Reset23/the-stack-v2-processed · Datasets at Hugging Face

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/src/primaires/combat/commandes/bander/__init__.py

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# -*-coding:Utf-8 -* # Copyright (c) 2013 LE GOFF Vincent # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # * Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT # OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. """Package contenant la commande 'bander'. """ from primaires.interpreteur.commande.commande import Commande from primaires.objet.conteneur import SurPoids class CmdBander(Commande): """Commande 'bander'. """ def __init__(self): """Constructeur de la commande""" Commande.__init__(self, "charger", "bend") self.nom_categorie = "combat" self.schema = "<jet:nom_objet> (avec/with <projectile:nom_objet>)" self.aide_courte = "charge une arme de jet" self.aide_longue = \ "Cette commande permet de charger une arme de jet que " \ "vous équipez. Elle prend en paramètre obligatoire le " \ "nom de l'arme. Si rien n'est précisé ensuite, le système " \ "cherchera le bon projectile dans vos conteneurs équipés " \ "et le placera automatiquement sur l'arme de jet. Sinon, " \ "vous pouvez préciser après le nom de l'arme de jet le " \ "mot-clé |cmd|avec|ff| (ou |cmd|with|ff| en anglais) suivi " \ "du nom du projectile. Vous devez dans tous les cas " \ "posséder le projectile indiqué." def ajouter(self): """Méthode appelée lors de l'ajout de la commande à l'interpréteur""" arme_de_jet = self.noeud.get_masque("jet") arme_de_jet.proprietes["conteneurs"] = \ "(personnage.equipement.equipes, )" projectile = self.noeud.get_masque("projectile") projectile.proprietes["conteneurs"] = \ "(personnage.equipement.inventaire_simple.iter_objets_qtt(" \ "True), )" projectile.proprietes["quantite"] = "True" projectile.proprietes["conteneur"] = "True" def interpreter(self, personnage, dic_masques): """Interprétation de la commande""" personnage.agir("charger") arme_de_jet = dic_masques["jet"].objet if not arme_de_jet.est_de_type("arme de jet"): personnage << "|err|Ceci n'est pas une arme de jet.|ff|" return if dic_masques["projectile"]: projectiles = list(dic_masques["projectile"].objets_qtt_conteneurs) projectile, qtt, conteneur = projectiles[0] if not projectile.est_de_type("projectile"): personnage << "|err|Ceci n'est pas un projectile.|ff|" return else: projectile = conteneur = None for objet in personnage.equipement.inventaire: if objet.est_de_type("projectile") and objet.cle in \ arme_de_jet.projectiles_autorises: projectile = objet conteneur = objet.contenu break if projectile is None or conteneur is None: personnage << "|err|Aucun projectile pour cette arme " \ "de jet ne peut être trouvé sur vous.|ff|" return if projectile.cle not in arme_de_jet.projectiles_autorises: personnage << "|err|Vous ne pouvez utiliser {} avec " \ "{}.|ff|".format(arme_de_jet.get_nom(), projectile.get_nom()) personnage << "Vous commencez à recharger {}.".format( arme_de_jet.get_nom()) personnage.etats.ajouter("charger") yield 1 if "charger" not in personnage.etats: return personnage.etats.retirer("charger") # Si l'arme de jet est déjà chargée if arme_de_jet.projectile: ancien_projectile = arme_de_jet.projectile try: personnage.ramasser(objet=ancien_projectile) except SurPoids: personnage.salle.objets_sol.ajouter(objet=ancien_projectile) personnage << "{} glisse à terre.".format( ancien_projectile.get_nom().capitalize()) else: personnage << "Vous récupérez {}.".format( ancien_projectile.get_nom()) arme_de_jet.projectile = None conteneur.retirer(projectile) arme_de_jet.script["charge"].executer(personnage=personnage, arme=arme_de_jet, projectile=projectile) arme_de_jet.projectile = projectile personnage << "Vous bandez {} avec {}.".format( arme_de_jet.get_nom(), projectile.get_nom()) personnage.salle.envoyer("{{}} bande {} avec {}.".format( arme_de_jet.get_nom(), projectile.get_nom()), personnage)

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nolar/shortener

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# coding: utf-8 from ._base import Storage, StorageID from ._base import StorageExpectationError, StorageItemAbsentError, StorageUniquenessError from .wrapped import WrappedStorage from .sdb import SDBStorage from .mysql import MysqlStorage

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import os import sys DISTNAME = 'iced' DESCRIPTION = 'ICE normalization' MAINTAINER = 'Nelle Varoquaux' MAINTAINER_EMAIL = 'nelle.varoquaux@gmail.com' VERSION = "0.6.0a0.dev0" SCIPY_MIN_VERSION = '0.19.0' NUMPY_MIN_VERSION = '1.16.0' # Optional setuptools features # We need to import setuptools early, if we want setuptools features, # as it monkey-patches the 'setup' function # For some commands, use setuptools SETUPTOOLS_COMMANDS = set([ 'develop', 'release', 'bdist_egg', 'bdist_rpm', 'bdist_wininst', 'install_egg_info', 'build_sphinx', 'egg_info', 'easy_install', 'upload', 'bdist_wheel', '--single-version-externally-managed', ]) if SETUPTOOLS_COMMANDS.intersection(sys.argv): import setuptools extra_setuptools_args = dict( zip_safe=False, # the package can run out of an .egg file include_package_data=True, extras_require={ 'alldeps': ( 'numpy >= {0}'.format(NUMPY_MIN_VERSION), 'scipy >= {0}'.format(SCIPY_MIN_VERSION), ), }, ) else: extra_setuptools_args = dict() def configuration(parent_package='', top_path=None): if os.path.exists('MANIFEST'): os.remove('MANIFEST') from numpy.distutils.misc_util import Configuration config = Configuration(None, parent_package, top_path) # Avoid non-useful msg: # "Ignoring attempt to set 'name' (from ... " config.set_options(ignore_setup_xxx_py=True, assume_default_configuration=True, delegate_options_to_subpackages=True, quiet=True) config.add_subpackage('iced') return config def setup_package(): metadata = dict( configuration=configuration, name=DISTNAME, maintainer=MAINTAINER, maintainer_email=MAINTAINER_EMAIL, description=DESCRIPTION, version=VERSION, scripts=['iced/scripts/ice'], classifiers=[ 'Intended Audience :: Science/Research', 'Intended Audience :: Developers', 'License :: OSI Approved', 'Programming Language :: C', 'Programming Language :: Python', 'Topic :: Software Development', 'Topic :: Scientific/Engineering', 'Operating System :: Microsoft :: Windows', 'Operating System :: POSIX', 'Operating System :: Unix', 'Operating System :: MacOS'], **extra_setuptools_args) if len(sys.argv) == 1 or ( len(sys.argv) >= 2 and ('--help' in sys.argv[1:] or sys.argv[1] in ('--help-commands', 'egg_info', '--version', 'clean'))): # For these actions, NumPy is not required # # They are required to succeed without Numpy for example when # pip is used to install Scikit-learn when Numpy is not yet present in # the system. try: from setuptools import setup except ImportError: from distutils.core import setup metadata['version'] = VERSION else: from numpy.distutils.core import setup metadata['configuration'] = configuration setup(**metadata) if __name__ == "__main__": setup_package()

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/Inpainting/contextual_attention_baseline/celebahq/model.py

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from __future__ import print_function # import numpy as np import tensorflow as tf from utils import spatial_discounting_mask from utils import random_bbox from utils import bbox2mask from utils import local_patch from utils import gan_wgan_loss from utils import random_interpolates from utils import gradient_penalty from utils import images_summary from utils import gradients_summary class CompletionModel(object): """Construct model.""" def __init__(self): print('Construct the model') def coarse_network(self, images, reuse=None): conv_layers = [] cnum = 32 with tf.variable_scope('coarse', reuse=reuse): conv1 = tf.layers.conv2d(images, cnum, 5, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv1') conv2 = tf.layers.conv2d(conv1, 2 * cnum, 3, strides=2, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv2_downsample') conv3 = tf.layers.conv2d(conv2, 2 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv3') conv4 = tf.layers.conv2d(conv3, 4 * cnum, 3, strides=2, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv4_downsample') conv5 = tf.layers.conv2d(conv4, 4 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv5') conv6 = tf.layers.conv2d(conv5, 4 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv6') conv7 = tf.layers.conv2d(conv6, 4 * cnum, 3, padding='same', dilation_rate=2, activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv7_atrous') conv8 = tf.layers.conv2d(conv7, 4 * cnum, 3, padding='same', dilation_rate=4, activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv8_atrous') conv9 = tf.layers.conv2d(conv8, 4 * cnum, 3, padding='same', dilation_rate=8, activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv9_atrous') conv10 = tf.layers.conv2d(conv9, 4 * cnum, 3, padding='same', dilation_rate=16, activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv10_atrous') conv11 = tf.layers.conv2d(conv10, 4 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv11') conv12 = tf.layers.conv2d(conv11, 4 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv12') conv13 = tf.layers.conv2d( inputs=tf.image.resize_nearest_neighbor(conv12, (conv3.get_shape().as_list()[1], conv3.get_shape().as_list()[2])), filters=2 * cnum, kernel_size=3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv13_upsample') conv14 = tf.layers.conv2d(conv13, 2 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv14') conv15 = tf.layers.conv2d( inputs=tf.image.resize_nearest_neighbor(conv14, (conv1.get_shape().as_list()[1], conv1.get_shape().as_list()[2])), filters=cnum, kernel_size=3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv15_upsample') conv16 = tf.layers.conv2d(conv15, int(cnum / 2), 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv16') conv17 = tf.layers.conv2d(conv16, 3, 3, strides=1, padding='same', kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv17') conv_output = tf.clip_by_value(conv17, -1., 1.) for i in range(1, 18): conv_layers.append(eval('conv{}'.format(i))) for conv in conv_layers: print('conv:{}, output_shape:{}'.format(conv_layers.index(conv) + 1, conv.get_shape().as_list())) return conv_output def refine_network(self, images, reuse=None): """Construct refine network.""" conv_layers = [] cnum = 32 with tf.variable_scope('refine', reuse=reuse): conv1 = tf.layers.conv2d(images, cnum, 5, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv1') conv2 = tf.layers.conv2d(conv1, cnum, 3, strides=2, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv2_downsample') conv3 = tf.layers.conv2d(conv2, 2 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv3') conv4 = tf.layers.conv2d(conv3, 2 * cnum, 3, strides=2, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv4_downsample') conv5 = tf.layers.conv2d(conv4, 4 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv5') conv6 = tf.layers.conv2d(conv5, 4 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv6') conv7 = tf.layers.conv2d(conv6, 4 * cnum, 3, padding='same', dilation_rate=2, activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv7_atrous') conv8 = tf.layers.conv2d(conv7, 4 * cnum, 3, padding='same', dilation_rate=4, activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv8_atrous') conv9 = tf.layers.conv2d(conv8, 4 * cnum, 3, padding='same', dilation_rate=8, activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv9_atrous') conv10 = tf.layers.conv2d(conv9, 4 * cnum, 3, padding='same', dilation_rate=16, activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv10_atrous') conv11 = tf.layers.conv2d(conv10, 4 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv11') conv12 = tf.layers.conv2d(conv11, 4 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv12') conv13 = tf.layers.conv2d( inputs=tf.image.resize_nearest_neighbor(conv12, (conv3.get_shape().as_list()[1], conv3.get_shape().as_list()[2])), filters=2 * cnum, kernel_size=3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv13_upsample') conv14 = tf.layers.conv2d(conv13, 2 * cnum, 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv14') conv15 = tf.layers.conv2d( inputs=tf.image.resize_nearest_neighbor(conv14, (conv1.get_shape().as_list()[1], conv1.get_shape().as_list()[2])), filters=cnum, kernel_size=3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv15_upsample') conv16 = tf.layers.conv2d(conv15, int(cnum / 2), 3, strides=1, padding='same', activation=tf.nn.elu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv16') conv17 = tf.layers.conv2d(conv16, 3, 3, strides=1, padding='same', kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv17') conv_output = tf.clip_by_value(conv17, -1., 1.) for i in range(1, 18): conv_layers.append(eval('conv{}'.format(i))) for conv in conv_layers: print('conv:{}, output_shape:{}'.format(conv_layers.index(conv) + 1, conv.get_shape().as_list())) return conv_output def global_discriminator(self, x, reuse=None): cnum = 64 with tf.variable_scope('global_discriminator', reuse=reuse): conv1 = tf.layers.conv2d(x, cnum, 5, strides=2, padding='same', activation=tf.nn.leaky_relu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv1') conv2 = tf.layers.conv2d(conv1, 2 * cnum, 5, strides=2, padding='same', activation=tf.nn.leaky_relu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv2') conv3 = tf.layers.conv2d(conv2, 4 * cnum, 5, strides=2, padding='same', activation=tf.nn.leaky_relu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv3') conv4 = tf.layers.conv2d(conv3, 4 * cnum, 5, strides=2, padding='same', activation=tf.nn.leaky_relu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv4') return tf.contrib.layers.flatten(tf.nn.leaky_relu(conv4)) def local_discriminator(self, x, reuse=None): cnum = 64 with tf.variable_scope('local_discriminator', reuse=reuse): conv1 = tf.layers.conv2d(x, cnum, 5, strides=2, padding='same', activation=tf.nn.leaky_relu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv1') conv2 = tf.layers.conv2d(conv1, 2 * cnum, 5, strides=2, padding='same', activation=tf.nn.leaky_relu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv2') conv3 = tf.layers.conv2d(conv2, 4 * cnum, 5, strides=2, padding='same', activation=tf.nn.leaky_relu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv3') conv4 = tf.layers.conv2d(conv3, 8 * cnum, 5, strides=2, padding='same', activation=tf.nn.leaky_relu, kernel_initializer=tf.keras.initializers.glorot_normal(), name='conv4') return tf.contrib.layers.flatten(tf.nn.leaky_relu(conv4)) def build_wgan_discriminator(self, global_input, local_input, reuse=None): with tf.variable_scope('wgan_discriminator', reuse=reuse): dglobal = self.global_discriminator(global_input, reuse=reuse) dlocal = self.local_discriminator(local_input, reuse=reuse) dout_global = tf.layers.dense(dglobal, 1, name='dout_global_fc') dout_local = tf.layers.dense(dlocal, 1, name='dout_local_fc') return dout_global, dout_local def build_graph_with_losses(self, batch_data, cfg, summary=True, reuse=None): # batch_pos = batch_data / 127.5 - 1 batch_pos = batch_data bbox = random_bbox(cfg) mask = bbox2mask(bbox, cfg) batch_incomplete = batch_pos * (1. - mask) ones_x = tf.ones_like(batch_incomplete)[:, :, :, 0:1] coarse_network_input = tf.concat([batch_incomplete, ones_x, ones_x * mask], axis=3) coarse_output = self.coarse_network(coarse_network_input, reuse) batch_complete_coarse = coarse_output * mask + batch_incomplete * (1. - mask) refine_network_input = tf.concat([batch_complete_coarse, ones_x, ones_x * mask], axis=3) refine_output = self.refine_network(refine_network_input, reuse) batch_complete_refine = refine_output * mask + batch_incomplete * (1. - mask) losses = {} # local patches local_patch_pos = local_patch(batch_pos, bbox) local_patch_coarse = local_patch(coarse_output, bbox) local_patch_refine = local_patch(refine_output, bbox) local_patch_mask = local_patch(mask, bbox) l1_alpha = cfg['coarse_l1_alpha'] losses['coarse_l1_loss'] = l1_alpha * tf.reduce_mean(tf.abs(local_patch_pos - local_patch_coarse) * spatial_discounting_mask(cfg)) losses['coarse_ae_loss'] = l1_alpha * tf.reduce_mean(tf.abs(batch_pos - coarse_output) * (1. - mask)) losses['refine_l1_loss'] = losses['coarse_l1_loss'] + \ tf.reduce_mean(tf.abs(local_patch_pos - local_patch_refine) * spatial_discounting_mask(cfg)) losses['refine_ae_loss'] = losses['coarse_ae_loss'] + \ tf.reduce_mean(tf.abs(batch_pos - refine_output) * (1. - mask)) losses['coarse_ae_loss'] /= tf.reduce_mean(1. - mask) losses['refine_ae_loss'] /= tf.reduce_mean(1. - mask) # wgan batch_pos_neg = tf.concat([batch_pos, batch_complete_refine], axis=0) # local discriminator patch local_patch_pos_neg = tf.concat([local_patch_pos, local_patch_refine], axis=0) # wgan with gradient penalty pos_neg_global, pos_neg_local = self.build_wgan_discriminator(batch_pos_neg, local_patch_pos_neg, reuse) pos_global, neg_global = tf.split(pos_neg_global, 2) pos_local, neg_local = tf.split(pos_neg_local, 2) # wgan loss g_loss_global, d_loss_global = gan_wgan_loss(pos_global, neg_global) g_loss_local, d_loss_local = gan_wgan_loss(pos_local, neg_local) losses['refine_g_loss'] = cfg['global_wgan_loss_alpha'] * g_loss_global + g_loss_local losses['refine_d_loss'] = d_loss_global + d_loss_local # gradient penalty interpolates_global = random_interpolates(batch_pos, batch_complete_refine) interpolates_local = random_interpolates(local_patch_pos, local_patch_refine) dout_global, dout_local = self.build_wgan_discriminator(interpolates_global, interpolates_local, reuse=True) # apply penalty penalty_global = gradient_penalty(interpolates_global, dout_global, mask=mask, norm=750.) penalty_local = gradient_penalty(interpolates_local, dout_local, mask=local_patch_mask, norm=750.) losses['gp_loss'] = cfg['wgan_gp_lambda'] * (penalty_global + penalty_local) losses['refine_d_loss'] += losses['gp_loss'] g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'coarse') +\ tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'refine') d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'wgan_discriminator') if summary: # stage1 tf.summary.scalar('rec_loss/coarse_rec_loss', losses['coarse_l1_loss'] + losses['coarse_ae_loss']) # tf.summary.scalar('rec_loss/coarse_l1_loss', losses['coarse_l1_loss']) # tf.summary.scalar('rec_loss/coarse_ae_loss', losses['coarse_ae_loss']) tf.summary.scalar('rec_loss/refine_rec_loss', losses['refine_l1_loss'] + losses['refine_ae_loss']) # tf.summary.scalar('rec_loss/refine_l1_loss', losses['refine_l1_loss']) # tf.summary.scalar('rec_loss/refine_ae_loss', losses['refine_ae_loss']) visual_img = [batch_pos, batch_incomplete, batch_complete_coarse, batch_complete_refine] visual_img = tf.concat(visual_img, axis=2) images_summary(visual_img, 'raw_incomplete_coarse_refine', 4) # stage2 gradients_summary(g_loss_global, refine_output, name='g_loss_global') gradients_summary(g_loss_local, refine_output, name='g_loss_local') tf.summary.scalar('convergence/refine_d_loss', losses['refine_d_loss']) # tf.summary.scalar('convergence/refine_g_loss', losses['refine_g_loss']) tf.summary.scalar('convergence/local_d_loss', d_loss_local) tf.summary.scalar('convergence/global_d_loss', d_loss_global) tf.summary.scalar('gradient_penalty/gp_loss', losses['gp_loss']) tf.summary.scalar('gradient_penalty/gp_penalty_local', penalty_local) tf.summary.scalar('gradient_penalty/gp_penalty_global', penalty_global) # summary the magnitude of gradients from different losses w.r.t. predicted image # gradients_summary(losses['g_loss'], refine_output, name='g_loss') gradients_summary(losses['coarse_l1_loss'] + losses['coarse_ae_loss'], coarse_output, name='rec_loss_grad_to_coarse') gradients_summary(losses['refine_l1_loss'] + losses['refine_ae_loss'] + losses['refine_g_loss'], refine_output, name='rec_loss_grad_to_refine') gradients_summary(losses['coarse_l1_loss'], coarse_output, name='l1_loss_grad_to_coarse') gradients_summary(losses['refine_l1_loss'], refine_output, name='l1_loss_grad_to_refine') gradients_summary(losses['coarse_ae_loss'], coarse_output, name='ae_loss_grad_to_coarse') gradients_summary(losses['refine_ae_loss'], refine_output, name='ae_loss_grad_to_refine') return g_vars, d_vars, losses def build_infer_graph(self, batch_data, cfg, bbox=None, name='val'): cfg['max_delta_height'] = 0 cfg['max_delta_width'] = 0 if bbox is None: bbox = random_bbox(cfg) mask = bbox2mask(bbox, cfg) batch_pos = batch_data batch_incomplete = batch_pos * (1. - mask) ones_x = tf.ones_like(batch_incomplete)[:, :, :, 0:1] coarse_network_input = tf.concat([batch_incomplete, ones_x, ones_x * mask], axis=3) # inpaint coarse_output = self.coarse_network(coarse_network_input, reuse=True) batch_complete_coarse = coarse_output * mask + batch_incomplete * (1. - mask) refine_network_input = tf.concat([batch_complete_coarse, ones_x, ones_x * mask], axis=3) refine_output = self.refine_network(refine_network_input, reuse=True) # apply mask and reconstruct # batch_complete = batch_predicted * mask + batch_incomplete * (1. - mask) batch_complete_coarse = coarse_output * mask + batch_incomplete * (1. - mask) batch_complete_refine = refine_output * mask + batch_incomplete * (1. - mask) # global image visualization visual_img = [batch_pos, batch_incomplete, batch_complete_coarse, batch_complete_refine] images_summary(tf.concat(visual_img, axis=2), name + '_raw_incomplete_coarse_refine', 10) return (batch_complete_coarse, batch_complete_refine) def build_static_infer_graph(self, batch_data, cfg, name): bbox = (tf.constant(cfg['hole_height'] // 2), tf.constant(cfg['hole_width'] // 2), tf.constant(cfg['hole_height']), tf.constant(cfg['hole_width'])) return self.build_infer_graph(batch_data, cfg, bbox, name) if __name__ == '__main__': import yaml with open('config.yaml', 'r') as f: cfg = yaml.load(f) model = CompletionModel() x = tf.random_uniform([10, 256, 256, 3]) g_vars, d_vars, losses = model.build_graph_with_losses(x, cfg) print(len(g_vars), len(d_vars))

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/[114][Flatten Binary Tree to Linked List][Medium].py

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# https://leetcode.com/problems/flatten-binary-tree-to-linked-list

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#Answer to Polynomials import numpy a=list(map(float,input().split())) x=int(input()) print(numpy.polyval(a,x)) """ poly The poly tool returns the coefficients of a polynomial with the given sequence of roots. print numpy.poly([-1, 1, 1, 10]) #Output : [ 1 -11 9 11 -10] roots The roots tool returns the roots of a polynomial with the given coefficients. print numpy.roots([1, 0, -1]) #Output : [-1. 1.] polyint The polyint tool returns an antiderivative (indefinite integral) of a polynomial. print numpy.polyint([1, 1, 1]) #Output : [ 0.33333333 0.5 1. 0. ] polyder The polyder tool returns the derivative of the specified order of a polynomial. print numpy.polyder([1, 1, 1, 1]) #Output : [3 2 1] polyval The polyval tool evaluates the polynomial at specific value. print numpy.polyval([1, -2, 0, 2], 4) #Output : 34 polyfit The polyfit tool fits a polynomial of a specified order to a set of data using a least-squares approach. print numpy.polyfit([0,1,-1, 2, -2], [0,1,1, 4, 4], 2) #Output : [ 1.00000000e+00 0.00000000e+00 -3.97205465e-16] The functions polyadd, polysub, polymul, and polydiv also handle proper addition, subtraction, multiplication, and division of polynomial coefficients, respectively. """

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/interview_cake/CAKE_203_find_rotation_point.py

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import unittest # class Solution: # naive solution with O(n) time # def findRotationPoint(self, words): # """ # :type A: List[List[int]] # :rtype: List[List[int]] # """ # length = len(words) # if length == 0: # return None # elif length == 1: # return 0 # index = 1 # prev = now = words[0] # while index < length: # prev, now = now, words[index] # if prev > now: # return index # index += 1 class Solution: # with binary search O(logn) def findRotationPoint(self, words): """ :type A: List[List[int]] :rtype: List[List[int]] """ length = len(words) left, right = 0, length-1 while True: middle = (left + right)//2 if words[left] < words[middle] > words[right]: left = middle elif words[left] > words[middle] < words[right]: right = middle else: break if right-left == length-1: # middle index never moved return 0 else: return middle+1 class BasicTest(unittest.TestCase): def test_not_rotated(self): input_ = [ 'asymptote', # <-- rotates here! 'babka', 'banoffee', 'engender', 'karpatka', 'othellolagkage', 'ptolemaic', 'retrograde', 'supplant', 'undulate', 'xenoepist', ] expected_output = 0 output = Solution().findRotationPoint(input_) self.assertEqual(output, expected_output) def test_1(self): input_ = [ 'ptolemaic', 'retrograde', 'supplant', 'undulate', 'xenoepist', 'asymptote', # <-- rotates here! 'babka', 'banoffee', 'engender', 'karpatka', 'othellolagkage', ] expected_output = 5 output = Solution().findRotationPoint(input_) self.assertEqual(output, expected_output) def test_2(self): input_ = [ 'retrograde', 'supplant', 'undulate', 'xenoepist', 'asymptote', # <-- rotates here! 'babka', 'banoffee', 'engender', 'karpatka', 'othellolagkage', ] expected_output = 4 output = Solution().findRotationPoint(input_) self.assertEqual(output, expected_output) def test_3(self): input_ = [ 'ptolemaic', 'retrograde', 'supplant', 'undulate', 'xenoepist', 'zzzzzz', 'asymptote', # <-- rotates here! 'babka', 'banoffee', 'engender', 'karpatka', 'othellolagkage', ] expected_output = 6 output = Solution().findRotationPoint(input_) self.assertEqual(output, expected_output) if __name__ == '__main__': unittest.main(verbosity=2)

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models from apps.Linea_Investigacion.models import linea_investigacion from apps.Sub_Lin_Investigacion.models import sub_lin_investigacion from apps.baseDatos.models import baseDatos from apps.Revista.models import revista from apps.palabraClave.models import palabraClave from apps.ciudad.models import ciudad from apps.pais.models import pais from django.contrib.auth.models import User # Create your models here. class articulos_cientificos(models.Model): Estado = ( ('Receptado', 'Receptado'), ('En revisión', 'En revisión'), ('Aceptado', 'Aceptado'), ('Publicado', 'Publicado'), ) Tipo = ( ('Científico', 'Científico'), ('De revisión', 'De revisión'), ('Ensayo', 'Ensayo'), ('Reflexión', 'Reflexión'), ) titulo = models.CharField(max_length=300, null=True, blank=True, unique=True) estado = models.CharField(max_length=30, blank=True, null=True, choices=Estado) iSSN = models.CharField(max_length=60, blank=True, null=True) url = models.CharField(max_length=300, blank=True, null=True) doi = models.CharField(max_length=300, blank=True, null=True) fechaPublicacion = models.DateField(blank=True, null=True) pais = models.ForeignKey(pais, blank=True, null=True) ciudad = models.ForeignKey(ciudad, blank=True, null=True) baseDatos = models.ManyToManyField(baseDatos, blank=True) revista = models.ForeignKey(revista, blank=True) volumen = models.CharField(max_length=150, blank=True, null=True) numero = models.CharField(max_length=150, blank=True, null=True) lineaInves = models.ForeignKey(linea_investigacion, max_length=150, blank=True, null=True) SubLinea = models.ForeignKey(sub_lin_investigacion, max_length=150, blank=True, null=True) resumen = models.TextField(blank=True, null=True) palabraClave = models.ManyToManyField(palabraClave, blank=True) documento = models.FileField(upload_to='articulo/', blank=True, null=True) tipoArticulo = models.CharField(max_length=150, blank=True, null=True, choices=Tipo) aprobado = models.CharField(max_length=150, blank=True, null=True) comiteEditorial = models.CharField(max_length=150, blank=True, null=True) estPub = models.BooleanField(blank=True) desEstado = models.TextField(null=True, blank=True) class Meta: permissions = ( ("ver_articulo", "ver articulo"), )

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# Copyright (c) 2017-present, Facebook, 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. ############################################################################## # # Based on: # -------------------------------------------------------- # Faster R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Sean Bell # -------------------------------------------------------- import numpy as np from detectron.core.config import cfg import detectron.utils.boxes as box_utils class GenerateProposalsOp(object): """Output object detection proposals by applying estimated bounding-box transformations to a set of regular boxes (called "anchors"). """ def __init__(self, anchors, spatial_scale, train): self._anchors = anchors self._num_anchors = self._anchors.shape[0] self._feat_stride = 1. / spatial_scale self._train = train def forward(self, inputs, outputs): """See modeling.detector.GenerateProposals for inputs/outputs documentation. """ # 1. for each location i in a (H, W) grid: # generate A anchor boxes centered on cell i # apply predicted bbox deltas to each of the A anchors at cell i # 2. clip predicted boxes to image # 3. remove predicted boxes with either height or width < threshold # 4. sort all (proposal, score) pairs by score from highest to lowest # 5. take the top pre_nms_topN proposals before NMS # 6. apply NMS with a loose threshold (0.7) to the remaining proposals # 7. take after_nms_topN proposals after NMS # 8. return the top proposals # predicted probability of fg object for each RPN anchor scores = inputs[0].data # predicted achors transformations bbox_deltas = inputs[1].data # input image (height, width, scale), in which scale is the scale factor # applied to the original dataset image to get the network input image im_info = inputs[2].data # 1. Generate proposals from bbox deltas and shifted anchors height, width = scores.shape[-2:] # Enumerate all shifted positions on the (H, W) grid shift_x = np.arange(0, width) * self._feat_stride shift_y = np.arange(0, height) * self._feat_stride shift_x, shift_y = np.meshgrid(shift_x, shift_y, copy=False) # Convert to (K, 4), K=H*W, where the columns are (dx, dy, dx, dy) # shift pointing to each grid location shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose() # Broacast anchors over shifts to enumerate all anchors at all positions # in the (H, W) grid: # - add A anchors of shape (1, A, 4) to # - K shifts of shape (K, 1, 4) to get # - all shifted anchors of shape (K, A, 4) # - reshape to (K*A, 4) shifted anchors num_images = inputs[0].shape[0] A = self._num_anchors K = shifts.shape[0] all_anchors = self._anchors[np.newaxis, :, :] + shifts[:, np.newaxis, :] all_anchors = all_anchors.reshape((K * A, 4)) rois = np.empty((0, 5), dtype=np.float32) roi_probs = np.empty((0, 1), dtype=np.float32) for im_i in range(num_images): im_i_boxes, im_i_probs = self.proposals_for_one_image( im_info[im_i, :], all_anchors, bbox_deltas[im_i, :, :, :], scores[im_i, :, :, :] ) batch_inds = im_i * np.ones( (im_i_boxes.shape[0], 1), dtype=np.float32 ) im_i_rois = np.hstack((batch_inds, im_i_boxes)) rois = np.append(rois, im_i_rois, axis=0) roi_probs = np.append(roi_probs, im_i_probs, axis=0) outputs[0].reshape(rois.shape) outputs[0].data[...] = rois if len(outputs) > 1: outputs[1].reshape(roi_probs.shape) outputs[1].data[...] = roi_probs def proposals_for_one_image( self, im_info, all_anchors, bbox_deltas, scores ): # Get mode-dependent configuration cfg_key = 'TRAIN' if self._train else 'TEST' pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N nms_thresh = cfg[cfg_key].RPN_NMS_THRESH min_size = cfg[cfg_key].RPN_MIN_SIZE # Transpose and reshape predicted bbox transformations to get them # into the same order as the anchors: # - bbox deltas will be (4 * A, H, W) format from conv output # - transpose to (H, W, 4 * A) # - reshape to (H * W * A, 4) where rows are ordered by (H, W, A) # in slowest to fastest order to match the enumerated anchors bbox_deltas = bbox_deltas.transpose((1, 2, 0)).reshape((-1, 4)) # Same story for the scores: # - scores are (A, H, W) format from conv output # - transpose to (H, W, A) # - reshape to (H * W * A, 1) where rows are ordered by (H, W, A) # to match the order of anchors and bbox_deltas scores = scores.transpose((1, 2, 0)).reshape((-1, 1)) # 4. sort all (proposal, score) pairs by score from highest to lowest # 5. take top pre_nms_topN (e.g. 6000) if pre_nms_topN <= 0 or pre_nms_topN >= len(scores): order = np.argsort(-scores.squeeze()) else: # Avoid sorting possibly large arrays; First partition to get top K # unsorted and then sort just those (~20x faster for 200k scores) inds = np.argpartition( -scores.squeeze(), pre_nms_topN )[:pre_nms_topN] order = np.argsort(-scores[inds].squeeze()) order = inds[order] bbox_deltas = bbox_deltas[order, :] all_anchors = all_anchors[order, :] scores = scores[order] # Transform anchors into proposals via bbox transformations proposals = box_utils.bbox_transform( all_anchors, bbox_deltas, (1.0, 1.0, 1.0, 1.0)) # 2. clip proposals to image (may result in proposals with zero area # that will be removed in the next step) proposals = box_utils.clip_tiled_boxes(proposals, im_info[:2]) # 3. remove predicted boxes with either height or width < min_size keep = _filter_boxes(proposals, min_size, im_info) proposals = proposals[keep, :] scores = scores[keep] # 6. apply loose nms (e.g. threshold = 0.7) # 7. take after_nms_topN (e.g. 300) # 8. return the top proposals (-> RoIs top) if nms_thresh > 0: keep = box_utils.nms(np.hstack((proposals, scores)), nms_thresh) if post_nms_topN > 0: keep = keep[:post_nms_topN] proposals = proposals[keep, :] scores = scores[keep] return proposals, scores def _filter_boxes(boxes, min_size, im_info): """Only keep boxes with both sides >= min_size and center within the image. """ # Scale min_size to match image scale min_size *= im_info[2] ws = boxes[:, 2] - boxes[:, 0] + 1 hs = boxes[:, 3] - boxes[:, 1] + 1 x_ctr = boxes[:, 0] + ws / 2. y_ctr = boxes[:, 1] + hs / 2. keep = np.where( (ws >= min_size) & (hs >= min_size) & (x_ctr < im_info[1]) & (y_ctr < im_info[0]))[0] return keep

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# Converted from EC2InstanceSample.template located at: # http://aws.amazon.com/cloudformation/aws-cloudformation-templates/ import troposphere.ec2 as ec2 from troposphere import Base64, FindInMap, GetAtt, Output, Parameter, Ref, Template template = Template() keyname_param = template.add_parameter( Parameter( "KeyName", Description="Name of an existing EC2 KeyPair to enable SSH " "access to the instance", Type="String", ) ) template.add_mapping( "RegionMap", { "us-east-1": {"AMI": "ami-7f418316"}, "us-west-1": {"AMI": "ami-951945d0"}, "us-west-2": {"AMI": "ami-16fd7026"}, "eu-west-1": {"AMI": "ami-24506250"}, "sa-east-1": {"AMI": "ami-3e3be423"}, "ap-southeast-1": {"AMI": "ami-74dda626"}, "ap-northeast-1": {"AMI": "ami-dcfa4edd"}, }, ) ec2_instance = template.add_resource( ec2.Instance( "Ec2Instance", ImageId=FindInMap("RegionMap", Ref("AWS::Region"), "AMI"), InstanceType="t1.micro", KeyName=Ref(keyname_param), SecurityGroups=["default"], UserData=Base64("80"), ) ) template.add_output( [ Output( "InstanceId", Description="InstanceId of the newly created EC2 instance", Value=Ref(ec2_instance), ), Output( "AZ", Description="Availability Zone of the newly created EC2 instance", Value=GetAtt(ec2_instance, "AvailabilityZone"), ), Output( "PublicIP", Description="Public IP address of the newly created EC2 instance", Value=GetAtt(ec2_instance, "PublicIp"), ), Output( "PrivateIP", Description="Private IP address of the newly created EC2 instance", Value=GetAtt(ec2_instance, "PrivateIp"), ), Output( "PublicDNS", Description="Public DNSName of the newly created EC2 instance", Value=GetAtt(ec2_instance, "PublicDnsName"), ), Output( "PrivateDNS", Description="Private DNSName of the newly created EC2 instance", Value=GetAtt(ec2_instance, "PrivateDnsName"), ), ] ) print(template.to_json())

[ "mark@peek.org" ]

mark@peek.org

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/tests/opc/test_rels.py

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[ "MIT" ]

permissive

amisa/python-pptx

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refs/heads/master

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# encoding: utf-8 """Test suite for pptx.part module.""" from __future__ import absolute_import import pytest from mock import call, Mock, patch, PropertyMock from pptx.opc.constants import RELATIONSHIP_TYPE as RT from pptx.opc.oxml import CT_Relationships from pptx.opc.package import Part, _Relationship, RelationshipCollection from pptx.opc.packuri import PackURI from ..unitutil import class_mock, instance_mock, loose_mock class Describe_Relationship(object): def it_remembers_construction_values(self): # test data -------------------- rId = 'rId9' reltype = 'reltype' target = Mock(name='target_part') external = False # exercise --------------------- rel = _Relationship(rId, reltype, target, None, external) # verify ----------------------- assert rel.rId == rId assert rel.reltype == reltype assert rel.target_part == target assert rel.is_external == external def it_should_raise_on_target_part_access_on_external_rel(self): rel = _Relationship(None, None, None, None, external=True) with pytest.raises(ValueError): rel.target_part def it_should_have_target_ref_for_external_rel(self): rel = _Relationship(None, None, 'target', None, external=True) assert rel.target_ref == 'target' def it_should_have_relative_ref_for_internal_rel(self): """ Internal relationships (TargetMode == 'Internal' in the XML) should have a relative ref, e.g. '../slideLayouts/slideLayout1.xml', for the target_ref attribute. """ part = Mock(name='part', partname=PackURI('/ppt/media/image1.png')) baseURI = '/ppt/slides' rel = _Relationship(None, None, part, baseURI) # external=False assert rel.target_ref == '../media/image1.png' class DescribeRelationshipCollection(object): def it_also_has_dict_style_get_rel_by_rId(self, rels_with_known_rel): rels, rId, known_rel = rels_with_known_rel assert rels[rId] == known_rel def it_should_raise_on_failed_lookup_by_rId(self, rels): with pytest.raises(KeyError): rels['rId666'] def it_has_a_len(self, rels): assert len(rels) == 0 def it_can_add_a_relationship(self, _Relationship_): baseURI, rId, reltype, target, is_external = ( 'baseURI', 'rId9', 'reltype', 'target', False ) rels = RelationshipCollection(baseURI) rel = rels.add_relationship(reltype, target, rId, is_external) _Relationship_.assert_called_once_with( rId, reltype, target, baseURI, is_external ) assert rels[rId] == rel assert rel == _Relationship_.return_value def it_can_add_a_relationship_if_not_found( self, rels_with_matching_rel_, rels_with_missing_rel_): rels, reltype, part, matching_rel = rels_with_matching_rel_ assert rels.get_or_add(reltype, part) == matching_rel rels, reltype, part, new_rel = rels_with_missing_rel_ assert rels.get_or_add(reltype, part) == new_rel def it_knows_the_next_available_rId(self, rels_with_rId_gap): rels, expected_next_rId = rels_with_rId_gap next_rId = rels._next_rId assert next_rId == expected_next_rId def it_can_find_a_related_part_by_reltype( self, rels_with_target_known_by_reltype): rels, reltype, known_target_part = rels_with_target_known_by_reltype part = rels.part_with_reltype(reltype) assert part is known_target_part def it_can_find_a_related_part_by_rId(self, rels_with_known_target_part): rels, rId, known_target_part = rels_with_known_target_part part = rels.related_parts[rId] assert part is known_target_part def it_raises_KeyError_on_part_with_rId_not_found(self, rels): with pytest.raises(KeyError): rels.related_parts['rId666'] def it_can_compose_rels_xml(self, rels_with_known_rels, rels_elm): rels_with_known_rels.xml rels_elm.assert_has_calls( [ call.add_rel( 'rId1', 'http://rt-hyperlink', 'http://some/link', True ), call.add_rel( 'rId2', 'http://rt-image', '../media/image1.png', False ), call.xml() ], any_order=True ) # def it_raises_on_add_rel_with_duplicate_rId(self, rels, rel): # with pytest.raises(ValueError): # rels.add_rel(rel) # fixtures --------------------------------------------- @pytest.fixture def _Relationship_(self, request): return class_mock(request, 'pptx.opc.package._Relationship') @pytest.fixture def rel(self, _rId, _reltype, _target_part, _baseURI): return _Relationship(_rId, _reltype, _target_part, _baseURI) @pytest.fixture def rels(self, _baseURI): return RelationshipCollection(_baseURI) @pytest.fixture def rels_elm(self, request): """ Return a rels_elm mock that will be returned from CT_Relationships.new() """ # create rels_elm mock with a .xml property rels_elm = Mock(name='rels_elm') xml = PropertyMock(name='xml') type(rels_elm).xml = xml rels_elm.attach_mock(xml, 'xml') rels_elm.reset_mock() # to clear attach_mock call # patch CT_Relationships to return that rels_elm patch_ = patch.object(CT_Relationships, 'new', return_value=rels_elm) patch_.start() request.addfinalizer(patch_.stop) return rels_elm @pytest.fixture def rels_with_known_rel(self, rels, _rId, rel): rels[_rId] = rel return rels, _rId, rel @pytest.fixture def rels_with_known_rels(self): """ Populated RelationshipCollection instance that will exercise the rels.xml property. """ rels = RelationshipCollection('/baseURI') rels.add_relationship( reltype='http://rt-hyperlink', target='http://some/link', rId='rId1', is_external=True ) part = Mock(name='part') part.partname.relative_ref.return_value = '../media/image1.png' rels.add_relationship(reltype='http://rt-image', target=part, rId='rId2') return rels @pytest.fixture def rels_with_known_target_part(self, rels, _rel_with_known_target_part): rel, rId, target_part = _rel_with_known_target_part rels.add_relationship(None, target_part, rId) return rels, rId, target_part @pytest.fixture def rels_with_matching_rel_(self, request, rels): matching_reltype_ = instance_mock( request, str, name='matching_reltype_' ) matching_part_ = instance_mock( request, Part, name='matching_part_' ) matching_rel_ = instance_mock( request, _Relationship, name='matching_rel_', reltype=matching_reltype_, target_part=matching_part_, is_external=False ) rels[1] = matching_rel_ return rels, matching_reltype_, matching_part_, matching_rel_ @pytest.fixture def rels_with_missing_rel_(self, request, rels, _Relationship_): missing_reltype_ = instance_mock( request, str, name='missing_reltype_' ) missing_part_ = instance_mock( request, Part, name='missing_part_' ) new_rel_ = instance_mock( request, _Relationship, name='new_rel_', reltype=missing_reltype_, target_part=missing_part_, is_external=False ) _Relationship_.return_value = new_rel_ return rels, missing_reltype_, missing_part_, new_rel_ @pytest.fixture def rels_with_rId_gap(self, request, rels): rel_with_rId1 = instance_mock( request, _Relationship, name='rel_with_rId1', rId='rId1' ) rel_with_rId3 = instance_mock( request, _Relationship, name='rel_with_rId3', rId='rId3' ) rels['rId1'] = rel_with_rId1 rels['rId3'] = rel_with_rId3 return rels, 'rId2' @pytest.fixture def rels_with_target_known_by_reltype( self, rels, _rel_with_target_known_by_reltype): rel, reltype, target_part = _rel_with_target_known_by_reltype rels[1] = rel return rels, reltype, target_part @pytest.fixture def _baseURI(self): return '/baseURI' @pytest.fixture def _rel_with_known_target_part( self, _rId, _reltype, _target_part, _baseURI): rel = _Relationship(_rId, _reltype, _target_part, _baseURI) return rel, _rId, _target_part @pytest.fixture def _rel_with_target_known_by_reltype( self, _rId, _reltype, _target_part, _baseURI): rel = _Relationship(_rId, _reltype, _target_part, _baseURI) return rel, _reltype, _target_part @pytest.fixture def _reltype(self): return RT.SLIDE @pytest.fixture def _rId(self): return 'rId6' @pytest.fixture def _target_part(self, request): return loose_mock(request)

[ "scanny@cisco.com" ]

scanny@cisco.com

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/src/main/output/business_word_uml_president_state_father/history.py

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[]

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matkosoric/GenericNameTesting

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refs/heads/master

2021-01-08T22:35:20.022350

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[ "soric.matko@gmail.com" ]

soric.matko@gmail.com

adcdc60dec982562dd7453dba24bea1c337f66f5

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/server.py

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StevenAWillis/counter.py

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refs/heads/master

2020-06-20T18:46:19.194569

2019-07-16T14:43:04

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from flask import Flask, render_template, request, redirect, session app =Flask(__name__) app.secret_key = 'keep it secret, keep it safe' @app.route("/") def count(): if 'count' not in session: session['count'] =0 else: session['count'] +=1 if 'countv' not in session: session['countv'] =0 else: session['countv'] +=1 return render_template("index.html", count_visits = session['count'],count_visits2 = session['countv']) @app.route("/by2") def count_2(): session['count'] +=1 return redirect("/" ) @app.route("/reset") def count_reset(): session['count'] = 0 return redirect("/" ) @app.route("/manual_count",methods=['POST']) def manual_count(): # session['manual_counter'] = request.form['number'] # session['count'] += int(session['manual_counter'])-1 session['count'] += int(request.form['number'])-1 return redirect("/" ) app.run(debug=True)

[ "you@example.com" ]

you@example.com

37d399628b1fda5d720d6cfa764e556e40257814

dea7bc1fe176d090ffa426ffd982f3f8ddb8afa7

/Histogram_Equalization.py

48c9fb6dd1c7f0db61f7ddb0227333f5d4228d5f

[]

no_license

cool229/Ashish-Kumar

f03f99a02d6c96ff94931b9026b079b7f6c8ffad

72b40c7916b2447c11a6548fbb0d72a25098a6eb

refs/heads/master

2020-03-29T20:10:41.810904

2019-03-10T09:42:47

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import cv2 import matplotlib.pyplot as plt def main(): imgpath = "D:\\COURSES\\OpenCV\\Action\\standard_test_images\\lena_color_256.tif" img = cv2.imread(imgpath, 1) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) R, G, B = cv2.split(img) output1_R = cv2.equalizeHist(R) output1_G = cv2.equalizeHist(G) output1_B = cv2.equalizeHist(B) output1 = cv2.merge((output1_R, output1_G, output1_B)) # clahe = cv2.createCLAHE() clahe = cv2.createCLAHE(clipLimit = 2.0, tileGridSize = (8,8)) output2_R = clahe.apply(R) output2_G = clahe.apply(G) output2_B = clahe.apply(B) output2 = cv2.merge((output2_R, output2_G, output2_B)) outputs = [img, output1, output2] titles = ['Original Image', 'Adjusted Histogram','CLAHE'] # outputs = [img, box, blur, gaussian] for i in range(3): plt.subplot(1, 3, i+1) plt.imshow(outputs[i]) plt.title(titles[i]) plt.xticks([]) plt.yticks([]) plt.show() if __name__ == "__main__": main()

[ "noreply@github.com" ]

cool229.noreply@github.com

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/alipay/aop/api/domain/OrderJourneyElement.py

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alipay/alipay-sdk-python-all

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2023-08-27T21:35:01.778771

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#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * from alipay.aop.api.domain.JourneyLocation import JourneyLocation from alipay.aop.api.domain.JourneyLocation import JourneyLocation from alipay.aop.api.domain.OrderExtInfo import OrderExtInfo from alipay.aop.api.domain.FunctionalService import FunctionalService from alipay.aop.api.domain.UserInfomation import UserInfomation from alipay.aop.api.domain.JourneyServiceChangeInfo import JourneyServiceChangeInfo from alipay.aop.api.domain.JourneyMerchantInfo import JourneyMerchantInfo class OrderJourneyElement(object): def __init__(self): self._arrival = None self._departure = None self._duration = None self._end_time = None self._end_time_desc = None self._ext_info = None self._functional_services = None self._passagers = None self._service_change_info = None self._service_provider = None self._start_time = None self._start_time_desc = None @property def arrival(self): return self._arrival @arrival.setter def arrival(self, value): if isinstance(value, JourneyLocation): self._arrival = value else: self._arrival = JourneyLocation.from_alipay_dict(value) @property def departure(self): return self._departure @departure.setter def departure(self, value): if isinstance(value, JourneyLocation): self._departure = value else: self._departure = JourneyLocation.from_alipay_dict(value) @property def duration(self): return self._duration @duration.setter def duration(self, value): self._duration = value @property def end_time(self): return self._end_time @end_time.setter def end_time(self, value): self._end_time = value @property def end_time_desc(self): return self._end_time_desc @end_time_desc.setter def end_time_desc(self, value): self._end_time_desc = value @property def ext_info(self): return self._ext_info @ext_info.setter def ext_info(self, value): if isinstance(value, list): self._ext_info = list() for i in value: if isinstance(i, OrderExtInfo): self._ext_info.append(i) else: self._ext_info.append(OrderExtInfo.from_alipay_dict(i)) @property def functional_services(self): return self._functional_services @functional_services.setter def functional_services(self, value): if isinstance(value, list): self._functional_services = list() for i in value: if isinstance(i, FunctionalService): self._functional_services.append(i) else: self._functional_services.append(FunctionalService.from_alipay_dict(i)) @property def passagers(self): return self._passagers @passagers.setter def passagers(self, value): if isinstance(value, list): self._passagers = list() for i in value: if isinstance(i, UserInfomation): self._passagers.append(i) else: self._passagers.append(UserInfomation.from_alipay_dict(i)) @property def service_change_info(self): return self._service_change_info @service_change_info.setter def service_change_info(self, value): if isinstance(value, JourneyServiceChangeInfo): self._service_change_info = value else: self._service_change_info = JourneyServiceChangeInfo.from_alipay_dict(value) @property def service_provider(self): return self._service_provider @service_provider.setter def service_provider(self, value): if isinstance(value, JourneyMerchantInfo): self._service_provider = value else: self._service_provider = JourneyMerchantInfo.from_alipay_dict(value) @property def start_time(self): return self._start_time @start_time.setter def start_time(self, value): self._start_time = value @property def start_time_desc(self): return self._start_time_desc @start_time_desc.setter def start_time_desc(self, value): self._start_time_desc = value def to_alipay_dict(self): params = dict() if self.arrival: if hasattr(self.arrival, 'to_alipay_dict'): params['arrival'] = self.arrival.to_alipay_dict() else: params['arrival'] = self.arrival if self.departure: if hasattr(self.departure, 'to_alipay_dict'): params['departure'] = self.departure.to_alipay_dict() else: params['departure'] = self.departure if self.duration: if hasattr(self.duration, 'to_alipay_dict'): params['duration'] = self.duration.to_alipay_dict() else: params['duration'] = self.duration if self.end_time: if hasattr(self.end_time, 'to_alipay_dict'): params['end_time'] = self.end_time.to_alipay_dict() else: params['end_time'] = self.end_time if self.end_time_desc: if hasattr(self.end_time_desc, 'to_alipay_dict'): params['end_time_desc'] = self.end_time_desc.to_alipay_dict() else: params['end_time_desc'] = self.end_time_desc if self.ext_info: if isinstance(self.ext_info, list): for i in range(0, len(self.ext_info)): element = self.ext_info[i] if hasattr(element, 'to_alipay_dict'): self.ext_info[i] = element.to_alipay_dict() if hasattr(self.ext_info, 'to_alipay_dict'): params['ext_info'] = self.ext_info.to_alipay_dict() else: params['ext_info'] = self.ext_info if self.functional_services: if isinstance(self.functional_services, list): for i in range(0, len(self.functional_services)): element = self.functional_services[i] if hasattr(element, 'to_alipay_dict'): self.functional_services[i] = element.to_alipay_dict() if hasattr(self.functional_services, 'to_alipay_dict'): params['functional_services'] = self.functional_services.to_alipay_dict() else: params['functional_services'] = self.functional_services if self.passagers: if isinstance(self.passagers, list): for i in range(0, len(self.passagers)): element = self.passagers[i] if hasattr(element, 'to_alipay_dict'): self.passagers[i] = element.to_alipay_dict() if hasattr(self.passagers, 'to_alipay_dict'): params['passagers'] = self.passagers.to_alipay_dict() else: params['passagers'] = self.passagers if self.service_change_info: if hasattr(self.service_change_info, 'to_alipay_dict'): params['service_change_info'] = self.service_change_info.to_alipay_dict() else: params['service_change_info'] = self.service_change_info if self.service_provider: if hasattr(self.service_provider, 'to_alipay_dict'): params['service_provider'] = self.service_provider.to_alipay_dict() else: params['service_provider'] = self.service_provider if self.start_time: if hasattr(self.start_time, 'to_alipay_dict'): params['start_time'] = self.start_time.to_alipay_dict() else: params['start_time'] = self.start_time if self.start_time_desc: if hasattr(self.start_time_desc, 'to_alipay_dict'): params['start_time_desc'] = self.start_time_desc.to_alipay_dict() else: params['start_time_desc'] = self.start_time_desc return params @staticmethod def from_alipay_dict(d): if not d: return None o = OrderJourneyElement() if 'arrival' in d: o.arrival = d['arrival'] if 'departure' in d: o.departure = d['departure'] if 'duration' in d: o.duration = d['duration'] if 'end_time' in d: o.end_time = d['end_time'] if 'end_time_desc' in d: o.end_time_desc = d['end_time_desc'] if 'ext_info' in d: o.ext_info = d['ext_info'] if 'functional_services' in d: o.functional_services = d['functional_services'] if 'passagers' in d: o.passagers = d['passagers'] if 'service_change_info' in d: o.service_change_info = d['service_change_info'] if 'service_provider' in d: o.service_provider = d['service_provider'] if 'start_time' in d: o.start_time = d['start_time'] if 'start_time_desc' in d: o.start_time_desc = d['start_time_desc'] return o

