(): print('spam') do_twice(print_spam) def do_twice(f, a): f(a) f(a) def print_spam(a): print(a) print(a) do_twice(print_spam, 'spamm') def do_four(f, a): do_twice(f,a) do_twice(f,a) do_four(print_spam, "SPAM") def hor_line(): print('+','-'*4,'+','-'*4) hor_line hor_line() def hor_line(): print('+','-'*4,'+','-'*4, end="") hor_line() def hor_line(): print('+','-'*4,'+','-'*4,'+' end="") def hor_line(): print('+','-'*4,'+','-'*4,'+', end="") hor_line() def print_main_line(): print('+','-'*4,'+','-'*4,'+', end="") def print_second_line(): print('|', ''*4, '|',''*4,'|', end='') print('|', ''*4, '|',''*4,'|', end='') print('|', ''*4, '|',''*4,'|', end='') print('|', ''*4, '|',''*4,'|', end='') def square_print(): print_main_line() print_second_line() print_main_line() print_second_line() print_main_line() square_print() def print_main_line(): print('+','-'*4,'+','-'*4,'+') def print_second_line(): print('|', ''*4, '|',''*4,'|') print('|', ''*4, '|',''*4,'|') print('|', ''*4, '|',''*4,'|') print('|', ''*4, '|',''*4,'|') def square_print(): print_main_line() print_second_line() print_main_line() print_second_line() print_main_line() square_print() def print_second_line(): print('|', ' '*4, '|',' '*4,'|') print('|', ' '*4, '|',' '*4,'|') print('|', ' '*4, '|',' '*4,'|') print('|', ' '*4, '|',' '*4,'|') square_print() def print_main_line(): print('+','-'*4,'+','-'*4,'+') def print_second_line(): print('|', ' '*4, '|',' '*4,'|') print('|', ' '*4, '|',' '*4,'|') print('|', ' '*4, '|',' '*4,'|') print('|', ' '*4, '|',' '*4,'|') def square_print(): print_main_line() print_second_line() print_main_line() def double_square(): square_print() square_print() double_square double_square() def print_main_line(): print('+','-'*4,'+','-'*4,'+') def print_second_line(): print('|', ' '*4, '|',' '*4,'|') print('|', ' '*4, '|',' '*4,'|') print('|', ' '*4, '|',' '*4,'|') print('|', ' '*4, '|',' '*4,'|') def square_print(): print_main_line() print_second_line() def double_square(): square_print() square_print() double_square() def square_print(): print_main_line() print_second_line() print_main_line() print_second_line() print_main_line() square_print() def print_main_line(): print('+','-'*4,'+','-'*4,'+','-'*4,'+') def print_second_line(): print('|', ' '*4, '|',' '*4,'|', ' '*4,'|') print('|', ' '*4, '|',' '*4,'|', ' '*4,'|') print('|', ' '*4, '|',' '*4,'|', ' '*4,'|') print('|', ' '*4, '|',' '*4,'|', ' '*4,'|') def square_print(): print_main_line() print_second_line() print_main_line() print_second_line() print_main_line() print_second_line() print_main_line() square_print()

18.679245

47

0.476431

0

538

0.181145

bef16a350cb321f3059e524b8af8bbcaac507956

123

py

Python

email_log/apps.py

bernd-wechner/django-email-log

dbbe0ef6cee8b8067d6420dccc7a8f2061662a68

[ "MIT" ]

26

2015-04-14T18:24:54.000Z

2022-03-07T13:01:34.000Z

email_log/apps.py

bernd-wechner/django-email-log

dbbe0ef6cee8b8067d6420dccc7a8f2061662a68

[ "MIT" ]

23

2015-06-23T02:40:39.000Z

2022-02-08T05:07:42.000Z

email_log/apps.py

bernd-wechner/django-email-log

dbbe0ef6cee8b8067d6420dccc7a8f2061662a68

[ "MIT" ]

25

2015-02-04T16:16:05.000Z

2021-09-28T10:53:00.000Z

from django.apps import AppConfig class EmailLogConfig(AppConfig): name = 'email_log' verbose_name = "Email log"

17.571429

33

0.731707

86

0.699187

0

22

0.178862

bef17e7d48e784a47058c04dd63db533f851c334

83

py

Python

gawain/tests/test_numerics.py

henrywatkins/gawain

c556be20242249504fc0e04a5d3b7168a8369043

[ "MIT" ]

1

2021-11-20T06:16:13.000Z

gawain/tests/test_numerics.py

henrywatkins/gawain

c556be20242249504fc0e04a5d3b7168a8369043

[ "MIT" ]

null

gawain/tests/test_numerics.py

henrywatkins/gawain

c556be20242249504fc0e04a5d3b7168a8369043

[ "MIT" ]

null

import pytest from gawain.numerics import Clock, SolutionVector, MHDSolutionVector

27.666667

68

0.86747

0

bef2574ded37985d33b872832104339ea2dcbc78

384

py

Python

project_9/util.py

sople1/project_9

7d91d786533d508572feae1ffbd1b4a6a80208ab

[ "CC0-1.0" ]

null

project_9/util.py

sople1/project_9

7d91d786533d508572feae1ffbd1b4a6a80208ab

[ "CC0-1.0" ]

null

project_9/util.py

sople1/project_9

7d91d786533d508572feae1ffbd1b4a6a80208ab

[ "CC0-1.0" ]

null

""" utility for project 9 :author: Seongsu Yoon <sople1@snooey.net> :license: CC0 """ def clear(): """ clear cmd/term :return: void """ import os import sys if sys.platform == 'win32': os.system('cls') # on windows else: os.system('clear') # on linux / os x if __name__ == '__main__': raise Exception("please run main py")

14.769231

45

0.570313

0

213

0.554688

bef2add5e5d23f2bc354f97f806087052f88a9fd

2,383

py

Python

api/views.py

HigoOliveira/DesafioFinalServer

284d3cea59f28f6da229345496896106e3d2048a

[ "MIT" ]

null

api/views.py

HigoOliveira/DesafioFinalServer

284d3cea59f28f6da229345496896106e3d2048a

[ "MIT" ]

null

api/views.py

HigoOliveira/DesafioFinalServer

284d3cea59f28f6da229345496896106e3d2048a

[ "MIT" ]

null

from rest_framework.views import APIView from .models import User, Event from .serializer import UserSerializer, EventSerializer from rest_framework.permissions import AllowAny, IsAuthenticated from rest_framework.authentication import TokenAuthentication from rest_framework import status, mixins, generics from rest_framework.response import Response class UserVerify(mixins.RetrieveModelMixin, generics.GenericAPIView): queryset = User.objects.all() serializer_class = UserSerializer lookup_field = 'phone' def get(self, request, *args, **kwargs): return self.retrieve(request, *args, **kwargs) class Create(mixins.CreateModelMixin, generics.GenericAPIView): serializer_class = UserSerializer def post(self, request, *args, **kwargs): return self.create(request, *args, **kwargs) class Update(mixins.UpdateModelMixin, generics.GenericAPIView): permissioon_classes = (IsAuthenticated,) serializer_class = UserSerializer queryset = User.objects.all() def get_object(self): return self.request.user def post(self, request, *args, **kwargs): return self.partial_update(request, *args, **kwargs) class CreateEvent(mixins.CreateModelMixin, generics.GenericAPIView): permissioon_classes = (IsAuthenticated,) serializer_class = EventSerializer def post(self, request, *args, **kwargs): data = request.data.copy() data['user'] = self.request.user.id serializer = self.get_serializer(data=data) serializer.is_valid(raise_exception=True) self.perform_create(serializer) headers = self.get_success_headers(serializer.data) return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers) class ListEvent(mixins.ListModelMixin, generics.GenericAPIView): permissioon_classes = (IsAuthenticated,) queryset = Event.objects.all() serializer_class = EventSerializer def get_queryset(self): queryset = super(ListEvent, self).get_queryset() return queryset.filter(user=self.request.user) def get(self, request, *args, **kwargs): return self.list(request, *args, **kwargs) class DeleteEvent(mixins.DestroyModelMixin, generics.GenericAPIView): serializer_class = EventSerializer permissioon_classes = (IsAuthenticated,) queryset = Event.objects.all() def post(self, request, *args, **kwargs): print(request.POST) return self.destroy(request, *args, **kwargs)

36.661538

85

0.768779

2,018

0.846832

0

13

0.005455

bef317cba640175d733bcaa55e5644bbab2602a1

1,582

py

Python

Searching_Sorting/MinimumMovesToEqualAllArrayElements2.py

PK-100/Competitive_Programming

d0863feaaa99462b2999e85dcf115f7a6c08bb8d

[ "MIT" ]

70

2018-06-25T21:20:15.000Z

2022-03-24T03:55:17.000Z

Searching_Sorting/MinimumMovesToEqualAllArrayElements2.py

An3sha/Competitive_Programming

ee7eadf51939a360d0b004d787ebabda583e92f0

[ "MIT" ]

4

2018-09-04T13:12:20.000Z

2021-06-20T08:29:12.000Z

Searching_Sorting/MinimumMovesToEqualAllArrayElements2.py

An3sha/Competitive_Programming

ee7eadf51939a360d0b004d787ebabda583e92f0

[ "MIT" ]

24

2018-12-26T05:15:32.000Z

2022-01-23T23:04:54.000Z

# LC 462 #from statistics import median from random import randrange from math import floor class Solution: #Quick Select Algorithm def partition(self,x, pivot_index = 0): i = 0 if pivot_index !=0: x[0],x[pivot_index] = x[pivot_index],x[0] for j in range(len(x)-1): if x[j+1] < x[0]: x[j+1],x[i+1] = x[i+1],x[j+1] i += 1 x[0],x[i] = x[i],x[0] return x,i def RSelect(self,x,k): if len(x) == 1: return x[0] else: xpart = self.partition(x,randrange(len(x))) x = xpart[0] # partitioned array j = xpart[1] # pivot index if j == k: return x[j] elif j > k: return self.RSelect(x[:j],k) else: k = k - j - 1 return self.RSelect(x[(j+1):], k) def median(self,lst): lstLen = len(lst) index = (lstLen - 1) // 2 if (lstLen % 2): return self.RSelect(lst,index) else: return (self.RSelect(lst,index) + self.RSelect(lst,index+1))/2.0 def sorting_median(self,lst): lstLen = len(lst) lst.sort() index = (lstLen - 1) // 2 if (lstLen % 2): return lst[index] else: return (lst[index] + lst[index+1])/2.0 def minMoves2(self, nums: List[int]) -> int: mdn = floor(self.sorting_median(nums)) movesmdn = 0 for i in nums: movesmdn += abs(i-mdn) return movesmdn

28.763636

76

0.474083

1,489

0.941214

0

93

0.058786

bef32dc0efa2656e8a84216ea747c7b952e1b452

43

py

Python

moban/_version.py

CLiu13/moban

5deada1af7ff24a6adf698de6a8b589a258d4dc2

[ "MIT" ]

1

2018-12-16T01:16:22.000Z

moban/_version.py

CLiu13/moban

5deada1af7ff24a6adf698de6a8b589a258d4dc2

[ "MIT" ]

null

moban/_version.py

CLiu13/moban

5deada1af7ff24a6adf698de6a8b589a258d4dc2

[ "MIT" ]

null

__version__ = "0.3.9" __author__ = "C. W."

14.333333

21

0.604651

0

14

0.325581

bef3d9edefde231b494e39df17d4acf13c5e8797

666

py

Python

Gif_Rick/rick_random_gif.py

BiceCold/Citadel_of_Ricks

72f1a447accc2c11d1fa1cbf3c3342913913e50e

[ "Apache-2.0" ]

2

2018-04-13T17:41:08.000Z

2018-09-20T22:19:52.000Z

Gif_Rick/rick_random_gif.py

BiceCold/Citadel_of_Ricks

72f1a447accc2c11d1fa1cbf3c3342913913e50e

[ "Apache-2.0" ]

null

Gif_Rick/rick_random_gif.py

BiceCold/Citadel_of_Ricks

72f1a447accc2c11d1fa1cbf3c3342913913e50e

[ "Apache-2.0" ]

null

import imgurpython from Environment_Handlers.configs import get_config import random client_id = get_config("client_id") client_secret = get_config("client_secret") client_refresh_token = get_config("client_refresh") client_access_token = get_config("client_access_token") username = 'antipoliticsrick' client = imgurpython.ImgurClient(client_id, client_secret, client_access_token, client_refresh_token) # album_ids = client.get_account_album_ids(username, page=0) img_lst = client.get_album_images('GebVe10') giflink = [] def random_gif(): for gif in img_lst: giflink.append(gif.link) randomgif = random.choice(giflink) return randomgif

26.64

101

0.792793

0

150

0.225225

bef57e6edf7a67698588bda9e271df4b1e689682

131

py

Python

catalyst/dl/experiment/__init__.py

andrey-avdeev/catalyst

fd17aaba7775c99b7e2b1ce86e60aa8f2379acc3

[ "Apache-2.0" ]

3

2019-11-02T05:37:06.000Z

2020-01-13T02:26:07.000Z

catalyst/dl/experiment/__init__.py

andrey-avdeev/catalyst

fd17aaba7775c99b7e2b1ce86e60aa8f2379acc3

[ "Apache-2.0" ]

null

catalyst/dl/experiment/__init__.py

andrey-avdeev/catalyst

fd17aaba7775c99b7e2b1ce86e60aa8f2379acc3

[ "Apache-2.0" ]

1

2021-12-20T07:32:25.000Z

# flake8: noqa from .base import BaseExperiment from .config import ConfigExperiment from .supervised import SupervisedExperiment

21.833333

44

0.839695

0

14

0.10687

bef59cacc937701b313af0467ffa47a4f4a7a929

3,217

py

Python

home_directories/Library/Application Support/Alfred 3/Alfred.alfredpreferences/workflows/user.workflow.BC9837BA-F60E-475C-B524-E761FBD0E1EB/common.py

joekallen/dotfiles

9318c168fdb9ce0b1066b032e93a7e465d0ffcee

[ "Apache-2.0" ]

null

home_directories/Library/Application Support/Alfred 3/Alfred.alfredpreferences/workflows/user.workflow.BC9837BA-F60E-475C-B524-E761FBD0E1EB/common.py

joekallen/dotfiles

9318c168fdb9ce0b1066b032e93a7e465d0ffcee

[ "Apache-2.0" ]

null

home_directories/Library/Application Support/Alfred 3/Alfred.alfredpreferences/workflows/user.workflow.BC9837BA-F60E-475C-B524-E761FBD0E1EB/common.py

joekallen/dotfiles

9318c168fdb9ce0b1066b032e93a7e465d0ffcee

[ "Apache-2.0" ]

null

import os import argparse import subprocess from workflow import Workflow def get_kubectl_cmd_path(): wf = Workflow() return wf.settings.get("KUBECTL_CMD_PATH") or os.environ.get("KUBECTL_CMD_PATH", '/usr/local/bin/kubectl') class KService: def __init__(self, type, name, age, status): self.type = type self.name = name self.age = age self.status = status def get_args(args): parser = argparse.ArgumentParser() parser.add_argument('query', nargs='?', default="") return parser.parse_args(args) def get_pods(): res = [] pods = subprocess.Popen("%s get pods" % get_kubectl_cmd_path(), shell=True, stdout=subprocess.PIPE).stdout.read().split( '\n')[ 1:-1] for pod_str in pods: try: dep_name, _, status, _, age = " ".join(pod_str.split()).split(' ') res.append(KService("Pod", dep_name, age, status)) except: print("ASd") return res def get_deployments(): res = [] deps = subprocess.Popen("%s get deploy" % get_kubectl_cmd_path(), shell=True, stdout=subprocess.PIPE).stdout.read().split( '\n')[1:-1] for dep_str in deps: dep_name, _, current, _, _, age = " ".join(dep_str.split()).split(' ') res.append(KService("Deploy", dep_name, age, current)) return res def get_replica_sets(): res = [] deps = subprocess.Popen("%s get rs" % get_kubectl_cmd_path(), shell=True, stdout=subprocess.PIPE).stdout.read().split( '\n')[1:-1] for dep_str in deps: dep_name, desired, current, _, age = " ".join(dep_str.split()).split(' ') res.append(KService("Deploy", dep_name, age, "%s/%s" % (desired, current))) return res def get_services(): res = [] res += get_pods() res += get_deployments() return res def search_key_for_service(service): return u' '.join([ service.name ]) def process_and_feedback(wf, wf_cached_data_key, data_func, icon, include_type_in_arg=False): args = get_args(wf.args) data = wf.cached_data(wf_cached_data_key, data_func, max_age=60) query = args.query.strip() if query: data = wf.filter(query, data, key=search_key_for_service, min_score=20) for d in data: if include_type_in_arg: arg = "{type} {name}".format(type=d.type.lower(), name=d.name) else: arg = d.name wf.add_item(title=d.name, subtitle="%s - Age: %s | Extra: %s" % (d.type, d.age, d.status), arg=arg, valid=True, icon=icon) wf.send_feedback() def update_local_path_vars(wf): set_path_to = os.environ.get('set_path_to') configured_path = os.environ.get('configured_path') wf.settings[set_path_to] = configured_path wf.settings.save() print("Successfully set path to %s with %s" % (set_path_to, wf.settings[set_path_to])) def _report_missing_var(wf, var_name): print("Missing dashbaord url; use *ksetenv*") """ wf.add_item(title="Hit enter to set %s environment variable." % var_name, arg="setenv", valid=True) wf.send_feedback() """

27.732759

126

0.608952

166

0.051601

0

494

0.153559

bef59fb3dbc590e868cbbe9ba87904ee2be92c5d

528

py

Python

dependencies/generate maps/pythongis/app/tk2/__init__.py

karimbahgat/AutoMap

eae52f16b7ce71cb2b4b7ae67cf6e4680ea2194f

[ "MIT" ]

4

2015-12-05T14:31:55.000Z

2018-02-09T05:54:36.000Z

dependencies/generate maps/pythongis/app/tk2/__init__.py

karimbahgat/AutoMap

eae52f16b7ce71cb2b4b7ae67cf6e4680ea2194f

[ "MIT" ]

1

2022-01-13T02:52:09.000Z

dependencies/generate maps/pythongis/app/tk2/__init__.py

karimbahgat/AutoMap

eae52f16b7ce71cb2b4b7ae67cf6e4680ea2194f

[ "MIT" ]

1

2018-10-24T01:08:11.000Z

""" Tk2 Tk2 is a convenience library for extending the functionality of Tkinter, to make it easier and more flexible to create GUI applications. """ from .basics import * from .scrollwidgets import * from .texteditor import Text, MultiTextSearch from .variables import * # Later from .multiwidgets import * from .progbar import * from .ribbon import * #from orderedlist import * #from calendar import * from web import * from . import filedialog from . import messagebox from . import colorchooser from . import dispatch

20.307692

73

0.767045

0

206

0.390152

bef5aaf1ff9723ae8680002976dbc5ebda4fccc9

37

py

Python

pp/web/base/tests/test_forjenkins.py

oisinmulvihill/pp-web-base

0be51b1d98c4923e1f4ccbfaea59ae662a8c5cdc

[ "BSD-3-Clause" ]

null

pp/web/base/tests/test_forjenkins.py

oisinmulvihill/pp-web-base

0be51b1d98c4923e1f4ccbfaea59ae662a8c5cdc

[ "BSD-3-Clause" ]

null

pp/web/base/tests/test_forjenkins.py

oisinmulvihill/pp-web-base

0be51b1d98c4923e1f4ccbfaea59ae662a8c5cdc

[ "BSD-3-Clause" ]

null

def test_nonop(): assert 1 == 1

9.25

17

0.567568

0

bef5e1efda3dd7f954a3c06d34cbd2bd9106ec13

803

py

Python

tools/deepke/relation_extraction/standard/models/BasicModule.py

dfface/DoctorKG

6bd6ebec8244a9ce0a2c8c278a704f02b9afaaf8

[ "MIT" ]

1

2022-03-26T16:08:08.000Z

tools/deepke/relation_extraction/standard/models/BasicModule.py

dfface/DoctorKG

6bd6ebec8244a9ce0a2c8c278a704f02b9afaaf8

[ "MIT" ]

null

tools/deepke/relation_extraction/standard/models/BasicModule.py

dfface/DoctorKG

6bd6ebec8244a9ce0a2c8c278a704f02b9afaaf8

[ "MIT" ]

null

import os import time import torch import torch.nn as nn class BasicModule(nn.Module): ''' 封装nn.Module, 提供 save 和 load 方法 ''' def __init__(self): super(BasicModule, self).__init__() def load(self, path, device): ''' 加载指定路径的模型 ''' self.load_state_dict(torch.load(path, map_location=device)) def save(self, epoch=0, cfg=None): ''' 保存模型，默认使用“模型名字+时间”作为文件名 ''' time_prefix = time.strftime('%Y-%m-%d_%H-%M-%S') prefix = os.path.join(cfg.cwd, 'checkpoints',time_prefix) os.makedirs(prefix, exist_ok=True) name = os.path.join(prefix, cfg.model_name + '_' + f'epoch{epoch}' + '.pth') torch.save(self.state_dict(), name) return name

22.942857

85

0.555417

808

0.919226

0

264

0.300341

bef6dbd81f470e4f916903c6f30ebc2cb970bd0a

310

py

Python

url_shortener_client/exceptions/__init__.py

Andrelpoj/hire.me

79428e2094a6b56e762a7f958e1b75f395f59cef

[ "Apache-2.0" ]

null

url_shortener_client/exceptions/__init__.py

Andrelpoj/hire.me

79428e2094a6b56e762a7f958e1b75f395f59cef

[ "Apache-2.0" ]

null

url_shortener_client/exceptions/__init__.py

Andrelpoj/hire.me

79428e2094a6b56e762a7f958e1b75f395f59cef

[ "Apache-2.0" ]

null

class AliasNotFound(Exception): def __init__(self, alias): self.alias = alias class AliasAlreadyExists(Exception): def __init__(self, alias): self.alias = alias class UnexpectedServerResponse(Exception): def __init__(self, response): self.response = response

25.833333

43

0.670968

298

0.96129

0

bef786a72fbb29131b60f5c806a5c2a1d2c1e463

3,135

py

Python

software/nuke/init.py

kei-iketani/plex

cf09c8ef93984e5a69b23bf56248b87e4cfd98b0

[ "MIT" ]

153

2018-03-22T18:29:17.000Z

2022-03-07T03:43:09.000Z

software/nuke/init.py

kei-iketani/plex

cf09c8ef93984e5a69b23bf56248b87e4cfd98b0

[ "MIT" ]

30

2018-08-16T16:27:42.000Z

2021-02-24T05:37:25.000Z

software/nuke/init.py

alexanderrichter/arPipeline

3466f70a79e4d32c0647ba21d9689157a0f7772e

[ "MIT" ]

34

2018-03-24T03:54:05.000Z

2022-03-10T11:36:52.000Z

#********************************************************************* # content = init Nuke # version = 0.1.0 # date = 2019-12-01 # # license = MIT <https://github.com/alexanderrichtertd> # author = Alexander Richter <alexanderrichtertd.com> #********************************************************************* import os import errno import nuke import pipefunc from tank import Tank #********************************************************************* # VARIABLE TITLE = os.path.splitext(os.path.basename(__file__))[0] LOG = Tank().log.init(script=TITLE) PROJECT_DATA = Tank().data_project RESOLUTION = (' ').join([str(PROJECT_DATA['resolution'][0]), str(PROJECT_DATA['resolution'][1]), PROJECT_DATA['name'].replace(' ', '')]) #********************************************************************* # FOLDER CREATION def create_write_dir(): file_name = nuke.filename(nuke.thisNode()) file_path = os.path.dirname(file_name) os_path = nuke.callbacks.filenameFilter(file_path) # cope with the directory existing already by ignoring that exception try: os.makedirs(os_path) except OSError, e: if e.errno != errno.EEXIST: raise def add_plugin_paths(): # ADD all IMG paths for img in os.getenv('IMG_PATH').split(';'): for img_sub in pipefunc.get_deep_folder_list(path=img, add_path=True): nuke.pluginAddPath(img_sub) # ADD sub software paths for paths in os.getenv('SOFTWARE_SUB_PATH').split(';'): nuke.pluginAddPath(paths) #********************************************************************* # PIPELINE Tank().init_software() add_plugin_paths() try: from scripts import write_node except: LOG.warning('FAILED loading write_node') # LOAD paths try: for paths in os.getenv('SOFTWARE_SUB_PATH').split(';'): nuke.pluginAddPath(paths) except: LOG.warning('FAILED loading SOFTWARE_SUB_PATH') print('SETTINGS') # RESOLUTION ********************************************************************* try: nuke.addFormat(RESOLUTION) nuke.knobDefault('Root.format', PROJECT_DATA['name'].replace(' ', '')) print(' {} ON - {}'.format(chr(254), RESOLUTION)) except: LOG.error(' OFF - {}'.format(RESOLUTION), exc_info=True) print(' {} OFF - {}'.format(chr(254), RESOLUTION)) # FPS ********************************************************************* try: nuke.knobDefault("Root.fps", str(PROJECT_DATA['fps'])) print(' {} ON - {} fps'.format(chr(254), PROJECT_DATA['fps'])) except: LOG.error(' OFF - {} fps'.format(PROJECT_DATA['fps']), exc_info=True) print(' {} OFF - {} fps'.format(chr(254), PROJECT_DATA['fps'])) # createFolder ********************************************************************* try: nuke.addBeforeRender(create_write_dir) print(' {} ON - create_write_dir (before render)'.format(chr(254))) except: LOG.error(' OFF - create_write_dir (before render)'.format(chr(254)), exc_info=True) print(' {} OFF - create_write_dir (before render)'.format(chr(254))) print('')

30.436893

89

0.536204

0

1,382

0.440829

bef8b3cc0e57e7d1cb77a57410c13eec81de3df9

589

py

Python

main.py

gaoshanyu/web_ui_test_sample

8a6cc9b54b5f728af7ef0725dea42d759bd115d0

[ "MIT" ]

null

main.py

gaoshanyu/web_ui_test_sample

8a6cc9b54b5f728af7ef0725dea42d759bd115d0

[ "MIT" ]

null

main.py

gaoshanyu/web_ui_test_sample

8a6cc9b54b5f728af7ef0725dea42d759bd115d0

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Created at 03/10/2020 __author__ = 'raniys' import pytest if __name__ == '__main__': # -v: verbose; -s: shortcut for --capture=no; # -m: only run tests matching given mark expression. example: -m 'mark1 and not mark2'; # --html=path: create html report file at given path. # pytest.main(["-v", "-s", "-m", "smoke", "--html=./reports/smoke_tests_report.html"]) # pytest.main(["-v", "-s", "-m", "sample", "--html=./reports/sample_tests_report.html"]) pytest.main(["-v", "-s", "-m", "search", "--html=./reports/search_tests_report.html"])

39.266667

92

0.614601

0

486

0.825127

bef9a72ceb82bbb48832da89c306ea29b20a4752

863

py

Python

rnd/HaskellRSLCompiler/test/parse/test.py

syoyo/lucille

ff81b332ae78181dbbdc1ec3c3b0f59992e7c0fa

[ "BSD-3-Clause" ]

77

2015-01-29T21:02:10.000Z

2022-03-04T11:23:12.000Z

rnd/HaskellRSLCompiler/test/parse/test.py

syoyo/lucille

ff81b332ae78181dbbdc1ec3c3b0f59992e7c0fa

[ "BSD-3-Clause" ]

1

2018-11-08T02:11:24.000Z

2018-11-08T04:31:17.000Z

rnd/HaskellRSLCompiler/test/parse/test.py

syoyo/lucille

ff81b332ae78181dbbdc1ec3c3b0f59992e7c0fa

[ "BSD-3-Clause" ]

13

2015-04-20T08:17:29.000Z

2020-06-17T18:35:06.000Z

#!/usr/bin/env python import os, sys import subprocess import re import glob errlog = [] def run(f): cmd = "../../lslc" p = subprocess.Popen([cmd, f], stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True) outs = [l for l in p.stdout] errs = [l for l in p.stderr] errline = re.compile("TODO") failed = False for l in errs: if errline.search(l): failed = True if failed: print "[FAIL] ", f errlog.append("==== [" + f + "] ====") for l in errs: errlog.append(l[:-1]) errlog.append("=====================") errlog.append("\n") else: print "[OK ] ", f def main(): for f in glob.glob("*.sl"): run(f) f = open("errlog.log", "w") for l in errlog: print >>f, l if __name__ == '__main__': main()

17.979167

98

0.499421

0

131

0.151796

befb15db729fb7dcc8145ec21e9b327a9461b95c

657

py

Python

glasses/models/classification/base/__init__.py

rentainhe/glasses

34300a76985c7fc643094fa8d617114926a0ee75

[ "MIT" ]

271

2020-10-20T12:30:23.000Z

2022-03-17T03:02:38.000Z

glasses/models/classification/base/__init__.py

rentainhe/glasses

34300a76985c7fc643094fa8d617114926a0ee75

[ "MIT" ]

212

2020-07-25T13:02:23.000Z

2022-02-20T10:33:32.000Z

glasses/models/classification/base/__init__.py

rentainhe/glasses

34300a76985c7fc643094fa8d617114926a0ee75

[ "MIT" ]

23

2021-01-03T13:53:36.000Z

2022-03-17T05:40:34.000Z

from torch import Tensor, nn from ...base import VisionModule class ClassificationModule(VisionModule): """Base Classification Module class""" def __init__( self, encoder: nn.Module, head: nn.Module, in_channels: int = 3, n_classes: int = 1000, **kwargs ): super().__init__() self.encoder = encoder(in_channels=in_channels, **kwargs) self.head = head(self.encoder.widths[-1], n_classes) self.initialize() def initialize(self): pass def forward(self, x: Tensor) -> Tensor: x = self.encoder(x) x = self.head(x) return x

21.9

65

0.584475

591

0.899543

0

38

0.057839

befc1052790c2cb39af3f31238e68ac4213b7a50

3,202

py

Python

lib/data.py

PEDIA-Charite/classifier

13e9d6108f9691b089aac59c7392f7940033b8af

[ "MIT" ]

2

2019-04-04T03:44:25.000Z

2019-12-23T17:08:51.000Z

lib/data.py

PEDIA-Charite/classifier

13e9d6108f9691b089aac59c7392f7940033b8af

[ "MIT" ]

9

2017-05-23T09:55:15.000Z

2019-11-22T11:24:20.000Z

lib/data.py

PEDIA-Charite/classifier

13e9d6108f9691b089aac59c7392f7940033b8af

[ "MIT" ]

2

2017-05-24T12:23:13.000Z

2019-09-03T08:36:18.000Z

# -*- coding: utf-8 -*- import os import numpy as np import sys import logging import csv # Setup logging logger = logging.getLogger(__name__) console_handle = logging.StreamHandler() console_handle.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s: %(message)s', datefmt='%m-%d %H:%M') console_handle.setFormatter(formatter) logger.addHandler(console_handle) class Data: """Common class for a list of instances of the class Samples Attributes: name: name of the data as a string samples: a list of samples as instances of class Sample casedisgene: a list of lists [[case,gene]] containing each case in samples and the respective disease causing gene """ # index for each score FM_IDX = 0 CADD_IDX = 1 GESTALT_IDX = 2 BOQA_IDX = 3 PHENO_IDX = 4 # FEATURE_IDX is for feature vector which contain the above feature score # LABEL_IDX is for pathogenic gene label (0, 1) # GENE_IDX is for gene symbol FEATURE_IDX = 0 LABEL_IDX = 1 GENE_IDX = 2 GENE_NAME_IDX = 3 def __init__(self): self.data = {} # Filter dict self.filter_dict = {0: "feature_score", 1: "cadd_phred_score", 2: "gestalt_score", 3: "boqa_score", 4: "pheno_score"} def loadData(self, input_file, filter_field=None): filter_cases = [] with open(input_file) as csvfile: reader = csv.DictReader(csvfile) case = "" for row in reader: case = row["case"] if not case in self.data: self.data.update({case:[[], [], [], []]}) x = self.data[case][self.FEATURE_IDX] y = self.data[case][self.LABEL_IDX] gene = self.data[case][self.GENE_IDX] gene_name = self.data[case][self.GENE_NAME_IDX] x.append([row["feature_score"], row["cadd_phred_score"], row["gestalt_score"], row["boqa_score"], row["pheno_score"]]) y.append(int(row["label"])) gene.append(row["gene_id"]) gene_name.append(row["gene_symbol"]) # filter the sample which has no the feature we assigned if filter_field != None: if int(row["label"]) == 1: if row[self.filter_dict[filter_field[0]]] == 'nan' or row[self.filter_dict[filter_field[0]]] == '0': logger.debug("%s - %s has no %s score", case, row["gene_symbol"], self.filter_dict[filter_field[0]]) filter_cases.append(case) for key in list(self.data): if key in filter_cases: del self.data[key] else: x = self.data[key][self.FEATURE_IDX] y = self.data[key][self.LABEL_IDX] x = np.array(x) y = np.array(y) self.data[key][self.FEATURE_IDX] = x self.data[key][self.LABEL_IDX] = y logger.info("Input %s: total %d cases", input_file, len(self.data))