[ "jiandong.jd@antfin.com" ]

jiandong.jd@antfin.com

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refs/heads/master

2022-12-14T01:51:06.414952

2020-09-08T21:23:38

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import numpy as np from bokeh.io import curdoc from bokeh.layouts import column, row from bokeh.models import ( ColumnDataSource, Div, Select, MultiSelect, Slider, TextInput, ) from bokeh.plotting import figure from bokeh.models.tools import TapTool from bokeh.models.callbacks import CustomJS import os PATH = os.path.abspath(os.path.dirname(__file__)) # Load an example dataset data = np.loadtxt( os.path.join(PATH, "data", "TESS-Gaia-mini.csv"), delimiter=",", skiprows=1 ) ra, dec, par, sid, _, _, ticid, tmag, dist = data.T data = dict(ra=ra, dec=dec, dist=dist, ticid=ticid) # Things the user can plot (label: parameter name) axis_map = {"Right Ascension": "ra", "Declination": "dec", "Distance": "dist"} # Input controls x_axis = Select( title="X Axis", options=sorted(axis_map.keys()), value="Right Ascension", name="x_axis", ) y_axis = Select( title="Y Axis", options=sorted(axis_map.keys()), value="Declination" ) s_axis = Select( title="Marker Size", options=sorted(axis_map.keys()), value="Distance" ) controls = [s_axis, x_axis, y_axis] # Primary plot source1 = ColumnDataSource(data=dict(x=[], y=[], size=[])) plot1 = figure( plot_height=600, plot_width=700, title="", tooltips=[("TIC ID", "@ticid")], tools="tap", sizing_mode="scale_both", ) plot1.circle( x="x", y="y", source=source1, size="size", line_color=None, ) taptool = plot1.select(type=TapTool) # Secondary plot source2 = ColumnDataSource(data=dict(x=[], y=[])) plot2 = figure( plot_height=300, plot_width=700, title="", sizing_mode="scale_both", ) plot2.circle( x="x", y="y", source=source2, line_color=None, color="black", alpha=0.1 ) # Events def callback1(attr, old, new): """ Triggered when the user changes what we're plotting on the main plot. """ # Get the parameters to plot (x axis, y axis, and marker size) x_name = axis_map[x_axis.value] y_name = axis_map[y_axis.value] s_name = axis_map[s_axis.value] # Update the labels plot1.xaxis.axis_label = x_axis.value plot1.yaxis.axis_label = y_axis.value # Update the data source source1.data = dict( x=data[x_name], y=data[y_name], size=data[s_name] / np.min(data[s_name]), ticid=data["ticid"], ) def callback2(attr, old, new): """ Triggered when the user selects a point on the main plot. """ # Get the TIC ID ticid = source1.data["ticid"][source1.selected.indices[0]] print("Fetching data for TIC ID {0}".format(ticid)) # TODO: Actually fetch the data from MAST. # For now just populate with random numbers source2.data = dict(x=np.arange(10000), y=np.random.randn(10000)) # Register the callbacks source1.selected.on_change("indices", callback2) for control in controls: control.on_change("value", callback1) # Display things on the page inputs = column(*controls, width=320) inputs.sizing_mode = "fixed" l = column(row(inputs, plot1), plot2) # Load and display the data callback1(None, None, None) # Go! curdoc().add_root(l) curdoc().title = "delicatessen"

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from rest_framework.serializers import ModelSerializer, \ SlugRelatedField from api.models.model_year_report_vehicle import ModelYearReportVehicle from api.serializers.vehicle import VehicleZevTypeSerializer from api.serializers.vehicle import ModelYearSerializer from api.serializers.credit_transaction import CreditClassSerializer from api.models.vehicle_zev_type import ZevType from api.models.model_year import ModelYear from api.models.model_year_report import ModelYearReport from api.models.credit_class import CreditClass class ModelYearReportVehicleSerializer(ModelSerializer): zev_class = SlugRelatedField( slug_field='credit_class', queryset=CreditClass.objects.all() ) model_year = SlugRelatedField( slug_field='name', queryset=ModelYear.objects.all() ) vehicle_zev_type = SlugRelatedField( slug_field='vehicle_zev_code', queryset=ZevType.objects.all() ) class Meta: model = ModelYearReportVehicle fields = ( 'id', 'pending_sales', 'sales_issued', 'make', 'model_name', 'range', 'zev_class', 'model_year', 'vehicle_zev_type', 'update_timestamp', ) class ModelYearReportVehicleSaveSerializer(ModelSerializer): """ Model Year Report Vehicle save serializer """ zev_class = SlugRelatedField( slug_field='credit_class', queryset=CreditClass.objects.all() ) model_year = SlugRelatedField( slug_field='name', queryset=ModelYear.objects.all() ) vehicle_zev_type = SlugRelatedField( slug_field='vehicle_zev_code', queryset=ZevType.objects.all() ) def create(self, validated_data): request = self.context.get('request') model_id = request.data.get('model_year_report_id') model_year_report_vehicle = ModelYearReportVehicle.objects.create( **validated_data, model_year_report=ModelYearReport.objects.get(id=model_id) ) return model_year_report_vehicle class Meta: model = ModelYearReportVehicle fields = ( 'pending_sales', 'sales_issued', 'make', 'model_name', 'range', 'zev_class', 'model_year', 'vehicle_zev_type', 'model_year_report_id' )

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#!/usr/bin/python # _*_ coding: UTF-8 _*_ import time import urllib import httplib import hashlib import json #from lib.ChannelException import ChannelException from lib.RequestCore import * from json.tests.test_pass1 import JSON class Channel(object): '''Channel类提供百度云PUSH服务的Python版本SDK，用户首先实例化这个类，设置自己的apiKey,和 secretKey，即可使用PUSH服务接口 ''' #发起请求时的时间戳 TIMESTAMP = 'timestamp' #请求过期的时间, 默认为10分钟 EXPIRES = 'expires' #API版本号 VERSION = 'v' #消息通道ID号 CHANNEL_ID = 'channel_id' #用户类型 USER_ID = 'user_id' #用户ID的类型 USER_TYPE = 'user_type' #设备类型 DEVICE_TYPE = 'device_type' #第几页，批量查询是，需要指定start, 默认为0 START = 'start' #每页多少条记录，默认为100 LIMIT = 'limit' #消息 MESSAGES = 'messages' #消息id MSG_IDS = 'msg_ids' #消息key，可以按key去除重复消息 MSG_KEYS = 'msg_keys' #消息类型，0：消息（透传）， 1：通知，默认为0 MESSAGE_TYPE = 'message_type' #消息过期时间，默认86400秒 MESSAGE_EXPIRES = 'message_expires' #消息标签名，可按标签分组 TAG_NAME = 'tag' #消息标签扩展信息 TAG_INFO = 'info' #消息标签id TAG_ID = 'tid' #应用id，从百度开发者中心获得 APPID = 'appid' #应用key，从百度开发者中心获得,是创建Channel的必须参数 API_KEY = 'apikey' #从百度开发者中心获得，是创建Channel的必须参数 SECRET_KEY = 'secret_key' #Channel常量，用户不必关注 SIGN = 'sign' METHOD = 'method' HOST = 'host' PRODUCT = 'channel' DEFAULT_HOST = 'channel.api.duapp.com' #证书相关常量 NAME = 'name' DESCRIPTION = 'description' CERT = 'cert' RELEASE_CERT = 'release_cert' DEV_CERT = 'dev_cert' #推送类型 PUSH_TYPE = 'push_type' #可选推送类型 PUSH_TO_USER = 1 PUSH_TO_TAG = 2 PUSH_TO_ALL = 3 #Channel 错误常量 CHANNEL_SDK_SYS = 1 CHANNEL_SDK_INIT_FAIL = 2 CHANNEL_SDK_PARAM = 3 CHANNEL_SDK_HTTP_STATUS_ERROR_AND_RESULT_ERROR = 4 CHANNEL_SDK_HTTP_STATUS_OK_BUT_RESULT_ERROR = 5 ### # 对外接口 ### def __init__(self, apiKey, secretKey, arr_curlOpts = None): self._apiKey = apiKey self._secretKey = secretKey self._requestId = 0 if arr_curlOpts is None: self._curlOpts = dict(IMEOUT = 30, CONNECTTIMEOUT = 5) else: self._curlOpts = arr_curlOpts self._arrayErrorMap = { '0' : 'python sdk error', Channel.CHANNEL_SDK_SYS : 'php sdk error', Channel.CHANNEL_SDK_INIT_FAIL : 'php sdk init error', Channel.CHANNEL_SDK_PARAM : 'lack param', Channel.CHANNEL_SDK_HTTP_STATUS_ERROR_AND_RESULT_ERROR : 'http status is error, and the body returned is not a json string', Channel.CHANNEL_SDK_HTTP_STATUS_OK_BUT_RESULT_ERROR :'http status is ok, but the body returned is not a json string'} self._method_channel_in_body = ['push_msg', 'set_tag', 'fetch_tag', 'delete_tag', 'query_user_tags'] if(not isinstance(self._curlOpts, dict)): raise ChannelExcepthion('invalid param -arr_curlopt is not an dict',Channel.CHANNEL_SDK_INIT_FAIL) def setApiKey(self, apiKey): self._apiKey = apiKey def setSecretKey(self, secretKey): self._secretKey = secretKey def getRequestId(self): return self._requestId # 服务器端根据userId, 查询绑定信息 # 用户关注：是 def queryBindList(self, optional = None): """ 服务器端根据userId, 查询绑定信息参数： str userId: 用户ID号 dict optional: 可选参数返回值：成功:python字典；失败：False """ try: arrArgs = dict() arrArgs[Channel.METHOD] = 'query_bindlist' arrArgs[Channel.API_KEY] = self._apiKey if(not arrArgs.has_key(Channel.TIMESTAMP)): arrArgs[Channel.TIMESTAMP] = int(time.time()) if(arrArgs.has_key(Channel.SECRET_KEY)): del arrArgs[Channel.SECRET_KEY] resource = 'channel' if(arrArgs.has_key(Channel.CHANNEL_ID) ): if(arrArgs[Channel.CHANNEL_ID] is not None and arrArgs[Channel.METHOD] not in self._method_channel_in_body): resource = arrArgs[Channel.CHANNEL_ID] del arrArgs[Channel.CHANNEL_ID] url = 'http://' + Channel.DEFAULT_HOST + '/rest/2.0/' + Channel.PRODUCT + '/' + resource arrArgs[Channel.SIGN] = self._genSign('POST', url, arrArgs) print arrArgs self.send_request1(arrArgs) self.send_request(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) return False # 服务器端根据userId, 查询绑定信息 # 用户关注：是 def queryBindList_byId(self, userId, optional = None): """ 服务器端根据userId, 查询绑定信息参数： str userId: 用户ID号 dict optional: 可选参数返回值：成功:python字典；失败：False """ try: #tmpArgs = [userId, optional] #arrArgs = self._mergeArgs([Channel.USER_ID], tmpArgs) arrArgs = dict() arrArgs[Channel.METHOD] = 'query_bindlist' return self._commonProcess(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) return False # 推送消息 # 用户关注：是 def pushMessage(self, push_type, messages, message_keys, optional = None): """ 推送消息参数: push_type: 推送消息的类型 messages：消息内容 message_keys: 消息key optional: 可选参数返回值成功：python字典; 失败：False """ try: tmpArgs = [push_type, messages, message_keys, optional] arrArgs = self._mergeArgs([Channel.PUSH_TYPE, Channel.MESSAGES, Channel.MSG_KEYS], tmpArgs) arrArgs[Channel.METHOD] = 'push_msg' if(push_type == Channel.PUSH_TO_USER): if(not arrArgs.has_key(Channel.USER_ID) or arrArgs[Channel.USER_ID] is None): raise ChannelException("userId should be specified in optional when pushType is PUSH_TO_USER", self.CHANNEL_SDK_PARAM) elif(push_type == Channel.PUSH_TO_TAG): if(not arrArgs.has_key(Channel.TAG_NAME) or arrArgs[Channel.TAG_NAME] is None): raise ChannelException("tagName should be specified in optional when pushType is PUSH_TO_TAG", self.CHANNEL_SDK_PARAM) elif(push_type == Channel.PUSH_TO_ALL): pass else: raise ChannelException("pushType value is invalid", self.CHANNEL_SDK_PARAM) arrArgs[Channel.PUSH_TYPE] = push_type if(isinstance(arrArgs[Channel.MESSAGES], list)): arrArgs[Channel.MESSAGES] = json.dumps(arrArgs[Channel.MESSAGES]) if(isinstance(arrArgs[Channel.MSG_KEYS], list)): arrArgs[Channel.MSG_KEYS] = json.dumps(arrArgs[Channel.MSG_KEYS]) return self._commonProcess(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) return False #校验userId是否已经绑定 #用户关注：是 def verifyBind(self, userId, optional = None): """ 校验userId是否已经绑定参数： userId:用户id optional:可选参数返回值：成功：python数组；失败False """ try: tmpArgs = [userId, optional] arrArgs = self._mergeArgs([Channel.USER_ID], tmpArgs) arrArgs[Channel.METHOD] = 'verify_bind'; return self._commonProcess(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) return False #根据userId查询消息 #用户关注：是 def fetchMessage(self, userId, optional = None): """ 根据userId查询消息参数： userId:用户id optional:可选参数返回值：成功：python字典；失败：False """ try: tmpArgs = [userId, optional] arrArgs = self._mergeArgs([Channel.USER_ID], tmpArgs) arrArgs[Channel.METHOD] = 'fetch_msg'; return self._commonProcess(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) return False #根据userId查询消息个数 #用户关注：是 def fetchMessageCount(self, userId, optional = None): """ 根据userId查询消息个数参数： userId:用户id optional:可选参数返回值：成功：python字典；失败：False """ try: tmpArgs = [userId, optional] arrArgs = self._mergeArgs([Channel.USER_ID], tmpArgs) arrArgs[Channel.METHOD] = 'fetch_msgcount'; return self._commonProcess(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) return False #根据userId, msgIds删除消息 #用户关注：是 def deleteMessage(self, userId, msgId, optional = None): """ 根据userId, msgIds删除消息参数： userId:用户id msgIds:消息id optional:可选参数返回值：成功：python字典；失败：False """ try: tmpArgs = [userId, msgId, optional] arrArgs = self._mergeArgs([Channel.USER_ID, Channel.MSG_IDS], tmpArgs) arrArgs[Channel.METHOD] = 'delete_msg'; if(isinstance(arrArgs[Channel.MSG_IDS], list)): arrArgs[Channel.MSG_IDS] = json.dumps(arrArgs[Channel.MSG_IDS]) return self._commonProcess(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) #设置消息标签 #用户关注：是 def setTag(self, tagName, optional = None): """ 设置消息标签参数： tagName:标签 optional:可选参数返回值：成功：python字典；失败：False """ try: tmpArgs = [tagName, optional] arrArgs = self._mergeArgs([Channel.TAG_NAME], tmpArgs) arrArgs[Channel.METHOD] = 'set_tag'; return self._commonProcess(arrArgs) except ChannelException, e: self._channelExceptionHandler(e) return False #查询消息标签信息 #用户关注：是 def fetchTag(self, optional = None): """ 查询消息标签信息参数: optional:可选参数返回值：成功：python字典；失败：False """ try: tmpArgs = [optional] arrArgs = self._mergeArgs([], tmpArgs) arrArgs[Channel.METHOD] = 'fetch_tag'; return self._commonProcess(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) return False #删除消息标签 #用户关注：是 def deleteTag(self, tagName, optional = None): """ 删除消息标签参数： tagName:标签 optional:可选参数返回值：成功：python字典；失败：False """ try: tmpArgs = [tagName, optional] arrArgs = self._mergeArgs([Channel.TAG_NAME], tmpArgs) arrArgs[Channel.METHOD] = 'delete_tag'; return self._commonProcess(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) return False #查询用户相关的标签 #用户关注：是 def queryUserTag(self, userId, optional = None): """ 查询用户相关的标签参数： userId:用户id optional:可选参数返回值：成功：python字典；失败：False """ try: tmpArgs = [userId, optional] arrArgs = self._mergeArgs([Channel.USER_ID], tmpArgs) arrArgs[Channel.METHOD] = 'query_user_tags'; return self._commonProcess(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) return False #根据channelId查询设备类型 #用户关注：是 def queryDeviceType(self, channelId, optional = None): """ 根据channelId查询设备类型参数： ChannelId:用户Channel的ID号 optional:可选参数返回值：成功：python字典；失败：False """ try: tmpArgs = [channelId, optional] arrArgs = self._mergeArgs([Channel.CHANNEL_ID], tmpArgs) arrArgs[Channel.METHOD] = 'query_device_type'; return self._commonProcess(arrArgs) except ChannelException, e: #self._channelExceptionHandler(e) return False # # 内部函数 # def _checkString(self, string, minLen, maxLen): if( isinstance(string, str) and len(string) >= minLen and len(string) <= maxLen ): return True else: return False def _adjustOpt(self, opt): if(not ((opt is not None) and isinstance(opt, dict))): raise ChannelException('no params are set', Channel.CHANNEL_SDK_PARAM) if(not opt.has_key(Channel.TIMESTAMP)): opt[Channel.TIMESTAMP] = int(time.time()) opt[Channel.HOST] = Channel.DEFAULT_HOST opt[Channel.API_KEY] = self._apiKey if(opt.has_key(Channel.SECRET_KEY)): del opt[Channel.SECRET_KEY] def _genSign(self, method, url, arrContent): gather = method + url keys = arrContent.keys() keys.sort() for key in keys: gather += key + '=' + str(arrContent[key]) gather += self._secretKey sign = hashlib.md5(urllib.quote_plus(gather)) return sign.hexdigest() def _baseControl(self, opt): resource = 'channel' if(opt.has_key(Channel.CHANNEL_ID) ): if(opt[Channel.CHANNEL_ID] is not None and opt[Channel.METHOD] not in self._method_channel_in_body): resource = opt[Channel.CHANNEL_ID] del opt[Channel.CHANNEL_ID] host = opt[Channel.HOST] del opt[Channel.HOST] url = 'http://' + host + '/rest/2.0/' + Channel.PRODUCT + '/' url += resource http_method = 'POST' opt[Channel.SIGN] = self._genSign(http_method, url, opt) request = RequestCore(url) headers = dict() headers['Content-Type'] = 'application/x-www-form-urlencoded' headers['User-Agent'] = 'Baidu Channel Service Pythonsdk Client' for (headerKey , headerValue) in headers.items(): headerValue = headerValue.replace('\r', '') headerValue = headerValue.replace('\n', '') if (headerValue is not None): request.add_header(headerKey, headerValue) request.set_method(http_method) request.set_body(urllib.urlencode(opt)) if(isinstance(self._curlOpts, dict)): request.set_curlopts(self._curlOpts) request.handle_request() return ResponseCore(request.get_response_header(),request.get_response_body(),request.get_response_code()) def _commonProcess(self, paramOpt): self._adjustOpt(paramOpt) ret = self._baseControl(paramOpt) if( ret is None): raise ChannelException('base control returned None object', Channel.CHANNEL_SDK_SYS) if(ret.isOK()): result = json.loads(ret.body) if (result is None): raise ChannelException(ret.body, Channel.CHANNEL_SDK_HTTP_STATUS_OK_BUT_RESULT_ERROR) self._requestId = result['request_id'] return result result = json.loads(ret.body) if(result is None): raise ChannelException('ret body:' + ret.body, Channel.CHANNEL_SDK_HTTP_STATUS_ERROR_AND_RESULT_ERROR) self._requestId = result['request_id'] raise ChannelException(result['error_msg'], result['error_code']) def _mergeArgs(self, arrNeed, tmpArgs): arrArgs = dict() if( len(arrNeed) == 0 and len(tmpArgs) == 0): return arrArgs if(len(tmpArgs)-1 != len(arrNeed) and len(tmpArgs) != len(arrNeed)): keys = '(' for key in arrNeed: keys += key + ',' if(key[-1] == '' and key[-2] == ','): keys = keys[0:-2] keys += ')' raise ChannelException('invalid sdk, params, params' + keys + 'are need', Channel.CHANNEL_SDK_PARAM) if(len(tmpArgs)-1 == len(arrNeed) and tmpArgs[-1] is not None and (not isinstance(tmpArgs[-1], dict)) ): raise ChannelException ('invalid sdk params, optional param must bean dict', Channel.CHANNEL_SDK_PARAM) idx = 0 if(isinstance(arrNeed, list)): for key in arrNeed: if(tmpArgs[idx] is None): raise ChannelException ('lack param ' + key, Channel.CHANNEL_SDK_PARAM) arrArgs[key] = tmpArgs[idx] idx = idx + 1 if(len(tmpArgs) == idx + 1 and tmpArgs[idx] is not None): for (key, value) in tmpArgs[idx].items(): if(not arrArgs.has_key(key) and value is not None): arrArgs[key] = value return arrArgs # send Http Request def send_request(self, arrArgs, echo = False): conn = httplib.HTTPConnection("channel.api.duapp.com") #hostname, port #headers = dict() #headers['Content-Type'] = 'application/x-www-form-urlencoded' #headers['User-Agent'] = 'Baidu Channel Service Pythonsdk Client' headers = {"Content-type": "application/x-www-form-urlencoded", "User-Agent": "Baidu Channel Service Pythonsdk Client"} conn.request("POST", "rest/2.0/channel/channel", urllib.urlencode(arrArgs), headers) response = conn.getresponse() if response.status == 200 and response.reason == "OK": return response.read() if echo else "@Reviced Data OK." else: raise RuntimeError("Http request exception:", response.status) def send_request1(self, opt): request = RequestCore("http://channel.api.duapp.com/rest/2.0/channel/channel") headers = dict() headers['Content-Type'] = 'application/x-www-form-urlencoded' headers['User-Agent'] = 'Baidu Channel Service Pythonsdk Client' for (headerKey , headerValue) in headers.items(): headerValue = headerValue.replace('\r', '') headerValue = headerValue.replace('\n', '') if (headerValue is not None): request.add_header(headerKey, headerValue) request.set_method('POST') request.set_body(urllib.urlencode(opt)) if(isinstance(self._curlOpts, dict)): request.set_curlopts(self._curlOpts) request.handle_request() ret = ResponseCore(request.get_response_header(),request.get_response_body(),request.get_response_code()) if( ret is None): raise ChannelException('base control returned None object', Channel.CHANNEL_SDK_SYS) if(ret.isOK()): result = ret.body print result if (result is None): raise ChannelException(ret.body, Channel.CHANNEL_SDK_HTTP_STATUS_OK_BUT_RESULT_ERROR) #self._requestId = result['request_id'] return result result = json.loads(ret.body) if(result is None): raise ChannelException('ret body:' + ret.body, Channel.CHANNEL_SDK_HTTP_STATUS_ERROR_AND_RESULT_ERROR) self._requestId = result['request_id'] raise ChannelException(result['error_msg'], result['error_code']) def _channelExceptionHandler(self, ex): print ex.error_msg, self._arrayErrorMap[ex.error_code]

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# -*- coding: utf-8 -*- # Generated by Django 1.9.3 on 2016-04-13 19:35 from __future__ import unicode_literals import datetime from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('hello', '0002_sitemessage'), ] operations = [ migrations.AddField( model_name='sitemessage', name='event_date', field=models.DateField(default=datetime.datetime(2016, 4, 13, 21, 35, 9, 134000)), ), ]

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""" WSGI config for contacts project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.1/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'contacts.settings') application = get_wsgi_application()

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MrHamdulay/csc3-capstone

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# Program to check if time is valid def main(): h=eval(input("Enter the hours: \n")) m=eval(input("Enter the minutes: \n")) s=eval(input("Enter the seconds: \n")) if 0<=h<=23 and 0<=m<=59 and 0<=s<=59: print("Your time is valid. ") else: print("Your time is invalid. ") main()

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m-hahn/fairseq

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from fairseq.models.roberta import RobertaModel roberta = RobertaModel.from_pretrained( 'checkpoints_STS-B/', checkpoint_file='checkpoint_best.pt', data_name_or_path='STS-B-bin' ) import torch label_fn = lambda label: roberta.task.label_dictionary.string( torch.LongTensor([label + roberta.task.label_dictionary.nspecial]) ) ncorrect, nsamples = 0, 0 roberta.cuda() roberta.eval() evaluatedSoFar = set() lineNumbers = 0 with open('/u/scr/mhahn/PRETRAINED/GLUE/glue_data/STS-B/dev_alternatives_c.tsv', "r") as fin: with open('/u/scr/mhahn/PRETRAINED/GLUE/glue_data/STS-B/dev_alternatives_c_predictions_fairseq.tsv', "w") as outFile: while True: lineNumbers += 1 try: line = next(fin).strip() except UnicodeDecodeError: print("UnicodeDecodeError", lineNumbers) continue if line == "#####": originalSentences = next(fin) # the original separation = int(next(fin).strip()) # position of separation tokenizedSentences = next(fin) line = next(fin) #print(line) subset, sentences = line.strip().split("\t") sentences = sentences.strip().split(" ") # print(sentences, separation) sentences = [sentences[:separation], sentences[separation:]] # print(sentences) assert len(sentences[1]) > 1, (line, separation, sentences) # quit() for i in range(2): sentences[i] = ("".join(sentences[i])).replace("▁", " ").replace("</s>", "").strip() assert len(sentences[1]) > 1, (line, separation, sentences) assert sentences[0].endswith("."), (line, separation, sentences) # print(sentences) if tuple(sentences) in evaluatedSoFar: continue evaluatedSoFar.add(tuple(sentences)) if len(evaluatedSoFar) % 100 == 0: print(len(evaluatedSoFar), sentences) tokens = roberta.encode(sentences[0], sentences[1]) # https://github.com/pytorch/fairseq/issues/1009 features = roberta.extract_features(tokens) prediction = float(5.0 * roberta.model.classification_heads['sentence_classification_head'](features)) print("\t".join([sentences[0], sentences[1], str(prediction)]), file=outFile)

[ "mhahn29@gmail.com" ]

mhahn29@gmail.com

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no_license

Amos-x/Blog-scripts

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refs/heads/master

2022-04-19T21:19:12.070218

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# -*- coding:utf-8 -*- # __author__ = Amos # Email = 379833553@qq.com # Create_at = 2018/11/8 下午3:48 # FileName = build from config import config as CONFIG from utils.common import exec_shell,container_is_exist def build_sonarqube(): if not container_is_exist('sonarqube'): pull = 'docker pull sonarqube:7.1' exec_shell(pull) build = 'docker run -d --name sonarqube \ -p 9000:9000 \ -e SONARQUBE_JDBC_USERNAME={mysql_username} \ -e SONARQUBE_JDBC_PASSWORD={mysql_password} \ -e SONARQUBE_JDBC_URL=jdbc:mysql://{mysql_host}:3306/{soanr_db_name}?useUnicode=true\&characterEncoding=utf8\&rewriteBatchedStatements=true\&useConfigs=maxPerformance \ sonarqube:7.1'.format(mysql_host=CONFIG.MYSQL_HOST,mysql_username=CONFIG.MYSQL_USERNAME, mysql_password=CONFIG.MYSQL_PASSWORD,soanr_db_name=CONFIG.MYSQL_NAME_SONARQUBE) exec_shell(build) exec_shell('docker start sonarqube') else: print('sonarqube 容器已存在，跳过安装')

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379833553@qq.com

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/Week_4/Assignment_3/assignment3.py

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no_license

Nidhiks2000/eyrc-mooc

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refs/heads/master

2023-07-18T05:26:22.311527

2021-09-08T16:48:23

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# No other modules apart from 'csv' and 'datetime' need to be imported # as they aren't required to solve the assignment # Import required module/s import csv from datetime import datetime as dt def dayofweek(d, m, y): t = [ 0, 3, 2, 5, 0, 3, 5, 1, 4, 6, 2, 4 ] y -= m < 3 return (( y + int(y / 4) - int(y / 100) + int(y / 400) + t[m - 1] + d) % 7) def readWorkSheet(file_name): """Reads the input CSV file of Work Sheet and creates a mapping of date and office name where he worked. Parameters ---------- file_name : str CSV file name of Work Sheet Returns ------- dict Mapping of the date and office name where he worked as { Key : Value } pair Example ------- >>> csv_file_name = 'week4_assignment3_sample.csv' >>> print( readWorkSheet( csv_file_name ) ) { '2021-03-26': 'A', '2021-04-01': 'B', '2021-04-20': 'B', '2021-04-04': '-', '2021-04-12': 'A', '2021-04-23': 'A', '2021-04-03': 'B', '2021-03-29': 'A', '2021-03-28': '-', '2021-03-31': 'A', '2021-04-10': 'B', '2021-04-16': 'A', '2021-04-24': 'B', '2021-04-11': '-', '2021-04-13': 'B' } """ date_office_name_mapping = {} input_file_obj = open(file_name, 'r') ############## ADD YOUR CODE HERE ############## reader=csv.DictReader(input_file_obj) for rows in reader: now = rows['date'] x = now.split("-") #print(x) #print(dt.date(2020,7,24).strftime('%A')) res = dayofweek(int(x[2]),int(x[1]),int(x[0])) if(res!=0 and res%2!=0): date_office_name_mapping[now] = "A" elif (res!=0 and res%2 == 0): date_office_name_mapping[now] = "B" elif (res == 0): date_office_name_mapping[now] = "-" ################################################## input_file_obj.close() return date_office_name_mapping def calculateOfficeHrs(mapping_dict): """Calculate the number of hours worked in office A and B with the given mapping of date and office name. Parameters ---------- mapping_dict : dict Mapping of the date and office name where he worked as { Key : Value } pair Returns ------- tuple Number of hours worked in office A and B as pair Example ------- >>> date_office_name_mapping = { '2021-03-26': 'A', '2021-04-01': 'B', '2021-04-20': 'B', '2021-04-04': '-', '2021-04-12': 'A', '2021-04-23': 'A', '2021-04-03': 'B', '2021-03-29': 'A', '2021-03-28': '-', '2021-03-31': 'A', '2021-04-10': 'B', '2021-04-16': 'A', '2021-04-24': 'B', '2021-04-11': '-', '2021-04-13': 'B' } >>> print( calculateOfficeHrs( date_office_name_mapping ) ) (48, 36) """ no_hrs_office_A, no_hrs_office_B = 0, 0 ############## ADD YOUR CODE HERE ############## for key,value in mapping_dict.items(): if (value == "A"): no_hrs_office_A+=8 elif(value == "B"): no_hrs_office_B+=6 ################################################## return (no_hrs_office_A, no_hrs_office_B) def writeOfficeWorkSheet(mapping_dict, out_file_name): """Writes a CSV file with date and office name where the person worked on each day. Parameters ---------- mapping_dict : dict Mapping of the date and office name where he worked as { Key : Value } pair out_file_name : str File name of CSV file for writing the data to """ output_file_obj = open(out_file_name, 'w') ############## ADD YOUR CODE HERE ############## writer = csv.writer(output_file_obj,delimiter=',') writer.writerow(['date','office_name']) for key,value in mapping_dict.items(): writer.writerow([key,value]) ################################################## output_file_obj.close() if __name__ == "__main__": """Main function, code begins here. """ csv_file_name = 'week4_assignment3_sample.csv' date_office_name_mapping = readWorkSheet(csv_file_name) print(date_office_name_mapping) total_hrs_office_A_B = calculateOfficeHrs(date_office_name_mapping) print(total_hrs_office_A_B) out_csv_file_name = 'output_week4_assignment3_sample.csv' writeOfficeWorkSheet(date_office_name_mapping, out_csv_file_name)

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Happysunshine.disroot.org

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rephrase-ai/pytorch-lightning

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2023-06-06T11:32:41.765882

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# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from argparse import Namespace import pytest import torch from pytorch_lightning.core.saving import load_hparams_from_yaml from pytorch_lightning.loggers import CSVLogger from pytorch_lightning.loggers.csv_logs import ExperimentWriter def test_file_logger_automatic_versioning(tmpdir): """Verify that automatic versioning works""" root_dir = tmpdir.mkdir("exp") root_dir.mkdir("version_0") root_dir.mkdir("version_1") logger = CSVLogger(save_dir=tmpdir, name="exp") assert logger.version == 2 def test_file_logger_manual_versioning(tmpdir): """Verify that manual versioning works""" root_dir = tmpdir.mkdir("exp") root_dir.mkdir("version_0") root_dir.mkdir("version_1") root_dir.mkdir("version_2") logger = CSVLogger(save_dir=tmpdir, name="exp", version=1) assert logger.version == 1 def test_file_logger_named_version(tmpdir): """Verify that manual versioning works for string versions, e.g. '2020-02-05-162402' """ exp_name = "exp" tmpdir.mkdir(exp_name) expected_version = "2020-02-05-162402" logger = CSVLogger(save_dir=tmpdir, name=exp_name, version=expected_version) logger.log_hyperparams({"a": 1, "b": 2}) logger.save() assert logger.version == expected_version assert os.listdir(tmpdir / exp_name) == [expected_version] assert os.listdir(tmpdir / exp_name / expected_version) @pytest.mark.parametrize("name", ['', None]) def test_file_logger_no_name(tmpdir, name): """Verify that None or empty name works""" logger = CSVLogger(save_dir=tmpdir, name=name) logger.save() assert logger.root_dir == tmpdir assert os.listdir(tmpdir / 'version_0') @pytest.mark.parametrize("step_idx", [10, None]) def test_file_logger_log_metrics(tmpdir, step_idx): logger = CSVLogger(tmpdir) metrics = { "float": 0.3, "int": 1, "FloatTensor": torch.tensor(0.1), "IntTensor": torch.tensor(1), } logger.log_metrics(metrics, step_idx) logger.save() path_csv = os.path.join(logger.log_dir, ExperimentWriter.NAME_METRICS_FILE) with open(path_csv, 'r') as fp: lines = fp.readlines() assert len(lines) == 2 assert all([n in lines[0] for n in metrics]) def test_file_logger_log_hyperparams(tmpdir): logger = CSVLogger(tmpdir) hparams = { "float": 0.3, "int": 1, "string": "abc", "bool": True, "dict": { 'a': { 'b': 'c' } }, "list": [1, 2, 3], "namespace": Namespace(foo=Namespace(bar='buzz')), "layer": torch.nn.BatchNorm1d } logger.log_hyperparams(hparams) logger.save() path_yaml = os.path.join(logger.log_dir, ExperimentWriter.NAME_HPARAMS_FILE) params = load_hparams_from_yaml(path_yaml) assert all([n in params for n in hparams])

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rephrase-ai.noreply@github.com

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/codes/CodeJamCrawler/16_0_2/Omsg/PyRunPancakeLarge.py

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[]

no_license

DaHuO/Supergraph

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refs/heads/master

2021-06-14T16:07:52.405091

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import numpy as np # ---- config ---- # FileInput="dataPancakesLarge.in" FileOutput="dataPancakesLarge.out" # ---------------- # def start(pancakes): pancakes=pancakes[::-1] pan=[] turns=0 for p in pancakes: pan.append(p) i=0 for p in pan: if p=="-": pan=turn_pancakes(pan,i) turns=turns+1 i=i+1 return str(turns) def build_pancakes(pan): pancakes="" for p in pan: pancakes=pancakes+p return pancakes def turn_pancakes(pan,start): i=0 for p in pan: if i>=start: if pan[i]=="-": pan[i]="+" else: pan[i]="-" i=i+1 return pan def file_load(): check=[] with open(FileInput) as f: for line in f: check.append(line) return check def normal_mode(): result = start("+-+") print "------------------------------------" print "Result: "+str(result) print "------------------------------------" def array_mode(): f = open(FileOutput, 'w') check = file_load() print check for i in range(np.size(check)): if i>0: writeString = "Case #"+str(i)+": "+str(start(str(check[i]).replace("\n",""))) f.write(writeString+"\n") print writeString print "------------------------------------" f.close() if __name__ == "__main__": print "------------------------------------" print "Start program" print "------------------------------------" array_mode()

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whisnos/weix

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# -*- coding: utf-8 -*- from application import app,db from common.models.food.FoodStockChangeLog import FoodStockChangeLog from common.models.food.Food import Food from common.libs.Helper import geneTime class FoodService(): @staticmethod def setStockChangeLog( food_id = 0,quantity = 0,note = '' ): if food_id < 1: return False food_info = Food.query.filter_by( id = food_id ).first() if not food_info: return False model_stock_change = FoodStockChangeLog() model_stock_change.food_id = food_id model_stock_change.unit = quantity model_stock_change.total_stock = food_info.stock model_stock_change.note = note model_stock_change.created_time = geneTime() db.session.add(model_stock_change) db.session.commit() return True

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whisnos@163.com

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# Copyright (c) 2016 Red Hat, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. # See the License for the specific language governing permissions and # limitations under the License. import abc import collections import copy import functools import itertools from os import path import jsonschema import six import yaml from sahara import exceptions as ex from sahara.i18n import _ from sahara.plugins import exceptions as p_ex from sahara.utils import files def transform_exception(from_type, to_type, transform_func=None): """Decorator to transform exception types. :param from_type: The type of exception to catch and transform. :param to_type: The type of exception to raise instead. :param transform_func: A function to transform from_type into to_type, which must be of the form func(exc, to_type). Defaults to: lambda exc, new_type: new_type(exc.message) """ if not transform_func: transform_func = lambda exc, new_type: new_type(exc.message) def decorator(func): @functools.wraps(func) def handler(*args, **kwargs): try: func(*args, **kwargs) except from_type as exc: raise transform_func(exc, to_type) return handler return decorator def validate_instance(instance, validators, reconcile=True, **kwargs): """Runs all validators against the specified instance. :param instance: An instance to validate. :param validators: A sequence of ImageValidators. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :raises ImageValidationError: If validation fails. """ with instance.remote() as remote: for validator in validators: validator.validate(remote, reconcile=reconcile, **kwargs) class ImageArgument(object): """An argument used by an image manifest.""" SPEC_SCHEMA = { "type": "object", "items": { "type": "object", "properties": { "target_variable": { "type": "string", "minLength": 1 }, "description": { "type": "string", "minLength": 1 }, "default": { "type": "string", "minLength": 1 }, "required": { "type": "boolean", "minLength": 1 }, "choices": { "type": "array", "minLength": 1, "items": { "type": "string" } } } } } @classmethod def from_spec(cls, spec): """Constructs and returns a set of arguments from a specification. :param spec: The specification for the argument set. :return A dict of arguments built to the specification. """ jsonschema.validate(spec, cls.SPEC_SCHEMA) arguments = {name: cls(name, arg.get('description'), arg.get('default'), arg.get('required'), arg.get('choices')) for name, arg in six.iteritems(spec)} reserved_names = ['distro', 'reconcile'] for name, arg in six.iteritems(arguments): if name in reserved_names: raise p_ex.ImageValidationSpecificationError( _("The following argument names are reserved: " "{names}").format(reserved_names)) if not arg.default and not arg.required: raise p_ex.ImageValidationSpecificationError( _("Argument {name} is not required and must specify a " "default value.").format(name=arg.name)) if arg.choices and arg.default and arg.default not in arg.choices: raise p_ex.ImageValidationSpecificationError( _("Argument {name} specifies a default which is not one " "of its choices.").format(name=arg.name)) return arguments def __init__(self, name, description=None, default=None, required=False, choices=None): self.name = name self.description = description self.default = default self.required = required self.choices = choices @six.add_metaclass(abc.ABCMeta) class ImageValidator(object): """Validates the image spawned to an instance via a set of rules.""" @abc.abstractmethod def validate(self, remote, reconcile=True, **kwargs): """Validates the image. :param remote: A remote socket to the instance. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :raises ImageValidationError: If validation fails. """ pass @six.add_metaclass(abc.ABCMeta) class SaharaImageValidatorBase(ImageValidator): """Base class for Sahara's native image validation.""" DISTRO_KEY = 'distro' RECONCILE_KEY = 'reconcile' ORDERED_VALIDATORS_SCHEMA = { "type": "array", "items": { "type": "object", "minProperties": 1, "maxProperties": 1 } } _DISTRO_FAMILES = { 'centos': 'redhat', 'centos7': 'redhat', 'fedora': 'redhat', 'redhatenterpriseserver': 'redhat', 'ubuntu': 'debian' } @staticmethod def get_validator_map(custom_validator_map=None): """Gets the map of validator name token to validator class. :param custom_validator_map: A map of validator names and classes to add to the ones Sahara provides by default. These will take precedence over the base validators in case of key overlap. :return A map of validator names and classes. """ default_validator_map = { 'package': SaharaPackageValidator, 'script': SaharaScriptValidator, 'any': SaharaAnyValidator, 'all': SaharaAllValidator, 'os_case': SaharaOSCaseValidator, 'argument_case': SaharaArgumentCaseValidator, 'argument_set': SaharaArgumentSetterValidator, } if custom_validator_map: default_validator_map.update(custom_validator_map) return default_validator_map @classmethod def from_yaml(cls, yaml_path, validator_map=None, resource_roots=None): """Constructs and returns a validator from the provided yaml file. :param yaml_path: The relative path to a yaml file. :param validator_map: A map of validator name to class. :param resource_roots: The roots from which relative paths to resources (scripts and such) will be referenced. Any resource will be pulled from the first path in the list at which a file exists. :return A SaharaImageValidator built to the yaml specification. """ validator_map = validator_map or {} resource_roots = resource_roots or [] file_text = files.get_file_text(yaml_path) spec = yaml.safe_load(file_text) validator_map = cls.get_validator_map(validator_map) return cls.from_spec(spec, validator_map, resource_roots) @classmethod def from_spec(cls, spec, validator_map, resource_roots): """Constructs and returns a validator from a specification object. :param spec: The specification for the validator. :param validator_map: A map of validator name to class. :param resource_roots: The roots from which relative paths to resources (scripts and such) will be referenced. Any resource will be pulled from the first path in the list at which a file exists. :return A validator built to the specification. """ pass @classmethod def from_spec_list(cls, specs, validator_map, resource_roots): """Constructs a list of validators from a list of specifications. :param specs: A list of validator specifications, each of which will be a dict of size 1, where the key represents the validator type and the value respresents its specification. :param validator_map: A map of validator name to class. :param resource_roots: The roots from which relative paths to resources (scripts and such) will be referenced. Any resource will be pulled from the first path in the list at which a file exists. :return: A list of validators. """ validators = [] for spec in specs: validator_class, validator_spec = cls.get_class_from_spec( spec, validator_map) validators.append(validator_class.from_spec( validator_spec, validator_map, resource_roots)) return validators @classmethod def get_class_from_spec(cls, spec, validator_map): """Gets the class and specification from a validator dict. :param spec: A validator specification including its type: a dict of size 1, where the key represents the validator type and the value respresents its configuration. :param validator_map: A map of validator name to class. :return: A tuple of validator class and configuration. """ key, value = list(six.iteritems(spec))[0] validator_class = validator_map.get(key, None) if not validator_class: raise p_ex.ImageValidationSpecificationError( _("Validator type %s not found.") % validator_class) return validator_class, value class ValidationAttemptFailed(object): """An object representing a failed validation attempt. Primarily for use by the SaharaAnyValidator, which must aggregate failures for error exposition purposes. """ def __init__(self, exception): self.exception = exception def __bool__(self): return False def __nonzero__(self): return False def try_validate(self, remote, reconcile=True, image_arguments=None, **kwargs): """Attempts to validate, but returns rather than raising on failure. :param remote: A remote socket to the instance. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :param image_arguments: A dictionary of image argument values keyed by argument name. :return True if successful, ValidationAttemptFailed object if failed. """ try: self.validate( remote, reconcile=reconcile, image_arguments=image_arguments, **kwargs) return True except p_ex.ImageValidationError as exc: return self.ValidationAttemptFailed(exc) class SaharaImageValidator(SaharaImageValidatorBase): """The root of any tree of SaharaImageValidators. This validator serves as the root of the tree for SaharaImageValidators, and provides any needed initialization (such as distro retrieval.) """ SPEC_SCHEMA = { "title": "SaharaImageValidator", "type": "object", "properties": { "validators": SaharaImageValidatorBase.ORDERED_VALIDATORS_SCHEMA }, "required": ["validators"] } def get_argument_list(self): return [argument for name, argument in six.iteritems(self.arguments)] @classmethod def from_spec(cls, spec, validator_map, resource_roots): """Constructs and returns a validator from a specification object. :param spec: The specification for the validator: a dict containing the key "validators", which contains a list of validator specifications. :param validator_map: A map of validator name to class. :param resource_roots: The roots from which relative paths to resources (scripts and such) will be referenced. Any resource will be pulled from the first path in the list at which a file exists. :return A SaharaImageValidator containing all specified validators. """ jsonschema.validate(spec, cls.SPEC_SCHEMA) arguments_spec = spec.get('arguments', {}) arguments = ImageArgument.from_spec(arguments_spec) validators_spec = spec['validators'] validator = SaharaAllValidator.from_spec( validators_spec, validator_map, resource_roots) return cls(validator, arguments) def __init__(self, validator, arguments): """Constructor method. :param validator: A SaharaAllValidator containing the specified validators. """ self.validator = validator self.validators = validator.validators self.arguments = arguments @transform_exception(ex.RemoteCommandException, p_ex.ImageValidationError) def validate(self, remote, reconcile=True, image_arguments=None, **kwargs): """Attempts to validate the image. Before deferring to contained validators, performs one-time setup steps such as distro discovery. :param remote: A remote socket to the instance. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :param image_arguments: A dictionary of image argument values keyed by argument name. :raises ImageValidationError: If validation fails. """ argument_values = {} for name, argument in six.iteritems(self.arguments): if name not in image_arguments: if argument.required: raise p_ex.ImageValidationError( _("Argument {name} is required for image " "processing.").format(name=name)) else: argument_values[name] = argument.default else: value = image_arguments[name] choices = argument.choices if choices and value not in choices: raise p_ex.ImageValidationError( _("Value for argument {name} must be one of " "{choices}.").format(name=name, choices=choices)) else: argument_values[name] = value argument_values[self.DISTRO_KEY] = remote.get_os_distrib() self.validator.validate(remote, reconcile=reconcile, image_arguments=argument_values) class SaharaPackageValidator(SaharaImageValidatorBase): """A validator that checks package installation state on the instance.""" class Package(object): def __init__(self, name, version=None): self.name = name self.version = version def __str__(self): return ("%s-%s" % (self.name, self.version) if self.version else self.name) _SINGLE_PACKAGE_SCHEMA = { "oneOf": [ { "type": "object", "minProperties": 1, "maxProperties": 1, "additionalProperties": { "type": "object", "properties": { "version": { "type": "string", "minLength": 1 }, } }, }, { "type": "string", "minLength": 1 } ] } SPEC_SCHEMA = { "title": "SaharaPackageValidator", "oneOf": [ _SINGLE_PACKAGE_SCHEMA, { "type": "array", "items": _SINGLE_PACKAGE_SCHEMA, "minLength": 1 } ] } @classmethod def _package_from_spec(cls, spec): """Builds a single package object from a specification. :param spec: May be a string or single-length dictionary of name to configuration values. :return: A package object. """ if isinstance(spec, six.string_types): return cls.Package(spec, None) else: package, properties = list(six.iteritems(spec))[0] version = properties.get('version', None) return cls.Package(package, version) @classmethod def from_spec(cls, spec, validator_map, resource_roots): """Builds a package validator from a specification. :param spec: May be a string, a single-length dictionary of name to configuration values, or a list containing any number of either or both of the above. Configuration values may include: version: The version of the package to check and/or install. :param validator_map: A map of validator name to class. :param resource_roots: The roots from which relative paths to resources (scripts and such) will be referenced. Any resource will be pulled from the first path in the list at which a file exists. :return: A validator that will check that the specified package or packages are installed. """ jsonschema.validate(spec, cls.SPEC_SCHEMA) packages = ([cls._package_from_spec(package_spec) for package_spec in spec] if isinstance(spec, list) else [cls._package_from_spec(spec)]) return cls(packages) def __init__(self, packages): self.packages = packages @transform_exception(ex.RemoteCommandException, p_ex.ImageValidationError) def validate(self, remote, reconcile=True, image_arguments=None, **kwargs): """Attempts to validate package installation on the image. Even if reconcile=True, attempts to verify previous package installation offline before using networked tools to validate or install new packages. :param remote: A remote socket to the instance. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :param image_arguments: A dictionary of image argument values keyed by argument name. :raises ImageValidationError: If validation fails. """ env_distro = image_arguments[self.DISTRO_KEY] env_family = self._DISTRO_FAMILES[env_distro] check, install = self._DISTRO_TOOLS[env_family] if not env_family: raise p_ex.ImageValidationError( _("Unknown distro: cannot verify or install packages.")) try: check(self, remote) except (ex.SubprocessException, ex.RemoteCommandException, RuntimeError): if reconcile: install(self, remote) check(self, remote) else: raise def _dpkg_check(self, remote): check_cmd = ("dpkg -s %s" % " ".join(str(package) for package in self.packages)) return _sudo(remote, check_cmd) def _rpm_check(self, remote): check_cmd = ("rpm -q %s" % " ".join(str(package) for package in self.packages)) return _sudo(remote, check_cmd) def _yum_install(self, remote): install_cmd = ( "yum install -y %s" % " ".join(str(package) for package in self.packages)) _sudo(remote, install_cmd) def _apt_install(self, remote): install_cmd = ( "apt-get -y install %s" % " ".join(str(package) for package in self.packages)) return _sudo(remote, install_cmd) _DISTRO_TOOLS = { "redhat": (_rpm_check, _yum_install), "debian": (_dpkg_check, _apt_install) } class SaharaScriptValidator(SaharaImageValidatorBase): """A validator that runs a script on the instance.""" _DEFAULT_ENV_VARS = [SaharaImageValidatorBase.RECONCILE_KEY, SaharaImageValidatorBase.DISTRO_KEY] SPEC_SCHEMA = { "title": "SaharaScriptValidator", "oneOf": [ { "type": "object", "minProperties": 1, "maxProperties": 1, "additionalProperties": { "type": "object", "properties": { "env_vars": { "type": "array", "items": { "type": "string" } }, "output": { "type": "string", "minLength": 1 }, "inline": { "type": "string", "minLength": 1 } }, } }, { "type": "string" } ] } @classmethod def from_spec(cls, spec, validator_map, resource_roots): """Builds a script validator from a specification. :param spec: May be a string or a single-length dictionary of name to configuration values. Configuration values include: env_vars: A list of environment variable names to send to the script. output: A key into which to put the stdout of the script in the image_arguments of the validation run. :param validator_map: A map of validator name to class. :param resource_roots: The roots from which relative paths to resources (scripts and such) will be referenced. Any resource will be pulled from the first path in the list at which a file exists. :return: A validator that will run a script on the image. """ jsonschema.validate(spec, cls.SPEC_SCHEMA) script_contents = None if isinstance(spec, six.string_types): script_path = spec env_vars, output_var = cls._DEFAULT_ENV_VARS, None else: script_path, properties = list(six.iteritems(spec))[0] env_vars = cls._DEFAULT_ENV_VARS + properties.get('env_vars', []) output_var = properties.get('output', None) script_contents = properties.get('inline') if not script_contents: for root in resource_roots: file_path = path.join(root, script_path) script_contents = files.try_get_file_text(file_path) if script_contents: break if not script_contents: raise p_ex.ImageValidationSpecificationError( _("Script %s not found in any resource roots.") % script_path) return SaharaScriptValidator(script_contents, env_vars, output_var) def __init__(self, script_contents, env_vars=None, output_var=None): """Constructor method. :param script_contents: A string representation of the script. :param env_vars: A list of environment variables to send to the script. :param output_var: A key into which to put the stdout of the script in the image_arguments of the validation run. :return: A SaharaScriptValidator. """ self.script_contents = script_contents self.env_vars = env_vars or [] self.output_var = output_var @transform_exception(ex.RemoteCommandException, p_ex.ImageValidationError) def validate(self, remote, reconcile=True, image_arguments=None, **kwargs): """Attempts to validate by running a script on the image. :param remote: A remote socket to the instance. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :param image_arguments: A dictionary of image argument values keyed by argument name. Note that the key SIV_RECONCILE will be set to 1 if the script should reconcile and 0 otherwise; all scripts should act on this input if possible. The key SIV_DISTRO will also contain the distro representation, per `lsb_release -is`. :raises ImageValidationError: If validation fails. """ arguments = copy.deepcopy(image_arguments) arguments[self.RECONCILE_KEY] = 1 if reconcile else 0 script = "\n".join(["%(env_vars)s", "bash <<_SIV_", "%(script)s", "_SIV_"]) env_vars = "\n".join("export %s=%s" % (key, value) for (key, value) in six.iteritems(image_arguments) if key in self.env_vars) script = script % {"env_vars": env_vars, "script": self.script_contents} code, stdout = _sudo(remote, script) if self.output_var: image_arguments[self.output_var] = stdout @six.add_metaclass(abc.ABCMeta) class SaharaAggregateValidator(SaharaImageValidatorBase): """An abstract class representing an ordered list of other validators.""" SPEC_SCHEMA = SaharaImageValidator.ORDERED_VALIDATORS_SCHEMA @classmethod def from_spec(cls, spec, validator_map, resource_roots): """Builds the aggregate validator from a specification. :param spec: A list of validator definitions, each of which is a single-length dictionary of name to configuration values. :param validator_map: A map of validator name to class. :param resource_roots: The roots from which relative paths to resources (scripts and such) will be referenced. Any resource will be pulled from the first path in the list at which a file exists. :return: An aggregate validator. """ jsonschema.validate(spec, cls.SPEC_SCHEMA) validators = cls.from_spec_list(spec, validator_map, resource_roots) return cls(validators) def __init__(self, validators): self.validators = validators class SaharaAnyValidator(SaharaAggregateValidator): """A list of validators, only one of which must succeed.""" def _try_all(self, remote, reconcile=True, image_arguments=None, **kwargs): results = [] for validator in self.validators: result = validator.try_validate(remote, reconcile=reconcile, image_arguments=image_arguments, **kwargs) results.append(result) if result: break return results def validate(self, remote, reconcile=True, image_arguments=None, **kwargs): """Attempts to validate any of the contained validators. Note that if reconcile=True, this validator will first run all contained validators using reconcile=False, and succeed immediately should any pass validation. If all fail, it will only then run them using reconcile=True, and again succeed immediately should any pass. :param remote: A remote socket to the instance. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :param image_arguments: A dictionary of image argument values keyed by argument name. :raises ImageValidationError: If validation fails. """ results = self._try_all(remote, reconcile=False, image_arguments=image_arguments) if reconcile and not any(results): results = self._try_all(remote, reconcile=True, image_arguments=image_arguments) if not any(results): raise p_ex.AllValidationsFailedError(result.exception for result in results) class SaharaAllValidator(SaharaAggregateValidator): """A list of validators, all of which must succeed.""" def validate(self, remote, reconcile=True, image_arguments=None, **kwargs): """Attempts to validate all of the contained validators. :param remote: A remote socket to the instance. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :param image_arguments: A dictionary of image argument values keyed by argument name. :raises ImageValidationError: If validation fails. """ for validator in self.validators: validator.validate(remote, reconcile=reconcile, image_arguments=image_arguments) class SaharaOSCaseValidator(SaharaImageValidatorBase): """A validator which will take different actions depending on distro.""" _distro_tuple = collections.namedtuple('Distro', ['distro', 'validator']) SPEC_SCHEMA = { "type": "array", "minLength": 1, "items": { "type": "object", "minProperties": 1, "maxProperties": 1, "additionalProperties": SaharaImageValidator.ORDERED_VALIDATORS_SCHEMA, } } @classmethod def from_spec(cls, spec, validator_map, resource_roots): """Builds an os_case validator from a specification. :param spec: A list of single-length dictionaries. The key of each is a distro or family name and the value under each key is a list of validators (all of which must succeed.) :param validator_map: A map of validator name to class. :param resource_roots: The roots from which relative paths to resources (scripts and such) will be referenced. Any resource will be pulled from the first path in the list at which a file exists. :return: A SaharaOSCaseValidator. """ jsonschema.validate(spec, cls.SPEC_SCHEMA) distros = itertools.chain(*(six.iteritems(distro_spec) for distro_spec in spec)) distros = [ cls._distro_tuple(key, SaharaAllValidator.from_spec( value, validator_map, resource_roots)) for (key, value) in distros] return cls(distros) def __init__(self, distros): """Constructor method. :param distros: A list of distro tuples (distro, list of validators). """ self.distros = distros def validate(self, remote, reconcile=True, image_arguments=None, **kwargs): """Attempts to validate depending on distro. May match the OS by specific distro or by family (centos may match "centos" or "redhat", for instance.) If multiple keys match the distro, only the validators under the first matched key will be run. If no keys match, no validators are run, and validation proceeds. :param remote: A remote socket to the instance. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :param image_arguments: A dictionary of image argument values keyed by argument name. :raises ImageValidationError: If validation fails. """ env_distro = image_arguments[self.DISTRO_KEY] family = self._DISTRO_FAMILES.get(env_distro) matches = {env_distro, family} if family else {env_distro} for distro, validator in self.distros: if distro in matches: validator.validate( remote, reconcile=reconcile, image_arguments=image_arguments) break class SaharaArgumentCaseValidator(SaharaImageValidatorBase): """A validator which will take different actions depending on distro.""" SPEC_SCHEMA = { "type": "object", "properties": { "argument_name": { "type": "string", "minLength": 1 }, "cases": { "type": "object", "minProperties": 1, "additionalProperties": SaharaImageValidator.ORDERED_VALIDATORS_SCHEMA, }, }, "additionalProperties": False, "required": ["argument_name", "cases"] } @classmethod def from_spec(cls, spec, validator_map, resource_roots): """Builds an argument_case validator from a specification. :param spec: A dictionary with two items: "argument_name", containing a string indicating the argument to be checked, and "cases", a dictionary. The key of each item in the dictionary is a value which may or may not match the argument value, and the value is a list of validators to be run in case it does. :param validator_map: A map of validator name to class. :param resource_roots: The roots from which relative paths to resources (scripts and such) will be referenced. Any resource will be pulled from the first path in the list at which a file exists. :return: A SaharaArgumentCaseValidator. """ jsonschema.validate(spec, cls.SPEC_SCHEMA) argument_name = spec['argument_name'] cases = {key: SaharaAllValidator.from_spec( value, validator_map, resource_roots) for key, value in six.iteritems(spec['cases'])} return cls(argument_name, cases) def __init__(self, argument_name, cases): """Constructor method. :param argument_name: The name of an argument. :param cases: A dictionary of possible argument value to a sub-validator to run in case of a match. """ self.argument_name = argument_name self.cases = cases def validate(self, remote, reconcile=True, image_arguments=None, **kwargs): """Attempts to validate depending on argument value. :param remote: A remote socket to the instance. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :param image_arguments: A dictionary of image argument values keyed by argument name. :raises ImageValidationError: If validation fails. """ arg = self.argument_name if arg not in image_arguments: raise p_ex.ImageValidationError( _("Argument {name} not found.").format(name=arg)) value = image_arguments[arg] if value in self.cases: self.cases[value].validate( remote, reconcile=reconcile, image_arguments=image_arguments) class SaharaArgumentSetterValidator(SaharaImageValidatorBase): """A validator which sets a specific argument to a specific value.""" SPEC_SCHEMA = { "type": "object", "properties": { "argument_name": { "type": "string", "minLength": 1 }, "value": { "type": "string", "minLength": 1 }, }, "additionalProperties": False, "required": ["argument_name", "value"] } @classmethod def from_spec(cls, spec, validator_map, resource_roots): """Builds an argument_set validator from a specification. :param spec: A dictionary with two items: "argument_name", containing a string indicating the argument to be set, and "value", a value to which to set that argument. :param validator_map: A map of validator name to class. :param resource_roots: The roots from which relative paths to resources (scripts and such) will be referenced. Any resource will be pulled from the first path in the list at which a file exists. :return: A SaharaArgumentSetterValidator. """ jsonschema.validate(spec, cls.SPEC_SCHEMA) argument_name = spec['argument_name'] value = spec['value'] return cls(argument_name, value) def __init__(self, argument_name, value): """Constructor method. :param argument_name: The name of an argument. :param value: A value to which to set that argument. """ self.argument_name = argument_name self.value = value def validate(self, remote, reconcile=True, image_arguments=None, **kwargs): """Attempts to validate depending on argument value. :param remote: A remote socket to the instance. :param reconcile: If false, all validators will only verify that a desired state is present, and fail if it is not. If true, all validators will attempt to enforce the desired state if possible, and succeed if this enforcement succeeds. :param image_arguments: A dictionary of image argument values keyed by argument name. """ image_arguments[self.argument_name] = self.value def _sudo(remote, cmd, **kwargs): return remote.execute_command(cmd, run_as_root=True, **kwargs)