37.232558

135

0.553716

2,805

0.876015

0

908

0.283573

befd00f2e7be9ee982348d730c3ed4d4bbdd8988

177

py

Python

iris_sdk/models/data/tn_status.py

NumberAI/python-bandwidth-iris

0e05f79d68b244812afb97e00fd65b3f46d00aa3

[ "MIT" ]

2

2020-04-13T13:47:59.000Z

2022-02-23T20:32:41.000Z

iris_sdk/models/data/tn_status.py

bandwidthcom/python-bandwidth-iris

dbcb30569631395041b92917252d913166f7d3c9

[ "MIT" ]

5

2020-09-18T20:59:24.000Z

2021-08-25T16:51:42.000Z

iris_sdk/models/data/tn_status.py

bandwidthcom/python-bandwidth-iris

dbcb30569631395041b92917252d913166f7d3c9

[ "MIT" ]

5

2018-12-12T14:39:50.000Z

2020-11-17T21:42:29.000Z

#!/usr/bin/env python from iris_sdk.models.base_resource import BaseData from iris_sdk.models.maps.tn_status import TnStatusMap class TnStatus(TnStatusMap, BaseData): pass

25.285714

54

0.813559

47

0.265537

0

21

0.118644

befd8dcdbdb6d9ed65837be1a16b79168d010d75

8,437

py

Python

venv/lib/python3.6/site-packages/ansible_collections/f5networks/f5_modules/plugins/modules/bigip_device_group_member.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

1

2020-01-22T13:11:23.000Z

venv/lib/python3.6/site-packages/ansible_collections/f5networks/f5_modules/plugins/modules/bigip_device_group_member.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

12

2020-02-21T07:24:52.000Z

2020-04-14T09:54:32.000Z

venv/lib/python3.6/site-packages/ansible_collections/f5networks/f5_modules/plugins/modules/bigip_device_group_member.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- # # Copyright: (c) 2017, F5 Networks Inc. # GNU General Public License v3.0 (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = r''' --- module: bigip_device_group_member short_description: Manages members in a device group description: - Manages members in a device group. Members in a device group can only be added or removed, never updated. This is because the members are identified by unique name values and changing that name would invalidate the uniqueness. version_added: "1.0.0" options: name: description: - Specifies the name of the device that you want to add to the device group. Often this will be the hostname of the device. This member must be trusted by the device already. Trusting can be done with the C(bigip_device_trust) module and the C(peer_hostname) option to that module. type: str required: True device_group: description: - The device group to which you want to add the member. type: str required: True state: description: - When C(present), ensures the device group member exists. - When C(absent), ensures the device group member is removed. type: str choices: - present - absent default: present extends_documentation_fragment: f5networks.f5_modules.f5 author: - Tim Rupp (@caphrim007) - Wojciech Wypior (@wojtek0806) ''' EXAMPLES = r''' - name: Add the current device to the "device_trust_group" device group bigip_device_group_member: name: "{{ inventory_hostname }}" device_group: device_trust_group provider: password: secret server: lb.mydomain.com user: admin delegate_to: localhost - name: Add the hosts in the current scope to "device_trust_group" bigip_device_group_member: name: "{{ item }}" device_group: device_trust_group provider: password: secret server: lb.mydomain.com user: admin loop: "{{ hostvars.keys() }}" run_once: true delegate_to: localhost ''' RETURN = r''' # only common fields returned ''' from datetime import datetime from ansible.module_utils.basic import AnsibleModule from ..module_utils.bigip import F5RestClient from ..module_utils.common import ( F5ModuleError, AnsibleF5Parameters, f5_argument_spec ) from ..module_utils.icontrol import tmos_version from ..module_utils.teem import send_teem class Parameters(AnsibleF5Parameters): api_map = {} api_attributes = [] returnables = [] updatables = [] class ApiParameters(Parameters): pass class ModuleParameters(Parameters): pass class Changes(Parameters): def to_return(self): result = {} try: for returnable in self.returnables: change = getattr(self, returnable) if isinstance(change, dict): result.update(change) else: result[returnable] = change result = self._filter_params(result) except Exception: raise return result class UsableChanges(Changes): pass class ReportableChanges(Changes): pass class Difference(object): pass class ModuleManager(object): def __init__(self, *args, **kwargs): self.module = kwargs.get('module', None) self.client = F5RestClient(**self.module.params) self.want = Parameters(params=self.module.params) self.have = None self.changes = Changes() def _set_changed_options(self): changed = {} for key in Parameters.returnables: if getattr(self.want, key) is not None: changed[key] = getattr(self.want, key) if changed: self.changes = Changes(params=changed) def _announce_deprecations(self, result): warnings = result.pop('__warnings', []) for warning in warnings: self.module.deprecate( msg=warning['msg'], version=warning['version'] ) def exec_module(self): start = datetime.now().isoformat() version = tmos_version(self.client) changed = False result = dict() state = self.want.state if state == "present": changed = self.present() elif state == "absent": changed = self.absent() reportable = ReportableChanges(params=self.changes.to_return()) changes = reportable.to_return() result.update(**changes) result.update(dict(changed=changed)) self._announce_deprecations(result) send_teem(start, self.client, self.module, version) return result def present(self): if self.exists(): return False else: return self.create() def absent(self): if self.exists(): return self.remove() return False def create(self): self._set_changed_options() if self.module.check_mode: return True self.create_on_device() return True def remove(self): if self.module.check_mode: return True self.remove_from_device() if self.exists(): raise F5ModuleError("Failed to remove the member from the device group.") return True def exists(self): errors = [401, 403, 409, 500, 501, 502, 503, 504] uri = "https://{0}:{1}/mgmt/tm/cm/device-group/{2}/devices/{3}".format( self.client.provider['server'], self.client.provider['server_port'], self.want.device_group, self.want.name ) resp = self.client.api.get(uri) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) if resp.status == 404 or 'code' in response and response['code'] == 404: return False if resp.status in [200, 201] or 'code' in response and response['code'] in [200, 201]: return True if resp.status in errors or 'code' in response and response['code'] in errors: if 'message' in response: raise F5ModuleError(response['message']) else: raise F5ModuleError(resp.content) def create_on_device(self): params = self.changes.api_params() params['name'] = self.want.name params['partition'] = self.want.partition uri = "https://{0}:{1}/mgmt/tm/cm/device-group/{2}/devices/".format( self.client.provider['server'], self.client.provider['server_port'], self.want.device_group ) resp = self.client.api.post(uri, json=params) try: response = resp.json() except ValueError as ex: raise F5ModuleError(str(ex)) if resp.status in [200, 201] or 'code' in response and response['code'] in [200, 201]: return True raise F5ModuleError(resp.content) def remove_from_device(self): uri = "https://{0}:{1}/mgmt/tm/cm/device-group/{2}/devices/{3}".format( self.client.provider['server'], self.client.provider['server_port'], self.want.device_group, self.want.name ) response = self.client.api.delete(uri) if response.status == 200: return True raise F5ModuleError(response.content) class ArgumentSpec(object): def __init__(self): self.supports_check_mode = True argument_spec = dict( name=dict(required=True), device_group=dict(required=True), state=dict( default='present', choices=['absent', 'present'] ), ) self.argument_spec = {} self.argument_spec.update(f5_argument_spec) self.argument_spec.update(argument_spec) def main(): spec = ArgumentSpec() module = AnsibleModule( argument_spec=spec.argument_spec, supports_check_mode=spec.supports_check_mode ) try: mm = ModuleManager(module=module) results = mm.exec_module() module.exit_json(**results) except F5ModuleError as ex: module.fail_json(msg=str(ex)) if __name__ == '__main__': main()

28.6

94

0.6174

5,482

0.649757

0

2,528

0.299633

befdd813dce9c8916652b3514805d92fc7258e7d

793

py

Python

comrade/blueprints/rest.py

sp3c73r2038/elasticsearch-comrade

ed0c94e071d2fe701a14429981390b9a89df79a7

[ "MIT" ]

256

2019-09-09T10:09:34.000Z

2022-03-28T04:15:21.000Z

comrade/blueprints/rest.py

sp3c73r2038/elasticsearch-comrade

ed0c94e071d2fe701a14429981390b9a89df79a7

[ "MIT" ]

503

2019-07-31T17:01:12.000Z

2022-03-28T13:19:26.000Z

comrade/blueprints/rest.py

nmeisels/elasticsearch-comrade

57dc600e5ffd7f9d4c055b584124bef9365e538c

[ "MIT" ]

25

2019-08-30T13:04:31.000Z

2022-03-09T09:50:32.000Z

from elasticsearch import TransportError from sanic import Blueprint from sanic.request import Request from sanic.response import HTTPResponse, json from ..connections import get_client rest_bp = Blueprint('rest') def format_es_exception(e: TransportError): return json({"status_code": e.status_code, "error": e.error, "info": e.info}) @rest_bp.route('/query', methods=['POST']) async def close_index(request: Request) -> HTTPResponse: client = get_client(request) body = request.json['body'] method = request.json['method'] path = request.json['path'] try: resp = await client.transport.perform_request(method, path, body=body) except TransportError as e: return format_es_exception(e) return json(resp)

28.321429

78

0.693569

0

412

0.519546

369

0.465322

66

0.083228

befebe8c408a00b9be09490e9fa3fb8d41c06ce6

1,081

py

Python

tests/test_utils.py

tedeler/pyexchange

58042f473cbd4f00769249ce9ca20c6a376eddb6

[ "Apache-2.0" ]

128

2015-01-11T10:29:40.000Z

2021-06-25T05:27:45.000Z

tests/test_utils.py

tedeler/pyexchange

58042f473cbd4f00769249ce9ca20c6a376eddb6

[ "Apache-2.0" ]

52

2015-01-02T15:24:28.000Z

2020-08-07T04:49:49.000Z

tests/test_utils.py

tedeler/pyexchange

58042f473cbd4f00769249ce9ca20c6a376eddb6

[ "Apache-2.0" ]

96

2015-01-02T15:16:20.000Z

2021-12-25T01:37:46.000Z

from datetime import datetime from pytz import timezone, utc from pytest import mark from pyexchange.utils import convert_datetime_to_utc def test_converting_none_returns_none(): assert convert_datetime_to_utc(None) is None def test_converting_non_tz_aware_date_returns_tz_aware(): utc_time = datetime(year=2014, month=1, day=1, hour=1, minute=1, second=1) assert utc_time.tzinfo is None assert convert_datetime_to_utc(utc_time) == datetime(year=2014, month=1, day=1, hour=1, minute=1, second=1, tzinfo=utc) def test_converting_tz_aware_date_returns_tz_aware_date(): # US/Pacific timezone is UTC-07:00 (In April we are in DST) # We use localize() because according to the pytz documentation, using the tzinfo # argument of the standard datetime constructors does not work for timezones with DST. pacific_time = timezone("US/Pacific").localize(datetime(year=2014, month=4, day=1, hour=1, minute=0, second=0)) utc_time = utc.localize(datetime(year=2014, month=4, day=1, hour=8, minute=0, second=0)) assert convert_datetime_to_utc(pacific_time) == utc_time

43.24

121

0.781684

0

238

0.220167

befed480f20eb883fd15d6235756ef7750bbee56

786

py

Python

vidpub/__main__.py

gary9630/session-video-publisher

6602f53d722af8e569c82b7de8ef79a63293c766

[ "0BSD" ]

null

vidpub/__main__.py

gary9630/session-video-publisher

6602f53d722af8e569c82b7de8ef79a63293c766

[ "0BSD" ]

5

2020-11-15T12:45:03.000Z

2021-12-07T08:29:40.000Z

vidpub/__main__.py

gary9630/session-video-publisher

6602f53d722af8e569c82b7de8ef79a63293c766

[ "0BSD" ]

4

2018-06-23T16:48:03.000Z

2021-04-18T09:51:29.000Z

import argparse from .upload_video import upload_video from .generate_playlist import generate_playlist def parse_args(argv): parser = argparse.ArgumentParser() parser.add_argument( "-u", "--upload", action="store_true", help="Upload videos to YouTube channel" ) parser.add_argument( "-p", "--playlist", action="store_true", help="Generate playlist information in json files" ) parser.add_argument( "-o", "--output_dir", default="./videos", help="Output path of video information" ) return parser.parse_args(argv) def main(argv=None): options = parse_args(argv) if options.upload: upload_video() if options.playlist: generate_playlist(options.output_dir) if __name__ == "__main__": main()

23.818182

99

0.675573

0

205

0.260814

beff85e9c6691647f15d3bfe260f151e7cc2041f

3,275

py

Python

ally/utils/option.py

rjfranssen/PyAlly

f24d4d449dd0578f52e75365ad0ba69a572d3237

[ "MIT" ]

53

2019-08-11T20:39:16.000Z

2022-02-01T02:05:12.000Z

ally/utils/option.py

rjfranssen/PyAlly

f24d4d449dd0578f52e75365ad0ba69a572d3237

[ "MIT" ]

53

2019-12-11T06:39:59.000Z

2022-02-13T05:06:44.000Z

ally/utils/option.py

rjfranssen/PyAlly

f24d4d449dd0578f52e75365ad0ba69a572d3237

[ "MIT" ]

31

2019-10-05T02:28:16.000Z

2022-02-03T03:41:42.000Z

# MIT License # # Copyright (c) 2020 Brett Graves # # 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 datetime import math from .utils import * ############################################################################ def option_format(symbol="", exp_date="1970-01-01", strike=0, direction=""): """Returns the OCC standardized option name. Args: symbol: the underlying symbol, case insensitive exp_date: date of expiration, in string-form. strike: strike price of the option direction: 'C' or 'call' or the like, for call, otherwise 'p' or 'Put' for put Returns: OCC string, like 'IBM201231C00301000' .. code-block:: python # Construct the option's OCC symbol >>> ibm_call = ally.utils.option_format( exp_date = '2020-12-31', symbol = 'IBM', # case insensitive direction = 'call', strike = 301 ) >>> ibm_call 'IBM201231C00301000' """ if not ( check(symbol) and check(exp_date) and check(str(strike)) and check(direction) ): return "" # direction into C or P direction = "C" if "C" in direction.upper() else "P" # Pad strike with zeros def format_strike(strike): x = str(math.floor(float(strike) * 1000)) return "0" * (8 - len(x)) + x # Assemble return ( str(symbol).upper() + datetime.datetime.strptime(exp_date, "%Y-%m-%d").strftime("%y%m%d") + direction + format_strike(strike) ) def option_strike(name): """Pull apart an OCC standardized option name and retreive the strike price, in integer form""" return int(name[-8:]) / 1000.0 def option_maturity(name): """Given OCC standardized option name, return the date of maturity""" return datetime.datetime.strptime(name[-15:-9], "%y%m%d").strftime("%Y-%m-%d") def option_callput(name): """Given OCC standardized option name, return whether its a call or a put""" return "call" if name.upper()[-9] == "C" else "put" def option_symbol(name): """Given OCC standardized option name, return option ticker""" return name[:-15]

31.796117

90

0.635725

0

2,376

0.725496

8300d2d4159d348f8f2a81357e0afeb556ced95e

460

py

Python

examples/104-python3-9-pipeline.py

marviniter/argo-dataflow

89a060b1c6ea70f7c26bc58a01ba675c3acc1c06

[ "Apache-2.0" ]

null

examples/104-python3-9-pipeline.py

marviniter/argo-dataflow

89a060b1c6ea70f7c26bc58a01ba675c3acc1c06

[ "Apache-2.0" ]

null

examples/104-python3-9-pipeline.py

marviniter/argo-dataflow

89a060b1c6ea70f7c26bc58a01ba675c3acc1c06

[ "Apache-2.0" ]

null

from argo_dataflow import pipeline, kafka def handler(msg, context): return ("hi! " + msg.decode("UTF-8")).encode("UTF-8") if __name__ == '__main__': (pipeline("104-python3-9") .owner('argoproj-labs') .describe("""This example is of the Python 3.9 handler. [Learn about handlers](../docs/HANDLERS.md)""") .step( (kafka('input-topic') .code('main', handler) .kafka('output-topic') )) .save())

23

60

0.582609

0

186

0.404348

8300f1e857cc9e2e0c3bf9685d4664e9e4c8faa9

2,195

py

Python

djangur.py

JerryPopi/djangur-py

0ba76a1a9c0f77ded014f0f3a0b3a98bf7835f51

[ "MIT" ]

null

djangur.py

JerryPopi/djangur-py

0ba76a1a9c0f77ded014f0f3a0b3a98bf7835f51

[ "MIT" ]

null

djangur.py

JerryPopi/djangur-py

0ba76a1a9c0f77ded014f0f3a0b3a98bf7835f51

[ "MIT" ]

null

import asyncio import discord from commands import Commands, Guild_Instance, leave, play_search import os from pymongo import MongoClient from dotenv import load_dotenv load_dotenv() CONNECTION_STRING = f"mongodb+srv://{os.environ['mongo_user']}:{os.environ['mongo_pass']}@djangur.erogd.mongodb.net/djangur?retryWrites=true&w=majority" db_client = MongoClient(CONNECTION_STRING) db = db_client['djangur'] client = discord.Client() @client.event async def on_ready(): print('Logged in as {0.user}'.format(client)) print(os.environ['prefix']) @client.event async def on_message(msg): if msg.author == client.user: return ginst = Guild_Instance.by_id(msg.guild.id) ginst.tc = msg.channel ginst.db = db[str(msg.guild.id)] if msg.content.isdigit() and ginst.searching: await play_search(msg.content, msg=msg, client=client, ginst=ginst) if not msg.content.startswith(os.environ['prefix']): return no_prefix = msg.content[len(os.environ['prefix']):] split = no_prefix.split(' ', 1) cmd = split[0] args = split[1] if (len(split) == 2) else '' if cmd in Commands.command_map: await Commands.command_map[cmd].fn(args, msg=msg, client=client, ginst=ginst) else: await msg.channel.send(f'{cmd}: Command not found.') @client.event async def on_voice_state_update(member, before, after): if not member.name == 'Tramvai': return elif before.channel is None: ginst = Guild_Instance.by_id(after.channel.guild.id) voice = after.channel.guild.voice_client time = 0 while True: await asyncio.sleep(1) time = time + 1 if voice.is_playing() and not voice.is_paused(): time = 0 if time == 600: print(await Commands.command_map['leave'].fn(None, None, None, ginst)) if not voice.is_connected(): break elif before.channel is not None: if after.channel is None: ginst = Guild_Instance.by_id(before.channel.guild.id) await Commands.command_map['leave'].fn(None, None, None, ginst) client.run(os.environ['token'])

29.662162

152

0.653303

0

1,719

0.783144

1,677

0.764009

251

0.114351

830374b559d44b39454687ae70bffd40d78c9944

44,236

py

Python

membership/models.py

str4nd/sikteeri

34dd5a4dc35558cdba9e6f97fd38fb661a36b8a5

[ "MIT" ]

22

2015-03-30T19:33:15.000Z

2022-01-10T03:52:43.000Z

membership/models.py

str4nd/sikteeri

34dd5a4dc35558cdba9e6f97fd38fb661a36b8a5

[ "MIT" ]

66

2015-05-15T13:54:59.000Z

2021-05-27T20:28:39.000Z

membership/models.py

str4nd/sikteeri

34dd5a4dc35558cdba9e6f97fd38fb661a36b8a5

[ "MIT" ]

13

2015-03-09T18:59:29.000Z

2022-01-10T04:08:38.000Z

# -*- coding: utf-8 -*- from datetime import datetime, timedelta from decimal import Decimal import logging from django.core.files.storage import FileSystemStorage from membership.billing.pdf_utils import get_bill_pdf, create_reminder_pdf from membership.reference_numbers import barcode_4, group_right,\ generate_membership_bill_reference_number import traceback from io import StringIO, BytesIO from django.core.exceptions import ObjectDoesNotExist from django.db import models from django.db import transaction from django.db.models import Q, Sum, Count from django.utils.translation import ugettext_lazy as _ import django.utils.timezone from django.conf import settings from django.template.loader import render_to_string from django.forms import ValidationError from django.db.models.query import QuerySet from django.contrib.contenttypes.models import ContentType from .utils import log_change, tupletuple_to_dict from membership.signals import send_as_email, send_preapprove_email, send_duplicate_payment_notice from .email_utils import bill_sender, preapprove_email_sender, duplicate_payment_sender, format_email logger = logging.getLogger("membership.models") class BillingEmailNotFound(Exception): pass class MembershipOperationError(Exception): pass class MembershipAlreadyStatus(MembershipOperationError): pass class PaymentAttachedError(Exception): pass MEMBER_TYPES = (('P', _('Person')), ('J', _('Junior')), ('S', _('Supporting')), ('O', _('Organization')), ('H', _('Honorary'))) MEMBER_TYPES_DICT = tupletuple_to_dict(MEMBER_TYPES) STATUS_NEW = 'N' STATUS_PREAPPROVED = 'P' STATUS_APPROVED = 'A' STATUS_DIS_REQUESTED = 'S' STATUS_DISASSOCIATED = 'I' STATUS_DELETED = 'D' MEMBER_STATUS = ((STATUS_NEW, _('New')), (STATUS_PREAPPROVED, _('Pre-approved')), (STATUS_APPROVED, _('Approved')), (STATUS_DIS_REQUESTED, _('Dissociation requested')), (STATUS_DISASSOCIATED, _('Dissociated')), (STATUS_DELETED, _('Deleted'))) MEMBER_STATUS_DICT = tupletuple_to_dict(MEMBER_STATUS) BILL_EMAIL = 'E' BILL_PAPER = 'P' BILL_SMS = 'S' BILL_TYPES = ( (BILL_EMAIL, _('Email')), (BILL_PAPER, _('Paper')), (BILL_SMS, _('SMS')) ) BILL_TYPES_DICT = tupletuple_to_dict(BILL_TYPES) def logging_log_change(sender, instance, created, **kwargs): operation = "created" if created else "modified" logger.info('%s %s: %s' % (sender.__name__, operation, repr(instance))) def _get_logs(self): '''Gets the log entries related to this object. Getter to be used as property instead of GenericRelation''' my_class = self.__class__ ct = ContentType.objects.get_for_model(my_class) object_logs = ct.logentry_set.filter(object_id=self.id) return object_logs class Contact(models.Model): logs = property(_get_logs) last_changed = models.DateTimeField(auto_now=True, verbose_name=_('contact changed')) created = models.DateTimeField(auto_now_add=True, verbose_name=_('contact created')) first_name = models.CharField(max_length=128, verbose_name=_('First name'), blank=True) # Primary first name given_names = models.CharField(max_length=128, verbose_name=_('Given names'), blank=True) last_name = models.CharField(max_length=128, verbose_name=_('Last name'), blank=True) organization_name = models.CharField(max_length=256, verbose_name=_('Organization name'), blank=True) street_address = models.CharField(max_length=128, verbose_name=_('Street address')) postal_code = models.CharField(max_length=10, verbose_name=_('Postal code')) post_office = models.CharField(max_length=128, verbose_name=_('Post office')) country = models.CharField(max_length=128, verbose_name=_('Country')) phone = models.CharField(max_length=64, blank=True, verbose_name=_('Phone')) sms = models.CharField(max_length=64, blank=True, verbose_name=_('SMS number')) email = models.EmailField(blank=True, verbose_name=_('E-mail')) homepage = models.URLField(blank=True, verbose_name=_('Homepage')) def save(self, *args, **kwargs): if self.homepage: if '://' not in self.homepage: self.homepage = "http://{homepage}".format(homepage=self.homepage) if self.organization_name: if len(self.organization_name) < 5: raise Exception("Organization's name should be at least 5 characters.") super(Contact, self).save(*args, **kwargs) def delete_if_no_references(self, user): person = Q(person=self) org = Q(organization=self) billing = Q(billing_contact=self) tech = Q(tech_contact=self) refs = Membership.objects.filter(person | org | billing | tech) if refs.count() == 0: logger.info("Deleting contact %s: no more references (by %s)" % ( str(self), str(user))) self.logs.delete() self.delete() def find_memberid(self): # Is there better way to find a memberid? try: return Membership.objects.get(person_id=self.id).id except Membership.DoesNotExist: pass try: return Membership.objects.get(organization_id=self.id).id except Membership.DoesNotExist: pass try: return Membership.objects.get(billing_contact_id=self.id).id except Membership.DoesNotExist: pass try: return Membership.objects.get(tech_contact_id=self.id).id except Membership.DoesNotExist: return None def email_to(self): if self.email: return format_email(name=self.name(), email=self.email) return None def name(self): if self.organization_name: return self.organization_name else: return '%s %s' % (self.first_name, self.last_name) def __str__(self): if self.organization_name: return self.organization_name else: return '%s %s' % (self.last_name, self.first_name) class MembershipManager(models.Manager): def sort(self, sortkey): qs = MembershipQuerySet(self.model) return qs.sort(sortkey) def get_query_set(self): return MembershipQuerySet(self.model) class MembershipQuerySet(QuerySet): def sort(self, sortkey): sortkey = sortkey.strip() reverse = False if sortkey == "name": return self.order_by("person__first_name", "organization__organization_name") elif sortkey == "-name": return self.order_by("person__first_name", "organization__organization_name" ).reverse() elif sortkey == "last_name": return self.order_by("person__last_name", "organization__organization_name") elif sortkey == "-last_name": return self.order_by("person__last_name", "organization__organization_name").reverse() return self.order_by(sortkey) class Membership(models.Model): class Meta: permissions = ( ("read_members", "Can read member details"), ("manage_members", "Can change details, pre-/approve"), ("delete_members", "Can delete members"), ("dissociate_members", "Can dissociate members"), ("request_dissociation_for_member", "Can request dissociation for member"), ) logs = property(_get_logs) type = models.CharField(max_length=1, choices=MEMBER_TYPES, verbose_name=_('Membership type')) status = models.CharField(max_length=1, choices=MEMBER_STATUS, default=STATUS_NEW, verbose_name=_('Membership status')) created = models.DateTimeField(auto_now_add=True, verbose_name=_('Membership created')) approved = models.DateTimeField(blank=True, null=True, verbose_name=_('Membership approved')) last_changed = models.DateTimeField(auto_now=True, verbose_name=_('Membership changed')) public_memberlist = models.BooleanField(_('Show in the memberlist'), default=False) municipality = models.CharField(_('Home municipality'), max_length=128, blank=True) nationality = models.CharField(_('Nationality'), max_length=128) birth_year = models.IntegerField(_('Year of birth'), null=True, blank=True) organization_registration_number = models.CharField(_('Business ID'), blank=True, max_length=15) person = models.ForeignKey('Contact', related_name='person_set', verbose_name=_('Person'), blank=True, null=True, on_delete=models.PROTECT) billing_contact = models.ForeignKey('Contact', related_name='billing_set', verbose_name=_('Billing contact'), blank=True, null=True, on_delete=models.PROTECT) tech_contact = models.ForeignKey('Contact', related_name='tech_contact_set', verbose_name=_('Technical contact'), blank=True, null=True, on_delete=models.PROTECT) organization = models.ForeignKey('Contact', related_name='organization_set', verbose_name=_('Organization'), blank=True, null=True, on_delete=models.PROTECT) extra_info = models.TextField(blank=True, verbose_name=_('Additional information')) locked = models.DateTimeField(blank=True, null=True, verbose_name=_('Membership locked')) dissociation_requested = models.DateTimeField(blank=True, null=True, verbose_name=_('Dissociation requested')) dissociated = models.DateTimeField(blank=True, null=True, verbose_name=_('Member dissociated')) objects = MembershipManager() def primary_contact(self): if self.organization: return self.organization else: return self.person def name(self): if self.primary_contact(): return self.primary_contact().name() else: return str(self) def email(self): return self.primary_contact().email def email_to(self): return self.primary_contact().email_to() def get_billing_contact(self): '''Resolves the actual billing contact. Useful for billing details.''' if self.billing_contact: return self.billing_contact elif self.person: return self.person else: return self.organization def billing_email(self): '''Finds the best email address for billing''' contact_priority_list = [self.billing_contact, self.person, self.organization] for contact in contact_priority_list: if contact: if contact.email: return str(contact.email_to()) raise BillingEmailNotFound("Neither billing or administrative contact " "has an email address") # https://docs.djangoproject.com/en/dev/ref/models/instances/#django.db.models.Model.clean def clean(self): if self.type not in list(MEMBER_TYPES_DICT.keys()): raise ValidationError("Illegal member type '%s'" % self.type) if self.status not in list(MEMBER_STATUS_DICT.keys()): raise ValidationError("Illegal member status '%s'" % self.status) if self.status != STATUS_DELETED: if self.type == 'O' and self.person: raise ValidationError("Organization may not have a person contact.") if self.type != 'O' and self.organization: raise ValidationError("Non-organization may not have an organization contact.") if self.person and self.organization: raise ValidationError("Person-contact and organization-contact are mutually exclusive.") if not self.person and not self.organization: raise ValidationError("Either Person-contact or organization-contact must be defined.") if not self.municipality: raise ValidationError("Municipality can't be null.") else: if self.person or self.organization or self.billing_contact or self.tech_contact: raise ValidationError("A membership may not have any contacts if it is deleted.") def save(self, *args, **kwargs): try: self.full_clean() except ValidationError as ve: raise ve super(Membership, self).save(*args, **kwargs) def _change_status(self, new_status): # Allowed transitions From State: [TO STATES] _allowed_transitions = { STATUS_NEW: [ STATUS_PREAPPROVED, STATUS_DELETED ], STATUS_PREAPPROVED: [ STATUS_APPROVED, STATUS_DELETED ], STATUS_APPROVED: [ STATUS_DIS_REQUESTED, STATUS_DISASSOCIATED ], STATUS_DISASSOCIATED: [ STATUS_DELETED ], STATUS_DIS_REQUESTED: [ STATUS_DISASSOCIATED, STATUS_APPROVED ], } with transaction.atomic(): me = Membership.objects.select_for_update().filter(pk=self.pk)[0] current_status = me.status if new_status == current_status: raise MembershipAlreadyStatus("Membership is already {status}".format(status=new_status)) elif new_status not in _allowed_transitions[current_status]: raise MembershipOperationError("Membership status can't change from {current} to {new}".format( current=current_status, new=new_status)) me.status = new_status if new_status == STATUS_APPROVED: # Preserve original approve time (cancel dissociation) if not me.approved: me.approved = datetime.now() me.dissociation_requested = None elif new_status == STATUS_DIS_REQUESTED: me.dissociation_requested = datetime.now() elif new_status == STATUS_DISASSOCIATED: me.dissociated = datetime.now() me.cancel_outstanding_bills() elif new_status == STATUS_DELETED: me.person = None me.billing_contact = None me.tech_contact = None me.organization = None me.municipality = '' me.birth_year = None me.organization_registration_number = '' me.save() self.refresh_from_db() def preapprove(self, user): assert user is not None self._change_status(new_status=STATUS_PREAPPROVED) log_change(self, user, change_message="Preapproved") ret_items = send_preapprove_email.send_robust(self.__class__, instance=self, user=user) for item in ret_items: sender, error = item if error is not None: raise error logger.info("Membership {membership} preapproved.".format(membership=self)) def approve(self, user): assert user is not None self._change_status(new_status=STATUS_APPROVED) log_change(self, user, change_message="Approved") def request_dissociation(self, user): assert user is not None self._change_status(new_status='S') log_change(self, user, change_message="Dissociation requested") def cancel_dissociation_request(self, user): assert user is not None if not self.approved: raise MembershipOperationError("Can't cancel dissociation request unless approved as member") self._change_status(new_status=STATUS_APPROVED) log_change(self, user, change_message="Dissociation request state reverted") def dissociate(self, user): assert user is not None self._change_status(new_status=STATUS_DISASSOCIATED) log_change(self, user, change_message="Dissociated") def cancel_outstanding_bills(self): try: latest_billingcycle = self.billingcycle_set.latest('start') if not latest_billingcycle.is_paid: bill = latest_billingcycle.first_bill() if not bill.is_reminder(): CancelledBill.objects.get_or_create(bill=bill) logger.info("Created CancelledBill for Member #{member.pk} bill {bill.pk}".format( bill=bill, member=bill.billingcycle.membership)) except ObjectDoesNotExist: return # No billing cycle, no need to cancel bills @transaction.atomic def delete_membership(self, user): assert user is not None me = Membership.objects.select_for_update().filter(pk=self.pk)[0] if me.status == STATUS_DELETED: raise MembershipAlreadyStatus("Membership already deleted") elif me.status == STATUS_NEW: # must be imported here due to cyclic imports from services.models import Service logger.info("Deleting services of the membership application %s." % repr(self)) for service in Service.objects.filter(owner=self): service.delete() logger.info("Deleting aliases of the membership application %s." % repr(self)) for alias in self.alias_set.all(): alias.delete() else: logger.info("Not deleting services of membership %s." % repr(self)) logger.info("Expiring aliases of membership %s." % repr(self)) for alias in self.alias_set.all(): alias.expire() contacts = [self.person, self.billing_contact, self.tech_contact, self.organization] self._change_status(new_status=STATUS_DELETED) for contact in contacts: if contact is not None: contact.delete_if_no_references(user) log_change(self, user, change_message="Deleted") def duplicates(self): """ Finds duplicates of memberships, looks for similar names, emails, phone numbers and contact details. Returns a QuerySet object that doesn't include the membership of which duplicates are search for itself. """ matches = Membership.objects.none() if self.person and not self.organization: # Matches by first or last name matches |= Membership.objects.filter( person__first_name__icontains=self.person.first_name.strip(), person__last_name__icontains=self.person.last_name.strip()) # Matches by email address matches |= Membership.objects.filter( person__email__contains=self.person.email.strip()) # Matches by phone or SMS number phone_number = self.person.phone.strip() sms_number = self.person.sms.strip() if phone_number: matches |= Membership.objects.filter(person__phone__icontains=phone_number) if sms_number: matches |= Membership.objects.filter(person__sms__icontains=sms_number) elif self.organization and not self.person: organization_name = self.organization.organization_name.strip() matches = Membership.objects.filter( organization__organization_name__icontains=organization_name) return matches.exclude(id=self.id) @classmethod def search(cls, query): person_contacts = Contact.objects org_contacts = Contact.objects # Split into words and remove duplicates words = set(query.split(" ")) # Each word narrows the search further for word in words: # Exact match for membership id (for Django admin) if word.startswith('#'): try: mid = int(word[1:]) person_contacts = person_contacts.filter(person_set__id=mid) org_contacts = org_contacts.filter(organization_set__id=mid) continue except ValueError: pass # Continue processing normal search # Exact word match when word is "word" if word.startswith('"') and word.endswith('"'): word = word[1:-1] # Search query for people f_q = Q(first_name__iexact=word) l_q = Q(last_name__iexact=word) g_q = Q(given_names__iexact=word) person_contacts = person_contacts.filter(f_q | l_q | g_q) # Search for organizations o_q = Q(organization_name__iexact=word) org_contacts = org_contacts.filter(o_q) else: # Common search parameters email_q = Q(email__icontains=word) phone_q = Q(phone__icontains=word) sms_q = Q(sms__icontains=word) common_q = email_q | phone_q | sms_q # Search query for people f_q = Q(first_name__icontains=word) l_q = Q(last_name__icontains=word) g_q = Q(given_names__icontains=word) person_contacts = person_contacts.filter(f_q | l_q | g_q | common_q) # Search for organizations o_q = Q(organization_name__icontains=word) org_contacts = org_contacts.filter(o_q | common_q) # Finally combine matches; all membership for which there are matching # contacts or aliases person_q = Q(person__in=person_contacts) org_q = Q(organization__in=org_contacts) alias_q = Q(alias__name__in=words) qs = Membership.objects.filter(person_q | org_q | alias_q).distinct() qs = qs.order_by("organization__organization_name", "person__last_name", "person__first_name") return qs @classmethod def paper_reminder_sent_unpaid_after(cls, days=14): unpaid_filter = Q(billingcycle__is_paid=False) type_filter = Q(type=BILL_PAPER) date_filter = Q(due_date__lt=datetime.now() - timedelta(days=days)) not_deleted_filter = Q(billingcycle__membership__status__exact=STATUS_APPROVED) bill_qs = Bill.objects.filter(unpaid_filter, type_filter, date_filter, not_deleted_filter) membership_ids = set() for bill in bill_qs: membership_ids.add(bill.billingcycle.membership.id) return Membership.objects.filter(id__in=membership_ids) def __repr__(self): return "<Membership(%s): %s (%i)>" % (self.type, str(self), self.id) def __str__(self): if self.organization: return str(self.organization) else: if self.person: return str(self.person) else: return "#%d" % self.id class Fee(models.Model): type = models.CharField(max_length=1, choices=MEMBER_TYPES, verbose_name=_('Fee type')) start = models.DateTimeField(_('Valid from date')) sum = models.DecimalField(_('Sum'), max_digits=6, decimal_places=2) vat_percentage = models.IntegerField(_('VAT percentage')) def __str__(self): return "Fee for %s, %s euros, %s%% VAT, %s--" % \ (self.get_type_display(), str(self.sum), str(self.vat_percentage), str(self.start)) class BillingCycleManager(models.Manager): def get_query_set(self): return BillingCycleQuerySet(self.model) class BillingCycleQuerySet(QuerySet): def sort(self, sortkey): sortkey = sortkey.strip() reverse = False if sortkey == "name": return self.order_by("membership__person__first_name", "membership__organization__organization_name") elif sortkey == "-name": return self.order_by("membership__person__first_name", "memership__organization__organization_name").reverse() elif sortkey == "last_name": return self.order_by("membership__person__last_name", "membership__organization__organization_name") elif sortkey == "-last_name": return self.order_by("membership__person__last_name", "membership__organization__organization_name" ).reverse() elif sortkey == "reminder_count": return self.annotate(reminder_sum=Sum('bill__reminder_count') ).order_by('reminder_sum') elif sortkey == "-reminder_count": return self.annotate(reminder_sum=Sum('bill__reminder_count') ).order_by('reminder_sum').reverse() return self.order_by(sortkey) class BillingCycle(models.Model): class Meta: permissions = ( ("read_bills", "Can read billing details"), ("manage_bills", "Can manage billing"), ) membership = models.ForeignKey('Membership', verbose_name=_('Membership'), on_delete=models.PROTECT) start = models.DateTimeField(default=django.utils.timezone.now, verbose_name=_('Start')) end = models.DateTimeField(verbose_name=_('End')) sum = models.DecimalField(_('Sum'), max_digits=6, decimal_places=2) # This limits sum to 9999,99 is_paid = models.BooleanField(default=False, verbose_name=_('Is paid')) # NOT an integer since it can begin with 0 XXX: format reference_number = models.CharField(max_length=64, verbose_name=_('Reference number')) logs = property(_get_logs) objects = BillingCycleManager() def first_bill_sent_on(self): try: first_sent_date = self.bill_set.order_by('created')[0].created return first_sent_date except IndexError: # No bills sent yet return None def last_bill(self): try: return self.bill_set.latest("due_date") except ObjectDoesNotExist: return None def first_bill(self): try: return self.bill_set.order_by('due_date')[0] except IndexError: return None def is_first_bill_late(self): if self.is_paid: return False try: first_due_date = self.bill_set.order_by('due_date')[0].due_date except IndexError: # No bills sent yet return False if datetime.now() > first_due_date: return True return False def is_last_bill_late(self): if self.is_paid or self.last_bill() is None: return False if datetime.now() > self.last_bill().due_date: return True return False def amount_paid(self): data = self.payment_set.aggregate(Sum('amount'))['amount__sum'] if data is None: data = Decimal('0') return data def update_is_paid(self, user=None): was_paid = self.is_paid total_paid = self.amount_paid() if not was_paid and total_paid >= self.sum: self.is_paid = True self.save() logger.info("BillingCycle %s marked as paid, total paid: %.2f." % ( repr(self), total_paid)) elif was_paid and total_paid < self.sum: self.is_paid = False self.save() logger.info("BillingCycle %s marked as unpaid, total paid: %.2f." % ( repr(self), total_paid)) if user: log_change(self, user, change_message="Marked as paid") def get_fee(self): for_this_type = Q(type=self.membership.type) not_before_start = Q(start__lte=self.start) fees = Fee.objects.filter(for_this_type, not_before_start) valid_fee = fees.latest('start').sum return valid_fee def get_vat_percentage(self): for_this_type = Q(type=self.membership.type) not_before_start = Q(start__lte=self.start) fees = Fee.objects.filter(for_this_type, not_before_start) vat_percentage = fees.latest('start').vat_percentage return vat_percentage def is_cancelled(self): first_bill = self.first_bill() if first_bill: return first_bill.is_cancelled() return False def get_rf_reference_number(self): """ Get reference number in international RFXX format. For example 218012 is formatted as RF28218012 where 28 is checksum :return: RF formatted reference number """ # Magic 2715 is "RF" in number encoded format and # zeros are placeholders for modulus calculation. reference_number_int = int(''.join(self.reference_number.split()) + '271500') modulo = reference_number_int % 97 return "RF%02d%s" % (98 - modulo, reference_number_int) @classmethod def get_reminder_billingcycles(cls, memberid=None): """ Get queryset for BillingCycles with missing payments and witch have 2 or more bills already sent. :param memberid: :return: """ if not settings.ENABLE_REMINDERS: return cls.objects.none() qs = cls.objects # Single membership case if memberid: logger.info('memberid: %s' % memberid) qs = qs.filter(membership__id=memberid) qs = qs.exclude(bill__type=BILL_PAPER) return qs # For all memberships in Approved state qs = qs.annotate(bills=Count('bill')) qs = qs.filter(bills__gt=2, is_paid__exact=False, membership__status=STATUS_APPROVED, membership__id__gt=-1) qs = qs.exclude(bill__type=BILL_PAPER) qs = qs.order_by('start') return qs @classmethod def get_pdf_reminders(cls, memberid=None): buffer = BytesIO() cycles = cls.create_paper_reminder_list(memberid) if len(cycles) == 0: return None create_reminder_pdf(cycles, buffer, payments=Payment) pdf_content = buffer.getvalue() buffer.close() return pdf_content @classmethod def create_paper_reminder_list(cls, memberid=None): """ Create list of BillingCycles with missing payments and which already don't have paper bill. :param memberid: optional member id :return: list of billingcycles """ datalist = [] for cycle in cls.get_reminder_billingcycles(memberid).all(): # check if paper reminder already sent cont = False for bill in cycle.bill_set.all(): if bill.type == BILL_PAPER: cont = True break if cont: continue datalist.append(cycle) return datalist def end_date(self): """Logical end date This is one day before actual end since actual end is a timestamp. The end date is the previous day. E.g. 2015-01-01 -- 2015-12-31 """ day = timedelta(days=1) return self.end.date()-day def __str__(self): return str(self.start.date()) + "--" + str(self.end_date()) def save(self, *args, **kwargs): if not self.end: self.end = self.start + timedelta(days=365) if (self.end.day != self.start.day): # Leap day self.end += timedelta(days=1) if not self.reference_number: self.reference_number = generate_membership_bill_reference_number(self.membership.id, self.start.year) if not self.sum: self.sum = self.get_fee() super(BillingCycle, self).save(*args, **kwargs) cache_storage = FileSystemStorage(location=settings.CACHE_DIRECTORY) class CancelledBill(models.Model): """List of bills that have been cancelled""" bill = models.OneToOneField('Bill', verbose_name=_('Original bill'), on_delete=models.PROTECT) created = models.DateTimeField(auto_now_add=True, verbose_name=_('Created')) exported = models.BooleanField(default=False) logs = property(_get_logs) def save(self, *args, **kwargs): if self.bill.is_reminder(): raise ValueError("Can not cancel reminder bills") super(CancelledBill, self).save(*args, **kwargs) class Bill(models.Model): billingcycle = models.ForeignKey(BillingCycle, verbose_name=_('Cycle'), on_delete=models.PROTECT) reminder_count = models.IntegerField(default=0, verbose_name=_('Reminder count')) due_date = models.DateTimeField(verbose_name=_('Due date')) created = models.DateTimeField(auto_now_add=True, verbose_name=_('Created')) last_changed = models.DateTimeField(auto_now=True, verbose_name=_('Last changed')) pdf_file = models.FileField(upload_to="bill_pdfs", storage=cache_storage, null=True) type = models.CharField(max_length=1, choices=BILL_TYPES, blank=False, null=False, verbose_name=_('Bill type'), default='E') logs = property(_get_logs) def is_due(self): return self.due_date < datetime.now() def __str__(self): return '{sent_on} {date}'.format(sent_on=_('Sent on'), date=str(self.created)) def save(self, *args, **kwargs): if not self.due_date: self.due_date = datetime.now() + timedelta(days=settings.BILL_DAYS_TO_DUE) # Second is from reminder_count so that tests can assume due_date # is monotonically increasing self.due_date = self.due_date.replace(hour=23, minute=59, second=self.reminder_count % 60) super(Bill, self).save(*args, **kwargs) def is_reminder(self): return self.reminder_count > 0 def is_cancelled(self): try: if self.cancelledbill is not None: return True except CancelledBill.DoesNotExist: pass return False # FIXME: different template based on class? should this code be here? def render_as_text(self): """ Renders the object as text suitable for sending as e-mail. """ membership = self.billingcycle.membership vat = Decimal(self.billingcycle.get_vat_percentage()) / Decimal(100) if not self.is_reminder(): non_vat_amount = (self.billingcycle.sum / (Decimal(1) + vat)) return render_to_string('membership/bill.txt', { 'membership_type' : MEMBER_TYPES_DICT[membership.type], 'membership_type_raw' : membership.type, 'bill_id': self.id, 'member_id': membership.id, 'member_name': membership.name(), 'billing_contact': membership.billing_contact, 'billing_name': str(membership.get_billing_contact()), 'street_address': membership.get_billing_contact().street_address, 'postal_code': membership.get_billing_contact().postal_code, 'post_office': membership.get_billing_contact().post_office, 'country': membership.get_billing_contact().country, 'billingcycle': self.billingcycle, 'iban_account_number': settings.IBAN_ACCOUNT_NUMBER, 'bic_code': settings.BIC_CODE, 'due_date': self.due_date, 'today': datetime.now(), 'reference_number': group_right(self.billingcycle.reference_number), 'sum': self.billingcycle.sum, 'vat_amount': vat * non_vat_amount, 'non_vat_amount': non_vat_amount, 'vat_percentage': self.billingcycle.get_vat_percentage(), 'barcode': barcode_4(iban = settings.IBAN_ACCOUNT_NUMBER, refnum = self.billingcycle.reference_number, duedate = self.due_date, euros = self.billingcycle.sum) }) else: amount_paid = self.billingcycle.amount_paid() sum = self.billingcycle.sum - amount_paid non_vat_amount = sum / (Decimal(1) + vat) return render_to_string('membership/reminder.txt', { 'membership_type' : MEMBER_TYPES_DICT[membership.type], 'membership_type_raw' : membership.type, 'bill_id': self.id, 'member_id': membership.id, 'member_name': membership.name(), 'billing_contact': membership.billing_contact, 'billing_name': str(membership.get_billing_contact()), 'street_address': membership.get_billing_contact().street_address, 'postal_code': membership.get_billing_contact().postal_code, 'post_office': membership.get_billing_contact().post_office, 'municipality': membership.municipality, 'billing_email': membership.get_billing_contact().email, 'email': membership.primary_contact().email, 'billingcycle': self.billingcycle, 'iban_account_number': settings.IBAN_ACCOUNT_NUMBER, 'bic_code': settings.BIC_CODE, 'today': datetime.now(), 'latest_recorded_payment': Payment.latest_payment_date(), 'reference_number': group_right(self.billingcycle.reference_number), 'original_sum': self.billingcycle.sum, 'amount_paid': amount_paid, 'sum': sum, 'vat_amount': vat * non_vat_amount, 'non_vat_amount': non_vat_amount, 'vat_percentage': self.billingcycle.get_vat_percentage(), 'barcode': barcode_4(iban = settings.IBAN_ACCOUNT_NUMBER, refnum = self.billingcycle.reference_number, duedate = None, euros = sum) }) def generate_pdf(self): """ Generate pdf and return pdf content """ return get_bill_pdf(self, payments=Payment) # FIXME: Should save sending date def send_as_email(self): membership = self.billingcycle.membership if self.billingcycle.sum > 0: ret_items = send_as_email.send_robust(self.__class__, instance=self) for item in ret_items: sender, error = item if error != None: logger.error("%s" % traceback.format_exc()) logger.exception("Error while sending email") raise error else: self.billingcycle.is_paid = True logger.info('Bill not sent: membership fee zero for %s: %s' % ( membership.email, repr(Bill))) self.billingcycle.save() def bill_subject(self): if not self.is_reminder(): subject = settings.BILL_SUBJECT else: subject = settings.REMINDER_SUBJECT return subject.format(id=self.id) def reference_number(self): return self.billingcycle.reference_number class Payment(models.Model): class Meta: permissions = ( ("can_import_payments", "Can import payment data"), ) """ Payment object for billing """ # While Payment refers to BillingCycle, the architecture scales to support # recording payments that are not related to any billingcycle for future # extension billingcycle = models.ForeignKey('BillingCycle', verbose_name=_('Cycle'), null=True, on_delete=models.PROTECT) ignore = models.BooleanField(default=False, verbose_name=_('Ignored payment')) comment = models.CharField(max_length=64, verbose_name=_('Comment'), blank=True) reference_number = models.CharField(max_length=64, verbose_name=_('Reference number'), blank=True) message = models.CharField(max_length=256, verbose_name=_('Message'), blank=True) transaction_id = models.CharField(max_length=30, verbose_name=_('Transaction id'), unique=True) payment_day = models.DateTimeField(verbose_name=_('Payment day')) # This limits sum to 9999999.99 amount = models.DecimalField(max_digits=9, decimal_places=2, verbose_name=_('Amount')) type = models.CharField(max_length=64, verbose_name=_('Type')) payer_name = models.CharField(max_length=64, verbose_name=_('Payer name')) duplicate = models.BooleanField(verbose_name=_('Duplicate payment'), blank=False, null=False, default=False) logs = property(_get_logs) def __str__(self): return "%.2f euros (reference '%s', date '%s')" % (self.amount, self.reference_number, self.payment_day) def attach_to_cycle(self, cycle, user=None): if self.billingcycle: raise PaymentAttachedError("Payment %s already attached to BillingCycle %s." % (repr(self), repr(cycle))) self.billingcycle = cycle self.ignore = False self.save() logger.info("Payment %s attached to member %s cycle %s." % (repr(self), cycle.membership.id, repr(cycle))) if user: log_change(self, user, change_message="Attached to billing cycle") cycle.update_is_paid(user=user) def detach_from_cycle(self, user=None): if not self.billingcycle: return cycle = self.billingcycle logger.info("Payment %s detached from cycle %s." % (repr(self), repr(cycle))) self.billingcycle = None self.save() if user: log_change(self, user, change_message="Detached from billing cycle") cycle.update_is_paid() def send_duplicate_payment_notice(self, user, **kwargs): if not user: raise Exception('send_duplicate_payment_notice user objects as parameter') billingcycle = BillingCycle.objects.get(reference_number=self.reference_number) if billingcycle.sum > 0: ret_items = send_duplicate_payment_notice.send_robust(self.__class__, instance=self, user=user, billingcycle=billingcycle) for item in ret_items: sender, error = item if error is not None: logger.error("%s" % traceback.format_exc()) raise error log_change(self, user, change_message="Duplicate payment notice sent") @classmethod def latest_payment_date(cls): try: return Payment.objects.latest("payment_day").payment_day except Payment.DoesNotExist: return None class ApplicationPoll(models.Model): """ Store statistics taken from membership application "where did you hear about us" poll. """ membership = models.ForeignKey('Membership', verbose_name=_('Membership'), on_delete=models.PROTECT) date = models.DateTimeField(auto_now=True, verbose_name=_('Timestamp')) answer = models.CharField(max_length=512, verbose_name=_('Service specific data')) models.signals.post_save.connect(logging_log_change, sender=Membership) models.signals.post_save.connect(logging_log_change, sender=Contact) models.signals.post_save.connect(logging_log_change, sender=BillingCycle) models.signals.post_save.connect(logging_log_change, sender=Bill) models.signals.post_save.connect(logging_log_change, sender=Fee) models.signals.post_save.connect(logging_log_change, sender=Payment) # These are registered here due to import madness and general clarity send_as_email.connect(bill_sender, sender=Bill, dispatch_uid="email_bill") send_preapprove_email.connect(preapprove_email_sender, sender=Membership, dispatch_uid="preapprove_email") send_duplicate_payment_notice.connect(duplicate_payment_sender, sender=Payment, dispatch_uid="duplicate_payment_notice")

41.149767

128

0.628651

40,604

0.917895

0

6,739

0.152342

0

8,305

0.187743

830421c0eef174df1951cc79db82af6869f9e1bc

177

py

Python

napari_imc/io/__init__.py

neuromusic/napari-imc

ce2ff998b33b49f19a786585cc2cb8e59db74c24

[ "MIT" ]

4

2021-01-29T15:11:37.000Z

2021-03-01T02:04:24.000Z

napari_imc/io/__init__.py

neuromusic/napari-imc

ce2ff998b33b49f19a786585cc2cb8e59db74c24

[ "MIT" ]

25

2021-01-19T01:49:13.000Z

2022-02-09T10:46:41.000Z

napari_imc/io/__init__.py

neuromusic/napari-imc

ce2ff998b33b49f19a786585cc2cb8e59db74c24

[ "MIT" ]

3

2021-01-29T17:31:05.000Z

2022-03-25T10:23:32.000Z

from .imaxt import ImaxtFileReader from .mcd import McdFileReader from .txt import TxtFileReader __all__ = [ 'ImaxtFileReader', 'McdFileReader', 'TxtFileReader', ]

17.7

34

0.734463

0

47

0.265537

83043d6bcc47235264f0457736e61baf87cbac95

2,449

py

Python

eval.py

ldzhangyx/TCN-for-beat-tracking

8e09ba5b2f222a4944a8bd039987a01240ae778d

[ "BSD-3-Clause" ]

3

2021-03-22T01:59:52.000Z

2022-01-22T11:08:56.000Z

eval.py

ldzhangyx/TCN-for-beat-tracking

8e09ba5b2f222a4944a8bd039987a01240ae778d

[ "BSD-3-Clause" ]

1

2021-06-21T19:14:35.000Z

eval.py

ldzhangyx/TCN-for-beat-tracking

8e09ba5b2f222a4944a8bd039987a01240ae778d

[ "BSD-3-Clause" ]

1

2021-03-22T01:59:57.000Z

import torch from torch.utils.data import Dataset import numpy as np import os import pickle from madmom.features import DBNBeatTrackingProcessor import torch from model import BeatTrackingNet from utils import init_single_spec from mir_eval.beat import evaluate from data import BallroomDataset from beat_tracker import predict_beats_from_spectrogram import yaml import sys import pdb # import config with open('config.yaml', 'r') as f: config = yaml.load(f, Loader=yaml.FullLoader) def evaluate_model( model_checkpoint, spectrogram, ground_truth): """ Given a model checkpoint, a single spectrogram, and the corresponding ground truth, evaluate the model's performance on all beat tracking metrics offered by mir_eval.beat. """ prediction = predict_beats_from_spectrogram( spectrogram, model_checkpoint) scores = evaluate(ground_truth, prediction) return scores def evaluate_model_on_dataset( model_checkpoint, dataset, ground_truths): """ Run through a whole instance of torch.utils.data.Dataset and compare the model's predictions to the given ground truths. """ # Create dicts to store scores and histories mean_scores = {} running_scores = {} # Iterate over dataset for i in range(len(dataset)): spectrogram = dataset[i]["spectrogram"].unsqueeze(0) ground_truth = ground_truths[i] scores = evaluate_model( model_checkpoint, spectrogram, ground_truth) beat_scores = scores for metric in beat_scores: if metric not in running_scores: running_scores[metric] = 0.0 running_scores[metric] += beat_scores[metric] # Each iteration, pass our current index and our running score total # to a print callback function. print(f"{i}, {str(running_scores)}") # After all iterations, calculate mean scores. for metric in running_scores: mean_scores[metric] = running_scores[metric] / (i + 1) # Return a dictionary of helpful information return { "total_examples": i + 1, "scores": mean_scores } dataset = BallroomDataset() ground_truths = (dataset.get_ground_truth(i) for i in range(len(dataset))) # Run evaluation evaluate_model_on_dataset(config['default_checkpoint_path'], dataset, ground_truths)

25.510417

79

0.685178

0

728

0.297264

830448984e5a77e90d22cacc683d54197d1adc44

130,468

py

Python

pycity_calc/cities/scripts/city_generator/city_generator.py

RWTH-EBC/pyCity_calc

99fd0dab7f9a9030fd84ba4715753364662927ec

[ "MIT" ]

4

2020-06-22T14:14:25.000Z

2021-11-08T11:47:01.000Z

pycity_calc/cities/scripts/city_generator/city_generator.py

RWTH-EBC/pyCity_calc

99fd0dab7f9a9030fd84ba4715753364662927ec

[ "MIT" ]

4

2019-08-28T19:42:28.000Z

2019-08-28T19:43:44.000Z

pycity_calc/cities/scripts/city_generator/city_generator.py

RWTH-EBC/pyCity_calc

99fd0dab7f9a9030fd84ba4715753364662927ec

[ "MIT" ]