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/352. Data Stream as Disjoint Intervals.py

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import bisect from typing import List class SummaryRanges: def __init__(self): """ Initialize your data structure here. """ self.res = [] def addNum(self, val: int) -> None: loc = bisect.bisect_left(self.res, [val]) if loc < len(self.res): if self.res[loc][0] == val: return if self.res[loc][0] > val: if loc >= 1: if self.res[loc - 1][1] >= val : return if self.res[loc - 1][1] + 1 == val and self.res[loc][0] - 1 == val: self.res[loc - 1:loc + 1] = [[self.res[loc - 1][0], self.res[loc][1]]] elif self.res[loc - 1][1] + 1 == val: self.res[loc-1:loc] = [[self.res[loc-1][0], val]] elif self.res[loc][0] - 1 == val: self.res[loc:loc+1] = [[val, self.res[loc][1]]] else: if self.res[loc][0] - 1 == val: self.res[loc:loc+1] = [[val, self.res[loc][1]]] else: self.res.insert(loc, [val, val]) else: self.res.insert(loc, [val, val]) else: if self.res[loc - 1][1] >= val: return elif self.res[loc - 1][1] + 1 == val: self.res[loc - 1:loc] = [[self.res[loc - 1][0], val]] else: self.res.insert(loc, [val, val]) def getIntervals(self) -> List[List[int]]: return self.res # Your SummaryRanges object will be instantiated and called as such: # obj = SummaryRanges() # obj.addNum(val) # param_2 = obj.getIntervals() a = SummaryRanges() a.addNum(1) print(a.res) a.addNum(3) print(a.res) a.addNum(7) print(a.res) a.addNum(2) a.addNum(6) print(a.getIntervals())

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762307667@qq.com

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Yang-yc/Python

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2022-12-31T00:47:01.659889

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class Tool(object): # 使用赋值语句定义类属性，记录所有工具对象的数量 count = 0 def __init__(self, name): self.name = name # 让类属性的值+1 Tool.count += 1 # 1.创建工具对象 tool1 = Tool("斧头") tool2 = Tool("榔头") tool3 = Tool("水桶") # 2.输出工具对象的总数 print(Tool.count)

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ycc20121404@163.com

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/uvrnmt/imagesfromsentence.py

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no_license

zubairabid/hybrid-mt

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#!/usr/bin/env python # coding: utf-8 # In[1]: from sklearn.feature_extraction.text import TfidfTransformer from sklearn.feature_extraction.text import CountVectorizer import numpy as np import pickle # In[2]: def load_stopwords(path_to_stopwords): stopwords = [] with open(path_to_stopwords, 'rb') as f: stopwords = pickle.load(f) return stopwords # In[3]: def load_index_from_word(path_to_en2id): en2id = {} with open(path_to_en2id, 'rb') as f: en2id = pickle.load(f) return en2id # In[4]: def load_lookup_table(path_to_lookup_table): lookup_table = [] with open(path_to_lookup_table, 'rb') as f: lookup_table = pickle.load(f) return lookup_table # In[5]: def preprocess(sentences): processed_sentences = [] for sentence in sentences: processed_sentences.append(sentence.lower()) return processed_sentences # In[6]: def topics_from_dataset(sentences): print("Generating topics and weights for dataset") vectorizer = CountVectorizer() transformer = TfidfTransformer() tfidf = transformer.fit_transform(vectorizer.fit_transform(sentences)) topics = vectorizer.get_feature_names() weights = tfidf.toarray() return topics, weights # In[7]: def sentence_remove_stopwords(sentence, stopwords): filtered_words = [] reduced_sentence = '' wordlist = sentence.strip().split(' ') for word in wordlist: if word not in stopwords: filtered_words.append(word) reduced_sentence = ' '.join(filtered_words) return reduced_sentence # In[8]: def topics_from_sentence(sentence_id, sentence, weights, topics): top_topics = [] sentence_topics = [] weight = weights[sentence_id] location = np.argsort(-weight) limit = min(10, len(weight)) for i in range(limit): if weight[location[i]] > 0.0: top_topics.append(topics[location[i]]) for word in sentence.split(): if word.lower() in top_topics: sentence_topics.append(word) return sentence_topics # In[9]: def images_from_topics(sentence_topics, stopwords, en2id, lookup_table): imagelist = [] for topic in sentence_topics: if topic in en2id.keys() and not topic in stopwords: if en2id[topic] in lookup_table: #print('<', topic, '> is in lookup table') #print(topic, lookup_table[en2id[topic]]) for image in lookup_table[en2id[topic]]: if image > 0.0 and not image in imagelist: imagelist.append(image) else: pass #print('>', topic, '< not in lookup table') else: if topic not in en2id.keys(): pass #print('|', topic, '| not in dictionary') return imagelist # In[10]: def get_features(sentences, cap): path_to_en2id = 'en2id.pkl' path_to_stopwords = 'stopwords-en.pkl' path_to_lookup_table = 'cap2image_en2fr.pickle' sentences = preprocess(sentences) images_for_sentence = [] en2id = load_index_from_word(path_to_en2id) stopwords = load_stopwords(path_to_stopwords) lookup_table = load_lookup_table(path_to_lookup_table) topics, weights = topics_from_dataset(sentences) for sentence_id, sentence in enumerate(sentences): sentence_topics = topics_from_sentence(sentence_id, sentence, weights, topics) imagelist = images_from_topics(sentence_topics, stopwords, en2id, lookup_table) if not imagelist: imagelist=[0] images_for_sentence.append(imagelist) feature_index = np.load('./data/train-resnet50-avgpool.npy') batch_sentence_features = [] for i, dummy in enumerate(sentences): sentence = sentences[i] images = images_for_sentence[i] sentence_features = [] for image in images: image_feature = feature_index[image-1] sentence_features.append(image_feature) if len(sentence_features) > cap: sentence_features = sentence_features[:cap] elif len(sentence_features) < cap: for j in range(cap-len(sentence_features)): sentence_features.append(np.zeros((2048,), dtype=float )) batch_sentence_features.append(sentence_features) pt = np.array(batch_sentence_features) return pt

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#!/usr/bin/env python3 """ Precision Module """ import numpy as np def precision(confusion): """ Function that calculates the sensitivity for each class in a confussion matrix """ classes, _ = confusion.shape classPrecision = np.zeros(classes) for classItem in range(classes): classPrecision[classItem] = np.divide( confusion[classItem][classItem], np.sum(confusion[:, classItem])) return classPrecision

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"""Helper functions for writing to terminals and files.""" import os import shutil import sys from typing import Optional from typing import Sequence from typing import TextIO from .wcwidth import wcswidth from _pytest.compat import final def get_terminal_width() -> int: width, _ = shutil.get_terminal_size(fallback=(80, 24)) if width < 40: width = 80 return width def should_do_markup(file: TextIO) -> bool: if os.environ.get("PY_COLORS") == "1": return True if os.environ.get("PY_COLORS") == "0": return False if "NO_COLOR" in os.environ: return False if "FORCE_COLOR" in os.environ: return True return ( hasattr(file, "isatty") and file.isatty() and os.environ.get("TERM") != "dumb" ) @final class TerminalWriter: _esctable = dict( black=30, red=31, green=32, yellow=33, blue=34, purple=35, cyan=36, white=37, Black=40, Red=41, Green=42, Yellow=43, Blue=44, Purple=45, Cyan=46, White=47, bold=1, light=2, blink=5, invert=7, ) def __init__(self, file: Optional[TextIO] = None) -> None: if file is None: file = sys.stdout if hasattr(file, "isatty") and file.isatty() and sys.platform == "win32": try: import colorama except ImportError: pass else: file = colorama.AnsiToWin32(file).stream assert file is not None self._file = file self.hasmarkup = should_do_markup(file) self._current_line = "" self._terminal_width: Optional[int] = None self.code_highlight = True @property def fullwidth(self) -> int: if self._terminal_width is not None: return self._terminal_width return get_terminal_width() @fullwidth.setter def fullwidth(self, value: int) -> None: self._terminal_width = value @property def width_of_current_line(self) -> int: """Return an estimate of the width so far in the current line.""" return wcswidth(self._current_line) def markup(self, text: str, **markup: bool) -> str: for name in markup: if name not in self._esctable: raise ValueError(f"unknown markup: {name!r}") if self.hasmarkup: esc = [self._esctable[name] for name, on in markup.items() if on] if esc: text = "".join("\x1b[%sm" % cod for cod in esc) + text + "\x1b[0m" return text def sep( self, sepchar: str, title: Optional[str] = None, fullwidth: Optional[int] = None, **markup: bool, ) -> None: if fullwidth is None: fullwidth = self.fullwidth if sys.platform == "win32": fullwidth -= 1 if title is not None: N = max((fullwidth - len(title) - 2) // (2 * len(sepchar)), 1) fill = sepchar * N line = f"{fill} {title} {fill}" else: line = sepchar * (fullwidth // len(sepchar)) if len(line) + len(sepchar.rstrip()) <= fullwidth: line += sepchar.rstrip() self.line(line, **markup) def write(self, msg: str, *, flush: bool = False, **markup: bool) -> None: if msg: current_line = msg.rsplit("\n", 1)[-1] if "\n" in msg: self._current_line = current_line else: self._current_line += current_line msg = self.markup(msg, **markup) try: self._file.write(msg) except UnicodeEncodeError: msg = msg.encode("unicode-escape").decode("ascii") self._file.write(msg) if flush: self.flush() def line(self, s: str = "", **markup: bool) -> None: self.write(s, **markup) self.write("\n") def flush(self) -> None: self._file.flush() def _write_source(self, lines: Sequence[str], indents: Sequence[str] = ()) -> None: """Write lines of source code possibly highlighted. Keeping this private for now because the API is clunky. We should discuss how to evolve the terminal writer so we can have more precise color support, for example being able to write part of a line in one color and the rest in another, and so on. """ if indents and len(indents) != len(lines): raise ValueError( "indents size ({}) should have same size as lines ({})".format( len(indents), len(lines) ) ) if not indents: indents = [""] * len(lines) source = "\n".join(lines) new_lines = self._highlight(source).splitlines() for indent, new_line in zip(indents, new_lines): self.line(indent + new_line) def _highlight(self, source: str) -> str: """Highlight the given source code if we have markup support.""" from _pytest.config.exceptions import UsageError if not self.hasmarkup or not self.code_highlight: return source try: from pygments.formatters.terminal import TerminalFormatter from pygments.lexers.python import PythonLexer from pygments import highlight import pygments.util except ImportError: return source else: try: highlighted: str = highlight( source, PythonLexer(), TerminalFormatter( bg=os.getenv("PYTEST_THEME_MODE", "dark"), style=os.getenv("PYTEST_THEME"), ), ) return highlighted except pygments.util.ClassNotFound: raise UsageError( "PYTEST_THEME environment variable had an invalid value: '{}'. " "Only valid pygment styles are allowed.".format( os.getenv("PYTEST_THEME") ) ) except pygments.util.OptionError: raise UsageError( "PYTEST_THEME_MODE environment variable had an invalid value: '{}'. " "The only allowed values are 'dark' and 'light'.".format( os.getenv("PYTEST_THEME_MODE") ) )

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from rest_framework import serializers from .models import Listings class ListingsSerializers(serializers.ModelSerializer): class Meta: model = Listings fields = ('title', 'adress', 'city', 'state', 'price', 'house_type', 'sqft', 'open_house', 'sale_type', 'photo_main', 'bathrooms', 'bedrooms', 'photo_main', 'slug') class ListingsDetailSerializers(serializers.ModelSerializer): class Meta: model = Listings fields = '__all__' lookup_field = 'slug'

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# Copyright 2020 Google LLC. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for tfx.orchestration.portable.python_executor_operator.""" import os from typing import Any, Dict, List, Text import tensorflow as tf from tfx import types from tfx.dsl.components.base import base_executor from tfx.dsl.io import fileio from tfx.orchestration.portable import data_types from tfx.orchestration.portable import python_executor_operator from tfx.proto.orchestration import executable_spec_pb2 from tfx.proto.orchestration import execution_result_pb2 from tfx.types import standard_artifacts from tfx.utils import test_case_utils from google.protobuf import text_format class InprocessExecutor(base_executor.BaseExecutor): """A Fake in-process executor what returns execution result.""" def Do( self, input_dict: Dict[Text, List[types.Artifact]], output_dict: Dict[Text, List[types.Artifact]], exec_properties: Dict[Text, Any]) -> execution_result_pb2.ExecutorOutput: executor_output = execution_result_pb2.ExecutorOutput() python_executor_operator._populate_output_artifact(executor_output, output_dict) return executor_output class NotInprocessExecutor(base_executor.BaseExecutor): """A Fake not-in-process executor what writes execution result to executor_output_uri.""" def Do(self, input_dict: Dict[Text, List[types.Artifact]], output_dict: Dict[Text, List[types.Artifact]], exec_properties: Dict[Text, Any]) -> None: executor_output = execution_result_pb2.ExecutorOutput() python_executor_operator._populate_output_artifact(executor_output, output_dict) with fileio.open(self._context.executor_output_uri, 'wb') as f: f.write(executor_output.SerializeToString()) class InplaceUpdateExecutor(base_executor.BaseExecutor): """A Fake noop executor.""" def Do(self, input_dict: Dict[Text, List[types.Artifact]], output_dict: Dict[Text, List[types.Artifact]], exec_properties: Dict[Text, Any]) -> None: model = output_dict['output_key'][0] model.name = 'my_model' class PythonExecutorOperatorTest(test_case_utils.TfxTest): def testRunExecutor_with_InprocessExecutor(self): executor_sepc = text_format.Parse( """ class_path: "tfx.orchestration.portable.python_executor_operator_test.InprocessExecutor" """, executable_spec_pb2.PythonClassExecutableSpec()) operator = python_executor_operator.PythonExecutorOperator(executor_sepc) input_dict = {'input_key': [standard_artifacts.Examples()]} output_dict = {'output_key': [standard_artifacts.Model()]} exec_properties = {'key': 'value'} stateful_working_dir = os.path.join(self.tmp_dir, 'stateful_working_dir') executor_output_uri = os.path.join(self.tmp_dir, 'executor_output') executor_output = operator.run_executor( data_types.ExecutionInfo( execution_id=1, input_dict=input_dict, output_dict=output_dict, exec_properties=exec_properties, stateful_working_dir=stateful_working_dir, execution_output_uri=executor_output_uri)) self.assertProtoPartiallyEquals( """ output_artifacts { key: "output_key" value { artifacts { } } }""", executor_output) def testRunExecutor_with_NotInprocessExecutor(self): executor_sepc = text_format.Parse( """ class_path: "tfx.orchestration.portable.python_executor_operator_test.NotInprocessExecutor" """, executable_spec_pb2.PythonClassExecutableSpec()) operator = python_executor_operator.PythonExecutorOperator(executor_sepc) input_dict = {'input_key': [standard_artifacts.Examples()]} output_dict = {'output_key': [standard_artifacts.Model()]} exec_properties = {'key': 'value'} stateful_working_dir = os.path.join(self.tmp_dir, 'stateful_working_dir') executor_output_uri = os.path.join(self.tmp_dir, 'executor_output') executor_output = operator.run_executor( data_types.ExecutionInfo( execution_id=1, input_dict=input_dict, output_dict=output_dict, exec_properties=exec_properties, stateful_working_dir=stateful_working_dir, execution_output_uri=executor_output_uri)) self.assertProtoPartiallyEquals( """ output_artifacts { key: "output_key" value { artifacts { } } }""", executor_output) def testRunExecutor_with_InplaceUpdateExecutor(self): executor_sepc = text_format.Parse( """ class_path: "tfx.orchestration.portable.python_executor_operator_test.InplaceUpdateExecutor" """, executable_spec_pb2.PythonClassExecutableSpec()) operator = python_executor_operator.PythonExecutorOperator(executor_sepc) input_dict = {'input_key': [standard_artifacts.Examples()]} output_dict = {'output_key': [standard_artifacts.Model()]} exec_properties = { 'string': 'value', 'int': 1, 'float': 0.0, # This should not happen on production and will be # dropped. 'proto': execution_result_pb2.ExecutorOutput() } stateful_working_dir = os.path.join(self.tmp_dir, 'stateful_working_dir') executor_output_uri = os.path.join(self.tmp_dir, 'executor_output') executor_output = operator.run_executor( data_types.ExecutionInfo( execution_id=1, input_dict=input_dict, output_dict=output_dict, exec_properties=exec_properties, stateful_working_dir=stateful_working_dir, execution_output_uri=executor_output_uri)) self.assertProtoPartiallyEquals( """ output_artifacts { key: "output_key" value { artifacts { custom_properties { key: "name" value { string_value: "my_model" } } } } }""", executor_output) if __name__ == '__main__': tf.test.main()

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#!/var/www/html/python-rest/myenv/bin/python3.6 from django.core import management if __name__ == "__main__": management.execute_from_command_line()

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# [4,5,2,25] def nextGreater(arr): for i in range(len(arr)): for j in range(i+1,len(arr)): print('i -------->',arr[i]) print('j--->',arr[j]) next = -1 if arr[i] nextGreater([4,5,2,25])

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# https://leetcode.com/problems/paint-house-iii/ """ There is a row of m houses in a small city, each house must be painted with one of the n colors (labeled from 1 to n), some houses that have been painted last summer should not be painted again. A neighborhood is a maximal group of continuous houses that are painted with the same color. For example: houses = [1,2,2,3,3,2,1,1] contains 5 neighborhoods [{1}, {2,2}, {3,3}, {2}, {1,1}]. Given an array houses, an m x n matrix cost and an integer target where: houses[i]: is the color of the house i, and 0 if the house is not painted yet. cost[i][j]: is the cost of paint the house i with the color j + 1. Return the minimum cost of painting all the remaining houses in such a way that there are exactly target neighborhoods. If it is not possible, return -1. Example 1: Input: houses = [0,0,0,0,0], cost = [[1,10],[10,1],[10,1],[1,10],[5,1]], m = 5, n = 2, target = 3 Output: 9 Explanation: Paint houses of this way [1,2,2,1,1] This array contains target = 3 neighborhoods, [{1}, {2,2}, {1,1}]. Cost of paint all houses (1 + 1 + 1 + 1 + 5) = 9. Example 2: Input: houses = [0,2,1,2,0], cost = [[1,10],[10,1],[10,1],[1,10],[5,1]], m = 5, n = 2, target = 3 Output: 11 Explanation: Some houses are already painted, Paint the houses of this way [2,2,1,2,2] This array contains target = 3 neighborhoods, [{2,2}, {1}, {2,2}]. Cost of paint the first and last house (10 + 1) = 11. Example 3: Input: houses = [3,1,2,3], cost = [[1,1,1],[1,1,1],[1,1,1],[1,1,1]], m = 4, n = 3, target = 3 Output: -1 Explanation: Houses are already painted with a total of 4 neighborhoods [{3},{1},{2},{3}] different of target = 3. Constraints: m == houses.length == cost.length n == cost[i].length 1 <= m <= 100 1 <= n <= 20 1 <= target <= m 0 <= houses[i] <= n 1 <= cost[i][j] <= 104 """ from functools import cache from math import inf # bottom up def min_cost( houses: list[int], cost: list[list[int]], m: int, n: int, target: int ) -> int: # dp[k][i][c] := min cost to form k groups with first i houses and last house paint with c dp = [ [[inf for _ in range(n + 1)] for _ in range(m + 1)] for _ in range(target + 1) ] # init values: 0 groups with first 0 houses is dummy for c in range(n + 1): dp[0][0][c] = 0 for k in range(1, target + 1): for i in range(k, m + 1): hi = houses[i - 1] hj = houses[i - 2] if i >= 2 else 0 si, ei = (hi, hi) if hi else (1, n) sj, ej = (hj, hj) if hj else (1, n) for ci in range(si, ei + 1): v = 0 if ci == hi else cost[i - 1][ci - 1] for cj in range(sj, ej + 1): # when ci == cj: same group # when ci != cj: form new group dp[k][i][ci] = min( dp[k][i][ci], dp[k - int(ci != cj)][i - 1][cj] + v ) ans = min(dp[target][m]) return -1 if ans == inf else ans # top down def min_cost( houses: list[int], cost: list[list[int]], m: int, n: int, target: int ) -> int: @cache def dp(i: int, p: int, h: int) -> int: """ Args: i (int): index p (int): previous color h (int): neighborhoods Returns: int: cost """ if (h > target) or (i == m and h != target): return inf if i == m: return 0 if houses[i] != 0: return dp(i + 1, houses[i], h + int(p != houses[i])) best = inf for nxt_c, cst in enumerate(cost[i], start=1): best = min(best, dp(i + 1, nxt_c, h + int(p != nxt_c)) + cst) return best res = dp(0, 0, 0) return res if res != inf else -1

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import sys import StringIO stdout = sys.stdout sys.stdout = f = StringIO.StringIO() print('Sample output') print('good') print('Good') sys.stdout = stdout s = f.getvalue() print 'Done-------------' print(s)

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# Natural Language Toolkit: K-Means Clusterer # # Copyright (C) 2001-2021 NLTK Project # Author: Trevor Cohn <tacohn@cs.mu.oz.au> # URL: <https://www.nltk.org/> # For license information, see LICENSE.TXT import copy import random import sys try: import numpy except ImportError: pass from nltk.cluster.util import VectorSpaceClusterer class KMeansClusterer(VectorSpaceClusterer): """ The K-means clusterer starts with k arbitrary chosen means then allocates each vector to the cluster with the closest mean. It then recalculates the means of each cluster as the centroid of the vectors in the cluster. This process repeats until the cluster memberships stabilise. This is a hill-climbing algorithm which may converge to a local maximum. Hence the clustering is often repeated with random initial means and the most commonly occurring output means are chosen. """ def __init__( self, num_means, distance, repeats=1, conv_test=1e-6, initial_means=None, normalise=False, svd_dimensions=None, rng=None, avoid_empty_clusters=False, ): """ :param num_means: the number of means to use (may use fewer) :type num_means: int :param distance: measure of distance between two vectors :type distance: function taking two vectors and returning a float :param repeats: number of randomised clustering trials to use :type repeats: int :param conv_test: maximum variation in mean differences before deemed convergent :type conv_test: number :param initial_means: set of k initial means :type initial_means: sequence of vectors :param normalise: should vectors be normalised to length 1 :type normalise: boolean :param svd_dimensions: number of dimensions to use in reducing vector dimensionsionality with SVD :type svd_dimensions: int :param rng: random number generator (or None) :type rng: Random :param avoid_empty_clusters: include current centroid in computation of next one; avoids undefined behavior when clusters become empty :type avoid_empty_clusters: boolean """ VectorSpaceClusterer.__init__(self, normalise, svd_dimensions) self._num_means = num_means self._distance = distance self._max_difference = conv_test assert not initial_means or len(initial_means) == num_means self._means = initial_means assert repeats >= 1 assert not (initial_means and repeats > 1) self._repeats = repeats self._rng = rng if rng else random.Random() self._avoid_empty_clusters = avoid_empty_clusters def cluster_vectorspace(self, vectors, trace=False): if self._means and self._repeats > 1: print("Warning: means will be discarded for subsequent trials") meanss = [] for trial in range(self._repeats): if trace: print("k-means trial", trial) if not self._means or trial > 1: self._means = self._rng.sample(list(vectors), self._num_means) self._cluster_vectorspace(vectors, trace) meanss.append(self._means) if len(meanss) > 1: # sort the means first (so that different cluster numbering won't # effect the distance comparison) for means in meanss: means.sort(key=sum) # find the set of means that's minimally different from the others min_difference = min_means = None for i in range(len(meanss)): d = 0 for j in range(len(meanss)): if i != j: d += self._sum_distances(meanss[i], meanss[j]) if min_difference is None or d < min_difference: min_difference, min_means = d, meanss[i] # use the best means self._means = min_means def _cluster_vectorspace(self, vectors, trace=False): if self._num_means < len(vectors): # perform k-means clustering converged = False while not converged: # assign the tokens to clusters based on minimum distance to # the cluster means clusters = [[] for m in range(self._num_means)] for vector in vectors: index = self.classify_vectorspace(vector) clusters[index].append(vector) if trace: print("iteration") # for i in range(self._num_means): # print ' mean', i, 'allocated', len(clusters[i]), 'vectors' # recalculate cluster means by computing the centroid of each cluster new_means = list(map(self._centroid, clusters, self._means)) # measure the degree of change from the previous step for convergence difference = self._sum_distances(self._means, new_means) if difference < self._max_difference: converged = True # remember the new means self._means = new_means def classify_vectorspace(self, vector): # finds the closest cluster centroid # returns that cluster's index best_distance = best_index = None for index in range(len(self._means)): mean = self._means[index] dist = self._distance(vector, mean) if best_distance is None or dist < best_distance: best_index, best_distance = index, dist return best_index def num_clusters(self): if self._means: return len(self._means) else: return self._num_means def means(self): """ The means used for clustering. """ return self._means def _sum_distances(self, vectors1, vectors2): difference = 0.0 for u, v in zip(vectors1, vectors2): difference += self._distance(u, v) return difference def _centroid(self, cluster, mean): if self._avoid_empty_clusters: centroid = copy.copy(mean) for vector in cluster: centroid += vector return centroid / (1 + len(cluster)) else: if not len(cluster): sys.stderr.write("Error: no centroid defined for empty cluster.\n") sys.stderr.write( "Try setting argument 'avoid_empty_clusters' to True\n" ) assert False centroid = copy.copy(cluster[0]) for vector in cluster[1:]: centroid += vector return centroid / len(cluster) def __repr__(self): return "<KMeansClusterer means=%s repeats=%d>" % (self._means, self._repeats) ################################################################################# def demo(): # example from figure 14.9, page 517, Manning and Schutze from nltk.cluster import KMeansClusterer, euclidean_distance vectors = [numpy.array(f) for f in [[2, 1], [1, 3], [4, 7], [6, 7]]] means = [[4, 3], [5, 5]] clusterer = KMeansClusterer(2, euclidean_distance, initial_means=means) clusters = clusterer.cluster(vectors, True, trace=True) print("Clustered:", vectors) print("As:", clusters) print("Means:", clusterer.means()) print() vectors = [numpy.array(f) for f in [[3, 3], [1, 2], [4, 2], [4, 0], [2, 3], [3, 1]]] # test k-means using the euclidean distance metric, 2 means and repeat # clustering 10 times with random seeds clusterer = KMeansClusterer(2, euclidean_distance, repeats=10) clusters = clusterer.cluster(vectors, True) print("Clustered:", vectors) print("As:", clusters) print("Means:", clusterer.means()) print() # classify a new vector vector = numpy.array([3, 3]) print("classify(%s):" % vector, end=" ") print(clusterer.classify(vector)) print() if __name__ == "__main__": demo()

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# うーん分からん # ある特定の問題を入れたい人の気持ちになって考えた場合、トップにする必要はない # 上位P問が使われるので、同率P位に滑り込めればOK # 明らかに、ソートしても問題ない # 投票して数字を増やすより減らしたほうが見通しが立てやすい気がするぞ # （P問の値を1引き上げるのではなく、N-P問の値を1引き下げる、と考える。これでも同じ。） # 降順にソートする。 # 「ある特定の問題」がk問目だとする。 # 上位（P-1）問は確定。P問目〜k-1問目をk問目と同じ数まで引き下げられたらOK。 # この引き下げが可能である条件は # ・P問目の数 - k問目の数 <= 投票者の数M # ・引き下げに必要な票数合計 <= 投票者の数M * 引き下げ票数(N-V) # これが必要条件なのは分かるけど、十分性は……? この2つを満たせば必ず引き下げができるのか……? # まぁ多分できそうな気がする # 実際は毎回この判定をやると計算量が間に合わない # 差分だけ調べて合計を更新する（累積和っぽい感じ） n, m, v, p = list(map(int, input().split())) vote = list(map(int, input().split())) vote.sort(reverse=True) target_score = vote[p-1] # P問目 ans = p # 最初p問は明らかに条件を満たす vote_num_to_match = 0 # print(vote) for i in range(p, n): vote_num_to_match += (vote[i-1] - vote[i]) * (i-(p-1)) # print(vote_num_to_match) if target_score - vote[i] <= m and vote_num_to_match <= m * (n-v): ans += 1 else: break print(ans)

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#전처리 하나와 모델을 합침 import numpy as np import tensorflow as tf import pandas as pd from sklearn.datasets import load_boston from sklearn.preprocessing import MinMaxScaler, StandardScaler from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV, RandomizedSearchCV from sklearn.metrics import accuracy_score from sklearn.svm import LinearSVC, SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.pipeline import Pipeline, make_pipeline import timeit start_time = timeit.default_timer() import warnings warnings.filterwarnings('ignore') dataset = load_boston() x = dataset.data y = dataset.target x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=44) # Pipeline은 전처리 + 모델해줘서 MinMaxScaler문 생략 가능 # from sklearn.preprocessing import MinMaxScaler # scale = MinMaxScaler() # scale.fit(x_train) # x_train = scale.transform(x_train) # x_test = scale.transform(x_test) parameters = [ {"svc__C" :[1,10,100,1000], "svc__kernel":["linear"]}, # 1주고 linear, 10주고 linear, ... 4번 {"svc__C" :[1,10,100], "svc__kernel":["rbf"], "svc__gamma":[0.001, 0.0001]}, #3x2 6번 {"svc__C" :[1,10,100,1000], "svc__kernel":["sigmoid"],"svc__gamma":[0.001, 0.0001]}] #4x2 8번 parameters = [ {"mal__C" :[1,10,100,1000], "mal__kernel":["linear"]}, # 1주고 linear, 10주고 linear, ... 4번 {"mal__C" :[1,10,100], "mal__kernel":["rbf"], "mal__gamma":[0.001, 0.0001]}, #3x2 6번 {"mal__C" :[1,10,100,1000], "mal__kernel":["sigmoid"],"mal__gamma":[0.001, 0.0001]}] #4x2 8번 # 언더바 (_) 두개 써줘야한다 # 2. 모델 Pipe = Pipeline([('scale', MinMaxScaler()), ('mal', SVC())]) #SVC모델과 MinMax 를합친다 , 괄호 조심 # pipe = make_pipeline(StandardScaler(), SVC()) # 두가지 방법이 있다. # Pipeline 써주는 이유 : 트레인만 하는게 효과적, cv만큼 스케일링, 과적합 방지, 모델에 적합해서 성능이 강화 ..... model = GridSearchCV(Pipe, parameters, cv=5) model.fit(x_train, y_train) results = model.score(x_test, y_test) print('results : ', results) # MinMaxScaler # results : 0.9666666666666667 # StandardScaler # results : 0.9666666666666667

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''' Created on Dec 7, 2018 @author: _patels13 ''' import time def follow(thefile): thefile.seek(0, 2) while True: line = thefile.readline() print(line) if not line: time.sleep(0.1) continue yield line # Example use if __name__ == '__main__': logfile = open("access-log.log") print(logfile) for line in follow(logfile): print(line)

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#!/usr/bin/env python import codecs import re import os from setuptools import setup, find_packages def read(*parts): filename = os.path.join(os.path.dirname(__file__), *parts) with codecs.open(filename, encoding='utf-8') as fp: return fp.read() def find_version(*file_paths): version_file = read(*file_paths) version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M) if version_match: return version_match.group(1) raise RuntimeError("Unable to find version string.") setup( name="froide", version=find_version("froide", "__init__.py"), url='https://github.com/okfde/froide', license='MIT', description="German Freedom of Information Portal", long_description=read('README.md'), author='Stefan Wehrmeyer', author_email='mail@stefanwehrmeyer.com', packages=find_packages(), scripts=['manage.py'], install_requires=[ 'Django', 'Markdown', 'celery', 'geoip2', 'django-elasticsearch-dsl', 'django-taggit', 'pytz', 'requests', 'python-magic', 'djangorestframework', 'djangorestframework-csv', 'djangorestframework-jsonp', 'python-mimeparse', 'django-configurations', 'django-storages', 'dj-database-url', 'django-cache-url', 'django-filter', 'phonenumbers', 'django-filingcabinet', 'icalendar', ], include_package_data=True, classifiers=[ 'Development Status :: 5 - Production/Stable', 'Environment :: Web Environment', 'Framework :: Django', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Internet :: WWW/HTTP' ] )

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import argparse from datetime import datetime import signal import json import subprocess import sys import time instances = {} instance_types = { ("v100", 1): "p3.2xlarge", ("v100", 4): "p3.8xlarge", ("v100", 8): "p3.16xlarge", ("k80", 1): "p2.xlarge", ("k80", 8): "p2.8xlarge", ("k80", 16): "p2.16xlarge", } def signal_handler(sig, frame): global instances # Clean up all instances when program is interrupted. for (zone, gpu_type, num_gpus) in instances: [instance_id, _] = instances[(zone, gpu_type, num_gpus)] if instance_id is not None: delete_spot_instance(zone, instance_id) sys.exit(0) def launch_spot_instance(zone, gpu_type, num_gpus, instance_id): instance_type = instance_types[(gpu_type, num_gpus)] with open("specification.json.template", 'r') as f1, open("specification.json", 'w') as f2: template = f1.read() specification_file = template % (instance_type, zone) f2.write(specification_file) command = """aws ec2 request-spot-instances --instance-count 1 --type one-time --launch-specification file://specification.json""" try: spot_instance_request_id = None print("[%s] Trying to create instance with %d GPU(s) of type %s in zone %s" % ( datetime.now().strftime('%Y-%m-%dT%H:%M:%S.000Z'), num_gpus, gpu_type, zone), file=sys.stderr) output = subprocess.check_output(command, shell=True).decode() return_obj = json.loads(output) spot_instance_request_id = return_obj["SpotInstanceRequests"][0]["SpotInstanceRequestId"] command = """aws ec2 describe-spot-instance-requests --spot-instance-request-id %s""" % ( spot_instance_request_id) time.sleep(30) output = subprocess.check_output(command, shell=True).decode() return_obj = json.loads(output) instance_id = return_obj["SpotInstanceRequests"][0]["InstanceId"] print("[%s] Created instance %s with %d GPU(s) of type %s in zone %s" % ( datetime.now().strftime('%Y-%m-%dT%H:%M:%S.000Z'), instance_id, num_gpus, gpu_type, zone)) return [instance_id, True] except Exception as e: pass if spot_instance_request_id is not None: command = """aws ec2 cancel-spot-instance-requests --spot-instance-request-ids %s""" % ( spot_instance_request_id) subprocess.check_output(command, shell=True) print("[%s] Instance with %d GPU(s) of type %s creation failed" % ( datetime.now().strftime('%Y-%m-%dT%H:%M:%S.000Z'), num_gpus, gpu_type)) return [None, False] def monitor_spot_instance(zone, instance_id): command = """aws ec2 describe-instances --instance-id %(instance_id)s""" % { "instance_id": instance_id, } try: output = subprocess.check_output(command, shell=True).decode() if "running" in output: print("[%s] Instance %s running in zone %s" % ( datetime.now().strftime('%Y-%m-%dT%H:%M:%S.000Z'), instance_id, zone)) return True except Exception as e: pass print("[%s] Instance %s not running in zone %s" % ( datetime.now().strftime('%Y-%m-%dT%H:%M:%S.000Z'), instance_id, zone)) # Delete spot instance in case it exists. delete_spot_instance(zone, instance_id) return False def delete_spot_instance(zone, instance_id): command = """aws ec2 terminate-instances --instance-ids %(instance_id)s""" % { "instance_id": instance_id, } try: output = subprocess.check_output(command, shell=True) print("[%s] Successfully deleted instance %s" % ( datetime.now().strftime('%Y-%m-%dT%H:%M:%S.000Z'), instance_id)) except: return def main(args): global instances for zone in args.zones: for gpu_type in args.gpu_types: for num_gpus in args.all_num_gpus: instances[(zone, gpu_type, num_gpus)] = [None, False] while True: # Spin in a loop; try to launch spot instances of particular type if # not running already. Check on status of instances, and update to # "not running" as needed. for (zone, gpu_type, num_gpus) in instances: [instance_id, running] = instances[(zone, gpu_type, num_gpus)] if instance_id is not None: running = \ monitor_spot_instance(zone, instance_id) if not running: [instance_id, running] = \ launch_spot_instance(zone, gpu_type, num_gpus, instance_id) instances[(zone, gpu_type, num_gpus)] = [instance_id, running] time.sleep(600) if __name__ == '__main__': parser = argparse.ArgumentParser( description='Get AWS spot instance availability') parser.add_argument('--zones', type=str, nargs='+', default=["us-east-1b", "us-east-1c"], help='AWS availability zones') parser.add_argument('--gpu_types', type=str, nargs='+', default=["v100", "k80"], help='GPU types') parser.add_argument('--all_num_gpus', type=int, nargs='+', default=[1, 8], help='Number of GPUs per instance') args = parser.parse_args() signal.signal(signal.SIGINT, signal_handler) main(args)

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from rest_framework.serializers import ModelSerializer from .models import Report class ReportSerializer(ModelSerializer): class Meta: model = Report

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__author__ = 'brunocatao' from django import forms from portal.models import Discipline from django.utils.translation import ugettext as _ class DisciplineForm(forms.ModelForm): class Meta: model = Discipline fields = ('name', 'acronym', 'description', 'feed_url', 'twitter_id', 'registration_type', 'access_type', )

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#!/usr/bin/env python import unittest import random import time import math import sys import array import os import cv2.cv as cv def find_sample(s): for d in ["../samples/c/", "../doc/pics/"]: path = os.path.join(d, s) if os.access(path, os.R_OK): return path return s class TestTickets(unittest.TestCase): def test_2542670(self): xys = [(94, 121), (94, 122), (93, 123), (92, 123), (91, 124), (91, 125), (91, 126), (92, 127), (92, 128), (92, 129), (92, 130), (92, 131), (91, 132), (90, 131), (90, 130), (90, 131), (91, 132), (92, 133), (92, 134), (93, 135), (94, 136), (94, 137), (94, 138), (95, 139), (96, 140), (96, 141), (96, 142), (96, 143), (97, 144), (97, 145), (98, 146), (99, 146), (100, 146), (101, 146), (102, 146), (103, 146), (104, 146), (105, 146), (106, 146), (107, 146), (108, 146), (109, 146), (110, 146), (111, 146), (112, 146), (113, 146), (114, 146), (115, 146), (116, 146), (117, 146), (118, 146), (119, 146), (120, 146), (121, 146), (122, 146), (123, 146), (124, 146), (125, 146), (126, 146), (126, 145), (126, 144), (126, 143), (126, 142), (126, 141), (126, 140), (127, 139), (127, 138), (127, 137), (127, 136), (127, 135), (127, 134), (127, 133), (128, 132), (129, 132), (130, 131), (131, 130), (131, 129), (131, 128), (132, 127), (133, 126), (134, 125), (134, 124), (135, 123), (136, 122), (136, 121), (135, 121), (134, 121), (133, 121), (132, 121), (131, 121), (130, 121), (129, 121), (128, 121), (127, 121), (126, 121), (125, 121), (124, 121), (123, 121), (122, 121), (121, 121), (120, 121), (119, 121), (118, 121), (117, 121), (116, 121), (115, 121), (114, 121), (113, 121), (112, 121), (111, 121), (110, 121), (109, 121), (108, 121), (107, 121), (106, 121), (105, 121), (104, 121), (103, 121), (102, 121), (101, 121), (100, 121), (99, 121), (98, 121), (97, 121), (96, 121), (95, 121)] #xys = xys[:12] + xys[16:] pts = cv.CreateMat(len(xys), 1, cv.CV_32SC2) for i,(x,y) in enumerate(xys): pts[i,0] = (x, y) storage = cv.CreateMemStorage() hull = cv.ConvexHull2(pts, storage) hullp = cv.ConvexHull2(pts, storage, return_points = 1) defects = cv.ConvexityDefects(pts, hull, storage) vis = cv.CreateImage((1000,1000), 8, 3) x0 = min([x for (x,y) in xys]) - 10 x1 = max([x for (x,y) in xys]) + 10 y0 = min([y for (y,y) in xys]) - 10 y1 = max([y for (y,y) in xys]) + 10 def xform(pt): x,y = pt return (1000 * (x - x0) / (x1 - x0), 1000 * (y - y0) / (y1 - y0)) for d in defects[:2]: cv.Zero(vis) # First draw the defect as a red triangle cv.FillConvexPoly(vis, [xform(p) for p in d[:3]], cv.RGB(255,0,0)) # Draw the convex hull as a thick green line for a,b in zip(hullp, hullp[1:]): cv.Line(vis, xform(a), xform(b), cv.RGB(0,128,0), 3) # Draw the original contour as a white line for a,b in zip(xys, xys[1:]): cv.Line(vis, xform(a), xform(b), (255,255,255)) self.snap(vis) def test_2686307(self): lena = cv.LoadImage(find_sample("lena.jpg"), 1) dst = cv.CreateImage((512,512), 8, 3) cv.Set(dst, (128,192,255)) mask = cv.CreateImage((512,512), 8, 1) cv.Zero(mask) cv.Rectangle(mask, (10,10), (300,100), 255, -1) cv.Copy(lena, dst, mask) self.snapL([lena, dst, mask]) m = cv.CreateMat(480, 640, cv.CV_8UC1) print "ji", m print m.rows, m.cols, m.type, m.step def snap(self, img): self.snapL([img]) def snapL(self, L): for i,img in enumerate(L): cv.NamedWindow("snap-%d" % i, 1) cv.ShowImage("snap-%d" % i, img) cv.WaitKey() cv.DestroyAllWindows() if __name__ == '__main__': random.seed(0) if len(sys.argv) == 1: suite = unittest.TestLoader().loadTestsFromTestCase(TestTickets) unittest.TextTestRunner(verbosity=2).run(suite) else: suite = unittest.TestSuite() suite.addTest(TestTickets(sys.argv[1])) unittest.TextTestRunner(verbosity=2).run(suite)

[ "slacealic@gmail.com" ]

slacealic@gmail.com

f8ce9dd28dcb500d0b0228e76c9a387e5584278c

cee11bed1fd868fc87ef113f6062440cd190a40c

/detect/model/backbone/MobilenetV2.py

95ec1e952dae01010670f264bbf36bfb9f651726

[]

no_license

Peiiii/plate_detect_recongnize_tf_py3

f1d41270c7e6ed1718cb9d0d46784d8c83701439

39a04ef6475cdbaf8b4ff6e6f729e5b28b24daf1

refs/heads/master

2020-07-10T12:30:17.863818

2019-08-25T07:52:44

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# coding: utf-8 import tensorflow as tf from ...model.layers import * def MobilenetV2(input_data, training): with tf.variable_scope('MobilenetV2'): conv = convolutional(name='Conv', input_data=input_data, filters_shape=(3, 3, 3, 32), training=training, downsample=True, activate=True, bn=True) conv = inverted_residual(name='expanded_conv', input_data=conv, input_c=32, output_c=16, training=training, t=1) conv = inverted_residual(name='expanded_conv_1', input_data=conv, input_c=16, output_c=24, downsample=True, training=training) conv = inverted_residual(name='expanded_conv_2', input_data=conv, input_c=24, output_c=24, training=training) conv = inverted_residual(name='expanded_conv_3', input_data=conv, input_c=24, output_c=32, downsample=True, training=training) conv = inverted_residual(name='expanded_conv_4', input_data=conv, input_c=32, output_c=32, training=training) feature_map_s = inverted_residual(name='expanded_conv_5', input_data=conv, input_c=32, output_c=32, training=training) conv = inverted_residual(name='expanded_conv_6', input_data=feature_map_s, input_c=32, output_c=64, downsample=True, training=training) conv = inverted_residual(name='expanded_conv_7', input_data=conv, input_c=64, output_c=64, training=training) conv = inverted_residual(name='expanded_conv_8', input_data=conv, input_c=64, output_c=64, training=training) conv = inverted_residual(name='expanded_conv_9', input_data=conv, input_c=64, output_c=64, training=training) conv = inverted_residual(name='expanded_conv_10', input_data=conv, input_c=64, output_c=96, training=training) conv = inverted_residual(name='expanded_conv_11', input_data=conv, input_c=96, output_c=96, training=training) feature_map_m = inverted_residual(name='expanded_conv_12', input_data=conv, input_c=96, output_c=96, training=training) conv = inverted_residual(name='expanded_conv_13', input_data=feature_map_m, input_c=96, output_c=160, downsample=True, training=training) conv = inverted_residual(name='expanded_conv_14', input_data=conv, input_c=160, output_c=160, training=training) conv = inverted_residual(name='expanded_conv_15', input_data=conv, input_c=160, output_c=160, training=training) conv = inverted_residual(name='expanded_conv_16', input_data=conv, input_c=160, output_c=320, training=training) feature_map_l = convolutional(name='Conv_1', input_data=conv, filters_shape=(1, 1, 320, 1280), training=training, downsample=False, activate=True, bn=True) return feature_map_s, feature_map_m, feature_map_l