null

# coding=utf-8 """ Script to generate city object. """ from __future__ import division import os import numpy as np import pickle import warnings import random import datetime import shapely.geometry.point as point import pycity_base.classes.Weather as weath import pycity_base.classes.demand.SpaceHeating as SpaceHeating import pycity_base.classes.demand.ElectricalDemand as ElectricalDemand import pycity_base.classes.demand.Apartment as Apartment import pycity_base.classes.demand.DomesticHotWater as DomesticHotWater import pycity_base.classes.demand.Occupancy as occup import pycity_calc.environments.timer as time # import pycity_calc.environments.market as price import pycity_calc.environments.germanmarket as germanmarket import pycity_calc.environments.environment as env import pycity_calc.environments.co2emissions as co2 import pycity_calc.buildings.building as build_ex import pycity_calc.cities.city as city import pycity_calc.visualization.city_visual as citvis import pycity_calc.toolbox.modifiers.slp_th_manipulator as slpman import pycity_calc.toolbox.teaser_usage.teaser_use as tusage import pycity_calc.toolbox.mc_helpers.user.user_unc_sampling as usunc try: import teaser.logic.simulation.VDI_6007.weather as vdiweather except: # pragma: no cover msg = 'Could not import teaser.logic.simulation.VDI_6007.weather. ' \ 'If you need to use it, install ' \ 'it via pip "pip install TEASER". Alternatively, you might have ' \ 'run into trouble with XML bindings in TEASER. This can happen ' \ 'if you try to re-import TEASER within an active Python console.' \ 'Please close the active Python console and open another one. Then' \ ' try again. You might also be on the wrong TEASER branch ' \ '(without VDI 6007 core).' warnings.warn(msg) def load_data_file_with_spec_demand_data(filename): """ Function loads and returns data from .../src/data/BaseData/Specific_Demand_Data/filename. Filename should hold float (or int) values. Other values (e.g. strings) will be loaded as 'nan'. Parameter --------- filename : str String with name of file, e.g. 'district_data.txt' Returns ------- dataset : numpy array Numpy array with data """ src_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname ( os.path.abspath( __file__))))) input_data_path = os.path.join(src_path, 'data', 'BaseData', 'Specific_Demand_Data', filename) dataset = np.genfromtxt(input_data_path, delimiter='\t', skip_header=1) return dataset def convert_th_slp_int_and_str(th_slp_int): """ Converts thermal slp type integer into string Parameters ---------- th_slp_int : int SLP type integer number Returns ------- th_slp_tag : str SLP type string Annotations ----------- - `HEF` : Single family household - `HMF` : Multi family household - `GBA` : Bakeries - `GBD` : Other services - `GBH` : Accomodations - `GGA` : Restaurants - `GGB` : Gardening - `GHA` : Retailers - `GHD` : Summed load profile business, trade and services - `GKO` : Banks, insurances, public institutions - `GMF` : Household similar businesses - `GMK` : Automotive - `GPD` : Paper and printing - `GWA` : Laundries """ if th_slp_int is None: msg = 'th_slp_int is None. Going to return None.' warnings.warn(msg) return None slp_th_profile_dict_tag = {0: 'HEF', 1: 'HMF', 2: 'GMF', 3: 'GMK', 4: 'GPD', 5: 'GHA', 6: 'GBD', 7: 'GKO', 8: 'GBH', 9: 'GGA', 10: 'GBA', 11: 'GWA', 12: 'GGB', 13: 'GHD'} th_slp_tag = slp_th_profile_dict_tag[th_slp_int] return th_slp_tag def convert_el_slp_int_and_str(el_slp_int): """ Converts el slp type integer into string Parameters ---------- el_slp_int : int SLP type integer number Returns ------- el_slp_tag : str SLP type string Annotations ----------- # 0: H0 : Residential # 1: G0 : Commercial # 2: G1 : Commercial Mo-Sa 08:00 to 18:00 # 3: G2 : Commercial, mainly evening hours # 4: G3 : Commercial 24 hours # 5: G4 : Shop / hairdresser # 6: G5 : Backery # 7: G6 : Commercial, weekend # 8: L0 : Farm # 9: L1 : Farm, mainly cattle and milk # 10: L2 : Other farming """ if el_slp_int is None: msg = 'el_slp_int is None. Going to return None.' warnings.warn(msg) return None slp_el_profile_dict_tag = {0: 'H0', 1: 'G0', 2: 'G1', 3: 'G2', 4: 'G3', 5: 'G4', 6: 'G5', 7: 'G6', 8: 'L0', 9: 'L1', 10: 'L2'} el_slp_tag = slp_el_profile_dict_tag[el_slp_int] return el_slp_tag def convert_method_3_nb_into_str(method_3_nb): """ Converts method_3_nb into string Parameters ---------- method_3_nb : int Number of method 3 Returns ------- method_3_str : str String of method 3 """ if method_3_nb is None: msg = 'method_3_nb is None. Going to return None.' warnings.warn(msg) return None dict_method_3 = {0: 'food_pro', 1: 'metal', 2: 'rest', 3: 'sports', 4: 'repair'} method_3_str = dict_method_3[method_3_nb] return method_3_str def convert_method_4_nb_into_str(method_4_nb): """ Converts method_4_nb into string Parameters ---------- method_4_nb : int Number of method 4 Returns ------- method_4_str : str String of method 4 """ if method_4_nb is None: msg = 'method_4_nb is None. Going to return None.' warnings.warn(msg) return None dict_method_4 = {0: 'metal_1', 1: 'metal_2', 2: 'warehouse'} method_4_str = dict_method_4[method_4_nb] return method_4_str def conv_build_type_nb_to_name(build_type): """ Convert build_type number to name / explanation Parameters ---------- build_type : int Building type number, based on Spec_demands_non_res.txt Returns ------- build_name : str Building name / explanation """ if build_type is None: msg = 'build_type is None. Going to return None for build_name.' warnings.warn(msg) return None dict_b_name = { 0: 'Residential', 1: 'Office (simulation)', 2: 'Main construction work', 3: 'Finishing trade construction work', 4: 'Bank and insurance', 5: 'Public institution', 6: 'Non profit organization', 7: 'Small office buildings', 8: 'Other services', 9: 'Metal', 10: 'Automobile', 11: 'Wood and timber', 12: 'Paper', 13: 'Small retailer for food', 14: 'Small retailer for non-food', 15: 'Large retailer for food', 16: 'Large retailer for non-food', 17: 'Primary school', 18: 'School for physically handicapped', 19: 'High school', 20: 'Trade school', 21: 'University', 22: 'Hotel', 23: 'Restaurant', 24: 'Childrens home', 25: 'Backery', 26: 'Butcher', 27: 'Laundry', 28: 'Farm primary agriculture ', 29: 'Farm with 10 - 49 cattle units', 30: 'Farm with 50 - 100 cattle units', 31: 'Farm with more than 100 cattle units', 32: 'Gardening', 33: 'Hospital', 34: 'Library', 35: 'Prison', 36: 'Cinema', 37: 'Theater', 38: 'Parish hall', 39: 'Sports hall', 40: 'Multi purpose hall', 41: 'Swimming hall', 42: 'Club house', 43: 'Fitness studio', 44: 'Train station smaller 5000m2', 45: 'Train station equal to or larger than 5000m2' } return dict_b_name[build_type] def constrained_sum_sample_pos(n, total): """ Return a randomly chosen list of n positive integers summing to total. Each such list is equally likely to occur. Parameters ---------- n : int Number of chosen integers total : int Sum of all entries of result list Returns ------- results_list : list (of int) List with result integers, which sum up to value 'total' """ dividers = sorted(random.sample(range(1, int(total)), int(n - 1))) list_occ = [a - b for a, b in zip(dividers + [total], [0] + dividers)] for i in range(len(list_occ)): list_occ[i] = int(list_occ[i]) return list_occ def redistribute_occ(occ_list): """ Redistribute occupants in occ_list, so that each apartment is having at least 1 person and maximal 5 persons. Parameters ---------- occ_list Returns ------- occ_list_new : list List holding number of occupants per apartment """ occ_list_new = occ_list[:] if sum(occ_list_new) / len(occ_list_new) > 5: # pragma: no cover msg = 'Average number of occupants per apartment is higher than 5.' \ ' This is not valid for usage of Richardson profile generator.' raise AssertionError(msg) # Number of occupants to be redistributed nb_occ_redist = 0 # Find remaining occupants # ############################################################### for i in range(len(occ_list_new)): if occ_list_new[i] > 5: # Add remaining occupants to nb_occ_redist nb_occ_redist += occ_list_new[i] - 5 # Set occ_list_new entry to 5 persons occ_list_new[i] = 5 if nb_occ_redist == 0: # Return original list return occ_list_new # Identify empty apartments and add single occupant # ############################################################### for i in range(len(occ_list_new)): if occ_list_new[i] == 0: # Add single occupant occ_list_new[i] = 1 # Remove occupant from nb_occ_redist nb_occ_redist -= 1 if nb_occ_redist == 0: # Return original list return occ_list_new # Redistribute remaining occupants # ############################################################### for i in range(len(occ_list_new)): if occ_list_new[i] < 5: # Fill occupants up with remaining occupants for j in range(5 - occ_list_new[i]): # Add single occupant occ_list_new[i] += 1 # Remove single occupant from remaining sum nb_occ_redist -= 1 if nb_occ_redist == 0: # Return original list return occ_list_new if nb_occ_redist: # pragma: no cover raise AssertionError('Not all occupants could be distributed.' 'Check inputs and/or redistribute_occ() call.') def generate_environment(timestep=3600, year_timer=2017, year_co2=2017, try_path=None, location=(51.529086, 6.944689), altitude=55, new_try=False): """ Returns environment object. Total number of timesteps is automatically generated for one year. Parameters ---------- timestep : int Timestep in seconds year_timer : int, optional Chosen year of analysis (default: 2010) (influences initial day for profile generation) year_co2 : int, optional Chose year with specific emission factors (default: 2017) try_path : str, optional Path to TRY weather file (default: None) If set to None, uses default weather TRY file (2010, region 5) location : Tuple, optional (latitude , longitude) of the simulated system's position, (default: (51.529086, 6.944689) for Bottrop, Germany. altitude : float, optional Altitute of location in m (default: 55 - City of Bottrop) new_try : bool, optional Defines, if TRY dataset have been generated after 2017 (default: False) If False, assumes that TRY dataset has been generated before 2017. If True, assumes that TRY dataset has been generated after 2017 and belongs to the new TRY classes. This is important for extracting the correct values from the TRY dataset! Returns ------- environment : object Environment object """ # Create environment timer = time.TimerExtended(timestep=timestep, year=year_timer) weather = weath.Weather(timer, useTRY=True, pathTRY=try_path, location=location, altitude=altitude, new_try=new_try) market = germanmarket.GermanMarket() co2em = co2.Emissions(year=year_co2) environment = env.EnvironmentExtended(timer=timer, weather=weather, prices=market, location=location, co2em=co2em) return environment def generate_res_building_single_zone(environment, net_floor_area, spec_th_demand, th_gen_method, el_gen_method, annual_el_demand=None, el_random=False, use_dhw=False, dhw_method=1, number_occupants=None, build_year=None, mod_year=None, build_type=None, pv_use_area=None, height_of_floors=None, nb_of_floors=None, neighbour_buildings=None, residential_layout=None, attic=None, cellar=None, construction_type=None, dormer=None, dhw_volumen=None, do_normalization=True, slp_manipulate=True, curr_central_ahu=None, dhw_random=False, prev_heat_dev=True, season_mod=None): """ Function generates and returns extended residential building object with single zone. Parameters ---------- environment : object Environment object net_floor_area : float Net floor area of building in m2 spec_th_demand : float Specific thermal energy demand in kWh/m2*a th_gen_method : int Thermal load profile generation method 1 - Use SLP 2 - Load Modelica simulation output profile (only residential) Method 2 is only used for residential buildings. For non-res. buildings, SLPs are generated instead el_gen_method : int, optional Electrical generation method (default: 1) 1 - Use SLP 2 - Generate stochastic load profile (only valid for residential building) annual_el_demand : float, optional Annual electrical energy demand in kWh/a (default: None) el_random : bool, optional Defines, if random value should be chosen from statistics or if average value should be chosen. el_random == True means, use random value. (default: False) use_dhw : bool, optional Boolean to define, if domestic hot water profile should be generated (default: False) True - Generate dhw profile dhw_method : int, optional Domestic hot water profile generation method (default: 1) 1 - Use Annex 42 profile 2 - Use stochastic profile number_occupants : int, optional Number of occupants (default: None) build_year : int, optional Building year of construction (default: None) mod_year : int, optional Last year of modernization of building (default: None) build_type : int, optional Building type (default: None) pv_use_area : float, optional Usable pv area in m2 (default: None) height_of_floors : float average height of single floor nb_of_floors : int Number of floors above the ground neighbour_buildings : int neighbour (default = 0) 0: no neighbour 1: one neighbour 2: two neighbours residential_layout : int type of floor plan (default = 0) 0: compact 1: elongated/complex attic : int type of attic (default = 0) 0: flat roof 1: non heated attic 2: partly heated attic 3: heated attic cellar : int type of cellar (default = 0) 0: no cellar 1: non heated cellar 2: partly heated cellar 3: heated cellar construction_type : str construction type (default = "heavy") heavy: heavy construction light: light construction dormer : str construction type 0: no dormer 1: dormer dhw_volumen : float, optional Volume of domestic hot water in liter per capita and day (default: None). do_normalization : bool, optional Defines, if stochastic profile (el_gen_method=2) should be normalized to given annualDemand value (default: True). If set to False, annual el. demand depends on stochastic el. load profile generation. If set to True, does normalization with annualDemand slp_manipulate : bool, optional Defines, if thermal space heating SLP profile should be modified (default: True). Only used for residential buildings! Only relevant, if th_gen_method == 1 True - Do manipulation False - Use original profile Sets thermal power to zero in time spaces, where average daily outdoor temperature is equal to or larger than 12 °C. Rescales profile to original demand value. curr_central_ahu : bool, optional Defines, if building has air handling unit (AHU) (default: False) dhw_random : bool, optional Defines, if hot water volume per person and day value should be randomized by choosing value from gaussian distribution (20 % standard deviation) (default: False) If True: Randomize value If False: Use reference value prev_heat_dev : bool, optional Defines, if heating devices should be prevented within chosen appliances (default: True). If set to True, DESWH, E-INST, Electric shower, Storage heaters and Other electric space heating are set to zero. Only relevant for el_gen_method == 2 season_mod : float, optional Float to define rescaling factor to rescale annual lighting power curve with cosine wave to increase winter usage and decrease summer usage. Reference is maximum lighting power (default: None). If set to None, do NOT perform rescaling with cosine wave Returns ------- extended_building : object BuildingExtended object """ assert net_floor_area > 0 assert spec_th_demand >= 0 if annual_el_demand is not None: assert annual_el_demand >= 0 else: assert number_occupants is not None assert number_occupants > 0 # Define SLP profiles for residential building with single zone th_slp_type = 'HEF' el_slp_type = 'H0' if number_occupants is not None: assert number_occupants > 0 assert number_occupants <= 5 # Max 5 occupants for stochastic profile if el_gen_method == 2 or (dhw_method == 2 and use_dhw == True): # Generate occupancy profile (necessary for stochastic, el. or # dhw profile) occupancy_object = occup.Occupancy(environment, number_occupants=number_occupants) else: # Generate occupancy object without profile generation # Just used to store information about number of occupants occupancy_object = occup.Occupancy(environment, number_occupants=number_occupants, do_profile=False) else: occupancy_object = None # Dummy object to prevent error with # apartment usage if el_gen_method == 2: warnings.warn('Stochastic el. profile cannot be generated ' + 'due to missing number of occupants. ' + 'SLP is used instead.') # Set el_gen_method to 1 (SLP) el_gen_method = 1 elif dhw_method == 2: raise AssertionError('DHW profile cannot be generated' + 'for residential building without' + 'occupants (stochastic mode).' + 'Please check your input file ' + '(missing number of occupants) ' + 'or disable dhw generation.') if (number_occupants is None and dhw_method == 1 and use_dhw == True): # Set number of occupants to 2 to enable dhw usage number_occupants = 2 # Create space heating demand if th_gen_method == 1: # Use SLP heat_power_curve = SpaceHeating.SpaceHeating(environment, method=1, profile_type=th_slp_type, livingArea=net_floor_area, specificDemand=spec_th_demand) if slp_manipulate: # Do SLP manipulation timestep = environment.timer.timeDiscretization temp_array = environment.weather.tAmbient mod_curve = \ slpman.slp_th_manipulator(timestep, th_slp_curve=heat_power_curve.loadcurve, temp_array=temp_array) heat_power_curve.loadcurve = mod_curve elif th_gen_method == 2: # Use Modelica result profile heat_power_curve = SpaceHeating.SpaceHeating(environment, method=3, livingArea=net_floor_area, specificDemand=spec_th_demand) # Calculate el. energy demand for apartment, if no el. energy # demand is given for whole building to rescale if annual_el_demand is None: # Generate annual_el_demand_ap annual_el_demand = calc_el_dem_ap(nb_occ=number_occupants, el_random=el_random, type='sfh') print('Annual electrical demand in kWh: ', annual_el_demand) if number_occupants is not None: print('El. demand per person in kWh: ') print(annual_el_demand / number_occupants) print() # Create electrical power curve if el_gen_method == 2: if season_mod is not None: season_light_mod = True else: season_light_mod = False el_power_curve = ElectricalDemand.ElectricalDemand(environment, method=2, total_nb_occupants=number_occupants, randomizeAppliances=True, lightConfiguration=0, annualDemand=annual_el_demand, occupancy=occupancy_object.occupancy, do_normalization=do_normalization, prev_heat_dev=prev_heat_dev, season_light_mod=season_light_mod, light_mod_fac=season_mod) else: # Use el. SLP el_power_curve = ElectricalDemand.ElectricalDemand(environment, method=1, annualDemand=annual_el_demand, profileType=el_slp_type) # Create domestic hot water demand if use_dhw: if dhw_volumen is None or dhw_random: dhw_kwh = calc_dhw_dem_ap(nb_occ=number_occupants, dhw_random=dhw_random, type='sfh') # Reconvert kWh/a to Liters per day dhw_vol_ap = dhw_kwh * 1000 * 3600 * 1000 / (955 * 4182 * 35 * 365) # DHW volume per person and day dhw_volumen = dhw_vol_ap / number_occupants if dhw_method == 1: # Annex 42 dhw_power_curve = DomesticHotWater.DomesticHotWater(environment, tFlow=60, thermal=True, method=1, # Annex 42 dailyConsumption=dhw_volumen * number_occupants, supplyTemperature=25) else: # Stochastic profile dhw_power_curve = DomesticHotWater.DomesticHotWater(environment, tFlow=60, thermal=True, method=2, supplyTemperature=25, occupancy=occupancy_object.occupancy) # Rescale to reference dhw volume (liters per person # and day) curr_dhw_vol_flow = dhw_power_curve.water # Water volume flow in Liter/hour curr_volume_year = sum(curr_dhw_vol_flow) * \ environment.timer.timeDiscretization / \ 3600 curr_vol_day = curr_volume_year / 365 curr_vol_day_and_person = curr_vol_day / \ occupancy_object.number_occupants print('Curr. volume per person and day: ', curr_vol_day_and_person) dhw_con_factor = dhw_volumen / curr_vol_day_and_person print('Conv. factor of hot water: ', dhw_con_factor) print('New volume per person and day: ', curr_vol_day_and_person * dhw_con_factor) # Normalize water flow and power load dhw_power_curve.water *= dhw_con_factor dhw_power_curve.loadcurve *= dhw_con_factor # Create apartment apartment = Apartment.Apartment(environment, occupancy=occupancy_object, net_floor_area=net_floor_area) # Add demands to apartment if th_gen_method == 1 or th_gen_method == 2: if use_dhw: apartment.addMultipleEntities([heat_power_curve, el_power_curve, dhw_power_curve]) else: apartment.addMultipleEntities([heat_power_curve, el_power_curve]) else: if use_dhw: apartment.addMultipleEntities([el_power_curve, dhw_power_curve]) else: apartment.addEntity(el_power_curve) # Create extended building object extended_building = \ build_ex.BuildingExtended(environment, build_year=build_year, mod_year=mod_year, build_type=build_type, roof_usabl_pv_area=pv_use_area, net_floor_area=net_floor_area, height_of_floors=height_of_floors, nb_of_floors=nb_of_floors, neighbour_buildings=neighbour_buildings, residential_layout=residential_layout, attic=attic, cellar=cellar, construction_type=construction_type, dormer=dormer, with_ahu= curr_central_ahu) # Add apartment to extended building extended_building.addEntity(entity=apartment) return extended_building def generate_res_building_multi_zone(environment, net_floor_area, spec_th_demand, th_gen_method, el_gen_method, nb_of_apartments, annual_el_demand=None, el_random=False, use_dhw=False, dhw_method=1, total_number_occupants=None, build_year=None, mod_year=None, build_type=None, pv_use_area=None, height_of_floors=None, nb_of_floors=None, neighbour_buildings=None, residential_layout=None, attic=None, cellar=None, construction_type=None, dormer=None, dhw_volumen=None, do_normalization=True, slp_manipulate=True, curr_central_ahu=False, dhw_random=False, prev_heat_dev=True, season_mod=None): """ Function generates and returns extended residential building object with multiple apartments. Occupants are randomly distributed over number of apartments. Parameters ---------- environment : object Environment object net_floor_area : float Net floor area of building in m2 spec_th_demand : float Specific thermal energy demand in kWh/m2*a annual_el_demand : float, optional Annual electrical energy demand in kWh/a (default: None) el_random : bool, optional Defines, if random value should be chosen from statistics or if average value should be chosen. el_random == True means, use random value. (default: False) th_gen_method : int Thermal load profile generation method 1 - Use SLP 2 - Load Modelica simulation output profile (only residential) Method 2 is only used for residential buildings. For non-res. buildings, SLPs are generated instead el_gen_method : int, optional Electrical generation method (default: 1) 1 - Use SLP 2 - Generate stochastic load profile (only valid for residential building) nb_of_apartments : int Number of apartments within building use_dhw : bool, optional Boolean to define, if domestic hot water profile should be generated (default: False) True - Generate dhw profile dhw_method : int, optional Domestic hot water profile generation method (default: 1) 1 - Use Annex 42 profile 2 - Use stochastic profile total_number_occupants : int, optional Total number of occupants in all apartments (default: None) build_year : int, optional Building year of construction (default: None) mod_year : int, optional Last year of modernization of building (default: None) build_type : int, optional Building type (default: None) pv_use_area : float, optional Usable pv area in m2 (default: None) height_of_floors : float average height of the floors nb_of_floors : int Number of floors above the ground neighbour_buildings : int neighbour (default = 0) 0: no neighbour 1: one neighbour 2: two neighbours residential_layout : int type of floor plan (default = 0) 0: compact 1: elongated/complex attic : int type of attic (default = 0) 0: flat roof 1: non heated attic 2: partly heated attic 3: heated attic cellar : int type of cellar (default = 0) 0: no cellar 1: non heated cellar 2: partly heated cellar 3: heated cellar construction_type : str construction type (default = "heavy") heavy: heavy construction light: light construction dormer : str construction type 0: no dormer 1: dormer dhw_volumen : float, optional Volume of domestic hot water in liter per capita and day (default: None). do_normalization : bool, optional Defines, if stochastic profile (el_gen_method=2) should be normalized to given annualDemand value (default: True). If set to False, annual el. demand depends on stochastic el. load profile generation. If set to True, does normalization with annualDemand slp_manipulate : bool, optional Defines, if thermal space heating SLP profile should be modified (default: True). Only used for residential buildings! Only relevant, if th_gen_method == 1 True - Do manipulation False - Use original profile Sets thermal power to zero in time spaces, where average daily outdoor temperature is equal to or larger than 12 °C. Rescales profile to original demand value. curr_central_ahu : bool, optional Defines, if building has air handling unit (AHU) (default: False) dhw_random : bool, optional Defines, if hot water volume per person and day value should be randomized by choosing value from gaussian distribution (20 % standard deviation) (default: False) If True: Randomize value If False: Use reference value prev_heat_dev : bool, optional Defines, if heating devices should be prevented within chosen appliances (default: True). If set to True, DESWH, E-INST, Electric shower, Storage heaters and Other electric space heating are set to zero. Only relevant for el_gen_method == 2 season_mod : float, optional Float to define rescaling factor to rescale annual lighting power curve with cosine wave to increase winter usage and decrease summer usage. Reference is maximum lighting power (default: None). If set to None, do NOT perform rescaling with cosine wave Returns ------- extended_building : object BuildingExtended object Annotation ---------- Raise assertion error when share of occupants per apartment is higher than 5 (necessary for stochastic, el. profile generation) """ assert net_floor_area > 0 assert spec_th_demand >= 0 if annual_el_demand is not None: assert annual_el_demand >= 0 if total_number_occupants is not None: assert total_number_occupants > 0 assert total_number_occupants / nb_of_apartments <= 5, ( 'Number of occupants per apartment is ' + 'at least once higher than 5.') # Distribute occupants to different apartments occupancy_list = constrained_sum_sample_pos(n=nb_of_apartments, total=total_number_occupants) # While not all values are smaller or equal to 5, return run # This while loop might lead to large runtimes for buildings with a # large number of apartments (not finding a valid solution, see # issue #147). Thus, we add a counter to exit the loop count = 0 while all(i <= 5 for i in occupancy_list) is not True: occupancy_list = constrained_sum_sample_pos(n=nb_of_apartments, total=total_number_occupants) if count == 100000: # Take current occupancy_list and redistribute occupants # manually until valid distribution is found occupancy_list = redistribute_occ(occ_list=occupancy_list) # Exit while loop break count += 1 print('Current list of occupants per apartment: ', occupancy_list) else: msg = 'Number of occupants is None for current building!' warnings.warn(msg) # Define SLP profiles for residential building with multiple zone th_slp_type = 'HMF' el_slp_type = 'H0' # Create extended building object extended_building = \ build_ex.BuildingExtended(environment, build_year=build_year, mod_year=mod_year, build_type=build_type, roof_usabl_pv_area=pv_use_area, net_floor_area=net_floor_area, height_of_floors=height_of_floors, nb_of_floors=nb_of_floors, neighbour_buildings= neighbour_buildings, residential_layout= residential_layout, attic=attic, cellar=cellar, construction_type= construction_type, dormer=dormer, with_ahu=curr_central_ahu) if annual_el_demand is not None: # Distribute el. demand equally to apartments annual_el_demand_ap = annual_el_demand / nb_of_apartments else: annual_el_demand_ap = None # Loop over apartments # #--------------------------------------------------------------------- for i in range(int(nb_of_apartments)): # Dummy init of number of occupants curr_number_occupants = None # Check number of occupants if total_number_occupants is not None: # Get number of occupants curr_number_occupants = occupancy_list[i] # Generate occupancy profiles for stochastic el. and/or dhw if el_gen_method == 2 or (dhw_method == 2 and use_dhw): # Generate occupancy profile (necessary for stochastic, el. or # dhw profile) occupancy_object = occup.Occupancy(environment, number_occupants= curr_number_occupants) else: # Generate occupancy object without profile occupancy_object = occup.Occupancy(environment, number_occupants= curr_number_occupants, do_profile=False) else: if el_gen_method == 2: warnings.warn('Stochastic el. profile cannot be generated ' + 'due to missing number of occupants. ' + 'SLP is used instead.') # Set el_gen_method to 1 (SLP) el_gen_method = 1 elif dhw_method == 2: raise AssertionError('DHW profile cannot be generated' + 'for residential building without' + 'occupants (stochastic mode).' + 'Please check your input file ' + '(missing number of occupants) ' + 'or disable dhw generation.') if (curr_number_occupants is None and dhw_method == 1 and use_dhw == True): # If dhw profile should be generated, but current number of # occupants is None, number of occupants is samples from # occupancy distribution for apartment curr_number_occupants = usunc.calc_sampling_occ_per_app( nb_samples=1) # Assumes equal area share for all apartments apartment_area = net_floor_area / nb_of_apartments # Create space heating demand (for apartment) if th_gen_method == 1: # Use SLP heat_power_curve = \ SpaceHeating.SpaceHeating(environment, method=1, profile_type=th_slp_type, livingArea=apartment_area, specificDemand=spec_th_demand) if slp_manipulate: # Do SLP manipulation timestep = environment.timer.timeDiscretization temp_array = environment.weather.tAmbient mod_curve = \ slpman.slp_th_manipulator(timestep, th_slp_curve=heat_power_curve.loadcurve, temp_array=temp_array) heat_power_curve.loadcurve = mod_curve elif th_gen_method == 2: # Use Modelica result profile heat_power_curve = SpaceHeating.SpaceHeating(environment, method=3, livingArea=apartment_area, specificDemand=spec_th_demand) # Calculate el. energy demand for apartment, if no el. energy # demand is given for whole building to rescale if annual_el_demand_ap is None: # Generate annual_el_demand_ap annual_el_demand_ap = calc_el_dem_ap(nb_occ=curr_number_occupants, el_random=el_random, type='mfh') print('Annual el. demand (apartment) in kWh: ', annual_el_demand_ap) if curr_number_occupants is not None: print('El. demand per person in kWh: ') print(annual_el_demand_ap / curr_number_occupants) print() # Create electrical power curve if el_gen_method == 2: if season_mod is not None: season_light_mod = True else: season_light_mod = False el_power_curve = ElectricalDemand.ElectricalDemand(environment, method=2, total_nb_occupants=curr_number_occupants, randomizeAppliances=True, lightConfiguration=0, annualDemand=annual_el_demand_ap, occupancy=occupancy_object.occupancy, do_normalization=do_normalization, prev_heat_dev=prev_heat_dev, season_light_mod=season_light_mod, light_mod_fac=season_mod) else: # Use el. SLP el_power_curve = ElectricalDemand.ElectricalDemand(environment, method=1, annualDemand=annual_el_demand_ap, profileType=el_slp_type) # Create domestic hot water demand if use_dhw: if dhw_volumen is None or dhw_random: dhw_kwh = calc_dhw_dem_ap(nb_occ=curr_number_occupants, dhw_random=dhw_random, type='mfh') # Reconvert kWh/a to Liters per day dhw_vol_ap = dhw_kwh * 1000 * 3600 * 1000 / ( 955 * 4182 * 35 * 365) # DHW volume per person and day dhw_volumen = dhw_vol_ap / curr_number_occupants if dhw_method == 1: # Annex 42 dhw_power_curve = DomesticHotWater.DomesticHotWater( environment, tFlow=60, thermal=True, method=1, # Annex 42 dailyConsumption=dhw_volumen * curr_number_occupants, supplyTemperature=25) else: # Stochastic profile dhw_power_curve = DomesticHotWater.DomesticHotWater( environment, tFlow=60, thermal=True, method=2, supplyTemperature=25, occupancy=occupancy_object.occupancy) # Rescale to reference dhw volume (liters per person # and day) curr_dhw_vol_flow = dhw_power_curve.water # Water volume flow in Liter/hour curr_volume_year = sum(curr_dhw_vol_flow) * \ environment.timer.timeDiscretization / \ 3600 curr_vol_day = curr_volume_year / 365 curr_vol_day_and_person = curr_vol_day / \ occupancy_object.number_occupants print('Curr. volume per person and day: ', curr_vol_day_and_person) dhw_con_factor = dhw_volumen / curr_vol_day_and_person print('Conv. factor of hot water: ', dhw_con_factor) print('New volume per person and day: ', curr_vol_day_and_person * dhw_con_factor) # Normalize water flow and power load dhw_power_curve.water *= dhw_con_factor dhw_power_curve.loadcurve *= dhw_con_factor # Create apartment apartment = Apartment.Apartment(environment, occupancy=occupancy_object, net_floor_area=apartment_area) # Add demands to apartment if th_gen_method == 1 or th_gen_method == 2: if use_dhw: apartment.addMultipleEntities([heat_power_curve, el_power_curve, dhw_power_curve]) else: apartment.addMultipleEntities([heat_power_curve, el_power_curve]) else: if use_dhw: apartment.addMultipleEntities([el_power_curve, dhw_power_curve]) else: apartment.addEntity(el_power_curve) # Add apartment to extended building extended_building.addEntity(entity=apartment) return extended_building def generate_nonres_building_single_zone(environment, net_floor_area, spec_th_demand, annual_el_demand, th_slp_type, el_slp_type=None, build_year=None, mod_year=None, build_type=None, pv_use_area=None, method_3_type=None, method_4_type=None, height_of_floors=None, nb_of_floors=None): """ Function generates and returns extended nonresidential building object with single zone. Parameters ---------- environment : object Environment object net_floor_area : float Net floor area of building in m2 spec_th_demand : float Specific thermal energy demand in kWh/m2*a annual_el_demand : float Annual electrical energy demand in kWh/a th_slp_type : str Thermal SLP type (for non-residential buildings) - `GBA` : Bakeries - `GBD` : Other services - `GBH` : Accomodations - `GGA` : Restaurants - `GGB` : Gardening - `GHA` : Retailers - `GHD` : Summed load profile business, trade and services - `GKO` : Banks, insurances, public institutions - `GMF` : Household similar businesses - `GMK` : Automotive - `GPD` : Paper and printing - `GWA` : Laundries el_slp_type : str, optional (default: None) Electrical SLP type - H0 : Household - L0 : Farms - L1 : Farms with breeding / cattle - L2 : Farms without cattle - G0 : Business (general) - G1 : Business (workingdays 8:00 AM - 6:00 PM) - G2 : Business with high loads in the evening - G3 : Business (24 hours) - G4 : Shops / Barbers - G5 : Bakery - G6 : Weekend operation number_occupants : int, optional Number of occupants (default: None) build_year : int, optional Building year of construction (default: None) mod_year : int, optional Last year of modernization of building (default: None) build_type : int, optional Building type (default: None) pv_use_area : float, optional Usable pv area in m2 (default: None) method_3_type : str, optional Defines type of profile for method=3 (default: None) Options: - 'food_pro': Food production - 'metal': Metal company - 'rest': Restaurant (with large cooling load) - 'sports': Sports hall - 'repair': Repair / metal shop method_4_type : str, optional Defines type of profile for method=4 (default: None) - 'metal_1' : Metal company with smooth profile - 'metal_2' : Metal company with fluctuation in profile - 'warehouse' : Warehouse height_of_floors : float average height of the floors nb_of_floors : int Number of floors above the ground Returns ------- extended_building : object BuildingExtended object """ assert net_floor_area > 0 assert spec_th_demand >= 0 assert annual_el_demand >= 0 assert th_slp_type != 'HEF', ('HEF thermal slp profile only valid for ' + 'residential buildings.') assert th_slp_type != 'HMF', ('HMF thermal slp profile only valid for ' + 'residential buildings.') assert el_slp_type != 'H0', ('H0 thermal slp profile only valid for ' + 'residential buildings.') # Create space heating demand heat_power_curve = SpaceHeating.SpaceHeating(environment, method=1, profile_type=th_slp_type, livingArea=net_floor_area, specificDemand=spec_th_demand) if method_3_type is not None: el_power_curve = \ ElectricalDemand.ElectricalDemand(environment, method=3, annualDemand=annual_el_demand, do_normalization=True, method_3_type=method_3_type) elif method_4_type is not None: el_power_curve = \ ElectricalDemand.ElectricalDemand(environment, method=4, annualDemand=annual_el_demand, do_normalization=True, method_4_type=method_4_type) else: # Use el. SLP for el. power load generation assert el_slp_type is not None, 'el_slp_type is required!' el_power_curve = \ ElectricalDemand.ElectricalDemand(environment, method=1, annualDemand=annual_el_demand, profileType=el_slp_type) # Create apartment apartment = Apartment.Apartment(environment) # Add demands to apartment apartment.addMultipleEntities([heat_power_curve, el_power_curve]) # Create extended building object extended_building = build_ex.BuildingExtended(environment, net_floor_area=net_floor_area, build_year=build_year, mod_year=mod_year, build_type=build_type, roof_usabl_pv_area=pv_use_area, height_of_floors=height_of_floors, nb_of_floors=nb_of_floors, ) # Add apartment to extended building extended_building.addEntity(entity=apartment) return extended_building def get_district_data_from_txt(path, delimiter='\t'): """ Load city district data from txt file (see annotations below for further information of required inputs). naN are going to be replaced with Python None. Parameters ---------- path : str Path to txt file delimiter : str, optional Defines delimiter for txt file (default: '\t') Returns ------- district_data : ndarray Numpy 2d-array with city district data (each column represents different parameter, see annotations) Annotations ----------- File structure Columns: 1: id (int) 2: x in m (float) 3: y in m (float) 4: building_type (int, e.g. 0 for residential building) 5: net floor area in m2 (float) 6: Year of construction (int, optional) 7: Year of modernization (int, optional) 8: Annual (final) thermal energy demand in kWh (float, optional) 9: Annual electrical energy demand in kWh (float, optional) 10: Usable pv roof area in m2 (float, optional) 11: Number of apartments (int, optional) 12: Total number of occupants (int, optional) 13: Number of floors above the ground (int, optional) 14: Average Height of floors (float, optional) 15: If building has a central AHU or not (boolean, optional) 16: Residential layout (int, optional, e.g. 0 for compact) 17: Neighbour Buildings (int, optional) (0 - free standing) (1 - double house) (2 - row house) 18: Type of attic (int, optional, e.g. 0 for flat roof) (1 - regular roof; unheated) (2 - regular roof; partially heated) (3 - regular roof; fully heated) 19: Type of cellar (int, optional, e.g. 1 for non heated cellar) (0 - no basement) (1 - non heated) (2 - partially heated) (3 - fully heated) 20: Dormer (int, optional, 0: no dormer/ 1: dormer) 21: Construction Type(heavy/light, optional) (0 - heavy; 1 - light) 22: Method_3_nb (for usage of measured, weekly non-res. el. profile (optional) 23: Method_4_nb (for usage of measured, annual non-res. el. profile (optional) """ district_data = np.genfromtxt(path, delimiter=delimiter, skip_header=1) # Replace nan with None values of Python district_data = np.where(np.isnan(district_data), None, district_data) return district_data def calc_el_dem_ap(nb_occ, el_random, type): """ Calculate electric energy demand per apartment per year in kWh/a (residential buildings, only) Parameters ---------- nb_occ : int Number of occupants el_random : bool Defines, if random value should be chosen from statistics or if average value should be chosen. el_random == True means, use random value. type : str Define residential building type (single family or multi- family) Options: - 'sfh' : Single family house - 'mfh' : Multi family house Returns ------- el_dem : float Electric energy demand per apartment in kWh/a """ assert nb_occ > 0 assert nb_occ <= 5, 'Number of occupants cannot exceed 5 per ap.' assert type in ['sfh', 'mfh'] if el_random: # Choose first entry of random sample list el_dem = usunc.calc_sampling_el_demand_per_apartment( nb_samples=1, nb_persons=nb_occ, type=type)[0] else: # Choose average value depending on nb_occ # Class D without hot water (Stromspiegel 2017) dict_sfh = {1: 2500, 2: 3200, 3: 3900, 4: 4200, 5: 5400} dict_mfh = {1: 1500, 2: 2200, 3: 2800, 4: 3200, 5: 4000} if type == 'sfh': el_dem = dict_sfh[nb_occ] elif type == 'mfh': el_dem = dict_mfh[nb_occ] return el_dem def calc_dhw_dem_ap(nb_occ, dhw_random, type, delta_t=35, c_p_water=4182, rho_water=995): """ Calculate hot water energy demand per apartment per year in kWh/a (residential buildings, only) Parameters ---------- nb_occ : int Number of occupants dhw_random : bool Defines, if random value should be chosen from statistics or if average value should be chosen. dhw_random == True means, use random value. type : str Define residential building type (single family or multi- family) Options: - 'sfh' : Single family house - 'mfh' : Multi family house delta_t : float, optional Temperature split of heated up water in Kelvin (default: 35) c_p_water : float, optional Specific heat capacity of water in J/kgK (default: 4182) rho_water : float, optional Density of water in kg/m3 (default: 995) Returns ------- dhw_dem : float Electric energy demand per apartment in kWh/a """ assert nb_occ > 0 assert nb_occ <= 5, 'Number of occupants cannot exceed 5 per ap.' assert type in ['sfh', 'mfh'] if dhw_random: # Choose first entry of random sample list # DHW volume in liters per apartment and day dhw_volume = usunc.calc_sampling_dhw_per_apartment( nb_samples=1, nb_persons=nb_occ, b_type=type)[0] dhw_dem = dhw_volume * 365 * rho_water * c_p_water * delta_t / \ (1000 * 3600 * 1000) else: # Choose average value depending on nb_occ # Class D without hot water (Stromspiegel 2017) dict_sfh = {1: 500, 2: 800, 3: 1000, 4: 1300, 5: 1600} dict_mfh = {1: 500, 2: 900, 3: 1300, 4: 1400, 5: 2000} if type == 'sfh': dhw_dem = dict_sfh[nb_occ] elif type == 'mfh': dhw_dem = dict_mfh[nb_occ] return dhw_dem def run_city_generator(generation_mode, timestep, year_timer, year_co2, location, th_gen_method, el_gen_method, district_data, use_dhw=False, dhw_method=1, try_path=None, pickle_city_filename=None, do_save=True, path_save_city=None, eff_factor=0.85, show_city=False, altitude=55, dhw_volumen=None, do_normalization=True, slp_manipulate=True, call_teaser=False, teaser_proj_name='pycity', do_log=True, log_path=None, project_name='teaser_project', air_vent_mode=1, vent_factor=0.5, t_set_heat=20, t_set_cool=70, t_night=16, vdi_sh_manipulate=False, city_osm=None, el_random=False, dhw_random=False, prev_heat_dev=True, season_mod=None, merge_windows=False, new_try=False): """ Function generates city district for user defined input. Generated buildings consist of only one single zone! Parameters ---------- generation_mode : int Integer to define method to generate city district (so far, only csv/txt file import has been implemented) generation_mode = 0: Load data from csv/txt file (tab seperated) timestep : int Timestep in seconds year_timer : int Chosen year of analysis (influences initial day for profile generation) year_co2 : int, optional Chose year with specific emission factors location : Tuple (latitude, longitude) of the simulated system's position. th_gen_method : int Thermal load profile generation method 1 - Use SLP 2 - Load Modelica simulation output profile (only residential) Method 2 is only used for residential buildings. For non-res. buildings, SLPs are generated instead 3 - Use TEASER VDI 6007 core to simulate thermal loads‚ el_gen_method : int Electrical generation method 1 - Use SLP 2 - Generate stochastic load profile (only valid for residential building). Requires number of occupants. district_data : ndarray Numpy 2d-array with city district data (each column represents different parameter, see annotations) use_dhw : bool, optional Defines if domestic hot water