[ "1535376447@qq.com" ]

1535376447@qq.com

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refs/heads/master

2022-11-17T15:09:29.113493

2020-07-18T13:47:34

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import warnings import matplotlib.pyplot as plt import mmcv import torch from mmcv.parallel import collate, scatter from mmcv.runner import load_checkpoint from mmdet.core import get_classes from mmdet.datasets.pipelines import Compose from mmdet.models import build_detector from mmdet.ops import RoIAlign, RoIPool def init_detector(config, checkpoint=None, device='cuda:0'): """Initialize a detector from config file. Args: config (str or :obj:`mmcv.Config`): Config file path or the config object. checkpoint (str, optional): Checkpoint path. If left as None, the model will not load any weights. Returns: nn.Module: The constructed detector. """ if isinstance(config, str): config = mmcv.Config.fromfile(config) elif not isinstance(config, mmcv.Config): raise TypeError('config must be a filename or Config object, ' f'but got {type(config)}') config.model.pretrained = None model = build_detector(config.model, test_cfg=config.test_cfg) if checkpoint is not None: checkpoint = load_checkpoint(model, checkpoint) if 'CLASSES' in checkpoint['meta']: model.CLASSES = checkpoint['meta']['CLASSES'] else: warnings.simplefilter('once') warnings.warn('Class names are not saved in the checkpoint\'s ' 'meta data, use COCO classes by default.') model.CLASSES = get_classes('coco') model.cfg = config # save the config in the model for convenience model.to(device) model.eval() return model class LoadImage(object): def __call__(self, results): if isinstance(results['img'], str): results['filename'] = results['img'] results['ori_filename'] = results['img'] else: results['filename'] = None results['ori_filename'] = None img = mmcv.imread(results['img']) results['img'] = img results['img_fields'] = ['img'] results['img_shape'] = img.shape results['ori_shape'] = img.shape return results def inference_detector(model, img): """Inference image(s) with the detector. Args: model (nn.Module): The loaded detector. imgs (str/ndarray or list[str/ndarray]): Either image files or loaded images. Returns: If imgs is a str, a generator will be returned, otherwise return the detection results directly. """ cfg = model.cfg device = next(model.parameters()).device # model device # build the data pipeline test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:] test_pipeline = Compose(test_pipeline) # prepare data data = dict(img=img) data = test_pipeline(data) data = collate([data], samples_per_gpu=1) if next(model.parameters()).is_cuda: # scatter to specified GPU data = scatter(data, [device])[0] else: # Use torchvision ops for CPU mode instead for m in model.modules(): if isinstance(m, (RoIPool, RoIAlign)): if not m.aligned: # aligned=False is not implemented on CPU # set use_torchvision on-the-fly m.use_torchvision = True warnings.warn('We set use_torchvision=True in CPU mode.') # just get the actual data from DataContainer data['img_metas'] = data['img_metas'][0].data # forward the model with torch.no_grad(): result = model(return_loss=False, rescale=True, **data) return result async def async_inference_detector(model, img): """Async inference image(s) with the detector. Args: model (nn.Module): The loaded detector. imgs (str/ndarray or list[str/ndarray]): Either image files or loaded images. Returns: Awaitable detection results. """ cfg = model.cfg device = next(model.parameters()).device # model device # build the data pipeline test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:] test_pipeline = Compose(test_pipeline) # prepare data data = dict(img=img) data = test_pipeline(data) data = scatter(collate([data], samples_per_gpu=1), [device])[0] # We don't restore `torch.is_grad_enabled()` value during concurrent # inference since execution can overlap torch.set_grad_enabled(False) result = await model.aforward_test(rescale=True, **data) return result def show_result_pyplot(model, img, result, score_thr=0.3, fig_size=(15, 10)): """Visualize the detection results on the image. Args: model (nn.Module): The loaded detector. img (str or np.ndarray): Image filename or loaded image. result (tuple[list] or list): The detection result, can be either (bbox, segm) or just bbox. score_thr (float): The threshold to visualize the bboxes and masks. fig_size (tuple): Figure size of the pyplot figure. """ if hasattr(model, 'module'): model = model.module img = model.show_result(img, result, score_thr=score_thr, show=False) plt.figure(figsize=fig_size) plt.imshow(mmcv.bgr2rgb(img)) plt.show()

[ "1654388696@qq.com" ]

1654388696@qq.com

bfd6a2a39dc4018db6176fae203a4a0bded8c670

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/vgrabber/datalayer/serializer/test.py

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[]

no_license

janjanech/vzdelavanieGui

dff17add6e6946063597d4c1eba5d6d76b6f5374

b2015f41f7cb1be1ecccf1c4778a91f43f8fba12

refs/heads/master

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from lxml.etree import Element from vgrabber.model import Test class TestSerializer: __test: Test def __init__(self, test): self.__test = test def serialize(self): test_element = Element( 'test', id=str(self.__test.id), name=self.__test.name, moodleid=str(self.__test.moodle_id) ) return test_element

[ "janik@janik.ws" ]

janik@janik.ws

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/tests/test_permissions_sql.py

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simonw/datasette-permissions-sql

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e0103ea1c13389391a3e40241485df45739aa638

refs/heads/master

2022-10-09T19:47:28.383910

2020-06-12T07:03:35

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from datasette.app import Datasette import httpx import sqlite_utils import pytest def create_tables(conn): db = sqlite_utils.Database(conn) db["table_access"].insert_all( [ {"user_id": 1, "database": "test", "table": "dogs"}, {"user_id": 2, "database": "test", "table": "dogs"}, {"user_id": 1, "database": "test", "table": "cats"}, ] ) db["cats"].insert({"name": "Casper"}) db["dogs"].insert({"name": "Cleo"}) db["other"].insert({"name": "Other"}) # user_id = 3 is banned from 'sqlite_master' db["banned"].insert({"table": "other", "user_id": 3}) @pytest.fixture async def ds(tmpdir): filepath = tmpdir / "test.db" ds = Datasette( [filepath], metadata={ "plugins": { "datasette-permissions-sql": [ { "action": "view-query", "fallback": True, "resource": ["test", "sqlite_master"], "sql": """ SELECT -1 FROM banned WHERE user_id = :actor_id """, }, { "action": "view-table", "sql": """ SELECT * FROM table_access WHERE user_id = :actor_id AND "database" = :resource_1 AND "table" = :resource_2 """, }, ] }, "databases": { "test": { "allow_sql": {}, "queries": {"sqlite_master": "select * from sqlite_master"}, } }, }, ) await ds.get_database().execute_write_fn(create_tables, block=True) return ds @pytest.mark.asyncio async def test_ds_fixture(ds): assert {"table_access", "cats", "dogs", "banned", "other"} == set( await ds.get_database().table_names() ) @pytest.mark.parametrize( "actor,table,expected_status", [ (None, "dogs", 403), (None, "cats", 403), ({"id": 1}, "dogs", 200), ({"id": 2}, "dogs", 200), ({"id": 1}, "cats", 200), ({"id": 2}, "cats", 403), ], ) @pytest.mark.asyncio async def test_permissions_sql(ds, actor, table, expected_status): async with httpx.AsyncClient(app=ds.app()) as client: cookies = {} if actor: cookies = {"ds_actor": ds.sign({"a": actor}, "actor")} response = await client.get( "http://localhost/test/{}".format(table), cookies=cookies ) assert expected_status == response.status_code @pytest.mark.parametrize( "actor,expected_status", [(None, 200), ({"id": 1}, 200), ({"id": 3}, 403),], ) @pytest.mark.asyncio async def test_fallback(ds, actor, expected_status): async with httpx.AsyncClient(app=ds.app()) as client: cookies = {} if actor: cookies = {"ds_actor": ds.sign({"a": actor}, "actor")} response = await client.get( "http://localhost/test/sqlite_master", cookies=cookies ) assert expected_status == response.status_code

[ "swillison@gmail.com" ]

swillison@gmail.com

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/remo/remozilla/admin.py

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[]

no_license

seocam/remo

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879cbbb0132f12dff64dfbd4ed118d0f5169615f

refs/heads/master

2021-01-15T13:06:39.844096

2014-05-13T10:23:06

null

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null

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false

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from django.contrib import admin from remo.remozilla.models import Bug, Status class BugAdmin(admin.ModelAdmin): """Bug Admin.""" list_display = ('__unicode__', 'summary', 'status', 'resolution') list_filter = ('status', 'resolution', 'council_vote_requested') search_fields = ('bug_id', ) admin.site.register(Bug, BugAdmin) admin.site.register(Status)

[ "giorgos@mozilla.com" ]

giorgos@mozilla.com

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/synapse/lib/scrape.py

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williballenthin/synapse

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refs/heads/master

2020-12-24T14:19:12.530026

2017-03-16T20:30:38

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2

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2015-08-28T02:01:50

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import re import synapse.data as s_data import synapse.cortex as s_cortex import synapse.lib.datfile as s_datfile from synapse.common import * tldlist = list(s_data.get('iana.tlds')) tldlist.sort(key=lambda x: len(x)) tldlist.reverse() tldcat = '|'.join(tldlist) fqdn_re = r'((?:[a-z0-9_-]{1,63}\.){1,10}(?:%s))' % tldcat scrape_types = [ ('hash:md5', r'(?=(?:[^A-Za-z0-9]|^)([A-Fa-f0-9]{32})(?:[^A-Za-z0-9]|$))',{}), ('hash:sha1', r'(?=(?:[^A-Za-z0-9]|^)([A-Fa-f0-9]{40})(?:[^A-Za-z0-9]|$))',{}), ('hash:sha256', r'(?=(?:[^A-Za-z0-9]|^)([A-Fa-f0-9]{64})(?:[^A-Za-z0-9]|$))',{}), ('inet:url', r'\w+://[^ \'"\t\n\r\f\v]+',{}), ('inet:ipv4', r'(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)',{}), ('inet:tcp4', r'((?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?):[0-9]{1,5})',{}), ('inet:fqdn', r'(?:[^a-z0-9_.-]|^)((?:[a-z0-9_-]{1,63}\.){1,10}(?:%s))(?:[^a-z0-9_.-]|$)' % tldcat, {}), ('inet:email', r'(?:[^a-z0-9_.+-]|^)([a-z0-9_\.\-+]{1,256}@(?:[a-z0-9_-]{1,63}\.){1,10}(?:%s))(?:[^a-z0-9_.-]|$)' % tldcat, {} ), ] regexes = { name:re.compile(rule,re.IGNORECASE) for (name,rule,opts) in scrape_types } def scrape(text, data=None): ''' Scrape types from a blob of text and return an ingest compatible dict. ''' if data == None: data = {} for ptype,rule,info in scrape_types: regx = regexes.get(ptype) for valu in regx.findall(text): yield (ptype,valu) def getsync(text, tags=()): ret = [] core = s_cortex.openurl('ram://') with s_cortex.openurl('ram://'): core.setConfOpt('enforce',1) core.on('core:sync', ret.append) for form,valu in scrape(text): tufo = core.formTufoByFrob(form,valu) for tag in tags: core.addTufoTag(tufo,tag) return ret if __name__ == '__main__': import sys data = {} for path in sys.argv[1:]: byts = reqbytes(path) text = byts.decode('utf8') data = scrape(text,data=data) #FIXME options for taging all / tagging forms / form props print( json.dumps( {'format':'syn','data':data} ) ) # #print( repr( data ) ) #def scanForEmailAddresses(txt): #return [ m[0] for m in email_regex.findall(txt) ]

[ "invisigoth.kenshoto@gmail.com" ]

invisigoth.kenshoto@gmail.com

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/get_er2_mvis.py

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[]

no_license

srbrodzik/impacts-scripts

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refs/heads/master

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#!/usr/bin/python3 # Inconsistent naming of daily subdirectories after unzip. Sometimes HH, othertimes HHMM import os import sys import shutil import requests from datetime import datetime from bs4 import BeautifulSoup from ftplib import FTP from zipfile import ZipFile def listFD(url, ext=''): page = requests.get(url).text #print page soup = BeautifulSoup(page, 'html.parser') return [url + '/' + node.get('href') for node in soup.find_all('a') if node.get('href').endswith(ext)] def getImageHHMM(path): flist = os.listdir(path) hhmmList = [] for file in flist: (base,ext) = os.path.splitext(file) # assumes base is YYYYMMDDhhmmss hhmm = base[8:12] if hhmm not in hhmmList: hhmmList.append(hhmm) return hhmmList if len(sys.argv) != 2: print('Usage: sys.argv[0] [YYYY-MM-DD]') sys.exit() else: date = sys.argv[1] # User inputs debug = 1 file_ext = 'zip' #url = 'https://asp-archive.arc.nasa.gov/IMPACTS/N809NA/video_2022/'+date+'/MVIS' url = 'https://asp-archive.arc.nasa.gov/IMPACTS/N809NA/still-images_2022/'+date+'/MVIS' tempDir = "/tmp" targetDirBase = "/home/disk/bob/impacts/images/MVIS" catPrefix = 'aircraft.NASA_ER2' catSuffix = 'MVIS' ftpCatalogServer = 'catalog.eol.ucar.edu' ftpCatalogUser = 'anonymous' catalogDestDir = '/pub/incoming/catalog/impacts' # Create image directory, if needed targetDir = targetDirBase+'/'+date.replace('-','') if not os.path.exists(targetDir): os.makedirs(targetDir) # Get filelist from url urlFlist = listFD(url, file_ext) # Save first file every minute os.chdir(targetDir) for file in urlFlist: command = 'wget '+file os.system(command) # naming convention is: # IMPACTS-MVIS_ER2_2022010815_R0_still-images-jpeg.zip fname = os.path.basename(file) (proj,plane,dateHour,junk,suffix) = fname.split('_') # ONE OR THE OTHER - DUE TO INCONSISTENT DIRECTORY NAMING CONVENTIONS #time = dateHour[-2:]+'00' time = dateHour[-2:] try: with ZipFile(fname, 'r') as zip: zip.extractall() os.remove(fname) if os.path.exists('__MACOSX'): shutil.rmtree('__MACOSX') os.chdir(targetDir+'/'+time) for imgFile in os.listdir(): print(imgFile) if '_' in imgFile or os.path.getsize(imgFile) == 0: print(' {} removed'.format(imgFile)) os.remove(targetDir+'/'+time+'/'+imgFile) else: (base,ext) = os.path.splitext(imgFile) hhmm = base[8:12] if hhmm not in getImageHHMM(targetDir): shutil.move(targetDir+'/'+time+'/'+imgFile, targetDir+'/'+imgFile) else: os.remove(targetDir+'/'+time+'/'+imgFile) os.chdir(targetDir) os.rmdir(time) except: print('Unable to unzip {}'.format(fname)) """ # Open ftp connection catalogFTP = FTP(ftpCatalogServer,ftpCatalogUser) catalogFTP.cwd(catalogDestDir) # Rename jpg files & upload to catalog for file in os.listdir(targetDir): print(file) (imageTime,ext) = os.path.splitext(file) imageTime = imageTime[:-2] catName = catPrefix+'.'+imageTime+'.'+catSuffix+ext shutil.copy(targetDir+'/'+file, tempDir+'/'+catName) ftpFile = open(tempDir+'/'+catName,'rb') catalogFTP.storbinary('STOR '+catName,ftpFile) ftpFile.close() os.remove(tempDir+'/'+catName) # Close ftp connection catalogFTP.quit() """

[ "brodzik@uw.edu" ]

brodzik@uw.edu

20591bf17f76442d97811693898a12de521db530

2cbf3aaad62f4922d827af658fb5dbb7ac651bef

/teledusite/teledu/admin/gameSystem.py

6ec61995a947768d4240c49b802f454dea506c89

[]

no_license

tctimmeh/teledu

0266240aa864cd2eed75857e66eaeb8270f44c1a

04135ffb04f397f29152ca48f868a957b18d504a

refs/heads/master

2021-01-23T08:52:32.817693

2013-10-29T01:34:41

2,566,893

0

null

UTF-8

Python

false

104

py

from django.contrib import admin from teledu.models import GameSystem admin.site.register(GameSystem)

[ "tctimmeh@gmail.com" ]

tctimmeh@gmail.com

e6371302f8591fb2a405866ff5192a52cc735e72

9743d5fd24822f79c156ad112229e25adb9ed6f6

/xai/brain/wordbase/nouns/_quadricepses.py

05b399424fc9c4dc2fe68ec222b1600fea37c16f

[ "MIT" ]

permissive

cash2one/xai

de7adad1758f50dd6786bf0111e71a903f039b64

e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6

refs/heads/master

2021-01-19T12:33:54.964379

2017-01-28T02:00:50

null

0

null

UTF-8

Python

false

268

py

from xai.brain.wordbase.nouns._quadriceps import _QUADRICEPS #calss header class _QUADRICEPSES(_QUADRICEPS, ): def __init__(self,): _QUADRICEPS.__init__(self) self.name = "QUADRICEPSES" self.specie = 'nouns' self.basic = "quadriceps" self.jsondata = {}

[ "xingwang1991@gmail.com" ]

xingwang1991@gmail.com

4dd9fda777a418611e522466a3fcad13b7b456bf

080a6b7be74dc2d2fac61e0bb60a5402533294de

/week7/bc-ints-avg-float.py

3104214222f46aab04b7ba40ece6c87de394fb3c

[]

no_license

rosmoke/DCU-Projects

cfec4c59ba00beb68d174cf869952b7a88e5c1dc

1478f476e1d81756d00a206b8f5bfcd0a1094649

refs/heads/master

2021-01-20T17:03:59.642966

2016-06-23T15:06:46

61,814,159

0

null

UTF-8

Python

false

184

py

i = 0 integer = raw_input() total = 0.0 while integer != "end": total = total + int(integer) integer = raw_input() i = i + 1 if i > 1: print total / i else: print total

[ "danielasofiei@yahoo.ie" ]

danielasofiei@yahoo.ie

6f7318386169ac167772e18f4034f3b8da28d5a7

f93fde3ad0c7f96710f8f8f8495adfa14484763b

/ld12/gene.py

d2678af23a96a69bc93a9d4e8569b51c75c8e227

[ "MIT" ]

permissive

xapple/ld12

0f80b0b4fc353327779e3189d7152f110cc0cf78

e2dfc98beaec8d6dcecaec86fb7854ea5bb6f333

refs/heads/master

2021-01-10T01:52:50.282298

2016-04-04T19:19:34

53,348,264

0

null

UTF-8

Python

false

1,169

py

# Built-in modules # import re # First party modules # # Third party modules # ############################################################################### class Gene(object): """A DNA sequence with an ID associated and belonging to a genome.""" def __repr__(self): return '<%s object %s>' % (self.__class__.__name__, self.name) def __str__(self): return str(self.seq.seq) def __len__(self): return len(self.seq) def __init__(self, seq, genome): self.seq = seq self.name = seq.id self.genome = genome self.annotation = None # Filled in by the __init__.py self.raw_hits = [] # Filled in by the duplications.py self.best_tax = None # Filled in by the duplications.py @property def long_name(self): """A more descriptive name""" return self.name + " (from " + self.genome.long_name + ")" @property def ribo_group(self): """If it is a ribosomal protein, what group is it part of ?""" results = re.findall("ribosomal protein ([LS][1-9]+)", self.annotation) if not results: return False else: return results[0]

[ "lucas.sinclair@me.com" ]

lucas.sinclair@me.com

06e0ddc21cdd990cd36cfa9d2d2fcbe3eddc2d2e

10d89b6e07a7c72c385eb1d1c60a3e0ed9f9fc3c

/boss/report/views/phone_fee.py

ead5501cffbbe306fc0cb441b004269ec0037dac

[]

no_license

cash2one/pt

2a4998a6627cf1604fb64ea8ac62ff1c227f0296

8a8c12375610182747099e5e60e15f1a9bb3f953

refs/heads/master

2021-01-20T00:36:43.779028

2016-11-07T03:27:18

null

0

null

UTF-8

Python

false

2,983

py

#coding: utf-8 """ 服务质量追踪-充话费 """ from report_pub import * @login_required @permission_required(u'man.%s' % ReportConst.PHONE_FEE, raise_exception=True) @add_common_var def phone_fee(request, template_name): app = request.GET.get("app") report_check_app(request, app) vers = get_app_versions(app) channels = get_app_channels(app) operators = get_report_filters("gy_fee_prod_isptype") provinces = get_report_filters("gy_fee_prod_province") faces = get_report_filters("gy_fee_prod_content") faces.sort(key=lambda a: int(a)) product_type = get_product_type(ReportConst.PHONE_FEE) cps = get_cp_info(product_type) return report_render(request, template_name,{ "currentdate": get_datestr(1, "%Y-%m-%d"), "operators": operators, "provinces": provinces, "faces": faces, "cps": cps, "vers": vers, "channels": channels }) @login_required @permission_required(u'man.%s' % ReportConst.PHONE_FEE, raise_exception=True) def phone_fee_ajax(request): start_date = request.POST.get("start_date") end_date = request.POST.get("end_date") app = request.POST.get("app") report_check_app(request, app) ver = request.POST.get("ver") channel = request.POST.get("channel") operator = request.POST.get("operator") province = request.POST.get("province") face = request.POST.get("face") cp = request.POST.get("cp") result = get_service_quality_data(start_date, end_date, app, ver, channel, operator, province, face, cp, ReportConst.PHONE_FEE) return HttpResponse(json.dumps(result)) @login_required @permission_required(u'man.%s' % ReportConst.PHONE_FEE, raise_exception=True) def phone_fee_csv(request): start_date = request.GET.get("start_date") end_date = request.GET.get("end_date") app = request.GET.get("app") report_check_app(request, app) ver = request.GET.get("ver") channel = request.GET.get("channel") operator = request.GET.get("operator") province = request.GET.get("province") face = request.GET.get("face") cp = request.GET.get("cp") filename = '%s-质量追踪(%s-%s-%s).csv' % (ReportConst.PHONE_FEE, str(get_app_name(app)), str(start_date), str(end_date)) csv_data = [["日期", "总单数", "成功数", "失败数", "失败率", "1分钟到账数", "1分钟到账率", "3分钟到账数", "3分钟到账率", "10分钟到账数", "10分钟到账率", "30分钟到账数", "30分钟到账率", "30分钟以上到账数", "30分钟以上到账率"]] csv_data.extend(get_service_quality_data(start_date, end_date, app, ver, channel, operator, province, face, cp, ReportConst.PHONE_FEE)) return get_csv_response(filename, csv_data)

[ "xl@putao.cn" ]

xl@putao.cn

2032fcdbc5f7bfd3980087825cefef8a1b0f3e7e

9b9a02657812ea0cb47db0ae411196f0e81c5152

/repoData/arneb-django-export/allPythonContent.py

c1f0cbe7c45766156c8d3fdd4513c94e9d1ed073

[]

no_license

aCoffeeYin/pyreco

cb42db94a3a5fc134356c9a2a738a063d0898572

0ac6653219c2701c13c508c5c4fc9bc3437eea06

refs/heads/master

2020-12-14T14:10:05.763693

2016-06-27T05:15:15

null

0

null

UTF-8

Python

false

5,667

py

__FILENAME__ = models ########NEW FILE######## __FILENAME__ = urls from django.conf.urls.defaults import * urlpatterns = patterns('export.views', url(r'^database/$', 'export_database', {}, name="export_database"), url(r'^database_s3/$', 'export_to_s3', {}, name="export_database_s3"), url(r'^media/$', 'export_media', {}, name="export_mediaroot"), url(r'^list_s3/$', 'list_s3', {}, name="export_list_s3"), url(r'^$', 'export_index', {}, name="export_index"), ) ########NEW FILE######## __FILENAME__ = views import os, time from datetime import date from django.conf import settings from django.http import HttpResponse, HttpResponseRedirect from django.core.exceptions import ImproperlyConfigured from django.utils.translation import ugettext_lazy as _ from django.views.generic.simple import direct_to_template from django.contrib.admin.views.decorators import staff_member_required try: import S3 except ImportError: S3 = None # default dump commands, you can overwrite these in your settings. MYSQLDUMP_CMD = getattr(settings, 'MYSQLDUMP_CMD', '/usr/bin/mysqldump -h %s --opt --compact --skip-add-locks -u %s -p%s %s | bzip2 -c') SQLITE3DUMP_CMD = getattr(settings, 'SQLITE3DUMP_CMD', 'echo ".dump" | /usr/bin/sqlite3 %s | bzip2 -c') DISABLE_STREAMING = getattr(settings, 'DISABLE_STREAMING', False) @staff_member_required def export_database(request): """ Dump the database directly to the browser """ if request.method == 'POST': if settings.DATABASE_ENGINE == 'mysql': cmd = MYSQLDUMP_CMD % (settings.DATABASE_HOST, settings.DATABASE_USER, settings.DATABASE_PASSWORD, settings.DATABASE_NAME) elif settings.DATABASE_ENGINE == 'sqlite3': cmd = SQLITE3DUMP_CMD % settings.DATABASE_NAME else: raise ImproperlyConfigured, "Sorry, django-export only supports mysql and sqlite3 database backends." stdin, stdout = os.popen2(cmd) stdin.close() if DISABLE_STREAMING: stdout = stdout.read() response = HttpResponse(stdout, mimetype="application/octet-stream") response['Content-Disposition'] = 'attachment; filename=%s' % date.today().__str__()+'_db.sql.bz2' return response return direct_to_template(request, 'export/export.html', {'what': _(u'Export Database')}) @staff_member_required def export_media(request): """ Tar the MEDIA_ROOT and send it directly to the browser """ if request.method == 'POST': stdin, stdout = os.popen2('tar -cf - %s' % settings.MEDIA_ROOT) stdin.close() if DISABLE_STREAMING: stdout = stdout.read() response = HttpResponse(stdout, mimetype="application/octet-stream") response['Content-Disposition'] = 'attachment; filename=%s' % date.today().__str__()+'_media.tar' return response return direct_to_template(request, 'export/export.html', {'what': _(u'Export Media Root')}) @staff_member_required def export_to_s3(request): """ Dump the database and upload the dump to Amazon S3 """ if request.method == 'POST': if settings.DATABASE_ENGINE == 'mysql': cmd = MYSQLDUMP_CMD % (settings.DATABASE_HOST, settings.DATABASE_USER, settings.DATABASE_PASSWORD, settings.DATABASE_NAME) elif settings.DATABASE_ENGINE == 'sqlite3': cmd = SQLITE3DUMP_CMD % settings.DATABASE_NAME else: raise ImproperlyConfigured, "Sorry, django-export only supports mysql and sqlite3 database backends." stdin, stdout = os.popen2(cmd) stdin.close() file_name = 'dump_%s.sql.bz2' % time.strftime('%Y%m%d-%H%M') conn = S3.AWSAuthConnection(settings.AWS_ACCESS_KEY_ID, settings.AWS_SECRET_ACCESS_KEY) res = conn.put(settings.AWS_BUCKET_NAME, file_name, S3.S3Object(stdout.read()), {'Content-Type': 'application/x-bzip2',}) if res.http_response.status == 200: request.user.message_set.create(message="%s" % _(u"%(filename)s saved on Amazon S3") % {'filename': file_name}) else: request.user.message_set.create(message="%s" % _(u"Upload failed with %(status)s") % {'status': res.http_response.status}) stdout.close() return HttpResponseRedirect('/admin/') return direct_to_template(request, 'export/export.html', {'what': _(u'Export Database to S3'), 's3support': (S3 is not None), 's3': True}) @staff_member_required def list_s3(request): """ List Amazon S3 bucket contents """ if S3 is not None: conn = S3.AWSAuthConnection(settings.AWS_ACCESS_KEY_ID, settings.AWS_SECRET_ACCESS_KEY) generator = S3.QueryStringAuthGenerator(settings.AWS_ACCESS_KEY_ID, settings.AWS_SECRET_ACCESS_KEY, calling_format=S3.CallingFormat.VANITY) generator.set_expires_in(300) bucket_entries = conn.list_bucket(settings.AWS_BUCKET_NAME).entries entries = [] for entry in bucket_entries: entry.s3url = generator.get(settings.AWS_BUCKET_NAME, entry.key) entries.append(entry) return direct_to_template(request, 'export/list_s3.html', {'object_list': entries, 's3support': True}) else: return direct_to_template(request, 'export/list_s3.html', {'object_list': [], 's3support': False}) @staff_member_required def export_index(request): """ List all available export views. """ return direct_to_template(request, 'export/index.html', {'s3support': (S3 is not None),}) ########NEW FILE########

[ "dyangUCI@github.com" ]

dyangUCI@github.com

c6fa6a2edb99f5bcef6bebbe9f0f17b78178e9aa

dc8a337ea1d8a285577d33e5cfd4dbbe846ee1a0

/src/main/scala/mock/25092020/ShortestPathInGridWithObstaclesElimination.py

d006e85a23d9c40ffbbddf03061da34dabd8a5b3

[]

no_license

joestalker1/leetcode

8a5cdda17abd33c3eef859732f75d7bec77a9d0e

ae392ddbc7eb56cb814b9e9715043c98a89a6314

refs/heads/master

2023-04-13T22:09:54.407864

2023-04-09T19:22:54

131,803,943

0

null

UTF-8

Python

false

1,824

py

from heapq import heappop, heappush class Solution: def shortestPath(self, grid, k): if not grid or not grid[0]: return -1 n = len(grid) m = len(grid[0]) q = [[0, 0, 0, 0]] # len, row,col, eliminated obstacles < k seen = set() seen.add((0,0)) while q: d, r, c, elim = q.pop(0) if r == n - 1 and c == m - 1: return d for dr, dc in [[0, 1], [0, -1], [1, 0], [-1, 0]]: nr = r + dr nc = c + dc if 0 <= nr < n and 0 <= nc < m: # if (nr, nc) in seen: # continue if grid[nr][nc] == 0 or grid[nr][nc] == 1 and elim < k: paths[nr][nc] = d + 1 q.append([d + 1, nr, nc, elim + 1 if grid[nr][nc] == 1 else elim]) return -1 sol = Solution() # print(sol.shortestPath([[0, 0, 0], # [1, 1, 0], # [0, 0, 0], # [0, 1, 1], # [0, 0, 0]], 1)) print(sol.shortestPath([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 1, 1, 1, 1, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]], 1))

[ "denys@dasera.com" ]

denys@dasera.com

47f4c767c2a7b9c7065929c3f93e659bcb8be2b3

fd6525073b8bd0f9387ccd14b801fdb6bfecd374

/nur/path.py

260f0d7957dea351b7c4bc2b1ca3abc02dcda391

[ "MIT" ]

permissive

demyanrogozhin/NUR

5a92757c52d28ff0bbe8684b4bf25fc8998bfc43

a7746bf35b2fda77e2cb7a3a1f22db3e4d21f399

refs/heads/master

2020-12-12T17:40:47.783164

2020-01-26T21:10:26

2020-01-26T21:38:16

234,187,682

1

0

MIT

2020-01-15T22:40:27

null

UTF-8

Python

false

1,245

py

import os import subprocess from pathlib import Path from .error import NurError def _is_repo(path: Path) -> bool: return path.joinpath("lib/evalRepo.nix").exists() def _find_root() -> Path: source_root = Path(__file__).parent.parent.resolve() if _is_repo(source_root): # if it was not build with release.nix return source_root else: root = Path(os.getcwd()).resolve() while True: if _is_repo(root): return root new_root = root.parent.resolve() if new_root == root: if _is_repo(new_root): return new_root else: raise NurError("NUR repository not found in current directory") ROOT = _find_root() LOCK_PATH = ROOT.joinpath("repos.json.lock") MANIFEST_PATH = ROOT.joinpath("repos.json") EVALREPO_PATH = ROOT.joinpath("lib/evalRepo.nix") _NIXPKGS_PATH = None def nixpkgs_path() -> str: global _NIXPKGS_PATH if _NIXPKGS_PATH is not None: return _NIXPKGS_PATH cmd = ["nix-instantiate", "--find-file", "nixpkgs"] path = subprocess.check_output(cmd).decode("utf-8").strip() _NIXPKGS_PATH = str(Path(path).resolve()) return _NIXPKGS_PATH

[ "joerg@thalheim.io" ]

joerg@thalheim.io

3b293693b6b6ec55a2f05ceb0a7ce642c441bdba

bad62c2b0dfad33197db55b44efeec0bab405634

/sdk/security/azure-mgmt-security/azure/mgmt/security/v2020_01_01/operations/_server_vulnerability_assessment_operations.py

a9f17636c842d926979789c6b493a74d6cec9412

[ "MIT", "LicenseRef-scancode-generic-cla", "LGPL-2.1-or-later" ]

permissive

test-repo-billy/azure-sdk-for-python

20c5a2486456e02456de17515704cb064ff19833

cece86a8548cb5f575e5419864d631673be0a244

refs/heads/master

2022-10-25T02:28:39.022559

2022-10-18T06:05:46

182,325,031

0

MIT

2019-07-25T22:28:52

2019-04-19T20:59:15

Python

UTF-8

Python

false

27,727

py

# pylint: disable=too-many-lines # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, Callable, Dict, Optional, TypeVar, Union, cast from azure.core.exceptions import ( ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, ResourceNotModifiedError, map_error, ) from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpResponse from azure.core.polling import LROPoller, NoPolling, PollingMethod from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.core.utils import case_insensitive_dict from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.arm_polling import ARMPolling from .. import models as _models from ..._serialization import Serializer from .._vendor import _convert_request, _format_url_section T = TypeVar("T") ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() _SERIALIZER.client_side_validation = False def build_list_by_extended_resource_request( resource_group_name: str, resource_namespace: str, resource_type: str, resource_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) api_version = kwargs.pop("api_version", _params.pop("api-version", "2020-01-01")) # type: str accept = _headers.pop("Accept", "application/json") # Construct URL _url = kwargs.pop( "template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceNamespace}/{resourceType}/{resourceName}/providers/Microsoft.Security/serverVulnerabilityAssessments", ) # pylint: disable=line-too-long path_format_arguments = { "subscriptionId": _SERIALIZER.url( "subscription_id", subscription_id, "str", pattern=r"^[0-9A-Fa-f]{8}-([0-9A-Fa-f]{4}-){3}[0-9A-Fa-f]{12}$" ), "resourceGroupName": _SERIALIZER.url( "resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._]+$" ), "resourceNamespace": _SERIALIZER.url("resource_namespace", resource_namespace, "str"), "resourceType": _SERIALIZER.url("resource_type", resource_type, "str"), "resourceName": _SERIALIZER.url("resource_name", resource_name, "str"), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") # Construct headers _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) def build_get_request( resource_group_name: str, resource_namespace: str, resource_type: str, resource_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) server_vulnerability_assessment = kwargs.pop("server_vulnerability_assessment", "default") # type: str api_version = kwargs.pop("api_version", _params.pop("api-version", "2020-01-01")) # type: str accept = _headers.pop("Accept", "application/json") # Construct URL _url = kwargs.pop( "template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceNamespace}/{resourceType}/{resourceName}/providers/Microsoft.Security/serverVulnerabilityAssessments/{serverVulnerabilityAssessment}", ) # pylint: disable=line-too-long path_format_arguments = { "subscriptionId": _SERIALIZER.url( "subscription_id", subscription_id, "str", pattern=r"^[0-9A-Fa-f]{8}-([0-9A-Fa-f]{4}-){3}[0-9A-Fa-f]{12}$" ), "resourceGroupName": _SERIALIZER.url( "resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._]+$" ), "resourceNamespace": _SERIALIZER.url("resource_namespace", resource_namespace, "str"), "resourceType": _SERIALIZER.url("resource_type", resource_type, "str"), "resourceName": _SERIALIZER.url("resource_name", resource_name, "str"), "serverVulnerabilityAssessment": _SERIALIZER.url( "server_vulnerability_assessment", server_vulnerability_assessment, "str" ), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") # Construct headers _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) def build_create_or_update_request( resource_group_name: str, resource_namespace: str, resource_type: str, resource_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) server_vulnerability_assessment = kwargs.pop("server_vulnerability_assessment", "default") # type: str api_version = kwargs.pop("api_version", _params.pop("api-version", "2020-01-01")) # type: str accept = _headers.pop("Accept", "application/json") # Construct URL _url = kwargs.pop( "template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceNamespace}/{resourceType}/{resourceName}/providers/Microsoft.Security/serverVulnerabilityAssessments/{serverVulnerabilityAssessment}", ) # pylint: disable=line-too-long path_format_arguments = { "subscriptionId": _SERIALIZER.url( "subscription_id", subscription_id, "str", pattern=r"^[0-9A-Fa-f]{8}-([0-9A-Fa-f]{4}-){3}[0-9A-Fa-f]{12}$" ), "resourceGroupName": _SERIALIZER.url( "resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._]+$" ), "resourceNamespace": _SERIALIZER.url("resource_namespace", resource_namespace, "str"), "resourceType": _SERIALIZER.url("resource_type", resource_type, "str"), "resourceName": _SERIALIZER.url("resource_name", resource_name, "str"), "serverVulnerabilityAssessment": _SERIALIZER.url( "server_vulnerability_assessment", server_vulnerability_assessment, "str" ), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") # Construct headers _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs) def build_delete_request( resource_group_name: str, resource_namespace: str, resource_type: str, resource_name: str, subscription_id: str, **kwargs: Any ) -> HttpRequest: _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) server_vulnerability_assessment = kwargs.pop("server_vulnerability_assessment", "default") # type: str api_version = kwargs.pop("api_version", _params.pop("api-version", "2020-01-01")) # type: str accept = _headers.pop("Accept", "application/json") # Construct URL _url = kwargs.pop( "template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceNamespace}/{resourceType}/{resourceName}/providers/Microsoft.Security/serverVulnerabilityAssessments/{serverVulnerabilityAssessment}", ) # pylint: disable=line-too-long path_format_arguments = { "subscriptionId": _SERIALIZER.url( "subscription_id", subscription_id, "str", pattern=r"^[0-9A-Fa-f]{8}-([0-9A-Fa-f]{4}-){3}[0-9A-Fa-f]{12}$" ), "resourceGroupName": _SERIALIZER.url( "resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._]+$" ), "resourceNamespace": _SERIALIZER.url("resource_namespace", resource_namespace, "str"), "resourceType": _SERIALIZER.url("resource_type", resource_type, "str"), "resourceName": _SERIALIZER.url("resource_name", resource_name, "str"), "serverVulnerabilityAssessment": _SERIALIZER.url( "server_vulnerability_assessment", server_vulnerability_assessment, "str" ), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") # Construct headers _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs) class ServerVulnerabilityAssessmentOperations: """ .. warning:: **DO NOT** instantiate this class directly. Instead, you should access the following operations through :class:`~azure.mgmt.security.v2020_01_01.SecurityCenter`'s :attr:`server_vulnerability_assessment` attribute. """ models = _models def __init__(self, *args, **kwargs): input_args = list(args) self._client = input_args.pop(0) if input_args else kwargs.pop("client") self._config = input_args.pop(0) if input_args else kwargs.pop("config") self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer") self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer") @distributed_trace def list_by_extended_resource( self, resource_group_name: str, resource_namespace: str, resource_type: str, resource_name: str, **kwargs: Any ) -> _models.ServerVulnerabilityAssessmentsList: """Gets a list of server vulnerability assessment onboarding statuses on a given resource. :param resource_group_name: The name of the resource group within the user's subscription. The name is case insensitive. Required. :type resource_group_name: str :param resource_namespace: The Namespace of the resource. Required. :type resource_namespace: str :param resource_type: The type of the resource. Required. :type resource_type: str :param resource_name: Name of the resource. Required. :type resource_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: ServerVulnerabilityAssessmentsList or the result of cls(response) :rtype: ~azure.mgmt.security.v2020_01_01.models.ServerVulnerabilityAssessmentsList :raises ~azure.core.exceptions.HttpResponseError: """ error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, 304: ResourceNotModifiedError, } error_map.update(kwargs.pop("error_map", {}) or {}) _headers = kwargs.pop("headers", {}) or {} _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) api_version = kwargs.pop("api_version", _params.pop("api-version", "2020-01-01")) # type: str cls = kwargs.pop("cls", None) # type: ClsType[_models.ServerVulnerabilityAssessmentsList] request = build_list_by_extended_resource_request( resource_group_name=resource_group_name, resource_namespace=resource_namespace, resource_type=resource_type, resource_name=resource_name, subscription_id=self._config.subscription_id, api_version=api_version, template_url=self.list_by_extended_resource.metadata["url"], headers=_headers, params=_params, ) request = _convert_request(request) request.url = self._client.format_url(request.url) # type: ignore pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access 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("ServerVulnerabilityAssessmentsList", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized list_by_extended_resource.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceNamespace}/{resourceType}/{resourceName}/providers/Microsoft.Security/serverVulnerabilityAssessments"} # type: ignore @distributed_trace def get( self, resource_group_name: str, resource_namespace: str, resource_type: str, resource_name: str, **kwargs: Any ) -> _models.ServerVulnerabilityAssessment: """Gets a server vulnerability assessment onboarding statuses on a given resource. :param resource_group_name: The name of the resource group within the user's subscription. The name is case insensitive. Required. :type resource_group_name: str :param resource_namespace: The Namespace of the resource. Required. :type resource_namespace: str :param resource_type: The type of the resource. Required. :type resource_type: str :param resource_name: Name of the resource. Required. :type resource_name: str :keyword server_vulnerability_assessment: ServerVulnerabilityAssessment status. only a 'default' value is supported. Default value is "default". Note that overriding this default value may result in unsupported behavior. :paramtype server_vulnerability_assessment: str :keyword callable cls: A custom type or function that will be passed the direct response :return: ServerVulnerabilityAssessment or the result of cls(response) :rtype: ~azure.mgmt.security.v2020_01_01.models.ServerVulnerabilityAssessment :raises ~azure.core.exceptions.HttpResponseError: """ error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, 304: ResourceNotModifiedError, } error_map.update(kwargs.pop("error_map", {}) or {}) _headers = kwargs.pop("headers", {}) or {} _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) server_vulnerability_assessment = kwargs.pop("server_vulnerability_assessment", "default") # type: str api_version = kwargs.pop("api_version", _params.pop("api-version", "2020-01-01")) # type: str cls = kwargs.pop("cls", None) # type: ClsType[_models.ServerVulnerabilityAssessment] request = build_get_request( resource_group_name=resource_group_name, resource_namespace=resource_namespace, resource_type=resource_type, resource_name=resource_name, subscription_id=self._config.subscription_id, server_vulnerability_assessment=server_vulnerability_assessment, api_version=api_version, template_url=self.get.metadata["url"], headers=_headers, params=_params, ) request = _convert_request(request) request.url = self._client.format_url(request.url) # type: ignore pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access 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("ServerVulnerabilityAssessment", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceNamespace}/{resourceType}/{resourceName}/providers/Microsoft.Security/serverVulnerabilityAssessments/{serverVulnerabilityAssessment}"} # type: ignore @distributed_trace def create_or_update( self, resource_group_name: str, resource_namespace: str, resource_type: str, resource_name: str, **kwargs: Any ) -> _models.ServerVulnerabilityAssessment: """Creating a server vulnerability assessment on a resource, which will onboard a resource for having a vulnerability assessment on it. :param resource_group_name: The name of the resource group within the user's subscription. The name is case insensitive. Required. :type resource_group_name: str :param resource_namespace: The Namespace of the resource. Required. :type resource_namespace: str :param resource_type: The type of the resource. Required. :type resource_type: str :param resource_name: Name of the resource. Required. :type resource_name: str :keyword server_vulnerability_assessment: ServerVulnerabilityAssessment status. only a 'default' value is supported. Default value is "default". Note that overriding this default value may result in unsupported behavior. :paramtype server_vulnerability_assessment: str :keyword callable cls: A custom type or function that will be passed the direct response :return: ServerVulnerabilityAssessment or the result of cls(response) :rtype: ~azure.mgmt.security.v2020_01_01.models.ServerVulnerabilityAssessment :raises ~azure.core.exceptions.HttpResponseError: """ error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, 304: ResourceNotModifiedError, } error_map.update(kwargs.pop("error_map", {}) or {}) _headers = kwargs.pop("headers", {}) or {} _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) server_vulnerability_assessment = kwargs.pop("server_vulnerability_assessment", "default") # type: str api_version = kwargs.pop("api_version", _params.pop("api-version", "2020-01-01")) # type: str cls = kwargs.pop("cls", None) # type: ClsType[_models.ServerVulnerabilityAssessment] request = build_create_or_update_request( resource_group_name=resource_group_name, resource_namespace=resource_namespace, resource_type=resource_type, resource_name=resource_name, subscription_id=self._config.subscription_id, server_vulnerability_assessment=server_vulnerability_assessment, api_version=api_version, template_url=self.create_or_update.metadata["url"], headers=_headers, params=_params, ) request = _convert_request(request) request.url = self._client.format_url(request.url) # type: ignore pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize("ServerVulnerabilityAssessment", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized create_or_update.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceNamespace}/{resourceType}/{resourceName}/providers/Microsoft.Security/serverVulnerabilityAssessments/{serverVulnerabilityAssessment}"} # type: ignore def _delete_initial( # pylint: disable=inconsistent-return-statements self, resource_group_name: str, resource_namespace: str, resource_type: str, resource_name: str, **kwargs: Any ) -> None: error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, 304: ResourceNotModifiedError, } error_map.update(kwargs.pop("error_map", {}) or {}) _headers = kwargs.pop("headers", {}) or {} _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) server_vulnerability_assessment = kwargs.pop("server_vulnerability_assessment", "default") # type: str api_version = kwargs.pop("api_version", _params.pop("api-version", "2020-01-01")) # type: str cls = kwargs.pop("cls", None) # type: ClsType[None] request = build_delete_request( resource_group_name=resource_group_name, resource_namespace=resource_namespace, resource_type=resource_type, resource_name=resource_name, subscription_id=self._config.subscription_id, server_vulnerability_assessment=server_vulnerability_assessment, api_version=api_version, template_url=self._delete_initial.metadata["url"], headers=_headers, params=_params, ) request = _convert_request(request) request.url = self._client.format_url(request.url) # type: ignore pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access 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) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceNamespace}/{resourceType}/{resourceName}/providers/Microsoft.Security/serverVulnerabilityAssessments/{serverVulnerabilityAssessment}"} # type: ignore @distributed_trace def begin_delete( self, resource_group_name: str, resource_namespace: str, resource_type: str, resource_name: str, **kwargs: Any ) -> LROPoller[None]: """Removing server vulnerability assessment from a resource. :param resource_group_name: The name of the resource group within the user's subscription. The name is case insensitive. Required. :type resource_group_name: str :param resource_namespace: The Namespace of the resource. Required. :type resource_namespace: str :param resource_type: The type of the resource. Required. :type resource_type: str :param resource_name: Name of the resource. Required. :type resource_name: str :keyword server_vulnerability_assessment: ServerVulnerabilityAssessment status. only a 'default' value is supported. Default value is "default". Note that overriding this default value may result in unsupported behavior. :paramtype server_vulnerability_assessment: 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 ARMPolling. 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.PollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of LROPoller that returns either None or the result of cls(response) :rtype: ~azure.core.polling.LROPoller[None] :raises ~azure.core.exceptions.HttpResponseError: """ _headers = kwargs.pop("headers", {}) or {} _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) server_vulnerability_assessment = kwargs.pop("server_vulnerability_assessment", "default") # type: str api_version = kwargs.pop("api_version", _params.pop("api-version", "2020-01-01")) # type: str cls = kwargs.pop("cls", None) # type: ClsType[None] polling = kwargs.pop("polling", True) # type: Union[bool, PollingMethod] 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 = self._delete_initial( # type: ignore resource_group_name=resource_group_name, resource_namespace=resource_namespace, resource_type=resource_type, resource_name=resource_name, server_vulnerability_assessment=server_vulnerability_assessment, api_version=api_version, cls=lambda x, y, z: x, headers=_headers, params=_params, **kwargs ) kwargs.pop("error_map", None) def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements if cls: return cls(pipeline_response, None, {}) if polling is True: polling_method = cast( PollingMethod, ARMPolling(lro_delay, lro_options={"final-state-via": "location"}, **kwargs) ) # type: PollingMethod elif polling is False: polling_method = cast(PollingMethod, NoPolling()) else: polling_method = polling if cont_token: return LROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output, ) return LROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceNamespace}/{resourceType}/{resourceName}/providers/Microsoft.Security/serverVulnerabilityAssessments/{serverVulnerabilityAssessment}"} # type: ignore

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import yfinance as yf import numpy as np import pandas as pd from pandasgui import show from scipy.optimize import minimize import matplotlib.pyplot as plt ''' A library for running Dual Moving Average Crossover trading strategy, with backtesting, period optimization, and vizualization tools. ''' #Period of time (in years) that we look back when optimizing in return calculation HINDSIGHT = 2 def clean_data(data): ''' Removes row (days) with no data from dataframe or series ''' incomplete_idxs = False if isinstance(data, pd.DataFrame): for col in data.columns: incomplete_idxs |= np.isnan(data[col]) else: incomplete_idxs |= np.isnan(data) return data[~incomplete_idxs] def calc_crossovers(sma, lma): ''' Returns a dataframe containing only the rows where a crossover of the sma and lma is detected. 1 indicates a buy point (sma moving above lma), -1 a sell point ''' num_points = len(clean_data(lma)) high = (sma > lma)[-num_points:] crossovers = high.astype(int).diff()[1:] trimmed = crossovers[crossovers != 0] return trimmed def profit(data, crossovers): ''' Calculates profit assuming data covers a continuous time period with the given crossovers ''' if len(crossovers) == 0: return 0 total = 0 # If first crossover is a sell point assume implicit buy point at very start of data print(crossovers.iloc[0]) if crossovers.iloc[0] == -1: total += data.loc[crossovers.index[0]] - data.iloc[0] # Add the difference between value at sell points and value at buy points to our profit for i in range(1,len(crossovers)): left_bound = crossovers.index[i-1] if crossovers.loc[left_bound] == 1: right_bound = crossovers.index[i] total += data.loc[right_bound] - data.loc[left_bound] # If last crossover is a buy point assume implicit sell point at end of data (include # profit we have made on current holding) if crossovers.iloc[-1] == 1: total += data.iloc[-1] - data.loc[crossovers.index[-1]] return total def optimize(data): ''' Uses scipy's convex minimization library to find optimal short period and long period for moving averages. Because the profit certainly isn't a truly convex function I use a wide range of seeds as initial guesses in hopes of detecting all the local minimums and comparing them to get a good guess of the global min ''' cons = ({'type': 'ineq', 'fun': lambda x: x[1] - x[0]}, {'type': 'ineq', 'fun': lambda x: x[0] - 5}) # Ranges of initial guesses for short and long periods #30 and 40 step size for max accuracy, larger for faster runtime short_seeds = range(5, 300, 50) long_seeds = range(20, 800, 70) # short_seeds = [100] # long_seeds = [750] minimum = float('inf') best_short = 0 best_long = 0 for short_seed in short_seeds: for long_seed in long_seeds: # Use all combinations of ranges where long_seed > short_seed as initial guesses if long_seed > short_seed: res = minimize(run_analysis, [short_seed, long_seed], args=(data,), method='COBYLA', constraints=cons, options={'rhobeg': 10.0, 'catol': 0.0}) if res.fun < minimum: best_short = res.x[0] best_long = res.x[1] minimum = res.fun return (int(round(best_short)), int(round(best_long)), minimum) def run_analysis(periods, data): ''' Objective function for minimization, runs profit calculation with given periods and data Returns negative profit for minimization (maximization of profit) ''' short_period = int(round(periods[0])) long_period = int(round(periods[1])) sma = data.rolling(short_period).mean() lma = data.rolling(long_period).mean() print('sma') print(sma) print('lma') print(lma) crossovers = calc_crossovers(sma, lma) return -1 * profit(data, crossovers) def visualize(data, short_period, long_period): ''' Useful for visualizing the algorithm's decisions. Plots the stock price with colored vertical bars at buy and sell points ''' sma = data.rolling(short_period).mean() lma = data.rolling(long_period).mean() crossovers = calc_crossovers(sma, lma) buys = pd.DataFrame(crossovers[crossovers == 1.0]) sells = pd.DataFrame(crossovers[crossovers == -1.0]) data.plot(color='black') for buy in buys.index: plt.axvline(buy, color="green") for sell in sells.index: plt.axvline(sell, color="red") plt.show() def split_year(data): ''' Split dataframe into a list of dataframes, each corresponding to the data for each year ''' years = np.unique(data.index.year) split = [] for year in years: split.append(data[data.index.year == year]) return split def calc_returns(split_data): ''' Calculate annual returns for periods optimized over slices (of size HINDSIGHT) of past data. Gives an idea of what kind of results to realistically expect ''' annual_returns = [] max_return = float('-inf') min_return = float('inf') for i in range(2, len(split_data)): test_year = split_data[i] optimize_period = pd.DataFrame(np.concatenate(split_data[i-HINDSIGHT:i])) print('optimize period:') print(optimize_period) periods = optimize(optimize_period) print('periods:') print(periods) profit = run_analysis(periods, test_year) annual_returns.append(profit) if profit > max_return: max_return = profit if profit < min_return: min_return = profit return annual_returns, max_return, min_return def main(): ''' Main's current functionality: Find optimal windows for TSLA and print them, along with profit since 6/29/2010 ''' ticker = yf.Ticker('MRNA') # data = yf.download(tickers, period='max', group_by='ticker') data = ticker.history(period="max")[:-4] dirty = pd.DataFrame(data) #Currently using only closing prices frame = clean_data(dirty)['Close'] periods = optimize(frame) # periods = calc_returns(split_year(frame)) print(periods) # visualize(frame, periods[0], periods[1]) if __name__ == "__main__": main() ''' how to quantify number of shares you want to buy (steepness of trend, volatility, top 20 stocks?) '''