profiles should be generated. (default: False) dhw_method : int, optional Defines method for dhw profile generation (default: 1) Only relevant if use_dhw=True. Options: - 1: Generate profiles via Annex 42 - 2: Generate stochastic dhw profiles try_path : str, optional Path to TRY weather file (default: None) If set to None, uses default weather TRY file (2010, region 5) pickle_city_filename : str, optional Name for file, which should be pickled and saved, if no path is handed over to save object to(default: None) do_save : bool, optional Defines, if city object instance should be saved as pickle file (default: True) path_save_city : str, optional Path to save (pickle and dump) city object instance to (default: None) If None is used, saves file to .../output/... eff_factor : float, optional Efficiency factor of thermal boiler system (default: 0.85) show_city : bool, optional Boolean to define if city district should be printed by matplotlib after generation (default: False) True: Print results False: Do not print results altitude : float, optional Altitude of location in m (default: 55 - City of Bottrop) dhw_volumen : float, optional Volume of domestic hot water in liter per capita and day (default: None). do_normalization : bool, optional Defines, if stochastic profile (el_gen_method=2) should be normalized to given annualDemand value (default: True). If set to False, annual el. demand depends on stochastic el. load profile generation. If set to True, does normalization with annualDemand slp_manipulate : bool, optional Defines, if thermal space heating SLP profile should be modified (default: True). Only used for residential buildings! Only relevant, if th_gen_method == 1 True - Do manipulation False - Use original profile Sets thermal power to zero in time spaces, where average daily outdoor temperature is equal to or larger than 12 °C. Rescales profile to original demand value. call_teaser : bool, optional Defines, if teaser should be called to generate typeBuildings (currently, residential typeBuildings only). (default: False) If set to True, generates typeBuildings and add them to building node as attribute 'type_building' teaser_proj_name : str, optional TEASER project name (default: 'pycity'). Only relevant, if call_teaser is set to True do_log : bool, optional Defines, if log file of inputs should be generated (default: True) log_path : str, optional Path to log file (default: None). If set to None, saves log to .../output air_vent_mode : int Defines method to generation air exchange rate for VDI 6007 simulation Options: 0 : Use constant value (vent_factor in 1/h) 1 : Use deterministic, temperature-dependent profile 2 : Use stochastic, user-dependent profile vent_factor : float, optional Ventilation rate factor in 1/h (default: 0.5). Only used, if array_vent_rate is None (otherwise, array_vent_rate array is used) t_set_heat : float, optional Heating set temperature in degree Celsius. If temperature drops below t_set_heat, model is going to be heated up. (default: 20) (Related to constraints for res. buildings in DIN V 18599) t_set_cool : float, optional Cooling set temperature in degree Celsius. If temperature rises above t_set_cool, model is going to be cooled down. (default: 70) t_night : float, optional Night set back temperature in degree Celsius (default: 16) (Related to constraints for res. buildings in DIN V 18599) project_name : str, optional TEASER project name (default: 'teaser_project') vdi_sh_manipulate : bool, optional Defines, if VDI 6007 thermal space heating load curve should be normalized to match given annual space heating demand in kWh (default: False) el_random : bool, optional Defines, if annual, eletrical demand value for normalization of el. load profile should randomly diverge from reference value within specific boundaries (default: False). If False: Use reference value for normalization If True: Allow generating values that is different from reference value dhw_random : bool, optional Defines, if hot water volume per person and day value should be randomized by choosing value from gaussian distribution (20 % standard deviation) (default: False) If True: Randomize value If False: Use reference value prev_heat_dev : bool, optional Defines, if heating devices should be prevented within chosen appliances (default: True). If set to True, DESWH, E-INST, Electric shower, Storage heaters and Other electric space heating are set to zero. Only relevant for el_gen_method == 2 season_mod : float, optional Float to define rescaling factor to rescale annual lighting power curve with cosine wave to increase winter usage and decrease summer usage. Reference is maximum lighting power (default: None). If set to None, do NOT perform rescaling with cosine wave merge_windows : bool, optional Defines TEASER project setting for merge_windows_calc (default: False). If set to False, merge_windows_calc is set to False. If True, Windows are merged into wall resistances. new_try : bool, optional Defines, if TRY dataset have been generated after 2017 (default: False) If False, assumes that TRY dataset has been generated before 2017. If True, assumes that TRY dataset has been generated after 2017 and belongs to the new TRY classes. This is important for extracting the correct values from the TRY dataset! Returns ------- city_object : object City object of pycity_calc Annotations ----------- Non-residential building loads are automatically generated via SLP (even if el_gen_method is set to 2). Furthermore, dhw profile generation is automatically neglected (only valid for residential buildings) Electrical load profiles of residential buildings without occupants are automatically generated via SLP (even if el_gen_method is set to 2) File structure (district_data np.array) Columns: 1: id (int) 2: x in m (float) 3: y in m (float) 4: building_type (int, e.g. 0 for residential building) 5: net floor area in m2 (float) 6: Year of construction (int, optional) 7: Year of modernization (int, optional) 8: Annual (final) thermal energy demand in kWh (float, optional) For residential: space heating, only! For non-residential: Space heating AND hot water! (SLP usage) 9: Annual electrical energy demand in kWh (float, optional) 10: Usable pv roof area in m2 (float, optional) 11: Number of apartments (int, optional) 12: Total number of occupants (int, optional) 13: Number of floors above the ground (int, optional) 14: Average Height of floors (float, optional) 15: If building has a central AHU or not (boolean, optional) 16: Residential layout (int, optional, e.g. 0 for compact) 17: Neighbour Buildings (int, optional); 0 - free standing; 1 - Double house; 2 - Row house; 18: Type of attic (int, optional, e.g. 0 for flat roof); 1 - Roof, non heated; 2 - Roof, partially heated; 3- Roof, fully heated; 19: Type of basement (int, optional, e.g. 1 for non heated basement 0 - No basement; 1 - basement, non heated; 2 - basement, partially heated; 3- basement, fully heated; 20: Dormer (int, optional, 0: no dormer/ 1: dormer) 21: Construction Type(heavy/light, optional) (0 - heavy; 1 - light) 22: Method_3_nb (for usage of measured, weekly non-res. el. profile (optional) (0 to 4) 23: Method_4_nb (for usage of measured, annual non-res. el. profile (optional) (0 - 2) method_3_type : str, optional Defines type of profile for method=3 (default: None) Options: 0 - 'food_pro': Food production 1 - 'metal': Metal company 2 - 'rest': Restaurant (with large cooling load) 3 - 'sports': Sports hall 4 - 'repair': Repair / metal shop method_4_type : str, optional Defines type of profile for method=4 (default: None) 0 - 'metal_1' : Metal company with smooth profile 1 - 'metal_2' : Metal company with fluctuation in profile 2 - 'warehouse' : Warehouse """ assert eff_factor > 0, 'Efficiency factor has to be larger than zero.' assert eff_factor <= 1, 'Efficiency factor cannot increase value 1.' if dhw_volumen is not None: # pragma: no cover assert dhw_volumen >= 0, 'Hot water volume cannot be below zero.' if generation_mode == 1: # pragma: no cover assert city_osm is not None, 'Generation mode 1 requires city object!' if vdi_sh_manipulate is True and th_gen_method == 3: # pragma: no cover msg = 'Simulated profiles of VDI 6007 call (TEASER --> ' \ 'space heating) is going to be normalized with annual thermal' \ ' space heating demand values given by user!' warnings.warn(msg) if do_log: # pragma: no cover # Write log file # ################################################################ # Log file path if log_path is None: # If not existing, use default path this_path = os.path.dirname(os.path.abspath(__file__)) log_path = os.path.join(this_path, 'output', 'city_gen_log.txt') log_file = open(log_path, mode='w') log_file.write('PyCity_Calc city_generator.py log file') log_file.write('\n############## Time and location ##############\n') log_file.write('Date: ' + str(datetime.datetime.now()) + '\n') log_file.write('generation_mode: ' + str(generation_mode) + '\n') log_file.write('timestep in seconds: ' + str(timestep) + '\n') log_file.write('Year for timer: ' + str(year_timer) + '\n') log_file.write('Year for CO2 emission factors: ' + str(year_co2) + '\n') log_file.write('Location: ' + str(location) + '\n') log_file.write('altitude: ' + str(altitude) + '\n') if generation_mode == 0: log_file.write('Generation mode: csv/txt input, only.\n') elif generation_mode == 1: log_file.write('Generation mode: csv/txt plus city osm object.\n') log_file.write('\n############## Generation methods ##############\n') log_file.write('th_gen_method: ' + str(th_gen_method) + '\n') if th_gen_method == 1: log_file.write('Manipulate SLP: ' + str(slp_manipulate) + '\n') elif th_gen_method == 3: log_file.write('t_set_heat: ' + str(t_set_heat) + '\n') log_file.write('t_set_night: ' + str(t_night) + '\n') log_file.write('t_set_cool: ' + str(t_set_cool) + '\n') log_file.write('air_vent_mode: ' + str(air_vent_mode) + '\n') log_file.write('vent_factor: ' + str(vent_factor) + '\n') log_file.write('el_gen_method: ' + str(el_gen_method) + '\n') log_file.write( 'Normalize el. profile: ' + str(do_normalization) + '\n') log_file.write( 'Do random el. normalization: ' + str(el_random) + '\n') log_file.write( 'Prevent el. heating devices for el load generation: ' '' + str(prev_heat_dev) + '\n') log_file.write( 'Rescaling factor lighting power curve to implement seasonal ' 'influence: ' + str(season_mod) + '\n') log_file.write('use_dhw: ' + str(use_dhw) + '\n') log_file.write('dhw_method: ' + str(dhw_method) + '\n') log_file.write('dhw_volumen: ' + str(dhw_volumen) + '\n') log_file.write( 'Do random dhw. normalization: ' + str(dhw_random) + '\n') log_file.write('\n############## Others ##############\n') log_file.write('try_path: ' + str(try_path) + '\n') log_file.write('eff_factor: ' + str(eff_factor) + '\n') log_file.write('timestep in seconds: ' + str(timestep) + '\n') log_file.write('call_teaser: ' + str(call_teaser) + '\n') log_file.write('teaser_proj_name: ' + str(teaser_proj_name) + '\n') # Log file is closed, after pickle filename has been generated # (see code below) if generation_mode == 0 or generation_mode == 1: # ################################################################## # Load specific demand files # Load specific thermal demand input data spec_th_dem_res_building = load_data_file_with_spec_demand_data( 'RWI_res_building_spec_th_demand.txt') start_year_column = (spec_th_dem_res_building[:, [0]]) # Reverse start_year_column = start_year_column[::-1] """ Columns: 1. Start year (int) 2. Final year (int) 3. Spec. thermal energy demand in kWh/m2*a (float) """ # ################################################################## # Load specific electrical demand input data spec_el_dem_res_building = load_data_file_with_spec_demand_data( 'AGEB_res_building_spec_e_demand.txt') """ Columns: 1. Start year (int) 2. Final year (int) 3. Spec. thermal energy demand in kWh/m2*a (float) """ # ################################################################## # Load specific electrical demand input data # (depending on number of occupants) spec_el_dem_res_building_per_person = \ load_data_file_with_spec_demand_data( 'Stromspiegel2017_spec_el_energy_demand.txt') """ Columns: 1. Number of persons (int) ( 1 - 5 SFH and 1 - 5 MFH) 2. Annual electrical demand in kWh/a (float) 3. Specific electrical demand per person in kWh/person*a (float) """ # ################################################################### # Load specific demand data and slp types for # non residential buildings spec_dem_and_slp_non_res = load_data_file_with_spec_demand_data( 'Spec_demands_non_res.txt') """ Columns: 1. type_id (int) 2. type_name (string) # Currently 'nan', due to expected float 3. Spec. thermal energy demand in kWh/m2*a (float) 4. Spec. electrical energy demand in kWh/m2*a (float) 5. Thermal SLP type (int) 6. Electrical SLP type (int) """ # ################################################################### # Generate city district # Generate extended environment of pycity_calc environment = generate_environment(timestep=timestep, year_timer=year_timer, year_co2=year_co2, location=location, try_path=try_path, altitude=altitude, new_try=new_try) print('Generated environment object.\n') if generation_mode == 0: # Generate city object # ############################################################ city_object = city.City(environment=environment) print('Generated city object.\n') else: # Overwrite city_osm environment print('Overwrite city_osm.environment with new environment') city_osm.environment = environment city_object = city_osm # Check if district_data only holds one entry for single building # In this case, has to be processed differently if district_data.ndim > 1: multi_data = True else: # Only one entry (single building) multi_data = False # If multi_data is false, loop below is going to be exited with # a break statement at the end. # Generate dummy node id and thermal space heating demand dict dict_id_vdi_sh = {} # Loop over district_data # ############################################################ for i in range(len(district_data)): if multi_data: # Extract data out of input file curr_id = int( district_data[i][0]) # id / primary key of building curr_x = district_data[i][1] # x-coordinate in m curr_y = district_data[i][2] # y-coordinate in m curr_build_type = int( district_data[i][3]) # building type nb (int) curr_nfa = district_data[i][4] # Net floor area in m2 curr_build_year = district_data[i][5] # Year of construction curr_mod_year = district_data[i][ 6] # optional (last year of modernization) curr_th_e_demand = district_data[i][ 7] # optional: Final thermal energy demand in kWh # For residential buildings: Space heating only! # For non-residential buildings: Space heating AND hot water! (SLP) curr_el_e_demand = district_data[i][ 8] # optional (Annual el. energy demand in kWh) curr_pv_roof_area = district_data[i][ 9] # optional (Usable pv roof area in m2) curr_nb_of_apartments = district_data[i][ 10] # optional (Number of apartments) curr_nb_of_occupants = district_data[i][ 11] # optional (Total number of occupants) curr_nb_of_floors = district_data[i][ 12] # optional (Number of floors above the ground) curr_avg_height_of_floors = district_data[i][ 13] # optional (Average Height of floors) curr_central_ahu = district_data[i][ 14] # optional (If building has a central air handling unit (AHU) or not (boolean)) curr_res_layout = district_data[i][ 15] # optional Residential layout (int, optional, e.g. 0 for compact) curr_nb_of_neighbour_bld = district_data[i][ 16] # optional Neighbour Buildings (int, optional) curr_type_attic = district_data[i][ 17] # optional Type of attic (int, optional, e.g. 0 for flat roof); # 1 - Roof, non heated; 2 - Roof, partially heated; 3- Roof, fully heated; curr_type_cellar = district_data[i][ 18] # optional Type of basement # (int, optional, e.g. 1 for non heated basement 0 - No basement; 1 - basement, non heated; 2 - basement, partially heated; 3- basement, fully heated; curr_dormer = district_data[i][ 19] # optional Dormer (int, optional, 0: no dormer/ 1: dormer) curr_construction_type = district_data[i][ 20] # optional Construction Type(heavy/light, optional) (0 - heavy; 1 - light) curr_method_3_nb = district_data[i][ 21] # optional Method_3_nb (for usage of measured, weekly non-res. el. profile curr_method_4_nb = district_data[i][ 22] # optional Method_4_nb (for usage of measured, annual non-res. el. profile else: # Single entry # Extract data out of input file curr_id = int(district_data[0]) # id / primary key of building curr_x = district_data[1] # x-coordinate in m curr_y = district_data[2] # y-coordinate in m curr_build_type = int( district_data[3]) # building type nb (int) curr_nfa = district_data[4] # Net floor area in m2 curr_build_year = district_data[5] # Year of construction curr_mod_year = district_data[ 6] # optional (last year of modernization) curr_th_e_demand = district_data[ 7] # optional: Final thermal energy demand in kWh # For residential buildings: Space heating only! # For non-residential buildings: Space heating AND hot water! (SLP) curr_el_e_demand = district_data[ 8] # optional (Annual el. energy demand in kWh) curr_pv_roof_area = district_data[ 9] # optional (Usable pv roof area in m2) curr_nb_of_apartments = district_data[ 10] # optional (Number of apartments) curr_nb_of_occupants = district_data[ 11] # optional (Total number of occupants) curr_nb_of_floors = district_data[ 12] # optional (Number of floors above the ground) curr_avg_height_of_floors = district_data[ 13] # optional (Average Height of floors) curr_central_ahu = district_data[ 14] # optional (If building has a central air handling unit (AHU) or not (boolean)) curr_res_layout = district_data[ 15] # optional Residential layout (int, optional, e.g. 0 for compact) curr_nb_of_neighbour_bld = district_data[ 16] # optional Neighbour Buildings (int, optional) curr_type_attic = district_data[ 17] # optional Type of attic (int, optional, e.g. 0 for flat roof); # 1 - Roof, non heated; 2 - Roof, partially heated; 3- Roof, fully heated; curr_type_cellar = district_data[ 18] # optional Type of basement # (int, optional, e.g. 1 for non heated basement 0 - No basement; 1 - basement, non heated; 2 - basement, partially heated; 3- basement, fully heated; curr_dormer = district_data[ 19] # optional Dormer (int, optional, 0: no dormer/ 1: dormer) curr_construction_type = district_data[ 20] # optional Construction Type(heavy/light, optional) (0 - heavy; 1 - light) curr_method_3_nb = district_data[ 21] # optional Method_3_nb (for usage of measured, weekly non-res. el. profile curr_method_4_nb = district_data[ 22] # optional Method_4_nb (for usage of measured, annual non-res. el. profile print('Process building', curr_id) print('########################################################') # Assert functions # ############################################################ assert curr_build_type >= 0 assert curr_nfa > 0 for m in range(5, 9): if multi_data: if district_data[i][m] is not None: assert district_data[i][m] > 0 else: if district_data[m] is not None: assert district_data[m] > 0 if curr_nb_of_apartments is not None: assert curr_nb_of_apartments > 0 # Convert to int curr_nb_of_apartments = int(curr_nb_of_apartments) if curr_nb_of_occupants is not None: assert curr_nb_of_occupants > 0 # Convert curr_nb_of_occupants from float to int curr_nb_of_occupants = int(curr_nb_of_occupants) if (curr_nb_of_occupants is not None and curr_nb_of_apartments is not None): assert curr_nb_of_occupants / curr_nb_of_apartments <= 5, ( 'Average share of occupants per apartment should ' + 'not exceed 5 persons! (Necessary for stochastic, el.' + 'profile generation.)') if curr_method_3_nb is not None: curr_method_3_nb >= 0 if curr_method_4_nb is not None: curr_method_4_nb >= 0 if curr_build_type == 0 and curr_nb_of_apartments is None: # pragma: no cover # Define single apartment, if nb of apartments is unknown msg = 'Building ' + str(curr_id) + ' is residential, but' \ ' does not have a number' \ ' of apartments. Going' \ ' to set nb. to 1.' warnings.warn(msg) curr_nb_of_apartments = 1 if (curr_build_type == 0 and curr_nb_of_occupants is None and use_dhw and dhw_method == 2): raise AssertionError('DHW profile cannot be generated' + 'for residential building without' + 'occupants (stochastic mode).' + 'Please check your input file ' + '(missing number of occupants) ' + 'or disable dhw generation.') # Check if TEASER inputs are defined if call_teaser or th_gen_method == 3: if curr_build_type == 0: # Residential assert curr_nb_of_floors is not None assert curr_avg_height_of_floors is not None assert curr_central_ahu is not None assert curr_res_layout is not None assert curr_nb_of_neighbour_bld is not None assert curr_type_attic is not None assert curr_type_cellar is not None assert curr_dormer is not None assert curr_construction_type is not None if curr_nb_of_floors is not None: assert curr_nb_of_floors > 0 if curr_avg_height_of_floors is not None: assert curr_avg_height_of_floors > 0 if curr_central_ahu is not None: assert 0 <= curr_central_ahu <= 1 if curr_res_layout is not None: assert 0 <= curr_res_layout <= 1 if curr_nb_of_neighbour_bld is not None: assert 0 <= curr_nb_of_neighbour_bld <= 2 if curr_type_attic is not None: assert 0 <= curr_type_attic <= 3 if curr_type_cellar is not None: assert 0 <= curr_type_cellar <= 3 if curr_dormer is not None: assert 0 <= curr_dormer <= 1 if curr_construction_type is not None: assert 0 <= curr_construction_type <= 1 # Check building type (residential or non residential) # #------------------------------------------------------------- if curr_build_type == 0: # Is residential print('Residential building') # Get spec. net therm. demand value according to last year # of modernization or build_year # If year of modernization is defined, use curr_mod_year if curr_mod_year is not None: use_year = int(curr_mod_year) else: # Use year of construction use_year = int(curr_build_year) # Get specific, thermal energy demand (based on use_year) for j in range(len(start_year_column)): if use_year >= start_year_column[j]: curr_spec_th_demand = spec_th_dem_res_building[len( spec_th_dem_res_building) - 1 - j][2] break # # Get spec. electr. demand # if curr_nb_of_occupants is None: # # USE AGEB values, if no number of occupants is given # # Set specific demand value in kWh/m2*a # curr_spec_el_demand = spec_el_dem_res_building[1] # # Only valid for array like [2012 38.7] # else: # # Use Stromspiegel 2017 values # # Calculate specific electric demand values depending # # on number of occupants # # if curr_nb_of_apartments == 1: # btype = 'sfh' # elif curr_nb_of_apartments > 1: # btype = 'mfh' # # # Average occupancy number per apartment # curr_av_occ_per_app = \ # curr_nb_of_occupants / curr_nb_of_apartments # print('Average number of occupants per apartment') # print(round(curr_av_occ_per_app, ndigits=2)) # # if curr_av_occ_per_app <= 5 and curr_av_occ_per_app > 0: # # Correctur factor for non-int. av. number of # # occupants (#19) # # # Divide annual el. energy demand with net floor area # if btype == 'sfh': # row_idx_low = math.ceil(curr_av_occ_per_app) - 1 # row_idx_high = math.floor(curr_av_occ_per_app) - 1 # elif btype == 'mfh': # row_idx_low = math.ceil(curr_av_occ_per_app) - 1 \ # + 5 # row_idx_high = math.floor(curr_av_occ_per_app) - 1 \ # + 5 # # cur_spec_el_dem_per_occ_high = \ # spec_el_dem_res_building_per_person[row_idx_high][2] # cur_spec_el_dem_per_occ_low = \ # spec_el_dem_res_building_per_person[row_idx_low][2] # # print('Chosen reference spec. el. demands per person ' # 'in kWh/a (high and low value):') # print(cur_spec_el_dem_per_occ_high) # print(cur_spec_el_dem_per_occ_low) # # delta = round(curr_av_occ_per_app, 0) - \ # curr_av_occ_per_app # # if delta < 0: # curr_spec_el_dem_occ = cur_spec_el_dem_per_occ_high + \ # (cur_spec_el_dem_per_occ_high - # cur_spec_el_dem_per_occ_low) * delta # elif delta > 0: # curr_spec_el_dem_occ = cur_spec_el_dem_per_occ_low + \ # (cur_spec_el_dem_per_occ_high - # cur_spec_el_dem_per_occ_low) * delta # else: # curr_spec_el_dem_occ = cur_spec_el_dem_per_occ_high # # # print('Calculated spec. el. demand per person in ' # # 'kWh/a:') # # print(round(curr_spec_el_dem_occ, ndigits=2)) # # # Specific el. demand per person (dependend on av. # # number of occupants in each apartment) # # --> Multiplied with number of occupants # # --> Total el. energy demand in kWh # # --> Divided with net floor area # # --> Spec. el. energy demand in kWh/a # # curr_spec_el_demand = \ # curr_spec_el_dem_occ * curr_nb_of_occupants \ # / curr_nfa # # # print('Spec. el. energy demand in kWh/m2:') # # print(curr_spec_el_demand) # # else: # raise AssertionError('Invalid number of occupants') # if el_random: # if curr_nb_of_occupants is None: # # Randomize curr_spec_el_demand with normal distribution # # with curr_spec_el_demand as mean and 10 % standard dev. # curr_spec_el_demand = \ # np.random.normal(loc=curr_spec_el_demand, # scale=0.10 * curr_spec_el_demand) # else: # # Randomize rounding up and down of curr_av_occ_per_ap # if round(curr_av_occ_per_app) > curr_av_occ_per_app: # # Round up # delta = round(curr_av_occ_per_app) - \ # curr_av_occ_per_app # prob_r_up = 1 - delta # rnb = random.random() # if rnb < prob_r_up: # use_occ = math.ceil(curr_av_occ_per_app) # else: # use_occ = math.floor(curr_av_occ_per_app) # # else: # # Round down # delta = curr_av_occ_per_app - \ # round(curr_av_occ_per_app) # prob_r_down = 1 - delta # rnb = random.random() # if rnb < prob_r_down: # use_occ = math.floor(curr_av_occ_per_app) # else: # use_occ = math.ceil(curr_av_occ_per_app) # # sample_el_per_app = \ # usunc.calc_sampling_el_demand_per_apartment(nb_samples=1, # nb_persons=use_occ, # type=btype)[0] # # # Divide sampled el. demand per apartment through # # number of persons of apartment (according to # # Stromspiegel 2017) and multiply this value with # # actual number of persons in building to get # # new total el. energy demand. Divide this value with # # net floor area to get specific el. energy demand # curr_spec_el_demand = \ # (sample_el_per_app / curr_av_occ_per_app) * \ # curr_nb_of_occupants / curr_nfa # conversion of the construction_type from int to str if curr_construction_type == 0: new_curr_construction_type = 'heavy' elif curr_construction_type == 1: new_curr_construction_type = 'light' else: new_curr_construction_type = 'heavy' # #------------------------------------------------------------- else: # Non-residential print('Non residential') # Get spec. demands and slp types according to building_type curr_spec_th_demand = \ spec_dem_and_slp_non_res[curr_build_type - 2][2] curr_spec_el_demand = \ spec_dem_and_slp_non_res[curr_build_type - 2][3] curr_th_slp_type = \ spec_dem_and_slp_non_res[curr_build_type - 2][4] curr_el_slp_type = \ spec_dem_and_slp_non_res[curr_build_type - 2][5] # Convert slp type integers into strings curr_th_slp_type = convert_th_slp_int_and_str(curr_th_slp_type) curr_el_slp_type = convert_el_slp_int_and_str(curr_el_slp_type) # If curr_el_e_demand is not known, calculate it via spec. # demand if curr_el_e_demand is None: curr_el_e_demand = curr_spec_el_demand * curr_nfa # #------------------------------------------------------------- # If curr_th_e_demand is known, recalc spec e. demand if curr_th_e_demand is not None: # Calc. spec. net thermal energy demand with efficiency factor curr_spec_th_demand = eff_factor * curr_th_e_demand / curr_nfa else: # Spec. final energy demand is given, recalculate it to # net thermal energy demand with efficiency factor curr_spec_th_demand *= eff_factor # # If curr_el_e_demand is not known, calculate it via spec. demand # if curr_el_e_demand is None: # curr_el_e_demand = curr_spec_el_demand * curr_nfa if th_gen_method == 1 or th_gen_method == 2 or curr_build_type != 0: print('Used specific thermal demand value in kWh/m2*a:') print(curr_spec_th_demand) # #------------------------------------------------------------- # Generate BuildingExtended object if curr_build_type == 0: # Residential if curr_nb_of_apartments > 1: # Multi-family house building = generate_res_building_multi_zone(environment, net_floor_area=curr_nfa, spec_th_demand=curr_spec_th_demand, annual_el_demand=curr_el_e_demand, th_gen_method=th_gen_method, el_gen_method=el_gen_method, nb_of_apartments=curr_nb_of_apartments, use_dhw=use_dhw, dhw_method=dhw_method, total_number_occupants=curr_nb_of_occupants, build_year=curr_build_year, mod_year=curr_mod_year, build_type=curr_build_type, pv_use_area=curr_pv_roof_area, height_of_floors=curr_avg_height_of_floors, nb_of_floors=curr_nb_of_floors, neighbour_buildings=curr_nb_of_neighbour_bld, residential_layout=curr_res_layout, attic=curr_type_attic, cellar=curr_type_cellar, construction_type=new_curr_construction_type, dormer=curr_dormer, dhw_volumen=dhw_volumen, do_normalization=do_normalization, slp_manipulate=slp_manipulate, curr_central_ahu=curr_central_ahu, dhw_random=dhw_random, prev_heat_dev=prev_heat_dev, season_mod=season_mod) elif curr_nb_of_apartments == 1: # Single-family house building = generate_res_building_single_zone(environment, net_floor_area=curr_nfa, spec_th_demand=curr_spec_th_demand, annual_el_demand=curr_el_e_demand, th_gen_method=th_gen_method, el_gen_method=el_gen_method, use_dhw=use_dhw, dhw_method=dhw_method, number_occupants=curr_nb_of_occupants, build_year=curr_build_year, mod_year=curr_mod_year, build_type=curr_build_type, pv_use_area=curr_pv_roof_area, height_of_floors=curr_avg_height_of_floors, nb_of_floors=curr_nb_of_floors, neighbour_buildings=curr_nb_of_neighbour_bld, residential_layout=curr_res_layout, attic=curr_type_attic, cellar=curr_type_cellar, construction_type=new_curr_construction_type, dormer=curr_dormer, dhw_volumen=dhw_volumen, do_normalization=do_normalization, slp_manipulate=slp_manipulate, curr_central_ahu=curr_central_ahu, dhw_random=dhw_random, prev_heat_dev=prev_heat_dev, season_mod=season_mod) else: raise AssertionError('Wrong number of apartments') else: # Non-residential method_3_str = None method_4_str = None # Convert curr_method numbers, if not None if curr_method_3_nb is not None: method_3_str = \ convert_method_3_nb_into_str(int(curr_method_3_nb)) if curr_method_4_nb is not None: method_4_str = \ convert_method_4_nb_into_str(int(curr_method_4_nb)) building = generate_nonres_building_single_zone(environment, th_slp_type=curr_th_slp_type, net_floor_area=curr_nfa, spec_th_demand=curr_spec_th_demand, annual_el_demand=curr_el_e_demand, el_slp_type=curr_el_slp_type, build_year=curr_build_year, mod_year=curr_mod_year, build_type=curr_build_type, pv_use_area=curr_pv_roof_area, method_3_type=method_3_str, method_4_type=method_4_str, height_of_floors=curr_avg_height_of_floors, nb_of_floors=curr_nb_of_floors ) # Generate position shapely point position = point.Point(curr_x, curr_y) if generation_mode == 0: # Add building to city object id = city_object.add_extended_building( extended_building=building, position=position, name=curr_id) elif generation_mode == 1: # Add building as entity to corresponding building node # Positions should be (nearly) equal assert position.x - city_object.nodes[int(curr_id)][ 'position'].x <= 0.1 assert position.y - city_object.nodes[int(curr_id)][ 'position'].y <= 0.1 city_object.nodes[int(curr_id)]['entity'] = building id = curr_id # Save annual thermal net heat energy demand for space heating # to dict (used for normalization with VDI 6007 core) dict_id_vdi_sh[id] = curr_spec_th_demand * curr_nfa print('Finished processing of building', curr_id) print('#######################################################') print() # If only single building should be processed, break loop if multi_data is False: break # #------------------------------------------------------------- print('Added all buildings with data to city object.') # VDI 6007 simulation to generate space heating load curves # Overwrites existing heat load curves (and annual heat demands) if th_gen_method == 3: print('Perform VDI 6007 space heating load simulation for every' ' building') if el_gen_method == 1: # Skip usage of occupancy and electrial load profiles # as internal loads within VDI 6007 core requ_profiles = False else: requ_profiles = True tusage.calc_and_add_vdi_6007_loads_to_city(city=city_object, air_vent_mode=air_vent_mode, vent_factor=vent_factor, t_set_heat=t_set_heat, t_set_cool=t_set_cool, t_night=t_night, alpha_rad=None, project_name=project_name, requ_profiles=requ_profiles) # Set call_teaser to False, as it is already included # in calc_and_add_vdi_6007_loads_to_city call_teaser = False if vdi_sh_manipulate: # Normalize VDI 6007 load curves to match given annual # thermal space heating energy demand for n in city_object.nodes(): if 'node_type' in city_object.nodes[n]: # If node_type is building if city_object.nodes[n]['node_type'] == 'building': # If entity is kind building if city_object.nodes[n][ 'entity']._kind == 'building': # Given value (user input) ann_sh = dict_id_vdi_sh[n] # Building pointer curr_b = city_object.nodes[n]['entity'] # Current value on object curr_sh = curr_b.get_annual_space_heat_demand() norm_factor = ann_sh / curr_sh # Do normalization # Loop over apartments for apart in curr_b.apartments: # Normalize apartment space heating load apart.demandSpaceheating.loadcurve \ *= norm_factor print('Generation results:') print('###########################################') for n in city_object.nodes(): if 'node_type' in city_object.nodes[n]: if city_object.nodes[n]['node_type'] == 'building': if 'entity' in city_object.nodes[n]: if city_object.nodes[n]['entity']._kind == 'building': print('Results of building: ', n) print('################################') print() curr_b = city_object.nodes[n]['entity'] sh_demand = curr_b.get_annual_space_heat_demand() el_demand = curr_b.get_annual_el_demand() dhw_demand = curr_b.get_annual_dhw_demand() nfa = curr_b.net_floor_area print('Annual space heating demand in kWh:') print(sh_demand) if nfa is not None and nfa != 0: print( 'Specific space heating demand in kWh/m2:') print(sh_demand / nfa) print() print('Annual electric demand in kWh:') print(el_demand) if nfa is not None and nfa != 0: print('Specific electric demand in kWh/m2:') print(el_demand / nfa) nb_occ = curr_b.get_number_of_occupants() if nb_occ is not None and nb_occ != 0: print('Specific electric demand in kWh' ' per person and year:') print(el_demand / nb_occ) print() print('Annual hot water demand in kWh:') print(dhw_demand) if nfa is not None and nfa != 0: print('Specific hot water demand in kWh/m2:') print(dhw_demand / nfa) volume_year = dhw_demand * 1000 * 3600 / ( 4200 * 35) volume_day = volume_year / 365 if nb_occ is not None and nb_occ != 0: v_person_day = \ volume_day / nb_occ print('Hot water volume per person and day:') print(v_person_day) print() # Create and add TEASER type_buildings to every building node if call_teaser: # Create TEASER project project = tusage.create_teaser_project(name=teaser_proj_name, merge_windows=merge_windows) # Generate typeBuildings and add to city tusage.create_teaser_typecity(project=project, city=city_object, generate_Output=False) if do_save: # pragma: no cover if path_save_city is None: if pickle_city_filename is None: msg = 'If path_save_city is None, pickle_city_filename' \ 'cannot be None! Instead, filename has to be ' \ 'defined to be able to save city object.' raise AssertionError this_path = os.path.dirname(os.path.abspath(__file__)) path_save_city = os.path.join(this_path, 'output', pickle_city_filename) try: # Pickle and dump city objects pickle.dump(city_object, open(path_save_city, 'wb')) print('Pickled and dumped city object to: ') print(path_save_city) except: warnings.warn('Could not pickle and save city object') if do_log: # pragma: no cover if pickle_city_filename is not None: log_file.write('pickle_city_filename: ' + str(pickle_city_filename) + '\n') print('Wrote log file to: ' + str(log_path)) # Close log file log_file.close() # Visualize city if show_city: # pragma: no cover # Plot city district try: citvis.plot_city_district(city=city_object, plot_street=False) except: warnings.warn('Could not plot city district.') return city_object if __name__ == '__main__': this_path = os.path.dirname(os.path.abspath(__file__)) # User inputs ######################################################### # Choose generation mode # ###################################################### # 0 - Use csv/txt input to generate city district # 1 - Use csv/txt input file to enrich existing city object, based on # osm call (city object should hold nodes, but no entities. City # generator is going to add building, apartment and load entities to # building nodes generation_mode = 0 # Generate environment # ###################################################### year_timer = 2017 year_co2 = 2017 timestep = 3600 # Timestep in seconds # location = (51.529086, 6.944689) # (latitude, longitude) of Bottrop location = (50.775346, 6.083887) # (latitude, longitude) of Aachen altitude = 266 # Altitude of location in m (Aachen) # Weather path try_path = None # If None, used default TRY (region 5, 2010) new_try = False # new_try has to be set to True, if you want to use TRY data of 2017 # or newer! Else: new_try = False # Space heating load generation # ###################################################### # Thermal generation method # 1 - SLP (standardized load profile) # 2 - Load and rescale Modelica simulation profile # (generated with TRY region 12, 2010) # 3 - VDI 6007 calculation (requires el_gen_method = 2) th_gen_method = 3 # For non-residential buildings, SLPs are generated automatically. # Manipulate thermal slp to fit to space heating demand? slp_manipulate = False # True - Do manipulation # False - Use original profile # Only relevant, if th_gen_method == 1 # Sets thermal power to zero in time spaces, where average daily outdoor # temperature is equal to or larger than 12 °C. Rescales profile to # original demand value. # Manipulate vdi space heating load to be normalized to given annual net # space heating demand in kWh vdi_sh_manipulate = False # Electrical load generation # ###################################################### # Choose electric load profile generation method (1 - SLP; 2 - Stochastic) # Stochastic profile is only generated for residential buildings, # which have a defined number of occupants (otherwise, SLP is used) el_gen_method = 2 # If user defindes method_3_nb or method_4_nb within input file # (only valid for non-residential buildings), SLP will not be used. # Instead, corresponding profile will be loaded (based on measurement # data, see ElectricalDemand.py within pycity) # Do normalization of el. load profile # (only relevant for el_gen_method=2). # Rescales el. load profile to expected annual el. demand value in kWh do_normalization = True # Randomize electrical demand value (residential buildings, only) el_random = True # Prevent usage of electrical heating and hot water devices in # electrical load generation (only relevant if el_gen_method == 2) prev_heat_dev = True # True: Prevent electrical heating device usage for profile generation # False: Include electrical heating devices in electrical load generation # Use cosine function to increase winter lighting usage and reduce # summer lighting usage in richadson el. load profiles # season_mod is factor, which is used to rescale cosine wave with # lighting power reference (max. lighting power) season_mod = 0.3 # If None, do not use cosine wave to estimate seasonal influence # Else: Define float # (only relevant if el_gen_method == 2) # Hot water profile generation # ###################################################### # Generate DHW profiles? (True/False) use_dhw = True # Only relevant for residential buildings # DHW generation method? (1 - Annex 42; 2 - Stochastic profiles) # Choice of Anex 42 profiles NOT recommended for multiple builings, # as profile stays the same and only changes scaling. # Stochastic profiles require defined nb of occupants per residential # building dhw_method = 2 # Only relevant for residential buildings # Define dhw volume per person and day (use_dhw=True) dhw_volumen = None # Only relevant for residential buildings # Randomize choosen dhw_volume reference value by selecting new value dhw_random = True # Input file names and pathes # ###################################################### # Define input data filename filename = 'city_3_buildings.txt' # filename = 'city_clust_simple.txt' # filename = 'aachen_forsterlinde_mod_6.txt' # filename = 'aachen_frankenberg_mod_6.txt' # filename = 'aachen_huenefeld_mod_6.txt' # filename = 'aachen_kronenberg_mod_8.txt' # filename = 'aachen_preusweg_mod_8.txt' # filename = 'aachen_tuerme_mod_6.txt' # Output filename pickle_city_filename = filename[:-4] + '.pkl' # For generation_mode == 1: # city_osm_input = None # city_osm_input = 'aachen_forsterlinde_mod_7.pkl' city_osm_input = 'aachen_frankenberg_mod_7.pkl' # city_osm_input = 'aachen_huenefeld_mod_7.pkl' # city_osm_input = 'aachen_kronenberg_mod_7.pkl' # city_osm_input = 'aachen_preusweg_mod_7.pkl' # city_osm_input = 'aachen_tuerme_mod_7.pkl' # Pickle and dump city object instance? do_save = True # Path to save city object instance to path_save_city = None # If None, uses .../output/... # Efficiency factor of thermal energy systems # Used to convert input values (final energy demand) to net energy demand eff_factor = 1 # For VDI 6007 simulation (th_gen_method == 3) # ##################################### t_set_heat = 20 # Heating set temperature in degree Celsius t_set_night = 16 # Night set back temperature in degree Celsius t_set_cool = 70 # Cooling set temperature in degree Celsius # Air exchange rate (required for th_gen_method = 3 (VDI 6007 sim.)) air_vent_mode = 2 # int; Define mode for air ventilation rate generation # 0 : Use constant value (vent_factor in 1/h) # 1 : Use deterministic, temperature-dependent profile # 2 : Use stochastic, user-dependent profile # False: Use static ventilation rate value vent_factor = 0.3 # Constant. ventilation rate # (only used, if air_vent_mode is 0. Otherwise, estimate vent_factor # based on last year of modernization) # TEASER typebuilding generation # ###################################################### # Use TEASER to generate typebuildings? call_teaser = False teaser_proj_name = filename[:-4] # Requires additional attributes (such as nb_of_floors, net_floor_area..) merge_windows = False # merge_windows : bool, optional # Defines TEASER project setting for merge_windows_calc # (default: False). If set to False, merge_windows_calc is set to False. # If True, Windows are merged into wall resistances. txt_path = os.path.join(this_path, 'input', filename) if generation_mode == 1: path_city_osm_in = os.path.join(this_path, 'input', city_osm_input) # Path for log file log_f_name = log_file_name = str('log_' + filename) log_f_path = os.path.join(this_path, 'output', log_file_name) # End of user inputs ################################################ print('Run city generator for ', filename) assert generation_mode in [0, 1] if generation_mode == 1: assert city_osm_input is not None if air_vent_mode == 1 or air_vent_mode == 2: assert el_gen_method == 2, 'air_vent_mode 1 and 2 require occupancy' \ ' profiles!' # Load district_data file district_data = get_district_data_from_txt(txt_path) if generation_mode == 1: # Load city input file city_osm = pickle.load(open(path_city_osm_in, mode='rb')) else: # Dummy value city_osm = None # Generate city district city = run_city_generator(generation_mode=generation_mode, timestep=timestep, year_timer=year_timer, year_co2=year_co2, location=location, th_gen_method=th_gen_method, el_gen_method=el_gen_method, use_dhw=use_dhw, dhw_method=dhw_method, district_data=district_data, pickle_city_filename=pickle_city_filename, eff_factor=eff_factor, show_city=True, try_path=try_path, altitude=altitude, dhw_volumen=dhw_volumen, do_normalization=do_normalization, slp_manipulate=slp_manipulate, call_teaser=call_teaser, teaser_proj_name=teaser_proj_name, air_vent_mode=air_vent_mode, vent_factor=vent_factor, t_set_heat=t_set_heat, t_set_cool=t_set_cool, t_night=t_set_night, vdi_sh_manipulate=vdi_sh_manipulate, city_osm=city_osm, el_random=el_random, dhw_random=dhw_random, prev_heat_dev=prev_heat_dev, log_path=log_f_path, season_mod=season_mod, merge_windows=merge_windows, new_try=new_try, path_save_city=path_save_city, do_save=do_save)