[ "nathantung@Nathans-MacBook-Pro.local" ]

nathantung@Nathans-MacBook-Pro.local

e56fe457c611b069400b8d96e73af45fe2389bdb

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/project 2/task2.py

856b3e4454d95c47243c65920022d9319d6751e3

[]

no_license

hariprasath95/computer_vision_image_processing

0a6a8f107028c498ba47de23d0e744eb9f9b34a4

3dbc2a82911afec1238206495507447997a63a23

refs/heads/master

2020-03-30T05:09:23.556754

2018-12-03T09:56:32

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py

UBIT = 'hparthas' import cv2 import numpy as np from matplotlib import pyplot as plt import random np.random.seed(sum([ord(c) for c in UBIT])) # read image 1 and convert to BW m1_clr = cv2.imread('data/tsucuba_left.png') image1_bw= cv2.cvtColor(m1_clr,cv2.COLOR_BGR2GRAY) # read image 2 and convert to BW m2_clr = cv2.imread('data/tsucuba_right.png') image2_bw = cv2.cvtColor(m2_clr,cv2.COLOR_BGR2GRAY) # Extract Sift features and compute Descriptors for image 1 and image 2 sift = cv2.xfeatures2d.SIFT_create() keypoints_mountain1 ,m1_des= sift.detectAndCompute(image1_bw,None) image1_withkp = cv2.drawKeypoints(m1_clr,keypoints_mountain1,None) cv2.imwrite('output/task2/task2_sift1.jpg',image1_withkp) keypoints_mountain2,m2_des = sift.detectAndCompute(image2_bw,None) image2_withkp = cv2.drawKeypoints(m2_clr,keypoints_mountain2,None) cv2.imwrite('output/task2/task2_sift2.jpg',image2_withkp) def drawlines(img1,img2,lines,pts1,pts2,color): r,c = (cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)).shape i = 0 for r,pt1,pt2 in zip(lines,pts1,pts2): x0,y0 = map(int, [0, -r[2]/r[1] ]) x1,y1 = map(int, [c, -(r[2]+r[0]*c)/r[1] ]) img1 = cv2.line(img1, (x0,y0), (x1,y1), color[i],1) img1 = cv2.circle(img2,tuple(pt1),5,color[i],-1) i = i+1 return img1 pts1 = [] pts2 = [] bf = cv2.BFMatcher() matches = bf.knnMatch(m1_des,m2_des, k=2) for i,(m,n) in enumerate(matches): pts2.append(keypoints_mountain2[m.trainIdx].pt) pts1.append(keypoints_mountain1[m.queryIdx].pt) fundamentalmat, mask = cv2.findFundamentalMat(np.array(pts1),np.array(pts2),cv2.FM_RANSAC) print(fundamentalmat) pts1 = np.array(pts1)[mask.ravel() == 1] pts2 = np.array(pts2)[mask.ravel() == 1] random_points = np.random.randint(0, len(pts1), 10) selected_point1,selected_point2 = list(), list() for i, (p1, p2) in enumerate(zip(pts1, pts1)): if i in random_points: selected_point1.append(p1) selected_point2.append(p2) selected_point1 = np.float32(selected_point1) selected_point2 = np.float32(selected_point2) colors = [] for i in range(0,10): colors.append(tuple(np.random.randint(0,255,3).tolist())) img1_lines = cv2.computeCorrespondEpilines(selected_point1.reshape(-1, 1, 2), 2, fundamentalmat) img1_lines = img1_lines.reshape(-1, 3) img1_lines1 = drawlines(m1_clr,m2_clr,img1_lines,selected_point1,selected_point2,colors) img2_lines = cv2.computeCorrespondEpilines(selected_point2.reshape(-1, 1, 2), 2, fundamentalmat) img2_lines = img1_lines.reshape(-1, 3) img2_lines1 = drawlines(m2_clr,m1_clr,img2_lines,selected_point2,selected_point1,colors) stereo = cv2.StereoBM_create(96, blockSize=17) stereo.setMinDisparity(16) stereo.setDisp12MaxDiff(0) stereo.setUniquenessRatio(10) stereo.setSpeckleRange(32) stereo.setSpeckleWindowSize(100) disparity_map = stereo.compute(image1_bw, image2_bw).astype(np.float32) / 16.0 disp_map = (disparity_map - 16)/96 # printing out all the output plt.imsave('output/task2/task2_disparity.jpg', disp_map, cmap=plt.cm.gray) cv2.imwrite('output/task2/task2_epi_right.jpg', img2_lines1) cv2.imwrite('output/task2/task2_epi_left.jpg', img1_lines1) cv2.imwrite("output/task2/merged.jpg", np.hstack([img2_lines1, img1_lines1]))

[ "-" ]

-

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/host_im/mount/malware-classification-master/samples/not/sample_good810.py

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no_license

Barnsa/Dissertation

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import difflib import textwrap import random import readline import datetime nterms = 422 n1, n2 = 0, 1 if nterms <= 0: print("Please provide a positive integer.") elif nterms == 1: print("Fibonacci sequence upto", nterms, ":") print(n1) else: print("Fibonacci sequence:") count = 0 while 0 == True & 0 < 422: print(n1) nth = n1 + n2 n1 = n2 n2 = nth count = count - -1

[ "barnsa@uni.coventry.ac.uk" ]

barnsa@uni.coventry.ac.uk

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effce116340b7d937bd285e43b49e1ef83d56156

/data_files/673 Number of Longest Increasing Subsequence.py

324b988bc2e0f1fbbc1e5247543d6f56b9c932ca

[]

no_license

DL2021Spring/CourseProject

a7c7ef57d69bc1b21e3303e737abb27bee3bd585

108cdd906e705e9d4d05640af32d34bfc8b124da

refs/heads/master

2023-04-11T18:52:30.562103

2021-05-18T09:59:59

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py

from typing import List class LenCnt: def __init__(self, l, c): self.l = l self.c = c def __repr__(self): return repr((self.l, self.c)) class Solution: def findNumberOfLIS(self, A: List[int]) -> int: if not A: return 0 n = len(A) F = [LenCnt(l=1, c=1) for _ in A] mx = LenCnt(l=1, c=1) for i in range(1, n): for j in range(i): if A[i] > A[j]: if F[i].l < F[j].l + 1: F[i].l = F[j].l + 1 F[i].c = F[j].c elif F[i].l == F[j].l + 1: F[i].c += F[j].c if F[i].l > mx.l: mx.l = F[i].l mx.c = F[i].c elif F[i].l == mx.l: mx.c += F[i].c return mx.c if __name__ == "__main__": assert Solution().findNumberOfLIS([1,1,1,2,2,2,3,3,3]) == 27 assert Solution().findNumberOfLIS([1, 3, 5, 4, 7]) == 2 assert Solution().findNumberOfLIS([2, 2, 2, 2, 2]) == 5

[ "1042448815@qq.com" ]

1042448815@qq.com

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/myia/operations/macro_dtype.py

3a8c6fd0f5874ab38ee713d174cf5ba6a72c6c5f

[ "MIT" ]

permissive

zangmunger/myia

1f2e9045af62da5a5d832eed0436de7c5813cd99

0aa38aa3c43648ee408dc031352ba442f6bed59f

refs/heads/master

2020-12-13T04:10:28.154027

2020-01-15T20:33:05

null

0

null

UTF-8

Python

false

377

py

"""Implementation of the 'dtype' macro.""" from ..lib import AbstractArray, Constant, macro @macro async def dtype(info, arr: AbstractArray): """Macro implementation for 'dtype'.""" return Constant((await arr.get()).element) __operation_defaults__ = { 'name': 'dtype', 'registered_name': 'dtype', 'mapping': dtype, 'python_implementation': None, }

[ "abergeron@gmail.com" ]

abergeron@gmail.com

5bf4d8bfacc9a85401f649b40ed3654d51bcc16f

b231c58c841dfc3e90677ce15cb6fe246b56bac5

/backend/oloffers_24608/urls.py

c4b31a37d38eccfb3cdf67e3a3ddc7782d33c132

[]

no_license

crowdbotics-apps/oloffers-24608

025688272bd3322ce8748160fbe89ebd0a8691d7

0900f3d64303e932aca702335024bfe398e68cd9

refs/heads/master

2023-03-19T04:33:32.594244

2021-02-18T22:08:13

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0

null

UTF-8

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false

1,967

py

"""oloffers_24608 URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include from allauth.account.views import confirm_email from rest_framework import permissions from drf_yasg.views import get_schema_view from drf_yasg import openapi urlpatterns = [ path("", include("home.urls")), path("accounts/", include("allauth.urls")), path("modules/", include("modules.urls")), path("api/v1/", include("home.api.v1.urls")), path("admin/", admin.site.urls), path("users/", include("users.urls", namespace="users")), path("rest-auth/", include("rest_auth.urls")), # Override email confirm to use allauth's HTML view instead of rest_auth's API view path("rest-auth/registration/account-confirm-email/<str:key>/", confirm_email), path("rest-auth/registration/", include("rest_auth.registration.urls")), ] admin.site.site_header = "OLOffers" admin.site.site_title = "OLOffers Admin Portal" admin.site.index_title = "OLOffers Admin" # swagger api_info = openapi.Info( title="OLOffers API", default_version="v1", description="API documentation for OLOffers App", ) schema_view = get_schema_view( api_info, public=True, permission_classes=(permissions.IsAuthenticated,), ) urlpatterns += [ path("api-docs/", schema_view.with_ui("swagger", cache_timeout=0), name="api_docs") ]

[ "team@crowdbotics.com" ]

team@crowdbotics.com

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23ed45d816476bc2c73a0e0b0d6bf96c713c979d

/bearsong.py

45b321d8c2af89b38a3356ca648e9b1100f6e379

[]

no_license

jason12360/zixue

e5fd0c6cd0ba8d4c72420360697ad23a5479615d

acc696732bb770f6fc0f0a8d4d076305ae39a9a6

refs/heads/master

2020-03-17T20:22:02.306897

2018-06-01T09:46:13

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UTF-8

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py

word = 'bottles' for beer_num in range(99,0,-1): print(beer_num,word,'of beer on the wall.') print(beer_num,word,'of beer.') print('Take it down.') print('Pass it around.') if beer_num == 1: print('No more bottle of beer on the wall') else: new_num = beer_num - 1 if new_num == 1: word = 'bottle' print(new_num,word,'of beer on the wall.')

[ "370828117@qq.com" ]

370828117@qq.com

0c1bef08943f239f67d9037534080ced61668cfd

f6e83bc298b24bfec278683341b2629388b22e6c

/scripts/check_db_integrity.py

3a994897b46425ba6bb41e00d88a664abd1712c1

[ "LicenseRef-scancode-generic-cla", "Apache-2.0" ]

permissive

noaOrMlnx/sonic-utilities

8d8ee86a9c258b4a5f37af69359ce100c29ad99c

9881f3edaa136233456408190367a09e53386376

refs/heads/master

2022-08-17T23:15:57.577454

2022-05-18T21:49:32

225,886,772

1

0

NOASSERTION

2022-07-19T08:49:40

2019-12-04T14:31:32

Python

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Python

false

3,007

py

#!/usr/bin/env python3 """ This is to verify if Database has critical tables present before warmboot can proceed. If warmboot is allowed with missing critical tables, it can lead to issues in going down path or during the recovery path. This test detects such issues before proceeding. The verification procedure here uses JSON schemas to verify the DB entities. In future, to verify new tables or their content, just the schema modification is needed. No modification may be needed to the integrity check logic. """ import os, sys import json, jsonschema import syslog import subprocess import traceback DB_SCHEMA = { "COUNTERS_DB": { "$schema": "http://json-schema.org/draft-06/schema", "type": "object", "title": "Schema for COUNTERS DB's entities", "required": ["COUNTERS_PORT_NAME_MAP"], "properties": { "COUNTERS_PORT_NAME_MAP": {"$id": "#/properties/COUNTERS_PORT_NAME_MAP", "type": "object"} } } } def main(): if not DB_SCHEMA: return 0 for db_name, schema in DB_SCHEMA.items(): db_dump_file = "/tmp/{}.json".format(db_name) dump_db_cmd = "sonic-db-dump -n 'COUNTERS_DB' -y > {}".format(db_dump_file) p = subprocess.Popen(dump_db_cmd, shell=True, text=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) (_, err) = p.communicate() rc = p.wait() if rc != 0: print("Failed to dump db {}. Return code: {} with err: {}".format(db_name, rc, err)) try: with open(db_dump_file) as fp: db_dump_data = json.load(fp) except ValueError as err: syslog.syslog(syslog.LOG_DEBUG, "DB json file is not a valid json file. " +\ "Error: {}".format(str(err))) return 1 # What: Validate if critical tables and entries are present in DB. # Why: This is needed to avoid warmbooting with a bad DB; which can # potentially trigger failures in the reboot recovery path. # How: Validate DB against a schema which defines required tables. try: jsonschema.validate(instance=db_dump_data, schema=schema) except jsonschema.exceptions.ValidationError as err: syslog.syslog(syslog.LOG_ERR, "Database is missing tables/entries needed for reboot procedure. " +\ "DB integrity check failed with:\n{}".format(str(err.message))) return 1 syslog.syslog(syslog.LOG_DEBUG, "Database integrity checks passed.") return 0 if __name__ == '__main__': res = 0 try: res = main() except KeyboardInterrupt: syslog.syslog(syslog.LOG_NOTICE, "SIGINT received. Quitting") res = 1 except Exception as e: syslog.syslog(syslog.LOG_ERR, "Got an exception %s: Traceback: %s" % (str(e), traceback.format_exc())) res = 2 finally: syslog.closelog() try: sys.exit(res) except SystemExit: os._exit(res)

[ "noreply@github.com" ]

noaOrMlnx.noreply@github.com

bc67cc314ee03bbe982d2d0f9e6123cef9b45598

8550f21689b0549d899b738411d2f94e1e5cc7e2

/catkin_ws/devel/lib/python2.7/dist-packages/mavros_msgs/srv/_CommandTriggerControl.py

2ecd8ccdd401bf391ec50284ebbee533562d2a0e

[]

no_license

pratyusv/px4_data_collection

fb21c0e53b9b9c5b7d3d4f1db25589ca852561b9

cb45a180ff2c2b3cf7d9693c54997123dbfd46b5

refs/heads/master

2020-03-20T01:27:21.756306

2018-06-12T17:34:01

137,076,821

0

null

UTF-8

Python

false

145

py

/home/pratyush/git/px4_data_collection/catkin_ws/devel/.private/mavros_msgs/lib/python2.7/dist-packages/mavros_msgs/srv/_CommandTriggerControl.py

[ "pratyushvarshney91@gmail.com" ]

pratyushvarshney91@gmail.com

33b9a78047691b1cba997ab1118c3c51209a9d79

1d928c3f90d4a0a9a3919a804597aa0a4aab19a3

/python/sentry/2016/8/mail.py

04a7cd3cd597f4687513a9b5f50d11221517a4a9

[]

no_license

rosoareslv/SED99

d8b2ff5811e7f0ffc59be066a5a0349a92cbb845

a062c118f12b93172e31e8ca115ce3f871b64461

refs/heads/main

2023-02-22T21:59:02.703005

2021-01-28T19:40:51

306,497,459

1

null

2020-11-24T20:56:18

2020-10-23T01:18:07

null

UTF-8

Python

false

17,616

py

from __future__ import absolute_import, print_function import itertools import logging import time import traceback import uuid from datetime import datetime, timedelta from random import Random import six from django.contrib.webdesign.lorem_ipsum import WORDS from django.core.urlresolvers import reverse from django.utils import timezone from django.utils.safestring import mark_safe from django.views.generic import View from sentry.constants import LOG_LEVELS from sentry.digests import Record from sentry.digests.notifications import Notification, build_digest from sentry.digests.utilities import get_digest_metadata from sentry.http import get_server_hostname from sentry.models import ( Activity, Event, Group, GroupStatus, Organization, OrganizationMember, Project, Release, Rule, Team ) from sentry.plugins.sentry_mail.activity import emails from sentry.utils.dates import to_datetime, to_timestamp from sentry.utils.email import inline_css from sentry.utils.http import absolute_uri from sentry.utils.samples import load_data from sentry.web.decorators import login_required from sentry.web.helpers import render_to_response, render_to_string logger = logging.getLogger(__name__) def get_random(request): seed = request.GET.get('seed', six.text_type(time.time())) return Random(seed) def make_message(random, length=None): if length is None: length = int(random.weibullvariate(8, 3)) return ' '.join(random.choice(WORDS) for _ in range(length)) def make_culprit(random): def make_module_path_components(min, max): for _ in range(random.randint(min, max)): yield ''.join(random.sample(WORDS, random.randint(1, int(random.paretovariate(2.2))))) return '{module} in {function}'.format( module='.'.join(make_module_path_components(1, 4)), function=random.choice(WORDS) ) def make_group_metadata(random, group): return { 'type': 'error', 'metadata': { 'type': '{}Error'.format( ''.join(word.title() for word in random.sample(WORDS, random.randint(1, 3))), ), 'value': make_message(random), } } def make_group_generator(random, project): epoch = to_timestamp(datetime(2016, 6, 1, 0, 0, 0, tzinfo=timezone.utc)) for id in itertools.count(1): first_seen = epoch + random.randint(0, 60 * 60 * 24 * 30) last_seen = random.randint(first_seen, first_seen + (60 * 60 * 24 * 30)) group = Group( id=id, project=project, culprit=make_culprit(random), level=random.choice(LOG_LEVELS.keys()), message=make_message(random), first_seen=to_datetime(first_seen), last_seen=to_datetime(last_seen), status=random.choice(( GroupStatus.UNRESOLVED, GroupStatus.RESOLVED, )), ) if random.random() < 0.8: group.data = make_group_metadata(random, group) yield group # TODO(dcramer): use https://github.com/disqus/django-mailviews class MailPreview(object): def __init__(self, html_template, text_template, context=None): self.html_template = html_template self.text_template = text_template self.context = context if context is not None else {} def text_body(self): return render_to_string(self.text_template, self.context) def html_body(self): try: return inline_css(render_to_string(self.html_template, self.context)) except Exception: traceback.print_exc() raise def render(self, request): return render_to_response('sentry/debug/mail/preview.html', { 'preview': self, 'format': request.GET.get('format'), }) class ActivityMailPreview(object): def __init__(self, activity): self.email = emails.get(activity.type)(activity) def get_context(self): context = self.email.get_base_context() context.update(self.email.get_context()) return context def text_body(self): return render_to_string(self.email.get_template(), self.get_context()) def html_body(self): try: return inline_css(render_to_string( self.email.get_html_template(), self.get_context())) except Exception: import traceback traceback.print_exc() raise class ActivityMailDebugView(View): def get(self, request): org = Organization( id=1, slug='organization', name='My Company', ) team = Team( id=1, slug='team', name='My Team', organization=org, ) project = Project( id=1, organization=org, team=team, slug='project', name='My Project', ) group = next( make_group_generator( get_random(request), project, ), ) event = Event( id=1, project=project, group=group, message=group.message, data=load_data('python'), datetime=datetime(2016, 6, 13, 3, 8, 24, tzinfo=timezone.utc), ) activity = Activity( group=event.group, project=event.project, **self.get_activity(request, event) ) return render_to_response('sentry/debug/mail/preview.html', { 'preview': ActivityMailPreview(activity), 'format': request.GET.get('format'), }) @login_required def new_event(request): platform = request.GET.get('platform', 'python') org = Organization( id=1, slug='example', name='Example', ) team = Team( id=1, slug='example', name='Example', organization=org, ) project = Project( id=1, slug='example', name='Example', team=team, organization=org, ) random = get_random(request) group = next( make_group_generator(random, project), ) event = Event( id=1, project=project, group=group, message=group.message, data=load_data(platform), datetime=to_datetime( random.randint( to_timestamp(group.first_seen), to_timestamp(group.last_seen), ), ), ) rule = Rule(label="An example rule") interface_list = [] for interface in six.itervalues(event.interfaces): body = interface.to_email_html(event) if not body: continue interface_list.append((interface.get_title(), mark_safe(body))) return MailPreview( html_template='sentry/emails/error.html', text_template='sentry/emails/error.txt', context={ 'rule': rule, 'group': group, 'event': event, 'link': 'http://example.com/link', 'interfaces': interface_list, 'tags': event.get_tags(), 'project_label': project.name, 'tags': [ ('logger', 'javascript'), ('environment', 'prod'), ('level', 'error'), ('device', 'Other') ] }, ).render(request) @login_required def digest(request): random = get_random(request) # TODO: Refactor all of these into something more manageable. org = Organization( id=1, slug='example', name='Example Organization', ) team = Team( id=1, slug='example', name='Example Team', organization=org, ) project = Project( id=1, slug='example', name='Example Project', team=team, organization=org, ) rules = {i: Rule( id=i, project=project, label="Rule #%s" % (i,), ) for i in range(1, random.randint(2, 4))} state = { 'project': project, 'groups': {}, 'rules': rules, 'event_counts': {}, 'user_counts': {}, } records = [] event_sequence = itertools.count(1) group_generator = make_group_generator(random, project) for i in range(random.randint(1, 30)): group = next(group_generator) state['groups'][group.id] = group offset = timedelta(seconds=0) for i in range(random.randint(1, 10)): offset += timedelta(seconds=random.random() * 120) event = Event( id=next(event_sequence), event_id=uuid.uuid4().hex, project=project, group=group, message=group.message, data=load_data('python'), datetime=to_datetime( random.randint( to_timestamp(group.first_seen), to_timestamp(group.last_seen), ), ) ) records.append( Record( event.event_id, Notification( event, random.sample(state['rules'], random.randint(1, len(state['rules']))), ), to_timestamp(event.datetime), ) ) state['event_counts'][group.id] = random.randint(10, 1e4) state['user_counts'][group.id] = random.randint(10, 1e4) digest = build_digest(project, records, state) start, end, counts = get_digest_metadata(digest) return MailPreview( html_template='sentry/emails/digests/body.html', text_template='sentry/emails/digests/body.txt', context={ 'project': project, 'counts': counts, 'digest': digest, 'start': start, 'end': end, }, ).render(request) @login_required def report(request): from sentry.tasks import reports random = get_random(request) duration = 60 * 60 * 24 * 7 timestamp = random.randint( to_timestamp(datetime(2016, 6, 1, 0, 0, 0, tzinfo=timezone.utc)), to_timestamp(datetime(2016, 7, 1, 0, 0, 0, tzinfo=timezone.utc)), ) organization = Organization( id=1, slug='example', name='Example', ) team = Team( id=1, slug='example', name='Example', organization=organization, ) project = Project( id=1, organization=organization, team=team, slug='project', name='My Project', ) start, stop = reports._to_interval(timestamp, duration) group_instances = {} def fetch_group_instances(id_list): results = {} for id in id_list: instance = group_instances.get(id) if instance is not None: results[id] = instance return results def make_group_id_generator(): group_generator = make_group_generator(random, project) while True: group = next(group_generator) if random.random() < 0.95: group_instances[group.id] = group yield group.id group_id_sequence = make_group_id_generator() def make_release_generator(): id_sequence = itertools.count(1) while True: dt = to_datetime( random.randint( timestamp - (30 * 24 * 60 * 60), timestamp, ), ) yield Release( id=next(id_sequence), project=project, version=''.join([ random.choice('0123456789abcdef') for _ in range(40) ]), date_added=dt, date_started=dt, ) release_instances = {} def make_release_id_generator(): release_generator = make_release_generator() while True: release = next(release_generator) release_instances[release.id] = release yield release.id release_id_generator = make_release_id_generator() def build_issue_list(): summaries = [] for i in range(3): summaries.append( int(random.weibullvariate(10, 1) * random.paretovariate(0.5)) ) return summaries, [( next(group_id_sequence), ( int(random.paretovariate(0.3)), int(random.paretovariate(0.3)), ), ) for _ in xrange(0, random.randint(1, 5))] def build_release_list(): return reports.trim_release_list([ ( next(release_id_generator), max(1, int(random.weibullvariate(20, 0.15))), ) for _ in range(random.randint(0, 10)) ]) def build_report(): daily_maximum = random.randint(1000, 10000) rollup = 60 * 60 * 24 series = [( timestamp + (i * rollup), (random.randint(0, daily_maximum), random.randint(0, daily_maximum)) ) for i in xrange(0, 7)] aggregates = [ random.randint(0, daily_maximum * 7) if random.random() < 0.9 else None for _ in xrange(0, 4) ] return series, aggregates, build_issue_list(), build_release_list() report = reduce( reports.merge_reports, [build_report() for _ in xrange(0, random.randint(1, 3))] ) if random.random() < 0.85: personal = { 'resolved': random.randint(0, 100), 'users': int(random.paretovariate(0.2)), } else: personal = { 'resolved': 0, 'users': 0, } return MailPreview( html_template='sentry/emails/reports/body.html', text_template='sentry/emails/reports/body.txt', context={ 'duration': reports.durations[duration], 'interval': { 'start': reports.date_format(start), 'stop': reports.date_format(stop), }, 'report': reports.to_context( report, fetch_group_instances, ), 'organization': organization, 'personal': personal, 'user': request.user, }, ).render(request) @login_required def request_access(request): org = Organization( id=1, slug='example', name='Example', ) team = Team( id=1, slug='example', name='Example', organization=org, ) return MailPreview( html_template='sentry/emails/request-team-access.html', text_template='sentry/emails/request-team-access.txt', context={ 'email': 'foo@example.com', 'name': 'George Bush', 'organization': org, 'team': team, 'url': absolute_uri(reverse('sentry-organization-members', kwargs={ 'organization_slug': org.slug, }) + '?ref=access-requests'), }, ).render(request) @login_required def invitation(request): org = Organization( id=1, slug='example', name='Example', ) om = OrganizationMember( id=1, email='foo@example.com', organization=org, ) return MailPreview( html_template='sentry/emails/member-invite.html', text_template='sentry/emails/member-invite.txt', context={ 'email': 'foo@example.com', 'organization': org, 'url': absolute_uri(reverse('sentry-accept-invite', kwargs={ 'member_id': om.id, 'token': om.token, })), }, ).render(request) @login_required def access_approved(request): org = Organization( id=1, slug='example', name='Example', ) team = Team( id=1, slug='example', name='Example', organization=org, ) return MailPreview( html_template='sentry/emails/access-approved.html', text_template='sentry/emails/access-approved.txt', context={ 'email': 'foo@example.com', 'name': 'George Bush', 'organization': org, 'team': team, }, ).render(request) @login_required def confirm_email(request): email = request.user.emails.first() email.set_hash() email.save() return MailPreview( html_template='sentry/emails/confirm_email.html', text_template='sentry/emails/confirm_email.txt', context={ 'confirm_email': 'foo@example.com', 'user': request.user, 'url': absolute_uri(reverse( 'sentry-account-confirm-email', args=[request.user.id, email.validation_hash] )), 'is_new_user': True, }, ).render(request) @login_required def recover_account(request): return MailPreview( html_template='sentry/emails/recover_account.html', text_template='sentry/emails/recover_account.txt', context={ 'user': request.user, 'url': absolute_uri(reverse( 'sentry-account-confirm-email', args=[request.user.id, 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'] )), 'domain': get_server_hostname(), }, ).render(request)

[ "rodrigosoaresilva@gmail.com" ]

rodrigosoaresilva@gmail.com

b93802b90b96587bbc604e99f8afd807224026b6

d752046f2a056ca1b26323d431dc0a02153fe071

/corphub_app/views.py

81be1004dd1aa6fa1e8611c3b38e9714b8818496

[]

no_license

calixo888/corphub

2cecac4a116ce49df64428da2f602cc00c7ed2d6

bc8e811b0edef18a906595e93c3ef8abf2198fca

refs/heads/master

2020-07-06T07:41:49.775105

2019-08-18T00:24:22

202,943,344

0

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UTF-8

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false

2,735

py

from django.shortcuts import render from django.http import HttpResponse from corphub_app import forms import requests from bs4 import BeautifulSoup from fake_useragent import UserAgent import re # Create your views here. links = [] agent = UserAgent() header = {'user-agent': agent.chrome} query = "" def index(request): global links global query if request.method == "POST": form = forms.SearchForm(request.POST) if form.is_valid(): query = form.cleaned_data['search'] links = [] queries = [] queries.append(query) queries.append("\"{}\"".format(query)) for new_query in queries: links = search_web(links, new_query, False) links = search_web(links, new_query, True) else: form = forms.SearchForm() query = "" midpoint = len(links) // 2 return render(request, "corphub_app/index.html", context={"form": form, "links1": links[:20], "links2": links[20:40]}) def search_web(links, query, news): if news: page = requests.get("https://news.google.com/search?q=" + query + "&hl=en-US&gl=US&ceid=US%3Aen", headers=header) soup = BeautifulSoup(page.content) for i in soup.find_all('a', href=True): if str(i['href']).startswith("./articles/"): link = "https://news.google.com" + i['href'][1:] links.append(link) else: page = requests.get("https://www.google.dz/search?q=see") soup = BeautifulSoup(page.content) for link in soup.find_all("a",href=re.compile("(?<=/url\?q=)(htt.*://.*)")): new_link = re.split(":(?=http)",link["href"].replace("/url?q=","")) links.append(new_link[0]) return list(set(links)) def viewall(request): global query links = [] queries = [] queries.append(query) # queries.append(query + " news") # queries.append(query + " speculations") # queries.append(query + " stock") # queries.append(query + " startup") # queries.append(query + " development") # queries.append(query + " founder") # queries.append(query + " funding") # queries.append(query + " products") # queries.append(query + " market") # queries.append(query + " evaluation") # queries.append(query + " launches") # queries.append("\"{}\"".format(query)) # queries.append("\"{} CEO\"".format(query)) for new_query in queries: links = search_web(links, new_query, False) links = search_web(links, new_query, True) midpoint = len(links) // 2 return render(request, "corphub_app/viewall.html", context={"links1": links[:midpoint], "links2": links[midpoint:-1]})

[ "calix.huang1@gmail.com" ]

calix.huang1@gmail.com

706653fd415c83d411ace8de9143ce7840d68d3b

631847fafbcfa07bf33eee078d9b59b464ce4b50

/optimization/first_sdEta_mjj_optimization/sdEta_mistake_analyses/dEta_mmjj_cuts_plots/loose_analysis_sdeta_2.6_mmjj_1250/Output/Histos/MadAnalysis5job_0/selection_6.py

676e32566983b0ec5ab94df2e16d9c835f8efb95

[ "MIT" ]

permissive

sheride/axion_pheno

7b46aeb7cc562800d78edd9048504fdbc0f5fa42

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

refs/heads/master

2021-07-01T08:47:59.981416

2021-02-03T23:03:50

219,261,636

0

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def selection_6(): # Library import import numpy import matplotlib import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec # Library version matplotlib_version = matplotlib.__version__ numpy_version = numpy.__version__ # Histo binning xBinning = numpy.linspace(0.0,15.0,76,endpoint=True) # Creating data sequence: middle of each bin xData = numpy.array([0.1,0.3,0.5,0.7,0.9,1.1,1.3,1.5,1.7,1.9,2.1,2.3,2.5,2.7,2.9,3.1,3.3,3.5,3.7,3.9,4.1,4.3,4.5,4.7,4.9,5.1,5.3,5.5,5.7,5.9,6.1,6.3,6.5,6.7,6.9,7.1,7.3,7.5,7.7,7.9,8.1,8.3,8.5,8.7,8.9,9.1,9.3,9.5,9.7,9.9,10.1,10.3,10.5,10.7,10.9,11.1,11.3,11.5,11.7,11.9,12.1,12.3,12.5,12.7,12.9,13.1,13.3,13.5,13.7,13.9,14.1,14.3,14.5,14.7,14.9]) # Creating weights for histo: y7_DELTAR_0 y7_DELTAR_0_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,14.2269397856,29.2686068716,42.0216839226,53.8740337468,65.345663898,74.9462956554,84.5878873777,85.8406463021,68.0355015886,53.7389538628,43.5119226432,35.2910057012,30.0505782003,25.2645903093,21.7518693251,18.7017759437,16.0078702566,13.977204,12.4255453321,10.7838187416,9.2853840281,7.80332530051,6.9026260738,6.07561878382,4.93746376098,4.22918836906,3.48815940527,2.97230504815,2.34590998594,1.75636209209,1.32648326116,0.978485959928,0.704182595429,0.425784834446,0.266115451529,0.155575186432,0.077787613216,0.0163763339402,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_1 y7_DELTAR_1_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0243513843729,0.437219398517,1.90818329045,4.98180620258,10.6690529283,14.0212372097,14.7747180546,13.4634543873,11.1414093722,9.25864852021,7.3992469442,4.88447188044,3.72933124821,2.67278182098,1.785830671,0.886755287737,0.437538625934,0.206430031749,0.14567736925,0.0364983561051,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_2 y7_DELTAR_2_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.010015500946,0.210827354383,1.1043367519,3.92531257812,13.5234914809,30.2912178236,46.0838361656,58.5243306794,62.5391968847,63.3436328869,56.3138671526,44.9199493321,32.1881132876,22.5490791347,14.7893253303,10.0407480386,7.01801405988,4.31735199282,2.72113783099,1.69689179851,0.984039934326,0.491984844366,0.160645101078,0.060271545181,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_3 y7_DELTAR_3_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0495711330339,0.274957426964,0.973627117883,2.55762299097,7.76663885706,19.9214122151,34.6961896523,50.4545726139,63.3301783937,72.7695267728,75.0469451217,63.9590999488,52.4348162857,40.451246417,31.5271822715,21.8594217387,14.9992489287,8.81199090124,5.32442770432,3.04691429202,1.76012762759,1.02287320723,0.401628955731,0.159564926421,0.0825047904422,0.0109912867624,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_4 y7_DELTAR_4_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00887767014811,0.0759772789329,0.232925657028,0.637530473781,1.5829580809,4.51377023518,11.9062611638,19.6372610107,25.9343437817,24.8418055666,21.3486646516,17.5619678984,14.2514731715,10.7506924374,7.89660274505,5.41992652692,3.46179761542,2.11585149266,1.21774025906,0.610805536993,0.273377576975,0.125312833378,0.0602072654643,0.0167761688189,0.00493094604148,0.000987661857571,0.000986317874714,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_5 y7_DELTAR_5_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00529321572864,0.032265123723,0.100087042889,0.237969571997,0.472390264535,1.07840164214,2.87399061276,8.78438888738,9.35412207477,8.02839354176,6.7025049691,5.44290482357,4.16685260101,3.08921594844,2.17438844212,1.41491783529,0.837907644561,0.467092151593,0.242753117423,0.106387124132,0.0375573952177,0.0148761385032,0.00554593435565,0.00126014468047,0.000504083718591,0.000504337781551,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_6 y7_DELTAR_6_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.011726445518,0.0343623072018,0.0667015745523,0.123708920398,0.226725995141,0.459739007655,1.30382609683,4.19569814032,3.99820256236,3.11386650599,2.34601586286,1.6786639287,1.14890441592,0.701102953853,0.394525310231,0.200446400474,0.106762134161,0.0389371717401,0.0134482051709,0.00487045362199,0.00142975738105,0.000856878251999,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_7 y7_DELTAR_7_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00203093481187,0.00457892515879,0.00807620683047,0.0138846935016,0.0262791762867,0.0514614332342,0.158958463992,0.632041840455,0.546463122043,0.363087015128,0.221461048474,0.118847596591,0.0580068739514,0.0272951566595,0.00932449750143,0.00302238162131,0.000777307441283,0.000323807246285,0.000151288016484,4.31956675371e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_8 y7_DELTAR_8_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000397353899821,0.000851817282859,0.00130394912405,0.00133173945735,0.00363008249248,0.00767978756739,0.0298868199775,0.149025608528,0.108398517773,0.0580069166798,0.0272855297697,0.00966175853683,0.00356842933188,0.00104881704937,0.000311672595736,8.51463370875e-05,2.82221564232e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_9 y7_DELTAR_9_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.8176445648,7.81885224201,26.0706471194,20.8588238205,28.6702607706,15.6291007454,7.82206758643,2.60887622775,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_10 y7_DELTAR_10_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.05238929114,2.10669359492,9.48357970457,23.1623222351,41.0651894164,73.7367210511,83.2093963036,71.6096602945,69.5256940557,62.1407558123,24.2184303523,16.8530462465,8.42834886866,6.31964175934,1.05389798404,2.10719341437,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_11 y7_DELTAR_11_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.460173181188,2.53547733378,5.52939261055,21.1876541413,55.0573097014,94.6651205523,121.15612909,123.455281276,129.442122021,101.815908052,74.8570845222,38.2410033778,23.261106246,11.2831135567,5.99121726511,2.99425878938,1.61274751646,0.69062134378,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_12 y7_DELTAR_12_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0831404702383,0.249176732371,0.664691804604,2.10478467023,5.06703074763,21.1848242564,67.0946490953,105.806841104,117.522932757,84.8151703951,53.1401353085,33.5614589539,19.3015896713,10.162149492,5.78678133508,2.79690199338,1.24588250771,0.609061720901,0.13844381602,0.0831275053638,0.0276859124705,0.0,0.0276409432069,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_13 y7_DELTAR_13_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100785377154,0.120920679639,0.383225938119,0.826641328569,1.61310465394,4.58649534825,17.3819593289,78.708592968,64.5751717463,40.2059961189,23.6527862349,15.1750273073,7.92495183879,4.46651719202,2.51018279784,0.917429901511,0.453710935501,0.201587745459,0.0906670862141,0.0100661645167,0.0100795814575,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_14 y7_DELTAR_14_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0566037567452,0.11036622211,0.178201717181,0.410221686761,0.76113021364,1.79387172429,6.78141036292,32.8407576729,24.3281294271,14.3691203548,7.7406395668,3.85625760331,1.86444847881,0.959156873108,0.339487305005,0.138659905559,0.0679308331898,0.019809310375,0.0,0.00565236542584,0.0,0.00282703721639,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_15 y7_DELTAR_15_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00608990105335,0.0137132010778,0.025935347506,0.0472171441118,0.0608769864765,0.140025149504,0.674488634573,4.3170093677,2.92640403151,1.45929345457,0.717569533183,0.272545577971,0.0822253042415,0.0304505391214,0.0106678754096,0.00762122995213,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y7_DELTAR_16 y7_DELTAR_16_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00180640904432,0.00198614334736,0.00270663264263,0.00469407696684,0.00686384291275,0.0173364292146,0.0996748146434,0.970327686842,0.603058611542,0.224992506803,0.0707826916138,0.0189580556419,0.00361129180941,0.000902789349561,0.000360840331731,0.0,0.000180186409828,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating a new Canvas fig = plt.figure(figsize=(12,6),dpi=80) frame = gridspec.GridSpec(1,1,right=0.7) pad = fig.add_subplot(frame[0]) # Creating a new Stack pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights+y7_DELTAR_16_weights,\ label="$bg\_dip\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#e5e5e5", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights,\ label="$bg\_dip\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#f2f2f2", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights,\ label="$bg\_dip\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights,\ label="$bg\_dip\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights,\ label="$bg\_dip\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#c1bfa8", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights,\ label="$bg\_dip\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#bab5a3", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights,\ label="$bg\_dip\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b2a596", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights,\ label="$bg\_dip\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b7a39b", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights,\ label="$bg\_vbf\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ad998c", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights,\ label="$bg\_vbf\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#9b8e82", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights,\ label="$bg\_vbf\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#876656", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights,\ label="$bg\_vbf\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#afcec6", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights,\ label="$bg\_vbf\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#84c1a3", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights,\ label="$bg\_vbf\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#89a8a0", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights,\ label="$bg\_vbf\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#829e8c", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights+y7_DELTAR_1_weights,\ label="$bg\_vbf\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#adbcc6", linewidth=4, linestyle="dashdot",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y7_DELTAR_0_weights,\ label="$signal$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#7a8e99", linewidth=3, linestyle="dashed",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") # Axis plt.rc('text',usetex=False) plt.xlabel(r"\Delta R [ j_{1} , j_{2} ] ",\ fontsize=16,color="black") plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\ fontsize=16,color="black") # Boundary of y-axis ymax=(y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights+y7_DELTAR_16_weights).max()*1.1 ymin=0 # linear scale #ymin=min([x for x in (y7_DELTAR_0_weights+y7_DELTAR_1_weights+y7_DELTAR_2_weights+y7_DELTAR_3_weights+y7_DELTAR_4_weights+y7_DELTAR_5_weights+y7_DELTAR_6_weights+y7_DELTAR_7_weights+y7_DELTAR_8_weights+y7_DELTAR_9_weights+y7_DELTAR_10_weights+y7_DELTAR_11_weights+y7_DELTAR_12_weights+y7_DELTAR_13_weights+y7_DELTAR_14_weights+y7_DELTAR_15_weights+y7_DELTAR_16_weights) if x])/100. # log scale plt.gca().set_ylim(ymin,ymax) # Log/Linear scale for X-axis plt.gca().set_xscale("linear") #plt.gca().set_xscale("log",nonposx="clip") # Log/Linear scale for Y-axis plt.gca().set_yscale("linear") #plt.gca().set_yscale("log",nonposy="clip") # Legend plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.) # Saving the image plt.savefig('../../HTML/MadAnalysis5job_0/selection_6.png') plt.savefig('../../PDF/MadAnalysis5job_0/selection_6.png') plt.savefig('../../DVI/MadAnalysis5job_0/selection_6.eps') # Running! if __name__ == '__main__': selection_6()

[ "elijah.sheridan@vanderbilt.edu" ]

elijah.sheridan@vanderbilt.edu

1767d3910fac27679191cb881124e7753f02b9dc

d29a5ce285083043a37cb0da2abb5a3045e05551

/reviewboard/admin/tests/test_related_user_widget.py

81f98b7213a9063a1f659f54219a18d85eaa4dc9

[ "MIT" ]

permissive

wen501271303/reviewboard

83fa35123b851a5b42e3c2a3eb44f477a3da6198

a3b548437deb703792b805cf80f80313c7dd7f8a

refs/heads/master

2020-06-12T06:16:28.241753

2019-06-24T22:25:09

null

0

null

UTF-8

Python

false

5,736

py

"""Unit tests for reviewboard.admin.form_widgets.RelatedUserWidget.""" from __future__ import unicode_literals from django import forms from django.contrib.auth.models import User from reviewboard.admin.form_widgets import RelatedUserWidget from reviewboard.testing.testcase import TestCase class TestForm(forms.Form): """A Test Form with a field that contains a RelatedUserWidget.""" my_multiselect_field = forms.ModelMultipleChoiceField( queryset=User.objects.filter(is_active=True), label=('Default users'), required=False, widget=RelatedUserWidget()) class LocalSiteTestForm(forms.Form): """A Test Form with a field that contains a RelatedUserWidget. The RelatedUserWidget is defined to have a local_site_name. """ my_multiselect_field = forms.ModelMultipleChoiceField( queryset=User.objects.filter(is_active=True), label=('Default users'), required=False, widget=RelatedUserWidget(local_site_name='supertest')) class SingleValueTestForm(forms.Form): """A Test Form with a field that contains a RelatedUserWidget. The RelatedUserWidget is defined as setting multivalued to False. """ my_select_field = forms.ModelMultipleChoiceField( queryset=User.objects.filter(is_active=True), label=('Default users'), required=False, widget=RelatedUserWidget(multivalued=False)) class RelatedUserWidgetTests(TestCase): """Unit tests for RelatedUserWidget.""" fixtures = ['test_users'] def test_render_empty(self): """Testing RelatedUserWidget.render with no initial data""" my_form = TestForm() html = my_form.fields['my_multiselect_field'].widget.render( 'Default users', [], {'id': 'default-users'}) self.assertHTMLEqual( """<input id="default-users" name="Default users" type="hidden" /> <script> $(function() { var view = new RB.RelatedUserSelectorView({ $input: $('#default\\u002Dusers'), initialOptions: [], useAvatars: true, multivalued: true }).render(); }); </script>""", html) def test_render_with_data(self): """Testing RelatedUserWidget.render with initial data""" my_form = TestForm() html = my_form.fields['my_multiselect_field'].widget.render( 'Default users', [1, 2, 3], {'id': 'default-users'}) self.assertHTMLEqual( """<input id="default-users" name="Default users" type="hidden" value="1,2,3" /> <script> $(function() { var view = new RB.RelatedUserSelectorView({ $input: $('#default\\u002Dusers'), initialOptions: [{"avatarURL": "https://secure.gravatar.com/avatar/e64c7d89f26bd1972efa854d13d7dd61?s=40\\u0026d=mm", "fullname": "Admin User", "id": 1, "username": "admin"}, {"avatarURL": "https://secure.gravatar.com/avatar/b0f1ae4342591db2695fb11313114b3e?s=40\\u0026d=mm", "fullname": "Doc Dwarf", "id": 2, "username": "doc"}, {"avatarURL": "https://secure.gravatar.com/avatar/1a0098e6600792ea4f714aa205bf3f2b?s=40\\u0026d=mm", "fullname": "Dopey Dwarf", "id": 3, "username": "dopey"}], useAvatars: true, multivalued: true }).render(); }); </script>""", html) def test_render_with_local_site(self): """Testing RelatedUserWidget.render with a local site defined""" my_form = LocalSiteTestForm() html = my_form.fields['my_multiselect_field'].widget.render( 'Default users', [], {'id': 'default-users'}) self.assertIn("localSitePrefix: 's/supertest/',", html) def test_value_from_datadict(self): """Testing RelatedUserWidget.value_from_datadict""" my_form = TestForm() value = ( my_form.fields['my_multiselect_field'] .widget .value_from_datadict( {'people': ['1', '2']}, {}, 'people')) self.assertEqual(value, ['1', '2']) def test_value_from_datadict_single_value(self): """Testing RelatedUserWidget.value_from_datadict with a single value""" my_form = SingleValueTestForm() value = ( my_form.fields['my_select_field'] .widget .value_from_datadict( {'people': ['1']}, {}, 'people')) self.assertEqual(value, ['1']) def test_value_from_datadict_with_no_data(self): """Testing RelatedUserWidget.value_from_datadict with no data""" my_form = TestForm() value = ( my_form.fields['my_multiselect_field'] .widget .value_from_datadict( {'people': []}, {}, 'people')) self.assertEqual(value, []) def test_value_from_datadict_with_missing_data(self): """Testing RelatedUserWidget.value_from_datadict with missing data""" my_form = TestForm() value = ( my_form.fields['my_multiselect_field'] .widget .value_from_datadict( {}, {}, 'people')) self.assertIsNone(value)

[ "christian@beanbaginc.com" ]

christian@beanbaginc.com

62ae74f067f9e799dea7e452ee0644d0e64f3f79

53fab060fa262e5d5026e0807d93c75fb81e67b9

/backup/user_091/ch25_2020_09_30_19_23_04_594122.py

56f82a63d1ac642611703c6c1b15d4481a550f7c

[]

no_license

gabriellaec/desoft-analise-exercicios

b77c6999424c5ce7e44086a12589a0ad43d6adca

01940ab0897aa6005764fc220b900e4d6161d36b

refs/heads/main

2023-01-31T17:19:42.050628

2020-12-16T05:21:31

306,735,108

0

null

UTF-8

Python

false

360

py

import math v=int(input('Digite o valor da velocidade da jaca: ')) m=math.degrees(input('Digite em graus o ângulo de lançamento: ')) d=((v**2)*math.sin(math.degrees(2*m)))/9.8 if d<98: print('Muito perto') elif d>=98 and d<=102: print('Acertou') elif d>102: print('Muito longe')

[ "you@example.com" ]

you@example.com

7595dee2388e7d0424519ce001bd1a177e831d2c

de707c94c91f554d549e604737b72e6c86eb0755

/supervised_learning/0x12-transformer_apps/1-dataset.py

ecbc295eff78c8f60161c3dbab97b078db9527a8

[]

no_license

ejonakodra/holbertonschool-machine_learning-1

885cf89c1737573228071e4dc8e26304f393bc30

8834b201ca84937365e4dcc0fac978656cdf5293

refs/heads/main

2023-07-10T09:11:01.298863

2021-08-11T03:43:59

null

0

null

UTF-8

Python

false

3,979

py

#!/usr/bin/env python3 """ Defines class Dataset that loads and preps a dataset for machine translation """ import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds class Dataset: """ Loads and preps a dataset for machine translation class constructor: def __init__(self) public instance attributes: data_train: contains the ted_hrlr_translate/pt_to_en tf.data.Dataset train split, loaded as_supervided data_valid: contains the ted_hrlr_translate/pt_to_en tf.data.Dataset validate split, loaded as_supervided tokenizer_pt: the Portuguese tokenizer created from the training set tokenizer_en: the English tokenizer created from the training set instance method: def tokenize_dataset(self, data): that creates sub-word tokenizers for our dataset def encode(self, pt, en): that encodes a translation into tokens """ def __init__(self): """ Class constructor Sets the public instance attributes: data_train: contains the ted_hrlr_translate/pt_to_en tf.data.Dataset train split, loaded as_supervided data_valid: contains the ted_hrlr_translate/pt_to_en tf.data.Dataset validate split, loaded as_supervided tokenizer_pt: the Portuguese tokenizer created from the training set tokenizer_en: the English tokenizer created from the training set """ self.data_train = tfds.load("ted_hrlr_translate/pt_to_en", split="train", as_supervised=True) self.data_valid = tfds.load("ted_hrlr_translate/pt_to_en", split="validation", as_supervised=True) self.tokenizer_pt, self.tokenizer_en = self.tokenize_dataset( self.data_train) def tokenize_dataset(self, data): """ Creates sub_word tokenizers for our dataset parameters: data [tf.data.Dataset]: dataset to use whose examples are formatted as tuple (pt, en) pt [tf.Tensor]: contains the Portuguese sentence en [tf.Tensor]: contains the corresponding English sentence returns: tokenizer_pt, tokenizer_en: tokenizer_pt: the Portuguese tokenizer tokenizer_en: the English tokenizer """ SubwordTextEncoder = tfds.deprecated.text.SubwordTextEncoder tokenizer_pt = SubwordTextEncoder.build_from_corpus( (pt.numpy() for pt, en in data), target_vocab_size=(2 ** 15)) tokenizer_en = SubwordTextEncoder.build_from_corpus( (en.numpy() for pt, en in data), target_vocab_size=(2 ** 15)) return tokenizer_pt, tokenizer_en def encode(self, pt, en): """ Encodes a translation into tokens parameters: pt [tf.Tensor]: contains the Portuguese sentence en [tf.Tensor]: contains the corresponding English sentence returns: pt_tokens, en_tokens: pt_tokens [np.ndarray]: the Portuguese tokens en_tokens [np.ndarray]: the English tokens """ pt_start_index = self.tokenizer_pt.vocab_size pt_end_index = pt_start_index + 1 en_start_index = self.tokenizer_en.vocab_size en_end_index = en_start_index + 1 pt_tokens = [pt_start_index] + self.tokenizer_pt.encode( pt.numpy()) + [pt_end_index] en_tokens = [en_start_index] + self.tokenizer_en.encode( en.numpy()) + [en_end_index] return pt_tokens, en_tokens

[ "eislek02@gmail.com" ]

eislek02@gmail.com

4a8e80cd7e903d4068d0a5758d594014ae06a533

477a669e9ab041f411a728dacd983b20e20073a5

/cosmos.py

e1d5ab469f7f67c126c1e466a0fd5e1f10ce8cb4

[]

no_license

DriftingPig/postrunbrick

01c2a008bfd29ed93ac058b5eb39d553517999fd

39456e64ded3bd12234719057df047c024db7213

refs/heads/main

2023-07-03T03:36:50.463758

2021-08-04T18:11:11

390,432,082

0

null

UTF-8

Python

false

9,761

py

from filesystem import LegacySimData import subprocess from glob import glob import astropy.io.fits as fits from astropy.table import Table,vstack from SurveySource import BaseSource import os import numpy as np class CosmosRepeats(LegacySimData): def __init__(self, survey_dir=None, outdir=None, subsection=None, brick=None, startid=None, **kwargs): self.reference_surveydir = '/global/cscratch1/sd/dstn/cosmos-subs-lsd/' self.reference_outdir_top = '/global/cscratch1/sd/dstn/dr9-cosmos-subs/' self.reference_outdir = '/global/cscratch1/sd/dstn/dr9-cosmos-subs/80/' super(CosmosRepeats,self).__init__(survey_dir=survey_dir, outdir=outdir, subsection=subsection, brick=brick) self.total_sets = ['80', '81', '82', '83', '84', '85', '86', '87', '88', '89'] self.set_bricklist() #self.maskdir = self.find_file('masks') self.startid = startid def get_legacypipe_ver(self): #docker version used in this cosmos repeats run, documentation purpose return 'legacysurvey/legacypipe:DR9.7.1' def set_bricklist(self): fns = glob(os.path.join(self.reference_outdir,'coadd','*','*')) bricklist = [] for fn in fns: bricklist.append(os.path.basename(fn)) self.bricklist = bricklist def stack_all_rs(self): startids = ['0','50','100','150','200','250','300','350','400','450','500'] for one_set in self.total_sets: print(one_set) final_tab = None for startid in startids: catalog = BaseSource(survey_dir=self.survey_dir, outdir=self.outdir,subsection = 'rs%s_cosmos%s'%(startid,one_set)) fn = catalog.find_file("processed_one") tab = Table.read(fn) tab['startid'] = np.array([startid]*len(tab),dtype=np.str) if final_tab is None: final_tab = tab else: final_tab = vstack((final_tab,tab)) #just setting 9999 as stacking 'all' catalog = BaseSource(survey_dir=self.survey_dir, outdir=self.outdir,subsection = 'rs9999_cosmos%s'%(one_set)) fn = catalog.find_file("processed_one") final_tab.write(fn,overwrite=True) print("written fn %s"%fn) def set_maskbits_cross(self,mp=None): if mp is None: print('mp needs to be set') return None subprocess.call(["mkdir","-p",self.outdir+'/maskbits']) mp.map(self._set_maskbits_cross,self.bricklist) def _set_maskbits_cross(self,brickname): maskbits_img = np.zeros((3600,3600),dtype=np.int) for one_set in self.total_sets: cosmos_catalog = LegacySimData(survey_dir=self.survey_dir,outdir=self.outdir,brick=brickname, subsection='rs%d_cosmos%s'%(self.startid,one_set)) maskbits_fn = cosmos_catalog.find_file('maskbits') maskbits = fits.getdata(maskbits_fn) maskbits_img |= maskbits hdu = fits.ImageHDU(data=maskbits_img) fn = cosmos_catalog.find_file('maskbits_cross') fn = fn.replace('.fits','_rs%d.fits'%self.startid) hdu.writeto(fn,overwrite=True) print("written %s"%fn) def mask_tractor(self,mp=None): #add an additial colum to tractor file, 'matched_cosmos': the sources that's in the common maskbits==0 footprint if mp is None: print('mp needs to be set') return None mp.map(self._mask_tractor_core,self.bricklist) return True def _mask_tractor_core(self,brickname): if brickname is None: return None cosmos_catalog = LegacySimData(survey_dir=self.survey_dir,outdir=self.outdir,brick=brickname, subsection='rs%d_cosmos80'%(self.startid)) cosmos_catalog.get_mask(brick=brickname) cosmos_catalog.get_maskbits_corss(brick=brickname,startid=self.startid) for one_set in self.total_sets: cosmos_catalog.subsection = 'rs%d_cosmos%s'%(self.startid, one_set) tractor_fn = cosmos_catalog.find_file('tractor') tractor = Table.read(tractor_fn) bx = (tractor['bx']+0.5).astype(int) by = (tractor['by']+0.5).astype(int) mask_flag = cosmos_catalog.mask[(by),(bx)] maskbits_corss_flag = cosmos_catalog.maskbits_cross[(by),(bx)] sel = (mask_flag==1) tractor['matched_cosmos'] = sel tractor['maskbits_cross'] = maskbits_corss_flag tractor.write(tractor_fn,overwrite=True) return True def match_catalog(self,mp=None,south=True): #adding additial columns to sources, showing the corresponding values in other sets if mp is None: #for debug X = (self.bricklist[0],self.total_sets[1],south) self._match_catalog_core(X) return None inputs = [] for brickname in self.bricklist: for one_set in self.total_sets: inputs.append((brickname,one_set,south)) mp.map(self._match_catalog_core,inputs) def _match_catalog_core(self,X): (brickname,set_num,south) = X self.brick = brickname self.subsection = 'rs%d_cosmos%s'%(self.startid,set_num) tractor_fn = self.find_file('tractor') T = Table.read(tractor_fn) catalog = BaseSource(filetype='tractor', survey_dir=self.survey_dir, outdir=self.outdir,subsection='rs%d_cosmos%s'%(self.startid,set_num),brick=brickname) LRG = catalog.target_selection('LRG_sv3',south=south) #lrg T['set_%s_lrg_sv3'%set_num] = LRG LRG_like = catalog.target_selection('LRG_sv3_like', south=south) T['set_%s_lrg_sv3_like'%set_num] = LRG_like for one_set in self.total_sets: #if one_set == set_num: #continue catalog_i = BaseSource(filetype='tractor', survey_dir=self.survey_dir, outdir=self.outdir,subsection='rs%d_cosmos%s'%(self.startid,one_set),brick=brickname) cat1, cat2, matched = catalog.match_catalog(catalog.source, catalog_i.source) catalog_i.source = cat2 catalog_i._construct_class() LRG = catalog_i.target_selection('LRG_sv3',south=south) T['set_%s_lrg_sv3'%one_set] = LRG T['set_%s_matched'%one_set]=matched T['set_%s_flux_g'%one_set]=cat2['flux_g'] T['set_%s_flux_r'%one_set]=cat2['flux_r'] T['set_%s_flux_z'%one_set]=cat2['flux_z'] T['set_%s_flux_w1'%one_set]=cat2['flux_w1'] T['set_%s_flux_w2'%one_set]=cat2['flux_w2'] T['set_%s_fiberflux_z'%one_set]=cat2['fiberflux_z'] T['set_%s_fiberflux_g'%one_set]=cat2['fiberflux_g'] tmp_fn = tractor_fn.replace('tractor-','tmp-tractor-') T.write(tmp_fn, overwrite=True) subprocess.call(["mv",tmp_fn,tractor_fn]) def _match_catalog_core_old(self,X): #not used now (brickname,set_num,south) = X print(X) self.brick = brickname self.subsection = 'cosmos%s'%set_num tractor_fn = self.find_file('tractor') T = Table.read(tractor_fn) catalog = BaseSource(filetype='tractor', survey_dir=self.survey_dir, outdir=self.outdir,subsection='cosmos%s'%set_num,brick=brickname) #select ELG, LRG in this set LRG = catalog.target_selection('LRG',south=south) #lrg_opt, lrg_ir, lrg_sv_opt, lrg_sv_ir T['set_%s_lrgopt'%set_num] = LRG[0] T['set_%s_lrgir'%set_num] = LRG[1] T['set_%s_lrgsvopt'%set_num] = LRG[2] T['set_%s_lrgsvir'%set_num] = LRG[3] ELG = catalog.target_selection('ELG',south=south) #svgtot, svgfib, fdrgtot, fdrgfib T['set_%s_svgtot'%set_num] = ELG[0] T['set_%s_svgfib'%set_num] = ELG[1] T['set_%s_fdrgtot'%set_num] = ELG[2] T['set_%s_fdrgfib'%set_num] = ELG[3] for one_set in self.total_sets: #if one_set == set_num: #continue catalog_i = BaseSource(filetype='tractor', survey_dir=self.survey_dir, outdir=self.outdir,subsection='cosmos%s'%one_set,brick=brickname) cat1, cat2, matched = catalog.match_catalog(catalog.source, catalog_i.source) catalog_i.source = cat2 catalog_i._construct_class() LRG_i = catalog_i.target_selection('LRG',south=south) ELG_i = catalog_i.target_selection('ELG',south=south) T['set_%s_lrgopt'%one_set] = LRG_i[0] T['set_%s_lrgir'%one_set] = LRG_i[1] T['set_%s_lrgsvopt'%one_set] = LRG_i[2] T['set_%s_lrgsvir'%one_set] = LRG_i[3] T['set_%s_svgtot'%one_set] = ELG_i[0] T['set_%s_svgfib'%one_set] = ELG_i[1] T['set_%s_fdrgtot'%one_set] = ELG_i[2] T['set_%s_fdrgfib'%one_set] = ELG_i[3] T['set_%s_matched'%one_set]=matched T['set_%s_flux_g'%one_set]=cat2['flux_g'] T['set_%s_flux_r'%one_set]=cat2['flux_r'] T['set_%s_flux_z'%one_set]=cat2['flux_z'] T['set_%s_flux_w1'%one_set]=cat2['flux_w1'] T['set_%s_flux_w2'%one_set]=cat2['flux_w2'] T['set_%s_fiberflux_z'%one_set]=cat2['fiberflux_z'] T['set_%s_fiberflux_g'%one_set]=cat2['fiberflux_g'] tmp_fn = tractor_fn.replace('tractor-','tmp-tractor-') T.write(tmp_fn, overwrite=True) subprocess.call(["mv",tmp_fn,tractor_fn])

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#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import torch.nn.functional as F from pytext.config import ConfigBase from pytext.config.component import Component, ComponentType from pytext.utils import loss_utils from pytext.utils.cuda_utils import FloatTensor from torch import nn class Loss(Component): """Base class for loss functions""" __COMPONENT_TYPE__ = ComponentType.LOSS def __init__(self, config=None, *args, **kwargs): super().__init__(config) def __call__(self, logit, targets, reduce=True): raise NotImplementedError class CrossEntropyLoss(Loss): def __init__(self, config, ignore_index=-100, weight=None, *args, **kwargs): self.ignore_index = ignore_index self.weight = weight def __call__(self, logits, targets, reduce=True): return F.cross_entropy( logits, targets, ignore_index=self.ignore_index, reduction="elementwise_mean" if reduce else "none", weight=self.weight, ) class BinaryCrossEntropyLoss(Loss): class Config(ConfigBase): reweight_negative: bool = True reduce: bool = True def __call__(self, m_out, targets, reduce=True): """ Computes 1-vs-all binary cross entropy loss for multiclass classification. """ # Converts targets to one-hot representation. Dim: [batch, n_classes] one_hot_targets = ( FloatTensor(targets.size(0), m_out.size(1)) .zero_() .scatter_(1, targets.unsqueeze(1).data, 1) ) # This weighting applies uniform class weights. # examples_per_class = one_hot_target.sum(0).clamp(min=1) # total_positive = examples_per_class.sum() # weights = total_positive.unsqueeze(0) / examples_per_class loss = F.binary_cross_entropy_with_logits( m_out, one_hot_targets, reduction="none" ) if self.config.reweight_negative: # This makes sure we have same weights for all negative classes and # single positive class. Weight is 1 for the correct class and # 1 / (n - 1) for other ones. weights = one_hot_targets + (1.0 - one_hot_targets) / max( 1, one_hot_targets.size(1) - 1.0 ) loss = loss * weights return loss.sum(1).mean() if reduce else loss.sum(1) class AUCPRHingeLoss(nn.Module, Loss): """area under the precision-recall curve loss, Reference: "Scalable Learning of Non-Decomposable Objectives", Section 5 \ TensorFlow Implementation: \ https://github.com/tensorflow/models/tree/master/research/global_objectives\ """ class Config(ConfigBase): """ Attributes: precision_range_lower (float): the lower range of precision values over which to compute AUC. Must be nonnegative, `\leq precision_range_upper`, and `leq 1.0`. precision_range_upper (float): the upper range of precision values over which to compute AUC. Must be nonnegative, `\geq precision_range_lower`, and `leq 1.0`. num_classes (int): number of classes(aka labels) num_anchors (int): The number of grid points used to approximate the Riemann sum. """ precision_range_lower: float = 0.0 precision_range_upper: float = 1.0 num_classes: int = 1 num_anchors: int = 20 def __init__(self, config, weights=None, *args, **kwargs): """Args: config: Config containing `precision_range_lower`, `precision_range_upper`, `num_classes`, `num_anchors` """ nn.Module.__init__(self) Loss.__init__(self, config) self.num_classes = self.config.num_classes self.num_anchors = self.config.num_anchors self.precision_range = ( self.config.precision_range_lower, self.config.precision_range_upper, ) # Create precision anchor values and distance between anchors. # coresponding to [alpha_t] and [delta_t] in the paper. # precision_values: 1D `Tensor` of shape [K], where `K = num_anchors` # delta: Scalar (since we use equal distance between anchors) self.precision_values, self.delta = loss_utils.range_to_anchors_and_delta( self.precision_range, self.num_anchors ) # notation is [b_k] in paper, Parameter of shape [C, K] # where `C = number of classes` `K = num_anchors` self.biases = nn.Parameter( FloatTensor(self.config.num_classes, self.config.num_anchors).zero_() ) self.lambdas = nn.Parameter( FloatTensor(self.config.num_classes, self.config.num_anchors).data.fill_( 1.0 ) ) def forward(self, logits, targets, reduce=True, size_average=True, weights=None): """ Args: logits: Variable :math:`(N, C)` where `C = number of classes` targets: Variable :math:`(N)` where each value is `0 <= targets[i] <= C-1` weights: Coefficients for the loss. Must be a `Tensor` of shape [N] or [N, C], where `N = batch_size`, `C = number of classes`. size_average (bool, optional): By default, the losses are averaged over observations for each minibatch. However, if the field sizeAverage is set to False, the losses are instead summed for each minibatch. Default: ``True`` reduce (bool, optional): By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per input/target element instead and ignores size_average. Default: True """ C = 1 if logits.dim() == 1 else logits.size(1) if self.num_classes != C: raise ValueError( "num classes is %d while logits width is %d" % (self.num_classes, C) ) labels, weights = AUCPRHingeLoss._prepare_labels_weights( logits, targets, weights=weights ) # Lagrange multipliers # Lagrange multipliers are required to be nonnegative. # Their gradient is reversed so that they are maximized # (rather than minimized) by the optimizer. # 1D `Tensor` of shape [K], where `K = num_anchors` lambdas = loss_utils.lagrange_multiplier(self.lambdas) # print("lambdas: {}".format(lambdas)) # A `Tensor` of Shape [N, C, K] hinge_loss = loss_utils.weighted_hinge_loss( labels.unsqueeze(-1), logits.unsqueeze(-1) - self.biases, positive_weights=1.0 + lambdas * (1.0 - self.precision_values), negative_weights=lambdas * self.precision_values, ) # 1D tensor of shape [C] class_priors = loss_utils.build_class_priors(labels, weights=weights) # lambda_term: Tensor[C, K] # according to paper, lambda_term = lambda * (1 - precision) * |Y^+| # where |Y^+| is number of postive examples = N * class_priors lambda_term = class_priors.unsqueeze(-1) * ( lambdas * (1.0 - self.precision_values) ) per_anchor_loss = weights.unsqueeze(-1) * hinge_loss - lambda_term # Riemann sum over anchors, and normalized by precision range # loss: Tensor[N, C] loss = per_anchor_loss.sum(2) * self.delta loss /= self.precision_range[1] - self.precision_range[0] if not reduce: return loss elif size_average: return loss.mean() else: return loss.sum() @staticmethod def _prepare_labels_weights(logits, targets, weights=None): """ Args: logits: Variable :math:`(N, C)` where `C = number of classes` targets: Variable :math:`(N)` where each value is `0 <= targets[i] <= C-1` weights: Coefficients for the loss. Must be a `Tensor` of shape [N] or [N, C], where `N = batch_size`, `C = number of classes`. Returns: labels: Tensor of shape [N, C], one-hot representation weights: Tensor of shape broadcastable to labels """ N, C = logits.size() # Converts targets to one-hot representation. Dim: [N, C] labels = FloatTensor(N, C).zero_().scatter(1, targets.unsqueeze(1).data, 1) if weights is None: weights = FloatTensor(N).data.fill_(1.0) if weights.dim() == 1: weights.unsqueeze_(-1) return labels, weights

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sds1vrk/Algo_Study

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# 단어 찾기 # 시에 쓰이지 않는 단어 찾기 import sys # sys.stdin=open("3190.txt","r") n=int(input()) node=[] content=[] for i in range(n): node.append(input()) for j in range(n-1): content.append(input()) # 오름차순으로 정렬 ==> 무조건 답은 1개이기 때문에 node.sort() content.sort() # print(node) # print(content) result="" for i in range(len(content)): if content[i]!=node[i]: # print(content[i]) result=node[i] break if result=="none": result=content[-1] print(result)

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/ijvine_ebay/ijvine_ebay_base/wizard/imports/__init__.py

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# -*- coding: utf-8 -*- ############################################################################## # Copyright (c) 2021-Present IjVine Corporation (<https://ijvine.com/>) ############################################################################## from . import import_operation from . import import_attribute from . import import_attribute_value from . import import_category from . import import_order from . import import_partner from . import import_template from . import import_product

[ "komolafetosin@gmail.com" ]

komolafetosin@gmail.com

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no_license

BerilBBJ/scraperwiki-scraper-vault

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65ea6a943cc348a9caf3782b900b36446f7e137d

refs/heads/master

2021-12-02T23:55:58.481210

2013-09-30T17:02:59

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import scraperwiki #!/usr/bin/python # -*- coding: utf-8 -*- import re import glob import datetime import locale import sqlite3 import sys import traceback import urllib import cgi print "Content-Type: text/plain\n\n" def fOpen(filename): buff = [] f = open(filename,"r") for line in f: buff.append(line) f.close() return ''.join(buff) ################################################################################ # Main ################################################################################ deTag = re.compile('<.*?>') # 一時格納用のレコード配列 dataRows = [] # ファイル名 html =scraperwiki.scrape('http://line-friends.com/category/woman/page/') # 必要部分を取得 posts = re.findall('<div class="postmetadata">.*?<p class="admin_del">', html, re.DOTALL) for post in posts: # 空白を除外 buff = re.sub('\t| ','',post) # レコード格納用 dr= {} # deTag.sub('置換文字列',検索対象文字列変数) # deTag部分が正規表現 # 投稿年月日を抽出 dr["y"] = str(deTag.sub('',re.search('<spanclass="date-year">.*?</span>',buff).group())) dr["m"] = str(deTag.sub('',re.search('<spanclass="date-month">.*?</span>',buff).group())).replace('月','') dr["d"] = str(deTag.sub('',re.search('<spanclass="date-day">.*?</span>',buff).group())) dr["t"] = str(deTag.sub('',re.search('<pclass="posted_time">.*?</p>',buff).group())) # 投稿内容を抽出 dr["name"] = deTag.sub('',re.search('<pclass="poster_name">.*?</p>',buff).group()) dr["age"] = deTag.sub('',re.search('<pclass="poster_age">.*?</p>',buff).group()) dr["area"] = deTag.sub('',re.search('<pclass="poster_area">.*?</p>',buff).group()) work = re.search('<pclass="poster_line">.*?readonly/></p>',buff).group() work = re.sub('.*"type="text"value="','',work) dr["ID"] = re.sub('"readonly/></p>','',work) dr["txt"] = deTag.sub('',re.search('<pclass="poster_txt"><p>.*?</p>',buff,re.DOTALL).group()) # 写真 result = re.search('<imgsrc="http://line-friends.com/uploads/.*?/>',buff) if not result is None: dr["img"] = re.sub('<imgsrc="|"/>','',result.group()) else: dr["img"] = "" dataRows.append(dr) print dr["ID"] import scraperwiki #!/usr/bin/python # -*- coding: utf-8 -*- import re import glob import datetime import locale import sqlite3 import sys import traceback import urllib import cgi print "Content-Type: text/plain\n\n" def fOpen(filename): buff = [] f = open(filename,"r") for line in f: buff.append(line) f.close() return ''.join(buff) ################################################################################ # Main ################################################################################ deTag = re.compile('<.*?>') # 一時格納用のレコード配列 dataRows = [] # ファイル名 html =scraperwiki.scrape('http://line-friends.com/category/woman/page/') # 必要部分を取得 posts = re.findall('<div class="postmetadata">.*?<p class="admin_del">', html, re.DOTALL) for post in posts: # 空白を除外 buff = re.sub('\t| ','',post) # レコード格納用 dr= {} # deTag.sub('置換文字列',検索対象文字列変数) # deTag部分が正規表現 # 投稿年月日を抽出 dr["y"] = str(deTag.sub('',re.search('<spanclass="date-year">.*?</span>',buff).group())) dr["m"] = str(deTag.sub('',re.search('<spanclass="date-month">.*?</span>',buff).group())).replace('月','') dr["d"] = str(deTag.sub('',re.search('<spanclass="date-day">.*?</span>',buff).group())) dr["t"] = str(deTag.sub('',re.search('<pclass="posted_time">.*?</p>',buff).group())) # 投稿内容を抽出 dr["name"] = deTag.sub('',re.search('<pclass="poster_name">.*?</p>',buff).group()) dr["age"] = deTag.sub('',re.search('<pclass="poster_age">.*?</p>',buff).group()) dr["area"] = deTag.sub('',re.search('<pclass="poster_area">.*?</p>',buff).group()) work = re.search('<pclass="poster_line">.*?readonly/></p>',buff).group() work = re.sub('.*"type="text"value="','',work) dr["ID"] = re.sub('"readonly/></p>','',work) dr["txt"] = deTag.sub('',re.search('<pclass="poster_txt"><p>.*?</p>',buff,re.DOTALL).group()) # 写真 result = re.search('<imgsrc="http://line-friends.com/uploads/.*?/>',buff) if not result is None: dr["img"] = re.sub('<imgsrc="|"/>','',result.group()) else: dr["img"] = "" dataRows.append(dr) print dr["ID"] import scraperwiki #!/usr/bin/python # -*- coding: utf-8 -*- import re import glob import datetime import locale import sqlite3 import sys import traceback import urllib import cgi print "Content-Type: text/plain\n\n" def fOpen(filename): buff = [] f = open(filename,"r") for line in f: buff.append(line) f.close() return ''.join(buff) ################################################################################ # Main ################################################################################ deTag = re.compile('<.*?>') # 一時格納用のレコード配列 dataRows = [] # ファイル名 html =scraperwiki.scrape('http://line-friends.com/category/woman/page/') # 必要部分を取得 posts = re.findall('<div class="postmetadata">.*?<p class="admin_del">', html, re.DOTALL) for post in posts: # 空白を除外 buff = re.sub('\t| ','',post) # レコード格納用 dr= {} # deTag.sub('置換文字列',検索対象文字列変数) # deTag部分が正規表現 # 投稿年月日を抽出 dr["y"] = str(deTag.sub('',re.search('<spanclass="date-year">.*?</span>',buff).group())) dr["m"] = str(deTag.sub('',re.search('<spanclass="date-month">.*?</span>',buff).group())).replace('月','') dr["d"] = str(deTag.sub('',re.search('<spanclass="date-day">.*?</span>',buff).group())) dr["t"] = str(deTag.sub('',re.search('<pclass="posted_time">.*?</p>',buff).group())) # 投稿内容を抽出 dr["name"] = deTag.sub('',re.search('<pclass="poster_name">.*?</p>',buff).group()) dr["age"] = deTag.sub('',re.search('<pclass="poster_age">.*?</p>',buff).group()) dr["area"] = deTag.sub('',re.search('<pclass="poster_area">.*?</p>',buff).group()) work = re.search('<pclass="poster_line">.*?readonly/></p>',buff).group() work = re.sub('.*"type="text"value="','',work) dr["ID"] = re.sub('"readonly/></p>','',work) dr["txt"] = deTag.sub('',re.search('<pclass="poster_txt"><p>.*?</p>',buff,re.DOTALL).group()) # 写真 result = re.search('<imgsrc="http://line-friends.com/uploads/.*?/>',buff) if not result is None: dr["img"] = re.sub('<imgsrc="|"/>','',result.group()) else: dr["img"] = "" dataRows.append(dr) print dr["ID"] import scraperwiki #!/usr/bin/python # -*- coding: utf-8 -*- import re import glob import datetime import locale import sqlite3 import sys import traceback import urllib import cgi print "Content-Type: text/plain\n\n" def fOpen(filename): buff = [] f = open(filename,"r") for line in f: buff.append(line) f.close() return ''.join(buff) ################################################################################ # Main ################################################################################ deTag = re.compile('<.*?>') # 一時格納用のレコード配列 dataRows = [] # ファイル名 html =scraperwiki.scrape('http://line-friends.com/category/woman/page/') # 必要部分を取得 posts = re.findall('<div class="postmetadata">.*?<p class="admin_del">', html, re.DOTALL) for post in posts: # 空白を除外 buff = re.sub('\t| ','',post) # レコード格納用 dr= {} # deTag.sub('置換文字列',検索対象文字列変数) # deTag部分が正規表現 # 投稿年月日を抽出 dr["y"] = str(deTag.sub('',re.search('<spanclass="date-year">.*?</span>',buff).group())) dr["m"] = str(deTag.sub('',re.search('<spanclass="date-month">.*?</span>',buff).group())).replace('月','') dr["d"] = str(deTag.sub('',re.search('<spanclass="date-day">.*?</span>',buff).group())) dr["t"] = str(deTag.sub('',re.search('<pclass="posted_time">.*?</p>',buff).group())) # 投稿内容を抽出 dr["name"] = deTag.sub('',re.search('<pclass="poster_name">.*?</p>',buff).group()) dr["age"] = deTag.sub('',re.search('<pclass="poster_age">.*?</p>',buff).group()) dr["area"] = deTag.sub('',re.search('<pclass="poster_area">.*?</p>',buff).group()) work = re.search('<pclass="poster_line">.*?readonly/></p>',buff).group() work = re.sub('.*"type="text"value="','',work) dr["ID"] = re.sub('"readonly/></p>','',work) dr["txt"] = deTag.sub('',re.search('<pclass="poster_txt"><p>.*?</p>',buff,re.DOTALL).group()) # 写真 result = re.search('<imgsrc="http://line-friends.com/uploads/.*?/>',buff) if not result is None: dr["img"] = re.sub('<imgsrc="|"/>','',result.group()) else: dr["img"] = "" dataRows.append(dr) print dr["ID"]

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# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...file_utils import _LazyModule, is_torch_available, is_vision_available _import_structure = { "configuration_poolformer": ["POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PoolFormerConfig"], } if is_vision_available(): _import_structure["feature_extraction_poolformer"] = ["PoolFormerFeatureExtractor"] if is_torch_available(): _import_structure["modeling_poolformer"] = [ "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "PoolFormerForImageClassification", "PoolFormerModel", "PoolFormerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_poolformer import POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, PoolFormerConfig if is_vision_available(): from .feature_extraction_poolformer import PoolFormerFeatureExtractor if is_torch_available(): from .modeling_poolformer import ( POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, PoolFormerForImageClassification, PoolFormerModel, PoolFormerPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

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wangjiangben@huawei.com

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/testlints/test_lint_ext_ian_space_dns_name.py

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846468230/Plint

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c7e7ca27e5d04bbaa4e7ad71d8e86ec5c9388987

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import sys sys.path.append("..") from lints import base from lints import lint_ext_ian_space_dns_name import unittest import os from cryptography import x509 from cryptography.hazmat.backends import default_backend class TestIANEmptyDNS(unittest.TestCase): '''test lint_ext_ian_space_dns_name.py''' def test_IANEmptyDNS(self): certPath ='..\\testCerts\\IANEmptyDNS.pem' lint_ext_ian_space_dns_name.init() with open(certPath, "rb") as f: cert = x509.load_pem_x509_certificate(f.read(), default_backend()) out = base.Lints["e_ext_ian_space_dns_name"].Execute(cert) self.assertEqual(base.LintStatus.Error,out.Status) def test_IANNotEmptyDNS(self): certPath ='..\\testCerts\\SANNoEntries.pem' lint_ext_ian_space_dns_name.init() with open(certPath, "rb") as f: cert = x509.load_pem_x509_certificate(f.read(), default_backend()) out = base.Lints["e_ext_ian_space_dns_name"].Execute(cert) self.assertEqual(base.LintStatus.Pass,out.Status) if __name__=="__main__": unittest.main(verbosity=2)

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846468230@qq.com

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/appsflyer_processor.py

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import pandas as pd import numpy as np from datetime import date from pathlib import Path from typing import Optional, Dict class AppsFlyerProcessor: source_directory_path: Path platform_directory_map: Dict[str, str] processed_data: Optional[pd.DataFrame]=None def __init__(self, source_directory_path: Path, platform_directory_map: Dict[str, str]): self.source_directory_path = source_directory_path self.platform_directory_map = platform_directory_map def process(self): processed_data = pd.DataFrame() for platform, app_id in self.platform_directory_map.items(): files_path = self.source_directory_path / app_id for path in files_path.glob('*.csv'): file_name = path.absolute() df = pd.read_csv(file_name) day_list = [ x for x in df.columns if x not in ('Cohort Day', 'Media Source', 'Ltv Country', 'Campaign Id', 'Users', 'Cost', 'Average eCPI', 'Users') ] df_final = pd.DataFrame() for i in day_list: event_day = i.split(' ')[-1] if event_day == 'partial': event_day = i.split(' ')[-3] df_temp = df[['Cohort Day', 'Media Source', 'Ltv Country', 'Campaign Id']] # Ensure Campaign Id can be read as a string df_temp['Campaign Id'] = df_temp['Campaign Id'].astype(str) df_temp['Campaign Id'] = '"' + df_temp['Campaign Id'] + '"' df_temp['event_day'] = event_day df_temp['cohort_revenue'] = df[[i]] df_temp.cohort_revenue = df_temp.cohort_revenue.apply(lambda s: float(s.split('/')[0]) / float(s.split('/')[1]) if isinstance(s, str) and '/' in s else s) df_temp['platform'] = platform df_temp['install'] = df[['Users']] df_final = df_temp.append(df_final, sort=True) processed_data = processed_data.append(df_final, sort=True) self.processed_data = processed_data def process_old(self): today = date.today() file_name = input('Please enter file name: ') platform = '' if file_name.find('ios') != -1: platform = 'ios' elif file_name.find('android') != -1: platform = 'android' else: platform = 'error' df = pd.read_csv('{}.csv'.format(file_name)) day_list = [x for x in df.columns if x not in ('Cohort Day', 'Media Source', 'Ltv Country', 'Campaign Id', 'Users', 'Cost', 'Average eCPI','Users')] df_final = pd.DataFrame() for i in day_list: event_day = i.split(' ')[-1] df_temp = df[['Cohort Day', 'Media Source', 'Ltv Country', 'Campaign Id']] # Ensure Campaign Id can be read as a string df_temp['Campaign Id'] = df_temp['Campaign Id'].astype(str) df_temp['Campaign Id'] = '"' + df_temp['Campaign Id'] + '"' df_temp['event_day'] = event_day df_temp['cohort_revenue'] = df[[i]] df_temp['platform'] = platform df_temp['install'] = df[['Users']] df_final = df_temp.append(df_final, sort = True) df_final.to_csv('AF Total Revenue Data Lot - {}.csv'.format(today), index=False) print('Exported CSV')

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leif@leifmeyer.io

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/Algorithms/pySINDy/env/lib/python3.6/site-packages/matplotlib/backends/_gtk3_compat.py

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""" GObject compatibility loader; supports ``gi`` and ``pgi``. The binding selection rules are as follows: - if ``gi`` has already been imported, use it; else - if ``pgi`` has already been imported, use it; else - if ``gi`` can be imported, use it; else - if ``pgi`` can be imported, use it; else - error out. Thus, to force usage of PGI when both bindings are installed, import it first. """ import importlib import sys if "gi" in sys.modules: import gi elif "pgi" in sys.modules: import pgi as gi else: try: import gi except ImportError: try: import pgi as gi except ImportError: raise ImportError("The GTK3 backends require PyGObject or pgi") from .backend_cairo import cairo # noqa # The following combinations are allowed: # gi + pycairo # gi + cairocffi # pgi + cairocffi # (pgi doesn't work with pycairo) # We always try to import cairocffi first so if a check below fails it means # that cairocffi was unavailable to start with. if gi.__name__ == "pgi" and cairo.__name__ == "cairo": raise ImportError("pgi and pycairo are not compatible") if gi.__name__ == "pgi" and gi.version_info < (0, 0, 11, 2): raise ImportError("The GTK3 backends are incompatible with pgi<0.0.11.2") gi.require_version("Gtk", "3.0") globals().update( {name: importlib.import_module("{}.repository.{}".format(gi.__name__, name)) for name in ["GLib", "GObject", "Gtk", "Gdk"]})

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XihangJ/leetcode

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''' You are given two lists of closed intervals, firstList and secondList, where firstList[i] = [starti, endi] and secondList[j] = [startj, endj]. Each list of intervals is pairwise disjoint and in sorted order. Return the intersection of these two interval lists. A closed interval [a, b] (with a <= b) denotes the set of real numbers x with a <= x <= b. The intersection of two closed intervals is a set of real numbers that are either empty or represented as a closed interval. For example, the intersection of [1, 3] and [2, 4] is [2, 3]. ''' class Solution: # method 1. 2 pointers. O(m + n), S(1) def intervalIntersection(self, firstList: List[List[int]], secondList: List[List[int]]) -> List[List[int]]: if not firstList or not secondList: return [] res = [] i1 = 0 i2 = 0 while i1 < len(firstList) and i2 < len(secondList): first = firstList[i1] second = secondList[i2] left, right = max(first[0], second[0]), min(first[1], second[1]) if left <= right: res.append([left, right]) if first[1] < second[1]: i1 += 1 elif first[1] > second[1]: i2 += 1 else: i1 += 1 i2 += 1 return res

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recuraki/PythonJunkTest

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import sys from io import StringIO import unittest class TestClass(unittest.TestCase): def assertIO(self, input, output): stdout, stdin = sys.stdout, sys.stdin sys.stdout, sys.stdin = StringIO(), StringIO(input) resolve() sys.stdout.seek(0) out = sys.stdout.read()[:-1] sys.stdout, sys.stdin = stdout, stdin self.assertEqual(out, output) def test_入力例_1(self): input = """4 3 6""" output = """safe""" self.assertIO(input, output) def test_入力例_2(self): input = """6 5 1""" output = """delicious""" self.assertIO(input, output) def test_入力例_3(self): input = """3 7 12""" output = """dangerous""" self.assertIO(input, output) if __name__ == "__main__": unittest.main() def resolve(): x,a,b = map(int, input().split()) if b <= a: print("delicious") elif b <= (a+x): print("safe") else: print("dangerous")

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kanai@wide.ad.jp

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/leetcode/501~600/501. Find Mode in Binary Search Tree.py

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Parkyes90/algo

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# Definition for a binary tree node. from typing import List class TreeNode: def __init__(self, x): self.val = x self.left = None self.right = None class Solution: def findMode(self, root: TreeNode) -> List[int]: if not root: return [] values = [] values_map = {} nodes = [root] ret = [] while nodes: node = nodes.pop(0) if node.left: nodes.append(node.left) if node.right: nodes.append(node.right) if isinstance(node.val, int): values.append(node.val) if not values: return [] for value in values: values_map[value] = values_map.get(value, 0) + 1 maximum = max(values_map.values()) for key, value in values_map.items(): if maximum == value: ret.append(key) return ret if __name__ == "__main__": r = TreeNode(0) s = Solution() answer = s.findMode(r) print(answer)

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parkyes90@gmail.com

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from django.conf import settings from rest_framework_jwt.authentication import JSONWebTokenAuthentication from rest_framework_jwt.settings import api_settings from .user_utils import get_or_create_user def patch_jwt_settings(): """Patch rest_framework_jwt authentication settings from allauth""" defaults = api_settings.defaults defaults['JWT_PAYLOAD_GET_USER_ID_HANDLER'] = ( __name__ + '.get_user_id_from_payload_handler') if 'allauth.socialaccount' not in settings.INSTALLED_APPS: return from allauth.socialaccount.models import SocialApp try: app = SocialApp.objects.get(provider='helsinki') except SocialApp.DoesNotExist: return defaults['JWT_SECRET_KEY'] = app.secret defaults['JWT_AUDIENCE'] = app.client_id # Disable automatic settings patching for now because it breaks Travis. # patch_jwt_settings() class JWTAuthentication(JSONWebTokenAuthentication): def authenticate_credentials(self, payload): return get_or_create_user(payload) def get_user_id_from_payload_handler(payload): return payload.get('sub')

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juha.yrjola@iki.fi

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/tensorflow/tensorflow-master/tensorflow/python/kernel_tests/control_flow_ops_py_test.py