44.482782

173

0.52153

0

61,217

0.469189

830541d7c666d087b745fabc733309dfe46fdeb0

14,092

py

Python

cpgan_data.py

basilevh/object-discovery-cp-gan

170cdcf14aa0b5f7258d15e177485ee4fd697afb

[ "MIT" ]

14

2020-06-04T15:50:38.000Z

2021-10-03T02:59:54.000Z

cpgan_data.py

basilevh/object-discovery-cp-gan

170cdcf14aa0b5f7258d15e177485ee4fd697afb

[ "MIT" ]

null

cpgan_data.py

basilevh/object-discovery-cp-gan

170cdcf14aa0b5f7258d15e177485ee4fd697afb

[ "MIT" ]

1

2021-01-19T15:50:47.000Z

# Basile Van Hoorick, March 2020 # Common code for PyTorch implementation of Copy-Pasting GAN import copy import itertools import matplotlib.pyplot as plt import numpy as np import os, platform, time import torch import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.transforms as transforms from PIL import Image, ImageDraw from torch.utils.data import Dataset from tqdm import tqdm def read_image_robust(img_path, monochromatic=False): ''' Returns an image that meets conditions along with a success flag, in order to avoid crashing. ''' try: # image = plt.imread(img_path).copy() image = np.array(Image.open(img_path)).copy() # always uint8 success = True if np.any(np.array(image.strides) < 0): success = False # still negative stride elif not(monochromatic) and (image.ndim != 3 or image.shape[2] != 3): success = False # not RGB elif monochromatic: # width, height = image.shape[1], image.shape[0] # image = np.broadcast_to(x[:, :, np.newaxis], (height, width, 3)) image = image[:, :, np.newaxis] # one channel <=> only one ground truth except IOError: # Probably corrupt file image = None success = False return image, success def paint_squares(image, noisy=False, channels=10): ''' Paints one or more squares at random locations to create an artificial foreground image. Generates multiple associated ground truth masks; one per object. ''' width, height = image.shape[1], image.shape[0] image = image.copy() # do not overwrite background object_count = np.random.randint(1, 5) # [1, 4] inclusive masks = np.zeros((height, width, channels), dtype=np.uint8) for i in range(object_count): sq_w, sq_h = 9, 9 x1 = np.random.randint(0, width - sq_w + 1) y1 = np.random.randint(0, height - sq_h + 1) x2 = x1 + sq_w y2 = y1 + sq_h masks[y1:y2, x1:x2, i] = 255 if not(noisy): # Pick one fixed (not necessarily saturated) color for the whole square clr = np.random.randint(0, 256, 3) image[y1:y2, x1:x2] = clr else: # Pick a random fully saturated (extremal) color for every pixel image[y1:y2, x1:x2] = np.random.choice([0, 255], (sq_h, sq_w, 3)) return image, masks, object_count def create_random_gfake_mask(width, height): ''' See Appendix D. ''' x0, y0 = np.random.rand(2) * 0.8 + 0.1 num_verts = np.random.randint(4, 7) # TODO possible improvement: allow up to more vertices? # TODO possible improvement: encourage convex (currently many "sharp" objects) radii = np.random.rand(num_verts) * 0.4 + 0.1 # radii = np.random.rand(num_verts) * 0.8 + 0.2 # TODO: not very clear from paper angles = np.sort(np.random.rand(num_verts)) * 2.0 * np.pi poly_polar = list(zip(radii, angles)) poly_cart = [(int(width * (x0 + r * np.cos(a)) / 1), int(height * (y0 + r * np.sin(a)) / 1)) for (r, a) in poly_polar] # poly_cart = [(x1, y1), (x2, y2), ...] img = Image.new('L', (width, height), 0) ImageDraw.Draw(img).polygon(poly_cart, outline=1, fill=255) mask = np.array(img, dtype='uint8') assert(mask.shape == (height, width)) return mask def apply_border_zero(masks): ndim = len(masks.shape) if ndim == 2: masks[0, :] = 0 masks[-1, :] = 0 masks[:, 0] = 0 masks[:, -1] = 0 elif ndim == 3: masks[:, 0, :] = 0 masks[:, -1, :] = 0 masks[:, :, 0] = 0 masks[:, :, -1] = 0 elif ndim == 4: masks[:, :, 0, :] = 0 masks[:, :, -1, :] = 0 masks[:, :, :, 0] = 0 masks[:, :, :, -1] = 0 else: raise Exception('Mask has too many dimensions') return masks def copy_paste(fores, masks, backs, border_zero=True): # TODO possible improvement: poisson blending # if hard_thres > 0: # used_masks = (masks > hard_thres).float() # force binary # else: used_masks = masks.clone() # Border zeroing implemented in April 2020 if border_zero: used_masks = apply_border_zero(used_masks) return used_masks * fores + (1.0 - used_masks) * backs class MyCopyPasteDataset(Dataset): ''' Custom dataset class with foreground, background, and optional mask folders as image sources. Only one object may appear per image, since the object count is not kept track of. Returns irrelevant foreground anti-shortcuts as well. Enforces color (RGB) images. ''' def __init__(self, fore_dir, back_dir, mask_dir=None, rand_horz_flip=True, post_resize=-1, center_crop=False): self.fore_dir = fore_dir self.back_dir = back_dir self.rand_horz_flip = rand_horz_flip if post_resize <= 0: self.post_tf = transforms.ToTensor() # converts [0, 255] to [0.0, 1.0] elif center_crop: # Resize + square center crop self.post_tf = transforms.Compose([ transforms.ToPILImage(), transforms.Resize(post_resize), transforms.CenterCrop(post_resize), transforms.ToTensor() ]) else: # Resize both dimensions, possibly distorting the images self.post_tf = transforms.Compose([ transforms.ToPILImage(), transforms.Resize((post_resize, post_resize)), transforms.ToTensor() ]) self.has_masks = (mask_dir is not None) # Load all file paths; file names must be the same across all 2 or 3 given directories # self.all_fore_files = [] # self.all_mask_files = [] # self.all_back_files = [] # for fn in os.listdir(fore_dir): # fore_fp = os.path.join(fore_dir, fn) # if os.path.isfile(fore_fp): # back_fp = os.path.join(back_dir, fn) # assert(os.path.isfile(back_fp)) # self.all_fore_files.append(fore_fp) # self.all_back_files.append(back_fp) # if self.has_masks: # mask_fp = os.path.join(mask_dir, fn) # assert(os.path.isfile(mask_fp)) # self.all_mask_files.append(mask_fp) # Load all file paths; file names must be the same across foreground and segmentation masks self.all_fore_files = [] self.all_mask_files = [] self.all_back_files = [] for fn in os.listdir(fore_dir): fore_fp = os.path.join(fore_dir, fn) self.all_fore_files.append(fore_fp) if self.has_masks: mask_fp_jpg = os.path.join(mask_dir, fn[:-4] + '.jpg') mask_fp_png = os.path.join(mask_dir, fn[:-4] + '.png') if os.path.isfile(mask_fp_jpg): self.all_mask_files.append(mask_fp_jpg) elif os.path.isfile(mask_fp_png): self.all_mask_files.append(mask_fp_png) else: raise Exception('No matching mask file found for ' + fore_fp) for fn in os.listdir(back_dir): back_fp = os.path.join(back_dir, fn) self.all_back_files.append(back_fp) self.fore_count = len(self.all_fore_files) self.back_count = len(self.all_back_files) print('Image file count: ' + str(self.fore_count) + ' foreground, ' + str(self.back_count) + ' background, has masks: ' + str(self.has_masks)) def __len__(self): return self.fore_count def __getitem__(self, idx): # Force randomness (especially if num_workers > 0) np.random.seed(idx + int((time.time() * 654321) % 123456)) # Read random pair of images from file system success = False while not(success): file_idx = np.random.choice(self.fore_count) fp = self.all_fore_files[file_idx] fore, success = read_image_robust(fp) if not(success): continue if self.has_masks: fp = self.all_mask_files[file_idx] mask, success = read_image_robust(fp, monochromatic=True) assert(success) # must match fore # mask = ((mask > 0) * 255.0).astype('uint8') # convert soft masks to hard else: mask = None # Read random background image success = False while not(success): file_idx2 = np.random.choice(self.back_count) fp = self.all_back_files[file_idx2] back, success = read_image_robust(fp) # Read irrelevant foreground image success = False while not(success): file_idx3 = np.random.choice(self.fore_count) if file_idx3 == file_idx: continue # try again, cannot pick same image fp = self.all_fore_files[file_idx3] irrel, success = read_image_robust(fp) # Transform foregrounds (+ masks) and backgrounds # NOTE: identical random choices must be made for some images if self.rand_horz_flip: if np.random.rand() < 0.5: fore = fore[:, ::-1, :].copy() if self.has_masks: mask = mask[:, ::-1, :].copy() if np.random.rand() < 0.5: irrel = irrel[:, ::-1, :].copy() if np.random.rand() < 0.5: back = back[:, ::-1, :].copy() fore = self.post_tf(fore) irrel = self.post_tf(irrel) back = self.post_tf(back) if self.has_masks: mask = self.post_tf(mask) # Verify sizes assert(fore.shape[1:] == irrel.shape[1:]) assert(fore.shape[1:] == back.shape[1:]) if self.has_masks: assert(fore.shape[1:] == mask.shape[1:]) # Create grounded fake mask and composite width, height = fore.shape[2], fore.shape[1] # fore is (C, H, W) gfake_mask = self.post_tf(create_random_gfake_mask(width, height)) comp_gfake = copy_paste(fore, gfake_mask, back) # Construct dictionary; object count is unknown result = {'fore': fore, 'back': back, 'irrel': irrel, 'object_cnt': 1, 'gfake_mask': gfake_mask, 'comp_gfake': comp_gfake} if self.has_masks: result['mask'] = mask # don't set None, otherwise crash return result class MySquaresDataset(Dataset): ''' Custom dataset class with just a collection of background images as source. One or more artificial objects are painted to create a foreground, keeping track of object count. Returns irrelevant foreground anti-shortcuts as well. Enforces color (RGB) images. ''' def __init__(self, back_dir, rand_horz_flip=True, noisy=False, max_objects=10): self.back_dir = back_dir self.rand_horz_flip = rand_horz_flip self.post_tf = transforms.ToTensor() # converts [0, 255] to [0.0, 1.0] self.noisy = noisy self.max_objects = max_objects # Load all file paths; file names must be the same across all 2 or 3 given directories self.all_back_files = [] for fn in os.listdir(back_dir): back_fp = os.path.join(back_dir, fn) self.all_back_files.append(back_fp) self.file_count = len(self.all_back_files) print('Image file count: ' + str(self.file_count) + ', noisy: ' + str(self.noisy) + ', max objects: ' + str(self.max_objects)) def __len__(self): return self.file_count def __getitem__(self, idx): # Read a random triplet (relevant + background + irrelevant) of non-overlapping backgrounds from file system success = False while not(success): file_idx = np.random.choice(self.file_count) fp = self.all_back_files[file_idx] fore, success = read_image_robust(fp) success = False while not(success): file_idx2 = np.random.choice(self.file_count) if file_idx2 == file_idx: continue # try again, cannot pick same image fp = self.all_back_files[file_idx2] back, success = read_image_robust(fp) success = False while not(success): file_idx3 = np.random.choice(self.file_count) if file_idx3 == file_idx or file_idx3 == file_idx2: continue # try again, cannot pick same image fp = self.all_back_files[file_idx3] irrel, success = read_image_robust(fp) # Create corresponding foregrounds and masks; leave actual background unchanged fore, masks, object_cnt = paint_squares(fore, noisy=self.noisy, channels=self.max_objects) irrel, _, _ = paint_squares(irrel, noisy=self.noisy, channels=self.max_objects) # Transform foregrounds (+ masks) and backgrounds # NOTE: identical random choices must be made for some images if self.rand_horz_flip: if np.random.rand() < 0.5: fore = fore[:, ::-1, :].copy() masks = masks[:, ::-1, :].copy() if np.random.rand() < 0.5: irrel = irrel[:, ::-1, :].copy() if np.random.rand() < 0.5: back = back[:, ::-1, :].copy() fore = self.post_tf(fore) masks = self.post_tf(masks) irrel = self.post_tf(irrel) back = self.post_tf(back) # Create grounded fake mask and composite width, height = fore.shape[2], fore.shape[1] # fore is (C, H, W) gfake_mask = self.post_tf(create_random_gfake_mask(width, height)) comp_gfake = copy_paste(fore, gfake_mask, back) # Construct dictionary result = {'fore': fore, 'back': back, 'irrel': irrel, 'mask': masks, 'object_cnt': object_cnt, 'gfake_mask': gfake_mask, 'comp_gfake': comp_gfake} return result

39.92068

154

0.590477

9,761

0.692663

0

4,107

0.291442

8305a58a05e7a9623ae618b46a183f5331e34e3b

3,207

py

Python

provision/env/lib/python3.6/site-packages/ansible/plugins/become/dzdo.py

brightkan/tukole-frontend

45e1d82a4ae5a65e88e7434f67d4d1a88f462e96

[ "MIT" ]

1

2020-03-29T18:41:01.000Z

ansible/ansible/plugins/become/dzdo.py

SergeyCherepanov/ansible

875711cd2fd6b783c812241c2ed7a954bf6f670f

[ "MIT" ]

7

2020-09-07T17:27:56.000Z

2022-03-02T06:25:46.000Z

ansible/ansible/plugins/become/dzdo.py

SergeyCherepanov/ansible

875711cd2fd6b783c812241c2ed7a954bf6f670f

[ "MIT" ]

1

2020-10-30T12:48:24.000Z

# -*- coding: utf-8 -*- # Copyright: (c) 2018, Ansible Project # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import (absolute_import, division, print_function) __metaclass__ = type DOCUMENTATION = """ become: dzdo short_description: Centrify's Direct Authorize description: - This become plugins allows your remote/login user to execute commands as another user via the dzdo utility. author: ansible (@core) version_added: "2.8" options: become_user: description: User you 'become' to execute the task ini: - section: privilege_escalation key: become_user - section: dzdo_become_plugin key: user vars: - name: ansible_become_user - name: ansible_dzdo_user env: - name: ANSIBLE_BECOME_USER - name: ANSIBLE_DZDO_USER become_exe: description: Sudo executable default: dzdo ini: - section: privilege_escalation key: become_exe - section: dzdo_become_plugin key: executable vars: - name: ansible_become_exe - name: ansible_dzdo_exe env: - name: ANSIBLE_BECOME_EXE - name: ANSIBLE_DZDO_EXE become_flags: description: Options to pass to dzdo default: -H -S -n ini: - section: privilege_escalation key: become_flags - section: dzdo_become_plugin key: flags vars: - name: ansible_become_flags - name: ansible_dzdo_flags env: - name: ANSIBLE_BECOME_FLAGS - name: ANSIBLE_DZDO_FLAGS become_pass: description: Options to pass to dzdo required: False vars: - name: ansible_become_password - name: ansible_become_pass - name: ansible_dzdo_pass env: - name: ANSIBLE_BECOME_PASS - name: ANSIBLE_DZDO_PASS ini: - section: dzdo_become_plugin key: password """ from ansible.plugins.become import BecomeBase class BecomeModule(BecomeBase): name = 'dzdo' # messages for detecting prompted password issues fail = ('Sorry, try again.',) def build_become_command(self, cmd, shell): super(BecomeModule, self).build_become_command(cmd, shell) if not cmd: return cmd becomecmd = self.get_option('become_exe') or self.name flags = self.get_option('become_flags') or '' if self.get_option('become_pass'): self._prompt = '[dzdo via ansible, key=%s] password:' % self._id flags = '%s -p "%s"' % (flags.replace('-n', ''), self._prompt) user = self.get_option('become_user') or '' if user: user = '-u %s' % (user) return ' '.join([becomecmd, flags, user, self._build_success_command(cmd, shell)])

32.72449

131

0.558154

811

0.252884

0

2,434

0.758965

830713faff66a018b4d3b736c65a71173ebb4219

3,078

py

Python

templates/php/functionsTest.py

anconaesselmann/LiveUnit

8edebb49cb02fa898550cbafdf87af7fc22f106b

[ "MIT" ]

null

templates/php/functionsTest.py

anconaesselmann/LiveUnit

8edebb49cb02fa898550cbafdf87af7fc22f106b

[ "MIT" ]

null

templates/php/functionsTest.py

anconaesselmann/LiveUnit

8edebb49cb02fa898550cbafdf87af7fc22f106b

[ "MIT" ]

null

import unittest import os if __name__ == '__main__' and __package__ is None: from os import sys, path sys.path.append(path.abspath(path.join(__file__, "..", ".."))) sys.path.append(path.abspath(path.join(__file__, "..", "..", "..", "classes_and_tests"))) from php.functions import * from src.mocking.MockFileSystem import MockFileSystem class PhpFunctionsTest(unittest.TestCase): def test_get_doc_block_tag(self): settings = "{\"author\": \"Axel\"}" args = {"settings" : settings} expected = "@author Axel" fc = FunctionCollection() result = fc.get_doc_block_tag(args) self.assertEqual(expected, result) def test_get_doc_block_tag_with_empty_value(self): settings = "{\"author\": None}" args = {"settings" : settings} expected = None fc = FunctionCollection() result = fc.get_doc_block_tag(args) self.assertEqual(expected, result) def test_get_class_name(self): args = {"dir" : path.join("Folder1", "Folder2", "FileName.php")} expected = "FileName" fc = FunctionCollection() result = fc.get_class_name(args) self.assertEqual(expected, result) def test_get_py_package_name(self): args = {"dir" : path.join(os.sep, "MyProject", "library", "aae", "mvc", "Controller.php")} expected = path.join("aae\\mvc") mockFileSystem = MockFileSystem() mockFileSystem.createFile(path.join(os.sep, "MyProject", "libraryTest", "SomeFileTest.php")) fc = FunctionCollection() fc.fileSystem = mockFileSystem result = fc.get_php_namespace(args) self.assertEqual(expected, result) """def test_get_relative_autoloader_path(self): settings = "{\"php_autoloader_dir\": \"relative/path/to/Autoloader.php\"}" args = {"settings" : settings} expected = "require_once strstr(__FILE__, 'Test', true).'/relative/path/to/Autoloader.php';" result = FunctionCollection.get_php_autoloader(args) self.assertEqual(expected, result) def test_get_absolute_autoloader_path(self): settings = "{\"php_autoloader_dir\": \"/absolute/path/to/Autoloader.php\"}" args = {"settings" : settings} expected = "require_once \"/absolute/path/to/Autoloader.php\";" result = FunctionCollection.get_php_autoloader(args) self.assertEqual(expected, result) def test_getautoloader_path_with_no_value(self): settings = "{\"php_autoloader_dir\": None}" args = {"settings" : settings} expected = None result = FunctionCollection.get_php_autoloader(args) self.assertEqual(expected, result) def test_get_php_namespace(self): settings = "{\"base_dir\": \"/MyProject/library\"}" args = {"settings" : settings, "dir": "/MyProject/library/aae/mvc/Controller.php"} expected = "aae\\mvc" result = FunctionCollection.get_php_namespace(args) self.assertEqual(expected, result)""" if __name__ == '__main__': unittest.main()

34.58427

100

0.649448

2,677

0.869721

0

1,611

0.523392

8307eb589ed701e9bef2d35aecb16eec594af392

5,629

py

Python

app.py

opeyemibami/decision_support_system

15ffdd795c8f2704b577a9c84db9dafb1fcf792d

[ "MIT" ]

1

2021-10-31T13:07:24.000Z

app.py

opeyemibami/decision_support_system

15ffdd795c8f2704b577a9c84db9dafb1fcf792d

[ "MIT" ]

null

app.py

opeyemibami/decision_support_system

15ffdd795c8f2704b577a9c84db9dafb1fcf792d

[ "MIT" ]

1

2022-02-03T13:12:59.000Z

import sys import numpy as np import matplotlib.pyplot as plt from PIL import Image import efficientnet.keras as efn import streamlit as st import SessionState from skimage.transform import resize import skimage import skimage.filters import reportgenerator import style from keras.models import Model, load_model st.set_option('deprecation.showPyplotGlobalUse', False) model = load_model('classifier.h5') st.markdown( f""" <style> .reportview-container .main .block-container{{ max-width: {1000}px; padding-top: {5}rem; padding-right: {0}rem; padding-left: {0}rem; padding-bottom: {0}rem; }} .reportview-container .main {{ }} [data-testid="stImage"] img {{ margin: 0 auto; max-width: 500px; }} </style> """, unsafe_allow_html=True, ) # main panel logo = Image.open('dss_logo.png') st.image(logo, width=None) style.display_app_header(main_txt='Gleason Score Prediction for Prostate Cancer', sub_txt='The intensity of prostate cancer metastasis in using artificial intelligence', is_sidebar=False) # session state ss = SessionState.get(page='home', run_model=False) st.markdown('**Upload biopsy image to analyze**') st.write('') uploaded_file = st.file_uploader("Choose an image...", type=['png', 'jpg']) med_opinion_list = ["The cancer cells look like healthy cells and PSA levels are low. However, cancer in this early stage is usually slow growing.", "Well differentiated cells and PSA levels are medium. This stage also includes larger tumors found only in the prostate, as long as the cancer cells are still well differentiated. ", "Moderately diffentiated cells and the PSA level is medium. The tumor is found only inside the prostate, and it may be large enough to be felt during DRE.", "Moderately or poorly diffentiated cells and the PSA level is medium. The tumor is found only inside the prostate, and it may be large enough to be felt during DRE.", "Poorly diffentiated cells. The cancer has spread beyond the outer layer of the prostate into nearby tissues. It may also have spread to the seminal vesicles. The PSA level is high.", "Poorly diffentiated cells. The tumor has grown outside of the prostate gland and may have invaded nearby structures, such as the bladder or rectum.", "Poorly diffentiated cells. The cancer cells across the tumor are poorly differentiated, meaning they look very different from healthy cells.", "Poorly diffentiated cells. The cancer has spread to the regional lymph nodes.", "Poorly diffentiated cells. The cancer has spread to distant lymph nodes, other parts of the body, or to the bones.", ] if uploaded_file is not None: # uploaded_file.read() image = Image.open(uploaded_file) st.image(image, caption='Biopsy image', use_column_width=True) im_resized = image.resize((224, 224)) im_resized = resize(np.asarray(im_resized), (224, 224, 3)) # grid section col1, col2, col3 = st.columns(3) col1.header('Resized Image') col1.image(im_resized, caption='Biopsy image', use_column_width=False) with col2: st.header('Gray Image') gray_image = skimage.color.rgb2gray(im_resized) st.image(gray_image, caption='preprocessed image', use_column_width=False) with col3: st.header('Spotted Pattern') # sigma = float(sys.argv[2]) gray_image = skimage.color.rgb2gray(im_resized) blur = skimage.filters.gaussian(gray_image, sigma=1.5) # perform adaptive thresholding t = skimage.filters.threshold_otsu(blur) mask = blur > t sel = np.zeros_like(im_resized) sel[mask] = im_resized[mask] st.image(sel, caption='preprocessed image', use_column_width=False) preds = model.predict(np.expand_dims(im_resized, 0)) data = (preds[0]*100).round(2) isup_data = [data[0], data[1], data[2], data[3], data[4]+data[5]+data[6], data[7]+data[8]+data[9]] gleason_label = ['0+0', '3+3', '3+4', '4+3', '4+4', '3+5', '5+3', '4+5', '5+4', '5+5'] gleason_colors = ['yellowgreen', 'red', 'gold', 'lightskyblue', 'cyan', 'lightcoral', 'blue', 'pink', 'darkgreen', 'yellow'] isup_label = ['0', '1', '2', '3', '4', '5'] isup_colors = ['gold', 'lightskyblue', 'cyan', 'lightcoral', 'blue'] col1, col2, = st.columns(2) with col1: reportgenerator.visualize_confidence_level(data, label=gleason_label, ylabel='GleasonScore Pattern Scale', title='GleasonScore Prediction ') with col2: reportgenerator.pieChart(data, label=gleason_label, colors=gleason_colors, title='GleasonScore Prediction Distribution', startangle=120) col1, col2, = st.columns(2) with col1: reportgenerator.pieChart(isup_data, label=isup_label, colors=isup_colors, title='ISUP Pattern Scale Prediction Distribution', startangle=45) with col2: reportgenerator.visualize_confidence_level(isup_data, label=isup_label, ylabel='ISUP Pattern Scale', title='ISUP Prediction') opinion = list(data).index(max(list(data))) style.display_app_header(main_txt='Medical Report Proposition:', sub_txt=med_opinion_list[opinion], is_sidebar=False)

45.032

203

0.650382

0

2,568

0.456209

83080191fabbc152072cd0019bf81fd6f737d375

7,129

py

Python

richardson_extrapolation.py

PrabalChowdhury/CSE330-NUMERICAL-METHODS

aabfea01f4ceaecfbb50d771ee990777d6e1122c

[ "MIT" ]

null

richardson_extrapolation.py

PrabalChowdhury/CSE330-NUMERICAL-METHODS

aabfea01f4ceaecfbb50d771ee990777d6e1122c

[ "MIT" ]

null

richardson_extrapolation.py

PrabalChowdhury/CSE330-NUMERICAL-METHODS

aabfea01f4ceaecfbb50d771ee990777d6e1122c

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Richardson-Extrapolation.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1oNlSL2Vztk9Fc7tMBgPcL82WGaUuCY-A Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\rightarrow$Run All). Make sure you fill in any place that says `YOUR CODE HERE` or "YOUR ANSWER HERE", as well as your name and collaborators below: """ NAME = "Prabal Chowdhury" COLLABORATORS = "" """--- ## CSE330 Lab: Richardson Extrapolation --- ## Instructions Today's assignment is to: 1. Implement Richardson Extrapolation method using Python ## Richardson Extrapolation: We used central difference method to calculate derivatives of functions last task. In this task we will use Richardson extrapolation to get a more accurate result. Let, $$ D_h = \frac{f(x_1+h) -f(x_1-h)}{2h}\tag{5.1}$$ General Taylor Series formula: $$ f(x) = f(x_1) + f'(x_1)(x - x_1) + \frac{f''(x_1)}{2}(x - x_1)^2+... $$ Using Taylor's theorem to expand we get, \begin{align} f(x_1+h) &= f(x_1) + f^{\prime}(x_1)h + \frac{f^{\prime \prime}(x_1)}{2}h^2 + \frac{f^{\prime \prime \prime}(x_1)}{3!}h^3 + \frac{f^{(4)}(x_1)}{4!}h^4 + \frac{f^{(5)}(x_1)}{5!}h^5 + O(h^6)\tag{5.2} \\ f(x_1-h) &= f(x_1) - f^{\prime}(x_1)h + \frac{f^{\prime \prime}(x_1)}{2}h^2 - \frac{f^{\prime \prime \prime}(x_1)}{3!}h^3 + \frac{f^{(4)}(x_1)}{4!}h^4 - \frac{f^{(5)}(x_1)}{5!}h^5 + O(h^6)\tag{5.3} \end{align} Subtracting $5.3$ from $5.2$ we get, $$ f(x_1+h) - f(x_1-h) = 2f^{\prime}(x_1)h + 2\frac{f^{\prime \prime \prime}(x_1)}{3!}h^3 + 2\frac{f^{(5)}(x_1)}{5!}h^5 + O(h^7)\tag{5.4}$$ So, \begin{align} D_h &= \frac{f(x_1+h) - f(x_1-h)}{2h} \\ &= \frac{1}{2h} \left( 2f^{\prime}(x_1)h + 2\frac{f^{\prime \prime \prime}(x_1)}{3!}h^3 + 2\frac{f^{(5)}(x_1)}{5!}h^5 + O(h^7) \right) \\ &= f^{\prime}(x_1) + \frac{f^{\prime \prime \prime}(x_1)}{6}h^2 + \frac{f^{(5)}(x_1)}{120}h^4 + O(h^6) \tag{5.5} \end{align} We get our derivative $f'(x)$ plus some error terms of order $>= 2$ Now, we want to bring our error order down to 4. If we use $h, \text{and} \frac{h}{2}$ as step size in $5.5$, we get, \begin{align} D_h &= f^{\prime}(x_1) + f^{\prime \prime \prime}(x_1)\frac{h^2}{6} + f^{(5)}(x_1) \frac{h^4}{120} + O(h^6) \tag{5.6} \\ D_{h/2} &= f^{\prime}(x_1) + f^{\prime \prime \prime}(x_1)\frac{h^2}{2^2 . 6} + f^{(5)}(x_1) \frac{h^4}{2^4 . 120} + O(h^6) \tag{5.7} \end{align} Multiplying $5.7$ by $4$ and subtracting from $5.6$ we get, \begin{align} D_h - 4D_{h/2} &= -3f^{\prime}(x) + f^{(5)}(x_1) \frac{h^4}{160} + O(h^6)\\ \Longrightarrow D^{(1)}_h = \frac{4D_{h/2} - D_h}{3} &= f^{\prime}(x) - f^{(5)}(x_1) \frac{h^4}{480} + O(h^6) \tag{5.8} \end{align} Let's calculate the derivative using $5.8$ ### 1. Let's import the necessary headers """ import numpy as np import pandas as pd import matplotlib.pyplot as plt from numpy.polynomial import Polynomial """### 2. Let's create a function named `dh(f, h, x)` function `dh(f, h, x)` takes three parameters as input: a function `f`, a value `h`, and a set of values `x`. It returns the derivatives of the function at each elements of array `x` using the Central Difference method. This calculates equation $(5.1)$. """ def dh(f, h, x): ''' Input: f: np.polynomial.Polynonimial type data. h: floating point data. x: np.array type data. Output: return np.array type data of slope at each point x. ''' # -------------------------------------------- return (f(x+h) - f(x-h)) / (2*h) # -------------------------------------------- """### 3. Let's create another funtion `dh1(f, h, x)`. `dh1(f, h, x)` takes the same type of values as `dh(f, h, x)` as input. It calculates the derivative using previously defined `dh(f, h, x)` function and using equation $5.8$ and returns the values. """ def dh1(f, h, x): ''' Input: f: np.polynomial.Polynonimial type data. h: floating point data. x: np.array type data. Output: return np.array type data of slope at each point x. ''' # -------------------------------------------- # YOUR CODE HERE return (4 * dh(f, h/2, x) - dh(f, h, x)) / 3 # -------------------------------------------- """### 4. Now let's create the `error(f, hs, x_i)` function The `error(f, hs, x_i)` function takes a function `f` as input. It also takes a list of different values of h as `hs` and a specific value as `x_i` as input. It calculates the derivatives as point `x_i` using both functions described in **B** and **C**, i.e. `dh` and `dh1` """ def error(f, hs, x_i): #Using the functions we wrote dh() my c_diff and dh1() which is my first order c diff, we find the error through appending their diffrences with Y_actual ny f(x) ''' Input: f : np.polynomial.Polynonimial type data. hs : np.array type data. list of h. x_i: floating point data. single value of x. Output: return two np.array type data of errors by two methods.. ''' f_prime = f.deriv(1) #first order derivitive f^1(x) Y_actual = f_prime(x_i) diff_error = [] diff2_error = [] for h in hs: #where h is my loop counter iterating through hs # for each values of hs calculate the error using both methods # and append those values into diff_error and diff2_error list. # -------------------------------------------- # YOUR CODE HERE e1 = Y_actual - dh(f, hs, x_i) diff_error.append(e1) e2 = Y_actual - dh1(f, hs, x_i) diff2_error.append(e2) # -------------------------------------------- print(pd.DataFrame({"h": hs, "Diff": diff_error, "Diff2": diff2_error})) return diff_error, diff2_error """### 5. Finally let's run some tests function to draw the actual function """ def draw_graph(f, ax, domain=[-10, 10], label=None): data = f.linspace(domain=domain) ax.plot(data[0], data[1], label='Function') """### Draw the polynomial and it's actual derivative function""" fig, ax = plt.subplots() ax.axhline(y=0, color='k') p = Polynomial([2.0, 1.0, -6.0, -2.0, 2.5, 1.0]) p_prime = p.deriv(1) draw_graph(p, ax, [-2.4, 1.5], 'Function') draw_graph(p_prime, ax, [-2.4, 1.5], 'Derivative') ax.legend() """### Draw the actual derivative and richardson derivative using `h=1` and `h=0.1` as step size.""" fig, ax = plt.subplots() ax.axhline(y=0, color='k') draw_graph(p_prime, ax, [-2.4, 1.5], 'actual') h = 1 x = np.linspace(-2.4, 1.5, 50, endpoint=True) y = dh1(p, h, x) ax.plot(x, y, label='Richardson; h=1') h = 0.1 x = np.linspace(-2.4, 1.5, 50, endpoint=True) y = dh1(p, h, x) ax.plot(x, y, label='Richardson; h=0.1') ax.legend() """### Draw error-vs-h cuve""" fig, ax = plt.subplots() ax.axhline(y=0, color='k') hs = np.array([1., 0.55, 0.3, .17, 0.1, 0.055, 0.03, 0.017, 0.01]) e1, e2 = error(p, hs, 2.0) ax.plot(hs, e1, label='e1') ax.plot(hs, e2, label='e2') ax.legend()

36.747423

273

0.591668

0

5,533

0.776126

83092d72acd08ca21db99e040f029c6dead0fb17

9,050

py

Python

src/mlshell/blocks/pipeline/steps.py

nizaevka/mlshell

36893067f598f6b071b61604423d0fd15c2a7c62

[ "Apache-2.0" ]

8

2020-10-04T15:33:58.000Z

2020-11-24T15:10:18.000Z

src/mlshell/blocks/pipeline/steps.py

nizaevka/mlshell

36893067f598f6b071b61604423d0fd15c2a7c62

[ "Apache-2.0" ]

5

2020-03-06T18:13:10.000Z

2022-03-12T00:52:48.000Z

src/mlshell/blocks/pipeline/steps.py

nizaevka/mlshell

36893067f598f6b071b61604423d0fd15c2a7c62

[ "Apache-2.0" ]

null

"""The :mod:`mlshell.pipeline.steps` contains unified pipeline steps.""" import inspect import mlshell import numpy as np import pandas as pd import sklearn import sklearn.impute import sklearn.compose __all__ = ['Steps'] class Steps(object): """Unified pipeline steps. Parameters ---------- estimator : :mod:`sklearn` estimator Estimator to use in the last step. If ``estimator_type=regressor``: ``sklearn.compose.TransformedTargetRegressor(regressor=`estimator`)`` If ``estimator_type=classifier`` and ``th_step=True``: ``sklearn.pipeline.Pipeline(steps=[ ('predict_proba', mlshell.model_selection.PredictionTransformer(`estimator`)), ('apply_threshold', mlshell.model_selection.ThresholdClassifier(threshold=0.5, kwargs='auto')), ])`` If ``estimator_type=classifier`` and ``th_step=False``: ``sklearn.pipeline.Pipeline(steps=[('classifier', `estimator`)])`` estimator_type : str {'classifier`, 'regressor'}, optional (default=None) Either regression or classification task. If None, get from :func:`sklearn.base.is_classifier` on ``estimator``. th_step : bool If True and ``estimator_type=classifier``: ``mlshell.model_selection. ThresholdClassifier`` sub-step added, otherwise ignored. Notes ----- Assembling steps in class are made for convenience. Use steps property to access after initialization. Only OneHot encoder and imputer steps are initially activated. By default, 4 parameters await for resolution ('auto'): 'process_parallel__pipeline_categoric__select_columns__kw_args' 'process_parallel__pipeline_numeric__select_columns__kw_args' 'estimate__apply_threshold__threshold' 'estimate__apply_threshold__params' Set corresponding parameters with ``set_params()`` to overwrite default in created pipeline or use :class:`mlshell.model_selection.Resolver` . 'pass_custom' step allows brute force arbitrary parameters in uniform style with pipeline hp (as if score contains additional nested loops). Step name is hard-coded and could not be changed. 'apply_threshold' allows grid search classification thresholds as pipeline hyper-parameter. 'estimate' step should be the last. """ _required_parameters = ['estimator', 'estimator_type'] def __init__(self, estimator, estimator_type=None, th_step=False): if estimator_type is None: estimator_type = 'classifier' if sklearn.base.is_classifier(estimator)\ else 'regressor' self._steps = [ ('pass_custom', mlshell.preprocessing.FunctionTransformer(func=self.scorer_kwargs, validate=False, skip=True, kw_args={})), ('select_rows', mlshell.preprocessing.FunctionTransformer(func=self.subrows, validate=False, skip=True)), ('process_parallel', sklearn.pipeline.FeatureUnion(transformer_list=[ ('pipeline_categoric', sklearn.pipeline.Pipeline(steps=[ ('select_columns', mlshell.preprocessing.FunctionTransformer(self.subcolumns, validate=False, skip=False, kw_args='auto')), # {'indices': dataset.meta['categoric_ind_name']} ('encode_onehot', mlshell.preprocessing.OneHotEncoder(handle_unknown='ignore', categories='auto', sparse=False, drop=None, skip=False)), # x could be []. ])), ('pipeline_numeric', sklearn.pipeline.Pipeline(steps=[ ('select_columns', mlshell.preprocessing.FunctionTransformer(self.subcolumns, validate=False, skip=False, kw_args='auto')), # {'indices': dataset.meta['numeric_ind_name']} ('impute', sklearn.pipeline.FeatureUnion([ ('indicators', sklearn.impute.MissingIndicator(missing_values=np.nan, error_on_new=False)), ('gaps', sklearn.impute.SimpleImputer(missing_values=np.nan, strategy='constant', fill_value=0, copy=True)), ])), ('transform_normal', mlshell.preprocessing.PowerTransformer(method='yeo-johnson', standardize=False, copy=False, skip=True)), ('scale_row_wise', mlshell.preprocessing.FunctionTransformer(func=None, validate=False, skip=True)), ('scale_column_wise', sklearn.preprocessing.RobustScaler(quantile_range=(0, 100), copy=False)), ('add_polynomial', sklearn.preprocessing.PolynomialFeatures(degree=1, include_bias=False)), # x => degree=1 => x, x => degree=0 => [] ('compose_columns', sklearn.compose.ColumnTransformer([ ("discretize", sklearn.preprocessing.KBinsDiscretizer(n_bins=5, encode='onehot-dense', strategy='quantile'), self.bining_mask)], sparse_threshold=0, remainder='passthrough')) ])), ])), ('select_columns', sklearn.feature_selection.SelectFromModel(estimator=CustomSelector(estimator_type=estimator_type, verbose=False, skip=True), prefit=False)), ('reduce_dimensions', mlshell.decomposition.PCA(random_state=42, skip=True)), ('estimate', self.last_step(estimator, estimator_type, th_step=th_step)), ] def last_step(self, estimator, estimator_type, th_step): """Prepare estimator step.""" if estimator_type == 'regressor': last_step =\ sklearn.compose.TransformedTargetRegressor(regressor=estimator) elif estimator_type == 'classifier' and th_step: last_step = sklearn.pipeline.Pipeline(steps=[ ('predict_proba', mlshell.model_selection.PredictionTransformer( estimator)), ('apply_threshold', mlshell.model_selection.ThresholdClassifier( params='auto', threshold=None)), ]) elif estimator_type == 'classifier' and not th_step: last_step = sklearn.pipeline.Pipeline(steps=[('classifier', estimator)]) else: raise ValueError(f"Unknown estimator type `{estimator_type}`.") if sklearn.base.is_classifier(estimator=last_step)\ ^ (estimator_type == "classifier"): raise TypeError(f"{self.__class__.__name__}:" f"{inspect.stack()[0][3]}:" f" wrong estimator type: {last_step}") return last_step @property def steps(self): """list : access steps to pass in `sklearn.pipeline.Pipeline` .""" return self._steps def scorer_kwargs(self, x, **kw_args): """Mock function to custom kwargs setting. Parameters ---------- x : :class:`numpy.ndarray` or :class:`pandas.DataFrame` Features of shape [n_samples, n_features]. **kw_args : dict Step parameters. Could be extracted from pipeline in scorer if needed. Returns ------- result: :class:`numpy.ndarray` or :class:`pandas.DataFrame` Unchanged ``x``. """ return x def subcolumns(self, x, **kw_args): """Get sub-columns from x. Parameters ---------- x : :class:`numpy.ndarray` or :class:`pandas.DataFrame` Features of shape [n_samples, n_features]. **kw_args : dict Columns indices to extract: {'indices': array-like}. Returns ------- result: :class:`numpy.ndarray` or :class:`pandas.DataFrame` Extracted sub-columns of ``x``. """ indices = kw_args['indices'] if isinstance(x, pd.DataFrame): return x.iloc[:, indices] else: return x[:, indices] def subrows(self, x): """Get rows from x.""" # For example to delete outlier/anomalies. return x def bining_mask(self, x): """Get features indices which need bining.""" # Use slice(0, None) to get all. return [] class CustomSelector(sklearn.base.BaseEstimator): """Custom feature selector template.""" def __init__(self, estimator_type='classifier', verbose=True, skip=False): self.skip = skip self.verbose = verbose self.feature_importances_ = None self.estimator_type = estimator_type super().__init__() if not self.skip: raise NotImplementedError def fit(self, x, y): if self.skip: self.feature_importances_ = np.full(x.shape[1], fill_value=1) return self # TODO: some logic self.feature_importances_ = np.full(x.shape[1], fill_value=1) return self if __name__ == '__main__': pass

43.301435

206

0.612818

8,780

0.970166

0

132

0.014586

0

4,305

0.475691

830a09b0fe214d145afe8c3a467c3effd538a38b

2,283

py

Python

paste/application/repositories.py

Afonasev/Paste

ca1dcb566f15a9cf1aa0e97c6fc4cf4d450ec89d

[ "MIT" ]

null

paste/application/repositories.py

Afonasev/Paste

ca1dcb566f15a9cf1aa0e97c6fc4cf4d450ec89d

[ "MIT" ]

1

2018-05-07T00:12:59.000Z

paste/application/repositories.py

Afonasev/Paste

ca1dcb566f15a9cf1aa0e97c6fc4cf4d450ec89d

[ "MIT" ]

null

from datetime import datetime import peewee from paste import domain from . import db class AbstractRepository(domain.IRepository): _model = NotImplemented _entity = NotImplemented def count(self): return self._model.count() def save(self, entity): model = _entity_to_model(entity) if model.pk is None: model.created_at = datetime.utcnow() model.updated_at = datetime.utcnow() model.save() return _model_to_entity(model) def get(self, **kw): try: return _model_to_entity(self._model.get(**kw)) except peewee.DoesNotExist: raise domain.DoesNotExist('%s: %s' % (self._entity, kw)) def find(self, page, size, **kw): if kw: for k, v in kw.items(): if isinstance(v, domain.Entity): kw[k] = v.pk query = self._model.filter(**kw) else: query = self._model.select() return [_model_to_entity(i) for i in query.paginate(page, size)] def delete(self, entity): _entity_to_model(entity).delete_instance() class UserRepository(AbstractRepository): _model = db.User _entity = domain.User class SnippetRepository(AbstractRepository): _model = db.Snippet _entity = domain.Snippet def _by_object(obj): name = obj.__class__.__name__ fields = ('pk', 'created_at', 'updated_at') if name == 'User': return domain.User, db.User, fields + ('name', 'passhash') if name == 'Snippet': fields += ('author', 'name', 'syntax', 'raw', 'html') return domain.Snippet, db.Snippet, fields raise NotImplementedError def _entity_to_model(entity): _, model_cls, fields = _by_object(entity) attrs = {} for field in fields: value = getattr(entity, field) if isinstance(value, domain.Entity): value = value.pk attrs[field] = value return model_cls(**attrs) def _model_to_entity(model): entity_cls, _, fields = _by_object(model) attrs = {} for f in fields: value = getattr(model, f) if isinstance(value, db.AbstractModel): value = _model_to_entity(value) attrs[f] = value return entity_cls(**attrs)

23.78125

72

0.610162

1,234

0.540517

0

100

0.043802

830a2f904f214eab34723ae65f4d0799f4773a77

3,278

py

Python

example/example_nursery.py

airysen/racog

8751436437e9e82d80d54617a8b39fae5fd0ebdd

[ "MIT" ]

3

2019-03-06T07:58:22.000Z

2021-03-12T18:10:46.000Z

example/example_nursery.py

airysen/racog

8751436437e9e82d80d54617a8b39fae5fd0ebdd

[ "MIT" ]

1

2019-08-19T18:51:02.000Z

example/example_nursery.py

airysen/racog

8751436437e9e82d80d54617a8b39fae5fd0ebdd

[ "MIT" ]

1

2019-08-19T19:07:05.000Z

# Dataset https://archive.ics.uci.edu/ml/datasets/Nursery import numpy as np import pandas as pd from collections import Counter from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold from imblearn.metrics import geometric_mean_score from sklearn.metrics import mean_squared_error, make_scorer, roc_auc_score, log_loss from imblearn.over_sampling import SMOTE, RandomOverSampler from imblearn.under_sampling import RandomUnderSampler from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, LabelEncoder from sklearn.ensemble import RandomForestClassifier from racog import RACOG RS = 334 nurseryurl = 'https://archive.ics.uci.edu/ml/machine-learning-databases/nursery/nursery.data' attribute_list = ['parents', 'has_nurs', 'form', 'children', 'housing', 'finance', 'social', 'health', 'target'] nursery = pd.read_csv(nurseryurl, header=None, names=attribute_list) LE = LabelEncoder() X = nursery.drop('target', axis=1) y = nursery['target'] ii = y[y == 'recommend'].index.values X.drop(ii, inplace=True) y.drop(ii, inplace=True) for col in X: if X[col].dtype == 'object': X[col] = LE.fit_transform(X[col]) X = X.values LE = LabelEncoder() y = LE.fit_transform(y) rf = RandomForestClassifier() params = {'class_weight': 'balanced', 'criterion': 'entropy', 'max_depth': 15, 'max_features': 0.9, 'min_samples_leaf': 11, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0, 'n_estimators': 30} rf.set_params(**params) gscore = make_scorer(geometric_mean_score, average='multiclass') def gmean(y_true, y_pred): return geometric_mean_score(y_true, y_pred, average='multiclass') strf = StratifiedKFold(n_splits=3, shuffle=True, random_state=RS) count = 0 for train_index, test_index in strf.split(X, y): print(Counter(y[test_index]), Counter(y[train_index])) # swap train/test X_train, X_test, y_train, y_test = X[test_index], X[train_index], y[test_index], y[train_index] rf.set_params(**params) rf.fit(X_train, y_train) y_pred = rf.predict(X_test) print('#####################################################') print('Count', count) print('') print('Without oversampling | Gmean:', gmean(y_test, y_pred)) rnd_over = RandomOverSampler(random_state=RS + count) X_rndo, y_rndo = rnd_over.fit_sample(X_train, y_train) print('') rf.fit(X_rndo, y_rndo) y_pred = rf.predict(X_test) print('Random oversampling | Gmean:', gmean(y_test, y_pred)) smote = SMOTE(random_state=RS + count, kind='regular', k_neighbors=5, m=None, m_neighbors=10, n_jobs=1) X_smote, y_smote = smote.fit_sample(X_train, y_train) rf.fit(X_smote, y_smote) y_pred = rf.predict(X_test) print('') print('SMOTE oversampling | Gmean:', gmean(y_test, y_pred)) racog = RACOG(categorical_features='all', warmup_offset=100, lag0=20, n_iter='auto', threshold=10, eps=10E-5, verbose=0, n_jobs=1) X_racog, y_racog = racog.fit_sample(X_train, y_train) rf.fit(X_racog, y_racog) y_pred = rf.predict(X_test) print('RACOG oversampling | Gmean:', gmean(y_test, y_pred)) print('') count = count + 1