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# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=g-long-lambda """Tests for tensorflow.ops.control_flow_ops.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import math import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf from tensorflow.python.framework import function from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import gen_data_flow_ops from tensorflow.python.ops import logging_ops from tensorflow.python.ops import state_ops from tensorflow.python.util import nest def check_op_order(graph): """Sanity check on the ordering of op id.""" for op in graph.get_operations(): for v in op.inputs: assert v.op._id < op._id or op.type == "Merge", ( "The id of %s must be less than the id of %s" % (v.op.name, op.name)) return True def check_consumers(graph): """Sanity check on the consumer list of the tensors.""" consumer_count = {} for op in graph.get_operations(): for v in op.inputs: cnt = consumer_count.get(v, 0) consumer_count[v] = cnt + 1 for k, v in consumer_count.items(): if len(k.consumers()) != v: return False return True def isum(s): i = tf.constant(0, name="i") c = lambda i, s: tf.less(i, 10) b = lambda i, s: [tf.add(i, 1), tf.add(i, s)] _, r_s = tf.while_loop(c, b, [i, s]) return r_s class ControlFlowTest(tf.test.TestCase): def testRefIdentity(self): with self.test_session(): v = tf.Variable(7) v = control_flow_ops._Identity(v) op = tf.assign(v, 9) v2 = control_flow_ops.with_dependencies([op], v) self.assertTrue(check_op_order(v.graph)) self.assertTrue(isinstance(v2, tf.Tensor)) tf.initialize_all_variables().run() self.assertEqual(9, v2.eval()) def testRefEnter(self): with self.test_session(): v = tf.Variable(7) enter_v = control_flow_ops._Enter(v, "foo_1", is_constant=True) nine = tf.constant(9) enter_nine = control_flow_ops.enter(nine, "foo_1") op = tf.assign(enter_v, enter_nine) v2 = control_flow_ops.with_dependencies([op], enter_v) v3 = control_flow_ops.exit(v2) tf.initialize_all_variables().run() self.assertEqual(9, v3.eval()) def testRefSwitch(self): with self.test_session(): v = tf.Variable(7) p = tf.constant(True) v1 = control_flow_ops._SwitchRefOrTensor(v.ref(), p) v2 = tf.assign(v1[1], 9) tf.initialize_all_variables().run() self.assertEqual(9, v2.eval()) def testEnterMulExit(self): with self.test_session(): data = tf.constant([1, 2, 3, 4, 5, 6], name="data") enter_data = control_flow_ops.enter(data, "foo_1", False) five = tf.constant(5) enter_five = control_flow_ops.enter(five, "foo_1", False) mul_op = tf.mul(enter_data, enter_five) exit_op = control_flow_ops.exit(mul_op) result = exit_op.eval() self.assertAllEqual(np.array([x * 5 for x in [1, 2, 3, 4, 5, 6]]), result) def testSwitchMergeIndexedSlices(self): with self.test_session(): values = tf.constant([1, 2, 3, 4, 5, 6]) indices = tf.constant([0, 2, 4, 6, 8, 10]) data = tf.IndexedSlices(values, indices) pred = tf.convert_to_tensor(True) switch_op = control_flow_ops.switch(data, pred) merge_op = control_flow_ops.merge(switch_op)[0] val = merge_op.values.eval() ind = merge_op.indices.eval() self.assertAllEqual(np.arange(1, 7), val) self.assertAllEqual(np.arange(0, 12, 2), ind) def testSwitchDeadBranch(self): with self.test_session(): data = tf.constant([1, 2, 3, 4, 5, 6], name="data") ports = tf.convert_to_tensor(True, name="ports") switch_op = control_flow_ops.switch(data, ports) dead_branch = tf.identity(switch_op[0]) with self.assertRaisesWithPredicateMatch( tf.errors.InvalidArgumentError, lambda e: "The tensor returned for" in str(e)): dead_branch.eval() def testSwitchMergeLess(self): with self.test_session(): data = tf.constant([1, 2, 3, 4, 5, 6], name="data") zero = tf.convert_to_tensor(0) one = tf.convert_to_tensor(1) less_op = tf.less(zero, one) switch_op = control_flow_ops.switch(data, less_op) merge_op = control_flow_ops.merge(switch_op)[0] result = merge_op.eval() self.assertAllEqual(np.arange(1, 7), result) def testSwitchMergeAddIdentity(self): with self.test_session(): data = tf.constant([1, 2, 3, 4, 5, 6], name="data") ports = tf.convert_to_tensor(False, name="ports") switch_op = control_flow_ops.switch(data, ports) one = tf.constant(1) add_op = tf.add(switch_op[0], one) id_op = tf.identity(switch_op[1]) merge_op = control_flow_ops.merge([add_op, id_op])[0] result = merge_op.eval() self.assertAllEqual(np.array([x + 1 for x in [1, 2, 3, 4, 5, 6]]), result) def testSwitchMergeAddMul(self): with self.test_session(): data = tf.constant([1, 2, 3, 4, 5, 6], name="data") ports = tf.convert_to_tensor(True, name="ports") switch_op = control_flow_ops.switch(data, ports) one = tf.constant(1) add_op = tf.add(switch_op[0], one) five = tf.constant(5) mul_op = tf.mul(switch_op[1], five) merge_op = control_flow_ops.merge([add_op, mul_op])[0] result = merge_op.eval() self.assertAllEqual(np.array([x * 5 for x in [1, 2, 3, 4, 5, 6]]), result) def testLoop_false(self): with self.test_session(): false = tf.convert_to_tensor(False) n = tf.constant(10) enter_false = control_flow_ops.enter(false, "foo_1", False) enter_n = control_flow_ops.enter(n, "foo_1", False) merge_n = control_flow_ops.merge([enter_n, enter_n], name="merge_n")[0] switch_n = control_flow_ops.switch(merge_n, enter_false) exit_n = control_flow_ops.exit(switch_n[0]) next_n = control_flow_ops.next_iteration(switch_n[0]) merge_n.op._update_input(1, next_n) result = exit_n.eval() self.assertAllEqual(10, result) def testLoop_1(self): with self.test_session(): zero = tf.constant(0) one = tf.constant(1) n = tf.constant(10) enter_i = control_flow_ops.enter(zero, "foo", False) enter_one = control_flow_ops.enter(one, "foo", True) enter_n = control_flow_ops.enter(n, "foo", True) with tf.device("/gpu:0"): merge_i = control_flow_ops.merge([enter_i, enter_i])[0] less_op = tf.less(merge_i, enter_n) cond_op = control_flow_ops.loop_cond(less_op) switch_i = control_flow_ops.switch(merge_i, cond_op) add_i = tf.add(switch_i[1], enter_one) next_i = control_flow_ops.next_iteration(add_i) merge_i.op._update_input(1, next_i) exit_i = control_flow_ops.exit(switch_i[0]) result = exit_i.eval() self.assertAllEqual(10, result) def testLoop_2(self): with self.test_session(): zero = tf.constant(0) one = tf.constant(1) n = tf.constant(10) enter_i = control_flow_ops.enter(zero, "foo", False) enter_one = control_flow_ops.enter(one, "foo", True) enter_n = control_flow_ops.enter(n, "foo", True) merge_i = control_flow_ops.merge([enter_i, enter_i])[0] less_op = tf.less(merge_i, enter_n) cond_op = control_flow_ops.loop_cond(less_op) switch_i = control_flow_ops.switch(merge_i, cond_op) add_i = tf.add(switch_i[1], enter_one) with tf.device("/gpu:0"): next_i = control_flow_ops.next_iteration(add_i) merge_i.op._update_input(1, next_i) exit_i = control_flow_ops.exit(switch_i[0]) result = exit_i.eval() self.assertAllEqual(10, result) def testCondBool(self): values = tf.constant(10) fn1 = lambda: tf.add(values, 1) fn2 = lambda: tf.sub(values, 1) with self.assertRaisesRegexp(TypeError, "must not be a Python bool"): _ = tf.cond(False, fn1, fn2) def testCondIndexedSlices(self): with self.test_session(): values = tf.constant(10) indices = tf.constant(0) x = tf.IndexedSlices(values, indices) pred = tf.less(1, 2) fn1 = lambda: tf.IndexedSlices(tf.add(x.values, 1), indices) fn2 = lambda: tf.IndexedSlices(tf.sub(x.values, 1), indices) r = tf.cond(pred, fn1, fn2) val = r.values.eval() ind = r.indices.eval() self.assertTrue(check_op_order(x.values.graph)) self.assertAllEqual(11, val) self.assertAllEqual(0, ind) def testCondIndexedSlicesDifferentTypes(self): with self.test_session(): values = tf.constant(10) i_32 = tf.convert_to_tensor(0, name="one", dtype=tf.int32) i_64 = tf.convert_to_tensor(0, name="one", dtype=tf.int64) x = tf.IndexedSlices(values, i_32) pred = tf.less(1, 2) fn1 = lambda: tf.IndexedSlices(tf.add(x.values, 1), i_32) fn2 = lambda: tf.IndexedSlices(tf.sub(x.values, 1), i_64) r = tf.cond(pred, fn1, fn2) val = r.values.eval() ind = r.indices.eval() self.assertTrue(check_op_order(x.values.graph)) self.assertAllEqual(11, val) self.assertAllEqual(0, ind) self.assertTrue(ind.dtype == np.int64) def testCondColocation(self): with self.test_session(use_gpu=True): with tf.device("/cpu:0"): v = tf.Variable(7.0) x = tf.constant(10.0) pred = tf.less(1.0, 2.0) fn1 = lambda: tf.add(v, 1.0) fn2 = lambda: tf.sub(x, 1.0) r = tf.cond(pred, fn1, fn2) for op in x.graph.get_operations(): if op.name == "cond/Add/Switch": self.assertDeviceEqual(op.device, "/cpu:0") def _testCond_1(self, use_gpu): with self.test_session(use_gpu=use_gpu): x = tf.constant(10) pred = tf.less(1, 2) fn1 = lambda: tf.add(x, 1) fn2 = lambda: tf.sub(x, 1) r = tf.cond(pred, fn1, fn2) result = r.eval() self.assertTrue(check_op_order(x.graph)) self.assertAllEqual(11, result) def testCond_1(self): self._testCond_1(use_gpu=False) self._testCond_1(use_gpu=True) def testCond_2(self): with self.test_session(): x = tf.constant(10) r = tf.cond(tf.less(1, 0), lambda: tf.add(x, 1), lambda: tf.sub(x, 1)) result = r.eval() self.assertTrue(check_op_order(x.graph)) self.assertAllEqual(9, result) def testCond_3(self): with self.test_session(): x = tf.constant(10) pred = tf.less(1, 2) fn1 = lambda: tf.add(x, 1) fn2 = lambda: tf.sub(x, 1) fn3 = lambda: tf.add(tf.cond(pred, fn1, fn2), 1) r = tf.cond(pred, fn3, fn2) result = r.eval() self.assertTrue(check_op_order(x.graph)) self.assertAllEqual(12, result) def testCond_4(self): with self.test_session(): v1 = tf.Variable(7) v2 = tf.Variable(7) v3 = tf.Variable(7) age = tf.constant(3) max_age = tf.constant(2) pred = tf.greater(age, max_age) fn1 = lambda: [tf.assign(v1, 1).op, tf.assign(v2, 2).op] fn2 = lambda: [tf.assign(v3, 3).op, tf.constant(10).op] r = tf.cond(pred, fn1, fn2) tf.initialize_all_variables().run() self.assertEqual(len(r), 2) result = r[1].eval() self.assertTrue(check_op_order(age.graph)) self.assertAllEqual(True, result) self.assertAllEqual(7, v1.eval()) self.assertAllEqual(2, v2.eval()) self.assertAllEqual(7, v3.eval()) def testCond_5(self): with self.test_session(): alive = tf.constant(True, name="alive") count = tf.constant(0, name="count") def body(i): return tf.cond( alive, lambda: [tf.less(i, 3), tf.add(count, 1)], lambda: [alive, count]) for i in range(10): alive, count = body(i) self.assertAllEqual(4, count.eval()) def testCond_6(self): with self.test_session(): v1 = tf.Variable([7]) age = tf.constant(3) pred = tf.greater(age, 4) fn1 = lambda: age fn2 = lambda: v1 r = tf.cond(pred, fn1, fn2) tf.initialize_all_variables().run() result = r.eval() self.assertAllEqual(np.array([7]), result) def testCond_7(self): with self.test_session() as sess: x = tf.constant(10) y = tf.constant(200) pred = tf.less(1, 2) fn1 = lambda: [tf.add(x, 1), tf.add(x, 2)] fn2 = lambda: [y, y] r = tf.cond(pred, fn1, fn2) self.assertAllEqual([11, 12], sess.run(r)) def testCondRef(self): with self.test_session(): x = state_ops.variable_op([1], tf.float32) true_fn = lambda: x false_fn = lambda: tf.constant([2.0]) r = tf.cond(tf.constant(False), true_fn, false_fn) self.assertAllEqual([2.0], r.eval()) def testUninitializedRefIdentity(self): with self.test_session() as sess: v = state_ops.variable_op([1], tf.float32) inited = state_ops.is_variable_initialized(v) v_f, v_t = control_flow_ops.ref_switch(v, inited) # Both v_f and v_t are uninitialized references. However, an actual use # of the reference in the 'true' branch in the 'tf.identity' op will # not 'fire' when v is uninitialized, so this is a valid construction. # This test tests that _ref_identity allows uninitialized ref as input # so that this construction is allowed. v_f_op = gen_array_ops._ref_identity(v_f) v_t_op = gen_array_ops._ref_identity(v_t) with tf.control_dependencies([v_f_op]): assign_v = tf.assign(v, [1.0]) with tf.control_dependencies([v_t_op]): orig_v = tf.identity(v) merged_op = control_flow_ops.merge([assign_v, orig_v]) self.assertAllEqual([1.0], sess.run(merged_op.output)) def testCondGrad_1(self): with self.test_session(): x = tf.constant(10.0, name="x") pred = tf.less(1, 2) fn1 = lambda: tf.identity(x) fn2 = lambda: tf.identity(x) r = tf.cond(pred, fn1, fn2) grad = tf.gradients(r, [x])[0] result = grad.eval() self.assertAllEqual(1.0, result) def testCondGrad_2(self): with self.test_session(): c = tf.placeholder(tf.int32, shape=[]) x = tf.constant(10.0) pred = tf.less(c, 2) fn1 = lambda: tf.mul(x, 42.0) fn2 = lambda: tf.mul(x, 3.0) r = tf.cond(pred, fn1, fn2) grad = tf.gradients(r, [x])[0] self.assertAllEqual(42.0, grad.eval(feed_dict={c: 1})) self.assertAllEqual(3.0, grad.eval(feed_dict={c: 3})) def testNestedCond_Simple(self): with self.test_session(): x = tf.constant(0., name="X") y = tf.cond(tf.constant(True), lambda: x, lambda: tf.cond(x < 1., lambda: x, lambda: x)) result = tf.gradients(y, x)[0] self.assertEqual(1.0, result.eval()) z = tf.cond(tf.constant(False), lambda: x, lambda: tf.cond(x < 1., lambda: x, lambda: x)) result = tf.gradients(z, x)[0] self.assertEqual(1.0, result.eval()) def testCondGrad_Gather(self): with self.test_session() as sess: v1 = tf.Variable([1.0, 42.0]) c = tf.placeholder(tf.int32, shape=[]) pred = tf.less(c, 2) fn1 = lambda: tf.identity(v1) fn2 = lambda: tf.gather(v1, [1, 1]) r = tf.cond(pred, fn1, fn2) grad = tf.gradients(r, [v1])[0] tf.initialize_all_variables().run() # Should just be [1, 1], but possibly a sparse representation gv, gi = sess.run([grad.values, grad.indices], feed_dict={c: 1}) dense_gv = [sum([y for (x, y) in zip(gi, gv) if x == i]) for i in range(2) ] self.assertAllEqual(dense_gv, [1.0, 1.0]) # Should be [0, 2], as the else forwards v1[1] twice gv, gi = sess.run([grad.values, grad.indices], feed_dict={c: 3}) dense_gv = [sum([y for (x, y) in zip(gi, gv) if x == i]) for i in range(2) ] self.assertAllEqual(dense_gv, [0.0, 2.0]) # Microbenchmark: 10,000 iterations took 0.21s. def testWhile_1(self): with self.test_session(): n = tf.constant(0) c = lambda x: tf.less(x, 10000) b = lambda x: tf.add(x, 1) r = tf.while_loop(c, b, [n], parallel_iterations=20) self.assertEqual(10000, r.eval()) def testWhileWithRefs_1(self): with self.test_session() as sess: x = tf.Variable(0).ref() i = tf.constant(0) c = lambda i, x: tf.less(i, 100) self.assertEqual(x.dtype, tf.int32_ref) def b(i, x): self.assertEqual(x.dtype, tf.int32_ref) return (i+1, gen_array_ops._ref_identity(x)) r = tf.while_loop(c, b, [i, x], parallel_iterations=5) tf.initialize_all_variables().run() self.assertEqual(r[0].dtype, tf.int32) self.assertEqual(r[1].dtype, tf.int32_ref) value_i, value_x = sess.run(r) self.assertEqual(100, value_i) self.assertEqual(0, value_x) def testWhile_2(self): with self.test_session(): s = tf.constant(0) r = isum(s) self.assertAllEqual(45, r.eval()) # Have more than 10 parallel iterations and hence exercise k-bound # most of the time. def testWhile_3(self): with self.test_session(): def compute(i, m, c, o): m, c = [tf.add(m, 1), tf.add(c, 1)] o = tf.add(o, m) o = tf.add(o, c) i = tf.add(i, 1) return [i, m, c, o] i = tf.convert_to_tensor(0) m = tf.convert_to_tensor(0) c = tf.convert_to_tensor(0) o = tf.convert_to_tensor(0) d = tf.convert_to_tensor(100) r = tf.while_loop( lambda i, m, c, o: tf.less(i, d), compute, [i, m, c, o]) result = r[3].eval() self.assertTrue(check_op_order(i.graph)) self.assertAllEqual(10100, result) def testWhile_4(self): with self.test_session(): def compute(i, m, c, o): m, c = [tf.gather(x, i), tf.gather(x, i)] o = tf.add(o, m) o = tf.add(o, c) i = tf.add(i, 1) return [i, m, c, o] i = tf.convert_to_tensor(0) m = tf.convert_to_tensor(0) c = tf.convert_to_tensor(0) o = tf.convert_to_tensor(0) x = tf.convert_to_tensor([1, 2, 3, 4, 5, 6]) s = tf.size(x) r = tf.while_loop( lambda i, m, c, o: tf.less(i, s), compute, [i, m, c, o]) result = r[3].eval() self.assertTrue(check_op_order(i.graph)) self.assertAllEqual(42, result) def testWhile_5(self): with self.test_session(): def compute(i, c, o): c = tf.slice(x, tf.expand_dims(i, 0), [1]) o = tf.concat(0, [o, c]) i = tf.add(i, 1) return [i, c, o] i = tf.convert_to_tensor(0) c = tf.convert_to_tensor(0) o = tf.convert_to_tensor([0]) x = tf.convert_to_tensor([1, 2, 3, 4, 5, 6]) s = tf.size(x) r = tf.while_loop( lambda i, c, o: tf.less(i, s), compute, [i, c, o]) result = r[2].eval() self.assertTrue(check_op_order(i.graph)) self.assertAllEqual(np.array([0, 1, 2, 3, 4, 5, 6]), result) def _testWhile_Gpu_1(self, use_gpu): with self.test_session(use_gpu=use_gpu): n = tf.constant(1.0) c = lambda x: tf.less(x, 10.0) b = lambda x: tf.add(x, 1.0) r = tf.while_loop(c, b, [n]) self.assertAllClose(10.0, r.eval()) def testWhile_Gpu_1(self): self._testWhile_Gpu_1(use_gpu=False) self._testWhile_Gpu_1(use_gpu=True) def _testWhile_Gpu_2(self, use_gpu): with self.test_session(use_gpu=use_gpu): n = tf.constant(1.0) c = lambda x: tf.less(x, 10.0) def b(x): with tf.device("/cpu:0"): return tf.add(x, 1.0) r = tf.while_loop(c, b, [n]) self.assertAllClose(10.0, r.eval()) def testWhile_Gpu_2(self): self._testWhile_Gpu_1(use_gpu=False) self._testWhile_Gpu_1(use_gpu=True) def testWhileShape(self): with self.test_session(): i = tf.constant(0) m = tf.ones([2, 2]) c = lambda i, j: tf.less(i, 2) def _b(i, j): new_i = tf.add(i, 1) new_j = tf.tile(j, [2, 2]) return [new_i, new_j] r = tf.while_loop(c, _b, [i, m]) r = r[1] * tf.ones([8, 8]) self.assertAllEqual(np.ones((8, 8)), r.eval()) def testWhileWithNonTensorInput_Scalar(self): with self.test_session(): n = 0 c = lambda x: x < 10000 b = lambda x: x + 1 r = tf.while_loop(c, b, [n], parallel_iterations=20) self.assertEqual(10000, r.eval()) def testWhileWithNonTensorInput_Vector(self): with self.test_session(): n = np.array([0]) # Note, [0] would not work here; that is a list c = lambda x: x[0] < 10000 b = lambda x: tf.pack([x[0] + 1]) r = tf.while_loop(c, b, [n], parallel_iterations=20) self.assertEqual([10000], r.eval()) def testWhileShapeInference(self): with self.test_session(): i = tf.constant(0) m = tf.ones([2, 2]) c = lambda i, j: tf.less(i, 2) def _b(i, j): new_i = tf.add(i, 1) new_j = tf.concat(0, [j, j]) return [new_i, new_j] r = tf.while_loop(c, _b, [i, m]) self.assertTrue(r[1].get_shape()[0].value is None) self.assertEqual(r[1].get_shape()[1], tf.Dimension(2)) def _testNestedWhile_1(self, use_gpu): with self.test_session(use_gpu=use_gpu): n = tf.constant(0) def cpu_sum(s): c = lambda i, s: tf.less(i, 10) def b(i, s): i1 = tf.add(i, 1) with tf.device("/cpu:0"): s1 = tf.add(i, s) return i1, s1 _, r_s = tf.while_loop(c, b, [n, s]) return r_s c = lambda x: tf.less(x, 200) b = lambda x: tf.add(x, cpu_sum(n)) r = tf.while_loop(c, b, [n]) self.assertEqual(225, r.eval()) def testNestedWhile_1(self): self._testNestedWhile_1(use_gpu=False) self._testNestedWhile_1(use_gpu=True) def testWhileWithControl_1(self): with self.test_session(): n = tf.constant(0) r = tf.constant(0) condition = lambda n_, r_: tf.less(n_, 10) def body(n_, r_): n_ = tf.add(n_, 1) with r_.graph.control_dependencies([r_]): r_ = tf.constant(12) return [n_, r_] res = tf.while_loop(condition, body, [n, r], parallel_iterations=1) self.assertAllEqual(12, res[1].eval()) def testWhileWithControl_2(self): with self.test_session(): r = tf.constant(0) condition = lambda r_: tf.less(r_, 10) def body(r_): with r_.graph.control_dependencies([r_]): r_ = tf.constant(12) return [r_] res = tf.while_loop(condition, body, [r], parallel_iterations=1) self.assertAllEqual(12, res.eval()) def testWhileWithControl_3(self): with self.test_session() as sess: b = tf.placeholder(tf.bool) c = tf.constant(0) with tf.control_dependencies([b]): c = tf.while_loop(lambda x: x < 10, lambda x: x + 1, [c]) self.assertEqual(10, sess.run(c, {b: True})) def testWhileWithControl_4(self): with self.test_session() as sess: b = tf.placeholder(tf.bool) c = tf.constant(1) x0 = tf.constant(0) with tf.control_dependencies([b]): r = tf.while_loop(lambda x: x < 10, lambda x: x + tf.identity(c), [x0]) self.assertEqual(10, sess.run(r, {b: True})) def testCondWhile_1(self): with self.test_session(): n = tf.convert_to_tensor(0, name="n") c = lambda x: tf.less(x, 10) b = lambda x: tf.add(x, 1) r = tf.cond(tf.less(0, 1), lambda: tf.while_loop(c, b, [n]), lambda: n) self.assertAllEqual(10, r.eval()) def testCondWhile_2(self): with self.test_session(): n = tf.convert_to_tensor(0) c = lambda x: tf.less(x, 10) b = lambda x: tf.add(x, 1) r = tf.cond(tf.less(1, 0), lambda: tf.add(n, 1), lambda: tf.while_loop(c, b, [n])) self.assertAllEqual(10, r.eval()) def testWhileCond_1(self): with self.test_session(): i = tf.convert_to_tensor(0, name="i") n = tf.convert_to_tensor(10, name="n") one = tf.convert_to_tensor(1, name="one") c = lambda x: tf.less(x, n) # pylint: disable=undefined-variable # for OSS build b = lambda x: tf.cond( tf.constant(True), lambda: tf.add(x, one), lambda: tf.sub(x, one)) # pylint: enable=undefined-variable r = tf.while_loop(c, b, [i]) self.assertAllEqual(10, r.eval()) def testWhileCond_2(self): with self.test_session(): n = tf.convert_to_tensor(0, name="n") c = lambda x: tf.less(x, 10) b = lambda x: tf.cond(tf.constant(True), lambda: tf.add(x, 1), lambda: n) r = tf.while_loop(c, b, [n]) self.assertAllEqual(10, r.eval()) def testWhileCond_3(self): with self.test_session(): n = tf.convert_to_tensor(0) c = lambda x: tf.less(x, 10) # pylint: disable=undefined-variable # for OSS build b = lambda x: tf.cond(tf.less(0, 1), lambda: tf.add(x, 1), lambda: tf.sub(x, 1)) # pylint: enable=undefined-variable r = tf.while_loop(c, b, [n]) self.assertAllEqual(10, r.eval()) # NOTE: It is ok to have parallel_iterations > 1 def testWhileUpdateVariable_1(self): with self.test_session(): select = tf.Variable([3.0, 4.0, 5.0]) n = tf.constant(0) def loop_iterator(j): return tf.less(j, 3) def loop_body(j): ns = tf.scatter_update(select, j, 10.0) nj = tf.add(j, 1) op = control_flow_ops.group(ns) nj = control_flow_ops.with_dependencies([op], nj) return [nj] r = tf.while_loop(loop_iterator, loop_body, [n], parallel_iterations=1) self.assertTrue(check_op_order(n.graph)) tf.initialize_all_variables().run() self.assertEqual(3, r.eval()) result = select.eval() self.assertAllClose(np.array([10.0, 10.0, 10.0]), result) def testWhileUpdateVariable_2(self): with self.test_session(): select1 = tf.Variable([3.0, 4.0, 5.0]) select2 = tf.Variable([3.0, 4.0, 5.0]) n = tf.constant(0) def loop_iterator(j): return tf.less(j, 3) def loop_body(j): ns1 = tf.scatter_update(select1, j, 10.0) ns2 = tf.scatter_update(select2, j, 10.0) nj = tf.add(j, 1) op = control_flow_ops.group(ns1, ns2) nj = control_flow_ops.with_dependencies([op], nj) return [nj] r = tf.while_loop(loop_iterator, loop_body, [n], parallel_iterations=1) self.assertTrue(check_op_order(n.graph)) tf.initialize_all_variables().run() self.assertEqual(3, r.eval()) result1 = select1.eval() self.assertAllClose(np.array([10.0, 10.0, 10.0]), result1) result2 = select2.eval() self.assertAllClose(np.array([10.0, 10.0, 10.0]), result2) def testWhileUpdateVariable_3(self): with self.test_session(): select = tf.Variable([3.0, 4.0, 5.0]) n = tf.constant(0) def loop_iterator(j, _): return tf.less(j, 3) def loop_body(j, _): ns = tf.scatter_update(select, j, 10.0) nj = tf.add(j, 1) return [nj, ns] r = tf.while_loop(loop_iterator, loop_body, [n, tf.identity(select)], parallel_iterations=1) tf.initialize_all_variables().run() result = r[1].eval() self.assertTrue(check_op_order(n.graph)) self.assertAllClose(np.array([10.0, 10.0, 10.0]), result) # b/24814703 def testWhileUpdateVariable_4(self): with self.test_session(): var_a = tf.Variable(0, name="a") var_b = tf.Variable(0, name="b") tf.initialize_all_variables().run() c = tf.constant(0, name="c") asn1 = tf.assign_add(var_a, 1, name="a_add") # Loop condition def pred(i): return tf.less(i, 10) # Loop body def loop_body(i): asn2 = tf.assign_add(var_b, asn1, name="b_add") with tf.control_dependencies([asn2]): ni = tf.add(i, 1, name="i_add") return ni lpa = tf.while_loop(pred, loop_body, [c], parallel_iterations=1) self.assertEqual(0, var_b.eval()) lpa.eval() # Run the loop self.assertEqual(10, var_b.eval()) # b/24736492 def testWhileUpdateVariable_5(self): with self.test_session(): # Create some variables. var_a = tf.Variable(0, name="a") var_b = tf.Variable(0, name="b") tf.initialize_all_variables().run() # Change condition to check var_b def pred(_): return tf.less(var_b, 10) # Change body to increment var_b def loop_body(i): asn1 = tf.assign_add(var_a, tf.constant(1), name="a_add") asn2 = tf.assign_add(var_b, tf.constant(1), name="b_add") with tf.control_dependencies([asn1, asn2]): inc_b = tf.identity(var_b) return inc_b lpa = tf.while_loop(pred, loop_body, [var_b], 1, name="loop") self.assertEqual(0, var_b.eval()) lpa.eval() # Run the loop self.assertEqual(10, var_a.eval()) self.assertEqual(10, var_b.eval()) # b/24814668 def testWhileUpdateVariable_6(self): with self.test_session(): # Create some variables. var_a = tf.Variable(0, name="a") var_b = tf.Variable(0, name="b") c = tf.constant(0) tf.initialize_all_variables().run() # Loop condition def pred(i): return tf.less(i, 10) # Loop body def loop_body(i): asn1 = tf.assign_add(var_a, 1, name="a_add") with tf.control_dependencies([asn1]): asn2 = tf.assign_add(var_b, var_a, name="b_add") with tf.control_dependencies([asn2]): ni = tf.add(i, 1, name="i_add") return ni lpa = tf.while_loop(pred, loop_body, [c], 1, name="loop") self.assertEqual(0, var_b.eval()) lpa.eval() # Run the loop self.assertEqual(55, var_b.eval()) self.assertEqual(10, var_a.eval()) def testWhileQueue_1(self): with self.test_session(): q = tf.FIFOQueue(-1, tf.int32) i = tf.constant(0) def c(i): return tf.less(i, 10) def b(i): ni = tf.add(i, 1) ni = control_flow_ops.with_dependencies([q.enqueue((i,))], ni) return ni r = tf.while_loop(c, b, [i], parallel_iterations=1) self.assertEqual([10], r.eval()) for i in xrange(10): self.assertEqual([i], q.dequeue().eval()) def testWhileStack_1(self): with self.test_session(): s = gen_data_flow_ops._stack(tf.int32, stack_name="foo") i = tf.constant(0) def c(i): return tf.less(i, 10) def b(i): ni = tf.add(i, 1) ni = control_flow_ops.with_dependencies( [gen_data_flow_ops._stack_push(s, i)], ni) return ni r = tf.while_loop(c, b, [i], parallel_iterations=1) x = tf.constant(0) def c1(i, _): return tf.greater(i, 0) def b1(i, x): ni = tf.sub(i, 1) nx = x + gen_data_flow_ops._stack_pop(s, tf.int32) return [ni, nx] _, rx = tf.while_loop(c1, b1, [r, x], parallel_iterations=1) self.assertEqual(45, rx.eval()) def _testWhileGrad_ColocateGradients(self, colocate): with self.test_session(graph=tf.Graph()) as sess: v = tf.constant(2.0, name="v") c = lambda v: tf.less(v, 100.0) def b(x): with tf.device("/gpu:0"): return tf.square(x) loop = tf.while_loop(c, b, [v], parallel_iterations=1) r = tf.gradients(loop, v, colocate_gradients_with_ops=colocate)[0] r_ops = r.graph.get_operations() r_devices = [(op.name, op.device.lower()) for op in r_ops] self.assertTrue(any("Square" in op.name for op in r_ops)) for (name, dev) in r_devices: if not colocate and name.endswith("Square"): # Only forward graph contain gpu in Square device self.assertTrue("gpu:0" in dev) elif colocate and "Square" in name: # Forward and backward graphs contain gpu in Square/Square_grad devices self.assertTrue("gpu:0" in dev) else: self.assertFalse("gpu:0" in dev) self.assertAllClose(1024.0, sess.run(r)) def testWhileGrad_ColocateGradients(self): self._testWhileGrad_ColocateGradients(colocate=False) self._testWhileGrad_ColocateGradients(colocate=True) def testWhileGrad_Square(self): with self.test_session(): v = tf.constant(2.0, name="v") c = lambda v: tf.less(v, 100.0) b = tf.square r = tf.while_loop(c, b, [v], parallel_iterations=1) r = control_flow_ops.cond(tf.less(1, 2), lambda: r, lambda: v) r = tf.gradients(r, v)[0] self.assertAllClose(1024.0, r.eval()) def testWhileGrad_Shape(self): with self.test_session(): x = tf.placeholder(tf.float32, shape=[None]) v = tf.constant([2.0], name="v") n = tf.constant(0, name="n") c = lambda i, v: tf.less(i, 5) b = lambda i, v: [i + 1, tf.mul(x, v)] r = tf.while_loop(c, b, [n, v], parallel_iterations=1) r = tf.gradients(r[1], x)[0] self.assertEqual([None], r.get_shape().as_list()) self.assertAllClose([810.0, 2560.0], r.eval(feed_dict={x: [3.0, 4.0]})) def testWhileGrad_BaseShape(self): with self.test_session() as sess: x = tf.placeholder(tf.float32, [None]) v0 = tf.constant([2.0, 2.0], name="v") c = lambda v: tf.constant(False) b = lambda v: tf.mul(v, x) r = tf.while_loop(c, b, [v0]) y = tf.square(x) r = tf.gradients([r, y], x)[0] self.assertAllClose([2.0, 4.0], sess.run(r, feed_dict={x: [1.0, 2.0]})) def testWhileGrad_MultipleUses(self): with self.test_session(): v = tf.constant(2.0, name="v") c = lambda v: tf.less(v, 100.0) b = tf.square r = tf.while_loop(c, b, [v], parallel_iterations=1) r = tf.mul(r, r) r = tf.gradients(r, v)[0] self.assertEqual(524288.0, r.eval()) def testWhileGrad_LoopAdd(self): with self.test_session(): v = tf.constant(2.0, name="v") c = lambda v: tf.less(v, 100.0) b = tf.square r = tf.while_loop(c, b, [v], parallel_iterations=1) r = tf.add(r, r) r = tf.gradients(r, v)[0] self.assertAllClose(2048.0, r.eval()) def _testWhileGrad_Mul(self, use_gpu, p_iters): with self.test_session(use_gpu=use_gpu) as sess: a = tf.constant(3.0, name="a") v = tf.constant(2.0, name="v") c = lambda v: tf.less(v, 100.0) b = lambda v: tf.mul(v, a) r = tf.while_loop(c, b, [v], parallel_iterations=p_iters) grad_a, grad_v = tf.gradients(r, [a, v]) grad_a_val, grad_v_val = sess.run([grad_a, grad_v]) self.assertAllClose(216.0, grad_a_val) self.assertAllClose(81.0, grad_v_val) def testWhileGrad_Mul(self): self._testWhileGrad_Mul(use_gpu=False, p_iters=1) self._testWhileGrad_Mul(use_gpu=False, p_iters=10) self._testWhileGrad_Mul(use_gpu=True, p_iters=1) self._testWhileGrad_Mul(use_gpu=True, p_iters=10) def testWhileGrad_Variable(self): with self.test_session(): a = tf.Variable(3.0) v = tf.constant(2.0, name="v") c = lambda v: tf.less(v, 100.0) b = lambda v: tf.mul(v, a) r = tf.while_loop(c, b, [v], parallel_iterations=1) r = tf.gradients(r, a) tf.initialize_all_variables().run() self.assertAllClose(216.0, r[0].eval()) def testWhile_NestedInput(self): with self.test_session() as sess: named = collections.namedtuple("named", ("a", "b")) loop_vars = [named(a=tf.constant(0.0), b=tf.constant(1.0)), (tf.constant(2.0), tf.constant(3.0)), tf.constant(4.0)] c = lambda lv0, _1, _2: lv0.a < 100.0 def b(lv0, lv1, lv2): lv0 = named(a=lv0.a + 1, b=lv0.b) lv1 = (lv1[0] + 1, lv1[1]) lv2 += 2 return [lv0, lv1, lv2] r = tf.while_loop(c, b, loop_vars) self.assertTrue(isinstance(r, list)) self.assertTrue(isinstance(r[0], named)) self.assertTrue(isinstance(r[1], tuple)) self.assertTrue(isinstance(r[2], tf.Tensor)) r_flattened = nest.flatten(r) self.assertEqual( [100.0, 1.0, 102.0, 3.0, 4.0 + 100*2.0], sess.run(r_flattened)) def testWhile_NestedBadArityFails(self): with self.test_session(): named = collections.namedtuple("named", ("a", "b")) loop_vars = [named(a=tf.constant(0.0), b=tf.constant(1.0)), (tf.constant(2.0), tf.constant(3.0)), tf.constant(4.0)] c = lambda lv0, _1, _2: lv0.a < 100.0 def b(lv0, lv1, _): return [lv0, lv1] with self.assertRaisesRegexp(ValueError, "the same number of elements"): tf.while_loop(c, b, loop_vars) def testWhileGrad_ys_xs(self): with self.test_session(): x = tf.constant(3.0, name="x") y = tf.constant(2.0, name="y") c = lambda x, y: tf.less(x, 100.0) def b(x, y): y1 = tf.add(x, y) x1 = tf.mul(x, y1) return x1, y1 rx, ry = tf.while_loop(c, b, [x, y], parallel_iterations=1) r = tf.gradients([rx, ry], x) self.assertAllClose(304.0, r[0].eval()) r = tf.gradients([rx, ry], y) self.assertAllClose(124.0, r[0].eval()) r = tf.gradients([rx], x) self.assertAllClose(295.0, r[0].eval()) r = tf.gradients([rx], y) self.assertAllClose(120.0, r[0].eval()) def testWhileGrad_Dependency(self): with self.test_session(): i = tf.constant(0, name="i") x = tf.constant(2.0, name="x") c = lambda i, x: tf.less(i, 10) def b(i, x): x = tf.mul(x, 2.0) i = tf.add(i, 1) return i, x ri, rx = tf.while_loop(c, b, [i, x], parallel_iterations=1) r = tf.gradients([ri, rx], x) self.assertAllClose(1024.0, r[0].eval()) r = tf.gradients([rx], x) self.assertAllClose(1024.0, r[0].eval()) def testWhileGrad_NoGradient(self): with self.test_session(): v = tf.constant(2.0, name="v") c = lambda v: tf.less(v, 100.0) b = tf.square r = tf.while_loop(c, b, [v], back_prop=False) r = tf.add(r, v) r = tf.gradients(r, v) self.assertAllClose(1.0, r[0].eval()) def testWhileGrad_NoDependency(self): with self.test_session() as sess: variable = tf.Variable(tf.ones([2, 3])) time = tf.zeros([], dtype=tf.int32) def cond(time, tensor, _): return time < 10 def body(time, tensor, _): return (time+1, tensor, tensor) loop_vars = [time, variable, variable] tensors = tf.while_loop(cond=cond, body=body, loop_vars=loop_vars) cost = tf.reduce_sum(tensors[2]) grad = tf.gradients(cost, [variable]) tf.initialize_all_variables().run() self.assertAllClose(np.ones([2, 3]), sess.run(grad[0])) def testWhileGrad_Const(self): with self.test_session() as sess: c0 = tf.constant(0.0, name="c0") c1 = tf.constant(1.0, name="c1") time = tf.constant(0, name="t") def cond(time, _): return time < 1 def body(time, tensor): return time+1, c1 loop_vars = [time, c0] tensors = tf.while_loop(cond=cond, body=body, loop_vars=loop_vars) cost = tf.reduce_sum(tensors[1]) grad = tf.gradients(cost, [c0]) self.assertAllClose(0.0, sess.run(grad[0])) def testWhileGrad_SerialTwoLoops(self): with self.test_session(): i = tf.constant(0, name="i") x = tf.constant(2.0, name="x") c = lambda i, x: tf.less(i, 5) def b(i, x): x = tf.mul(x, 2.0) i = tf.add(i, 1) return i, x _, rx = tf.while_loop(c, b, [i, x], parallel_iterations=1) _, rx = tf.while_loop(c, b, [i, rx], parallel_iterations=1) r = tf.gradients([rx], x) self.assertAllClose(1024.0, r[0].eval()) def testWhileGrad_ParallelTwoLoops(self): with self.test_session(): i = tf.constant(0, name="i") x = tf.constant(2.0, name="x") c = lambda i, x: tf.less(i, 5) def b(i, x): x = tf.mul(x, 2.0) i = tf.add(i, 1) return i, x _, r1 = tf.while_loop(c, b, [i, x], parallel_iterations=1) _, r2 = tf.while_loop(c, b, [i, x], parallel_iterations=1) rx = tf.add(r1, r2) r = tf.gradients([rx], x) self.assertAllClose(64.0, r[0].eval()) def _testNestedWhileGrad_Simple(self, use_gpu): with self.test_session(use_gpu=use_gpu): v = tf.constant(1.0) def inner_loop(s): c = lambda x: tf.less(x, 4.0) b = lambda x: tf.mul(x, 2.0) return tf.while_loop(c, b, [s]) c = lambda x: tf.less(x, 2.0) b = lambda x: tf.mul(inner_loop(x), 2.0) r = tf.while_loop(c, b, [v]) r = tf.gradients(r, v)[0] self.assertAllClose(8.0, r.eval()) def testNestedWhileGrad_Simple(self): self._testNestedWhileGrad_Simple(use_gpu=False) self._testNestedWhileGrad_Simple(use_gpu=True) def testNestedWhileGrad_SerialInner(self): with self.test_session(): v = tf.constant(1.0) def inner_loop1(s): z = tf.constant(0) c = lambda i, x: tf.less(i, 4) b = lambda i, x: [tf.add(i, 1), tf.mul(x, 2.0)] return tf.while_loop(c, b, [z, s]) def inner_loop2(s): z = tf.constant(0) c = lambda i, x: tf.less(i, 4) b = lambda i, x: [tf.add(i, 1), tf.mul(x, 2.0)] return tf.while_loop(c, b, [z, s]) c = lambda x: tf.less(x, 128.0) b = lambda x: inner_loop2(inner_loop1(x)[1])[1] r = tf.while_loop(c, b, [v]) r = tf.gradients(r, v)[0] self.assertAllClose(256.0, r.eval()) def testNestedWhileGrad_ParallelInner(self): with self.test_session(): v = tf.constant(1.0) def inner_loop1(s): z = tf.constant(0) c = lambda i, x: tf.less(i, 4) b = lambda i, x: [tf.add(i, 1), tf.mul(x, 2.0)] return tf.while_loop(c, b, [z, s]) def inner_loop2(s): z = tf.constant(0) c = lambda i, x: tf.less(i, 4) b = lambda i, x: [tf.add(i, 1), tf.mul(x, 2.0)] return tf.while_loop(c, b, [z, s]) c = lambda x: tf.less(x, 128.0) b = lambda x: tf.mul(inner_loop1(x)[1], inner_loop2(x)[1]) r = tf.while_loop(c, b, [v]) r = tf.gradients(r, v)[0] self.assertAllClose(512.0, r.eval()) def _testWhileCondGrad_Simple(self, use_gpu): with self.test_session(use_gpu=use_gpu): v = tf.convert_to_tensor(2.0, name="v") n = tf.convert_to_tensor(100.0, name="n") one = tf.convert_to_tensor(1.0, name="one") c = lambda x: tf.less(x, n) # pylint: disable=undefined-variable # for OSS build b = lambda x: control_flow_ops.cond(tf.constant(True), lambda: tf.square(x), lambda: tf.sub(x, one)) # pylint: enable=undefined-variable r = tf.while_loop(c, b, [v]) r = tf.gradients(r, v)[0] self.assertAllClose(1024.0, r.eval()) def testWhileCondGrad_Simple(self): self._testWhileCondGrad_Simple(use_gpu=False) self._testWhileCondGrad_Simple(use_gpu=True) def testWhileCondGrad_UnknownShape(self): with self.test_session() as sess: v = tf.placeholder(tf.float32) n = tf.convert_to_tensor(100.0, name="n") one = tf.convert_to_tensor(1.0, name="one") c = lambda x: tf.less(x, n) # pylint: disable=undefined-variable # for OSS build b = lambda x: control_flow_ops.cond(tf.constant(True), lambda: tf.square(x), lambda: tf.sub(x, one)) # pylint: enable=undefined-variable r = tf.while_loop(c, b, [v]) r = tf.gradients(r, v)[0] r = sess.run(r, feed_dict={v: 2.0}) self.assertAllClose(1024.0, r) def testWhileWithRefsWithGradients_1(self): with self.test_session() as sess: x = tf.Variable(0).ref() i = tf.constant(0) c = lambda i, x: tf.less(i, 10) self.assertEqual(x.dtype, tf.int32_ref) # pylint: disable=protected-access def body(i, x): self.assertEqual(x.dtype, tf.int32_ref) return [i+1, gen_array_ops._ref_identity(x)] # pylint: enable=protected-access r = tf.while_loop(c, body, [i, x], parallel_iterations=5) grad_ys = [tf.Variable(73).ref()] grad = tf.gradients([r[1]], [x], grad_ys=grad_ys) tf.initialize_all_variables().run() self.assertEqual(r[0].dtype, tf.int32) self.assertEqual(r[1].dtype, tf.int32_ref) value_i, value_x, value_x_grad = sess.run(r + grad) self.assertEqual(10, value_i) self.assertEqual(0, value_x) self.assertEqual(73, value_x_grad) def testWhileGrad_IndexedSlices(self): with self.test_session(): values = tf.constant([2.0, 4.0], name="values") indices = tf.constant([0, 3], name="indices") shape = tf.constant([10], name="dense_shape") i = tf.constant(0) x = tf.IndexedSlices(values, indices, dense_shape=shape) def c(i, _): return i < 10 def b(i, x): return [i + 1, tf.IndexedSlices(x.values * 2.0, x.indices, x.dense_shape)] _, r = tf.while_loop(c, b, [i, x]) r = tf.gradients(r.values, values)[0] self.assertAllClose(np.array([1024.0, 1024.0]), r.eval()) def testWhileGrad_SparseTensor(self): with self.test_session(): values = tf.constant([2.0, 4.0], name="values") indices = tf.constant([[0], [3]], dtype=tf.int64, name="indices") shape = tf.constant([10], dtype=tf.int64, name="dense_shape") i = tf.constant(0) x = tf.SparseTensor(indices, values, shape=shape) def c(i, _): return i < 10 def b(i, x): return [i + 1, tf.SparseTensor(x.indices, x.values * 2.0, x.shape)] _, r = tf.while_loop(c, b, [i, x]) r = tf.gradients(r.values, values)[0] self.assertAllClose(np.array([1024.0, 1024.0]), r.eval()) def testCallGradInLoop(self): with self.test_session() as sess: i0 = tf.constant(0) params = tf.constant(5.0) params_1 = tf.square(params) def c(i, _): return i < 10 def b(i, x): data = tf.constant([1.0, 2.0, 3.0]) data = tf.mul(data, params_1) x1 = x + tf.gradients(data, params)[0] return i + 1, x1 output_grad = tf.while_loop(c, b, [i0, tf.constant(0.0)]) self.assertAllClose(600.0, sess.run(output_grad)[1]) def testWhileGrad_StopGrad(self): with self.test_session(): x = tf.constant(3.0, name="x") y = tf.constant(2.0, name="y") c = lambda x, y: tf.less(x, 100.0) def b(x, y): y1 = tf.stop_gradient(tf.square(y)) x1 = tf.add(tf.square(x), y1) return x1, y1 rx, _ = tf.while_loop(c, b, [x, y]) r = tf.gradients(rx, y)[0] self.assertAllClose(0.0, r.eval()) r = tf.gradients(rx, x)[0] self.assertAllClose(156.0, r.eval()) def testWhileGradGrad(self): theta = tf.Variable(initial_value=1.) def fn(prev, x): return prev + x * theta result = tf.scan(fn, np.array([1., 2., 3.], dtype=np.float32)) grad_theta = tf.gradients(result, theta) with self.assertRaisesRegexp(TypeError, "Second-order gradient"): tf.gradients(grad_theta, theta) def testOneValueCond(self): with self.test_session(): c = tf.placeholder(tf.int32, shape=[]) one = tf.convert_to_tensor(1, name="one") two = tf.convert_to_tensor(2, name="two") p = tf.greater_equal(c, 1) i = tf.cond(p, lambda: one, lambda: two) self.assertTrue(isinstance(i, tf.Tensor)) # True case: c = 2 is >= 1 self.assertEqual([1], i.eval(feed_dict={c: 2})) # False case: c = 0 is not >= 1 self.assertEqual([2], i.eval(feed_dict={c: 0})) def testExampleCond(self): with self.test_session(): x = tf.convert_to_tensor([-2.0, 2.0], name="x") d = tf.placeholder(tf.int32, shape=[]) def l2(): return tf.sqrt(tf.reduce_sum(tf.square(x))) def l1(): return tf.reduce_sum(tf.abs(x)) i = tf.cond(tf.equal(d, 2), l2, l1) self.assertAllClose(4.0, i.eval(feed_dict={d: 1})) self.assertAllClose(2.0 * math.sqrt(2), i.eval(feed_dict={d: 2})) def testCase(self): with self.test_session(): x = tf.constant(1) y = tf.constant(2) z = tf.constant(3) f1 = lambda: tf.constant(17) f2 = lambda: tf.constant(23) f3 = lambda: tf.constant(-1) r1 = tf.case({x < y: f1, x > z: f2}, default=f3, exclusive=True) self.assertAllEqual(r1.eval(), 17) r2 = tf.case([(y > z, f1), (y > x, f2)], default=f3) self.assertAllEqual(r2.eval(), 23) # Duplicate events can happen, first one is selected r3 = tf.case([(x < y, f1), (x < y, f2)], default=f3) self.assertAllEqual(r3.eval(), 17) # Duplicate events cause an error if exclusive = True r4 = tf.case([(x < y, f1), (x < y, f2)], default=f3, exclusive=True) with self.assertRaisesOpError( "More than one condition evaluated as True but exclusive=True."): r4.eval() # Check that the default is called if none of the others are r5 = tf.case({x > y: f1}, default=f3) self.assertAllEqual(r5.eval(), -1) ran_once = [False, False, False] def break_run_twice(ix): def _break(): ran_once[ix] = True return tf.constant(ix) return _break # Should not fail - each conditional gets called exactly once # except default. Default gets called twice: once to create an # empty output and once for the actual cond switch. r6 = tf.case([(x < y, break_run_twice(0)), (x > y, break_run_twice(1))], default=lambda: tf.constant(2)) self.assertAllEqual(r6.eval(), 0) def testCaseSideEffects(self): with self.test_session() as sess: v0 = tf.Variable(-1) v1 = tf.Variable(-1) v2 = tf.Variable(-1) a = lambda: control_flow_ops.with_dependencies([tf.assign(v0, 0)], 0) b = lambda: control_flow_ops.with_dependencies([tf.assign(v1, 1)], 1) c = lambda: control_flow_ops.with_dependencies([tf.assign(v2, 2)], 2) x = tf.constant(1) y = tf.constant(2) r0 = tf.case(((x < y, a), (x > y, b)), default=c, exclusive=True) r1 = tf.case(((x > y, a), (x < y, b)), default=c, exclusive=True) r2 = tf.case(((x > y, a), (x > y, b)), default=c, exclusive=True) tf.initialize_all_variables().run() self.assertAllEqual(sess.run([v0, v1, v2]), [-1] * 3) self.assertEqual(2, r2.eval()) self.assertAllEqual(sess.run([v0, v1, v2]), [-1, -1, 2]) tf.initialize_all_variables().run() self.assertAllEqual(sess.run([v0, v1, v2]), [-1] * 3) self.assertEqual(1, r1.eval()) self.assertAllEqual(sess.run([v0, v1, v2]), [-1, 1, -1]) tf.initialize_all_variables().run() self.assertAllEqual(sess.run([v0, v1, v2]), [-1] * 3) self.assertEqual(0, r0.eval()) self.assertAllEqual(sess.run([v0, v1, v2]), [0, -1, -1]) def testOneOpCond(self): with self.test_session(): v = tf.Variable(0) c = tf.convert_to_tensor(0) one = tf.convert_to_tensor(1) two = tf.convert_to_tensor(2) p = tf.greater_equal(c, 1) def a(): return tf.assign(v, one) def b(): return tf.assign(v, two) i = tf.cond(p, a, b) self.assertTrue(isinstance(i, tf.Tensor)) tf.initialize_all_variables().run() self.assertEqual(0, v.eval()) # True case: c = 2 is >= 1, v is set to 1. self.assertEqual(1, i.eval(feed_dict={c.name: 2})) self.assertEqual(1, v.eval()) # False case: c = 0 is not >= 1, v is set to 2. self.assertEqual(2, i.eval(feed_dict={c.name: 0})) self.assertEqual(2, v.eval()) def testWithOpsDependencies(self): with self.test_session() as sess: v = tf.Variable(0.0) c = tf.constant(10) # Fetching v directly will result in an uninitialized error with self.assertRaisesOpError("Attempting to use uninitialized value"): sess.run([c, v]) # Use a control dependency to ensure init_variable is run # while asking for c real_v = control_flow_ops.with_dependencies( name="real_tensor", output_tensor=v.ref(), dependencies=[v.initializer]) c_val, real_v_val = sess.run([c, real_v]) # Ensure the result of 'real_c' is the same as 'c' self.assertAllEqual(10, c_val) # Ensure that 'v' is initialized self.assertAllClose(0.0, real_v_val) def testWithTensorDependencies(self): with self.test_session(): v = tf.Variable(0.0) c1 = tf.constant(10) c2 = tf.constant(20) # c1_with_init_v depends on the init op for v c1_with_init_v = control_flow_ops.with_dependencies( name="c1_with_init_v", output_tensor=c1, dependencies=[v.initializer]) # c2_with_c1 depends on the value of c1_with_init_v c2_with_c1_dep = control_flow_ops.with_dependencies( name="c2_with_c1_dep", output_tensor=c2, dependencies=[c1_with_init_v]) # Fetching v directly will result in an uninitialized error with self.assertRaisesOpError("Attempting to use uninitialized value"): v.eval() # Get the value of 'c2_with_c1_dep', which should cause 'v' # to be initialized. self.assertAllEqual(20, c2_with_c1_dep.eval()) # Ensure that 'v' is initialized self.assertAllClose(0.0, v.eval()) def testWithIndexedSlicesDependencies(self): with self.test_session(): v = tf.Variable( np.array([[0.0, 1.0], [10.0, 11.0], [20.0, 21.0]]).astype(np.float32)) v_at_1 = tf.IndexedSlices(v, tf.constant([1])) gather_v_at_1 = tf.gather(v_at_1.values, v_at_1.indices) v_at_1_after_init = control_flow_ops.with_dependencies([v.initializer], v_at_1) gather_v_at_1_after_init = tf.gather( v_at_1_after_init.values, v_at_1_after_init.indices) # Fetching gather_v_at_1 will result in an uninitialized error with self.assertRaisesOpError("Attempting to use uninitialized value"): gather_v_at_1.eval() # Getting gather_v_at_1_after_init will work, and initialize v. self.assertAllEqual([[10.0, 11.0]], gather_v_at_1_after_init.eval()) # Double check that 'v' is initialized self.assertAllClose([[0.0, 1.0], [10.0, 11.0], [20.0, 21.0]], v.eval()) def testDependenciesDevice(self): with tf.Graph().as_default(): # device set on tensor => same device on dep. with tf.device("/job:ps"): vd = tf.Variable([0.0]) with_vd_dep = control_flow_ops.with_dependencies([vd.initializer], vd) self.assertTrue("/job:ps" in with_vd_dep.device) # No device set on tensor => no device on dep. vnod = tf.Variable([0.0]) with_vnod_dep = control_flow_ops.with_dependencies([vnod.initializer], vnod) self.assertDeviceEqual(None, with_vnod_dep.device) # device set on tensor, default device on graph => default device on dep. vdef = tf.Variable([0.0], name="vdef") with tf.device("/job:worker/gpu:1"): with_vdef_dep = control_flow_ops.with_dependencies([vdef.initializer], vdef) # The device is empty, but the colocation constraint is set. self.assertDeviceEqual("", with_vdef_dep.device) self.assertEqual([b"loc:@vdef"], with_vdef_dep.op.colocation_groups()) def testGroup(self): with self.test_session() as sess: v1 = tf.Variable([0.0]) v2 = tf.Variable([1.0]) # Group init1 and init2 and run. init = control_flow_ops.group(v1.initializer, v2.initializer) # Fetching v1 directly will result in an uninitialized error with self.assertRaisesOpError("Attempting to use uninitialized value"): v1.eval() # Runs "init" before fetching v1 and v2. init.run() v1_val, v2_val = sess.run([v1, v2]) # Ensure that v1 and v2 are initialized self.assertAllClose([0.0], v1_val) self.assertAllClose([1.0], v2_val) def testGroupEmpty(self): op = tf.group() self.assertEqual(op.type, "NoOp") self.assertEqual(op.control_inputs, []) def testMergeShapes(self): # All inputs unknown. p1 = tf.placeholder(tf.float32) p2 = tf.placeholder(tf.float32) p3 = tf.placeholder(tf.float32) m, index = control_flow_ops.merge([p1, p2, p3]) self.assertIs(None, m.get_shape().ndims) self.assertEqual([], index.get_shape()) # All inputs known with different ranks. p1 = tf.placeholder(tf.float32, shape=[1, 2]) p2 = tf.placeholder(tf.float32, shape=[1, 2, 3]) m, index = control_flow_ops.merge([p1, p2]) self.assertIs(None, m.get_shape().ndims) self.assertEqual([], index.get_shape()) # All inputs known with some dimensions different. p1 = tf.placeholder(tf.float32, shape=[1, 2]) p2 = tf.placeholder(tf.float32, shape=[2, 1]) m, index = control_flow_ops.merge([p1, p2]) self.assertEqual([None, None], m.get_shape().as_list()) self.assertEqual([], index.get_shape()) p1 = tf.placeholder(tf.float32, shape=[1, 2]) p2 = tf.placeholder(tf.float32, shape=[None, 2]) m, index = control_flow_ops.merge([p1, p2]) self.assertEqual([None, 2], m.get_shape().as_list()) self.assertEqual([], index.get_shape()) p1 = tf.placeholder(tf.float32, shape=[1, 2]) p2 = tf.placeholder(tf.float32, shape=[2, 2]) m, index = control_flow_ops.merge([p1, p2]) self.assertEqual([None, 2], m.get_shape().as_list()) self.assertEqual([], index.get_shape()) # All inputs known with same dimensions. p1 = tf.placeholder(tf.float32, shape=[1, 2]) p2 = tf.placeholder(tf.float32, shape=[1, 2]) m, index = control_flow_ops.merge([p1, p2]) self.assertEqual([1, 2], m.get_shape().as_list()) self.assertEqual([], index.get_shape()) p1 = tf.placeholder(tf.float32, shape=[None, 2]) p2 = tf.placeholder(tf.float32, shape=[None, 2]) m, index = control_flow_ops.merge([p1, p2]) self.assertEqual([None, 2], m.get_shape().as_list()) self.assertEqual([], index.get_shape()) p1 = tf.placeholder(tf.float32, shape=[None, None]) p2 = tf.placeholder(tf.float32, shape=[None, None]) m, index = control_flow_ops.merge([p1, p2]) self.assertEqual([None, None], m.get_shape().as_list()) self.assertEqual([], index.get_shape()) def testRefSelect(self): index = tf.placeholder(tf.int32) # All inputs unknown. p1 = tf.placeholder(tf.float32) p2 = tf.placeholder(tf.float32) p3 = tf.placeholder(tf.float32) v1 = tf.Variable(p1, validate_shape=False) v2 = tf.Variable(p2, validate_shape=False) v3 = tf.Variable(p3, validate_shape=False) s = control_flow_ops.ref_select(index, [v1, v2, v3]) self.assertIs(None, s.get_shape().ndims) # All inputs known but different. v1 = tf.Variable([[1, 2]]) v2 = tf.Variable([[2], [1]]) s = control_flow_ops.ref_select(index, [v1, v2]) self.assertIs(None, s.get_shape().ndims) # All inputs known and same. v1 = tf.Variable([[1, 2]]) v2 = tf.Variable([[1, 2]]) s = control_flow_ops.ref_select(index, [v1, v2]) self.assertEqual([1, 2], s.get_shape()) # Possibly the same but not guaranteed. v1 = tf.Variable([[1., 2.]]) p2 = tf.placeholder(tf.float32, shape=[None, 2]) v2 = tf.Variable(p2, validate_shape=False) s = control_flow_ops.ref_select(index, [v1, v2]) self.assertEqual(None, s.get_shape()) def testRunLoopTensor(self): with self.test_session() as sess: tensor_list = [] def condition(t): return t < tf.constant(5) def body(_): tensor_list.append(tf.constant(5)) return tf.constant(10) result = tf.while_loop(condition, body, [tf.constant(4)]) self.assertEqual(10, sess.run(result)) # Ensure that we cannot run a tensor that escapes the loop body # accidentally. with self.assertRaises(ValueError): sess.run(tensor_list[0]) class TupleTest(tf.test.TestCase): def testTensors(self): for v1_first in [True, False]: with self.test_session(): v1 = tf.Variable([1.0]) add1 = tf.add( control_flow_ops.with_dependencies([v1.initializer], v1.ref()), 2.0) v2 = tf.Variable([10.0]) add2 = tf.add( control_flow_ops.with_dependencies([v2.initializer], v2.ref()), 20.0) t1, _, t2 = control_flow_ops.tuple([add1, None, add2]) # v1 is not initialized. with self.assertRaisesOpError("Attempting to use uninitialized value"): v1.eval() # v2 is not initialized. with self.assertRaisesOpError("Attempting to use uninitialized value"): v2.eval() if v1_first: # Getting t1 initializes v2. self.assertAllClose([3.0], t1.eval()) self.assertAllClose([10.0], v2.eval()) else: # Getting t2 initializes v1. self.assertAllClose([30.0], t2.eval()) self.assertAllClose([1.0], v1.eval()) def testIndexedSlices(self): for v1_first in [True, False]: with self.test_session(): v1 = tf.Variable( np.array([[0.0, 1.0], [10.0, 11.0], [20.0, 21.0]]).astype( np.float32)) v1_at_1 = tf.IndexedSlices( control_flow_ops.with_dependencies([v1.initializer], v1.ref()), tf.constant([1])) v2 = tf.Variable( np.array([[0.1, 1.1], [10.1, 11.1], [20.1, 21.1]]).astype( np.float32)) v2_at_1 = tf.IndexedSlices( control_flow_ops.with_dependencies([v2.initializer], v2.ref()), tf.constant([1])) st1, st2 = control_flow_ops.tuple([v1_at_1, v2_at_1]) g1 = tf.gather(st1.values, st1.indices) g2 = tf.gather(st2.values, st2.indices) # v1 is not initialized. with self.assertRaisesOpError("Attempting to use uninitialized value"): v1.eval() # v2 is not initialized. with self.assertRaisesOpError("Attempting to use uninitialized value"): v2.eval() if v1_first: # Getting g1 initializes v2. self.assertAllClose([[10.0, 11.0]], g1.eval()) self.assertAllClose([[0.1, 1.1], [10.1, 11.1], [20.1, 21.1]], v2.eval()) else: # Getting g2 initializes v1. self.assertAllClose([[10.1, 11.1]], g2.eval()) self.assertAllClose([[0.0, 1.0], [10.0, 11.0], [20.0, 21.0]], v1.eval()) def testAcceptTensorsAsControlInputs(self): with self.test_session(): var = tf.Variable(0) assign = tf.assign(var, 1) t, = tf.tuple([tf.constant(0)], control_inputs=[assign]) # Should trigger the assign. t.eval() self.assertEquals(1, var.eval()) def testWhilePyFuncBasic(self): def func(x): return np.square(x) with self.test_session(): r = tf.while_loop( lambda i, v: i < 4, lambda i, v: [i + 1, tf.py_func(func, [v], [tf.float32])[0]], [tf.constant(0), tf.constant(2.0, tf.float32)]) self.assertEqual(r[1].eval(), 65536.0) def testWhileFuncBasic(self): @function.Defun(tf.float32) def func(x): return tf.square(tf.square(x)) with self.test_session(): x = tf.constant(2.0, tf.float32) r = tf.while_loop( lambda i, v: i < 2, lambda i, v: [i + 1, func(v)], [tf.constant(0), x]) self.assertEqual(r[1].eval(), 65536.0) r = tf.gradients(r, x)[0] self.assertEqual(r.eval(), 524288.0) self.assertEqual(len([op for op in x.graph.get_operations() if op.type == "Stack"]), 1) if __name__ == "__main__": tf.test.main()

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from collections import namedtuple Apple = namedtuple("Apple", ["species_name", "size", "price"]) def run(): apple = Apple("Gala", "M", 2.5) print(apple.species_name) print(apple.size) print(apple.price) print(apple[0]) print(apple[1]) print(apple[2]) for data in apple: print(data) if __name__ == '__main__': run()

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/snmp-mibs/DISMAN-EXPRESSION-MIB.py

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oriordan/pysnmp_mibs

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# PySNMP SMI module. Autogenerated from smidump -f python DISMAN-EXPRESSION-MIB # by libsmi2pysnmp-0.1.3 at Thu May 22 11:57:35 2014, # Python version sys.version_info(major=2, minor=7, micro=2, releaselevel='final', serial=0) # Imports ( Integer, ObjectIdentifier, OctetString, ) = mibBuilder.importSymbols("ASN1", "Integer", "ObjectIdentifier", "OctetString") ( NamedValues, ) = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ( ConstraintsIntersection, ConstraintsUnion, SingleValueConstraint, ValueRangeConstraint, ValueSizeConstraint, ) = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "ConstraintsUnion", "SingleValueConstraint", "ValueRangeConstraint", "ValueSizeConstraint") ( SnmpAdminString, ) = mibBuilder.importSymbols("SNMP-FRAMEWORK-MIB", "SnmpAdminString") ( ModuleCompliance, ObjectGroup, ) = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "ObjectGroup") ( sysUpTime, ) = mibBuilder.importSymbols("SNMPv2-MIB", "sysUpTime") ( Bits, Counter32, Counter64, Gauge32, Integer32, Integer32, IpAddress, ModuleIdentity, MibIdentifier, MibScalar, MibTable, MibTableRow, MibTableColumn, TimeTicks, TimeTicks, Unsigned32, mib_2, zeroDotZero, ) = mibBuilder.importSymbols("SNMPv2-SMI", "Bits", "Counter32", "Counter64", "Gauge32", "Integer32", "Integer32", "IpAddress", "ModuleIdentity", "MibIdentifier", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "TimeTicks", "TimeTicks", "Unsigned32", "mib-2", "zeroDotZero") ( RowStatus, TimeStamp, TruthValue, ) = mibBuilder.importSymbols("SNMPv2-TC", "RowStatus", "TimeStamp", "TruthValue") # Objects sysUpTimeInstance = MibIdentifier((1, 3, 6, 1, 2, 1, 1, 3, 0)) dismanExpressionMIB = ModuleIdentity((1, 3, 6, 1, 2, 1, 90)).setRevisions(("2000-10-16 00:00",)) if mibBuilder.loadTexts: dismanExpressionMIB.setOrganization("IETF Distributed Management Working Group") if mibBuilder.loadTexts: dismanExpressionMIB.setContactInfo("Ramanathan Kavasseri\nCisco Systems, Inc.\n170 West Tasman Drive,\nSan Jose CA 95134-1706.\nPhone: +1 408 527 2446\nEmail: ramk@cisco.com") if mibBuilder.loadTexts: dismanExpressionMIB.setDescription("The MIB module for defining expressions of MIB objects for\nmanagement purposes.") dismanExpressionMIBObjects = MibIdentifier((1, 3, 6, 1, 2, 1, 90, 1)) expResource = MibIdentifier((1, 3, 6, 1, 2, 1, 90, 1, 1)) expResourceDeltaMinimum = MibScalar((1, 3, 6, 1, 2, 1, 90, 1, 1, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(ValueRangeConstraint(-1,-1),ValueRangeConstraint(1,600),))).setMaxAccess("readwrite").setUnits("seconds") if mibBuilder.loadTexts: expResourceDeltaMinimum.setDescription("The minimum expExpressionDeltaInterval this system will\naccept. A system may use the larger values of this minimum to\nlessen the impact of constantly computing deltas. For larger\ndelta sampling intervals the system samples less often and\nsuffers less overhead. This object provides a way to enforce\nsuch lower overhead for all expressions created after it is\nset.\n\nThe value -1 indicates that expResourceDeltaMinimum is\nirrelevant as the system will not accept 'deltaValue' as a\nvalue for expObjectSampleType.\n\nUnless explicitly resource limited, a system's value for\nthis object should be 1, allowing as small as a 1 second\ninterval for ongoing delta sampling.\n\nChanging this value will not invalidate an existing setting\nof expObjectSampleType.") expResourceDeltaWildcardInstanceMaximum = MibScalar((1, 3, 6, 1, 2, 1, 90, 1, 1, 2), Unsigned32()).setMaxAccess("readwrite").setUnits("instances") if mibBuilder.loadTexts: expResourceDeltaWildcardInstanceMaximum.setDescription("For every instance of a deltaValue object, one dynamic instance\nentry is needed for holding the instance value from the previous\nsample, i.e. to maintain state.\n\nThis object limits maximum number of dynamic instance entries\nthis system will support for wildcarded delta objects in\nexpressions. For a given delta expression, the number of\ndynamic instances is the number of values that meet all criteria\nto exist times the number of delta values in the expression.\n\nA value of 0 indicates no preset limit, that is, the limit\nis dynamic based on system operation and resources.\n\nUnless explicitly resource limited, a system's value for\nthis object should be 0.\n\n\n\nChanging this value will not eliminate or inhibit existing delta\nwildcard instance objects but will prevent the creation of more\nsuch objects.\n\nAn attempt to allocate beyond the limit results in expErrorCode\nbeing tooManyWildcardValues for that evaluation attempt.") expResourceDeltaWildcardInstances = MibScalar((1, 3, 6, 1, 2, 1, 90, 1, 1, 3), Gauge32()).setMaxAccess("readonly").setUnits("instances") if mibBuilder.loadTexts: expResourceDeltaWildcardInstances.setDescription("The number of currently active instance entries as\ndefined for expResourceDeltaWildcardInstanceMaximum.") expResourceDeltaWildcardInstancesHigh = MibScalar((1, 3, 6, 1, 2, 1, 90, 1, 1, 4), Gauge32()).setMaxAccess("readonly").setUnits("instances") if mibBuilder.loadTexts: expResourceDeltaWildcardInstancesHigh.setDescription("The highest value of expResourceDeltaWildcardInstances\nthat has occurred since initialization of the managed\nsystem.") expResourceDeltaWildcardInstanceResourceLacks = MibScalar((1, 3, 6, 1, 2, 1, 90, 1, 1, 5), Counter32()).setMaxAccess("readonly").setUnits("instances") if mibBuilder.loadTexts: expResourceDeltaWildcardInstanceResourceLacks.setDescription("The number of times this system could not evaluate an\nexpression because that would have created a value instance in\nexcess of expResourceDeltaWildcardInstanceMaximum.") expDefine = MibIdentifier((1, 3, 6, 1, 2, 1, 90, 1, 2)) expExpressionTable = MibTable((1, 3, 6, 1, 2, 1, 90, 1, 2, 1)) if mibBuilder.loadTexts: expExpressionTable.setDescription("A table of expression definitions.") expExpressionEntry = MibTableRow((1, 3, 6, 1, 2, 1, 90, 1, 2, 1, 1)).setIndexNames((0, "DISMAN-EXPRESSION-MIB", "expExpressionOwner"), (0, "DISMAN-EXPRESSION-MIB", "expExpressionName")) if mibBuilder.loadTexts: expExpressionEntry.setDescription("Information about a single expression. New expressions\ncan be created using expExpressionRowStatus.\n\nTo create an expression first create the named entry in this\ntable. Then use expExpressionName to populate expObjectTable.\nFor expression evaluation to succeed all related entries in\nexpExpressionTable and expObjectTable must be 'active'. If\nthese conditions are not met the corresponding values in\nexpValue simply are not instantiated.\n\nDeleting an entry deletes all related entries in expObjectTable\nand expErrorTable.\n\nBecause of the relationships among the multiple tables for an\nexpression (expExpressionTable, expObjectTable, and\nexpValueTable) and the SNMP rules for independence in setting\nobject values, it is necessary to do final error checking when\nan expression is evaluated, that is, when one of its instances\nin expValueTable is read or a delta interval expires. Earlier\nchecking need not be done and an implementation may not impose\nany ordering on the creation of objects related to an\nexpression.\n\nTo maintain security of MIB information, when creating a new row in\nthis table, the managed system must record the security credentials\nof the requester. These security credentials are the parameters\nnecessary as inputs to isAccessAllowed from the Architecture for\n\nDescribing SNMP Management Frameworks. When obtaining the objects\nthat make up the expression, the system must (conceptually) use\nisAccessAllowed to ensure that it does not violate security.\n\nThe evaluation of the expression takes place under the\nsecurity credentials of the creator of its expExpressionEntry.\n\nValues of read-write objects in this table may be changed\n\n\nat any time.") expExpressionOwner = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 1, 1, 1), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(0, 32))).setMaxAccess("noaccess") if mibBuilder.loadTexts: expExpressionOwner.setDescription("The owner of this entry. The exact semantics of this\nstring are subject to the security policy defined by the\nsecurity administrator.") expExpressionName = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 1, 1, 2), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(1, 32))).setMaxAccess("noaccess") if mibBuilder.loadTexts: expExpressionName.setDescription("The name of the expression. This is locally unique, within\nthe scope of an expExpressionOwner.") expExpression = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 1, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1, 1024))).setMaxAccess("readcreate") if mibBuilder.loadTexts: expExpression.setDescription("The expression to be evaluated. This object is the same\nas a DisplayString (RFC 1903) except for its maximum length.\n\nExcept for the variable names the expression is in ANSI C\nsyntax. Only the subset of ANSI C operators and functions\nlisted here is allowed.\n\nVariables are expressed as a dollar sign ('$') and an\n\n\ninteger that corresponds to an expObjectIndex. An\nexample of a valid expression is:\n\n ($1-$5)*100\n\nExpressions must not be recursive, that is although an expression\nmay use the results of another expression, it must not contain\nany variable that is directly or indirectly a result of its own\nevaluation. The managed system must check for recursive\nexpressions.\n\nThe only allowed operators are:\n\n ( )\n - (unary)\n + - * / %\n & | ^ << >> ~\n ! && || == != > >= < <=\n\nNote the parentheses are included for parenthesizing the\nexpression, not for casting data types.\n\nThe only constant types defined are:\n\n int (32-bit signed)\n long (64-bit signed)\n unsigned int\n unsigned long\n hexadecimal\n character\n string\n oid\n\nThe default type for a positive integer is int unless it is too\nlarge in which case it is long.\n\nAll but oid are as defined for ANSI C. Note that a\nhexadecimal constant may end up as a scalar or an array of\n8-bit integers. A string constant is enclosed in double\nquotes and may contain back-slashed individual characters\nas in ANSI C.\n\nAn oid constant comprises 32-bit, unsigned integers and at\nleast one period, for example:\n\n 0.\n .0\n 1.3.6.1\n\n\nNo additional leading or trailing subidentifiers are automatically\nadded to an OID constant. The constant is taken as expressed.\n\nInteger-typed objects are treated as 32- or 64-bit, signed\nor unsigned integers, as appropriate. The results of\nmixing them are as for ANSI C, including the type of the\nresult. Note that a 32-bit value is thus promoted to 64 bits\nonly in an operation with a 64-bit value. There is no\nprovision for larger values to handle overflow.\n\nRelative to SNMP data types, a resulting value becomes\nunsigned when calculating it uses any unsigned value,\nincluding a counter. To force the final value to be of\ndata type counter the expression must explicitly use the\ncounter32() or counter64() function (defined below).\n\nOCTET STRINGS and OBJECT IDENTIFIERs are treated as\none-dimensioned arrays of unsigned 8-bit integers and\nunsigned 32-bit integers, respectively.\n\nIpAddresses are treated as 32-bit, unsigned integers in\nnetwork byte order, that is, the hex version of 255.0.0.0 is\n0xff000000.\n\nConditional expressions result in a 32-bit, unsigned integer\nof value 0 for false or 1 for true. When an arbitrary value\nis used as a boolean 0 is false and non-zero is true.\n\nRules for the resulting data type from an operation, based on\nthe operator:\n\nFor << and >> the result is the same as the left hand operand.\n\nFor &&, ||, ==, !=, <, <=, >, and >= the result is always\nUnsigned32.\n\nFor unary - the result is always Integer32.\n\nFor +, -, *, /, %, &, |, and ^ the result is promoted according\nto the following rules, in order from most to least preferred:\n\n If left hand and right hand operands are the same type,\n use that.\n\n If either side is Counter64, use that.\n\n If either side is IpAddress, use that.\n\n\n\n If either side is TimeTicks, use that.\n\n If either side is Counter32, use that.\n\n Otherwise use Unsigned32.\n\nThe following rules say what operators apply with what data\ntypes. Any combination not explicitly defined does not work.\n\nFor all operators any of the following can be the left hand or\nright hand operand: Integer32, Counter32, Unsigned32, Counter64.\n\nThe operators +, -, *, /, %, <, <=, >, and >= work with\nTimeTicks.\n\nThe operators &, |, and ^ work with IpAddress.\n\nThe operators << and >> work with IpAddress but only as the\nleft hand operand.\n\nThe + operator performs a concatenation of two OCTET STRINGs or\ntwo OBJECT IDENTIFIERs.\n\nThe operators &, | perform bitwise operations on OCTET STRINGs.\nIf the OCTET STRING happens to be a DisplayString the results\nmay be meaningless, but the agent system does not check this as\nsome such systems do not have this information.\n\nThe operators << and >> perform bitwise operations on OCTET\nSTRINGs appearing as the left hand operand.\n\nThe only functions defined are:\n\n counter32\n counter64\n arraySection\n stringBegins\n stringEnds\n stringContains\n oidBegins\n oidEnds\n oidContains\n average\n maximum\n minimum\n sum\n exists\n\n\n\nThe following function definitions indicate their parameters by\nnaming the data type of the parameter in the parameter's position\nin the parameter list. The parameter must be of the type indicated\nand generally may be a constant, a MIB object, a function, or an\nexpression.\n\ncounter32(integer) - wrapped around an integer value counter32\nforces Counter32 as a data type.\n\ncounter64(integer) - similar to counter32 except that the\nresulting data type is 'counter64'.\n\narraySection(array, integer, integer) - selects a piece of an\narray (i.e. part of an OCTET STRING or OBJECT IDENTIFIER). The\ninteger arguments are in the range 0 to 4,294,967,295. The\nfirst is an initial array index (one-dimensioned) and the second\nis an ending array index. A value of 0 indicates first or last\nelement, respectively. If the first element is larger than the\narray length the result is 0 length. If the second integer is\nless than or equal to the first, the result is 0 length. If the\nsecond is larger than the array length it indicates last\nelement.\n\nstringBegins/Ends/Contains(octetString, octetString) - looks for\nthe second string (which can be a string constant) in the first\nand returns the one-dimensioned arrayindex where the match began.\nA return value of 0 indicates no match (i.e. boolean false).\n\noidBegins/Ends/Contains(oid, oid) - looks for the second OID\n(which can be an OID constant) in the first and returns the\nthe one-dimensioned index where the match began. A return value\nof 0 indicates no match (i.e. boolean false).\n\naverage/maximum/minimum(integer) - calculates the average,\nminimum, or maximum value of the integer valued object over\nmultiple sample times. If the object disappears for any\nsample period, the accumulation and the resulting value object\ncease to exist until the object reappears at which point the\ncalculation starts over.\n\nsum(integerObject*) - sums all available values of the\nwildcarded integer object, resulting in an integer scalar. Must\nbe used with caution as it wraps on overflow with no\nnotification.\n\nexists(anyTypeObject) - verifies the object instance exists. A\nreturn value of 0 indicates NoSuchInstance (i.e. boolean\nfalse).") expExpressionValueType = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 1, 1, 4), Integer().subtype(subtypeSpec=SingleValueConstraint(4,6,7,5,3,8,2,1,)).subtype(namedValues=NamedValues(("counter32", 1), ("unsigned32", 2), ("timeTicks", 3), ("integer32", 4), ("ipAddress", 5), ("octetString", 6), ("objectId", 7), ("counter64", 8), )).clone(1)).setMaxAccess("readcreate") if mibBuilder.loadTexts: expExpressionValueType.setDescription("The type of the expression value. One and only one of the\nvalue objects in expValueTable will be instantiated to match\nthis type.\n\nIf the result of the expression can not be made into this type,\nan invalidOperandType error will occur.") expExpressionComment = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 1, 1, 5), SnmpAdminString().clone('')).setMaxAccess("readcreate") if mibBuilder.loadTexts: expExpressionComment.setDescription("A comment to explain the use or meaning of the expression.") expExpressionDeltaInterval = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 1, 1, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 86400)).clone(0)).setMaxAccess("readcreate") if mibBuilder.loadTexts: expExpressionDeltaInterval.setDescription("Sampling interval for objects in this expression with\nexpObjectSampleType 'deltaValue'.\n\nThis object has no effect if the the expression has no\ndeltaValue objects.\n\nA value of 0 indicates no automated sampling. In this case\nthe delta is the difference from the last time the expression\nwas evaluated. Note that this is subject to unpredictable\ndelta times in the face of retries or multiple managers.\n\nA value greater than zero is the number of seconds between\nautomated samples.\n\nUntil the delta interval has expired once the delta for the\n\n\nobject is effectively not instantiated and evaluating\nthe expression has results as if the object itself were not\ninstantiated.\n\nNote that delta values potentially consume large amounts of\nsystem CPU and memory. Delta state and processing must\ncontinue constantly even if the expression is not being used.\nThat is, the expression is being evaluated every delta interval,\neven if no application is reading those values. For wildcarded\nobjects this can be substantial overhead.\n\nNote that delta intervals, external expression value sampling\nintervals and delta intervals for expressions within other\nexpressions can have unusual interactions as they are impossible\nto synchronize accurately. In general one interval embedded\nbelow another must be enough shorter that the higher sample\nsees relatively smooth, predictable behavior. So, for example,\nto avoid the higher level getting the same sample twice, the\nlower level should sample at least twice as fast as the higher\nlevel does.") expExpressionPrefix = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 1, 1, 7), ObjectIdentifier()).setMaxAccess("readonly") if mibBuilder.loadTexts: expExpressionPrefix.setDescription("An object prefix to assist an application in determining\nthe instance indexing to use in expValueTable, relieving the\napplication of the need to scan the expObjectTable to\ndetermine such a prefix.\n\nSee expObjectTable for information on wildcarded objects.\n\nIf the expValueInstance portion of the value OID may\nbe treated as a scalar (that is, normally, 0) the value of\nexpExpressionPrefix is zero length, that is, no OID at all.\nNote that zero length implies a null OID, not the OID 0.0.\n\nOtherwise, the value of expExpressionPrefix is the expObjectID\nvalue of any one of the wildcarded objects for the expression.\nThis is sufficient, as the remainder, that is, the instance\nfragment relevant to instancing the values, must be the same for\nall wildcarded objects in the expression.") expExpressionErrors = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 1, 1, 8), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: expExpressionErrors.setDescription("The number of errors encountered while evaluating this\nexpression.\n\nNote that an object in the expression not being accessible,\nis not considered an error. An example of an inaccessible\nobject is when the object is excluded from the view of the\nuser whose security credentials are used in the expression\nevaluation. In such cases, it is a legitimate condition\nthat causes the corresponding expression value not to be\ninstantiated.") expExpressionEntryStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 1, 1, 9), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: expExpressionEntryStatus.setDescription("The control that allows creation and deletion of entries.") expErrorTable = MibTable((1, 3, 6, 1, 2, 1, 90, 1, 2, 2)) if mibBuilder.loadTexts: expErrorTable.setDescription("A table of expression errors.") expErrorEntry = MibTableRow((1, 3, 6, 1, 2, 1, 90, 1, 2, 2, 1)).setIndexNames((0, "DISMAN-EXPRESSION-MIB", "expExpressionOwner"), (0, "DISMAN-EXPRESSION-MIB", "expExpressionName")) if mibBuilder.loadTexts: expErrorEntry.setDescription("Information about errors in processing an expression.\n\nEntries appear in this table only when there is a matching\nexpExpressionEntry and then only when there has been an\nerror for that expression as reflected by the error codes\ndefined for expErrorCode.") expErrorTime = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 2, 1, 1), TimeStamp()).setMaxAccess("readonly") if mibBuilder.loadTexts: expErrorTime.setDescription("The value of sysUpTime the last time an error caused a\nfailure to evaluate this expression.") expErrorIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 2, 1, 2), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: expErrorIndex.setDescription("The one-dimensioned character array index into\nexpExpression for where the error occurred. The value\nzero indicates irrelevance.") expErrorCode = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 2, 1, 3), Integer().subtype(subtypeSpec=SingleValueConstraint(11,3,6,9,10,1,2,8,7,5,4,)).subtype(namedValues=NamedValues(("invalidSyntax", 1), ("resourceUnavailable", 10), ("divideByZero", 11), ("undefinedObjectIndex", 2), ("unrecognizedOperator", 3), ("unrecognizedFunction", 4), ("invalidOperandType", 5), ("unmatchedParenthesis", 6), ("tooManyWildcardValues", 7), ("recursion", 8), ("deltaTooShort", 9), ))).setMaxAccess("readonly") if mibBuilder.loadTexts: expErrorCode.setDescription("The error that occurred. In the following explanations the\nexpected timing of the error is in parentheses. 'S' means\nthe error occurs on a Set request. 'E' means the error\n\n\noccurs on the attempt to evaluate the expression either due to\nGet from expValueTable or in ongoing delta processing.\n\ninvalidSyntax the value sent for expExpression is not\n valid Expression MIB expression syntax\n (S)\nundefinedObjectIndex an object reference ($n) in\n expExpression does not have a matching\n instance in expObjectTable (E)\nunrecognizedOperator the value sent for expExpression held an\n unrecognized operator (S)\nunrecognizedFunction the value sent for expExpression held an\n unrecognized function name (S)\ninvalidOperandType an operand in expExpression is not the\n right type for the associated operator\n or result (SE)\nunmatchedParenthesis the value sent for expExpression is not\n correctly parenthesized (S)\ntooManyWildcardValues evaluating the expression exceeded the\n limit set by\n expResourceDeltaWildcardInstanceMaximum\n (E)\nrecursion through some chain of embedded\n expressions the expression invokes itself\n (E)\ndeltaTooShort the delta for the next evaluation passed\n before the system could evaluate the\n present sample (E)\nresourceUnavailable some resource, typically dynamic memory,\n was unavailable (SE)\ndivideByZero an attempt to divide by zero occurred\n (E)\n\nFor the errors that occur when the attempt is made to set\nexpExpression Set request fails with the SNMP error code\n'wrongValue'. Such failures refer to the most recent failure to\nSet expExpression, not to the present value of expExpression\nwhich must be either unset or syntactically correct.\n\nErrors that occur during evaluation for a Get* operation return\nthe SNMP error code 'genErr' except for 'tooManyWildcardValues'\nand 'resourceUnavailable' which return the SNMP error code\n'resourceUnavailable'.") expErrorInstance = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 2, 1, 4), ObjectIdentifier()).setMaxAccess("readonly") if mibBuilder.loadTexts: expErrorInstance.setDescription("The expValueInstance being evaluated when the error\noccurred. A zero-length indicates irrelevance.") expObjectTable = MibTable((1, 3, 6, 1, 2, 1, 90, 1, 2, 3)) if mibBuilder.loadTexts: expObjectTable.setDescription("A table of object definitions for each expExpression.\n\nWildcarding instance IDs:\n\nIt is legal to omit all or part of the instance portion for\nsome or all of the objects in an expression. (See the\nDESCRIPTION of expObjectID for details. However, note that\nif more than one object in the same expression is wildcarded\nin this way, they all must be objects where that portion of\nthe instance is the same. In other words, all objects may be\nin the same SEQUENCE or in different SEQUENCEs but with the\nsame semantic index value (e.g., a value of ifIndex)\nfor the wildcarded portion.") expObjectEntry = MibTableRow((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1)).setIndexNames((0, "DISMAN-EXPRESSION-MIB", "expExpressionOwner"), (0, "DISMAN-EXPRESSION-MIB", "expExpressionName"), (0, "DISMAN-EXPRESSION-MIB", "expObjectIndex")) if mibBuilder.loadTexts: expObjectEntry.setDescription("Information about an object. An application uses\nexpObjectEntryStatus to create entries in this table while\nin the process of defining an expression.\n\nValues of read-create objects in this table may be\nchanged at any time.") expObjectIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1, 1), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(1, 4294967295))).setMaxAccess("noaccess") if mibBuilder.loadTexts: expObjectIndex.setDescription("Within an expression, a unique, numeric identification for an\nobject. Prefixed with a dollar sign ('$') this is used to\nreference the object in the corresponding expExpression.") expObjectID = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1, 2), ObjectIdentifier()).setMaxAccess("readcreate") if mibBuilder.loadTexts: expObjectID.setDescription("The OBJECT IDENTIFIER (OID) of this object. The OID may be\nfully qualified, meaning it includes a complete instance\nidentifier part (e.g., ifInOctets.1 or sysUpTime.0), or it\nmay not be fully qualified, meaning it may lack all or part\nof the instance identifier. If the expObjectID is not fully\nqualified, then expObjectWildcard must be set to true(1).\nThe value of the expression will be multiple\nvalues, as if done for a GetNext sweep of the object.\n\nAn object here may itself be the result of an expression but\nrecursion is not allowed.\n\nNOTE: The simplest implementations of this MIB may not allow\nwildcards.") expObjectIDWildcard = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1, 3), TruthValue().clone('false')).setMaxAccess("readcreate") if mibBuilder.loadTexts: expObjectIDWildcard.setDescription("A true value indicates the expObjecID of this row is a wildcard\nobject. False indicates that expObjectID is fully instanced.\nIf all expObjectWildcard values for a given expression are FALSE,\n\n\nexpExpressionPrefix will reflect a scalar object (i.e. will\nbe 0.0).\n\nNOTE: The simplest implementations of this MIB may not allow\nwildcards.") expObjectSampleType = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1, 4), Integer().subtype(subtypeSpec=SingleValueConstraint(3,1,2,)).subtype(namedValues=NamedValues(("absoluteValue", 1), ("deltaValue", 2), ("changedValue", 3), )).clone(1)).setMaxAccess("readcreate") if mibBuilder.loadTexts: expObjectSampleType.setDescription("The method of sampling the selected variable.\n\nAn 'absoluteValue' is simply the present value of the object.\n\nA 'deltaValue' is the present value minus the previous value,\nwhich was sampled expExpressionDeltaInterval seconds ago.\nThis is intended primarily for use with SNMP counters, which are\nmeaningless as an 'absoluteValue', but may be used with any\ninteger-based value.\n\nA 'changedValue' is a boolean for whether the present value is\ndifferent from the previous value. It is applicable to any data\ntype and results in an Unsigned32 with value 1 if the object's\nvalue is changed and 0 if not. In all other respects it is as a\n'deltaValue' and all statements and operation regarding delta\nvalues apply to changed values.\n\nWhen an expression contains both delta and absolute values\nthe absolute values are obtained at the end of the delta\nperiod.") expObjectDeltaDiscontinuityID = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1, 5), ObjectIdentifier().clone((1, 3, 6, 1, 2, 1, 1, 3, 0))).setMaxAccess("readcreate") if mibBuilder.loadTexts: expObjectDeltaDiscontinuityID.setDescription("The OBJECT IDENTIFIER (OID) of a TimeTicks, TimeStamp, or\nDateAndTime object that indicates a discontinuity in the value\nat expObjectID.\n\n\n\nThis object is instantiated only if expObjectSampleType is\n'deltaValue' or 'changedValue'.\n\nThe OID may be for a leaf object (e.g. sysUpTime.0) or may\nbe wildcarded to match expObjectID.\n\nThis object supports normal checking for a discontinuity in a\ncounter. Note that if this object does not point to sysUpTime\ndiscontinuity checking must still check sysUpTime for an overall\ndiscontinuity.\n\nIf the object identified is not accessible no discontinuity\ncheck will be made.") expObjectDiscontinuityIDWildcard = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1, 6), TruthValue().clone('false')).setMaxAccess("readcreate") if mibBuilder.loadTexts: expObjectDiscontinuityIDWildcard.setDescription("A true value indicates the expObjectDeltaDiscontinuityID of\nthis row is a wildcard object. False indicates that\nexpObjectDeltaDiscontinuityID is fully instanced.\n\nThis object is instantiated only if expObjectSampleType is\n'deltaValue' or 'changedValue'.\n\nNOTE: The simplest implementations of this MIB may not allow\nwildcards.") expObjectDiscontinuityIDType = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1, 7), Integer().subtype(subtypeSpec=SingleValueConstraint(2,1,3,)).subtype(namedValues=NamedValues(("timeTicks", 1), ("timeStamp", 2), ("dateAndTime", 3), )).clone(1)).setMaxAccess("readcreate") if mibBuilder.loadTexts: expObjectDiscontinuityIDType.setDescription("The value 'timeTicks' indicates the expObjectDeltaDiscontinuityID\nof this row is of syntax TimeTicks. The value 'timeStamp' indicates\nsyntax TimeStamp. The value 'dateAndTime indicates syntax\nDateAndTime.\n\nThis object is instantiated only if expObjectSampleType is\n'deltaValue' or 'changedValue'.") expObjectConditional = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1, 8), ObjectIdentifier().clone((0, 0))).setMaxAccess("readcreate") if mibBuilder.loadTexts: expObjectConditional.setDescription("The OBJECT IDENTIFIER (OID) of an object that overrides\nwhether the instance of expObjectID is to be considered\nusable. If the value of the object at expObjectConditional\nis 0 or not instantiated, the object at expObjectID is\ntreated as if it is not instantiated. In other words,\nexpObjectConditional is a filter that controls whether or\nnot to use the value at expObjectID.\n\nThe OID may be for a leaf object (e.g. sysObjectID.0) or may be\nwildcarded to match expObjectID. If expObject is wildcarded and\nexpObjectID in the same row is not, the wild portion of\nexpObjectConditional must match the wildcarding of the rest of\nthe expression. If no object in the expression is wildcarded\nbut expObjectConditional is, use the lexically first instance\n(if any) of expObjectConditional.\n\nIf the value of expObjectConditional is 0.0 operation is\nas if the value pointed to by expObjectConditional is a\nnon-zero (true) value.\n\nNote that expObjectConditional can not trivially use an object\nof syntax TruthValue, since the underlying value is not 0 or 1.") expObjectConditionalWildcard = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1, 9), TruthValue().clone('false')).setMaxAccess("readcreate") if mibBuilder.loadTexts: expObjectConditionalWildcard.setDescription("A true value indicates the expObjectConditional of this row is\na wildcard object. False indicates that expObjectConditional is\nfully instanced.\n\nNOTE: The simplest implementations of this MIB may not allow\nwildcards.") expObjectEntryStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 2, 3, 1, 10), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: expObjectEntryStatus.setDescription("The control that allows creation/deletion of entries.\n\nObjects in this table may be changed while\nexpObjectEntryStatus is in any state.") expValue = MibIdentifier((1, 3, 6, 1, 2, 1, 90, 1, 3)) expValueTable = MibTable((1, 3, 6, 1, 2, 1, 90, 1, 3, 1)) if mibBuilder.loadTexts: expValueTable.setDescription("A table of values from evaluated expressions.") expValueEntry = MibTableRow((1, 3, 6, 1, 2, 1, 90, 1, 3, 1, 1)).setIndexNames((0, "DISMAN-EXPRESSION-MIB", "expExpressionOwner"), (0, "DISMAN-EXPRESSION-MIB", "expExpressionName"), (1, "DISMAN-EXPRESSION-MIB", "expValueInstance")) if mibBuilder.loadTexts: expValueEntry.setDescription("A single value from an evaluated expression. For a given\ninstance, only one 'Val' object in the conceptual row will be\ninstantiated, that is, the one with the appropriate type for\nthe value. For values that contain no objects of\nexpObjectSampleType 'deltaValue' or 'changedValue', reading a\nvalue from the table causes the evaluation of the expression\nfor that value. For those that contain a 'deltaValue' or\n'changedValue' the value read is as of the last sampling\ninterval.\n\nIf in the attempt to evaluate the expression one or more\nof the necessary objects is not available, the corresponding\nentry in this table is effectively not instantiated.\n\nTo maintain security of MIB information, when creating a new\nrow in this table, the managed system must record the security\ncredentials of the requester. These security credentials are\nthe parameters necessary as inputs to isAccessAllowed from\n[RFC2571]. When obtaining the objects that make up the\nexpression, the system must (conceptually) use isAccessAllowed to\nensure that it does not violate security.\n\nThe evaluation of that expression takes place under the\n\n\nsecurity credentials of the creator of its expExpressionEntry.\n\nTo maintain security of MIB information, expression evaluation must\ntake place using security credentials for the implied Gets of the\nobjects in the expression as inputs (conceptually) to\nisAccessAllowed from the Architecture for Describing SNMP\nManagement Frameworks. These are the security credentials of the\ncreator of the corresponding expExpressionEntry.") expValueInstance = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 3, 1, 1, 1), ObjectIdentifier()).setMaxAccess("noaccess") if mibBuilder.loadTexts: expValueInstance.setDescription("The final instance portion of a value's OID according to\nthe wildcarding in instances of expObjectID for the\nexpression. The prefix of this OID fragment is 0.0,\nleading to the following behavior.\n\nIf there is no wildcarding, the value is 0.0.0. In other\nwords, there is one value which standing alone would have\nbeen a scalar with a 0 at the end of its OID.\n\nIf there is wildcarding, the value is 0.0 followed by\na value that the wildcard can take, thus defining one value\ninstance for each real, possible value of the wildcard.\nSo, for example, if the wildcard worked out to be an ifIndex,\nthere is an expValueInstance for each applicable ifIndex.") expValueCounter32Val = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 3, 1, 1, 2), Counter32()).setMaxAccess("readonly") if mibBuilder.loadTexts: expValueCounter32Val.setDescription("The value when expExpressionValueType is 'counter32'.") expValueUnsigned32Val = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 3, 1, 1, 3), Unsigned32()).setMaxAccess("readonly") if mibBuilder.loadTexts: expValueUnsigned32Val.setDescription("The value when expExpressionValueType is 'unsigned32'.") expValueTimeTicksVal = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 3, 1, 1, 4), TimeTicks()).setMaxAccess("readonly") if mibBuilder.loadTexts: expValueTimeTicksVal.setDescription("The value when expExpressionValueType is 'timeTicks'.") expValueInteger32Val = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 3, 1, 1, 5), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: expValueInteger32Val.setDescription("The value when expExpressionValueType is 'integer32'.") expValueIpAddressVal = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 3, 1, 1, 6), IpAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: expValueIpAddressVal.setDescription("The value when expExpressionValueType is 'ipAddress'.") expValueOctetStringVal = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 3, 1, 1, 7), OctetString()).setMaxAccess("readonly") if mibBuilder.loadTexts: expValueOctetStringVal.setDescription("The value when expExpressionValueType is 'octetString'.") expValueOidVal = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 3, 1, 1, 8), ObjectIdentifier()).setMaxAccess("readonly") if mibBuilder.loadTexts: expValueOidVal.setDescription("The value when expExpressionValueType is 'objectId'.") expValueCounter64Val = MibTableColumn((1, 3, 6, 1, 2, 1, 90, 1, 3, 1, 1, 9), Counter64()).setMaxAccess("readonly") if mibBuilder.loadTexts: expValueCounter64Val.setDescription("The value when expExpressionValueType is 'counter64'.") dismanExpressionMIBConformance = MibIdentifier((1, 3, 6, 1, 2, 1, 90, 3)) dismanExpressionMIBCompliances = MibIdentifier((1, 3, 6, 1, 2, 1, 90, 3, 1)) dismanExpressionMIBGroups = MibIdentifier((1, 3, 6, 1, 2, 1, 90, 3, 2)) # Augmentions # Groups dismanExpressionResourceGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 90, 3, 2, 1)).setObjects(*(("DISMAN-EXPRESSION-MIB", "expResourceDeltaMinimum"), ("DISMAN-EXPRESSION-MIB", "expResourceDeltaWildcardInstancesHigh"), ("DISMAN-EXPRESSION-MIB", "expResourceDeltaWildcardInstances"), ("DISMAN-EXPRESSION-MIB", "expResourceDeltaWildcardInstanceResourceLacks"), ("DISMAN-EXPRESSION-MIB", "expResourceDeltaWildcardInstanceMaximum"), ) ) if mibBuilder.loadTexts: dismanExpressionResourceGroup.setDescription("Expression definition resource management.") dismanExpressionDefinitionGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 90, 3, 2, 2)).setObjects(*(("DISMAN-EXPRESSION-MIB", "expObjectID"), ("DISMAN-EXPRESSION-MIB", "expObjectConditional"), ("DISMAN-EXPRESSION-MIB", "expExpressionComment"), ("DISMAN-EXPRESSION-MIB", "expObjectDiscontinuityIDWildcard"), ("DISMAN-EXPRESSION-MIB", "expErrorInstance"), ("DISMAN-EXPRESSION-MIB", "expExpressionErrors"), ("DISMAN-EXPRESSION-MIB", "expExpression"), ("DISMAN-EXPRESSION-MIB", "expErrorTime"), ("DISMAN-EXPRESSION-MIB", "expErrorIndex"), ("DISMAN-EXPRESSION-MIB", "expExpressionEntryStatus"), ("DISMAN-EXPRESSION-MIB", "expExpressionValueType"), ("DISMAN-EXPRESSION-MIB", "expObjectEntryStatus"), ("DISMAN-EXPRESSION-MIB", "expObjectDeltaDiscontinuityID"), ("DISMAN-EXPRESSION-MIB", "expObjectSampleType"), ("DISMAN-EXPRESSION-MIB", "expExpressionDeltaInterval"), ("DISMAN-EXPRESSION-MIB", "expExpressionPrefix"), ("DISMAN-EXPRESSION-MIB", "expObjectDiscontinuityIDType"), ("DISMAN-EXPRESSION-MIB", "expObjectConditionalWildcard"), ("DISMAN-EXPRESSION-MIB", "expObjectIDWildcard"), ("DISMAN-EXPRESSION-MIB", "expErrorCode"), ) ) if mibBuilder.loadTexts: dismanExpressionDefinitionGroup.setDescription("Expression definition.") dismanExpressionValueGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 90, 3, 2, 3)).setObjects(*(("DISMAN-EXPRESSION-MIB", "expValueTimeTicksVal"), ("DISMAN-EXPRESSION-MIB", "expValueIpAddressVal"), ("DISMAN-EXPRESSION-MIB", "expValueInteger32Val"), ("DISMAN-EXPRESSION-MIB", "expValueOidVal"), ("DISMAN-EXPRESSION-MIB", "expValueUnsigned32Val"), ("DISMAN-EXPRESSION-MIB", "expValueCounter64Val"), ("DISMAN-EXPRESSION-MIB", "expValueCounter32Val"), ("DISMAN-EXPRESSION-MIB", "expValueOctetStringVal"), ) ) if mibBuilder.loadTexts: dismanExpressionValueGroup.setDescription("Expression value.") # Compliances dismanExpressionMIBCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 90, 3, 1, 1)).setObjects(*(("DISMAN-EXPRESSION-MIB", "dismanExpressionDefinitionGroup"), ("DISMAN-EXPRESSION-MIB", "dismanExpressionResourceGroup"), ("DISMAN-EXPRESSION-MIB", "dismanExpressionValueGroup"), ) ) if mibBuilder.loadTexts: dismanExpressionMIBCompliance.setDescription("The compliance statement for entities which implement\nthe Expression MIB.") # Exports # Module identity mibBuilder.exportSymbols("DISMAN-EXPRESSION-MIB", PYSNMP_MODULE_ID=dismanExpressionMIB) # Objects mibBuilder.exportSymbols("DISMAN-EXPRESSION-MIB", sysUpTimeInstance=sysUpTimeInstance, dismanExpressionMIB=dismanExpressionMIB, dismanExpressionMIBObjects=dismanExpressionMIBObjects, expResource=expResource, expResourceDeltaMinimum=expResourceDeltaMinimum, expResourceDeltaWildcardInstanceMaximum=expResourceDeltaWildcardInstanceMaximum, expResourceDeltaWildcardInstances=expResourceDeltaWildcardInstances, expResourceDeltaWildcardInstancesHigh=expResourceDeltaWildcardInstancesHigh, expResourceDeltaWildcardInstanceResourceLacks=expResourceDeltaWildcardInstanceResourceLacks, expDefine=expDefine, expExpressionTable=expExpressionTable, expExpressionEntry=expExpressionEntry, expExpressionOwner=expExpressionOwner, expExpressionName=expExpressionName, expExpression=expExpression, expExpressionValueType=expExpressionValueType, expExpressionComment=expExpressionComment, expExpressionDeltaInterval=expExpressionDeltaInterval, expExpressionPrefix=expExpressionPrefix, expExpressionErrors=expExpressionErrors, expExpressionEntryStatus=expExpressionEntryStatus, expErrorTable=expErrorTable, expErrorEntry=expErrorEntry, expErrorTime=expErrorTime, expErrorIndex=expErrorIndex, expErrorCode=expErrorCode, expErrorInstance=expErrorInstance, expObjectTable=expObjectTable, expObjectEntry=expObjectEntry, expObjectIndex=expObjectIndex, expObjectID=expObjectID, expObjectIDWildcard=expObjectIDWildcard, expObjectSampleType=expObjectSampleType, expObjectDeltaDiscontinuityID=expObjectDeltaDiscontinuityID, expObjectDiscontinuityIDWildcard=expObjectDiscontinuityIDWildcard, expObjectDiscontinuityIDType=expObjectDiscontinuityIDType, expObjectConditional=expObjectConditional, expObjectConditionalWildcard=expObjectConditionalWildcard, expObjectEntryStatus=expObjectEntryStatus, expValue=expValue, expValueTable=expValueTable, expValueEntry=expValueEntry, expValueInstance=expValueInstance, expValueCounter32Val=expValueCounter32Val, expValueUnsigned32Val=expValueUnsigned32Val, expValueTimeTicksVal=expValueTimeTicksVal, expValueInteger32Val=expValueInteger32Val, expValueIpAddressVal=expValueIpAddressVal, expValueOctetStringVal=expValueOctetStringVal, expValueOidVal=expValueOidVal, expValueCounter64Val=expValueCounter64Val, dismanExpressionMIBConformance=dismanExpressionMIBConformance, dismanExpressionMIBCompliances=dismanExpressionMIBCompliances, dismanExpressionMIBGroups=dismanExpressionMIBGroups) # Groups mibBuilder.exportSymbols("DISMAN-EXPRESSION-MIB", dismanExpressionResourceGroup=dismanExpressionResourceGroup, dismanExpressionDefinitionGroup=dismanExpressionDefinitionGroup, dismanExpressionValueGroup=dismanExpressionValueGroup) # Compliances mibBuilder.exportSymbols("DISMAN-EXPRESSION-MIB", dismanExpressionMIBCompliance=dismanExpressionMIBCompliance)

[ "oriordan@devel.hu" ]

oriordan@devel.hu

ac7050e1295509d0dead45d69060787b34fbc2c0

26933a384f41d0ebb3fb341d0b30a37983cb8bd8

/src/026.py

cf27c3a412306c4594e21af586da4a8470e6a276

[]

no_license

KBRI-Biology-Experiment-Computation/bioinfo-lecture-2021-07-05

bbd085139730fd947c716bc46596f22f521eb92c

c46bb213bc41ad4f119abdf35c758da641228beb

refs/heads/main

2023-08-09T19:34:13.823250

2021-07-07T08:03:11

null

0

null

UTF-8

Python

false

67

py

s = "ATGTTATAG" for i in range(0, len(s), 3): print(s[i:i+3])

[ "kenneth.jh.han@gmail.com" ]

kenneth.jh.han@gmail.com

10c9ca0234965420c1d7890a0676eac38518ad78

d40fbefbd5db39f1c3fb97f17ed54cb7b6f230e0

/ibm_db2/tests/test_unit.py

b9afacce63cced4dd32e2536a2f191f692792ab6

[]

permissive

slightilusion/integrations-core

47a170d791e809f3a69c34e2426436a6c944c322

8f89e7ba35e6d27c9c1b36b9784b7454d845ba01

refs/heads/master

2020-05-20T18:34:41.716618

2019-05-08T21:51:17

185,708,851

2

0

BSD-3-Clause

2019-05-09T02:05:19

2019-05-09T02:05:18

null

UTF-8

Python

false

616

py

# (C) Datadog, Inc. 2019 # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) import pytest from datadog_checks.ibm_db2.utils import scrub_connection_string pytestmark = pytest.mark.unit class TestPasswordScrubber: def test_start(self): s = 'pwd=password;...' assert scrub_connection_string(s) == 'pwd=********;...' def test_end(self): s = '...;pwd=password' assert scrub_connection_string(s) == '...;pwd=********' def test_no_match_within_value(self): s = '...pwd=password;...' assert scrub_connection_string(s) == s

[ "noreply@github.com" ]

slightilusion.noreply@github.com

5f697b884de06a2a759dbaf8eff4e587d4b61385

711756b796d68035dc6a39060515200d1d37a274

/output_cog_tags/initial_8302.py

ada636326405c8ccbeb036a5d50f5d02b405d7d7

[]

no_license

batxes/exocyst_scripts

8b109c279c93dd68c1d55ed64ad3cca93e3c95ca

a6c487d5053b9b67db22c59865e4ef2417e53030

refs/heads/master

2020-06-16T20:16:24.840725

2016-11-30T16:23:16

75,075,164

0

null

UTF-8

Python

false

4,331

py

import _surface import chimera try: import chimera.runCommand except: pass from VolumePath import markerset as ms try: from VolumePath import Marker_Set, Link new_marker_set=Marker_Set except: from VolumePath import volume_path_dialog d= volume_path_dialog(True) new_marker_set= d.new_marker_set marker_sets={} surf_sets={} if "Cog1_Anch" not in marker_sets: s=new_marker_set('Cog1_Anch') marker_sets["Cog1_Anch"]=s s= marker_sets["Cog1_Anch"] mark=s.place_marker((440, 791, 782), (0, 0, 1), 21.9005) if "Cog2_GFPN" not in marker_sets: s=new_marker_set('Cog2_GFPN') marker_sets["Cog2_GFPN"]=s s= marker_sets["Cog2_GFPN"] mark=s.place_marker((225, 173, 486), (1, 0.5, 0), 21.9005) if "Cog2_GFPC" not in marker_sets: s=new_marker_set('Cog2_GFPC') marker_sets["Cog2_GFPC"]=s s= marker_sets["Cog2_GFPC"] mark=s.place_marker((189, 410, 398), (1, 0.5, 0), 21.9005) if "Cog2_Anch" not in marker_sets: s=new_marker_set('Cog2_Anch') marker_sets["Cog2_Anch"]=s s= marker_sets["Cog2_Anch"] mark=s.place_marker((223, 777, 905), (1, 0.5, 0), 21.9005) if "Cog3_GFPN" not in marker_sets: s=new_marker_set('Cog3_GFPN') marker_sets["Cog3_GFPN"]=s s= marker_sets["Cog3_GFPN"] mark=s.place_marker((478, 789, 82), (1, 0.87, 0), 21.9005) if "Cog3_GFPC" not in marker_sets: s=new_marker_set('Cog3_GFPC') marker_sets["Cog3_GFPC"]=s s= marker_sets["Cog3_GFPC"] mark=s.place_marker((333, 343, 863), (1, 0.87, 0), 21.9005) if "Cog3_Anch" not in marker_sets: s=new_marker_set('Cog3_Anch') marker_sets["Cog3_Anch"]=s s= marker_sets["Cog3_Anch"] mark=s.place_marker((31, 931, 648), (1, 0.87, 0), 21.9005) if "Cog4_GFPN" not in marker_sets: s=new_marker_set('Cog4_GFPN') marker_sets["Cog4_GFPN"]=s s= marker_sets["Cog4_GFPN"] mark=s.place_marker((870, 666, 358), (0.97, 0.51, 0.75), 21.9005) if "Cog4_GFPC" not in marker_sets: s=new_marker_set('Cog4_GFPC') marker_sets["Cog4_GFPC"]=s s= marker_sets["Cog4_GFPC"] mark=s.place_marker((808, 521, 926), (0.97, 0.51, 0.75), 21.9005) if "Cog4_Anch" not in marker_sets: s=new_marker_set('Cog4_Anch') marker_sets["Cog4_Anch"]=s s= marker_sets["Cog4_Anch"] mark=s.place_marker((930, 997, 266), (0.97, 0.51, 0.75), 21.9005) if "Cog5_GFPN" not in marker_sets: s=new_marker_set('Cog5_GFPN') marker_sets["Cog5_GFPN"]=s s= marker_sets["Cog5_GFPN"] mark=s.place_marker((361, 198, 927), (0.39, 0.31, 0.14), 21.9005) if "Cog5_GFPC" not in marker_sets: s=new_marker_set('Cog5_GFPC') marker_sets["Cog5_GFPC"]=s s= marker_sets["Cog5_GFPC"] mark=s.place_marker((324, 359, 244), (0.39, 0.31, 0.14), 21.9005) if "Cog5_Anch" not in marker_sets: s=new_marker_set('Cog5_Anch') marker_sets["Cog5_Anch"]=s s= marker_sets["Cog5_Anch"] mark=s.place_marker((138, 760, 307), (0.39, 0.31, 0.14), 21.9005) if "Cog6_GFPN" not in marker_sets: s=new_marker_set('Cog6_GFPN') marker_sets["Cog6_GFPN"]=s s= marker_sets["Cog6_GFPN"] mark=s.place_marker((105, 308, 227), (0.6, 0.31, 0.64), 21.9005) if "Cog6_GFPC" not in marker_sets: s=new_marker_set('Cog6_GFPC') marker_sets["Cog6_GFPC"]=s s= marker_sets["Cog6_GFPC"] mark=s.place_marker((981, 431, 237), (0.6, 0.31, 0.64), 21.9005) if "Cog6_Anch" not in marker_sets: s=new_marker_set('Cog6_Anch') marker_sets["Cog6_Anch"]=s s= marker_sets["Cog6_Anch"] mark=s.place_marker((90, 179, 506), (0.6, 0.31, 0.64), 21.9005) if "Cog7_GFPN" not in marker_sets: s=new_marker_set('Cog7_GFPN') marker_sets["Cog7_GFPN"]=s s= marker_sets["Cog7_GFPN"] mark=s.place_marker((62, 80, 966), (0.89, 0.1, 0.1), 21.9005) if "Cog7_GFPC" not in marker_sets: s=new_marker_set('Cog7_GFPC') marker_sets["Cog7_GFPC"]=s s= marker_sets["Cog7_GFPC"] mark=s.place_marker((941, 518, 40), (0.89, 0.1, 0.1), 21.9005) if "Cog7_Anch" not in marker_sets: s=new_marker_set('Cog7_Anch') marker_sets["Cog7_Anch"]=s s= marker_sets["Cog7_Anch"] mark=s.place_marker((148, 603, 718), (0.89, 0.1, 0.1), 21.9005) if "Cog8_GFPC" not in marker_sets: s=new_marker_set('Cog8_GFPC') marker_sets["Cog8_GFPC"]=s s= marker_sets["Cog8_GFPC"] mark=s.place_marker((755, 407, 323), (0.3, 0.69, 0.29), 21.9005) if "Cog8_Anch" not in marker_sets: s=new_marker_set('Cog8_Anch') marker_sets["Cog8_Anch"]=s s= marker_sets["Cog8_Anch"] mark=s.place_marker((819, 809, 563), (0.3, 0.69, 0.29), 21.9005) for k in surf_sets.keys(): chimera.openModels.add([surf_sets[k]])

[ "batxes@gmail.com" ]

batxes@gmail.com

641a6e7fb1755fe782bd4cfa3de6704b19fd36e6

53fab060fa262e5d5026e0807d93c75fb81e67b9

/gaussiana/ch3_2020_09_09_11_54_52_542373.py

b6266cc9513e5cad35ebf1d45a0ba2b74ee8442f

[]

no_license

gabriellaec/desoft-analise-exercicios

b77c6999424c5ce7e44086a12589a0ad43d6adca

01940ab0897aa6005764fc220b900e4d6161d36b

refs/heads/main

2023-01-31T17:19:42.050628

2020-12-16T05:21:31

306,735,108

0

null

UTF-8

Python

false

230

py

import math def calcula_gaussiana(x, mi, sigma): form = (1/ (sigma((2*math.pi)**(1/2))))* (e**(((-0.5((z-mi)/sigma))**2)) return form

[ "you@example.com" ]

you@example.com

24f9ced0b5dd1c194216331452babe0cbcb1b786

52e05d43e6c37ee7a586118cf0f390b04e92ada3

/76. Minimum Window Substring _ Hash Table.py

81639a8ff54abd07d4ebc6079e31945e94369bcd

[]

no_license

CaizhiXu/LeetCode-Python-Solutions

8f7a856e11e0804f32c43ed98bc08525a950ac13

63120dbaabd7c3c19633ebe952bcee4cf826b0e0

refs/heads/master

2021-05-18T04:57:16.412834

2020-08-05T04:33:13

251,121,107

0

null

UTF-8

Python

false

1,795

py

class Solution: def minWindow(self, s: str, t: str) -> str: ans = float('inf') minstr = '' sourcehash = [0] * 256 targethash = [0] * 256 self.init_target_hash(targethash, t) j = 0 for i in range(len(s)): while not self.valid(sourcehash, targethash) and j < len(s): sourcehash[ord[j]] += 1 j += 1 if self.valid(sourcehash, targethash): if ans > j - i: ans = j - i minstr = s[i:j] sourcehash[ord[i]] -= 1 return minstr def init_target_hash(self, targethash, t): for ch in t: targethash[ord[ch]] = targethash.get(ord[ch], 0) + 1 def valid(self, sourcehash, targethash): # to check whether it is includes in targethash for i in range(256): if targethash[i] > sourcehash[i]: return False return True ## time, space - O(N) from collections import Counter, defaultdict class Solution: def minWindow(self, s: str, t: str) -> str: t_cnts = Counter(t) s_cnts = defaultdict(int) start, end = 0, 0 match = 0 minstr = '' minLen = float('inf') while end < len(s): s_cnts[s[end]] += 1 if s[end] in t_cnts and s_cnts[s[end]] == t_cnts[s[end]]: match += 1 end += 1 while match == len(t_cnts): if end - start < minLen: minLen = end - start minstr = s[start:end] s_cnts[s[start]] -= 1 if s[start] in t_cnts and s_cnts[s[start]] < t_cnts[s[start]]: match -= 1 start += 1 return minstr

[ "xucaizhi@gmail.com" ]

xucaizhi@gmail.com