31.519231

99

0.682123

0

645

0.196766

830a7a30cf722db0418fa36cfcde2cb40ad3323f

8,187

py

Python

channels/piratestreaming.py

sodicarus/channels

d77402f4f460ea6daa66959aa5384aaffbff70b5

[ "MIT" ]

null

channels/piratestreaming.py

sodicarus/channels

d77402f4f460ea6daa66959aa5384aaffbff70b5

[ "MIT" ]

null

channels/piratestreaming.py

sodicarus/channels

d77402f4f460ea6daa66959aa5384aaffbff70b5

[ "MIT" ]

null

# -*- coding: utf-8 -*- # StreamOnDemand Community Edition - Kodi Addon # ------------------------------------------------------------ # streamondemand.- XBMC Plugin # Canale piratestreaming # http://www.mimediacenter.info/foro/viewforum.php?f=36 # ------------------------------------------------------------ import re import urlparse from core import config, httptools from platformcode import logger from core import scrapertools from core import servertools from core.item import Item from core.tmdb import infoSod __channel__ = "piratestreaming" host = "https://www.piratestreaming.watch/" def mainlist(item): logger.info() itemlist = [Item(channel=__channel__, title="[COLOR azure]Film[/COLOR]", action="peliculas", extra="movie", url="%s/category/films/" % host, thumbnail="http://orig03.deviantart.net/6889/f/2014/079/7/b/movies_and_popcorn_folder_icon_by_matheusgrilo-d7ay4tw.png"), Item(channel=__channel__, title="[COLOR yellow]Cerca...[/COLOR]", action="search", extra="movie", thumbnail="http://dc467.4shared.com/img/fEbJqOum/s7/13feaf0c8c0/Search"), Item(channel=__channel__, title="[COLOR azure]Serie TV[/COLOR]", extra="serie", action="peliculas_tv", url="%s/category/serie/" % host, thumbnail="http://orig03.deviantart.net/6889/f/2014/079/7/b/movies_and_popcorn_folder_icon_by_matheusgrilo-d7ay4tw.png"), Item(channel=__channel__, title="[COLOR azure]Anime[/COLOR]", extra="serie", action="peliculas_tv", url="%s/category/anime-cartoni-animati/" % host, thumbnail="http://orig03.deviantart.net/6889/f/2014/079/7/b/movies_and_popcorn_folder_icon_by_matheusgrilo-d7ay4tw.png"), Item(channel=__channel__, title="[COLOR yellow]Cerca SerieTV...[/COLOR]", action="search", extra="serie", thumbnail="http://dc467.4shared.com/img/fEbJqOum/s7/13feaf0c8c0/Search")] return itemlist def peliculas(item): logger.info() itemlist = [] # Carica la pagina data = httptools.downloadpage(item.url).data # Estrae i contenuti patron = 'data-placement="bottom" title="(.*?)" alt=[^=]+="([^"]+)"> <img' matches = re.compile(patron, re.DOTALL).findall(data) for scrapedtitle, scrapedurl in matches: scrapedthumbnail = "" scrapedplot = "" scrapedtitle = scrapertools.decodeHtmlentities(scrapedtitle).strip() itemlist.append(infoSod( Item(channel=__channel__, action="findvideos", contentType="movie", fulltitle=scrapedtitle, show=scrapedtitle, title="[COLOR azure]" + scrapedtitle + "[/COLOR]", url=scrapedurl, thumbnail=scrapedthumbnail, plot=scrapedplot, extra=item.extra, folder=True), tipo='movie')) # Paginazione patronvideos = '<a\s*class="nextpostslink" rel="next" href="([^"]+)">Avanti' matches = re.compile(patronvideos, re.DOTALL).findall(data) if len(matches) > 0: scrapedurl = urlparse.urljoin(item.url, matches[0]) itemlist.append( Item(channel=__channel__, action="HomePage", title="[COLOR yellow]Torna Home[/COLOR]", folder=True)), itemlist.append( Item(channel=__channel__, action="peliculas", title="[COLOR orange]Successivo >>[/COLOR]", url=scrapedurl, thumbnail="http://2.bp.blogspot.com/-fE9tzwmjaeQ/UcM2apxDtjI/AAAAAAAAeeg/WKSGM2TADLM/s1600/pager+old.png", folder=True)) return itemlist def peliculas_tv(item): logger.info() itemlist = [] # Carica la pagina data = httptools.downloadpage(item.url).data # Estrae i contenuti patron = 'data-placement="bottom" title="(.*?)" alt=[^=]+="([^"]+)"> <img' matches = re.compile(patron, re.DOTALL).findall(data) for scrapedtitle, scrapedurl in matches: scrapedthumbnail = "" scrapedplot = "" scrapedtitle = scrapertools.decodeHtmlentities(scrapedtitle).strip() itemlist.append(infoSod( Item(channel=__channel__, action="episodios", fulltitle=scrapedtitle, show=scrapedtitle, title="[COLOR azure]" + scrapedtitle + "[/COLOR]", url=scrapedurl, thumbnail=scrapedthumbnail, plot=scrapedplot, extra=item.extra, folder=True), tipo='tv')) # Paginazione patronvideos = '<a\s*class="nextpostslink" rel="next" href="([^"]+)">Avanti' matches = re.compile(patronvideos, re.DOTALL).findall(data) if len(matches) > 0: scrapedurl = urlparse.urljoin(item.url, matches[0]) itemlist.append( Item(channel=__channel__, action="HomePage", title="[COLOR yellow]Torna Home[/COLOR]", folder=True)), itemlist.append( Item(channel=__channel__, action="peliculas_tv", title="[COLOR orange]Successivo >>[/COLOR]", url=scrapedurl, thumbnail="http://2.bp.blogspot.com/-fE9tzwmjaeQ/UcM2apxDtjI/AAAAAAAAeeg/WKSGM2TADLM/s1600/pager+old.png", folder=True)) return itemlist def HomePage(item): import xbmc xbmc.executebuiltin("ReplaceWindow(10024,plugin://plugin.video.streamondemand)") def search(item, texto): logger.info("[piratestreaming.py] " + item.url + " search " + texto) item.url = host + "/?s=" + texto try: if item.extra == "movie": return peliculas(item) if item.extra == "serie": return peliculas_tv(item) # Continua la ricerca in caso di errore except: import sys for line in sys.exc_info(): logger.error("%s" % line) return [] def episodios(item): itemlist = [] data = httptools.downloadpage(item.url).data patron = 'link-episode">(.*?)<\/span> <a\s*ref="nofollow" target=[^=]+="([^"]+)"' matches = re.compile(patron, re.DOTALL).findall(data) for scrapedtitle, scrapedurl in matches: scrapedtitle = scrapertools.decodeHtmlentities(scrapedtitle).strip() scrapedtitle = re.sub(r'\s+', ' ', scrapedtitle) scrapedtitle = scrapedtitle.replace(" -", "") scrapedtitle = scrapedtitle.replace("", "") scrapedtitle = scrapedtitle.replace("", " ") itemlist.append( Item(channel=__channel__, action="findvid_serie", contentType="episode", title=scrapedtitle, url=scrapedurl, thumbnail=item.thumbnail, extra=item.extra, fulltitle=scrapedtitle, show=item.show)) if config.get_library_support() and len(itemlist) != 0: itemlist.append( Item(channel=__channel__, title="[COLOR yellow]""Aggiungi alla libreria""[/COLOR]", url=item.url, action="add_serie_to_library", extra="episodios", show=item.show)) return itemlist def findvid_serie(item): logger.info() itemlist = servertools.find_video_items(data=item.url) for videoitem in itemlist: videoitem.title = "".join([item.title, '[COLOR green][B]' + videoitem.title + '[/B][/COLOR]']) videoitem.fulltitle = item.fulltitle videoitem.show = item.show videoitem.thumbnail = item.thumbnail videoitem.channel = __channel__ return itemlist

36.386667

142

0.561011

0

2,368

0.289239

830be7742fbd411e52ef441a27dec4480a075f6e

5,259

py

Python

test/IECore/LayeredDictTest.py

gcodebackups/cortex-vfx

72fa6c6eb3327fce4faf01361c8fcc2e1e892672

[ "BSD-3-Clause" ]

5

2016-07-26T06:09:28.000Z

2022-03-07T03:58:51.000Z

test/IECore/LayeredDictTest.py

turbosun/cortex

4bdc01a692652cd562f3bfa85f3dae99d07c0b15

[ "BSD-3-Clause" ]

null

test/IECore/LayeredDictTest.py

turbosun/cortex

4bdc01a692652cd562f3bfa85f3dae99d07c0b15

[ "BSD-3-Clause" ]

3

2015-03-25T18:45:24.000Z

2020-02-15T15:37:18.000Z

########################################################################## # # Copyright (c) 2008-2010, Image Engine Design Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # * Neither the name of Image Engine Design nor the names of any # other contributors to this software may be used to endorse or # promote products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS # IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ########################################################################## import unittest import IECore class LayeredDictTest( unittest.TestCase ) : def testDict( self ) : dict1 = { "a" : 10, "b" : { "c" : 20, "d" : 30, }, "e" : 40, } dict2 = { "a" : 20, "b" : { "c" : 100, "f" : { "g" : 1000, }, "h" : 1 }, } d = IECore.LayeredDict( [ dict1, dict2 ] ) self.assertEqual( d["a"], 10 ) self.assertEqual( d["b"]["c"], 20 ) self.assertEqual( d["b"]["d"], 30 ) self.assertEqual( d["b"]["f"]["g"], 1000 ) self.assertEqual( d["e"], 40 ) self.assertEqual( d["b"]["h"], 1 ) self.assertRaises( KeyError, d.__getitem__, "z" ) def testCompoundObject( self ) : dict1 = IECore.CompoundObject( { "a" : IECore.IntData( 10 ), "b" : { "c" : IECore.IntData( 20 ), "d" : IECore.IntData( 30 ), }, "e" : IECore.IntData( 40 ), } ) dict2 = IECore.CompoundObject( { "a" : IECore.IntData( 20 ), "b" : { "c" : IECore.IntData( 100 ), "f" : { "g" : IECore.IntData( 1000 ), }, "h" : IECore.IntData( 1 ) }, } ) d = IECore.LayeredDict( [ dict1, dict2 ] ) self.assertEqual( d["a"], IECore.IntData( 10 ) ) self.assertEqual( d["b"]["c"], IECore.IntData( 20 ) ) self.assertEqual( d["b"]["d"], IECore.IntData( 30 ) ) self.assertEqual( d["b"]["f"]["g"], IECore.IntData( 1000 ) ) self.assertEqual( d["e"], IECore.IntData( 40 ) ) self.assertEqual( d["b"]["h"], IECore.IntData( 1 ) ) self.assertRaises( KeyError, d.__getitem__, "z" ) def testKeys( self ) : dict1 = { "a" : 10, "b" : { "c" : 20, "d" : 30, }, "e" : 40, } dict2 = IECore.CompoundObject( { "a" : IECore.IntData( 20 ), "b" : { "c" : IECore.IntData( 100 ), "f" : { "g" : IECore.IntData( 1000 ), }, "h" : IECore.IntData( 1 ) }, "i" : IECore.IntData( 1 ) } ) d = IECore.LayeredDict( [ dict1, dict2 ] ) self.assertEqual( set( d.keys() ), set( [ "a", "b", "e", "i" ] ) ) self.assertEqual( set( d["b"].keys() ), set( [ "c", "d", "f", "h" ] ) ) def testContains( self ) : dict1 = { "a" : 10, "b" : { }, "e" : 40, } dict2 = IECore.CompoundObject( { "b" : IECore.CompoundObject(), "i" : IECore.IntData( 1 ) } ) d = IECore.LayeredDict( [ dict1, dict2 ] ) self.assert_( "a" in d ) self.assert_( "b" in d ) self.assert_( "e" in d ) self.assert_( "i" in d ) self.assert_( not "x" in d ) def testGet( self ) : dict1 = { "a" : 10, "e" : 40, } dict2 = IECore.CompoundObject( { "a" : IECore.StringData( "hello" ), "b" : IECore.FloatData( 10 ), "i" : IECore.IntData( 1 ) } ) d = IECore.LayeredDict( [ dict1, dict2 ] ) self.assertEqual( d.get( "a", None ), 10 ) self.assertEqual( d.get( "b", None ), IECore.FloatData( 10 ) ) self.assertEqual( d.get( "i", None ), IECore.IntData( 1 ) ) self.assertEqual( d.get( "e", None ), 40 ) self.assertEqual( d.get( "x", 11 ), 11 ) def testLayerEditing( self ) : dict1 = { "a" : 10, "e" : 40, } dict2 = IECore.CompoundObject( { "a" : IECore.StringData( "hello" ), "b" : IECore.FloatData( 10 ), "i" : IECore.IntData( 1 ) } ) layers = [ dict1, dict2 ] d = IECore.LayeredDict( layers ) self.failUnless( d.layers is layers ) self.assertEqual( d["a"], 10 ) layers.insert( 0, { "a" : 100 } ) self.assertEqual( d["a"], 100 ) if __name__ == "__main__": unittest.main()

24.460465

76

0.56969

3,389

0.644419

0

2,064

0.39247

830c809918b4ad486fc0af2abc3ed71d8ce032a1

1,161

py

Python

www/courses/cs1120/spring2017/code/day10.py

ic4f/sergey.cs.uni.edu

52bbf121f73603fdb465dae36dbe691fe39e6e47

[ "Unlicense" ]

null

www/courses/cs1120/spring2017/code/day10.py

ic4f/sergey.cs.uni.edu

52bbf121f73603fdb465dae36dbe691fe39e6e47

[ "Unlicense" ]

null

www/courses/cs1120/spring2017/code/day10.py

ic4f/sergey.cs.uni.edu

52bbf121f73603fdb465dae36dbe691fe39e6e47

[ "Unlicense" ]

null

def makePic(): file = pickAFile() return makePicture(file) def decreaseRed(picture): for pixel in getPixels(picture): setRed(pixel, getRed(pixel) * 0.2) repaint(picture) def decreaseRed2(picture): pixels = getPixels(picture) for i in range(len(pixels)): pixel = pixels[i] setRed(pixel, getRed(pixel) * 0.2) repaint(picture) def decreaseRedHalf(picture): pixels = getPixels(picture) for i in range((len(pixels)/2) * 0.9): pixel = pixels[i] setRed(pixel, getRed(pixel) * 0.2) repaint(picture) def makeNetherlands(picture): pixels = getPixels(picture) color1 = makeColor(174,28,40) color2 = makeColor(255, 255, 255) color3 = makeColor(33,70,139) point1 = len(pixels)/3 point2 = point1 * 2 point3 = len(pixels) for i in range(0, point1): pixel = pixels[i] setColor(pixel, color1) print i for i in range(point1, point2): pixel = pixels[i] setColor(pixel, color2) print i for i in range(point2, point3): pixel = pixels[i] setColor(pixel, color3) print i repaint(picture)

23.22

41

0.612403

0

830ca47819b03f644d5fc932f9eb92819146316f

1,425

py

Python

nuplan/database/utils/boxes/box.py

MCZhi/nuplan-devkit

3c4f5b8dcd517b27cfd258915ca5fe5c54e3cb0c

[ "Apache-2.0" ]

null

nuplan/database/utils/boxes/box.py

MCZhi/nuplan-devkit

3c4f5b8dcd517b27cfd258915ca5fe5c54e3cb0c

[ "Apache-2.0" ]

null

nuplan/database/utils/boxes/box.py

MCZhi/nuplan-devkit

3c4f5b8dcd517b27cfd258915ca5fe5c54e3cb0c

[ "Apache-2.0" ]

null

from __future__ import annotations import abc from typing import Any, Dict class BoxInterface(abc.ABC): """ Interface for box. """ @property # type: ignore @abc.abstractmethod def label(self) -> int: """ Label id. :return: Label id. """ pass @label.setter # type: ignore @abc.abstractmethod def label(self, label: int) -> None: """ Sets label id. :param label: label id. """ pass @property # type: ignore @abc.abstractmethod def score(self) -> float: """ Classification score. :return: Classification score. """ pass @score.setter # type: ignore @abc.abstractmethod def score(self, score: float) -> None: """ Sets classification score. :param score: Classification score. """ pass @abc.abstractmethod def serialize(self) -> Dict[str, Any]: """ Serializes the box instance to a JSON-friendly vector representation. :return: Encoding of the box. """ pass @classmethod @abc.abstractmethod def deserialize(cls, data: Dict[str, Any]) -> BoxInterface: """ Instantiates a Box3D instance from serialized vector representation. :param data: Output from serialize. :return: Deserialized box. """ pass

22.619048

77

0.562105

1,346

0.944561

0

1,251

0.877895

0

692

0.485614

830e1e09e8a968bc1c2ae3714f7a575834f1f2be

4,627

py

Python

tests/test_training.py

Hilly12/masters-code

60b20a0e5e4c0ab9152b090b679391d8d62ec88a

[ "MIT" ]

null

tests/test_training.py

Hilly12/masters-code

60b20a0e5e4c0ab9152b090b679391d8d62ec88a

[ "MIT" ]

null

tests/test_training.py

Hilly12/masters-code

60b20a0e5e4c0ab9152b090b679391d8d62ec88a

[ "MIT" ]

null

import torch import prifair as pf N_SAMPLES = 10000 VAL_SAMPLES = 1000 STUDENT_SAMPLES = 5000 INPUTS = 1000 OUTPUTS = 5 BATCH_SIZE = 256 MAX_PHYSICAL_BATCH_SIZE = 128 EPSILON = 2.0 DELTA = 1e-5 MAX_GRAD_NORM = 1.0 N_TEACHERS = 4 N_GROUPS = 10 EPOCHS = 2 class MockModel(torch.nn.Module): def __init__(self): super(MockModel, self).__init__() self.model = torch.nn.Sequential( torch.nn.Linear(in_features=INPUTS, out_features=OUTPUTS), torch.nn.LogSoftmax(dim=1), ) def forward(self, x): return self.model(x) X = torch.randn(N_SAMPLES + VAL_SAMPLES, INPUTS) Y = torch.randint(0, OUTPUTS, (N_SAMPLES + VAL_SAMPLES,)) student = torch.randn(STUDENT_SAMPLES, INPUTS) groups = torch.randint(0, N_GROUPS, (N_SAMPLES,)) weights = torch.ones(N_SAMPLES) / N_SAMPLES train_data = torch.utils.data.TensorDataset(X[:N_SAMPLES], Y[:N_SAMPLES]) val_data = torch.utils.data.TensorDataset(X[N_SAMPLES:], Y[N_SAMPLES:]) student_data = torch.utils.data.TensorDataset(student, torch.zeros(STUDENT_SAMPLES)) train_loader = torch.utils.data.DataLoader(train_data, batch_size=BATCH_SIZE) val_loader = torch.utils.data.DataLoader(val_data, batch_size=BATCH_SIZE) student_loader = torch.utils.data.DataLoader(student_data, batch_size=BATCH_SIZE) model_class = MockModel optim_class = torch.optim.NAdam criterion = torch.nn.NLLLoss() def test_vanilla(): model, metrics = pf.training.train_vanilla( train_loader=train_loader, val_loader=val_loader, model_class=model_class, optim_class=optim_class, loss_fn=criterion, epochs=EPOCHS, ) assert model is not None and metrics is not None def test_dpsgd(): model, metrics = pf.training.train_dpsgd( train_loader=train_loader, val_loader=val_loader, model_class=model_class, optim_class=optim_class, loss_fn=criterion, target_epsilon=EPSILON, target_delta=DELTA, max_grad_norm=MAX_GRAD_NORM, epochs=EPOCHS, max_physical_batch_size=MAX_PHYSICAL_BATCH_SIZE, ) assert model is not None and metrics is not None def test_dpsgd_weighted(): model, metrics = pf.training.train_dpsgd_weighted( train_loader=train_loader, val_loader=val_loader, model_class=model_class, optim_class=optim_class, loss_fn=criterion, target_epsilon=EPSILON, target_delta=DELTA, max_grad_norm=MAX_GRAD_NORM, epochs=EPOCHS, max_physical_batch_size=MAX_PHYSICAL_BATCH_SIZE, weighting="sensitive_attr", labels=groups.numpy(), ) assert model is not None and metrics is not None model, metrics = pf.training.train_dpsgd_weighted( train_loader=train_loader, val_loader=val_loader, model_class=model_class, optim_class=optim_class, loss_fn=criterion, target_epsilon=EPSILON, target_delta=DELTA, max_grad_norm=MAX_GRAD_NORM, epochs=EPOCHS, max_physical_batch_size=MAX_PHYSICAL_BATCH_SIZE, weighting="custom", weights=weights, ) assert model is not None and metrics is not None def test_dpsgdf(): model, metrics = pf.training.train_dpsgdf( train_loader=train_loader, val_loader=val_loader, model_class=model_class, optim_class=optim_class, loss_fn=criterion, target_epsilon=EPSILON, target_delta=DELTA, base_clipping_threshold=MAX_GRAD_NORM, epochs=EPOCHS, group_labels=groups, max_physical_batch_size=MAX_PHYSICAL_BATCH_SIZE, ) assert model is not None and metrics is not None def test_pate(): model, metrics = pf.training.train_pate( train_loader=train_loader, val_loader=val_loader, student_loader=student_loader, model_class=model_class, optim_class=optim_class, loss_fn=criterion, n_teachers=N_TEACHERS, target_epsilon=EPSILON, target_delta=DELTA, epochs=EPOCHS, ) assert model is not None and metrics is not None def test_reweighed_sft_pate(): model, metrics = pf.training.train_reweighed_sftpate( train_loader=train_loader, val_loader=val_loader, student_loader=student_loader, model_class=model_class, optim_class=optim_class, loss_fn=criterion, n_teachers=N_TEACHERS, target_epsilon=EPSILON, target_delta=DELTA, epochs=EPOCHS, weights=weights, ) assert model is not None and metrics is not None

28.91875

84

0.690944

318

0.068727

0

24

0.005187

830e39c22c34be264cb1928c1b6da3f32584283d

177

py

Python

problem/01000~09999/02164/2164.py3.py

njw1204/BOJ-AC

1de41685725ae4657a7ff94e413febd97a888567

[ "MIT" ]

1

2019-04-19T16:37:44.000Z

problem/01000~09999/02164/2164.py3.py

njw1204/BOJ-AC

1de41685725ae4657a7ff94e413febd97a888567

[ "MIT" ]

1

2019-04-20T11:42:44.000Z

problem/01000~09999/02164/2164.py3.py

njw1204/BOJ-AC

1de41685725ae4657a7ff94e413febd97a888567

[ "MIT" ]

3

2019-04-19T16:37:47.000Z

2021-10-25T00:45:00.000Z

from collections import deque n,x=int(input()),deque() for i in range(1,n+1): x.append(i) while len(x)>1: x.popleft() if len(x)==1: break x.append(x.popleft()) print(x.pop())

22.125

34

0.661017

0

830e650277386eb71938c69ac25104bf879b279f

2,430

py

Python

craft_ai/timezones.py

craft-ai/craft-ai-client-python

3d8b3d9a49c0c70964deaeb9645130dd54f9a0b3

[ "BSD-3-Clause" ]

14

2016-08-26T07:06:57.000Z

2020-09-22T07:41:21.000Z

craft_ai/timezones.py

craft-ai/craft-ai-client-python

3d8b3d9a49c0c70964deaeb9645130dd54f9a0b3

[ "BSD-3-Clause" ]

94

2016-08-02T14:07:59.000Z

2021-10-06T11:50:52.000Z

craft_ai/timezones.py

craft-ai/craft-ai-client-python

3d8b3d9a49c0c70964deaeb9645130dd54f9a0b3

[ "BSD-3-Clause" ]

8

2017-02-07T12:05:57.000Z

2021-10-14T09:45:30.000Z

import re _TIMEZONE_REGEX = re.compile(r"^([+-](2[0-3]|[01][0-9])(:?[0-5][0-9])?|Z)$") TIMEZONES = { "UTC": "+00:00", "GMT": "+00:00", "BST": "+01:00", "IST": "+01:00", "WET": "+00:00", "WEST": "+01:00", "CET": "+01:00", "CEST": "+02:00", "EET": "+02:00", "EEST": "+03:00", "MSK": "+03:00", "MSD": "+04:00", "AST": "-04:00", "ADT": "-03:00", "EST": "-05:00", "EDT": "-04:00", "CST": "-06:00", "CDT": "-05:00", "MST": "-07:00", "MDT": "-06:00", "PST": "-08:00", "PDT": "-07:00", "HST": "-10:00", "AKST": "-09:00", "AKDT": "-08:00", "AEST": "+10:00", "AEDT": "+11:00", "ACST": "+09:30", "ACDT": "+10:30", "AWST": "+08:00", } def is_timezone(value): # Valid time zone range is -12:00 (-720 min) and +14:00 (+840 min) # cf. https://en.wikipedia.org/wiki/List_of_UTC_time_offsets if isinstance(value, int) and value <= 840 and value >= -720: return True if not isinstance(value, str): return False if value in TIMEZONES: return True result_reg_exp = _TIMEZONE_REGEX.match(value) is not None return result_reg_exp def get_timezone_key(configuration): for key in configuration: if configuration[key]["type"] == "timezone": return key return None def timezone_offset_in_sec(timezone): if isinstance(timezone, int): # If the offset belongs to [-15, 15] it is considered to represent hours. # This reproduces Moment's utcOffset behaviour. if timezone > -16 and timezone < 16: return timezone * 60 * 60 return timezone * 60 if timezone in TIMEZONES: timezone = TIMEZONES[timezone] if len(timezone) > 3: timezone = timezone.replace(":", "") offset = (int(timezone[-4:-2]) * 60 + int(timezone[-2:])) * 60 else: offset = (int(timezone[-2:]) * 60) * 60 if timezone[0] == "-": offset = -offset return offset def timezone_offset_in_standard_format(timezone): if isinstance(timezone, int): sign = "+" if timezone >= 0 else "-" absolute_offset = abs(timezone) if absolute_offset < 16: return "%s%02d:00" % (sign, absolute_offset) return "%s%02d:%02d" % ( sign, int(absolute_offset / 60), int(absolute_offset % 60), ) return timezone

26.413043

81

0.530864

0

746

0.306996

830ebcb1b5a538ed7758db2770eff5e0ab51ebf3

2,066

py

Python

gpvdm_gui/gui/json_fdtd.py

roderickmackenzie/gpvdm

914fd2ee93e7202339853acaec1d61d59b789987

[ "BSD-3-Clause" ]

12

2016-09-13T08:58:13.000Z

2022-01-17T07:04:52.000Z

gpvdm_gui/gui/json_fdtd.py

roderickmackenzie/gpvdm

914fd2ee93e7202339853acaec1d61d59b789987

[ "BSD-3-Clause" ]

3

2017-11-11T12:33:02.000Z

2019-03-08T00:48:08.000Z

gpvdm_gui/gui/json_fdtd.py

roderickmackenzie/gpvdm

914fd2ee93e7202339853acaec1d61d59b789987

[ "BSD-3-Clause" ]

6

2019-01-03T06:17:12.000Z

2022-01-01T15:59:00.000Z

# # General-purpose Photovoltaic Device Model - a drift diffusion base/Shockley-Read-Hall # model for 1st, 2nd and 3rd generation solar cells. # Copyright (C) 2008-2022 Roderick C. I. MacKenzie r.c.i.mackenzie at googlemail.com # # https://www.gpvdm.com # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License v2.0, as published by # the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along # with this program; if not, write to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. # ## @package json_transfer_matrix # Store the cv domain json data # import sys import os import shutil import json from json_base import json_base class json_fdtd_simulation(json_base): def __init__(self): json_base.__init__(self,"fdtd_segment") self.var_list=[] self.var_list.append(["english_name","FDTD (beta)"]) self.var_list.append(["icon","fdtd"]) self.var_list.append(["fdtd_lambda_start",520e-9]) self.var_list.append(["fdtd_lambda_stop",700e-9]) self.var_list.append(["fdtd_lambda_points",1]) self.var_list.append(["use_gpu",False]) self.var_list.append(["max_ittr",100000]) self.var_list.append(["zlen",1]) self.var_list.append(["xlen",60]) self.var_list.append(["ylen",60]) self.var_list.append(["xsize",8.0]) self.var_list.append(["lam_jmax",12]) self.var_list.append(["plot",1]) self.var_list.append(["fdtd_xzy","zy"]) self.var_list.append(["dt",1e-19]) self.var_list.append(["id",self.random_id()]) self.var_list_build() class json_fdtd(json_base): def __init__(self): json_base.__init__(self,"fdtd",segment_class=True,segment_example=json_fdtd_simulation())

31.30303

91

0.727009

969

0.469022

0

1,181

0.571636

830eef9810e77b134c4cc2e988eadd23436bf9ed

4,637

py

Python

gru/plugins/base/inventory.py

similarweb/gru

49ef70c2b5e58302c84dbe7d984a7d49aebc0384

[ "BSD-2-Clause-FreeBSD" ]

7

2016-12-11T19:58:33.000Z

2020-07-11T08:55:34.000Z

gru/plugins/base/inventory.py

similarweb/gru

49ef70c2b5e58302c84dbe7d984a7d49aebc0384

[ "BSD-2-Clause-FreeBSD" ]

null

gru/plugins/base/inventory.py

similarweb/gru

49ef70c2b5e58302c84dbe7d984a7d49aebc0384

[ "BSD-2-Clause-FreeBSD" ]

1

2019-12-09T19:31:50.000Z

from . import BasePlugin from gru.config import settings class Host(object): def __init__(self, host_id, host_data=None): self.host_id = host_id if host_data: self.host_data = host_data else: self.host_data = {} def __repr__(self): return 'Host(host_id="{}")'.format( self.host_id, self.host_data ) def __str__(self): return self.__repr__() def get_identifier(self): return self.host_id def get_display_name(self): display_name = self.field(settings.get('inventory.host_display_name_field'), None) if not display_name: display_name = self.host_id return display_name def field(self, field_name, default=None): """ Return a (possibly nested) field :param field_name: the name of the field to return. If it contains periods ("."), a nested lookup will be performed :param default: a default value to return if the field is not found :return: The value matching the field name inside the host data """ try: return self.host_data[field_name] except KeyError: pass parts = field_name.split('.') current_val = self.host_data for part in parts: try: current_val = current_val[part] except (KeyError, TypeError): return default except TypeError: return default return current_val class HostCategory(object): def __init__(self, category, group, count=0): self.category = category self.group = group self.count = count def __repr__(self): return 'HostCategory(category="{}", group="{}", count={})'.format( self.category, self.group, self.count ) def __str__(self): return self.__repr__() class HostList(object): def __init__(self, hosts=None, total_hosts=0): if hosts: self.hosts = hosts else: self.hosts = [] self.total_hosts = total_hosts def __repr__(self): return 'HostList(hosts={}..., total_hosts={})'.format( self.hosts[:5], self.total_hosts ) def append(self, host): self.hosts.append(host) def __str__(self): return self.__repr__() def __iter__(self): for host in self.hosts: yield host def __nonzero__(self): return self.hosts.__nonzero__() def __len__(self): return len(self.hosts) class InventoryProvider(BasePlugin): def host_group_breakdown(self, category): """ Returns a list of groups belonging to a category. Example: if category = "datacenter", an expected return value would be ["us-east-1", "us-west-2"] :param category: A category string to aggregate by :return: A list of HostCategory objects """ raise NotImplementedError('override me') def list(self, category, group, sort_by=None, from_ind=None, to_ind=None): """ Filter by a field and value. Example: provider.list("datacenter", "us-east-1") will return all Hosts in the us-east-1 datacenter :param category: Category to filter by (i.e. "datacenter") :param group: group to filter by (i.e. "us-east-1") :param sort_by: optional, a string representing a host attribute to sort by. hostname, for example :param from_ind: to support pagination, you may return only a subset of the results. this is the start index :param to_ind: to support pagination, you may return only a subset of the results. this is the end index :return: a list of Host objects """ raise NotImplementedError('override me') def host_search(self, query, from_ind=None, to_ind=None): """ Given a query string, perform a search of hosts :param query: a query string to perform the lookup by :param from_ind: to support pagination, you may return only a subset of the results. this is the start index :param to_ind: to support pagination, you may return only a subset of the results. this is the end index :return: a list of Host objects """ raise NotImplementedError('override me') def get_host_by_id(self, host_id): """ Return a Host object by its ID :param host_id: a host ID to query by :return: a Host object """ raise NotImplementedError('override me')

31.120805

117

0.607505

4,566

0.984688

74

0.015959

0

2,025

0.436705